Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model
This work addresses the challenge of parameter identification for lithium-ion battery models, which is critical for improving predictive accuracy in dynamic environments, representing an incremental advancement over traditional methods.
The paper tackles the problem of accurately identifying parameters for electrochemical models of lithium-ion battery cells by proposing a Reinforcement Learning (RL) approach that dynamically tailors current profiles to optimize parameter identifiability. The result is that the RL-based method outperforms conventional test protocols, reducing modeling errors and minimizing experiment duration, as validated in a Hardware-in-the-Loop setup.
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.