World Electr. Veh. J., Volume 15, Issue 7 (July 2024) – 29 articles

The effectiveness of government subsidies for electric vehicle (EV) enterprises and future improvements to subsidy policies to promote industry development have garnered widespread attention. Distinct mechanisms exist through which R&D and non-R&D subsidies impact enterprise innovation. This paper differentiates between R&D and non-R&D subsidies and uses data from listed companies and New Third Board companies in China from 2013 to 2022 to empirically analyze the effects of these two types of subsidies on the innovation of EV enterprises from the perspectives of innovation strategy and the industrial chain. The results show that both R&D and non-R&D subsidies effectively alleviate the inhibiting effects of financing constraints. R&D subsidies significantly incentivize innovation in EV enterprises, whereas the effect of non-R&D subsidies is not as pronounced. The incentivizing effect of R&D subsidies exhibits two distinct characteristics: first, R&D subsidies compel enterprises to choose an innovation strategy that prioritizes “quantity over quality”; second, R&D subsidies exert a more pronounced influence on enterprises in the upper and middle sectors of the EV industrial chain compared to downstream enterprises, which tend to engage in more strategic innovation behaviors. Full article

Transmission, energy management, and distribution systems are critical components of modern electric vehicles, encompassing all sectors of the power system through communication control technology. One widely used communication system in electric vehicles is the Controller Area Network (CAN). This research aims to investigate the development of CAN BUS technology, adapted from large trucks, to control the communication system within an ATV electric vehicle using a communication format similar to bus Communication. The communication control system includes several components: the engine switch, headlight, turn signal, emergency light, horn, forward/reverse gear, and accelerator. The system’s communication protocols were developed using MRS Developers Studio version 1.40 software to create the data transmission and reception formats for the vehicle’s components. The communication system employs three PLC 1.033.30B.00 type E control boxes, each with limited analog and digital input/output ports. The sequence of communication control begins with the engine start/stop operation, as the system will not function unless the engine is started first. The headlight operation is processed within the CAN BUS1 control box. Simultaneously, the turn signal and emergency light functions are controlled by CAN BUS1 and displayed on both the CAN BUS2 (front of the vehicle) and CAN BUS3 (rear of the vehicle) control boxes. Additionally, the accelerator function is managed within the CAN BUS2 control box and displayed on the CAN BUS3 control box. However, this operation is contingent upon the forward/reverse gear selection, managed by CAN BUS1 and processed by CAN BUS3. All system operations are designed within the software’s programming paths. The communication system operates using CAN-High and CAN-Low lines, and communication data fields can be monitored using the PCAN-View software version 4.2.1.533. This study demonstrates the feasibility and effectiveness of adapting CAN BUS technology for ATV electric vehicles, providing insights into the integration and control of various vehicular components within a unified communication framework. Full article

The power battery is a key component of electric vehicles and its performance is greatly affected by temperature. Battery thermal management systems based on phase change materials can effectively control the battery temperature and at the same time have the advantages of simple structures, energy savings, and good temperature uniformity, and has broad development prospects. In this paper, expanded graphite–paraffin composite phase change materials were prepared, phase change material cooling experiments were carried out, and a phase change material cooling simulation model was also established using the Fluent software to study the influence of phase change material thermophysical parameters on thermal management performance. The results show that the phase change material thermal management method has excellent cooling performance. The best thermal management performance is achieved at the 3C discharge rate, with a phase change material filling thickness of 4 mm, a melting point of 40 °C above ambient temperature, and a thermal conductivity of 3 W/(m·K). When the phase change latent heat was increased from 150 J/g to 250 J/g, the liquid phase ratio decreased from 0.84 to 0.51, and the subsequent cooling performance was greatly improved, so the phase change latent heat should be increased as much as possible. Full article

Despite the steady rise of electric vehicles (EVs) in other countries, the Philippines has yet to capitalize on its proliferation due to several mixed concerns. Status, socio-demographic characteristics, and availability have been the main concerns with purchasing EVs in the country. Consumer segmentation and analysis for EV acceptance and utility in the Philippines were determined in this study due to the need for understanding consumer preferences and market segmentation towards EVs in the Philippines. A total of 311 valid responses coming from EV owners were collected through purposive and snowball sampling approaches. The data were collected via face-to-face distribution and online distribution of a questionnaire covering demographic characteristics for market segmentation. Demographic data such as gender, age, residence type, car ownership, and income were used to identify consumer segments using the K-means clustering approach. Jupyter Notebook v7.1.3 was used for the overall analysis, and the number of clusters was optimized, ensuring precise segmentation. The results indicated a strong correlation between car ownership and the ability to purchase EVs, where K-means clustering effectively identified consumer groups. The groupings also included “Not Capable at All” to “Highly Capable” individuals based on their likelihood to purchase EVs. Based on the results, the core-value customers of EVs are male, older than 55 years old, live in urban areas, own a vehicle and car insurance, and have a monthly income of more than PHP 130,000. Following those are high-value customers, considered target users expected to use EVs frequently. It could be posited that customers are frequent purchasers of products and services. Based on the results, high-value customers are male, aged 36–45 years old, live in urban areas, own a car, have car insurance, and have a monthly income of PHP 100,001–130,000. Both of these should be highly considered by EV industries, as these characteristics would be the driving market of EVs in the Philippines. The constructed segmentation provided valuable insights for the EV industry, academic institutions, and policymakers, offering a foundation for targeted marketing strategies and promoting EV adoption in the Philippines. Moreover, the sustainable marketing strategies developed could be adopted and extended among other developing countries wanting to adopt EVs for utility. Future works are also suggested based on the study limitations for researchers to consider as study extensions, such as a holistic approach to EV adoption that considers environmental, social, and economic factors, as well as policies and promotion development. Full article

Electric tilt-wing flying cars are an efficient, economical, and environmentally friendly solution to urban traffic congestion and travel efficiency issues. This article addresses the high energy consumption and obstacle interference during the takeoff phase of the tilt-wing eVTOL (electric Vertical Takeoff and Landing), proposing a trajectory planning method based on energy minimization and obstacle avoidance. Firstly, based on the dynamics analysis, the relationship between energy consumption, spatial trajectory, and obstacles is sorted out and the decision variables for the trajectory planning problem with obstacle avoidance are determined. Secondly, based on the power discretization during the takeoff phase, the energy minimization objective function is established and the constraints of performance limitations and spatial obstacles are derived. Thirdly, by integrating the optimization model with the SLSQP (Sequential Least Squares Quadratic Programming algorithm), the second-order sequential quadratic programming model and decision variable update equations are derived, establishing the solution process for the trajectory planning problem of the tilt-wing eVTOL takeoff with obstacle avoidance. Finally, the Airbus Vahana A³ is taken as an example to verify and validate the effectiveness, stability, and robustness of the model and optimization algorithm proposed. The validation results show that the OTPEM (obstacle avoidance trajectory planning methodology based on energy minimization) can effectively handle changes in the takeoff end state and exhibits good stability and robustness in different obstacle environments. It can provide a certain reference for the three-dimensional obstacle avoidance trajectory planning of Airbus Vahana A³ and other tilt-wing eVTOL trajectory planning problems. Full article

Advanced Driver Assistance System (ADAS) is the latest buzzword in the automotive industry aimed at reducing human errors and enhancing safety. In ADAS systems, the choice of control strategy is not straightforward due to the highly complex nonlinear dynamics, control objectives, and safety critical constraints. Nonlinear Model Predictive Control (NMPC) has evolved as a favorite option for optimal control due to its ability to handle such constrained, Multi-Input Multi-Output (MIMO) systems efficiently. However, NMPC suffers from a bottleneck of high computational complexity, making it unsuitable for fast real-time applications. This paper presents a generic framework using Successive Online Linearization-based NMPC (SOL-NMPC) for for the control in ADAS. The nonlinear system is linearized and solved using Linear Model Predictive Control every iteration. Furthermore, offset-free MPC is developed with the Extended Kalman Filter for reducing model mismatch. The developed SOL-NMPC is validated using the 14-Degrees-of-Freedom (DoF) model of a D-class light motor vehicle. The performance is simulated in matlab/Simulink and validated using the CarSim® software (Version 2016). The real-time implementation of the proposed strategy is tested in the Hardware-In-the-Loop (HIL) co-simulation using the STM32-Nucleo-144 development board. The detailed performance analysis is presented along with time profiling. It can be seen that the loss of accuracy can be counteracted by the fast response of the proposed framework. Full article

Manufacturers of electric cars provide their vehicles with many technical data that are important for the user. This includes information on dimensions, mass, performance, consumption, battery capacity, range, payload, etc. However, some interesting parameters are usually withheld from the end user. These parameters include, for example, the loss in the energy flow from the battery to the driving wheels or the rolling resistance of the vehicle. However, since these loss parameters have a significant influence on the vehicle’s consumption, it is of interest to know them. This article presents a method for determining these two parameters. The basis for this are simple driving tests that can be carried out by anyone on public roads. Full article

To realize accurate environment perception, which is the technological key to enabling autonomous vehicles to interact with their external environments, it is primarily necessary to solve the issues of object detection and tracking in the vehicle-movement process. Multi-sensor fusion has become an essential process in efforts to overcome the shortcomings of individual sensor types and improve the efficiency and reliability of autonomous vehicles. This paper puts forward moving object detection and tracking methods based on LiDAR—camera fusion. Operating based on the calibration of the camera and LiDAR technology, this paper uses YOLO and PointPillars network models to perform object detection based on image and point cloud data. Then, a target box intersection-over-union (IoU) matching strategy, based on center-point distance probability and the improved Dempster–Shafer (D–S) theory, is used to perform class confidence fusion to obtain the final fusion detection result. In the process of moving object tracking, the DeepSORT algorithm is improved to address the issue of identity switching resulting from dynamic objects re-emerging after occlusion. An unscented Kalman filter is utilized to accurately predict the motion state of nonlinear objects, and object motion information is added to the IoU matching module to improve the matching accuracy in the data association process. Through self-collected data verification, the performances of fusion detection and tracking are judged to be significantly better than those of a single sensor. The evaluation indexes of the improved DeepSORT algorithm are 66% for MOTA and 79% for MOTP, which are, respectively, 10% and 5% higher than those of the original DeepSORT algorithm. The improved DeepSORT algorithm effectively solves the problem of tracking instability caused by the occlusion of moving objects. Full article

The rapid advancement of artificial intelligence technology has significantly enhanced the intelligence of mobile robots, facilitating their widespread utilization in unmanned driving, smart home systems, and various other domains. As the scope, scale, and complexity of robot deployment continue to expand, there arises a heightened demand for enhanced computational power and real-time performance, with path planning emerging as a prominent research focus. In this study, we present an adaptive Lévy flight–rotation slime mold algorithm (LRSMA) for global robot motion planning, which incorporates LRSMA with the cubic Hermite interpolation. Unlike traditional methods, the algorithm eliminates the need for a priori knowledge of appropriate interpolation points. Instead, it autonomously detects the convergence status of LRSMA, dynamically increasing interpolation points to enhance the curvature of the motion curve when it surpasses the predefined threshold. Subsequently, it compares path lengths resulting from two different objective functions to determine the optimal number of interpolation points and the best path. Compared to LRSMA, this algorithm reduced the minimum path length and average processing time by (2.52%, 3.56%) and (38.89%, 62.46%), respectively, along with minimum processing times. Our findings demonstrate that this method effectively generates collision-free, smooth, and curvature-constrained motion curves with the least processing time. Full article

With the rapid development of autonomous driving technology, estimating and controlling key vehicle state parameters under complex road conditions have become critical challenges. This study combines Unscented Kalman Filtering (UKF) and Sliding Mode Control (SMC) methods to propose an integrated control model for achieving more efficient control. First, a three-degrees-of-freedom vehicle dynamics model based on the Dugoff tire model is constructed to accurately estimate key vehicle state parameters. Next, UKF is used to estimate road friction coefficients and key vehicle state parameters, and its performance is compared with Extended Kalman Filtering (EKF) under various conditions. The results show the superiority of UKF in identifying road friction coefficients. Based on SMC theory, a sliding surface is designed, and the functional relationship between state variables and control variables is derived to establish the corresponding control model. Joint simulations using Carsim and Simulink under different conditions validate the real-time performance and effectiveness of the designed UKF-SMC integrated control strategy in the presence of external disturbances and system uncertainties. Simulation results indicate that this strategy effectively enhances the overall performance and safety of autonomous vehicles, providing an accurate real-time solution capable of handling complex and variable road conditions. The proposed UKF-SMC integrated control strategy not only proves its theoretical superiority but also demonstrates promising practical applications in simulation experiments. This study provides reliable technical support for the development of autonomous driving technology under complex road conditions. Full article

This research on electric vehicle purchasing intentions in Thailand using Structural Equation Modeling aimed to achieve the following objectives: Firstly, to investigate the factors influencing consumers’ intentions to purchase electric vehicles. Secondly, to examine the impact of consumer characteristics on supply chain agility (SCA). Thirdly, to analyze how electric vehicle characteristics influence supply chain agility. Lastly, to assess the influence of supply chain agility on consumers’ purchasing intentions. The study sampled individuals in Thailand holding personal driver’s licenses and intending to purchase electric cars, totaling 350 respondents selected randomly. Data analysis employed descriptive statistics including frequency, percentage, and mean values. The validity and reliability of the questionnaires were ensured through factor loading and Cronbach’s Alpha tests. Our findings indicated that consumer characteristics, electric vehicle features, and supply chain agility significantly affect purchasing intentions. Consumer-specific factors like social influence, environmental concern, and perceptions of electric vehicles were found to impact purchase intentions. Electric vehicle characteristics such as battery longevity, perceived benefits, and value also influenced purchase intentions. Additionally, supply chain agility factors including flexibility, speed in innovation, and responsiveness to customer needs were identified as influential. The research underscores the importance for manufacturers to prioritize initiatives that enhance customer experience with electric vehicles, alleviating concerns and fostering confidence in their use, thereby encouraging adoption without apprehensions about potential issues. Full article

This paper proposes a robust lateral control scheme for the path tracking of autonomous vehicles. Considering the discrepancies between the model parameters and the actual values of the vehicle and the fluctuation of parameters during driving, the norm-bounded uncertainty is utilized to deal with the uncertainty of model parameters. Because some state variables in the model are difficult to measure, an $H_{\infty }$ observer is designed to estimate state variables and provide accurate state information to improve the robustness of path tracking. An $H_{\infty }$ state feedback controller is proposed to suppress system nonlinearity and uncertainty and produce the desired steering wheel angle to solve the path tracking problem. A feedforward control is designed to deal with road curvature and further reduce tracking errors. In summary, a path tracking method with $H_{\infty }$ performance is established based on the linear matrix inequality (LMI) technique, and the gains in observer and controller can be obtained directly. The hardware-in-the-loop (HIL) test is built to validate the real-time processing performance of the proposed method to ensure excellent practical application potential, and the effectiveness of the proposed control method is validated through the utilization of urban road and highway scenes. The experimental results indicate that the suggested control approach can track the desired trajectory more precisely compared with the model predictive control (MPC) method and make tracking errors within a small range in both urban and highway scenarios. Full article

Aiming at the problem that the traditional rapidly exploring random tree (RRT) algorithm only considers the global path of unmanned vehicles in a static environment, which has the limitation of not being able to avoid unknown dynamic obstacles in real time, and that the traditional dynamic window approach (DWA) algorithm is prone to fall into a local optimum during local path planning, this paper proposes a path planning method for unmanned vehicles that integrates improved RRT and DWA algorithms. The RRT algorithm is improved by introducing strategies such as target-biased random sampling, adaptive step size, and adaptive radius node screening, which enhance the efficiency and safety of path planning. The global path key points generated by the improved RRT algorithm are used as the subtarget points of the DWA algorithm, and the DWA algorithm is optimized through the design of an adaptive evaluation function weighting method based on real-time obstacle distances to achieve more reasonable local path planning. Through simulation experiments, the fusion algorithm shows promising results in a variety of typical static and dynamic mixed driving scenarios, can effectively plan a path that meets the driving requirements of an unmanned vehicle, avoids unknown dynamic obstacles, and shows higher path optimization efficiency and driving stability in complex environments, which provides strong support for an unmanned vehicle’s path planning in complex environments. Full article

In modern electric vehicle applications, understanding the evolution of the internal electrochemical reaction throughout the aging of batteries is as relevant as knowing their state of health. This article demonstrates the feasibility of correlating a mechanistic model of the battery internal electrochemical reactions with an equivalent electrical circuit (EEC) model, providing a practical and understandable interpretation of the internal reactions for electrical specialists. By way of electrochemical impedance spectroscopy analysis and automatic control theory, a methodology for correlating the resistance and capacitance variations of the EEC model and how they reflect the electrochemical reaction changes is proposed. These changes are represented through the time constants of the three $RC$ parallel arrays from an EEC model. PS-260 lead–acid batteries were analyzed throughout the SOC and their useful life to validate this methodology. The result analysis allows us to establish that the first $RC$ array corresponds to the negative electrode reactions in the range of 1.48 Hz to 10 kHz, the second $RC$ array to the positive electrode reactions and generation of sulfates in the range of 0.5 to 1.48 Hz, and the third $RC$ array to the generation of sulfates and their diffusion in the range of 0.01 to 0.5 Hz. Full article

Environment perception technology is the most important part of driverless technology, and driverless vehicles need to realize decision planning and control by virtue of perception feedback. This paper summarizes the most promising technology methods in the field of perception, namely visual perception technology, radar perception technology, state perception technology, and information fusion technology. Regarding the current development status in the field, the development of the main perception technology is mainly the innovation of information fusion technology and the optimization of algorithms. Multimodal perception and deep learning are becoming popular. The future of the field can be transformed by intelligent sensors, promote edge computing and cloud collaboration, improve system data processing capacity, and reduce the burden of data transmission. Regarding driverless vehicles as a future development trend, the corresponding technology will become a research hotspot. Full article

This paper describes an effective operating strategy for electric vehicles (EVs) in a hybrid facility that leverages renewable energy sources. The method is to enhance the profit of the wind–thermal–EV hybrid plant while maintaining the grid frequency (f_PG) and energy level of the EV battery storage system. In a renewable-associated power network, renewable energy producers must submit power supply proposals to the system operator at least one day before operations begin. The market managers then combine the power plans for the next several days based on bids from both power providers and distributors. However, due to the unpredictable nature of renewable resources, the electrical system cannot exactly adhere to the predefined power supply criteria. When true and estimated renewable power generation diverges, the electrical system may experience an excess or shortage of electricity. If there is a disparity between true and estimated wind power (TWP, EWP), the EV plant operates to minimize this variation. This lowers the costs associated with the discrepancy between actual and projected wind speeds (TWS, EWS). The proposed method effectively reduces the uncertainty associated with wind generation while being economically feasible, which is especially important in a deregulated power market. This study proposes four separate energy levels for an EV battery storage system (E_EV,max, E_EV,opt, E_EV,low, and E_EV,min) to increase system profit and revenue, which is unique to this work. The optimum operating of these EV battery energy levels is determined by the present electric grid frequency and the condition of TWP and EWP. The proposed approach is tested on a modified IEEE 30 bus system and compared to an existing strategy to demonstrate its effectiveness and superiority. The entire work was completed using the optimization technique called sequential quadratic programming (SQP). Full article

One of the most important problems in the widespread use of electric vehicles is the lack of charging infrastructure. Especially in tourist areas where historical buildings are located, the installation of a power grid for the installation of electric vehicle charging stations or generating electrical energy by installing renewable energy production systems such as large-sized PV (photovoltaic) and wind turbines poses a problem because it causes the deterioration of the historical texture. Considering the need for renewable energy sources in the transportation sector, our aim in this study is to model an electric vehicle charging station using PVPS (photovoltaic power system) and FC (fuel cell) power systems by using irradiation and temperature data from historical regions. This designed charging station model performs electric vehicle charging, meeting the energy demand of a house and hydrogen production by feeding the electrolyzer with the surplus energy from producing electrical energy with the PVPS during the daytime. At night, when there is no solar radiation, electric vehicle charging and residential energy demand are met with an FC power system. One of the most important advantages of this system is the use of hydrogen storage instead of a battery system for energy storage and the conversion of hydrogen into electrical energy with an FC. Unlike other studies, in our study, fossil energy sources such as diesel generators are not included for the stable operation of the system. The system in this study may need hydrogen refueling in unfavorable climatic conditions and the energy storage capacity is limited by the hydrogen fuel tank capacity. Full article

With the rapid development of global energy transformation and new power system, ensuring the stability of distributed energy grid connections is the key to maintaining the reliable operation of the whole power system. This paper constructs detailed impedance models of grid-following (GFL) and grid-forming (GFM) inverters using a harmonic linearization method and thoroughly investigates the mechanisms of resonance when inverters are connected to the grid, as well as the impact of model parameters on the stability of the grid system. This paper also briefly analyzes the scenario where distributed energy and electric vehicles are integrated into the grid simultaneously, demonstrating that grid system stability can be ensured in complex grid situations through reasonable parameter design. Lastly, the accuracy of the proposed impedance models and analysis is verified through MATLAB/Simulink simulations. Full article

This study thoroughly analyses Smart Electromobility Charging Infrastructure (SECI), exploring its multifaceted dimensions and advancements. Delving into the intricate landscape of SECI, the study critically evaluates existing technologies, integration methodologies, and emerging trends. Through a systematic examination of literature and empirical studies, the article elucidates the evolving ecosystem of smart charging solutions, considering aspects including advancements in charging protocols. Additionally, the review highlights challenges and prospects in the SECI domain, providing insightful information for scholars, practitioners, and policymakers involved in the dynamic field of electromobility. Technical potentials, including functionalities and integration with the smart grid, have been thoroughly reviewed. An analysis is conducted on the effects of intelligent charging on power distribution systems and strategies to lessen these effects. This study also examines the development of intelligent charging algorithms, optimisation methods, and security analysis. This paper, therefore, contributes to fostering a more thorough comprehension of the current state and future trajectories of Smart Electromobility Charging Infrastructure. Full article

Traffic-sign detection and recognition (TSDR) is crucial to avoiding harm to pedestrians, especially children, from intelligent connected vehicles and has become a research hotspot. However, due to motion blurring, partial occlusion, and smaller sign sizes, pedestrian TSDR faces increasingly significant challenges. To overcome these difficulties, a CTM-YOLOv8n model is proposed based on the YOLOv8n model. With the aim of extracting spatial features more efficiently and making the network faster, the C2f Faster module is constructed to replace the C2f module in the head, which applies filters to only a few input channels while leaving the remaining ones untouched. To enhance small-sign detection, a tiny-object-detection (TOD) layer is designed and added to the first C2f layer in the backbone. Meanwhile, the seventh Conv layer, eighth C2f layer, and connected detection head are deleted to reduce the quantity of model parameters. Eventually, the original CIoU is replaced by the MPDIoU, which is better for training deep models. During experiments, the dataset is augmented, which contains the choice of categories ‘w55’ and ‘w57’ in the TT100K dataset and a collection of two types of traffic signs around the schools in Tianjin. Empirical results demonstrate the efficacy of our model, showing enhancements of 5.2% in precision, 10.8% in recall, 7.0% in F1 score, and 4.8% in mAP@0.50. However, the number of parameters is reduced to 0.89M, which is only 30% of the YOLOv8n model. Furthermore, the proposed CTM-YOLOv8n model shows superior performance when tested against other advanced TSDR models. Full article

The implementation of e-jeepneys stands as a change process that will eventually transition to the modernization of the public transport system in the Philippines. To address concerns about jeepneys’ effects on the environment, energy use, society, the economy, and policies, their acceptability in the Philippines must be considered. This research study aims to identify the sources of influence on Filipinos’ adoption of e-jeepney utilization as a mode of transportation using the extended Pro-Environmental Planned Behavior (PEBP) model. A total of 502 commuters voluntarily answered the survey questionnaire. Based on the findings, perceived environmental concern (PEC) is the most significant determinant influencing attitude (AT) and, thus, affecting the Filipinos’ behavioral intention (BI) towards the adoption of e-jeepneys. Conversely, AT was the primary determinant of BI, which strongly supported the notion of AT as a strong driving force shaping behavioral decisions. Moreover, perceived authority support (PAS) emerged as a strong predictor of subjective norms (SNs), demonstrating the influence of institutional support on societal perceptions. As a result, more environmentally conscious people are more likely to view e-jeepneys positively and intend to use them as a mode of transportation. The endorsement or support from authoritative figures or institutions notably influences subjective norms, which are individuals’ perceptions of social pressures regarding the use of e-jeepneys. Full article

Due to their excellent mechanical strength, corrosion resistance, and ease of processing, carbon fiber and carbon-fiber-reinforced plastics are finding wide application in diverse fields, including aerospace, industry, and automobiles. This research explores the feasibility of integrating carbon fiber solutions into the rotors of 85-kilowatt electric vehicle interior permanent magnet synchronous motors. Two novel configurations are proposed: a carbon fiber wire-wound rotor and a carbon fiber injection-molded rotor. A finite element analysis compares the performance of these models against a basic designed rotor, considering factors like no-load back electromotive force, no-load voltage harmonics, cogging torque, load torque, torque ripple, efficiency, and manufacturing cost. Additionally, a comprehensive analysis of system efficiency and energy loss based on hypothetical electric vehicle parameters is presented. Finally, mechanical strength simulations assess the feasibility of the proposed carbon fiber composite rotor designs. Full article

This study investigates the impact of temperature variations on the torque capacity of shrink-fit junctions in water-jacketed permanent magnet synchronous motors. Focusing on both baseline and improved designs; torque capacities were evaluated across a temperature range from −40 °C to 120 °C under different material conditions: Least material condition, nominal, and maximum material condition. The baseline design exhibited torque capacities from 7648 Nm to 9032 Nm at −40 °C, decreasing significantly to 549 Nm to 1533 Nm at 120 °C. The improved design showed enhanced performance, with torque capacities ranging from 8055 Nm to 9247 Nm at −40 °C and from 842 Nm to 1618 Nm at 120 °C. The maximum improvement was observed at 120 °C for least material conditions, with a 55.4% increase, and the minimum improvement at −40 °C for maximum material conditions, with a 2.4% increase. Our findings demonstrate a significant increase in torque capacity by up to 20% under varied thermal conditions. These results underscore the effectiveness of design modifications in enhancing thermal stability and torque capacity, making the improved design a more reliable choice for high-performance applications subject to significant thermal fluctuations. This research highlights the critical role of material selection, thermal management, and precise design adjustments in optimizing the performance and reliability of permanent magnet synchronous motors. Full article

As they are the core components of the drive motor in electric vehicles, the accurate fault diagnosis of rolling bearings is the key to ensuring the safe operation of electric vehicles. At present, intelligent diagnostic methods based on current signals (CSs) are widely used owing to the advantages of the easy collection, low cost, and non-invasiveness of CSs. However, in practical applications, the fault characteristics of the CS are weak, resulting in diagnostic performance that fails to meet the expected standards. In this paper, a diagnosis method is proposed to address this problem and enhance the diagnosis accuracy. Firstly, CSs from two phases are processed by periodic resampling to enhance data features, which are then fused through splicing operations. Subsequently, a feature enhancement module is constructed using multi-scale feature fusion for decomposing the input. Finally, a diagnosis model is constructed by using an improved channel attention module (CAM) for enhancing the diagnosis performance. The results from experiments containing two different types of bearing datasets show that the proposed method can extract high-quality fault features and improve the diagnosis accuracy, presenting great potential in intelligent fault diagnosis and the maintenance of electric vehicles. Full article

Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long Short-Term Memory–Multi-head Attention (CNN-BiLSTM-MHA)-based lithium-ion battery SOH estimation method is proposed in this paper. First, the voltage, energy, and temperature data of the battery in the constant current charging phase are measured. Then, based on the voltage and energy data, the incremental energy analysis (IEA) is performed to calculate the incremental energy (IE) curve. The IE curve features including IE, peak value, average value, and standard deviation are extracted and combined with the thermal features of the battery to form a complete multi-feature sequence. A CNN-BiLSTM-MHA model is set up to map the features to the battery SOH. Experiments were conducted using batteries with different charging currents, and the results showed that even if the nonlinearity of battery SOH degradation is significant, this method can still achieve a fast and accurate estimation of the battery SOH. The Mean Absolute Error (MAE) is 0.1982%, 0.1873%, 0.1652%, and 0.1968%, and the Root-Mean-Square Error (RMSE) is 0.2921%, 0.2997%, 0.2130%, and 0.2625%, respectively. The average Coefficient of Determination (R²) is above 96%. Compared to the BiLSTM model, the training time is reduced by an average of about 36%. Full article

Unmanned vehicles (UVs) have become increasingly important in various scenarios of civil and military operations. The present work aims at the conceptual design of a modular Amphibious Unmanned Ground Vehicle (A-UGV) that can be easily adapted for different types of land and/or water missions with low monetary cost (EUR < 5 k, without sensors). Basing the design on the needs highlighted in the 2021 review of the Strategic Directive of the Portuguese Navy, the necessary specifications and requirements are established for two mission scenarios. Then, a market research analysis focused on vehicles currently available and their technological advances is conducted to identify existing UV solutions and respective characteristics/capabilities of interest to the current work. To study and define the geometry of the hull and the configuration of the A-UGV itself, preliminary computational structural and fluid analyses are carried out to ensure it complies with the specifications initially established. As a result, one obtains a fully electric vehicle with approximate dimensions of 1050 × 670 × 450 mm (length–width–height), enabled with 6 × 6 traction capable of reaching 20 km/h on land, which possesses amphibious capabilities of independent propulsion in water up to 8 kts and an estimated autonomy of over 60 min. Full article

The integration of the Internet of Vehicles (IoV) and low-orbit satellite Internet not only increases the efficiency of traffic management but also contributes to the emergence of new cyberattacks. Spoofing interference occupies a special place among them. To prevent a rogue satellite from imposing unauthorized content on vehicle owners, a zero-knowledge authentication protocol (ZKAP) based on a modular polyalphabetic polynomial code (MPPC) was developed. The use of MPPC allowed for increasing the authentication speed of the satellite performing the role of RSU. As a result, a reduction in the time needed to guess the prover’s signal also reduces the probability of granting a rogue satellite the communication session and increases the imitation resistance of the satellite IoV. At the same time, the MPPC allows for improving the noise resistance of the ZKAP. An algorithm for calculating the control residuals for a noise-resistant MPPC was developed for this purpose, as well as an algorithm for correcting errors arising in the communication channel due to interference. Thus, the developed authentication protocol based on a noise-resistant modular code allows for simultaneously reducing the probabilities of the first-order and second-order errors, which leads to the increased cybersecurity of satellite IoV. Full article

Finite-set model predictive control (FS-MPC) is an easy and intuitive control technique. However, parametric uncertainties reduce the accuracy of the prediction. Classical MPC requires many calculations; therefore, the calculation time generates a considerable time delay in the actuation. This delay deteriorates the performance of the system and generates a significant current ripple. This paper proposes a finite-set integral sliding modes predictive control (FS-ISMPC) for a permanent magnet synchronous motor (PMSM). The conventional decision function is replaced by an integral sliding cost function, which has several advantages, such as robustness to parameter uncertainties, and convergence in finite time. The proposed decision function does not require the inductance and resistance parameters of the motor. In addition, the proposal includes compensation for the calculation delay of the control vector. The proposed control strategy was compared with traditional predictive control with delay compensation using a real-time hardware-in-the-loop (HIL) simulation. The results obtained from the comparison indicated that the proposed controller has a lower THD and computational burden. Full article

Due to the reduction in fossil fuel abundance and the harmful environmental effects of burning them, the renewable resource potentials of microgrid (MG) structures have become highly highly. However, the uncertainty and variability of MGs leads to system frequency deviations in islanded or stand-alone mode. Usually, battery energy storage systems (BESSs) reduce this frequency deviation, despite limitations such as reducing efficiency in the long term and increasing expenses. A suitable solution is to use electric vehicles (EVs) besides BESSs in systems with different energy sources in the microgrid structure. In this field, due to the fast charging and discharging of EVs and the fluctuating character of renewable energy sources, controllers based on the traditional model cannot ensure the stability of MGs. For this purpose, in this research, an ultra-local model (ULM) controller with an extended state observer (ESO) for load frequency control (LFC) of a multi-microgrid (MMG) has been systematically developed. Specifically, a compensating controller based on the single-input interval type fuzzy logic controller (FLC) was used to remove the ESO error and improve the LFC performance. Since the performance of the ULM controller based on SIT2-FLC depends on specific parameters, all of these coefficients were adjusted by an improved harmony search algorithm (IHSA). Simulation and statistical analysis results show that the proposed controller performs well in reducing the frequency fluctuations and power of the system load line and offers a higher level of resistance than conventional controllers in different MG scenarios. Full article