A SOH estimation model for energy storage batteries based on multiple cycle features

doi:10.19799/j.cnki.2095-4239.2023.0485

Abstract

Abstract:

Accurate estimation of the state of health (SOH) of electrochemical energy storage batteries is crucial for ensuring their safe and reliable operation. Data-driven methods have been widely used for SOH estimations. However, existing methods overlook the temporal health information and feature extraction between multiple consecutive cycles of battery operation and the relationship between these features and the SOH value. This study proposes a novel SOH estimation model called multi-cycle net (MCNet) to address these issues. This model does not require manual extraction of health features; it only takes current and voltage measurements during the charging phase of the battery as input. It automatically extracts features relevant to the SOH estimation within each cycle, extracts relevant features between multiple consecutive cycles, and then combines them for SOH estimation. First, to construct the multicycle tensor input data and improve the convergence speed of the model, the sampled data from the charging phase of each cycle with varying lengths were preprocessed through length alignment, maximum-minimum normalization, and concatenation of multiple consecutive historical cycle data. Second, the preprocessed tensor data were used as input to build the MCNet model for predicting the SOH using a publicly available battery dataset. The average-absolute- and root-mean-square errors were within 1% and 1.4%, respectively. Finally, the proposed model was compared with other commonly used sequence prediction models, and a comparative experiment was conducted using only single-cycle current and voltage data as inputs. The results demonstrate that the proposed model achieves higher accuracy in SOH estimation, and using multiple cycles' current and voltage data as input improves the estimation accuracy.

Key words: electrochemical energy storage batteries, state of health, multi-cycle features, transformer, GRU

CLC Number:

TM 911

Xuanliang ZHANG, Ting HE, Wenlong ZHU, Shen WANG, Jianhua ZENG, Quan XU, Yingchun NIU. A SOH estimation model for energy storage batteries based on multiple cycle features[J]. Energy Storage Science and Technology, 2023, 12(11): 3488-3498.

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实验组	训练集	测试集
组1	CS2_36、CS2_37、CS2_38	CS2_35
组2	CS2_35、CS2_37、CS2_38	CS2_36
组3	CS2_35、CS2_36、CS2_38	CS2_37
组4	CS2_35、CS2_36、CS2_37	CS2_38

实验组	方法	MAE	RMSE
组1	单循环	0.65%	1.14%
组1	10循环	0.55%	1.01%
组2	单循环	0.72%	1.00%
组2	10循环	0.65%	1.04%
组3	单循环	0.85%	1.26%
组3	10循环	0.60%	0.86%
组4	单循环	1.02%	1.56%
组4	10循环	0.89%	1.36%

模型	组1		组2		组3		组4
模型	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE
BiLSTM	1.71%	0.91%	1.49%	1.06%	1.09%	0.78%	1.46%	0.95%
LSTM	1.59%	0.93%	1.33%	0.92%	1.09%	0.81%	1.54%	0.98%
BiGRU	1.72%	1.02%	1.16%	0.77%	1.14%	0.79%	1.37%	1.02%
GRU	1.66%	0.97%	1.32%	0.95%	1.35%	1.13%	1.81%	1.15%
MCNet	1.01%	0.55%	1.04%	0.65%	0.86%	0.60%	1.36%	0.89%

电池	10循环		1循环
电池	RMSE	MAE	RMSE	MAE
电池2	1.39%	0.73%	1.47%	0.72%
电池4	0.30%	0.22%	0.35%	0.27%
电池5	0.48%	0.45%	0.59%	0.54%
电池6	0.47%	0.25%	0.46%	0.28%

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