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Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions
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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.
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“ML methods have also been applied to battery energy management (for example charge estimation [330] or optimization in hybrid EVs [15]), and to detect faults and lateral misalignment in wireless charging of EVs [780].”
From Tackling climate change with machine learning (2023) · cites this paper as [15]
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“Hybrid EVs, which are more efficient than combustion engines alone, rely on power management methods that can be improved with ML [15].”
From Tackling climate change with machine learning (2023) · cites this paper as [15]
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Self-citationBoujneh, M., Majdoub, N., Ladhari, T., & Sakly, A. (2020) · International Conference on Sciences and Techniques of Automatic Control and Computer Engineering · doi:10.1109/sta50679.2020.9329308 · Cited by 2
The Study for Equivalent Consumption Minimization Strategy Considering Drivability of Parallel HEV
Jang, J., & Lee, H. (2020) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc49601.2020.9330848 · Cited by 2
Metadata
- Authors
- Ali, A., & Söffker, D.
- Affiliations
- University of Duisburg-Essen (DE)
- Corresponding author
- Ali, Ahmed
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2018-02-25
- Volume
- 11
- Issue
- 3
- Pages
- 476
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2789725222
- Cited by
- 134
- References
- 125
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 7 · 2023: 23 · 2022: 24 · 2021: 22 · 2020: 22 · 2019: 17 · 2018: 3
- FWCI
- 8.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Engineering › Automotive Engineering › Vehicle Dynamics and Control Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Keywords
- hybrid electric vehicles, power management, real-time control, powertrain efficiency, rule-based control, optimization-based control, artificial intelligence, adaptive control, fuel consumption reduction, emission reduction
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