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

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%.