Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

• Published in: Energies (Basel) Vol. 11; no. 3; p. 476
• Main Authors: Ali, Ahmed, Söffker, Dirk
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25-02-2018
• Subjects: Air pollution; Artificial intelligence; Computer applications; Electric vehicles; Emissions control; Energy flow; Energy management; Energy resources; Energy sources; Environmental degradation; hybrid electric vehicles; Identification methods; intelligent transportation systems; Management methods; Optimization; pattern recognition; Power management; Powertrain; Real time; real-time optimal power management; Trends
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en11030476

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.