Surrogate-Driven Multi-Objective Predictive Control for Electric Vehicular Platoon

This paper proposes a surrogate-driven multi-objective predictive control (SMPC) strategy to address the dynamics uncertainty and multi-objective optimization issues of electric vehicular platoon (EVP). A surrogate-driven model is established with subspace identification to alleviate the adverse effects of uncertain dynamics for EVP. Then, a subspace predictor-based distributed surrogate-driven model predictive controller is developed for EVP. To mitigate conflicts among multiple optimization objectives involving driving safety, driving comfort and energy economy, a multi-objective cost function with the predictive sequence is designed. To this end, a grey wolf optimizer is suggested to guide the search towards diverse solutions, aiming to achieve globally optimal trade-offs among conflicting multiple objectives. In this way, the SMPC strategy is constructed, and its stability is theoretically proven. Finally, several experiments are carried out on a co-simulation vehicular platoon platform with the IPG-CarMaker software. The experimental results validate the effectiveness of the proposed SMPC strategy.