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Battery aging-aware adaptive model predictive control based on coupled semi-empirical electro-thermal and aging models

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  • Dorronsoro, Xabier
  • de Castro, Ricardo
  • Varela Barreras, Jorge
  • Garayalde, Erik
  • Iraola, Unai

Abstract

This paper presents an aging-rate aware nonlinear model predictive control (MPC) strategy for battery energy storage systems, integrating a semi-empirical, experimentally validated electro-thermal and degradation model to account for both calendar and cycle aging factors, often neglected in conventional energy management approaches. A key contribution is the introduction of a adaptive weighting method that dynamically adjusts the weights of the MPC cost function according to the battery’s aging state, primarily driven by time-dependent degradation factors. This adaptive mechanism improves control decisions across varying prediction horizons, leading to reductions in both battery degradation and total operating costs by up to 262.7 % and 44.51 %, respectively, when compared to a standard MPC.

Suggested Citation

  • Dorronsoro, Xabier & de Castro, Ricardo & Varela Barreras, Jorge & Garayalde, Erik & Iraola, Unai, 2025. "Battery aging-aware adaptive model predictive control based on coupled semi-empirical electro-thermal and aging models," Applied Energy, Elsevier, vol. 401(PB).
    • Handle: RePEc:eee:appene:v:401:y:2025:i:pb:s0306261925012243
    DOI: 10.1016/j.apenergy.2025.126494
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