A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter

Shichun Yang,Yuwei Pan,Xinhua Liu,Yang Hua,Heping Ling,Billy Wu,Xinan Zhou,Sida Zhou

doi:10.1038/s41598-021-84729-1

Abstract

An accurate state of charge (SOC) estimation in battery management systems (BMS) is of crucial importance to guarantee the safe and effective operation of automotive batteries. However, the BMS consistently suffers from inaccuracy of SOC estimation. Herein, we propose a SOC estimation approach with both high accuracy and robustness based on an improved extended Kalman filter (IEKF). An equivalent circuit model is established, and the simulated annealing-particle swarm optimization (SA-PSO) algorithm is used for offline parameter identification. Furthermore, improvements have been made with noise adaptation, a fading filter and a linear-nonlinear filtering based on the traditional EKF method, and rigorous mathematical proof has been carried out accordingly. To deal with model mismatch, online parameter identification is achieved by a dual Kalman filter. Finally, various experiments are performed to validate the proposed IEKF. Experimental results show that the IEKF algorithm can reduce the error to 2.94% under dynamic stress test conditions, and robustness analysis is verified with noise interference, hence demonstrating its practicability for extending to state estimation of battery packs applied in real-world operating conditions.

Highlights

Due to the global energy crisis and environmental pollution issues, electric vehicles (EVs) are being developed as alternatives to traditional internal combustion engine powered v ehicles[1,2]
To address the issues of state of charge (SOC) estimation of lithium-ion batteries (LIBs), this paper proposes an improved extended Kalman filter (EKF) (IEKF) algorithm based on equivalent circuit models (ECM), which has a precise filtering system and time-varying battery model parameters, to effectively improve the model accuracy, robustness and convergence of the algorithm
The experimental results show that maximum error (ME) of improved extended Kalman filter (IEKF) can be reduced to 1.43% under 1C constant current discharge conditions, which is better than the 1.93% for the EKF algorithm

Summary

Introduction

Due to the global energy crisis and environmental pollution issues, electric vehicles (EVs) are being developed as alternatives to traditional internal combustion engine powered v ehicles[1,2]. To address the issues of SOC estimation of LIBs, this paper proposes an improved EKF (IEKF) algorithm based on ECM, which has a precise filtering system and time-varying battery model parameters, to effectively improve the model accuracy, robustness and convergence of the algorithm. To speed up the fitting process, a SA-PSO method has been developed for offline parameter identification, which can be used as the initial value of online estimation and implemented for model correction.

Results

Conclusion