World Electr. Veh. J., Volume 14, Issue 9 (September 2023) – 35 articles

Electric vehicles (EVs) are inducing revolutionary developments to the transportation and power sectors. Their innumerable benefits are forcing nations to adopt this sustainable mode of transport. Governments are framing and implementing various green energy policies. Nonetheless, there exist several critical challenges and concerns to be resolved in order to reap the complete benefits of E-mobility. The impacts of unplanned EV charging are a major concern. Accurate EV load forecasting followed by an efficient charge scheduling system could, to a large extent, solve this problem. This work focuses on short-term EV demand forecasting using three learning frameworks, which were applied to real-time adaptive charging network (ACN) data, and performance was analyzed. Auto-regressive (AR) forecasting, support vector regression (SVR), and long short-term memory (LSTM) frameworks demonstrated good performance in EV charging demand forecasting. Among these, LSTM showed the best performance with a mean absolute error (MAE) of 4 kW and a root-mean-squared error (RMSE) of 5.9 kW. Full article

The proliferation of autonomous vehicles (AVs) emphasises the pressing need to navigate challenging road networks riddled with anomalies like unapproved speed bumps, potholes, and other hazardous conditions, particularly in low- and middle-income countries. These anomalies not only contribute to driving stress, vehicle damage, and financial implications for users but also elevate the risk of accidents. A significant hurdle for AV deployment is the vehicle’s environmental awareness and the capacity to localise effectively without excessive dependence on pre-defined maps in dynamically evolving contexts. Addressing this overarching challenge, this paper introduces a specialised deep learning model, leveraging YOLO v4, which profiles road surfaces by pinpointing defects, demonstrating a mean average precision ([email protected]) of 95.34%. Concurrently, a comprehensive solution—RA-SLAM, which is an enhanced Visual Simultaneous Localisation and Mapping (V-SLAM) mechanism for road scene modeling, integrated with the YOLO v4 algorithm—was developed. This approach precisely detects road anomalies, further refining V-SLAM through a keypoint aggregation algorithm. Collectively, these advancements underscore the potential for a holistic integration into AV’s intelligent navigation systems, ensuring safer and more efficient traversal across intricate road terrains. Full article

The accurate identification of a driver’s braking intention is crucial to the formulation of regenerative braking control strategies for electric vehicles. In this paper, a braking intention recognition model based on the sample entropy of the braking signal and a probabilistic neural network (PNN) is proposed to achieve the accurate recognition of different braking intentions. Firstly, the brake pedal travel signal is decomposed to extract the effective components via variational modal decomposition (VMD); then, the features of the decomposed signal are extracted using sample entropy to obtain the multidimensional feature vector of the braking signal; finally, the sparrow search algorithm (SSA) and probabilistic neural network are combined to optimize the smoothing factor with the sparrow search algorithm and the cross-entropy loss function as the fitness function to establish a braking intention recognition model. The experimental validation results show that combining the sample entropy features of the braking signal with the probabilistic neural network can effectively identify the braking intention, and the SSA-PNN algorithm has higher recognition accuracy compared with the traditional machine learning algorithm. Full article

Over the years, attention to climate change has meant that international agreements have been drawn up and increasingly stringent regulations aimed at reducing the environmental impact of the marine sector have been issued. A possible alternative technology to the conventional and polluting diesel internal combustion engines is represented by the Fuel Cells. In the present article, the preliminary design of two energy systems based on Solid Oxide Fuel Cells (SOFCs) fed by bio-methane was carried out for a particular cruise ship. The SOFC systems were sized to separately supply the electric energies required for the ship propulsion and to power the other ship electrical utilities. The SOFC systems operate in nominal conditions at constant load and other electrical storage systems (batteries) cover the fluctuations in the electrical energy demand. Furthermore, the heat produced by the SOFCs is exploited for co-/tri-generation purposes, to satisfy the ship thermal energy needs. The preliminary design of the new energy systems was made using electronic spreadsheets. The new energy system has obtained the primary energy consumption and CO₂ emissions reductions of 12.74% and 40.23% compared to the conventional energy system. Furthermore, if bio-methane is used, a reduction of 95.50% could be obtained in net CO₂ emissions. Full article

In the current context of the ban on fossil fuel vehicles (diesel and petrol) adopted by several European cities, the question arises of the development of the infrastructure for the distribution of alternative energies, namely hydrogen (for fuel cell electric vehicles) and electricity (for battery electric vehicles). First, we compare the main advantages/constraints of the two alternative propulsion modes for the user. The main advantages of hydrogen vehicles are autonomy and fast recharging. The main advantages of battery-powered vehicles are the lower price and the wide availability of the electricity grid. We then review the existing studies on the deployment of new hydrogen distribution networks and compare the deployment costs of hydrogen and electricity distribution networks. Finally, we conclude with some personal conclusions on the benefits of developing both modes and ideas for future studies on the subject. Full article

The transit service in a city should be the most efficient, least polluting, most accessible, and sustainable means of transportation for its citizens. However, serious shortcomings have been detected, mainly in medium-sized cities in developing countries. These shortcomings are related to a lack of user information, insecurity, low service availability, and repeated stops in inappropriate and/or unauthorized places. Some of these shortcomings contribute to high accident rates and traffic congestion. The development of tools to improve the characteristics and conditions of transit service in cities has become an imperative need to improve the quality of life of citizens and city sustainability. Transit service tracking is relevant in aspects such as online location information to travelers and control by transport companies for compliance with speed limits, schedules, routes, and stops. This research proposes a transit vehicle tracking system based on the Internet of Vehicles (IoV) in Vehicle-to-Roadside (V2R) classification. The proposed system is ideal for the use of electric vehicles due to the low power consumption of the tracking device. This system uses Intelligent Transportation Systems (ITS) tracking service architecture, Long Range (LoRa) communication technology, and its LoRa Wide Area Network (LoRaWAN) protocol. Additionally, the system offers real-time location prediction in the absence of position data. The IoV tracking device integrates a GPS-LoRa module card with an Inertial Measurement Unit (IMU). A location prediction algorithm was implemented to train and store a prediction model with previously collected data from tracking devices. To evaluate the developed model, a case study in the city of Popayán (Colombia) was implemented, using three routes for testing. The results of the system implementation were satisfactory, obtaining an average coverage of 60.4% of the routes in the final field tests through LoRa communication. For the remaining 39.6% of the routes, location data prediction was used, with an average accuracy of 177 m with respect to the real location. Considering the obtained results, a tracking system such as the one proposed in this article can be used in the transit systems of medium-sized cities in developing countries to improve service quality and fleet control. Full article

For unmanned electric drive chassis parameter optimization problems, an unmanned electric drive chassis model containing power systems and energy systems was built using CRUISE, and as the traditional genetic algorithm is prone to falling into the local optima, an improved isolation niche genetic algorithm based on KOHONEN network clustering (KIGA) is proposed. The simulation results show that the proposed KIGA can reasonably divide the initial niche populations. Compared with the traditional genetic algorithm (GA) and the isolation niche genetic algorithm (IGA), KIGA can achieve faster convergence and a better global search ability. The comprehensive performance of the unmanned electric drive chassis in terms of power and economy was increased by 8.26% with a set of better solutions. The results show that simultaneous power system and energy system parameter optimization can enhance unmanned electric drive chassis performance and that KIGA is an efficient method for optimizing the parameters of unmanned electric drive chassis. Full article

Electric vehicle (EV) technology has revolutionized the transportation sector in the last few decades. The adoption of EVs, along with the advancement of smart grid technologies and Renewable Energy Sources (RES), has introduced new concepts in the automobile and power industries. Vehicle-Grid Integration (VGI) or Vehicle-to-Grid (V2G) is a technology revolutionizing both the transport and electric power sectors. From a V2G perspective, these sectors are complementary and mutually beneficial. For the power sector, mitigation of voltage and frequency excursions and the prospect of grid stabilization on the brink of uncertainties owing to the dynamics in the grid scenario are very important. This article focuses on various aspects of EV-power grid integration. The tremendous benefits of this technology, as presented in the literature, are reviewed. Furthermore, the concerns and the implementation challenges are reviewed in detail in this work. Full article

Energy management strategies are crucial for improving fuel economy and reducing the exhaust emissions of hybrid tractors. The authors study a series diesel-electric hybrid tractor (SDEHT) and propose a multi-operating point Fuzzy PID control strategy (MOPFPCS) aimed to achieve better fuel economy and improved control. To further improve the vehicle economy, the adaptive particle swarm optimization method is used to optimize the key parameters of the Fuzzy PID controller. A co-simulation model in AVL-Cruise and Matlab/Simulink environment is developed for plowing mode and transportation mode. The simulation results show that under the two operation modes, the equivalent fuel consumption of the adaptive particle swarm optimization multi-operating points Fuzzy PID control strategy (APSO-MOPFPCS) is reduced by 18.3% and 15.0%, respectively, compared to the engine single-operating point control strategy (ESOPCS). Also, it was found to be reduced by 9.5% and 4.6%, respectively, compared to the MOPFPCS. Full article

The multi-power source coupled transmission system is a high-performance and energy-saving potential power transmission system, and most of the commonly used pure electric vehicles in the market that use multi-power source coupled drive adopt the motor dual-axis distributed independent drive scheme. The configuration design method for multi-power source fusion hybrid systems mainly focuses on the search and selection of power split hybrid systems based on planetary gear mechanisms. But it has not yet covered the configuration design of transmission systems, resulting in a lack of universal expression and generation methods for the configuration of multi-power source fusion hybrid systems in pure electric vehicles. Therefore, to solve the configuration optimization design problem of a dual-motor single-planetary-array power system, an improved general matrix topology design method is proposed to generate all feasible topology structures. And energy consumption, economy, and the dynamic performance of alternative configurations are optimized and simulated through the control strategy based on a dynamic programming algorithm. Under comprehensive testing conditions, 25 alternative options that met the screening criteria were selected, and, ultimately, five optimized configuration options were obtained. Configuration 1 has the best economy, reducing energy consumption by about 6.3%and increasing driving range by about 6.7%. Its 0–100 km/h acceleration time is about 31.4% faster than the reference configuration. In addition, the energy consumption economy during actual driving is almost the same as the theoretical optimal energy consumption economy, with a difference of only 0.3%. The success of this study not only provides an innovative method for optimizing the configuration of dual-motor single-row star train power systems, but also has a positive impact on improving energy utilization efficiency, reducing energy consumption, and improving the overall performance of electric vehicles. Full article

This paper presents a new control strategy for vehicle stability based on brake-by-wire. However, there are few studies in the literature that compare the stability of a vehicle by systematic experimentation with or without controllers. In this paper, the complete experimental procedure is designed, and the experimental results are analyzed in detail. Firstly, the hydraulic model of the brake-by-wire is established based on its structure and working principles, and the yaw moment control method is proposed for the vehicle’s stability. The deviation between the desired values and actual values of the yaw rate and sideslip angle is taken as the input, and the fuzzy controller calculates the additional yaw moment for the vehicle stability. Next, the simulation under different conditions which contain the steering wheel step input, double lane change and turning is conducted, and the yaw rates and sideslip angles with and without stability control are compared, and the effectiveness of the control method is verified. Finally, the turning test is conducted based on brake-by-wire chassis to verify the proposed method. The experimental results show that the yaw rate decreased by 14% and the sideslip angle decreased by 25% when the brake control was applied. Furthermore, the proposed method performed well in improving the stability of the brake-by-wire chassis. Full article

In modern electrode manufacturing for lithium-ion batteries, the drying of the electrode pastes consumes a considerable amount of space and energy. To increase the efficiency of the drying process and reduce the footprint of the drying equipment, a laser-based drying process is investigated. Evaporation rates of up to 318 g m⁻² s⁻¹ can be measured, which is orders of magnitude higher than the evaporation rates in conventional furnace drying processes. Optical measurements of the slurry components in the visible and near-infrared spectrum are conducted. Thermal analyses the of laser-dried samples reveal that the commonly used binders carboxymethyl-cellulose (CMC) and styrene–butadiene rubber (SBR) are not affected by the laser drying process within the investigated process window. The results indicated that with the combination of a fast laser drying step and a subsequent convection drying step, high evaporation rates can be achieved while maintaining the integrity and adhesion of the anode. Full article

Fog, haze, or smoke are standard atmospheric phenomena that dramatically compromise the overall visibility of any scene, critically affecting features such as the illumination, contrast, and contour detection of objects. The decrease in visibility compromises the performance of computer vision algorithms such as pattern recognition and segmentation, some of which are very relevant to decision-making in the field of autonomous vehicles. Several dehazing methods have been proposed that either need to estimate fog parameters through physical models or are statistically based. But physical parameters greatly depend on the scene conditions, and statistically based methods require large datasets of natural foggy images together with the original images without fog, i.e., the ground truth, for evaluation. Obtaining proper fog-less ground truth images for pixel-to-pixel evaluation is costly and time-consuming, and this fact hinders progress in the field. This paper aims to tackle this issue by proposing gradient-based metrics for image defogging evaluation that do not require a ground truth image without fog or a physical model. A comparison of the proposed metrics with metrics already used in the NTIRE 2018 defogging challenge as well as several state-of-the-art defogging evaluation metrics is performed to prove its effectiveness in a general situation, showing comparable results to conventional metrics and an improvement in the no-reference scene. A Matlab implementation of the proposed metrics has been developed and it is open-sourced in a public GitHub repository. Full article

Zero-emission trucks for regional and long-haul missions are an option for fossil-free freight. The viability of such powertrains and system solutions was studied conceptually in project ESCALATE for trucks with GVW of 40 tonnes and beyond through various battery electric and fuel cell prime mover combinations. The study covers battery and fuel cell power sources with different degrees of battery electric as well as H₂ and fuel cell operation. As a design basis, two different missions with a single-charge/H₂ refill were analysed. The first mission was the VECTO long-haul profile repeated up to 750 km, whereas the second was a real 520 km on-road mission in Finland. Based on the simulated energy consumption on the driving cycle, on-board energy demand was estimated, and the initial single-charge and H₂ refill operational scenarios were produced with five different power source topologies and on-board storage capacities. The traction motors of the tractor were dimensioned so that a secondary mission of GVW up to 76 tonnes on a shorter route or a longer route with more frequent battery recharge and/or H₂ refill can be operated. Based on the powertrain and vehicle model, various infrastructure options for charging and H₂ refuelling strategies as well as various operative scenarios with indicative total cost of ownership (TCO) were analysed. Full article

To enhance the safety and stability of lane change maneuvers for autonomous vehicles in adverse weather conditions, this paper proposes a quadratic programming−based trajectory planning algorithm for lane changing in rainy weather. Initially, in order to mitigate the risk of potential collisions on wet and slippery road surfaces, we incorporate the concept of road adhesion coefficients and delayed reaction time to refine the establishment of the minimum safety distance. This augmentation establishes constraints on lane change safety distances and delineates the boundaries of viable lane change domains within inclement weather contexts. Subsequently, adopting a hierarchical trajectory planning framework, we incorporate visibility cost functions and safety distance constraints during dynamic programming sampling to ensure the safety of vehicle operation. Furthermore, the vehicle lane change sideslip phenomenon is considered, and the optimal lane change trajectory is obtained based on the quadratic programming algorithm by introducing the maneuverability objective function. In conclusion, to verify the effectiveness of the algorithm, lateral linear quadratic regulator (LQR) and longitudinal double proportional−integral−derivative (DPID) controllers are designed for trajectory tracking. The results demonstrate the algorithm’s capability to produce continuous, stable, and collision−free trajectories. Moreover, the lateral acceleration varies within the range of ±1.5 m/s², the center of mass lateral deflection angle varies within the range of ±0.15°, and the yaw rate remains within the ±0.1°/s range. Full article

The present study reports a critical review of NIO′s business model considering the evolving landscape of the electric vehicle market and servicing. The objective of this study is to develop a comprehensive framework that facilitates the identification of key elements characterizing a company’s business model and highlights ongoing transformations crucial for adaptation and survival in a rapidly changing environmental context. Focusing on the case study of NIO, a relatively young Chinese original equipment manufacturer (OEM) specializing in high-tech electric cars, the research delves into the challenging scenario of the Chinese electric vehicle market, which recently faced a bubble in 2023. The market proliferation, supply chain disruptions, and price wars triggered by Tesla have resulted in a survival struggle for numerous automotive startups, leaving larger companies with increasing market shares. Despite facing adversities, NIO managed to secure a promising segment catering to premium-range battery electric vehicles (BEVs), establishing a competitive advantage through differentiation. By pursuing ambitious investments, the company aims to create economies of scope and achieve cost leadership, venturing into new market sectors and vertically integrating the production chain. Given NIO’s agility in adapting to market conditions, aggressive entry into new segments, and a strategic vision for the future, it serves as an excellent candidate for testing and validating the proposed framework. The research sheds light on NIO’s trajectory and offers insights into its potential for sustained growth in the dynamic electric vehicle market. Full article

In this study, a multibody system (MBS) computational framework is developed to determine the exact location of the contact point and wear prediction resulting from the pantograph–catenary interaction. The railroad vehicle models in the MBS computational framework comprise rigid-body railroad vehicles, rigid-body pantograph systems, and flexible catenary systems. To avoid incremental rotation, the nonlinear finite element absolute nodal coordinate formulation is used to model a flexible catenary system in the MBS computational framework. To avoid co-simulation processes, the rigid-body railroad vehicle and the pantograph and flexible catenary systems were integrated into the MBS algorithms. The pantograph–catenary interaction is modeled using an elastic contact formulation developed to include the effect of pantograph–catenary separation and sliding contact. The proposed MBS approach evaluates the location of the contact point, contact force, and normal wear rate (NWR) from the mechanical and electrical contributions. This investigation considers the vibration caused by a crosswind scenario and determines the numerical result in the case of a steady crosswind scenario. The steady crosswind scenario contains the advantage of pantograph–catenary aerodynamic design, and the vibration of the catenary system remains significant after the excitation of a steady crosswind. In the case of a steady crosswind, the higher value of the steady crosswind effect significantly increases the mean contact force and the NWR from the mechanical contribution. After crosswind load disturbances, the mean contact force decreases, but the standard deviation of the contact force increases. Therefore, the NWR from the electrical contribution increases significantly. However, the total NWR increases with the crosswind velocity. Full article

The driving smoothness of vehicles is heavily influenced by their suspension system, and implementing active suspension control can effectively minimize the vibration movement of the vehicle and ensure a comfortable driving experience. An 8-DoF active suspension model of the full vehicle is established, and a fuzzy-PID controller is designed to autonomously regulate the parameters of the PID controller. Using the MATLAB/Simulink environment, a simulation model for suspension is created, and the vibration characteristics of passive, PID control, and fuzzy-PID control suspensions are compared with the help of the continuous crossing road hump model and C-level road model as road inputs. The results show that the utilization of fuzzy-PID control considerably diminishes the vertical, pitch, and roll oscillations of the suspension body and modifies the suspension dynamic deflection and tire dynamic load in contrast to the other two scenarios, thus enhancing ride comfort. Fuzzy-PID control led to a decrease of approximately 40% in acceleration, 25% in suspension workspace, and 30% in tire deflection compared to passive suspension. In addition, the reduction in acceleration is about 20%, the reduction in suspension workspace is approximately 10%, and the reduction in tire deflection is about 15% compared to the PID control suspension system. Full article

Compared with batteries, ultracapacitors have higher specific power and longer cycle life. They can act as power buffers to absorb peak power during charging and discharging, playing a role in peak shaving and valley filling, thereby extending the cycle life of the battery. In this article, a replaceable battery electric coupe SUV equipped with a lithium iron phosphate (LiFePO₄) power battery is taken as the research object, and a vehicle dynamics simulation model is established on the MATLAB/Simulink platform. Parameter matching and control optimization for a hybrid energy storage system (HESS) are conducted. Through a proven semiempirical cycle model of the LiFePO₄ power battery, the operating cycle life model is derived and used to estimate the battery cycle life. World Light Vehicle Test Cycle (WLTC) simulation results show that the HESS with 308 ultracapacitors can extend the cycle life of the LiFePO₄ power battery by 34.24%, thus significantly reducing the operation cost of the battery replacement station. Full article

In recent years, artificial intelligence and machine learning have captured the attention of researchers and industrialists in order to estimate and predict the state of batteries. The quality of data must be good, and the source of data must be the same for different models’ performance comparisons. The lithium-ion battery is popularly used because of its high energy density and its compact size. Due to the non-linear and complex characteristics of lithium-ion batteries, electric vehicle users have to know about battery health conditions. Different types of state estimation methods are used, namely, electrochemical-based, equivalent circuit model (ECM) based, and data-driven approaches. This paper is a survey of electric vehicle history, different battery chemistries, battery management system (BMS) basics and key challenges and solutions in BMS, and in-depth discussions about other battery state of charge and state of health estimation methods. Research trend analysis, critical analysis of this work, limitations, and future directions of existing works are discussed. This paper also provides information on the open-access available datasets of different battery chemistry for a data-driven approach. This paper highlights the key challenges of state estimation techniques. Knowledge of accurate battery state of charge (SOC) provides critical information about remaining available energy. In comparison, battery state of health (SOH) indicates its current health condition, remaining lifetime, performance, and proper energy management of the electric vehicles. Full article

In order to counter the problems of cracks and large area fractures in the welding points of quick-replacement battery boxes for electric vehicles (which may lead to the concentration of stress), in this study, a fatigue analysis of the welding points, based on a load spectrum, was used to predict welding points’ fatigue and improve the structural life of quick-replacement battery boxes. Firstly, a model of the quick-replacement battery box was established in SolidWorks software; secondly, the welding points’ fatigue was analyzed using the Optistruct module of HyperMesh software, and the topology of the quick-replacement battery box was optimized according to the results of the analysis; finally, for testing purposes and to achieve a lighter weight and an improved structural life, the fatigue of the welding points of the optimized battery box was analyzed. The results of the analysis showed that the force of the quick-replacement battery box was primarily concentrated at the connection between the middle bottom plate and the partition. Additionally, retaining the number of welding points at the hanging ear was shown to be beneficial for maintaining stiffness during electric vehicle operation; however, the number of welding points at the partition connection could be appropriately reduced. Before optimization, the maximum fatigue damage values of the welding points were 2.763 × 10⁻⁶, 3.833 × 10⁻⁶, and 6.728 × 10⁻⁶, respectively, satisfying the criteria of fatigue damage to the welding points. After optimization, the fatigue damage values of the welding points in the quick-replacement battery box were significantly reduced to 4.431 × 10⁻⁸, 4.562 × 10⁻⁸, and 8.885 × 10⁻⁸, respectively, compared with their pre-optimized levels. Consequently, the stress concentration was alleviated effectively, thereby meeting the conditions for fatigue damage. These results have important theoretical and engineering significance for the design and optimization of quick-replacement battery boxes for electric vehicles. Full article

In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in industrial item-sorting systems and proposes an object-recognition and sorting system for autonomous driving. In industrial sorting lines, goods-sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot’s real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consumption. These issues mean that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array)-based item-sorting system that achieves similar performance to current mainstream sorting systems but at a lower cost and consumption. The recognition component employs a morphological-recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degrees-of-freedom robotic arm is introduced into the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26 ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs. Full article

To address traffic congestion in urban expressways during the transition from wide to narrow sections, this study proposed a car-following strategy based on deep reinforcement learning. Firstly, a car-following strategy was developed based on a twin-delayed deep deterministic policy gradient (TD3) algorithm, and a multi-objective constrained reward function was designed by comprehensively considering safety, traffic efficiency, and ride comfort. Secondly, 214 car-following periods and 13 platoon-following periods were selected from the natural driving database for the strategies training and testing. Finally, the effectiveness of the proposed strategy was verified through simulation experiments of car-following and platoon-following. The results showed that compared to human-driven vehicles (HDV), the TD3 and deep deterministic policy gradient (DDPG)-based strategies enhanced traffic efficiency by over 29% and ride comfort by more than 60%. Furthermore, compared to DDPG, the relative errors between the following distance and desired safety distance using TD3 could be reduced by 1.28% and 1.37% in simulation experiments of car-following and platoon-following, respectively. This study provides a new approach to alleviate traffic congestion for wide-to-narrow road sections in urban expressways. Full article

Multiple object tracking (MOT), as a core technology for environment perception in autonomous driving, has attracted attention from researchers. Combing the advantages of batch global optimization, we present a novel online MOT framework for autonomous driving, consisting of feature extraction and data association on a temporal window. In the feature extraction stage, we design a three-channel appearance feature extraction network based on metric learning by using ResNet50 as the backbone network and the triplet loss function and employ a Kalman Filter with a constant acceleration motion model to optimize and predict the object bounding box information, so as to obtain reliable and discriminative object representation features. For data association, to reduce the ID switches, the min-cost flow of global association is introduced within the temporal window composed of consecutive multi-frame images. The trajectories within the temporal window are divided into two categories, active trajectories and inactive trajectories, and the appearance, motion affinities between each category of trajectories, and detections are calculated, respectively. Based on this, a sparse affinity network is constructed, and the data association is achieved using the min-cost flow problem of the network. Qualitative experimental results on KITTI MOT public benchmark dataset and real-world campus scenario sequences validate the effectiveness and robustness of our method. Compared with the homogeneous, vision-based MOT methods, quantitative experimental results demonstrate that our method has competitive advantages in terms of higher order tracking accuracy, association accuracy, and ID switches. Full article

To solve the problem of smooth switching between the car-following model and lane-changing model, the Intelligent Driver Model (IDM) for a single lane was used to study the driver’s behavior switching mechanism of normally following, generating intentions to change lanes, creating space and speed gains, and performing lane change. In the case of sufficient lane-changing space and speed gains, the ego vehicle’s intention to change lanes was considered to solve the switching boundary between car-following behavior and lane-changing behavior, which is also the IDM failure point. In the event that there are no lane-changing gains, the IDM was optimized by incorporating the constraint components of the target lane vehicles in conjunction with the actual motion state of the ego vehicle, and the Stepless Switching Intelligent Driver Model (SSIDM) was constructed. Drivers’ natural driving information was collected, and scenario mining was performed on structured roads. On the basis of the collected data, an elliptic equation was used to fit the behavior switching boundary, and the two component balance coefficients of the front and rear vehicles on the target lane were identified. According to the test set verification results, the Mean Square Error (MSE) of the SSIDM is 2.172, which is 57.98% less than that of the conventional single-lane IDM. The SSIDM can accomplish stepless switching comparable to the driver’s behavior between the car-following behavior and the lane-changing behavior, with greater precision than IDM. This research can provide theoretical support for the construction of the point-to-point driving model and the development of L2+ autonomous driving functions. It can provide assistance for the landing and application of full-behavior and full-scene autonomous driving. Full article

During wireless charging of the traction battery of electrically powered vehicles, the active area between the ground and vehicle assemblies must be monitored for inductive power transfer. If metallic foreign objects enter this area, they interact with the magnetic field and can heat up strongly, and thus become a potential source of hazard. To detect such foreign objects, measurements based on passive inductive sensors have already been carried out in advance. However, a large number of factors influence the detectability of metallic foreign objects, such as the characteristics of the magnetic field of the ground assembly coil, the size, shape, position, orientation, and material composition of the foreign objects, or the design of the sensor coils. The related practical testing effort can be reduced if the characteristics of the charging system and the foreign object detection system can be simulated. Therefore, simulation models were developed within the scope of this work and validated with the help of practical measurements. These models were used in the next step to analyze new test arrangements that had not yet been investigated by measurement. In the simulations described here, precision in the range of 1 mV could be achieved. Cumulatively, many influencing factors can be easily investigated, and results can be generated in a largely automated manner and typically in a wider variety than with practical measurements. Full article

The charging and discharging process of batteries generates a significant amount of heat, which can adversely affect their lifespan and safety. This study aims to enhance the performance of a lithium-ion battery (LIB) pack with a high discharge rate (5C) by proposing a combined battery thermal management system (BTMS) consisting of improved phase change materials (paraffin/aluminum composite) and forced-air convection. Battery thermal performance is simulated using computational fluid dynamics (CFD) to study the effects of heat transfer and flow parameters. To evaluate the impact of essential parameters on the thermal performance of the battery module, temperature uniformity and maximum temperature in the cells are evaluated. For the proposed cooling system, an ambient temperature of 24.5 °C and the application of a 3 mm thick paraffin/aluminum composite showed the best cooling effect. In addition, a 2 m/s inlet velocity with 25 mm cell spacing provided the best cooling performance, thus reducing the maximum temperature. The paraffin can effectively manage thermal parameters maintaining battery temperature stability and uniformity. Simulation results demonstrated that the proposed cooling system combined with forced-air convection, paraffin, and metal foam effectively reduced the maximum temperature and temperature difference in the battery by 308 K and 2.0 K, respectively. Full article

Building an autonomous driving system requires a detailed and unified semantic representation from multiple cameras. The bird’s eye view (BEV) has demonstrated remarkable potential as a comprehensive and unified perspective. However, most current research focuses on innovating the view transform module, ignoring whether the crucial image encoder can construct long-range feature relationships. Hence, we redesign an image encoder with a large kernel attention mechanism to encode image features. Considering the performance gains obtained by the complex view transform module are insignificant, we propose a simple and effective Bilinear-Attention Transform module to lift the dimension completely. Finally, we redesign a BEV encoder with a CNN block of a larger kernel size to reduce the distortion of BEV features away from the ego vehicle. The results on the nuScenes dataset confirm that our model outperforms other models with equivalent training settings on the segmentation task and approaches state-of-the-art performance. Full article

Currently, autonomous vehicles, non-autonomous vehicles, and VIP (emergency) autonomous cars are using intelligent road management techniques to interact with one another and enhance the effectiveness of the traffic system. All sorts of vehicles are managed and under control using the intersection management unit approach. This study focuses on transportation networks where VIP cars are a major disruption, accounting for 40% of accidents and 80% of delays. Intelligent Mobility (IM) is a strategy promoted in this study that proposes setting up intelligent channels for all vehicle communication. As part of its function, the IM unit keeps tabs on how often each junction is used so that it may notify drivers on traffic conditions and ease their workload. The suggested layout may drastically cut average wait times at crossings, as shown in SUMO simulations. The entrance of a VIP car should disrupt all traffic, but the IM (intersection management) unit effectively manages all traffic by employing preemptive scheduling and non-preemptive scheduling techniques for all types of vehicles. We are employing Nishtar roads, the M4 motorway, Mexico, and Washington roads in our scenario. In comparison to all other routes, the simulation results demonstrate that the Washington road route is better able to manage all vehicle kinds. Washington’s traffic delays for 50 cars of all sorts are 4.02 s for autonomous vehicles, 3.62 s for VIP autonomous vehicles, and 4.33 s for non-autonomous vehicles. Full article

Permanent magnet motors have become an important component of industrial production, transportation, and aerospace due to their advantages of high torque density, high power density, high reliability, low losses, and high efficiency. Permanent magnet claw pole motor (PMCPM) is a special type of transverse flux motor which has a higher torque density compared to traditional permanent magnet motors. Due to the absence of winding ends, its axial space utilization is high, and the usage of windings is greatly reduced, reducing the cost and weight of the motor. PMCPM has the advantages of small space, a light weight, a high torque density, a high efficiency, and simple production, which have potential for use in the field of electric vehicles. The double-stator structure design can improve the torque density, efficiency, and radial space utilization of PMCPM, which helps to expand their applications in the field of electric vehicles. This article designs two PMCPM with concentrated winding while different rotor structures (PMCPM1 and PMCPM2) and a three-dimensional finite element method is employed to compare and analyze the performance of PMCPM1 and PMCPM2 and the traditional PMCPM (TPMCPM). Multiphysics analysis is carried out for PMCPM1 and PMCPM2. The stress of the inner and outer stators during interference assembly are analyzed. In this paper, a hybrid material core design is proposed, in which the stator yoke is rolled by silicon steel material and the stator claw pole is pressed by the SMC die method. The multiphysics simulation performance of the PMCPM1 and PMCPM2 with hybrid cores is analyzed. Full article