IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i2p251-d1562681.html
   My bibliography  Save this article

A Double Resistive–Capacitive Approach for the Analysis of a Hybrid Battery–Ultracapacitor Integration Study

Author

Listed:
  • Adrian Chmielewski

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

  • Piotr Piórkowski

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

  • Krzysztof Bogdziński

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

  • Paweł Krawczyk

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

  • Jakub Lorencki

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

  • Artur Kopczyński

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

  • Jakub Możaryn

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

  • Ramon Costa-Castelló

    (ETSEIB, ESAII, Universitat Politècnica de Catalunya, Av. Diagonal 647, 08028 Barcelona, Spain)

  • Stepan Ozana

    (Faculty of Electrical Engineering and Computer Science, Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 708 00 Ostrava-Poruba, Czech Republic)

Abstract

The development of energy storage systems is significant for solving problems related to climate change. A hybrid energy storage system (HESS), combining batteries with ultracapacitors, may be a feasible way to improve the efficiency of electric vehicles and renewable energy applications. However, most existing research requires comprehensive modelling of HESS components under different operating conditions, hindering optimisation and real-world application. This study proposes a novel approach to analysing the set of differential equations of a substitute model of HESS and validates a model-based approach to investigate the performance of an HESS composed of a Valve-Regulated Lead Acid (VRLA) Absorbent Glass Mat (AGM) battery and a Maxwell ultracapacitor in a parallel configuration. Consequently, the set of differential equations describing the HESS dynamics is provided. The dynamics of this system are modelled with a double resistive–capacitive (2-RC) scheme using data from Hybrid Pulse Power Characterisation (HPPC) and pseudo-random cycles. Parameters are identified using the Levenberg–Marquardt algorithm. The model’s accuracy is analysed, estimated and verified using Mean Square Errors (MSEs) and Normalised Root Mean Square Errors (NRMSEs) in the range of a State of Charge (SoC) from 0.1 to 0.9. Limitations of the proposed models are also discussed. Finally, the main advantages of HESSs are highlighted in terms of energy and open-circuit voltage (OCV) characteristics.

Suggested Citation

  • Adrian Chmielewski & Piotr Piórkowski & Krzysztof Bogdziński & Paweł Krawczyk & Jakub Lorencki & Artur Kopczyński & Jakub Możaryn & Ramon Costa-Castelló & Stepan Ozana, 2025. "A Double Resistive–Capacitive Approach for the Analysis of a Hybrid Battery–Ultracapacitor Integration Study," Energies, MDPI, vol. 18(2), pages 1-24, January.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:251-:d:1562681
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/2/251/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/2/251/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Wang, Yujie & Liu, Chang & Pan, Rui & Chen, Zonghai, 2017. "Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator," Energy, Elsevier, vol. 121(C), pages 739-750.
    3. 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.
    4. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    5. P., Naresh & N., Sai Vinay Kishore & V., Seshadri Sravan Kumar, 2024. "A new configuration for enhanced integration of a battery–ultracapacitor system," Renewable Energy, Elsevier, vol. 229(C).
    6. 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).
    7. Chmielewski, Adrian & Gumiński, Robert & Mączak, Jędrzej & Radkowski, Stanisław & Szulim, Przemysław, 2016. "Aspects of balanced development of RES and distributed micro-cogeneration use in Poland: Case study of a µCHP with Stirling engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 930-952.
    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.
    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. 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.
    2. 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.
    3. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
    4. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Luo, Guangzhao & Sui, Xin & Teodorescu, Remus, 2019. "Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles," Energy, Elsevier, vol. 185(C), pages 1054-1062.
    5. Tian, Jiaqiang & Wang, Yujie & Liu, Chang & Chen, Zonghai, 2020. "Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles," Energy, Elsevier, vol. 194(C).
    6. Linghu, Jinqing & Kang, Longyun & Liu, Ming & Luo, Xuan & Feng, Yuanbin & Lu, Chusheng, 2019. "Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter," Energy, Elsevier, vol. 189(C).
    7. 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.
    8. Yang, Yang & Yuan, Wei & Zhang, Xiaoqing & Ke, Yuzhi & Qiu, Zhiqiang & Luo, Jian & Tang, Yong & Wang, Chun & Yuan, Yuhang & Huang, Yao, 2020. "A review on structuralized current collectors for high-performance lithium-ion battery anodes," Applied Energy, Elsevier, vol. 276(C).
    9. Jinqing Linghu & Longyun Kang & Ming Liu & Bihua Hu & Zefeng Wang, 2019. "An Improved Model Equation Based on a Gaussian Function Trinomial for State of Charge Estimation of Lithium-ion Batteries," Energies, MDPI, vol. 12(7), pages 1-15, April.
    10. Kim, Jaewon & Sin, Seunghwa & Kim, Jonghoon, 2024. "Early remaining-useful-life prediction applying discrete wavelet transform combined with improved semi-empirical model for high-fidelity in battery energy storage system," Energy, Elsevier, vol. 297(C).
    11. Li, Xining & Ju, Lingling & Geng, Guangchao & Jiang, Quanyuan, 2023. "Data-driven state-of-health estimation for lithium-ion battery based on aging features," Energy, Elsevier, vol. 274(C).
    12. Berrueta, Alberto & Urtasun, Andoni & Ursúa, Alfredo & Sanchis, Pablo, 2018. "A comprehensive model for lithium-ion batteries: From the physical principles to an electrical model," Energy, Elsevier, vol. 144(C), pages 286-300.
    13. Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
    14. Song, Yuchen & Liu, Datong & Liao, Haitao & Peng, Yu, 2020. "A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 261(C).
    15. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    16. Wang, Yujie & Sun, Zhendong & Li, Xiyun & Yang, Xiaoyu & Chen, Zonghai, 2019. "A comparative study of power allocation strategies used in fuel cell and ultracapacitor hybrid systems," Energy, Elsevier, vol. 189(C).
    17. Jinrui Nan & Bo Deng & Wanke Cao & Jianjun Hu & Yuhua Chang & Yili Cai & Zhiwei Zhong, 2022. "Big Data-Based Early Fault Warning of Batteries Combining Short-Text Mining and Grey Correlation," Energies, MDPI, vol. 15(15), pages 1-19, July.
    18. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    19. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    20. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    21. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.

    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:18:y:2025:i:2:p:251-:d:1562681. 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.