Estimation of Lithium-Ion Battery SOC Model Based on AGA-FOUKF Algorithm

Chao Fang,Jingjin Wu,Chenguang Liu,Zhiyang Jin

doi:10.3389/fenrg.2021.769818

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.

Highlights

Lithium (Li)–ion batteries are an essential energy source for new energy electric vehicles, and the precise estimation of state of charge (SOC) can effectively estimate the vehicle’s mileage (Shen et al, 2019)
The results show that the identification results based on the genetic algorithm (GA) can accurately represent the highly dynamic characteristics of Li-ion batteries, the GA has the disadvantage of fixed probabilities of crossover and mutation
The adaptive genetic algorithm (AGA) was run by setting the initial population size at 200, the initial value of adaptive probability of crossover (PC) at 0.5, and the initial value of adaptive PM at 0.05

Summary

Introduction

Lithium (Li)–ion batteries are an essential energy source for new energy electric vehicles, and the precise estimation of state of charge (SOC) can effectively estimate the vehicle’s mileage (Shen et al, 2019). The Ah counting and the OCV method are easy to implement, but the Ah counting will produce cumulative errors and decrease the veracity of SOC estimation. To solve the shortcomings of traditional algorithms, Plett adopted EKF to assess the battery SOC (Plett, 2004). The EKF algorithm adopted Taylor’s first-order formula to linearize the nonlinear system, ignoring higher-order terms, which will reduce the estimation authenticity of the nonlinear battery system. Compares the SOC estimation results of EKF and UKF, demonstrating that the UKF algorithm has a faster convergence speed and higher Researchers have been proposed the unscented Kalman filter (UKF) algorithm (He et al, 2013). compares the SOC estimation results of EKF and UKF, demonstrating that the UKF algorithm has a faster convergence speed and higher

Methods

Results

Conclusion