Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm
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 base...
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-04-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/8/5/3661 |
| _version_ | 1811184394513154048 |
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| author | Zeyu Chen Rui Xiong Kunyu Wang Bin Jiao |
| author_facet | Zeyu Chen Rui Xiong Kunyu Wang Bin Jiao |
| author_sort | Zeyu Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T13:11:33Z |
| format | Article |
| id | doaj.art-584c4a76ae8c4108b90bdc747a452720 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T13:11:33Z |
| publishDate | 2015-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-584c4a76ae8c4108b90bdc747a4527202022-12-22T04:22:34ZengMDPI AGEnergies1996-10732015-04-01853661367810.3390/en8053661en8053661Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization AlgorithmZeyu Chen0Rui Xiong1Kunyu Wang2Bin Jiao3School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, ChinaNational Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, ChinaSchool of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, ChinaSchool of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, ChinaPlug-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.http://www.mdpi.com/1996-1073/8/5/3661plug-in hybrid electric vehicleenergy management strategyparticle swarm optimizationglobal optimal control |
| spellingShingle | Zeyu Chen Rui Xiong Kunyu Wang Bin Jiao Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm Energies plug-in hybrid electric vehicle energy management strategy particle swarm optimization global optimal control |
| title | Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm |
| title_full | Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm |
| title_fullStr | Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm |
| title_full_unstemmed | Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm |
| title_short | Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm |
| title_sort | optimal energy management strategy of a plug in hybrid electric vehicle based on a particle swarm optimization algorithm |
| topic | plug-in hybrid electric vehicle energy management strategy particle swarm optimization global optimal control |
| url | http://www.mdpi.com/1996-1073/8/5/3661 |
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