Free-piston engines are new and efficient energy conversion devices that eliminate mechanical crankshafts. A wide-input power converter was needed as an electronic crankshaft for a free-piston engine to achieve efficient power generation control. A 20 kW single-phase full-bridge power converter that can operate over a wide-input voltage range was proposed in this paper to solve this problem. A current controller was designed by discussing the current flow of the power converters in four working modes, including forward electric, reverse electric, forward generation, and reverse generation. A model that considers the parasitic inductance on the wires in the circuit and the parasitic inductance and capacitance of each pole of the insulated gate bipolar transistor (IGBT) switch was established in this paper, and the accuracy of the model was verified through simulation in MATLAB/Simulink. The main parameters of the power converter, such as the absorption resistance and capacitance of the

Li, Mengfei, Xu, Zhaoping, Liu, Liang

Deep Reinforcement Learning–Based Control Strategy for Electro-Hydrostatic Active Suspension

15-18-03-0016

07/09/2025

A DRL (deep reinforcement learning) algorithm, DDPG (deep deterministic policy gradient), is proposed to address the problems of slow response speed and nonlinear feature of electro-hydrostatic actuator (EHA), a new type of actuation method for active suspension. The model-free RL (reinforcement learning) and the flexibility of optimizing general reward functions are combined with the ability of neural networks to deal with complex temporal problems through the introduction of a new framework called “actor-critic”. A EHA active suspension model is developed and incorporated into a 7-degrees-of-freedom dynamics model of the vehicle, with a reward function consisting of the vehicle dynamics parameters and the EHA pump–valve control signals. The simulation results show that the strategy proposed in this article can be highly adapted to the nonlinear hydraulic system. Compared with iLQR (iterative linear quadratic regulator), DDPG controller exhibits better control performance, achieves

Wang, Jiawei, Guo, Huiru, Deng, Xiaohe

Data-Driven Vehicle Specification of Conventional and Electric Heavy-Duty Commercial Trucks for Improved Energy Consumption

02-18-03-0018

07/09/2025

In the heavy-duty commercial trucks sector, selecting the most energy-efficient vehicle can enable great reductions of the fleet operating costs associated with energy consumption and emissions. Customization and selection of the vehicle design among all possible options, also known as “vehicle specification,” can be formulated as a design space exploration problem where the objective is to find the optimal vehicle configuration in terms of minimum energy consumption for an intended application. A vehicle configuration includes both vehicle characteristics and powertrain components. The design space is the set of all possible vehicle configurations that can be obtained by combining the different powertrain components and vehicle characteristics. This work considers Class 8 heavy-duty trucks (gross combined weight up to 36,000 kg). The driving characteristics, such as the desired speed profile and the road elevation along the route, define the intended application. The objective of the

Villani, Manfredi, Pandolfi, Alfonso, Ahmed, Qadeer, Pianese, Cesare

Thermal Interaction Based on Battery Electrochemical Modeling for In-Wheel Hub Motor Electric Vehicles: A Performance Analysis

14-14-03-0016

07/09/2025

As the adoption of battery electric vehicles (BEVs) continues to rise, analyzing their performance under varying environmental conditions that affect energy consumption has become increasingly important. A critical factor influencing the efficiency of BEVs is the heat loss from the operation and interaction between the vehicle components, such as the battery and motor, and the surrounding temperature. This study presents a comprehensive analysis of the thermal interaction in BEVs by integrating hub motor vehicle and battery electrochemical model with environmental factors. It explores how ambient temperature variations influence the performance of EV components, particularly the motors and battery systems, in both hot and cold weather conditions. The simulations also consider the passenger comfort inside the cabin as it investigates the effects of operating the air-conditioning system on overall energy consumption, revealing significant energy consumption shifts during extreme ambient

Abdullah, Mohamed, Zhang, Xi

A New Electrified Planetary Gear Set - The Cost- and Fuel-Efficient Alternative to Continuously Variable Transmissions

2025-01-0309

07/02/2025

In order to mitigate the effects of climate change, the global transport sector, one of the largest emitters of CO2, needs to drastically reduce its emissions. Although hybridization and electrification are becoming increasingly popular as a solution for a variety of applications, their use in two- and three-wheelers, as well as in recreational and powersports vehicles, remains limited due to their high costs and complexity compared to conventional drivetrains with continuously variable transmissions (CVTs). Despite their affordability and simplicity, CVTs suffer from low mechanical efficiency, with transmission losses ranging from 20–50 %, highlighting a significant opportunity for improvement. In response to these limitations, this study presents the development and experimental evaluation of an electrified planetary gear set (ePGS) in a lightweight off-road vehicle. It is designed to overcome the efficiency limitations of CVTs while maintaining high driving comfort and low system

Jakoby, Moritz, Engels, Michael, Fahrbach, Timm, Andert, Jakob

Improving Speed Trajectory Optimization for Electric Vehicles with Pre-Calculated Energy Maps and Dynamic Profile Discretization

2025-01-0318

07/02/2025

Ongoing research and development in the field of electric vehicles (EVs) have resulted in a continuous expansion of their range. Additionally, advancements in vehicle connectivity have created new opportunities for intelligent driving assistance and energy optimization, particularly through the use of cloud data. However, the integration of eco-driving assistance with numerical optimization of speed trajectories remains challenging due to the high computational demands of these methods. To address this challenge and make such a system feasible for integration into vehicle systems, the computational effort required for an optimized driving trajectory must be minimized. This paper presents a method to accelerate speed trajectory optimization using pre-calculated energy and time consumption maps. For this purpose, a dynamic discretization of the anticipated driving profile is applied. Initial results show a substantial reduction in computation time, varying with different scenarios

Schilling Johnson, Rene, Henke, Markus

Effect of Gasoline Component on Fuel Economy of WLTC Drive with EGR and Lean Combustion

2025-01-0305

07/02/2025

It is becoming increasingly clear that research into alternative fuels, including drop-in fuels, is essential for the continued survival of the internal combustion engine. In this study, the authors have evaluated olefinic and oxygenated fuels as drop-in fuels using a single-cylinder engine and considering fuel characteristic parameters. The authors have assessed thermal efficiency by adding EGR or excess air from zero to the maximum value that allows stable combustion. Next, we attempted to predict fuel efficiency for four types of passenger cars (Japanese small K-car N/A, K-car T/C, Series HV, and Power-split HV) by changing the fuels. We created a model to estimate fuel efficiency during WLTC driving. The results indicated that fuel economy could potentially be improved by adding an olefin fuel that burns stably even with a large amount of EGR or air and an oxygen fuel whose octane number increases. It was observed that the fuel economy improvement rate was particularly notable for

Moriyoshi, Yasuo, Xu, Fuguo, Wang, Zhiyuan, Tanaka, Kotaro, Kuboyama, Tatsuya

Optimized Cabin Heating Strategy for Battery Electric CEP Vehicles

2025-01-0263

07/02/2025

Electromobility is gaining importance in the courier, express and parcel (CEP) sector, as parcel service providers increasingly rely on zero-emission vehicles to improve their CO₂ footprint. A common drawback of battery electric vehicles is their reduced range under cold operating conditions, due to the increased energy demand for cabin heating. Another CEP-specific factor influencing both energy consumption and cabin comfort is the frequent opening of doors during parcel delivery. Additionally, during delivery phases, the cabin cools down in the driver’s repeated absence from the cabin, as the heating is inactive. Nonetheless, a sufficient level of thermal comfort must be maintained during the driving phases between delivery stops. This paper presents an optimization-based strategy for the cabin heating of battery electric CEP vehicles. The objective is to maximize cabin comfort during driving phases while maintaining efficient energy consumption. For this purpose, a nonlinear model

Rehm, Dominik, Krost, Jonathan, Meywerk, Martin, Czarnetzki, Walter

Applying Real World Traffic Data on Machine Learning Methods for Graph Based Cooperation Strategies of Automated Vehicles

2025-01-0286

07/02/2025

With the increasing distribution of smart mobility systems, automated & connected vehicles are more and more interacting with each other and with smart infrastructure using V2X-communication. Hereby, the vehicles’ position, driving dynamics data, or driving intention are exchanged. Previous research has explored graph-based cooperation strategies for automated vehicles in mixed traffic environments based on current V2X-communication standards. Thereby, the focus is set on cooperation optimization and maneuver negotiation. These strategies can be implemented through both centralized and decentralized computational approaches and are conflict-free by design. To enhance these previously established cooperation models, real-world traffic data is used to derive vehicle trajectories, providing a more accurate representation of actual traffic scenarios in order to enhance the practical application of the described methodology. Additionally, machine learning algorithms are employed to train

Flormann, Maximilian, Meyer, Felix, Henze, Roman

Simulation-Based Investigation and its Experimental Validation of the Thermal Behaviour of a Battery Electric City Bus

2025-01-0266

07/02/2025

Electrification of city busses is an important factor for decarbonisation of the public transport sector. Due to its strictly scheduled routes and regular idle times, the public transport sector is an ideal use case for battery electric vehicles (BEV). In this context, the thermal management has a high potential to decrease the energy demand or to increase the vehicles range. The thermal management of an electric city bus controls the thermal behaviour of the components of the powertrain, such as motor and inverters, as well as the conditioning of the battery system and the heating, ventilation, and air conditioning (HVAC) of the drivers’ front box and the passenger room. The focus of the research is the modelling of the thermal behaviour of the important components of an electric city bus in MATLAB/Simscape including real-world driving cycles and the thermal management. The heating of the components, geometry and behaviour of the cooling circuits as well as the different mechanisms of

Schäfer, Henrik, Meywerk, Martin, Hellberg, Tobias

Investigation on Thermal Management of Heavy-Duty Commercial FCEV with Focus on Vehicle System Efficiency

2025-01-0265

07/02/2025

The primary approach to meet the objectives of the EU Heavy Duty CO2 Regulation involves decarbonizing the road transport sector by battery electric vehicles (BEV) or hydrogen-fueled vehicles. Even though the well-to-wheel efficiency of hydrogen-fueled powertrains like fuel cell electric vehicles (FCEV) and H2-internal combustion engines (H2-ICE) is much lower in comparison to BEV, they are better suited for on-road heavy-duty trucks, long haul transport missions and regions with scarce charging infrastructure. Hence, this paper focuses on heavy-duty FCEVs and their overall energetic efficiency enhancement by intelligently managing energy transfer across coolant circuit boundaries through waste heat recovery, while ensuring that all relevant components remain within required temperature boundaries under both cold and hot ambient conditions. Results were obtained using a 1D-model that comprises all thermal fluid circuits (refrigerant, coolant, air) created through GT-Suite software

Uhde, Sophia, Langhorst, Thorsten, Wuest, Marcel, Naber, Dirk

Experimental Evaluation of Purge Strategies in the Recirculation Path of a PEM Fuel Cell System

2025-01-0316

07/02/2025

PEM fuel cell technology plays a vital role in realizing an emission-free mobility and, depending on the considered use case, offers significant advantages over battery electric solutions as well as hydrogen combustion engines. When high performance over a longer period of time as well as short refueling times are key requirements, fuel cell powertrains show their core strengths. However, the adaption of fuel cells in the mobility sector strongly depends on their efficiency which directly relates to the vehicle’s fuel consumption, range and ultimately cost to operate. Therefore, the influence on efficiency and power of different purge strategies used to operate PEM fuel cells is experimentally investigated and compared. A concentration-dependent purge strategy is developed and examined in reference to a charge-dependent strategy. The measurements are carried out on a fuel cell system test bench which corresponds to a fully functional fuel cell system including all commonly used

Hauser, Tobias, Allmendinger, Frank

Modular Thermal Management Framework for Electric Drive System Development

2025-01-0262

07/02/2025

The automotive industry is undergoing a major shift from internal combustion engines to hybrid and battery electric vehicles, which has led to significant advancements and increased complexity in drivetrain design and thermal management systems. This complexity reflects the growing need to optimize energy efficiency, extend vehicle range, and ensure system reliability in modern electric vehicles. At the Institute of Automotive Engineering, a specialized synthesis tool for drivetrain optimization is used to identify the best drivetrain configurations based on specific boundaries and requirements. Building up on this toolchain a modular and adaptable thermal management framework has been developed, addressing another critical aspect of vehicle and drive development: efficient thermal circuit layout and its impact on energy consumption and overall system reliability. The thermal framework emphasizes the dynamic interactions between key components, such as electric machines, power

Notz, Fabian, Sturm, Axel, Sander, Marcel, Kässens, Christoph, Henze, Roman

Computational Investigation of Advanced Compression Ignition with Wet Ethanol in an OP-2S

04-19-01-0002

06/30/2025

Alcohol fuels have inherent properties that make them suitable candidates to replace conventional fossil fuels in internal combustion engines by reducing the formation of harmful emissions such as lifecycle carbon dioxide (CO2), nitrogen oxides (NOX), and particulate matter (PM). There is an increasing amount of work to use fuels such as ethanol or methanol in mixing-controlled compression ignition (MCCI) as a replacement for diesel fuel. However, employing these fuels in a strictly MCCI strategy results in an evaporative cooling penalty that lowers indicated fuel efficiency. This work proposes the use of an advanced compression ignition (ACI) strategy with a high autoignition resistant fuel, where a fraction of the fuel is premixed and autoignited in conjunction with a fraction of fuel that is burned in a mixing-controlled manner to achieve diesel-like efficiencies with significant emission reductions. A computational model for MCCI with diesel and wet ethanol in an opposed piston two

O’Donnell, Patrick Christopher, Gainey, Brian, Bhatt, Ankur, Huo, Ming, Lawler, Benjamin

Learning-Based Predictive Controller for Connected and Automated Hybrid Electric Vehicles

14-14-02-0015

06/19/2025

This article aims at presenting a learning-based predictive control strategy for hybrid electric vehicles (HEVs) in the presence of uncertainty, where the controller structure and energy efficiency of the HEV is simultaneously optimized. The proposed approach includes development of a Bayesian optimization (BO)–based control structure optimization method, followed by an eco-driving–based hierarchical robust energy management strategy (EMS) development for connected and automated HEVs. To apply the learning-based strategy online, we also introduce an approach with approximate cost function for the BO to reduce training and computation time and improve energy in a given trip. The control structure is described by a parameter vector, which is updated, using BO, in an episodic fashion with the performance of the EMS and the computation time. With the current control structure, the hierarchical EMS includes a high-level powertrain energy manager that takes long-term decisions, and a low

HomChaudhuri, Baisravan, Iranzo Juan, Ignacio

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