Browse Topic: Energy management

Items (7,359)

Energy-saving and Economic Evaluation of Vacuum Insulation Panels with Glass Wool Core-polyimide Membrane Structure in Hotel Buildings under Different Climatic Conditions

2026-99-1652

To be published on 07/24/2026

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 Analysis

2026-99-1639

To be published on 07/24/2026

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 Engineering

2026-99-1645

To be published on 07/24/2026

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

Research on the Hysteresis Performance of Welded Hollow Spherical Joints under Low Temperature

2026-99-1641

To 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 Optimization

2026-99-1811

7/17/2026

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

Sustainable Airport Emissions Reduction through Optimized End-Around Taxiway Operations: A Simulation Approach

2026-99-0582

7/10/2026

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 Optimization

2026-99-0555

7/10/2026

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

Simulation Analysis of Pure Electric Vehicle Equipped with Continuously Variable Transmission

2026-99-0522

7/10/2026

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

Optimization of Preheating Energy Distribution for Low-Temperature Cold Starts in Diesel-Electric Hybrid Vehicles

2026-99-0516

7/10/2026

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

Multi-Objective Optimization Research and Design of Seat Structure in Vehicle Collision

2026-99-0518

7/10/2026

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

Temperature Control Technology for Solid-state Hydrogen Proton Exchange Membrane Fuel Cell

2026-99-0528

7/10/2026

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 Consumption

2026-99-0507

7/10/2026

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

Fuel Savings from Predictive Charge Control Based on Short-Trip Duration in Series Hybrid Electric Vehicles

14-15-03-0015

7/10/2026

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

Improved Thermal Management for Operating a Battery Electric City Bus under Real Driving Conditions

2026-01-0758

7/1/2026

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 Approach

2026-01-0761

7/1/2026

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

Synergistic Integration of Advanced Combustion and Air-Path Technologies to Maximize Internal Combustion Engines Efficiency in Hybrid Applications

2026-01-0733

7/1/2026

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

Multiphysical Modeling of a Direct-Cooled Litz Wire Winding for a PMSM Using Surrogate Models

2026-01-0731

7/1/2026

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 Algorithm

2026-01-0774

7/1/2026

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

Towards a Smarter E-Bike: Enhancing Energy Efficiency through Adaptive Speed Control

2026-01-0736

7/1/2026

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

Enhancing Efficiency in Megawatt-Scale EV Charging via Adaptive Multi-Agent Control of DC/DC Converters

2026-01-0769

7/1/2026

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 Trucks

14-15-03-0013

6/29/2026

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 Development

02-19-03-0015

6/10/2026

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 Vehicles

2026-37-0026

6/9/2026

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-Driving

13-07-02-0006

6/9/2026

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

Hybridized Diesel Powertrains - An Important Enabler for Commercial Applications on the Long Journey towards Full Electrification

2026-37-0022

6/9/2026

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

Passenger Car Powertrain for Voltages above 800V

2026-37-0020

6/9/2026

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

Experimental Investigation on Cold Start and Warm-Up Phases for an Ethanol Fueled SI Engine

2026-37-0031

6/9/2026

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

Potential of ORC-Based Waste Heat Recovery in Conventional and Hybrid Heavy-Duty Powertrains

2026-37-0018

6/9/2026

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 Vehicles

2026-37-0034

6/9/2026

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 Trucks

2026-37-0019

6/9/2026

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 Mobility

2026-37-0025

6/9/2026

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 Outlook

2026-37-0042

6/9/2026

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

Electrical and Copper Compatibility Studies of Transmission Lubricants for Advanced Electric Vehicles

2026-01-5036

6/3/2026

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 Evaluation

02-19-03-0016

6/2/2026

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 Aircraft

2026-26-0776

6/1/2026

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 Efficiency

2026-26-0791

6/1/2026

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 Nanocomposites

2026-26-0762

6/1/2026

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

ZF bets big on hybrid transmissions

26TOFHP06_06

6/1/2026

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

Hitting the road IN THE ALL-NEW VNR

26TOFHP06_01

6/1/2026

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

Q&A

26TOFHP06_15

6/1/2026

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

Analysis on the Technical Path and Engineering Application of Ship Carbon Capture

2026-99-1730

5/22/2026

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) Technology

2026-99-1736

5/22/2026

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 Tree

2026-99-1714

5/22/2026

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

Multi-Physics Modeling of Aircraft Hybrid Propulsion Systems Integrating Batteries, SOFC, and Jet Engine

2026-99-1703

5/22/2026

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

Research on Carbon Emission Calculation Algorithm for Trucks on Expressways in Xinjiang

2026-99-1718

5/22/2026

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 Vehicles

10-10-04-0033

5/22/2026

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

Effect of Burning Biomass Fuel Cyclohexanol on Diesel Engine Performance

2026-99-1708

5/22/2026

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

An Energy-Saving Coupling-Decoupling Optimization Strategy for Electric Modular Buses Considering Vehicle to Vehicle Charging

2026-99-0730

5/15/2026

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 Performance Prediction Methods for Centrifugal Refrigeration Compressors in Data Centers

2026-99-0725

5/15/2026

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

Design and Performance Research of an Integrated Thermal Management System for Hybrid Electric Light Trucks

2026-99-0735

5/15/2026

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

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