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Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles
Open AccessArticle

Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm

by 1, 2,3,*, 1 and 1
1
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
2
National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
3
Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Academic Editor: Joeri Van Mierlo
Energies 2015, 8(5), 3661-3678; https://doi.org/10.3390/en8053661
Received: 25 February 2015 / Revised: 11 April 2015 / Accepted: 24 April 2015 / Published: 29 April 2015
(This article belongs to the Special Issue Advances in Plug-in Hybrid Vehicles and Hybrid Vehicles)
Plug-in hybrid electric vehicles (PHEVs) have been recognized as one of the most promising vehicle categories nowadays due to their low fuel consumption and reduced emissions. Energy management is critical for improving the performance of PHEVs. This paper proposes an energy management approach based on a particle swarm optimization (PSO) algorithm. The optimization objective is to minimize total energy cost (summation of oil and electricity) from vehicle utilization. A main drawback of optimal strategies is that they can hardly be used in real-time control. In order to solve this problem, a rule-based strategy containing three operation modes is proposed first, and then the PSO algorithm is implemented on four threshold values in the presented rule-based strategy. The proposed strategy has been verified by the US06 driving cycle under the MATLAB/Simulink software environment. Two different driving cycles are adopted to evaluate the generalization ability of the proposed strategy. Simulation results indicate that the proposed PSO-based energy management method can achieve better energy efficiency compared with traditional blended strategies. Online control performance of the proposed approach has been demonstrated through a driver-in-the-loop real-time experiment. View Full-Text
Keywords: plug-in hybrid electric vehicle; energy management strategy; particle swarm optimization; global optimal control plug-in hybrid electric vehicle; energy management strategy; particle swarm optimization; global optimal control
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MDPI and ACS Style

Chen, Z.; Xiong, R.; Wang, K.; Jiao, B. Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm. Energies 2015, 8, 3661-3678. https://doi.org/10.3390/en8053661

AMA Style

Chen Z, Xiong R, Wang K, Jiao B. Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm. Energies. 2015; 8(5):3661-3678. https://doi.org/10.3390/en8053661

Chicago/Turabian Style

Chen, Zeyu; Xiong, Rui; Wang, Kunyu; Jiao, Bin. 2015. "Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm" Energies 8, no. 5: 3661-3678. https://doi.org/10.3390/en8053661

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