A novel intelligent method for fault diagnosis of electric vehicle
This paper proposes a method of fault detection of Lithium-ion batteries based on wavelet-neural for guaranteeing the safety and reliability of electric vehicles (EVS).
Survey of Lithium-Ion Battery Anomaly Detection Methods in
This paper provides a comprehensive review of the anomaly types and detection methods for lithium-ion batteries in electric vehicles. We classify battery anomalies into energy
Fault detection method for electric vehicle battery pack based on
DOI: 10.1080/15435075.2024.2422463 Corpus ID: 274018926; Fault detection method for electric vehicle battery pack based on improved kurtosis and isolation forest @article{Wu2024FaultDM,
A novel semi-supervised fault detection and isolation method for
This paper proposes a semi-supervised fault detection and isolation method for vehicle battery systems, which can accurately detect and isolate early or minor short-circuit
Modified Relative Entropy-Based Lithium-Ion Battery Pack Online
The curve of voltage range for the selected period. The data of vehicle No.9 was collected from 17:58:35 on June 13, 2020 to 06:38:29 on November 17, 2020 with a
A method for battery fault diagnosis and early warning
1 INTRODUCTION. Lithium-ion batteries are widely used as power sources for new energy vehicles due to their high energy density, high power density, and long service life. 1, 2 However, it usually requires hundreds
A fault detection method of electric vehicle battery through
A thermal runaway happened in a battery cell of an electric vehicle during driving, and the fire spreads to other batteries in a few minutes. Based on the recorded battery
Safety management system of new energy vehicle power battery
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
A novel semi-supervised fault detection and isolation method for
Global problems such as environmental pollution and energy depletion have been greatly alleviated by the arrival of electric vehicles (EVs) [1, 2].Lithium-ion batteries have
A real-time insulation detection method for battery packs used in
Finally, the proposed method is tested with voltage data from four faulty vehicles. The tests prove that the method has good advance detection ability for both progressive and
Adaptive internal short-circuit fault detection for lithium-ion
Electric vehicles (EVs) have emerged as a promising solution for reducing energy consumption and global emissions [1], [2].Lithium-ion batteries, due to their high energy
High-Precision Fault Detection for Electric Vehicle Battery System
Fault detection of the electric vehicle battery system is vital for safe driving, energy economy, and lifetime extension. This paper proposes a data-driven method to achieve
Anomaly Detection Method for Lithium-Ion Battery Cells Based on
Abnormalities in individual lithium-ion batteries can cause the entire battery pack to fail, thereby the operation of electric vehicles is affected and safety accidents even
A real-time insulation detection method for battery packs used in
The equivalent circuit of battery pack insulation detection is shown in Fig. 1. Fig. 1 (a) consists of Part 1 and Part 2. Part 1 represents the equivalent circuit of the electric vehicle
Precision-Concentrated Battery Defect Detection Method in Real
Abstract: Hundreds of electric vehicle (EV) battery thermal runaway accidents resulting from untreated defects restrict further development of EV industry. Battery defect
A fault detection method of electric vehicle battery through
Lithium-ion battery (LIB) is the preferred battery type for new energy electric vehicles (EVs) owing to the high energy density, low self-discharge rate and high cycle life [2],
An Improved Non-Intrusive Load Monitoring Method for Recognition
Keywords: Electric Vehicle Battery, Non-intrusive Load Monitoring, Pattern Recognition, Smart Metering. 1. Introduction Knowledge of the load being used in a property
Evaluating fault detection strategies for lithium-ion batteries in
Electric Vehicles (EVs) are a rapidly growing segment in India''s automotive sector, with an expected 70% growth by 2030. Lithium-ion (Li-ion) rechargeable batteries are
Efficient battery fault monitoring in electric vehicles: Advancing
Battery fault monitoring relies on fault-sensitive data gathered by sensors, such as voltage and temperature, because abnormal changes in voltage and temperature are typical
Battery safety issue detection in real-world electric vehicles by
In recent years, electric vehicles (EVs) have gained widespread recognition as a means of reducing fossil fuel consumption and greenhouse gas emissions [1].Lithium-ion
High-Precision Fault Detection for Electric Vehicle Battery System
Abstract: Fault detection of the electric vehicle battery system is vital for safe driving, energy economy, and lifetime extension. This paper proposes a data-driven method to achieve early
detection in electric vehicle battery system
However, this method is easily disturbed by external noise. In above researches, most of detection methods of DC arc are mainly used in the photovoltaic industry. Although there are a
Research on power battery anomaly detection method based on
Health monitoring and abnormality detection of power batteries for new energy vehicles has been one of the hot topics in recent years. Accurate and efficient power battery
(PDF) An Accurate Activate Screw Detection Method for
An Accurate Activate Screw Detection Method for Automatic Electric Vehicle Battery Disassembly This research proposes a systematic method to complete screw
Fault Diagnosis and Detection for Battery System in Real-World
This work proposes a novel data-driven method to detect long-term latent fault and abnormality for electric vehicles (EVs) based on real-world operation data. Specifically, the
Anomaly Detection Method for Lithium-Ion Battery Cells Based on
and regions.1 New electric energy vehicles are playing an increasingly important role in decarbonization in the trans-portation industry. They constitute a promising
Research on a fast detection method of self-discharge of lithium battery
The existing self-discharge rate detection methods include the definition method, capacity retention method, and open-circuit voltage decay method [5].The definition method is
DCS-YOLO: Defect detection model for new energy vehicle battery
The future trend in global automobile development is electrification, and the current collector is an essential component of the battery in new energy vehicles. Aiming at the
Safety management system of new energy vehicle power battery
The continuous progress of society has deepened people''s emphasis on the new energy economy, and the importance of safety management for New Energy Vehicle
Rapid diagnosis of power battery faults in new energy vehicles
Research can achieve real-time monitoring and timely reminders of potential faults. By early detection of issues such as battery overheating and voltage imbalance, this
Detection and Fault Diagnosis of High-Voltage System of New Energy Vehicles
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
Efficient battery fault monitoring in electric vehicles: Advancing
Real-time monitoring of battery fault risk in battery management systems (BMS) is the key to ensuring the safe and stable operation of EVs. The operational data of
Multi-fault detection and diagnosis method for battery packs
Due to the growing pressure of environmental pollution and energy crisis, electric vehicles (EVs) have become the future development trend. At the same time, due to
Machine Learning Applied to Lithium‐Ion Battery State Estimation
LIBs exhibit dynamic and nonlinear characteristics, which raise significant safety concerns for electric vehicles. Accurate and real-time battery state estimation can enhance
A novel battery abnormality detection method using interpretable
The abnormality detection of lithium-ion battery pack is crucial to ensure the safety of electric vehicles (EVs). However, the dynamic and complex operating conditions of EVs making it
Cyberattack detection methods for battery energy storage systems
For instance, types of attacks against the particular BESS component and the methods for their detection and mitigation were studied (e.g. battery management system
An Accurate Activate Screw Detection Method for Automatic
With the increasing popularity of electric vehicles, the number of end-of-life (EOF) electric vehicle batteries (EVBs) is also increasing day by day. Efficient dismantling and
6 FAQs about [Energy-changing vehicle battery detection method]
Can a fault diagnosis model improve the safety of new energy battery vehicles?
Traditional FDM falls far short of the expected results and cannot meet the requirements. 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.
Can wavelet-neural detection of lithium-ion batteries guarantee the safety of electric vehicles?
The voltage difference value has a strong correlation with the fault occurrence. Experimental results verify the feasibility and advantages of the proposed method. This paper proposes a method of fault detection of Lithium-ion batteries based on wavelet-neural for guaranteeing the safety and reliability of electric vehicles (EVS).
What is the experience-based method of battery fault diagnosis?
The experience-based method is based on the existing prior knowledge, using logical analysis and reasoning the relationship between events to achieve battery fault diagnosis. It can be divided into the expert system , fuzzy logic , and graph theory .
Can lithium-ion battery fault diagnose EV based on real-time voltage?
In this paper, the novel method for lithium-ion battery fault diagnosis of EV based on real-time voltage is presented. The effectiveness of the method is verified based on the real-time data collected by EVs. The related conclusions are drawn as follows:
Can kurtosis detect faults in lithium-ion batteries of electric vehicles?
In this paper, a novel fault diagnosis method for lithium-ion batteries of electric vehicles based on real-time voltage is proposed. Firstly, the voltage distribution of battery cells is confirmed in electric vehicles, and the reasons are analyzed. Furthermore, kurtosis is utilized to discover cell faults for the first time.
How to diagnose lithium-ion battery fault?
To enhance the reliability and safety of lithium-ion batteries, many scholars have proposed different methods for lithium-ion battery fault diagnosis. Current fault diagnosis methods can be divided into three categories: experience-based methods, model-based methods, and data-driven methods [5, 8, 9].