A Comprehensive Review of Energy Management Strategies in Hybrid Electric Vehicles: Comparative Analysis and Challenges

被引:1
作者
Tella, Vaishnavi Chandra [1 ]
Alzayed, Mohamad [2 ]
Chaoui, Hicham [2 ]
机构
[1] Texas Tech Univ, Dept Elect & Comp Engn, Lubbock, TX 79409 USA
[2] Carleton Univ, Dept Elect, Intelligent Robot & Energy Syst Res Grp IRES, Ottawa, ON K1S 5B6, Canada
关键词
Batteries; Fuels; Energy management; Dynamic programming; Hybrid electric vehicles; Engines; Electric motors; Optimization; Hybrid power systems; Combustion; Climate change; Energy management strategies; hybrid electric vehicles; plug-in hybrid; offline and online control strategies; rule-based; optimization-based; deep learning; reinforcement learning; LEARNING-BASED ENERGY; POWER MANAGEMENT; DIFFERENTIAL EVOLUTION; TORQUE DISTRIBUTION; OPTIMIZATION; MODEL; MINIMIZATION; SYSTEMS; ARCHITECTURE; OPERATION;
D O I
10.1109/ACCESS.2024.3509737
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As air pollution, greenhouse gases, and global warming worsen, finding clean energy sources is critical. Renewable energy is a promising solution, especially in the transportation sector, which consumes significant energy. Hybrid electric vehicles (HEVs), combining an internal combustion engine and an electric battery, are key to reducing fossil fuel use and mitigating environmental harm. Effectively managing power distribution between these sources to enhance efficiency and minimize fuel consumption is crucial, known as an Energy Management Strategy (EMS). This article provides an overview of various EMS approaches for HEVs, analyzing their advantages and disadvantages. Rule-based strategies offer simplicity, optimization-based strategies provide superior performance, and advanced techniques like machine learning promise significant improvements. Current trends include integrating sophisticated sensors, data analytics, and artificial intelligence for real-time decision-making. Future directions aim at robust EMS frameworks integrating smart grid technologies and vehicle-to-everything (V2X) communication. The article reviews EMS methodologies, comparing their strengths and weaknesses, and discusses the main challenges and future trends in energy management for hybrid electric vehicles.
引用
收藏
页码:181858 / 181878
页数:21
相关论文
共 159 条
[31]   Calibration methodology for energy management system of a plug-in hybrid electric vehicle [J].
Duan, Benming ;
Wang, Qingnian ;
Zeng, Xiaohua ;
Gong, Yinsheng ;
Song, Dafeng ;
Wang, Junnian .
ENERGY CONVERSION AND MANAGEMENT, 2017, 136 :240-248
[32]   Hybrid electric vehicles: Architecture and motor drives [J].
Ehsani, Mehrdad ;
Gao, Yimin ;
Miller, John M. .
PROCEEDINGS OF THE IEEE, 2007, 95 (04) :719-728
[33]   Modelling and control of hybrid electric vehicles (A comprehensive review) [J].
Enang, Wisdom ;
Bannister, Chris .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 74 :1210-1239
[34]   A hybrid model predictive and fuzzy logic based control method for state of power estimation of series-connected Lithium-ion batteries in HEVs [J].
Esfandyari, M. J. ;
Yazdi, M. R. Hairi ;
Esfahanian, V ;
Masih-Tehrani, M. ;
Nehzati, H. ;
Shekoofa, O. .
JOURNAL OF ENERGY STORAGE, 2019, 24
[35]   Deep learning in the development of energy Management strategies of hybrid electric Vehicles: A hybrid modeling approach [J].
Estrada, Pedro Maroto ;
de Lima, Daniela ;
Bauer, Peter H. ;
Mammetti, Marco ;
Bruno, Joan Carles .
APPLIED ENERGY, 2023, 329
[36]   Design of an adaptive EMS for fuel cell vehicles [J].
Ettihir, K. ;
Cano, M. Higuita ;
Boulon, L. ;
Agbossou, K. .
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2017, 42 (02) :1481-1489
[37]   A Hybrid Energy Management Strategy Based on ANN and GA Optimization for Electric Vehicles [J].
Farajpour, Yashar ;
Chaoui, Hicham ;
Khayamy, Mehdy ;
Kelouwani, Sousso ;
Alzayed, Mohamad .
2022 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC), 2022,
[38]   A Mixed-Integer Linear Programming Model for the Electric Vehicle Charging Coordination Problem in Unbalanced Electrical Distribution Systems [J].
Franco, John F. ;
Rider, Marcos J. ;
Romero, Ruben .
IEEE TRANSACTIONS ON SMART GRID, 2015, 6 (05) :2200-2210
[39]   A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution [J].
Ganesh, Akhil Hannegudda ;
Xu, Bin .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2022, 154
[40]  
Gao, 2018, Modern Electric, Hybrid Electric, and Fuel Cell Vehicles