IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i21p4429-d1267304.html
   My bibliography  Save this article

Real-Time Management for an EV Hybrid Storage System Based on Fuzzy Control

Author

Listed:
  • Dimitrios Rimpas

    (Department of Electrical & Electronics Engineering, University of West Attica, 250 Thivon Av., 122-44 Egaleo, Greece)

  • Stavrοs D. Kaminaris

    (Department of Electrical & Electronics Engineering, University of West Attica, 250 Thivon Av., 122-44 Egaleo, Greece)

  • Dimitrios D. Piromalis

    (Department of Electrical & Electronics Engineering, University of West Attica, 250 Thivon Av., 122-44 Egaleo, Greece)

  • George Vokas

    (Department of Electrical & Electronics Engineering, University of West Attica, 250 Thivon Av., 122-44 Egaleo, Greece)

Abstract

Following the European Climate Law of 2021 and the climate neutrality goal for zero-emission transportation by 2050, electric vehicles continue to gain market share, reaching 2.5 million vehicles in Q1 of 2023. Electric vehicles utilize an electric motor for propulsion powered by lithium batteries, which suffer from high temperatures caused by peak operation conditions and rapid charging, so hybridization with supercapacitors is implemented. In this paper, a fuzzy logic controller is employed based on a rule-based scheme and the Mamdani model to control the power distribution of the hybrid system, driven by the state of charge and duty cycle parameters. An active topology with one bi-directional DC-to-DC converter at each source is exploited in the MATLAB/Simulink environment, and five power states like acceleration and coasting are identified. Results show that the ideal duty cycle is within 0.40–0.50 as a universal value for all power states, which may vary depending on the available state of charge. Total efficiency is enhanced by 6%, sizing is increased by 22%, leading to a more compact layout, and battery life is extended by 20%. Future work includes testing with larger energy sources and the application of this management strategy in real-time operations.

Suggested Citation

  • Dimitrios Rimpas & Stavrοs D. Kaminaris & Dimitrios D. Piromalis & George Vokas, 2023. "Real-Time Management for an EV Hybrid Storage System Based on Fuzzy Control," Mathematics, MDPI, vol. 11(21), pages 1-18, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4429-:d:1267304
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/21/4429/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/21/4429/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xinyu Zhao & Yunxiao Zhang & Xueying Cui & Le Wan & Jinlong Qiu & Erfa Shang & Yongchang Zhang & Haisen Zhao, 2023. "Wavelet Packet-Fuzzy Optimization Control Strategy of Hybrid Energy Storage Considering Charge–Discharge Time Sequence," Sustainability, MDPI, vol. 15(13), pages 1-17, July.
    2. Tsemekidi Tzeiranaki, Sofia & Economidou, Marina & Bertoldi, Paolo & Thiel, Christian & Fontaras, Georgios & Clementi, Enrico Luca & Franco De Los Rios, Camilo, 2023. "“The impact of energy efficiency and decarbonisation policies on the European road transport sector”," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    3. Dong, Ao & Ma, Ruifei & Deng, Yelin, 2023. "Optimization on charging of the direct hybrid lithium-ion battery and supercapacitor for high power application through resistance balancing," Energy, Elsevier, vol. 273(C).
    4. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    5. Adrian Chmielewski & Piotr Piórkowski & Krzysztof Bogdziński & Jakub Możaryn, 2023. "Application of a Bidirectional DC/DC Converter to Control the Power Distribution in the Battery–Ultracapacitor System," Energies, MDPI, vol. 16(9), pages 1-40, April.
    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. Zhang, Wei & Wang, Jixin & Liu, Yong & Gao, Guangzong & Liang, Siwen & Ma, Hongfeng, 2020. "Reinforcement learning-based intelligent energy management architecture for hybrid construction machinery," Applied Energy, Elsevier, vol. 275(C).
    2. Yao He & Changchang Miao & Ji Wu & Xinxin Zheng & Xintian Liu & Xingtao Liu & Feng Han, 2021. "Research on the Power Distribution Method for Hybrid Power System in the Fuel Cell Vehicle," Energies, MDPI, vol. 14(3), pages 1-15, January.
    3. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
    4. Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
    5. Chi T. P. Nguyen & Bảo-Huy Nguyễn & Minh C. Ta & João Pedro F. Trovão, 2023. "Dual-Motor Dual-Source High Performance EV: A Comprehensive Review," Energies, MDPI, vol. 16(20), pages 1-28, October.
    6. Wang, Yujie & Sun, Zhendong & Chen, Zonghai, 2019. "Development of energy management system based on a rule-based power distribution strategy for hybrid power sources," Energy, Elsevier, vol. 175(C), pages 1055-1066.
    7. Berrueta, Alberto & Heck, Michael & Jantsch, Martin & Ursúa, Alfredo & Sanchis, Pablo, 2018. "Combined dynamic programming and region-elimination technique algorithm for optimal sizing and management of lithium-ion batteries for photovoltaic plants," Applied Energy, Elsevier, vol. 228(C), pages 1-11.
    8. Bo, Lin & Han, Lijin & Xiang, Changle & Liu, Hui & Ma, Tian, 2022. "A Q-learning fuzzy inference system based online energy management strategy for off-road hybrid electric vehicles," Energy, Elsevier, vol. 252(C).
    9. Mirela Sichigea & Daniel Cîrciumaru & Valeriu Brabete & Cătălin Mihail Barbu, 2024. "Sustainable Transport in the European Union: Exploring the Net-Zero Transition through Confirmatory Factor Analysis and Gaussian Graphical Modeling," Energies, MDPI, vol. 17(11), pages 1-17, May.
    10. Mohammad Pourmatin & Moein Moeini-Aghtaie & Erfan Hassannayebi & Elizabeth Hewitt, 2024. "Transition to Low-Carbon Vehicle Market: Characterization, System Dynamics Modeling, and Forecasting," Energies, MDPI, vol. 17(14), pages 1-36, July.
    11. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    12. Xiaogang Wu & Zhihao Cui & Xuefeng Li & Jiuyu Du & Ye Liu, 2019. "Control Strategy for Active Hierarchical Equalization Circuits of Series Battery Packs," Energies, MDPI, vol. 12(11), pages 1-18, May.
    13. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
    14. Wu, Yue & Huang, Zhiwu & Liao, Hongtao & Chen, Bin & Zhang, Xiaoyong & Zhou, Yanhui & Liu, Yongjie & Li, Heng & Peng, Jun, 2020. "Adaptive power allocation using artificial potential field with compensator for hybrid energy storage systems in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    15. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    16. Lu, Xin & Chen, Ning & Li, Hui & Guo, Shiyu & Chen, Zengtao, 2023. "Simulation of the temperature distribution of lithium-ion battery module considering the time-delay effect of the porous electrodes," Energy, Elsevier, vol. 284(C).
    17. Xu, Bin & Shi, Junzhe & Li, Sixu & Li, Huayi & Wang, Zhe, 2021. "Energy consumption and battery aging minimization using a Q-learning strategy for a battery/ultracapacitor electric vehicle," Energy, Elsevier, vol. 229(C).
    18. Ramya Kuppusamy & Srete Nikolovski & Yuvaraja Teekaraman, 2023. "Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems," Sustainability, MDPI, vol. 15(20), pages 1-29, October.
    19. Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
    20. Tian, Yu & Lin, Cheng & Li, Hailong & Du, Jiuyu & Xiong, Rui, 2021. "Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods," Applied Energy, Elsevier, vol. 300(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:jmathe:v:11:y:2023:i:21:p:4429-:d:1267304. 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.