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.
Highlights
In recent years, Li-Ion battery (LIB) has been widely used in electric vehicles (EVs) and hybrid electric vehicles (HEVs) due to their high energy density, long cycle-life, low self-discharge and high specific power [1,2]
A large quantity of battery SOC estimation methods based on Li-Ion battery equivalent circuit mode (ECM) have been reported in the literature [3,4,5,6,7,8,9,10,11,12], which can be classified as coulomb counting [3,4,5], open circuit voltage (OCV)-based method [6,7], and model-based method [8,9,10,11,12]
2.6%, which illustrates that the adaptive extended Kalman Filter (AEKF)-based algorithm has higher accuracy in estimating battery SOC
Summary
Li-Ion battery (LIB) has been widely used in electric vehicles (EVs) and hybrid electric vehicles (HEVs) due to their high energy density, long cycle-life, low self-discharge and high specific power [1,2]. A large quantity of battery SOC estimation methods based on Li-Ion battery equivalent circuit mode (ECM) have been reported in the literature [3,4,5,6,7,8,9,10,11,12], which can be classified as coulomb counting [3,4,5], open circuit voltage (OCV)-based method [6,7], and model-based method [8,9,10,11,12].
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