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

Jia Wang,Shenglong Zhang,Xia Hu

doi:10.3389/fenrg.2021.702139

Abstract

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%.

Highlights

With the increasing attractiveness of new energy vehicles, the safety of the electric vehicle battery is crucial
To compare the calculation accuracy for the improved RBFNN, a traditional radial basis function (RBF) neural network is employed in this study to diagnose the fault of the battery
It can be conclusion that the GRNN and Probabilistic Neural Network (PNN) algorithms are suitable for battery diagnosis problem

Summary

Introduction

With the increasing attractiveness of new energy vehicles, the safety of the electric vehicle battery is crucial. This paper presented a fault diagnosis method for lithium-ion battery packs using improved RBF neural networks. To obtain the sample data, the test equipment of the lithium iron phosphate battery pack is shown in details in Data Preparing for Lithium-Ion Battery Packs, as well as the fault levels and treatment methods. A pure electric passenger car was employed to collect the fault information of a lithium iron phosphate battery pack.

Results

Conclusion