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 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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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 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.

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.

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.

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 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.

With the intelligent development of new energy vehicles，the number of low-voltage controllers and static power consumption have increased. The traditional management mode is facing problems such as battery depletion and redundant selection，and the extension of international transportation cycles has raised the requirements for static power consumption. The article mainly elaborates on the causes of low-voltage static power consumption during the parking period of new energy vehicles，the power consumption characteristics of the controller，and proposes optimization measures and management methods to achieve refined management，providing a basis for battery selection and meeting customer needs.

Calculus can precisely describe the continuous changes in vehicle energy consumption with operating conditions, and multi-objective optimization algorithms can strike a balance among conflicting objectives, such as range and performance. This article focuses on the energy consumption prediction and energy-saving control of electric vehicles,elaborates on their core demands, and proposes technical paths from three aspects: dynamic prediction modeling, energy-saving control strategies, and full-scenario implementation. It points out that future energy consumption management will evolve towards "predictive energy conservation" and "personalized optimization".

Energy recovery technology can enhance the pure electric range of new energy vehicles and is one of the core technologies of new energy vehicles. This paper systematically analyzes the influencing factors of energy recovery function and introduces the energy recovery control strategy of a hybrid electric vehicle with a series parallel structure. The energy recovery function can be divided into three categories：braking energy recovery，coasting energy recovery，and the switching control between braking energy recovery and coasting energy recovery. Through the development of energy recovery strategies，the fuel economy and pure electric range of the vehicle can be effectively improved.

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.

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.

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.

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.

With the rapid development of intelligent vehicles, the concept of "software-defined vehicles" has increasingly taken root in people's hearts. Under the framework of this concept, only by achieving rapid iterative updates of software can the competitiveness of vehicles be ensured. However, in the increasingly frequent OTA upgrade activities,the overly long OTA upgrade time has suppressed users' willingness to trigger upgrades. Therefore, how to shorten the perceptible upgrade time for users has become the key to enhancing user experience. Based on this, the article proposes an OTA parallel upgrade method based on FA collaborative scheduling, optimizing the original serial upgrade mode to a parallel upgrade. Verified through real vehicle tests, the method proposed in this paper can significantly shorten the upgrade time.

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.

With the increasing popularity of electric vehicles, liquid lithium batteries in power batteries have the risk of fire caused by thermal runaway in application, and the charging speed is difficult to meet the needs of users for rapid energy replenishment, which restricts the further promotion of electric vehicles and the improvement of user experience. Based on this, this paper discusses the technical characteristics of the new solid-state lithium battery, its compatibility with the existing electric vehicle system, the current application bottleneck and its optimization strategy, from the perspective of material innovation and system integration, a feasible development path is proposed to provide a useful reference for the application optimization of solid-state batteries in electric vehicles.

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.

With the rapid development of the automobile industry in the direction of intelligence and networking, as the"Neural network" of the vehicle, the structure of the automobile wiring harness is becoming more and more complex, and the types are becoming more and more numerous, which brings new challenges to the warehouse management of wire harness manufacturing enterprises. This paper first expounds the significance of the introduction of intelligent systems, and then systematically proposes a hierarchical overall architecture design scheme, furthermore, a feasible optimization strategy is proposed from the perspective of intelligent scheduling of work flow and system life cycle maintenance, in order to help enterprises in the fierce market competition through the intelligent innovation of internal logistics to build a solid core advantage of supply chain.

This paper first explains the role of cloud computing in automotive electrical data analysis, and then proposes optimization paths for automotive electrical data analysis empowered by cloud computing, providing a reference for the intelligent development of automotive electrical systems.

This paper proposes a parallel hybrid vehicle AMT transmission shift control optimization method to dynamically learn and adjust shift positions, to avoid wear on the forks, and to improve the reliability of the transmission assembly. The control optimization process is explained in terms of symptoms, cause analysis, structure layout, and test verification.

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.

With the continuous advancement of vehicle electrification and intelligence, traditional low-voltage power distribution technology can no longer meet the complex and variable power demands. This paper proposes a scenario engine-based intelligent low-voltage power distribution technology for vehicles. By real-time sensing of vehicle status, user habits, and environmental information, it dynamically optimizes power distribution strategies to achieve efficient energy allocation. The article elaborates on the technical principles, system architecture, and key technologies of the scenario engine, while analyzing its performance advantages through practical application cases. Research shows that vehicles equipped with this intelligent power distribution technology based on the scenario engine can reduce energy consumption by 15%~30%, significantly improving electric vehicle range and system reliability, providing important references for the future evolution of automotive electrical architectures.

The global new energy vehicle industry is developing rapidly, but the shortage of lithium resources and the rising prices have restricted its sustainable development process. Sodium resources are abundant, low-cost and have good low-temperature performance. Sodium-ion batteries have become an important alternative solution. Studying the optimization of electrode materials and performance matching of sodium-ion batteries is of great significance for promoting breakthroughs in the industry. This article analyzes the advantages of sodium-ion batteries in the application of new energy vehicles, and then conducts an analysis from aspects such as electrode material optimization and performance matching.Sodium-ion batteries have the characteristics of abundant resources, low cost, good low-temperature performance and high safety, and have unique competitiveness in new energy vehicles. After optimizing the structure of the positive electrode material, the composite of the negative electrode material, and the matching of the electrolyte system, the performance of the battery has been improved. According to the characteristics of the operating conditions of new energy vehicles, such as power demand, range requirements, and low-temperature adaptability, sodium-ion batteries have good compatibility in terms of energy density, cycle life, and low-temperature performance. The optimization of electrode materials and system matching are the keys to promoting the large-scale application of sodium-ion batteries in the field of new energy vehicles.The research aims to enhance the application value of sodium-ion batteries in new energy vehicles.