Abstract
Lithium-ion electrochemical batteries are being used more in a large number of applications, such as electric vehicles. However, increasing their efficiency lies in the accuracy of their model. For this, extracting the best values of parameters of the battery model is needed. A recent metaheuristic optimizer named the red-tail hawk (RTH) is used in the current research to extract the battery parameters. The idea of this algorithm is extracted from hunting techniques of red-tail hawks. The RTH algorithm is more likely to avoid entangled local optimums because of its high diversity, fast convergence rate, and appropriate exploitation-exploration balance. The RTH optimizer is compared with other algorithms to check and approve its performance. Using the proposed method, the root mean squared error (RMSE) between the model outputs and the measured voltage dataset was decreased to 8.12E-03, much better than all the other considered algorithms.
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