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

R Punyavathi; A Pandian; Arvind R Singh; Mohit Bajaj; Milkias Berhanu Tuka; Vojtech Blazek

doi:10.1038/s41598-024-55988-5

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

Sci Rep. 2024 Mar 7;14(1):5661. doi: 10.1038/s41598-024-55988-5.

Authors

R Punyavathi¹, A Pandian¹, Arvind R Singh², Mohit Bajaj^{3

4

5

6}, Milkias Berhanu Tuka⁷, Vojtech Blazek⁸

Affiliations

¹ Department of EEE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522302, India.
² Department of Electrical Engineering, School of Physics and Electronic Engineering, Hanjiang Normal University, Hubei Shiyan, 442000, People's Republic of China.
³ Department of Electrical Engineering, Graphic Era (Deemed to Be University), Dehradun, 248002, India. thebestbajaj@gmail.com.
⁴ Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan. thebestbajaj@gmail.com.
⁵ Graphic Era Hill University, Dehradun, 248002, India. thebestbajaj@gmail.com.
⁶ Applied Science Research Center, Applied Science Private University, Amman, 11937, Jordan. thebestbajaj@gmail.com.
⁷ Department of Electrical and Computer Engineering, College of Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia. milkias.berhanu@aastu.edu.et.
⁸ ENET Centre, VSB-Technical University of Ostrava, 708 00, Ostrava, Czech Republic.

Abstract

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.

Keywords: Hybrid energy storage solution; Light electric vehicles; Machine learning; PV-battery interface; SRM EV drive; Solar electric vehicle; Supercapacitors; Sustainable power management.