However, if it is well maintained during use, it is also prone to malfunctions, which can affect the practicality of new energy vehicles. This article studies the current status and innovation of fault diagnosis and maintenance technology for new energy vehicles. Based on the actual situation, faults can be divided into two types: mechanical
According to statistics, 60% of fire accidents in new energy vehicles are caused by power batteries. The development of advanced fault diagnosis technology for power battery system has...
According to statistics, 60% of fire accidents in new energy vehicles are caused by power batteries. The development of advanced fault diagnosis technology for power battery
Developing reliable battery fault diagnosis and fault warning algorithms is essential to ensure the safety of battery systems. After years of development, traditional fault diagnosis techniques based on three-dimensional information of voltage, current and temperature have gradually encountered bottlenecks. It is necessary to adopt a proactive
The emergence of new energy vehicles (NEVs) has revolutionized the transportation sector by offering a sustainable and environmentally friendly alternative to traditional fuel-driven vehicles. NEVs have demonstrated remarkable potential in reducing energy consumption and curbing exhaust emissions, thereby contributing to the advancement of a
Big data analysis in New Energy Automobile (NEA) maintenance and fault diagnosis improves efficiency and quality of maintenance, benefiting the future of the automobile industry. Based on big data analysis and combined with the current status of NEA maintenance technical support and fault diagnosis, the paper provides an in-depth analysis of the application value of big data in
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explore the new technology of fault diagnosis and maintenance of new energy vehicles, especially the use of electronic diagnosis technology for the battery voltage fault diagnosis, to help the development
explore the new technology of fault diagnosis and maintenance of new energy vehicles, especially the use of electronic diagnosis technology for the battery voltage fault diagnosis, to help the
DOI: 10.25236/ajets.2023.060904 Corpus ID: 261499317; Battery voltage fault diagnosis mechanism of new energy vehicles based on electronic diagnosis technology @article{Sun2023BatteryVF, title={Battery voltage fault diagnosis mechanism of new energy vehicles based on electronic diagnosis technology}, author={Baowen Sun}, journal={Academic
In this regard, this article diagnoses new energy vehicle faults based on Markov models and designs maintenance algorithms. According to the switch closure characteristics of relay devices, various relay fault modes are analyzed, and relay fault diagnosis is clearly handled. This enables new energy vehicles to more accurately identify the
The proposed WOA-LSTM fault diagnosis model can reduce the diagnosis time and cost of battery fault diagnosis, improve the diagnosis accuracy, and achieve the
In this regard, this article diagnoses new energy vehicle faults based on Markov models and designs maintenance algorithms. According to the switch closure characteristics of relay
The proposed method can efficiently and accurately detect internal short-circuit faults and has great potential for application in fault diagnosis of large energy storage battery
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The proposed WOA-LSTM fault diagnosis model can reduce the diagnosis time and cost of battery fault diagnosis, improve the diagnosis accuracy, and achieve the purpose of optimizing the diagnosis model. The innovation of this research is to integrate WOA and LSTM algorithm, obtain the optimal solution by simulating the principle of whale hunting
The proposed method can efficiently and accurately detect internal short-circuit faults and has great potential for application in fault diagnosis of large energy storage battery packs. Meanwhile, Tran et al. proposed a real-time model-based sensor fault detection and isolation scheme for lithium-ion battery degradation [ 161 ].
The experimental results show that the application of big data can reduce the failure rate of the battery system to a minimum of 11%, the power system to 10%, and the work efficiency to 89.5%, laying a good foundation for the healthy development of the NEA industry.
Research on Battery Management System and Common Fault Diagnosis and Maintenance Countermeasures of New Energy Vehicles
2 Key Points of Application of Electronic Diagnostic Technology in the Maintenance of New Energy Vehicles fault diagnosis of electronic control system New energy vehicles mainly rely on the circuit system for the whole vehicle control, so the electronic control system is very complex, if there is a fault is also need accurate diagnosis to find out the
Once the failure occurs, it will seriously affect the reliability of new energy vehicles, which is not conducive to the service of new energy vehicles. It is necessary to cooperate with maintenance and fault diagnosis technology to achieve fault disposal, so as to improve the serviceability of new energy vehicles. Therefore, this paper analyses
In this context, this study delves into the application of electronic diagnosis technology for the precise identification of battery voltage faults in NEVs, aiming to foster the
In this context, this study delves into the application of electronic diagnosis technology for the precise identification of battery voltage faults in NEVs, aiming to foster the continued growth of the NEV sector
The new energy vehicle system is in the initial stage of application, so the probability of fault is greater. Therefore, its reliability urgently needs to be improved. In order to improve the fault diagnosis effect of new energy vehicles, this paper proposes a fault diagnosis system of new energy vehicle electric drive system based on improved machine learning and
Developing reliable battery fault diagnosis and fault warning algorithms is essential to ensure the safety of battery systems. After years of development, traditional fault
Developing reliable battery fault diagnosis and fault warning algorithms is essential to ensure the safety of battery systems. After years of development, traditional fault diagnosis techniques based on three-dimensional information of voltage, current and temperature have gradually encountered bottlenecks.
Yet the faults of batteries are coupled with each other, and the actual faults usually are the simultaneous occurrence of multiple faults, so the combination of information fusion technology and battery system fault diagnosis is the future tendency. The advantages and disadvantages of data-driven fault diagnosis methods are compared in Table 7.
The integration of battery management systems (BMSs) with fault diagnosis algorithms has found extensive applications in EVs and energy storage systems [12, 13]. Currently, the standard fault diagnosis systems include data collection, fault diagnosis and fault handling , and reliable data acquisition [, , ] is the foundation.
In addition, Zhou et al. also performed real-time fault diagnosis for battery open faults based on a dual-expansion Kalman filtering method, which uses only the current of the battery pack and the terminal voltages of the parallel battery modules in addition to other sensor data .
In battery system fault diagnosis, finding a suitable extraction method of fault feature parameters is the basis for battery system fault diagnosis in real-vehicle operation conditions. At present, model-based fault diagnosis methods are still the hot spot of research.
Remedial measures include controlling the charging rate, performing battery equalization, regular inspection and maintenance and controlling the depth of discharge to effectively manage the charging and discharging state of the battery system.
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