Identifying the unknown parameters of ECM Model for Li-Ion Battery Using Rao-1 algorithm

Nowadays, lithium-ion batteries (LiBs) have emerged as the most popular type of Energy Storage Systems (ESS) in electric vehicles (EVs). Accurately estimating the parameters for the equivalent circuit model (ECM) of LiBs, especially those that are not provided in the manufacturer's datasheets, is crucial for improving their behavior modeling and understanding. Therefore, this study focuses on investigating a precise method named Rao-1 algorithm, for extracting the optimal values of the ECM's parameters. The Rao-1 technique is a simple metaphor less algorithm that involves only arithmetic operation, such as addition and multiplication. The primary objective is to minimize the difference between the estimated voltage derived from the ECM and the measured voltage of the battery. To evaluate the effectiveness of this approach, a real-world driving data-based test profile is employed. Moreover, a comparative analysis is conducted against state-of-the-art optimization algorithms. Simulation results show that the applied method is capable of accurately estimating the parameters of the ECM and surpasses other methods in terms of accuracy and convergence speed. Finally, the Rao-1 approach can be suggested to improve the accuracy of battery models used in various applications since it offers a compromise between simplicity and precision.