Model Prediction and Rule Based Energy Management Strategy for a Plug-in Hybrid Electric Vehicle With Hybrid Energy Storage System

Abstract

This article presents an energy management strategy (EMS) design and optimization approach for a plug-in hybrid electric vehicle (PHEV) with a hybrid energy storage system (HESS) which contains a Li–Ti–O battery pack and a Ni–Co–Mn battery pack. The EMS shares power flows within the hybrid powertrain, and it employs a dual fuzzy logical controller whose inputs are predictions for PHEV powertrain states. An elitist nondominant genetic algorithm using a model in loop simulation approach as fitness functions is implemented to multiobjective optimization for the EMS under worldwide light-duty test cycles. The optimal objectives are improving PHEV mileage, minimizing battery packs capacity fades, reducing HESS degradation inconsistency, and minimizing driving cost unit distance. A hardware in loop test bench has been established to verify EMS performances in embedded systems. The test results under new European driving cycles demonstrate that optimized EMSs remain appropriate for different driving cycles and their performances are close to dynamic programming based offline optimal solutions. Due to the contributions of both the HESS and the optimized EMS, the PHEV energy efficiency has been improved by 1.6%–2.5% and the PHEV energy storage system cycle life can be improved by 159%–203%.