Search Results (237)

The successful establishment of battery electric vehicles (BEVs) is strongly linked to criteria such as cost and range. In particular, the need for air conditioning strains battery capacities and limits the availability of BEVs. Thermal energy storage systems (TESs) open up alternative paths for heat and cold supply with excellent scalability and cost efficiency. Previous TES concepts have largely focused on heat during cold seasons, but storage-based air conditioning systems for all seasons are still missing. To fill this gap, a concept based on a Brayton cycle allowing heat and cold supply and, simultaneously, an output of electrical energy at times when no air conditioning is needed was investigated. Central thermal components include water-based cold storage and electrically heated, high-temperature, solid-medium storage, both with innovative TPMS structures and flexible operation managements. With transient simulation studies a system was identified with effective storage densities of up to 100 Wh/kg, reaching a constant heat and cold supply of 5 kW and 2.5 kW, respectively, over 41 min. In addition, the underlying cycle allows an electrical output of up to 1.7 kW during times of inactive air conditioning requirements. Compared to a reference system designed only for winter operation, the moderately lower storage densities are compensated by proportionately longer discharging times. By combining a compact and dynamic Brayton cycle with a TES in BEVs, a storage-based air conditioning system with high utilization potential and high operational flexibility was developed. In addition to further optimizations, the knowledge for TES solutions can also be transferred to today’s air conditioning systems, extending the solution space for storage-supported thermomanagement options in BEVs. Full article

This paper evaluates a novel series-type suspension mount designed for electric vehicles (EVs), in which the spring and damper are arranged in series rather than in a conventional parallel configuration. This structurally simple yet innovative design avoids the need for additional mechanical components, such as inerters or costly active devices, while effectively mitigating vibration. Comparative quarter-car simulations demonstrated that the series-type configuration provided a faster reduction in transmissibility across the analyzed frequency range, highlighting its superior isolation capability compared to conventional mounts. An extended series-type model was also investigated by incorporating auxiliary sub-mount elements to assess the parametric effects. The results showed that damping variations had a limited influence, whereas the sub-mount stiffness played a decisive role in shaping the transmissibility curves and generating the secondary resonance behavior. To validate the concept experimentally, a prototype consisting of four coil springs and a vibration isolation pad was prepared and tested using impact-hammer excitation. The measured transmissibility confirmed improved vibration isolation up to 100 Hz under the given specimen conditions, with resonance features attributable to the inherent stiffness of the isolation pad. Overall, the findings verified that a simple series-type mount can provide efficient and practical vibration isolation tailored to EV applications. Full article

A fundamental challenge in lithium-ion battery charging is the inherent trade–off between charging speed and battery health. Fast charging tends to accelerate battery degradation, while slow charging extends downtime and intensifies range anxiety, heightening concerns over inadequate driving range during operation. This contradiction has become a key bottleneck restricting the advancement of electric vehicles. In response to the limitations of conventional charging strategies and optimization methods, which typically intensify this trade–off, this study proposes a novel two–stage fast charging optimization strategy for lithium–ion batteries. The proposed method first introduces a hybrid clustering algorithm that combines the canopy algorithm with bisecting K–means to achieve adaptive SOC staging. This staging is guided by the nonlinear characteristics of the internal resistance with respect to the state of charge (SOC), allowing for a data–driven division of charging phases. Following staging, a closed–loop optimization framework is developed. A wavelet neural network (WNN) is employed to precisely capture and approximate the nonlinear characteristics of the charging process for performance prediction, upon which a multi–strategy enhanced multi–objective particle swarm optimization (MOPSO) algorithm is applied to efficiently search for Pareto–optimal solutions that balance charging time and ohmic loss. In addition, an active learning mechanism is incorporated to refine the WNN using selectively sampled data iteratively, thereby improving prediction accuracy and the robustness of the optimization process. Experimental results demonstrate that when the SOC reaches 70%, the proposed method shortens the charging time by 12.5% and reduces ohmic loss by 31% compared with the conventional constant current–constant voltage (CC–CV) strategy, effectively achieving a balance between charging efficiency and battery health. Full article

With the growing deployment of electric buses (e-buses), accurate energy use modelling has become essential for fleet optimisation and operational planning. Using the SORT methodology, this study analyses instantaneous energy consumption and recuperation (IECR). Three vehicle configurations were tested (one battery with pantograph, four batteries, and eight batteries), each with ten repeatable runs. Four approaches were compared: a baseline linear regression, an extended linear model (ELM) due to the state, a feed-forward neural network, and a recurrent neural network (RNN). The extended linear model achieved a determination coefficient of R² = 0.9124 (residual standard deviation 4.26) compared with R² = 0.7859 for the baseline, while the determination coefficient for the RNN is 0.9343, and the RNN provided the highest accuracy on the test set (the correlation coefficient between real and predicted values is 0.9666). The results confirm the dominant influence of speed and acceleration on IECR and show that battery configuration mainly affects consumption during acceleration. Literature-consistent findings indicate that regenerative systems can recover 25–51% of braking energy, with advanced control methods further improving recovery. Despite non-normality and temporal dependence of residuals, the state-aware linear model remains interpretable and competitive, whereas recurrent networks offer superior fidelity. These results support real-time energy management, charging optimisation, and reliable range prediction for electric buses in urban public transport. Full article

Battery electric vehicles (BEVs) are gaining market share, yet range anxiety and sparse charging still create demand for hybrids with combustion-engine range extenders. Range-extender vehicles face high customer expectations for noise, vibration, and harshness (NVH) due to their direct comparability with fully electric vehicles. Key challenges include the vibrations of the internal combustion engine, especially from vehicle-induced starts, and the discontinuous operating principle. A technological concept to reduce vibrations in the drivetrain and on the engine mounts, called “FEVcom,” relies on rolling moment compensation. In this concept, a counter-rotating electric machine is coupled to the internal combustion engine via a gear stage to minimize external mount forces. However, due to high speed fluctuations of the crankshaft, the gear drive tends to rattle, which is perceived as disturbing and must be avoided. As part of this work, the rolling moment compensation system was examined regarding its vibration excitation, and an extension to prevent gear rattling was simulated and optimized. For the simulation, the extension, based on a chain or belt drive, was set up as a multi-body simulation model in combination with the range extender and examined dynamically at different speeds. Variations of the extended system were simulated, and recommendations for an optimized layout were derived. This work demonstrates the feasibility of successful rattling avoidance in a range-extender drivetrain with full utilization of the rolling moment compensation. It also provides a solid foundation for further detailed investigations and for developing a prototype for experimental validation based on the understanding gained of the system. Full article

Batteries are ubiquitous, with their presence ranging from electric vehicles to portable electronics. Research focused on increasing average voltage, improving stability, and extending cycle longevity of batteries is pivotal for the advancement of battery technology. These advancements can be accelerated through research into battery chemistries. The traditional approach, which examines each material combination individually, poses significant challenges in terms of resources and financial investment. Physics-based simulations, while detailed, are both time-consuming and resource-intensive. Researchers aim to mitigate these concerns by employing Machine Learning (ML) techniques. In this study, we propose a Transformer-based deep learning model for predicting the average voltage of battery electrodes. Transformers, known for their ability to capture complex dependencies and relationships, are adapted here for tabular data and regression tasks. The model was trained on data from the Materials Project database. The results demonstrated strong predictive performance, with lower mean absolute error (MAE) and mean squared error (MSE), and higher R² values, indicating high accuracy in voltage prediction. Additionally, we conducted detailed per-ion performance analysis across ten working ions and apply sample-wise loss weighting to address data imbalance, significantly improving accuracy on rare-ion systems (e.g., Rb and Y) while preserving overall performance. Furthermore, we performed SHAP-based feature attribution to interpret model predictions, revealing that gravimetric energy and capacity dominate prediction influence, with architecture-specific differences in learned feature importance. This work highlights the potential of Transformer architectures in accelerating the discovery of advanced materials for sustainable energy storage. Full article

For electric vehicles widely used recently, lithium-ion batteries serve as the primary energy storage units, affecting the vehicles’ performance, safety, and lifespan. Accurate state of charge (SOC) estimation is pivotal for the battery management system (BMS) to enhance the predictability of the vehicle’s range and avert thermal runaway due to improper charging methods. In this study, an adaptive SOC estimation methodology was developed using parameter identification with forgetting factor recursive least squares (FFRLS). These parameters are then incorporated into a dual adaptive extended Kalman filter (DAEKF) for SOC estimation under varying load conditions. DAEKF is used to dynamically adjust the covariance matrices for process and measurement noises, significantly enhancing the filter’s adaptability and precision. The integration of FFRLS and DAEKF enables a robust SOC estimation of electric vehicles, featuring rapid computation speeds, high accuracy, and excellent adaptability, positioning them as ideal candidates for enhancements in battery management system technology. Full article

To address the demands for wide-range and high-precision current measurement, this paper proposes a novel current sensor design that integrates spin sensing technology, magnetic shunt effect, and a multi-sensor data fusion algorithm. The spin valve sensors accurately detect the magnetic field generated by the signal current, while the soft magnetic shunt structure attenuates the magnetic field to a level suitable for the spin valve sensors. Consequently, the detection current range can be extended by 6.8 times. Using four spin valve sensors and data fusion with an averaging algorithm, the system can calibrate the errors caused by the displacement or tilt of the current-carrying wire. Experimental results demonstrate that the current sensor achieves a sensitivity of 61.6 mV/V/A, an excellent linearity of 0.55%, and robust measurement performance, as well as strong anti-interference capability. Our study offers a novel solution for high-precision, wide-range current measurement in applications such as those in new energy vehicle electronics and precision electric energy metering. Full article

This study presents an advanced simulation-based methodology for evaluating the dynamic vibrational behavior of the operator–vehicle assembly in autonomous electric agricultural tractors. Using the TE-0 electric tractor as the experimental platform, the research is structured into three integrated stages. In the first stage, a seated anthropometric virtual model of the human operator is developed based on experimental data and biomechanical validation. The second stage involves a detailed modal analysis of the TE-0 electric tractor using Altair Sim Solid, with the objective of determining the natural frequencies and vibration modes in the [0–80] Hz range, in compliance with ISO 2631-1. This analysis captures both the structural-induced frequencies—associated with the chassis, wheelbase, and metallic frame—and the operational-induced frequencies, influenced by the velocity and terrain profile. Subsequently, the modal analysis of the “Grammer Cabin Seat” is conducted to assess its dynamic response and identify critical vibration modes, highlighting how the seat behaves under vibrational stimuli from the tractor and terrain. The third stage extends the analysis to the virtual operator model seated on the tractor seat, investigating the biomechanical response of the human body and the operator–seat–vehicle interaction during simulated motion. Simulations were carried out using SolidWorks 2023 and Altair Sim Solid over a frequency range of [0–80] Hz, corresponding to operation on unprocessed soil covered with grass, at a constant forward speed of 7 km/h. The results reveal critical resonance modes and vibration transmission paths that may impact operator health, comfort, and system performance. The research contributes to the development of safer, more ergonomic, and sustainable autonomous agricultural transport systems. By simulating real-world operation scenarios and integrating a rigorously validated experimental protocol—including vibration data acquisition, biomechanical modeling, and multi-stage modal analysis—this study demonstrates the importance of advanced modeling in optimizing system-level performance, minimizing harmful vibrations, and supporting the transition toward resilient and eco-efficient electric tractor platforms in smart agricultural mobility. Full article

With the advancement of autonomous and electric vehicles, an increasing demand has been observed for the automatic robot-controlled charging of electric vehicles. The idea of developing such charging stations was raised at several research institutions and universities as early as the 2010s, however the appearance of automatic charging stations with higher Technology Readiness Levels (TRL) can only be dated from 2019 onwards. In most of the developed concepts and solutions, a dedicated parking system is required by vehicle drivers, since the operating range of the robots used for charging is limited. In most cases, solutions do not incorporate robots with unique geometries; instead, proven industrial solutions are applied. The robots in these prototypes are typically installed in a fixed position, similar to industrial applications, and are not mobile. The charging of one vehicle is usually performed by one robot. A high-level summary of the developed mechanical system is presented in this project overview. In this research, an automated, robot-controlled electric vehicle charging system was designed, in which vehicles are parked perpendicularly adjacent to each other, and multiple vehicles are charged using a single collaborative robot. The mechanical system was implemented with a robot mounted on an extendable arm attached to a carriage, which is guided in two directions along rails. In this manner, the automatic charging system is positioned precisely at the parking location of the vehicle to be charged. Full article

Dynamic wireless charging systems have emerged as a promising solution to extend the driving range of electric vehicles by enabling energy transfer while the vehicle is in motion. However, the segment-based charging lane structure introduces challenges such as pulsation of the output power and the need for precise switching control of the transmitting segments. This paper proposes a position-sensorless control method for managing transmitting lines in a dynamic wireless charging system. The proposed approach uses a segmented charging lane structure combined with two receiving coils and LCC compensation circuits on both the transmitting and receiving sides. Based on theoretical analysis, the study determines the optimal switching positions and signals to reduce the current fluctuation. To validate the proposed method, a dynamic wireless charging system prototype with a power rating of $3kW$ was designed, constructed, and tested in a laboratory environment. The results demonstrate that the proposed position-sensorless control method effectively mitigates power fluctuations and enhances the stability and efficiency of the wireless charging process. Full article

The transition to electric mobility has significantly increased the demand for lithium-ion batteries, raising concerns about their end-of-life management. Therefore, this study presents the design, development and first implementation steps of a stationary energy storage system utilizing second-life electric vehicle (EV) batteries. These batteries, no longer suitable for traction applications due to a reduced state of health (SoH) below 80%, retain sufficient capacity for less demanding stationary applications. The proposed system is designed to be flexible and scalable, serving both research and commercial purposes. Key challenges include heterogeneous battery characteristics, safety considerations due to increased internal resistance and battery aging, and the need for flexible power electronics. An optimized dual active bridge (DAB) converter topology is introduced to connect several batteries in parallel and to ensure efficient bidirectional power flow over a wide voltage range. A first prototype, rated at 50 $\mathrm{k}$$\mathrm{W}$, has been built and tested in the laboratory. This study contributes to sustainable energy storage solutions by extending battery life cycles, reducing waste, and promoting economic viability for industrial partners. Full article

Electric buses are key in the strategy towards a greenhouse-gas-neutral fleet. However, their restrictions in terms of range and refueling as well as their increased price point present new challenges for public transport companies. This study aims to address, based on real-world operational data, how energy consumption and charging behavior affect battery aging and how operational strategies can be optimized to extend battery life under realistic conditions. This article presents an energy consumption analysis with respect to ambient temperatures and average vehicle speed based exclusively on real-world data of an urban bus fleet, providing a data foundation for range forecasting and infrastructure planning optimized for public transport needs. Additionally, the State of Charge (SOC) window during operation and vehicle idle time as well as the charging power were analyzed in this case study to formulate recommendations towards a more battery-friendly treatment. The central research question is whether battery-friendly operational strategies—such as reduced charging power and lower SOC windows—can realistically be implemented in daily public transport operations. The impact of the recommendations on battery lifetime is estimated using a battery aging model on drive cycles. Finally, the reduction in CO₂ emissions compared to diesel buses is estimated. Full article

In air-conditioning compressors operating under ultra-low temperature conditions, both the rotational speed and load torque are at high levels, demanding pump motors that offer high efficiency and high power at high speeds. Electrically excited synchronous motors (EESMs) satisfy these operational requirements by leveraging their inherent wide-speed field-weakening capability and superior high-speed performance characteristics. Current research on EESM primarily targets electric vehicle applications, with a high-efficiency design focused on medium and low speeds. Excitation design under constant-power–speed extension remains insufficiently explored. To address it, this paper proposes an EESM design methodology optimized for high-speed efficiency and constant-power excitation control. Key EESM parameters are determined through a dynamic phasor diagram, and design methods for turn number, split ratio, and other parameters are proposed to extend the high-efficiency region into the high-speed range. Additionally, a power output modulation strategy in the field-weakening region is introduced, enabling dynamic high-power regulation at high speed through excitation adjustment. Compared to similarly sized PMSMs, the proposed EESM exhibits consistently superior efficiency beyond 10,000 rpm, delivering 19% and 49% higher power output at 12,000 rpm and 14,000 rpm, respectively, relative to conventional pump-drive PMSMs. Experimental validation via a prototype confirms excellent high-speed efficiency and sustained constant-power performance, in alignment with the design targets. Full article

Due to the limitations of current battery technologies—such as lower energy density and high cost compared to fossil fuels—electric vehicles (EVs) face constraints in applications requiring extended range or heavy payloads, such as refuse trucks. As a midterm solution, hybrid electric vehicles (HEVs) combine internal combustion engines (ICEs) and electric powertrains to enable flexible energy usage, particularly in urban duty cycles characterized by frequent stopping and idling. This study introduces a model-based energy management strategy using the Equivalent Consumption Minimization Strategy (ECMS), tailored for a retrofitted series hybrid refuse truck. A conventional ISUZU NPR 10 truck was instrumented to collect real-world driving and operational data, which guided the development of a vehicle-specific ECMS controller. The proposed strategy was evaluated over five driving cycles—including both standardized and measured urban scenarios—under varying load conditions: Tare Mass (TM) and Gross Vehicle Mass (GVM). Compared with a rule-based control approach, ECMS demonstrated up to 14% improvement in driving range and significant reductions in exhaust gas emissions (CO, NO_x, and CO₂). The inclusion of auxiliary load modeling further enhances the realism of the simulation results. These findings validate ECMS as a viable strategy for optimizing fuel economy and reducing emissions in hybrid refuse truck applications. Full article