Energy Economy Optimization for Platooning of Autonomous Electric Mining Trucks via Distributed Economic Model Predictive Control

This paper studies the problem of multi-objective energy economy optimization for electric mining truck platooning considering tracking performance, driving safety stability, and electric energy economy. We propose a dual distributed economic model predictive control (DEMPC) algorithm to optimize the transient battery energy efficiency and to ensure closed-loop stability and string stability of the platoon. Within the framework of DEMPC, each truck first solves an open-loop control optimization problem for platoon formation and then solves an open-loop economic optimization problem for direct electric energy economy improvement. Specifically, we present two modified constraint domains for the second optimization, which are conducive to finding a superior solution to the economic optimization problem. Numerical simulation results demonstrate that the two dual-DEMPC methods can save energy by 1.72% and 1.70% for autonomous electric trucks compared with the traditional methods.