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| Remaining Life Prediction of Lithium Battery Based on Discharge Process Information |
| ZHANG Xuan,FENG Hai-lin |
| School of Mathematics and Statistics, Xidian University, Xi’an, Shaanxi 710126, China |
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Abstract Capacity or internal resistance is an important indicator to measure the health status of lithium-ion batteries. But in the actual operation of lithium-ion batteries, the battery capacity and internal resistance are difficult to obtain in real time. So, a method to obtain new health indicators based on the discharge process information is proposed and the remaining useful life of lithium-ion batteries is predicted. The law of voltage change during the discharging process of lithium-ion batteries is studied. On the above basis, two new health indicators which can be measured online are proposed. The accuracy of the new health indicators is corrected by Box-Cox transformation. The results show that there is a strong correlation between the extracted health indicators and the capacity, which can solve the problem that the battery capacity is difficult to measure online to some extent. In addition, the battery degradation process model based on new health indicators is established, and the remaining useful life of lithium-ion batteries is predicted by the relevance vector machine. The experimental results show that the relevance vector machine algorithm is better than other algorithms in life prediction performance. And the later the prediction time, the more accurate the prediction results. The extracted health indicators can describe the degradation process of lithium-ion batteries well and predict the remaining useful life accurately.
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Received: 30 September 2020
Published: 14 November 2022
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2022, Vol. 43 
