Accurately identifying the parameters of electrochemical models of li-ion
battery (LiB) cells is a critical task for enhancing the fidelity and
predictive ability. Traditional parameter identification methods often require
extensive data collection experiments and lack adaptability in dynamic
environments. This paper describes a Reinforcement Learning (RL) based approach
that dynamically tailors the current profile applied to a LiB cell to optimize
the parameters identifiability of the electrochemical model. The proposed
framework is implemented in real-time using a Hardware-in-the-Loop (HIL) setup,
which serves as a reliable testbed for evaluating the RL-based design strategy.
The HIL validation confirms that the RL-based experimental design outperforms
conventional test protocols used for parameter identification in terms of both
reducing the modeling errors on a verification test and minimizing the duration
of the experiment used for parameter identification.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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