Editor’s Choice Articles

Automated charging technologies are becoming increasingly important in the electrification of heavy road freight transport, especially in combination with autonomous driving. This study provides a comprehensive analysis of automated charging technologies for electric heavy-duty vehicles (HDVs). It encompasses the entire spectrum of feasible technologies, including static and dynamic approaches, with each charging technology evaluated for its advantages, potentials, challenges and technology readiness level (TRL). Static conductive charging methods such as charging robots, underbody couplers, or pantographs show good potential, with pantographs being the most mature option. These technologies are progressing towards higher TRLs, with a focus on standardization and adaptability. While static wireless charging is operational for some prototype solutions, it encounters challenges related to implementation and efficiency. Dynamic conductive charging through an overhead contact line or contact rails holds promise for high-traffic HDV routes with the overhead contact line being the most developed option. Dynamic wireless charging, although facing efficiency challenges, offers the potential for seamless integration into roads and minimal wear and tear. Battery swapping is emerging as a practical solution to reduce downtime for charging, with varying levels of readiness across different implementations. To facilitate large-scale deployment, further standardization efforts are required. This study emphasizes the necessity for continued research and development to enhance efficiency, decrease costs and ensure seamless integration into existing infrastructures. Technologies that achieve this best will have the highest potential to significantly contribute to the creation of an efficiently automated and environmentally friendly transport sector. Full article

This paper is based on the work presented at EVS36 in Sacramento. The core of the work deals with the cabin climate control of battery electric vehicles (BEV) using model predictive control (MPC) approaches. These aim to reduce the energy demand for cabin air conditioning while maintaining comfort and air quality. The first step briefly overviews model predictive control approaches and the respective fundamentals. Afterward, the modeling for the system dynamics is explained. The challenge for the system model considering humid air is discussed, and the first implementation method is presented. With the added equations for the air quality and humidity, a logic to prevent window fogging was developed to improve safety. Ultimately, model-in-the-loop (MiL) investigations identified an energy-saving potential of up to 15.4% for cold and 39.7% for hot conditions compared to a rule-based strategy. In addition, the investigations carried out showed that it was also possible to improve indoor comfort by specifically influencing the air quality and humidity. Together with the safety criteria introduced to prevent window fogging, it was possible to present a strategy that can significantly improve thermal management for the cabin in modern BEVs. Full article

Battery cell finalization is a crucial process chain in battery manufacturing, contributing to a significant share of CAPEX and OPEX. Thus, there is a high cost-saving potential by improving the process chain. This research paper investigates various crucial facets of the cell finalization process in battery cell production through an expert survey. These include investment cost allocation, potential cost savings in sub-processes, reject generation, early detection of faulty cells, quality measurement techniques, and the utilization of inline data for early quality determination and real-time process control during the formation process. A solution approach for the implementation of electrochemical impedance spectroscopy for inline early quality determination is given. The results yield valuable insights for optimizing the formation process and enhancing product quality. Full article

A networked microgrid is composed of multiple nearby microgrids linked together to gain additional flexibility for resilient operations. Networked microgrids collaborate to prevent power shortages in microgrid clusters by sharing critical renewable and energy storage resources. However, controlling the local resources of each microgrid, including the energy storage systems’ charging and discharging, maintaining the DC bus voltage, and even overseeing the power shared by multiple microgrids, is challenging. Therefore, a microgrid control technique and distributed energy management are used cooperatively in this study to handle the shared power between a system of networked microgrids incorporating photovoltaics and battery energy storage systems. Numerical simulation results from a networked microgrid system verify the accuracy and soundness of the suggested distributed energy management under several operating conditions, including renewable uncertainties and sequential load variations in different zones. The applicability of the suggested technique is confirmed by hardware implementation, and several operational scenarios further evaluate the proposed system on a practical two-microgrid system located in the Florida International University (FIU) testbed. Full article

This paper examines the extent to which it is possible to replace conventional light commercial vehicles in the heating, ventilation and air conditioning and plumbing trade with battery electric vehicles with an unchanged usage profile. GPS trackers are used to record the position data of 22 craft vehicles with combustion engines from eleven companies over the duration of one working week. Within this paper, various assumptions (battery capacity and average consumption) are made for battery electric vehicles and the charging power on the company premises. The potential of battery electric vehicles is evaluated based on the assumption that they are charged only on company premises. Using the collected data and the assumptions made, theoretical state of charge curves are calculated for the vehicles. The driving profiles of the individual vehicles differ greatly, and the suitability of battery electric vehicles should be considered individually. Battery capacity, vehicle energy consumption and charging power at the company have a substantial influence on the suitability of battery electric vehicles. Furthermore, there are differences between vehicles that can charge on the company premises at night and those that cannot or can only do so on some days. Full article

The idea of electric-drive-reconstructed onboard charger (EDROC) systems, along with the concept of dual-channel charging, offers a novel design, thought to enhance the integration and fault tolerance of the charging system of electric vehicles (EVs). This article investigates a dual-channel EDROC incorporating an asymmetrical six-phase permanent magnet synchronous machine (ASPMSM). A unique operation mode, called the unbalanced charging voltage operation mode, exists in this topology, in case the voltages of the two batteries are unequal. This unbalance results in different winding currents following through two channels, leading to an undesired charging torque in the machine. To ensure the safety of the system, an effective charging torque elimination method, based on dual-channel winding current balance, is proposed, which achieves a dot-shaped current path of torque generation-associated subspace (i.e., α–β subspace) by balancing the dual-channel charging power. Eventually, a controller is designed for the system and a prototype is created, to validate the effectiveness of the proposed method. Full article

In road transport, most vehicles today still rely on internal combustion engines. However, these engines have lower efficiency and generate higher pollution levels compared to electric motors. Consequently, there is a growing interest in the transition from conventional vehicles to electric ones. However, the transition to an electrified road transport system is not without challenges. Among these, the impact that electric vehicle charging will have on the electricity grid is of particular concern. This paper analyzes different charging scenarios for plug-in hybrid electric vehicles (PHEVs) and proposes charging strategies to minimize their impact on the electricity grid. The analysis is based on a large dataset of trips in urban areas in Italy. The study shows that smart charging of PHEVs can be implemented to minimize the impact on the electricity grid. The implementation of optimized charging strategies can contribute to making PHEVs a valid, eco-sustainable alternative to conventional vehicles while also promoting the stability and efficiency of the electricity grid. The study aims to verify the effectiveness and efficiency of the flexible charging strategy by comparing the common charging operation (first in–first out) with other, less impactful charging schemes. Full article

The current trend in distributed electric aircraft propulsion systems is to utilize the DC bus system at higher voltage levels than conventional aircraft systems. With Boeing and Airbus utilizing the +/−270 V bipolar DC bus system, the research on high-voltage systems is increasing gradually, with voltage levels ranging from 1 to 10 kV systems or +/−0.5 to +/−5 kV DC bus systems. These voltage levels present considerable challenges to the distributed electric aircraft propulsion systems. In addition to partial discharge effects, there are other challenges, particularly the challenge associated with effectively limiting short-circuit fault currents due to the low cable impedance of the distribution system. The cable impedance is a significant factor that determines the fault current during fault conditions. Due to the low impedance, there is a sharp increase in fault current, necessitating an enhanced protection strategy, which ensures that the system is adequately protected. This paper introduces a coordinated protection strategy specifically designed for distributed electric aircraft propulsion systems to mitigate or prevent short-circuit faults. The proposed algorithm utilizes an I²t-based strategy and the current-limiting-based strategy to protect the system from short-circuit faults and overload conditions. Redundant backup protection is also included in the algorithm in case the circuit breaker fails to operate. Full article

Accurate extraction of polarization resistance is crucial in the application of proton exchange membrane fuel cells. It is generally assumed that the steady-state resistance obtained from the polarization curve model is equivalent to the AC impedance obtained from the electrochemical impedance spectroscopy (EIS) when the frequency approaches zero. However, due to the low-frequency stability and nonlinearity issues of the EIS method, this dynamic process leads to an additional rise in polarization resistance compared to the steady-state method. In this paper, a semi-empirical model and equivalent circuit models are developed to extract the steady-state and dynamic polarization resistances, respectively, while a static internal resistance correction method is proposed to represent the systematic error between the two. With the correction, the root mean square error of the steady-state resistance relative to the dynamic polarization resistance decreases from 26.12% to 7.42%, indicating that the weighted sum of the static internal resistance and the steady-state resistance can better correspond to the dynamic polarization resistance. The correction method can also simplify the EIS procedure by directly generating an estimate of the dynamic polarization resistance in the full current interval. Full article

This study investigates for the first time how public charging infrastructure usage differs under the presence of diverse pricing models. About 3 million charging events from different European countries were classified according to five different pricing models (cost-free, flat-rate, time-based, energy-based, and mixed) and evaluated using various performance indicators such as connection duration; transferred energy volumes; average power; achievable revenue; and the share of charging and idle time for AC, DC, and HPC charging infrastructure. The study results show that the performance indicators differed for the classified pricing models. In addition to the quantitative comparison of the performance indicators, a Kruskal–Wallis one-way analysis of variance and a pairwise comparison using the Mann–Whitney-U test were used to show that the data distributions of the defined pricing models were statistically significantly different. The results are discussed from various perspectives on the efficient design of public charging infrastructure. The results show that time-based pricing models can improve the availability of public charging infrastructure, as the connection duration per charging event can be roughly halved compared to other pricing models. Flat-rate pricing models and AC charging infrastructure can support the temporal shift of charging events, such as shifting demand peaks, as charging events usually have several hours of idle time per charging process. By quantifying various performance indicators for different charging technologies and pricing models, the study is relevant for stakeholders involved in the development and operation of public charging infrastructure. Full article

The convergence of vehicular communications, 5th generation mobile network (5G) technology, and edge computing marks a paradigm shift in intelligent transportation. Vehicular communication systems, including Vehicle-to-Vehicle and Vehicle-to-Infrastructure, are integral to Intelligent Transportation Systems. The advent of 5G enhances connectivity, while edge computing brings computational processes closer to data sources. This synergy holds the potential to revolutionize transportation efficiency and safety. This research investigates vehicular communication and edge computing dynamics within a 5G network, considering varying distances between On Board Units and Roadside Units. Energy consumption patterns and CPU load at the RSU are analyzed through meticulous real-world experiments and simulations. Our results show stable energy consumption at shorter distances, with fluctuations increasing at greater ranges. CPU load correlates with communication distance, highlighting the need for adaptive algorithms. While experiments exhibit higher variability, our simulations validate these findings, emphasizing the importance of considering transmission range in vehicular communication network design. Full article

Based on an investigation of how vehicle structural characteristics and system parameters influence the motion stability of high-speed steering intelligent vehicle platooning, a control strategy for ensuring motion stability is proposed. This strategy is based on a virtual articulated concept and is validated using both characteristic equation analysis and time domain analysis methods. To create a system, any two adjacent front and rear vehicles in the intelligent vehicle platooning are connected using a virtual articulated model constructed through the virtual structure method. A ten-degrees-of-freedom model of the intelligent vehicle platooning system is established, taking into account the nonlinearities of the tire and steering systems, utilizing the principles of the second Lagrange equation theory. The system damping ratio is determined through characteristic equation analysis, and the system’s dynamic critical speed is assessed by examining the relationship between the damping ratio and the motion stability of the intelligent vehicle platooning, serving as an indicator of system stability. By applying sensitivity analysis, control variable analysis, and time domain analysis methods, the influence of vehicle structural characteristics and system parameters on the system’s dynamic critical speed and motion stability under lateral disturbances within the intelligent vehicle platooning is thoroughly investigated, thereby validating the soundness of the proposed control strategy. Full article

This article provides a comprehensive overview of state-of-the-art techniques for detecting and diagnosing stator winding inter-turn short faults (ITSFs) in permanent-magnet synchronous motors (PMSMs) for electric vehicles (EVs). The review focuses on the following three main categories of diagnostic approaches: motor model-based, signal processing-based, and artificial intelligence (AI)-based fault detection and diagnosis methods. Motor model-based methods utilize motor state estimation and motor parameter estimation as the primary strategies for ITSF diagnosis. Signal processing-based techniques extract fault signatures from motor measured data across time, frequency, or time-frequency domains. In contrast, AI-based methods automatically extract higher-order fault signatures from large volumes of preprocessed data, thereby enhancing the effectiveness of fault diagnosis. The strengths and limitations of each approach are thoroughly examined, providing valuable insights into the advancements in ITSF detection and diagnosis techniques for PMSMs in EV applications. The emphasis is placed on the application of signal processing methods and deep learning techniques in the diagnosis of ITSF in PMSMs in EV applications. Full article

Due to their reliability, design and analysis models with PID and PID fuzzy controllers for six-phase drive are being applied in new areas in various industries, including transportation. First, the development of any system with multiphase motors requires an elaborate model to define the control mode and controllers. The modeling of a control system for six-phase drive is based on its conventional d-q mathematical model and indirect field-oriented control. In this study, a Simulink six-phase drive model is designed with indirect field-oriented control and simulated with two types of fuzzy controller, PID and PID fuzzy. The simulation results are presented and analyzed; these results reflect the step response and performance at the provided speed reference law while keeping the load application at a constant speed. A fuzzy controller with 49 rules is considered and applied. With field-oriented control and a well-tuned PID controller, the six-phase electric drive has good step response specifications: a short settling time when starting without a load, no overshoot in the step response, small size, and a slight decrease in speed when loaded. The system employing a PID fuzzy controller shows slightly better results in response to the application of torque: the decrease in speed is eliminated more quickly. The simulation results were tabulated with the PID and with the results of previous research that rearranged some models to only operate in the classical controller mode. The simulation results indicate the robustness to disturbance of both the systems with six-phase drive and provide high-quality transient specifications at the provided reference speed. Full article

This paper advocates for a proactive approach to traffic safety by introducing a collaborative Misbehaviour Response System (MBR) designed to preemptively address hazardous driving behaviours such as wrong-way driving and distracted driving. The system integrates with electric vehicles (EVs), leveraging advanced technologies like ADAS, edge computing, and cloud services to enhance road safety. Upon detection of misbehaviour, the MBR system utilizes data from interconnected parking facilities to identify the nearest safe location and provides navigation guidance to authorities and nearby vehicles. The paper presents a prototype of the MBR system, demonstrating its efficiency in detecting misbehaviours and coordinating swift responses. It also discusses the system’s limitations and societal implications, outlining future research directions, including integration with autonomous vehicle systems and variable speed limit technologies, to further improve road safety through proactive and context-aware response mechanisms. Full article

Brushless DC machines have demonstrated significant advantages in electrical engineering by eliminating commutators and brushes. Every year, these machines increase their presence in transportation applications. In this sense, early fault identification in these systems, specifically in the electronic speed controllers, is relevant for correct device operation. In this context, the techniques reported in the literature for fault identification based on the Hilbert–Huang transform have shown efficiency in electrical systems. This manuscript proposes a novel technique for early fault identification in electronic speed controllers based on the Hilbert–Huang transform algorithm. Initially, currents from the device are captured with non-invasive sensors in a time window during motor operation. Subsequently, the signals are processed to obtain pertinent information about amplitudes and frequencies using the Hilbert–Huang transform, focusing on fundamental components. Then, estimated parameters are evaluated by computing the error between signals. The existing electrical norms of a balanced system are used to identify a healthy or damaged driver. Through amplitude and frequency error analysis between three-phase signals, early faults caused by system imbalances such as current increasing, torque reduction, and speed reduction are detected. The proposed technique is implemented through data acquisition devices at different voltage conditions and then physical signals are evaluated offline through several simulations in the Matlab environment. The method’s robustness against signal variations is highlighted, as each intrinsic mode function serves as a component representation of the signal and instantaneous frequency computation provides resilience against these variations. Two study cases are conducted in different conditions to validate this technique. The experimental results demonstrate the effectiveness of the proposed method in identifying early faults in brushless DC motor drivers. This study provides data from each power line within the electronic speed controller to detect early faults and extend different approaches, contributing to addressing early failures in speed controllers while expanding beyond the conventional focus on motor failure analysis. Full article

Incorporating battery electric buses into bus fleets faces three primary challenges: a BEB’s extended refuel time, the cost of charging, both by the consumer and the power provider, and large compute demands for planning methods. When BEBs charge, the additional demands on the grid may exceed hardware limitations, so power providers divide a consumer’s energy needs into separate meters even though doing so is expensive for both power providers and consumers. Prior work has developed a number of strategies for computing charge schedules for bus fleets; however, prior work has not worked to reduce costs by aggregating meters. Additionally, because many works use mixed integer linear programs, their compute needs make planning for commercial-sized bus fleets intractable. This work presents a multi-program approach to computing charge plans for electric bus fleets. The proposed method solves a series of subproblems where the solution to the charge problem becomes more refined with each problem, moving closer to the optimal schedule. The results demonstrate how runtimes are reduced by using intermediate subproblems to refine the bus charge solution so that the proposed method can be applied to large bus fleets of 100+ buses. Not only will we demonstrate that runtimes scale linearly with the number of buses but we will also show how the proposed method scales to large bus fleets of over 100 buses while managing the monthly cost of energy. Full article

As a newly conceived vibration isolation element, an inerter can be implemented in different forms, and it is easily introduced in different vibration isolation scenarios. This paper focuses on a novel inerter device called an electromagnetic inerter (EMI), which combines a linear generator with a fluid inerter. Firstly, the structure and the working principle of the EMI is stated. Then, the parameter sensitivity of the fluid inerter is analyzed, and two parameters that have great influence on the inertance coefficient are obtained. Subsequently, the influence of the change of the external circuit on the output characteristics of the device is also discussed. This proves that the introduction of external circuits can simplify complex mechanical topologies. Finally, the topological structures of vehicle suspension are changed in the form of an EMI (including external circuit), and the dynamic performance of these structures in the typical vibration isolation system of a vehicle’s suspension is obtained. It is found that an L4 layout should be considered as the best suspension structure. Compared with traditional passive suspension, it not only ensures that its handling stability is not weakened, but also reduces the root mean square value of body acceleration and the peak of suspension work space by 4.56% and 11.62%, respectively. Full article

With more electric vehicles introduced in society, there is a need for the further implementation of charging infrastructure. Innovation in electromobility may result in new charging and discharging strategies, including concepts such as smart charging and vehicle-to-grid. This article provides an overview of vehicle charging and discharging innovations with a cable connection. A MATLAB/Simulink model is developed to show the difference between an electric vehicle with and without the vehicle-to-grid capabilities for electricity grid prices estimated for Sweden for three different electric vehicle user profiles and four different electric vehicle models. The result includes the state-of-charge values and price estimations for the different vehicles charged with or without a bidirectional power flow to and from the electric grid. The results show that there is a greater difference in state-of-charge values over the day investigated for the electric vehicles with vehicle-to-grid capabilities than for vehicles without vehicle-to-grid capabilities. The results indicate potential economic revenues from using vehicle-to-grid if there is a significant variation in electricity prices during different hours. Therefore, the vehicle owner can potentially receive money from selling electricity to the grid while also supporting the electric grid. The study provides insights into utilizing vehicle-to-grid in society and taking steps towards its implementation. Full article

The worldwide commitment to the electrification of road transport will require a broad overhaul of equipment and infrastructure. Heavy-duty trucks account for over one-third of on-road energy use. Electrified roadways (e-Hwys) are an emerging technology where electric vehicles receive electricity while driving via dynamic wireless power transfer (DWPT), which is becoming highly efficient, and can bypass the battery to directly serve the motor. A modeling study was undertaken to compare long-haul trucks on e-Hwys with conventional battery technology requiring off-road recharging to assess the most favorable pathway to electrification. Detailed data taken from on-road driving trips from five diesel transport trucks were obtained for this study. This on-road data provided the simulations with both real-life duty cycles as well as performance targets for electric trucks, enabling an assessment and comparison of their performance on e-Hwys or with fast recharging. Battery-only trucks were found to have lifetimes down to 60% original battery capacity (60% SOH) of up to 9 years with 1600 kWh packs, and were similar to conventional diesel truck performance. On e-Hwys smaller pack sizes in the 500 to 900 kWh capacity range were sufficient for the driving duty, and showed lifetimes upwards of 20 years, comparing favorably to the battery calendar life limit of about 26 years. For a 535 kWh battery pack, an e-Hwy DWPT level of 250 kW was sufficient for a 36 tonne truck to complete all the daily driving as defined by the diesel reference trucks, and reach a battery pack end of life point of 60% SOH. Full article

This study presents a decision-support methodology to design and optimize modular Boost converters in the context of fuel-cell electric vehicles. It involves the utilization of interleaved techniques to reduce fuel-cell current ripple, enhance system efficiency, tackle issues related to weight and size concerns, and offer better flexibility and modularity within the converter. The methodology incorporates emerging technologies by wide-bandgap semiconductors, providing better efficiency and higher temperature tolerance. It employs a multiphysical approach, considering electrical, thermal, and efficiency constraints to achieve an optimal power architecture for FCHEVs. Results demonstrate the advantages of wide-bandgap semiconductor utilization in terms of volume reduction and efficiency enhancements for different power levels. Results from one of the considered power levels highlight the feasibility of certain architectures through the utilization of WBG devices. These architectures reveal improvements in both efficiency and volume reduction as a result of incorporating WBG devices. Additionally, the analysis presents a comparison of manufacturing cost between standard and wide-bandgap semiconductors to demonstrate the market penetration potential. Full article

During the operation of a wheel loader, the external load acting on the bucket undergoes many changes, resulting in significant changes in the load ratio on the front and rear axles. For this reason, controlling a standard wheel loader is not trivial. In addition, in the case of a distributed electric wheel loader (DEWL), the operating control algorithm is often complex and is, therefore, the subject of optimization studies. This study compared the electric power consumption across different vertical loads, speeds, and travel directions for single-front, single-rear, and dual-motor configurations, both during transporting and pre-shoveling operations. The analysis led to the development of control rules based on energy-saving objectives. Under the shoveling condition, it was observed that vertical loads can lead to an insufficient driving force and skidding, necessitating the proposal of a new optimized control scheme. The results revealed that the optimal solution for transporting is the single-motor drive control scheme without a mechanical connection between the front and rear motor. With the single-motor control scheme, comparing the preferred controlled motor with the unselected motor under different loads, the average electrical power savings for forward, backward, and circling were at least 3.51%, 3.12%, and 0.34%, respectively. Under the pre-shoveling condition, the optimal control scheme was identified as the single rear motor control scheme, effectively reducing electrical power consumption. In response to the issues encountered during the shoveling condition, an economical solution involving the modification of the front axle transmission ratio has been proposed, along with an optimized control scheme based on vertical load variations. Full article

The diffusion of electric vehicles is fundamental for transport sector decarbonization. However, a major concern about electric vehicles is their compatibility with power grids. Adopting a whole-power-system approach, this work presents a comprehensive analysis of the impacts and benefits of electric vehicles’ diffusion on a national power system, i.e., Italy. Demand and flexibility profiles are estimated with a detailed review of the literature on the topic, allowing us to put forward reliable charging profiles and the resulting flexibility, compatible with the Italian regulatory framework. Distribution network planning and power system dispatching are handled with dedicated models, while the uncertainty associated with EV charging behavior is managed with a Monte Carlo approach. The novelty of this study is considering a nationwide context, considering both transmission and distribution systems, and proposing a set of policies suitable for enabling flexibility provision. The results show that the power and energy demand created by the spread of EVs will have localized impacts on power and voltage limits of the distribution network, while the consequences for transmission grids and dispatching will be negligible. In 2030 scenarios, smart charging reduces grid elements’ violations (−23%, −100%), dispatching costs (−43%), and RES curtailment (−50%). Full article

In recent years, many countries have actively promoted sustainable mobility as part of their efforts to decarbonize transportation through automotive electrification. Therefore, identifying the factors that influence individuals’ interest in using electric cars (ECs) is crucial for guiding public opinion toward choosing this sustainable mode of transportation. Consequently, the present study mobilized the theory of planned behavior and the technology acceptance model to interpret the various factors influencing the intention to adopt ECs in a developing country. Following the developed model, data were collected from individuals using cars in Morocco through an online questionnaire. Data analysis using structural equation modeling revealed a positive influence of relative advantage on both the perceived ease of use and green perceived usefulness. Furthermore, the perceived ease of use, green perceived usefulness, environmental concern, and social influence positively affected attitudes toward using ECs. Similarly, these results confirmed that green perceived usefulness and individual attitudes positively enhance ECs adoption intention. These findings contribute to the literature related to ECs adoption and offer guidance to policymakers on promoting ECs adoption in developing countries. Full article

Further advances in hardware and software features are needed to optimize battery and thermal management systems to allow for the execution of longer trips in electric vehicles. This paper assesses the economic and environmental impacts of the following features: eco-charging, eco-driving, smart fast charging, predictive thermal powertrain and cabin conditioning, and an advanced heat pump system. A Total Cost of Ownership (TCO) and externalities calculation is carried out on two passenger cars and one light commercial vehicle (LCV). The energy consumption data from the vehicles are based on experiments. The analysis shows more benefits for the LCV, while the smart fast-charging feature on the car shows a slight increase in TCO. However, negative results did not contribute significantly compared to the ability to install a smaller battery capacity for similar use. Full article

The evolution of electric propulsion systems in the maritime sector has been influenced significantly by technological advancements in power electronics and machine design. Traditionally, these systems have employed surface-mounted permanent magnet synchronous motors (PMSMs) in podded configurations. However, the advent of permanent magnet Vernier motors (PMVMs), which leverage magnetic gearing effects, presents a novel approach with promising potential. This study conducts a comparative analysis between PMVMs and conventional PMSMs at a power level of 5 MW for podded ship propulsion, with a particular focus on the impact of gear ratios ($Gr$). An objective function was developed that integrates motor dimension constraints and the power factor (PF), a critical yet frequently neglected parameter in existing research. The findings indicate that PMVMs with lower $Gr$ have lower mass and cost compared to those with higher $Gr$ and traditional PMSMs, at a PF level of 0.7, which is high for Vernier machines. Moreover, PMVMs with lower $Gr$ achieve efficiencies exceeding 99%, outperforming both their higher $Gr$ counterparts and conventional PMSMs. The superior performance of PMVMs is attributed to lower current density and reduced copper loss, which contribute to their enhanced thermal performance. These details are elaborated on further in the paper. Consequently, these findings suggest that PMVMs with lower $Gr$ are particularly well suited for high-power maritime propulsion applications, offering advantages in terms of compactness, efficiency (EF), cost-effectiveness, and thermal performance. Full article

With the rapid advancement of autonomous vehicles reshaping urban transportation, the importance of innovative traffic management solutions has escalated. This research addresses these challenges through the deployment of roadside units (RSUs), aimed at enhancing traffic flow and safety within the autonomous driving era. Our research, conducted in diverse road settings such as straight and traffic circle roads, delves into the RSUs’ capacity to diminish traffic density and alleviate congestion. Employing vehicle-to-infrastructure communication, we can scrutinize its essential role in navigating autonomous vehicles, incorporating basic safety messages (BSMs) and probe vehicle data (PVD) to accurately monitor vehicle presence and status. This paper presupposes the connectivity of all vehicles, contemplating the integration of on-board units or on-board diagnostics in legacy vehicles to extend connectivity, albeit this aspect falls beyond the work’s current ambit. Our detailed experiments on two types of roads demonstrate that vehicle behavior is significantly impacted when density reaches critical thresholds of 3.57% on straight roads and 34.41% on traffic circle roads. However, it is important to note that the identified threshold values are not absolute. In our experiments, these thresholds represent points at which the behavior of one vehicle begins to significantly impact the flow of two or more vehicles. At these levels, we propose that RSUs intervene to mitigate traffic issues by implementing measures such as prohibiting lane changes or restricting entry to traffic circles. We propose a new message set in PVD for RSUs: road balance. Using this message, RSUs can negotiate between vehicles. This approach underscores the RSUs’ capability to actively manage traffic flow and prevent congestion, highlighting their critical role in maintaining optimal traffic conditions and enhancing road safety. Full article

In this study, we present an innovative, fully automated, and digitalized methodology to optimize the energy efficiency and cost effectiveness of Li-ion battery modules. Advancing on from today’s optimization schemes that rely on user experience and other limitations, the mechanical and thermal designs are optimized simultaneously in this study by coupling 3D multi-physical behavior models to multi-objective heuristic optimization algorithms. Heat generation at various loading and ambient conditions are estimated with a physics-based, fractional-order battery cell-level model, which is extrapolated to a module that further accounts for the interconnected cells’ heat transfer phenomena. Several key performance indicators such as the surface temperature increase, the temperature variations on the cells, and heat uniformity within the module are recorded. For the air-cooled study case, the proposed coupled framework performs more than 250 module evaluations in a relatively short time for the whole available electro-thermal-mechanical design space, thereby ensuring global optimal results in the final design. The optimal module design proposed by this method is built in this work, and it is experimentally evaluated with a module composed of 12 series-connected Li-ion NMC/C 43Ah prismatic battery cells. The performance is validated at various conditions, which is achieved by accounting the thermal efficiency and pressure drop with regard to power consumption improvements. The validations presented in this study verify the applicability and overall efficiency of the proposed methodology, as well as paves the way toward better energy and cost-efficient battery systems. Full article

Addressing the pressing challenge of global warming, reducing greenhouse gas emissions in the transportation sector is a critical imperative. Battery and fuel cell electric vehicles have emerged as promising solutions for curbing emissions in this sector. In this study, we conducted a comprehensive life cycle assessment (LCA) for typical passenger vehicles, heavy-duty trucks, and city buses using either proton-exchange membrane fuel cells or Li-ion batteries with different cell chemistries. To ensure accuracy, we supplemented existing studies with data from the literature, particularly for the recycling phase, as database limitations were encountered. Our results highlight that fuel cell and battery systems exhibit large emissions in the production phase. Recycling can significantly offset some of these emissions, but a comparison of the technologies examined revealed considerable differences. Overall, battery electric vehicles consistently outperform fuel cell electric vehicles regarding absolute greenhouse gas emissions. Hence, we recommend prioritizing battery electric over fuel cell vehicles. However, deploying fuel cell electric vehicles could become attractive in a hydrogen economy scenario where other factors, e.g., the conversion and storage of surplus renewable electricity via electrolysis, become important. Full article

Over the last 50 years since Whittingham created the world’s first lithium-ion battery (LIB) in 1970, LIBs have continued to develop and have become mainstream for electric vehicle (EV) batteries. However, when an LIB for an EV reaches 80% of its state of health (SOH), although it still retains about 80% of its capacity, it is no longer suitable for use in general EVs and must be retired. This is problematic because not only is a retired LIB still viable for use and not totally obsolete, if not properly disposed of, a retired LIB may cause environmental pollution on top of being a waste of resources. Therefore, the reuse of retired LIBs from EVs is increasingly important. This paper uses circular economy (CE) and extended producer responsibility (EPR) as a theoretical basis to deal with the disposal of retired LIBs from EVs in Taiwan from legal, technical, and economic perspectives, and hopes to provide suggestions for the reuse of retired LIBs from EVs in Taiwan. Full article

The growing awareness about climate change and environmental pollution is pushing the industrial and academic world to investigate more sustainable solutions to reduce the impact of anthropic activities. As a consequence, a process of electrification is involving all kind of vehicles with a view to gradually substitute traditional powertrains that emit several pollutants in the exhaust due to the combustion process. In this context, fuel cell powertrains are a more promising strategy, with respect to battery electric alternatives where productivity and endurance are crucial. It is important to replace internal combustion engines in those vehicles, such as the those in the sector of Non-Road Mobile Machinery. In the present paper, a preliminary analysis of a fuel cell powertrain for a telehandler is proposed. The analysis focused on performance, fuel economy, durability, applicability and environmental impact of the vehicle. Numerical models were built in MATLAB/Simulink and a simple power follower strategy was developed with the aim of reducing components degradation and to guarantee a charge sustaining operation. Simulations were carried out regarding both peak power conditions and a typical real work scenario. The simulations’ results showed that the fuel cell powertrain was able to achieve almost the same performances without excessive stress on its components. Indeed, a degradation analysis was conducted, showing that the fuel cell system can achieve satisfactory durability. Moreover, a Well-to-Wheel approach was adopted to evaluate the benefits, in terms of greenhouse gases, of adopting the fuel cell system. The results of the analysis demonstrated that, even if considering grey hydrogen to feed the fuel cell system, the proposed powertrain can reduce the equivalent ${\mathrm{CO}}_2$ emissions of 69%. This reduction can be further enhanced using hydrogen from cleaner production processes. The proposed preliminary analysis demonstrated that fuel cell powertrains can be a feasible solution to substitute traditional systems on off-road vehicles, even if a higher investment cost might be required. Full article

The digitalization of the automotive industry presents significant potential for technical advantages, such as the online collection of customer driving data. These data can be used for customer-oriented development to improve the durability of components or systems. However, due to current limitations in data transfer, the sampling frequency is typically lower than that of classic dataloggers. This paper examines the importance of low-frequency data in the development of drivetrain durability and investigates the extent to which these data can be utilized for a drivetrain durability analysis. Real driving data were utilized as a database to demonstrate the impact of downsampling on data significance, with the deviation in damage serving as the criteria. The findings suggest that low-frequency data, when available in sufficient quantities, can provide valuable information for predicting durability in rollover and time at level classification. The deviation in the damage prediction is less than 2% for distances exceeding 5000 km. However, low-frequency data are not suitable for rainflow analysis. Finally, the database size was adjusted to assess the statistical stability of the durability prediction. A larger dataset typically reduces variance. The paper presents evidence for the quality and usability of cloud data in drivetrain durability design. Cloud data from a significant number of customer vehicles can be used for certain analyses of representative customer load collectives, which can reduce development time and costs. Full article

The objectives of this research were to develop simulation models for agricultural tractors with different powertrain technologies and evaluate the energy consumption in typical agricultural operations. Simulation models were developed for conventional, parallel hybrid electric, series hybrid electric, fuel cell hybrid, and battery electric powertrains. Autonomie vehicle simulation software (version 2022) was used for the simulations and the tractor models were simulated in two tilling cycles and in a road transport cycle with a trailer. The alternative powertrains were configured to have at least the same tractive performance as the conventional, diesel engine-powered tractor model. The simulation results showed that the potential of the parallel and series hybrid powertrains to improve energy efficiency depends heavily on the tractor size and the operating cycle conditions. The fuel cell hybrid and battery electric powertrains have a higher potential to reduce energy consumption and emissions but still have inherent technical challenges for practical operation. The battery-powered electric tractor would require improvements in the storage energy density to have a comparable operational performance in comparison to other powertrains. The fuel cell hybrid tractor already provided an adequate operating performance but the availability of hydrogen and refueling infrastructure could be challenging to resolve in the farming context. Full article

In 2020, only 0.9% of buses running in European Union countries were electric, with 93.5% still being diesel-powered. The Sustainable and Smart Mobility Strategy set out by the European Commission targets a reduction of at least 55% in greenhouse gas emissions by 2023 and the achievement of climate neutrality by 2050. These targets will only be met by a shift to sustainable mobility, which comprises the introduction of electric vehicles in cities and the adoption of battery electric vehicles (BEV) for urban public transport. Thus, a literature review on “electrification of bus fleets” was conducted, focusing on the practices adopted for the replacement of polluting buses with electric-powered ones. A total of 62 documents were included in the final investigation, and their results were used to conduct a SWOT analysis. It is possible to conclude that BEBs are an important asset for cities to decarbonize the transport sector and that they are more cost-effective than diesel buses. On the other hand, some attention needs to be given to the generation of energy that will feed the charging of batteries because the use of fossil fuel energy sources can jeopardize the environmental benefits of BEBs. Full article

The core goal of the BANULA research project is to combine customer-oriented and grid-compatible charging of electric vehicles. It addresses the current challenges of the e-mobility ecosystem from the perspective of grid operators and charging infrastructure users and creates added value for every mass market role involved. In the project, the idea of a virtual balancing group based on blockchain technology is implemented. Thereby, it enables extended data acquisition, a real-time data exchange between grid and market participants, proper balancing and grid node-specific load flow determination and, thus, load management. Full article

In light of the widespread use of electric vehicles for urban distribution, this paper delves into the electric vehicle routing problem (EVRP): specifically addressing multiple trips per vehicle, diverse vehicle types, and simultaneous pickup and delivery. The primary objective is to minimize the overall cost, which encompasses travel expenses, waiting times, recharging costs, and fixed vehicle costs. The focal problem is formulated as a heterogeneous and multi-trip electric vehicle routing problem with pickup and delivery (H-MT-EVRP-PD). Additionally, we introduce two heuristic algorithms to efficiently approximate solutions within a reasonable computational time. The variable neighborhood search (VNS) algorithm and the adaptive large neighborhood search (ALNS) algorithm are presented and compared based on our computational experiences with both. Through solving a series of large-scale real-world instances for the H-MT-EVRP-PD and smaller instances using an exact method, we demonstrate the efficacy of the proposed approaches. Full article

In a simulation study, it was investigated how the costs of supplying H2 for the refuelling of long-haul trucks along highways in Canada can be minimized by optimizing the design of the refuelling infrastructure. Scenarios using local or centralized blue H2 production were evaluated using two different modes of H2 transportation (liquid H2 tanker trucks and pipelines). For each scenario, the average H2 supply costs were determined considering H2 production costs from facilities of different sizes and transportation costs for H2 that was not produced locally. Average H2 supply costs were 2.83 CAD/kg H2 for the scenario with local H2 production at each refuelling site, 3.22–3.27 CAD/kg H2 for scenarios using centralized H2 production and tanker truck transportation, and 2.92–2.96 CAD/kg H2 for centralized H2 production scenarios with pipeline transportation. Optimized scenarios using the cheaper transportation option (tanker truck or pipeline) for each highway segment had average H2 supply costs (2.82–2.88 CAD/kg H2) similar to those of using only local H2 production, with slightly lower costs for the scenario using the largest H2 production volumes. Follow-on research is recommended to include the impact of CO2 transportation (from blue H2 production) on the design of an optimum supply infrastructure. Full article

A radical transformation of the transport industry is required in order to achieve a fossil-fuel-free vehicle fleet and reach the greenhouse gas emissions goals. Electrification plays a crucial role in this radical process. An electric road system (ERS) is a road that supplies power to electric vehicles as they drive on it, offering numerous advantages. These include an extended driving range, decreased reliance on batteries, and increased flexibility and convenience for drivers, eliminating the need to stop for recharging. This paper highlights the transformative potential of ERS in revolutionizing the land transport sector. Through thorough testing with a conductive ERS demonstrator, the viability of the presented technology is validated. Essential aspects like power transfer, efficiency, safety, and environmental impact showcase ERS’s adaptability and scalability across diverse vehicle types. This study recommends widespread ERS support for battery electric vehicles, emphasizing the route toward a sustainable future. Full article

In addition to passenger vehicles, battery-electric trucks and buses could offer substantial flexibility to the energy system. Using a Bass diffusion model, we extrapolated the unidirectional charging needs and availability of trucks in five of eleven typical applications, as well as city buses, for Germany until 2040. Combined, these heavy-duty vehicles could provide up to 23 GW of down-regulating flexibility potential (i.e., in case of excess power supply) in 2040. The resulting revenues could contribute to reducing electricity costs for depot operators. These results illustrate the need to provide easy and automated market access to heavy-duty vehicle fleets. Full article

In electric vehicle fast charging systems, it is important to minimize the effect of fast charging on the grid and it is also important to operate the charging system at high efficiencies. In order to achieve these objectives, in this paper, a sinusoidal half-wave DC current charging protocol and a sinusoidal half-wave pulsed current charging protocol are proposed for the fast charging of Li-ion batteries. A detailed procedure is presented for implementing the following proposed methods: (a) a pre-defined half-sine wave current function and (b) a pulsed half-sine wave current method. Unlike the conventional full-wave sinusoidal ripple current charging protocols, the proposed study does not utilize any sinusoidal full-wave ripple. The performance of these new charging methods on Ni-Co-Al-type Li-cells is studied and compared with the existing constant current and positive pulsed current charging protocols, which have been discussed in the existing literature. In addition, the changes in the electrochemical impedance spectrograph of each cell are examined to study the effects of the applied charging methods on the internal resistance of the Li cell. Finally, the test results are presented for 250 life cycles of charging at 2C (C: charging rate) and the degradation in cell capacities are compared among the four different methods for the Ni-Co-Al-type Li cell. Full article

In this paper, we present a comprehensive assessment of the effects of residential loads, electric vehicles (EVs), and electric heat pumps (HPs) on low-voltage (LV) grids in urban, suburban, and rural areas of Germany. Firstly, real data are used to determine the typical structures for each LV grid region. Secondly, nine scenarios are defined with different levels of EV and HP penetration. Thirdly, the Low Voltage Load Flow Calculation in the DIgSILENT PowerFactory is performed for all scenarios while taking the simultaneity factor (SF) for each load type into consideration to calculate the minimum voltage and maximum loadings of transformer and lines in each grid; this allows for the grid’s potential bottlenecks to be identified. The network simulations are carried out with the consideration of charging powers of 11 kW and 22 kW in order to evaluate how an increasing EV load in the future may affect the grid’s parameters. To the best of our knowledge, no study in the literature has simultaneously addressed all of the aforementioned topics. The results of this study provide a useful framework that distribution system operators (DSOs) may apply to anticipate the forthcoming challenges and figure out when grid reinforcement will be required. Full article

This paper presents the design and simulation of a neural sliding mode controller (NSMC) for a regenerative braking system in an electric vehicle (EV). The NSMC regulates the required current and voltage of the bidirectional DC-DC buck–boost converter, an element of the auxiliary energy system (AES), to improve the state of charge (SOC) of the battery of the EV. The controller is based on a recurrent high-order neural network (RHONN) trained using the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) as the tools to train the neural networks to obtain a higher SOC in the battery. The performance of the controller with the two training algorithms is compared with a proportional integral (PI) controller illustrating the differences and improvements obtained with the EKF and the UKF. Furthermore, robustness tests considering Gaussian noise and varying of parameters have demonstrated the outcome of the NSMC over a PI controller. The proposed controller is a new strategy with better results than the PI controller applied to the same buck–boost converter circuit, which can be used for the main energy system (MES) efficiency in an EV architecture. Full article

Nowadays, electric vehicles have gained significant attention as a promising solution to the environmental concerns associated with traditional combustion engine vehicles. With the increasing demand for high-performance hypercars, the need for advanced torque control strategies has become paramount. Field-Oriented Control using Current Vector Control represents a consolidated solution to implement torque control. However, this kind of control must take into account the DC link voltage variation and the variation of motor parameters depending on the magnets’ temperature while providing the maximum torque production for specific inverter current and voltage limitations. Multidimensional lookup tables are needed to provide a robust torque control from zero speed up to maximum speed under deep flux-weakening operation. Therefore, this article aims to explore the application of FOC 4D control in electrical hypercars and its impact on enhancing their overall performance and control stability. The article will delve into the principles underlying FOC 4D control and its advantages, challenges, and potential solutions to optimize the operation of electric hypercars. An electric powertrain model has been developed in the Simulink environment with the Simscape tool using a S-function block for the implementation of digital control in C-code. High-power electric motor electromagnetic parameters, derived from a Finite Element Method magnetic model, have been used in the simulation. The 4D LUTs have been computed from the motor flux maps and implemented in C-code in the S-function. The choice of FOC 4D control has been validated in the main load points of a hypercar application and compared to the conventional FOC. The final part of the research underlines the benefits of the FOC 4D on reliability, critical in motorsport applications. Full article

Sensorless control of permanent magnet synchronous motors is preferable in some applications due to cost and mounting space concerns. The performance of most existing position estimation methods greatly depends on the accuracy of the motor inductance. As the estimated position should not be involved in the parameter identification process in a sensorless control system, an online inductance identification method independent of the rotor position information is developed in this paper. The proposed method utilizes the recursive least square algorithm and the particle swarm optimization algorithm to realize real-time identification of the inductance along the direct axis and the quadrature axis, respectively, based on the deduced parametric equations without position information. The proposed method is efficient enough to be implemented within 0.2 ms and does not introduce any additional signal injection. A test bench is built to validate the characteristics of the method, and the experimental results show that the identified inductance can converge to the actual value rapidly and is robust to changes in the initial values and stator current. With the proposed method, accurate estimation of the rotor position and speed can be obtained using traditional model-based position estimators, and the stability of the sensorless control system can be improved significantly. Full article

The successful introduction of battery electric trucks heavily depends on public charging infrastructure. But even as the first trucks capable of long-haul transportation are being built, no coherent fast-charging networks are yet available. This paper presents a methodology for assessing fast charging networks for electric trucks in Germany from the literature. It aims to establish a quantitative understanding of the networks’ performance and robustness to deviations from idealized system parameters and identify crucial charging sites from a transportation planning perspective. Additionally, the study explores the quantification of adaptation effects displayed by agents in response to charging site outages. To achieve these objectives, a comprehensive methodology incorporating infrastructure, vehicle and operational strategy modeling, simulation, and subsequent evaluation is presented. Factors such as charging station locations, C-rates, mandatory rest periods, and vehicle parameters are taken into account, along with the distribution of traffic according to publicly available data. The study aims to offer a comprehensive understanding of charging networks’ performance and resilience. This will be applied in a case study on two proposed networks and newly created derivatives. The proposed network offers over 99% coverage for long-haul transport but leads to a time loss of approximately 7% under reference conditions. This study advances the understanding of the performance and resilience of proposed charging networks, providing a solid foundation for the design and implementation of robust and efficient charging infrastructure for electric trucks. Full article

As the adoption of battery electric buses (BEBs) in public transportation systems grows, the need for precise energy consumption forecasting becomes increasingly important. Accurate predictions are essential for optimizing routes, charging schedules, and ensuring adequate operational range. This paper introduces an innovative forecasting methodology that combines a propulsion and auxiliary energy model with a novel concept, the environment generator. This approach addresses the primary challenge in electric bus energy forecasting: estimating future environmental conditions, such as weather, passenger load, and traffic patterns, which significantly impact energy demand. The environment generator plays a crucial role by providing the energy models with realistic input data. This study validates various models with different levels of model complexity against real-world operational data from a case study of over one year with 16 electric buses in Göttingen, Germany. Our analysis thoroughly examines influencing factors on energy consumption, like altitude, temperature, passenger load, and driving patterns. In order to comprehensively understand energy demands under varying operational conditions, the methodology integrates data-driven models and physical simulations into a modular and highly accurate energy predictor. The results demonstrate the effectiveness of our approach in providing more accurate energy consumption forecasts, which is essential for efficient electric bus fleet management. This research contributes to the growing body of knowledge in electric vehicle energy prediction and offers practical insights for transit authorities and operators in optimizing electric bus operations. Full article

The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the foundation for high-performance driving in predictive control systems. The traditional PMSM multi-parameter identification method suffers from insufficient rank of the identification equation and is prone to getting stuck in local optimal solutions. This article combines the bacterial foraging optimization algorithm (BFOA) to establish a built-in PMSM predictive control parameter compensation model. Firstly, we analyzed the reasons why the distortion of PMSM motor parameters affects the actual speed and calculated the deviation of d-axis and q-axis currents caused by the distortion. Secondly, parameter compensation was applied to the prediction model, and BFOA was combined to optimize the compensation parameters. This algorithm does not use the traditional voltage equation as the fitness function but instead uses a brand-new set of four equations for parameter iteration optimization. The optimized compensation parameters can reduce current deviation and improve the robustness of the PMSM predictive control system. The proposed model can cover four kinds of motor distortion parameters, including stator resistance, D-axis inductance, Q-axis inductance, and permanent magnet flux linkage. Finally, the traditional PMSM predictive control model is compared with the predictive control model combined with BFOA. The simulation results show that the dynamic and static performance of the compensated system is improved when single or multiple parameters are distorted. Full article

Subcooled liquid hydrogen (sLH2) is an onboard storage, as well as a hydrogen refueling technology that is currently being developed by Daimler Truck and Linde to boost the mileage of heavy-duty trucks, while also improving performance and reducing the complexity of hydrogen refueling stations. In this article, the key technical aspects, advantages, challenges and future developments of sLH2 at vehicle and infrastructure levels will be explored and highlighted. Full article

The pursuit of autonomous driving relies on developing perception systems capable of making accurate, robust, and rapid decisions to interpret the driving environment effectively. Object detection is crucial for understanding the environment at these systems’ core. While 2D object detection and classification have advanced significantly with the advent of deep learning (DL) in computer vision (CV) applications, they fall short in providing essential depth information, a key element in comprehending driving environments. Consequently, 3D object detection becomes a cornerstone for autonomous driving and robotics, offering precise estimations of object locations and enhancing environmental comprehension. The CV community’s growing interest in 3D object detection is fueled by the evolution of DL models, including Convolutional Neural Networks (CNNs) and Transformer networks. Despite these advancements, challenges such as varying object scales, limited 3D sensor data, and occlusions persist in 3D object detection. To address these challenges, researchers are exploring multimodal techniques that combine information from multiple sensors, such as cameras, radar, and LiDAR, to enhance the performance of perception systems. This survey provides an exhaustive review of multimodal fusion-based 3D object detection methods, focusing on CNN and Transformer-based models. It underscores the necessity of equipping fully autonomous vehicles with diverse sensors to ensure robust and reliable operation. The survey explores the advantages and drawbacks of cameras, LiDAR, and radar sensors. Additionally, it summarizes autonomy datasets and examines the latest advancements in multimodal fusion-based methods. The survey concludes by highlighting the ongoing challenges, open issues, and potential directions for future research. Full article

In this study, we focused on the eco-driving of electric vehicles (EVs). The target vehicle is an electric bus developed by our research team. Using the parameters of the bus and speed pattern optimization algorithm, we derived the EV’s eco-driving speed pattern. Compared to the eco-driving of internal combustion engine vehicles (ICVs), we found several different characteristics. We verified these characteristics with actual vehicle driving test data of the target bus, and the results confirmed its rationality. The EV’s eco-driving method can improve electricity consumption by about 10–20% under the same average speed. Full article