Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook

Fengqi Zhang, Lihua Wang, Serdar Coskun, Hui Pang, Yahui Cui and Junqiang Xi
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Fengqi Zhang: School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Lihua Wang: School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Serdar Coskun: Department of Mechanical Engineering, Tarsus University, Tarsus, Mersin 33400, Turkey
Hui Pang: School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Yahui Cui: School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Junqiang Xi: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Energies, 2020, vol. 13, issue 13, 1-35

Abstract: Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.

Keywords: Hybrid Electric Vehicles (HEVs); energy management strategies (EMSs); driving cycle prediction; optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

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