Power Split Strategy Optimization of a Plug-in Parallel Hybrid Electric Vehicle

Abstract

Hybrid electric vehicles (HEV), plug-in HEV (PHEV) need an energy management system (EMS) to ensure good fuel economy while maintaining battery state-of-charge (SOC) within a safe range. The EMS is in charge of the power split decision between the engine and the electrical motor. For a PHEV, the optimal power split scenario will depend on the driving cycle, initial SOC, and trip length. Heavy computation and accurate knowledge of the future trip are required to find the optimal power split control and this represents a significant difficulty for the development of an EMS. The aim of this paper is to propose a genetic algorithm (GA) that optimizes the power split control parameters for a given driving cycle in a relatively short computation time, thus, overcoming the problem of heavy computation. The methodology consists in 1) defining the control laws and their associated control parameters based on the observation of optimality obtained by dynamic programming; and 2) developing a GA that will be able to compute the near-optimal values of these parameters in a short time and for a given driving cycle. It is demonstrated that the GA provides short computational burden and near-optimality for a wide variety of driving cycles. It then offers a promising tool for a future real-time implementation.