TWO-STAGE DC-DC ISOLATED CONVERTER FOR EV BATTERY CHARGING WITH AI CONTROLLER
DOI:
https://doi.org/10.63458/ijerst.v3i1.102Keywords:
Electric Vehicle (EV), AI-Controlled Charging, DC-DC, ANN, Sustainable Transportation, Battery PerformanceAbstract
The Electric vehicles (EVs) require efficient and reliable charging systems to optimize battery performance and lifespan. Traditional converters, however, face limitations in meeting these demands, especially under dynamic charging conditions. This project presents a novel approach to EV battery charging using an AI-controlled two-stage DC-DC isolated converter, enhanced with an artificial neural network (ANN) for real-time optimization. The proposed system utilizes a two-stage converter architecture, where the first stage boosts the input voltage, and the second isolated stage provides galvanic separation, ensuring safe and efficient power transfer to the EV battery. An ANN- based controller dynamically adjusts the converter parameters based on input voltage, battery state-of-charge (SOC), and output current. This intelligent control minimizes energy losses, improves voltage regulation, and reduces thermal stress on the converter components, leading to enhanced system efficiency. Simulation results indicate that the ANN controller adapts effectively to varying input conditions, maintaining optimal charging rates while safeguarding battery health. This AI-driven approach offers a promising solution for fast, efficient, and adaptive EV battery charging, aligning with the increasing demand for smart and sustainable transportation technologies.
References
S. Rajesh et al., “DC-DC Converters for EV Charging: Topologies and Control,” 2021.
R. Smith et al., “AI-Driven Control for EV Charging Systems,” Journal of Power Electronics, vol. 16, no. 4, pp. 255–267, 2020, doi: 10.1109/JPE.2020.2978140.
J. Chen et al., “Thermal Management in DC-DC Converters for EVs,” Energy eports, vol. 8, pp. 543–551, 2022, doi: 10.1016/j.egyr.2022.01.045.
X. Wang et al., “Emerging Trends in EV Charging Technologies,” IEEE Power and Energy Society General Meeting, 2023.
R. Li et al., “Performance Comparison of DC-DC Converter Topologies,” Renewable Energy, vol. 168, pp. 60–70, 2022, doi: 10.1016/j.renene.2021.12.007.
S. Brown et al., “AI Applications in Energy Storage Systems,” Energy Storage Materials, vol. 34, pp. 149– 158, 2024, doi: 10.1016/j.ensm.2023.12.005.
Y. Kim et al., “Innovative Control Systems for EV Charging,” Journal of Power Electronics, vol. 18, no. 3, pp. 400–411, 2023,doi: 10.1109/JPE.2023.3078890.
Z. Chen et al., “Reliability Assessment of EV Charging Systems,” Reliability Engineering & System Safety, vol. 213, 2021, doi: 10.1016/j.ress.2021.107785.
K. Lee et al., “Integration of Renewable Energy Sources in EV Charging,” Renewable Energy, vol. 10.1016/j.renene.2022.11.034. Vehicles, 182, pp. 925–934, 2023, doi: 11.
H. Patel et al., “Performance Optimization of Charging Systems,” Journal of Electric vol.. 7, no. 2, pp. 201–
, 2024, doi: 10.1109/JEVC.2024.3100217.
G. Li et al., “AI and Power Electronics: Future Trends,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 1, pp. 1–12, 2022, doi: 10.1109/JESTPE.2021.3081135.
A.Kumar et al., “Two-Stage DC-DC Converters: Design and Applications,” Journal of Electrical Engineering,vol. 12, no. 4, pp. 345–356, 2024,doi: 10.1109/JEE.2024.3100174.
M. Green et al., “Fault Detection in DC-DC Converters,” 2021,doi: 10.1016/j.ress.2021.107792.
P. Scott et al., “Energy Management Strategies for EV Charging,” Energy, vol. 238, 2022, doi: 10.1016/j.energy.2021.121699.
T. Johnson et al., “AI-Enhanced Smart Charging Solutions,” 2023.
V. Patel et al., “Decentralized Control in EV Charging,” IEEE Transactions on Smart Grid, vol. 15, no. 3, pp.2340–2350, 2024, doi: 10.1109/TSG.2023.3286001.
D. Williams et al., “Charging Infrastructure for Electric Vehicles,” International Journal of Energy Research, vol. 47, no. 2, pp. 698–712, 2023, doi: 10.1002/er.6365.
18.L. Zhang et al., “Modeling and Simulation of DC-DC Converters,” Journal of Power Sources, vol. 515,
, doi: 10.1016/j.jpowsour.2022.230362.
F. White et al., “Impact of AI on Energy Efficiency,” Journal of Cleaner Production, vol. 298, 2022, doi:
1016/j.jclepro.2021.126770.
M. Robinson et al., “Advances in Charging Technology for EVs,” Electric Power Systems Research, vol. 195,2023, doi: 10.1016/j.epsr.2022.107257.
