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

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%. • A PSO-LSTM model is established for SOC estimation of lithium-ion battery. • PSO is applied to optimize the hyper-parameters of LSTM. • Random noises are added to the sampled data, so as to prevent over-fitting of the PSO-LSTM model. • Results show that the proposed method has high estimation accuracy and robustness. [ABSTRACT FROM AUTHOR]