Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Energies

Yin Hua¹  Min Xu¹  Mian Li¹  Chengbin Ma²  Chen Zhao²  Paul Stewart³ 

[1] National Engineering Laboratory for the Automotive Electronic Control Technology, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, China; E-Mail:;University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, China; E-Mails:;National Engineering Laboratory for the Automotive Electronic Control Technology, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, China; E-Mail

关键词: state of charge;    lithium-ion battery;    electric vehicles;    nonlinear predictive filter;   

DOI  :  10.3390/en8053556

来源: mdpi

【 摘 要 】

Estimation of state of charge (SOC) is of great importance for lithium-ion (Li-ion) batteries used in electric vehicles. This paper presents a state of charge estimation method using nonlinear predictive filter (NPF) and evaluates the proposed method on the lithium-ion batteries with different chemistries. Contrary to most conventional filters which usually assume a zero mean white Gaussian process noise, the advantage of NPF is that the process noise in NPF is treated as an unknown model error and determined as a part of the solution without any prior assumption, and it can take any statistical distribution form, which improves the estimation accuracy. In consideration of the model accuracy and computational complexity, a first-order equivalent circuit model is applied to characterize the battery behavior. The experimental test is conducted on the LiCoO₂ and LiFePO₄ battery cells to validate the proposed method. The results show that the NPF method is able to accurately estimate the battery SOC and has good robust performance to the different initial states for both cells. Furthermore, the comparison study between NPF and well-established extended Kalman filter for battery SOC estimation indicates that the proposed NPF method has better estimation accuracy and converges faster.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.