A novel intelligent method for fault diagnosis of electric vehicle battery system based on wavelet neural network

Yao, Lei; Xiao, Yanqiu; Gong, X. G.; Hou, Junjian; Chen, Xiangtian

doi:10.1016/j.jpowsour.2020.227870

Cited by 106 publications

(24 citation statements)

References 33 publications

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“…The input to the ANN is the fault frequency matrix acquired by the 3s-MSS. In the work of(Yao et al, 2020), a wavelet-neural network system (containing discrete wavelet transform (DWT) and a general regression neural network (GRNN)) are used to detect the fault of Li-ion batteries for electric vehicles. The GRNN used is a highly parallel radial basis function network, which contains the input layer, pattern layer, summation layer, and output layer.…”

mentioning

confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

115

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Technology advancement demands energy storage devices (ESD) and systems (ESS) with better performance, longer life, higher reliability, and smarter management strategy. Designing such systems involve a trade-off among a large set of parameters, whereas advanced control strategies need to rely on the instantaneous status of many indicators. Machine learning can dramatically accelerate calculations, capture complex mechanisms to improve the prediction accuracy, and make optimized decisions based on comprehensive status information. The computational efficiency makes it applicable for real-time management. This paper reviews recent progresses in this emerging area, especially new concepts, approaches, and applications of machine learning technologies for commonly used energy storage devices (including batteries, capacitors/supercapacitors, fuel cells, other ESDs) and systems (including battery ESS, hybrid ESS, grid and microgrid-containing energy storage units, pumped-storage system, thermal ESS). The perspective on future directions is also discussed.

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confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

115

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“…Wavelet transform has the characteristics of being flexible and changeable, and can carry out multi-scale analysis. By adjusting the dilation factor

and the translation factor

, the signal can be observed step by step from the whole to the part, and the signal can be analyzed in the time and frequency domain, which has been widely used in the field of signal analysis [ 46 ].…”

Section: The Proposed Wpnfmentioning

confidence: 99%

Wavelet-Prototypical Network Based on Fusion of Time and Frequency Domain for Fault Diagnosis

Wang

Chen

Liu

et al. 2021

Sensors

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Neural networks for fault diagnosis need enough samples for training, but in practical applications, there are often insufficient samples. In order to solve this problem, we propose a wavelet-prototypical network based on fusion of time and frequency domain (WPNF). The time domain and frequency domain information of the vibration signal can be sent to the model simultaneously to expand the characteristics of the data, a parallel two-channel convolutional structure is proposed to process the information of the signal. After that, a wavelet layer is designed to further extract features. Finally, a prototypical layer is applied to train this network. Experimental results show that the proposed method can accurately identify new classes that have never been used during the training phase when the number of samples in each class is very small, and it is far better than other traditional machine learning models in few-shot scenarios.

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“…Although the prediction results for the LSTM neural network is good for diagnosing the fault of the battery and photovoltaic array problem, the structure of this method is very complex and it has a low prediction efficiency. Yao et al (2020) used the waveletneural to identify the lithium-ion batteries of the electric vehicles. To improve the safety and reliability of the vehicles, four parameters were selected as the variables, influencing the performance of the electric vehicles.…”

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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A novel intelligent method for fault diagnosis of electric vehicle battery system based on wavelet neural network

Cited by 106 publications

References 33 publications

Machine learning toward advanced energy storage devices and systems

Machine learning toward advanced energy storage devices and systems

Wavelet-Prototypical Network Based on Fusion of Time and Frequency Domain for Fault Diagnosis

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

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