Editor’s Choice Articles

The rapid proliferation of Electric Vehicles (EVs) offers a promising pathway toward reducing greenhouse gas emissions and fostering a sustainable environment. However, the large-scale integration of EVs presents significant challenges to distribution networks, potentially increasing stress on grid infrastructure. To address these challenges, this study proposes the integration of a Virtual Power Plant (VPP) framework within EV charging stations as a novel approach to facilitate dynamic power management. The proposed framework integrates electric vehicle (EV) scheduling, battery energy storage (BES) charging, and vehicle-to-grid (V2G) support, while dynamically monitoring energy generation and consumption. This approach aims to enhance voltage regulation and minimize both EV charging durations and waiting periods. A modified IEEE 13-bus test network, equipped with six strategically placed EV charging stations, has been employed to evaluate the performance of the proposed model. Simulation results indicate that the proposed VPP-based method enables dynamic power coordination through EV scheduling, significantly improving the voltage stability margin of the distribution system and efficiently reduces charging times for EV users. Full article

In terms of their high efficiency and low environmental impact, proton exchange membrane fuel cells (PEMFC) are becoming increasingly essential in the development of hydrogen electric vehicles. Despite these advantages, optimizing hydrogen consumption remains difficult because of the highly nonlinear behavior of PEMFC systems and their sensitivity to variations in operating conditions. This article outlines an intelligent control approach based on extremum seeking control (ESC), based on an artificial neural network (ANN) model, to improve hydrogen utilization in hydrogen electric vehicles. Experimental data on current, voltage, and temperature are collected, preprocessed, and used to train the ANN model of the PEMFC. The ESC algorithm uses this predictive ANN model to adjust the fuel cell current in real time, ensuring voltage stability while reducing hydrogen consumption. The simulation results demonstrate that the ANN-based ESC system provides voltage stability under dynamic load variations and achieves approximately 2.7% hydrogen savings without affecting the experimental current profile, validating the efficacy of the suggested strategy for effective hydrogen management in fuel cell electric vehicles. Full article

Two-speed transmissions can improve battery electric vehicle (BEV) drivetrain efficiency. However, the additional losses caused by shifting actuators offset these efficiency gains. Particularly hydraulic actuated wet-running multi-plate clutches, which enable powershifts, typically require rotary feedthroughs. Commonly used rectangular sealing rings (RSR) demand continuous hydraulic power due to leakage and cause friction torque. This leads to high RSR temperatures, especially at high angular velocities of electric machines. This article introduces a two-speed BEV transmission concept using wet-running multi-plate clutches actuated via a rotating 5/3-way valve that can shut off, i.e., lock up the actuating pressure directly in the rotating system. Consequently, the rotary feedthrough is depressurized and contactless gap seals are usable. This reduces supply pressure requirements and minimizes hydraulic and friction losses while retaining powershift capability. Component-level tests evaluate leakage, pressure shut off, actuator dynamics and power consumption. Results show that actuating pressure in a shut-off clutch is maintained for longer than 60 min and electrical actuator power consumption is less than 20 W. During overlapping gearshifts, gap seal leakage is less than 1 L/min at 10 bar and sufficient pressure dynamics are achieved. These findings confirm the feasibility of the proposed actuator for multi-plate clutches in two-speed BEV transmissions. Full article

The integration of electric buses into urban transportation networks is a priority for policymakers aiming to promote sustainable public mobility. Among available technologies, electric Bus Rapid Transit (eBRT) systems offer an environmentally friendly and operationally effective alternative to conventional modes. This study introduces a Machine Learning Decision Support Framework designed to assess the feasibility of deploying eBRT systems in urban environments. Using a dataset of 28 routes in the Athens Metropolitan Area, the framework integrates diverse variables such as land use, population coverage, proximity to public transport, points of interest, road characteristics, and safety indicators. The XGBoost model demonstrated strong predictive performance, outperforming traditional approaches and highlighting the significance of points of interest, land use diversity, green spaces, and roadway infrastructure in forecasting travel times. Overall, the proposed framework provides urban planners and policymakers with a robust, data-driven tool for evaluating the practical and environmental viability of eBRT systems. Full article

Creating trust in society for new technologies, such as a new types of powertrains, and making them marketable requires transparent, neutral, and independent technical verification. This is crucial for the acceptance and success of electrified vehicles in the used car markets. A key component of electric vehicles is the traction battery, whose current and future condition, particularly regarding aging, determines its residual value and safe operation. This review aims to identify and evaluate methods for predicting the lifetime of onboard traction batteries, focusing on their applicability in technical inspections. A systematic literature and patent review was conducted using targeted keywords, yielding 22 patents and 633 publications. From these, 150 distinct lifetime prediction methods were extracted and categorized into a four-level mind map. These methods are summarized, cited, and structured in detailed tables. The relationships between approaches are explained to clarify the current research landscape. Long Short-Term Memory, Convolutional Neural Networks, and Particle Filters were identified as the most frequently used techniques. However, no methods were found suitable for predicting the lifetime of traction batteries during technical vehicle inspections, which operate under short test durations, limited data access, and diverse real-world operating conditions. Most studies focused on cell-level testing and did not address complete battery systems in operational vehicles. This gap highlights the need for applied research and the development of practical methods to support battery assessment in real-world conditions. Advancing this field is essential to foster confidence in battery systems and enable a sustainable transition to electromobility. Full article

Electrifying port horizontal transportation is constrained by downtime and deadheading from fixed charging/swapping systems, large battery sizes, and the lack of integrated decision tools for life-cycle emissions. This study develops a carbon-efficiency-centered bi-objective optimization framework benchmarking Mobile Charging Stations (MCSs) against Fixed Charging Stations (FCSs) and Battery Swapping Stations (BSWSs). The framework integrates operational parameters such as charging power, range, dispatch, and non-operational mileage, along with grid carbon intensity, battery embodied emissions, and carbon-market factors. It generates Pareto fronts using the NSGA-II algorithm with real port data. Port horizontal transportation refers to the movement of goods within the port area, typically involving the use of specialized vehicles to transport containers short distances across the terminal. Results show that MCSs can reuse idle windows to reduce deadheading and infrastructure demand, yielding significant economic improvements. The trade-off between emissions and profitability is context-dependent: at low-to-moderate reuse levels, low-carbon and profitable solutions coexist; beyond a threshold of approximately 0.5–0.75, the Pareto fronts shift to high emissions and high profits, highlighting the context-specific advantages of MCSs for port-infrastructure planning. MCSs thus provide context-dependent advantages over FCSs and BSWSs, offering practical guidance for port infrastructure planning and carbon-informed policy design. Full article

The global shift toward sustainable transport electric vehicles (EVs) is at the core of decarbonization efforts. While advanced economies have achieved their rapid adoption through strong policies and incentives, emerging markets face structural and behavioral barriers. This study investigates the paradox in Morocco, whereby a significant automotive capacity contrasts with a minimal domestic BEV market share of 0.6%, despite 143% growth from a small base, using a four-dimensional framework encompassing financial, infrastructural and energy, policy and institutional, and behavioral–social factors. The research integrates a literature review, a survey (n = 522), and secondary data on charging infrastructure and EV sales. Findings reveal a strong value–action gap: 69% of respondents acknowledged EVs’ environmental benefits yet only 1.1% owned one and 42% had considered buying. The high upfront costs of EVs influenced over 70% of participants, and a significant association was confirmed between charging availability and purchase intent (χ² = 34.80, p < 0.05). Urban-centric charging, fragmented governance, and skepticism persist as barriers. The study concludes that industrial strength alone cannot ensure adoption without targeted incentives, equitable infrastructure, and cultural shifts in ownership perception, offering key insights for policymakers in emerging economies pursuing sustainable mobility. Full article

Heavy-duty trucking is central to the U.S. economy, and improving its long-term sustainability requires cost-effective, energy-efficient, and reliable operations. Emerging technologies—advanced powertrains, batteries, and alternative fuels—offer potential solutions, but their economic and operational viability remains uncertain. This study evaluates the performance of Class 8 battery electric (BEV), plug-in hybrid (PHEV), fuel cell electric (FCEV), and diesel trucks in terms of energy use and the levelized cost of driving (LCOD) to determine when these technologies become competitive without compromising operational reliability. The analysis explores how evolving fuel prices and vehicle technology improvements in 2023, 2035, and 2050 influence the cost competitiveness of each powertrain. By comparing the results at both the technology level and the fleet level, the study demonstrates that powertrains that appear cost-effective on individual routes may not always scale to fleet-wide viability, and vice versa. The analysis is based on real-world data from over 15,700 Class 8 truck trips recorded in California in 2022, capturing diverse driving scenarios, payload conditions, and operational constraints. The results show that BEV250 can deliver cost-effective performance in short-haul operations (0–250 miles) under depot electricity prices below USD 0.34/kWh and maintain this advantage through 2050 as battery costs decline. In the 250–500-mile segment, the technology-level analysis indicates that BEV500 often achieves the lowest LCOD on individual tours, particularly under low electricity prices, while the fleet-level results show that FCEVs provide a more consistent cost performance across all tours, especially when the route variability is high. For long-haul operations (>500 miles), where BEVs are assumed to operate without en-route charging, FCEVs emerge as the most cost-effective non-diesel option by 2050, provided hydrogen prices fall below USD 6/kg. PHEVs show a limited long-term competitiveness and are mainly viable under transitional fuel price conditions. Overall, the findings underscore that there is no one-size-fits-all solution. Powertrain adoption must be range-aware, infrastructure-sensitive, and fleet-structured. By integrating technology-level and fleet-level perspectives, this study provides actionable insights for fleet operators, policymakers, and industry stakeholders seeking to balance cost, reliability, and sustainability in heavy-duty freight. Full article

The switching action of the main circuit breaker of the train will produce switching overvoltage. In order to suppress the switching overvoltage, the phase selection control of the circuit breaker is required. However, the mechanical structure of the train-mounted electronically controlled pneumatic vacuum main circuit breaker is too complicated, resulting in a large dispersion of its closing action time, which is not suitable for the traditional phase selection control system. In order to obtain the accurate closing action time, a method for predicting the closing action time of train electronically controlled pneumatic vacuum main circuit breaker based on the PCA and GBDT algorithm is proposed. The relationship between the closing phase of AC25 kV power supply train and the peak value of switching overvoltage is obtained by simulation and field test, and the accuracy requirement of the prediction model is determined, that is, the prediction error should be within ±3.3 ms. The final prediction results show that the prediction error of the on-board electronically controlled pneumatic vacuum main circuit breaker closing action time prediction model based on the PCA and GBDT algorithm is controlled within ±3.3 ms, and the probability is 92%, which meets the accuracy requirements of phase selection control. Full article

This paper develops and applies an ex-ante methodological framework to assess the societal optimisation of eBRT innovations within the Horizon Europe eBRT2030 project, using Multi-Criteria Decision Analysis (MCDA) and the PROMETHEE method. The study evaluates 11 eBRT innovations to be deployed in five demonstration sites in Europe and one in Colombia. Twenty social parameters, including 10 risks and 10 benefits, were weighted and scored through expert and stakeholder engagement, to calculate the Societal Optimisation Index (SOI). Positive SOI values indicate that societal benefits outweigh risks, and negative values indicate the opposite, while close-to-zero values indicate socially neutral or ambiguous options requiring case-specific judgement. The results indicate that innovations such as Adaptive Fleet Scheduling and Planning, Intelligent Driver Support Systems, and IoT Monitoring Platforms provide strong societal benefits with manageable risks, while charging-related innovations are associated with social concerns. The study emphasises the importance of social impact assessment prior to implementing innovations, to enable inclusive decision-making for policymakers and transport planners and enable the development of socially optimised eBRT systems. Embedding experts’ perspectives and social criteria ensures that technological innovations are aligned with societal needs, assisting the transition towards more equitable, low-carbon transport systems. Full article

To operate electric vehicle (EV) fleets in a safe and efficient manner, many companies have been deploying charging infrastructures (CIs) at their premises. Forecasting of different system parameters of a CI, such as how many charging points will be occupied during the day, can help create accurate charge plans. In this paper, we deal with the applicability of continuous Nowcasting, i.e., frequently executed short-term forecasts, to predict the next few data points based on the past and current situation in a CI. Specifically, we forecast the number of charging EVs over a rolling two-hour horizon using XGBoost and LSTM. In the experiments, we apply different weighting schemes to emphasize the relevance of the most recent observations combined with different multi-horizon forecasting strategies. Experimental results using a real-world dataset show that a linear weighting schema combined with a direct forecasting strategy using XGBoost achieves the lowest RMSE value of 0.906 for the 15 min forecasting horizon when predicting the number of active charging stations. For the 2 h horizon, the best RMSE of 2.545 is achieved with XGBoost using the strategy Direct, but with an exponential weighting strategy. We then illustrate how short-term predictions can be used to improve the operational efficiency of an example CI by dynamically adjusting power limits based on the latest prediction results. Full article

The lack of standardized functional safety information limits the adoption of electric vehicles (EVs) in Brazil. This study proposes a voluntary Brazilian safety labeling program for critical EV subsystems, based on ISO 26262:2018 (Functional Safety) and ISO 21448:2022 (Safety of the Intended Functionality, SOTIF), adapted to the Brazilian regulatory context. The framework integrates (i) comparative analysis of international vehicle labeling programs; (ii) hazard analysis and risk assessment (HARA) for four critical subsystems (battery management, electric powertrain, charging system, HV cables/connectors); and (iii) a document reliability index (DRI) that weights generic relative risk (RRI_gen) by the robustness of technical documentation (Evidence Score). The DRI calculation assumes statistical independence among subsystems as a simplification, to be validated in the pilot phase. Application to a simulated dataset of 100 BEV models yielded DRI scores ranging from 1.6 to 9.3 (mean = 5.0, SD = 1.8, CV = 36.7%). Vehicles were classified into five safety classes (1–5), with approximately 85% distributed across intermediate classes 2–4, demonstrating strong discriminatory power. Results are communicated via a physical label integrated into Brazil’s National Energy Conservation Label (ENCE), with QR codes linking to detailed subsystem data. The proposal can reduce consumer risk perceptions, stimulate industrial innovation in safety documentation, support regulatory harmonization with ISO standards, and advance electric mobility adoption in emerging markets. Full article

This research presents the design of a robust nonlinear controller for the lateral dynamics of a self-driving car. It is based on the block control and super-twisting sliding mode control techniques in order to effectively mitigate the uncertainties and disturbances of the vehicle. The dynamic model of the system is composed of the standard bicycle dynamic model (not kinematic) for the vehicle and the dynamics of a BLDC motor connected to a steering rack system as the steering actuator. Moreover, the control scheme considers an inner loop for controlling the actuator position based on the field-oriented control (FOC) and PID control approaches. The controller’s overall performance is validated through its application to a mathematical model of a brushless direct current (BLDC) motor, acting as the actuator, plus the steering rack dynamics and the lateral dynamic model of the vehicle. Measurements of voltages and currents are taken in both the abc and dq reference frames, the latter being commonly used in the field-oriented control (FOC) technique. Additionally, the system’s performance is evaluated in terms of trajectory tracking, orientation, and lateral deviation from the lane center. Full article

With the increasing complexity of Autonomous Vehicle networks, enhanced cyber security has become a critical challenge. Traditional security techniques often struggle to adapt dynamically to evolving threats. Overcoming these limitations, this paper presents a novel domain ontology to structure knowledge concerning AV security threats, intrusion characteristics, and corresponding mitigation techniques. Unlike previous work, which mainly focused on static classifications or direct integration within Intrusion Detection Systems, our approach has the distinctive feature of creating a formalized and coherent semantic representation. The ontology was designed using Protégé 4.3 and Web Ontology Language (OWL), modeled from the core cyber security concepts of AVs, and it provides a more nuanced threat classification and significantly superior automated reasoning capability. An important feature of our design is that the ontology formalization was done independently of any real-time IDS integration. A PoC was carried out to prove that the ontology could select the most appropriate method of mitigation, using as input the output of machine-learning-based IDS; SPARQL queries retrieve mitigation instance, type, and effectiveness. This design choice enables us to concentrate strictly on validating the foundational semantic coherence and reasoning power of the knowledge structure, hence providing a robust and reliable analytical framework for further reactive and predictive security applications. The experimental evaluation confirms enhanced effectiveness in knowledge organization and reduces inconsistencies in security threat analysis. Specifically, class classification was performed in 1.049 s, while consistency check required just 0.044 s, hence validating the model’s robustness against classification principles and concept inferences. This work thus paves the way for the development of more intelligent and adaptive security frameworks. In the future, research will be focused on the integration with real-time security monitoring and IDS frameworks and on the study of optimization techniques, such as genetic algorithms, to improve the real-time selection of the countermeasures. Full article

Bidirectional electric vehicle (EV) charging represents an opportunity to leverage EVs as flexible energy assets within the power system. By enabling controlled power flow in both directions, bidirectional charging unlocks a wide range of grid services, thereby enhancing grid stability as the energy sector decarbonizes. This paper presents a comprehensive experimental evaluation of bidirectional charging systems (EVCS), focusing on response dynamics and controllability delays critical for grid services. A real ISO 15118–20–enabled EV and an EV emulator were used to conduct tests across configurations, utilizing the Watt & Well 22 kW bidirectional charging bay. The study compares CCS2 and CHAdeMO protocols under varying configuration conditions. Results show that modern chargers achieve sub-second responsiveness, with local communication delays typically below 0.4 s and ramping times around 0.5 s. However, power flow reversals introduce an additional delay of approximately 1 s. These updated controllability metrics are essential for validating bidirectional charging in time-critical applications such as primary frequency regulation. The findings highlight the influence of voltage level and modular configuration on dynamic performance, underscoring the need to integrate external control path delays for full-stack validation. This work provides a foundation for modeling and deploying bidirectional EVCS in fast-response grid services. Full article

Direct current fast charging is a necessary element of the transition to electric vehicles (EVs). Regulatory complexity, capital requirements, and challenging business models hinder charging infrastructure deployment, so focusing on the efficient use of such infrastructure is of paramount importance. A tool to improve this efficiency is an incentive to terminate charging events when charging power drops, the vehicle state of charge rises above some value, or time plugged in exceeds a threshold. A timeseries charging demand model was built based on observed EV population and charging behavior. This was used to explore these three incentive trigger metrics across a range of plausible values, to find their relative impacts on the vehicles charging, those waiting in line to access a cordset, and charging site operators. Results indicate that basing such a trigger on charging power would have little impact if the threshold power is low enough to accommodate older, slower-charging vehicles, but that more restrictive limits based on state of charge or charging duration can decrease wait times, increase vehicle throughput, and increase total energy sales for cordsets serving more than 1000 EVs per year. Full article

This study proposes a novel framework for analyzing Vehicle-Integrated Photovoltaic (VIPV) systems, integrating optical, thermal, and electrical models. The model modifies existing fixed PV methodologies for VIPV applications to assess received irradiance, PV module temperature, and energy production, and is available as an open-source MATLAB tool (VIPVLIB) enabling simulations via a smartphone. A key innovation is the integration of meteorological data and real-time driving, dynamically updating vehicle position and orientation every second. Different time resolutions were explored to balance accuracy and computational efficiency for optical model, while the thermal model, enhanced by vehicle speed, wind effects, and thermal inertia, improved temperature and power predictions. Validation on a minibus operating within the University of Palermo campus confirmed the applicability of the proposed framework. The roof received 45–47% of total annual irradiation, and the total yearly energy yield reached about 4.3 MWh/Year for crystalline-silicon, 3.7 MWh/Year for CdTe, and 3.1 MWh/Year for CIGS, with the roof alone producing up to 2.1 MWh/Year (c-Si). Under hourly operation, the generated solar energy was sufficient to fully meet daily demand from April to August, while during continuous operation it supplied up to 60% of total consumption. The corresponding CO₂-emission reduction ranged from about 3.5 ton/Year for internal-combustion vehicles to around 2 ton/Year for electric ones. The framework provides a structured, data-driven approach for VIPV analysis, capable of simulating dynamic optical, thermal, and electrical behaviors under actual driving conditions. Its modular architecture ensures both immediate applicability and long-term adaptability, serving as a solid foundation for advanced VIPV design, fleet-scale optimization, and sustainability-oriented policy assessment. Full article

The Internet of Vehicles (IoV) revolutionizes transportation by enabling real-time communication and data exchange among vehicles (V2V), infrastructure (V2I), and other entities (V2X). These capabilities are crucial for improving road safety and traffic efficiency. However, achieving reliable and secure consensus across network nodes remains a significant challenge. Consensus mechanisms are essential in IoV for ensuring agreement on the network’s state, enabling applications such as autonomous driving, traffic management, and emergency response. This paper presents a systematic review of IoV consensus mechanisms, examining 78 peer-reviewed publications from 2010 to June 2025 using the PRISMA framework. Our analysis highlights challenges, including scalability, latency, and energy efficiency and identifies trends such as the adoption of lightweight algorithms, edge computing, and AI-assisted techniques. Unlike previous reviews, this work introduces a structured comparative framework specifically designed for IoV environments, enabling a detailed evaluation of consensus mechanisms across key features such as latency, fault tolerance, communication overhead and scalability to identify their relative strengths and limitations. Full article

This paper investigates the feasibility of vehicle-integrated photovoltaic (VIPV) systems for light vehicles by developing and simulating an intelligent solar integration design based on the Tesla Model 3. The proposed system incorporates roof and bonnet-mounted photovoltaic modules, each managed by independent buck converters employing maximum power point tracking (MPPT) for optimal energy extraction. A novel fuzzy logic controller was designed to dynamically allocate auxiliary battery charging between the traction battery and the solar subsystem, using real-time irradiance and state-of-charge (SOC) inputs. The system was implemented in MATLAB/Simulink with location-specific data for Melbourne, Australia. Simulation results demonstrate high converter efficiencies of 94–95%, stable MPPT convergence within 0.5 s and an estimated annual solar contribution of 930 kWh, confirming effective control and energy management under varying conditions. This work highlights the innovative application of adaptive fuzzy control and dual MPPT coordination within VIPV systems and provides a validated basis for future optimisation and real-world integration. Full article

Electric Vehicles (EVs) are still facing prejudices about limited range, making them unattractive for many customers. However, their locally emission-free operation and the ability to recover kinetic energy during braking manoeuvres are significant advances against conventional drivetrains. Especially the function of one-pedal driving (OPD) can further reduce the energy consumption of EVs if properly realized and tuned. In this research, the optimisation and evaluation of an adaptive OPD strategy for a battery electric vehicle (BEV) with the aim of improving energy efficiency and driving comfort, which was previously introduced by the authors, is presented. Therefore, an adaptive pedal curve was designed first and extended through the integration of a fuzzy controller that considers the trade-off between efficient operation and driver intention based on vehicle speed and the drive pedal position signals. The strategy was extended by the incorporation of another input to represent the traffic area. The efficiency was evaluated in a proband study using virtual driving tests in a static simulator, in which different configurations were analysed and rated. It was found that the optimised strategy achieved the best overall result. Compared to pure regenerative braking as the benchmark, energy consumption as well as the amount of pedal changes were reduced by 8.45% as well as 62.27%, respectively, and the rate of energy recovery was increased by 67.8%. Full article

Current and potential electric vehicle (EV) owners express concerns about the charging infrastructure, mentioning non-functional chargers, prolonged charging times, inconvenient charger locations, long wait times, and high costs as major barriers. Addressing these issues often requires analyzing actual vehicle charging data, which is typically proprietary and inconsistent due to diverse standards and protocols. To understand and improve the EV charging experience, customer reviews are typically used to identify common customer pain points (CPPs). However, there is not a comprehensive method to map customer reviews to a standardized set of CPPs. In collaboration with the National Charging Experience (ChargeX) Consortium, this study bridges these gaps by proposing a Systematic Categorization and Analysis of Large-scale EV-charging Reviews (SCALER) framework. SCALER is an integrated, deep learning framework that segments, actively labels, analyzes, and classifies EV charging customer reviews into six CPP categories. To test its effectiveness, we used SCALER to analyze over 72,000 reviews from customers charging various EV models on different networks across the United States. SCALER achieves a classification accuracy of 92.5%, with an F1 score exceeding 85.7%. By demonstrating real-world applications of SCALER, we enhance the industry’s ability to understand and address CPPs to improve the EV charging experience. Full article

Achieving high-precision clamping force control is crucial for Electro-Mechanical Braking (EMB) systems but remains challenging due to significant nonlinear friction (e.g., static, Coulomb, and viscous friction) within the transmission mechanism. To address this, a comprehensive model integrating the electrical and mechanical dynamics of the EMB actuator is first established. This pressure-oriented model, which explicitly accounts for the nonlinear frictions, is developed and validated in MATLAB/Simulink 2022b. Furthermore, physical experiments under typical braking scenarios are conducted to investigate the system’s friction characteristics, leading to the identification of a displacement–pressure load curve for the actuator. This curve serves as a key reference for braking force observation. Finally, a braking force observer-based controller is designed, implemented via an Auto-Disturbance Rejection Control (ADRC) algorithm. Experimental results from step and sinusoidal braking force tests demonstrate that the proposed controller not only effectively compensates for nonlinear disturbances but also achieves robust and stable clamping force control. Full article

Mining articulated vehicles (MAVs) are widely used as primary transportation equipment in both underground and open-pit mines. These include various machines such as Load–Haul–Dump machines and mining trucks. Path tracking control for MAVs has been an important research topic. Most current research focuses on path tracking control during forward driving. However, there are relatively limited studies on reverse path tracking control. Reversing plays a crucial role in the operation of MAVs. Nevertheless, existing methods typically use the center of the front axle as the control point; therefore, the positioning system is usually installed at the front axle. In practice, however, this means the positioning system is actually located at the rear axle during reverse operations. While it is theoretically possible to infer the position and orientation of the front axle from the rear axle, a strong nonlinear relationship exists between the motion states of the front and rear axles, which introduces significant errors in the system. As a result, these existing methods are not suitable for reverse driving conditions. To address this issue, this paper proposes a nonlinear model predictive control (NMPC) method for path tracking during mining-articulated vehicle (MAV) reverse operations. This method innovatively reconstructs the reverse-motion model by selecting the center of the rear axle as the control point, effectively addressing the instability issues encountered in traditional control methods during reverse maneuvers without requiring additional positioning devices. A comparative analysis with other control strategies, such as NMPC for forward driving, reverse NMPC using the front axle model, and reverse linear model predictive control (LMPC), reveals that the proposed NMPC method achieves excellent control accuracy. Displacement and heading error amplitudes do not exceed 0.101 m and 0.0372 rad, respectively. The maximum solution time per control period is 0.007 s. In addition, as the complexity of the reverse path increases, it continues to perform excellently. Simulation results show that as the curvature of the U-shaped curve increases, the proposed NMPC method consistently maintains high accuracy under various operational conditions. Full article

High-precision speed measurement at low speeds in PMSM drives is hindered by encoder quantization noise. This paper proposes an enhanced extended state observer (ESO)-based method to overcome limitations of conventional approaches such as direct differentiation with the low-pass filter (high noise), the phase-locked loop (PLL)-based method (limited dynamic response), and standard ESO (sensitivity to disturbance). The improved ESO incorporates reference torque feedforward and disturbance feedback, significantly suppressing noise and enhancing robustness. Simulations and experiments demonstrate that the proposed method reduces steady-state speed fluctuation by up to 42% compared to standard ESO and over 90.1% relative to differentiation-based methods, while also improving transient performance. It exhibits superior accuracy and stability across various low-speed conditions, offering a practical solution for high-performance servo applications. Full article

Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for the safe and efficient operation of electric vehicles (EVs). Conventional approaches, including Coulomb counting, electrochemical impedance spectroscopy, and equivalent circuit models, provide useful insights but face practical limitations such as error accumulation, high equipment requirements, and limited applicability across different conditions. These challenges have encouraged the use of machine learning (ML) methods, which can model nonlinear relationships and temporal degradation patterns directly from cycling data. This paper reviews four machine learning algorithms that are widely applied in SOH estimation: support vector regression (SVR), random forest (RF), convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). Their methodologies, advantages, limitations, and recent extensions are discussed with reference to the existing literature. To complement the review, MATLAB-based simulations were carried out using the NASA Prognostics Center of Excellence (PCoE) dataset. Training was performed on three cells (B0006, B0007, B0018), and testing was conducted on an unseen cell (B0005) to evaluate cross-battery generalisation. The results show that the LSTM model achieved the highest accuracy (RMSE = 0.0146, MAE = 0.0118, R² = 0.980), followed by CNN and RF, both of which provided acceptable accuracy with errors below 2% SOH. SVR performed less effectively (RMSE = 0.0457, MAPE = 4.80%), reflecting its difficulty in capturing sequential dependencies. These outcomes are consistent with findings in the literature, indicating that deep learning models are better suited for modelling long-term battery degradation, while ensemble approaches such as RF remain competitive when supported by carefully engineered features. This review also identifies ongoing and future research directions, including the use of optimisation algorithms for hyperparameter tuning, transfer learning for adaptation across battery chemistries, and explainable AI to improve interpretability. Overall, LSTM and hybrid models that combine complementary methods (e.g., CNN-LSTM) show strong potential for deployment in battery management systems, where reliable SOH prediction is important for safety, cost reduction, and extending battery lifetime. Full article

As the global automobile industry undergoes rapid intelligent transformation, understanding public acceptance of autonomous driving emerges as a critical research challenge. This study adopts the Technology Acceptance Model (TAM) as its theoretical framework to conduct a comparative analysis between China and Europe, two major automotive markets and central arenas for the development of autonomous driving. It investigates how contextual factors—including policy support, infrastructure, social trust, and cultural values—influence acceptance patterns. The findings show that in China, strong policy guidance, rapid infrastructure deployment, and large-scale demonstration projects have substantially increased willingness to adopt, while the widespread use of L2-level systems has enhanced public familiarity with the technology. Nonetheless, high-profile accidents have also exposed vulnerabilities in public trust. In contrast, European consumers demonstrate a more cautious stance, emphasizing legal liability, data privacy, and ethical compliance, while simultaneously regarding autonomous driving as a means of achieving carbon reduction, traffic safety, and sustainable mobility. The results further indicate that in the European context, institutional guarantees and prior experience are decisive, with accident memory and institutional trust serving as critical moderators within TAM pathways. Full article

VDI/VDE 2206 introduces the “V-Model”, a standard in the field of automotive development that uses systems engineering to derive requirements for (sub-)systems and components based on vehicle characteristics. These characteristics, which are directly experienced by drivers, are crucial in the concept phase, where virtual methods are increasingly applied. Regarding the battery electric vehicle’s energy storage, commonly a lithium-ion battery, vehicle metrics, especially for charging, range, and longitudinal dynamics, are of particular relevance. This publication will demonstrate a method to derive the requirements for the battery system based on those metrics. The core of the method is a static battery model, which considers the needed effects and dependencies in order to adequately represent the defined vehicle metrics, e.g., the battery’s open-circuit voltage and internal resistance. This paper also discusses the necessity of the relevant effects and dependencies and also why some of them can be ignored at this particular vehicle development stage. The result is a consistent method for requirement definition, from vehicle level to battery system level, applicable in the concept phase of the vehicle development process. Full article

Electric vehicles are key to sustainable mobility, but their limited range remains a major obstacle to widespread adoption. Extending driving distance requires optimizing energy use across subsystems. This study combines bibliometric mapping (2017–2024, Scopus) with a focused qualitative review to structure recent research. Results highlight a strong emphasis on energy efficiency, with China leading due to its market size, industrial base, and supportive policies. Major research directions tied to range extension include energy storage, motion control, thermal regulation, cooperative driving, and grid interaction. Among these, hybrid energy storage systems and motor control stand out for their measurable impact and industrial relevance, while thermal management, regenerative braking, and systemic approaches (V2V and V2G) remain underexplored. Beyond mapping contributions, the study identifies ongoing gaps and calls for integrated strategies that combine electrical, thermal, and mechanical aspects. As EV adoption accelerates and battery demand increases, the findings emphasize the need for battery-aware, multi-objective energy management strategies. This synthesis provides a vital framework to guide future research and support the development of robust, integrated, and industry-ready solutions for optimizing EV energy use and extending driving range. Full article

The study evaluates the impact of massive electric vehicle (EV) penetration on Quito’s 138 kV distribution system in Ecuador, employing a probabilistic approach to support a sustainable energy transition. The rapid adoption of EVs, as projected by Ecuador’s National Electromobility Strategy, poses significant challenges to the capacity and reliability of the city’s electrical infrastructure. The objective is to analyze the system’s response to increased EV load and assess its readiness for this scenario. A methodology integrating dynamic battery modeling, Monte Carlo simulations, and power flow analysis was employed, evaluating two penetration levels: 800 and 25,000 EVs, under homogeneous and non-homogeneous distribution scenarios. The results indicate that while the system can handle moderate penetration, high penetration levels lead to overloads in critical lines, such as L10–15 and L11–5, compromising normal system operation. It is concluded that specific infrastructure upgrades and the implementation of smart charging strategies are necessary to mitigate operational risks. This approach provides a robust framework for effective planning of EV integration into the system, contributing key insights for a transition toward sustainable mobility. Full article

This study presents a comparative analysis of efficiency and harmonic generation in Triple Active Bridge (TAB) converters under two operating configurations: Case I, with one input source and two loads, and Case II, with two input sources and one load. Two modulation strategies, Single-Phase Shift (SPS) and Dual-Phase Shift (DPS), are evaluated through frequency-domain modeling and simulations performed in MATLAB/Simulink. The analysis is complemented by experimental validation on a laboratory prototype. The results show that DPS reduces harmonic amplitudes, decreases conduction losses, and improves output waveform quality, leading to higher efficiency compared to SPS. Harmonic current spectra and total harmonic distortion (THD) are analyzed to quantify the impact of each modulation method. The findings highlight that DPS is more suitable for applications requiring stable power transfer and improved efficiency, such as renewable energy systems, electric vehicles, and multi-source DC microgrids. Full article

In response to the rising demand for sustainable transportation, electric vehicles (EVs) are increasingly regarded as viable alternatives to conventional vehicles. This study investigates the intention of Malaysian consumers to choose EVs as their preferred mode of transportation. Consumption values were conceptualized as a multi-dimensional construct comprising functional value, symbolic value, emotional value, novelty value, and conditional value. This study examines the relationships between these consumption values, consumer attitudes, and intention to purchase EVs. In addition, this study also examines the mediating role of attitude and the moderating role of infrastructure readiness. Data were gathered using a proportionate stratified sampling method from 264 respondents in Klang Valley, Malaysia. Of the twelve (12) hypotheses tested, four (4) were supported. The analysis indicates positive relationship between attitude and emotional value with consumers’ intention to purchase EVs. Consumers’ attitudes mediate the relationship between functional value, emotional value, and intention to purchase EVs. Infrastructure readiness does not moderate the relationship between consumers’ attitudes towards EVs and their purchase intentions. This study enhances the existing knowledge of consumers’ multifaceted value views about EVs and offers practical guidance for marketers and serves as a reference for policymakers to improve the marketability of EVs. Full article

Additive manufacturing technologies enable the design of complex lightweight structures for electric powertrain applications. This study evaluates the topology optimization and conceptual additive manufacturing of a real electric vehicle gearbox housing, aiming to reduce weight while maintaining structural stiffness. Based on an existing industrial component, a topology-optimized design featuring an X-shaped rib structure was developed. The manufacturing concept combines Laser Metal Deposition (LMD) with a pre-machined turned part. A comparative material study was carried out using finite element simulations to assess aluminum, magnesium, titanium, and stainless steel in terms of weight, deformation, and natural frequency. The results indicate that aluminum alloys offer the best balance of stiffness and weight due to their high specific modulus and favorable processability. The optimized design achieved a simulated weight reduction of approximately 21% with only a minor increase in rotational deformation. A cost analysis of different manufacturing methods suggests that, while conventional casting remains more economical at higher volumes, additive processes are becoming increasingly viable for small series. The study provides a theoretical foundation for future development of lightweight functionally integrated gearbox housings in electric mobility. Full article

Public electric vehicle (EV) charging infrastructure is crucial for accelerating EV adoption and reducing transportation emissions; however, disparities in infrastructure access have raised significant equity concerns. This review synthesizes existing knowledge and identifies gaps regarding equity in EV public charging research. Following structured review protocols, 91 peer-reviewed studies from Scopus and Google Scholar were analyzed, focusing explicitly on equity considerations. The findings indicate that current research on EV public charging equity mainly adopts geographic information systems (GIS), network optimization, behavioral modeling, and hybrid analytical frameworks, yet lacks consistent normative frameworks for assessing equity outcomes. Equity assessments highlight four key dimensions: spatial accessibility, cost burdens, reliability and usability, and user awareness and trust. Socio-economic disparities, particularly income, housing tenure, and ethnicity, frequently exacerbate inequitable access, disproportionately disadvantaging low-income, renter, and minority populations. Additionally, infrastructure-specific choices, including charger reliability, strategic location, and pricing strategies, significantly influence adoption patterns and equity outcomes. However, the existing literature primarily reflects the contexts of North America, Europe, and China, revealing substantial geographical and methodological limitations. This review suggests the need for more robust normative evaluations of equity, comprehensive demographic data integration, and advanced methodological frameworks, thereby guiding targeted, inclusive, and context-sensitive infrastructure planning and policy interventions. Full article

Achieving climate protection goals in the transport sector requires the adoption of innovative mobility solutions and new vehicle concepts. In Germany, commercial transport accounts for one-quarter of the total car mileage. Many of these trips are comparatively short, often involve a single passenger, and require the transport of only small or lightweight goods—yet they are typically carried out by car. Substituting cars with small and light electric vehicles (LEVs) wherever feasible could make commercial transport more efficient and environmentally friendly. LEVs combine a favorable weight-to-payload ratio with the high efficiency of electric drivetrains. This study estimates the share of car trips in commercial transport in Germany that could theoretically be substituted by LEVs. The analysis is based on a comparison of trip characteristics from a national travel survey with the technical capabilities of selected LEV categories. Our results indicate that up to 73% of commercial car trips and 44% of mileage could theoretically be covered by LEVs, with particularly high potential for trips in commercial passenger transport. Although limitations in range and payload restrict the universal applicability of LEVs, the findings reveal substantial opportunities to make commercial transport cleaner and more sustainable. These insights highlight the relevance of LEVs for sustainable commercial transport and offer a data-driven basis for further discussion of their potential and for guiding targeted policy and planning. Full article

Electric vehicle (EV) drivetrains operate at high rotational speeds, which makes the noise, vibration, and harshness (NVH) performance of gear transmissions a critical design factor. Without the masking effect of an internal combustion engine, gear whine can become a prominent source of passenger discomfort. This paper provides the first comprehensive review focused specifically on gear tooth surface waviness, a subtle manufacturing-induced deviation that can excite tonal noise. Periodic, micron-scale undulations caused by finishing processes such as grinding may generate non-meshing frequency “ghost orders,” leading to tonal complaints even in high-quality gears. The article compares finishing technologies including honing and superfinishing, showing their influence on waviness and acoustic behavior. It also summarizes modern waviness detection techniques, from single-flank rolling tests to optical scanning systems, and highlights data-driven predictive approaches using machine learning. Industrial case studies illustrate the practical challenges of managing waviness, while recent proposals such as controlled surface texturing are also discussed. The review identifies gaps in current research: (i) the lack of standardized waviness metrics for consistent comparison across studies; (ii) the limited validation of digital twin approaches against measured data; and (iii) the insufficient integration of machine learning with physics-based models. Addressing these gaps will be essential for linking surface finish specifications with NVH performance, reducing development costs, and improving passenger comfort in EV transmissions. Full article

As electric scooters (e-scooters) continue to populate city streets and gain popularity as a key mode of micro-mobility, issues such as their energy consumption and demand from the power grid, as well as optimizing their electrical systems, become increasingly important. Improving performance requires a deep understanding of their electrical behavior and the design of smart control strategies. This paper presents a detailed analysis of the entire electrical system of commercial electric scooters, with a particular focus on the performance of key components such as the permanent magnet brushless direct current motor and the lithium-ion battery system. The study involves modeling and simulation of motor control, battery management, and DC-link voltage stabilization using MATLAB/Simulink. The simulations are complemented by laboratory measurements of the motor performance in an SXT Scooters MAX unit under various operating conditions. Additionally, a complete battery charging cycle is analyzed to evaluate charging characteristics and usable energy storage capacity. This paper presents a first step for researchers interested in studying the electrical systems of e-scooters. Additionally, it can serve as educational material for electrical engineers in the field of e-scooters. Full article

With the growing adoption of electric buses in urban transportation systems, ensuring the safety and structural integrity of their battery systems under accident scenarios has become increasingly important. Among potential accidents, rollover events pose a particular risk, as they can lead to the penetration or deformation of the battery pack and, consequently, trigger thermal runaway. In this context, this study evaluates the structural performance of rechargeable energy storage systems (REESS) in electric buses under rollover conditions, following the guidelines of United Nations Economic Commission for Europe (UNECE) Regulations No. 100 and No. 66. The analysis focuses on the structural safety of uniformly distributing the battery pack beneath the vehicle floor during rollover scenarios. The methodology adopted includes detailed finite element modeling to accurately represent the vehicle structure and battery modules, as well as virtual instrumentation using accelerometers. Simulations were conducted to evaluate structural deformations, battery retention integrity, and acceleration levels within the REESS compartments under rollover impact conditions. The results demonstrated compliance with both regulations and highlighted the importance of properly positioning and securing the battery module to the vehicle floor. The findings contribute to the improvement of design and validation criteria for electric buses, reinforcing the need to align technological innovation with international safety standards. Finally, this research supports the development of safer and more reliable vehicles, promoting sustainable mobility solutions for urban transportation systems. Full article

The transition to heavy-duty electric vehicles (HDEVs) offers substantial environmental benefits but raises concerns about increased pavement deterioration due to the added mass of large battery packs. A key research question is whether additional structural demands on road infrastructure could offset these benefits. This study investigates the impact of battery weight distribution on asphalt pavement performance by comparing conventional diesel trucks with electric trucks under equivalent gross vehicle weight (36 tons). Three battery placement scenarios were evaluated: (i) concentration at the steering axle, (ii) concentration at the rear tractor axles, and (iii) uniform distribution across all tractor axles. Pavement elastic response was analyzed using a representative cross-section using mechanistic–empirical modeling, with fatigue damage estimated according to the Mechanistic–Empirical Pavement Design Guide (MEPDG) fatigue law. Results indicate that tensile strains at the bottom of asphalt layers may increase by up to 60%, with relative fatigue damage rising by 185% and 34% for scenarios (i) and (iii), respectively, while scenario (ii) produced nearly equivalent damage to conventional trucks. These findings highlight the critical role of battery placement; the optimal performance seems to be achieved when weight is concentrated at the rear tractor axles. Full article

The rapid development of the new energy vehicle industry has resulted in a large number of retired power batteries. Creating products from second-use retired batteries (SURB) is crucial for sustainability by extending the batteries’ operational life, which, in turn, conserves resources and protects the environment. Consequently, this paper establishes a structural equation model (SEM) based on an interpretive structural model (ISM). It investigates consumers’ willingness to pay (WTP) for secondary utilization of retired batteries (SURB) products by extending the theory of planned behavior (TPB)with incentive policy, knowledge, and perceived risk. The study reveals that incentive policies and knowledge are fundamental factors, while subjective norms, perceived risk, and perceived behavioral control exert moderate influence. Attitude emerges as the most significant predictor, directly affecting consumers’ WTP, with perceived behavioral control also playing a key role. Incentive policies and knowledge have an indirect influence through perceived behavioral control and perceived risk. Finally, this paper discusses the theoretical and practical significance of the findings and provides relevant policy recommendations. Full article

This paper presents a novel ultrafast DC circuit breaker concept based on a railgun actuator, designed for ultrafast charging stations operating at 800 $\mathrm{V}$ and delivering up to 640 $\mathrm{k}$$\mathrm{W}$. The proposed breaker achieves contact opening speeds exceeding 190 $\mathrm{m}$/$\mathrm{s}$, enabling fault current interruption within 200 $\mathrm{\mu }\mathrm{s}$ and limiting the peak fault current to 2200 $\mathrm{A}$. This performance significantly reduces breaker stress compared with conventional mechanical solutions. System-level simulations demonstrate a dramatic reduction in energy dissipation during faults—from 11,000 J with a conventional fast breaker to just 250 $\mathrm{J}$ using the proposed design. A 3D finite element method model of the railgun actuator confirms the feasibility of achieving a 15 $\mathrm{m}$$\mathrm{m}$ stroke in 150 $\mathrm{\mu }\mathrm{s}$. The evolution of current density and magnetic field is analyzed, highlighting the influence of skin and velocity skin effects. Results confirm that the proposed solution acts both as a circuit breaker and a fault current limiter, enhancing safety, reliability, and durability in high-power DC systems. Full article

Established hairpin stators for electric traction motors are made up of a large number of so-called hairpins. To produce these stators, the individual hairpins must first be pre-assembled into an auxiliary device in order to achieve the desired winding scheme. The resulting hairpin basket must then be picked up and transported to the lamination stack. Automated solutions for both processes are characterized by a high degree of complexity and low flexibility. Manual assembly, however, is prone to errors. The new approach presented in this paper is therefore based on the collaborative assembly of the hairpins and a flexible hairpin basket gripper. A cobot hands the hairpins in the correct sequence to the operator. The correct positioning of the hairpins in the auxiliary device is ensured by the use of a monitor located under it. The creation of the correct assembly sequence is partly automated by a collision detection program. In addition, a new and flexible hairpin basket gripping concept is presented. Tests show that the cycle times of both new processes are slow due to hardware limitations. This restricts their use to specific applications, such as complex winding patterns or very small quantities. Full article

The increasing shift towards battery electric vehicles (BEVs) in urban environments raises the question of how real-world traffic conditions affect their energy consumption. While BEVs are expected to reduce local emissions, their total energy demand, particularly in city traffic with with low average speeds, and therefore a higher impact of secondary consumption, remains insufficiently understood. To address this, a simulative framework to analyze the average energy consumption of an all-electric vehicle fleet in a mid-sized city, using Darmstadt, Germany, as a case study, is presented. A validated microscopic traffic simulation is built based on 2024 data and enriched with representative powertrain models for various vehicle classes, including passenger cars, trucks, and buses. The simulation allows the assessment of consumption under different traffic densities and speeds, revealing the substantial influence of secondary consumers and traffic flow on total energy demand. Furthermore, the study compares the $C{O}_2$ emissions of an all-BEV fleet with those of a fully combustion-based fleet. The findings aim to highlight the role of secondary consumers in urban traffic and to identify the potential for energy-saving. Full article

This paper presents a detailed, measurement-based assessment of interharmonic, harmonic, and supraharmonic emissions from a Battery Energy Storage System (BESS) supporting electric vehicle (EV) fast charging. In contrast to prior literature, which is largely simulation-based and often neglects interharmonic and even harmonic components, this study provides real-world data under dynamic operating conditions. Emission limits are established in accordance with relevant international standards, with the observed deviations from standard practices highlighted in existing studies. The operation of the BESS-assisted fast-charging system is classified into five distinct operating stages, and the variations in spectral emissions across these stages are analyzed. A comparative evaluation with a grid-fed fast charger reveals the influence of BESS integration on power quality. Notably, the analysis shows a significant increase in even harmonics during EV charging events. This component is identified as the limiting factor in the network’s harmonic hosting capacity, underscoring the need to account for even harmonics in future grid compatibility assessments. These findings provide valuable insights for grid operators, EV infrastructure planners, and standardization bodies aiming to ensure compliance with power quality standards in evolving charging scenarios. Full article

Lithium-ion batteries are pivotal components in battery electric vehicles, significantly influencing vehicle design and performance. This study investigates the interactions between cell properties and battery pack characteristics through statistical correlation analysis of datasets derived from industry-leading benchmarking platforms. Findings indicate that energy densities are comparable across cell formats at the pack level. While NMC and NCA chemistries outperform LFP in energy density at both cell and pack levels, LFP’s favorable cell-to-pack factors mitigate these differences. Analysis of cell properties suggests that increases in cell-level volumetric and gravimetric energy density result in proportionally smaller gains at the pack level due to the growing proportion of required passive components. The impact of cell chemistry and format on the z-dimension of a battery pack is analyzed in order to identify dependencies and influences between nominal cell properties and the geometry of the battery pack. The analysis suggests no significant influence of the used cell chemistry on the vertical dimension of a battery pack. The consideration of cell formats shows a dependency between the battery pack z-dimension and cell geometry, with prismatic cells reaching the highest pack heights and cylindrical cells being observed in packs of smaller vertical dimensions. The study also investigates the emerging sodium-ion battery technology and assesses pack-level energy densities derived from cell-level properties. The insights of this study contribute to the understanding of cell-to-pack relationships, guiding R&D toward improved energy storage solutions for electric vehicles. Full article

This project presents the development of an open-source educational platform based on an automotive Electrically Excited Synchronous Machine (EESM) repurposed from a KIA Sportage mild-hybrid vehicle. The introduction provides an overview of hybrid drive systems and the primary configurations employed in automotive applications, including classifications based on power flow and the placement of electric motors. The focus is placed on the parallel hybrid configuration, where a belt-driven starter-generator assists the internal combustion engine (ICE). Due to the proprietary nature of the original control system, the unit was disassembled, and a custom control board was designed using a Texas Instruments C2000 Digital Signal Processor (DSP). The motor features a six-phase dual three-phase stator, offering improved torque smoothness, fault tolerance, and reduced current per phase. A compact Anisotropic Magneto Resistive (AMR) position sensor was implemented for position and speed measurements. Current sensing was achieved using both direct and magnetic field-based methods. The control algorithm was verified on a modified six-phase inverter under simulated vehicle conditions utilizing a dynamometer. Results confirmed reliable operation and validated the control approach. Future work will involve complete hardware testing with the new control board to finalize the platform as a flexible, open-source tool for research and education in hybrid drive technologies. Full article

The field of electric vehicles and electric mobility, like other modern engineering practice, not only requires deep analytical skills but increasingly demands the ability to synthesise and integrate knowledge across multiple disciplines (e.g., electrical engineering, mechanical engineering, sustainability engineering, design engineering) to create innovative systems. Education today, however, still has a strong analysis focus: learning, exploring, and understanding theories and concepts is the main drive. Design and synthesis build on those and aim at bringing together theories and concepts into creative and innovative systems. Teaching design and synthesis is notoriously hard. The design of electric vehicles exemplifies the complexity of contemporary engineering problems, requiring the integration of multiple domains to experience the challenges connected to design and synthesis. This paper presents the need for, rationale behind, setup of, and experiences with a 5 European Credit (140 h) Master’s-level (postgraduate) course named “Electric Vehicle System Design” that we developed as a joint effort for the University of Twente and the University of South-Eastern Norway. The course is specifically designed to immerse students in the multidisciplinary design and synthesis processes central to electric mobility. In the paper, the course framework, project-based approach, and lessons learned are discussed. This highlights how engineering students can be equipped for the challenges inherent to designing electric vehicles. Full article

The shift toward sustainable aviation has accelerated research into hybrid electric aircraft, particularly in the context of regional air mobility. To support this transition, we introduce the Soft Fixed Route Hybrid Electric Aircraft Charging Problem with Variable Speed (S-FRHACP-VS), a novel optimization problem for managing hybrid electric aircraft operations that considers variable speed. The objective is to minimize total costs by determining charging strategies, refueling decisions, hybridization ratios, and speed decisions while adhering to a soft schedule. This paper introduces an iterative variable-based fixation heuristic, named Iterative Two-Stage Mixed-Integer Programming Heuristic (ITS-MIP-H), that alternatively optimizes speed and hybridization ratios while considering the soft schedule constraints, nonlinear charging, and nonlinear energy consumption functions. In addition, a metaheuristic genetic algorithm is proposed as an alternative optimization approach. Experiments on ten realistic flight instances demonstrate that optimizing speed leads to an average cost reduction of 7.64% compared to the best non-speed-optimized model, with reductions of up to 18.64% compared to an all-fuel-based heuristic. Although genetic algorithm provides a viable alternative that performs better than the best non-speed-optimized model, the proposed iterative variable-based fixation heuristic approach consistently outperforms the metaheuristic, achieving the best solutions within seconds. These results provide new insights into the integration of hybrid electric aircraft within transportation networks, contributing to advancements in aircraft routing optimization, energy-efficient operations, and sustainable aviation policy development. Full article

Enhancing system durability and fuel economy stands as a crucial factor in the energy management of fuel cell hybrid vehicles. This paper proposes an Equivalent Consumption Minimization Strategy (ECMS) based on the Genetic Algorithm (GA), aiming to minimize the overall operating cost of the system. First, this study establishes a dynamic model of the hydrogen–electric hybrid vehicle, a static input–output model of the hybrid power system, and an aging model. Next, a speed prediction method based on an Autoregressive Integrated Moving Average (ARIMA) model is designed. This method fits a predictive model by collecting historical speed data in real time, ensuring the robustness of speed prediction. Finally, based on the speed prediction results, an adaptive Equivalence Factor (EF) method using a GA is proposed. This method comprehensively considers fuel consumption and the economic costs associated with the aging of the hydrogen–electric hybrid system, forming a total operating cost function. The GA is then employed to dynamically search for the optimal EF within the cost function, optimizing the system’s economic performance while ensuring real-time feasibility. Simulation outcomes demonstrate that the proposed energy management strategy significantly enhances both the durability and fuel economy of the fuel cell hybrid vehicle. Full article

This study presents the total cost of ownership (TCO) of battery electric buses across Europe (the EU27 + UK + Türkiye). A comprehensive review of the assumptions and data used for the TCO calculation of buses in the literature is provided, along with calculations of the different bus TCO excluding labor costs, across these countries. The calculated TCO is compared with diesel costs in each country to identify the countries in which bus electrification is financially most competitive. The study reveals that the financial case for bus electrification is strongest in Finland, France, Belgium and Greece (TCOs around €750k to €850k and high diesel costs in the range of €1.70 per liter) and is weakest in Malta, Bulgaria and Cyprus. These results are expected to be of interest for operators, academics, policy makers, and financial investors in bus electrification. Full article

To promote a more decentralized energy system, the European Commission introduced the concept of Renewable Energy Communities (RECs). Meanwhile, the increasing penetration of Electric Vehicles (EVs) may significantly increase peak power demand and consumption ramps when charging sessions are left uncontrolled. However, by integrating smart charging strategies, such as Vehicle-to-Grid (V2G), EV storage can actively support the energy balance within RECs. In this context, this work proposes a comprehensive and scalable model for leveraging smart charging capabilities in RECs. This approach focuses on an external cooperative framework to optimize incentive acquisition and reduce dependence on Medium Voltage (MV) grid substations. It adopts a hybrid strategy, combining Mixed-Integer Linear Programming (MILP) to solve the day-ahead global optimization problem with local rule-based controllers to manage power deviations. Simulation results for a six-month case study, using historical demand data and synthetic charging sessions generated from real-world events, demonstrate that V2G integration leads to a better alignment of overall power consumption with zonal pricing, smoother load curves with a 15.5% reduction in consumption ramps, and enhanced cooperation with a 90% increase in shared power redistributed inside the REC. Full article