State of Charge Estimation of Energy Storage Battery Pack under Typical Peak/Frequency Modulation Conditions

doi:10.11930/j.issn.1004-9649.202401003

Abstract

Abstract:

To address the issue of low estimation accuracy of the state of charge (SOC) for an energy storage battery pack under typical energy storage conditions of a power grid, this paper proposes a new SOC estimation model based on kernel principal component analysis (KPCA), pelican optimization algorithm (POA), and bidirectional gated recurrent unit (BiGRU). By designing the charge and discharge experiment of a battery pack under the condition of peak/frequency modulation, the paper extracts the fusion features of SOC change from the data as the model input. BiGRU networks are constructed under different working conditions, and POA is utilized to optimize its hyperparameters to improve the model's performance. The effectiveness of the model is further verified under mixed conditions. The results show that the proposed model has better SOC estimation performance and stronger robustness, which can improve the SOC estimation accuracy of energy storage battery packs under complex energy storage conditions.

Key words: energy storage battery pack, state of charge estimation, peak and frequency modulation, pelican optimization, bidirectional gated recurrent unit

Muyu ZHU, Hongzhong MA, Pengyu GUO, Wenjing XUAN. State of Charge Estimation of Energy Storage Battery Pack under Typical Peak/Frequency Modulation Conditions[J]. Electric Power, 2024, 57(6): 18-26.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202401003

https://www.electricpower.com.cn/EN/Y2024/V57/I6/18

Figures/Tables 14

Fig.1 BiGRU structure

Fig.2 Flow of SOC estimation

Fig.3 Lithium iron phosphate battery pack

Table 1 Battery pack parameters

型号	标称电压/V	标称容量/(A·h)
MCRSA08-LC	25.6	220

电池组尺寸/mm	工作温度/℃	质量/kg
555×430×154	–20~55	60

Fig.4 Experimental platform

Fig.5 Current instructions under different working conditions

Fig.6 Battery pack data monitoring

Fig.7 Contribution rates and eigenvalue distribution under different working conditions

Fig.8 Comparison of SOC estimation with different input features

Table 2 Error evaluation indexes of different input characteristics

工况	是否降维	E_RMS	E_MA	R²
调峰	是	0.008645	0.008527	0.9987
调峰	否	0.033260	0.022370	0.9406
调频	是	0.009983	0.010410	0.9930
调频	否	0.061020	0.047480	0.7753

Fig.9 Comparison of SOC estimation with different learning models

Table 3 Error evaluation indexes with different learning models

工况	模型	E_RMS	E_MA	R²
调峰	POA-BiGRU	0.008645	0.008527	0.9987
	KELM	0.024090	0.019680	0.9654
	BiLSTM	0.011050	0.012310	0.9892
	BiGRU	0.011360	0.012440	0.9875
调频	POA-BiGRU	0.009983	0.010410	0.9930
	KELM	0.037650	0.022560	0.9532
	BiLSTM	0.015780	0.011020	0.9785
	BiGRU	0.016620	0.012330	0.9771

Fig.10 SOC estimation effect under mixed conditions

Table 4 Error evaluation indexes under mixed conditions

模型	E_RMS	E_MA	R²
单模型	0.02872	0.025460	0.9755
双模型	0.01097	0.009862	0.9936

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