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

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  • Ren, Xiaoqing
  • Liu, Shulin
  • Yu, Xiaodong
  • Dong, Xia

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%.

Suggested Citation

  • Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:energy:v:234:y:2021:i:c:s0360544221014845
    DOI: 10.1016/j.energy.2021.121236
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    References listed on IDEAS

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