Cooperative Power Split Optimization for a Group of Intelligent Electric Vehicles Travelling on a Highway With Varying Slopes

Abstract

This paper proposes a cooperative optimal power split (COPS) method for a group of intelligent electric vehicles with battery/supercapacitor hybrid energy storage systems (HESSs). To achieve good performance, the proposed COPS method is made up of the upper and lower layers: the upper layer aims at obtaining the optimal power demand sequence and sending it to the lower layer; the lower layer attempts to optimize the power split for HESS. Firstly, to ensure the performance of the proposed method in practice, the resistance of the battery and supercapacitor packs as well as the DC/DC converter efficiency are not assumed to be constants, but modeled well in the paper. Secondly, to achieve the optimal power demand sequence, an upper power demand sequence optimization problem is formulated based on distributed model predictive control (DMPC), in which energy demand is incorporated into the cost function. Thirdly, after receiving the optimal power demand sequence, a hybrid power split optimization strategy is presented to obtain the optimal power split, in which the lower HESS power split optimization problem is formulated based on DMPC to decrease battery aging process and energy consumption. Finally, an improved particle swarm optimization algorithm with multiple dynamic populations is used to solve the formulated upper and lower optimization problems. Simulation results demonstrate that, compared with the benchmark, the proposed COPS method can significantly extend battery lifespan and slightly decrease energy consumption.