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| Remaining Useful Life Prediction of Lithium-ion Battery Based on Kendall Rank Correlation Coefficient Particle Filter |
| Received:January 15, 2021 Revised:March 11, 2021 |
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| DOI：10.7643/issn.1672-9242.2022.04.014 |
| KeyWord:lithium-ion battery state of health life prediction particle filter correlation coefficient |
| Author | Institution |
| CUI Xian |
State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University, Shanghai , China |
| CHEN Zi-qiang |
State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University, Shanghai , China |
| LU Di-hua |
State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University, Shanghai , China |
| LAN Jian-yu |
China Aerospace Science and Technology Corporation Shanghai Institute Space Power-Sources, Shanghai , China |
| DONG Meng-xue |
China Aerospace Science and Technology Corporation Shanghai Institute Space Power-Sources, Shanghai , China |
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| Abstract: |
| Aiming at the problem of low accuracy when the traditional particle filter (PF) algorithm is applied to the prediction of remaining useful life (RUL) of the lithium-ion battery, Kendall rank correlation coefficient (KCC) is introduced into the resampling process of traditional PF to solve the problem of particle shortage, and a method of RUL prediction of the lithium-ion battery based on KCC-PF is proposed. Firstly, the battery capacity degradation model was established to verify the accuracy and effectiveness of the model and determine the model’s initial parameters. KCC-PF algorithm was used to update the model parameters, and the prediction sequence of capacity was calculated step by step. Finally, the prediction results and uncertainty expression of RUL of the lithium-ion batteries were obtained according to the failure threshold. Based on the data of the NASA PCoE battery aging test, the RUL prediction test of the lithium-ion batteries based on PF and KCC-PF was carried out. The calculation results show that:when the prediction starting point is the 60th cycle, the relative error of RUL prediction based on KCC-PF is less than 10%, and with the prediction starting point moving backward, the relative error can be reduced to less than 3%. KCC-PF algorithm solves the problem of traditional PF particle shortage, and has high prediction accuracy and robustness when applied to the remaining useful life of the lithium-ion battery. |
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