Editor’s Choice Articles

A car cabin’s small volume makes it vulnerable to discomfort if temperature, humidity, and carbon dioxide levels are poorly regulated. In electric vehicles, the HVAC system draws energy from the car battery, reducing the driving range by several dozen kilometres under extreme conditions. A 1D simulation model calibrated for the Renault ZOE was used to evaluate the effects of ventilation parameters on thermal comfort, humidity, and power consumption. The results highlighted the interdependence of factors such as the recirculation ratio and blower flow rate, showing that energy-efficient settings depend on ambient conditions and other factors (such as occupancy, vehicle speed, infiltration). Adjustments can reduce heat pump energy use, but no single setting optimally balances power consumption and thermal comfort across all scenarios. The opti-CO₂ mode is proposed as a trade-off, offering energy savings while maintaining safety and comfort. This mode quickly achieves the cabin temperature target, limits carbon dioxide concentration at a safe level (1100 ppm), minimises fogging risks, and reduces heat pump power consumption. Compared to fresh air mode, the opti-CO₂ mode extends the driving range by 9 km in cold conditions and 26 km in hot conditions, highlighting its potential for improving energy efficiency and occupant comfort in electric vehicles. Full article

This study extends beyond conventional Battery Thermal Management System (BTMS) research by conducting a Life Cycle Analysis comparing the environmental impacts of two technologies: a traditional active cold plate system and an innovative passive Loop Heat Pipe (LHP) system. While active cold plate BTMS requires continuous energy input during operation and charging, leading to significant energy consumption and emissions, the passive LHP BTMS operates without external power or moving parts, substantially reducing the climate change impact. This analysis considered two materials for LHP construction: copper and stainless steel. The results demonstrated that the LHP design achieved a 9.9 kg reduction in overall BTMS mass compared to the cold plate system. The implementation of stainless steel effectively addressed the high resource consumption associated with copper while reducing environmental impact by over 50% across most impact categories, compared to the cold plate BTMS. The passive operation of the LHP system leads to substantially lower energy usage and emissions during the use phase compared to the active cold plate. These findings highlight the potential of passive LHP technology to enhance the environmental sustainability of Battery Thermal Management Systems while maintaining effective thermal performance. Full article

Standard driving cycles have been the method of choice for testing vehicle performance for decades, both in research and at the regulatory level. These methodologies offer the significant advantage of test reproducibility, allowing for consistent comparisons between vehicles. However, their inability to reflect real-world driving conditions has become increasingly evident. This issue was first exacerbated by the advent of hybrid and plug-in hybrid vehicles, which introduced new complexities in powertrain operation. Legislators attempted to adapt testing procedures to account for electric energy usage in emissions assessments, but these efforts have largely failed to address the technical challenges posed by modern vehicles. As a result, the gap between real-world fuel consumption and type-approval values has continued to grow. The introduction of ADAS technologies has further widened this discrepancy, as standard driving cycles are no longer capable of accurately representing modern vehicle performance. In light of these challenges, this paper critically evaluates the limitations of standard drive cycles and on-road testing procedures, explores how hybrid and connected vehicles further complicate performance assessment, and proposes directions for improving these methodologies. Full article

This article examines the rise and recent decline of Tesla in the U.S. electric vehicle market. Using qualitative, semi-quantitative, and statistical methods, the article traces how Tesla acquired a first-mover advantage and how second movers, both established automakers and start-ups, responded to Tesla’s rise. The recent decline in Tesla’s share of the U.S. electric vehicle market is linked to several factors: the proliferation of electric vehicle offerings from competitors, changes in public policy, and controversial decisions by Tesla and its CEO. The article concludes with a discussion of promising future strategies for both Tesla and its competitors. Full article

Continuous and accurate state-of-charge estimation is essential for optimal reliability and performance in electric vehicle battery management systems. This work reviews state-of-charge estimation strategies, from straightforward methods like lookup tables and ampere-hour counting to advanced mathematical models, such as electrochemical, observer-assisted equivalent circuit, and impedance-based models that capture cell dynamics. Additionally, data-driven models including fuzzy logic, neural networks, and support vector machines are explored for their ability to leverage large datasets. This review highlights the strengths and limitations of each method, emphasizing the specific contexts in which these strategies can be applied to achieve optimal effectiveness. Full article

The transition to electric vehicles (EVs) presents challenges and opportunities for the management of electrical networks. This paper focuses on developing and evaluating probabilistic forecasting algorithms to understand and predict EV charging behaviours, crucial for optimising grid operations and ensuring a balance between electricity demand and generation. Several forecasting approaches tailored to different time horizons are proposed across diverse model classes, including direct, bottom-up, and adaptive approaches. In all approaches, the target variable can be the load curve quantiles from 0.1 to 0.9 with 0.1 increments or prediction sets with a target coverage of 80%. Direct approaches learn from past load curves using GAMLSS or QGAM methods. Bottom-up approaches predict individual charging session characteristics (arrival time, charging duration, and energy demand) with mixture models before reconstructing the load curve. Adaptive approaches correct in real-time the prediction sets issued by direct or bottom-up approaches with conformal predictions. The experiments, conducted on real-world charging session data from Palo Alto, demonstrate the effectiveness of the proposed methods with regard to different metrics, including pinball loss, empirical coverage, and RPS. Overall, the results highlight the importance of quantifying uncertainty in load forecasts and the potential of probabilistic forecasting for EV load management. Full article

Electric road systems (ERSs) are a promising solution for electrifying heavy-duty freight transport by providing traction and charging power from the power lines installed along the road. Development of ERSs has been accelerated in the last decade, and several pilot projects have been successfully implemented, proving the high level of maturity that the technology has achieved. One crucial step that could be initiated before a rollout is the standardization and certification of ERS infrastructure and system components. For instance, pantographs for overhead ERSs face unique challenges, in that the power transfer should be safe and reliable in the presence of dynamic longitudinal and lateral movements of the vehicle. To tackle this problem, we outline the requirements for overhead ERSs and ERS pantograph testing. Among the key requirements are the rising and lowering times, response to lateral maneuvers, such as lane changes, and high electrical current during stillstand. We introduce our developed test stands capable of testing various aspects of an ERS pantograph. The lateral test stand was developed to test basic functionalities and simulate lateral movements. A second test stand was implemented, to test high currents and the subsequent temperature development. Furthermore, a digital test stand used for planning, design, and modeling is introduced. Full article

Decarbonizing long-haul, heavy-duty transport in Europe focuses on battery-electric trucks with high-power chargers or electric road systems and fuel-cell-electric vehicles with hydrogen refueling stations. We present a comparative life cycle assessment and total cost of ownership analysis of these technologies for 20% of Germany’s heavy-duty, long-haul transport alongside internal combustion engine vehicles. The results show that fuel cell vehicles with on-site hydrogen have the highest life cycle emissions (65 Mt CO₂e), followed by internal combustion engine vehicles (55 Mt CO₂e). Battery-electric vehicles using electric road systems achieve the lowest emissions (21 Mt CO₂e) and the lowest costs (EUR 45 billion). In contrast, fuel cell vehicles with on-site hydrogen have the highest costs (EUR 69 billion). Operational costs dominate total expenses, making them a compelling target for subsidies. The choice between battery and fuel cell technologies depends on the ratio of vehicles to infrastructure, transport performance, and range. Fuel cell trucks are better suited for remote areas due to their longer range, while integrating electric road systems with high-power charging could offer synergies. Recent advancements in battery and fuel cell durability further highlight the potential of both technologies in heavy-duty transport. This study provides insights for policymakers and industry stakeholders in the shift towards sustainable transport. The greenhouse gas emission savings from adopting battery-electric trucks are 54% in our high-power charging scenario and 62% in the electric road system scenario in comparison to the reference scenario with diesel trucks. Full article

The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) is driving advancements in highway transportation by targeting energy efficiency, environmental sustainability, and cost reductions. This study investigates the fuel economy potential and cost implications of advanced powertrain technologies using comprehensive system simulations. Leveraging tools such as Autonomie and TechScape, developed by Argonne National Laboratory, this study evaluates multiple timeframes (2023–2050) and powertrain types, including conventional internal combustion engines, hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). Simulations conducted across standard regulatory driving cycles provide detailed insights into fuel economy improvements, cost trajectories, and total cost of ownership. The findings highlight key innovations in battery energy density, lightweighting, and powertrain optimization, demonstrating the growing viability of BEVs and their projected economic competitiveness with conventional vehicles by 2050. This work delivers actionable insights for policymakers and industry stakeholders, underscoring the transformative potential of vehicle electrification in achieving sustainable transportation goals. Full article

The automotive industry is experiencing a period of transition from traditional internal combustion engine (ICE) vehicles to electric vehicles. Although electric machines have always been used in many applications, they are generally designed neglecting the sources of uncertainty, even such uncertainty can lead to significant deterioration of the motor performance. The aim of this paper is to compare the results obtained from the multi-objective optimization of an interior permanent magnet synchronous motor (IPMSM) using a robust approach versus a deterministic one. Unlike other studies in the literature, this research simultaneously considers different sources of uncertainty, such as geometric parameters, magnet properties, and operating temperature, to assess the variability of electric motor performance. Different designs of a 48 slot–8 pole motor are simulated with finite element analysis, then the outputs are used to train artificial neural networks that are employed to find the optimal design with different approaches. The method incorporates an innovative use of the neural network-based variance estimation (NNVE) technique to efficiently calculate the standard deviation of the objective functions. Finally, the results of the robust optimization are compared with those of the deterministic optimization. Due to the small margin of improvement in robustness, both methods lead to similar results. Full article

Lithium-ion batteries are a key technology in supply chains for modern electric vehicles. Their production is complex and can be prone to defects. As such, the detection of defective batteries is critical to ensure performance and consumer safety. Existing end-of-line testing relies heavily on electrical measurements for identifying defective cells. However, it is possible that not all pertinent information is encoded within the electrical measurements alone. Reversible expansion in lithium-ion cells is an indicator of lithiation within the cell, while irreversible expansion is a consequence of the ageing process; unexpected expansion may indicate the presence of undesirable defects. By measuring expansion in addition to electrical measurements, we aim to make better and faster quality predictions during end-of-line testing, thereby facilitating the early detection of potential defects. To make these predictions, we implement artificial intelligence algorithms to extract information from the measurements. Training these networks requires large training datasets, which are expensive to produce. In this paper, we demonstrate a first-order physical modelling approach for generating synthetic data to pre-train artificial intelligence algorithms that perform anomaly detection on lithium-ion battery cells at the end-of-line. The equivalent circuit model used to generate voltage curves could be fit to real data with a mean absolute error of less than 1%, and the expansion model could be fit to a mean absolute error of less than 2% of the measured values. By pretraining the artificial intelligence network using synthetic data, we can leverage existing physical models to reduce the amount of training data required. Full article

The imperative for electrification of road transport, driven by global climate targets, underscores the need for innovative powertrain systems in heavy-duty vehicles. When developing new electric drive modules, individual operational requirements need to be considered instead of generalized usage profiles, as heavy-duty vehicles experience significantly differing loads depending on their field of operation. Real driving data, representing the demands of different application scenarios, offers great potential for digital replication of driving conditions at different stages of simulation and physical validation. Application- and vehicle-specific longitudinal requirements during operation are particularly relevant for the dimensioning of powertrain components. Road gradient and mass estimation assist in the description of these operating conditions, allowing for detailed modeling of the real load conditions. An incorporation of real driving data instead of solely relying on standardized cycles has the potential of tailoring components to the target lead users and applications. While some operating conditions can be recorded by vehicle manufacturers, these are usually not accessible by third parties. In this paper, the authors present an innovative methodology of estimating vehicle parameters for the generation of representative driving profiles for implementation into a consecutive powertrain design process. The approach combines the measurement of real driving data with state estimation. The authors show that the presented methodology enables the generation of driving profiles with less than 25% deviation from the original data set. Full article

Advances in electric vehicles and charging infrastructure technology have given the electrification of road traffic a positive momentum. Nowadays, it is becoming more and more evident that the related energy and financial processes of the current e-mobility ecosystem are reaching their limits. This leads to usability losses for end users as well as administrative and non-causation-based financial burdens on various energy system participants. In this article, use cases are inferred from the literature, the aforementioned challenges are discussed in more detail, and strategies for addressing them are presented. Furthermore, the information system architecture of the BANULA project, with its core elements of open communication standards, virtual balancing areas, and blockchain components, is explained. BANULA addresses the aforementioned challenges by holistically considering the needs of all participants. A special focus of the project is implementing and investigating the concept of virtual balancing areas. This concept has been available since 2020 but has not been implemented in the market yet. To the best of the authors’ knowledge, BANULA is the first project that utilizes current legislation to transfer charging infrastructure to virtual balancing areas in conjunction with distributed ledger technology to support related processes. In the first step, the BANULA implementation prototype targets the German e-mobility ecosystem, but applicability to other states in the European Union is planned. Using an independent framework, the BANULA architecture and its prototypical implementation are evaluated. The authors show that the unique combination of virtual balancing areas and the related processes, enhanced through distributed ledger technology, has the potential to contribute to a user-centered, trustworthy, and grid-supportive e-mobility ecosystem. Full article

This article examines contacting by means of ultrasonic welding between a cast aluminum winding and a copper conductor of a wheel hub drive for a passenger car. The effect of thermal stress on the formation and growth of intermetallic phases (IMC) in the contact is analyzed. By using microscopy, the growth constant under the specific load conditions can be identified with the help of the parabolic time law and offer a possibility for predicting the service life of the corresponding contacts. As a result, it can be stated that the increase in electrical resistance of the present contact at load temperatures of 120 °C, 150 °C, and 180 °C does not reach a critical value. The growth rates of the IMC also show no critical tendencies at the usual operating temperatures (120 °C and 150 °C, e.g., at 150 °C = 4.59 × 10⁻⁷ μm²/s). The activation energy calculated using the Arrhenius plot of 155 kJ/mol (1.61 eV) can be classified as high in comparison to similar studies. In addition, it was found that future investigations of the IMC growth of corresponding electrical contacts should rather be carried out with electric current. The 180 °C sample series were carried out in the oven and with electric current; the samples in the oven did not show clear IMC, while the samples exposed to electric current already showed IMC under the microscope. Full article

The effective administration of lithium-ion batteries is key to the performance and durability of electric vehicles (EVs). This systematic mapping study (SMS) thoroughly examines optimization methodologies for battery management, concentrating on the estimation of state of health (SoH), remaining useful life (RUL), and state of charge (SoC). The findings disclose various methods that boost the accuracy and reliability of SoC, including enhanced variants of the Kalman filter, machine learning models like long short-term memory (LSTM) and convolutional neural networks (CNNs), as well as hybrid optimization frameworks that combine Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). For estimating SoH, prevalent data-driven techniques include support vector regression (SVR) and Gaussian process regression (GPR), alongside hybrid models merging machine learning with conventional estimation techniques to heighten predictive accuracy. RUL prediction sees advancements through deep learning techniques, especially LSTM and gated recurrent units (GRUs), improved using algorithms such as Harris Hawks Optimization (HHO) and Adaptive Levy Flight (ALF). This study underscores the critical role of integrating advanced filtering techniques, machine learning, and optimization algorithms in developing battery management systems (BMSs) that enhance battery reliability, extend lifespan, and optimize energy management for EVs. Moreover, innovations like hybrid models and synthetic data generation using generative adversarial networks (GANs) further augment the robustness and precision of battery management strategies. This review lays out a thorough framework for future exploration and development in the optimization of EV batteries. Full article

Freeway incidents block road lanes and result in increasing travel time delays. The intense lane changes of upstream vehicles may also lead to capacity drop and more congestion. Connected vehicles (CVs) offer a viable solution to minimize the impact of such incidents via monitoring the status of the incidents and providing real-time driving guidance. This paper evaluates the performance of an existing CV-enabled incident management system, which minimizes travel time by effectively leading CVs to bypass incident spots. This study comprehensively quantifies the effects of system parameters (speed weight and lane-changing inertia), control segment length, and road information-updating intervals. This analysis identifies the optimal settings for the incident management system to minimize vehicle travel time delays. Additionally, this paper evaluates the influence of CV market penetration rates (MPRs), network volume-to-capacity ratios, and incident settings to understand the system benefits under varying connected environments and traffic conditions. The results reveal that with the control of the proposed system, overall travel delays can be reduced by up to 45% and that road congestion caused by incidents can be mitigated quickly. Full article

As battery electric vehicles (BEVs) gain significance in the automotive industry, manufacturers must diversify their vehicle portfolios with a wide range of electric vehicle models. Electric powertrains must be designed to meet the unique requirements and boundary conditions of different vehicle concepts to provide satisfying solutions for their customers. During the early development phases, it is crucial to establish an initial powertrain component design that allows the respective divisions to develop their components independently and minimize interdependencies, avoiding time- and cost-intensive iterations. This study presents a holistic electric powertrain component design model, including the high-voltage battery, power electronics, electric machine, and transmission, which is meant to be used as a foundation for further development. This model’s simulation results and performance characteristics are validated against a reference vehicle, which was torn down and tested on a vehicle dynamometer. This tool is applicable for an optimization approach, focusing on achieving optimal energy consumption, which is crucial for the design of battery electric vehicles. Full article

Today, transportation plays a crucial role in economic development and establishing strong social relationships. Primary mobility challenges in cities include high levels of traffic, accidents, and pollution. Improvements in road infrastructure, technological advancements at traffic light intersections, and the adoption of electric or hybrid vehicles are insufficient to resolve these issues. Maximizing the use of public transit and shared transportation is essential for this purpose. Strategies aimed at reducing the number of private vehicles on city roads are beneficial in this regard. Ridesharing, particularly carpooling, is an effective strategy to achieve such a reduction in vehicle numbers. However, safety concerns related to carpooling tools present a significant barrier to the growth of this mode of transportation. The measures implemented in these tools often lack appropriate technology for the authentication process, which is crucial for enhancing safety for both passengers and drivers. This proposed research explores the benefits of improving the authentication processes for passengers and drivers within a shared transportation system to minimize information security risks. A thorough literature review was conducted on shared transportation, user registration, authentication processes within these systems, and technologies that could enhance security, such as blockchain. Subsequently, considering the identified criteria in the literature review, a proposal was developed for creating a registration and authentication module based on blockchain that could be applied across various systems. Finally, an analysis was conducted on how this module could be integrated into a carpooling application and the benefits it would provide regarding safety and increased user adoption. The findings from the review were organized and assessed to identify key aspects for improving user authentication in a system based on intelligent transportation systems (ITSs) and utilizing blockchain, recognized for its security and data integrity. The registration and authentication module developed in this work allows increased security, scalability, and user adoption for any type of application, e.g., carpooling. Full article

The rapid growth in e-commerce calls for research on the potential of electric vehicles in improving last-mile delivery. Whereas existing studies have examined aspects of last-mile delivery, such as challenges, acceptance/benefits, and feasibility, the studies are fragmented, with conflicting findings and regional differences. Thus, there is a need for a comprehensive understanding of the studies to map out current research trends and propose future research agendas. To address this research gap, a bibliometric review was conducted on 375 publications from the Scopus database. Findings reveal that pioneering countries such as the USA have researched integrating electric vehicles into last-mile delivery systems, focusing on technological advancements such as battery technologies and smart grids. The sustainability theme is common in most studies, focusing on controlling carbon emissions and energy efficiency. The electric micro-mobility theme has grown in recent years, while emerging technologies remain underexplored, especially in developing economies. Future research should address the underexplored areas. These include charging infrastructure optimisation, electric micro-mobility innovations, and integration in urban environments, alongside the social and ethical implications of electric vehicle adoption for last-mile delivery. Full article

Electric vehicles can substantially lower the overall carbon footprint of the transportation sector, and their batteries become key enablers of widespread electrification. Although high capacity and efficiency are essential for providing sufficient range and performance in electric vehicles, they can be compromised by the need to lower costs and environmental impacts and retain valuable materials. In the present work, multi-criteria decision analysis was adopted to assess the sustainability of different lithium-ion batteries. Life cycle carbon emissions and toxicity, material criticality, life cycle costs, specific energy, safety, and durability were considered in the analysis as key parameters of the transition to electric mobility. A subjective approach was chosen for the weight attribution of the criteria. Although certain alternatives, like lithium nickel cobalt manganese oxide (NCM) and lithium nickel cobalt aluminum oxide (NCA), outweigh others in specific energy, they lack in terms of safety, material preservation, and environmental impact. Addressing cost-related challenges is also important for making certain solutions competitive and largely accessible. Overall, while technical parameters are crucial for the development of lithium-ion batteries, it is equally important to consider the environmental burden, resource availability, and economic factors in the design process, alongside social aspects such as the ethical sourcing of materials to ensure their sustainability. Full article

The steering system plays a critical role in the vehicle’s handling and directly influences its lateral dynamics. Faults or abnormal behavior in this system can affect performance, cause vehicle instability, and even lead to accidents. Therefore, considering these potential events is essential for designing robust controllers for autonomous vehicles. For this reason, in this work, a fault-tolerant path-tracking Static Output-Feedback controller is designed to handle steering actuator faults in autonomous vehicle steering systems. The controller adopts a Linear Parameter Varying approach to effectively handle nonlinearities associated with varying vehicle speeds and tire behavior. Furthermore, it only uses information from sensors, avoiding estimation stages. This controller can operate in two modes: a no-fault mode where only the steering is controlled to follow the reference path and a fault mode where the controller manages both the steering and torque vectoring. In fault mode, torque vectoring compensates for faults in the steering actuator. The design of the controller is completed considering gain faults in the steering system. The simulation results show that the proposed controller successfully maintains vehicle stability and significantly reduces tracking errors during high-risk maneuvers, achieving reductions of up to 50.65% in lateral error and 47.26% in heading error under worst-case fault scenarios. Full article

The potential of electric vehicles (EVs) to support the decarbonization of the transportation sector, crucial for meeting greenhouse gas reduction targets under the Paris Agreement, is obvious. Despite their advantages, the adoption of electric vehicles faces limitations, particularly those related to battery range and charging times, which significantly impact the time needed for a trip compared to their combustion engine counterparts. However, recent improvements in fast charging technology have enhanced these aspects, making EVs more suitable for both daily and long-distance trips. EVs can now deal with long trips, with travel times only slightly longer than those of internal combustion engine (ICE) vehicles. Fast charging capabilities and infrastructure, such as 350 kW chargers, are essential for making EV travel times comparable to ICE vehicles, with brief stops every 2–3 h. Additionally, EVs help reduce noise pollution in urban areas, especially in noise-saturated environments, contributing to an overall decrease in urban sound levels. However, this research highlights a downside of DC (Direct Current) fast charging stations: high-frequency noise emissions during fast charging, which can disturb nearby residents, especially in urban and residential areas. This noise, a result of the growing fast charging infrastructure, has led to complaints and even operational restrictions for some charging stations. Noise-related disturbances are a significant urban issue. The World Health Organization identifies noise as a key contributor to health burdens in Europe, even when noise annoyance is subjective, influenced by individual factors like sensitivity, genetics, and lifestyle, as well as by the specific environment. This paper analyzes the sound emission of a broad sample of DC fast charging stations from leading EU market brands. The goal is to provide tools that assist manufacturers, installers, and operators of rapid charging stations in mitigating the aforementioned sound emissions in order to align these infrastructures with Sustainable Development Goals 3 and 11 adopted by all United Nations Member States in 2015. Full article

A smart regenerative braking system for EVs can reduce unnecessary brake operations by assisting in the braking of a vehicle according to the driving situation, road slope, and driver’s preference. Since the strength of regenerative braking is generally determined based on calibration data determined during the vehicle development process, some drivers could encounter inconveniences when the regenerative braking is activated differently from their driving habits. In order to solve this problem, various deep learning-based algorithms have been developed to provide driving stability by learning the driving data. Among those artificial intelligence algorithms, anomaly detection algorithms can successfully separate the deceleration data in abnormal driving situations, and the resulting refined deceleration data can be used to train the regression model to achieve better driving stability. This study evaluates the performance of a personalized driving assistance system by applying driver characteristic data, obtained through an anomaly detection algorithm, to vehicle control. Full article

In its draft proposal for the Road Transport Act, the Croatian government referred to European Union Directive 2022/738, which concerns the use of hired vehicles for goods transport, rather than the pertinent European Union regulations on automated and autonomous vehicles, specifically Regulation 2019/2144 and Implementing Regulation 2022/1426. This oversight highlights Croatia’s lack of preparedness to integrate highly automated and autonomous vehicles, which are crucial for safety and environmental performance as per European Union standards. This paper aims to clarify the safety and legal recommendations for the trafficking of these vehicles in Croatia. Level 2 and Level 3 automated vehicles, present in smaller numbers in road traffic in Croatia, were compared from the perspective of the lack of driving tasks and its impact on driver safety. The stages of road liability for traffic accidents were also investigated, with recommendations of strict (default) liability of manufacturers for fully autonomous vehicles as well as presumed liability of all road traffic participants for highly automated vehicles. The safety and traffic benefits of possible infrastructure upgrades for highly automated and fully autonomous vehicles were discussed, mostly in the segment of dedicated lines. Full article

The adoption of a new technology is well described by an S-curve. It starts with a slow initial introduction, faster growth, and a final low-pace stage that corresponds to saturation. Once the innovation is introduced and progressively adopted, prior to saturation, some of the initial owners will begin selling their initially owned goods for different reasons, including lack of satisfaction, upgrading to a newer model, or other special unrevealed reasons. In a given market, new and second-hand products will coexist that will find new owners. The evolution of the two qualities of the same product will progress to a given equilibrium and a final ratio specific to each market. With the hypothesis of second-hand goods viewed as a new technology for lower budgets in the market, their adoption can also be described by the S-curve. The questions to be answered will relate to the dynamics of adoption of the two technologies, the ratio at equilibrium between new and used products in a market, and the delay required before equilibrium is achieved. In this manuscript, a realistic model is presented to approach and analyze the adoption of electric vehicles (EVs) with the mix of new and used vehicles with new registrations. The EV transition is presented with an adoption represented by the S-curve; the ratio of new to used EVs with new registrations is also presented in a context of high demand of used EVs and a context of rapid depreciation of EVs corresponding to lower demand of pre-owned EVs. The model predicts the number of years required before an equilibrium is reached in the ratio between used and new EVs in new registrations for a given market. Full article

This paper demonstrates the use of an Excel-based tool called the “Electric Vehicle-Charging Infrastructure Financial Analysis Spreadsheet Tool”, or “EVCI-FAST”, developed to analyze public–private partnership approaches to deliver publicly accessible EV-charging infrastructure that would not be commercially viable without a government subsidy. To demonstrate the use of this tool, we conducted a high-level screening analysis for a hypothetical bundle of publicly accessible EV-charging stations to assess the financial viability of delivering electric vehicle-charging infrastructure (EVCI) using alternative public–private partnership (P3) structures. This demonstration suggests that the EVCI-FAST could assist public agencies in determining whether their budgetary resources are adequate to support a proposed P3 for an EVCI project. The demonstration suggests that the EVCI-FAST could also help agencies decide which P3 structuring option would best meet their financial objectives. The results from the analysis of the hypothetical project suggest that public agencies could benefit considerably from a P3 structure that uses a minimum revenue guarantee to reduce revenue risk for the private partner. Full article

A new graduate degree programme, Master of Science in Electric Vehicles (MScEV), for engineering students is presented, which is timely and vital for modern society. The purpose of this programme is to provide graduate students with up-to-date knowledge and skills that can enhance their career prospects in the fast-growing electric vehicle (EV) community. The programme not only provides technological knowledge in system design, operation, and management of EVs, but also involves research training in specific EV topics. This paper first outlines the rationale of the programme and reveals the shortcomings of existing EV education. Then, the curriculum structure of the newly developed MScEV programme as well as the corresponding core and elective courses are discussed. Finally, the findings of this programme are evaluated, indicating that the programme is attractive to an overwhelming number of students from diverse engineering backgrounds, as evidenced by the applicants’ and admittees’ degree qualifications and work experiences. Full article

The transition to sustainable mobility is one of the most pressing and complex challenges for the automotive industry, with impacts that extend beyond the mere reduction of emissions. Electric vehicles, while at the center of this evolution, raise questions about the consumption of natural resources, such as lithium, copper, and cobalt, and their long-term sustainability. In addition, the introduction of advanced technologies, including artificial intelligence (AI) and autonomous systems, brings new challenges related to the management of components and materials needed for their production, creating a significant impact on supply chains. The growing demand for electric and autonomous vehicles is pushing the industry to rethink production models, favoring the adoption of circular economy principles to minimize waste and optimize the use of resources. To better understand the implications of this transition, this study adopts a multiple case study methodology, which allows in-depth exploration of different contexts and scenarios, and analysis of real cases of dismantling and recycling of internal combustion engines (ICEs) and electric vehicles (EVs). The research includes a financial simulation and a comparison of revenues from the dismantling of ICE and EV vehicles, highlighting differences in the value of recycled materials and the effectiveness of circular economy practices applied to the two types of vehicles. This approach provides a detailed overview of the economic benefits and challenges related to the management of the end of life of vehicles, helping to outline optimal strategies for a sustainable and cost-effective future in the automotive sector. Full article

A ubiquitous network of charging stations is vital to facilitate the adoption of electric vehicles (EVs) and the achievement of a low-carbon transportation system. Currently, the availability of EV infrastructure differs significantly between communities as planning procedures are not necessarily equitable. Understanding the charging behavior of EV users is a crucial step toward creating an electric vehicle service equipment (EVSE) infrastructure that serves users efficiently, equitably, and sustainably. Presently, public charging station deployment efforts differ across communities, with little context surrounding urbanicity. This study analyzes data from 66 public Level 2 charging stations across Rhode Island. Motivated by the significant disparities in infrastructure availability between urban and rural areas, the research explores behavioral differences to inform infrastructure planning. Key findings reveal that urban stations are predominantly used during weekdays, with longer charging durations and higher energy consumption, whereas rural stations are primarily utilized on weekends and exhibit shorter, more efficient charging sessions. On average, dwell times at rural stations are approximately 50% shorter, while average energy demand is only 7% less. These results provide actionable insights for optimizing charging station deployment and utilization across diverse communities to support the growing demand for EVs. Full article

The research objective of this paper is to analyse what is behind the self-driving offer implemented in Phoenix (Arizona) by Waymo and a normal rent-a-car company by modelling both in e³value. A gap analysis proposes a new model of the rent-a-car business with the integration of a shared autonomous vehicle ride-hailing service. The goal is to encourage the growth of additional global shared autonomous vehicle trials and their incorporation into conventional businesses. The primary objective is to enhance shared autonomous mobility options, resulting in increased road safety, decreased traffic, and decreased emissions in urban areas. As a result, modelling Waymo can serve as a foundation for expanding the use of shared autonomous vehicles by other businesses in different geographic areas. Full article

In the Industry 4.0 era, integrating artificial intelligence (AI) with battery prognostics and health management (PHM) offers transformative solutions to the challenges posed by the complex nature of battery systems. These systems, known for their dynamic and nonl*-inear behavior, often exceed the capabilities of traditional PHM approaches, which struggle to account for the interplay of multiple physical domains and scales. By harnessing technologies such as big data analytics, cloud computing, the Internet of Things (IoT), and deep learning, AI provides robust, data-driven solutions for capturing and predicting battery degradation. These advancements address long-standing limitations in battery prognostics, enabling more accurate and reliable performance assessments. The convergence of AI with Industry 4.0 technologies not only resolves existing challenges but also introduces innovative approaches that enhance the adaptability and precision of battery health management. This perspective highlights recent progress in battery PHM and explores the shift from traditional methods to AI-powered, data-centric frameworks. By enabling more precise and scalable monitoring and prediction of battery health, this transition marks a significant step forward in advancing the field. Full article

This paper compares the electromagnetic performances of radial-flux, dual-rotor, permanent magnet (DRPM) machines with series (S) and parallel (P) magnetic circuits for two rotors, i.e., SDRPM and PDRPM, accounting for different slot/pole number combinations, stator winding configurations, and machine sizes. The machines are optimized using the finite element analysis (FEA) based on the genetic algorithm. It shows that the PDRPM machine with the tooth coil (TC) configuration has the highest permanent magnet (PM) utilisation compared to the PDRPM with toroidal winding (TW) configuration and the SDRPM machine with the TC configuration under different slot/pole number combinations. The scaling effects of the machine size on the torque have been investigated. The TW-PDRPM machine is suitable for large-radius and short-axial length applications due to the short end-winding length of the TW configuration, while the TC-PDRPM is better for small-radius and long-axial length applications. The TC-SDRPM performs well when both the machine outer radius and axial length increase. Finally, the TC-SDRPM and TW-PDRPM machines are prototyped and validated experimentally. Full article

Due to developments in vehicle engineering and communication technologies, vehicular networks have become an attractive and feasible solution for the future of electric, autonomous, and connected vehicles. Electric autonomous vehicles will require more data, computing resources, and communication capabilities to support them. The combination of vehicles, the Internet, and cloud computing together to form vehicular cloud computing (VCC), vehicular edge computing (VEC), and vehicular fog computing (VFC) can facilitate the development of electric autonomous vehicles. However, more connected and engaged nodes also increase the system’s vulnerability to cybersecurity and privacy breaches. Various security and privacy challenges in vehicular cloud computing and its variants (VEC, VFC) can be efficiently tackled using machine learning (ML). In this paper, we adopt a semi-systematic literature review to select 85 articles related to the application of ML for cybersecurity and privacy protection based on VCC. They were categorized into four research themes: intrusion detection system, anomaly vehicle detection, task offloading security and privacy, and privacy protection. A list of suitable ML algorithms and their strengths and weaknesses is summarized according to the characteristics of each research topic. The performance of different ML algorithms in the literature is also collated and compared. Finally, the paper discusses the challenges and future research directions of ML algorithms when applied to vehicular cloud computing. Full article

The integration of Full Electric Vehicles (FEVs) into the smart city ecosystem is an essential step towards achieving sustainable urban mobility. This study presents a comprehensive mobility network model designed to predict and optimize the energy supply for FEVs within smart cities. The model integrates advanced components such as a Charge Station Control Center (CSCC), smart charging infrastructure, and a dynamic user interface. Important aspects include analyzing power consumption, forecasting urban energy demand, and monitoring the State of Charge (SoC) of FEV batteries using innovative algorithms validated through real-world applications in Valencia (Spain) and Ljubljana (Slovenia). Results indicate high accuracies in SoC tracking (error < 0.05%) and energy demand forecasting (MSE ~6 × 10⁻⁴), demonstrating the model’s reliability and adaptability across diverse urban environments. This research contributes to the development of resilient, efficient, and sustainable smart city frameworks, emphasizing real-time data-driven decision-making in energy and mobility management. Full article

This study presents an adaptation of the YOLOv4 deep learning algorithm for 3D object detection, addressing a critical challenge in autonomous vehicle (AV) systems: accurate real-time perception of the surrounding environment in three dimensions. Traditional 2D detection methods, while efficient, fall short in providing the depth and spatial information necessary for safe navigation. This research modifies the YOLOv4 architecture to predict 3D bounding boxes, object depth, and orientation. Key contributions include introducing a multi-task loss function that optimizes 2D and 3D predictions and integrating sensor fusion techniques that combine RGB camera data with LIDAR point clouds for improved depth estimation. The adapted model, tested on real-world datasets, demonstrates a significant increase in 3D detection accuracy, achieving a mean average precision (mAP) of 85%, intersection over union (IoU) of 78%, and near real-time performance at 93–97% for detecting vehicles and 75–91% for detecting people. This approach balances high detection accuracy and real-time processing, making it highly suitable for AV applications. This study advances the field by showing how an efficient 2D detector can be extended to meet the complex demands of 3D object detection in real-world driving scenarios without sacrificing computational efficiency. Full article

This study aims to determine the type of vehicle with the lowest fuel consumption and greenhouse gas emissions by comparing spark ignition commercial vehicles against hybrid vehicles. The data were obtained through the OBD Link MX+ interface under traffic conditions in the Metropolitan District of Quito to determine the consumption and emissions delivered by each studied vehicle. Measurements were made while driving on two high-traffic routes during peak hours, with a duration of 2 to 3 h of stalling, and the engine fuel consumption parameters of each vehicle were obtained using 85 octane gasoline. Five measurements were generated per route and for each vehicle tested to reduce uncertainty and strengthen the prediction model with a factor of less than 10%. Statistical analysis was implemented to obtain a numerical model that allowed to analyse the estimate of the variation in fuel economy in each vehicle. The numerical model compared the values of fuel consumption measured with those calculated on all the routes with the highest traffic, finally indicating which vehicle with the smallest cylinder capacity is optimal, with an average consumption of 14 km/l on each route compared to a hybrid vehicle with an average consumption of 8.5 km/l per route, for better fuel performance within the Metropolitan District of Quito, in heavy traffic conditions. This study conducts a comparison of the consumption between a hybrid vehicle and spark ignition vehicles through the real driving cycle on routes considered to be of greater influx, to determine which vehicle has lower consumption and, therefore, greater energy efficiency in Quito City. Full article

Modern vehicles are equipped with various electronic control units (ECUs) for safety, entertainment, and autonomous driving. These ECUs operate independently according to their respective roles and generate considerable data. However, owing to capacity and security concerns, most of these data are not stored. In contrast, Tesla vehicles, equipped with multiple sensors and designed under the software-defined vehicle (SDV) concept, collect, store, and periodically transmit data to dedicated servers. The data stored inside and outside the vehicle by the manufacturer can be used for various purposes and can provide numerous insights to digital forensics researchers investigating incidents/accidents. In this study, various data stored inside and outside of Tesla vehicles are described sequentially from a digital forensics perspective. First, we identify the location and range of the obtainable storage media. Second, we explain how the data are acquired. Third, we describe how the acquired data are analyzed. Fourth, we verify the analyzed data by comparing them with one another. Finally, the cross-analysis of various data obtained from the actual accident vehicles and the data provided by the manufacturer revealed consistent trends across the datasets. Although the number of data points recorded during the same timeframe differed, the overall patterns remained consistent. This process enhanced the reliability of the vehicle data and improved the accuracy of the accident investigation. Full article

This comprehensive state-of-the-art literature review investigates the status of the electric vehicle (EV) market share and the key factors that affect EV adoption with a focus on the shared vision of vehicle electrification and the smart city movement. Investigating the current scenarios of EVs, this study observes a rapid increase in the number of EVs and charging stations in different parts of the world. It reports that people’s socio-economic features (e.g., age, gender, income, education, vehicle ownership, home ownership, and political affiliation) significantly influence EV adoption. Moreover, factors such as high driving range, fuel economy, safety technology, financial incentives, availability of free charging stations, and the capacity of EVs to contribute to decarbonization emerge as key motivators for EV purchases. The literature also indicates that EVs are predominantly used for short-distance travel and users commonly charge their vehicles at home. Most users prefer fast chargers and maintain a high state of charge (SOC) to avoid unforeseen situations. Despite the emergent trend, there is a disparity in charging infrastructure supply compared to the growing demand. Thus, there is a pressing need for more public charging stations to meet the surging charging demand. The integration of smart charging stations equipped with advanced technologies to optimize charging patterns based on energy demand, grid capacity, and people’s demand can help policymakers leverage the smart city movement. This paper makes valuable contributions to the literature by presenting a conceptual framework articulating the factors of EV adoption, outlying their role in achieving smart cities, suggesting policy recommendations to integrate EVs into smart cities, and proposing suggestions for future research directions. Full article

The transition to electric vehicles (EVs) is hindered by the insufficient development of charging infrastructure (CI) networks, particularly in urban areas. The existing literature highlights significant advancements in highway CI modeling, yet urban-specific models remain underdeveloped, due to the complexity of diverse driver behaviors and evolving environmental factors. To address this gap, this study investigates the influence of urban sprawl on future urban CI demand. Using a vector field analysis methodology, we first define the urban environment to capture its heterogeneity. A conceptual framework is then developed to analyze how changes in urban environments affect critical factors influencing CI demand. The results demonstrate that urban sprawl significantly impacts key variables shaping CI demand, including population distribution, transportation patterns, and land use. To quantify these impacts, geospatial metrics are derived from highly cited literature and integrated into the analysis, offering a novel approach to incorporating sprawl effects into CI planning. This study concludes that urban sprawl has a profound influence on future CI demand and emphasizes the importance of monitoring geospatial metrics over time. The proposed methodology provides a theoretical framework that enables stakeholders to anticipate changes in CI demand, thereby facilitating more effective infrastructure planning to accommodate urban sprawl. Full article

While IL has been successfully applied in RL-based approaches for autonomous driving, significant challenges, such as limited data for RL and poor generalization in IL, still need further investigation. To overcome these limitations, we propose in this paper a novel approach that effectively combines IL with DRL by incorporating expert demonstration data to control AV in roundabout and right-turn intersection scenarios. Instead of employing CNNs, we integrate a ViT into the perception module of the SAC algorithm to extract key features from environmental images. The ViT algorithm excels in identifying relationships across different parts of an image, thereby enhancing environmental understanding, which leads to more accurate and precise decision making. Consequently, our approach not only boosts the performance of the DRL model but also accelerates its convergence, improving the overall efficiency and effectiveness of AVs in roundabouts and right-turn intersections with dense traffic by a achieving high success rate and low collision compared to RL baseline algorithms. Full article

This research addresses the challenges and opportunities for electric vehicle charging stations in Latin America. The transition to electric mobility is crucial to reduce greenhouse gas emissions, modernize the quality of life in urban areas, update public policies related to transportation, and promote economic development. However, this is not an easy task in this region; it faces several obstacles, such as a lack of liquidity in governments, a lack of adequate infrastructure, high implementation costs, the need for clear regulatory frameworks, and limited public awareness of the benefits of electric mobility. To this end, the current panorama of electric mobility in the region is analyzed, including current policies, the state of the charging infrastructure, and the prospects for growth regarding electric vehicles in Latin America. Factors that could lead to their successful implementation are promoted, highlighting the importance of public policies adapted to Latin American countries, collaboration between the public–private industry, the industry’s adoption of new technologies in this region, and the education of the population, and the benefits of these policies are considered. Successful case studies from the region are presented to provide us with an idea of practices that can be carried out in other countries. The implementation of a charging system in Latin America is also studied; the successful implementation of charging systems is found to depend largely on the existence of integrated public policies that address aspects other than the charging infrastructure. Finally, the value of the work and the research findings are presented to indicate what this study can help with. These strategies are key to overcoming the challenges and maximizing the benefits of electric mobility in Latin America. Full article

The field of autonomous vehicle technology has experienced remarkable growth. A pivotal trend in this development is the enhancement of tracking performance and stability under high-speed conditions. Model predictive control (MPC), as a prevalent motion control method, necessitates an extended prediction horizon as vehicle speed increases and will lead to heightened online computational demands. To address this, a path preview strategy is integrated into the MPC framework that temporarily freezes the vehicle state within the prediction horizon. This approach assumes that the vehicle state will remain consistent for a specified preview distance and duration, effectively extending the prediction horizon for the MPC controller. In addition, a stability controller is designed to maintain handling stability under high-speed conditions, in which a square-root cubature Kalman filter (SRCKF) estimator is employed to predict tire forces to facilitate the cornering stiffness estimation of vehicle tires. The double lane change maneuver under high-speed conditions is conducted through the Carsim/Simulink co-simulation. The outcomes demonstrate that the SRCKF estimator could provide a reasonably accurate estimation of lateral tire forces throughout the whole traveling process and facilitates the stability controller to guarantee the handling stability. On the premise of ensuring handling stability, integrating the preview strategy could nearly double the prediction horizon for MPC, resulting in the limited increase of online computation burden brought while maintaining path tracking accuracy. Full article

Autonomous driving is a rising technology expected to revolutionize commuting. Even if the spread of autonomous vehicles is slower than expected some years ago, their progress will not stop and will become a reality shortly. Therefore, we must manage them both technologically and by considering their impact on other aspects such as safety, economy, society, and environment. Of these, trust in these vehicles by society is a crucial element that must be accounted for when designing the interaction between human passengers and autonomous vehicles. Economical and social impacts derived from the diffusion of autonomous vehicles hold both promises and challenges, as different sectors and professions might undergo considerable changes, along with our idea of transport infrastructure. This paper aims to analyze future developments and effects of this technology by starting with a review of the related work. For this purpose, we have analyzed several papers with contrasting perspectives and conclusions. This paper is not limited to summarizing them but also points out relevant research directions. Full article

Recently, the operation of electric charging stations has stopped being solely dependent on the state or centralised energy companies, instead depending on the decentralization of decisions made by the operators of these stations, whose goals are to maximise efficiency in the distribution and supply of energy for electric vehicles. Therefore, the operations of charging stations are exposed to increased complexity, leading to a growing need for decision-making based on more reliable and sustainable models. This research presents a review of key aspects, technologies, protocols, and case studies on the current and future trends of electric charging stations. A taxonomy of the technologies applied to charging stations and their applications in elements such as intelligent energy supply, electric vehicles, sustainability, the Industrial Internet of Things, and energy demand management is developed. Thus, this work synthesizes the essential features found in recent research regarding charging stations, aiming for a systemic approach that can lead toward sustainability in electromobility. Full article

Due to the significant increase in the number of EVs, this manuscript presents a study of the total ownership cost of electric vehicles in Romania. The total cost of ownership (TCO) includes the initial purchase price, maintenance costs, power prices, and government incentives or subsidies unique to the market in Romania. The TCO was calculated for battery electric vehicles (BEVs) and internal combustion vehicles (ICEs). Several vehicles were selected for the study, representing the models with the highest sales in Romania and a similar price range. The results show that EVs have a lower TCO compared with internal combustion vehicles if the battery replacement cost for EVs is not considered in the analysis. If this cost is considered, the TCO for the BEVs has a significant increase due to the high cost of the battery. Another analysis performed regards the CO₂ emissions. These are higher for ICEs compared to BEVs, so the BEVs help reduce emissions. Full article

Electrification of private cars is a key mechanism for reducing transport emissions and achieving net zero. Simultaneously, the development of public electric vehicle (EV) charging networks is essential for an equitable transition to EVs. This paper develops and analyses an extensive, nationally representative dataset of EV-charging sessions taking place on a key public charging network in Scotland between 2022 and 2024 to gain insights that can support the development of public charging infrastructure. Data were collated from 2786 chargers and analysed to establish a detailed characterisation of the network’s organisation and utilisation. The network considered is government-owned and was fundamental to the Scottish rollout of public chargers. Key insights from our analysis of the developed dataset include quantified disparities between urban and rural charger use-time behaviours, with the most rural areas tending to have charging activity more concentrated towards the middle of the day; an analysis of the numbers of deployed chargers in areas of greater/lesser deprivation; utilisation disparities between charger technologies, with 35% of slower chargers being used at least once daily compared to 86% of rapid/ultra-rapid chargers; and demonstration that charging tariff introductions resulted in a 51.3% average decrease in sessions. The implications of our findings for policy and practice are also discussed. Full article

Optimal planning depends on precise and exact estimation of the operation costs of mobile robots. Unfortunately, determining the current and future state of a vehicle implies identifying all the parameters in its model. Rather than broadening the number of factors, in this work we adopt the approach of using a higher-level abstraction model to identify only a few cost parameters. Based on the observation that arc travel times accurately reflect the effect of physical states, this work proposes using them as the key parameters to compute accurate path traversal costs in the context of indoor transportation. This approach eliminates the need to model all factors in order to derive the cost for every robot. The resulting model organizes those parameters in a bilinear state-space form and includes the evolution of actual travel times with changing states. We show that the proposed model accurately estimates arc travel times with respect to actual observations gathered from real robots traversing a few arcs of a traffic network until battery exhaustion. We experimentally obtained minimum-cost paths from random origin and destination nodes when using heuristics and the “closer-to-reality” (bilinear-state version of our model) path costs, finding that it can save an average of 15% in transportation time compared to conventional methods. Full article

Historically, practitioners and researchers have used selected count station data and survey-based methods along with demand modeling to forecast vehicle miles traveled (VMT). While these methods may suffer from self-reporting bias or spatial and temporal constraints, the widely available connected vehicle (CV) data at 3 s fidelity, independent of any fixed sensor constraints, present a unique opportunity to complement traditional VMT estimation processes with real-world data in near real-time. This study developed scalable methodologies and analyzed 238 billion records representing 16 months of connected vehicle data from January 2022 through April 2023 for Indiana, classified as internal combustion engine (ICE), hybrid (HVs) or electric vehicles (EVs). Year-over-year comparisons showed a significant increase in EVMT (+156%) with minor growth in ICEVMT (+2%). A route-level analysis enables stakeholders to evaluate the impact of their charging infrastructure investments at the federal, state, and even local level, unbound by jurisdictional constraints. Mean and median EV trip lengths on the six longest interstate corridors showed a 7.1 and 11.5 mile increase, respectively, from April 2022 to April 2023. Although the current CV dataset does not randomly sample the full fleet of ICE, HVs, and EVs, the methodologies and visuals in this study present a framework for future evaluations of the return on charging infrastructure investments on a regular basis using real-world data from electric vehicles traversing U.S. roads. This study presents novel contributions in utilizing CV data to compute performance measures such as VMT and trip lengths by vehicle type—EV, HV, or ICE, unattainable using traditional data collection practices that cannot differentiate among vehicle types due to inherent limitations. We believe the analysis presented in this paper can serve as a framework to support dialogue between agencies and automotive Original Equipment Manufacturers in developing an unbiased framework for deriving anonymized performance measures for agencies to make informed data-driven infrastructure investment decisions to equitably serve ICE, HV, and EV users. Full article

Solid-state batteries (SSBs) offer significant improvements in safety, energy density, and cycle life over conventional lithium-ion batteries, with promising applications in electric vehicles and grid storage due to their non-flammable electrolytes and high-capacity lithium metal anodes. However, challenges such as interfacial resistance, low ionic conductivity, and manufacturing scalability hinder their commercial viability. This study conducts a comprehensive scientometric analysis, examining 131 peer-reviewed SSB research articles from IEEE Xplore and Web of Science databases to identify key thematic areas and bibliometric patterns driving SSB advancements. Through a detailed analysis of thematic keywords and publication trends, this study uniquely identifies innovations in high-ionic-conductivity solid electrolytes and advanced cathode materials, providing actionable insights into the persistent challenges of interfacial engineering and scalable production, which are critical to SSB commercialization. The findings offer a roadmap for targeted research and strategic investments by researchers and industry stakeholders, addressing gaps in long-term stability, scalable production, and high-performance interface optimization that are currently hindering widespread SSB adoption. The study reveals key advances in electrolyte interface stability and ion transport mechanisms, identifying how solid-state electrolyte modifications and cathode coating methods improve charge cycling and reduce dendrite formation, particularly for high-energy-density applications. By mapping publication growth and clustering research themes, this study highlights high-impact areas such as cycling stability and ionic conductivity. The insights from this analysis guide researchers toward impactful areas, such as electrolyte optimization and scalable production, and provide industry leaders with strategies for accelerating SSB commercialization to extend electric vehicle range, enhance grid storage, and improve overall energy efficiency. Full article

In this paper, a fast and efficient control method is proposed for a pure sine wave inverter used in an electric vehicle system, which can provide better performance under transient and steady-state conditions. The proposed control technique consists of a fast terminal attractor (FTA) and a fractional multivariable grey model (FMGM). The FTA with finite time convergence offers a faster convergence rate of the system state and a singularity-free solution. However, if the uncertain system boundaries are overestimated or underestimated, chatter/steady-state errors can occur during the FTA, which can lead to significant harmonic distortion at the output of the pure sine wave inverter. A computationally efficient FMGM is incorporated into the FTA to solve the chatter/steady-state error problem when an uncertain estimate of the system boundary cannot be satisfied. Simulation results show that the proposed control technique exhibits low total harmonic distortion. Experimental results of a prototype pure sine wave inverter are presented to support the results of the simulation and mathematical analysis. Since the proposed pure sine wave inverter outperforms the classical TA (terminal attractor)-controlled pure sine wave inverter in terms of convergence speed, computational efficiency, and harmonic distortion elimination, this paper will serve as a useful reference for electric vehicle systems. Full article