Energy Conversion and Management, Vol.160, 74-84, 2018
Improving fuel economy and performance of a fuel-cell hybrid electric vehicle (fuel-cell, battery, and ultra-capacitor) using optimized energy management strategy
Fuel-Cell System (FCS) is the primary energy supply of a Fuel-Cell Vehicle (FCV). Battery or Ultra-Capacitor (UC), as a secondary power source, is used along the FCS to improve the FCV's power response. Battery and UC composition, as a hybrid power source presenting the term of Fuel-Cell Hybrid Electric Vehicle (FCHEV), provides the FCV with the advantages of high energy density and high dynamic response. The supervisory system of the FCHEV could be managed efficiently to exploit the benefits of battery and UC at the same time. As a matter of fact, in such a combination, the performance of the hybrid powertrain largely depends on how to distribute the requested power through different types of energy sources. In this paper, we design the powertrain elements of an FCHEV in advance, with FCS/Battery/UC considerations. The energy management strategy (EMS) is achieved by presenting a novel power sharing method and by implementing an intelligent control technique constructed based on Fuzzy Logic Control (FLC). The control parameters are accurately adjusted by the genetic algorithm (GA) while considering targets and restrictions within a multi-objective optimization function over a combined city/highway driving cycle. This optimized supervisory system is examined by Advanced Vehicle Simulator (ADVISOR) to evaluate the performance of the proposed EMS over 22 different driving cycles and some specific performance tests. The results of simulation show that the presented strategy progressively affects the vehicle characteristics. Fuel economy enhancement, vehicle performance improvement, battery charge-sustaining capability, and optimal energy distribution are some of the significant outcomes achieved by the optimized FLC-based EMS.
Keywords:Energy management strategy;Fuel-cell hybrid electric vehicle;Fuzzy logic control;Genetic algorithm;Multiple objective optimization