Lithium-ion battery capacity decline prediction based on ant colony optimization BP neural network algorithm

doi:10.19799/j.cnki.2095-4239.2019.0190

Abstract

Abstract:

Accurately predicting the declining trend with respect to the capacity of a battery is important for strengthening the management and maintenance of the battery system. Lithium-ion batteries are the research object; the battery capacity decline trend is predicted based on a source data set analysis published by NASA Laboratories. The data for a full-cycle charge-discharge test of a battery, obtained at room temperature and constant current, are denoised and optimized by a compact set orthogonal wavelet analysis to obtain a more stable and regular battery capacity decay process. The ant colony optimization (ACO) algorithm is subsequently used to optimize the initial weight of the BP neural network. And threshold, based on the ACO-BP neural network model to predict the capacity decline of lithium-ion batteries, and compared with BP neural network alone. The results denote that the ACO-BP neural network generates better prediction results when compared with that generated by the BP neural network alone; with more training samples, it contains more information on battery capacity degradation, and the prediction accuracy is significantly improved. The predicted average error is 1.46% when 80 charge and discharge cycles are used as training samples. If the training samples are further expanded, the prediction effect will improve. This study helps to strengthen the management of the battery systems and provides a technical reference for efficiently predicting the degradation trajectory of the lithium-ion batteries.

Key words: lithium-ion battery, capacity decline, wavelet analysis, ant colony algorithm, BP neural network

CLC Number:

TM 912

ZHANG Xinfeng, YAO Mengmeng, WANG Zhongyi, RAO Yongxiang. Lithium-ion battery capacity decline prediction based on ant colony optimization BP neural network algorithm[J]. Energy Storage Science and Technology, 2020, 9(1): 138-144.

Figures/Tables 10

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Table 1

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训练样本算法	60		70		80
训练样本算法	平均误差	RMSE	平均误差	RMSE	平均误差	RMSE
BP神经网络	5.23%	0.0156	4.85%	0.0132	2.82%	0.0894
ACO-BP神经网络	4.68%	0.0151	2.66%	0.0034	1.46%	0.0018

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