Lithium-ion battery state of charge estimation based on moving horizon

Author

Ma Yan ; Zhou Xiuwen ; Zhang Jixing

Author_Institution

State Key Lab. of Automotive Simulation & Control, Jilin Univ., Changchun, China

fYear

2014

Firstpage

5002

Lastpage

5007

Abstract

This paper is concentrate on state-of-charge(SOC) estimation of Lithium-ion battery which is used in electric vehicles. Due to constrains of battery(SOC constrain and disturbance constrain), moving horizon estimation(MHE) algorithm based on equivalent circuit model is proposed to estimate battery SOC. Compared with Kalman filter, MHE estimate state with more measured value, so it can filter noises better. Tuning parameters of the battery system are chosen to minimize the effects of measurement noises and SOC estimation error bounds. Compared with extended Kalman filter, the results of SOC estimated by MHE shows a better performance and can reduces SOC estimation error.

Keywords

battery powered vehicles; equivalent circuits; estimation theory; secondary cells; MHE algorithm; SOC constrain; SOC estimation error bound reduction; disturbance constrain; electric vehicles; equivalent circuit model; extended Kalman filter; lithium-ion battery system; moving horizon estimation algorithm; noise measurement effect minimization; state of charge estimation; tuning parameters; value measurement; Estimation error; System-on-chip; Battery Management System; Lithium-ion battery; Moving Horizon Estimation; State of Charge; electric vehicle;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2014 11th World Congress on

Type

conf

DOI

10.1109/WCICA.2014.7053563

Filename

7053563