Estimation of Lithium-Ion Battery SOC Model Based on AGA-FOUKF Algorithm
Chao Fang, Zhiyang Jin, Jingjin Wu, Chenguang Liu
Abstract
Aiming at the state estimation error caused by inaccurate battery model parameter estimation, a model-based state of charge (SOC) estimation method of lithium-ion battery is proposed. This method is derived from parameter identification using an adaptive genetic algorithm (AGA) and state estimation using fractional-order unscented Kalman filter (FOUKF). First, the fractional-order model is proposed to simulate the characteristics of lithium-ion batteries. Second, to tackle the problem of fixed values of probabilities of crossover and mutation in the genetic algorithm (GA) in model parameter identification, an AGA has been proposed. Then, the FOUKF method is used to assess battery SOC. For the data redundancy problem caused by the fractional-order algorithm, a time window is set to enhance the computational efficiency of the fractional-order operator. Finally, the experimental results show that the developed AGA-FOUKF algorithm can increase the correctness of SOC estimation.