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

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. Th...

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Bibliographic Details
Published in:Energies (Basel) 2018, Vol.11 (5), p.1033
Main Authors: Pang, Hui, Zhang, Fengqi
Format: Article
Language:English
Subjects:
Adaptive filters
Algorithms
Circuits
Commercialization
Comparative studies
Computer simulation
electric vehicles
Equivalent circuits
Experimental data
Extended Kalman filter
Feasibility studies
Genetic algorithms
Li-ion battery cell
Lithium-ion batteries
Parameter estimation
Parameter identification
Power management
sate of charge
State of charge
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en11051033