State of Charge Estimation for Lithium-Ion Batteries Using Extended Kalman Filter with Local Linearization

2017-01-1734

03/28/2017

Features
Event
The 13th International Conference on Automotive Engineering
Authors Abstract
Content
An accurate estimation of the state of charge (SOC) is necessary not only for optimal energy management but also for protecting the lithium-ion batteries (LIB) from being deeply discharged or overcharged. In this paper, an equivalent circuit model (ECM) is established to simulate the dynamic behavior of LIB. Parameters of internal resistance, diffusion resistance and diffusion capacitance are identified using the recursive least square method. Because open circuit voltage (OCV) and SOC have an obviously nonlinear relationship, an extended Kalman filter is proposed to estimate the SOC based on the ECM model. Local linearization is employed to approximate the nonlinear SOC-OCV curve by a straight line with the slope and intersection around the operating point. Simulation results show that the estimation error of the proposed algorithm is less than 5% for the test patterns.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-1734
Pages
7
Citation
Chen, B., and Chuang, G., "State of Charge Estimation for Lithium-Ion Batteries Using Extended Kalman Filter with Local Linearization," SAE Technical Paper 2017-01-1734, 2017, https://doi.org/10.4271/2017-01-1734.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-1734
Content Type
Technical Paper
Language
English