To meet the navigation needs of intelligent mobile robots in simple environments, an intelligent mobile platform control system based on the STM32F103C8 microcontroller was designed. This system integrates ultrasonic sensors, four infrared sensors, and photoresistors, and uses a TB6612FNG driver chip to control four-wheel DC geared motors. Remote control via Bluetooth is achieved through a mobile phone, enabling functions such as autonomous obstacle avoidance, infrared tracking, light source tracking, and Bluetooth remote control. Test results show that the obstacle avoidance success rate reaches 90%, the tracking accuracy is controlled within 5 cm, the light tracking response delay is less than 0.5 s, and the Bluetooth control delay is less than 0.3 s. This system provides a low-cost verification platform for the development of environmental perception modules in automotive driver assistance systems.

This study is grounded in the publicly accessible data from the NHTSA Autonomous Vehicle Accident Reports spanning from 2021—2025[1]. It undertakes a risk characteristic analysis of typical operating conditions of intelligent driving within complex traffic scenarios. Specifically, three typical operating conditions are investigated: intersections, high density scenarios, and mixed-driving scenarios. Risk characteristics are extracted, and the causes of accidents are analyzed in depth. The research reveals that distinct operating conditions exhibit unique risk patterns. For the intersection operating condition, the critical risk factors include non-compliant behaviors such as running red lights and making illegal turns. In the high-density traffic lane changing operating condition, the primary risk factors are insufficient vehicle spacing and frequent lane changing maneuvers. In the lane changing operating condition of the mixed driving scenario, the risk factors are attributed to the behavioral disparities between human driven and autonomous vehicles.This research offers data support and a decision-making foundation for enhancing the safety of intelligent driving in complex traffic scenarios. It is conducive to making targeted improvements to intelligent driving algorithms and traffic management strategies.

This study analyzes the energy density balancing mechanism of BYD Han EV's blade battery system:Adopting long strip-shaped cell design and module-free integration, volume utilization rate is significantly improved, with first-generation energy density of 140Wh/kg and second-generation reaching 190Wh/kg (lab data 210Wh/kg). Integrated with direct cooling thermal management and BMS active balancing, the temperature difference is controlled below 5℃ ,and cycle life exceeds 3000 times. Experimental data shows 92% range retention at -30℃ and 800V platform enabling 15-minute fast charging to 80%. The research reveals that through structural optimization with intelligent algorithms, the blade battery achieves balance between energy density, safety, and lifespan, providing references for high-energy-density battery design.

The problem of high voltage failure to power on BYD Qin EV pure electric new energy vehicles occurs from time to time, which has a significant impact on the normal use of the vehicle. Based on this, this article introduces the high-voltage system structure and control logic of BYD Qin EV, analyzes the types of high-voltage non power on faults, explains the diagnostic process and methods of such faults, and proposes common fault causes, manifestations, and maintenance methods based on practical cases.

This paper summarizes the current electric distribution mode based on 12V in Electric Vehicle (EV) and Hybrid Electric Vehicle (HEV) Zonal architecture, and analyzes the advantages and disadvantages of the 12V system. Then, the evolution of distribution mode of 48V based power system in Zonal architecture is analyzed, including the aspects of DC-DC conversion efficiency and cost, eFuse and harness use are comprehensively analyzed, and the advantages of 48V system and the challenges faced by future applications are summarized.

The service life attenuation of lithium batteries for new energy vehicles is highly correlated with the material structure, interfacial stability, thermodynamic environment, and operating conditions. Its performance retention capability directly affects the vehicle range, safety, and full life cycle cost. Focusing on the key technical chains such as electrode force-chemical attenuation, lithium plating side reaction expansion, thermal field gradient stress, interface aging caused by excessive voltage, and cyclic loss caused by high-rate fast charging, this paper establishes a systematic analysis framework for the life-impact mechanisms. On this basis, engineering solutions for life extension are proposed from five aspects: electrode structure enhancement, interface regulation, thermal control equilibrium, voltage window management, and dynamic charging control. This provides an operable technical path for the safe and reliable operation of power batteries and the establishment of a long-life system.

This paper systematically analyzes the key technologies, user demands, and policy environment that drive the innovation of exterior design for new energy vehicles. It explores core innovative elements such as closed front faces, intelligent interactive light language, aerodynamic optimization, lightweight materials and structures, and personalized colors and materials. It also looks forward to four major trends in the future exterior design of new energy vehicles: the widespread adoption of emotional and lifelike design languages, the extensive application of active aerodynamic systems,the deep integration of external user interfaces, and the comprehensive penetration of sustainable design concepts.

High-voltage system insulation faults represent a critical issue threatening the safety and user experience of new energy vehicles (NEVs). The traditional passive after-sales model struggles to address their suddenness and complexity, necessitating a transition towards a data-driven active service paradigm. This paper focuses on the high-value scenario of insulation fault early warning, systematically constructing an integrated after-sales big data platform encompassing data collection, real-time analysis, intelligent decision-making, and closed-loop management. The paper elaborates on the platform's layered architecture, including the data, computing, analytics, and application layers, and specifically investigates the development path of an early warning model that integrates deterministic rules with machine learning algorithms. By detailing the entire business closed-loop process from warning discovery and remote diagnosis to decision-making and push-notification, the study validates the platform's effectiveness and practical value in achieving early fault identification, precise intervention, and process optimization. It provides a key methodology and implementation case for automotive manufacturers building intelligent after-sales systems.

To address the concerns about the range anxiety and low energy recovery efficiency of electric vehicles and hydrogen fuel cell-powered commercial vehicles, this paper, based on the downhill sliding characteristics of the vehicle and the current operating status of the hydrogen fuel cell stack, proposes an energy recovery control method based on Predictive Cruise Control (PCC). This method relies on intelligent connected products to obtain information such as the slope and length of the road ahead, and transmits it through the Advanced Driver Assistance Systems Interface Specifications (ADASIS) v2 protocol to the Vehicle Control Unit (VCU). The VCU then adjusts the speed entering and exiting the slope based on the vehicle's operating status and simultaneously optimizes the power of the hydrogen fuel cell stack, making full use of inertia to climb the slope and reduce energy consumption. The multi-steep slope section test in Mohe verified that this method can significantly improve energy recovery efficiency and reduce the vehicle's energy consumption, providing technical support for the improvement of the range of electric and hydrogen fuel cell-powered commercial vehicles.

As electric vehicles rapidly develop, the safety and operational monitoring of charging stations have become pressing issues requiring urgent solutions. This article designs and implements an STM32-based intelligent charging station monitoring system. Adopting a three-layer IoT architecture, including an embedded lower-level machine, multi-sensor modules, wireless communication modules, and a mobile app upper-level machine, the system enables realtime data acquisition, anomaly handling, and closed-loop control for temperature, smoke, carbon monoxide, voltage/current, and vehicle traffic. System software, developed in C language, employs threshold logic for automatic control of fans, relays, and billing functions. The Wi-Fi module enables bidirectional communication between the lower-level device and the mobile app. Measurement results demonstrate the system's stability in monitoring multi-channel data, timely response to anomalies, and accurate execution of identity verification and billing. This ensures safe, intelligent, and efficient charging processes, providing a reliable technological foundation for remote monitoring and smart management.

With the continuous rise in the penetration rate of the new energy vehicle market, the reliability and efficiency of the drive system have increasingly become the core focus of the industry. As the key component of the drive system, the performance of the motor controller's control algorithm directly affects the power performance, economy, and ride comfort of the vehicle under various operating conditions. In typical operating conditions commonly encountered by new energy vehicles, such as low-speed high-load, high-speed cruising, and sudden acceleration/deceleration, the traditional vector control algorithm exhibits many prominent problems, including obvious torque ripple, significant decline in system efficiency, and lagging dynamic response, which severely restrict the improvement of vehicle performance and driving experience. To address these issues, this paper proposes a multi-mode adaptive control algorithm based on operating condition identification, which realizes dynamic adjustment of control strategies according to the characteristics of different operating conditions. Verified by both simulation analysis and bench tests, the optimized algorithm can effectively smooth the motor torque output, significantly reduce the iron loss and copper loss of the motor, and greatly improve the comprehensive efficiency of the drive system. Meanwhile, it plays an important role in enhancing the ride comfort and dynamic responsiveness of the vehicle, providing an effective solution for the upgrading of new energy vehicle drive control technology.

Aiming at the technical bottlenecks of traditional new energy vehicle battery management systems (BMS) in terms of monitoring accuracy, thermal management performance, energy balancing efficiency and operational reliability, this paper conducts multi-objective optimization design. By building a precise monitoring system, developing an air-cooled-liquid-cooled coupled thermal management system, optimizing the bidirectional DC/DC energy balancing technology and modular redundant architecture, and combining simulation analysis and real vehicle testing to verify the optimization effect. The results show that after optimization, the fault diagnosis accuracy of BMS has increased to 95.3%,the maximum battery temperature has decreased by 15.2 ℃ , the energy balancing efficiency has increased by 41.6%, the system communication bit error rate has decreased to below 10-6, and the operational reliability has increased by 23%.The research results have passed the ISO 26262 functional safety certification, which can provide theoretical and practical support for the technological upgrade of BMS and meet the conditions for industrial promotion.

Currently, the thermal management technology of intelligent vehicles is evolving from a single heat dissipation function to a key system for enhancing vehicle range, performance and safety. This article systematically reviews the development history of motor and electronic control thermal management technology, deeply analyzes the new demands of the industry, and elaborates in detail on the progress of core technologies from three dimensions: intelligent collaborative control strategies, efficient composite heat dissipation technology, and deep system integration and waste heat recovery and utilization. It also verifies the effectiveness of these technologies by analyzing the technical solutions of mainstream manufacturers such as Tesla and BYD. The research has summarized four major development trends in this field: integration, high efficiency, intelligence, and refinement. Meanwhile, it conducts a systematic outlook and analysis of the cutting-edge technological development trends in areas such as phase change material research and development, bionic heat dissipation design, and AI algorithm application.

As China continues to promote the development of the new energy vehicle industry, higher requirements have been put forward for the testing and debugging, management and monitoring of the whole vehicle and its components. To further meet the demand for real-time monitoring of the motor performance of new energy vehicles, improve the efficiency and convenience of monitoring, and enable the system to better serve the monitoring and management of drive motors in new energy vehicles, this paper discusses the research and design of a remote testing and monitoring system for new energy vehicle drive motors based on a cloud platform. The system can meet the real-time monitoring needs of new energy vehicle motor performance testing, improve monitoring efficiency and convenience,facilitate internal management and monitoring of laboratories, grasp the operation status of drive motor test benches in real time, query test status, and promote the development of modern laboratories towards intelligent and transparent management. Meanwhile, it is conducive to exploring the emerging "Internet +" model, reducing personnel costs, and promoting the transformation and upgrading of the quality inspection and testing industry.

This study focuses on enhancing the electrical contact reliability of electric vehicle charging interfaces and constructing a fault prediction model. By analyzing the failure mechanisms of electrical contacts, reliability improvement schemes such as modular wiring harness design and anti-corrosion coating application are proposed. A dynamic monitoring method for contact resistance is established based on IEC standards. A GCN-LSTM deep learning model is constructed to integrate current/voltage time-series data with user behavior features, enabling early fault warning.Experimental validation demonstrates that the model achieves an accuracy of 88.12% and an F1 Score of 0.844 in charging pile fault diagnosis, outperforming traditional models. This research provides theoretical support and technical pathways for intelligent operation and maintenance of charging facilities, which is of great significance for ensuring the healthy development of the new energy vehicle industry.

To analyze the charging power matching of battery electric vehicles (BEVs) at normal temperature and improve relevant testing methods, this study conducted tests on the charging process of five BEV models under charging piles of different specifications in a normal-temperature experimental environment. Before the test, the vehicles were discharged to 10% State of Charge (SOC) and left to stand for more than 12 hours. Parameters including charging power and vehicle demand power in the SOC 10%~80% (fast-charging range) and SOC 10%~100% (full-charging range) were collected and analyzed, and the power response coefficient k was proposed to comprehensively evaluate the charging adaptability of the models.

In response to the demand for domestic electric vehicles and charging piles to enter the overseas market, this paper focuses on the dynamic output characteristics of CCS charging piles. Using Keysight's SL1040A test equipment, data are collected from 9 models of CCS2 DC charging piles in different phases—voltage ramp-up, current ramp-up, stable output, and discharge phases. Then it analyzes the rate characteristics, deviation values, and accuracy performance of each phase, providing data support for the protocol adaptation of domestic electric vehicle charging systems and the design of domestic charging piles for overseas markets. Meanwhile, combined with the latest AFIR Regulation (Alternative Fuels Infrastructure Regulation) issued by the European Union, this paper conducts research and analysis on the export policies of the new energy charging pile industry.

With the rapid development of the new energy vehicle (NEV) industry, standardization of battery fault diagnosis and after-sales services has become crucial for ensuring vehicle safety and enhancing user experience. This study explores five dimensions: battery fault types, diagnostic technologies, standardized design of after-sales processes, quality management systems, and practical case studies. It proposes a multi-dimensional data fusion-based fault diagnosis model and a full-process standardized service solution, providing theoretical support and practical references for the industry.

With the rapid development of the automotive industry and the increasing demand for road traffic safety, the automotive lighting system has evolved from a simple lighting tool into a key component for ensuring driving safety. Therefore, this paper conducts research on an intelligent automotive lighting control system based on a single-chip microcomputer, aiming to design a solution that is both practical and economical. The system uses the STC89C52 single-chip microcomputer as the control core, perceives ambient light intensity, the distance of obstacles ahead, and the vehicle's steering state through multi-sensor fusion, realizes adaptive adjustment of lights in combination with intelligent control algorithms, and improves the system's stability in the complex electromagnetic environment of automobiles through hardware optimization and software anti-interference design.

This paper focuses on the faults of power batteries in new energy vehicles. It first analyzes their core characteristics and systematically elaborates on the application logic and operational key points of three key technologies: on-board real-time diagnosis, offline diagnosis, and intelligent diagnosis. Ultimately, a standardized troubleshooting process is constructed, which includes initial screening, in-depth detection, fault repair, and effect verification. The research aims to provide references for improving the efficiency of power battery fault handling and ensuring the safe operation of new energy vehicles.

The Battery Management System (BMS) is a critical control unit for ensuring the safe and efficient operation of electric vehicle batteries. Its electronic control component utilizes precision sensing, data acquisition, and algorithmic decision-making to achieve battery status monitoring, energy scheduling, and safety protection. Research indicates that optimizing the BMS's electronic control architecture and algorithm design can significantly enhance battery pack consistency, prolong battery life, and reduce system energy consumption. This paper focuses on electronic control as the core, systematically analyzing the BMS's structural composition, control mechanisms, and optimization design approaches, providing technical support and theoretical foundations for reliable electric vehicle operation.

Electromechanical Braking (EMB) technology is driving a generational shift from hydraulic to brake by-wire systems. Targeting autonomous driving requirements, this study employs theoretical comparison and empirical analysis to reveal EMB’s triple breakthroughs over hydraulic systems.Structural Reconstruction: Electromechanical direct-drive architecture eliminates hydraulic pipelines, reducing components by 40% and mass by 24.1%.Performance Leap: Response time ≤100ms 30% faster), control precision ±0.01MPa (85% higher), reducing collision kinetic energy by 62% in AEB scenarios.Energy Efficiency Innovation: Enhancing endurance by optimizing drag torque. Localization practices confirm feasibility: xxx company curbs torque attenuation at -40℃ (≤8%). The new regulation GB 21670—2025,to be implemented on January 1st, 2026, will accelerate industrial substitution. Future efforts must resolve redundancy costs challenges toward integrated "brake-drive-steer" smart actuators.

Charging efficiency and battery degradation will continue to restrict the development of pure electric passenger vehicles, and the battery swapping mode is a feasible solution to tackle the above challenges. This paper systematically analyzes the potential advantages and actual predicaments of the battery swapping mode, summarizes the key requirements for the planning and engineering design of battery swapping stations, and designs a ground-sunken battery swapping station. This design will increase the ground clearance of the chassis by three times, providing the necessary space for the automated circulation of batteries and thereby addressing the safety and user experience issues caused by lifting vehicles in traditional battery swapping. In addition, by optimizing the battery compartment plug-in mechanism, the battery capacity and ultimate service capability have been increased by 50% within the same space. The research will elaborate on the system architecture and operational mechanism, explore the technological evolution path, and provide decision support for the large-scale application of battery swapping mode and industrial progress.

With the continuous iteration of power battery technology for new energy vehicles, the control performance and reliability of the battery thermal management system have become the core factors restricting the range and safety performance of new energy vehicles. The electric water pump, as a key component of the thermal management system, and its drive control technology directly determines the operational efficiency and stability of the thermal management system. This paper focuses on the core component of the electric water pump drive chip, combines actual application schemes, systematically analyzes its technical requirements and selection logic, and mainly studies the key indicators such as reliability, temperature adaptation range, control accuracy, integration degree, and control algorithm of automotive-grade drive chips. It compares the technical advantages and disadvantages of three mainstream architecture schemes: fully integrated, pre-drive+separated power devices, and "MCU+"; verifies the application value of sensorless field-oriented control algorithm, and analyzes the technical implementation effectiveness through typical chip case studies. The research results show that: the high-integration single-chip solution has become the current mainstream selection; the wide temperature working range of -40~150℃, complete protection mechanism, and low power consumption characteristics are the core performance requirements of the drive chip; algorithm hardwareization and intelligent adaptive control will be the core direction of future technological evolution. The research in this paper can provide a reference for the selection design and technological innovation of the drive chip for electric water pumps in power battery.

Plug-in Hybrid Electric Vehicle (PHEV) achieves a balance among power performance, economy and environmental protection by integrating the advantages of internal combustion engines and electric motors. As a key technology in PHEV research and development, the matching of power system parameters and the simulation technology of vehicle performance directly determine the comprehensive performance and market competitiveness of its products. This paper systematically expounds the method of power system parameter matching, deeply integrates professional simulation tools such as MATLAB/Simulink and AVL-Cruise, and constructs a multi-dimensional vehicle performance simulation system architecture covering power performance, economy and thermal management. Through empirical analysis of typical engineering cases, the optimization efficiency of parameter matching on vehicle performance is quantitatively verified, and the future development trend of this technology is discussed.

Aiming at the longitudinal force control problem of pure electric pickup trucks under low-speed creep conditions, in order to improve control accuracy, driving smoothness and anti-disturbance robustness, this paper proposes a dynamic constraint model predictive control strategy integrating real-time slope feedforward. Firstly, establish a discrete state space model of the vehicle's longitudinal dynamics considering multi-source drag coupling; Secondly, a multi-objective cost function is constructed with speed tracking error, torque change rate and energy consumption as the optimization objectives. The core innovation lies in the design of a dynamic constraint adjustment mechanism based on slope feedforward. By estimating the slope resistance in real time and dynamically correcting the motor torque constraint boundary, the robustness of the controller under large slope disturbances and the feasibility of solving optimization problems are enhanced. Finally, a real vehicle test platform was built based on domestic pure electric pickup trucks to complete the verification. The results show that compared with the traditional proportional-integral-differential controller,the proposed MPC controller,reduces the average absolute error of speed tracking by 53.1% and the fluctuation of torque change rate (characterizing driving smoothness) by 52.4%. It shows excellent dynamic response characteristics under both slope and load change conditions, which can significantly improve the comprehensive control quality under creep conditions and has engineering application value.

This article investigates a front-wheel-drive battery electric vehicle. Using the coast-down resistance curve obtained from on-road tests, energy-flow measurements were conducted on a chassis dynamometer under both a standard ambient driving cycle and the subsequent ambient-temperature charging process. The results indicate that the efficiencies of the electric drivetrain, on-board charger, and battery pack during charge/discharge are within normal ranges, whereas the brake-energy recuperation rate is low and losses due to rolling resistance and aerodynamic drag account for a significant proportion of the total energy expenditure. Without altering the exterior styling, the optimisation effort therefore focused on reducing the tyre rolling-resistance coefficient and recalibrating the coast-down braking-torque map. Experimental validation shows that the recovered brake energy increased from 10.689 kW·h to 12.427 kW·h a gain of 16.3% and the driving range improved from 389 km to 410 km, corresponding to a 5.4 % enhancement.

The precise estimation and optimization of the driving range of electric vehicles is a key issue in the development of the new energy vehicle industry, which is influenced by multiple dimensions such as the driver's driving characteristics, battery states, environmental factors, and energy management strategies. This article takes AI algorithms as the core to explore their application in the field of electric vehicle driving range. It focuses on analyzing the impact of drivers' driving characteristics on driving range and AI modeling methods, sorts out the technological development of AI algorithms in driving range estimation, and elaborates on the practical application path through relevant cases. Finally, it looks forward to the challenges and future directions in this field, and provides a reference for the optimization of electric vehicle range technology.

Under the background of iterative 800 V high-voltage SiC electric drive platform, motor controllers face research pains, such as complex multi-physical field coupling, long bench test cycle, and high costs for extreme condition tests. Based on the AI digital twin five-dimensional model, this paper constructs a new research paradigm of "virtual rehearsal-bench verification-data loop", and builds a virtual calibration platform for electromagnetic-thermal-control multiphysical fields. It integrates the AI generative Hardware-in-the-Loop (HIL) testing framework and the Extended Kalman Filter (EKF) real-time junction temperature identification algorithm, and conducts verification based on the 800 V SiC three-level motor controller project. The results show that more than 80% of calibration parameters can be pre-simulated, reducing bench calibration workload by 75%; AI automated testing increases the efficiency of HIL scenario generation by 300%, and the single-wheel verification cycle is shortened by 35% to 40%; EKF integrates Foster's thermal network to achieve online estimation of power module junction temperature, with dynamic error <5 ℃ and response time <100 ms. The overall solution compresses the controller development cycle from 18~24 months to 12~15 months, reduces prototype iterations by 50%, and we advance the CLTC weighted efficiency compliance milestone from the 10th month after project initiation to the 6th month. Compared with enterprise projects such as Volkswagen and dSPACE, this paradigm has general applicability in the power electronics field and can provide a reusable digital technology path for the low-cost and rapid engineering implementation of high-voltage SiC motor controllers.

With the rapid popularization and large-scale development of China's electric vehicle industry, the problem of electromagnetic radiation interference from electric vehicles has become increasingly prominent, and it is more severe compared to traditional fuel vehicles, directly threatening the safety and reliability of vehicle operation. To clarify the main sources of electromagnetic radiation interference from electric vehicles, this study adopts a combination of literature research and laboratory testing techniques to conduct special research on Electromagnetic Compatibility (EMC) and system testing analysis on key electronic components and core systems of electric vehicles, and then proposes targeted and implementable measures for electromagnetic radiation interference control. The research results can provide reliable technical basis for the optimization of EMC design of electric vehicles and are of great significance for reducing the adverse effects of electromagnetic radiation on the vehicle itself and the surrounding environment.

This article explores the root causes of range capability decline from the perspectives of power battery lifespan evolution mechanisms, electric drive system energy consumption characteristics, and vehicle thermal management mechanisms. Based on measured data, it proposes maintenance optimization solutions, including a battery state of health assessment model, thermal management system maintenance strategies, and electric control parameter calibration optimization strategies. These measures provide technical support for the after-sales maintenance system and enhance the operational efficiency of vehicles throughout their entire lifecycle.

Elderly users face a significant "digital divide" when interacting with automotive human-machine interfaces. The emergence of embodied intelligence offers innovative ideas for developing interfaces that better align with the natural cognition and interaction habits of elderly users. This study, from the theoretical perspective of embodied intelligence, systematically analyzes the driving characteristics of elderly users and their core interaction needs and pain points in in-vehicle scenarios through user research. Furthermore, it proposes a set of age-friendly design principles for automotive HMI based on embodied intelligence, providing a theoretical foundation and practical approach for creating inclusive, safe, and enjoyable next-generation automotive interaction experiences.

As the primary mode of transportation, automobiles may develop electrical system failures during prolonged use that impair the proper functioning of electronic components. These malfunctions manifest in diverse forms with varied root causes, making the selection of appropriate diagnostic techniques and repair methodologies crucial. This article examines common electrical system failures, outlines diagnostic strategies, and proposes practical maintenance solutions to enhance repair efficiency and quality. The study provides actionable frameworks for implementing effective fault diagnosis and repair practices in automotive engineering.

In the current complex Internet of Vehicles environment, the contradiction between the uncertainty of wireless channels and the certainty of control systems has become extremely acute. Therefore, this paper launches a systematic study on the bottleneck mechanism and multi-dimensional enhancement path of 5G URLLC communication performance for intelligent connected vehicles under complex dynamic scenarios, with the expectation of providing theoretical support and technological paradigm for the construction of a resilient communication network architecture that integrates vehicle, road, and cloud, as well as for the application of L4-level or higher autonomous driving technology on the ground.

Under the guidance of the "dual carbon" goals, the energy consumption driving range performance of electric light trucks have become key areas for technological breakthroughs. This paper employs a coupled approach based on rolling resistance and energy recovery strategies to conduct energy consumption, range tests, and simulation analyses on a prototype electric light truck. The results show that under both 40 km/h constant-speed and complex driving conditions, energy consumption and driving range improved. Low-resistance design significantly enhances driving range, while high-energy recovery intensity, although reducing energy consumption, has a negative impact on driving experience.

With the rapid development of the new energy vehicle industry, the AC charging system of pure electric vehicles, as a key link in the energy supply of electric vehicles, its stability and safety directly affect the entire new energy vehicle industry. This paper conducts an in-depth analysis of the working logic of the AC charging system for pure electric vehicles, systematically sorts out the technical problems and fault types existing in the current charging system, and proposes corresponding optimization strategies in combination with actual application scenarios.

To adapt to the centralized computing electronic and electrical architecture under the trend of "software-defined vehicles", this paper conducts adaptive development and optimization design for the digital key system. First, sort out the evolution logic of automotive electronic and electrical architecture from distributed, domain control to vehicle-cloud integrated centralized (EEA 3.0); Furthermore, the pain points existing in the traditional digital key system under the distributed architecture, such as functional independence, insufficient log storage, and weak data collaboration ability, are analyzed, and an adaptation solution based on the EEA 3.0 architecture is proposed.

With the development of the "New Four Modernizations" (electricization, intelligence, networking, and sharing), automotive test development technology is facing new challenges. Based on the significance of automotive test development, this paper systematically analyzes the development path of automotive test development technology, focusing on the key technical points of core areas such as the test of the three core electric systems (battery, motor, and electronic control) and safety tests. In response to the test changes brought by new energy vehicles and intelligent driving technologies, this paper proposes countermeasures. The research results show that the collaborative optimization of virtual simulation technology, intelligent testing equipment, and big data platforms is the key direction for the development of test technology. The automation, intelligence, and digitalization of the test development system will help the domestic automotive industry gain a leading position in the fierce global competition.

This paper focuses on the problem of sheet metal cracking near the weld points in the side panel lock installation area of a new energy vehicle during the rear door opening and closing durability test (25,000 cycles). The rear half of the vehicle body is taken as the research object, and the fatigue stress distribution in this area is analyzed by combining tests with Finite Element Analysis (FEA). Aiming at the problem that the fatigue performance of the solder joint area is inferior to that of the general structural area, five improvement schemes centered on solder joint adjustment and structural adhesive addition are proposed. The durability performance of each scheme is evaluated through simulation analysis, and the optimal scheme is verified through bench tests. The results show that the hazardous areas identified by the finite element analysis are consistent with the crack areas in the test. The final selected scheme of "adding structural adhesive on the basis of the original scheme" reduced the stress of the side panel to 124MPa (lower than the elastic limit of 180MPa of DC06 cold-rolled steel plate), with over 50,000 test cycles, meeting the requirements of opening and closing durability, while also taking into account cost and process, providing a reference for similar body fatigue problems.