High-precision estimation of SOP of lithium-ion batteries using multiconstraint collaborative optimization and SSA-ELM dynamic compensation

doi:10.19799/j.cnki.2095-4239.2025.0447

Abstract

Abstract:

Accurate estimation of the peak power state [state of power (SOP)] for power batteries is essential for ensuring safe operation and extending the driving range of new energy vehicles. To address the insufficient accuracy of existing SOP estimation methods, this study proposes a joint estimation model—multi-constraint conditions (MCC)-sparrow search algorithm (SSA)-extreme learning machine (ELM)—which integrates MCC with an SSA-optimized ELM for error prediction and correction. Using a single lithium manganese oxide battery as the research subject, a second-order RC equivalent circuit model is established. Online parameter identification is performed with a forgetting factor-based recursive least squares method, and the battery's state of charge (SOC) is dynamically estimated using an adaptive extended Kalman filter algorithm. Furthermore, three-level operational conditions—30 s, 2 min, and 5 min—are defined based on discharge durations to simulate real-world scenarios. A preliminary SOP estimation model under varying durations is developed by comprehensively considering constraints such as SOC, voltage, and maximum allowable discharge current. Subsequently, absolute error datasets between estimated and measured SOP values under MCC are used to train ELM and SSA-ELM error prediction models, enabling dynamic compensation and correction of preliminary estimates. Experimental results demonstrate that the proposed MCC-SSA-ELM model markedly improves SOP estimation accuracy. Compared to the MCC and MCC-ELM models, the average relative errors of the MCC-SSA-ELM model under 30 s, 2 min, and 5 min durations are reduced by 0.382%, 6.215%, and 6.858%, respectively, with final errors consistently controlled within 0.15%. These results demonstrate the effectiveness and engineering applicability of the proposed method.

Key words: SOP, sparrow search algorithm, extreme learning machine, multi-constraint conditions

CLC Number:

TM 912

Chunling WU, Yao MA, Zhanhao CHANG, Taiping YANG, Jinhao MENG, Yating CHANG, Li WANG, Xiangming HE. High-precision estimation of SOP of lithium-ion batteries using multiconstraint collaborative optimization and SSA-ELM dynamic compensation[J]. Energy Storage Science and Technology, 2025, 14(11): 4309-4320.

Figures/Tables 16

Table 1

Main parameters of the experimental battery"

电池参数	数值
标称容量/ $A h$	31
标称电压/V	3.7
充电截止电压/V	4.2
放电截止电压/V	3

Table 1

Fig. 1

Fig. 2

Fig. 3

Table 2

Fig. 4

Fig. 5

Table 2

Fig. 6

Fig. 7

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Fig. 9

Fig. 10

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Table 3

Power state performance evaluation indexes of each model under different duration"

模型种类	平均绝对误差 $E M A E$			均方根误差 $E R M S E$			平均相对误差 $E M R E$
模型种类	30 s	2 min	5 min	30 s	2 min	5 min	30 s	2 min	5 min
MCC估计模型	1.5367	5.3814	5.8404	1.5962	10.4229	9.9889	0.384%	6.228%	6.900%
MCC-ELM估计模型	0.1203	2.7202	1.3610	0.1123	1.9641	0.8297	0.006%	0.192%	0.111%
MCC-SSA-ELM估计模型	0.0101	1.2682	0.1860	0.0639	1.2729	0.4852	0.002%	0.113%	0.042%

Table 3

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被比较算法	SSA比较结果实例
PSO	SSA收敛速度更快(迭代次数减少40%)，且在高维问题上更不易陷入局部最优
GWO	SSA的种群分工机制使其在复杂地形函数中寻优精度提高30%
GA	SSA适应度值更低，在风电功率预测中收敛速度比GA快5倍
WOA	SSA的警戒机制使其在动态环境中响应速度更快，抗干扰能力更强

参数	设置上限	设置下限
SOC限制	1	0
电压限制	4.2 V	3 V
电池设计限制	320 A	-160 A