World Electr. Veh. J., Volume 15, Issue 3 (March 2024) – 49 articles

To address the problem of system instability during vehicle low-speed driving, we propose improving the visual odometer using ORB (Oriented FAST and Rotated BRIEF) features. The homogeneity of ORB features leads to poor corner point properties of some feature points. When the environmental texture lacks richness, it leads to poor matching performance and low matching accuracy of the feature points. We solve the problem of the corner point properties of feature points using weight calculation for regions with different textures. When the vehicle speed is too low, the continuous frames captured by the camera will overlap significantly, causing large fluctuations in the system error. We use motion model estimation to solve this problem. Meanwhile, experimental validation using the KITTI dataset achieves good results. Full article

Intelligentization is the development trend of the future automobile industry. Intelligentization requires that the dynamic control of the vehicle can complete the trajectory tracking according to the trajectory output of the decision planning the driving state of the vehicle and ensure the driving safety and stability of the vehicle. However, trajectory limit planning and harsh road conditions caused by emergencies will increase the difficulty of trajectory tracking and stability control of unmanned vehicles. In view of the above problems, this paper studies the trajectory tracking and stability control of distributed drive unmanned vehicles. This paper applies a hierarchical control framework. Firstly, in the upper controller, an adaptive prediction time linear quadratic regulator (APT LQR) path following algorithm is proposed to acquire the desired front-wheel-steering angle considering the dynamic stability performance of the tires. The lateral stability of the DDAUV is determined based on the phase plane, and the sliding surface, in the improved sliding mode control (SMC), is further dynamically adjusted to obtain the desired additional yaw moment for coordinating the path following and lateral stability. Then, in the lower controller, considering the slip and the working load of four tires, a comprehensive cost function is established to reasonably distribute the driving torque of four in-wheel motors (IWMs) for producing the desired additional yaw moment. Finally, the proposed control algorithm is verified by the hardware-in-the-loop (HIL) experiment platform. The results show the path following and lateral stability can be coordinated effectively under different driving conditions. Full article

Data acquisition from a vehicle operating in real driving conditions is extremely useful for analyzing the real-time behavior of the vehicle and its components. A few studies have measured the real-time data for a four-wheeler electric vehicle. However, no attempts have been reported to measure the real-time data and find the inverter efficiency for a two-wheeler electric vehicle. The present work has accomplished successful real-time data acquisition from a two-wheeler electric vehicle. The real-time current and voltage coming in and going out from the inverter, frequency of the motor operation, power factor, distance covered, and velocity have been measured. The inverter efficiency is found to be over 95% for over 80% of the total drive time, and the power factor for the motor is over 0.8 for almost 50% of the total drive time. A few insights on driver behavior and finally the torque-speed characteristics and two quadrant operation of the motor are discussed. Full article

A position estimation method for unmanned tracked vehicles based on a steering dynamics model was developed during this study. This method can be used to estimate the position of a tracked vehicle in real time without relying on a high-precision positioning system. First, the relationship between the shear displacement of the track relative to the ground and the speed and yaw rate of the tracked vehicle during the steering process was analyzed. Next, the steering force of the tracked vehicle was calculated by using the shear force–displacement theory, and a steering dynamics model considering the acceleration of the vehicle was established. The experimental results show that this steering dynamics model produced more accurate position estimations for an unmanned tracked vehicle than did the kinematics model. This method can serve as a reference for the positioning of unmanned tracked vehicles working in special environments that cannot use precise positioning systems. Full article

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

This study compiles, reviews, and discusses the relevant history, present status, and growing trends in wireless electric vehicle charging. Various reported concepts, technologies, and available literature are discussed in this paper. The literature can be divided into two main groups: those that discuss the technical aspects and those that discuss the operations and systems involved in wireless electric vehicle charging systems. There may be an overlap of discussion in some studies. However, there is no single study that combines all the relevant topics into a guide for researchers, policymakers, and government entities. With the growing interest in wireless charging in the electric vehicle industry, this study aims to promote efforts to realize wireless power transfer in electric vehicles. Full article

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

Adaptive Cruise Control (ACC) systems have emerged as a significant advancement in automotive technology, promising safer and more efficient driving experiences. However, the performance of ACC systems can vary significantly depending on their type and underlying algorithms. This research presents a comprehensive comparative analysis of car-following distances in different types of Adaptive Cruise Control systems. We evaluate and compare three distinct categories of ACC systems using three different commercial vehicles brands. The study involves extensive real-world testing at Zalazone Proving Ground, to assess the performance of these systems under various driving conditions, including driving at multiple speeds and applying different car following scenarios. The study investigates how each ACC system manages the minimum following distances according to the type of ACC sensors in each tested vehicle. Our findings revealed that at low to medium ranges of constant driving speeds, there was an approximate linear increase in the average clearances between the two following vehicles for all applied scenarios, with comparatively shorter clearances obtained by the vision-based ACC system, while unstable measurements with a high level of dispersion for all ACC systems were observed at high range of driving speeds. Full article

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

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

Recent developments in vehicle-integrated photovoltaics (VIPV) offer prospects for enhancing electric vehicle range, lowering operating costs, and supporting carbon-neutral transport, particularly in urban settings. This study evaluates the solar energy potential of parking spaces in Berlin, considering challenges like building and tree shading using digital surface models and weather data for solar simulations. Utilizing open datasets and software, the analysis covered 48,827 parking spaces, revealing that VIPV could extend vehicle range by 7 to 14 km per day, equating to a median annual increase of 2527 km. The findings suggest median yearly cost savings of 164 euros from reduced grid charging. However, the environmental benefits of solar vehicle charging were found to be less than those of traditional grid-connected photovoltaic systems. The study introduces a method to pinpoint parking spaces that are most suitable for solar charging. Full article

To ensure the precise dimensioning and effective testing of drivetrain components, it is crucial to have a thorough understanding of customer requirements, with a particular emphasis on customer stress on these components. An accurate interpretation of customer data is essential for determining representative customer requirements, such as load collectives. The automobile industry has faced challenges in analyzing large amounts of customer driving data to obtain representative load collectives as target values in durability design. However, due to technical limitations and cost constraints, collecting data from a large sample size is not feasible. The ongoing digitalization of the automotive industry, driven by an increasing number of connected vehicles, enhances data-based and customer-oriented development. This paper investigates representative customer load collectives using cloud data from over 40,000 customer vehicles to lay the groundwork for realizing robust requirement engineering. A systematic method for analyzing big data on the cloud was introduced. The derived component-specific damage distribution from these collectives adopts a unique approach, utilizing the 1% vehicle term instead of the common 1% customer term to represent typical customer stress. This study shows that the driven mileage and the number of vehicles are crucial factors in 1% vehicle analysis. An analysis of the characteristics of the 1% vehicle is conducted, followed by an exploration to determine the required vehicle quantity for obtaining stable results. The shape parameter of the damage distribution determines the necessary number of vehicles for a reliable conclusion. Additionally, a comparative analysis of market-specific customer requirements between the US and Europe is presented, and real usage differences in customer operations are explained using an operating point frequency heatmap. The information presented in this paper provides valuable input for optimizing durability design and conducting efficient, customer-oriented tests, resulting in significant reductions in development time and costs. Full article

The wide-scale integration of electric vehicles (EVs) in developed countries represents a significant technological innovation and a step toward reducing carbon emissions from transportation. Conversely, in developing nations like the Philippines, the adoption and availability of EVs have not been as rapid or widespread compared to other countries. In identifying this gap, this study delved into the preferences and factors influencing Filipino consumers’ willingness to purchase EVs. The study gathered 311 valid responses utilizing conjoint analysis with an orthogonal approach to assess the attributes influencing customers’ purchase decisions. Conjoint analysis tools such as IBM SPSS v25 statistics were utilized to infer consumer preference. The results determined that cost is the primary concern for consumers by a considerable margin; followed by battery type and charging method; along with the type of EV, driving range, and charging speed; and most minor concern is regenerative brakes. Therefore, there is an apparent sensitivity to price and technology. This study is the first to apply conjoint analysis to the Philippine market, delivering in-depth consumer preference insights that can help manufacturers and policymakers customize their approach to making EVs more attractive and more viable in less developed markets. The results suggest that a targeted effort to overcome cost barriers and improve technological literacy among prospective buyers should be productive for speeding up EV adoption in the Philippines. The results could be extended in future research to a broader assessment of socioeconomic and environmental benefits, laying out a broader plan for promoting sustainable solutions in transportation. Full article

Wireless power transfer (WPT) systems, which have been around for decades, have recently become very popular with the widespread use of electric vehicles (EVs). In this study, an inductive coupling WPT system with a series–series compensation topology was designed and implemented for use in EVs. Initially, a 3D Maxwell (ANSYS Electromagnetics Suite 18) model of the system was generated. The impact of individual parameters on the coupling coefficient was analyzed through systematic variations in each parameter’s values. As a result, a system with a higher coupling coefficient was obtained. Using this system, three distinct load cases were investigated for their efficiency in the Simplorer (ANSYS Electromagnetics Suite 18) circuit. Subsequently, a prototype of the system was constructed, and the experimental results were compared with the model’s results. This study shows that both the output power and the efficiency of the system increase as the load resistance increases. The results obtained in this study are anticipated to offer valuable insights for the enhancement of WPT system design. Full article

The steering knuckle is a critical component of the suspension and steering drive systems of electric vehicles. The electrification of last-mile vehicles presents a challenge in terms of cost, driving range and compensation of battery weight. This work presents a numerical methodology to evaluate 60XX series aluminum metal matrix composites (AMMCs) with reinforcement ceramic particles for steering knuckle components in medium heavy-duty last-mile cargo vehicles. The use of AMMCs provides lightweight knuckles with sufficient strength, stiffness and safety conditions for electrical vehicle cargo configurations. The numerical study includes three aluminum alloys, two AMMC alloys and an Al 6061-T6 alloy as reference materials. The medium-duty heavy vehicle class < 12 t, such as electrical vehicle cargo configurations, is considered for the numerical study (class 1–4). The maximum von Mises stress for class 4 AMMC alloys exceeds 350 MPa, limited by fracture toughness. The weight reduction is about 65% when compared with commercial cast iron. Moreover, Al 6061-T6 alloys exhibit stress values surpassing 300 MPa, constraining their suitability for heavier vehicles. The study proposes assessing the feasibility of implementing AMMC alloys in critical components like steering knuckles and suggests solutions to enhance conventional vehicle suspension systems and overcome associated challenges. It aims to serve as a lightweight design guide, offering insights into stress variations with differing load conditions across various cargo vehicles. Full article

This study aimed to uncover the attitudes, preferences, and perceptions of Hail residents toward electric vehicles (EVs) by employing a real-life survey-based approach. This paper presents an in-depth analysis of the potential adoption and impact of EVs to clarify the picture of the transition from using traditional vehicles to using EVs in Hail City, Saudi Arabia. Hail is rapidly becoming a more urbanized city; in the past few decades, the city’s area has expanded from 3242 to 17,526 hectares, and its population has increased dramatically from 82,900 in 1984 to 344,111 in present day. As a result, the city is facing increasingly difficult challenges related to rising vehicle emissions and environmental degradation. A survey was conducted among a diverse group of 346 participants. The survey results show an average of 3.15 cars per family, which indicates a strong connection with personal vehicles. The survey provides a comprehensive picture of the respondents’ socioeconomic background, indicating an average household size of 5.8 people and an average monthly income of SR 13,350. The key findings from the survey reveal that approximately 52.3% of the respondents have 3–4 family members, and nearly half of the families own one or two cars. Government employees formed a major proportion of the respondents. The results show a significant inclination toward EVs, with 78.6% of the participants being aware of EV charging stations and 37.9% expressing a positive attitude towards switching to electric vehicles. Despite this, a large majority (88.7%) have never driven an electric car. The respondents’ driving habits are further explored in the survey, which reveals an average of 2.1 h of daily driving. Furthermore, the respondents disclosed an average weekly fuel expenditure of SR 235. The results also highlight that the average weekly cost for fuel varies among the respondents, with most respondents spending no more than SR 300 per week. Additionally, this study examined the daily vehicle mileage, revealing that 37.9% of the respondents have a daily mileage of 51 to 100 km, which impacts the planning of charging station capacities and locations. The findings suggest a growing interest in EVs and highlight the need for strategic infrastructure development to support the anticipated surge in EV adoption. Full article

The efficient development of electric vehicles is essential to drive society towards sustainable development. Designing a lightweight frame is a key strategy to improve the economy and environment, increase energy efficiency, and reduce carbon emissions. Taking an automatic loading and unloading mixer truck as the research object, a force analysis of its frame was conducted under six typical working conditions. A size optimization method based on a hybrid model of the Kriging model and the analytic hierarchy process (AHP) is proposed. An approximate model of the mass and maximum stress of the frame was established using the Kriging model, and the Kriging model was optimized by using the multi-objective genetic optimization algorithm and the AHP method. Meanwhile, topology optimization was introduced to improve the structural performance of the frame and reduce its weight. The optimization results show that the overall weight of the frame is reduced by 11.96% compared to the pre-optimization period, though it still meets the material performance specifications. By comparing the iterative curves of the single Kriging model with those of the AHP model, it can be seen that the initial optimization efficiency of the hybrid model is about twice as much as that of the AHP model, and the final optimization result is improved by about 3.6% compared with the Kriging model. This validates the hybrid model as an effective tool for the multi-objective optimization of electric vehicle frames, providing more efficient and accurate optimization results for frame design. Full article

Reliable motor control is important for electric vehicle applications. The control process requires accurate measurements of the current and rotor position information to establish correct motor control design, particularly in sensorless permanent magnet synchronous motor control systems. Practical issues regarding the motor control circuit, such as the effects of parasitic element behavior on the switching components in the insulated gate bipolar transistor-driven inverter, were discussed in this study. It analyzed the effects of parasitic elements that can cause the ringing of switching losses and affect the spike of the signal in the motor current, which must be avoided in the implementation of motor control. The gate driver circuit topology was improved to reduce this effect in motor control devices. The proposed gate driver circuit design with the ringing suppression circuit configuration achieved good performance by keeping the signal spike at less than 10% in the motor current. Furthermore, a signal spike or noise was not observed in the estimation results of rotor position when using current information as the parameter control process. Both conditions were verified by experiments on the designed motor control devices. Under these conditions, signal precision can be achieved in motor control. Full article

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

Unmanned tractors under ploughing conditions suffer from body tilting, violent shaking and limited hardware resources, which can reduce the detection accuracy of unmanned tractors for field obstacles. We optimize the YOLOv8 model in three aspects: improving the accuracy of detecting tilted obstacles, computational reduction, and adding a visual ranging mechanism. By introducing Funnel ReLU, a self-constructed inclined obstacle dataset, and embedding an SE attention mechanism, these three methods improve detection accuracy. By using MobileNetv2 and Bi FPN, computational reduction, and adding camera ranging instead of LIDAR ranging, the hardware cost is reduced. After completing the model improvement, comparative tests and real-vehicle validation are carried out, and the validation results show that the average detection accuracy of the improved model reaches 98.84% of the mAP value, which is 2.34% higher than that of the original model. The computation amount of the same image is reduced from 2.35 billion floating-point computations to 1.28 billion, which is 45.53% less than the model computation amount. The monitoring frame rate during the movement of the test vehicle reaches 67 FPS, and the model meets the performance requirements of unmanned tractors under normal operating conditions. Full article

An identification technique is proposed to create a relation between the accelerator pedal position and the corresponding driving moment. This step is beneficial to replace the complex physical model of the vehicle control unit, especially when the sufficient information needed to model certain functionalities of the vehicle control unit are unavailable. We utilized the nonlinear autoregressive exogenous model to regenerate the electric motor torque demand, given the accelerator pedal position, the motor’s angular speed, and the vehicle’s speed. This model proved to be extremely efficient in representing this highly complex relationship. The data employed for the identification process were chosen from an actual three-dimensional route with sudden changes of a dynamic nature in the driving mode, different speed limits, and elevations, as an attempt to thoroughly cover the driving moment scope based on the alternation of the given inputs. Analyzing the selected route data points showed the widespread coverage of the motor’s operational scope compared to a standard driving cycle. The training outcome revealed that linear modeling is inadequate for identifying the targeted system, and has a substantial estimation error. Adding the nonlinearity feature to the model led to an exceptionally high accuracy for the estimation and validation datasets. The main finding of this work is that the combined model from the nonlinear autoregressive exogenous and the sigmoid network enables the accurate modeling of highly nonlinear dynamic systems. Accordingly, the maximum absolute estimation error for the motor’s moment was less than 10 Nm during the real-world driving maneuver. The highest errors are found around the maximum motor’s moment. Finally, the model is validated with measurements from an actual field test maneuver. The identified model predicted the driving moment with a correlation of 0.994. Full article

Pavement defect detection technology stands as a pivotal component within intelligent driving systems, demanding heightened precision and rapid detection rates. Addressing the complexities arising from diverse defect types and intricate backgrounds in visual sensing, this study introduces an enhanced approach to augment the network structure and activation function within the foundational YOLOv5 algorithm. Initially, modifications to the YOLOv5′s architecture incorporate an adjustment to the Leaky ReLU activation function, thereby enhancing regression stability and accuracy. Subsequently, the integration of bi-level routing attention into the network’s head layer optimizes the attention mechanism, notably improving overall efficiency. Additionally, the replacement of the YOLOv5 backbone layer’s C3 module with the C3-TST module enhances initial convergence efficiency in target detection. Comparative analysis against the original YOLOv5s network reveals a 2% enhancement in map50 and a 1.8% improvement in F1, signifying an overall advancement in network performance. The initial convergence rate of the algorithm has been improved, and the accuracy and operational efficiency have also been greatly improved, especially on models with small-scale training sets. Full article

Decision-making and planning are the core aspects of autonomous driving systems. These factors are crucial for improving the safety, driving experience, and travel efficiency of autonomous vehicles. Intersections are crucial nodes in urban road traffic networks. The objective of this study is to comprehensively review the latest issues and research progress in decision-making and planning for autonomous vehicles in intersection environments. This paper reviews the research progress in the behavioral prediction of traffic participants in terms of machine learning-based behavioral prediction, probabilistic model behavioral prediction, and mixed-method behavioral prediction. Then, behavioral decision-making is divided into reactive decision-making, learning decision-making, and interactive decision-making, each of which is analyzed. Finally, a comparative analysis of motion planning and its applications is performed from a methodological viewpoint, including search, sampling, and numerical methods. First, key issues and major research progress related to end-to-end decision-making and path planning are summarized and analyzed. Second, the impact of decision-making and path planning on the intelligence level of autonomous vehicles in intersecting environments is discussed. Finally, future development trends and technical challenges are outlined. Full article

Electric vehicles (EVs) play a major role in the energy system because they are clean and environmentally friendly and can use excess electricity from renewable sources. In order to meet the growing charging demand for EVs and overcome its negative impact on the power grid, new EV charging stations integrating photovoltaic (PV) and energy storage systems (ESSs) have emerged. However, the output of solar PV systems and the charging demand of EVs are both characterized by uncertainty and dynamics. These may lead to large power fluctuations in the grid and frequent alternation of peak and valley loads, which are not conducive to the stability of the distribution network. The study of reasonable capacity configuration and control strategy issues is conducive to the efficient use of solar energy, fast charging of EVs, stability of the distribution network, and maximization of the economic benefits of the system. In this paper, the concept, advantages, capacity allocation methods and algorithms, and control strategies of the integrated EV charging station with PV and ESSs are reviewed. On the basis of the above research, the current problems and challenges are analyzed, and corresponding solutions and ideas are proposed. Full article

The increasing demand for efficient and cost-effective transportation solutions has led to the exploration of unconventional modes of transportation, such as ziplines. This paper presents the development of an electric prototype for a trolley that can be used for the simultaneous transport of goods and people on ziplines. The prototype is designed with a modular system that allows for easy customization based on the cargo’s weight and size. Two lightweight Maxon motors have been integrated for traction purposes with two Maxon inverters and a low-voltage swappable battery pack. The trolley’s chassis is made of lightweight materials, such as aluminum, making it highly maneuverable and capable of traveling at high speeds. The lightweight permits the operators to detach the trolley from the zipline when needed. The prototype’s traction and braking systems are controlled through a user-friendly interface, making it easy to operate, and with the possibility of a robust and automatic routine for goods transport. In this article, we present the simulation for the design and testing of the prototype, as well as its potential applications in various industries, such as mining, agriculture, and emergency services. Our results show that the prototype is a viable solution for zipline-based transportation, with high efficiency and performance standards. Further research and development are being conducted to optimize the prototype’s performance and expand its applications. Full article

The shift to electric transportation is crucial to fighting climate change. However, Germany’s goal of 15 million electric vehicles (EVs) by 2030 remains distant. Therefore, enhancing their economic viability is essential to promoting EV adoption. One promising option to increase the economics for the user is PV self-consumption optimization using smart charging EVs. Yet, more research is needed to explore the use case’s impacts on the German/European energy systems. Therefore, PV self-consumption optimization using EVs is integrated into an energy system model, assessing its impact on the energy system in 2030. For this purpose, the use case is modeled for different groups of people—personas—which are defined in a way that creates a diverse set of personas reflecting the distribution of different statistical values within Germany. The modified (dis)charging profiles are then aggregated and integrated into the energy system model. With a high implementation of PV self-consumption optimization in Germany in 2030, a positive system effect (with a system cost reduction of 53 million EUR/a) can be observed with a lower need for further storage and less curtailment of renewable energies (RES). Furthermore, the market values for RES increase by 0.7%, which fosters the integration of RES. Full article

The rise of electric mobility poses a challenge and an opportunity for the housing industry to provide charging infrastructure. The housing industry can take advantage of its large roof areas to install photovoltaic (PV) systems and use the electricity generated to charge electric vehicles. This study explores how the charging demand can be allocated to specific locations based on socio-economic parameters and determines whether PV integration is economically viable for EV charging. Two models are used, one with extensive spatial and traffic data to determine the charging demand for over 300 locations, and another with a time-series-based approach for four specific locations to analyze the seasonal dependencies. The results indicate that PV integration is economically advantageous when electricity purchase prices exceed 0.15 EUR/kWh. Higher electricity prices can lead to significant additional profits through PV integration. Slow charging and charging during the day are beneficial, as they increase self-consumption, making PV systems economically viable. However, fast-charging infrastructure should be combined with PV storage systems for effective PV integration. Full article

Precise trajectory prediction is pivotal for autonomous vehicles operating in real-world traffic conditions, and can help them make the right decisions to ensure safety on the road. However, state-of-the-art approaches consider limited information about the historical movements of vehicles. On highways, drivers make their next judgments according to the behavior of the ambient vehicles. Thus, vehicles need to consider temporal and spatial interactions to reduce the risk of future collisions. In the current work, a trajectory prediction method is put forward in accordance with a graph attention mechanism. We add the absolute and relative motion information of vehicles to the input of the model to describe the vehicles’ past motion states more accurately. LSTM models are employed to process the historical motion information of vehicles, as well as the temporal correlations in interactions. The graph attention mechanism is applied to capture the spatial correlations between vehicles. Utilizing a decoder rooted in an LSTM framework, the future trajectory distribution is generated. Evaluation on the NGSIM US-101 and I-80 datasets substantiates the superiority of our approach over existing state-of-the-art algorithms. Moreover, the predictions of our model are analyzed. Full article

For continuously changing road conditions and vehicle operating states, the exactitude of vehicle path tracking has not been secured by model predictive control based on linear lateral stiffness. An amended square root cubature Kalman filter method based on the minimization of a new covariance of interest is proposed to calculate the tire lateral deflection force in real time. The ratio of the estimated tire force to the linear tire force was used as a ratio to adjust the lateral deflection stiffness, and an adaptive model predictive controller was built based on the vehicle path-tracking error model to correct the tire lateral deflection stiffness. Finally, an analysis based on the joint CarSim and Simulink simulation platform shows that compared to a conventional model predictive control (MPC) controller, a trajectory-following controller built based on this method can effectively reduce the lateral distance error and heading error of an autonomous vehicle. Especially under low adhesion conditions, the conventional MPC controllers will demonstrate large instability during trajectory tracking due to the deviation of the linear tire force calculation results, whereas the adaptive model predictive control (AMPC) controllers can correct the side deflection stiffness by estimating the tire force and still achieve stable and effective tracking of the target trajectory. This suggests that the proposed algorithm can improve the effectiveness of trajectory tracking control for autonomous vehicles, which is an important reference value for the optimization of autonomous vehicle control systems. Full article

Extended-range electric vehicles have both a motor and an engine; the motor is used for driving, and the engine generates electricity via a range extender, which is connected to the motor. The permanent magnet generator is part of the range extender, and the output voltage is controlled by adjusting the engine’s speed; the generator’s rotating speed fluctuates, meaning that the engine’s fuel consumption increases. Meanwhile, considering the limited axial dimension of the range extender, an axial–radial disk hybrid generator that combines excitation is developed, making full use of the radial space; at the same time, the output voltage is adjusted without changing the engine’s speed. In this study, the generator’s magnetic field hybrid principle, the path of permanent magnetic circuit, and the electric excitation magnetic circuit under different loads were analyzed and verified via the finite element method. A comparative analysis method was also used, the technical index of the disk hybrid excitation generator was determined, and the main structural parameters were designed using theoretical calculations. The three-dimensional finite element model was established based on the results, and a finite element analysis was performed. An equivalent magnetic circuit model was established, and the formulas of synthetic permeability, leakage permeability, and effective permeability were determined. The finite element method (numerical method) and equivalent magnetic circuit method (analytical method) were used to calculate the synthetic magnetic fields of the air gap, rotor yoke, and rotor teeth under different excitation currents. A comparison between the two methods verified the design utility. The conclusions provide a valuable point of reference for the development of the disk hybrid excitation generator for use in range extenders in extended-range electric vehicles. Full article