Browse Topic: Energy conservation

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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
ZF bets big on hybrid transmissions26TOFHP06_066/1/2026
ZF foresees hybrid powertrain technology becoming more popular for commercial transport in the coming years, and it's working earnestly to be a major player in that realm. The supplier unveiled the TraXon 2 Hybrid transmission to the North American commercial vehicle market at last year's ACT Expo and is now evaluating the technology in real-world conditions. The next-gen automated manual transmission (AMT) is optimized to improve fuel efficiency for plug-in and full hybrid heavy-duty trucks and coaches, as well as special applications such as medium- to heavy-duty mobile cranes.
Gehm, Ryan
Q&A26TOFHP06_156/1/2026
Sustainability needs to be practical. That was a point Peter Voorhoeve, president of Volvo Trucks North America, made clear at CONEXPO 2026 in Las Vegas. “We're running a business, so we are focusing a lot on efficiency and uptime,” he said, referencing the up-to-10% improvement in fuel efficiency with the new VNL. “That helps our customers to run their operations at a better pace and a lower cost, but at the same time we have a very positive impact on the climate.” Voorhoeve also teased the launch of a new vocational truck. “We are strong in long haul. We are a leading sleeper manufacturer, very strong in regional haul, and we now have renewed focus on vocational,” he said. “In August we will launch a new truck specifically for the vocational segment that's built on the same platform as the VNL and VNR.” (See page 22 for our feature story on the new VNR.)
Gehm, Ryan
Adaptive HVAC Strategies for Enhanced Cabin Thermal Comfort, Air Quality and Energy Efficiency2026-26-07916/1/2026
Passenger comfort within vehicles and aerospace cabins relies on finely tuned management of temperature, air quality, and energy use. This paper proposes an integrated HVAC framework that combines zonal climate control, intelligent airflow distribution, and real-time sensor data to maintain thermal balance across different cabin zones. Leveraging predictive thermal load modelling and machine learning, the system anticipates environmental changes—such as sudden shifts in external temperature or passenger load—and proactively adjusts heating and cooling outputs. Simultaneously, air quality is enhanced through a multistage filtration system, active air purification technologies, and dynamic CO₂ concentration monitoring. Comfort assessment integrates PMV (Predicted Mean Vote) and PPD (Predicted Percentage Dissatisfied) indices to adapting environmental conditions. Simulations and early-stage prototypes improve energy savings and improve occupant comfort and air quality. The proposed HVAC approach is a promising avenue for enhancing passenger experience and operational efficiency in both ground and air mobility platforms.
Mudavath, Lehitha Sai, Patil, Ashish, Saha, Sudipta
AI Based Energy Sustained Series Hybrid Aircraft2026-26-07766/1/2026
This paper investigates the energy consumption characteristics of series hybrid aircraft with a focus on comparing conventional energy management approaches against an AI-powered optimization framework. The study comprehensively models the energy demands of a series hybrid aircraft across all major flight phases, including Idle & Ground Operations, Taxi, Takeoff, Climb, Cruise, Descent, Approach, Landing, and Rollout & Taxi. For each phase, detailed mathematical formulations are developed to capture power requirements and energy flow, incorporating real-time operational parameters to enhance the accuracy of the energy consumption estimations measured in kilowatt-hours (kWh). The AI-based optimization leverages advanced control strategies, specifically Model Predictive Control (MPC) and Reinforcement Learning (RL) algorithms, to dynamically manage the aircraft’s energy systems. MPC is employed to predict and optimize future energy usage by solving constrained optimization problems over a moving time horizon, ensuring efficient energy distribution while satisfying operational constraints. Concurrently, RL algorithms enable adaptive learning from operational data to improve decision-making in energy management, optimizing performance under varying flight conditions and uncertainties. Comparative analysis demonstrates that the AI-driven series hybrid aircraft achieves significant energy savings compared to conventional methods, quantified in both absolute kWh reductions and percentage improvements. These savings are particularly pronounced during overall phases such as Takeoff, Climb, and Cruise, etc. where optimal control of energy flows directly translates to improved efficiency and extended operational endurance. The findings underscore the potential of AI-integrated control systems in advancing sustainable aviation technologies by enabling smarter, energy-efficient hybrid propulsion. This paper provides a foundation for future development of intelligent energy management systems that can be deployed in next-generation hybrid series aircraft, contributing to reduced environmental impact and enhanced operational performance.
Kanchagar, Amogha
World’s First 2D, Non-Silicon Computer DevelopedTBMG-552826/1/2026
In a world first, researchers have used 2D materials, which are only an atom thick and retain their properties at that scale, unlike silicon, to develop a computer capable of simple operations. The development, published June 11 in Nature, represents a major leap toward the realization of thinner, faster and more energy-efficient electronics, the researchers said. They created a complementary metal-oxide semiconductor (CMOS), which is at the heart of nearly every modern electronic device, without relying on silicon. Instead, they used two different 2D materials to develop both types of transistors needed to control the electric current flow in CMOS computers: molybdenum disulfide for n-type transistors and tungsten diselenide for p-type transistors.
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
Multi-Physics Modeling of Aircraft Hybrid Propulsion Systems Integrating Batteries, SOFC, and Jet Engine2026-99-17035/22/2026
This paper presents a multi-physics modeling approach for a hybrid propulsion system designed for High-Altitude Long-Endurance Unmanned Aerial Vehicles (HALE UAVs), integrating solid oxide fuel cells (SOFCs), lithium-ion batteries, and a jet engine. A dynamic model was developed to analyze the coupled characteristics of pressure, temperature, and power under steady-state conditions. Simulation results demonstrate that the internally integrated system achieves efficient fuel and waste heat recovery, delivering a net power output of 300–700 kW, sufficient to meet the operational demands of HALE UAVs. Key innovations include a heat exchanger maintaining SOFC stack inlet temperatures above 850 K for optimal performance and a compressor-fan subsystem enhancing gas compression efficiency. Experimental validation confirmed the accuracy of the SOFC model, with simulated electrical characteristics aligning closely with empirical data. The proposed hybrid system addresses limitations in specific power and transient response while improving energy density, offering a viable solution for long-endurance flight missions. This study provides a foundational platform for advancing hybrid propulsion technologies in aviation.
Zhang, Lin, Zhang, Di, Zhao, Lulu, Li, Xi
Energy Saving and Personalized Thermal Comfort Control Based on Reinforcement Learning and Decision Tree2026-99-17145/22/2026
Indoor thermal comfort is closely related to people’s health and work efficiency. Control systems typically consume a large amount of energy to maintain a comfortable thermal environment. Currently, reinforcement learning is widely applied to optimize thermal comfort control systems. However, existing research mainly adopts universal thermal comfort evaluation models that aim to satisfy the majority of people, which makes it difficult to quickly and accurately reflect the specific thermal comfort needs of individuals. As a result, the hot environment is neither comfortable nor energy-efficient in practical use. Therefore, this paper proposes an energy-saving personalized thermal comfort control method based on decision trees and reinforcement learning. First, decision tree learning is used to obtain an individual thermal comfort evaluation model from a small amount of historical data. Then, this individual comfort model is combined with energy consumption to form a reward function, which is used in reinforcement learning to derive personalized thermal comfort control strategies. The experiments show that, compared to traditional methods, this approach can improve user thermal comfort by 43.8% and achieve an energy-saving effect of 30.7%.
Li, Xianying
Design of a Modular High-Power High-Repetition-Rate Semiconductor Pulsed Laser Power Supply2026-99-07165/15/2026
Pulsed lasers serve as critical components across a diverse spectrum of modern applications, ranging from precision manufacturing and medical equipment to advanced defense systems. Their performance is fundamentally governed by the pulsed power supplies that act as their energy source, where output characteristics such as stability, rise time, and efficiency directly dictate the quality and reliability of the laser output. Aligned with the prevailing industrial trend towards miniaturization and digital control in semiconductor laser pump drivers, this paper introduces a high-power, high-repetition-frequency pulsed laser power supply. The proposed design is architect ed around a phase-shifted full-bridge charging network for efficient energy transfer and a modular, switched-mode constant-current pulsed discharge network for precise output shaping. This integrated architecture provides versatile and independent control over key output parameters, including current amplitude, pulse width, and repetition frequency, offering significant flexibility for various operational requirements. The adopted switched-mode constant-current driving technique presents a substantial advantage over conventional linear constant-current methods. It drastically reduces conduction losses inherent in linear regulators, which is a decisive factor for enhancing overall system efficiency, particularly in demanding long-pulse application scenarios where thermal management is challenging. This work comprehensively details the systematic modeling, in-depth analysis, and tailored control design undertaken for both the front-end charging network and the rear-end pulse-forming modules. To validate the design methodology and practical performance, a functional prototype was developed and subjected to rigorous testing. Experimental results confirm that the prototype achieves a maximum constant-current pulsed output of 400 A, featuring a remarkably fast rise time of less than 10 μs. Furthermore, it demonstrates a wide range of operable pulse widths up to 1000 μs and sustains a maximum repetition frequency of 1000 Hz, thereby meeting the stringent demands of advanced high-power pulsed laser systems.
Huang, De, Lu, Jiawei, Yang, Zhiqing, Xv, Ziyi, Xing, Hui
Multi-Mode Control Strategy for Controllable Vehicle Suspension Systems2026-99-07125/15/2026
The suspension system with variable damping and variable stiffness actuators can realize four-quadrant mechanical output, effectively combining the energy efficiency of the semi-active suspension with the performance levels approaching those of active suspensions. However, the practical effectiveness of this system depends heavily on the ability of the control strategy to adapt to different driving conditions. In order to meet this challenge, this research has developed a multi-mode suspension collaborative control strategy to optimize energy efficiency and ride comfort in various operating scenarios. Based on the four-quadrant characteristics of the actuator, a suspension mode switching framework has been established, and the suspension work is divided into passive, semi-active, pseudo-active and active modes. In order to determine the appropriate switching boundary, first calculate the root mean square (RMS) value of the sprung mass acceleration and suspension dynamic deflection under passive conditions. With the existing human comfort sensitivity as a reference, the switching threshold of sprung mass acceleration is 0.527 m/s2, and the switching threshold of suspension dynamic deflection is 8.31×10−3m, and the corresponding conversion rules are formulated. Then, the LQR controller optimized by the genetic algorithm is used to allocate the control force adaptively according to the suspension mode to realize cooperative multi-mode operation. The simulation results on B-D composite road surfaces show that compared with traditional passive suspension, this method can reduce the sprung mass acceleration, suspension dynamic deflection and tire dynamic load by 10.59%, 16.65% and 32.9% respectively. These results confirm that the collaborative control strategy significantly improves the ride comfort, vehicle adaptability and overall performance in complex road conditions.
Li, Zhiying, Li, Jei, Zhu, Anding, Bai, Xianxu, Li, Weihan, Li, Rui
Research on Performance Prediction Methods for Centrifugal Refrigeration Compressors in Data Centers2026-99-07255/15/2026
As the “digital brain” and core foundational support for the development of intelligent transportation and connected vehicles, the performance of data centers directly determines the operational capability of intelligent transportation systems. In the process of advancing the vehicle-road-cloud collaborative architecture, the demand for high-performance computing power in data centers has experienced explosive growth. The substantial increase in computing tasks has posed severe challenges to thermal management, making efficient and reliable cooling systems an indispensable core component. Centrifugal compressor water-cooling units are the mainstream cooling solution for large-capacity scenarios, and their design optimization is crucial for improving the energy efficiency and performance of the entire cooling system. This paper proposes a one-dimensional performance prediction method for centrifugal compressors based on an empirical loss model, and realizes the iterative calculation of parameters in the entire flow path from the impeller inlet to the diffuser outlet through Python programming. A systematic impact assessment was carried out for major loss mechanisms such as surface friction, tip clearance, and wake mixing under standard operating conditions and critical operating conditions. The results show that the original model has high prediction accuracy under standard operating conditions, with isentropic efficiency error not exceeding 5%; however, under critical operating conditions, the efficiency prediction deviation reaches 7.54% due to the neglect of coupling effects between various losses. To address this issue, this paper introduces deviation correction factors related to flow rate, rotational speed, and density, which significantly improve the model’s prediction capability under extreme operating conditions: the efficiency error under critical operating conditions is reduced to 1.54%, and only 0.3% under rated operating conditions. This model provides a reliable tool for compressor performance prediction and extreme operating boundary identification, and has high application value in engineering practice.
Zhu, Minhao, Jiang, Bin, Li, Min, Zeng, Zihui, Gu, Yunhui
An Energy-Saving Coupling-Decoupling Optimization Strategy for Electric Modular Buses Considering Vehicle to Vehicle Charging2026-99-07305/15/2026
As an emerging innovative mode of public transportation, electric modular buses (EMBs) offer a novel solution to the problems of existing public transportation systems, due to the coupling-decoupling processes. In this paper, we study the energy consumption characteristics of EMBs by joining vehicle-to-vehicle (V2V) charging and reduction in aerodynamic drag due to coupling. For the pursuit of energy economy, ride comfort, and operational efficiency, we constructed an optimization scheme based on the simulated annealing (SA) algorithm to facilitate the coupling-decoupling process. The simulation results show that EMBs can meet 82.5 % of service requests compared with 61.8 % for the benchmark group, and V2V presents a significant contribution to energy efficiency, especially at low battery state of charge (SOC). Additionally, sensitivity analysis is conducted to study the impact of initial SOC, operation interval, and route type. The results provide insights for optimizing EMBs’ operations and emphasize the potential role of EMBs in supporting low-carbon and sustainable urban mobility systems.
Liao, Peng, Guo, Jiahe, Ning, Donghong, Li, Sijia, Wang, Tao
Research on Waste Heat Recovery Characteristics of Electric Drive System in Hybrid Electric Light-Duty Trucks2026-99-07225/15/2026
Aimed at the high energy consumption for battery heating of a light hybrid truck in low-temperature winter, this paper proposes an optimized battery thermal management scheme based on motor waste heat and PTC cooperation. Then it verifies its energy-saving performance based on multi-condition simulation and testing. Taking the constant-speed condition at -5°C as an example, firstly, the accuracy of the battery thermal management model is verified by comparative simulation and test. Then, based on the verified model, the battery thermal management model is simulated under typical winter conditions at 0°C and 5°C. The analysis results show that, when the battery temperature is raised from the initial state to a certain target, the energy consumption of the motor waste heat-assisted PTC heating scheme is obviously less than that of PTC heating. The energy saving rates are 33.137% at -5°C, 32.45% at 0°C, and 32.56% at 5°C, respectively. The research results have proved that the effective utilization of motor waste heat can reduce PTC energy consumption.
Meng, Shun, Zhang, Dong, Zhang, Yu, Zhang, Chunyu, Yao, Mingyao, Qiu, Liang, Qian, Yejian
Battery Aging Studies Under UAM UsageF-0082-2026-02715/5/2026
Electric vertical takeoff and landing aircraft impose significantly higher electrochemical and thermal demands on Li-ion batteries than conventional electric vehicles, yet publicly available aging datasets for this application remain limited in applicability, cell technology, and statistical robustness. This study experimentally characterizes the degradation behavior of state-of-the-art Molicel P45B 21700 cells under realistic Urban Air Mobility operating conditions involving high power demand, rapid turnaround, and repeated cycling. Eight cells are subjected to over 3,000 cycles using a fast constant-current charging protocol and a multi-segment constant-power discharge profile. The discharge profile is derived from a representative 7000-lb winged eVTOL with a 20-mile range, requiring normalized power rates of 6.4E during takeoff and landing and 1.6E during cruise. Periodic Reference Performance Tests are conducted to track capacity fade, internal resistance evolution, and energy efficiency. The cells retained over 90% of their initial capacity after 3,270 cycles, while total energy efficiency remained stable at 91%, comprising impedance and hysteresis-driven components of approximately 94% and 97%, respectively. Direct current internal resistance exhibited an initial decrease before stabilizing, yet total discharged capacity increased from 1.81 Ah to 1.84 Ah, indicating aging-driven polarization effects not captured by DCIR. A zero-order equivalent circuit model underpredicts discharged capacity by approximately 4% for fresh cells, increasing to nearly 6% at cycle 3,270 due to unmodeled time-dependent polarization effects. These results demonstrate that while modern Li-ion cells exhibit strong durability under repetitive high-power usage, the accuracy of battery performance prediction is strongly dependent on dynamic impedance effects beyond conventional DCIR-based models.
Halder, Anubhav, Gandhi, Farhan
Optimization Using Analytic Sensitivities Derived from an Advanced Coupling Framework: Applications to Rotor Blade TwistF-0082-2026-02345/5/2026
An advanced coupling framework was leveraged to assemble analytic sensitivities of lifting line theory aerodynamic loads with respect to externally-defined blade geometry parameters for optimization of main rotor performance of conventional helicopter configurations. Three vehicle weights and two flat-plate-equivalent drag configurations were examined across the flight envelope from hover to an advance ratio of 0.3. Two types of twist controls were investigated: quasi-static and fully active. Power savings were strongly correlated to the forward flight to hover power, ranging between 1.5 and 3.5% for quasi-static geometries and 2.0 and 4.5% for fully active controls when the installed power is twice of that required in hover. Blade twists optimized at higher power ratios were observed to favor high shaft tilt angles. Optimal twist deformation relative to hover-optimized designs is nonlinear across the blade span. Minimal penalties to aerodynamic vibrations were incurred through the use of either quasi-static or fully active twist controls as measured with a vibration intrusion index.
Hansen, Josh, Reveles, Nicolas
Yarn-Shaped Supercapacitors Promise Efficient Energy StorageTBMG-550875/1/2026
As interest in wearable technology has surged, research into creating energy-storage devices that can be woven into textiles has also increased. Researchers at North Carolina State University have now identified a “sweet spot” at which the length of a threadlike energy storage technology called a “yarn-shaped supercapacitor” (YSC) yields the highest and most efficient flow of energy per unit length.
A Comparison of Road Grade Preview Signals from Lidar and Maps2026-01-00264/7/2026
Road grade can impact the energy efficiency, safety, and comfort associated with automated vehicle control systems. Currently, control systems that attempt to compensate for road grade are designed with one of two assumptions. Either the grade is only known once the vehicle is driving over the road segment through proprioception, or complete knowledge of the oncoming road grade is known from a pre-made map. Both assumptions limit the performance of a control system, as not having a preview signal prevents proactive grade compensation, whereas relying only on map data potentially subjects the control system to missing or outdated information. These limits can be avoided by measuring the oncoming grade in real-time using on-board lidar sensors. In this work, we use point returns accumulated during travel to estimate the grade at each waypoint along a path. The estimated grade is defined as the difference in height between the front and rear wheelbase at a given waypoint. Kalman filtering techniques are used to mitigate the effects of odometry and motion uncertainty on the grade estimates. This estimator’s performance is compared to the measurements of a map created with a GNSS/INS system via a field experiment. When compared to the map-based system, the lidar-based estimator produces an unbiased error with a standard deviation of 0.6 degrees at an average range of 52.7 meters. By having similar precision to map-based systems, automotive lidar-based grade estimation systems are shown to be a valid approach for measuring road grade when a map is unavailable or inaccurate. In using lidar as an input signal for grade-based control system tasks, autonomous vehicles achieve higher redundancy and independence in contrast to existing methods.
Schexnaydre, Logan, Poovalappil, Aman, Robinette, Darrell, Bos, Jeremy
Simulation Verification and Design Guide for P1+P2 Hybrid Built-in Damper System2026-01-00064/7/2026
The damper system in a hybrid TMED system reduces engine-induced vibration and damps the rapid torsional torque applied by the motor through spring stiffness. Furthermore, the built-in damper system of the P1+P2 TMED-II hybrid system offers improved fuel efficiency compared to the external damper system of the existing P0+P2 TMED-I. Although the internal layout of the transmission is limited, the built-in damper system was redesigned to accommodate installation between the P1 and P1 motor. However, CAE analysis techniques for damper systems are currently not clearly defined, and research data on their strength under rotational torque loads are lacking. To reduce development costs and provide direction, CAE analysis technology development and validation are necessary. In this study, a finite element model of the damper system was developed and compared with experimental results to ensure CAE reliability. Furthermore, based on the validated model, structural and fatigue durability analyses were performed, attempting to replace test-driven R&D methods with CAE. Additionally, we used Design of Experiments (DOE) to determine the impact of structural analysis stresses on the design parameters of the embedded damper system. Through this process, we selected an optimal model that minimized stresses, and verified the improvement effects through analysis of this model.
Sun, Hyang Sun, Ganesan, Karthikeyan
Real-time Parameter Optimization for Eco-driving Control in Connected and Automated Vehicles Using Reinforcement Learning2026-01-00414/7/2026
This paper introduces a novel methodology to enhance the energy efficiency of eco-driving controllers in Connected and Automated Vehicles (CAVs) by leveraging reinforcement learning (RL) techniques for real-time parameter optimization. Traditional eco-driving strategies rely on fixed control parameters, which limit adaptability across diverse traffic and road conditions. To address this, we apply continuous action space RL algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), to dynamically tune four key parameters within a model predictive control framework that is grounded in Pontryagin’s Maximum Principle (PMP). These parameters influence acceleration, braking, cruising, and intersection-approach behaviors, making them critical for achieving optimal eco-driving performance. Our study employs Argonne National Laboratory’s RoadRunner simulator, a Simulink-based environment designed for high-fidelity CAV analysis, incorporating realistic traffic signals, road gradients, and vehicle interactions. RL agents are trained to interpret vehicle states, road attributes, and traffic light information to adjust control parameters in real time. This integration enables the controller to anticipate and respond to dynamic driving scenarios, thereby improving both energy efficiency and operational robustness. Simulation experiments across multiple driving scenarios demonstrate that the RL-enhanced eco-driving controller achieves substantial energy savings without compromising travel time. On average, our approach surpasses a baseline eco-driving controller without RL by 12% and outperforms a high-fidelity human driver model by 24.2% in terms of energy consumption reduction. These results highlight the potential of continuous action space RL to advance real-time eco-driving control in CAVs. Overall, this work provides a pathway toward more intelligent, adaptive, and sustainable vehicle control systems that can accelerate the deployment of energy-efficient mobility solutions.
Zhang, Yaozhong, Ammourah, Rami, Han, Jihun, Moawad, Ayman, Shen, Daliang, Karbowski, Dominik
Look-Ahead Information based Predictive Cruise Controller for Energy Efficient Powertrain Operation of Heavy-Duty Electric Trucks2026-01-00394/7/2026
Heavy-duty electric trucks represent a growing innovation in the transport and logistics sector, aiming to reduce emissions and reliance on fossil fuels. A major challenge with battery electric trucks is the long recharging time which takes significantly longer than refueling conventional diesel trucks. This limitation highlights the importance of optimizing powertrain operations to reduce energy losses and maximize efficiency. One effective approach is implementing optimal speed control through a predictive cruise controller. By anticipating road conditions, traffic, and elevation changes, the predictive cruise controller can adjust the truck’s speed in real time to minimize energy consumption, enhancing the range and reducing the need for frequent charging. Many problem formulations for electric trucks focus primarily on minimizing the energy required at the wheels, often overlooking the impact of powertrain efficiencies. This simplification neglects critical factors such as the efficiency of the traction electric machine (EM), gear losses, and battery dynamics, which are essential for optimizing overall energy consumption and improving vehicle performance. This research paper shows the impact of powertrain efficiencies on the optimal speed profile generation with a predictive cruise controller (PCC). The PCC optimizes electric truck operation by focusing on three primary factors in its cost function: 1) battery energy consumption, 2) total trip time, and 3) battery state of charge (SOC). To achieve the optimal speed profile, Sequential Quadratic Programming (SQP) is used. A comparison was made with a conventional cruise controller, which simplifies the vehicle model by minimizing the required energy at wheels and ignores powertrain losses in its energy calculations. The results show that the proposed PCC offers a 12.85% improvement in battery SOC and 10.83 % improvement in energy consumption as compared to baseline.
Safder, Ahmad Hussain, Villani, Manfredi, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer
Energy-Efficient Predictive Cruise Control in Heavy-Duty Electric Trucks with Optimization of the Penalty Factor as a Function of Payload2026-01-00424/7/2026
Heavy-duty Class 8 battery electric trucks not only offer the potential to significantly reduce greenhouse gas (GHG) emissions compared to conventional diesel trucks but can also provide significant savings in fuel costs. To further enhance energy and freight efficiency, Predictive Cruise Control (PCC) algorithms can be developed that generate optimal acceleration profiles for the vehicle by minimizing a cost function which combines both energy consumption and deviation from the desired velocity. A critical component of the cost function is the penalty factor, which governs the tradeoff between energy use and travel time, which are two conflicting objectives in freight logistics. Selecting an appropriate penalty factor is essential, as freight deliveries are time sensitive, but minimizing energy consumption remains a priority. Moreover, variations in payload significantly affect vehicle dynamics and energy usage, making it critical to adapt the penalty factor to different payload conditions and maintain consistent performance. This study presents a method for optimally selecting the penalty factor for various payload scenarios. A validated powertrain simulator which is calibrated using data from an actual electric truck, was used to conduct 100 simulations across a spectrum of payloads, from no load to fully loaded. The resulting discrete search space of energy and time was used to perform a brute-force (exhaustive) search to determine the optimal penalty factor for each scenario. The proposed algorithm incorporates adjustable weightings of the penalty factor for energy and time preferences. This allows flexibility for the driver or fleet operator to prioritize either objective. The results demonstrate that using a fixed penalty factor is suboptimal for heavy-duty electric trucks. In contrast, the optimal selection of the penalty factor significantly improves consistency across different payloads. A reduction of the variation in travel time to within approximately 4% across all loading conditions was observed. This work shows the importance of adaptive penalty tuning in PCC for real-world deployment in freight applications, ensuring both energy efficiency and timely deliveries under varying payload demands.
Safder, Ahmad Hussain, Villani, Manfredi, Wang, Eric, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer
Standardization of On-Vehicle Measurement of BEV Thermal System Energy Consumption and Range Impacts2026-01-01324/7/2026
This paper presents research and digital twin modeling results to support work on a methodology to properly account for the energy consumed by the thermal system of a BEV, for use within both existing Petroleum-Equivalent Fuel Economy (PEFE) calculations, and the proposed addition of hot and cold weather range values to the consumer-facing Monroney label [1]. Properly accounting for thermal system impacts would incentivize minimizing energy consumption of these systems, since 1) BEV PEFE is a direct input to an OEMs overall CAFE performance, and 2) the values on the Monroney label has some impact on consumer vehicle choice. The impetus for this work was Final Rules issued by the EPA and NHTSA in early 2024 eliminating A/C Efficiency Credits for BEVs from the 2027 MY, thus eliminating regulatory incentives to minimize energy consumption of these systems. Higher energy consumption will produce a number of negative secondary effects, including higher real-world greenhouse gas emissions, reduced vehicle range, greater strain on the nation’s electrical grid, and higher vehicle mass leading to reduced vehicle safety - should OEMs opt to merely install larger batteries to address cold and hot weather range impacts instead of implementing lower energy-consuming technology. The results from the analysis, which ideally would be confirmed with follow-up vehicle tests, show that for a baseline, PTC-heat based system, thermal system energy consumption represents 19.2% of the total energy consumed by a BEV on an annual basis, using an ambient-VMT weighted approach. It seems to be the technical equivalent of “straining at a gnat while swallowing a camel” to focus so much time and energy on identifying incremental improvements in energy consumption from the propulsion-portion of a BEV, while by comparison ignoring the system that according to this analysis can account for nearly 20% of the total on an annual basis.
Taylor, Dwayne
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