World Electr. Veh. J., Volume 14, Issue 10 (October 2023) – 33 articles

Static frequency converters (SFCs) are very important for starting the connection of synchronous capacitors to the power grid, which is beneficial for ensuring the impact of electric vehicle connection on the inertia of the power grid. In the traditional sensorless initial rotor position detection method, the signal-to-noise ratio of the induced voltage at the machine terminal is small, making it difficult to accurately extract the rotor position. In this study, a reliable initial position detection method for a sensorless-controlled synchronous machine drive is proposed. A step excitation voltage was applied to the excitation circuit before the motor was started, and the three-phase induction voltage at the terminals was sampled in real time. The sampling signal was processed in two ways: digital filter processing and stator flux calculation. The accuracy of the initial rotor position is determined by comparing the differences between the two results. This algorithm does not depend on additional hardware circuits and has fewer setting parameters; therefore, it is easy to apply in engineering applications. Finally, a comparative experiment was conducted using a real-time digital system (RTDS) to verify the feasibility and effectiveness of the proposed method. The proposed rotor position detection method can effectively improve the detection reliability and ensure the start-up reliability of SFCS. Full article

The presented research on electric wheel loaders lacks a detailed analysis of drive energy-saving during the shovel preparation phase, which is characterized by a high probability of loader tire skidding. To address this issue, this study examines the energy consumption efficiency of a two-motor distributed drive wheel loader under three drive modes including front motor drive, rear motor drive, and dual-motor drive, taking into account the change in the drive force demand caused by the bucket landing. This study finds that the motor energy conversion efficiency is the greatest in single-motor drive mode when the bucket does not generate positive pressure with the ground. In dual-motor drive mode, the total torque overcome is greater, but the motor energy conversion efficiency is the greatest when the bucket generates the greatest positive pressure with the ground. This study suggests that in future designs of electric loaders, two motors can be used to distribute the drive, but the front and rear motors should be designed to participate in the drive with a certain torque distribution ratio at different speeds and resistance to avoid the phenomenon of the bucket pressing the ground too much. Full article

Anti-roll and anti-pitch are important directions in the comprehensive research of automobiles. In order to improve the anti-roll and anti-pitch performance of the vehicle, an inerter was applied to the vehicle suspension system, and a 14 DOF vehicle nonlinear dynamics model was established. The influence of the change in inertance in the eight kinds of improved ISD (Inerter-Spring-Damper) suspension structures on the RMS (root mean square) value of performance indexes of roll, vertical, and pitch motion of the vehicle was studied. Based on this, the vehicle’s ISD structure with better performance was selected, and the NSGA-Ⅱ algorithm was adopted to optimize the selected structural parameters. The simulation results showed that the four kinds of suspension hadbetter comprehensive performance, and their structureswere, respectively, excluding the supporting spring in parallel, (1) an inerter in series with a spring and a damper in parallel, (2) a damper in series with a spring and an inerter in parallel, (3) an inerter and a damper in series, and (4) the damper in parallel with a spring and an inerter in series. The ISD suspension structure had better comprehensive performance under step steering braking, which was obviously better than the passive suspension, and effectively improved the vehicle ride comfort, anti-roll and anti-pitch performance. Under the hook steering braking, the lateral load transfer rate was used to evaluate the vehicle’s anti-rollover ability. The results showed that the ride comfort and anti-rollover ability of ISD suspension were better than those of passive suspension. Under the condition of taking into account the anti-pitching ability, the suspension consists of a supporting spring in parallel with an inerter, and a damper in series was better. Full article

The nascent electrification of transport has heralded a new chapter in the driving force of mobility. Developing regions such as sub-Saharan Africa already lag in this transformative transport transition. A potential transitional step towards full-scale electric mobility is the retrofitting of the existing fleet of internal combustion-based vehicles. This paper proposes a novel approach to the design of a retrofit electric drivetrain for an internal combustion engine vehicle. Specifically, a minibus taxi, which dominates the region’s informal paratransit industry, is electrified. This retrofit is the first formal research presented with a focus on sub-Saharan Africa and its unique challenges. A generic methodology is presented to systematically specify and select drivetrain components and assess the suitability and characteristics of those components. Unique about the presented methodology is the application of driving-cycle data of internal combustion engine vehicles, which provides quantitative insights into the performance and characteristics of the selected components for a retrofit. Finally, a real-world use case is presented to provide a tangible example and to validate the feasibility of the presented approach. Full article

The Dual Credit Policy is an important policy to promote the development of new energy vehicles unique to China. There is a lack of research that intuitively reflects the impact of the Dual Credit Policy on industrial development through an industry-based factual comparison of this policy. Based on the Taylor expansion and Cross-Entropy description, this article obtains the development regression function by the quantitative analysis of five indicators—the number of new energy vehicle-related patents, sales volume, production volume, the number of newly registered enterprises, infrastructure construction (the number of charging piles) before and after the implementation of the policy, and describes them quantitatively using the Taylor expansion to obtain the CPTI index. The CPCEI index is obtained by calculating the Cross-Entropy of the distribution of each indicator before and after policy implementation. The above two indices were compared for the growth trend and growth quantity, respectively. Finally, the following conclusions were obtained: 1. the Dual Credit Policy is more significantly promoted at the market level than the impact on the technical level; 2. although there is also incentive in infrastructure construction, it cannot fully react to the market demand; 3. the number of start-up’s operating in the new energy field increases, but the overall growth trend gradually slows down and fails to significantly change the existing structure of the market. This study suggests that the government should launch a special incentive policy for charging piles, and new energy manufacturers should expand their production capacity to meet the market demand. Full article

Given the influence of the randomness of driving conditions on the energy management strategy of vehicles, deep reinforcement learning considering driving conditions prediction was proposed. A working condition prediction model based on the BP neural network was established, and the correction coefficient of vehicle demand torque was determined according to the working condition prediction results. An energy management strategy and deep reinforcement learning were integrated to build an energy management strategy with deep reinforcement learning based on driving condition prediction. Simulation experiments were conducted according to the actual collected working condition data. The experimental results show that the energy management strategy, i.e., deep reinforcement learning considering working condition prediction, has faster convergence speed and more vital self-learning ability, and the equivalent fuel consumption per 100 km under different driving conditions is 6.411 L/100 km, 6.327 L/100 km, and 6.388 L/100 km, respectively. Compared with the unimproved strategy, the fuel economy can be improved by 3.18%, 3.08%, and 2.83%. The research shows that the energy management strategy, the deep reinforcement learning based on driving condition prediction, is effective and adaptive. Full article

Recently, there has been renewed interest in lightweight structures; however, a small structure change can strongly affect vehicle dynamic behavior. Therefore, this study provides new insights into non-parametric modeling based on artificial neural networks (ANNs). This work is then motivated by the requirement for a reliable substitute for virtual instrumentation in electric car development to enable the prediction of the current value of the vehicle slip from a given time history of the vehicle (input) and previous values of synthetic data (feedback). The training data are generated from a multi-body simulation using MSC Adams Car; the simulation involves a double lane-change maneuver. This test is commonly used to evaluate vehicle stability. Based on dynamic considerations, this study implements the nonlinear autoregressive exogenous (NARX) identification scheme used in time-series modeling. This work presents an ANN that is able to predict the side slip angle from simulated training data generated employing MSC Adams Car. This work is a specific solution to overtake maneuvers, avoiding the loss of vehicle control and increasing driving safety. Full article

A collaborative control strategy for distributed drive electric vehicles (DDEVs) focusing on differential drive assisted steering (DDAS) and active front steering (AFS) is proposed to address the issues of sudden torque changes, reduced steering characteristics, and weak collaborative control capabilities caused by the coupling of the AFS and DDAS systems in DDEVs. This paper establishes a coupled dynamic model of the AFS and DDAS systems and, on this basis, designs AFS controllers for yaw velocity feedback control and DDAS controllers for steering wheel torque control, respectively. Additionally, it analyzes the interference factors of the two control systems and develops a collaborative control strategy for DDAS and AFS; this control strategy establishes a corner motor correction module, steering wheel torque correction module, and assistance correction module. Co-simulation is carried out on Matlab/Simulink and the Carsim platform to verify the correctness of the model under typical working conditions; to reduce the sudden change in the steering wheel torque caused by AFS additional angle interventions; to improve the poor steering characteristics caused by DDAS, introducing additional yaw torque; to greatly enhance the collaborative control effect; and to meet the requirements for vehicle handling stability, portability, and safety. Full article

For autonomous vehicles driving in off-road environments, it is crucial to have a sensitive environmental perception ability. However, semantic segmentation in complex scenes remains a challenging task. Most current methods for off-road environments often have the problems of single scene and low accuracy. Therefore, this paper proposes a semantic segmentation network based on LiDAR called Multi-scale Augmentation Point-Cylinder Network (MAPC-Net). The network uses a multi-layer receptive field fusion module to extract features from objects of different scales in off-road environments. Gated feature fusion is used to fuse PointTensor and Cylinder for encoding and decoding. In addition, we use CARLA to build off-road environments for obtaining datasets, and employ linear interpolation to enhance the training data to solve the problem of sample imbalance. Finally, we design experiments to verify the excellent semantic segmentation ability of MAPC-Net in an off-road environment. We also demonstrate the effectiveness of the multi-layer receptive field fusion module and data augmentation. Full article

Sustainability emphasises the crucial need to incorporate environmentally conscious practises across the entire supply chain management process in the modern age. A great emphasis is placed on minimising environmental consequences, eliminating waste, conserving energy, and sourcing materials responsibly in the production, distribution, and disposal of electric vehicles. Electric vehicle manufacturers must prioritise sustainability to ensure that their products contribute significantly to a brighter future while also meeting the ethical and environmental demands of consumers as well as regulatory bodies. Green supply chain management (GSCM) incorporates environmentally friendly practises to reduce environmental effects. This study incorporates fuzzy TOPSIS for analysing and rating GSCM practises, assisting decision-makers in prioritising sustainability in the supply chains of electric vehicle manufacturers. We develop a multi-criteria decision-making framework to evaluate GSCM criteria while accounting for inherent uncertainty. Fuzzy TOPSIS handles linguistic problems as well as ambiguity while providing a precise GSCM representation. Real-world case studies from various sectors demonstrate the applicability and benefits of our approach to finding improvement areas and expediting GSCM assessments. This research presents a systematic, quantitative way for evaluating GSCM practises, allowing supply chain alignment with sustainability goals. This promotes environmentally sustainable practises and increases the sustainability of supply chains for electric car manufacturing. Full article

Research on manufacturing components for electric vehicles plays a vital role in their development. Furthermore, significant advancements in additive manufacturing processes have revolutionized the production of various parts. By establishing a system that enables the recovery, processing, and reuse of metal powders essential for additive manufacturing, we can achieve sustainable production of electric vehicles. This approach holds immense importance in terms of reducing manufacturing costs, expanding the market, and safeguarding the environment. In this study, we developed an additive manufacturing system for recycled metal powders, encompassing powder variety, properties, processing, manufacturing, component properties, and applications. This system was used to create a knowledge graph providing a convenient resource for researchers to understand the entire procedure from recycling to application. To improve the graph’s accuracy, we employed ChatGPT and BERT training. We also demonstrated the knowledge graph’s utility by processing recycled 316 L stainless steel powders and assessing their quality through image processing. This experiment serves as a practical example of recycling and analyzing powders using the established knowledge graph. Full article

Vehicle dynamics play an important role in determining a vehicle’s stability. It is necessary to identify and obtain models related to vehicle dynamics to evaluate the performance of electric vehicles, as well as how to control them. This paper presents fundamentals of vehicle dynamics, proposing a three-degree-of-freedom nonlinear observer and controller to control lateral velocity and tire torque in comparison to a PID control, while also utilizing a Lyapunov function to determine the stability of the controlled state feedback system concerning the observer, which estimates state errors. This work demonstrates the mathematical development of estimations that will be fed into the algorithms of two active nonlinear controls (state feedback and PID), utilizing the results from Matlab-Simulink simulations of tire torque, lateral and angular velocities based on longitudinal velocity measurements, and employing dynamic gains, such as response to a steering maneuver by the driver following the international standards ISO 7401/2011 and ISO 3888-2. It is concluded that the observer is robust and exhibits energy-saving efficiency in tire torque, even under conditions of variable tire-ground friction. Full article

The adoption of fully battery-powered electric vehicles (EVs) in Kuwait is currently at less than one percent of the population, and hence Kuwait has the lowest index rank among countries around the globe. Effectively, only Kuwaiti nationals have the opportunity to own EVs, as there are no fast direct-current charging stations in Kuwait, and Kuwaiti landlords (expats cannot own real estate) do not allow the installation of EV-charging home wall boxes at their rented properties. Given these facts, this paper only focuses on the preferences of Kuwaiti nationals, even though they only constitute 25 percent of the population of Kuwait. To accomplish the present objectives, a quantitative descriptive method (closed-ended questions) was used to collect data from a sample of 227 Kuwaiti nationals who were representative of the owners of half a million internal combustion engine (ICE) cars and that were categorized as early majority consumers. The findings of the present study indicate that over 50 percent of the respondents would prefer to buy an EV in the following three years when certain criteria were satisfied, including government-controlled pricing policies and recharging point availability, high-speed roads, and free EV-dedicated parking spaces. Furthermore, over 40 percent of respondents stated that they would contemplate purchasing an electric vehicle if the price of gasoline or diesel increased by 19 to 50 percent. The findings also indicated that more than 40 percent of respondents believed that EVs are fire- and crash-safe, and roughly 50 percent of the respondents would be willing to pay between 6 and 20% more for an EV because they believe that EVs are ecofriendlier vehicles and are significantly faster than conventional petrol vehicles. Additionally, respondents rewarded those vehicles with an excellent mark because of their ecological, economical, and technological attributes and benefits. Full article

The armature reaction of the hybrid excitation starter generator (HESG) under load conditions will affect the distribution of the main magnetic field and the output performance. However, using the conventional field-circuit combination method to study the armature reaction has the problem of low accuracy and inaccurate influencing factors. Therefore, this paper proposed a graphical method to analyze the armature reaction and a new type of HESG with a combined-pole permanent magnet (PM) rotor and claw-pole electromagnetic rotor. The analytical formula of the voltage regulation rate under the armature reaction was derived using the graphical method. The main influencing parameters of the armature reaction magnetic field (ARMF) were analyzed, and the overall output performance was analyzed using finite element software. On this basis, comparison analyses before and after optimization and the prototype test were carried out. The results show that the direct-axis armature reaction reactance, quadrature-axis armature reaction reactance, and voltage regulation rate of the optimized HESG were significantly reduced, the output voltage range of the whole machine was wide, and the voltage regulation performance was good. Full article

With the decarbonization of the transportation sector and the diversification of travel demand, the development of shared electric vehicles has become crucial. Based on survey data of travel mode and destination of shared electric vehicles in Beijing, this paper aims to explore the formation and distribution mechanisms of the demand for shared electric vehicles. First of all, a multi-index and multi-cause (MIMIC) model was established to quantify the psychological latent variables that cannot be directly observed and to analyze the mechanisms between individual socio-demographic attributes and latent variables. Secondly, these psychological latent variables were added to mixed logit (ML) models as explanatory variables to form hybrid choice models to explore the travel mode choice behavior and travel destination choice behavior, respectively, when using shared electric vehicles for leisure travel. The results show that potential users of shared electric vehicles are characterized by higher education, employees of enterprises, no car availability and high driving years, and most of them travel for the purpose of connecting to transport hubs. Latent variables such as individual carbon trading, subjective norms, risks and behavioral intentions all affect the demand for shared electric vehicles; in-car time, out-of-car time, travel cost and the number of subway stations have negative impacts on the demand, while mall properties and the number of parking lots have positive impacts on the demand. Furthermore, the use of shared electric vehicles is highly correlated with the use of cars and subways, and part of the travel demand could be transferred to shared electric vehicles by taking certain measures. Full article

The safety performance test of intelligent driving vehicles needs to rely on the collision scenarios in a real road traffic environment. In order to study the collision scenarios and accident characteristics of vehicles and two wheelers (TWs) in line with the complex traffic conditions in China, this paper proposes using clustering analysis to initially cluster traffic accident data to obtain the base scenarios and then applying the association rule algorithm to each base scenario to obtain the potential connection of its accident attributes and describe the collision scenarios in more detail. This study is based on data from 335 vehicle and two-wheeler crashes in the National Automobile Accident In-Depth Investigation System (NAIS). It used clustering analysis to cluster the crash data into different partitions to obtain eight clusters of vehicle and two-wheeler base scenarios and applied association rules to analyze the rest of the accident attributes, revealing common crash characteristics to describe the base scenarios in more detail. In the end, it constructed eleven types of detailed vehicle and two-wheeler collision scenarios covering straight roads, intersections, and T-junctions. The results provide richer and more suitable crash scenarios of vehicles and two wheelers in China’s complex traffic and is an important reference for the development of intelligent driving testing scenarios in the future. Full article

Although the four-wheel steering system expands the flexibility of vehicle control, it also brings the problem of difficult coordination between driver comfort and vehicle stability. To this end, this paper proposes robust coordinated control for a four-wheel steering (4WS) vehicle considering driving comfort and vehicle stability. First, the vehicle dynamics model is constructed to reflect the lateral motion characteristics of a 4WS vehicle. Then, the driver model is coupled into the 4WS vehicle model to describe the driver’s handling characteristics. To suppress the system perturbation caused by the uncertainties of driver behavior and vehicle states, the Takagi-Sugeno fuzzy robust control method is developed to design the human-machine co-driving system. Moreover, the robust positive invariant set theory is used to guarantee the stability and safety constraints of the vehicle. Finally, the proposed human-machine shared robust control for 4WS vehicle is verified through the driving simulator platform. The results indicate that the fuzzy robust shared control approach comprehensively improves the driving comfort, vehicle stability, and path tracking. Full article

To improve energy utilization efficiency and extend the driving range of electric vehicles, this paper proposes a Dual-Motor Coupled Drive System (DMCDS) with a simple structure and establishes a dynamic mathematical model to analyze power flow characteristics under different driving modes. Considering the interdependence between the optimization of component sizes and system control in multi-motor drive systems, a two-layer hybrid optimization method is proposed to determine the optimal component sizes, balancing vehicle performance with minimal system energy losses. To evaluate the effectiveness of the proposed optimization design method, extensive simulation analysis was carried out in MATLAB. The results demonstrate that the optimization of motor sizes and gear ratios can enhance the energy efficiency of the drive system. In comparison with prototype scheme before optimization, the high-efficiency region utilization of motors EM_R and EM_S increased by 45% and 48%, respectively. Compared with the prototype and single-motor drive system, the average drive efficiency after optimization increased by 2.5% and 4.2%, respectively, and the energy consumption per 100 km decreased by 3.6% and 6.8%, respectively. These results confirm the efficacy of the proposed optimization design method in achieving an energy-saving effect. Full article

The drying of electrodes for lithium-ion batteries is one of the most energy- and cost-intensive process steps in battery production. Laser-based drying processes have emerged as promising candidates for electrode manufacturing due to their direct energy input, spatial homogeneity within the laser spot, and rapid controllability. However, it is unclear to what extent electrode and cell quality are affected by higher heating and drying rates. Hybrid systems as a combination of laser- and convection-based drying were investigated in an experimental study with water-processed LFP cathodes. The manufactured electrodes were compared with purely laser-dried and purely convection-dried samples in terms of drying times and quality characteristics. The electrodes were characterized with regard to physical properties like adhesion and electronic conductivity, as well as electrochemical performance using the rate capability. Regarding adhesion and electronic conductivity, the LFP-based cathodes dried in the hybrid-drying process by laser and convection showed similar quality characteristics compared to conventionally dried cathodes, while, at the same time, significantly reducing the overall drying time. In terms of electrochemical performance, measured by the rate capability, no significant differences were found between the drying technologies used. These findings demonstrate the great potential of laser- and convection-based hybrid drying of LFP cathodes to enhance the electrode-drying process in terms of energy efficiency and operational costs. Full article

The electric bus is an extremely important part of urban public transportation and has a huge impact on the ecosystem. However, the battery capacity is still a tough problem, and electric buses often face a booming demand for charging during peak periods. This paper focuses on the problem of electric bus battery swapping station (BSS) location. Based on the traffic flow assignment theory, this paper proposes a hybrid traffic assignment method based on GA and Frank–Wolfe algorithm, which has proved to be closer to the global optimum than the traditional method. This paper proposes a BSS selection model considering service quality as an evaluating indicator and a simulation is made based on a virtual road network. Compared with the traditional method, the result from the hybrid method is more suitable for electric buses when considering the situation at peak hours. Full article

Higher-energy-density, Wh L⁻¹ or Wh kg⁻¹, lithium-ion cells are one of the critical advancements required for the implementation of electric vehicles. This increase leads to a longer drive distance between recharges. Aside from material development, full lithium-ion cell design parameters have the potential to greatly influence fabricated cell energy density. The following work highlights the impact of these full-cell design parameters, investigating the effect of a negative to positive capacity ratio, positive electrode porosity, positive electrode active material content, and overall charge voltage on stack volumetric energy density. Decreasing the N:P ratio or increasing active material content results in an almost identical volumetric energy density increase: ~4%. Decreasing the positive electrode porosity from 40–30% or increasing the charge voltage from 4.2–4.35 V also results in an almost identical increase in volumetric energy density: ~5.5%. Combining all design changes has the potential to increase stack volumetric energy density by 20% compared to the baseline cell design. Full article

Electric motorcycles (EMs) are gaining popularity in densely populated Asian countries, offering environmentally friendly solutions to combat traffic-related pollution. Governments and authorities are eager to promote EMs to reduce reliance on traditional fuel-based motorcycles. While prior research has explored the potential impacts of EMs, limited attention has been given to the adoption intentions of the Pakistani public. This study investigates the factors influencing the behavioral intentions of adopting EMs in Pakistan, employing an extended technology acceptance model (TAM) framework. The extended model incorporates perceived values and environmental concerns, along with perceived usefulness and perceived ease of use, to assess their impact on EM adoption intentions. Based on data collected from 228 respondents in Karachi, Pakistan, structural equation models were estimated to identify significant factors affecting EM adoption. Findings highlight the substantial influence of perceived value and environmental concern on behavioral intentions, with perceived ease of use playing a mediated role through perceived usefulness. Results suggest that effective marketing and user-friendly EM designs, coupled with well-crafted policies and education, can substantially boost EM adoption by the public, facilitating a shift toward sustainable transportation alternatives. Full article

The algorithm function designed in this paper can make a car maintain stability during automatic vehicle hold through the model input of multi-level target fluid pressure combined with slope judgment modules of different levels after the automatic vehicle hold software works. At the same time, a complete parking function module is designed, which can monitor the whole parking process in real time. Through the design of this function, the functional diversity of the electro-hydraulic braking system can be increased. When judging that the driver intends to start, the automatic vehicle hold system will automatically release the fluid pressure according to the opening of the accelerator pedal pressed by the driver so that the vehicle does not happen to brake when the vehicle starts in the slippery slope condition. Finally, real vehicle verification proves that the function can effectively meet the parking requirements and start on the flat and on a ramp. Also, it can effectively control the vehicle according to the driver’s driving intention. Full article

Currently, in the process of autonomous parking, the algorithm detection accuracy and rate of parking spaces are low due to the diversity of parking scenes, changes in lighting conditions, and other unfavorable factors. An improved algorithm based on YOLOv5-OBB is proposed to reduce the computational effort of the model and increase the speed of model detection. Firstly, the backbone module is optimized, the Focus module and SSP (Selective Spatial Perception) module are replaced with the general convolution and SSPF (Selective Search Proposals Fusion) modules, and the GELU activation function is introduced to reduce the number of model parameters and enhance model learning. Secondly, the RFB (Receptive Field Block) module is added to fuse different feature modules and increase the perceptual field to optimize the small target detection. After that, the CA (coordinate attention) mechanism is introduced to enhance the feature representation capability. Finally, the post-processing is optimized using spatial location correlation to improve the accuracy of the vehicle position and bank angle detection. The implementation results show that by using the improved method proposed in this paper, the FPS of the model is improved by 2.87, algorithm size is reduced by 1 M, and the mAP is improved by 8.4% on the homemade dataset compared with the original algorithm. The improved model meets the requirements of perceived accuracy and speed of parking spaces in autonomous parking. Full article

With the increasingly serious problem of environmental pollution, new energy vehicles have become a hot spot in today’s research. The lithium-ion battery has become the mainstream power battery of new energy vehicles as it has the advantages of long service life, high-rated voltage, low self-discharge rate, etc. The battery management system is the key part that ensures the efficient and safe operation of the vehicle as well as the long life of the power battery. The accurate estimation of the power battery state directly affects the whole vehicle’s performance. As a result, this paper established a lithium-ion battery charge state estimation model based on BP, PSO-BP and LSTM neural networks, which tried to combine the PSO algorithm with the LSTM algorithm. The particle swarm algorithm was utilized to obtain the optimal parameters of the model in the process of repetitive iteration so as to establish the PSO-LSTM prediction model. The superiority of the LSTM neural network model in SOC estimation was demonstrated by comparing the estimation accuracies of BP, PSO-BP and LSTM neural networks. The comparative analysis under constant flow conditions in the laboratory showed that the PSO-LSTM neural network predicts SOC more accurately than BP, PSO-BP and LSTM neural networks. The comparative analysis under DST and US06 operating conditions showed that the PSO-LSTM neural network has a greater prediction accuracy for SOC than the LSTM neural network. Full article

The inevitable electrification of the sub-Saharan African paratransit system poses substantial threats to an already crippled electricity supply network. The integration of any electric vehicle fleet in this region will require in-depth analyses and understanding of the grid impact due to charging. This allows informative decisions for sufficient planning to be made for the required network infrastructure or the implementation of applicable ‘load-shifting’ techniques. This paper presents Grid-Sim, a software tool that enables comprehensive analysis of the grid impact implications of electrifying vehicle fleets. Grid-Sim is applied to assess the load profiles, energy demand, load-shifting techniques, and associated emissions for two charging stations serving an electrified minibus taxi fleet of 202 vehicles in Johannesburg, South Africa. It is found that the current operation patterns result in a peak grid power draw of 12 kW/taxi, grid-drawn energy of 87.4 kWh/taxi/day, and, subsequently, 93 kg CO₂/taxi/day of emissions. However, when using the built-in option of including external batteries and a solar charging station, the average peak power draw reduces by 66%, and both grid-drawn energy and emissions reduce by 58%. Full article

With the development of autonomous driving technology, truck platooning control has become a reality. Truck platooning can improve road capacity by maintaining a minor headway. Platooning systems can significantly reduce fuel consumption and emissions, especially for trucks. In this study, we designed a Platoon-MAPPO algorithm to implement truck platooning control based on multi-agent reinforcement learning for a platooning facing an on-ramp scenario on highway. A centralized training, decentralized execution algorithm was used in this paper. Each truck only computes its actions, avoiding the data computation delay problem caused by centralized computation. Each truck considers the truck status in front of and behind itself, maximizing the overall gain of the platooning and improving the global operational efficiency. In terms of performance evaluation, we used the traditional rule-based platooning following model as a benchmark. To ensure fairness, the model used the same network structure and traffic scenario as our proposed model. The simulation results show that the algorithm proposed in this paper has good performance and improves the overall efficiency of the platoon while guaranteeing traffic safety. The average energy consumption decreased by 14.8%, and the road occupancy rate decreased by 43.3%. Full article

Aiming at the problem of insufficient tracking accuracy caused by object occlusion in the process of multi-object tracking, this paper proposes a multi-order semantic fusion pedestrian multi-object tracking network. Firstly, the feature pyramid attention module is used in the backbone network to enlarge the receptive field and obtain more abundant feature information to improve the detection accuracy of different scale objects. Secondly, a size-aware module is integrated into the pedestrian re-identification branch network to fuse semantic features from different resolutions and extract more basic pedestrian features, thereby improving the tracking accuracy. Finally, the detection head is reconstructed and the small object detection layer is fused to make the proposed network adapt to objects of different sizes. Experiments on the MOT16 and MOT17 datasets show that the multi-object tracking accuracy of the proposed network reaches 75.4% (MOT16) and 74.3% (MOT17), which effectively deals with the problem of low tracking accuracy caused by occlusion in the field of autonomous driving, and achieves good tracking results. The network proposed in this paper improves the tracking accuracy of pedestrians and provides a basis for further practical applications. Full article

Large-format lithium-ion (Li-ion) batteries are increasingly applied in energy storage systems for electric vehicles, owing to their flexible shape design, lighter weight, higher specific energy, and compact layouts. Nevertheless, the large thermal gradient of Li-ion batteries leads to performance degradation and irreversible safety issues. The difference in the highest temperature position at various operational modes makes accurate temperature monitoring complicated. Accordingly, a full understanding of the temperature inconsistency of large-format Li-ion batteries is crucial. In this study, these inconsistent characteristics are analyzed by establishing an electrothermal model and conducting experiments based on an 8-Ah pouch-type ternary Li-ion battery with contraposition tabs. Regarding the characteristic of inhomogeneous temperature distribution, the analysis results demonstrate that it is primarily attributable to the uneven heat generation within the battery system and the effects of the two tabs. For the evolution of the highest temperature position, this study compares the maximum temperature rise of the positive tab and main battery body. The results illustrate that the operating temperature has a greater impact on the maximum temperature rise of the main battery body since its resistance strongly depends on the operating temperature compared to the positive and negative tabs. In addition, the electrothermal model is expected to be employed for the battery thermal management system (BTMS) to mitigate the battery temperature inconsistency. Full article

As an important component of new energy vehicles, the safety of lithium-ion batteries has attracted extensive attention. To reveal the mechanism and characteristics of ternary lithium-ion batteries under different trigger modes, an experimental system was established. The effects of different trigger modes on battery surface temperature, battery internal temperature, injection time, and battery voltage were analyzed. Among them, acupuncture, overheating, and overcharging are used as trigger conditions for mechanical, thermal, and electrical abuse. The results show that the injection time and surface peak temperature are positively correlated with the energy input before thermal runaway. Before the cell triggers abuse, the more input energy, the higher the cell surface temperature, the more serious the thermal runaway, and the higher the damage to the surrounding battery system. Under the same conditions, the intensity and damage degree of overcharge thermal runaway are greater than those of internal short circuit and overtemperature. The abnormal change of voltage suddenly rising and rapidly falling can be used as a condition to judge whether overcharge thermal runaway occurs. Finally, according to the temperature curves at different positions, the thermal diffusion law under different abuse conditions is summarized, which provides a basis for the safety design of the battery module. Full article