State of Charge Estimation for Lithium-Ion Batteries Using Extended Kalman Filter with Local Linearization National Taipei Univ. of Technology

Format:

Conference/Event

Author/Creator:

Chen, Chen, author.

Contributor:

Chuang, Guo-Shun

Conference Name:

The 13th International Conference on Automotive Engineering (2017-04-03 : Bangkok, Thailand)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2017

Summary:

AbstractAn 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

Notes:

Vendor supplied data

Publisher Number:

2017-01-1734

Access Restriction:

Restricted for use by site license