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

Fengqi Zhang,Junqiang Xi,Hui Pang,Serdar Coskun,Yahui Cui,Lihua Wang

doi:10.3390/en13133352

Fengqi Zhang, Junqiang Xi + Show 4 more

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https://doi.org/10.3390/en13133352

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Journal: Energies	Publication Date: Jun 30, 2020
Citations: 104	License type: CC BY 4.0

Affiliation: Xi'an University of Technology

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.

Highlights

Hybrid Electric Vehicles (HEVs) are composed of different types of energy sources and power converters, which generally refer to vehicles consisting of an internal combustion engine (ICE) with an electric motor
The results showed that the computation efficiency of the pseudospectral method is higher than that of dynamic programming (DP), while the optimization performance is close to DP
The results show that the Artificial Neural Network (ANN)-based velocity predictor yields the best performance for predictive energy management

Summary

Introduction

Hybrid Electric Vehicles (HEVs) are composed of different types of energy sources and power converters, which generally refer to vehicles consisting of an internal combustion engine (ICE) with an electric motor. The general goal to develop HEVs is to reduce fuel consumption and emissions while ensuring drivers’ power demands by investigating the appropriate energy management strategies (EMSs). Energy management aims to obtain an optimal power split in view of complex driving conditions, as well as to minimize fuel consumption and emissions. Energies 2020, 13, 3352 that improvements in the fuel economy of HEVs, and the consequent reduction in emissions, depend crucially on their energy management strategies (EMSs) [1].

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