Multi-Objective Optimization Energy Management Strategy for Fuel Cell Hybrid Vehicles Based on Rule Learning

Abstract

In this paper, a rule learning-based energy management strategy is proposed to achieve optimal energy consumption economy and prolong the batteries lifetime for a fuel cell hybrid electric vehicle (FCHEV). First, the Pontryagin’s minimum principle (PMP) is used to obtain the optimal global solution. Then, K-means algorithm is adopted to simplify the optimal database composed of the optimized data based on PMP and the corresponding driving cycle features. According to the simplification data set, the improved repeated incremental pruning to produce error reduction (RIPPER) algorithm based on rule learning theory is used to learn the rules. Finally, the multiple linear regression algorithms are utilized to fit the data in the rule set. Simulation results validate that the proposed strategy can achieve more than 94% of the PMP strategy in the fuel consumption and also can prolong the batteries lifetime.