Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917994568876032 |
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| author | Mikesell, Ian da Silva, Samuel Filgueira Ozkan, Mehmet Fatih Idrissi, Faissal El Ramesh, Prashanth Canova, Marcello |
| author_facet | Mikesell, Ian da Silva, Samuel Filgueira Ozkan, Mehmet Fatih Idrissi, Faissal El Ramesh, Prashanth Canova, Marcello |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15578 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model Mikesell, Ian da Silva, Samuel Filgueira Ozkan, Mehmet Fatih Idrissi, Faissal El Ramesh, Prashanth Canova, Marcello Systems and Control Machine Learning 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. |
| title | Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2504.15578 |