Browse Topic: Energy management

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Study on the Impact of Vehicle Fixation Methods on the Range Testing of Electric Vehicles2026-99-03268/14/2026
The range energy consumption testing of electric vehicles is usually completed in an environment where the environmental chamber and chassis dynamometer are built. The vehicle is bound to the chassis dynamometer to simulate the range performance on a real road, and the vehicle's fixing method is particularly important, as it even affects the test results. In order to investigate the impact of vehicle fixation as a key testing factor on the range test results of electric vehicles, this study conducted comparative experiments using rigid fixation test vehicles at different positions. By relying on a chassis dynamometer to simulate road resistance and following the Chinese Light Vehicle Test Code (CLTC-P), a range test is conducted on the same electric vehicle under strictly controlled environmental conditions. The experiment collected data on endurance mileage, total energy consumption, and segmented energy consumption. By comparing the differences in simulated resistance and electric energy change trends of chassis dynamometer under different binding methods of test vehicles during the test process, the comprehensive energy consumption results were different. The results showed that the rigid fixation at different positions significantly affected the sliding results of the test vehicle chassis dynamometer, leading to differences in the comprehensive endurance energy consumption results. The comprehensive endurance mileage difference reached 24 kilometers, and the comprehensive energy consumption difference reached 4Wh/km. This study reveals potential sources of system bias in laboratory testing and analyzes the impact of vehicle fixation methods on comprehensive range energy consumption results. The research conclusions can provide a theoretical basis and empirical reference for improving the current standards for energy consumption and range testing of electric vehicles, and enhancing the accuracy and reproducibility of test results.
Zhou, Meng, Jiang, Zhijie, Geng, Peilin
A Foundational CFD Validation for an Occupant-Centric HVAC Digital Twin: Paving the Way for Real-Time Control2026-99-10028/7/2026
The heating, ventilation, and air-conditioning (HVAC) systems are one of the main factors that contribute to the building’s energy usage. Achieving an effective balance between reducing energy use and maintaining acceptable thermal comfort is the key challenge in conventional HVAC systems. To overcome this challenge, integrating the occupant-centric controls coupled with digital twins into HVAC systems is another potential technique for this effective balance. For this purpose, computational fluid dynamics (CFD) offers the potential, in combination with other surrogate models for real- time applications to enhance the system's performance further. In general, the CFD is applied to investigate indoor airflow/temperature distributions. These are essential for occupant health, comfort, and energy optimisation for the HVAC design state. The objective of this study is to propose an initial step toward building an occupant-centric HVAC digital twin by validating a CFD model of an office against dense in-situ sensing data. The model has been used to resolve airflow and temperature stratification under conventional HVAC operations, using ANSYS Fluent. The boundary conditions have been derived from measured supply parameters, internal gains, and local weather conditions. The results from this study show that the air velocity and temperature at selected durations follow the same trend with low errors, compared to the sensing and measurement data. The model validation from this study establishes the basis for a weather- aware, occupant-feedback digital twin for larger floorplates and multi-zone systems. To achieve the target of the energy and comfort co-optimisation in Industry 4.0-ready buildings, the future work will focus on surrogate modelling to enable near-real-time inference for closed-loop occupant-centric controls, which will directly support dynamic set-point adjustments and multi-zone system ventilation.
Larpruenrudee, Puchanee, Hellany, Ali, Famakinwa, Tosin, Shrestha, Surendra, Attwater, Roger, Calheiros, Rodrigo Neves
Research on Parameter Matching of Electric Vehicle Powertrain System2026-99-09058/7/2026
This paper focuses on the parameter matching of key components and the improvement of overall vehicle performance for a certain front-wheel drive pure electric vehicle. Firstly, based on the target performance of the vehicle, the rated/peak power, speed, and torque of the permanent magnet synchronous drive motor, as well as the capacity, voltage, and series-parallel scheme of the LiFePO4 power battery, are systematically calculated. Meanwhile, the gear ratio of the transmission system is determined based on the dual constraints of the maximum speed and the maximum gradeability. Subsequently, the vehicle model is built using AVL Cruise, and the maximum speed, 0-100 Km/h acceleration time, maximum gradeability, and NEDC range are simulated and verified under steady-state and transient conditions. The results show that the maximum speed of the prototype vehicle reaches 139 Km/h, the 0-100 Km/h acceleration is 7.98 s, the maximum gradeability is 33.2%, the power consumption per 100 Km is 12.12 KWh, and the range is 485 Km, all of which are superior to the design indicators. The research verifies the rationality of the proposed parameter matching scheme and can provide a theoretical basis and engineering reference for the forward development of the power system of pure electric vehicles of the same level.
He, Yuefan, Zhang, Baoping, Tang, Shujian, Chen, Hanbang, Jin, Biao
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
Solid-State System Efficiently Generates Purified OxygenTBMG-555398/1/2026
Innovators at NASA Johnson Space Center, in collaboration with innovators at American Oxygen, have developed a solid-state system and process that separates oxygen from ambient air and compresses the resulting purified oxygen — with a significant reduction in power consumption compared to prior state-of-the-art. It is based upon a proven solid oxide electrochemical oxygen separation and compression technique that derives purified oxygen from ambient air and compresses it using an electrochemical pumping method.
Research on Reliability Test Methods for Electric Drive Axle Differentials2026-01-50447/31/2026
With the rapid development of the global economy, issues such as the energy crisis and environmental pollution have become increasingly severe. Owing to their environmental friendliness, structural simplicity, and high energy efficiency, electric vehicles have attracted widespread attention. Electric drive technology serves as the most promising and versatile propulsion solution for battery electric vehicles, hybrid electric vehicles, and fuel cell vehicles. As an advanced mechatronic transmission system, the electric drive axle offers high transmission efficiency, flexible packaging, and ease of digital and active chassis control integration, and has thus been increasingly adopted in modern vehicle architectures. The differential is a key component within the electric drive axle, responsible for regulating the rotational speed difference between the left and right wheels and ensuring balanced torque distribution. It plays a decisive role in vehicle stability and traction performance. This study focuses on the reliability testing methodology for differentials in electric drive axles, primarily including the extraction of reliability test conditions and the feasibility analysis of the proposed testing scheme. Specifically, based on the parameters of a given electric vehicle, a Simulink model of the motor and differential is established, and a complete four-wheel-drive vehicle model is constructed. Through simulation under typical driving conditions, operational data of the rear-drive axle differential are obtained. The collected data are then preprocessed and subjected to dimensionality reduction using Principal Component Analysis. The selected principal components are further analyzed using K-means clustering to construct representative differential reliability test conditions. The limitations of existing testing methods are analyzed based on the simulated results and relevant literature. Finally, a reinforced fatigue testing method for the differential is designed according to the extracted test conditions, and the feasibility of the corresponding test bench is evaluated.
Zheng, Hongyu, Li, Ziyu, Wang, Dajiang, Tian, Kai
Development of Electric Vehicle Oils with High Energy Efficiency and Cooling Performance by Applying Novel Friction Modifier2026-01-50647/31/2026
Improving the efficiency of electric vehicle (EV) transmissions can help to extend the driving range of EVs, and the EV oil used in these transmissions plays an important role. In this study, in order to enhance energy efficiency, we examined the effects of lowering viscosity, traction, and friction in EV oil. While friction modifiers (FMs) have been widely used as friction reduction technologies in the field of tribology for many years, we previously developed a new FM that reduces friction in drive units. We found that a combination of lowering viscosity and using the developed FM was effective for better energy efficiency. The oil formulated with the developed FM improved efficiency by approximately +0.8% to +0.9% compared to commercial EV oil. EV oil also requires cooling performance. We assumed that reducing heat generation through friction reduction would improve cooling performance and examined the effect of lowering viscosity, traction, and friction. Consequently, it was found that a combination of lowering traction and applying the developed FM is effective for reduction in parasitic heat losses. We also examined durability, which is an issue when reducing viscosity. The results suggested that the oil formulated with the developed FM had good durability for gears and bearings. Thus, we succeeded in developing an ultra-low-viscosity EV oil that has excellent energy efficiency and high cooling performance.
Nakamura, Toshitaka, Furuse, Takashi, Hasegawa, Shinji, Akahori, Shinya, Itou, Kimikazu, Sakurada, Soichiro, Akiguchi, Junnosuke
Advanced Damping Force Modeling Using Machine Learning for Next-Generation Electric Vehicle Suspensions2026-01-50537/31/2026
The rapid evolution of electric vehicles (EVs) has led to the development of innovative approaches to optimize ride comfort, handling, and the overall suspension performance. EVs introduce unique challenges due to their distinct weight distribution, powertrain dynamics, and noise characteristics, unlike their conventional internal combustion engine (ICE) counterparts. This paper outlines an advanced damping force modeling methodology using machine learning (ML) techniques to enhance the suspension design process for next-generation EVs. The analysis is based on data-driven ML algorithms, i.e., Gradient Boosting, Random Forest, and Neural Networks, to simulate the nonlinear and frequency-dependent phenomenon of dampers in different operating conditions. A comprehensive dataset, generated through simulation and experimental testing, captures the effects of road profiles, vehicle dynamics, and damping settings. Additionally, this research evaluates the impact of machine-learned damping force predictions on critical ride and handling metrics, including ride comfort, road-holding ability, and energy efficiency. The results demonstrate that the ML models can enhance the iterative design process considerably and help to create the adaptive suspension systems that will address the particular requirements of EVs. This paper contributes to advancing the state-of-the-art of the suspension modeling, incorporating the ML-based insights in the development cycle. It highlights the possibility of artificial intelligence to transform suspension design, paving the way for superior ride quality and vehicle performance in electric mobility.
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
Characterization of Dynamic Characteristics of Pneumatic Systems Based on Aerodynamic Frequency Ratio2026-99-02307/31/2026
To address the ambiguity in the relationship between design parameters and energy characteristics in pneumatic systems caused by gas compressibility and low viscosity, which leads to design redundancy, this paper proposes a dynamic characteristic characterisation method based on the pneumatic frequency ratio. This aims to establish a correlation mechanism between system energy consumption and dynamic performance. By constructing a nonlinear dynamic model of a double-acting cylinder, the dimensionless aerodynamic frequency ratio (Ω) is defined to characterise the matching relationship between the system’s natural and operating frequencies. Analytical relationships between Ω and key design parameters—such as cylinder diameter and valve sound velocity conductance—are derived, thereby establishing a normalised similarity criterion. Through combined simulation analysis and experimental validation, the regulatory patterns of Ω on the dynamic characteristics of displacement, velocity, and pressure are systematically investigated. Results indicate that under consistent Ω conditions, the normalised dynamic characteristic error across aerodynamic systems with varying parameters can be controlled within 4%. A significant linear correlation exists between the frequency ratio and the amplitude of cylinder chamber pressure differentials, with errors below 3%. The study further reveals that Ω exerts a nonlinear regulatory effect on system responsiveness and stability: increasing Ω enhances dynamic response speed but exacerbates pressure fluctuations, whereas decreasing Ω slows response but improves pressure stability. This methodology provides a theoretical foundation for energy-efficient design, parameter matching, and intelligent control of pneumatic systems, effectively addressing a gap in existing research on energy-dynamics coupling analysis.
Li, Mengru, Du, Hongwang, Wang, Jiajia, Yuan, Tingting, Xiong, Wei
Design of an Automatic Device for Snow Clearing of Solar Greenhouses2026-99-01467/31/2026
Solar greenhouses in winter or mountainous areas can be at risk of roof snow accumulation, leading to collapse, poor lighting, and sudden drops in temperature. The snow removal technologies presently employed on these greenhouses have the disadvantages of being cumbersome to adjust, being intricately structured, having a high cost, having high energy consumption, and being poorly adaptable to the curvature of the plastic. An intelligent snow removal device for removing snow on a northern solar greenhouse roof, and an automatic alarm safety system were designed to solve the problems. The device consists of a snow-clearing mechanism, a traversing mechanism, and detection-alarm modules. The mechanism for snow removal consists of a crank-slider with a curved guide rail. The snow removal rod is driven by the gear motor, which goes back and forth on the arched top. A bevel gear transmission system drives the gear motor mechanism. Due to this, the transverse mechanism moves with an interrupting jump-action on transverse rails around many different zones. The system for monitoring snow pressure has a distributed sensor that is programmed as a shield using an Arduino software system. The sensors detect the pressure of the snow in real-time. When the snow pressure hits the threshold, it activates the mechanism for coordinated functioning. This mechanism triggers snow clearing when the pressure threshold is achieved to avoid energy consumed through “premature clearing”. It also fits well on the curved surfaces of the greenhouse without any jamming. The snow removal machine’s various components and operations would accomplish full span snow removal and make it possible to overcome high labour intensity, slow manual response, energy waste, and others. The technology can enhance the safety of winter production of northern greenhouse crops and improve the disaster-resistant capacity of modern agriculture facilities. This technology has been granted a patent for invention.
Fu, Chengguo, Wei, Shanxiang, Zhang, Rongxian, Ding, Xuefeng, Gao, Yulan
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
Maximizing Autonomy, Minimizing Environmental Impact: The Cooperative Regenerative Braking2026-36-03167/30/2026
In conventional braking systems, the kinetic energy of a vehicle is predominantly converted into heat through friction, a thermodynamically inefficient process. This not only causes progressive wear of components but also leads to the release of various materials, including heavy metals and organic compounds. With increasing concern over non-exhaust emissions, the search for innovative solutions becomes imperative. In electrified vehicles (xEVs), regenerative braking emerges as a strategic technology, converting kinetic energy into electrical energy to recharge the battery and extend range. This process not only enhances the vehicle's energy efficiency but also results in reduced frequency and intensity of mechanical brake usage. Consequently, there is a direct reduction in the wear of friction braking components, which translates into a significant mitigation of particulate matter emissions associated with this wear. The optimization of these systems occurs through Cooperative Regenerative Braking (CRB), which intelligently integrates with hydraulic braking. The primary challenge lies in managing the transition between modes to recover maximum energy without compromising safety and driver comfort. This technical paper explores how CRB employs 'torque blending' via advanced ECUs and software to adjust in real-time the proportion of each braking type, aiming for maximum energy recovery in diverse driving scenarios. To verify the effectiveness of this system, practical tests were conducted on a vehicle. The results obtained from these tests were conclusive, demonstrating significant gains in energy efficiency, with an increased battery recharging capacity during decelerations, optimized by the braking system. This improvement in efficiency directly impacts the reduction in the use of the conventional friction brake system and, consequently, a sharp decrease in particulate matter emissions. In this context, the intelligent and cooperative management of regenerative braking is a strategic and fundamental component for building a more sustainable future in vehicular mobility.
Batagini, Emerson, Romão, Bruno
Co-optimization of Energy Efficiency and Drivability for Dual-Motor Electric Commercial Vehicles: A Topology-Aware Hierarchical Approach02-19-04-00267/29/2026
Dual-motor architectures provide additional operating degrees of freedom for electric commercial vehicles (ECVs), but the integration of automated manual transmissions (AMTs) introduces torque discontinuities during gear-related mode transitions. Existing energy management strategies usually focus on steady-state efficiency optimization, while the mechanical feasibility of mode transitions is often considered separately or neglected. To address this issue, this study proposes a topology-aware hierarchical control framework for dual-motor ECVs. The framework combines an offline global efficiency map with an online transition-feasibility arbitration mechanism. In the offline layer, the energy-oriented operating mode and torque split are extracted over the vehicle-speed and wheel-torque domain. In the online layer, a topology-based transition matrix is used to identify mechanically singular mode transitions, and potentially torque-interrupting commands are re-routed through feasible bridge modes. The proposed method embeds powertrain topology constraints into the real-time implementation of an offline optimal map, thereby complementing conventional global optimization methods with transition-feasibility arbitration. Simulation results under the CHTC driving cycle show that the proposed strategy improves torque continuity during mode transitions while retaining most of the energy-saving benefit of the unconstrained efficiency-oriented strategy. Compared with the rule-based strategy, the proposed method reduces SOC-equivalent energy consumption by 10.7%, and recovers 65.5% of the DP-achievable energy-saving potential. Hardware-in-the-Loop (HIL) results further demonstrate that the proposed online arbitration logic can be executed within the controller sampling period.
Song, Dafeng, Chen, Lexin, Zeng, Xiaohua, Ni, Lixin
Model-Based Optimization and Evaluation of Hybrid Powertrains for Commercial Heavy-Duty Class 8 Truck Applications02-19-04-00257/27/2026
In an ever-evolving landscape of emission regulations, charging infrastructure, customer demands, fuel/energy costs and decarbonization goals, heavy-duty on-road vehicle manufacturers continue to evaluate alternative powertrain technologies. While most heavy-duty vehicle manufacturers now have battery electric vehicles (BEVs) in their portfolio, significant challenges of charging infrastructure, range anxiety, payload capacity reduction and upfront costs have contributed to their lower adoption rates. Plug-in hybrid electric vehicles (PHEVs) have significant potential of leveraging upcoming BEV infrastructure and component supply chains to reduce operating costs while still maintaining longer range and payload capacity benefits of conventional ICE powertrains. This article applies a model-based approach to evaluate multiple Class 7–8 heavy-duty powertrain configurations. A system-level (1D) model of the conventional diesel ICE-based truck was developed in GT-Suite and validated against on-road test data. Using the diesel ICE model as a baseline, system-level models for different hybrid configurations were adapted, and their powertrain architecture was optimized at the system level. Additionally, an equivalent consumption minimization strategy (ECMS) for energy management was also optimized for each of the hybrid powertrain configurations to maximize fuel efficiency and emission benefits. All the hybrid configurations were then compared against the conventional diesel ICE Class 8 truck in terms of performance (acceleration, top speed, gradeability and startability), fuel economy (real-world cycles and certification cycles), emissions, and range for long-haul applications. Unique to this approach is the simultaneous co-optimization of powertrain component sizing and supervisory control logic by utilizing a Genetic Algorithm–based optimization approach. Results indicate that all parallel hybrid architectures (P2, P2–P3, and P4) achieve performance (acceleration, top speed, gradeability, and startability) parity or improvement compared to baseline diesel architecture. P2-based architectures demonstrated a 12–14% improvement in fuel economy on representative real-world cycles when operating in a blended charge-depleting–charge-sustaining mode of operation, and a 4–6% improvement in fuel economy when operating in charge-sustaining mode alone. By quantifying these results across diverse topologies, this work addresses a significant research gap in the holistic evaluation of Class 8 hybrids, specifically, the trade-off between multi-speed electric drives, system-level mass increases, and real-world fuel economy, that remains underexplored in current literature.
Baburaj, Adithya, Paul, Sumit, Dhanraj, Fnu, Joshi, Satyum, Franke, Michael
Research on the Hysteresis Performance of Welded Hollow Spherical Joints under Low Temperature2026-99-16417/24/2026
The extreme cold environment has a significant impact on the mechanical properties of welded hollow ball nodes, which are crucial components in large-span steel structures. In this paper, based on the comprehensive test data of drum-shaped welded hollow sphere nodes from Beijing Daxing International Airport, a sophisticated finite element model incorporating welding residual stress is established. Through detailed static loading analysis and systematic hysteresis performance studies, the research thoroughly explores the influence mechanisms of low temperature on node bearing capacity, deformation capability, and energy dissipation performance. The investigation reveals that while the bearing capacity of the nodes increases significantly in low-temperature environments, both their plastic deformation capacity and energy consumption performance are notably reduced. These findings provide valuable theoretical references for the design and optimization of large-span mesh frame structures in cold regions, enabling engineers to better account for temperature effects in structural calculations and safety assessments. The results have important implications for improving the reliability and durability of steel structures in extreme cold environments.
Luo, Yanzhi, Jin, Changming
Energy-saving and Economic Evaluation of Vacuum Insulation Panels with Glass Wool Core-polyimide Membrane Structure in Hotel Buildings under Different Climatic Conditions2026-99-16527/24/2026
This study prepares high-performance PI/VIP composite thermal insulation materials for buildings by integrating polyimide (PI) composite membranes and vacuum insulation panels (VIPs), and uses EnergyPlus to explore their impacts on building energy conservation, operating costs, and carbon emissions under different climates. Experimental results show the materials have low thermal conductivity, long service life, and excellent thermal insulation and flame-retardant properties due to their internal vacuum structure inhibiting heat transfer. Simulations in Jinan (tropical monsoon), Heilongjiang (cold temperate), and Shenzhen (subtropical humid) climates indicate that compared with traditional XPS and rock wool boards, buildings using PI/VIP composites achieve 21.3%, 34.7%, and 18.9% higher annual energy-saving efficiency respectively, with 27%-41% lower carbon emissions; the most significant effects in Heilongjiang highlight the material’s great promotion potential in severe cold areas.
Bian, Chenqian, Chen, Zhaofeng
Lightweight Optimization Design of Subframe Structure Based on Sensitivity Analysis2026-99-16397/24/2026
In order to meet the needs of national energy conservation and environmental protection policies, a typical chassis structure lightweight method based on sensitivity analysis of 13 strength conditions was proposed. Firstly, the finite element model of the subframe of a certain model is established, and the impact strength and static strength of the subframe structure are analyzed by the finite element method. Secondly, the sensitivity analysis of 13 strength conditions was carried out for the 12 main sheet thicknesses in the finite element model. Based on the results of the sensitivity analysis, the plate thickness of the components that is conducive to lightweight and has little impact on the 13 strength conditions of the subframe was selected as the design variable. The size optimization was carried out with the goal of minimizing the mass of the subframe and the constraint that the maximum Von Mises stress of the unit, where each material is located, did not exceed the yield strength of the material. The optimization results show that the performance of the subframe under 13 strength conditions meets the requirements of the index, and the weight of the subframe is reduced by 1.37 kg / 9.6%.
Jing, Min, Jia, Zhilong, Zhang, Hualei, Liu, Minjie, Gan, Xinhua, Gao, Jinyu
Evaluation of Structural Service Performance of Low-temperature Modified Asphalt Pavement based on Peavy-Duty traffic Engineering2026-99-16457/24/2026
Relying on the reconstruction project, the low-temperature modified asphalt pavement significantly reduces the construction temperature of the asphalt mixture by 40 °C compared with the traditional asphalt pavement, and improves the road performance of the material. By comparing the two mixture rolling schemes, the compaction effect of scheme 2 is better. For AC-13 mixture, the flexural tensile strength of USP-SBS composite modified asphalt mixture is 0.67 MPa higher than that of SBS modified asphalt mixture, and compared with SBS modified asphalt mixture, the final rut depth of USP-SBS composite modified asphalt mixture is 2.68 mm shallower than that of SBS modified asphalt mixture, and the total deformation rate is 43.8% lower than that of the latter. The post-construction quality evaluation shows that the stability of the low-temperature modified asphalt pavement test section under the bearing capacity and high-temperature-water coupling is better than that of the conventional road section, and the low-temperature stability is comparable to that of the two. This innovative application not only achieves energy saving and emission reduction but also provides a new solution for road construction under heavy traffic conditions.
Liu, Chuanfeng, Xu, Ke, Shi, Zheng, Hao, Jidong, Zhao, Liandi, Xianwei, Wang
On-orbit Computing Framework for LEO Satellites Based on MADDPG and Lyapunov Optimization2026-99-18117/17/2026
This paper solves the problem of resource and energy constraints on orbit computing for LEO satellites. By combining MADDPG reinforcement learning and Lyapunov optimization, the paper proposes a computing framework and implements an adaptive task offloading model for space flight using a multi-agent deep actor critic algorithm, MADDPG. The joint optimization mechanism is implemented by multi-agent dynamic task offloading. Through the transformation from the state with long-term constraints into optimization of the status of queue stability, the load scheduling under threshold energy in accordance with the characteristics of energy constraints was realized by introducing Lyapunov virtual queues into the process of policy evaluation of deep reinforcement learning. The experimental results show that the proposed framework enables a lightweight preliminary calculation, balanced energy consumption to reduce resource allocation, and realizes the stable queues through adaptability of tasks under energy balance conditions, which can provide high-efficiency computing assistance and support for space orbit tasks such as monitoring remote sensing of Earth.
Yan, Ming, Zhao, Liang, Xu, Lexi, Zhou, Xiaofei, Hawbani, Ammar, Sun, Yunhe
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
Temperature Control Technology for Solid-state Hydrogen Proton Exchange Membrane Fuel Cell2026-99-05287/10/2026
The global trend towards green and low-carbon development is that hydrogen fuel cells, as a new type of green power device, have the characteristics of zero emissions and no pollution. Its basic principle is that hydrogen fuel directly converts chemical energy into electrical energy through electrochemical reactions, achieving energy conversion between fuel cells and internal combustion engines, thereby providing sustained and stable power. The PEMFC has attracted significant attention due to advantages such as fast start-up times and long lifespans. However, excessive temperature during the reaction process of solid-state hydrogen proton fuel cells can lead to a decrease in efficiency. This article studies the temperature control device of solid-state hydrogen fuel cells and finds that active temperature control technology can achieve precise temperature regulation, but it consumes more energy; the passive temperature control scheme can reduce energy consumption, but the response speed to low-temperature start-up is limited; The application of intelligent algorithm fuzzy PID significantly improves the temperature control accuracy under dynamic loads and effectively enhances the hydrogen release rate.
Ma, Yueyue, Liu, Jingyi, Shi, Jian, Lu, Zhaona, Bao, Xueqin
Distributed Control Method and System of Formation Train Based on Multi-Objective Operation Curve Optimization2026-99-05557/10/2026
In order to improve the transportation efficiency of high-speed trains, reduce the operational energy consumption and ensure the on-time arrival of trains, the operation curve optimization is regarded as a key way to achieve the above objectives. In this paper, a distributed control method and system for grouped trains based on multi-objective running curve optimization is introduced. Firstly, the train dynamics equations are established by considering the combined forces during train operation and the train driving maneuvering strategy, combining with the line conditions, and dividing the train operating conditions; secondly, combining with the virtual grouping technology, the train units are kept in a high safety and smooth tracking operation with small intervals between the train units; and then the constraints, such as setting up safety protection distance and Then, the constraints of safety protection distance and space-time safety protection are set, and with energy-saving and comfort as the optimization goals, the multi-objective hiking optimization algorithm (MOHOA) is adopted to optimize the operation curve according to the train's working conditions; finally, the high-speed train tracking and operation system model is considered to have nonlinear and parameter-variable characteristics, and is susceptible to external factors. Finally, considering that the high-speed train tracking system model has nonlinear and time-varying characteristics and is easily affected by external disturbances, a distributed control law is designed for the optimized running curve, and a sliding mode control method is adopted for tracking operation. By optimizing the running curve of the train and realizing the precise protection strategy, the control method established based on the optimized curve can ensure the smooth running of the train while improving the efficiency of railroad transportation.
Jiang, Qiqi, Chen, Guangwu, Shi, Jianqiang, Wang, Dong, Si, Yongbo, Li, Peng, Zhang, Wentao, Yang, Yang
Development of a Hybrid-Driven Computational Model for Cylinder Pressure in Marine Low-Speed Engines2026-99-05257/10/2026
To address the high operating cost of online cylinder pressure monitoring systems for low-speed engines in ships and the limitations of existing alternatives - i.e., the lack of flexibility of the mechanical model under different operating conditions and the lack of physical interpretability of the data-driven model - this study proposes a hybrid-driven based in-cylinder pressure calculation model. Taking the 6EX340EF marine low-speed engine as the object of study, the method first constructs a mechanical model and optimizes the Wiebe function parameters using the Dung Beetle Optimizer (DBO). Subsequently, the mapping relationships between operating parameters, Wiebe parameters, initial compression stage temperature and charge mass are learned by constructing a combined neural network of Convolutional Neural Network (CNN) and Bi-directional Long and Short-Term Memory Network (Bi-LSTM). Finally, the overall calculation of in-cylinder pressure was realized by integrating a multidimensional parametric framework of engine configuration parameters, real-time running inputs and dynamic MAP maps. The results show IMEP R2 = 0.9864 and peak pressure error ≤ 2%, confirming that the model can provide technical support for long-term real-time pressure measurement and closed-loop optimization control based on in-cylinder pressure for marine low-speed engines.
Huang, Jialong
Impact of Driving Style on Electric Vehicle Energy Consumption2026-99-05077/10/2026
The way we drive has a big effect on how much energy electric cars use, so making better driving habits can help make electric cars use less energy. By utilizing a set of real EV driving data, this paper classifies and analyzes EVs from the perspective of energy consumption, and establishes an intelligent scoring system for EV driving behavior based on a decision tree model. Experimental results show that this method is able to successfully distinguish different driving behaviours and the critical driving behavior factors, such as vehicle speed, accelerator pedal change rate, etc., and braking behavior are identified. Use intelligent scoring to give driver suggestions; this way, they can improve on their driving techniques and lower their energy consumption.
Liang, Yongkai, Zhang, Hao, Liu, Yu, Yu, Hanzhengnan
Multi-Objective Optimization Research and Design of Seat Structure in Vehicle Collision2026-99-05187/10/2026
The vehicles often accompanied by a huge impact in the collision process, high-quality and high-strength car-seats can better protect the safety of passengers. However, in the call for vehicle energy saving and emission reduction, the lightweight design of car-seats is imminent. Therefore, it is necessary to achieve lightweight seat weight while ensuring vehicle safety. Based on the dynamic condition of vehicle collision, this paper takes the rear seat of a certain model as the research object, takes multiple responses of the seat skeleton system as the target, establishes a multi-objective optimization model of the seat skeleton, determines the optimization result with the greatest comprehensive satisfaction, verifies the optimization result of the seat skeleton. The correctness and feasibility of the design method are proved.
Shao, Youlin, Ni, Weiyu, Chen, Daojiong, Cheng, Zhiqing
Optimization of Preheating Energy Distribution for Low-Temperature Cold Starts in Diesel-Electric Hybrid Vehicles2026-99-05167/10/2026
To minimize energy input and preheating time, this study first analyzed the energy consumption of intake air, lubricating oil, and coolant preheating through simulations. Temperature rise data were collected under various heating parameters. Next, simulations evaluated the hybrid power system’s resistance characteristics immediately after startup and the combustion parameters during the first cycle post-ignition under different temperatures. The temperature thresholds for successful start-up were identified, defining the feasible domain for optimization. Optimization calculations aimed to minimize preheating time and energy input, constrained by maximum preheating power. Results show that intake air heating has the greatest impact on start-up success, followed by lubricating oil heating. It is recommended to increase energy allocation to intake air and lubricating oil heating. This optimized strategy reduces preheating time and energy input by approximately 26% without changing the preheating equipment.
Wei, Shengchen, Zhao, Zhenfeng
Simulation Analysis of Pure Electric Vehicle Equipped with Continuously Variable Transmission2026-99-05227/10/2026
This study looks into the performance traits of a pure electric car that has a continuously variable transmission (CVT) system by doing careful simulations. The research is mostly about checking how well it performs dynamically and how much better its energy efficiency is compared to regular designs. With the help of AVL Cruise software, a detailed drivetrain model was made to test things like how fast it can accelerate, its top speed, how well it climbs hills, and how much energy it uses when driven in standard ways. The simulation results show some big improvements: the CVT car can go from 0 to 100 km/h in 12.92 seconds, which is 14% quicker than expected; it can reach a top speed of 179 km/h, 15% higher than planned; and it can climb really steep hills at a 41.33% gradient. The energy efficiency analysis also found that it uses less power, consuming just 15.88 kWh per 100km under NEDC conditions and 13.72 kWh per 100km in UDC cycles, which are 21% and 24% less than before. These results prove that the CVT works well in keeping the motor running efficiently by changing ratios all the time. The study points out the technical benefits of CVT systems in making performance and energy saving balanced, but it also finds some practical problems like environmental factors and system integration issues. This work gives useful ideas for making new electric vehicle transmission systems and hints at good ways to improve them in the future.
Chen, Haishan, Gong, Naifa, Pan, Yulong, Cai, Zhicheng, Gao, Yujie, Shen, Xiaobing, Fu, Xianlan, Chen, Keren
Improved Thermal Management for Operating a Battery Electric City Bus under Real Driving Conditions2026-01-07587/1/2026
The goal of reducing global CO2 emissions requires actions especially for the transportation sector. To achieve the goal, electric traction motors are frequently implemented in passenger vehicles, as well as in commercial vehicles like heavy-duty trucks or buses. Particularly electric city buses have the potential to reduce the local emissions in urban areas and provide local exhaust-emission-free mobility. While their number of registrations rises, research focusses on the improvement of the overall system in order to increase energy efficiency. High importance is gained by the thermal management of the whole system. This research investigates a simulative approach to improve the thermal management and therefore the energy efficiency of an electric city bus. The different thermal components of an electric city bus like drive system, battery system and heating, ventilation and air conditioning system (HVAC system) are modelled. Their thermal behavior has been validated in previous research. Based on the validated model, this study proposes an improved thermal management that, state-dependent, combines the thermal circuits of the single components to reduce the overall energy demand. Cooling or heating is provided by the HVAC system. Furthermore, the simulation utilizes real driving cycles of a city bus in the Hamburg area. Measurement data from an entire year are examined by a cluster analysis that results in typical application profiles for urban bus traffic. These profiles are used as basis for further research. An operating strategy for the thermal management of an electric city bus under real driving conditions is developed using the simulation model. Results are presented, which show that the overall energy demand decreases due to an improved, application profile-dependent thermal management system.
Schäfer, Henrik, Hellberg, Tobias, Meywerk, Martin
Efficient Testing of Vehicle-Integrated Thermal Management Systems on a Chassis Dynamometer Using an Innovative Energy Efficient Approach2026-01-07617/1/2026
Hybrid-electric (xHEV) and fuel cell electric vehicles (FCEVs) are expected to play a crucial role in the transition towards sustainable mobility in both the individual and commercial transportation sectors. As their market share increases, there is a need for advanced research to enhance overall vehicle efficiency – particularly through optimized energy management systems. For FCEVs, an optimal energy management strategy is essential to ensure safe and durable operation. For xHEVs, thermal management serves as a central lever for improving efficiency and controlling emissions, making it an integral part of the overall powertrain development process. Considering today’s regulatory landscape, these aspects must be addressed early in development. Consequently, a holistic methodological framework is required, enabling not only technical robustness but also economic benefits, such as reducing engineering effort through effective frontloading. This methodology is composed of integrated simulation and testing approaches to develop components, systems, and operation strategies for future vehicles. Building on component- and system-level evaluations conducted at a dedicated thermal system testbed (ThermoLab), vehicle-level testing is required to calibrate and validate the laboratory results. To bridge the gap between the testbed and real driving events, an innovative approach is developed to replicate essential real-world boundary conditions, with particular focus on thermal and hydraulic conditions. The combination of a dedicated low-temperature extension chamber and an innovative dynamic coolant conditioning unit enables the energy-efficient transfer of thermal and hydraulic boundary conditions to a classic chassis dynamometer that was previously incapable of low-temperature testing. While the dedicated low temperature extension chamber transfers low temperature boundary conditions to the vehicles surrounding, the dynamic conditioning unit (Dynamic Module III) enables the accurate reproduction of relevant temperatures within the vehicle’s powertrain. This study demonstrates an innovative approach for the energy-efficient transfer of real-world low-temperature boundary conditions on a chassis dynamometer incorporating low-temperature extension and dynamic conditioning units as part of a holistic development methodology.
Lavall, Philipp, Beidl, Christian, Fiore, Luis, Papavasileiou, Ioannis, Hohenberg, Günter, Kalski, Christian
Enhancing Efficiency in Megawatt-Scale EV Charging via Adaptive Multi-Agent Control of DC/DC Converters2026-01-07697/1/2026
The rapid adoption of electric vehicles (EVs) with longer driving range demands high-power charging solutions that are efficient, scalable, and reliable. This work introduces a comprehensive simulation framework for megawatt-scale charging systems, focusing on the integration and control of multiple DC/DC converters. With the primary objective of maximizing overall system efficiency during megawatt-scale charging operations. A multi-agent adaptive control strategy is implemented to dynamically optimize operating points and allocate charging currents across converters in real time so that each participating converter operates at its optimal operating point where the maximum possible efficiency is delivered. This multi-agent adaptive control strategy allocates not only the individual optimal operating points of the multiple DC/DC converters but rather determines the optimal number of participating DC/DC converters at each time instance during the charging session. In addition to that, the strategy provides the option of delivering the optimal charging current during each time instance, so that maximized system efficiency is guaranteed during the charging process. Simulation results demonstrate that even a small efficiency improvement of 0.5% can yield substantial environmental benefits at a scale, where a 10 MW charging park avoids nearly 0.9 GWh of energy use and more than 350 t of CO₂ emissions over 10 years. By fully passing these efficiency gains to customers, charging becomes more affordable without compromising service provider margins, while the resulting climate benefits scale directly with utilization, installed capacity, electricity prices, and system lifetime. The proposed approach enables intelligent supervisory control for next-generation high-power charging stations, combining efficiency, cost-effectiveness, and sustainability. These findings support the development of modular, resource-efficient infrastructure for future EV ecosystems.
Salah, Alia, Abu Mohareb, Omar
Methodology for Developing a Learning-Based Energy Management Strategy for Hybrid Electric Vehicles via Soft Actor-Critic (SAC) Deep Reinforcement Learning Algorithm2026-01-07747/1/2026
The optimization of energy management strategies for hybrid electric vehicles is crucial for minimizing fuel and electrical energy consumption while maintaining the energetic stability of the electrical system. Conventional heuristic, rule-based approaches typically rely on classical optimization techniques and manual calibration by experienced engineers. These methods often suffer from simplified assumptions, sub-optimality, and are increasingly time-consuming given the growing complexity of modern hybrid powertrain architectures. This research proposes a novel methodology for the development of a learning-based energy management strategy (EMS) via deep reinforcement learning (DRL) to transition toward highly automated, data-based, and optimization-based development approaches. The methodology utilizes the Soft Actor-Critic (SAC) algorithm, an off-policy actor-critic method, to train an agent through experiences by interacting with an environment. The environment consists of a backward-looking, quasi-static vehicle longitudinal dynamics simulation model of an exemplary P2 plug-in hybrid electric vehicle (PHEV) combined with a database of customer-representative driving profiles. The agent learns optimal control policies through defined states and actions, optimizing a multi-criteria reward function that balances fuel efficiency against energetic stability. The framework permits the definition of both non-predictive and predictive states. Additionally, a shield function is implemented to consider hard constraints ensuring safe and stable operation. Variation calculations and sensitivity analyses regarding reward function shaping and hyperparameter tuning are conducted. The agent is trained in an offline simulation environment, and the learned policy of the trained deep neural network (DNN) is transferred into deterministic control maps, applicable to vehicle control units, ensuring interpretability, reproducibility, and compliance with certification requirements. Finally, exemplary simulation results of the DRL-EMS approach are presented and compared to benchmark equivalent consumption minimization strategy (ECMS). In conclusion, the proposed methodology enables a generally valid approach for the development of learning-based energy management strategies towards close-to-optimal strategies while reducing manual calibration effort.
Metzler, Sebastian, Winke, Florian, Jungen, Mario, Schmiedler, Stefan, Hofmann, Peter, Geringer, Bernhard
Multiphysical Modeling of a Direct-Cooled Litz Wire Winding for a PMSM Using Surrogate Models2026-01-07317/1/2026
In permanent magnet synchronous machines (PMSMs) ohmic losses occur in the stator windings. Reducing these losses contributes to a higher efficiency and increases the vehicles range. An effective approach to reduce frequency-dependent AC conduction loss is the use of litz wires. In addition, direct cooling helps to reduce DC conduction loss and winding temperatures. Therefore, this work presents a multiphysical modeling approach of a direct-cooled litz wire winding in a PMSM. It combines loss modeling of the winding with novel thermal and hydraulic calculation methods. AC conduction loss due to skin and proximity effect and DC conduction loss are modeled temperature dependent. Scaled-down conjugate heat transfer simulations are used to determine the heat transfer coefficient (HTC) between wires and coolant. Additionally, the pressure drop is derived and converted into parameters for use in a porous media model. The derived parameters are used to generate surrogate models to enable computationally efficient predictions. Using the developed methods a case study is carried out. The influence of the number of turns per slot, litz wire diameter and number of parallel litz wires is investigated. In order to isolate the influence of the winding configuration, the geometry of the PMSM and the coolant volume flow remain constant. Performance indicators are energy consumption during a duty cycle, winding mass and pressure drop. Based on this study it is shown that the stator winding design is a multiphysical compromise. The method enables a targeted design of the winding configuration with respect to various objectives and facilitates the assessment of their influencing factors on the overall machine characteristics under conflicting performance requirements.
Blaschke, Wolfgang Maximilian, Mengoni, Leonard, List, Adrian, Kulzer, André Casal
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
Towards a Smarter E-Bike: Enhancing Energy Efficiency through Adaptive Speed Control2026-01-07367/1/2026
This paper presents the development of a speed controller for e-bikes, designed as part of an energy-adaptive assistance system. The controller provides riders with appropriate support along planned routes, based on the available battery capacity. The control concept is intended for integration into existing commercial e-bikes without requiring extensive modifications to the drive system. Therefore, the rider remains part of the control loop, adjusting the support mode according to instructions from the controller. The speed controller is implemented as a rule-based state machine, enabling comprehensible design and parameterization. Since the rider must manually switch between support modes while riding, the control logic incorporates hysteresis and dead times to ensure stability, prevent oscillations, and avoid frequent mode switching. The user interface is a smartphone application that issues visual and audio instructions for switching support modes. An initial, system-independent version that relied on GPS-based speed measurement was found to be insufficiently accurate for the control task. Furthermore, it was found that detection of the pedaling state was essential for proper operation. To address these issues, a Bluetooth-based hardware adapter was developed to access relevant signals from the e-bike’s CAN bus communication system. These include pedal power, cadence and speed, which are made accessible through reverse engineering of the CAN bus. The proposed concept is evaluated in a chassis dynamometer study with 13 participants on two test profiles: a synthetic gradient profile for assessing control stability and a realistic elevation profile for dynamic evaluation. Additional measurements taken with one of the test riders at different speeds demonstrate the system’s reliability and its potential to improve the energy efficiency. The results show that, with approximately the same power brought in by the rider, only 27% more electrical energy is required to increase the average speed by 45%.
Rauch, Yannick, Simmann, Gabriel, Schneider, Manuel, Goss, Christian, Kriesten, Reiner
Assessing Geographical and Seasonal Influences on Energy Consumption and Driving Range for Electric Drayage Trucks14-15-03-00136/29/2026
The electrification of heavy-duty vehicles is a critical pathway toward improved energy efficiency in the freight sector. The current battery electric truck technology poses several challenges to commercial vehicle operations, such as limited driving range, sensitivity to climate conditions, and long recharging times. Estimating the energy consumption of heavy-duty electric trucks is crucial to assessing the feasibility of fleet electrification and its impact on the electric grid. This article focuses on developing a model-based simulation approach to predict and analyze the energy consumption of electric trucks by considering the impact of weather and geographical conditions on vehicle road load and auxiliary components power consumption, as well as the impact these factors have on driving range. Specifically, drayage trucks employed in logistics around maritime ports are used as a case study, with consideration of seasonal climate variations and geographical characteristics at different locations. The article includes results for three major container ports within the United States, providing region-specific insights into the energy requirements and driving range of the electric drayage trucks in these regions, which will inform decision-makers in integrating electric trucks into the existing drayage operations and plan investments for electric grid development.
Shiledar, Ankur, Villani, Manfredi, Lucero, Joseph N. E., Sun, Ruixiao, Sujan, Vivek A., Onori, Simona, Rizzoni, Giorgio
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
Can Large Language Models Enable Sustainable Driving Behavior? A Study on Large Language Model-Based Eco-Driving13-07-02-00066/9/2026
Large language models (LLMs) have shown remarkable capabilities for perceiving driving environments and making interpretable, logical decisions for autonomous driving. However, their potential for more comprehensive driving strategies, especially concerning energy efficiency, remains underexplored. Most existing studies primarily focus on driving safety, which may inadvertently increase energy consumption. To address this issue, this study explores the use of LLMs as high-level controllers to jointly optimize driving safety and energy efficiency. A textual prompt is designed for the LLM, incorporating few-shot examples that describe scenarios, states, and actions. The LLM processes the scenario and state prompts describing the surrounding traffic environment. It generates a high-level control signal, which is then translated into low-level vehicle motion commands in a high-fidelity traffic simulator with realistic physics, vehicle dynamics, road slopes, and network topology. Experiments in campus-scale digital twin car-following scenarios demonstrate that the proposed LLM-based framework achieves an average reduction of 4.16% in energy consumption compared to the reinforcement learning paradigm, while maintaining driving safety and providing interpretable high-level decision-making. This study highlights the potential of LLMs for longitudinal eco-driving applications under the evaluated simulation settings, extending previous LLM-based autonomous driving research that primarily focused on safety to also consider energy efficiency.
Wang, Haoyu, Li, Zhenning, Wang, Siying, Zhou, Zijing, Zhang, Xiang, Yang, Zhifeng, Ou, Shiqi (Shawn), Qi, Hao
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
Low-Voltage Electric Vehicle with a Reconfigurable Powertrain Architecture for Urban and Extra-Urban Mobility2026-37-00256/9/2026
This paper presents a novel concept for battery electric vehicles (BEVs), referred to as the low-voltage reconfigurable electric vehicle (LVREV). The LVREV is designed to bridge the gap between L- and M-class vehicles by adopting a <60 V multi-phase powertrain combined with a swappable battery system, maintaining the overall vehicle mass below one ton. This configuration enables adaptable driving range, optimized energy consumption in urban environments, and enhanced safety. The LVREV features two distinct operating modes. Frugal mode is intended for urban use and employs a smaller battery pack to maximize efficiency and reduce vehicle mass, while Dual mode is tailored for longer extra-urban trips through the use of a dual-battery configuration. The key innovations of the LVREV concept include a reconfigurable vehicle architecture capable of meeting both urban and extra-urban mobility requirements, thus providing a highly versatile transportation solution. In addition, the low-voltage powertrain improves safety and lowers system costs, facilitating manual battery replacement and compatibility with domestic charging infrastructure. By integrating these technological solutions, the LVREV expands the potential of low-voltage electric vehicles and supports the development of more flexible, efficient, and user-oriented mobility concepts. Experimental and simulation results demonstrate the feasibility of the proposed solution and provide initial validation of the reconfigurable powertrain and battery architecture.
Tramacere, Eugenio, Favelli, Stefano, Galluzzi, Renato, Tonoli, Andrea
Hybridized Diesel Powertrains - An Important Enabler for Commercial Applications on the Long Journey towards Full Electrification2026-37-00226/9/2026
The ongoing efforts for reduction of the traffic-related greenhouse gas emissions and, at the same time, the mitigation of harmful pollutant emissions from vehicle exhaust emissions are important development tasks for the entire automotive industry worldwide according to demand to provide clean and efficient products. Further tightened fleet average FE standards and ultra-low limits for exhaust emissions require the continuous development of new propulsion system types. Due to the given reluctance of the end customer and corresponding low acceptance of fully electrified vehicles, especially in the commercial vehicle segment, new and innovative topologies are needed to meet regulatory requirements and maintain the high versatility of today’s dominating solutions. For further optimization of operating conditions with enhanced fuel efficiency, the technical strategy is also determined by uplifting the attractiveness of electric driving incl. the avoidance of areas with poor ICE efficiency and as well as the coverage of emission-critical operations by electric propulsion. In this context, the support provided by an electric drive on board the vehicle in a combined drive system is becoming increasingly important. This article discusses accordingly various platform strategies for hybridized Diesel powertrains in different sectors of commercial vehicle applications and delivers a comprehensive comparative analysis of different hybrid drive concepts. Specifically, several hybrid powertrain configurations that extend an electric drive platform (hybridized BEVs), such as series and parallel-series topologies, are compared with traditional parallel hybrid powertrain topologies based on internal combustion engines (ICE). The study focuses mainly on two different cornerstone applications: a large light commercial vehicle, ranging from 3,5 to 6,5 to. and a heavy-duty long-haul truck with 40…44 to. gross vehicle weight. It evaluates the advantages in terms of CO2 emissions and Diesel fuel savings and investigates the effects on emission controls aspects. In addition to technical comparisons, the paper addresses also regulatory demands and end customer merits, assessing the integrational effort and commonalities in components with pure ICE and battery electric topologies. Furthermore, it explores the additional impact of advanced operational strategies for Hybrid Diesel powertrains, incorporating insights from innovative observations from executed hybrid technology demonstrator vehicles.
Koerfer, Thomas
Optimal Charging Planning with Energy Consumption Simulation for Battery Electric Long-Haul Trucks2026-37-00196/9/2026
Vehicle fleet decarbonization is a key objective for the coming years, with electrification representing the primary pathway to achieving the targets set by the European Union. The share of battery electric trucks in new registrations has been gradually increasing especially in light and medium size trucks. The replacement rate of diesel long-haul trucks with zero emission trucks is still low due to challenges posed by added complexity and limitations of battery charging. Depot overnight charging is not sufficient to cover the energy needs of a truck covering large distances and careful planning of the route using public charging infrastructure is crucial for an optimized route minimizing extra costs and range anxiety. The current work aims to develop a methodology to propose the optimal charging locations for a given route of a battery electric truck based on nearby stations along the route. Our study uses an open-source optimization algorithm for the fixed route vehicle charging problem coupled with a powertrain simulation model that is used to calculate the energy consumption and the electric range of the vehicles. A variety of constraints, such as initial State of Charge, lowest allowed State of Charge threshold, maximum trip duration, distance deviation, have been implemented in different scenarios from real world locations with a goal to investigate the impact of planning constraints and charging infrastructure in the optimal planning of electric truck routing. The results of our analysis indicate that the integration of an accurate energy consumption calculation model to a route and charging optimisation algorithm can be proven beneficial for minimizing the time penalty due to charging.
Perdikopoulos, Michail, Doulgeris, Stylianos, Livitsanos, Georgios, Kazakis, Thomas, Mellios, Giorgos, Ntziachristos, Leonidas
Development of a Load Responsive Mechanical Shifting Mechanism and its Benefits for Battery Electric Vehicles2026-37-00266/9/2026
Many high-end electric vehicles use an automatic two-speed transmission. The ability of the drivetrain to switch between two gear ratios improves vehicle performance and increases driving range. The aim of the presented research work is to transfer these advantages to small and lightweight battery-electric vehicles, which face significant cost and weight constraints and therefore cannot rely on highly sophisticated electric motors. Direct-drive systems are widely used in this vehicle class due to their simplicity and high baseline efficiency. However, they offer limited flexibility in adapting the operating point of the electric motor under varying load conditions. A two-speed transmission can overcome this limitation by enabling load point shifting, allowing the motor to operate closer to its optimal efficiency region during both urban and extra-urban driving. This results in improved energy consumption without adding substantial system complexity. Currently, only actuated transmissions are offered on the market, with automation adding a high degree of complexity and representing a major cost driver. Therefore, the focus during the concept development phase was on designing a fully mechanical, self-shifting system to meet the cost pressures of the targeted vehicle classes. Hence, the team at ITnA developed and patented a solution that enables automatic gear changes solely based on output torque, which reflects the motor load and the current driving situation. In the present work, both the operating principle of the technology and the advantages regarding the performance and efficiency of electric vehicles are described. Owing to its simple architecture and the absence of electronics, the transmission is inherently robust and durable, making it a significant contribution to the development of sustainable and affordable e-mobility for the mass market.
Napetschnig, Christof, Tromayer, Juergen, Stückler, David
Analysis of a Sorption Energy Storage System for Cabin Air Heating and Dehumidification in Electric Vehicles2026-37-00346/9/2026
This work investigates the integration of a Sorption Thermal Energy Storage (TES) into the Heating, Ventilation and Air Conditioning (HVAC) system of electric vehicles. The proposed device reduces the energy demand for cabin heating under winter conditions, leading to a driving range increase. The TES dehumidifies the cabin air through a desiccant bed (zeolite 4A), preventing window fogging, enabling higher air recirculation rates, and consequently reducing the required heating power. An experimentally validated numerical model was used to analyze the adsorption and regeneration processes and to identify suitable operating conditions. Regeneration was found to be effective at moderate temperatures (from 120°C), with a counter-current airflow configuration providing faster and more efficient desorption compared to parallel-flow one. A simplified model integrating TES, HVAC unit and cabin was developed and used to compare different configurations. Heating energy consumption with and without TES under different ambient conditions, passenger loads, airflow rates, and regeneration states was evaluated. Heating energy savings ranged from 19% to 71%, increasing with higher external humidity. Considering the desiccant bed volume, equal to 1.65 L, electric energy savings up to 1.7 kWh L-1 for heat pump systems and 3.3 kWh L-1 for electric heaters were estimated, corresponding to a potential driving range increase of 13.4 km L-1 and 33.5 km L-1, respectively. Preliminary TES tests on a mock-up vehicle confirmed the effective dehumidification capacity of the proposed technology.
Verlingieri, Rebecca, Calabrese, Luigi, Freni, Angelo, Marocco, Luca, Scudeler, Gabriele, De Antonellis, Stefano
Experimental Investigation on Cold Start and Warm-Up Phases for an Ethanol Fueled SI Engine2026-37-00316/9/2026
The reduction of Greenhouse Gas (GHG) emissions represents a key challenge for the transportation sector, requiring the adoption of renewable fuels capable of ensuring both environmental benefits and compatibility with existing internal combustion engine technologies. In this context, bioethanol emerges as a viable solution for Spark Ignition (SI) engines, offering a low life-cycle CO₂ footprint and favorable combustion characteristics. Nevertheless, despite its well-known advantages under steady-state operation, the widespread use of high-ethanol-content fuels is still limited by critical issues during engine cold start. The aim of this work is to experimentally investigate the influence of ethanol content on cold-start behavior and idle warm-up transient operation of a Naturally Aspirated (NA), Port Fuel Injected (PFI) SI engine. The experimental campaign was carried out under idle conditions using four fuels with increasing ethanol content, namely commercial gasoline (E5), E30, E60, and neat ethanol (E100). Cold-start and full warm-up tests were performed starting from ambient temperature, while additional dedicated experiments were conducted on E100 to evaluate startability under different initial engine wall temperatures. The results show that increasing ethanol content has a limited impact on the overall warm-up duration, while slightly reducing engine wall and exhaust gas temperatures. Conversely, E100 exhibits pronounced startability issues at low initial wall temperatures, requiring multiple cranking attempts to achieve stable idle operation. A minimum wall temperature threshold in the range of 25-30 °C was identified as necessary to ensure reliable cold start with E100. The outcomes of this study provide experimental evidence of the key role played by engine thermal conditions in enabling stable operation of ethanol fueled SI engines during cold start.
Falbo, Luigi, Falbo, Biagio, Perrone, Diego, Castiglione, Teresa
Alcohol-Based Energy Carriers in Transportation: Status, Standards, and Outlook2026-37-00426/9/2026
The global transport sector accounts for approximately 30 % of total final energy consumption and 15.9 % of worldwide greenhouse gas (GHG) emissions, with road transport alone accounting for the largest share at 11.8 %. Decarbonizing this sector requires energy sources that combine scalable generation from renewable sources with compatibility with various modes of transportation and existing infrastructure. Methanol and ethanol emerge as promising alternative energy carriers that can leverage existing logistics infrastructure while reducing dependence on fossil fuels. Global methanol production reached 112 million metric tons, and global ethanol production totaled approximately 93.5 million metric tons in 2024, compared to more than 2 billion metric tons of gasoline and diesel produced annually. The review assesses production pathways and cost trajectories for both alcohols, evaluates fuel requirements across multiple transport modes, including passenger vehicles, light- and heavy-duty vehicles, maritime shipping, aviation, and rail, and provides regulatory frameworks governing fuel standards in six major markets, the European Union, the USA, Brazil, China, Japan, and India. From a technical perspective, the internal combustion engine is examined in greater detail as the energy conversion system, synthesizing current combustion research on engine performance, emissions characteristics, and cold-start behavior. Current standards predominantly accommodate ethanol blending for spark-ignition (SI) engines in passenger vehicle applications, with permitted concentration limits ranging from 3 % in Japan to nearly pure ethanol in Brazil. Methanol applications remain more limited in road applications. In the maritime sector, recent ISO 8217:2024 specifications and International Maritime Organization (IMO) interim guidelines have established frameworks for the use of methanol and ethanol as marine fuels. Aviation remains the most restrictive sector, with alcohol fuels explicitly prohibited in certified aviation fuels due to material compatibility and safety concerns. To unlock the decarbonization potential of methanol and ethanol in the transport sector, coordinated policy support and continued technological innovation will be essential. As production scales and regulatory frameworks mature, both alcohol fuels may play an increasingly central role in the transition toward sustainable mobility.
Fitz, Patrick, Fellner, Felix, Rößlhuemer, Raphael, Härtl, Martin, Jaensch, Malte
Passenger Car Powertrain for Voltages above 800V2026-37-00206/9/2026
The EU funded innovation project High-Voltage fast-charging Efficient electric vehicle Powertrains (HiVEP) develops innovative technologies for mass-market electric vehicles (EVs) by advancing architectures operating above 800 V. These architectures integrate silicon carbide (SiC)-based power electronics, rare-earth-free electric machines with active winding reconfiguration, high C-rate batteries, and optimized thermal management systems. HiVEP aims to enable fast charging in less than ten minutes, reduce energy consumption by at least 25%, extend the driving range by 20%, and cut system costs by up to 20% in volume production. This article deals in detail with the project objectives, the methodological approach, and the expected key innovations, as well as the technical, environmental, and social impacts. The discussion situates HiVEP within the European research and innovation landscape, emphasizing its role in accelerating adoption of sustainable mobility solutions.
Schernus, Christof, Nada, Shady, Neuhaus, Christoph, Ewald, Jens, Swierc, Daniel, Kallur-Krishnamoorthy, Rajesh, Vasiliadis, Harilaos
Electrical and Copper Compatibility Studies of Transmission Lubricants for Advanced Electric Vehicles2026-01-50366/3/2026
Improved energy efficiency and lower CO2 emissions are the two major drivers for the emergence of E-mobility. Growth of electric vehicles (EVs) has sustained ever since their introduction till 2020 and has substantially increased thereafter. EVs require specialized lubricants, which are different from conventional lubricants mainly due to the addition of new hardware technology including e-motor, inverter, battery, and new materials (copper windings, elastomers, plastic, and other materials). Lubricant when used in an advanced powertrain electric vehicle specifically in E-powertrains may encounter the e-motor and must deliver unique performance attributes such as optimal electrical properties, thermal management, and material compatibility apart from the traditional features including extreme pressure, friction performance, oxidation, and wear control. In the current study, we have investigated conventional GL5, manual transmission fluid (MTF), automatic transmission fluid (ATF), and dedicated e-fluids to understand additive and viscosity effects on aforesaid performance traits. Our study emphasized that additive chemistry plays a significant role on key properties such as electrical properties, corrosion resistance, oxidation resistance, and tribological performance.
Katta, Lakshmi, Seth, Sarita, Singh, Sandeep, Bhardwaj, Anil, Arora, Ajay Kumar
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
Hitting the road IN THE ALL-NEW VNR26TOFHP06_016/1/2026
Volvo Trucks' revised VNR brings updated safety tech, improved fuel economy and driver comfort features to the regional haul segment. Volvo Trucks has continued its rollout of new models for every sector of the commercial truck market. The redesigned VNR is the latest model to see the spotlight. The new VNR naturally carries all of Volvo's latest safety tech, but also prioritizes maneuverability, fuel efficiency and configurability for a wide variety of fleet uses. “The VNR is an incredibly versatile truck,” said Maddie Sullivan, product marketing manager. “There are so many different configurations to meet our customer's needs. We offer four different cab sizes, three different axle configurations and two different chassis configurations.”
Wolfe, Matt
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