摘要：AbstractAn 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.
关键词：KeywordsDigital twinParticle swarm optimizationOptimal controlPlug-in hybrid electric vehicles
