Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

Energies

Zhongyue Zou²  Jun Xu²  Chris Mi¹  Binggang Cao² 

[1] Department of Electrical and Computer Engineering, University of Michigan, Dearborn, MI 48128, USA; E-Mails:;School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China; E-Mail:

关键词: model-based estimation;    state of charge (SOC);    battery management system (BMS);    Luenberger observer;    Kalman filter;    sliding mode observer;    proportional integral observer;   

DOI  :  10.3390/en7085065

来源: mdpi

【 摘 要 】

Four model-based State of Charge (SOC) estimation methods for lithium-ion (Li-ion) batteries are studied and evaluated in this paper. Different from existing literatures, this work evaluates different aspects of the SOC estimation, such as the estimation error distribution, the estimation rise time, the estimation time consumption, etc. The equivalent model of the battery is introduced and the state function of the model is deduced. The four model-based SOC estimation methods are analyzed first. Simulations and experiments are then established to evaluate the four methods. The urban dynamometer driving schedule (UDDS) current profiles are applied to simulate the drive situations of an electrified vehicle, and a genetic algorithm is utilized to identify the model parameters to find the optimal parameters of the model of the Li-ion battery. The simulations with and without disturbance are carried out and the results are analyzed. A battery test workbench is established and a Li-ion battery is applied to test the hardware in a loop experiment. Experimental results are plotted and analyzed according to the four aspects to evaluate the four model-based SOC estimation methods.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.