State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization

Energies

Zhihao Yu²  Ruituo Huai¹  Linjing Xiao² 

[1] College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China; E-Mail:;College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China; E-Mail:

关键词: state-of-charge;    local linearization;    equivalent circuit model;    Kalman filter;    Coulomb integral;    open-circuit voltage;   

DOI  :  10.3390/en8087854

来源: mdpi

【 摘 要 】

State of charge (SOC) estimation is of great significance for the safe operation of lithium-ion battery (LIB) packs. Improving the accuracy of SOC estimation results and reducing the algorithm complexity are important for the state estimation. In this paper, a zeroaxial straight line, whose slope changes along with SOC, is used to map the predictive SOC to the predictive open circuit voltage (OCV), and thus only one parameter is used to linearize the SOC-OCV curve near the present working point. An equivalent circuit model is used to simulate the dynamic behavior of a LIB, updating the linearization parameter in the measurement equation according to the present value of the state variables, and then a standard Kalman filter is used to estimate the SOC based on the local linearization. This estimation method makes the output equation of the nonlinear battery model contain only one parameter related to its dynamic variables. This is beneficial to simplify the algorithm structure and to reduce the computation cost. The linearization method do not essentially lose the main information of the dynamic model, and its effectiveness is verified experimentally. Fully and a partially charged battery experiments indicate that the estimation error of SOC is better than 0.5%.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.