A state of health estimation method for lithium-ion batteries using ICA-T features and CNN-LA-BiLSTM

doi:10.19799/j.cnki.2095-4239.2024.1124

Abstract

Abstract:

To address the challenges of insufficient estimation accuracy and inaccurate degradation modeling of lithium-ion battery state of health (SOH), this study proposes a lithium-ion battery SOH estimation method based on a convolutional neural network-local attention-bidirectional long short-term memory (CNN-LA-BiLSTM) model. First, the charging time, current, voltage, capacity, and temperature of the lithium-ion battery are measured during the charging phase. The lithium-ion battery undergoes incremental capacity (IC) analysis, and the IC curve area is extracted as an electrical characteristic of the lithium-ion battery. The temperature integral during charging the lithium-ion battery is calculated as a temperature characteristic. These features are combined into a joint IC area-temperature metric for SOH estimation of lithium-ion batteries. Then, the CNN-LA-BiLSTM model is constructed, incorporating LA to optimize the weights and biases of the CNN, while Huber loss function is used to optimize model parameters for enhanced SOH estimation accuracy. Results show that the proposed method effectively estimates the SOH of the battery, achieving a mean absolute percentage error of 0.5794%, root mean square difference of 0.0099, and a coefficient of determination of 0.9961. Compared with traditional methods, the proposed method shows better performance in battery SOH estimation.

Key words: lithium-ion battery, SOH, CNN-LA-BiLSTM, IC, Huber loss function

CLC Number:

TM 912

Chaolong ZHANG, Yang CHEN, Mengling LIU, Yufeng ZHANG, Guoqing HUA, Panpan YIN. A state of health estimation method for lithium-ion batteries using ICA-T features and CNN-LA-BiLSTM[J]. Energy Storage Science and Technology, 2025, 14(3): 1258-1269.

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电池编号	充电电流 /mA	放电电流 /mA	截止电压/V	充放电循环数/次
锂电池1	500	1250	3	1137
锂电池2	1250	1250	3	816

电池编号	滤波前皮尔逊系数	滤波后皮尔逊系数
锂电池1	0.9985	0.9985
锂电池2	0.9983	0.9983

电池编号	全电压范围皮尔逊系数	仅3.8 V前电压范围皮尔逊系数
锂电池1	0.9992	0.9982
锂电池2	0.9983	0.9867

电池编号	MAPE/%	RMSE	R²
锂电池1	0.5794	0.0099	0.9961
锂电池2	1.1265	0.0099	0.9933

特征	MAPE/%	RMSE	R²	皮尔逊系数
ICA	2.1124	0.0193	0.8746	0.9992
温度的积分	7.6104	0.0606	-0.2314	-0.7961
电流的积分	6.3893	0.0543	0.0114	-0.9442
充电时间	5.9775	0.0492	0.1882	-0.9373