Browse Topic: Fuel consumption

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Methods and Devices for Measuring the Waveform of Large Friction Forces and Energy between the Bodies and Needles of Injector Nozzles of Marine Piston Combustion Engines04-19-03-001410/21/2026
In marine combustion engines, it is important to minimize fuel consumption and reduce toxic emissions. One method to achieve this is minimizing friction losses in the injection subsystem, which is why measuring them is necessary. Some friction force values in precision pairs are so high that they require specialized methods and measurement stations. This article presents innovative methods and devices for measuring high friction and high power forces between the body and the needle of injector nozzles in the presence of a lubricant. The scientific method consists of increasing the load applied to the needle pin located in the nozzle body until it is completely ejected from the body. The devices that determine the course and maximum value of the friction force utilize test benches for dynamic tensile strength testing samples, employing specially designed and manufactured holders. When the fastening of the pin holder proved insufficient due to slippage, a semicircular groove was made around the circumferential needle pin. Round balls were inserted into the pin’s innovative mounting screw holes, recessed into the circumferential groove by about half their diameter, which is an important contribution by the authors. The maximum friction force was determined from the recorded signal, depending on the displacement or time waveform. The friction force depends on the needle’s position relative to the nozzle body; therefore, it is recommended to take measurements in at least four planes and compute the average. Different friction force values are caused by errors in the roundness and concentricity of the injector nozzle body guide part relative to the needle, as well as deposit formation. This article presents the influence of the state of the friction surfaces, as determined by modern geometric measurements of the cooperating elements, including computed tomography scans and silicone casts, on the maximum friction force value and modeling.
Monieta, Jan, Komorowski, Adam
Performance and Scheme Study of the Hybrid Engine Integrated PEMFC with Jet Engine for Commuter Aircraft2026-99-293410/19/2026
To support aviation decarbonization and improve overall propulsion efficiency, this paper proposes a hybrid propulsion architecture integrating a proton exchange membrane fuel cell (PEMFC) with a turbofan engine. The proposed parallel hybrid configuration achieves efficient energy conversion through the thermodynamic coupling of key components, including the heat exchanger, ducted fan, compressor, and combustor. A thermodynamic model is developed for parameter matching and system analysis. Results indicate that, at the design point, the overall system efficiency reaches 0.36, with a thermal efficiency of 0.42 and a propulsive efficiency of 0.82. Compared with the baseline CFM56-3C1 engine, the proposed system shows improved fuel economy and environmental performance, achieving a specific fuel consumption of 5.8 g/(s·kN), NOx emissions of 0.0063 g/kg-fuel, and a noise level of 92 dB. Exergy analysis further indicates that the primary exergy destruction occurs within the fuel cell (20%) and during combustor heat transfer (18%). These findings suggest that multi-energy pathway coupling can reduce fuel consumption and emissions, providing a potential approach for low-carbon aviation propulsion systems.
Huang, Mengxuan, Ji, Zhixing, Huang, Jiao, Xu, Zibo, Yao, Yunan, Cao, Zhipeng, Wang, Zhanxue
Physics-Constrained Procedural Generation of Synthetic Environments for the Robust Validation of Autonomous Maneuver and Logistics2026-01-75499/22/2026
The validation of Autonomous Ground Vehicles (AGVs) and intelligent logistics planners is frequently compromised by the ”Sim-to-Real” gap, where simulation environments fail to replicate the physical friction of operational deployment. Ideally, valid test cases must enforce strict mobility constraints and impose realistic sustainment penalties; however, many current generation tools rely on idealized terrain interactions and infinite-resource assumptions. We present a real-time procedural framework designed to generate high-friction validation environments that stress-test the robustness of the System Under Test (SUT). The architecture integrates gradient-based terrain analysis with a stochastic contested logistics model. It ingests synthetic heightmaps to precompute mobility corridors, ensuring that every generated evaluation episode adheres to vehicle-specific traversability limits. Simultaneously, a logistics kernel enforces fuel consumption scaled by terrain gradients and models supply chain interdiction as a parameterized Bernoulli process. We validate this framework through a ”Digital Twin” methodology, demonstrating that terrain-aware generation eliminates invalid initialization states (0% mobility violations) while the logistics model induces operationally relevant failure modes in the SUT. This unclassified, open-architecture approach supports DoD Verification, Validation, and Accreditation (VV&A) requirements by providing deterministic, reproducible edge cases for autonomous system evaluation.
Soykan, Bulent, Rabadi, Ghaith, Bochenek, Grace, Paul, Victor J.
Adaptive Neural Network Fuel Mass Correction for Structural Model Error Compensation in Diesel Engines with Variable Biodiesel Properties2026-24-00239/21/2026
Compression-ignition engines operating with biodiesel blends often exhibit variability in fuel properties, such as density, viscosity, and cetane number, which can lead to systematic deviations in injected fuel mass when using conventional physics-based models. These deviations can reduce combustion efficiency and increase brake-specific fuel consumption (BSFC). This study proposes a lightweight neural network–based approach to compensate for structural errors in baseline injection models, using a single-layer perceptron trained on the relative error (delta) between actual and modeled injected mass. By normalizing engine and fuel parameters and introducing a small amount of measurement noise, the network learns to predict a corrective factor that adapts the injected mass to match the desired target under varying fuel conditions. Simulation results demonstrate that the neural correction significantly reduces systematic bias: in test cases with intentionally introduced structural error, the average injection deviation of −1.7% in the baseline model is reduced to approximately 0.002% after correction. Root-mean-square (RMS) error over training and validation datasets remains below 0.16%, indicating robust generalization. The proposed method offers a computationally efficient solution suitable for embedded engine control units, requiring minimal additional complexity while ensuring precise fuel delivery. By eliminating bias caused by fuel property variability, the approach has the potential to improve fuel economy, reduce emissions, and maintain consistent engine performance under a wide range of operating conditions. This framework provides a practical path for integrating adaptive, data-driven correction mechanisms in diesel engines operating with heterogeneous or variable biofuels.
Gutierrez, Marcos, Taco, Diana
Analysis of Full Torque Determination in a Diesel Engine Operating in Dual Fuel mode with Hydrogen and Diesel2026-24-00149/21/2026
A 15kW diesel engine is modified in the laboratory to operate in dual fuel combustion mode. The engine is a three-cylinder, displacement of 1 Liter, originally fueled with diesel in its baseline configuration. The engine is modified by installing three PFI injectors, positioned toward the intake valves within the intake manifold. Hydrogen injection is synchronized with valve opening during the engine cycle using controlled delay units. The standard diesel injection system, managed by the original ECU, initiates combustion of the premixed air/hydrogen charge. The dual fuel operation is tested at 2000 rpm maximum torque. To maintain this condition, both diesel quantity through accelerator input and hydrogen flow via injectors duration are adjusted. Constraints included reducing diesel fuel and avoiding knock caused by excessive hydrogen. The engine operated reliably under all tested conditions. A maximum hydrogen energy substitution HES of 70% is achieved at high load, though higher values increased PPRR. A premixed equivalence ratio of 0.40 is identified as the limit before self-ignition occurred. To prevent this and achieve maximum power, an alternative strategy is introduced. Starting from diesel-only maximum torque, diesel is gradually reduced while hydrogen is increased. Rated torque is successfully achieved with an HES up to 45%. These results demonstrate that dual fuel operation can significantly reduce fossil fuel consumption while maintaining performance. It provided combustion stability and knock limits carefully managed through appropriate control of mixture composition. Further optimization could enhance efficiency and emissions performance in future applications.
Mancaruso, Ezio, Rossetti, Salvatore, Cameretti, Maria Cristina
Structural Robustness and Regime-Dependent Uncertainty in a Reduced-Order Physics Model for Vehicle Fuel Consumption Using Monte Carlo Analysis2026-24-00159/21/2026
Accurate prediction of vehicle fuel consumption typically relies either on simplified empirical correlations or on high-fidelity simulations that are computationally expensive. However, the structural robustness of reduced-order physics-based models under parametric uncertainty has not been systematically quantified. In particular, the interaction between model simplifications and uncertainty in vehicle and fuel properties across different operating regimes remains insufficiently investigated. This study presents a reduced-order physics-based framework derived from fundamental force and energy balances to estimate fuel consumption in L/100 km. The model includes aerodynamic drag, rolling resistance, inertial effects, drivetrain efficiency, and fuel lower heating value. Unlike purely empirical formulations, the proposed structure preserves physical interpretability while remaining computationally efficient. Monte Carlo simulations are employed to propagate simultaneous uncertainties in vehicle mass, drag coefficient, rolling resistance, engine efficiency, and fuel energy content. Thousands of randomized realizations are executed to quantify output variability, compute confidence intervals, and evaluate robustness indices. In addition, regime-dependent dominance transitions are analyzed by comparing urban and highway operating conditions. Results show that parameter influence is strongly dependent on speed regime: mass and rolling resistance dominate in low-speed conditions, while aerodynamic parameters become dominant at high speeds. Fuel energy content and efficiency exhibit nearly linear inverse relationships with consumption. The reduced-order structure demonstrates stable variance behavior under realistic uncertainty ranges, supporting its suitability for parametric studies and alternative fuel assessment. The proposed framework contributes a systematic evaluation of structural robustness in simplified physics-based fuel consumption models and provides a scalable methodology for uncertainty-aware automotive performance analysis.
Gutierrez, Marcos, Taco, Diana
Emissions Reduction Assessment in Buses During Real Driving Tests using Hydrogen-Methane Blends as Fuel2026-24-00069/21/2026
Increasing concern over climate change on planetary scale and urban pollution on a local spatial dimension are the pressing needs which invite to reduce greenhouse gas emissions in transportation as well as pollutant emissions. Both goals have prompted governments, industry stakeholders, and researchers to pursue innovative pathways toward sustainability in the on-the-road transportation sector trying to interpret this concept on the three requested dimensions, social, economic and environmental. Within this framework, hydrogen–methane mixtures have emerged as a promising alternative fuel solution which in someways match the three expectations. This primary solution matches the needs of urban transportation by buses, representing a further innovation step after the diesel-fuel to methane conversion. Hydrogen is characterized by carbon-free combustion, while methane is a comparatively clean and widely available fossil fuel. When blended, these two fuels can lower overall emissions relative to the use of pure CNG, while still being compatible with existing internal combustion engines if the content of hydrogen in the blend do not exceed 20 % by volume. Greater shares till to 35-40 % are compatible simply re-setting the ignition time according to the engine load. This compatibility makes the adoption of such blends both economically viable and technically achievable in the short to medium term, also increasing the market demand for hydrogen, reducing its cost. The social dimension of this choice is also saved, re-focusing attention on the reciprocating internal combustion engines which represent a great part of the industrial economy. This study describes the methodology adopted to assess the emissions performance of a hydrogen-methane-fueled (HCNG) bus for on-road emission testing. Two experimental campaigns were carried out: the first using conventional CNG, and the second employing an HCNG blend composed of 15% hydrogen and 85% CNG by volume. Tests were conducted along two routes, representing urban and extra-urban driving conditions, with different drivers and traffic conditions. The experimental results enabled a direct comparison between the two fuels. In both driving scenarios, a slight decrease in CO₂ emissions was observed when using the HCNG blend, corresponding to a reduction in fuel consumption. More significant decreases were recorded for pollutants such as CO, HC, and PN, whereas NOx emissions showed a modest increase of only a few percentage points. No modification has been implemented on the aftertreatment devices. The study indicates that the HCNG blend enhances vehicle responsiveness compared to conventional CNG and represents a step ahead in public urban transportation like the one from diesel fuel to methane.
Di Battista, Davide, Di Bartolomeo, Marco, Di Prospero, Federico, Di Diomede, Domenico, Cipollone, Roberto
Life Cycle Assessment of Greenhouse Gas Emissions from a Plug-in Hybrid Electric Vehicle with Different Powertrain Configurations in Europe2026-24-00199/21/2026
Plug in hybrid electric vehicles play an important role in transportation decarbonization. Compared with battery electric vehicles, plug in hybrid electric vehicles generally have a lower production carbon footprint due to their smaller batteries, which require far less raw material. Despite their smaller capacity, these batteries are typically sufficient to cover most daily travel distances in pure electric mode. The hybrid powertrain can be configured in multiple ways depending on the number and position of electric machines within the driveline. These configuration differences significantly influence both the total carbon footprint and the use phase greenhouse gas emissions. In this study, we evaluate the life cycle greenhouse gas emissions of a plug-in hybrid electric vehicle with various powertrain configurations in the European context. All configurations share the same premium mid-size sport utility vehicle glider. Battery capacity ranges from 20 kWh to 45 kWh, enabling an electric range of over 200 km under the Worldwide Harmonized Light Vehicles Test Cycle. The number of electric machines varies from one, as in the P2 configuration, to three, as in the P1+P3+P4 configuration. Use phase emissions for each configuration were estimated in accordance with the latest European Union emission legislation. The P2 powertrain exhibited the lowest weighted fuel and electricity consumption, whereas the P1+P3+P4 layout demonstrated the highest overall electric and fuel consumption. A sensitivity analysis of use phase emissions was performed, followed by projections for scenarios with increased renewable energy shares in both electricity generation and liquid fuel production. Finally, an extreme scenario assuming 100 % renewable electricity and fuel was analyzed.
Nguyen, Duc-Khanh, Andersson, Simon, Kristoffersson, Annika
Performance and Emission Control Enhancement Using Green-Synthesized Additives in Diesel Engine03-19-04-00208/24/2026
Cashew nut shell oil–based biodiesel (BD) is an environmentally friendly and sustainable alternative energy source that can help decrease the depletion of fossil fuels and reduce environmental pollution. In this research, the BD extracted from cashew nut shell was enriched with green-synthesized nanoparticles with various blends and evaluated for its performance. The BD20A blend recorded the best thermal efficiency of the brake, 29.5%, which was a boost of about 20.4% over diesel when using a medium load of 2.7 kW. Furthermore, the decrease in brake-specific fuel consumption was 36.2%, and exhaust gas temperature improved by 26.1% due to enhanced combustion, indicating better combustion and utilization of heat. The BD10A and BD20A recorded a considerable decrease in emissions compared to diesel under full-load conditions, with carbon monoxide and hydrocarbons reducing by 35.7% and 33.3%, respectively, and a moderate increase of nitrogen oxides. Among the multi-objective optimization approaches, the Jaya algorithm exhibited the fastest convergence rate and identified the optimum operating condition that achieved the best trade-off between engine performance and exhaust emissions. BD blends, particularly BD20A, provide greater thermal performance and better combustion behavior as well as lower exhaust emissions, making them viable as green alternatives to the traditional diesel fuel.
Victor Soosai Irudayaraj, S., Thanigaivelan, V., Brucely, Y., Lenin, N.
Research on Energy Consumption Optimization Control Strategy for Hybrid Electric Passenger Vehicles2026-01-50508/6/2026
The energy management strategy of hybrid electric vehicles (HEVs) is critical to achieving overall vehicle efficiency. Current global optimization methods involve significant computational complexity. Conventional or adaptive equivalent consumption minimization strategy (ECMS) also presents challenges in robustness and calibration efficiency, as it typically depends on real-time driving condition recognition and online parameter adjustment. To address these issues, this study proposes an engineering optimization framework based on a system efficiency lookup table approach. This method uses offline iterative simulation to determine a globally representative equivalent factor. It pre-computes optimal system efficiency, drive modes, and energy allocation across the entire operating range. These results are stored in a lookup table that the vehicle control unit (VCU) can access in real time. This approach eliminates the uncertainties associated with online optimization. It also provides calibration engineers with a quantitative tool to directly balance competing objectives, such as fuel economy and NVH. The optimized strategy increases the proportion of electric and parallel mode operation. This concentrates engine operating points within the high-efficiency zone and significantly reduces losses from multi-stage energy conversion. Simulations, dynamometer tests, and real-world road tests confirm the strategy’s effectiveness and repeatability. The approach achieves a fuel consumption reduction of approximately 0.2 L/100 km, with all test coefficients of variation (CV) remaining below 5%. The framework demonstrates good extensibility and can be adapted to different architectures, such as plug-in hybrid electric vehicles (PHEVs) and fuel cell vehicles (FCVs). It offers a universal and efficient engineering solution for optimizing energy consumption in hybrid powertrain systems.
Lin, Haoqiang, Wang, Jinhang, Chen, Lihua, Li, Huan
Axial flux motors evolve hybrid design for heavy-duty trucks26TOFHP08_078/1/2026
The trucking industry is dealing with a lot of uncertainty, and higher fuel and equipment costs are making it harder for companies to plan and buy new trucks. What can OEMs and fleet operators do to mitigate these challenges? Hybrid systems may be the answer. With internal combustion and electric power, hybrid systems reduce fuel consumption, improve transient performance, enable low-speed electric operation and create more flexible power management. Vehicles and equipment that could benefit from new or retrofit hybrid systems include refuse trucks, dump trucks, long-haul trucks, port equipment, excavators, terminal tractors and commercial delivery vehicles.
Glass, Jason
Influence of Aero Enablers on Brake Cooling Performance2026-36-03177/30/2026
Amid growing society concerns about environmental sustainability, fuel consumption has become a key factor in mitigating greenhouse gas emissions. As a result, modern vehicle design increasingly prioritizes aerodynamic drag reduction. However, aerodynamic enhancements can significantly affect brake cooling, since airflow distribution plays a crucial role in braking performance. This study explores the interplay between underbody aerodynamic features and brake cooling efficiency in production vehicles. Three body styles—compact sedan, midsize SUV, and minivan—were evaluated to determine how varying aerodynamic configurations influence airflow around the wheel assemblies. The findings highlight critical trade-offs between aerodynamic optimization and thermal management, offering valuable insights for achieving balanced vehicle development strategies.
Batista, Lorena, Motta, Daniel, Seren, Ericson, Bergel, André, Sarmento, Alisson, Terra, Rafael
Construction and Verification of Munich EV Driving Cycle Considering Road Slope2026-99-08697/30/2026
The driving cycle is the basic model of certification of vehicle fuel consumption and emissions, or calibration of powertrains. Standard regulatory driving cycles, such as WLTC, in general assume flat roads during their generation and fail to take into account the strong effect that road gradients have on vehicle operation and driving energy consumption. Such a shortcoming, then, leads to gross mismatches in adaptability when used for urban environments with typical hilly topography. To solve this problem, in this paper, we proposed a method for building driving cycles that consider the impact of slope with actual driving data. Initially, high-precision onboard data collectors were used to generate a total sum of 21, 350 km of driving data from the Munich area, thus creating a diversified driving data set with details such as vehicle speed, slope, and environmental information. Subsequently, joint probability distributions of “speed-acceleration” and “slope-slope change rate” are proposed by using the Micro-trip Method, and a novel chi-squared test algorithm is used to obtain a higher fidelity of urban driving cycle representative of typical conditions. Results of the driving cycle results show that the driving cycle built was close to the actual kinematics, indicating a deviation of less than 5%, and can capture the average uphill characteristic of 1.7%, which is quite well represented. Finally, in fact, validation of whole vehicle environmental chamber tests further demonstrates that the energy consumption prediction error of the developed driving cycle is just 2.2%, much lower than 19.1% error of WLTC. It highlights the importance of considering slope parameters in improving the accuracy of energy consumption calibration for an EV operating on complex slope terrains. Furthermore, it underscores that converting the real-world driving data into lab-based driving cycles can reduce the cost and time of actual road tests for Chinese companies going to the overseas markets, thereby offering support for the international marketing strategy of a global database.
Tian, Lichen, Jiang, Ping, Gao, Wang, Liang, Yongkai, Ma, Kunqi, Yu, Hanzhengnan
Fuel Savings from Predictive Charge Control Based on Short-Trip Duration in Series Hybrid Electric Vehicles14-15-03-00157/10/2026
Series hybrid electric vehicles (HEVs) employ an electric motor for propulsion, while the internal combustion engine operates solely as a generator under energy-efficient speed and load conditions. Owing to this architecture, series HEVs can achieve high fuel efficiency with a relatively simple control structure. However, conventional energy management systems (EMSs) often prioritize battery state-of-charge (SOC) stabilization, which can lead to frequent engine start–stop operations and unnecessary fuel consumption, particularly in short-trip driving. This study aims to enhance energy management performance in series HEVs by optimizing engine power generation timing based on predicted short-trip duration. A computationally efficient, rule-based prediction model is developed using real-world driving data, in which short-trip duration is estimated from vehicle speed and acceleration. Due to its low computational load, the proposed model is suitable for implementation in an onboard electronic control unit (ECU). The proposed control strategy initiates engine power generation when the battery SOC is low and the predicted trip duration is long, and suppresses generation when the SOC is sufficiently high or the predicted trip is short. A detailed vehicle model incorporating an engine, generator, electric motor, inverter, and battery is developed in Modelica to evaluate the proposed strategy. Simulation results demonstrate that the proposed EMS significantly reduces the frequency of engine start–stop events, leading to fuel economy improvements of 3.6% under the WLTC (excluding the extra-high phase) and 13.4% in a real-world urban–rural driving cycle, compared with a commercialized baseline vehicle. These results confirm the effectiveness and practical applicability of the proposed EMS for passenger vehicle applications.
Mizushima, Norifumi, Sato, Akira, Kuboyama, Tatsuya, Moriyoshi, Yasuo
Sustainable Airport Emissions Reduction through Optimized End-Around Taxiway Operations: A Simulation Approach2026-99-05827/10/2026
To mitigate the risks of runway incursions during aircraft transitions between closely spaced parallel runways, major hub airports globally have implemented End-Around Taxiway (EAT) as an effective safety solution. Operational data from leading international airports confirms that EAT installations have successfully enhanced surface safety while maintaining operational efficiency. However, the EAT involves a longer taxiing route, resulting in higher fuel consumption and pollutant emissions. This study takes the example of a set of closely spaced parallel runways at a domestic airport to analyze the ground taxiing process of arrival and departure flights, proposing a dynamic allocation strategy for EAT operations that can achieve energy conservation and emission reduction during the taxiing process. Through simulation, its effective operational performance is studied.
Wang, Zinan, Ye, Bojia
A Methodology for the Conversion from Physics-Based to Ultrafast-Running Data-Driven Powertrain Models for Real-Time Applications03-19-04-00177/3/2026
The virtualization of powertrain systems is a key enabler for modern powertrain development. While physics-based 0D/1D simulation models provide accuracy and interpretability, these models are typically computationally demanding, prolonging the development process and usage throughout the V-cycle. Moreover, achieving real-time-capable simulation models through model simplifications remains challenging, as it often leads to significant losses in accuracy. In contrast, data-driven approaches can achieve high computational efficiency without significantly compromising model accuracy. This opens the possibility for not only online control applications, such as model predictive control or reinforcement learning, but also for computational expensive offline control prototyping using ultrafast-running data-driven digital twins. This work focuses on the elaboration of a scalable methodology for the development of ultrafast-running powertrain models for stationary and transient engine operation. This includes the efficient generation of training data with great variance, data analysis, and preparation, an optimized partitioning method using the Jensen–Shannon distance, feature engineering, model training of a multilayer perceptron (MLP), a long short-term memory (LSTM), and gated recurrent unit (GRU) network, followed by the model evaluation using test data and the concluding model deployment. In order to demonstrate the concept, a calibrated 0D/1D model of a dual-fuel marine main engine provided by WinGD Ltd. for a pure car and truck carrier is utilized as the reference physics-based model. The case study provides a comprehensive examination of the development of ultrafast-running data-driven fuel consumption models in both stationary and transient engine operation. The results show that the proposed methodology yields robust results and minimizes the loss of accuracy to 1.80%–2.14% for the MLP predicting the steady-state fuel consumption and to 0.67%–0.96% (GRU) and 1.52%–1.68% (LSTM) for predicting the transient fuel consumption, while achieving a multiple 104-fold reduction of the real-time factor (RTF) on an identical CPU.
Weller, Louis, Zanelli, Alessandro, Yang, Qirui, Brutsche, Martin, Grill, Michael, Kulzer, André Casal
Multi-Scale Modelling of Localized Battery Degradation in Heavy-Duty Series Hybrids: From Electrochemical Impedance Spectroscopy to Powertrain and Battery Pack Performance2026-01-07477/1/2026
Hybrid electric vehicles rely heavily on battery pack power capability, which is often compromised by non-uniform aging and thermal gradients. Conventional battery models typically use bulk state-of-health metrics, failing to capture localized degradation that leads to current imbalances and reduced pack utility. This paper presents a multi-scale modelling framework that integrates Electrochemical Impedance Spectroscopy data into a fractional-order equivalent circuit model to simulate localized degradation in Lithium Iron Phosphate cells. Results show that the terminal voltage of LFP cells can be accurately modelled using the proposed fractional-order equivalent circuit with a discrete transfer-function implementation, maintaining root-mean-square errors below 20 mV across most state-of-health and state-of-charge conditions. The validated cell model is then extended to a degradation-aware battery pack representation. The battery pack in this work utilizes a 200-kWh, 800 V architecture consisting of five modules connected in parallel, each module composed of 13 parallel strings of 250 series cells, evaluated under multiple degradation scenarios. By integrating this pack model into a Class-8 series hybrid powertrain simulation, this study quantifies how cell-to-cell heterogeneity impacts vehicle performance under the VECTO regional delivery drive cycle. At the vehicle level, these battery constraints influence engine duty cycles and battery pack stress metrics. When localized degradation reaches up to 40% in one module while the remaining modules degrade up to 20% to 30%, such inhomogeneous degradation reduces the minimum pack terminal voltage by approximately 27% and increases peak discharge current by more than 30%, resulting in more rapid degradation. These battery-level limitations translate into higher fuel consumption by up to 6% in a charge-sustaining scenario.
Safavi, Seyed Reza, Homayouni, Hooman, Shoa, Tina, Wang, Jason, McTaggart-Cowan, Gordon
Experimental Study of Warm-Up And Startability Operations in a Spark Ignition Engine Fueled with Ethanol Gasoline Blends2026-01-07387/1/2026
The mitigation of Greenhouse Gas (GHG) emissions poses a major challenge for the transportation sector, driving the need for renewable fuels. Bioethanol represents a promising fuel for Spark-Ignition (SI) engines, combining a reduced life-cycle CO₂ impact with advantageous combustion properties. However, despite its proven performance under steady-state conditions, the widespread of fuels with high ethanol content is still constrained by significant difficulties during engine cold-start operation. This study aims to experimentally assess the effect of ethanol concentration on cold-start performance and warm-up transient behavior of a Naturally Aspirated (NA), Port Fuel Injected (PFI) SI engine. Warm-up tests were conducted at an operating condition of 2000 rpm engine speed and 20 Nm torque using three fuels with increasing ethanol content: commercial gasoline (E5), E30 and E60. In addition, dedicated startability tests were carried out for E60 and neat ethanol (E100) at different initial engine wall temperatures to evaluate fuel sensitivity to thermal conditions during engine start. The experimental results indicate that increasing ethanol concentration has a negligible effect on the overall duration of the warm-up process, while leading to a modest reduction in both engine wall and exhaust gas temperatures. At the same time, E100 displays severe startability limitations at low initial wall temperatures, requiring repeated cranking attempts before stable operation can be achieved. The same startability issues have been observed for E60 but with limited intensity. Two minimum engine wall temperature ranges were identified for reliable cold-start operation at 20-25 °C for E60 and 25-30°C for E100. Overall, these findings experimentally confirm the dominant influence of engine thermal conditions on the reliable startability of ethanol-fueled spark-ignition engines.
Falbo, Luigi, Falbo, Biagio, Perrone, Diego, Castiglione, Teresa
Synergistic Integration of Advanced Combustion and Air-Path Technologies to Maximize Internal Combustion Engines Efficiency in Hybrid Applications2026-01-07337/1/2026
This paper assesses the efficiency limits of light-duty vehicle propulsion systems based on reciprocating internal combustion engines (ICE) in the current state of the art and in the next five-year horizon, considering their combination with technologies such as electric turbocharging and hybridization, while excluding plug-in hybrid configurations so that fuel remains the primary onboard energy source. A systematic methodology is applied to evaluate the influence of key variables—heat transfer, air–fuel ratio, and compression ratio—on engine performance, integrating these variations into a simulation model to capture their interactions and effects. The resulting parametric study enables the generation of new engine maps that exploit synergies between parameters and enhance the prediction of engine behaviour across different operating conditions, forming the basis for assessing potential advancements in hybrid powertrain architectures. These maps are then used to define performance expectations for hybrid vehicles, identifying optimal parameter combinations to guide future technology development and improve efficiency in hybrid powertrain design. The proposed powertrain architectures are integrated into a representative vehicle model, considering two vehicle typologies: a compact passenger car and a sport utility vehicle (SUV). To quantify the potential fuel-consumption benefits, an intelligent energy-management algorithm is implemented to supervise and optimize system operation over a WLTC driving cycle. The results indicate that the proposed configurations can achieve fuel-consumption reductions exceeding 20%, demonstrating the effectiveness of both the powertrain designs and the control strategies. Overall, the findings highlight the significant efficiency potential of advanced ICE-based propulsion systems when combined with near-term technologies such as electric boosting and hybridization, confirming the viability of these improvements and providing a robust basis for future hybrid vehicle development focused on maximizing energy efficiency in transportation.
Pla, Benjamin, Dolz, Vicente, Serrano, Jose R., Gómez-Vilanova, Alejandro, Oliva, Fermin, Cardenas, Maria, Ariztegui, Javier
Experimental Investigation of Fuel Cutout Strategies in a Heavy-Duty Gasoline Compression Ignition Engine03-19-03-00156/26/2026
This study investigates Gasoline Compression Ignition (GCI), a family of advanced combustion strategies that can be used to achieve low engine-out criteria pollutant emissions in the heavy-duty transportation sector. In particular, high fuel stratification GCI (HFS-GCI) has been shown to have high thermal efficiencies while maintaining a highly controllable and responsive mixing-controlled combustion event. However, stable combustion at low loads has been shown to be the principal challenge to the implementation of HFS-GCI in production applications. It has also been observed that several strategies that achieve stable combustion at low loads result either in increased emissions or efficiency penalties. While the achievement and maintenance of high enough exhaust temperatures for efficient aftertreatment operation is a significant challenge at low loads even for traditional diesel engine operation, this challenge is exacerbated by the low reactivity and colder flame temperature of gasoline. In recent single-cylinder and 1D simulation studies, fuel cutout strategies have been proposed as an enabling strategy to simultaneously improve combustion stability at low loads and increase exhaust temperatures. In this study, fuel cutout strategies are studied in a prototype multicylinder heavy-duty GCI engine based on a Cummins ISX15 diesel engine. Steady-state engine studies are conducted at warm and cold idle conditions to identify combinations of cylinders that provide the most benefit. NOx and soot limits are set and the performance of cutout strategies are compared to a pre-optimized baseline. The most optimal strategies from steady-state testing are then implemented under transient test cycle conditions similar to those required under United States regulatory testing. The strategies were found to offer simultaneous improvements in stability, fuel consumption, criteria pollutants, and turbine outlet temperature. The choice of cylinders whose fuel supply was cut was seen to be important in realizing the observed benefits. The use of fuel cutout strategies offered optimal performance at all the conditions considered, offering an additional lever to improve the performance of HFS-GCI and highlighting a promising pathway to the use of gasoline-like fuels as alternatives to diesel in heavy-duty engines.
Viswanathan, Aravindh Babu, Zhang, Yu, Merritt, Brock
Heavy-Duty Nonroad Material Handler Electrification, Part 1: Real-World Drive Cycle Development02-19-03-00156/10/2026
Knowing a detailed operating cycle is critical for developing and testing equipment. Operating cycles can be separated by two clear distinctions: (1) regulatory or non-regulatory and (2) application at the engine-only or full machine level. The Environmental Protection Agency’s (EPA) Nonroad Transient Cycle (NRTC) may be a good representation of engine use in many types of equipment, but there is a gap in standardized and validated drive cycles specifically for nonroad material handlers. Lacking a standardized drive cycle makes it difficult to accurately benchmark machine performance and validate new powertrain technologies. The objective of this investigation is to illustrate the development of a custom drive cycle augmented with real-world customer use data that serves multiple purposes: (1) understand the range of operation and utilization that formulated inputs for electrified architecture analysis and (2) develop a repetitive and consistent maneuver to establish baseline energy consumption enabling equivalent comparison to future electrified prototype builds. This article presents a solution specifically for a 23-ton nonroad material handler in which material handling, machine transport, and extended idle were homologated to form representative short cycles defined by machine velocity and hydraulic cylinder position. The most intensive material handling short cycles had a load factor of 40% and an average fuel rate of 16 L/h. Combined with a visual aid, the short cycles exhibited low variability, having less than 5% root mean square (RMS) error in lift and reach position with respect to the average. The machine’s performance on these short cycles at the Advanced Power Systems Research Center (APSRC) was compared to results from two real-world customer locations operating the instrumented test machine in a cyclical manner, and for similar ground conditions were found to be comparable in fuel consumption.
Czarnecki, Alexander, Goodenough, Bryant, Worm, Jeremy, Robinette, Darrell, LaTendresse, Phil, Westman, John
Potential of ORC-Based Waste Heat Recovery in Conventional and Hybrid Heavy-Duty Powertrains2026-37-00186/9/2026
Heavy-duty vehicles significantly contribute to greenhouse gas emissions and urban air pollution, especially during cold-starts and transients when engine and aftertreatment efficiencies drop. Waste heat recovery (WHR) via Organic Rankine Cycle (ORC) systems offers a practical solution to improve fuel efficiency and cut CO₂ in real-world heavy-duty operations. This study examines ORC-based WHR integration into conventional and hybrid powertrains of an Isuzu FTR850 truck, analyzing four configurations: Shell-and-Tube or Plate heat exchangers with simple or regenerative ORC layouts. For hybrids, it compares two engine sizes and energy management strategies: an optimized fuzzy logic approach versus constant-power operation to enhance exhaust heat recovery. A validated quasi-static simulation framework is used to predict fuel consumption and exhaust properties over representative duty cycles. 2D performance maps using exhaust temperature and mass flow as inputs are used to model the WHR under off-design conditions. Results show that the recovery of waste heat WHR depends on the hybridization level and strategy. Conventional powertrains benefit most from Shell-and-Tube exchangers, recovering ~2 kWh of electrical energy per 8-hour cycle and reducing fuel consumption by 0.5%. Hybrid setups recover up to 3.9 kWh from exhaust gases with a simple layout coupled with a Shell-and-Tube heat exchanger under constant-power control. Electricity is used to support onboard auxiliaries and battery charging, further lowering fuel demand (-44%) and emissions. Finally, a multi-objective optimization was performed to exploit the synergy between hybridization and WHR while maintaining acceptable payload and battery operating conditions.
Donateo, Teresa, Morrone, Pietropaolo
Performance and Combustion Characteristics of Coryton SUSTAIN Classic Super 80 Carbon-Neutral Fuel versus EN 228 in a 1980s Fiat 500 Engine2026-37-00336/9/2026
The energy transition requires a rapid reduction in the use of fossil fuels, whose combustion generates substantial greenhouse-gas emissions. In Europe, transport alone accounts for roughly a quarter of total greenhouse-gas emissions, with road transport being the predominant component. In this context, the use of biofuels has emerged as a potential solution for limiting further increases in CO₂ emissions. However, most studies available in the literature evaluate the performance of these fuels on modern engines, while their effects on historic carburetted engines remain largely unexplored. This is particularly significant given the large fleet of historic vehicles across Europe, supported by a long-standing tradition of vehicle preservation, associations, and classic car collectors. The main historic-vehicle federations advise caution and the use of low-ethanol formulations so as not to damage elastomers, fuel tanks, and carburettor float bowls. For this reason, a few suppliers have developed fuels specifically for classic vehicles. Among this minority, in 2023 Coryton Advanced Fuels introduced the SUSTAIN Classic line, including the Super 80 variant. In the present study, the performance, fuel consumption, and emissions of an air-cooled, four-stroke Fiat 500 engine fueled with commercial RON 95 gasoline and Coryton SUSTAIN Classic Super 80 were analyzed. A first test comprised a complete sweep from 1000 to 5000 RPM and a second test evaluated four different main jets at maximum torque speed and maximum power speed. To evaluate the performance, the engine was installed on a test bench equipped with a torque meter. Static pressure and temperature sensors were employed to characterize the engine operating conditions, while a dynamic pressure sensor installed in the combustion chamber was used to analyze the combustion characteristics. Exhaust emissions were also measured using a gas analyzer, allowing for a detailed and accurate comparison of the effects associated with the use of the two fuels.
Tarchiani, Marco, Fossati, Federico, Raspanti, Sandro, Baroni, Alberto, Ferrara, Giovanni, Romani, Luca
An Open-Source Framework to Automate Virtual Optimization of Internal Combustion Engine Calibration Maps through Shape-Based Strategy2026-37-00016/9/2026
The automotive industry is facing increasingly stringent regulatory constraints, driving the need for faster and more efficient powertrain development. This results in higher systems complexity, making internal combustion engine calibration progressively more challenging to meet performance and emissions targets. This, combined with the manual nature of traditional calibration workflows, leads to a time-consuming process that heavily relies on human expertise. Although virtualization can reduce development time and costs, the overall workflow remains largely dependent on manual decision-making and iterative refinement. In this context, this work presents a virtual calibration framework based on a genetic algorithm, aimed at the automated optimization of engine calibration maps to satisfy performance and emissions constraints, while reducing manual effort. Each calibration map is represented through a polynomial parameterization. Specifically, a generic three-dimensional polynomial with map-specific order encodes the shape of each map, ensuring smoothness which directly impact on drivability. Accordingly, the calibration problem is reformulated as the optimization of a compact set of polynomial parameters that uniquely define the full set of calibration maps, rather than individual set-point. Each candidate solution is assessed by generating the corresponding calibration maps and simulating the engine behavior through a neural-network-based digital twin, providing predictions of operating conditions, hardware limits, performance metrics, and emissions. The proposed framework was validated on a passenger-car diesel engine, considering a reduced yet representative set of calibration maps, including main injection start of injection, air mass, boost pressure, and injection rail pressure. The objective of optimization was the minimization of brake mean fuel consumption, subject to an upper bound constraint on nitrogen oxides emissions. The global optimization process explored approximately 106 different calibration candidates within about 36 hours, leveraging parallel computation on a standard laptop. The results indicate that the procedure can deliver multiple near-optimal preliminary calibration solutions, providing an effective starting point for subsequent manual finetuning.
Romano, Gianvito, Aglietti, Filippo, Spedicato, Tonio, Cozza, Ivan Flaminio, Capra, Andrea
Development and Validation of a One-Dimensional Model for an Opposed-Piston Free-Piston Engine Generator with Parametric Analysis2026-37-00216/9/2026
Opposed-piston free-piston engine generators (OFPEGs) are emerging as a promising technology for next-generation hybrid and electrified transportation systems due to their high efficiency, reduced mechanical complexity, and improved noise, vibration, and harshness (NVH) characteristics. However, due to eliminating the conventional crankshaft mechanism and directly coupling a free-piston engine with linear generators, performance of OFPEG systems is governed by a strong coupling between piston dynamics, in-cylinder combustion processes, and electrical loading conditions. This coupling presents substantial challenges for system design, control, and optimization, limiting the further development and application of OFPEGs. Existing researches lack a comprehensive numerical model that integrates detailed in-cylinder thermodynamic process with control system of linear generator, and quantitative analysis of the effect of piston motion trajectory on system performance remains insufficiently explored. In this study, a novel one-dimensional OFPEG model is developed in Gasdyn and coupled with a linear motor model and a control strategy in MATLAB/Simulink, thus forming a complete numerical model for OFPEG. The model is validated against experimental measurements, demonstrating effective prediction of thermodynamic and dynamic performance with acceptable errors. Based on the validated model, the effects of varying piston motion trajectory on system performance are analyzed. Lower Rt and higher Ωcom and Ωexp are recommended for higher performance. When Rt is reduced to 2.5:1, thermal efficiency and indicated power improve to 36.3% and 3.4 kW, respectively. When Ωcom is increased to 0.6, thermal efficiency and indicated power improve to 35.5% and 3.22 kW, respectively. When Ωexp is increased to 0.6, thermal efficiency and indicated power improve to 36.0% and 3.41 kW, respectively. These improvements are primarily attributed to reduced heat transfer losses and enhanced scavenging efficiency under the modified trajectories. The results provide valuable insights into the optimization of piston motion trajectory to achieve higher performance. Furthermore, the proposed numerical model provides an effective tool for OFPEG design, optimization, and control strategy development, supporting the advancement of high-efficiency, low-carbon OFPEG systems for future transportation applications.
Wang, Jiayu, Morandi, Nicola, Lucchini, Tommaso, FENG, HUIHUA, Jia, Boru, Ren, Peirong
Heavy-Duty Nonroad Material Handler Electrification, Part 2: Energy Efficiency and Performance Evaluation02-19-03-00166/2/2026
Decarbonization efforts achieved through electrification in nonroad mobile machinery can realize a reduction in fuel consumption of more than 20%, thanks to concepts familiar to light-duty passenger vehicles. This case study compares the results of a hybrid-electric material handler to its conventional counterpart, utilizing machine-specific drive cycles presented in part one of this paper series. The hybrid prototype features an extended-range electric vehicle (EREV) powertrain that demonstrated substantial energy efficiency improvements. Specifically, there was a reduction in equivalent fuel consumption of 75% when operating in electric-only mode, and 33% when maintaining the battery by charging with an on-board generator. Together, the efficiency improvements can be extrapolated over a low-intensity, 8-h shift characterized by significant idle time and highly dynamic engine load for a 47% reduction in net energy consumption. Key technologies that led to this improvement included engine downsizing and decoupling, regenerative braking, and an electrohydraulic pump unit with advanced controls. This study explains details of the powertrain architecture and subsystems that were implemented on a demonstration vehicle, control strategies used to meet project goals, and an analysis of energy consumption from testing on a closed course. Also included in this study is a discourse on comparison metrics that can be used for quantifying the energy consumption differences between hybrid-electric and conventional diesel powertrains in nonroad mobile machinery.
Czarnecki, Alexander, Goodenough, Bryant, Worm, Jeremy, Robinette, Darrell, LaTendresse, Phil, Westman, John, Subert, David, Heath, Matthew, Kiefer, Dylan, Black, Andrew
Quantum-Inspired Optimization for Trajectory Planning of Reusable Launch Vehicles across Diverse Missions2026-26-07256/1/2026
Trajectory optimization for reusable launch vehicles is a critical challenge in space mission design, aiming to determine fuel-efficient paths for spacecraft during ascent, hover, and descent phases. Minimizing fuel consumption not only enhances cost-effectiveness but also improves mission sustainability. The optimization process is governed by nonlinear orbital mechanics, gravitational perturbations, atmospheric drag, and operational constraints such as thrust limits and collision avoidance. These factors make the problem highly non-convex and discontinuous, posing significant difficulties for classical gradient-based approaches, which often fail to identify global optima. In this work, we formulate the trajectory optimization problem for a reusable rocket executing an ascent–hover–descent cycle. The vehicle must ascend to a specified target altitude, maintain a stable hover for a given duration, and then return to the launch site. The primary decision variable is the throttle control profile, which is represented as a vector of throttle settings over a discretized time horizon and governs thrust levels throughout all flight phases. The objective is to minimize total fuel consumption while satisfying all physical and operational constraints. To address the problem’s complexity, we employ the BQPhy platform, which implements Quantum-Inspired Evolutionary Optimization (QIEO). This metaheuristic approach efficiently explores the search space, overcoming the limitations of traditional methods. Comparative analysis with a classical Genetic Algorithm (GA) shows that the QIEO-based method delivers solutions 5–10 times faster while achieving superior fuel-optimal trajectories. The proposed approach highlights the potential of quantum-inspired optimization for high-dimensional, nonlinear aerospace trajectory design problems, offering a promising solution for enhancing the efficiency of reusable spaceflight operations.
Eswara Sai Kumar, Kandula, Singh, Utkarsh, Pohankar, Pritam, A, Anoop, Maharana, Priyabrata, Lineswala, Rut
Data-Driven Air Traffic Management: Forecasting Demand, Modeling Delay Propagation, and Demand Capacity Balancing2026-26-07726/1/2026
Air Traffic Management (ATM) must be familiar with the exact Aircraft Take-off Weights (ATOWs) of airplanes to make the most use of runways, maintain safety margins high, and keep utilization and resources in balance. This paper aims to present a dependable ATOW forecasting methodology that can assist the air transport industry in enhancing operational decision-making. This research used datasets acquired from the EUROCONTROL Performance Review Commission (PRC) 2024 Aircraft Take-Off Weight Estimation dataset featuring 527,000 flights over Europe containing aircraft details, air trips and flight conditions. Technique comprises structured data input, inspection of missing data, timestamp aggregation to identify demand cycles over time, and domain-specific feature engineering using distance_per_minute, block_minutes, taxiout_ratio, and a strong wake turbulence metric The two supervised learning models used were Linear Regression (LR) for understanding and XGBoost for performance prediction In comparison to LR's 4,409 kg MAE (mean absolute error), 7,061 kg RMSE (root mean square error), and 0.9825 R2 value, XGBoost significantly excelled with validation results showing an R2 value of 0.9992 and an RMSE of 1,514 kg In the absence of labelled test targets, cross-validation nevertheless showed a constant degree of generalizability The residual diagnostics showed that the model was reliable for practical execution with low-variance deviations that were unbiased An accurate ATOW estimate improves the demand-capacity balance and On-Time Performance (OTP) in ATM, which in turn affects the runway schedule, wake turbulence diversion, slot allocation, and fuel planning The results highlight the need to include ATOW predictions in both tactical and strategic planning to reduce delays, increase airspace usage, and promote sustainable aviation operation and possesses significant improvements will consist of weather and runway conditions, stochastic ambiguity computation, and drift monitoring to keep up with ever-changing operating variables while maintaining accurate forecasts.
Senthilkumar, N., S, Gopalakrishnan, Gopinath, S
Experimental Investigation of Diesel, Aqueous Ammonia, and Compressed Natural Gas Mixtures on Performance and Emissions in a Compression Ignition Engine04-19-03-00115/30/2026
Abstract This study investigates and evaluates systematically the combustion, performance, and emissions characteristics of heavy-duty diesel engines fueled by diesel–ammonia–compressed natural gas triple blends. While dual-fuel systems are well-documented, the interactive effects of ammonia and CNG within a single compression ignition (CI) engine remain largely unexplored. Experiments were conducted on a 300 Nm, 660 rpm diesel engine by testing pure diesel, diesel–ammonia blends (10–20 wt.% aqueous ammonia), and triple-fuel mixtures containing 10% of the total energy from compressed natural gas. Pure diesel was first tested to provide baseline data, and subsequently blends were tested for a comparative study. The primary contribution of this work is the identification of a synergistic effect of the fuel triple blends on engine performance and emissions. Results indicate that all fuel blends improve thermal efficiency and reduce fuel consumption compared to conventional diesel. The blend containing 20% aqueous ammonia, 80% diesel, and 10% of the total fuel energy supplied by compressed natural gas achieved the highest thermal efficiency of 39.7% (7% higher than diesel) and the lowest brake specific fuel consumption of 211.22 g/kWh. Furthermore, emissions analysis revealed that carbon dioxide and nitrogen oxide emissions were significantly reduced with this triple blend. The blend decreased carbon dioxide by 26.6% and nitrogen oxide emissions by 32.1%, while hydrocarbon emissions were also lowered by up to 29.2%. Carbon monoxide emissions increased slightly for the triple blends, reaching a maximum value of 3.9028 g/kWh for the A20D80CNG10 mixture, compared to diesel operation. The slight increase in carbon monoxide emissions for triple blends highlights a trade-off in emission behavior. These findings address the combined utilization of diesel–ammonia–compressed natural gas triple-fuel mixtures in heavy-duty engines, demonstrating that strategic blending can simultaneously improve efficiency while mitigating environmental impact. Graphical Abstract
Sinkala, Happy, Sarıtaş, Mehmet, Kül, Volkan Sabri, Akansu, Selahaddin Orhan, Ünalan, Sebahattin
Research on Carbon Emission Calculation Algorithm for Trucks on Expressways in Xinjiang2026-99-17185/22/2026
In the field of measuring carbon emissions from road traffic, the carbon emission factor method has remarkable advantages in terms of standardization, operational simplicity, and adaptability. Backed by the IPCC international standard framework, this method offers convenient access to a dynamic factor database and incorporates an adaptive adjustment mechanism for real-world scenarios, such as technological advancements and regional disparities. Against this backdrop, this study employs the carbon emission factor method to establish refined measurement models based on load capacity and fuel consumption, respectively. These models are then applied to quantify carbon emissions from trucks on specific sections of the G30 highway in Xinjiang. The load-based model calculates emissions by integrating truck axle weight and driving distance, while the fuel-based model analyzes fuel consumption data in conjunction with driving mileage. A comparison of the two models in terms of measurement differences is also carried out in the research. Furthermore, it provides a granular breakdown of energy consumption data for fully loaded trucks exceeding 31 tons, as specified by national standards. This introduces a novel approach to precise carbon emission measurement in heavy-duty transportation in northwestern China. It also provides a method for establishing an emission mitigation policy that is region-specific on a scientific basis.
Li, Maowen, Han, Dongchen, Gao, Yansen, Bai, Haotian, Dai, Xiaomin
Effect of Burning Biomass Fuel Cyclohexanol on Diesel Engine Performance2026-99-17085/22/2026
Against the backdrop of growing global demands for energy sustainability and stricter emission regulations for diesel engines, this study investigates the performance implications of incorporating cyclohexanol—a renewable oxygenated fuel—into diesel fuel blends. Using a marine medium-speed diesel engine as the experimental platform, the research systematically evaluates engine performance and emission characteristics across a range of cyclohexanol-diesel blend ratios under low, medium, and high load conditions. Experimental findings reveal multifaceted effects of cyclohexanol blending on engine operation. Combustion of the blended fuels enhances the engine’s dynamic performance, particularly under medium and high loads, where the maximum in-cylinder burst pressure exhibits a noticeable increase. This improvement is attributed to cyclohexanol’s oxygen-carrying capacity, which promotes more vigorous and sustained combustion reactions. In terms of emissions, increasing the proportion of cyclohexanol in the fuel blend leads to significant reductions in soot and carbon monoxide (CO) emissions, reflecting the cleaner-burning properties of the oxygenated component. However, this is accompanied by an uptick in nitrogen oxide (NOx) emissions, likely due to the elevated combustion temperatures generated by the more efficient fuel oxidation process. From an economic perspective, cyclohexanol blending at consistent load levels induces a postponement in the crank angle at which peak heat release occurs during combustion. This temporal shift prolongs the effective combustion duration, enabling more complete fuel utilization within the cylinder. Consequently, fuel consumption rates decrease, and overall engine efficiency improves, highlighting the potential of cyclohexanol blends to enhance operational economy in marine propulsion systems. In summary, this study underscores the complex trade-offs associated with cyclohexanol-diesel blends: while they offer tangible benefits in power output, fuel efficiency, and reduced particulate emissions, managing the increase in NOx emissions remains a critical challenge. The results provide a foundational framework for advancing biofuel applications in marine engines, emphasizing the need for integrated emission control strategies to optimize the balance between performance and environmental sustainability.
Chen, Ke, Yang, Chenxi, Wang, Yibin, Fan, Jinyu, Liu, Yuchen, Ye, Zixiao, Huang, Jialiang
Development of a Variable Interface Finite Volume Method for Simulating One- and Three-Dimensional Unsteady Gas Flow and Performances of Underwater Marine Diesel Engines03-19-03-00125/6/2026
Diesel engines used for the main power supplier of submarine normally run in high back pressure and low intake pressure, causing unstable performances. Furthermore, when a submarine runs under the sea the exhaust pipe of the diesel engine is under the seawater. Once the lowest pressure in the exhaust pipe is not sufficient to push all the water out, the water will flow into the exhaust pipe and damage the diesel engine. Modeling can provide a useful guide for designing diesel engines, intake and exhaust pipes, and turbocharging systems to avoid water flowing into diesel engine. However, existing simulation methods cannot well simulate the exhaust system of an underwater diesel engine, in which the interface between the liquid water and the exhaust gas is variable. To overcome the drawbacks of existing simulation methods in handling the variable interface between the two phases, a variable interface finite volume method (FVM) is proposed, and a corresponding model is developed in this work. This is the major contribution of this work. A detailed model description and numerical treatment of governing equations are given. The new model is validated using the experiment conducted in this work on the procedure of gas pushing water in a pipe. The validation results show that the variable interface FVM is effective and reliable. Due to the complexity of the exhaust gas flow at the tailpipe, three-dimensional (3D) flow at the exit of exhaust pipe under different exhaust gas speeds is studied. Results show that, when the exhaust gas speed is below 20 m/s, after the bubble leaves the exit, a part of seawater will flow into the exhaust pipe and flow down along the pipe wall under gravity. With the increase in speed, this phenomenon disappears. Using the newly developed one-dimensional (1D) and 3D model, the 16V-MTU396SE84 underwater diesel engine’s performance was simulated under different back pressures. Also, the effect of silencer’s volume on the stability of diesel engine’s exhaust system was studied. Simulation results show that, with the increase in exhaust back pressure, the excess air factor becomes smaller, combustion turns worse, combustion pressure and maximum in-cylinder pressure become lower, the combustion temperature, maximum temperature, and brake specific fuel consumption go up. In addition, silencer’s volume is very important to the stability of engine performance. The bigger the silencer, the more stable the exhaust system. The flow in the 16V-MTU396SE84 diesel engine’s exhaust pipe under the seawater was also calculated. Simulation results are consistent with engine tests showing that when the engine runs under full load the exhaust gas pressure and the pushing water speed in the exhaust pipe are high, whereas in the part load, the exhaust gas pressure and the water speed become a little lower. The correct results of these simulated performances of underwater marine diesel engines indicate that the models newly developed in this work are reliable.
Guo, Dongshao, Zhang, Licheng, Yang, Shiyou, Sun, Yong, Abidin, Zainal, Lin, Shujun
Optimization of a Hybrid-Electric Multi-Tilt-Rotor Aircraft with Conventional and Vertical Takeoff and Landing CapabilitiesF-0082-2026-02065/5/2026
This paper demonstrates the sizing and optimization of a hybrid-electric multi-tilt rotor configuration of both conventional and vertical takeoff and landing capabilities. The study uses Parametric Energy-Based Aircraft Configuration Evaluator to design and optimize the aircraft. To explore the design space comprising both discrete and continuous design variables, a genetic algorithm is used for optimization. The design variables are not limited to conventional aero-propulsive parameters such as wing loading, aspect ratio, and disk loading. Battery-related parameters such as the maximum permissible depth of discharge, maximum permissible discharge rate, and the number of parallel strings in a battery pack are also considered in this work to study their impact on aircraft gross weight and fuel consumption. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) optimization framework is used to solve the multi-objective optimization problem, with objectives to minimize the maximum take-off mass and fuel weight. The sensitivity studies showed that higher wing-loading and lower-aspect-ratio designs resulted in lower gross weight. A higher permissible depth of discharge led to lower fuel consumption, despite a slight increase in gross weight. But increasing the number of parallel strings in a battery pack increased gross weight with negligible change in fuel consumption.
Bhandari, Rajan, Chakraborty, Imon
Effect of Passive Pre-Chamber Geometry on Performance and Emissions of a Single-Cylinder Natural Gas Large Bore Engine2026-01-03334/7/2026
The maritime industry is one of the most energy-intensive sectors, characterized by high fuel consumption and significant environmental impact. As global trade relies on shipping, the challenge of reducing pollutants and greenhouse gas emissions becomes ever more pressing. Natural gas (NG) is considered as a transitional fuel, capable of lowering CO₂ emissions by 20–30% compared to conventional marine fuels. However, to fully harness this potential, significant advances in combustion technology are necessary, particularly with ultra-lean combustion strategies. One of the most promising pathways is pre-chamber combustion, a solution that can simultaneously improve the efficiency and sustainability of NG marine engines. In this scenario, the passive pre-chamber geometry plays a key role, as it directly influences ignition behavior, combustion stability, and exhaust emissions. This work presents an experimental study conducted on a single-cylinder marine engine prototype, retrofitted from a diesel baseline, and equipped alternatively with four passive pre-chambers featuring different geometrical configurations. The tests were conducted at an engine speed of 1500 rpm and different loads to evaluate the influence of pre-chamber geometry on engine performance and exhaust emissions. Key parameters such as combustion phasing, efficiency, and pollutant formation were analyzed and compared between the four setups. Results showed that pre-chamber design affects the interaction between the turbulent jets and the main chamber mixture, leading to significant variations in both combustion efficiency and emission trends. These findings provide new insights into the role of passive pre-chamber geometry in optimizing large-bore NG marine engines, offering a valuable contribution to the development of cleaner and more efficient propulsion systems for the maritime sector.
Marchitto, Luca, Tornatore, Cinzia, Pennino, Vincenzo, Mariani PhD, Antonio, Beatrice, Carlo, Accurso, Francesco, Gorietti, Valentina, Pesce, Francesco, Giardino, Angelo, Vitti, Luciano
Establishing a Standard S-Factor: Improving Accuracy and Flexibility in SOC Corrections for HEV Testing2026-01-04224/7/2026
Hybrid-electric vehicle (HEV) fuel economy test procedures require that the net energy change (NEC) of the battery not interfere with measuring accurate fuel consumption results. SAE J1711-2010 required the NEC to stay within 1% of fuel energy consumption, assuming that residual changes in state of charge (SOC) would have negligible impact. In practice, however, the asymmetry between fuel and electricity conversion efficiencies means that an imbalance of one unit of battery energy can translate into a likely fuel consumption error of roughly three units. A standard S-Factor, a dimensionless ratio of marginal fuel change to marginal NEC change, was introduced in J1711-2023 to improve SOC correction procedures. The method improves upon the previous J1711 (2010) accuracy by correcting all results for NEC changes and expands the NEC-to-fuel ratio (NECFR) window, enabling HEVs to use electric propulsion more aggressively and potentially achieve higher fuel economy in testing and real-world usage. Using a standardized value (instead of requiring additional testing to determine the vehicle-specific value) provides a simple, low-burden approach across HEV designs. Empirical data, supported by simulations of HEV powertrains, indicate that there exists a practical range of S-Factors common to efficient HEV designs. The standard value represents a defensible best estimate within this range, close enough to provide corrected results with minimal error. The method also allows the NECFR window to be doubled (e.g., from ±1% to ±2%) while improving the fidelity of final fuel consumption results compared with the legacy procedure. This paper reviews the data and simulation work used to identify practical S-Factor values, evaluates the resulting correction accuracy across vehicle types and test cycles, and demonstrates that a single standardized S-Factor provides consistent results within an expanded NECFR window.
Duoba, Michael
BEV Incremental CO 2 Emissions in a Ranked Order Electric Grid2026-01-04244/7/2026
Battery Electric Vehicles (BEV) have been sold as ‘Zero Emissions Vehicles’ (ZEV) by governments to reduce transportation CO2. While they are not ZEV because they run on grid electricity, they could be ‘effectively ZEV’ if the incremental CO2 is ‘very small’. At the national level, this is estimated using following metrics: (1) Internal Combustion Engine Vehicle (ICEV) fuel consumption, from the total US gasoline consumption divided by the total fleet miles driven, 25 mpg or 350 g CO2/mi, (2) Strong Hybrid Electric Vehicles (HEV) about one third less, 240 g CO2/mi. (3) BEV energy consumption, using data from systematic on-road testing of a wide range of vehicles, estimated at 40 kWh/100 mi for a US sales mix. (4) Electricity marginal CO2: in a ranked order grid, zero-CO2 sources are prioritized and supplemented by fossil sources. IEA hourly data show that the US 48 contiguous states are self-contained, with zero-CO2 sources providing a third of total demand. The response to hourly demand changes comes largely from natural gas and coal power stations, with EPA data showing a combined marginal CO2 of 600 g CO2/kWh. On replacing an ICEV by a BEV, the reduction in gasoline use, - 350 g CO2/mi, is offset to two thirds by higher electricity consumption, 40 x 600 / 100 = + 240 g CO2/mi. BEV marginal CO2 is therefore similar to HEV, and not ‘much smaller’ than ICEV. This is because HEV engines and fossil power stations have similar efficiency and similar fuel CO2 intensity.
Phlips, Patrick
Energy Efficiency Analysis of U-Haul Truck and Trailer Systems: Impact of Tongue Weight and Load Variations2026-01-04664/7/2026
The demand for improved energy efficiency in real-world vehicle operations continues to grow with technology enhancement. When transporting large cargo loads with passenger pickup trucks and rental trailers, the interaction between vehicle payload, towing configuration, and fuel consumption becomes a key factor in overall system efficiency. Understanding how towing configurations and trailer loading influence fuel consumption and vehicle performance is critical for both consumer guidance and vehicle system design. This study investigates the energy efficiency of U-Haul truck and trailer systems, with a particular focus on the influence of trailer tongue weight. U-Haul truck and trailer simulation models were developed using AVL Vehicle Simulation Model (VSM) software, with an F-350 engine brake-specific fuel consumption (BSFC) map integrated to represent realistic engine performance. Two configurations with equal payload were evaluated: (1) a U-Haul truck alone, and (2) a U-Haul truck towing a trailer. Within these configurations, multiple scenarios were analyzed, including variations in payload levels and tongue weight distributions. Driving cycles were selected to capture common moving conditions such as urban stop-and-go traffic and extended highway operation. Simulation outputs quantified the interactions among vehicle dynamics, powertrain load, and fuel consumption. Results show clear differences in energy consumption between standalone and towing configurations, with tongue weight distribution exerting a significant influence on both efficiency and stability. The findings provide practical insights into the energy trade-offs between independent vehicle operation and towing scenarios. Moreover, the study highlights the importance of load distribution and driving cycle considerations in optimizing fuel consumption, offering a framework that can be extended to rental, commercial, and consumer towing applications where energy efficiency and vehicle performance are important.
Wang, Gang, Kathadi, Mohammad, Yang, William, Chen, Yan
Multi-Objective Reinforcement Learning Framework for Transmission Shift Schedule Optimization2026-01-01624/7/2026
Building upon previous work that successfully employed a Reinforcement Learning (RL) agent for the autonomous optimization of transmission shift programs to enhance fuel efficiency, this paper addresses a critical limitation of that approach: the neglect of human-centric factors. While the prior methodology achieved substantial fuel consumption reductions by training an RL agent in a Software-in-the-Loop (SiL) environment, it did not explicitly account for aspects such as driver comfort and preferences, which are paramount for real-world user acceptance and drivability. This work presents a multi-objective optimization framework extending the artificial calibrator to simultaneously maximize fuel efficiency and enhance driver comfort. The method introduces a modified RL reward function that penalizes undesirable shift behavior to ensure a smooth driving experience (drivability). This new methodology also incorporates a mechanism to capture and integrate driver preferences, moving beyond a purely quantitative fuel-economy-driven objective to a holistic, user-focused calibration. Experimental evaluation demonstrates that the extended framework successfully generates a shift strategy that achieves a favorable trade-off between fuel efficiency and drivability, resulting in a more balanced and practical calibration. The ability to integrate these qualitative factors into an automated, data-driven process represents a significant step forward, promising to accelerate the development of powertrain control systems that are both highly efficient and aligned with the expectations of human drivers. This work lays the foundation for future RL-based calibration tools that are capable of addressing the full spectrum of development objectives, from fuel economy to the subtleties of vehicle drivability.
Kengne Dzegou, Thierry Junior, Schober, Florian, Rebesberger, Ron, Henze, Roman, Sturm, Axel
Integrated DBSCAN-Based Segmentation of Tractor Activity into Productive and Non-Productive States from GPS Data2026-01-01694/7/2026
Accurate identification of Productive and Non-Productive States or tractor duty cycles—comprising working, idle, and transport states—is critical for performance analysis, fuel optimization, and emissions modeling in agriculture machinery and fleet monitoring. This study explores the application of integrated unsupervised machine learning (ML) techniques to classify duty cycles using GPS-derived parameters such as speed, location variance, and temporal patterns. Unlike supervised approaches, the proposed method does not rely on several labeled engine and vehicle parameters, making it scalable and adaptable across diverse operational contexts. Clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) in integration with hybrid rule-based and a road feature is employed to segment GPS data into distinct behavioral states. Feature engineering focuses on extracting motion signatures and spatial-temporal features that correlate with operational modes. Validation against manually annotated datasets demonstrates high accuracy in distinguishing idle, working, and transport phases. Furthermore, the present study demonstrates that by accurately determining the operational status of the tractor, unnecessary idling can be prevented through an idle avoidance system. Additionally, after assessing transport and working conditions, a movement-based control system for tire pressure adjustment is proposed. Both strategies have the potential to reduce fuel consumption by approximately 5-7%; however, this lies outside the scope of the present work. The framework offers a robust, data-driven solution for duty cycle monitoring and can be integrated into telematics systems for predictive maintenance and operational efficiency of the tractors.
Maharana, Devi prasad, Gangsar, Purushottam, Dharmadhikari, Nitin, Pandey, Anand Kumar
Evaluating the Impact of Drag Coefficient Changes on Driving Energy Using a Probability Distribution Model of Yaw Angle Based on Empirical Data from North American Highway2026-01-06154/7/2026
This study estimates the impact on driving energy of differences in aerodynamic characteristics for yaw angle from natural wind during North American Highway mode driving. A previous study [1] clarified the potential to estimate the fuel consumption impact of natural wind by integrating the drag coefficient yaw characteristics and yaw angle occurrence probability. The natural wind was measured on a vehicle while driving a representative North American Highway test course [2]. Driving energy is predicted from the obtained yaw probability and the drag coefficient yaw sweep data in a wind tunnel. Measurements were conducted every weekday for 8 hours in 2023, covering 70% of the traffic volume. The validity of the measurement period was evaluated by the deviation from the annual average of wind direction and speed. Since yaw probability varies depending on the road environment, it is necessary to weigh the road environment type probability when calculating the driving energy. The probability was calculated using machine learning from more than 490,000 images of North American Highways. Based on the obtained natural wind data, a yaw probability model was created for each vehicle speed in the US Highway driving mode. An evaluation method for the driving energy was constructed from data before and after the improvement of the drag coefficient yaw characteristics. This evaluation method is based on verification results from actual driving data. By using the yaw probability distribution that considers the road environment and traffic volume of the North American highway, the impact of the yaw angle due to natural wind on driving energy can be numerically estimated. According to this method, for a specific the drag coefficient yaw sweep characteristic with a 13 ct improvement in the drag coefficient at a 6-degree yaw angle, this would result in an improvement in drive energy of approximately 1% on real US highways. This is an important indicator for optimizing aerodynamic characteristics, and suggests a development direction that can improve fuel efficiency in the real world by optimizing the vehicle shape while taking into account the yaw angle caused by natural wind.
Onishi, Yasuyuki, Nucera, Fortunato, Nichols, Larry, Metka, Matt
Effect of Renewable Fuel Blends on Particulate Emissions in Warm-Up Conditions in a GDI Engine2026-01-03044/7/2026
Renewable gasoline offers significant benefits in reducing greenhouse gas (GHG) emissions. In this study, five gasolines with different renewable hydrocarbon classes and varying distillation curves were taken to investigate their effect on particle number (PN) emissions in a spark-ignition GDI engine at 10 bar indicated mean effective pressure (IMEP) and 2000 rpm. The engine coolant temperature was varied from 90°C to 35°C to investigate the effect of fuel evaporation on soot formation. Injectors with various spray plume targets and start of injection (SOI) timing (300° and 260° bTDC) were used to assess how different gasolines affect engine performance and to determine engine calibration requirements. A simplified transient cycle examines how engine motoring influences PN emissions for test gasolines. A high-speed camera and endoscope were used to identify the sources of soot during fuel combustion. Simulations were done to assess the quality of fuel-air mixing in support of the experimental data. The results revealed that the type of hydrocarbons in gasoline was crucially affecting PN emissions. Particles with >10 nm increased with increasing fuel’s aromatics. Paraffin-rich gasoline showed 71% and 98% lower PN than aromatics-rich gasoline under hot and cold engine conditions. Paraffin-rich gasoline showed lower PN in cold tests than in hot tests with retarded SOI. Replacing ~10% paraffins with olefins and naphthene reduced >10 nm PN by 15-77%. However, replacing 19% of paraffins with olefins and naphthene increases PN emissions. Optimal SOI timing reduces PN by 80% for aromatics-rich gasoline. Fuel consumption and hydrocarbon (HC) emissions increased with increasing aromatics and paraffins in gasoline under cold conditions. Yellow flames on the piston top and near the injector tip were the primary sources of soot. Simulation results showed that the liquid fuel mass increased by 14% when the coolant temperature was reduced by 55 K.
Muniappan, Krishnamoorthi, Dahlander, Petter, Helmantel, Ayolt, Alemahdi, Nika, Lehto, Kalle
Effect of Renewable Fuel Blends on Particulate Emissions under Medium Engine Load and Injector Coking Conditions in a GDI Engine2026-01-03014/7/2026
Renewable gasoline is blended with fossil gasoline as part of the effort to achieve zero net carbon emissions. This study examined how five gasoline fuels with different hydrocarbon compositions affect engine-out gaseous and particle number (PN) emissions. Gasolines F3 and F4 reduce GHG emissions by 54% and 35%, compared with fossil gasoline. The other three gasolines reduce GHG emissions by 4-9%. Tests were conducted on a single-cylinder GDI engine at 10-14 bar indicated mean effective pressure (IMEP) and 2000 rpm. The injector-tip coking behavior of the test fuels and the resulting PN emissions were also investigated at 10 bar IMEP. Spray plume targets and start-of-injection (SOI) timing were adjusted to examine how the test fuels affected PN emissions. An endoscope was used to identify the sources of soot during fuel combustion. The experimental results show that PN varies with gasoline composition and engine operating conditions. Aromatics and olefins contribute more to injector coking. Coked injector conditions showed 95% higher PN than clean injector conditions. Reducing the injector umbrella angle reduces coking. At 10-14 bar IMEP, PN emissions increased with higher aromatics content in the gasoline. Additionally, olefins and naphthene contributed to PN at higher IMEPs. 10-200 nm size particles accounted for 70-95% of total particles. Gasoline with higher C9+ aromatics and T50 to FBP values showed higher 10-200 nm particles. Replacing 10% of paraffins with olefins and naphthene in gasoline changed >10 nm particles by 25%. Increasing 4% paraffins and decreasing 4.5% aromatics in gasoline reduced PN emissions by 125%. Increasing the aromatics content of gasoline by 8% increased fuel consumption by 2% and hydrocarbon emissions by 30%. Retarding the SOI timing by 20 CAD reduced PN emissions by 60%.
Muniappan, Krishnamoorthi, Dahlander, Petter, Helmantel, Ayolt, Alemahdi, Nika, Lehto, Kalle
Achieving 70% Urban Fuel Consumption Reduction: An Opposed-Piston Engine-Based Mild-Hybrid Powertrain Architecture for Light Commercial Vehicles (JLA-2/JLA-T)2026-01-04324/7/2026
This paper proposes a novel powertrain architecture for the urban Light Commercial Vehicle (LCV) segment, leveraging the compact JLA-2 opposed-piston (OP) engine paired with the reconfigurable JLA-T mild-hybrid architecture. Within SAE literature, OP engines are consistently associated with simplicity. As highlighted by Tom Ryan III (2008 SAE President) in the foreword of Opposed Piston Engines: Evolution, Use, and Future Applications, this architecture is characterized by its manufacturing simplicity” and described as a “relatively simple, robust, and cost effective” power unit solution. The present work builds on this established view. The JLA-2 engine solves traditional packaging constraints by reducing the block width by 30% for horizontal installation and is volumetrically self-sufficient, eliminating external compressors. Although the gear train required for crank synchronization introduces design challenges, explicitly accounted for in our model, the elimination of the cylinder head and valve train reduces component count. The study utilizes a comprehensive computational methodology—incorporating 0D/1D thermodynamics, 3D CFD, and FEA—to evaluate the system against a standard Ford Escape baseline. The JLA-T module mechanically blends torque using a planetary gear-set and a low-voltage 48V electric assist, capturing electrification benefits without the high costs and safety complexities of high-voltage systems. Simulation results suggest significant performance improvements, notably achieving a sub-9-second 0-100 km/h acceleration and enabling Zero Emission Vehicle (ZEV) compliance in restricted zones. Most significantly, the analysis indicates that this platform delivers up to a 70% reduction in urban fuel consumption when operated as a PHEV, driven by the system’s modularity and optimized energy recovery. This paper presents the system architecture, control logic, and performance comparisons, demonstrating a feasible technical pathway for decarbonizing urban transport fleets. (Note: “JLA” serves as the proprietary designation for the engine and electromechanical hybrid system series proposed by the authors).
Nigro, Norberto, Aguerre, Horacio, Carignano, Mauro Guido, Alonso, José Luis, Juni, Carlos A.
Optimization Method for Vehicle Shape to Drastically Reduce Aerodynamic Drag in Heavy-Duty Vehicles2026-01-06034/7/2026
Since air drag is proportional to the square of the speed, it is expected that reducing air drag will significantly improve fuel efficiency for on-highway trucks and buses, which are often driven at high speeds. Therefore, the purpose of this study is to propose an optimization method for vehicle shape to drastically reduce aerodynamic drag in heavy-duty vehicles. Using NSGA-II, one of a genetic algorithm, the overall vehicle shape was optimized with drag coefficient (CD) and lift coefficient (CL) values as objective functions and design variables as parameters in a total of 13 locations. Among the Pareto solutions, an 86% reduction in CD was achieved compared to the base shape when the CD value was the lowest. Since the CL value remains low with this shape, it can be seen that driving stability does not deteriorate. Among the design variables in optimization, it was confirmed that the corner radius of the vehicle side was particularly effective in reducing the CD value. In addition, when optimizing only the cab shape, the optimal value for the front virtual angle was 30 deg., but in relation to the corner radius, the value for this optimized shape was around 40 deg. The CD value of a 1/20 model of the optimized shape was measured in a wind tunnel test and compared with the optimization results from the aforementioned numerical analysis. As a result, the CD value reduction effect of the optimization shape was confirmed in the wind tunnel test as well, demonstrating the validity of the optimization results described above. In addition, an investigation into the yaw angle dependency of the optimized shape revealed that adding a yaw angle provided a sailing effect that reduces CD. A program for calculating fuel consumption rates for heavy-duty vehicles was used to compare fuel efficiency when using the base shape and the optimization shape. The weighted average fuel economy in urban driving (JE05) and interurban driving (highway) modes was improved by approximately 21% using the optimization shape.
Kawano, Daisuke
Real-Time Observations of the Effect of Fuel Dilution in an Engine Transient Friction Rig. Along with Real-Time Observations of Fuel Entering and Leaving Internal Combustion Engine Oil, over Both Standard Engine, ICE and Plug-In Hybrid, PHEV Dynamic Drive Cycles2026-01-03514/7/2026
Hybrid electric vehicles (HEVs) with an increasing level of electrification, are becoming a major part of the global energy transition. To achieve lower engine tailpipe exhaust emissions and improve total fuel consumption, typically the HEV control system expertly and frequently switches between the internal combustion engine and electric motor drive, with multiple stops and restarts of the internal combustion engine (ICE). As a consequential result of this switching, are typically slower or even incomplete engine warm-up times, depending on the engine speed, load pattern and run time of the vehicle drive cycle. Along with the speed and load transient control, the engine stop and start processes are also challenging to control, with respect to cold start fuel and combustion by-products entering the oil. Consequently, contamination enters the engine oil but may not completely leave. These effects are highly transient over the drive cycle. Contaminants and in particular, fuel dilution, will affect the engine oil viscosity. To demonstrate this whilst yielding insights, a precisely controlled engine test cell, running the cold start Worldwide Harmonized Light Duty Transient Cycle (WLTC) for both, a non-hybridized ICE only vehicle and a HEV in charge sustaining mode operation is described. This also has on-line viscosity sensing and oil sampling. Typical data is shared along with engine oil comparisons. For complimentary insights, the impact of the fuel dilution on engine friction was investigated using a novel, precise, fully transient engine friction test rig, which measures gasoline direct injection high pressure fuel-pump friction and engine oil viscosity accurately. The cycle is based on measured data from vehicles tested on a chassis dynamometer. On-line friction data, with oil comparisons is used to show real-time data of the effect of fuel dilution on the frictional energy required, thus CO2 over the full WLTC.
Butcher, Richard, Bradley, Nathan, Thedering, Dennis
Study on the Regulation of the Three-Phase Microenvironment in Low-Platinum MEA2026-01-04434/7/2026
With the growth of energy demand, fuel cells as efficient and clean energy devices, have attracted increasing attention. However, the high cost of membrane electrode assembly (MEA) restricts their large-scale application. Therefore, reducing the platinum usage and improving performance have become key research point. In this work, MEA was prepared and excellent performance of 1.52 W·cm-2 was achieved at a low platinum loading. The influence of different ionomer/carbon (I/C) ratio on the performance of fuel cells was systematically investigated. It was found that the performance of the MEA was the highest when the I/C ratio is 0.6. Quantifying hydrophilic and hydrophobic characteristics of catalyst layers with varying ionomer contents revealed that the proton conduction efficiency is optimal when the I/C ratio is 0.6. This balance established efficient proton conduction pathways, from the results of proton conduction impedance testing. SEM analysis demonstrated that pore structure integrity was compromised at non-optimal I/C ratios, exhibiting pore blockage or cracking. The CV test results confirmed that the electrochemical active surface area (ECSA) reaches a maximum of 40 m2gPt-1 when the I/C ratio is controlled at 0.6. And the EIS tests indicated that the lowest charge transfer impedance. Combined the physical and electrochemical characterization results with I-V curves, it was clear that the proper ratio of the low I/C region benefits the mass transfer and proton conductions. This study provides theoretical and technical support for performance enhancement and has the potential for the large-scale application of low-platinum MEA in fuel cells in the future.
Li, Xin, Cai, Xin, Lin, Rui
Assessing Energy Use, Cost, and Emissions of Small Regional Rail Vehicles: Methodology and Case Study on a German Track2026-01-04194/7/2026
This work evaluates a standardized 30-ton, 16 m railbus platform optimized for unelectrified regional service, focusing on propulsion system design and trade-offs between range, cost, and emissions. A MATLAB/Simulink drive-cycle model was developed to simulate energy consumption and component performance under realistic operating conditions. The Erfurt–Rennsteig route in Germany (130 km round trip, gradients up to 6 %) was selected as a representative case study. The model incorporates detailed sub-models for traction motors, lithium-ion batteries (LFP and LTO), fuel storage, fuel cells, and ICE gensets across multiple fuel options (diesel, gasoline, methane, ethanol, methanol, HVO, FAME, and hydrogen). Battery lifetime is estimated using a combined cycle- and calendar-aging model using the rainflow algorithm to extract charge cycles, while cost models include capital, fuel, maintenance, track fees, and staffing. Results show that battery-electric configurations achieve 1 kWh/km energy use, while hybrid systems range from 2–4 kWh/km depending on fuel and secondary power unit. Control strategies that enable deeper cycling of the traction battery reduce fuel consumption by 7–18 %, with further savings possible from larger battery or genset capacities. Well-to-wheel greenhouse gas emissions vary widely: from near-zero for renewable fuels and clean electricity mixes to over 1,000 gCO2/kWh for fossil-based options. Lifecycle cost analysis indicates that while fuel may represent up to 25 % of total costs, track and station fees dominate operational expenses. Autonomous operation could eliminate oboard staffing costs, amounting to 25–35 %.
Ahrling, Christoffer, Tuner, Martin, Gainey, Brian, Torkiharchegani, Amir, Scharmach, Marcel, Hertel, Benedikt, Alaküla, Mats
Technical Outlook on the Potential of HVO and Biodiesel Blends for Cleaner Combustion2026-01-03164/7/2026
The increasing need to decarbonize the transport sector is accelerating the adoption of renewable and low-carbon fuels such as Hydrotreated Vegetable Oil (HVO) and biodiesel as sustainable substitutes for fossil diesel. These fuels are evaluated as drop-in solutions requiring no engine recalibration, enabling immediate GHG emission reduction in existing diesel fleets. This study experimentally investigates the combustion, performance, and emission characteristics of a turbocharged common-rail two-cylinder diesel engine (Kohler LWD 442 CRS) operated with conventional fossil Diesel, pure HVO (Hydrotreated Vegetable Oil), and an HVOB20 blend (80% HVO and 20% biodiesel produced from waste cooking oil and animal fats). Tests were carried out under steady-state conditions at the DIIEM Engine Laboratory of Roma Tre University. The analysis focused on in-cylinder pressure evolution, brake power, brake specific fuel consumption (BSFC), and both regulated and unregulated emissions. Regulated species include carbon monoxide (CO), nitrogen oxides (NOₓ) and particulate number concentration (PNC > 23 nm, PMP-compliant), while unregulated emissions cover non-methane hydrocarbons (NMHC), formaldehyde (HCHO), nitrous oxide (N₂O). CO and NMHC are key indicators of incomplete combustion: CO results from partial oxidation of carbon during fuel burning, and NMHC represents the fraction of unburned hydrocarbons excluding methane. Both pollutants decreased markedly with renewable fuels, indicating a more complete oxidation process promoted by HVO’s paraffinic composition and FAME’s oxygenated nature. Experimental results show that HVO and HVOB20 slightly increase brake torque and reduce BSFC compared with fossil diesel, despite their lower density and heating value. Combustion remained stable across all operating conditions, with negligible variations in ignition delay and pressure rise rate. NOₓ emissions were comparable or marginally higher at medium engine speeds, likely due to faster ignition and elevated combustion temperatures. Unregulated species such as HCHO and N₂O decreased or remained negligible with increasing renewable content, while PNC and count mean diameter (CMD) were significantly reduced, confirming cleaner combustion and reduced soot formation. Overall, both HVO and HVOB20 demonstrated improved combustion efficiency and emission performance while ensuring full engine operability without calibration adjustments. These findings confirm the technical viability of renewable diesel fuels as immediate, drop-in solutions for reducing GHG emissions.
Zaccai, Martina, Chiavola, Ornella, Palmieri, Fulvio, Verdoliva, Francesco
Simultaneous Reduction of NOx and Fuel Consumption for Off-Road Powertrains2026-01-02964/7/2026
Simultaneously reducing criteria pollutants and fuel consumption is important for clean air and improving vehicle total cost of ownership. The goal of this effort was focused on a 90% NOx reduction and 10% fuel savings for an off-road 407 kW diesel engine. The baseline was a production Fiat Powertrain 13L engine and aftertreatment system meeting 0.4 g/kW-hr NOx. The baseline system was quantified over the NRTC, RMC, new low load cycle and five field cycles. A next generation engine was built incorporating several fuel-efficient design features, including a higher compression ratio, increased fuel-rail pressure, low-friction piston rings, and a high-efficiency variable-geometry turbocharger. Cylinder deactivation and EGR pump technologies were added to this engine as well. The combination was optimized prior to adding advanced aftertreatment systems, showing the trade-off of engine out NOx and exhaust temperature. Two next-generation catalyst technologies were employed into a LO-SCR plus main SCR system, both with and without an electric heater upstream of the LO-SCR. These catalysts were hydrothermally aged to simulate significant field use. Dual SCR dosing with newly developed controls played a critical role in achieving the proper split between the upstream LO-SCR and the downstream main SCR. Adding a next generation mixer for the downstream SCR proved essential in obtaining the final results. The optimal configuration required adding an electric heater to elevate the exhaust temperature at the LO-SCR for early cycle NOx reduction. The final results showed a 94.8% NOx reduction and 15.7% fuel savings on the composite NRTC.
McCarthy, Jr.,, James, Wine, Jonathan, Bradley, Ryan, Hasseman, Andy, Prikhodko, Vitaly, Howell, Thomas
Effect of Pre-Chamber Geometry on Methanol Cold-Start Combustion in an SI Engine2026-01-03074/7/2026
Vehicle pollutant emissions are a major challenge in the development of internal combustion engines. To meet increasingly strict regulations, the automotive sector is exploring alternative fuels and lean-burn strategies. Methanol is gaining importance as a carbon-neutral fuel due to advances in green production technologies. Methanol, despite its potential for renewable production, faces severe limitations due to its inherent poor cold-start performance with conventional ignition systems. In this context, the present study aims to investigate the influence of pre-chamber ignition on cold-start combustion by using high-speed optical diagnostics to visualize flame propagation while simultaneously measuring in-cylinder pressure and engine performance. A major result concerns the significant cyclic variability of conventional spark ignition (SI) under cold-start conditions, which exhibits significant cyclic variability. Instead, passive pre-chamber ignition significantly enhances cold-start combustion stability, lowering CoV IMEP to below 3% at λ = 1.0 and sustaining stability under 5% even in ultra-lean conditions (λ = 1.6), where conventional SI operation fails. Flame visualization quantitatively confirms that this stability stems from distributed, multi-point ignition, which accelerates initial flame propagation by 3-4x compared to SI. These findings demonstrate that pre-chamber ignition can effectively overcome the traditional "cold-start" problem for methanol, enabling stable combustion from the first cycles. This provides an invaluable dataset for CFD model validation, as it captures a highly stable combustion process largely independent of the adverse thermal boundary conditions typical of cold start, thereby simplifying the modeling challenge.
Sementa, Paolo, Altieri, Nunzio, Tornatore, Cinzia
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