Robust Model Predictive Control of Fast Lithium-ion Battery Pretreatment for Safe Recycling
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912276727988224 |
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| author | Yuan, Meng Burman, Adam Zou, Changfu |
| author_facet | Yuan, Meng Burman, Adam Zou, Changfu |
| contents | The proper disposal and repurposing of end-of-life electric vehicle batteries are critical for maximizing their environmental benefits. This study introduces a robust model predictive control (MPC) framework designed to optimize the battery discharging process during pre-treatment, ensuring both efficiency and safety. The proposed method explicitly incorporates temperature constraints to prevent overheating and potential hazards. By leveraging a control-oriented equivalent circuit model integrated with thermal dynamics, the MPC algorithm dynamically adjusts the discharging profile to maintain safe operating temperatures. Additionally, the robust controller is designed to account for model mismatches between the nonlinear battery dynamics and the linearized model, ensuring reliable performance under varying conditions. The effectiveness of this approach is demonstrated through simulations comparing the robust MPC method with conventional discharging strategies, including constant current-constant voltage (CC-CV) and constant current-constant temperature (CC-CT) methods. Results indicate that the robust MPC framework significantly reduces discharging time while adhering to safety constraints, offering a promising solution for the recycling and second-life applications of lithium-ion batteries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11857 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Model Predictive Control of Fast Lithium-ion Battery Pretreatment for Safe Recycling Yuan, Meng Burman, Adam Zou, Changfu Systems and Control The proper disposal and repurposing of end-of-life electric vehicle batteries are critical for maximizing their environmental benefits. This study introduces a robust model predictive control (MPC) framework designed to optimize the battery discharging process during pre-treatment, ensuring both efficiency and safety. The proposed method explicitly incorporates temperature constraints to prevent overheating and potential hazards. By leveraging a control-oriented equivalent circuit model integrated with thermal dynamics, the MPC algorithm dynamically adjusts the discharging profile to maintain safe operating temperatures. Additionally, the robust controller is designed to account for model mismatches between the nonlinear battery dynamics and the linearized model, ensuring reliable performance under varying conditions. The effectiveness of this approach is demonstrated through simulations comparing the robust MPC method with conventional discharging strategies, including constant current-constant voltage (CC-CV) and constant current-constant temperature (CC-CT) methods. Results indicate that the robust MPC framework significantly reduces discharging time while adhering to safety constraints, offering a promising solution for the recycling and second-life applications of lithium-ion batteries. |
| title | Robust Model Predictive Control of Fast Lithium-ion Battery Pretreatment for Safe Recycling |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2503.11857 |