Therefore, the fault diagnosis model based on WOA-LSTM algorithm proposed in the study can improve the safety of the power battery of new energy battery vehicles and reduce the probability of safety accidents during the driving process of new energy vehicles.
Lithium-ion batteries are extensively used in electric vehicles, aerospace, communications, healthcare, and other sectors due to their high energy density, long lifespan, low self
Therefore, the fault diagnosis model based on WOA-LSTM algorithm proposed in the study can improve the safety of the power battery of new energy battery vehicles and
1. Supports various battery pack diagnostic methods including non-standard battery pack connector, jumper cables and 16 PIN OBD. 2. Quick reading for battery pack information, such as number of battery pack modules, SOC, SOH, temperature, single cell''s voltage and temperature of each module, etc., which will help technician to know about the battery status.
The use of electronic diagnostic technology to diagnose and maintain the battery voltage faults of new energy vehicles has various advantages, which can realize the accurate investigation of
Multiple sensors are implemented to monitor the new energy battery, taking measurements of the battery pack''s voltage, current, and temperature, and estimating its State of Charge (SOC) and State of Health (SOH). The data collection was conducted over a seven
Taking the leakage detection of byd-qin hybrid high-voltage system as an example, this paper analyzes the fault generation mechanism and puts forward the detection technology of new energy...
Fault detection and diagnosis (FDD) is a technique to monitor and determine the operating state of an electric motor, which allows early fault detection and prediction. With the use of FDD, various faults can be detected and identified, and by taking proper measures, the safety and reliability of EVs increase [5].
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
Echelon utilization refers to the process of essential detection, classification, and battery repair of retired power batteries of NEVs, intending to apply retired batteries to other fields, such as electric tools, solar/wind energy
In order to ensure the safety and reliability of NEV batteries, fault detection technologies for NEV battery have been proposed and developed rapidly in last few years (Chen, Liu, Alippi, Huang, & Liu, 2022) particular, fault detection methods based on machine learning using information extracted from large amounts of new energy vehicle operational data have
The use of electronic diagnostic technology to diagnose and maintain the battery voltage faults of new energy vehicles has various advantages, which can realize the accurate investigation of voltage faults and provide effective information reference for fault maintenance.
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
An improved target detection model DCS-YOLO (DC-SoftCBAM YOLO) based on YOLOv5 is proposed, which has high target detection model efficiency and meets the requirements of real-time detection of battery collector defects. The future trend in global automobile development is electrification, and the current collector is an essential component of the battery in new energy
DOI: 10.1016/j nengprac.2023.105738 Corpus ID: 264328871; Autoencoder-Enhanced Regularized Prototypical Network for New Energy Vehicle battery fault detection @article{Sun2023AutoencoderEnhancedRP, title={Autoencoder-Enhanced Regularized Prototypical Network for New Energy Vehicle battery fault detection}, author={Gangfeng Sun
As a high-energy carrier, a battery can cause massive damage if abnormal energy release occurs. Therefore, battery system safety is the priority for electric vehicles (EVs) [9].The most severe phenomenon is battery thermal runaway (BTR), an exothermic chain reaction that rapidly increases the battery''s internal temperature [10].BTR can lead to overheating, fire,
As the main component of the new energy battery, the safety vent usually is welded on the battery plate, which can prevent unpredictable explosion accidents caused by the increasing internal pressure of the battery. The welding quality of safety vent directly affects the safety and stability of the battery; so, the welding-defect detection is of great significance. In
Uncovering subtle battery behavior changes for improved fault detection. Specific focus on multidimensional signals to enhance safety strategies. Future trends in battery fault diagnosis driven by AI and multidimensional data.
Taking the leakage detection of byd-qin hybrid high-voltage system as an example, this paper analyzes the fault generation mechanism and puts forward the detection
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...
An end-to-end adaptive and lightweight defect detection model for the battery current collector (BCC), DGNet is proposed, which achieves higher detection accuracy and lower computational overhead, reaching the state-of-the-art (SOTA) level. As an essential component of the new energy vehicle battery, current collectors affect the performance of battery and are crucial to
As an essential component of the new energy vehicle battery, current collectors affect the performance of battery and are crucial to the safety of passengers. The significant differences in shape and scale among defect types make it challenging for the model detection of current collector defects. In order to reduce application costs and conduct real
With a swift detection time of 0.073 seconds per image, the model meets the stringent requirements for accuracy and real-time performance in identifying battery collector tray
Multiple sensors are implemented to monitor the new energy battery, taking measurements of the battery pack''s voltage, current, and temperature, and estimating its State of Charge (SOC) and State of Health (SOH). The data collection was conducted over a seven-month period from ten tested vehicles, with a set sampling cycle that resulted in an
Uncovering subtle battery behavior changes for improved fault detection. Specific focus on multidimensional signals to enhance safety strategies. Future trends in
Fault detection and diagnosis (FDD) is a technique to monitor and determine the operating state of an electric motor, which allows early fault detection and prediction. With the
Although the internal temperature detection of lithium-ion batteries is more reliable than surface temperature detection, surface temperature detection utilizing a thermographic camera, temperature sensor, and other tools is still an efficient, convenient, and low-cost battery temperature diagnosis method [25]. This method is more intuitive and also
Lithium-ion batteries are extensively used in electric vehicles, aerospace, communications, healthcare, and other sectors due to their high energy density, long lifespan, low self-discharge rate, and environmentally friendly characteristics (Xu et al., 2024a).However, complex operating conditions and improper handling can lead to various issues, including accelerated aging,
With a swift detection time of 0.073 seconds per image, the model meets the stringent requirements for accuracy and real-time performance in identifying battery collector tray defects within real-world industrial environments.
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
So far, many data-driven methods, including machine-learning tools, have been used in the case of battery state estimation. Nevertheless, for fault detection, just a few methods based on neural networks, SVM and deep learning are investigated. The conventional methods commonly used for fault detection of EVs are mainly model-based and signal-based.
Due to the same limitations of the model-based and signal-based methods, such as the inaccurate model and very nonlinear characteristic of the lithium-ion batteries, to reach higher accuracies and reliabilities, the data-driven methods and machine-learning-based FDDs are growing rapidly in the case of battery fault detection recently.
Herein, the development of advanced battery sensor technologies and the implementation of multidimensional measurements can strengthen battery monitoring and fault diagnosis capabilities.
There is a lack of research on the coupled evolution of multidimensional states in the battery fault process. Although numerous new sensors are believed to hold potential for early fault diagnosis, they are often applied to monitor different signals of a battery independently.
The presence of the RE serves as a valuable in-situ diagnostic tool in battery research and development, offering the following advantages: (1) Decoupling and distinguishing the potentials of the positive and negative electrodes, allowing for the assessment of each electrode's unique contribution to the overall battery capacity.
EVs’ Battery Fault Detection As it is obvious and has been discussed, the safety and reliability of an EV is one of the main factors affecting the electrification of transportation. The EV battery is one of the major parts in this regard, which can have many limitations.
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