State of Charge Estimation for Lithium-ion Battery Based on Relaxation Effect and Principal Component Analysis

State of charge(SOC) of lithium-ion battery is one of the most important parameters for battery management system. The open-circuit voltage method requires the battery to rest for a long time for to SOC estimation. To solve this problem, a SOC estimation model based on relaxation effect and principal component analysis(PCA) is proposed for lithium-ion batteries. First, a SOC estimation method based on the voltage relaxation curve is proposed, and a gated recurrent unit recurrent neural network(GRU-RNN) model is built, which significantly shorten the resting time compared with the open-circuit voltage method. Then, in order to solve the problem of high dimensionality of voltage relaxation curve data, PCA method is used to reduce the complexity of the GRU-RNN model by reducing the dimensionality of the input data. Finally, the lithium-ion battery periodic discharge experiment and dynamic discharge experiment is designed, and the battery voltage relaxation curve and SOC data are collected and used for model training and testing. The experiment results show that for each constant current discharge or dynamic discharge condition, the proposed SOC estimation method has high accuracy when the battery is rested for a short time, and the PCA method effectively reduces the model training time.