Lithium-Ion Battery State of Health Estimation Based on Multi-Dimensional Health Characteristics and GAPSO-BiGRU

Lv Zhou, Yu Zhang (), Kuiting Pan and Xiongfan Cheng
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Lv Zhou: Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China
Yu Zhang: Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China
Kuiting Pan: Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China
Xiongfan Cheng: Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China

Energies, 2025, vol. 18, issue 20, 1-16

Abstract: The state of health (SOH) of lithium-ion batteries (LIBs) is a key parameter that is crucial for delaying their lifespan degradation and ensuring safe use. To further explore the potential of charge curves in SOH estimation for LIBs, this paper proposes a method based on multi-dimensional health features and a genetic algorithm–particle swarm optimization (GAPSO)–bidirectional gated recurrent unit (BiGRU) neural network for SOH estimation. First, we extracted differential thermal voltammetry curves from the charging curve and defined the peak, valley, and their positions. Then, based on the charging temperature curve, we defined the time at which the maximum charging temperature occurs and the average charging temperature. Subsequently, we validated the correlation between the aforementioned six health features and SOH using the Pearson correlation coefficient. Finally, we used the multi-dimensional health features as model inputs to construct the BiGRU estimation model and employed the GAPSO hybrid strategy to achieve global adaptive optimization of the model’s hyperparameters. Experimental results on different LIBs show that the proposed method has relatively high accuracy, with an average absolute error and root mean square error of no more than 0.2771%. The comparison results with various methods further verify the superiority of the proposed method.

Keywords: lithium-ion battery; state of health; health characteristics; genetic algorithm-particle swarm optimization; bidirectional gated recurrent unit (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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