IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v324y2025ics0360544225016007.html
   My bibliography  Save this article

Optimal flexible power allocation energy management strategy for hybrid energy storage system with genetic algorithm based model predictive control

Author

Listed:
  • Ma, Bin
  • Li, Peng-Hui

Abstract

This paper proposes an optimal flexible power allocation-based energy management system (EMS) for hybrid energy storage systems (HESS) in electric vehicles (EVs). The main advantage of the proposed EMS is its use of a genetic algorithm-based model predictive control (GA-MPC) with optimal tuning of weights and constraints, which fully extends and balances ultracapacitors (UC) utilization over the actual driving cycle. Additionally, the GA-MPC gently isolates the battery from direct recharging, further extending the battery's lifetime. For the prediction aspect, a hybrid ARIMA-LSTM predictor is developed for real time prediction of more accurate power demand for the EMS optimization. Meanwhile, a MPC with a quadratic programming algorithm is designed to optimize the power allocation problem. Innovatively, a rule-based strategy and the GA are developed for constraint and weight regulation, gently preventing direct battery recharging and ensuring the UC has the highest priority in the dynamic power allocation process. The proposed approach is validated using a downscaled HESS experimental platform. Simulation results indicate that the UC utilization ratio is improved by 25.3 %, and the battery lifetime is prolonged by 13 % compared to conventional MPC, without altering the existing HESS topology.

Suggested Citation

  • Ma, Bin & Li, Peng-Hui, 2025. "Optimal flexible power allocation energy management strategy for hybrid energy storage system with genetic algorithm based model predictive control," Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:energy:v:324:y:2025:i:c:s0360544225016007
    DOI: 10.1016/j.energy.2025.135958
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225016007
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135958?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:energy:v:324:y:2025:i:c:s0360544225016007. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.