Version 1
: Received: 5 July 2024 / Approved: 5 July 2024 / Online: 5 July 2024 (14:48:19 CEST)
How to cite:
S, C. D.; Ramkumar, A. Design and Implementation of Adaptive Deep Learning-Based DC-DC Converters for Photovoltaic Systems with Battery Storage in Electric Vehicles. Preprints2024, 2024070545. https://doi.org/10.20944/preprints202407.0545.v1
S, C. D.; Ramkumar, A. Design and Implementation of Adaptive Deep Learning-Based DC-DC Converters for Photovoltaic Systems with Battery Storage in Electric Vehicles. Preprints 2024, 2024070545. https://doi.org/10.20944/preprints202407.0545.v1
S, C. D.; Ramkumar, A. Design and Implementation of Adaptive Deep Learning-Based DC-DC Converters for Photovoltaic Systems with Battery Storage in Electric Vehicles. Preprints2024, 2024070545. https://doi.org/10.20944/preprints202407.0545.v1
APA Style
S, C. D., & Ramkumar, A. (2024). Design and Implementation of Adaptive Deep Learning-Based DC-DC Converters for Photovoltaic Systems with Battery Storage in Electric Vehicles. Preprints. https://doi.org/10.20944/preprints202407.0545.v1
Chicago/Turabian Style
S, C. D. and A Ramkumar. 2024 "Design and Implementation of Adaptive Deep Learning-Based DC-DC Converters for Photovoltaic Systems with Battery Storage in Electric Vehicles" Preprints. https://doi.org/10.20944/preprints202407.0545.v1
Abstract
Photovoltaic (PV) systems, which are renewable energy sources, are increasingly being utilized in distributed generation. To maintain a stable energy balance, energy storage systems such as batteries (BAT) play a vital role. Electric vehicles (EVs) offer a promising solution with rechargeable battery systems for operation. The BATs must maintain their state of charge within design boundaries, despite intermittent PV and load power fluctuations, and advanced power control and management techniques are essential for their effective operation. The article evaluates an intelligent controller that uses a Sliding Mode Control adaptive deep learning algorithm Convolutional Neural Networks (SMC-CNN) and a unique Strategy for managing supervisory power. (SPMS) for photovoltaic systems with battery energy storage for better regulation of DC bus voltage. SMC-CNN offers exceptional resilience and seamless functionality, minimizing fluctuations in DC bus voltage in the control strategy design requirements. The primary goals are maintaining a consistent power supply and ensuring continuous service by preventing system components from exceeding capacity. This study aims to enhance the control of DC bus voltage in the PV and battery system, with the primary significance focusing on the following aspects. The advanced SPMS has been created, incorporating control system constraints to enhance SOC balancing speed and reduce fluctuations in DC bus voltage. Power flow management involves optimizing energy flow between the PV system, battery system, and load while minimizing battery capacity requirements. The proposed SMC-CNN and SPMS are demonstrated through real-time simulation using Matlab/Simulink, as demonstrated in comprehensive case studies.
Keywords
Photovoltaic (PV) Systems; Battery Storage; Electric Vehicles (EVs); DC-DC Converters; Adaptive Deep Learning; Sliding Mode Control (SMC); Convolutional Neural Networks (CNN)
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
