重庆理工大学学报2024，Vol.38Issue(13)：282-288,7.DOI:10.3969/j.issn.1674-8425(z).2024.07.035
扩展卡尔曼滤波优化的锂离子电池寿命预测
Lithium-ion battery life prediction based on extended Kalman filter
摘要
Abstract
Accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance in detecting the battery's health conditions and improving the safety of battery operation.However,predicting the RUL of lithium-ion batteries is difficult due to the non-linearity of battery degradation and the complexity of battery models.This paper employs a dual-exponential decay model integrated with the extended Kalman filter(EKF)algorithm to predict the RUL of lithium-ion batteries.Matlab is employed for the simulation of RUL prediction,and the simulation results are compared and analyzed with NASA capacity data.Our simulation results demonstrate the dual-exponential decay model integrated with the extended Kalman filter algorithm has a small deviation between the prediction and the actual RUL,with an overall average absolute error of about 10.9%,indicating a high accuracy.关键词
锂离子电池/RUL预测/双指数退化模型/扩展卡尔曼滤波Key words
lithium-ion batteries/RUL prediction/dual-exponential decay model/extended Kalman filter分类
信息技术与安全科学引用本文复制引用
张涌,张翔,李习龙,张伟,赵奉奎..扩展卡尔曼滤波优化的锂离子电池寿命预测[J].重庆理工大学学报,2024,38(13):282-288,7.基金项目
江苏省产业前瞻与关键核心技术项目(BE2022053-2) （BE2022053-2）
江苏省现代农业—重点及面上项目(BE2021339) （BE2021339）
南京林业大学青年科技创新基金项目(CX2019018) （CX2019018）
无人驾驶场车检验体系建立及关键技术研究(KJ(Y)2023042) （KJ(Y）
