Abstract
Abstract An autonomous calibration method based on particle swarm optimization (PSO) is studied for digital twin modelling of an electrified vehicle. To enhance the model robustness and mitigate the computational cost, a hybrid terminating strategy, which is built on a min-max function of the maximum iterations and minimal error, is implemented. A three-fold cross-validation experiment is designed to determine the setting of the terminating strategy. The proposed method is superior to the conventional PSO-based methods that are terminated by maximum iterations and minimal error. It can obtain a digital twin with at least 10% less error and save 45% computing time.
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