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

• Published in: Energies (Basel) Vol. 11; no. 5; p. 1033
• Main Authors: Pang, Hui, Zhang, Fengqi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2018
• 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; Mathematical models; Parameter estimation; Parameter identification; Power management; sate of charge; State of charge
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en11051033

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.