IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8540-d973379.html
   My bibliography  Save this article

Energy Management for PV Powered Hybrid Storage System in Electric Vehicles Using Artificial Neural Network and Aquila Optimizer Algorithm

Author

Listed:
  • Namala Narasimhulu

    (Department of Electrical and Electronics Engineering, Srinivasa Ramanujan Institute of Technology (Autonomous), Ananthapuramu 515701, Andhra Pradesh, India)

  • R. S. R. Krishnam Naidu

    (Department of Electrical and Electronics Engineering, N S Raju Institute of Technology, Sontyam, Visakhapatnam 531173, Andhra Pradesh, India)

  • Przemysław Falkowski-Gilski

    (Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland)

  • Parameshachari Bidare Divakarachari

    (Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore 560064, Karnataka, India)

  • Upendra Roy

    (Department of Electrical and Electronics Engineering, Channabasaveshwara Institute of Technology, Tumkur 572216, Karnataka, India)

Abstract

In an electric vehicle (EV), using more than one energy source often provides a safe ride without concerns about range. EVs are powered by photovoltaic (PV), battery, and ultracapacitor (UC) systems. The overall results of this arrangement are an increase in travel distance; a reduction in battery size; improved reaction, especially under overload; and an extension of battery life. Improved results allow the energy to be used efficiently, provide a comfortable ride, and require fewer energy sources. In this research, energy management between the PV system and the hybrid energy storage system (HESS), including the battery, and UC are discussed. The energy management control algorithms called Artificial Neural Network (ANN) and Aquila Optimizer Algorithm (AOA) are proposed. The proposed combined ANN–AOA approach takes full advantage of UC while limiting the battery discharge current, since it also mitigates high-speed dynamic battery charging and discharging currents. The responses’ behaviors are depicted and viewed in the MATLAB simulation environment to represent load variations and various road conditions. We also discuss the management among the PV system, battery, and UC to achieve the higher speed of 91 km/h when compared with existing Modified Harmony Search (MHS) and Genetic Algorithm-based Proportional Integral Derivative (GA-PID). The outcomes of this study could aid researchers and professionals from the automotive industry as well as various third parties involved in designing, maintaining, and evaluating a variety of energy sources and storage systems, especially renewable ones.

Suggested Citation

  • Namala Narasimhulu & R. S. R. Krishnam Naidu & Przemysław Falkowski-Gilski & Parameshachari Bidare Divakarachari & Upendra Roy, 2022. "Energy Management for PV Powered Hybrid Storage System in Electric Vehicles Using Artificial Neural Network and Aquila Optimizer Algorithm," Energies, MDPI, vol. 15(22), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8540-:d:973379
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8540/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/22/8540/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mikołaj Bartłomiejczyk & Leszek Jarzebowicz & Jiří Kohout, 2022. "Compensation of Voltage Drops in Trolleybus Supply System Using Battery-Based Buffer Station," Energies, MDPI, vol. 15(5), pages 1-15, February.
    2. Thanh Anh Huynh & Min-Fu Hsieh, 2018. "Performance Analysis of Permanent Magnet Motors for Electric Vehicles (EV) Traction Considering Driving Cycles," Energies, MDPI, vol. 11(6), pages 1-24, May.
    3. Szymon Potrykus & Filip Kutt & Janusz Nieznański & Francisco Jesús Fernández Morales, 2020. "Advanced Lithium-Ion Battery Model for Power System Performance Analysis," Energies, MDPI, vol. 13(10), pages 1-15, May.
    4. Wojciech Kurpiel & Przemysław Deja & Bartosz Polnik & Marcin Skóra & Bogdan Miedziński & Marcin Habrych & Grzegorz Debita & Monika Zamłyńska & Przemysław Falkowski-Gilski, 2021. "Performance of Passive and Active Balancing Systems of Lithium Batteries in Onerous Mine Environment," Energies, MDPI, vol. 14(22), pages 1-15, November.
    5. Robert Małkowski & Marcin Jaskólski & Wojciech Pawlicki, 2020. "Operation of the Hybrid Photovoltaic-Battery System on the Electricity Market—Simulation, Real-Time Tests and Cost Analysis," Energies, MDPI, vol. 13(6), pages 1-21, March.
    6. Aditya Kachhwaha & Ghamgeen Izat Rashed & Akhil Ranjan Garg & Om Prakash Mahela & Baseem Khan & Muhammed Badeaa Shafik & Mohamed G. Hussien, 2022. "Design and Performance Analysis of Hybrid Battery and Ultracapacitor Energy Storage System for Electrical Vehicle Active Power Management," Sustainability, MDPI, vol. 14(2), pages 1-14, January.
    7. Wang, Shuoqi & Lu, Languang & Han, Xuebing & Ouyang, Minggao & Feng, Xuning, 2020. "Virtual-battery based droop control and energy storage system size optimization of a DC microgrid for electric vehicle fast charging station," Applied Energy, Elsevier, vol. 259(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Jicheng & Sun, Jiakang & Yuan, Hanying & Su, Yihan & Feng, Shuxian & Lu, Chaoran, 2022. "Behavior analysis of photovoltaic-storage-use value chain game evolution in blockchain environment," Energy, Elsevier, vol. 260(C).
    2. Yi Du & Jiayan Zhou & Zhuofan He & Yandong Sun & Ming Kong, 2022. "A Dual-Harmonic Pole-Changing Motor with Split Permanent Magnet Pole," Energies, MDPI, vol. 15(20), pages 1-14, October.
    3. Ana Rita Silva & Ana Estanqueiro, 2022. "From Wind to Hybrid: A Contribution to the Optimal Design of Utility-Scale Hybrid Power Plants," Energies, MDPI, vol. 15(7), pages 1-19, April.
    4. Sijia Li & Arman Oshnoei & Frede Blaabjerg & Amjad Anvari-Moghaddam, 2023. "Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods," Sustainability, MDPI, vol. 15(11), pages 1-22, June.
    5. Diana Lemian & Florin Bode, 2022. "Battery-Supercapacitor Energy Storage Systems for Electrical Vehicles: A Review," Energies, MDPI, vol. 15(15), pages 1-13, August.
    6. Wang, Shuoqi & Guo, Dongxu & Han, Xuebing & Lu, Languang & Sun, Kai & Li, Weihan & Sauer, Dirk Uwe & Ouyang, Minggao, 2020. "Impact of battery degradation models on energy management of a grid-connected DC microgrid," Energy, Elsevier, vol. 207(C).
    7. Förster, Robert & Kaiser, Matthias & Wenninger, Simon, 2023. "Future vehicle energy supply - sustainable design and operation of hybrid hydrogen and electric microgrids," Applied Energy, Elsevier, vol. 334(C).
    8. Pavol Rafajdus & Valeria Hrabovcova & Pavel Lehocky & Pavol Makys & Filip Holub, 2018. "Effect of Saturation on Field Oriented Control of the New Designed Reluctance Synchronous Motor," Energies, MDPI, vol. 11(11), pages 1-10, November.
    9. Jiao, Feixiang & Zou, Yuan & Zhang, Xudong & Zhang, Bin, 2022. "Online optimal dispatch based on combined robust and stochastic model predictive control for a microgrid including EV charging station," Energy, Elsevier, vol. 247(C).
    10. Christoph Wenge & Robert Pietracho & Stephan Balischewski & Bartlomiej Arendarski & Pio Lombardi & Przemyslaw Komarnicki & Leszek Kasprzyk, 2020. "Multi Usage Applications of Li-Ion Battery Storage in a Large Photovoltaic Plant: A Practical Experience," Energies, MDPI, vol. 13(18), pages 1-18, September.
    11. Muhammad Waseem & Jingyuan Huang & Chak-Nam Wong & C. K. M. Lee, 2023. "Data-Driven GWO-BRNN-Based SOH Estimation of Lithium-Ion Batteries in EVs for Their Prognostics and Health Management," Mathematics, MDPI, vol. 11(20), pages 1-27, October.
    12. Yin, Linfei & Lu, Yuejiang, 2021. "Expandable deep width learning for voltage control of three-state energy model based smart grids containing flexible energy sources," Energy, Elsevier, vol. 226(C).
    13. Yang Sun & Shuhui Li & Malek Ramezani & Bharat Balasubramanian & Bian Jin & Yixiang Gao, 2019. "DSP Implementation of a Neural Network Vector Controller for IPM Motor Drives," Energies, MDPI, vol. 12(13), pages 1-17, July.
    14. Nicola Campagna & Vincenzo Castiglia & Rosario Miceli & Rosa Anna Mastromauro & Ciro Spataro & Marco Trapanese & Fabio Viola, 2020. "Battery Models for Battery Powered Applications: A Comparative Study," Energies, MDPI, vol. 13(16), pages 1-26, August.
    15. Dariusz Karkosiński & Wojciech Aleksander Rosiński & Piotr Deinrych & Szymon Potrykus, 2021. "Onboard Energy Storage and Power Management Systems for All-Electric Cargo Vessel Concept," Energies, MDPI, vol. 14(4), pages 1-16, February.
    16. Ammar Armghan & Muhammad Kashif Azeem & Hammad Armghan & Ming Yang & Fayadh Alenezi & Mudasser Hassan, 2021. "Dynamical Operation Based Robust Nonlinear Control of DC Microgrid Considering Renewable Energy Integration," Energies, MDPI, vol. 14(13), pages 1-23, July.
    17. Szymon Potrykus & Luis Fernando León-Fernández & Janusz Nieznański & Dariusz Karkosiński & Francisco Jesus Fernandez-Morales, 2021. "The Influence of External Load on the Performance of Microbial Fuel Cells," Energies, MDPI, vol. 14(3), pages 1-11, January.
    18. Pavić, Ivan & Čović, Nikolina & Pandžić, Hrvoje, 2022. "PV–battery-hydrogen plant: Cutting green hydrogen costs through multi-market positioning," Applied Energy, Elsevier, vol. 328(C).
    19. Jingang Han & Shiwei Lin & Boyu Pu, 2024. "Hierarchical Energy Management of DC Microgrid with Photovoltaic Power Generation and Energy Storage for 5G Base Station," Sustainability, MDPI, vol. 16(6), pages 1-19, March.
    20. Haupt, Leon & Schöpf, Michael & Wederhake, Lars & Weibelzahl, Martin, 2020. "The influence of electric vehicle charging strategies on the sizing of electrical energy storage systems in charging hub microgrids," Applied Energy, Elsevier, vol. 273(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8540-:d:973379. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.