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Advanced Lithium-ion battery models for electric vehicles: Assessing the optimal order reduction technique

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  • Obrador Rey, Sergi
  • Canals Casals, Lluc
  • Romero Baena, Juan Alberto
  • Valls Pau, Alessandro
  • Trilla, Lluís

Abstract

Physics-based models could greatly improve lithium-ion battery management system’s ability to estimate cell states. Integrating these models into battery embedded hardware can potentially extend the performance and lifespan of battery cells. Nevertheless, it is crucial to simplify this model to lessen the computational workload for a successful implementation in embedded microprocessors. This article uses the analytic hierarchy process to select the most suitable reduced-order technique for advanced models running in microcontrollers. The model’s implementation feasibility is assessed based on weighted criteria significant for control-oriented applications, such as accuracy, execution time, required memory, observability, and controllability. The application of the process and the corresponding boundary criteria are exemplified in different evaluation scenarios, representative of the electrical vehicle market. The successful results of the proposed process validate its scalability and reproducibility in similar decision-making scenarios. This approach not only advances the integration of advanced physics-based models into lithium-ion battery management systems but also broadens the applicability of decision-making tools in control field.

Suggested Citation

  • Obrador Rey, Sergi & Canals Casals, Lluc & Romero Baena, Juan Alberto & Valls Pau, Alessandro & Trilla, Lluís, 2026. "Advanced Lithium-ion battery models for electric vehicles: Assessing the optimal order reduction technique," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 245(C), pages 553-575.
    • Handle: RePEc:eee:matcom:v:245:y:2026:i:c:p:553-575
    DOI: 10.1016/j.matcom.2026.02.015
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    References listed on IDEAS

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