Fuel-Efficient Gear Shift and Power Split Strategy for Parallel HEVs Based on Heuristic Dynamic Programming and Neural Networks

Abstract

In this paper, an online gear shift and power split strategy for parallel hybrid electric vehicles (HEVs) is proposed to optimize the fuel consumption. The gear shift control is implemented with a neural network (NN). The training of the NN is performed with data obtained from dynamic programming (DP) for different driving cycles. Simulation results present that the gear shift sequences and fuel consumption obtained from the NN and DP are very similar, underlining the near-optimal gear shift control for HEVs based on the NN. Furthermore, the power split control is realized with action-dependent heuristic dynamic programming (ADHDP). ADHDP does not need a system model and allows learning continuously. The power split control is therefore robust with respect to uncertainties and disturbances, as well as adaptive with respect to various driving situations and driver behaviors. The gear shift control based on the NN and the power split control based on ADHDP are combined to an adaptive energy management strategy with possible real-time application. It enables online learning of the controller and prevents the curse of dimensionality from DP calculation. Simulations for different driving cycles and comparisons with optimal solutions by DP indicate that the proposed energy management strategy is robust, adaptive, and near optimal.