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Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control
by
Singh, Arvind R.
, Tuka, Milkias Berhanu
, Blazek, Vojtech
, Punyavathi, R.
, Pandian, A.
, Bajaj, Mohit
in
639/166
/ 639/4077
/ 639/705
/ Alternative energy sources
/ Control algorithms
/ Electric vehicles
/ Energy storage
/ Humanities and Social Sciences
/ Hybrid energy storage solution
/ Learning algorithms
/ Light electric vehicles
/ Machine learning
/ multidisciplinary
/ Photovoltaics
/ PV-battery interface
/ Renewable energy sources
/ Renewable resources
/ Science
/ Science (multidisciplinary)
/ Solar electric vehicle
/ Supercapacitors
/ Sustainability
/ Sustainable energy
/ Sustainable power management
/ Voltage
2024
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Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control
by
Singh, Arvind R.
, Tuka, Milkias Berhanu
, Blazek, Vojtech
, Punyavathi, R.
, Pandian, A.
, Bajaj, Mohit
in
639/166
/ 639/4077
/ 639/705
/ Alternative energy sources
/ Control algorithms
/ Electric vehicles
/ Energy storage
/ Humanities and Social Sciences
/ Hybrid energy storage solution
/ Learning algorithms
/ Light electric vehicles
/ Machine learning
/ multidisciplinary
/ Photovoltaics
/ PV-battery interface
/ Renewable energy sources
/ Renewable resources
/ Science
/ Science (multidisciplinary)
/ Solar electric vehicle
/ Supercapacitors
/ Sustainability
/ Sustainable energy
/ Sustainable power management
/ Voltage
2024
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Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control
by
Singh, Arvind R.
, Tuka, Milkias Berhanu
, Blazek, Vojtech
, Punyavathi, R.
, Pandian, A.
, Bajaj, Mohit
in
639/166
/ 639/4077
/ 639/705
/ Alternative energy sources
/ Control algorithms
/ Electric vehicles
/ Energy storage
/ Humanities and Social Sciences
/ Hybrid energy storage solution
/ Learning algorithms
/ Light electric vehicles
/ Machine learning
/ multidisciplinary
/ Photovoltaics
/ PV-battery interface
/ Renewable energy sources
/ Renewable resources
/ Science
/ Science (multidisciplinary)
/ Solar electric vehicle
/ Supercapacitors
/ Sustainability
/ Sustainable energy
/ Sustainable power management
/ Voltage
2024
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Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control
Journal Article
Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control
2024
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Overview
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.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
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