A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM

Xiaoqing Ren, Shulin Liu, Xiaodong Yu and Xia Dong

Energy, 2021, vol. 234, issue C

Abstract: State-of-charge (SOC) estimation of lithium-ion battery is one of the core functions of battery management system. In order to improve the estimation accuracy of SOC, this paper proposes a long short-term memory neural network based on particle swarm optimization (PSO-LSTM). Firstly, the key parameters of LSTM are optimized by PSO algorithm, so that the data characteristics of lithium-ion battery can match the network topology. In addition, random noise is added to the input layer of PSO-LSTM neural network to improve the anti-interference ability of the network. Finally, experiments show that the proposed method can achieve accurate estimation under different conditions. The estimates based on PSO-LSTM converge to the real state-of-charge within an error of 0.5%.

Keywords: Lithium-ion battery; SOC estimation; Particle swarm optimization algorithm; Long short-term memory neural network (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (58)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:234:y:2021:i:c:s0360544221014845

DOI: 10.1016/j.energy.2021.121236

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