The State of Charge Estimation of Lithium-ion Batteries Using an Improved Extreme Learning Machine Approach

Abstract

Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. In order to improve the estimation accuracy of SOC, this paper develops a SOC estimation model for a lithium-ion battery using a Particle Swarm Optimization-Extreme Learning Machine(PSO-ELM) algorithm. The PSO is applied to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal ELM model. The inputs to the PSO-ELM model are the battery voltage, current, and temperature, and the output is the actual SOC values. The performance of the proposed model is compared with BP neural network and ELM models and verified based on the mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The results demonstrate that the PSO-ELM model offers higher accuracy and lower SOC error rate than ELM and BP neural network models.