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Energies 2018, 11(5), 1033; https://doi.org/10.3390/en11051033

Experimental Data-Driven Parameter Identification and State of Charge Estimation for a Li-Ion Battery Equivalent Circuit Model

School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
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Received: 9 March 2018 / Revised: 12 April 2018 / Accepted: 17 April 2018 / Published: 24 April 2018
(This article belongs to the Collection Electric and Hybrid Vehicles Collection)
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Abstract

It is well known that accurate identification of the key state parameters and State of Charge (SOC) estimation method for a Li-ion battery cell is of great significance for advanced battery management system (BMS) of electric vehicles (EVs), which further facilitates the commercialization of EVs. This study proposed a systematic experimental data-driven parameter identification scheme and an adaptive extended Kalman Filter (AEKF)-based SOC estimation algorithm for a Li-Ion battery equivalent circuit model in EV applications. The key state parameters of Li-ion battery cell were identified based on the second-order resistor capacitor (RC) equivalent circuit model and the experimental battery test data using genetic algorithm (GA). Meanwhile, the proposed parameter identification procedure was validated by carrying out a comparative study of the simulated and experimental output voltage under the same input current profile. Then, SOC estimation was performed based on the AEKF algorithm. Finally, the effectiveness and feasibility of the proposed SOC estimator was verified by loading different operating profiles. View Full-Text
Keywords: electric vehicles; Li-ion battery cell; parameter identification; sate of charge; extended Kalman filter electric vehicles; Li-ion battery cell; parameter identification; sate of charge; extended Kalman filter
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Pang, H.; Zhang, F. Experimental Data-Driven Parameter Identification and State of Charge Estimation for a Li-Ion Battery Equivalent Circuit Model. Energies 2018, 11, 1033.

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