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Optimal EV Charging Using ANT Lion Optimized ANN Based MPPT with High Gain Modified Zeta Converter

International Journal of Electrical and Electronics Engineering
© 2024 by SSRG - IJEEE Journal
Volume 11 Issue 2
Year of Publication : 2024
Authors : S.L. Sreedevi, B.T. Geetha
10.14445/23488379/IJEEE-V11I2P104
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How to Cite?

S.L. Sreedevi, B.T. Geetha, "Optimal EV Charging Using ANT Lion Optimized ANN Based MPPT with High Gain Modified Zeta Converter," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 2, pp. 24-36, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I2P104

Abstract:

Photovoltaic (PV) based power systems can be integrated with charging loads for Electric Vehicles (EVs) owing to their similar power forms, interfaces, and locations. Due to the random nature of solar generation and EV charging, this integration involves a high level of uncertainty, which demands robust control design and optimization to provide a constant power supply to the EV battery. Therefore, this research implements a novel optimized Artificial Neural Network based Maximum Power Point Tracking (ANN-MPPT) with High gain DC to DC converter for efficient EV battery charging. A highgain modified Zeta converter is deployed to convert the minimal voltage output of PV to a higher level of increased voltage. To maximize the power from the PV panel, the Ant Lion Optimized ANN (ALO-ANN) based MPPT is established in this work, which helps to preserve the stabilized DC link voltage. DC-AC conversion is accomplished using the High Frequency (HF) full bridge inverter, which provides a regulated high output voltage. Similarly, the HF isolation transformer operates to convert power while ensuring sufficient voltage balancing and galvanic isolation. A Proportional Integral controller (PI) is implemented to control the output of a proposed interleaved synchronous rectifier, which achieves the highest possible output to the battery for EVs by decreasing HF rectification losses while providing quicker transition response and input noise rejection. Furthermore, the proposed system’s functionality is validated by employing MATLAB simulation. According to the computational findings, the proposed system has a high-efficiency value of 93.6% and a better transient response to the system.

Keywords:

Ant Lion Optimized ANN-based MPPT, EV battery, HF isolation transformer, High Frequency full bridge inverter, High gain modified zeta converter, PV system.

References:

[1] Yang Li et al., “Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems with an Electric Vehicle Charging Station: A Bi-Level Approach,” IEEE Transactions on Sustainable Energy, vol. 12, no. 4, pp. 2321-2331, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Shakil Ahamed Khan et al., “A New Isolated Multi-Port Converter with Multi-Directional Power Flow Capabilities for Smart Electric Vehicle Charging Stations,” IEEE Transactions on Applied Superconductivity, vol. 29, no. 2, pp. 1-4, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Zhaoxi Liu et al., “Transactive Real-Time Electric Vehicle Charging Management for Commercial Buildings with PV On-Site Generation,” IEEE Transactions on Smart Grid, vol. 10, no. 5, pp. 4939-4950, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Hiroshi Kikusato et al., “Electric Vehicle Charging Management Using Auction Mechanism for Reducing PV Curtailment in Distribution Systems,” IEEE Transactions on Sustainable Energy, vol. 11, no. 3, pp. 1394-1403, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Debasish Mishra, Bhim Singh, and Bijaya Ketan Panigrahi, “Sigma-Modified Power Control and Parametric Adaptation in a Grid-Integrated PV for EV Charging Architecture,” IEEE Transactions on Energy Conversion, vol. 37, no. 3, pp. 1965-1976, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Wei Jiang, and Yongqi Zhen, “A Real-Time EV Charging Scheduling for Parking Lots with PV System and Energy Store System,” IEEE Access, vol. 7, pp. 86184-86193, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Yu Wu et al., “Real-Time Energy Management of Photovoltaic-Assisted Electric Vehicle Charging Station by Markov Decision Process,” Journal of Power Sources, vol. 476, pp. 1-16, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Peng Zhuang, “Stochastic Energy Management and Cyber-Physical Security of Battery Energy Storage Systems in Smart Distribution Systems,” Thesis, University of Alberta, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Mohammad Ekramul Kabir et al., “Optimal Scheduling of EV Charging at a Solar Power-Based Charging Station,” IEEE Systems Journal, vol. 14, no. 3, pp. 4221-4231, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Larissa Affolabi et al., “Optimal Transactive Energy Trading of Electric Vehicle Charging Stations with On-Site PV Generation in Constrained Power Distribution Networks,” IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 1427-1440, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Abdul Rauf Bhatti et al., “Optimized Sizing of Photovoltaic Grid‐Connected Electric Vehicle Charging System Using Particle Swarm Optimization,” International Journal of Energy Research, vol. 43, no. 1, pp. 500-522, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Qin Yan, Bei Zhang, and Mladen Kezunovic, “Optimized Operational Cost Reduction for an EV Charging Station Integrated with Battery Energy Storage and PV Generation,” IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 2096-2106, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Kalpesh Chaudhari et al., “Agent-Based Aggregated Behavior Modeling for Electric Vehicle Charging Load,” IEEE Transactions on Industrial Informatics, vol. 15, no. 2, pp. 856-868, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Bhim Singh et al., “Implementation of Solar PV-Battery And Diesel Generator Based Electric Vehicle Charging Station,” IEEE Transactions on Industry Applications, vol. 56, no. 4, pp. 4007-4016, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Leon Fidele Nishimwe H., and Sung-Guk Yoon, “Combined Optimal Planning and Operation of a Fast EV-Charging Station Integrated with Solar PV and ESS,” Energies, vol. 14, no. 11, pp. 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Farzam Nejabatkhah et al., “Modeling and Control of a New Three-Input DC–DC Boost Converter for Hybrid pv/fc/Battery Power System,” IEEE Transactions on Power Electronics, vol. 27, no. 5, pp. 2309-2324, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Francarl Galea et al., “Design of a High Efficiency Wide Input Range Isolated Cuk DC-DC Converter for Grid Connected Regenerative Active Loads,” World Engineers’ Convention, pp. 1-11, 2011.
[Google Scholar] [Publisher Link]
[18] Patan Javeed et al., “SEPIC Converter for Low Power LED Applications,” Journal of Physics: Conference Series, vol. 1818, pp. 1-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Hassanien Ramadan et al., “An Efficient Variable-Step P&O Maximum Power Point Tracking Technique for Grid-Connected Wind Energy Conversion System,” SN Applied Sciences, vol. 1, pp. 1-15, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Nahla E. Zakzouk et al., “Improved Performance Low‐Cost Incremental Conductance PV MPPT Technique,” IET Renewable Power Generation, vol. 10, no. 4, pp. 561-574, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Doudou N. Luta, and Atanda K. Raji, “Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack,” Energies, vol. 12, no. 5, pp. 1-15, 2019.
[CrossRef] [Google Scholar] [Publisher Link]