Next Article in Journal
Experimental Investigation of the Transpired Solar Air Collectors and Metal Corrugated Packing Solar Air Collectors
Next Article in Special Issue
A Novel High Step-Up DC-DC Converter with Coupled Inductor and Switched Clamp Capacitor Techniques for Photovoltaic Systems
Previous Article in Journal
A Novel Parametric Modeling Method and Optimal Design for Savonius Wind Turbines
Previous Article in Special Issue
Design and Implementation of a High Efficiency, Low Component Voltage Stress, Single-Switch High Step-Up Voltage Converter for Vehicular Green Energy Systems
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Energies 2017, 10(3), 305;

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

Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan
Department of Vehicle Engineering, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Road, Taipei 106, Taiwan
Author to whom correspondence should be addressed.
Academic Editor: Hongwen He
Received: 15 January 2017 / Accepted: 1 March 2017 / Published: 3 March 2017
Full-Text   |   PDF [2573 KB, uploaded 7 March 2017]   |  


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. View Full-Text
Keywords: hybrid electric vehicle (HEV); control strategy; memetic algorithm (MA); parameters optimization hybrid electric vehicle (HEV); control strategy; memetic algorithm (MA); parameters optimization

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Cheng, Y.-H.; Lai, C.-M. Control Strategy Optimization for Parallel Hybrid Electric Vehicles Using a Memetic Algorithm. Energies 2017, 10, 305.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top