Vehicles, Volume 6, Issue 1 (March 2024) – 26 articles

Variable flux reluctance machines (VFRMs) are increasingly attracting research interest due to their magnetless and robust brushless structure. Under the modulation effect of the airgap permeance, the VFRM operates with a series of field harmonics, distinguishing it from conventional AC synchronous machines. This paper deals with the analysis and preliminary design of the VFRM from the perspective of multiple working airgap field harmonics. Firstly, the spatial and temporal order of the working field harmonics are defined. The systematic winding theory, including the unified star of slots and winding factor calculation method, is established to consider all these working harmonics. Then, an average torque model is built and simplified. The key role of 1st-order rotor permeance, 1st- and 3rd-order polarized stator permeance is deduced. The relationship between key parameters and average torque is computed, providing a guideline for the preliminary design of the VFRM. Full article

This article presents a case study on estimating the real service inspection intervals for German-brand passenger cars in Kazakhstan and Poland. This study aimed to identify disparities between the official recommendations of manufacturers for car maintenance and the real data collected in these two countries. The following passenger cars were examined: Audi A6, Q5, and Q8; Porsche Cayenne and Cayenne coupe; and Volkswagen Passat, Polo, Teramont, Tiguan, Touareg, Arteon, Golf, T-Cross, Tiguan all space, Touran, T-Roc, and Up. To assess the difference between real and recommended values, the manufacturer criteria of a recommended mileage of 15,000 and 30,000 km or a time frame of 365 and 730 days to the first service inspection were applied. The data analysis showed that in Kazakhstan, 31.4% of cars did not meet the warranty conditions, while in Poland, it was 21.0%. The dominant criterion that was not met was the time criterion. The assessment of these factors emphasizes the importance of customizing vehicle maintenance schedules to the specific conditions and driving behaviors prevalent in each country. The practical contribution of the article lies in uncovering the discrepancies between official manufacturer recommendations for car maintenance and the actual data collected in Kazakhstan and Poland. By identifying specific models, Volkswagen Touareg and Tiguan in Kazakhstan and Volkswagen Up in Poland, for which the maintenance intervals deviated significantly from those recommended, this study offers valuable insights for optimizing service schedules and improving the efficiency of maintenance practices in these countries. From a scientific perspective, this article contributes by providing empirical evidence of real-world maintenance behaviors for German-brand passenger cars. Full article

Curb detection tasks play a crucial role in the perception of the autonomous driving environment for logistics vehicles. With the popularity of multi-modal sensors under the BEV (Bird’s Eye View) paradigm, curb detection tasks are increasingly being integrated into multi-task perception networks, achieving robust detection results. This paper modifies and integrates the tri-plane spatial feature representation method of the EG3D network from the field of 3D reconstruction into a BEV-based multi-modal sensor detection network, including LiDAR, pinhole cameras, and fisheye cameras. The system collects a total of 24,350 frames of data under real road conditions for experimentation, proving the effectiveness of the proposed method. Full article

The performance of an electric vehicle (EV) notably depends on an energy management controller. This study developed several energy management controllers (EMCs) to optimize the efficiency of EVs in real-time driving conditions. Also, this study employed an innovative methodology to create EMCs, efficiency maps, and real-time driving cycles under actual driving conditions. The various EMCs such as PID, intelligent, hybrid, and supervisory controllers are designed using MATLAB/Simulink and examined under real-time conditions. In this instance, a mathematical model of an EV with a switched reluctance (SR) motor is developed to optimize energy consumption using different energy management controllers. Further, an inventive experimental approach is employed to generate efficiency maps for the SR motor and above-mentioned controllers. Then, the generated efficiency maps are integrated into a model-in-loop (MIL)-based EV test platform to analyze the performance under real-time conditions. Additionally, to verify EV model, a real-time driving cycle (DC) has been developed, encompassing various road conditions such as highway, urban, and rural. Subsequently, the developed models are included into an MIL-based EV test platform to optimize the performance of the electric motor and battery consumption in real-time conditions. The results indicate that the proposed supervisory controller (59.1%) has a lower EOT SOC drop compared to the PID (3.6%), intelligent (21.5%), and hybrid (44.9%) controllers. Also, the suggested controller achieves minimal energy consumption (44.67 Wh/km) and enhances energy recovery (−58.28 Wh) under different real-time conditions. Therefore, it will enhance the driving range and battery discharge characteristics of EVs across various real-time driving conditions. Full article

Connected and automated vehicles (CAV) are increasingly recognized as a critical component of intelligent transportation systems (ITS), contributing to advances in transportation safety and mobility. However, the implementation of CAV in a real-world environment comes with various threats, and cybersecurity is among the most vulnerable. As the technology becomes more advanced and complex, it is essential to develop a comprehensive cybersecurity framework that can address these concerns. This research proposes a novel framework based on complexity theory and employs the fuzzy set qualitative comparative analysis (fsQCA) technique to identify combinations of security attacks that lead to achieving cybersecurity in CAV. Compared to structural equation modelling (SEM), the fsQCA method offers the advantage of demonstrating all possible ways to achieve the outcome. The study’s findings suggest that in-vehicle networks and data storage security are the most crucial factors in ensuring the cybersecurity of CAV. The results can be useful for automotive designers in reducing the potential for attacks while developing secure networks. Full article

Wireless charging (WC) has gained popularity for the charging of electric vehicles in recent years of research, particularly dynamic wireless charging systems (DWCSs). Among the different topologies of DWCSs, this paper focuses on an inductively coupled wireless charging system (ICWCS). In this ICWCS, double-D (DD) coils create horizontal and vertical flux components between different pad configurations, which show optimal features in contrast to circular pad coils. In this work, the three-dimensional (3D) finite element technique (FEM) is used to establish the proposed design to observe the coupling coefficient, while the system design’s performance is evaluated using a circuit simulator. In the simulation, the proposed DD coil configuration is used for both the transmitter and receiver sides. It provides the maximum coupling coefficient and efficiency at perfect alignment when using an in-between air gap of 166 mm and six I-type ferrite bars on the transmitter side and five I-type ferrite bars on the receiver side. The coupling coefficient and system parameters, such as power and efficiency, are considered for different misalignments in the proposed configuration. The results of this work satisfy the Society of Automotive Engineers (SAE) J2954 Class 3 criteria. The best results obtained are on account of optimizing the ferrite core, which is achieved by varying its length and width. While varying the ferrite core’s dimensions, 0.2451, as the optimal k value, is obtained at the effective width and length of 57.5 mm and 400 mm, respectively. The simulation results of the Ansys Maxwell 3D software prove the feasibility of the proposed structure. Full article

Vehicle-to-vehicle (V2V) communication has played a pivotal role in modern intelligent transportation systems, enabling seamless information exchange among vehicles to enhance road safety, traffic efficiency, and overall driving experience. However, the secure transmission of sensitive data between vehicles remains a critical concern due to potential security threats and vulnerabilities. This research focused on investigating the security protocols that have been employed in vehicle-to-vehicle communication systems. A comprehensive review and analysis of relevant literature and research papers was conducted to gather information on existing V2V communication security protocols and techniques. The analysis encompassed key areas, including authentication mechanisms, encryption algorithms, key management protocols, and intrusion detection systems specifically applicable to V2V communication networks. Within the context of real-world V2V environments, this study delved into the challenges and limitations associated with implementing these protocols. The research aimed to provide a comprehensive understanding of the strengths and weaknesses of the current V2V communication security protocols. Furthermore, based on the findings, this paper proposes improvements and recommendations to enhance the security measures of the V2V communication protocol. Ultimately, this research contributes to the development of more secure and reliable V2V communication systems, propelling the advancement of intelligent transportation technology. Full article

Transforming intersections into roundabouts has shown that a sufficient degree of road safety and traffic capacity can be achieved. Because of the lower speeds at the area of a roundabout, drivers tend to become more easily adaptive to any kind of conflict with the surrounding environment. Despite the contribution to safety, the design elements of roundabouts are not uniformly fixed on a worldwide scale because of different traffic volumes, vehicle dimensions, drivers’ attitude, etc. The present study provides a brief overview of the contribution of roundabouts to road safety and the interactions between safety and the design elements of roundabouts. In addition, discussion points about current challenges and prospects are elaborated, including findings from the environmental assessment of roundabouts; their use and performance on the era of autonomous vehicles that will dominate in the near future; as well as the role and importance of simulation studies towards the improvement of the design and operation of roundabouts in favor of safer vehicle movement. The criticality of roundabouts, in terms of their geometric design as well as the provided road safety, lies upon the fact that roundabouts are currently used for the conventional vehicle fleet, which will be gradually replaced by new vehicle technologies. Such an action will directly impact the criteria for road network design and/or redesign, thereby continuously fostering new research initiatives. Full article

Multi-purpose agricultural tractors are vehicles that are usually involved in different operations, including road maintenance in small villages and cultivation of small agricultural plots, particularly in mountainous areas. These vehicles typically feature narrow tracks to enhance manoeuvrability, making rollover stability a critical consideration in operational settings. This characteristic arises from the interplay between the suspension and vehicle chassis. This paper introduces a numerical multi-body model designed to replicate the dynamics of a multi-purpose tractor with a torsional chassis. Model parameters were derived through experimental measurements conducted on an actual vehicle. Static measurements were performed to assess tire and suspension stiffness while tilting tests were performed to establish the static rollover limit of the vehicle. Dynamic tests conducted on a four-post test rig characterised the vehicle’s dynamics. The validated model was utilised to explore the vehicle’s stability by reproducing the static rollover tests and simulating the vehicle’s performance under working conditions on a banked road. The vehicle rollover stability was studied by performing a sensitivity analysis that considered both chassis torsional stiffness and suspension stiffness under different loading conditions. The results indicate a trade-off between frame and suspension stiffnesses that enhances overall vehicle stability. Full article

The development of vehicle drive systems targets different goals, which are partly contradictory. While the focus is often on increasing efficiency and—depending on the type of drive system—performance, the aim is to simultaneously reduce costs, weight, and volume as much as possible. This goal generally presents a conflict of objectives; for example, a gain in efficiency usually correlates with higher costs, or an increase in performance reduces the maximum achievable efficiency. Therefore, each drive system represents a compromise among these goals, and depending on the main focus, the development can be influenced. The methods presented in this work serve as a methodological framework for the evaluation of vehicle drive systems. The procedure involves evaluating different drive concepts based on defined criteria and comparing these evaluations with one another. These criteria can be selected freely and weighted differently, depending on the individual focus. In the sense of a holistic assessment, a system evaluation factor ultimately serves as an indicator, which is composed of the rating values of the individual criteria, taking into account their specific weightings. With the help of the novel method presented in this paper, the complexity of comparing differently designed powertrains is reduced, and a holistic assessment covering relevant viewpoints is possible. Such an all-encompassing view is helpful in the early development phase and is required as an evaluation basis for further, groundbreaking decisions in concept development. Full article

This paper focuses on the modeling and analysis of a four-groove passenger car tire, size 235/55R19, using finite element analysis. The Mooney–Rivlin material model is employed to define the hyperelastic behavior of the tire rubber compounds for all solid elements. The tire rim is modeled as a rigid body using aluminum alloy material, and the beads are modeled as beam elements using steel material. The tire model is validated in both static and dynamic domains through several simulations and is compared to published measured data. The tire is validated using footprint and vertical stiffness tests in the static domain. In the static footprint test, a steady-state vertical load is applied, and the tire–road contact area is computed. In the vertical stiffness test, a ramp vertical load is applied, and the tire’s vertical displacement is measured to calculate the tire’s vertical stiffness. In the dynamic domain, the tire is validated using drum-cleat and cornering tests. In the drum-cleat test, a drum with a 2.5 m diameter and a cleat with a 15 mm radius is used to excite the tire structure and obtain the frequency of the vertical and longitudinal first modes of vibration, that is, by applying the fast Fourier transformation (FFT) of the vertical and longitudinal reaction forces at the tire center. In addition to this test, the tire model is pre-steered on a flat surface with a two-degree slip angle and subjected to a steady state linear speed of 10 km/h to predict the cornering force and compute the cornering stiffness. In addition, the effect of tire longitudinal speed on the rolling resistance coefficient is then predicted at zero slip angle using the ISO 28580 rolling resistance test. The findings of this research work provide insights into passenger car tire–road interaction analysis and will be further used to perform tire rubber compound material model sensitivity analysis. Full article

This article focuses on the assessment of the efficiency of the modernization of railway wagons intended for passenger transport. The modernization of railway wagons is an important step towards ensuring the safer and more efficient operation of railways. However, this modernization is not without risks and, in addition to its many positive effects, it can also bring new challenges in the form of breakdowns and technical problems. Failures in passenger rail vehicles are a challenge facing manufacturers and transport operators, and identifying the relationships between these failures can help to improve the design, manufacture, and maintenance of these vehicles. Passenger rail vehicle failures can vary in nature, whether mechanical, electrical, or inside the vehicle. This paper attempts to provide a comprehensive view of the effectiveness of passenger rail car retrofitting based on the data collected and statistical analyses. The article further focuses on the description of selected rail passenger wagons, describes the analyses of the statistical data using the correlation between the selected rail wagon failures and their significance, in addition to whether the impact of modernization reduces the number of rail wagon failures. The results of this statistical analysis can be used to better understand the impact of modernization on different aspects of passenger transport and will help in making future investment and policy decisions in this area. © 2017 Elsevier Inc. All rights reserved. Full article

In the cooperative multi-sensor multi-vehicle (MSMV) localization domain, the data incest problem yields inconsistent data fusion results, thereby reducing the accuracy of vehicle localization. In order to address this problem, we propose the interval split covariance intersection filter (ISCIF). At first, the proposed ISCIF method is applied to the absolute positioning step. Then, we combine the interval constraint propagation (ICP) method and the proposed ISCIF method to realize relative positioning. Additionally, in order to enhance the robustness of the MSMV localization system, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. Three simulations were carried out: Simulation scenarios 1 and 2 aimed to assess the accuracy of the proposed ISCIF with various capabilities of absolute vehicle positioning, while simulation scenario 3 was designed to evaluate the localization performance when faults were present. The simulation results of scenarios 1 and 2 demonstrated that our proposed vehicle localization method reduced the root mean square error (RMSE) by 8.9% and 15.5%, respectively, compared to the conventional split covariance intersection filter (SCIF) method. The simulation results of scenario 3 indicated that the implemented FDE method could effectively reduce the RMSE of vehicles (by about 55%) when faults were present in the system. Full article

A lot of research has been conducted in recent years on stereo depth estimation techniques, taking the traditional approach to a new level such that it is in an appreciably good form for competing in the depth estimation market with other methods, despite its few demerits. Sufficient progress in accuracy and depth computation speed has manifested during the period. Over the years, stereo depth estimation has been provided with various training modes, such as supervised, self-supervised, and unsupervised, before deploying it for real-time performance. These modes are to be used depending on the application and/or the availability of datasets for training. Deep learning, on the other hand, has provided the stereo depth estimation methods with a new life to breathe in the form of enhanced accuracy and quality of images, attempting to successfully reduce the residual errors in stages in some of the methods. Furthermore, depth estimation from a single RGB image has been intricate since it is an ill-posed problem with a lack of geometric constraints and ambiguities. However, this monocular depth estimation has gained popularity in recent years due to the development in the field, with appreciable improvements in the accuracy of depth maps and optimization of computational time. The help is mostly due to the usage of CNNs (Convolutional Neural Networks) and other deep learning methods, which help augment the feature-extraction phenomenon for the process and enhance the quality of depth maps/accuracy of MDE (monocular depth estimation). Monocular depth estimation has seen improvements in many algorithms that can be deployed to give depth maps with better clarity and details around the edges and fine boundaries, which thus helps in delineating between thin structures. This paper reviews various recent deep learning-based stereo and monocular depth prediction techniques emphasizing the successes achieved so far, the challenges acquainted with them, and those that can be expected shortly. Full article

The transport sector is under an intensive renovation process. Innovative concepts such as shared and intermodal mobility, mobility as a service, and connected and autonomous vehicles (CAVs) will contribute to the transition toward carbon neutrality and are foreseen as crucial parts of future mobility systems, as demonstrated by worldwide efforts in research and industry communities. The main driver of CAVs development is road safety, but other benefits, such as comfort and energy saving, are not to be neglected. CAVs analysis and development usually focus on Information and Communication Technology (ICT) research themes and less on the entire vehicle system. Many studies on specific aspects of CAVs are available in the literature, including advanced powertrain control strategies and their effects on vehicle efficiency. However, most studies neglect the additional power consumption due to the autonomous driving system. This work aims to assess uncertain CAVs’ efficiency improvements and offers an overview of their architecture. In particular, a combination of the literature survey and proper statistical methods are proposed to provide a comprehensive overview of CAVs. The CAV layout, data processing, and management to be used in energy management strategies are discussed. The data gathered are used to define statistical distribution relative to the efficiency improvement, number of sensors, computing units and their power requirements. Those distributions have been employed within a Monte Carlo method simulation to evaluate the effect on vehicle energy consumption and energy saving, using optimal driving behaviour, and considering the power consumption from additional CAV hardware. The results show that the assumption that CAV technologies will reduce energy consumption compared to the reference vehicle, should not be taken for granted. In 75% of scenarios, simulated light-duty CAVs worsen energy efficiency, while the results are more promising for heavy-duty vehicles. Full article

This paper presents the design of a bidirectional wireless power and information transfer system. The wireless information transfer is based on near-field technology, utilizing communication coils integrated into power transfer coils. Compared with conventional far-field-based communication methods (e.g., Bluetooth and WLAN), the proposed near-field-based communication method provides a peer-to-peer feature, as well as lower latency, which enables the simple paring of a transmitter and a receiver for power transfer and the real-time updating of control parameters. Using the established communication, control parameters are transmitted from one side of the system to another side, and the co-control of the inverter and the active rectifier is realized. In addition, this work innovatively presents the communication-signal-based synchronization of an inverter and a rectifier, which requires no AC current sensing in the power path and no complex algorithm for stabilization, unlike conventional current-based synchronization methods. The proposed information and power transfer system was measured under different operating conditions, including aligned and misaligned positions, operating points with different charging powers, and forward and reverse power transfer. The results show that the presented prototype allows a bidirectional power transfer of up to 1.2 kW, and efficiency above 90% for the power ranges from 0.6 kW to 1.2 kW was obtained. Furthermore, the integrated communication is robust to the crosstalk from the power transfer and misalignment, and a zero BER (bit error rate) and ultra-low latency of 15.36 µs are achieved. The presented work thus provides a novel solution to the synchronization and real-time co-control of an active rectifier and an inverter in a wireless power transfer system, utilizing integrated near-field-based communication. Full article

Designing antennas for vehicular communication systems presents several unique challenges due to the dynamic nature of vehicular environments, mobility, and the need for reliable connectivity. A wider bandwidth is a critical requirement of vehicular antennas. In this paper, a super-wideband FR4 epoxy-based low-cost meander line patch antenna is designed for fifth-generation (5G) vehicular mobile frequency applications. The proposed antenna is excited through a microstrip feedline on top of the substrate with a continuous ground plane. The meander line is implemented through a theoretical formula to cover upper-5G frequency range 1 (FR1) and frequency range 2 (FR2). The proposed antenna has 7.5 dBi peak gain when operated at 28 GHz. The simulated bandwidth ratio (BWR) is 9.09:1 for a −10 dB reflection coefficient covering a 53.4 GHz (6.6 GHz to 60 GHz) frequency range. The proposed antenna has a linear meander line planar structure, occupies a small area of 34 mm × 20 mm × 1.6 mm, and satisfies the bandwidth requirements of 5G millimeter-wave and sub-bands of the sixth generation for vehicular applications. Full article

The increasing interest in environmental protection has propelled reverse logistics as a challenging field in supply chain optimization. This paper addresses the vehicle routing problem with simultaneous pick-up and delivery (VRPSDP) while considering fuzzy payloads, with the primary objective of minimizing fuzzy fuel consumption. The VRPSDP with fuzzy payloads poses a computationally intractable challenge, as it involves a fleet of vehicles departing from a central depot to both deliver and collect goods from a dispersed group of customers. To effectively tackle this problem, a genetic algorithm is applied that incorporates the concept of fuzziness. This problem diverges from the traditional VRPSDP by explicitly considering fuel consumption reduction towards environmental sustainability. To validate and assess the feasibility of the proposed approach, a series of test instances are utilized. The numerical results exhibit the efficiency of the proposed method and place emphasis on the influence of uncertainty in the quantities of goods collected and delivered by customers on the resulting solution. Full article

The study aims to improve the technique of motion planning for all-wheel drive (AWD) autonomous vehicles (AVs) by including torque vectoring (TV) models and extended physical constraints. Four schemes for realizing the TV drive were considered: with braking internal wheels, using a rear-axle sport differential (SD), with braking front internal wheel and rear-axle SD, and with SDs on both axles. The mathematical model combines 2.5D vehicle dynamics model and a simplified drivetrain model with the self-locking central differential. The inverse approach implies optimizing the distribution of kinematic parameters by imposing a set of constraints. The optimization procedure uses the sequential quadratic programming (SQP) technique for the nonlinear constrained minimization. The Gaussian N-point quadrature scheme provides numerical integration. The distribution of control parameters (torque, braking moments, SDs’ friction moment) is performed by evaluating linear and nonlinear algebraic equations inside of optimization. The technique proposed demonstrates an essential difference between forecasts built with a pure kinematic model and those considering the vehicle’s drive/control features. Therefore, this approach contributes to the predictive accuracy and widening model properties by increasing the number of references, including for actuators and mechanisms. Full article

When car following is controlled by human drivers (i.e., by their behavior), the traffic system does not meet stability conditions. In order to ensure the safety and reliability of self-driving vehicles, an additional hazard warning system should be incorporated into the adaptive control system in order to prevent any possible unavoidable collisions. The time to contact is a reasonable indicator of potential collisions. This research examines systems and solutions developed in this field to determine collision times and uses various alarms in self-driving cars that prevent collisions with obstacles. In the proposed analysis, we have tried to classify the various techniques and methods, including image processing, machine learning, deep learning, sensors, and so on, based on the solutions we have investigated. Challenges, future research directions, and open problems in this important field are also highlighted in the paper. Full article

Driver distraction detection not only helps to improve road safety and prevent traffic accidents, but also promotes the development of intelligent transportation systems, which is of great significance for creating a safer and more efficient transportation environment. Since deep learning algorithms have very strong feature learning abilities, more and more deep learning-based driver distraction detection methods have emerged in recent years. However, the majority of existing deep learning-based methods are optimized only through the constraint of classification loss, making it difficult to obtain features with high discrimination, so the performance of these methods is very limited. In this paper, to improve the discrimination between features of different classes of samples, we propose a high-discrimination feature learning strategy and design a driver distraction detection model based on Swin Transformer and the highly discriminative feature learning strategy (ST-HDFL). Firstly, the features of input samples are extracted through the powerful feature learning ability of Swin Transformer. Then, the intra-class distance of samples of the same class in the feature space is reduced through the constraint of sample center distance loss (SC loss), and the inter-class distance of samples of different classes is increased through the center vector shift strategy, which can greatly improve the discrimination of different class samples in the feature space. Finally, we have conducted extensive experiments on two publicly available datasets, AUC-DD and State-Farm, to demonstrate the effectiveness of the proposed method. The experimental results show that our method can achieve better performance than many state-of-the-art methods, such as Drive-Net, MobileVGG, Vanilla CNN, and so on. Full article

Due to the regular influence of human activities, traffic flow data usually exhibit significant periodicity, which provides a foundation for further research on traffic flow data. However, the temporal dependencies in traffic flow data are often obscured by entangled temporal regularities, making it challenging for general models to capture the intrinsic functional relationships within the data accurately. In recent years, a plethora of methods based on statistics, machine learning, and deep learning have been proposed to tackle these problems of traffic flow forecasting. In this paper, the Transformer is improved from two aspects: (1) an Efficient Attention mechanism is proposed, which reduces the time and memory complexity of the Scaled Dot Product Attention; (2) a Generative Decoding mechanism instead of a Dynamic Decoding operation, which accelerates the inference speed of the model. The model is named EGFormer in this paper. Through a lot of experiments and comparative analysis, the authors found that the EGFormer has better ability in the traffic flow forecasting task. The new model has higher prediction accuracy and shorter running time compared with the traditional model. Full article

Distributed-drive vehicles utilize independent drive motors on the four-wheel hubs. The working conditions of the wheel-hub motors are so harsh that the motors are prone to failing under different driving conditions. This study addresses the impact of drive motor faults on vehicle performance, particularly on slippery roads where sudden faults can lead to accidents. A fault-tolerant control system integrating motor fault diagnosis and a direct yaw moment control (DYC) based fault-tolerant controller are proposed to ensure the stability of the vehicle during various motor faults. Due to the difficulty of identifying the parameters of the popular permanent magnet synchronous wheel hub motors (PMSMs), the system employs a model-free backpropagation neural network (BPNN)-based fault detector. Turn-to-turn short circuits, open-phase faults, and diamagnetic faults are considered in this research. The fault detector is trained offline and utilizes rotor speed and phase currents for online fault detection. The system assigns the torque outputs from both healthy and faulted motors based on fault categories using sliding mode control (SMC)-based DYC. Simulations with four-wheel electric vehicle models demonstrate the accuracy of the fault detector and the effectiveness of the fault-tolerant controller. The proposed system is prospective and has potential for the development of distributed electric vehicles. Full article

The large majority of electric cars have a single-speed gearbox, because electrified powertrains provide maximal power across a wide operating range, and single-speed simplifies construction and reduces capital costs. Nevertheless, multi-speed transmissions have also been developed for electric cars, and some of them have recently appeared as commercial products. This paper aims to compare, through some practical examples, solutions with single-speed and dual-speed transmissions. In particular, given the very smooth driving of electric cars, for dual-speed solutions, a dual-clutch gearbox was considered. Finally, a continuously variable transmission (CVT) was also used. Different solutions were analyzed from a technical–economic point of view, based on a simulation of the vehicle under standardized driving cycles, thus evaluating the capital and running electricity costs. The obtained results show that the comparison between the two solutions is very open, and in the majority of cases, the advantages in terms of efficiency overcome the disadvantages due to the additional capital costs. For a rather low battery cost of 150 €/kWh, the total cost reduction moves from about 100–150 € up to 1500–2000 €, depending on the electricity cost, along the whole vehicle lifespan. Full article

In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs. Full article

In this work, a method is developed for the component design of chassis bushings with contoured inner cores, aided by artificial neural networks (ANNs) and design optimization. First, a model of a physical chassis bushing is generated using the finite element method (FEM). To determine the material parameters of the material model, a material parameter optimization is conducted. Based on the bushing model, different samples for a design study are generated using the design of experiments method. Due to invalid areas of the geometrical model definitions, constraints are established and the design parameter space is cleaned up. From the cleaned design parameter space, a database of several design parameter samples and three associated quasi-static stiffnesses, calculated with FEM simulations, is generated. The database is subsequently used for the training and hyper-parameter optimization of the ANN. Subsequently, the feed-forward ANN is employed in a design study, where stiffnesses are prescribed and design parameters identified. The design process is inverted with the help of a constrained design parameter optimization (DO), based on particle swarm optimization (PSO). Two usecases are defined for the evaluation of the design accuracy of the entire method. The design parameters found are validated by corresponding FEM simulations. Full article