Battery SOH estimation and RUL prediction framework based on variable forgetting factor online sequential extreme learning machine and particle filter

被引:68
作者
Duan, Wenxian [1 ]
Song, Shixin [2 ]
Xiao, Feng [1 ]
Chen, Yuan [3 ]
Peng, Silun [1 ]
Song, Chuanxue [1 ]
机构
[1] Jilin Univ, State Key Lab Automot Simulat & Control, Changchun 130022, Peoples R China
[2] Jilin Univ, Sch Mech & Aerosp Engn, Changchun 130022, Peoples R China
[3] Anhui Univ, Sch Artificial Intelligence, Hefei 230601, Peoples R China
关键词
State of health; Remaining useful life; Whale optimization algorithm; Extremely randomized trees; Online sequential extreme learning machine; OF-HEALTH ESTIMATION; LITHIUM-ION BATTERY; REMAINING USEFUL LIFE; INCREMENTAL CAPACITY; STATE; REGRESSION; ALGORITHM; CHARGE; MODEL;
D O I
10.1016/j.est.2023.107322
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Battery life prediction is of great practical significance to ensure the safety and reliability of equipment. This paper proposes a new framework to realize battery state of health (SOH) estimation and remaining useful life (RUL) prediction. The variable forgetting factor online sequential extreme learning machine (VFOS-ELM) is used to estimate battery SOH, and particle filter (PF) algorithm used to predict battery RUL. To improve the estimation accuracy, a new nonlinear decline method, adaptive weight and Gaussian variation are used to improve the standard whale optimization algorithm (WOA) algorithm. And the improved IWOA algorithm is used for parameter optimization of the VFOS-ELM and PF algorithm. The extremely randomized trees (ERT) algorithm is used to obtain the features with high correlation with the available capacity to reduce the complexity of the model and improve the estimation accuracy. Compared with other methods, the proposed IWOA-VFOS-ELM algorithm has higher estimation performance and noise anti-interference ability. The MAE of APR-3 and APR -4 for SOH estimation are both within 0.12 %, RMSE are within 0.15 %, and IA are both higher than 99.9 %. Compared with PF algorithm, the RUL prediction accuracy obtained by IWOA-PF algorithm is improved by 7.143 %, 6.445 % and 15.094, respectively. In summary, the IWOA-PF algorithm proposed in this paper can be used to predict the battery RUL, and the prediction performance is better than the PF algorithm.
引用
收藏
页数:21
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