Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control

被引:18
作者
Punyavathi, R. [1 ]
Pandian, A. [1 ]
Singh, Arvind R. [2 ]
Bajaj, Mohit [3 ,4 ,5 ,6 ]
Tuka, Milkias Berhanu [7 ]
Blazek, Vojtech [8 ]
机构
[1] Koneru Lakshmaiah Educ Fdn, Dept EEE, Vaddeswaram 522502, Andhra Pradesh, India
[2] Hanjiang Normal Univ, Sch Phys & Elect Engn, Dept Elect Engn, Hubei Shiyan 442000, Peoples R China
[3] Graph Era, Dept Elect Engn, Dehra Dun 248002, India
[4] Al Ahliyya Amman Univ, Hourani Ctr Appl Sci Res, Amman, Jordan
[5] Graph Era Hill Univ, Dehra Dun 248002, India
[6] Appl Sci Private Univ, Appl Sci Res Ctr, Amman 11937, Jordan
[7] Addis Ababa Sci & Technol Univ, Coll Engn, Dept Elect & Comp Engn, Addis Ababa, Ethiopia
[8] Tech Univ Ostrava, ENET Ctr, VSB, Ostrava 708 00, Czech Republic
关键词
Solar electric vehicle; Sustainable power management; Light electric vehicles; Hybrid energy storage solution; Supercapacitors; PV-battery interface; SRM EV drive; Machine learning; ALGORITHM; STRATEGY; OPTIMIZATION;
D O I
10.1038/s41598-024-55988-5
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions.
引用
收藏
页数:21
相关论文
共 50 条
[41]   Reinforcement learning based adaptive power sharing of battery/supercapacitors hybrid storage in electric vehicles [J].
Lahyani, Amine ;
Abdelhedi, Riadh ;
Ammari, Ahmed Chiheb ;
Sari, Ali ;
Venet, Pascal .
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2025, 47 (01) :2993-3014
[42]   A comprehensive review on energy management strategies of hybrid energy storage systems for electric vehicles [J].
Kumaresan, N. ;
Rammohan, A. .
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING, 2024, 46 (03)
[43]   Modular energy management system with Jaya algorithm for hybrid energy storage in electric vehicles [J].
Demircali, Akif ;
Koroglu, Selim .
INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2022, 46 (15) :21497-21510
[44]   Optimal Energy Management and Storage Sizing for Electric Vehicles With Dual Storage [J].
Deshpande, Alok ;
Taylor, Josh A. .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2023, 31 (02) :872-880
[45]   Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning [J].
Du, Guodong ;
Zou, Yuan ;
Zhang, Xudong ;
Kong, Zehui ;
Wu, Jinlong ;
He, Dingbo .
APPLIED ENERGY, 2019, 251
[46]   A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles [J].
Moura, Scott Jason ;
Fathy, Hosam K. ;
Callaway, Duncan S. ;
Stein, Jeffrey L. .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2011, 19 (03) :545-555
[47]   An Optimal Power and Energy Management by Hybrid Energy Storage Systems in Microgrids [J].
Serpi, Alessandro ;
Porru, Mario ;
Damiano, Alfonso .
ENERGIES, 2017, 10 (11)
[48]   A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles [J].
Xue, Qicheng ;
Zhang, Xin ;
Teng, Teng ;
Zhang, Jibao ;
Feng, Zhiyuan ;
Lv, Qinyang .
ENERGIES, 2020, 13 (20)
[49]   Powertrain Control for Hybrid-Electric Vehicles Using Supervised Machine Learning [J].
Harold, Craig K. D. ;
Prakash, Suraj ;
Hofman, Theo .
VEHICLES, 2020, 2 (02) :267-286
[50]   Energy management and storage systems on electric vehicles: A comprehensive review [J].
Rimpas, Dimitrios ;
Kaminaris, Stavros D. ;
Aldarraji, Izzat ;
Piromalis, Dimitrios ;
Vokas, Georgios ;
Papageorgas, Panagiotis G. ;
Tsaramirsis, Georgios .
MATERIALS TODAY-PROCEEDINGS, 2022, 61 :813-819