Affiliations: [a] Department of Electronics and Telecommunication Engineering, MIT Academy of Engineering, Alandi(D), Pune, Maharasta, India | [b] Department of General Engineering, Institute of Chemical Technology, Matunga, Mumbai, Maharashtra, India | [c] Department of Engineering Sciences, Marathwada Mitra Mandals Institute of Technology, Lohgaon, Pune, Maharashtra, India
Correspondence:
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Corresponding author: Vaishali Abhijeet Katkar, Department of Electronics and Telecommunication Engineering, MIT Academy of Engineering, Alandi(D), Pune 412105, India. E-mail: [email protected].
Abstract: When approaching a large-scale performance, the choice, size, and administration of an Energy Storage System (ESS) for an Electric Vehicle (EV) are crucial. As the peak-to-average power demand ratio is relatively large, particularly for an urban ride that is frequently marked by rapid deceleration as well as acceleration, the complementary characteristics of the battery and Ultra-Capacitor (UC)render this arrangement a viable Hybrid energy storage system (HESS) for EV. Enhanced dynamic responsiveness, increased miles per charge, and extended battery life provided by the HESS increase the Electric Vehicle (EV’s) effectiveness. The primary objective of the study that has been suggested is to create a smart Energy Management Strategy (EMS) for EVs. A battery package and a properly sized Ultra-Capacitor (UC) together give the required high power along with energy density. This research proposes a CNN-based power management technique to aid in efficient EMS. Additionally, by adjusting the Convolution Neural Network (CNN) classifier’s weights through Improved Honey Badger Optimization (IHBO), the adaptive approach of the Standard HBO Algorithm, the performance of the classifier is improved. In MATLAB, the suggested CNN-based model for BESS EM is simulated and experimental assessment and analysis are done in terms of converter current and battery SoC. By contrasting the suggested approach against several standardized models, the performance analysis of the proposed work is assessed to validate its performance.
Keywords: Electric vehicle, energy management, convolutional neural network, IHBO, battery, UC
