Abstract

The battery management system (BMS) of battery electric vehicles (BEVs) requires highly fidelity equivalent circuit model of battery to replicate its behavior in real-time applications. The estimation of parameters of battery model is a nonlinear, high-dimensional, and complex problem. In this paper, the determination of battery parameters is expressed as an optimization problem with an objective function defined as minimization of the Manhattan distance between catalog voltage and estimated voltage curve. The dependency of model parameters on C-rate and State of Charge (SOC) has also been accounted. Six different heuristic optimization approaches are implemented for minimizing the objective function, and their performances have been compared statistically using parametric and nonparametric tests and based on their convergence characteristics. The proposed method is tested on Energy Innovation Group (EIG) battery with nominal voltage 2.5 V and capacity 8 Ah. The battery parameters are also obtained using the pulse current charge–discharge test and used as a benchmark to compare the results of the proposed method.

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