A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles

RENEWABLE & SUSTAINABLE ENERGY REVIEWS

卷:151

Article

Chen, Z.^1,2,5,6  Liu, Y.^1,2  Ye, M.³  Zhang, Y.⁴  Li, G.⁶ 

[1] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China

[2] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China

[3] Chongqing Univ Technol, Key Lab Adv Manufacture Technol Automobile Parts, Minist Educ, Chongqing 400054, Peoples R China

[4] Queens Univ Belfast, Sir William Wright Technol Ctr, Belfast BT9 5BS, Antrim, North Ireland

[5] Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming 650500, Yunnan, Peoples R China

[6] Queen Mary Univ London, Sch Engn & Mat Sci, London E1 4NS, England

关键词: Hybrid electric vehicle (HEV);    Plug-in hybrid electric vehicle (PHEV);    Energy management strategy (EMS);    Optimal control strategy;    Equivalent consumption minimisation strategy (ECMS);    Equivalent factor (EF);   

DOI  :  10.1016/j.rser.2021.111607

来源: Elsevier

【 摘 要 】

Hybrid electric vehicles (HEVs), as a promising solution to mitigate environmental pollution and reduce fuel consumption, employ a combination of fuel and electric power as power supply for boosting the vehicle's fuel economy. Comparing to conventional internal combustion engine (ICE) driven vehicles, the additional propulsion power source in electrified powertrain systems of HEVs leads to the extra control degree of freedom. Thus, a well-designed energy management strategy (EMS) is indispensable to cope with the complexity of the power distribution existing in multiple power source system. Equivalent consumption minimisation strategy (ECMS) is one of the most promising EMS techniques due to its capability of achieving the real-time local optimal control. In ECMS, a key parameter - equivalent factor (EF) is usually employed to unify the ICE fuel consumption and the electric energy consumption into a single variable representing the equivalent fuel economy, thereby achieving the instantaneous fuel economy optimisation. This paper comprehensively surveys the state-of-the-art in ECMSs for PHEVs and HEVs. Firstly, the basic operation mechanism of ECMSs is discussed. Then, ECMSs are classified based on their dependence on either online computation or offline pre-computation. Moreover, the core technique of ECMSs - EF adaptation is elaborated in terms of their principles, key characteristics, advantages, and disadvantages. In addition, the key factors for the EF adaptation as well as the corresponding factor integration methods are analysed and summarised. Finally, future research trends and the gaps for the development of ECMSs are discussed.

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