An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine

Yao, Lei; Fang, Zhanpeng; Xiao, Yanqiu; Hou, Junjian; Zhi-jun, FU

doi:10.1016/j.energy.2020.118866

Cited by 196 publications

(56 citation statements)

References 37 publications

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“…Inspired by the satisfactory performance while predicting important states such as SOC, SOH and remaining useful life (RUL) of LIB, SVM has very recently been introduced into the domain of intelligent fault diagnosis of LIBs. In 2021, Yao et al [59] employed SVM to identify the fault state and the degree of fault. In addition, the discrete cosine filtering method based on white noise characteristics and the grid search method were used to enhance the prediction accuracy and reliability of SVM-based diagnostic scheme.…”

Section: Svm-based Fault Diagnosis Methodsmentioning

confidence: 99%

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

2021

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Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance.

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Section: Svm-based Fault Diagnosis Methodsmentioning

confidence: 99%

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

2021

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“…s s s (18) where the S2 is a high latitude matrix with the dimension (N-M+1)×k, and si 2 is the ith cell's variance value.…”

Section: ) Variance Matrixmentioning

confidence: 99%

“…The BMS is critical in the whole operation of electric vehicles, which can respond to any battery fault with the fastest speed, determine the fault location and cause, and give reasonable treatment methods [18]. Failures that the BMS cannot detect cause safety problems in electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network

Yao

Xiao

et al. 2022

IEEE Access

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The battery system is one of the core technologies of the new energy electric vehicle, so the frequent occurrence of safety accidents seriously limits the large-scale promotion and application. An innovative extreme learning machine optimized by genetic algorithm (GA-ELM)-based method is proposed to estimate the current system status, which improves the accuracy and timeliness of fault identification. It is feasible in the application of electric vehicles. To ensure the effectiveness of the signal, the proposed method is adopted using the simple mean filter to clean the data with eliminate wrong points. After the variance analysis, covariance, a horizontal variance of the filtered data, a modified feature parameters matrix is presented. The dimension is reduced by principal component analysis to improve the engineering application ability. Furthermore, a comprehensive GA-ELM-based identification method is proposed to reduce the resulting identification error of extreme learning machines due to the initial value change. More importantly, the sensitivity and accuracy of different solutions are compared and verified, which shows the technique has great potential in battery fault diagnosis based on the voltage signal.

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“…Meng et al (2017) used the BP neural network to diagnose the lithium battery using nine parameters. Yao et al (2021) used the Support Vector Machine (SVM) method to identify the Lithium-ion batteries. For the SVM algorithm, the kernel function parameter and penalty factor was optimized by using the grid search method.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method for Lithium-Ion Battery Packs Using Improved RBF Neural Network

Wang

Zhang

2021

Front. Energy Res.

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With the increasing demand for electric vehicles, the high voltage safety of electric vehicles has attracted significant attention. More than 30% of electric vehicle accidents are caused by the battery system; hence, it is vital to investigate the fault diagnosis method of lithium-ion battery packs. The fault types of lithium-ion battery packs for electric vehicles are complex, and the treatment is cumbersome. This paper presents a fault diagnosis method for the electric vehicle power battery using the improved radial basis function (RBF) neural network. First, the fault information of lithium-ion battery packs was collected using battery test equipment, and the fault levels were then determined. Subsequently, the improved RBF neural networks were employed to identify the fault of the lithium-ion battery pack system using the experimental data. The diagnosis test results showed that the improved RBF neural networks could effectively identify the fault diagnosis information of the lithium-ion battery packs, and the diagnosis accuracy was about 100%.

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An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine

Cited by 196 publications

References 37 publications

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network

A Fault Diagnosis Method for Lithium-Ion Battery Packs Using Improved RBF Neural Network

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