The State of Charge Estimation of Lithium Battery in Electric Vehicle Based on Extended Kalman Filter

Huan Huan Sun; Jun Bi; Sai Shao

doi:10.4028/www.scientific.net/AMR.953-954.796

Paper Titles

Model Identification of Power Lithium-Ion Battery Based on Laplace Transform
p.775

The Design of Monitoring System for Li-Ion Battery Formation Equipment
p.780

The Consistency and Safety of Power Li-Ion Batteries
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SOC Estimation Strategy and its Accuracy Analysis
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The State of Charge Estimation of Lithium Battery in Electric Vehicle Based on Extended Kalman Filter
p.796

An Improved Prediction Method of SOC Based on the GA-RBF Neural Network
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Application of Cold Water Cycle for Indoor Cooling System
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Comparative Test Research and Application of Coal Consumption On-Line Monitoring System for Energy-Saving Generation Dispatching
p.813

Influence of Condensed Water Temperature on Thermal Efficiency of Coal-Fired Power Plant and Energy Strategy
p.818

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 953-954The State of Charge Estimation of Lithium Battery...

The State of Charge Estimation of Lithium Battery in Electric Vehicle Based on Extended Kalman Filter

Abstract:

Accurate estimation of battery state of charge (SOC) is important to ensure operation of electric vehicle. Since a nonlinear feature exists in battery system and extended kalman filter algorithm performs well in solving nonlinear problems, the paper proposes an EKF-based method for estimating SOC. In order to obtain the accurate estimation of SOC, this paper is based on composite battery model that is a combination of three battery models. The parameters are identified using the least square method. Then a state equation and an output equation are identified. All experimental data are collected from operating EV in Beijing. The results of the experiment show that the relative error of estimation of state of charge is reasonable, which proves this method has good estimation performance.