Prognostic method of lithium-ion battery lifetime degradation under various working conditions

doi:10.19799/j.cnki.2095-4239.2023.0233

Abstract

Abstract:

Accurately predicting the degradation of lithium-ion battery life is crucial to ensure the safe and reliable operation of electric vehicles and electrochemical energy storage systems. However, predicting the life of lithium-ion batteries under various operating conditions remains a major challenge. This article presents a novel approach to construct feature matrices and predict battery life using deep convolutional neural networks. First, battery life degradation is defined in terms of capacity decay. A multidimensional feature parametric matrix is then built based on the dataset's characteristics, including variable operating conditions. This matrix combines historical capacity information, cycle count information, and operating conditions information. Next, a one-step ahead mapping relationship is established using the feature parameter matrix as input and the battery capacity to be predicted as output. To achieve this mapping relationship, a deep learning method with strong nonlinear capability is employed. It builds a dilated residual regression network by combining a dilated convolution module, a residual module, and a regression module. Furthermore, Bayesian Optimization is used to train the proposed network and find the best combination of hyperparameters, ensuring the model's optimality. Experimental results demonstrate that the proposed method significantly enhances prediction accuracy compared to commonly used model- and data-driven methods for the battery life prediction. Moreover, the proposed method exhibits robust prediction capability across different prediction starting points.

Key words: lithium-ion battery, varied working conditions, lifetime prediction, convolutional neural network, bayesian optimalization

CLC Number:

TM 912.9

Yubo ZHANG, Youyuan WANG, Dongning HUANG, Ziyi WANG, Weigen CHEN. Prognostic method of lithium-ion battery lifetime degradation under various working conditions[J]. Energy Storage Science and Technology, 2023, 12(7): 2238-2245.

Figures/Tables 6

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Table 3

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预测起点	方法	评价指标	测试集电芯
预测起点	方法	评价指标	46^#	15^#	3^#	61^#	20^#
30 (预测长度70)	PF	MAE	0.739	0.886	0.951	0.289	2.520
	PF	RMSE	0.895	1.018	1.056	0.376	2.995
	LSTM	MAE	0.198	0.267	0.311	0.367	0.179
	LSTM	RMSE	0.259	0.315	0.357	0.427	0.224
	DRRN	MAE	0.031	0.029	0.076	0.092	0.047
	DRRN	RMSE	0.039	0.035	0.087	0.105	0.056
50 (预测长度50)	PF	MAE	0.588	0.277	0.301	0.330	0.375
	PF	RMSE	0.694	0.345	0.348	0.387	0.484
	LSTM	MAE	0.210	0.298	0.343	0.429	0.195
	LSTM	RMSE	0.278	0.346	0.370	0.485	0.235
	DRRN	MAE	0.026	0.024	0.069	0.102	0.048
	DRRN	RMSE	0.031	0.032	0.074	0.117	0.057
70 (预测长度30)	PF	MAE	0.415	0.259	0.098	0.295	0.455
	PF	RMSE	1.518	0.290	0.119	0.320	0.564
	LSTM	MAE	0.228	0.334	0.370	0.520	0.227
	LSTM	RMSE	0.295	0.389	0.381	0.559	0.272
	DRRN	MAE	0.021	0.024	0.056	0.110	0.043
	DRRN	RMSE	0.026	0.030	0.066	0.125	0.051

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