A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles

A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles

The energy management strategy (EMS) and control algorithm of a hybrid electric vehicle (HEV) directly determine its energy efficiency, control effect, and system reliability. For a certain configuration of an HEV powertrain, the challenge is to develop an efficient EMS and an appropriate control al...

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Bibliographic Details
Main Authors: Qicheng Xue, Xin Zhang, Teng Teng, Jibao Zhang, Zhiyuan Feng, Qinyang Lv
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Energies
Subjects:
hybrid electric vehicles
energy management strategy
algorithm
optimization
classification
Online Access:https://www.mdpi.com/1996-1073/13/20/5355
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spelling doaj-3ca05372ceb84335bc738bf69dadf0f32020-11-25T03:58:29ZengMDPI AGEnergies1996-10732020-10-01135355535510.3390/en13205355A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric VehiclesQicheng Xue0Xin Zhang1Teng Teng2Jibao Zhang3Zhiyuan Feng4Qinyang Lv5Beijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaBeijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaBeijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaBeijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaBeijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaBeijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaThe energy management strategy (EMS) and control algorithm of a hybrid electric vehicle (HEV) directly determine its energy efficiency, control effect, and system reliability. For a certain configuration of an HEV powertrain, the challenge is to develop an efficient EMS and an appropriate control algorithm to satisfy a variety of development objectives while not reducing vehicle performance. In this research, a comprehensive, multi-level classification for HEVs is introduced in detail from the aspects of the degree of hybridization (DoH), the position of the motor, the components and configurations of the powertrain, and whether or not the HEV is charged by external power. The principle and research status of EMSs for each type of HEV are summarized and reviewed. Additionally, the EMSs and control algorithms of HEVs are compared and analyzed from the perspectives of characteristics, applications, real-time abilities, and historical development. Finally, some discussions about potential directions and challenges for future research on the energy management systems of HEVs are presented. This review is expected to bring contribution to the development of efficient, intelligent, and advanced EMSs for future HEV energy management systems.https://www.mdpi.com/1996-1073/13/20/5355hybrid electric vehiclesenergy management strategyalgorithmoptimizationclassification
collection DOAJ
language English
format Article
sources DOAJ
author Qicheng Xue
Xin Zhang
Teng Teng
Jibao Zhang
Zhiyuan Feng
Qinyang Lv
spellingShingle Qicheng Xue
Xin Zhang
Teng Teng
Jibao Zhang
Zhiyuan Feng
Qinyang Lv
A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles
Energies
hybrid electric vehicles
energy management strategy
algorithm
optimization
classification
author_facet Qicheng Xue
Xin Zhang
Teng Teng
Jibao Zhang
Zhiyuan Feng
Qinyang Lv
author_sort Qicheng Xue
title A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles
title_short A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles
title_full A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles
title_fullStr A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles
title_full_unstemmed A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles
title_sort comprehensive review on classification, energy management strategy, and control algorithm for hybrid electric vehicles
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2020-10-01
description The energy management strategy (EMS) and control algorithm of a hybrid electric vehicle (HEV) directly determine its energy efficiency, control effect, and system reliability. For a certain configuration of an HEV powertrain, the challenge is to develop an efficient EMS and an appropriate control algorithm to satisfy a variety of development objectives while not reducing vehicle performance. In this research, a comprehensive, multi-level classification for HEVs is introduced in detail from the aspects of the degree of hybridization (DoH), the position of the motor, the components and configurations of the powertrain, and whether or not the HEV is charged by external power. The principle and research status of EMSs for each type of HEV are summarized and reviewed. Additionally, the EMSs and control algorithms of HEVs are compared and analyzed from the perspectives of characteristics, applications, real-time abilities, and historical development. Finally, some discussions about potential directions and challenges for future research on the energy management systems of HEVs are presented. This review is expected to bring contribution to the development of efficient, intelligent, and advanced EMSs for future HEV energy management systems.
topic hybrid electric vehicles
energy management strategy
algorithm
optimization
classification
url https://www.mdpi.com/1996-1073/13/20/5355
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