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Application of a Bidirectional DC/DC Converter to Control the Power Distribution in the Battery–Ultracapacitor System

Author

Listed:
  • Adrian Chmielewski

    (Institute of Vehicles and Construction Machinery Engineering, Warsaw University of Technology, Narbutta 84 Str., 02-524 Warsaw, Poland)

  • Piotr Piórkowski

    (Institute of Vehicles and Construction Machinery Engineering, Warsaw University of Technology, Narbutta 84 Str., 02-524 Warsaw, Poland)

  • Krzysztof Bogdziński

    (Institute of Vehicles and Construction Machinery Engineering, Warsaw University of Technology, Narbutta 84 Str., 02-524 Warsaw, Poland)

  • Jakub Możaryn

    (Institute of Automatic Control and Robotics, Warsaw University of Technology, Sw. A. Boboli 8, 02-525 Warsaw, Poland)

Abstract

The article presents the use of the Texas Instruments LM5170EVM-BIDIR bidirectional DC/DC converter to control power distribution in a hybrid energy storage system based on a battery–ultracapacitor system. The paper describes typical topologies of connecting a battery with an ultracapacitor. The results of tests for calibration and identification of converter parameters are presented. The main innovation of the solution presented in this paper is the appropriate selection of the nominal voltage of the ultracapacitor so that the converter can be operated only in the constant current mode, in a cascade connection, excluding the low-efficiency constant voltage mode. This article demonstrated that such control allows for high efficiency and reduction of losses in the DC/DC converter, which is necessary in the case of mobile solutions. The amount of losses was determined depending on the control voltage in the operation modes of the converter: in the Step Up mode by increasing the voltage from 12 V to 24 V, from 12 V to 36 V, and from 12 V to 48 V and in the Step Down mode by decreasing the voltage from 48 V to 12 V, from 36 V to 12 V, and from 24 V to 12 V. For a calibrated converter in a semi-active topology, bench tests were carried out in a cycle with pulsating load. The tests were carried out using LiFePO4 cells with a voltage of 12 V and Maxwell ultracapacitors with a package voltage of 48 V. Power distribution in the range of 10% to 90% was achieved using the myRIO platform, which controlled the operation of the DC/DC converter based on an external current profile.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3687-:d:1132857
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    References listed on IDEAS

    as
    1. Qi, Nanjian & Yin, Yajiang & Dai, Keren & Wu, Chengjun & Wang, Xiaofeng & You, Zheng, 2021. "Comprehensive optimized hybrid energy storage system for long-life solar-powered wireless sensor network nodes," Applied Energy, Elsevier, vol. 290(C).
    2. Ba Hung, Nguyen & Jaewon, Sung & Lim, Ocktaeck, 2017. "A study of the effects of input parameters on the dynamics and required power of an electric bicycle," Applied Energy, Elsevier, vol. 204(C), pages 1347-1362.
    3. Song, Ziyou & Li, Jianqiu & Han, Xuebing & Xu, Liangfei & Lu, Languang & Ouyang, Minggao & Hofmann, Heath, 2014. "Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 135(C), pages 212-224.
    4. Capasso, Clemente & Lauria, Davide & Veneri, Ottorino, 2018. "Experimental evaluation of model-based control strategies of sodium-nickel chloride battery plus supercapacitor hybrid storage systems for urban electric vehicles," Applied Energy, Elsevier, vol. 228(C), pages 2478-2489.
    5. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    6. Blasius, Erik & Wang, Zhenqi, 2018. "Effects of charging battery electric vehicles on local grid regarding standardized load profile in administration sector," Applied Energy, Elsevier, vol. 224(C), pages 330-339.
    7. Adrian Chmielewski & Jakub Możaryn & Piotr Piórkowski & Krzysztof Bogdziński, 2018. "Comparison of NARX and Dual Polarization Models for Estimation of the VRLA Battery Charging/Discharging Dynamics in Pulse Cycle," Energies, MDPI, vol. 11(11), pages 1-28, November.
    8. Chia, Yen Yee & Lee, Lam Hong & Shafiabady, Niusha & Isa, Dino, 2015. "A load predictive energy management system for supercapacitor-battery hybrid energy storage system in solar application using the Support Vector Machine," Applied Energy, Elsevier, vol. 137(C), pages 588-602.
    9. Zhang, Cheng & Allafi, Walid & Dinh, Quang & Ascencio, Pedro & Marco, James, 2018. "Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique," Energy, Elsevier, vol. 142(C), pages 678-688.
    10. Piotr Piórkowski & Adrian Chmielewski & Krzysztof Bogdziński & Jakub Możaryn & Tomasz Mydłowski, 2018. "Research on Ultracapacitors in Hybrid Systems: Case Study," Energies, MDPI, vol. 11(10), pages 1-13, September.
    11. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    12. Li, J. & Adewuyi, K. & Lotfi, N. & Landers, R.G. & Park, J., 2018. "A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation," Applied Energy, Elsevier, vol. 212(C), pages 1178-1190.
    13. Chen, Haoqian & Sui, Yi & Shang, Wen-long & Sun, Rencheng & Chen, Zhiheng & Wang, Changying & Han, Chunjia & Zhang, Yuqian & Zhang, Haoran, 2022. "Towards renewable public transport: Mining the performance of electric buses using solar-radiation as an auxiliary power source," Applied Energy, Elsevier, vol. 325(C).
    14. Hsuan Liao & Yi-Tsung Chen & Linda Chen & Jiann-Fuh Chen, 2022. "Development of a Bidirectional DC–DC Converter with Rapid Energy Bidirectional Transition Technology," Energies, MDPI, vol. 15(13), pages 1-19, June.
    15. Yang, Ruixin & Xiong, Rui & Ma, Suxiao & Lin, Xinfan, 2020. "Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks," Applied Energy, Elsevier, vol. 260(C).
    16. Sharmila Sumsurooah & Yun He & Marcello Torchio & Konstantinos Kouramas & Beniamino Guida & Fabrizio Cuomo & Jason Atkin & Serhiy Bozhko & Alfredo Renzetti & Antonio Russo & Stefano Riverso & Alberto , 2021. "ENIGMA—A Centralised Supervisory Controller for Enhanced Onboard Electrical Energy Management with Model in the Loop Demonstration," Energies, MDPI, vol. 14(17), pages 1-17, September.
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