World Electric Vehicle Journal

The current literature highlights several state-of-health (SOH) prediction models for lithium-ion (Li-ion) batteries used in electric vehicles (EVs). However, a thorough comparative analysis remains absent. This study addresses this gap by conducting a comprehensive evaluation of SOH prediction methods for Li-ion batteries in EV applications, encompassing direct measurement techniques, physics-based approaches, and data-driven methodologies. The analysis identifies the strengths, limitations, and applicability of each modeling method. Additionally, this study explores key indicators of SOH, influential variables affecting battery health, and publicly available datasets that support SOH modeling. By synthesizing these insights, the research provides recommendations for improving existing models and outlines prospective directions for enhancing the accuracy and efficiency of SOH estimation in EV applications. This work aims to contribute to the development of robust, accurate, and practical SOH models, thereby advancing the reliability and sustainability of Li-ion battery systems in the growing EV industry. Full article

Nowadays, rapid advancements in computer vision, image processing, and artificial intelligence (AI) have significantly benefited autonomous vehicles. Visual perception is crucial for enhancing the functionality and safety of self-driving technology. However, adverse weather and illumination conditions can impair visual capabilities, affecting environmental awareness, decision-making, and safe navigation. This work provides a comprehensive review of AI image enhancement methods and benchmark datasets, including deblurring, deraining, dehazing, and low-light enhancement, along with the integration of multiple image enhancement techniques in computer vision tasks. Specifically, this review focuses on advancements for real-world applications and summarizes performance metrics for real-time operation in automotive vision systems. Furthermore, the paper highlights efforts and challenges in real-world testing to ensure the effectiveness and reliability of these solutions in practical applications, which is essential for enabling autonomous vehicles to operate safely and efficiently under various challenging conditions, thereby contributing to the future of intelligent transportation systems. 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

With the introduction of large distributed photovoltaic (PV) power and electric vehicles, the inherent volatility of their output makes it difficult for traditional power grid structures and reactive power optimization methods to meet the needs of the safe operation of distribution grids and economic benefits. Therefore, a multi-objective reactive power optimization method for a distributed grid is proposed under a distributed PV power generation scenario. Aiming at the two objectives of the network loss and voltage fluctuation rate, the improved multi-objective particle swarm optimization algorithm is used to solve the model under the condition that the output of each device does not exceed the constraint and the optimal solution that can reduce the distribution grid loss and improve the voltage stability of the distribution grid is obtained. The simulation was conducted on the IEEE 33-node and 113-node distribution networks to verify the proposed method’s feasibility. 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

This study aims to develop a theoretical formula to help bus operators easily predict electricity consumption while introducing a certain type of electric bus on a predetermined route. The formula requires vehicle-side information (such as air resistance coefficient, rolling resistance coefficient, vehicle weight, powertrain efficiency, kinetic energy recovery rate, auxiliary equipment electricity consumption, and other vehicle-related data) for construction and road-/operation-side information (such as average driving speed, number of starts and stops, road gradients, and other road-/operation-related data) for prediction. First, herein, as a basic study to construct the theoretical formula, a developed electric bus and its vehicle electricity consumption simulator are employed. We then perform a comparative analysis considering the comparison of loss between the actual operation on public roads and the assumed constant velocity when running on flat roads. Next, we develop theoretical equations for the generalization of velocity and gradient changes and simplified modeling of electricity consumption prediction. Considering the burden of information collection on operators, we categorize it into three stages. In this paper, we first organize the minimum necessary road-/operation-side information (route/operational indicators). Next, we propose a theoretical formula for electricity consumption prediction constructed based on vehicle-side information. Finally, we validate the validity and accuracy of the constructed formula using electric buses and their on-road operational data that we developed earlier. The verification results showed that, after obtaining vehicle-side and road-/operation-side information, the theoretical formula constructed in this study achieved an electricity consumption prediction with an average error of 6% (high-accuracy method). This result demonstrates the practicality of using the theoretical formula to predict the electricity consumption/range of electric buses operating on specific routes. Full article

High-fidelity driving scenario reconstruction can generate a lot of realistic virtual simulation environment samples, which can support effective training and testing for autonomous vehicles. Neural radiance fields (NeRFs) have demonstrated their excellence in high-fidelity scenario reconstruction; however, they still rely on dense-view data and precise camera poses, which are difficult to obtain in autonomous vehicles. To address the above issues, we propose a novel approach called SRNeRF, which can eliminate pose-based operations and perform scenario reconstruction from sparse views. To extract more scene knowledge from limited views, we incorporate an image super-resolution module based on a fully convolutional neural network and introduce a new texture loss to capture scene details for higher-quality scene reconstruction. On both object-centric and scene-level datasets, SRNeRF performs comparably to previous methods with ground truth poses and significantly outperforms methods with predicted poses, with a PSNR improvement of about 30%. Finally, we evaluate SRNeRF on our custom autonomous driving dataset, and the results show that SRNeRF can still generate stable images and novel views in the face of sparse views, demonstrating its scalability in autonomous driving scenario synthesis. Full article

As the world transitions towards sustainable transportation, the advancement of electric vehicles (EVs) has become imperative. Wireless power transfer (WPT) technology presents a promising solution to enhance the convenience and efficiency of EV charging while alleviating the challenges associated with traditional wired systems. This paper conducts an in-depth exploration of WPT technologies for EVs, focusing on their theoretical foundations, practical implementation, optimization strategies, development trends, and limitations. The theoretical principles of wireless charging are first elucidated, categorizing them into near-field methods, such as inductive and capacitive charging, and far-field methods, including microwave and laser-based charging. A comparative analysis reveals the advantages and limitations inherent to each technology. The implementation section examines various charging strategies, encompassing stationary, dynamic, and quasi-dynamic wireless charging, assessing their feasibility and effectiveness in practical applications. Furthermore, optimization techniques aimed at enhancing WPT system performance are examined in depth, with particular emphasis on coil structure optimizations, anti-misalignment solutions, compensation topology optimizations, modulation strategy optimizations, and parameter identifications. The discussion section outlines current development trends in wireless charging technologies for EVs, highlighting the limitations that hinder the widespread adoption of wireless charging technologies in the EV market. Finally, potential research directions and the implications of wireless charging technology on the development of EVs are summarized. This critical review aims to provide valuable insights for researchers and practitioners dedicated to advancing the field of wireless charging for EVs. Full article

This paper evaluated the acoustic characteristics of electric vehicles (EVs) using both psychoacoustic and soundscape methodologies by analyzing three key psychoacoustic parameters: loudness, roughness, and sharpness. Through correlation analysis between perceived values and objective parameters, we identified specific sound sources requiring improvement, including vehicle body acoustics, wheel noise, and acceleration-related sounds. The relationship between comfort perception and acoustic parameters showed varying correlations: loudness (0.0411), roughness (2.3452), and sharpness (0.9821). Notably, the overall correlation coefficient of 0.5 suggests that psychoacoustic parameters alone cannot fully explain human comfort perception in EVs. The analysis of sound propagation revealed elevated vibration levels specifically in the driver’s seat area compared to other vehicle regions, identifying key targets for improvement. The research identified significant acoustic events at three key frequencies (50 Hz, 250 Hz, and 450 Hz), requiring in-depth analysis to determine their sources and understand their effects on the vehicle’s NVH characteristics. The study successfully validated its results by demonstrating that a combined approach using both psychoacoustic and soundscape parameters provides a more comprehensive understanding of passenger acoustic perception. This integrated methodology effectively identified specific areas needing acoustic refinement, including: frame vibration noise during rough road operation; tire-generated noise; and acceleration-related sound emissions. Full article

This study investigates the impact of digital capabilities (DC) on building resilient supply chains in China’s electric vehicle (EV) industry. As the complexity of the EV sector continues to grow, improving supply chain resilience (SCR) is essential for sustaining long-term growth and maintaining competitiveness. This research focuses on how visibility and collaboration, supported by DC, contribute to the development of SCR. Using structural equation modeling (SEM), data from 399 Chinese EV supply chain enterprises were analyzed to examine the moderating effects of DC and their sub-dimensions—digital infrastructure capability, digital analytics capability, and strategic support capability—on the relationships between visibility, collaboration, and resilience. The results reveal that both visibility and collaboration significantly and positively influence resilience, with visibility having the strongest impact. Furthermore, digital analytics capability enhances the positive effect of collaboration on resilience, while overall DC and other dimensions, such as digital infrastructure and strategic support capabilities, show limited impact. The findings also underscore that digital infrastructure capability plays a vital role in amplifying the impact of visibility on resilience. Consequently, EV supply chain enterprises are encouraged to invest continuously in digital infrastructure and analytics capabilities to strengthen their SCR. Full article

In response to the rising frequency of traffic accidents and growing concerns regarding driving safety, the identification and analysis of dangerous driving behaviors have emerged as critical components in enhancing road safety. In this paper, the research progress in the recognition methods of dangerous driving behavior based on deep learning is analyzed. Firstly, the data collection methods are categorized into four types, evaluating their respective advantages, disadvantages, and applicability. While questionnaire surveys provide limited information, they are straightforward to conduct. The vehicle operation data acquisition method, being a non-contact detection, does not interfere with the driver’s activities but is susceptible to environmental factors and individual driving habits, potentially leading to inaccuracies. The recognition method based on dangerous driving behavior can be monitored in real time, though its effectiveness is constrained by lighting conditions. The precision of physiological detection depends on the quality of the equipment. Then, the collected big data are utilized to extract the features related to dangerous driving behavior. The paper mainly classifies the deep learning models employed for dangerous driving behavior recognition into three categories: Deep Belief Network (DBN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). DBN exhibits high flexibility but suffers from relatively slow processing speeds. CNN demonstrates excellent performance in image recognition, yet it may lead to information loss. RNN possesses the capability to process sequential data effectively; however, training these networks is challenging. Finally, this paper concludes with a comprehensive analysis of the application of deep learning-based dangerous driving behavior recognition methods, along with an in-depth exploration of their future development trends. As computer technology continues to advance, deep learning is progressively replacing fuzzy logic and traditional machine learning approaches as the primary tool for identifying dangerous driving behaviors. 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

It is necessary to maintain safe, efficient, and compatible energy storage systems to meet the high demand for electric vehicles (EVs). Lithium manganese nickel cobalt (NMC) and lithium ferro phosphate (LFP) batteries are the most commonly used lithium batteries in EVs. It is imperative to note that batteries are classified according to their electrochemical performance. A number of factors play a crucial role in determining how efficiently batteries can be used. These factors include the cell temperature, energy density, self-discharge, current limits, aging, and performance measurements. This paper offers a proposed electrothermal model for comparison between LFP and NMC batteries. This model demonstrates the different behaviors according to their application in EVs. This is carried out through studies of state of charge (SoC), state of health (SoH), thermal runaway, self-discharge, and remaining useful life (RUL) in EVs. According to numerical analysis, this paper examines how these different types of batteries behave in EVs to assist in the selection of the most suitable battery taking into account the operating temperature and discharge current using a helpful thermoelectric model reflecting battery safety and life span effectively. Using MATLAB Simulink, the data selected in the electrothermal model are combined from a number of references that are incorporated into lookup tables that affect the change in values in the electrothermal model. The cells are implemented in an EV system using a current test to examine the measured current that goes in and comes out of the battery cells during charging and discharging processes taking into account motoring and regenerative braking for a specified drive cycle time and a number of discharging cycles. It was found that LFP batteries have better stability for open circuit voltages of 3.34 volts over a wide range of conducted temperatures. NMC batteries, on the other hand, exhibit some open circuit voltage variation of 0.053 volts over the temperature range used. Furthermore, the self-discharging current of LFP batteries was about 12 times lower than that of NMC batteries. Compared to LFP batteries, NMC batteries have a higher energy density per unit of mass of 150%, which reflects their greater discharge range. As a result of temperature effects, it has been revealed that LFP batteries are about two times more stable during discharging than NMC batteries, particularly at higher temperatures, such as 45 degrees. 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

This paper presents the design of an electric amphibious vehicle with buoyancy provided by inflatable pontoons, referred to as the Electric Inflatable Pontoon amphibious vehicle (E-IPAMV). To investigate the effect of pontoon arrangements on resistance performance, maneuverability, seakeeping, transverse stability, and longitudinal stability of E-IPAMV, STAR-CCM+ and Maxsurf are used to solve the above performance parameters. A constrained space Latin hypercube experimental design is employed, using the lengths of the inflatable pontoons at five installation positions as input variables, and total resistance, steady turning diameter, maximum pitch angle, transverse metacentric height, and longitudinal metacentric height as output variables. A neural network model is then established and validated. Based on this model, NSGA-II is employed to optimize the pontoon lengths at the five installation positions, yielding Pareto-optimal solutions. Finally, considering project and manufacturing requirements, two optimized design schemes are proposed. Compared to the original design, optimization scheme 1 shows a slight reduction in seakeeping but improvements in other hydrodynamic performances. Meanwhile, optimization scheme 2 enhances all hydrodynamic performances. Specifically, in optimization scheme 2, maneuverability increases by the smallest amount, showing 23.43% improvement compared to the original design, while transverse stability sees the greatest improvement, increasing by 290.99% compared to the original design. 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

Simultaneous localization and mapping (SLAM) is one of the key technologies for mobile robots to achieve autonomous driving, and the lidar SLAM algorithm is the mainstream research scheme. Firstly, this paper introduces the overall framework of lidar SLAM, elaborates on the functions of front-end scan matching, loop closure detection, back-end optimization, and map building module, and summarizes the algorithms used. Then, the classical representative SLAM algorithms are described and compared from three aspects: pure lidar SLAM algorithm, multi-sensor fusion SLAM algorithm, and deep learning lidar SLAM algorithm. Finally, the challenges faced by the lidar SLAM algorithm in practical use are discussed. The development trend of the lidar SLAM algorithm is prospected from five dimensions: lightweight, multi-sensor fusion, combination of new sensors, multi-robot collaboration, and deep learning. This paper can provide a brief guide for novices entering the field of SLAM and provide a comprehensive reference for experienced researchers and engineers to explore new research directions. Full article

Battery modelling is essential for optimizing the performance and reliability of Unmanned Aerial Vehicles (UAVs), particularly given the challenges posed by their dynamic power demands and limited onboard computational resources. This study evaluates two widely adopted Equivalent Circuit Models (ECMs), the fixed resistance model and the Thevenin model to determine their suitability for UAV applications. Using the Specific Hybrid Pulse Power Characterization (SHPPC) method, key parameters, including Open Circuit Voltage (OCV), internal resistance (Ri), polarization resistance (R1), and polarization capacitance (C1), were estimated across multiple states of charge (SOC). The models were analyzed under nine parameterization scenarios, ranging from fully average parameters to configurations where selected parameters were tied to SOC. Results indicate that the Thevenin model, with selective SOC-dependent parameters, demonstrated superior predictive accuracy, achieving error reductions of up to 4.26 times compared to the fixed resistance model. Additionally, findings reveal that modelling all parameters as SOC-dependent is unnecessary, as simpler configurations can balance accuracy and computational efficiency, particularly for UAVs with constrained BMS capabilities. Full article