Efficient remaining capacity estimation method for LIB based on feature processing and the RBF neural network

doi:10.19799/j.cnki.2095-4239.2020.0314

Abstract

Abstract:

To solve the problem of the difficulty in balancing the accuracy and efficiency in the process of capacity estimation for an LIB, this paper proposes a remaining capacity estimation method for an LIB based on feature engineering and a radial basis neural network. First, the features associated with the remaining available capacity from the data during battery charging is extracted; then, the local anomaly factor algorithm is used to clean the abnormal points accurately in the features that increase the amount of effective information contained in the feature quantity; next, the dimensionality reduction process of the feature vector group is performed by the local linear embedding dimensionality reduction algorithm to reduce the computation complexity; and finally, a radial basis function neural network is introduced to establish an estimation model for the remaining capacity. The model is verified on different batteries; the results show that the model has strong robustness, the maximum average absolute error does not exceed 0.06, the maximum root mean square error is 0.05, and, when compared with the Elman neural network and the BP neural network algorithm, it has faster estimation efficiency while ensuring high accuracy.

Key words: lithium-ion battery, feature processing, RBF neural network, capacity estimation

CLC Number:

TM 911

Zheng CHEN, Leilei LI, Xing SHU, Shiquan SHEN, Yonggang LIU, Jiangwei SHEN. Efficient remaining capacity estimation method for LIB based on feature processing and the RBF neural network[J]. Energy Storage Science and Technology, 2021, 10(1): 261-270.

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电池编号	特征量
电池编号	CC-T	CV-T	CC-C	CV-C	CC-T/CV-T	CC-C/CV-C	max-IC
电池A-1	0.991/0.997	-0.985/-0.992	0.992/0.992	-0.955/-0.962	0.987/0.995	0.972/0.974	0.996/0.997
电池B-2	0.975/0.998	-0.967/-0.976	0.996/0.996	0.971/0.998	0.962/0.993	0.983/0.991	0.991/0.993
电池B-3	0.980/0.999	-0.954/-0.988	0.980/0.999	0.937/0.999	0.956/0.997	0.955/0.973	0.989/0.994
电池B-4	0.986/0.998	-0.936/-0.985	0.985/0.988	0.951/0.996	0.945/0.994	0.819/0.832	0.998/0.999

电池标号及训练量	电池A-1			电池B-2			电池B-3			电池B-4
电池标号及训练量	50%	60%	70%	50%	60%	70%	50%	60%	70%	50%	60%	70%
MAE	0.0475	0.0370	0.0327	0.1067	0.0460	0.0299	0.0292	0.0258	0.0239	0.0646	0.0494	0.0381 0.0022
MSE	0.0026	0.0018	0.0014	0.0137	0.0031	0.0011	0.0018	0.0012	0.0008	0.0056	0.0034	0.0381 0.0022
RMSE	0.0506	0.0428	0.0376	0.1171	0.0557	0.0330	0.0429	0.0343	0.0290	0.0748	0.0581	0.0472

方法	MAE	MSE	RMSE	Time/s
RBF NN	0.0612	0.0043	0.0570	0.99
Elman NN	0.0847	0.0076	0.0874	3.51
BP NN	0.0730	0.0070	0.0834	8.54

方法	MAE	MSE	RMSE	Time/s
RBF NN	0.0439	0.0021	0.0463	0.90
Elman NN	0.0518	0.0051	0.0714	3.59
BP NN	0.0643	0.0076	0.0885	9.45

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