IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i13p3435-d379783.html
   My bibliography  Save this article

An Energy Consumption Model for Designing an AGV Energy Storage System with a PEMFC Stack

Author

Listed:
  • Roman Niestrój

    (Department of Electrical Engineering and Computer Science, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, Poland)

  • Tomasz Rogala

    (Deparment of Fundamentals of Machinery Design, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, Poland)

  • Wojciech Skarka

    (Deparment of Fundamentals of Machinery Design, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, Poland)

Abstract

This article presents a methodology for building an AGV (automated guided vehicle) power supply system simulation model with a polymer electrolyte membrane fuel cell stack (PEMFC). The model focuses on selecting the correct parameters for the hybrid energy buffering system to ensure proper operating parameters of the vehicle, i.e., minimizing vehicle downtime. The AGV uses 2 × 1.18 kW electric motors and is a development version of a battery-powered vehicle in which the battery has been replaced with a hybrid power system using a 300 W PEMFC. The research and development of the new power system were initiated by the AGV manufacturer. The model-based design (MBD) methodology is used in the design and construction of a complete simulation model for the system, which consists of the fuel cell system, energy processing, a storage system, and an energy demand models. The energy demand model has been developed based on measurements from the existing AGV, and the remaining parts of the model are based on simulation models tuned to the characteristics obtained for the individual subsystems or from commonly available data. A parametric model is created with the possibility for development and determination by simulation of either the final system or from the parameters of the individual models’ elements (components of the designed system). The presented methodology can be used to develop alternative versions of the system, in particular the selection of the correct size of supercapacitors and batteries which depend on the energy demand profile and the development of the DC/DC converter and controllers. Additionally, the varying topology of the whole system was also analyzed. Minimization of downtime has been presented as one of many possible uses of the presented model.

Suggested Citation

  • Roman Niestrój & Tomasz Rogala & Wojciech Skarka, 2020. "An Energy Consumption Model for Designing an AGV Energy Storage System with a PEMFC Stack," Energies, MDPI, vol. 13(13), pages 1-31, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3435-:d:379783
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/13/3435/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/13/3435/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wee, Jung-Ho, 2007. "Applications of proton exchange membrane fuel cell systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(8), pages 1720-1738, October.
    2. Zhao, Hengbing & Burke, Andy, 2010. "Fuel Cell Powered Vehicles Using Supercapacitors: Device Characteristics, Control Strategies, and Simulation Results," Institute of Transportation Studies, Working Paper Series qt23w1m5bb, Institute of Transportation Studies, UC Davis.
    3. Alejandro Mendez & Teresa J. Leo & Miguel A. Herreros, 2014. "Current State of Technology of Fuel Cell Power Systems for Autonomous Underwater Vehicles," Energies, MDPI, vol. 7(7), pages 1-18, July.
    4. Simone Orcioni & Luca Buccolini & Adriana Ricci & Massimo Conti, 2017. "Lithium-ion Battery Electrothermal Model, Parameter Estimation, and Simulation Environment," Energies, MDPI, vol. 10(3), pages 1-20, March.
    5. Erdinc, O. & Uzunoglu, M., 2010. "Recent trends in PEM fuel cell-powered hybrid systems: Investigation of application areas, design architectures and energy management approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2874-2884, December.
    6. Alan Cruz Rojas & Guadalupe Lopez Lopez & J. F. Gomez-Aguilar & Victor M. Alvarado & Cinda Luz Sandoval Torres, 2017. "Control of the Air Supply Subsystem in a PEMFC with Balance of Plant Simulation," Sustainability, MDPI, vol. 9(1), pages 1-23, January.
    7. Bahman Shabani & Manu Biju, 2015. "Theoretical Modelling Methods for Thermal Management of Batteries," Energies, MDPI, vol. 8(9), pages 1-25, September.
    8. Omar, Noshin & Monem, Mohamed Abdel & Firouz, Yousef & Salminen, Justin & Smekens, Jelle & Hegazy, Omar & Gaulous, Hamid & Mulder, Grietus & Van den Bossche, Peter & Coosemans, Thierry & Van Mierlo, J, 2014. "Lithium iron phosphate based battery – Assessment of the aging parameters and development of cycle life model," Applied Energy, Elsevier, vol. 113(C), pages 1575-1585.
    9. Kurnia, Jundika C. & Sasmito, Agus P. & Shamim, Tariq, 2019. "Advances in proton exchange membrane fuel cell with dead-end anode operation: A review," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    10. Ayad, M.Y. & Becherif, M. & Henni, A., 2011. "Vehicle hybridization with fuel cell, supercapacitors and batteries by sliding mode control," Renewable Energy, Elsevier, vol. 36(10), pages 2627-2634.
    11. Zhao, Hengbing & Burke, Andy, 2008. "Optimization of Fuel Cell System Operating Conditions for Fuel Cell Vehicles," Institute of Transportation Studies, Working Paper Series qt5v34n31h, Institute of Transportation Studies, UC Davis.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aaron Shmaryahu & Nissim Amar & Alexander Ivanov & Ilan Aharon, 2021. "Sizing Procedure for System Hybridization Based on Experimental Source Modeling for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-21, August.
    2. Ruben Zieba Falama & Wojciech Skarka & Serge Yamigno Doka, 2022. "Optimal Design and Comparative Analysis of a PV/Mini-Hydropower and a PV/Battery Used for Electricity and Water Supply," Energies, MDPI, vol. 16(1), pages 1-22, December.
    3. Tadeusz Białoń & Roman Niestrój & Wojciech Korski, 2023. "PSO-Based Identification of the Li-Ion Battery Cell Parameters," Energies, MDPI, vol. 16(10), pages 1-22, May.
    4. Agnieszka A. Tubis & Honorata Poturaj, 2022. "Risk Related to AGV Systems—Open-Access Literature Review," Energies, MDPI, vol. 15(23), pages 1-23, November.
    5. Ángel Adrián Orta-Quintana & Rogelio Ernesto García-Chávez & Ramón Silva-Ortigoza & Magdalena Marciano-Melchor & Miguel Gabriel Villarreal-Cervantes & José Rafael García-Sánchez & Rocío García-Cortés , 2023. "Sensorless Tracking Control Based on Sliding Mode for the “Full-Bridge Buck Inverter–DC Motor” System Fed by PV Panel," Sustainability, MDPI, vol. 15(13), pages 1-27, June.
    6. Tadeusz Białoń & Roman Niestrój & Wojciech Skarka & Wojciech Korski, 2023. "HPPC Test Methodology Using LFP Battery Cell Identification Tests as an Example," Energies, MDPI, vol. 16(17), pages 1-21, August.
    7. Roberto Sánchez-Martinez & J. Enrique Sierra-García & Matilde Santos, 2022. "Performance and Extreme Conditions Analysis Based on Iterative Modelling Algorithm for Multi-Trailer AGVs," Mathematics, MDPI, vol. 10(24), pages 1-31, December.

    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. Adam Polak, 2020. "Simulation of Fuzzy Control of Oxygen Flow in PEM Fuel Cells," Energies, MDPI, vol. 13(9), pages 1-26, May.
    2. Hannan, M.A. & Azidin, F.A. & Mohamed, A., 2014. "Hybrid electric vehicles and their challenges: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 135-150.
    3. Sandoval, Cinda & Alvarado, Victor M. & Carmona, Jean-Claude & Lopez Lopez, Guadalupe & Gomez-Aguilar, J.F., 2017. "Energy management control strategy to improve the FC/SC dynamic behavior on hybrid electric vehicles: A frequency based distribution," Renewable Energy, Elsevier, vol. 105(C), pages 407-418.
    4. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    5. Erdinc, O. & Uzunoglu, M., 2010. "Recent trends in PEM fuel cell-powered hybrid systems: Investigation of application areas, design architectures and energy management approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2874-2884, December.
    6. Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
    7. Farouk Odeim & Jürgen Roes & Angelika Heinzel, 2015. "Power Management Optimization of an Experimental Fuel Cell/Battery/Supercapacitor Hybrid System," Energies, MDPI, vol. 8(7), pages 1-26, June.
    8. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Zhang, Jiye & Song, Pengyun, 2019. "Modeling and optimal energy management strategy for a catenary-battery-ultracapacitor based hybrid tramway," Energy, Elsevier, vol. 183(C), pages 1123-1135.
    9. Sun, Li & Walker, Paul & Feng, Kaiwu & Zhang, Nong, 2018. "Multi-objective component sizing for a battery-supercapacitor power supply considering the use of a power converter," Energy, Elsevier, vol. 142(C), pages 436-446.
    10. Ramin Sakipour & Hamdi Abdi, 2020. "Optimizing Battery Energy Storage System Data in the Presence of Wind Power Plants: A Comparative Study on Evolutionary Algorithms," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    11. Tie, Siang Fui & Tan, Chee Wei, 2013. "A review of energy sources and energy management system in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 82-102.
    12. Ma, Jian & Xu, Shu & Shang, Pengchao & ding, Yu & Qin, Weili & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2020. "Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method," Applied Energy, Elsevier, vol. 262(C).
    13. Danijel Pavković & Mihael Cipek & Zdenko Kljaić & Tomislav Josip Mlinarić & Mario Hrgetić & Davor Zorc, 2018. "Damping Optimum-Based Design of Control Strategy Suitable for Battery/Ultracapacitor Electric Vehicles," Energies, MDPI, vol. 11(10), pages 1-26, October.
    14. Shovon Goutam & Jean-Marc Timmermans & Noshin Omar & Peter Van den Bossche & Joeri Van Mierlo, 2015. "Comparative Study of Surface Temperature Behavior of Commercial Li-Ion Pouch Cells of Different Chemistries and Capacities by Infrared Thermography," Energies, MDPI, vol. 8(8), pages 1-18, August.
    15. Nicu Bizon & Mircea Raceanu & Emmanouel Koudoumas & Adriana Marinoiu & Emmanuel Karapidakis & Elena Carcadea, 2020. "Renewable/Fuel Cell Hybrid Power System Operation Using Two Search Controllers of the Optimal Power Needed on the DC Bus," Energies, MDPI, vol. 13(22), pages 1-26, November.
    16. Lopez Lopez, Guadalupe & Schacht Rodriguez, Ricardo & Alvarado, Victor M. & Gomez-Aguilar, J.F. & Mota, Juan E. & Sandoval, Cinda, 2017. "Hybrid PEMFC-supercapacitor system: Modeling and energy management in energetic macroscopic representation," Applied Energy, Elsevier, vol. 205(C), pages 1478-1494.
    17. Wang, Junye, 2015. "Theory and practice of flow field designs for fuel cell scaling-up: A critical review," Applied Energy, Elsevier, vol. 157(C), pages 640-663.
    18. Jiaming Zhou & Chunxiao Feng & Qingqing Su & Shangfeng Jiang & Zhixian Fan & Jiageng Ruan & Shikai Sun & Leli Hu, 2022. "The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    19. Zhou, Jianhao & Liu, Jun & Xue, Yuan & Liao, Yuhui, 2022. "Total travel costs minimization strategy of a dual-stack fuel cell logistics truck enhanced with artificial potential field and deep reinforcement learning," Energy, Elsevier, vol. 239(PA).
    20. Tao, Laifa & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou & Noktehdan, Azadeh, 2017. "Lithium-ion battery capacity fading dynamics modelling for formulation optimization: A stochastic approach to accelerate the design process," Applied Energy, Elsevier, vol. 202(C), pages 138-152.

    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:13:y:2020:i:13:p:3435-:d:379783. 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.