Browse Topic: Energy consumption

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Research on the Hysteresis Performance of Welded Hollow Spherical Joints under Low Temperature2026-99-1641To be published on 07/24/2026
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Predictive Model and Analysis of the Resultant Force in the Dry Machining of Mg-4Zn/Si 3 N 4 Nanocomposites2026-26-07626/1/2026
The development of lightweight materials for use in aerospace and automotive applications is extremely significant. Magnesium (Mg)-based alloys and composites are good candidate materials from the perspective of low density, good specific strength, and abundance. The Mg-4Zn alloy is one such alloy, which is a lightweight, biocompatible, and eco-friendly Mg-based alloy. In spite of these advantages, there is a strong need and scope to improve its wear resistance and mechanical properties. Mg-4Zn nanocomposites with Si3N4 reinforcements (a biocompatible bioceramic) are hypothesized to possess superior properties. Microstructural analysis of the vacuum stir-cast nanocomposites confirms grain refinement and a consequent increase in microhardness with an increase in Si3N4 reinforcement wt.%. The addition of Si3N4 reinforcement to improve the properties of the Mg-4Zn alloy could introduce challenges in machining. To make products from the nanocomposites, machining them with minimal subsurface defects with minimal energy consumption under sustainable conditions is necessary. The resultant machining force (Fr) is a good indicator of subsurface quality and energy consumption in machining. To investigate the effect of reinforcement wt.% and machining parameters on the resultant machining force, dry turning experiments on the vacuum stir-cast Mg-4Zn/Si3N4 nanocomposites were carried out based on the response surface methodology-based Box-Behnken design. It is observed that the regression model for Fr is influenced by the reinforcement wt.%, cutting speed, feed rate, and depth of cut and also their squares and their mutual interactions. Increase in microhardness, variation in porosity, thermal softening, and strain hardening contribute to the variation in Fr. Minimal Fr and hence better subsurface quality and lower energy consumption are obtained at mid values of Si3N4 reinforcement wt.% and cutting speed and low values of feed rate and depth of cut. The developed model is an excellent fit, with R2 and adjusted R2 values of 0.9907 and 0.9799, respectively.
N, Anand, Shaju, Tony M, G, Nagamalleswara Rao, D, Bijulal, K, Jayaprakash Reddy, K, Vijayan, Chaman, Joji J
Analysis on the Technical Path and Engineering Application of Ship Carbon Capture2026-99-17305/22/2026
This study focuses on the engineering application and performance evaluation of shipboard carbon capture systems. A process combining amine absorption and membrane separation was constructed, and the combined process was applied to a typical 7000 TEU container ship. After sea trials, the average carbon dioxide capture efficiency achieved by the system exceeded 87%, and the power consumption was maintained within an acceptable range. The integrated system greatly improved the EEXI and CII index levels and verified its economic feasibility in the medium and high carbon price scenario. The payback period of the investment costs was reduced to five years. After port coordination tests, the operability of ship-shore carbon dioxide transfer was verified, which promoted future scalability. The engineering layout, energy recovery design, and operation data worked together to provide a practical solution for maritime decarbonization. This study provides a valuable technical reference for the implementation of the International Maritime Organization (IMO) carbon reduction strategy, and also lays a solid foundation for subsequent legislation and system standardization.
Yang, Yongjian
Overcoming the Carbon Neutrality Bottleneck: Multidimensional Innovations and Future Prospects of Carbon Capture, Utilization, and Storage (CCUS) Technology2026-99-17365/22/2026
As the global pursuit of carbon neutrality accelerates, carbon capture, utilization, and storage (CCUS) technology is emerging as a critical strategic pillar for achieving significant emission reductions and facilitating the transition to green development. This review systematically summarizes the principal technological pathways and recent advances in carbon capture, resource utilization, and storage within CCUS systems, with particular attention to innovative directions including advanced adsorption and separation materials, synergistic catalytic conversion, biological carbon sequestration, and mineralization-based storage. By examining representative engineering practices and industrialization cases both domestically and internationally, this paper summarizes the major challenges currently facing CCUS, including material costs, energy consumption, environmental risks, and large-scale deployment. The positive impacts of interdisciplinary integration, process system optimization, and policy coordination on the commercialization of CCUS are also discussed. The review indicates that overcoming bottlenecks in core materials and process technologies, improving regulatory frameworks and market mechanisms, and establishing clustered industrial ecosystems are essential for CCUS to spearhead the forthcoming low-carbon energy and green industrial revolutions. This paper envisions future development trends for CCUS technology, highlights its multidimensional strategic value for global carbon governance, energy security, and the circular economy, and offers theoretical references and cutting-edge insights for scientific research, policy formulation, and industrial decision-making in related fields.
Wang, Yingfei
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
Research on Carbon Emission Calculation Algorithm for Trucks on Expressways in Xinjiang2026-99-17185/22/2026
In the field of measuring carbon emissions from road traffic, the carbon emission factor method has remarkable advantages in terms of standardization, operational simplicity, and adaptability. Backed by the IPCC international standard framework, this method offers convenient access to a dynamic factor database and incorporates an adaptive adjustment mechanism for real-world scenarios, such as technological advancements and regional disparities. Against this backdrop, this study employs the carbon emission factor method to establish refined measurement models based on load capacity and fuel consumption, respectively. These models are then applied to quantify carbon emissions from trucks on specific sections of the G30 highway in Xinjiang. The load-based model calculates emissions by integrating truck axle weight and driving distance, while the fuel-based model analyzes fuel consumption data in conjunction with driving mileage. A comparison of the two models in terms of measurement differences is also carried out in the research. Furthermore, it provides a granular breakdown of energy consumption data for fully loaded trucks exceeding 31 tons, as specified by national standards. This introduces a novel approach to precise carbon emission measurement in heavy-duty transportation in northwestern China. It also provides a method for establishing an emission mitigation policy that is region-specific on a scientific basis.
Li, Maowen, Han, Dongchen, Gao, Yansen, Bai, Haotian, Dai, Xiaomin
DDPG-Based Energy-Optimal Torque Split Strategy for Dual-Motor Electric Vehicles10-10-04-00335/22/2026
This study presents a torque distribution strategy for dual-motor electric vehicles utilizing a Deep Deterministic Policy Gradient reinforcement learning algorithm designed to optimize energy consumption. By using a simplified architecture and replicable reward functions, the proposed agents rely exclusively on standard CAN bus signals, commanded longitudinal force, and the motors’ velocities, eliminating the need for specialized sensors or complex plant models. Two reinforcement agents are trained using two different reward functions: power-based and State of Charge-based. These agents are validated through high-fidelity CarSim–Simulink co-simulations across soft, medium, and severe acceleration scenarios, in which they demonstrate superior performance to traditional adaptive methods. In the most demanding scenario, a typical adaptive strategy achieves an additional 7.8% of power consumption and 85% of optimal energy recovery, while the proposed reinforcement learning strategies reach 0.6% more consumption and 95% energy recovery during braking compared to the theoretical optimum. These results highlight a practical, reliable solution for maximizing efficiency in dual-motor powertrains without significant computational burden on existing electronic control units.
Meléndez-Useros, Miguel, Viadero-Monasterio, Fernando, López-Boada, María Jesús, López-Boada, Beatriz
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
Design and Performance Research of an Integrated Thermal Management System for Hybrid Electric Light Trucks2026-99-07355/15/2026
Addressing issues in traditional hybrid light trucks—such as low overall energy utilization efficiency and performance degradation of key components under extreme operating conditions—this study presents a novel, high-efficiency, integrated vehicle thermal management system. By coupling various subsystems, the system achieves efficient and rational utilization of the vehicle’s overall energy consumption. Comparative simulation analyses were conducted under different ambient temperatures and initial state-of-charge (SOC) levels to verify the reliability of the designed integrated thermal management system. Results show the system can meet the temperature requirements of all components under both high and low-temperature conditions. Meanwhile, findings indicate that ambient temperature and power modes have a substantial impact on the temperature of each component, and there is potential for utilizing motor waste heat. These outcomes provide a reference for the subsequent optimization of control strategies for thermal management systems in hybrid light trucks.
Meng, Shun, Zhang, Chunyu, Zhang, Yu, Zhang, Dong, Yao, Mingyao, Qiu, Liang, Wu, Yadong, Qian, Yejian
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
Quadcopter Mission Energy Prediction Using Machine LearningF-0082-2026-00565/5/2026
Battery energy awareness is an important aspect of tasking Unmanned Aerial Systems (UAS) safely and efficiently. By considering energy expenditure during mission planning, flight plans are assigned to the UAS only if there is sufficient energy onboard to complete the mission. In this work, several methods are developed for predicting the energy consumed during a flight, and their accuracy is assessed. Three simulation-based models derived from momentum-theory, blade-element theory, and computational fluid dynamics (CFD) are considered in addition to two data-driven models derived from flight test data (linear regression and Kriging), and four multi-fidelity models (Optimized Kirchstein, Hover-corrected, Additive Bridge, and Predictor-Corrector). Each model is used to predict the energy consumption of a representative mission and their predictions are compared to the measured energy consumption. From this analysis, it is found that a linear regression model trained on flight test data is able to deliver predictions within 3% of the measured value and outperforms other simulation-based and multi-fidelity models despite a simple architecture. The high level of accuracy and low computational requirements make this linear regression model desirable for energy-aware mission planning and energy-leash computations.
Healy, Richard, Nikolov, Daniel
A Comparative Assessment of Fuel Cell and Battery Powertrains for Formula Student Electric Applications2026-01-50324/27/2026
The organizers of the most prominent Formula Student competitions have recently initiated a preliminary feasibility study on the application of hydrogen-based propulsion technologies in future single-seater race vehicles. These include electric powertrains with electrochemically converted hydrogen in fuel cell–powered vehicles, competing within the electric championship league. Based on the initial set of regulations, this study presents a model-based comparison between battery-powered (BEVs) and fuel cell–powered electric vehicles (FCVs) for Formula Student. The analysis is conducted using energy, power, and efficiency metrics from four candidate models of propulsion systems, implemented in an open and publicly available MATLAB script: two BEVs with varying battery capacities, and two FCVs employing different hybridization strategies. The aim of this study is to pinpoint and quantify the advantages and disadvantages of each technology for the Formula Student use case, and to identify the optimal solution combining the different requirements of maximum acceleration and endurance race.
Martoccia, Lorenzo, Breda, Sebastiano, Fontanesi, Stefano, d’Adamo, Alessandro
Battery Electric Vehicle Energy Consumption and Range Test ProcedureJ1634_202604 (Current)4/13/2026
This SAE Recommended Practice establishes uniform procedures for testing BEVs that are capable of being operated on public and private roads. The procedure applies only to vehicles using batteries as their sole source of power. It is the intent of this document to provide standard tests that will allow for the determination of energy consumption and range for light-duty vehicles (LDVs) based on the federal test procedure (FTP) using the urban dynamometer driving cycle (UDDS) and the highway fuel economy driving schedule (HFEDS) and provide a flexible testing methodology that is capable of accommodating additional test cycles as needed. Additionally, this SAE Recommended Practice provides five-cycle testing guidelines for vehicles performing supplementary testing on the US06, SC03, and cold FTP procedures. Realistic alternatives should be allowed for new technology. Evaluations are based on the total vehicle system’s performance and not on subsystems apart from the vehicle.
Light Duty Vehicle Performance and Economy Measure Committee
Novel Air System for 300 kW Heavy Duty Fuel Cell2026-01-04344/7/2026
Hydrogen fuel cell powered vehicles for heavy duty trucking are a promising path for reducing future vehicle emissions due to their reduced mass for storage and faster refueling compared to battery electric trucks. These benefits come at the cost of increased system complexity stemming from the fact that fuel cells generate electricity through a chemical reaction which must be tightly controlled. The air handling system delivers the proper amount of air (oxygen) to react with fuel (hydrogen) in the fuel cell to produce power. Air delivery requires significant power and is the largest parasitic loss for a 300 kW fuel cell. Today’s systems use an electric motor driving an air compressor to supply pressurized air to the fuel cell stack. By operating at elevated pressure levels, fuel cells can achieve higher power density, which is important for vehicle powertrains. In addition to parasitic power loss, hydrogen fuel cell systems often have reliability issues associated with the air handling system. Reliability is of significant concern for heavy duty applications (especially long-haul applications). This project aims to improve both the electrical power consumption and reliability of hydrogen fuel cell air handling systems to meet the needs of heavy duty on-highway vehicle applications. The air handling is provided by a twin vortices series (TVS) compressor in addition to adding a TVS expander to recover waste heat energy back into the compressor. The final configuration includes a 600 V, 39 kW motor connected with a single shaft to the compressor and expander. This configuration reduced the total electrical power consumption from 48.6 kW to 37 kW at full load, 13.1 kW to 9 kW at half load and 0.44 kW to 0.22 kW at idle. The response time requirement was to be less than 2 sec while the final demonstration yielded 0.62 sec. Additional design changes, including water dosing into the compressor, addition of a recuperator, and elimination of the intercooler, were made to increase the energy efficiency of the air system.
Reich, Evan, Swartzlander, Matthew, Wine, Jonathan, McCarthy, Jr., James, Miller, Eric, Akhtar, Saad, Reddy, Sharan, Lawy, TJ
An Integrated Methodology for Sustainable Mobility Infrastructure Planning: The Atlanta Aerotropolis Case Study2026-01-00974/7/2026
By the early 2020s, more than 4.5 billion people have been living in urban areas worldwide, compared to just 1 billion in 1960. Rising growth in urban populations present challenges to infrastructure and transportation systems. Higher traffic levels and reliance on conventional vehicles have contributed to heightened greenhouse gas (GHG) emissions, rising global temperatures, and irreversible environmental degradation. In response, emerging transportation solutions—including intelligent ridesharing, autonomous vehicles, zero-tailpipe-emission transport, and urban air mobility—offer opportunities for safer and more sustainable transportation ecosystems. However, their widespread adoption depends not only on technological performance and efficiency, but also on integration with current infrastructure, safety, resilience to unexpected disruptions, and economic viability. A dynamic agent-based System-of-Systems (SoS) transportation model is developed to simulate vehicle traffic and human movement for assessing mobility solutions against different demand scenarios and possible disruptions within a well-defined metropolitan area. The analysis adopts the concept of an airport city—a cluster of residential, commercial, and industrial spaces surrounding major airports—as a representative urban context. Using the Atlanta Aerotropolis as a case study, this work introduces an interactive, parametric decision-support methodology for evaluating the impact and benefits of future mobility options, as part of transportation master planning. Given the multi-objective and multi-stakeholder nature of transportation planning (e.g. local government, urban planners, engineers, and technology providers), the proposed approach leverages simulation-enabled digital twins of mobility solution alternatives to analyze traffic performance across multiple criteria, including energy consumption, emissions, affordability, accessibility, and connectivity within the broader urban infrastructure. The study reveals cost-benefit trade-offs among mobility solutions in the context of disruptive scenarios, such as the 2026 FIFA World Cup hosted by Atlanta, GA. The results highlight the importance of deploying a mix of mobility options over the city’s transportation network to maximize sustainability while maintaining resilient operations.
Rana, Vishva, Balchanos, Michael, Mavris, Dimitri, Valenzuela Del Rio, Jose
Wind-Tunnel Investigation of Snow/Ice Drag on a 30%-Scale DrivAer Model2026-01-06044/7/2026
Wind-tunnel tests were conducted using a 30%-scale DrivAer model, in estateback and notchback rear-geometry configurations, to investigate aerodynamic performance changes associated with snow and ice buildup on passenger vehicles. Around 20 snow/ice accumulation patterns were tested, at a Reynolds number of 2.8 × 106 based on model wheelbase, for each of the notchback and estateback variants. 5 additional patterns were tested on the estateback with roof-rack support bars. Snow accumulation was modelled with foam, while ice accumulation was simulated with aluminum tape hand-formed to the desired shape. A simulated full-scale snow thickness of 58 mm on the hood, roof and trunk increased the wind-averaged drag coefficient by 16% for both model variants. With 90 mm of snow, the drag of the estateback variant increased by 19%. Drag changes increased with, but were not proportional to, snow thickness. Chamfered front and rear edges, representing windblown shapes, reduced the drag penalty compared to square-edged snow models. The largest drag increases, of 18% and 20%, respectively, for the notchback and estateback configurations, were due to simulated patchy snow and ice on multiple surfaces. Localized ice/snow patches sometimes caused stronger increases in drag than a similar or larger volume of precipitation elsewhere. Critical surfaces include the A and aft-most (C/D) pillars, the lower-front corners, the leading-edge of the hood and the leading- and trailing-edges of the roof. Simulated snow and ice at more upstream positions often caused higher increases in drag than accumulations further downstream. Drag and base pressure were more likely to be correlated for changes closer to the rear of the model. Some snow/ice patterns were found to increase side force and rolling moment in crosswinds, or to increase lift and change the pitching moment, potentially affecting vehicle stability and traction. The results are intended to support additional studies that will examine the impacts of snow/ice accumulation on fuel/energy use and safety.
de Souza, Fenella, McAuliffe, Brian
Generative Agents for High-Fidelity Simulation of Community-Scale Mobility and Energy Behavior2026-01-04654/7/2026
The transition to sustainable mobility and energy systems represents a complex socio-technical challenge, with the success of new technologies and policies critically dependent on their interaction with human behavior. Traditional models frequently struggle to capture the nuanced, heterogeneous, and adaptive characteristics of individual decision-making in mobility choices and energy usage, thereby introducing significant uncertainties into system design and policy evaluation. This paper presents a novel paradigm to bridge this gap: the Hierarchical Generative Agent-based Simulation Framework (HGA-Sim). The framework's core innovations are twofold: 1) It utilizes Large Language Models to generate agents endowed with intrinsic personality traits autonomously, enabling a realistic simulation of diverse, human-like responses to environmental stimuli and personal experiences. 2) It employs a hierarchical "Archetype -Individual" architecture, rendering large-scale community simulations computationally feasible. Validated through a case study of a 495-agent community, the HGA-Sim framework accurately reproduces aggregate mobility and energy consumption patterns, including critical peak loads and temporal dynamics. It demonstrates remarkable fidelity to real-world data with a Root Mean Square Error of 0.1983. By establishing a human-in-the-loop virtual testing environment, this research provides a foundational tool for evaluating the real-world viability of sustainable mobility designs, assessing the potential socioeconomic impacts of new transportation and energy policies, and mitigating risks associated with investments in future sustainable infrastructure.
Chen, Yongjian, Yang, Zhifeng, Ou, Shiqi(Shawn)
Lateral Stability and Torque Optimization Distribution for Distributed Drive Vehicles2026-01-06354/7/2026
To enhance the lateral stability and torque optimization of four-wheel hub motor distributed-drive vehicles under complex road conditions, a hierarchical control strategy for yaw stability is proposed. The upper-layer controller designs a yaw moment controller based on sliding mode control theory, establishing both a two-degree-of-freedom vehicle model and a seven-degree-of-freedom vehicle model to track the vehicle's desired yaw rate, desired sideslip angle, actual yaw rate, and actual sideslip angle. This enables the derivation of the corresponding additional yaw moment. The vehicle's operational state is analyzed using the phase plane method based on the sideslip angle and yaw rate, and the total additional yaw moment is computed through weighted calculations according to the identified state. Simultaneously, an unscented Kalman filter observer is implemented to improve the tracking accuracy of the actual yaw rate and actual sideslip angle in the seven-degree-of-freedom model. The lower-layer controller treats torque distribution as the design variable and allocates torque to each hub motor with the objective of minimizing the tire load rate. Finally, a co-simulation model is developed using CarSim and Simulink, and simulation analyses under double lane change and steering step input conditions are conducted to evaluate the vehicle's lateral stability.
Shi, Cheng'ao, Liu, Bingsen, Zou, Xiaojun, Wang, Tao, Zhang, Ming
Development of a Virtual Test Rig to Evaluate Thermal Management of a Battery-Electric Vehicle under Battery Heating-Cooling Modes2026-01-04164/7/2026
A battery-electric vehicle (BEV) has multiple powertrain components (battery, inverter, e-motor), a thermal management system (compressor, heat exchanger, cabin heating, ventilation, and air-conditioning), and a vehicle body, among others. Vehicle testing is time-consuming, and changing powertrain components during the testing and design process is costly. Simulation models (aka virtual or simulation test rig) have been widely used for efficient vehicle design. This work presents a systematic approach to developing a virtual test rig to evaluate the thermal performance of battery-electric vehicles. A Tesla Model Y is tested in a chassis dynamometer, and the measured vehicle performance data are used as boundary conditions for the complete vehicle model. The detailed lithium-ion battery (LIB) pack model, including its cooling system, was developed and calibrated using various transient driving cycle data. The HVAC model uses a simplified controller to maintain the cabin temperature at 25 °C in both battery heating and cooling modes. The predicted thermal and electrical performance of the BEV is well validated by test data. Then, the complete vehicle model is used to compare the thermal performances of the BEV under cabin heating and cooling modes for various transient driving cycles. The simulated results show that using an external cabin air circulation model can reduce the battery energy consumption and dissipated heat by 9.9% and 2.4%, respectively. This calibrated virtual test rig can be used to evaluate a new HVAC system.
Sok, Ratnak, Kusaka, Jin
Electric Vehicles & the Grid - Synergy or Strain?2026-01-03984/7/2026
As the demand for electrical power has surged over recent years due to the increasing popularity of data centers for Artificial Intelligence (AI) and Electric Vehicles (EVs), it is becoming evident that the aging electrical grid infrastructure is struggling to keep up. Some of the problems this aging infrastructure has resulted in include frequent blackouts due to weather related events, reduced efficiency resulting in higher maintenance costs and outdated communication systems causing poor monitoring and response times. Modernization of the grid in conjunction with integration of the transportation sector with the grid is essential to ensure the reliability and resiliency of the grid. Electric vehicles have dramatically increased in popularity, with most vehicle manufacturers offering at least one electric option in their lineups. Looking at recent developments in vehicle-to-grid (V2G) technology, a new possibility becomes evident; instead of straining the power grid, the electric vehicle can synergize with it. This becomes possible when EVs can facilitate charging during off-peak (low demand) hours and supplying power back to the grid during on-peak (high demand) hours. There are quite a few challenges associated with this approach, lack of standardized charging infrastructure and higher install costs, regulatory and policy hurdles, gaps in technological know-how particularly in relation to impact of power supplied by EVs on grid and effect of V2G on EV battery degradation in the long run, to name a few. This paper reviews the current power demand and supply along with existing and projected power consumption metrics. We also discuss the V2G strategy to effectively manage load requirements, incentives that can be provided to facilitate the execution, and the challenges associated with its widespread implementation. Finally, we discuss case studies of vehicles that incorporate V2G capability and their implications.
Dahlmann, Alexander Drake, Lele, Sneha
Development of Honda S+ Shift Utilizing e:HEV System2026-01-04184/7/2026
Honda is promoting mobility electrification to realize a carbon-neutral society by 2050. Hybrid vehicles will remain advantageous over electric vehicles in terms of manufacturing cost and driving range until renewable energy usage increases, charging infrastructure is sufficiently developed, and battery costs are reduced. In response to this situation, Honda has developed a new control system, “Honda S+ Shift”, which further enhances the “emotional value of driving pleasure” inherent to the e:HEV system and creates new value for hybrid vehicles. Honda S+ Shift synchronizes the engine and vehicle speed and selects a virtual gear position according to the driver's operation such as acceleration, cornering, and deceleration. Subsequently, the system achieves the required system output in cooperation with a dedicated energy management system. It also works with each vehicle system, such as drive force control, sound control, and meter cluster, to stimulate all five senses of the driver, greatly enhancing synchronization between the driver's senses and vehicle behavior. This paper explains how the concept of Honda S+ Shift is realized with the e:HEV system. Since there are no mechanical gear restrictions, Honda S+ Shift can select each gear ratio freely and enables fast shifting. On the other hand, there are difficulties in realizing realistic shifting behavior without direct connection between an engine and tires. Furthermore, e:HEV has three specific operation modes: EV, Hybrid and Engine, and Honda S+ Shift has to utilize them to keep its high efficiency. The first part of this paper describes the setting of the gear ratios, drive force and deceleration. Next, it explains engine speed control to realize sharp shifting and traction motor control to produce a realistic shift feel. Then, it describes how Honda S+ Shift utilizes three modes of e:HEV. It also refers to cooperation with sound and meter cluster control to maximize its effects.
Murata, Naoya, Narimoto, Ryosuke, Saito, Masatoshi, Ishida, Daichi, Gunji, Hiroki, Mitogawa, Terumasa, Ukai, Yohei, Kurachi, Shinobu, Nagakura, Akari, Shiki, Kazuki, Maeda, Sadaharu
Improving Winter Driving Range of EVs with a Novel Desiccant Device: Ceramic Humidity Regulator (CHR)2026-01-01174/7/2026
With rapid growth of Electric Vehicles (EVs) in the market, challenges such as driving range, charging infrastructure, and reducing charging time needs to be addressed. Unlike traditional Internal combustion vehicles, EVs have limited heating sources and primarily uses electricity from the running battery, which reduces driving range. Additionally, during winter operation, it is necessary to prevent window fogging to ensure better visibility, which requires introducing cold outside air into the cabin. This significantly increases the energy consumption for heating and the driving range can be reduced to half of the normal range. This study introduces the Ceramic Humidity Regulator (CHR), a compact and energy-efficient device developed to address driving range improvement. The CHR uses a desiccant system to dehumidify the cabin, which can prevent window fogging without introducing cold outside air, thereby reducing heating energy consumption. CHR is based on desiccant dehumidification technology. Unlike conventional desiccant rotors, it features an integrated structure that combines the desiccant material with a honeycomb-type Positive Temperature Coefficient (PTC) heater. This enables highly efficient direct heating regeneration and a compact design optimized for EVs installation. Previously, the heating power reduction achieved by CHR was measured, and the extended driving range was estimated based on those results. In contrast, this study conducted a complete driving test from full to empty battery charge in a cold laboratory environment. The test was performed using the CLTC (China Light-Duty Vehicle Test Cycle) driving mode. Using an EV equipped with a CHR prototype, tests were conducted with CHR turned ON and OFF respectively. A 13% improvement in winter driving range was actually observed, confirming the real-world benefits of the concept. In conclusion, this study demonstrates that CHR is a promising solution for extending EVs driving range under winter conditions while improving energy efficiency and passenger comfort.
Sakai, Naoki, Takahiko, Nakatani, Shinoda, Narimasa, Ihara, Yukio, Wakida, Norihiro, Kato, Kyohei, Anoop, Reghunathan-Nair
A Comprehensive Modelling Tool for Heavy-Duty Hybrid Fuel Cell/Battery Powertrains2026-01-04304/7/2026
The global transition towards sustainable transportation is driving the development of efficient, low-emission propulsion systems. Battery-electric solutions are effective in urban contexts, but face limitations in heavy-duty and long-haul applications due to the size and weight of the required energy storage. Hybrid battery/fuel cell powertrains offer a promising alternative for such use cases, reducing vehicle mass and charging times while maintaining high energy efficiency. This study presents an original zero-dimensional MATLAB/Simulink model, named HyPoST (Hydrogen Powertrain Simulation Tool), for a parallel hybrid fuel cell/battery system, here applied to heavy-duty vehicles. The model encompasses the main vehicle sub-systems, including the fuel cell stack with auxiliaries, battery pack, electric drive, transmission and the vehicle longitudinal dynamics, coordinated through a rule-based energy management strategy. Two representative heavy-duty vehicle configurations were analysed: a Group 5 long-haul truck, and a Group 2 urban delivery vehicle. A model-to-model validation strategy was performed using VECTO as a reference, the reference European Union’s tool for estimating energy consumption in heavy-duty vehicles. A graphical user interface (GUI) enables users to modify vehicle parameters and run customized simulations, and the model is made available to the scientific community in open and editable version upon request to the authors. The results demonstrate that HyPoST accurately reproduces heavy-duty vehicle behaviour for both truck models, as revealed by the analysis of gear selection, electric motor, and battery telemetries, providing a scalable and accessible tool for engineers, researchers and students.
Montecchi, Gianluca, Martoccia, Lorenzo, D'Adamo, Alessandro
Evaluating the Impact of Drag Coefficient Changes on Driving Energy Using a Probability Distribution Model of Yaw Angle Based on Empirical Data from North American Highway2026-01-06154/7/2026
This study estimates the impact on driving energy of differences in aerodynamic characteristics for yaw angle from natural wind during North American Highway mode driving. A previous study [1] clarified the potential to estimate the fuel consumption impact of natural wind by integrating the drag coefficient yaw characteristics and yaw angle occurrence probability. The natural wind was measured on a vehicle while driving a representative North American Highway test course [2]. Driving energy is predicted from the obtained yaw probability and the drag coefficient yaw sweep data in a wind tunnel. Measurements were conducted every weekday for 8 hours in 2023, covering 70% of the traffic volume. The validity of the measurement period was evaluated by the deviation from the annual average of wind direction and speed. Since yaw probability varies depending on the road environment, it is necessary to weigh the road environment type probability when calculating the driving energy. The probability was calculated using machine learning from more than 490,000 images of North American Highways. Based on the obtained natural wind data, a yaw probability model was created for each vehicle speed in the US Highway driving mode. An evaluation method for the driving energy was constructed from data before and after the improvement of the drag coefficient yaw characteristics. This evaluation method is based on verification results from actual driving data. By using the yaw probability distribution that considers the road environment and traffic volume of the North American highway, the impact of the yaw angle due to natural wind on driving energy can be numerically estimated. According to this method, for a specific the drag coefficient yaw sweep characteristic with a 13 ct improvement in the drag coefficient at a 6-degree yaw angle, this would result in an improvement in drive energy of approximately 1% on real US highways. This is an important indicator for optimizing aerodynamic characteristics, and suggests a development direction that can improve fuel efficiency in the real world by optimizing the vehicle shape while taking into account the yaw angle caused by natural wind.
Onishi, Yasuyuki, Nucera, Fortunato, Nichols, Larry, Metka, Matt
A Comparative Study of Combustion Stability Improvement via Prolonged Discharge Duration under Engine Idling Conditions2026-01-03084/7/2026
Proper control over combustion and emission characteristics under engine idling conditions remains to be challenging, especially when engine block temperature is low. A specially designed common-coil pack was demonstrated to improve engine idling stability in previous SAE congress. In this paper, the progress on further development of the ignition system was reported with improved system stability and enhanced ignition performances. The impact of the prolonged discharge duration on the combustion stability was investigated on a turbocharged 4-cylinder production engine, with special attention to cylinder-by-cylinder variation under cold and hot engine block temperatures. It is observed that a prolonged discharge duration can reduce both cycle-to-cycle and cylinder-to-cylinder variations significantly. Especially under cold engine block temperature conditions, prolonged discharge duration together with advanced spark timing can increase engine load and reduce carbon monoxide emissions effectively. Then, the total energy consumption of the ignition system under various operation conditions were calculated and compared with the engine power gained by the ignition system.
Yu, Xiao, Jin, Long, Leblanc, Simon, Ting, David, Zheng, Ming
MBSE-Driven XIL Workflow for Energy Evaluation of Automated Vehicles: From Simulation to VIL Testing2026-01-02894/7/2026
With the increasing market penetration of automated vehicles, there is a critical need for credible and repeatable methods to quantify their energy impacts. This paper presents a Model-Based Systems Engineering (MBSE)-driven Anything-in-the-Loop (XIL) methodology for quantifying the powertrain energy consumption and potential savings from various controls for automated vehicles in realistic road scenarios while preserving high-fidelity powertrain behavior. The novelty of this approach lies in its use of a unified MBSE backbone (AMBER: Argonne National Laboratory’s [Argonne’s] MBSE-centric platform for transportation energy analysis) to automate the seamless and traceable progression from pure simulation to Vehicle-in-the-Loop (VIL) testing. This work utilizes Argonne's multi-vehicle simulation tool, RoadRunner, which automatically constructs closed-loop road scenarios (road geometry, vehicle sensors, other vehicles, and traffic controls) and connects them to Argonne’s validated, high-fidelity vehicle and powertrain models in Autonomie. The MBSE backbone in AMBER organizes requirements, interfaces, plant and controller models, and test scenarios into a single set of models that is maintained across pure simulation, Software-in-the-Loop (SIL), Processor-in-the-Loop (PIL), and VIL stages. Each stage has a clear role: simulation enables rapid development and validation of advanced models or controls across a large number of scenarios; SIL supports standalone algorithm verification and scenario down-selection; PIL validates real-time execution, inputs/outputs, and timing on the target processor; and VIL provides closed-loop evaluation with a real vehicle under controlled laboratory conditions. AMBER’s automated build and configuration enable rapid retargeting across platforms and repeatable scenario reproduction, making validation fast and cost-effective. To demonstrate its practical application, the workflow is used to validate the functionality and quantify the energy savings of an eco-driving control against a calibrated human driver model. Experiments show strong repeatability and consistent energy gains for the eco-driving strategy while preserving trip time, yielding average energy savings of 7.8% across the evaluated scenarios. Overall, the MBSE-guided XIL workflow shortens development time and reduces test cost by limiting on-road testing and lowering integration risk before track evaluation, while producing credible, closed-loop energy assessments traceable from requirements to test evidence.
Jeong, Jongryeol, Sharer, Phillip, Di Russo, Miriam, Das, Debashis, Zhang, Yaozhong, Karbowski, Dominik
Adaptive Eco-Driving Feedback for Battery Electric Vehicles: A Reinforcement Learning Approach Using Context-Aware Policy Optimization2026-01-01654/7/2026
Energy efficiency and range optimization remain critical challenges to the widespread adoption of battery electric vehicles (BEVs). As a result, there is a growing demand for intelligent driver assistance systems that can extend the operating range and reduce range anxiety. This paper presents an adaptive eco-feedback and driver rating system based on proximal policy optimization (PPO) reinforcement learning, designed to support drivers with the target to reduce energy consumption and maximize driving range. The system processes real-time driving data, such as velocity, acceleration and powertrain status. Map data of high quality is used to anticipate traffic events, including but not limited to speed limits, curves, gradients, preceding vehicles and traffic lights. This contextual awareness allows the system to continuously assess driving behavior and provide personalized, context-aware visual feedback alongside a dynamic driving behavior rating. A PPO agent learns optimal feedback strategies through continuous interaction and evaluates the impact of specific guidance actions, such as but not limited to “release accelerator pedal”, “brake” and “recuperate”, on immediate energy efficiency and long-term driver adaptation patterns. Feedback intensity and modality are dynamically tailored to individual driver profiles based on observed reaction patterns and feedback adherence. This approach encourages drivers to prioritize energy efficiency while aiming to minimize cognitive distraction and discomfort. The algorithm is implemented and validated within a driving simulation environment that replicates diverse and realistic conditions. Virtual driving tests conducted in various scenarios, such as congested urban areas, suburban routes, mountain roads and highways demonstrate that the proposed PPO-based eco-driving assistance system can reduce energy losses by about 28% compared to conventional driving behavior.
Stocker, Christoph, Hirz, Mario, Martin, Michael, Kreis, Alexander, Stadler, Severin
From Propane to Pi: Affordable AI-Driven Solutions for Sustainable Bus Heating2026-01-01644/7/2026
This paper proposes an intelligent, artificial intelligence (AI) enabled seat heating system for school buses that saves energy by only activating heating elements when a passenger is identified. A custom-trained YOLOv8 deep learning model identifies passengers in real time and opens/closes real-time control of the individual electric seat heaters via a Raspberry Pi 5. The detector achieves around 10 frames-per-second (FPS) of inference on the Raspberry Pi 5 and 80–90 FPS on a laptop with over 92% detection confidence across various illumination conditions. Energy modeling shows the anticipated demand for a 10-kW propane-based heater is approximately 75% lower by implementing a 2.52 kW electric seat-heating system. In a typical operation schedule of 540 hours a year, this results in 4,000–5,000 kWh of annual savings, $465–$579 of annual cost savings and mitigates 0.9–1.3 t CO₂ per bus, annually. When implemented at the fleet level, the energy and cost saving will be in proportion. This approach offers a cost-effective, modular, and safe electrified public transportation solution that integrates comfort optimization with environmental accountability.
Chikkala, Daney Bhargav, Zadeh, Mehrdad, Tan, Teik-Khoon, Ponnam, Jitin, Batte, Jai Rathan
Assessing Energy Use, Cost, and Emissions of Small Regional Rail Vehicles: Methodology and Case Study on a German Track2026-01-04194/7/2026
This work evaluates a standardized 30-ton, 16 m railbus platform optimized for unelectrified regional service, focusing on propulsion system design and trade-offs between range, cost, and emissions. A MATLAB/Simulink drive-cycle model was developed to simulate energy consumption and component performance under realistic operating conditions. The Erfurt–Rennsteig route in Germany (130 km round trip, gradients up to 6 %) was selected as a representative case study. The model incorporates detailed sub-models for traction motors, lithium-ion batteries (LFP and LTO), fuel storage, fuel cells, and ICE gensets across multiple fuel options (diesel, gasoline, methane, ethanol, methanol, HVO, FAME, and hydrogen). Battery lifetime is estimated using a combined cycle- and calendar-aging model using the rainflow algorithm to extract charge cycles, while cost models include capital, fuel, maintenance, track fees, and staffing. Results show that battery-electric configurations achieve 1 kWh/km energy use, while hybrid systems range from 2–4 kWh/km depending on fuel and secondary power unit. Control strategies that enable deeper cycling of the traction battery reduce fuel consumption by 7–18 %, with further savings possible from larger battery or genset capacities. Well-to-wheel greenhouse gas emissions vary widely: from near-zero for renewable fuels and clean electricity mixes to over 1,000 gCO2/kWh for fossil-based options. Lifecycle cost analysis indicates that while fuel may represent up to 25 % of total costs, track and station fees dominate operational expenses. Autonomous operation could eliminate oboard staffing costs, amounting to 25–35 %.
Ahrling, Christoffer, Tuner, Martin, Gainey, Brian, Torkiharchegani, Amir, Scharmach, Marcel, Hertel, Benedikt, Alaküla, Mats
BEV Incremental CO 2 Emissions in a Ranked Order Electric Grid2026-01-04244/7/2026
Battery Electric Vehicles (BEV) have been sold as ‘Zero Emissions Vehicles’ (ZEV) by governments to reduce transportation CO2. While they are not ZEV because they run on grid electricity, they could be ‘effectively ZEV’ if the incremental CO2 is ‘very small’. At the national level, this is estimated using following metrics: (1) Internal Combustion Engine Vehicle (ICEV) fuel consumption, from the total US gasoline consumption divided by the total fleet miles driven, 25 mpg or 350 g CO2/mi, (2) Strong Hybrid Electric Vehicles (HEV) about one third less, 240 g CO2/mi. (3) BEV energy consumption, using data from systematic on-road testing of a wide range of vehicles, estimated at 40 kWh/100 mi for a US sales mix. (4) Electricity marginal CO2: in a ranked order grid, zero-CO2 sources are prioritized and supplemented by fossil sources. IEA hourly data show that the US 48 contiguous states are self-contained, with zero-CO2 sources providing a third of total demand. The response to hourly demand changes comes largely from natural gas and coal power stations, with EPA data showing a combined marginal CO2 of 600 g CO2/kWh. On replacing an ICEV by a BEV, the reduction in gasoline use, - 350 g CO2/mi, is offset to two thirds by higher electricity consumption, 40 x 600 / 100 = + 240 g CO2/mi. BEV marginal CO2 is therefore similar to HEV, and not ‘much smaller’ than ICEV. This is because HEV engines and fossil power stations have similar efficiency and similar fuel CO2 intensity.
Phlips, Patrick
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