LAPSE:2020.0250
Published Article
LAPSE:2020.0250
Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions
Ahmed M. Ali, Dirk Söffker
February 24, 2020
In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development.
Keywords
hybrid electric vehicles, intelligent transportation systems, pattern recognition, real-time optimal power management
Suggested Citation
Ali AM, Söffker D. Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions. (2020). LAPSE:2020.0250
Author Affiliations
Ali AM: Chair of Dynamics and Control, University of Duisburg-Essen, Lotharstr. 1, 47057 Duisburg, Germany [ORCID]
Söffker D: Chair of Dynamics and Control, University of Duisburg-Essen, Lotharstr. 1, 47057 Duisburg, Germany
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Journal Name
Energies
Volume
11
Issue
3
Article Number
E476
Year
2018
Publication Date
2018-02-25
Published Version
ISSN
1996-1073
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Original Submission
Other Meta
PII: en11030476, Publication Type: Review
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LAPSE:2020.0250
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doi:10.3390/en11030476
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Feb 24, 2020
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CC BY 4.0
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Feb 24, 2020
 
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Calvin Tsay
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