Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends

被引:84
作者
Dong, Peng [1 ,2 ]
Zhao, Junwei [1 ,2 ]
Liu, Xuewu [1 ,2 ,3 ]
Wu, Jian [3 ]
Xu, Xiangyang [1 ,2 ]
Liu, Yanfang [1 ,2 ]
Wang, Shuhan [1 ,2 ]
Guo, Wei [1 ,2 ]
机构
[1] Beihang Univ, Sch Transportat Sci & Engn, Dept Automot Engn, Beijing 100191, Peoples R China
[2] Beihang Univ, Ningbo Inst Technol, Ningbo 315832, Peoples R China
[3] GAC Automot Res & Dev Ctr, Guangzhou 511434, Peoples R China
基金
中国国家自然科学基金;
关键词
Hybrid electric vehicles(HEVs); Energy management strategies(EMSs); Development stages; Practical challenges; Implementation framework; MODEL-PREDICTIVE CONTROL; TRAFFIC SPEED PREDICTION; ADAPTIVE CRUISE CONTROL; POWER MANAGEMENT; NEURAL-NETWORK; SERIES-PARALLEL; FUEL-ECONOMY; VELOCITY PREDICTION; RECENT PROGRESS; OPTIMIZATION;
D O I
10.1016/j.rser.2022.112947
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The rapid development of intelligent and connected technologies is conducive to the efficient energy utilization of hybrid electric vehicles (HEVs). However, most energy management strategies (EMSs) with optimized, intelligent, and connected functions have not been directly applied to such vehicles because existing technical conditions cannot meet the theoretical requirements of complex EMSs. Therefore, based on the mapping rela-tionship between the information decision-making ability and the energy management effect, this study is the first to propose four development stages of HEV energy management practical application as follows: energy management based on instantaneous driving cycles (Stage 1 or S1); energy management based on forward driving cycle prediction (Stage 2 or S2); energy management based on global driving cycle prediction (Stage 3 or S3); and energy management based on autonomous velocity planning (Stage 4 or S4). The key technologies of each development stage are not independent, i.e., they complement each other in the process of practical application development. Furthermore, realizing energy management practical applications not only requires novel algorithm models but also involves several challenges such as acquiring and processing multi-source in-formation, predicting the vehicle power demand in the spatial-temporal domain during travel, and determining the vehicle control characteristics and ability. Finally, according to the development goals of energy manage-ment, this study proposes an implementation framework for HEV energy management in higher development stages, namely cooperative vehicle-edge-cloud for intelligent energy management, i.e., CVEC-IEM, which ex-ecutes information decision tasks on different computing platforms and realizes interconnection and interaction to provide development directions and goals for the efficient utilization of energy and successful deployment of HEV practical applications.
引用
收藏
页数:23
相关论文
共 176 条
[101]   A comparative study of supervisory control strategies for hybrid electric vehicles [J].
Pisu, Pierluigi ;
Rizzoni, Giorgio .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2007, 15 (03) :506-518
[102]   Optimal Energy Management for Hybrid Electric Vehicles Based on Dynamic Programming and Receding Horizon [J].
Polverino, Pierpaolo ;
Arsie, Ivan ;
Pianese, Cesare .
ENERGIES, 2021, 14 (12)
[103]  
Qiu CY, 2011, IEEE SYS MAN CYBERN, P2215, DOI 10.1109/ICSMC.2011.6084006
[104]   Features injected recurrent neural networks for short-term traffic speed prediction [J].
Qu, Licheng ;
Lyu, Jiao ;
Li, Wei ;
Ma, Dongfang ;
Fan, Haiwei .
NEUROCOMPUTING, 2021, 451 :290-304
[105]   Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles [J].
Rabinowitz, Aaron ;
Araghi, Farhang Motallebi ;
Gaikwad, Tushar ;
Asher, Zachary D. ;
Bradley, Thomas H. .
ENERGIES, 2021, 14 (18)
[106]   Energy Management Improvement of Hybrid Electric Vehicles via Combined GPS/Rule-Based Methodology [J].
Ramadan, H. S. ;
Becherif, M. ;
Claude, F. .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2017, 14 (02) :586-597
[107]   Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles [J].
Saiteja, Pemmareddy ;
Ashok, B. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2022, 157
[108]   Comparative analysis of hybrid vehicle energy management strategies with optimization of fuel economy and battery life [J].
Sarvaiya, Shradhdha ;
Ganesh, Sachin ;
Xu, Bin .
ENERGY, 2021, 228
[109]   Energy Management Strategy for Plug-in-Hybrid Electric Vehicles Based on Predictive PMP [J].
Schmid, Roland ;
Buerger, Johannes ;
Bajcinca, Naim .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2021, 29 (06) :2548-2560
[110]   Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures [J].
Seo, Minhwan ;
Song, Youngbin ;
Kim, Jake ;
Paek, Sung Wook ;
Kim, Gi-Heon ;
Kim, Sang Woo .
ENERGY, 2021, 226