Capacity prediction of lithium battery based on optimized Elman neural network

doi:10.19799/j.cnki.2095-4239.2021.0655

Abstract

Abstract:

Accurate prediction of lithium-ion battery capacity is advantageous for enhancing battery safety and avoiding battery abuse. An accurate capacity prediction has always been a challenge in battery management systems due to the effect of complicated internal electrochemical reactions and external variables. To achieve an effective and accurate prediction of lithium battery capacity in the full-service life, this study proposed an Elman neural network battery capacity prediction model optimized by a genetic algorithm. To begin, the typical characteristics of discharge capacity growth, internal resistance, and temperature data collected over various battery cycles were chosen to adequately depict the law of battery aging and capacity degradation. Secondly, the principal component analysis algorithm is used to reduce the dimension of the characteristic quantities to reduce the data dimension of the training quantity. The Elman neural network is then used to build the battery capacity prediction model. To ensure efficient and accurate prediction of battery capacity, a genetic algorithm is used to adjust the weights and thresholds of the Elman neural network. Finally, the model was verified on different batteries. The verification findings reveal that the GA-Elman neural network prediction model is more accurate and efficient than the classic Elman neural network and LSTM neural network prediction models. The maximum mean absolute error, maximum root mean square error, and minimum fitting coefficient of the model is 0.92%, 1.02%, and 0.9679, respectively, for various batteries, showing that the model can accurately predict the capacity of lithium battery in the process of decay and has high adaptability to different batteries.

Key words: aging characteristics, genetic algorithm, elman neural network, capacity prediction of lithium battery

CLC Number:

TM 911

Peng HUANG, Zhigen NIE, Zheng CHEN, Xing SHU, Shiquan SHEN, Jipeng YANG, Jiangwei SHEN. Capacity prediction of lithium battery based on optimized Elman neural network[J]. Energy Storage Science and Technology, 2022, 11(7): 2282-2294.

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电池编号	特征量
电池编号	F1	F2	F3	F4
电池1	0.931	0.885	0.693	0.832
电池2	0.948	0.893	0.764	0.811
电池3	0.936	0.872	0.782	0.829
电池4	0.927	0.884	0.685	0.823

电池误差评估	电池1训练量			电池2训练量			电池3训练量			电池4训练量
电池误差评估	40%	60%	80%	40%	60%	80%	40%	60%	80%	40%	60%	80%
R²	0.933	0.988	0.995	0.953	0.983	0.991	0.962	0.971	0.984	0.945	0.974	0.987
MAE/%	1.2	0.21	0.13	0.84	0.24	0.15	0.72	0.35	0.14	1.03	0.29	0.13
RMSE/%	1.97	0.41	0.24	1.75	0.54	0.36	1.53	0.93	0.57	1.86	0.75	0.44

不同方法	电池1		电池2		电池3		电池4
不同方法	RMSE/%	时间/s	RMSE/%	时间/s	RMSE/%	时间/s	RMSE/%	时间/s
LSTM NN	0.89/0.76	20.95/21.57	0.76/0.99	21.14/21.77	1.09/1.14	22.53/23.25	0.82/0.87	21.76/22.41
Elman NN	1.51/1.19	13.15/13.22	1.29/1.63	13.62/13.74	1.49/1.35	13.79/13.92	1.22/1.43	13.12/13.25
GA-Elman NN	0.66/0.41	10.79/10.73	0.57/0.33	10.51/10.39	0.34/0.38	11.16/11.02	0.43/0.46	10.14/10.25

电池	R²	MAE/%	RMSE/%
2	0.9679	0.92	1.02
3	0.9717	0.78	0.89
4	0.9863	0.53	0.61
5	0.9615	1.03	1.26
6	0.9587	1.15	1.47

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