Control Strategy Optimization for Parallel Hybrid Electric Vehicles Using a Memetic Algorithm

Yu-Huei Cheng,Ching-Ming Lai

doi:10.3390/en10030305

Abstract

Hybrid electric vehicle (HEV) control strategy is a management approach for generating, using, and saving energy. Therefore, the optimal control strategy is the sticking point to effectively manage hybrid electric vehicles. In order to realize the optimal control strategy, we use a robust evolutionary computation method called a “memetic algorithm (MA)” to optimize the control parameters in parallel HEVs. The “local search” mechanism implemented in the MA greatly enhances its search capabilities. In the implementation of the method, the fitness function combines with the ADvanced VehIcle SimulatOR (ADVISOR) and is set up according to an electric assist control strategy (EACS) to minimize the fuel consumption (FC) and emissions (HC, CO, and NOx) of the vehicle engine. At the same time, driving performance requirements are also considered in the method. Four different driving cycles, the new European driving cycle (NEDC), Federal Test Procedure (FTP), Economic Commission for Europe + Extra-Urban driving cycle (ECE + EUDC), and urban dynamometer driving schedule (UDDS) are carried out using the proposed method to find their respectively optimal control parameters. The results show that the proposed method effectively helps to reduce fuel consumption and emissions, as well as guarantee vehicle performance.

Highlights

The development of clean vehicles with high fuel economy and low emissions is gradually becoming mainstream in the automotive industry owing to the aggravation of the global energy crisis and environmental problems
This study proposes a robust evolutionary computation method called a “memetic algorithm (MA)” to optimize the control parameters in parallel Hybrid electric vehicle (HEV)
We combine the ADvanced VehIcle SimulatOR (ADVISOR) with the fitness function in the proposed MA method and use it to effectively evaluate the fuel consumption (FC) and emissions (HC, CO, and NOx ) of the vehicle engine based on the electric assist control strategy (EACS)

Summary

Introduction

The development of clean vehicles with high fuel economy and low emissions is gradually becoming mainstream in the automotive industry owing to the aggravation of the global energy crisis and environmental problems. In the field of vehicle engineering, conventional power systems driven by internal combustion engines (ICEs) have several disadvantages that adversely affect fuel economy and emissions. ICEs are generally over-designed approximately 10 times to meet the required vehicle driving performance that causes the cruising operating point to deviate away from the optimal operation point [1]. Hybrid electric vehicles (HEVs) do not certainly require external battery charging and new infrastructure; many researchers have focused on HEV in the past few years. Their superior fuel economy and lower emissions with no compromise in dynamic performance make HEVs a viable solution for providing cleaner and more fuel-efficient vehicles.

Methods

Results

Conclusion