Research on SOC Estimation Algorithm for Lithium Battery Based on EKF Algorithm and Ampere-hour Integration Method

DOI

10.2991/ecae-17.2018.22How to use a DOI?

Keywords

lithium battery; Stage of Charge (SOC); Extended Kalman Filter (EKF); composite model

Abstract

Accurate estimation of state of charge (SOC) in power battery is the key point of electric vehicle management system. In this paper, the problem of accurate estimate of the SOC for lithium battery is considered. The composite model of the battery is established, and the parameters of the model are identified by the recursive least square algorithm. The extended Kalman filtering algorithm (EKF) and current time integral method are combined to estimate the battery SOC, the working voltage and the battery SOC are considered as observation variables and state variables. The model is built in Matlab/Simulink, and the simulation of the current on the battery is carried out by simulating the FUDS working conditions. Simulation results show that the proposed algorithm can accurately estimate the SOC in the dynamic process of the lithium battery, and the error can be kept in the range of ñ2%.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bo Fan
AU  - Xinyu Luan
AU  - Rui Zhang
AU  - Tianlin Niu
AU  - Yijing Xie
PY  - 2017/12
DA  - 2017/12
TI  - Research on SOC Estimation Algorithm for Lithium Battery Based on EKF Algorithm and Ampere-hour Integration Method
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)
PB  - Atlantis Press
SP  - 101
EP  - 105
SN  - 2352-5401
UR  - https://doi.org/10.2991/ecae-17.2018.22
DO  - 10.2991/ecae-17.2018.22
ID  - Fan2017/12
ER  -