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Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Khayyam, Hamid and Bab-Hadiashar, Alireza 2014, Adaptive intelligent energy management system of plug-in hybrid electric vehicle, Energy, vol. 69, pp. 319-335, doi: 10.1016/j.energy.2014.03.020.

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Title Adaptive intelligent energy management system of plug-in hybrid electric vehicle
Author(s) Khayyam, Hamid
Bab-Hadiashar, Alireza
Journal name Energy
Volume number 69
Start page 319
End page 335
Total pages 17
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2014-05-01
ISSN 0360-5442
Keyword(s) Adaptive intelligent system
Complex systems
Energy management
Hybrid ANFIS-GA
Neuro-fuzzy
Plug-in hybrid electric vehicle
Science & Technology
Physical Sciences
Technology
Thermodynamics
Energy & Fuels
FUZZY INFERENCE SYSTEM
IDENTIFICATION
STRATEGIES
NETWORK
DESIGN
ANFIS
Summary Efficient energy management in hybrid vehicles is the key for reducing fuel consumption and emissions. To capitalize on the benefits of using PHEVs (Plug-in Hybrid Electric Vehicles), an intelligent energy management system is developed and evaluated in this paper. Models of vehicle engine, air conditioning, powertrain, and hybrid electric drive system are first developed. The effect of road parameters such as bend direction and road slope angle as well as environmental factors such as wind (direction and speed) and thermal conditions are also modeled. Due to the nonlinear and complex nature of the interactions between PHEV-Environment-Driver components, a soft computing based intelligent management system is developed using three fuzzy logic controllers. The crucial fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system with genetic algorithm optimization. For adaptive learning, a number of datasets were created for different road conditions and a hybrid learning algorithm based on the least squared error estimate using the gradient descent method was proposed. The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation. It is shown that the proposed intelligent energy management system is improving the performance of other existing systems. © 2014 Elsevier Ltd.
Language eng
DOI 10.1016/j.energy.2014.03.020
Field of Research 090602 Control Systems, Robotics and Automation
Socio Economic Objective 850799 Energy Conservation and Efficiency not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30069085

Document type: Journal Article
Collection: Institute for Frontier Materials
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