| 崔显,陈自强,卢地华,蓝建宇,董梦雪.基于KCC-PF的锂离子电池剩余使用寿命预测[J].装备环境工程,2022,19(4):86-94. CUI Xian,CHEN Zi-qiang,LU Di-hua,LAN Jian-yu,DONG Meng-xue.Remaining Useful Life Prediction of Lithium-ion Battery Based on Kendall Rank Correlation Coefficient Particle Filter[J].Equipment Environmental Engineering,2022,19(4):86-94. |
| 基于KCC-PF的锂离子电池剩余使用寿命预测 |
| Remaining Useful Life Prediction of Lithium-ion Battery Based on Kendall Rank Correlation Coefficient Particle Filter |
| 投稿时间：2021-01-15 修订日期：2021-03-11 |
| DOI：10.7643/issn.1672-9242.2022.04.014 |
| 中文关键词: 锂离子电池 健康状态 寿命预测 粒子滤波 相关系数中图分类号:TM912 文献标识码:A 文章编号:1672-9242(2022)04-0086-09 |
| 英文关键词:lithium-ion battery state of health life prediction particle filter correlation coefficient |
| 基金项目:国家自然科学基金(51677119) |
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| Author | Institution |
| CUI Xian | State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University, Shanghai 200240, China |
| CHEN Zi-qiang | State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University, Shanghai 200240, China |
| LU Di-hua | State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University, Shanghai 200240, China |
| LAN Jian-yu | China Aerospace Science and Technology Corporation Shanghai Institute Space Power-Sources, Shanghai 200245, China |
| DONG Meng-xue | China Aerospace Science and Technology Corporation Shanghai Institute Space Power-Sources, Shanghai 200245, China |
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| 中文摘要: |
| 目的 针对传统粒子滤波(PF)算法应用于锂电池剩余使用寿命(RUL)预测时准确性低的问题，将肯德尔秩次相关系数(KCC)引入传统PF的重采样过程，改善粒子匮乏问题，提出一种基于KCC-PF的锂电池RUL预测方法。方法 首先建立电池容量衰减模型，验证模型的准确性和有效性，并确定模型初始参数，利用KCC-PF算法循环更新模型参数，逐步计算出容量的预测序列，然后根据失效阈值获得锂电池RUL的预测结果及其不确定性表达。利用NASA PCoE的电池老化试验数据，进行基于PF和KCC-PF的锂电池RUL预测试验。结果 预测起点为第60次循环时，基于KCC-PF的锂电池RUL预测相对误差在10%以内。随着预测起点后移，相对误差可降低至3%以内。结论 KCC-PF算法解决了传统PF粒子匮乏的问题，应用于锂电池RUL预测时，具有较高的预测准确性和鲁棒性。 |
| 英文摘要: |
| 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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