Editor’s Choice Articles

During the early phases of EV market penetration, German policy makers supported the roll-out of a nation-wide charging infrastructure network by extensive state activities, most notably voluminous funding schemes to provide subsidies for publicly owned as well as business-driven charge point operators. An increasing EV adoption rate and therefore an increasing demand has since shifted the focus of policy making towards enabling a privately funded, competitive market. More recently, budgetary constraints have led to abrupt restrictions on policy making and market disruptions. This paper aims to provide insight into policy making during this transitional period, give reason for why a state-funded jump start was necessary for developing the charging infrastructure, and explore how policy makers now intend to foster the development of a functioning market while phasing out detrimental interventionist measures. Full article

Vehicle detection is essential for real-world applications such as road surveillance, intelligent transportation systems, and autonomous driving, where high accuracy and real-time performance are critical. However, achieving robust detection remains challenging due to scene complexity, occlusion, scale variation, and varying lighting conditions. Over the past two decades, numerous studies have been proposed to address these issues. This study presents a comprehensive and structured survey of image-based vehicle detection methods, systematically comparing classical machine learning techniques based on handcrafted features with modern deep learning approaches. Deep learning methods are categorized into one-stage detectors (e.g., YOLO, SSD, FCOS, CenterNet), two-stage detectors (e.g., Faster R-CNN, Mask R-CNN), transformer-based detectors (e.g., DETR, Swin Transformer), and GAN-based methods, highlighting architectural trade-offs concerning speed, accuracy, and practical deployment. We analyze widely adopted performance metrics from recent studies, evaluate characteristics and limitations of popular vehicle detection datasets, and explicitly discuss technical challenges, including domain generalization, environmental variability, computational constraints, and annotation quality. The survey concludes by clearly identifying open research challenges and promising future directions, such as efficient edge deployment strategies, multimodal data fusion, transformer-based enhancements, and integration with Vehicle-to-Everything (V2X) communication systems. Full article

In the automotive industry, battery electric vehicles (BEVs) represent the future of individual mobility. To establish a long-term market presence, innovative vehicle and powertrain concepts are essential, and therefore, identifying the most promising concepts is crucial to determine where to focus research and development further. Academia plays a significant role in this identification process; however, researchers often face restricted access to data from the industry, and identifying different technological approaches is often connected to significant costs. We present a comprehensive study of the Porsche Taycan Performance Battery Plus, which integrates two technological advancements: the first series-production implementation of a two-speed transmission in an electric vehicle allowing for high acceleration while reaching high top speeds and a 800 V battery system architecture providing more efficient charging capabilities. This study details vehicle dynamics, electric powertrain efficiencies, their impact on vehicle level, and the two technological advancements. This work aims to provide researchers access to vehicle dynamometer and real-world data from one of the most advanced and innovative battery electric sports cars. This allows for further analysis of cutting-edge technologies that have yet to reach the mass market. In addition to providing researchers with this study’s results, all data utilized in this study will be made available as open-access, enabling individual use of test data for parameter identification and the development of simulation models. Full article

The Department of Veterans Affairs’ (VA’s) transportation system plays an important role in ensuring access to transportation services for veterans, particularly those in rural or underserved areas. However, concerns remain regarding the effectiveness of collaboration among the various VA transportation stakeholders. Persistent transportation challenges hinder veterans’ access to essential healthcare services and resources. Electric autonomous ride-sharing services (ARSSs) offer a promising opportunity to enhance transportation access; however, their current limitations and the perspectives of VA transportation personnel must be considered. This study explored the current perspectives of the VA transportation system and assessed ARSSs as an innovative and sustainable alternative through interviews with eight VA transportation stakeholders representing seven transportation sectors. Our findings revealed the VA’s strengths, including personalized service, flexible accommodations, and collaborative care models, but also identified challenges, including limited funding, staff shortages, volunteer constraints, and restrictive eligibility criteria. The introduction of ARSSs was identified as an opportunity to alleviate some of these constraints by reallocating human resources and improving access to essential services, although concerns remain regarding ARSSs’ ability to accommodate veterans with disabilities and address rural route complexities. Effective communication strategies and streamlined coordination were key recommendations for improving service delivery and expanding transportation access for veterans. Full article

Integrating electric vehicles (EVs) has become integral to modern power grids to enhance grid stability and support green energy transportation solutions. EVs emerged as a promising energy solution that introduces a significant challenge to the unpredictable and dynamic nature of EV charging and discharging behaviors. These EV behaviors are performed by grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations that create unpredictable disturbances in the power grid. These disturbances introduced a nonlinear dynamic that compromises grid stability and power quality. Due to the unpredictable nature of these disturbances, the conventional control design with dynamic model prediction cannot manage these disturbances. To address these challenges, a Physics-Informed Machine Learning (PIML)-enhanced Model Predictive Control (MPC) framework is proposed to learn the stochastic behaviors of the EV-introduced disturbance in the power grid. The learned PIML model is integrated into an MPC framework to enable an accurate prediction of EV-driven disturbances with minimal data requirements. The MPC formulation optimizes pre-emptive control actions to mitigate the disturbance and ensure robust grid stability and enhanced EV integration. A comprehensive convergence and stability analysis of the proposed MPC formulation uses Lyapunov-based proofs. The efficacy of the proposed control design is evaluated on IEEE benchmark systems, demonstrating a significant improvement in performance metrics, such as frequency deviation, voltage stability, and scalability, compared to the conventional MPC design. The proposed MPC framework offers scalable and robust real-time EV grid integration in modern power grids. Full article

This work presents an innovative approach to battery thermal management for e-bikes by addressing heat generation at its source rather than relying on conventional cooling techniques. Traditional systems rely on heat sinks, fans, phase change materials, or cooling fluids, which increase cost and complexity. In contrast, this study integrates embedded thermal management algorithms into the e-bike’s motor controller, enabling temperature regulation through performance limitation. Two models are investigated: a reactive algorithm that reduces speed as battery temperature nears a critical threshold, and a predictive algorithm that forecasts future temperature evolution and adjusts speed accordingly. Experimental results show that the reactive algorithm successfully limited battery temperature to 26.7% below the critical value but at the cost of speed reductions up to 40%. The predictive model, tested in two configurations, demonstrated improved performance, limiting speed by a maximum of 20% while maintaining stable temperature profiles. These findings confirm that embedded algorithms can effectively manage battery temperature, with the reactive model being suitable for low-complexity applications and the predictive model offering enhanced performance when more computational resources are available. Full article

This study presents a practical methodology for systematically incorporating customer expectations and needs into the market implementation of electric vehicles (EVs). Utilising Quality Function Deployment (QFD), companies can evaluate and understand customer requirements, optimise product improvements, and allocate resources efficiently. Though not widely adopted in many Western contexts, QFD proves valuable in enhancing strategic decision making and improving market penetration. Moreover, the integration of EVs with renewable energy and advancements in battery and grid technologies strengthens their environmental and economic benefits. As technological progress and policy support continue, EVs are positioned to drive sustainable transportation and contribute to global carbon reduction goals. Full article

We present a reduced-order battery management system (BMS) for lithium-ion cells in electric and hybrid vehicles that couples a physics-based single-particle model (SPM) derived from the Cahn–Hilliard phase-field formulation with a lumped heat-transfer model. A three-dimensional COMSOL^® 5.0 simulation of a LiFePO₄ particle produced voltage and temperature data across ambient temperatures (253–298 K) and discharge rates (1 C–20.5 C). Principal component analysis (PCA) reduced this dataset to five latent variables, which we then mapped to experimental voltage–temperature profiles of an A123 Systems 26650 2.3 Ah cell using a self-normalizing neural network (SNN). The resulting ROM achieves real-time prediction accuracy comparable to detailed models while retaining essential electrothermal dynamics. Full article

This paper presents an optimization model for the charging infrastructure of electric vehicles (EV) designed to minimize installation costs, maximize the utilization of photovoltaic energy, reduce dependency on the electrical grid, and optimize charging times. The model utilizes methodologies such as Linear Optimal Power Flow (LOPF) to align EV charging schedules with the availability of renewable energy sources. Key inputs for the model include Photovoltaic (PV) production profiles, EV charging demands, specifications of the chargers, and the availability of grid energy. The framework integrates installation costs, grid energy consumption, and charging duration into a weighted objective function, ensuring energy balance and operational efficiency while adhering to budgetary constraints. Five distinct optimization scenarios are analyzed to evaluate the trade-offs between cost, charging duration, and reliance on various energy sources. The simulation results obtained from Cadi Ayyad University validate the model’s effectiveness in balancing costs, enhancing charging performance, and increasing dependence on solar energy. This approach provides a comprehensive solution for the development of sustainable and cost-effective EV charging infrastructure. Full article

This study presents a Model Predictive Control (MPC) strategy for the Battery Thermal Management System (BTMS) in electric vehicles (EVs) to optimize energy efficiency while maintaining battery temperature within the optimal range. Due to the complexity of BTMS dynamics, a high-fidelity model was developed using MATLAB/Simscape (2021a), and an artificial neural network (ANN)-based model was designed to achieve high accuracy with reduced computational load. To mitigate oscillatory control inputs observed in conventional MPC, an infinity-horizon MPC framework was introduced, incorporating a value function that accounts for system behavior beyond the prediction horizon. The proposed controller was evaluated using a simulation environment against a conventional rule-based controller under varying ambient temperatures. Results demonstrated significant energy savings, including a 78.9% reduction in low-temperature conditions, a 36% reduction in moderate temperatures, and a 27.8% reduction in high-temperature environments. Additionally, the controller effectively stabilized actuator operation, improving system longevity. These findings highlight the potential of ANN-assisted MPC for enhancing BTMS performance while minimizing energy consumption in EVs. Full article

Riding comfort and safety are essential requirements for any form of transportation but particularly for electric bicycles (e-bikes), which are highly affected by varying road conditions. These factors largely depend on the effectiveness of the e-bike’s control strategy. While several studies have proposed control approaches that address comfort and safety, vibration—an influential factor in both structural integrity and rider experience—has received limited attention during the design phase. Moreover, many commercially available e-bikes provide manual assistance-level settings, leaving comfort and safety management to the rider’s experience. This study proposes a Road-Adaptive Vibration Reduction System (RAVRS) that can be deployed on an e-bike rider’s smartphone to automatically maintain riding comfort and safety using manual assistance control. A fuzzy-based control algorithm is adopted to dynamically select the appropriate assistance level, aiming to minimize vibration while maintaining velocity and acceleration within thresholds associated with comfort and safety. This study presents a vibration analysis to highlight the significance of vibration control in improving electronic reliability, reducing mechanical fatigue, and enhancing user experience. A functional prototype of the RAVRS was implemented and evaluated using real-world data collected from experimental trips. The simulation results demonstrate that the proposed system achieves effective control of speed and acceleration, with success rates of 83.97% and 99.79%, respectively, outperforming existing control strategies. In addition, the proposed RAVRS significantly enhances the riding experience by improving both comfort and safety. Full article

This article presents an alternative variant of motion planning techniques for autonomous vehicles (AVs) centered on an inverse approach that concurrently optimizes both trajectory and speed. This method emphasizes searching for a trajectory and distributing its speed within a single road segment, regarded as a final element. The references for the road lanes are represented by splines that interpolate the path length, derivative, and curvature using Cartesian coordinates. This approach enables the determination of parameters at the final node of the road segment while varying the reference length. Instead of directly modeling the trajectory and velocity, the second derivatives of curvature and speed are modeled to ensure the continuity of all kinematic parameters, including jerk, at the nodes. A specialized inverse numerical integration procedure based on Gaussian quadrature has been adapted to reproduce the trajectory, speed, and other key parameters, which can be referenced during the motion tracking phase. The method emphasizes incorporating kinematic, dynamic, and physical restrictions into a set of nonlinear constraints that are part of the optimization procedure based on sequential quadratic optimization. The objective function allows for variation in multiple parameters, such as speed, longitudinal and lateral jerks, final time, final angular position, final lateral offset, and distances to obstacles. Additionally, several motion planning variants are calculated simultaneously based on the current vehicle position and the number of lanes available. Graphs depicting trajectories, speeds, accelerations, jerks, and other relevant parameters are presented based on the simulation results. Finally, this article evaluates the efficiency, speed, and quality of the predictions generated by the proposed method. The main quantitative assessment of the results may be associated with computing performance, which corresponds to time costs of 0.5–2.4 s for an average power notebook, depending on optimization settings, desired accuracy, and initial conditions. Full article

The complexity and shortening of development cycles in the automotive industry, particularly with the rise in hybrid electric vehicle sales, increases the need for efficient calibration and testing methods. Virtualization using hardware-in-the-loop testbenches has the potential to counteract these trends, specifically for the calibration of hybrid operating strategies. This paper presents a dynamic closed-loop validation of a hardware-in-the-loop testbench designed for the virtual calibration of hybrid operating strategies for a plug-in hybrid electric vehicle. Requirements regarding the hardware-in-the-loop testbench accuracy are defined based on the investigated use case. From this, a dedicated hardware-in-the-loop testbench setup is derived, including an electrical setup as well as a plant simulation model. The model is then operated in a closed loop with a series production hybrid control unit. The closed-loop validation results demonstrate that the chassis simulation reproduces driving resistance closely aligning with the reference data. The driver model follows target speed profiles within acceptable limits, achieving an R² = 0.9993, comparable to the R² reached by trained human drivers. The transmission model replicates the gear ratios, maintaining rotational speed deviations below 30 min⁻¹. Furthermore, the shift strategy is implemented in a virtual control unit, resulting in a gear selection comparable to reference measurements. The energy flow simulation in the complete powertrain achieves high accuracy. Deviations in the high-voltage battery state of charge remain below 50 Wh in a WLTC charge-sustaining drive cycle and are thus within the acceptable error margin. The net energy change criterion is satisfied with the hardware-in-the-loop testbench, achieving a net energy change of 0.202%, closely matching the reference measurement of 0.159%. Maximum deviations in cumulative high-voltage battery energy are proven to be below 10% in both the charging and discharging directions. Fuel consumption and CO₂ emissions are modeled with deviations below 3%, validating the simulation’s representation of vehicle efficiency. Real-time capability is achieved under all investigated operating conditions and test scenarios. The testbench achieves a real-time factor of at least 1.104, ensuring execution within the hard real-time criterion. In conclusion, the closed-loop validation confirms that the developed hardware-in-the-loop testbench satisfies all predefined requirements, accurately simulating the behavior of the reference vehicle. Full article

Power systems’ increased running costs and overuse of fossil fuels have resulted in continuing energy scarcity and momentous energy gap challenges worldwide. Renewable energy sources can meet exponential energy growth, lower reliance on fossil fuels, and mitigate global warming. An MINLP optimization method to solve the RES-hybrid system economic dispatch of electric vehicle charging stations is proposed in this paper. This technique bridges the gap between theoretical models and real-world implementation by balancing technical optimization with practical deployment constraints, making a timely and meaningful contribution. These contributions extend the practical application of MINLP in modern grid operations by aligning optimization outputs with the stochastic character of renewable energy, which is still a gap in the existing literature. The proposed economic dispatch simulation results over 24 h at an hourly resolution show that all generation units contributed proportionately to meeting EVCS demand: solar PV (51.29%), ESS (13.5%), grid (29.92%), and wind generator (8.29%). The RES-hybrid energy management systems at charging stations are designed to make the best use of solar PV power during the EVCS charging cycle. The supply–demand load profile problem dynamic in EVCS are designed to reduce reliance on grid electricity supplies while increasing renewable energy usage and reducing carbon impact. Full article

Assessing the impact of electric vehicle (EV) integration on power systems is crucial, particularly regarding frequency stability, which often remains largely unaddressed, especially in developing countries. This paper examines the effects of EV penetration on the frequency stability of Curaçao’s power network, an aspect not previously studied for the island. As a key contribution, we present a representative model of Curaçao’s power network, adjusting the dynamic models of the speed governors of synchronous machines, using data available to the academic community. Additionally, we analyze the impacts of EVs on the grid’s frequency stability under different EV participation scenarios. To achieve this, simulations were conducted considering various EV participation scenarios and different types of chargers to assess their impact on grid stability. The study evaluates key frequency stability metrics, including the rate of change of frequency (RoCoF) as well as the highest and lowest frequency values during the transient period. The results indicated that higher EV penetration can significantly impact frequency stability. The observed increase in the RoCoF and frequency zenith values suggests a weakening of the grid’s ability to withstand frequency disturbances, particularly in high-EV-penetration scenarios. Full article

The transition to electric public transportation is crucial for reducing the carbon footprint and promoting environmental sustainability. However, successful implementation requires strong public policies, including tax incentives and educational programs, to encourage widespread adoption. This study identifies the optimal electric bus model for Cuenca, Ecuador, using the multicriteria decision-making methods PROMETHEE and TOPSIS. The evaluation considers four key dimensions: technical (autonomy, passenger capacity, charging time, engine power), economic (acquisition, operation, and maintenance costs), social (community acceptance and accessibility), and environmental (reduction of pollutant emissions). The results highlight passenger capacity as the most influential criterion, followed by autonomy and engine power. The selected electric bus model emerges as the most suitable option due to its energy efficiency, low maintenance costs, and long service life, making it a cost-effective long-term investment. Additionally, its adoption would enhance air quality and improve the overall user experience. Beyond its relevance to Cuenca, this study provides a replicable methodology for evaluating electric bus feasibility in other cities with different geographic and socioeconomic contexts. Full article

The automotive 12 V power net is undergoing significant transitions driven by increasing power demand, higher availability requirements, and the aim to reduce wiring harness complexity. These changes are prompting a transformation of the power net architecture. To understand how future power net topologies will influence component requirements, electrical simulations are essential. They help with analyzing the transient behavior of the future power net, such as under- and over-voltages, over-currents, and other harmful electrical phenomena. The accurate parametrization of simulation models is crucial in order to obtain reliable results. This study focuses on the wiring harness, specifically its resistance and inductance, as well as the loads within the low-voltage power net, including their power profiles and input capacities. The parameters for this study were derived from vehicle measurements in three selected battery electric vehicles from different segments and were enriched by virtual vehicle analyses. As a result, an extensive database of vehicle parameters was created and is presented in this paper, and it can be used for power net simulations. As a next step, the collected data can be utilized to predict the parameters of various configurations in a zonal architecture setup. Full article

Electric vehicles have garnered substantial attention as an environmentally sustainable transportation alternative amid escalating global concerns regarding ecological preservation and energy resource management. While the proliferation of electric vehicles necessitates the development of efficient and secure charging infrastructure, the inherent communication-intensive nature of the charging processes has raised concerns regarding potential privacy vulnerabilities. Our paper introduces a privacy protection scheme specifically designed for electric vehicle charging reservations to address this issue. The primary goal of this scheme is to protect user privacy while maintaining operational efficiency and economic viability for charging providers. Our proposed solution ensures a secure and private environment for charging reservation transactions and subsequent deviation settlements by incorporating advanced technologies, including zero-knowledge proof, a consortium blockchain, and homomorphic encryption. The scheme encrypts charging reservation information and securely transmits it via a consortium blockchain, effectively shielding the sensitive data of all participating parties. Notably, the experimental findings establish the robustness of our scheme in terms of its security and privacy protection, aligning with the stringent demands of electric vehicle charging operations. Full article

Reducing carbon dioxide emissions in transportation has become a priority for achieving emission targets. Transitioning to electric vehicles significantly decreases global CO₂ emissions and reduces urban noise and air pollution. The selection of efficient charging strategies for electric bus fleets substantially influences their environmental impact. This study analyzes the charging strategy for electric bus fleets based on real operational data from Győr, Hungary. It evaluates the impact of different charging times and strategies on CO₂ emissions, considering the energy mixes of Hungary, Poland, Germany, and Sweden. A methodology has been developed for defining sustainable and environmentally friendly charging strategies by incorporating operational conditions as well as daily, monthly, and seasonal fluctuations in emission factors. Results indicate substantial potential for emission reduction through the recommended alternative charging strategies, although further studies regarding battery lifespan and economic feasibility of infrastructure investments are recommended. The novelty of this work lies in integrating real charging data with hourly country-specific emission intensity values to assess environmental impacts dynamically. A comparative framework of four charging strategies provides quantifiable insights into emission reduction potential under diverse national energy mixes. Full article

The increasing computational complexity of Model Predictive Control (MPC) in battery systems limits its practical adoption, despite its potential for optimizing performance under dynamic operating conditions. To address this challenge, this study introduces an Artificial Neural Network-based MPC framework (MPCANN) tailored for VTC6 3Ah lithium-ion cells, aiming to reduce computational burdens while retaining predictive accuracy. The framework synergizes MPC’s predictive capabilities with the daptive learning of Artificial Neural Network (ANN) by training the ANN offline using MPC-derived input–output data. Validation against prior MPC results demonstrates MPCANN’s ability to replicate MPC behavior across temperatures, achieving strong alignment in current and temperature predictions. While state of charge (SoC) estimation accuracy requires refinement at elevated temperatures, the framework reduces computation time by 94% compared to traditional MPC, highlighting its efficiency. These results underscore MPCANN’s potential to enable real-time implementation of advanced battery control strategies, offering a pathway to balance computational efficiency with performance in adaptive energy systems. Full article

This study presents the first investigation into the problem of autonomous vehicle (AV) merging into existing platoons, proposing a multi-agent deep reinforcement learning (MA-DRL)-based cooperative control framework. The developed MA-DRL architecture enables coordinated learning among multiple autonomous agents to address the multi-objective coordination challenge through synchronized control of platoon longitudinal acceleration, AV steering and acceleration. To enhance training efficiency, we develop a dual-layer multi-agent maximum Q-value proximal policy optimization (MAMQPPO) method, which extends the multi-agent PPO algorithm (a policy gradient method ensuring stable policy updates) by incorporating maximum Q-value action selection for platoon gap control and discrete command generation. This method simplifies the training process by using maximum Q-value action policy optimization to learn platoon gap selection and discrete action commands. Furthermore, a partially decoupled reward function (PD-Reward) is designed to properly guide the behavioral actions of both AVs and platoons while accelerating network convergence. Comprehensive highway simulation experiments show the proposed method reduces merging time by 37.69% (12.4 s vs. 19.9 s) and energy consumption by 58% (3.56 kWh vs. 8.47 kWh) compared to existing methods (the quintic polynomial-based + PID (Proportional–Integral–Differential)). Full article

Since permanent magnets and rotors are core components of electric vehicle drive motors, accurate diagnosis of demagnetization and eccentricity faults is crucial for ensuring the safe operation of electric vehicles. Currently, intelligent diagnostic methods based on three-phase current signals have been widely adopted due to their advantages of easy acquisition, low cost, and non-invasiveness. However, in practical applications, the fault characteristics in current signals are relatively weak, leading to diagnostic performance that falls short of expected standards. To address this issue and improve diagnostic accuracy, this paper proposes a novel diagnostic method. First, branch current is utilized as the data source for diagnosis to enhance the fault characteristics of the diagnostic signal. Next, a dual-modal feature extraction module is constructed, employing Variational Mode Decomposition (VMD) and Fast Fourier Transform (FFT) to concatenate the input branch current along the feature dimension in both the time and frequency domains, achieving nonlinear coupling of time–frequency features. Finally, to further improve diagnostic accuracy, a cascaded convolutional neural network based on dilated convolutional layers and multi-scale convolutional layers is designed as the diagnostic model. Experimental results show that the method proposed in this paper achieves a diagnostic accuracy of 98.6%, with a misjudgment rate of only about 2% and no overlapping feature results. Compared with existing methods, the method proposed in this paper can extract higher-quality fault features, has better diagnostic accuracy, a lower misjudgment rate, and more excellent feature separation ability, demonstrating great potential in intelligent fault diagnosis and maintenance of electric vehicles. Full article

As transportation systems evolve, ensuring safe and efficient mobility in Intelligent Transportation Systems remains a priority. Work zones, in particular, pose significant safety challenges due to lane closures, which can lead to abrupt braking and sudden lane changes. Most previous research on Connected and Autonomous Vehicles (CAVs) assumes ideal communication conditions, overlooking the effects of message loss and network unreliability. This study presents a comprehensive smart work zone (SWZ) framework that enhances lane-change safety by the integration of both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. Sensor-equipped SWZ barrels and Roadside Units (RSUs) collect and transmit real-time hazard alerts to approaching CAVs, ensuring coverage of critical roadway segments. In this study, a co-simulation framework combining VEINS, OMNeT++, and SUMO is implemented to assess lane-change safety and communication performance under realistic network conditions. Findings indicate that higher Market Penetration Rates (MPRs) of CAVs can lead to improved lane-change safety, with time-to-collision (TTC) values shifting toward safer time ranges. While lower transmission thresholds allow more frequent communication, they contribute to earlier network congestion, whereas higher thresholds maintain efficiency despite increased packet loss at high MPRs. These insights highlight the importance of incorporating realistic communication models when evaluating traffic safety in connected vehicle environments. Full article

With the rapid growth of renewable energy integration, battery energy storage technologies are playing an increasingly pivotal role in modern power systems. Among these, electric vehicle distributed energy storage systems (EV-DESSs) using vehicle-to-grid technology and commercial battery energy storage systems (BESSs) exhibit substantial potential for user-side energy storage applications. A comparative analysis of the cost competitiveness between these two types of energy storage systems is crucial for understanding their roles in the evolving power system. However, existing studies lack a unified framework for techno-economic comparisons between EV-DESSs and commercial BESSs. To address this research gap, we conduct a comprehensive, technology-rich techno-economic assessment of EV-DESSs and commercial BESSs, comparing their economic feasibility across various grid services. Based on the technical modeling, this research simulates the operational processes and the additional battery degradation of EV-DESSs and commercial BESSs for providing frequency regulation as well as peak shaving and valley filling services. Building on this foundation, the study evaluates the cost competitiveness and profitability of both technologies. The results indicate that the levelized cost of storage (LCOS) of EV-DESSs and commercial BESSs ranges from 0.057 to 0.326 USD/kWh and from 0.123 to 0.350 USD/kWh, respectively, suggesting significant overlap and thus intense competition. The benefit–cost ratio of EV-DESSs and commercial BESSs ranges from 26.3% to 270.1% and from 19.3% to 138.0%, respectively. Battery cost and cycle life are identified as the key factors enabling EV-DESSs to outperform commercial BESSs. This drives a strong preference for lithium iron phosphate (LFP) batteries in V2G applications, allowing for LCOS reductions of up to 4.2%–76.3% compared to commercial BESSs across different grid services. In contrast, ternary lithium-ion batteries exhibit weaker cost competitiveness in EV-DESSs compared to commercial BESSs. While solid-state and sodium–ion batteries are promising alternatives, they are less competitive in V2G applications due to higher costs or a shorter cycle life. These findings highlight the superiority of LFP batteries in current V2G applications and the need to align cost, cycle life, and safety performance in the development of next-generation battery chemistries. Full article

To suppress current and torque ripples, this paper proposes a novel deadbeat predictive current control strategy based on an adaptive sliding mode observer for permanent magnet-assisted synchronous reluctance motor (PMa-SynRM) drives. The parameter sensitivity of predictive current control is analyzed, and a sliding mode observer is employed to calculate the parameter disturbances for voltage compensation. The predicted current is utilized instead of the sampled current to address the one-step delay issue, effectively suppressing the adverse effects of parameter mismatch in predictive control. The adaptive control parameter module suppresses the chattering phenomenon in sliding mode control and enhances the observer’s adaptability under varying load conditions. The effectiveness of the proposed strategy is validated on a 2.2 kW PMa-SynRM platform. This strategy can suppress current and torque fluctuations under complex operating conditions, which has significant implications for electric vehicle drive control. Full article

The electrification trend characterizing the current automotive industry creates opportunities for the implementation of innovative functionalities, enhancing aspects of energy efficiency and vehicle dynamics. Active vehicle suspensions are an important subsystem in this process. To enable proper suspension control, vehicle sensors can be used to measure the system’s response and, in some cases, preview the road conditions and the presence of possible obstacles. When assessing the performance of a suspension system, the speed bump crossing represents a challenging maneuver. A suitable trade-off between comfort and road holding must be found through different phases of the profile. The proposed work uses a fixed-gain observer obtained from Kalman filtering to identify road unevenness and adapt the control strategy when the vehicle travels through a bump. To this end, the obstacle is identified through the use of affordable sensors available in high-end vehicles: accelerometers, inertial measurement units, and stroke sensors. The proposed technique is also affordable from the computational point of view, thus enabling its use in common microprocessors tailored for the automotive field. The bump identification technique is validated through experimental data captured in a vehicle demonstrator. Subsequently, numerical results show that the proposed technique is able to enhance comfort while keeping road holding and attenuating the transient after taking the bump. Full article

With the growing demand for advanced control algorithms in permanent magnet synchronous motor (PMSM) speed regulation, active disturbance rejection control (ADRC) has garnered significant attention for its simplicity and effectiveness as an alternative to traditional proportional-integral (PI) controllers. However, two key challenges limit its broader application: the lack of an intuitive equivalence analysis that highlights the advantages of ADRC over PI control and the complexity in selecting appropriate extended state observer (ESO) structures within ADRC. To address these issues, this paper develops an equivalent model of ADRC based on the structure of a generalized PI controller, offering a clearer understanding of its operational principles. The results demonstrate the relationship between ADRC and generalized PI control while highlighting ADRC’s superior capabilities. Additionally, this paper constructs a generalized model that incorporates all ADRC observer configurations, including both high-order ESO (HESO) and cascaded ESO (CESO), enabling a comprehensive analysis of ADRC with various observer structures and establishing equivalence relationships between them. The findings provide valuable insights into the efficacy and versatility of ADRC in PMSM speed regulation, supported by experimental validation on a test bench using the dSPACE DS1202 MicroLabBox. Full article

As the adoption of electric vehicles (EVs) rapidly increases, the expansion of charging infrastructure has become a critical issue. Unlike internal combustion engine vehicles, EV charging is sensitive to factors such as the time and location for charging, depending on the charging speed and capacity of the battery. Therefore, recommending an appropriate charging station that comprehensively considers not only the user’s preference but also the charging time, waiting time, charging fee rates, and power supply status is crucial for the user’s convenience. Currently, charging station recommendation services suggest suitable charging stations near a designated location and provide information on charging capacity, fee rates, and availability of chargers. Furthermore, research is being conducted on EV charging station recommendations that take into account various charging environments, such as power grid and renewable energy conditions. To solve these optimization problems, a large amount of information about the user’s history and conditions is required. In this paper, we propose a real-time charging station recommendation method based on minimal and simple current information while driving to the destination. We first propose a preference objective function that considers the factors of distance, time, and fees, and then analyze the recommendation results based on both synthetic and real-world charging environments. We also observe the recommendation results for different combinations of the weights for these factors. If we set all the weights equally, we can obtain appropriate recommendations for charging stations that reflect driving distance, trip time, and charging fees in a balanced way. On the other hand, as the number of charging stations in a given area increases, it has been found that gradually increasing the weighting of charging fees is necessary to alleviate the phenomenon of rising fee rates and provide balanced recommendations. Full article

This study focuses on Vehicle-Integrated Photovoltaic (VIPV) strategy adopted as an energy supply vector in disaster scenarios. As a matter of fact, energy supply may be a very critical issue in a disaster context, when grid networks may be damaged. Emergency vehicles, including ambulances and trucks, as well as mobile units such as containers and operating rooms, can be equipped with photovoltaic modules and can serve as mobile emergency energy sources, supporting both vehicle operations and disaster relief efforts. A methodology was developed to estimate energy production under unpredictable disaster conditions, by adapting existing VIPV simulation approaches. Obtained results show that VIPV strategy, even under minimal daily energy generation, can be a useful aid for disaster resilience and emergency prompt response. Ambulance performance, analyzed for worst-case scenarios (e.g., December), shows that they can power medical devices for 1 to 15 h daily. Additionally, the ambulance can generate up to 2 $\mathrm{M}\mathrm{W}\mathrm{h}$ annually, reducing CO₂ emissions by up to 0.5 tons. In optimal configurations, mobile operating rooms can generate up to 120 times the daily energy demand for medical devices. Full article

In the context of global energy conservation and emission reduction, electric vehicles (EVs) are essential for low-carbon transport. However, their rapid growth challenges power grids with load imbalances across networks and increases user charging costs. To address the issues of load balancing across large-scale distribution networks and the charging costs for users, this paper proposes an optimization strategy for EV charging behavior based on deep reinforcement learning (DRL). The strategy aims to minimize user charging costs while achieving load balancing across distribution networks. Specifically, the strategy divides the charging process into two stages: charging station selection and in-station charging scheduling. In the first stage, a Load Balancing Matching Strategy (LBMS) is employed to assist users in selecting a charging station. In the second stage, we use the DRL algorithm. In the DRL algorithm, we design a novel reward function that enables charging stations to meet user charging demands while minimizing user charging costs and reducing the load gap among distribution networks. Case study results demonstrate the effectiveness of the proposed strategy in a multi-distribution network environment. Moreover, even when faced with varying levels of EV user participation, the strategy continues to demonstrate strong performance. Full article

In addition to the widely recognized benefits of reducing carbon emissions and protecting the environment, the authors believe that bus electrification has potential advantages in enhancing driving safety, improving passenger comfort, and reducing driver fatigue—areas that have not yet been sufficiently studied and emphasized. Safety and comfort are fundamental objectives in the continuous development of transportation systems. They are directly and closely related to both passengers and drivers and are among the top priorities when individuals choose their mode of transportation. Therefore, these aspects deserve broader and more in-depth attention and research. This study aims to identify the potential advantages of route bus electrification in terms of safety and comfort. The results of a passive experiment on the speed profile of buses operating on actual routes are presented here. Firstly, we focus on the acceleration/deceleration at the starting/stopping stops, specifically for regular-route buses, and obtain the following information: I. Starting acceleration from a bus stop is particularly strong in the second half of the acceleration process, being suitable for motor-driven vehicles. II. The features of the stopping deceleration at a bus stop are “high intensity” and “low dispersion”, with the latter enabling the refinement of regenerative settings and significantly lowering electricity economy during electrification. And we compare the speed profile of an electric bus with those of a diesel bus and obtain the following information: III. Motor-driven vehicles offer the advantages of “high acceleration performance” and “no gear shifting”, making them particularly suitable for the high-intensity acceleration required when route buses depart from stations. This not only simplifies driving operations but also enhances lane-changing safety. And by calculating and analyzing the jerk amount, we could quantitatively demonstrate the comfortable driving experience while riding on this type of bus where there is no shock due to gear shifting. IV. While the “high acceleration performance” of motor-driven vehicles produces “individual differences in the speed change patterns”, this does not translate to “individual differences in electricity consumption”, owing to the characteristics of this type of vehicle. With engine-driven vehicles, measures such as “slow acceleration” and “shift up early” are strongly encouraged to realize eco-driving, and any driving style that deviates from these measures is avoided. However, with motor-driven vehicles, the driver does not need to be too concerned about the speed change patterns during acceleration. This characteristic also suggests a benefit in terms of the electrification of buses. Full article

Developing nations like Bangladesh have yet to adopt hybrid (HEVs) or electric vehicles (EVs) for goods carrying, whereas environmental pollution and fuel costs are hitting hard. The electrically powered cars and trucks market promises an excellent opportunity for environmentally friendly transportation. However, these countries’ inadequate infrastructure, substantial initial expenses, and insufficient policies impeding widespread acceptance hold market growth back. This study examines the current status of the electric car market in low- and middle-income developing nations like Bangladesh, focusing on the infrastructure and regulatory framework-related barriers and the aspects of growth promotion. To promote an expanding hybrid and EV ecosystem, this article outlines recent studies and identifies critical regions where support for policy and infrastructural developments is needed. It discusses how developing nations may adapt successful international practices to suit their specific needs. At the same time, the research adopted system dynamics and case study methods to assess the transportation fleet (142 vehicles) of a livestock farm and find the feasibility of adopting HEVs and EVs. Several instances are improving infrastructures for recharging, providing incentives for lowering the adoption process cost, and creating appropriate regulatory structures that promote corporate and consumer involvement. Findings highlight how crucial it is for governments, businesses, customers, and international bodies to collaborate to build an affordable and sustainable EV network. The investigation concludes with recommendations for more research and appropriate regulations that may accelerate the adoption of EVs, reduce their adverse impacts on the environment, and promote economic growth. Full article

This article presents the instrumentation of an electronic–mechanical differential prototype, consisting of an arrangement of three throttles to operate two hub motors on the rear wheels of an electric vehicle. Each motor is connected to its respective throttle, while a third throttle is connected in series with the other two. This configuration allows for speed control during both rectilinear and curvilinear motion, following Ackermann differential geometry, in a simple manner and without the need for complex electronic systems that make the electronic differential more expensive. The differential throttles are strategically positioned on the mass bars connected to the steering system, ensuring that the rear wheels maintain the appropriate differential ratio. For this reason, it is referred to as an “electronic–mechanical differential”. Additionally, this method can be extended to a four-wheel differential system. Full article

Research on lithium-ion batteries has been driven by the growing demand for electric vehicles to mitigate greenhouse gas emissions. Despite advances, batteries still face significant challenges in efficiency, lifetime, safety, and material optimization. In this context, the objective of this research is to develop a predictive model based on Deep deep-Learning learning techniques. Based on Deep Learning techniques that combine Transformer and Physicsphysics-Informed informed approaches for the optimization and design of electrochemical parameters that improve the performance of lithium batteries. Also, we present a training database consisting of three key components: numerical simulation using the Doyle–Fuller–Newman (DFN) mathematical model, experimentation with a lithium half-cell configured with a zinc oxide anode, and a set of commercial battery discharge curves using electronic monitoring. The results show that the developed Transformer–Physics physics-Informed informed model can effectively integrate deep deep-learning DNF to make predictions of the electrochemical behavior of lithium-ion batteries. The model can estimate the battery battery-charge capacity with an average error of 2.5% concerning the experimental data. In addition, it was observed that the Transformer could explore new electrochemical parameters that allow the evaluation of the behavior of batteries without requiring invasive analysis of their internal structure. This suggests that the Transformer model can assess and optimize lithium-ion battery performance in various applications, which could significantly impact the battery industry and its use in Electric Vehicles vehicles (EVs). Full article

Vehicle-grid integration (VGI) is critical for the future of electric power systems, with decarbonization targets anticipating millions of electric vehicles (EVs) by 2030. As EV adoption grows, charging demand—particularly during peak hours in cities—may place significant pressure on the electrical grid. Charging at high power, especially during the evening when most EVs are parked in residential areas, can lead to grid instability and increased costs. One promising solution is to leverage long-duration, low-power charging, which can align with typical user behavior and improve grid compatibility. This paper delves into how public slow charging stations (<7.4 kW) in metropolitan residential areas can alleviate grid pressures while fostering a host of additional benefits. We show that, with respect to a reference (22 kW infrastructure), such stations can increase EV user satisfaction by up to 20%, decrease grid costs by 40% owing to a peak load reduction of 10 to 55%, and provide six times the flexibility for energy markets. Cities can overcome the limitation of private garage scarcity with this charging approach, thus fostering the transition to EVs. Full article

Under California’s Clean Miles Standard (or SB 1014), transportation network companies (TNCs) must transition to zero-emission vehicles by 2030. One significant hurdle for TNC drivers is the electric vehicle (EV) acquisition and operating costs versus an internal combustion engine (ICE) vehicle. This study therefore evaluates net TNC driving earnings through EV acquisition pathways—financing, leasing, and renting—along with EV-favoring policy options. Key metrics assessed include (1) total TNC income when considering service fees, fuel costs, monthly vehicle payments, etc., and (2) the time EVs take to reach parity with their ICE counterparts. Monthly comparisons illustrate the earning differentials between new/used EVs and gas-powered vehicles. Our analyses employing TNC data from 2019 to 2020 suggest that EV leasing is optimal for short-term low-mileage drivers; EV financing is more feasible for those planning to drive for TNCs for over two years; EV rentals are only optimal for higher mileages, and they are not an economical pathway for longer-term driving. Sensitivity analyses further indicate that EV charging price discounts are effective in shortening the time for EVs to reach cost parity over ICEs. Drivers may experience a total asset gain when reselling their TNC vehicle after two to three years. Full article

Eco-driving is a key strategy for reducing energy consumption and emissions in electric vehicles (EVs) and internal combustion engine (ICE) vehicles. However, research gaps remain regarding its effectiveness across different driving environments, vehicle types, transmission systems, and contexts. This research evaluates eco-driving efficiency in urban and interurban settings, comparing small (Caceres) and large (Madrid) cities and assessing EVs ICE with direct, manual, and automatic transmissions. The authors conducted a large-scale driving experiment in Spain, with over 500 test runs across different road types. Results in the large city show that eco-driving reduces energy consumption by 30.4% in EVs on urban roads, benefiting from regenerative braking, compared to 10.75% in manual ICE vehicles. Automatic ICE vehicles also performed well, with 29.55% savings in local streets. In interurban settings, manual ICE vehicles achieved the highest savings (20.31%), while EVs showed more minor improvements (11.79%) due to already optimized efficiency at steady speeds. The small city showed higher savings due to smoother traffic flow, while single-speed transmissions in EVs enhanced efficiency across conditions. These findings provide valuable insights for optimizing eco-driving strategies and vehicle design. Future research should explore AI-driven eco-driving applications and real-time optimization to improve sustainable mobility. Full article

The feasibility of electric vertical take-off and landing aircraft (eVTOL) relies on high-performance batteries with elevated energy and power densities for long-distance flight. However, systemic evaluation of battery chemistries for eVTOLs remains limited. This paper fills this research gap through a comprehensive investigation of current and emerging battery technologies. First, the properties of current battery chemistries are benchmarked against eVTOL requirements, identifying nickel-rich lithium-ion batteries (LIB), such as NMC and NCA, as the best suited for this application. Through comparison of 300 commercial battery cells, the Molicel INR21700-P45B cell is identified as the best candidate. Among next-generation batteries, SiSu solid-state batteries (SSBs) emerge as the most promising alternative. The performance of these cells is evaluated using a custom eVTOL battery simulation model for two eVTOL aircraft: the Volocopter VoloCity and the Archer Midnight. Results indicate that the Molicel INR21700-P45B underperforms in high-load scenarios, with a state of charge (SoC) at the end of the flight below the 30% safety margin. Simulated SoC values for the SiSu cell remain above this threshold, reaching 64.9% for the VoloCity and 64.8% for the Midnight. These results highlight next-generation battery technologies for eVTOLs and demonstrate the potential of SSBs to enhance flight performance. Full article

This article presents a straightforward and effective way of adding the Internet of Vehicles function to vehicles with different drive systems. By equipping the vehicle with a transmission device that communicates with the vehicle’s on-board diagnostics system, the current parameters of the vehicle’s operation can be read. This allows for wireless transmission to the application installed on the mobile device. The current parameters related to the vehicle’s operation together with the location data from the Global Positioning System on the mobile device are transferred to the cloud server. In this way, each vehicle with a drive system acquires the Internet of Vehicles function. Using this setup, short trips in urban conditions were carried out in a vehicle with an internal combustion engine and a plug-in hybrid vehicle. The data from the cloud system were then processed using the KNIME analytical platform. Signatures characterizing the vehicles with two types of drive systems were created. The obtained results were analyzed using various analytical tools and experimentally validated. The presented method is universally applicable and allows for the quick recognition of different drive systems based on signatures implementing k-means analysis. Acquiring and processing data from vehicles with various drive systems can be used to obtain important information about the vehicle itself, the road infrastructure, and the vehicle’s immediate surroundings, which can translate into increased road safety. Full article

The usage rates of shared autonomous vehicles (SAVs) have become a pressing concern following their increased deployment. While prior research has focused on initial user acceptance, post-adoption behavior remains underexplored. As SAV deployment matures, public concerns have expanded beyond safety to encompass service requirements, challenging the relevance of earlier findings to current commercialization efforts. This study investigates the factors shaping SAV utilization through an empirical study in Norway, where autonomous buses have operated for several years. Through mixed methods, we first analyzed responses from 106 participants to 43 SAV users and 63 witnesses of SAV operations. The results revealed that concerns had shifted from technological anxiety to service-related factors. Through purposive interviews with individuals who showed acceptance of SAVs but did not adopt them as their primary mode of transportation, we explored the gap between high acceptance and low usage. Our findings provide insights into long-term SAV deployment and guidelines for improving usage rates, highlighting the importance of addressing service characteristics such as information transparency, vehicle appearance, speed, and convenience, rather than focusing solely on safety in commercial settings. Full article

Battery electric vehicles (BEVs) are one promising approach to mitigating local greenhouse gas emissions. However, they still lag behind conventional vehicles in terms of maximum driving range. Using the heating, ventilation, and air-conditioning (HVAC) system reduces the maximum driving range of the vehicle even further since the energy for the HVAC system must come from the battery. This work investigates the impact of (1) an air–air heat exchanger and (2) an improved thermal insulation of a truck cabin on the heating performance of the HVAC system. Additionally, the required fresh-air volume flow rate to keep the CO₂ level within the truck cabin below the critical value of 1000 ppm is factored in. The results show that the two simple measures proposed could increase the energy efficiency of the truck’s HVAC system by 22%. When two persons are present in the truck cabin, a fresh-air volume flow of around 100 m³/h is required to keep the CO₂ concentration around 1000 ppm. These results prove that, even with simple measures, the energy efficiency of vehicles’ subsystems can be increased. In the future, more research will be necessary to further improve the energy efficiency of other vehicular subsystems. Full article

The Arctic presents various challenges for a transition to electric vehicles compared to other regions of the world, including environmental conditions such as colder temperatures, differences in infrastructure, and cultural and economic factors. For this study, academic researchers partnered with three rural communities: Kotzebue, Galena, and Bethel, Alaska, USA. The study followed a co-production process that actively involved community partners to identify 21 typical vehicle use cases that were then empirically modeled to determine changes in fueling costs and greenhouse gas emissions related to a switch from an internal combustion engine to an electric vehicle. While most use cases showed decreases in fueling costs and climate emissions from a transition to electric versions of the vehicles, some common use profiles did not. Specifically, the short distances of typical commutes, when combined with low idling and engine block heater use, led to an increase in both fueling costs and emissions. Arctic communities likely need public investment and additional innovation in incentives, vehicle types, and power systems to fully and equitably participate in the transition to electrified transportation. More research on electric vehicle integration, user behavior, and energy demand at the community level is needed. Full article

Energy storage systems and intelligent charging infrastructures are critical components addressing the challenges arising with the growth of renewables and the rising energy demand. Hybrid energy storage systems, in particular, are promising, as they combine two or more types of energy storage technologies with complementary characteristics to enhance the overall performance. Managing electric vehicle charging enables the demand to align with fluctuating generation, while storage systems can enhance energy flexibility and reliability. In the case of bidirectional charging, EVs can even function as mobile, flexible storage systems that can be integrated into the grid. This paper introduces a novel testing environment that integrates unidirectional and bidirectional charging infrastructures into an existing hybrid energy storage system. It describes the test environment in technical detail, explains the functionality, and outlines its usefulness in practical applications. The test system not only supports grid integration but also expands the degrees of freedom for testing, enabling flexible and realistic experimental setups. This environment facilitates comprehensive investigations into EV behavior, charging strategies, control algorithms, and user interactions. It provides a platform for exploring the possibilities, limitations, and optimal use cases for smart charging and hybrid storage systems in practice. Full article

Battery parameter identification is a key challenge for battery management systems, as parameterizing lithium-ion batteries is resource-intensive. Electrical circuit models (ECMs) provide an alternative, but their parameters change with physical conditions and battery age, necessitating regular parameter identification. This paper presents two modular algorithms to improve data quality and enable fast, robust parameter identification. First, the dequantizer algorithm restores the time series generating the noisy, quantized data using the inverse normal distribution function. Then, the Linear Continuous-Time Regression (LCTR) algorithm extracts exponential parameters from first-order or overdamped second-order systems, deducing ECM parameters and guaranteeing optimality with respect to RMSE. The parameters have low sensitivity to measurement noise since they are continuous-time. Sensitivity analyses confirm the algorithms’ suitability for battery management across various Gaussian measurement noise, accuracy, time constants and state-of-charge (SoC), using evaluation metrics like root-mean-square-error (RMSE) (<2 mV), relative time constant errors, and steady-state error. If the coarseness of rounding is not extreme, the steady-state is restored within a fraction of a millivolt. While a slight overestimation in the lower time constants occurs for overdamped systems, the algorithms outperform the conventional benchmark for first-order systems. Their robustness is further validated in real-life applications, highlighting their potential to enhance commercial battery management systems. Full article

This work aims to study the technical and economical feasibility of a new hydrogen transport network by 2035 in France. The goal is to furnish charging stations for fuel cell electrical vehicles with hydrogen produced by electrolysis of water using low-carbon energy. Contrary to previous research works on hydrogen transport for road transport, we assume a more realistic assumption of the demand side: we assume that only drivers driving more than 20,000 km per year will switch to fuel cell electrical vehicles. This corresponds to a total demand of 100 TWh of electricity for the production of hydrogen by electrolysis. To meet this demand, we primarily use surplus electricity production from wind power. This surplus will satisfy approximately 10% of the demand. We assume that the rest of the demand will be produced using surplus from nuclear power plants disseminated in regions. We also assume a decentralized production, namely, that 100 MW electrolyzers will be placed near electricity production plants. Using an optimization model, we define the hydrogen transport network by considering decentralized production. Then we compare it with more centralized production. Our main conclusion is that decentralized production makes it possible to significantly reduce distribution costs, particularly due to significantly shorter transport distances. Full article

Modern mobility faces increasing challenges, like carbon-free transportation and the need for flexible transportation solutions. The U-Shift II project addresses these problems through a modular electric vehicle architecture, a drive unit (Driveboard) and a vehicle body (Capsule). This separation offers high flexibility in different use cases. Current architecture paradigms, like AUTOSAR, face limitations in cost and development speed. To address these issues, this paper introduces a hybrid software architecture that integrates signal-oriented architecture (e.g., CAN bus) with service-oriented architecture for enhanced flexibility. A integral component of the hybrid architecture is the dynamic link system, which bridges these architectures by dynamically integrating Capsule-specific components into the Driveboard software stack during runtime. The performance of the developed systen and its functionality were evaluated using a hardware setup integrated into a Driveboard prototype. The dynamic link aystem was evaluated including latency measurements, as well as functionality tests. Additionally, a cloud-based reconfiguration process enhances the versatility of the Driveboard by allowing for over-the-air software updates and resource allocation. The results show a promising hybrid, reconfigurable E/E architecture that aims to enable a robust transition towards a pure service-oriented architecture required in future electric autonomous vehicles. Full article

The increased demand for electricity is inevitable due to transport sector electrification. A major part of this demand is from electric vehicle (EV) charging on a large scale, which is now a growing concern for the grid power distribution system. The lack of insight into grid energy demand by EVs makes it difficult to manage these consumptions on a large scale. For any grid load management application to be effective in minimizing the impact of uncontrolled charging, there is a need to gain insight into EV energy demand. To address this issue, this study presents data-driven modeling of EV charging sessions based on machine learning (ML) techniques. The purpose of using ML as an approach is to provide insight for estimating future energy demand and minimizing the impact of EV charging on the grid. To achieve the aim of this study, firstly, we investigated the impact of large-scale charging of EVs on the grid. Based on this, we formulated an objective function, expressed as a sum of utility functions when EVs charge on the grid with constraints imposed on voltage levels and charging power. Secondly, we employed a graphical modeling approach to study the temporal distribution of EV energy consumption based on real-world datasets from EV charging sessions. Thirdly, using ML regression models, we predicted EV energy consumption using four different models of fine tree, linear regression, linear SVM (support vector machine), and neural network. We used 5-fold cross-validation to protect against overfitting and evaluated the performances of these models using regression analysis metrics. The results from our predictions showed better accuracy when compared with the results from the work of other authors. Full article

The vehicle fleets in Latin America are increasingly incorporating hybrid electric vehicles due to the economic and non-economic incentives provided by governments aiming to reduce energy consumption and emissions in the transportation sector. However, the impacts of implementing hybrid vehicles remain uncertain, especially in Latin American, which poses a risk to the achievement of environmental objectives in developing countries. The aim of this study is to evaluate the benefits of incorporating hybrid vehicles to replace internal combustion vehicles, considering the improvement in the level of emission standards. This study uses data reported by Colombian vehicle importers during the homologation process in Colombia and the number of vehicles registered in the country between 2010 and 2022. The Gompertz model and logistic growth curves are used to project the total number of vehicles, taking into account the level of hybridization and including conventional natural gas and electric vehicles. In this way, tailpipe emissions and energy efficiency up to 2040 are also projected for different hybrid vehicle penetration scenarios. Results show that the scenario in which the share of hybrid vehicles remains stable (Scenario 1) shows a slight increase in energy consumption compared to the baseline scenario, about 1.72% in 2035 and 2.87% in 2040. The scenario where the share of MHEVs, HEVs, and PHEVs reaches approximately 50% of the vehicle fleet in 2040 (Scenario 2) shows a reduction in energy consumption of 24.64% in 2035 and 33.81% in 2040. Finally, the scenario that accelerates the growth of HEVs and PHEVs while keeping MHEVs at the same level of participation from 2025 (Scenario 3) does not differ from Scenario 2. Results show that the introduction of full hybrids and plug-in hybrid vehicles improve fleet fuel consumption and emissions. Additionally, when the adoption rates of these technologies are relatively low, the benefits may be questionable, but when the market share of hybrid vehicles is high, energy consumption and emissions are significantly reduced. Nevertheless, this study also shows that Mild Hybrid Electric Vehicles (MHEVs) do not provide a significant improvement in terms of fuel consumption and emissions. Full article

Recent fire incidents following electric vehicle (EV) collisions have been increasing rapidly in Korea, corresponding to the growing distribution of EVs. While the overall number of EV fires is lower compared to those involving internal combustion engine (ICE) vehicles, EV fires can lead to more severe outcomes. Current regulations for post-crash fuel system integrity evaluation do not differentiate between EVs and ICE vehicles. However, the causes of fires in these vehicles differ due to variations in the design and construction of their fuel systems. This study analyzed seventeen cases of EV post-crash fires in Korea to derive two representative risk scenarios for EV post-crash fires. The first scenario involves significant intrusion into the EV front-end structure resulting from high-speed frontal collisions, while the second scenario involves direct impacts to the battery pack mounted under the vehicle from road curbs at low speeds (30–40 km/h). Based on these scenarios, we conducted tests to assess battery damage severity under two crash test modes, simulating both high-speed frontal collisions and low-speed curb impacts. The test results led to the development of a draft crash test concept to evaluate EV post-crash fire risks. Furthermore, we assessed the reproducibility of these test modes in relation to actual EV post-crash fires. Our findings indicate that square-shaped impactors provide higher reproducibility in simulating real EV post-crash fire incidents compared to hemisphere-shaped impactors. Additionally, a fire occurred 31 days after the storage of a crash-evaluated battery test specimen, which was determined to be caused by moisture invasion during post-crash storage, accelerating a micro-short circuit. This study aims to contribute to the development of new evaluation methods for the Korean New Car Assessment Program (KNCAP) to enhance EV post-crash fire safety by utilizing these test results to refine collision severity evaluation methods. Full article

Heavy goods transportation is responsible for around 27% of CO₂ emissions from road transport in the EU and for 5% of total CO₂ emissions in the EU. The decarbonization of long-distance transport in particular remains a major challenge. The combination of battery electric trucks (BETs) with on-route high-power charging (HPC) offers a promising solution. Planning and setting up the required infrastructure is a critical success factor here. We propose a methodology to evaluate the charging infrastructure needed to support the large-scale introduction of heavy-duty BETs in Germany, considering different levels of electrification, taking the European driving and rest time regulations into account. Our analysis employs MATSim, an activity-based multi-agent transport simulation, to assess potential bottlenecks in the charging infrastructure and to simulate the demand-based distribution of charging stations. The MATSim simulation is combined with an extensive pre-processing of transport-related data and a suitable post-processing. This approach allows for a detailed examination of the required charging infrastructure, considering the impacts of depot charging solutions and the dynamic nature of truck movements and charging needs. The results indicate a significant need to augment HPC with substantial low power overnight charging facilities and highlight the importance of strategic infrastructure development to accommodate the growing demand for chargers for BETs. By simulating various scenarios of electrification, we demonstrate the critical role of demand-oriented infrastructure planning in reducing emissions from the road freight sector until 2030. This study contributes to the ongoing discourse on sustainable transportation, offering insights into the infrastructure requirements and planning challenges associated with the transition to battery electric heavy-duty vehicles. Full article