State-of-health prediction of lithium-ion battery based on improved gate recurrent unit

Abstract

State of health (SOH) prediction of lithium-ion batteries is still a very important issue in evaluating the safety and reliability of battery-powered systems. This paper uses the lithiumion battery data in NASA Ames Research Center for research, analyzes the correlation between the relevant feature data, selects the data the most relevant to the capacity through the threshold, and uses it as the input of the neural network. We combine Particle Swarm Optimization (PSO) algorithm and Gate Recurrent Unit (GRU) to form PSO-GRU, and use the PSO-GRU method to find the time step and the number of neurons for the best prediction effect. The experimental results show that, compared with the LSTM method, the PSO-GRU method has higher prediction accuracy and has fewer weight parameters for the neural network training model.