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State-of-charge estimation based on improved back-propagation neural network for lithium-ion batteries in energy storage power stations

Zhu Xiaohong, Zhuang Mingwan, Yang Weirong, Li Xiuquan, Dai Hang

Archives of Electrical Engineering

2024

Vol. 73, nr 4

977--997

This paper develops a novel approach for the state of charge (SOC) estimation of Lithium-ion batteries in energy storage power stations, leveraging an improved back-propagation (BP) neural network optimized by an immune genetic algorithm (IGA). Addressing the paramount importance of accurate SOC estimation for enhancing battery management systems, this work proposes a methodological enhancement aimed at refining estimation precision and operational efficiency. First, the mechanisms of temperature, current, and voltage impacts on SOC are revealed, which serve as the inputs of the neural network. Second, the improved BP neural network’s structure and optimization through an IGA are designed, emphasizing the mitigation of traditional BP neural networks’ limitations including slow convergence speed and complex parameterization. Through an extensive experimental setup, the proposed model is validated against standard BP neural networks across various discharge experiments at different temperatures and discharge currents. Results prove that the estimation accuracy of the proposed method reaches as high as 98.15% and faster converges compared to the traditional BP network, thereby being valuable practically.