Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control
Status PubMed-not-MEDLINE Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články
PubMed
38454016
PubMed Central
PMC10920784
DOI
10.1038/s41598-024-55988-5
PII: 10.1038/s41598-024-55988-5
Knihovny.cz E-zdroje
- Klíčová slova
- Hybrid energy storage solution, Light electric vehicles, Machine learning, PV-battery interface, SRM EV drive, Solar electric vehicle, Supercapacitors, Sustainable power management,
- Publikační typ
- časopisecké články MeSH
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.
Applied Science Research Center Applied Science Private University Amman 11937 Jordan
Department of EEE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh 522302 India
Department of Electrical Engineering Graphic Era Dehradun 248002 India
ENET Centre VSB Technical University of Ostrava 708 00 Ostrava Czech Republic
Graphic Era Hill University Dehradun 248002 India
Hourani Center for Applied Scientific Research Al Ahliyya Amman University Amman Jordan
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