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Online Parameter Identification for State of Power Prediction of Lithium-Ion Batteries in Electric Vehicles Using Extremum Seeking

Chun Wei*, Mouhacine Benosman, and Taesic Kim
International Journal of Control, Automation, and Systems, vol. 17, no. 11, pp.2906-2916, 2019

Abstract : Accurate state-of-power (SOP) estimation is critical for building battery systems with optimized performance and longer life in electric vehicles and hybrid electric vehicles. This paper proposes a novel parameter identification method and its implementation on SOP prediction for lithium-ion batteries. The extremum seeking algorithm is developed for identifying the parameters of batteries on the basis of an electrical circuit model incorporating hysteresis effect. A rigorous convergence proof of the estimation algorithm is provided. In addition, based on the electrical circuit model with the identified parameters, a battery SOP prediction algorithm is derived, which considers both the voltage and current limitations of the battery. Simulation results for lithium-ion batteries based on real test data from urban dynamometer driving schedule (UDDS) are provided to validate the proposed parameter identification and SOP prediction methods. The proposed method is suitable for real operation of embedded battery management system due to its low complexity and numerical stability.

Keyword : Battery management system, extremum seeking, lithium-ion battery, parameter identification, state of power

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