Online State-of-Charge Estimation of Li-Ion Battery Based on the Second-Order RC Model

Ze Cheng; Qiu Yan Zhang; Yu Hui Zhang

doi:10.4028/www.scientific.net/AMR.805-806.1659

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Online State-of-Charge Estimation of Li-Ion Battery Based on the Second-Order RC Model
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Evaluation Method of Regenerative Braking for Electric Vehicles
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 805-806Online State-of-Charge Estimation of Li-Ion...

Online State-of-Charge Estimation of Li-Ion Battery Based on the Second-Order RC Model

Abstract:

The real-timely estimation of the SOC (state of charge) is the key technology in Li-ion battery management system. In this paper, to overcome the error of the SOC estimation of Extended Kalman filter (EKF), a new estimation method based on modified-strong tracking filter (MSTF) is applied to SOC estimation of Li-ion battery, based on the second-order RC equivalent circuit model. Experiments are made to compare the new filter with the EKF and Coulomb counting approach (Ah). The simulation results demonstrate that the new filter algorithm MSTF used in this paper has higher filtering accuracy under the same conditions.