Degradation-Aware Fast-Charging of Li-Ion Batteries Using Joint Electrical and Thermal Model Predictive Control
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866916042815569920 |
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| author | Fabry, Frederic Lodge, Alessio Medina, Robinson Hoekstra, Feye Wilkins, Steven Frunzete, Madalin |
| author_facet | Fabry, Frederic Lodge, Alessio Medina, Robinson Hoekstra, Feye Wilkins, Steven Frunzete, Madalin |
| contents | Fast-charging of lithium-ion batteries is essential for electric vehicle adoption, but aggressive charging can accelerate its degradation and create safety risks. This study investigates a control framework that coordinates charging current with active thermal management to minimise charging time, while respecting constraints on electrochemical degradation and thermal safety. A single particle model with electrolyte dynamics (SPMe), extended with a two-node thermal model, represents the battery dynamics and enables the prediction of internal states - used in the control strategy - including anode potential, core temperature, and cell voltage. Two multi-input multi-output control strategies are developed and compared: a classical approach using parallel proportional-integral-derivative (PID) controllers and an advanced model predictive control (MPC) with dual resolution prediction. Both controllers regulate the charging current and thermal resistance to minimise charging time while keeping within the limits of anode potential, core temperature, and cell voltage. The results demonstrate that coordinated thermal-electrochemical optimal control outperforms conventional approaches, achieving a 42.2% reduction in charging time compared to the manufacturer's charging recommendation, without increasing degradation. MPC reduces the charging time by 5.2% compared to PID control, but at a significant computational cost. This improvement demonstrates the untapped potential of integrated thermal management in fast-charging protocols. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24889 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Degradation-Aware Fast-Charging of Li-Ion Batteries Using Joint Electrical and Thermal Model Predictive Control Fabry, Frederic Lodge, Alessio Medina, Robinson Hoekstra, Feye Wilkins, Steven Frunzete, Madalin Systems and Control Fast-charging of lithium-ion batteries is essential for electric vehicle adoption, but aggressive charging can accelerate its degradation and create safety risks. This study investigates a control framework that coordinates charging current with active thermal management to minimise charging time, while respecting constraints on electrochemical degradation and thermal safety. A single particle model with electrolyte dynamics (SPMe), extended with a two-node thermal model, represents the battery dynamics and enables the prediction of internal states - used in the control strategy - including anode potential, core temperature, and cell voltage. Two multi-input multi-output control strategies are developed and compared: a classical approach using parallel proportional-integral-derivative (PID) controllers and an advanced model predictive control (MPC) with dual resolution prediction. Both controllers regulate the charging current and thermal resistance to minimise charging time while keeping within the limits of anode potential, core temperature, and cell voltage. The results demonstrate that coordinated thermal-electrochemical optimal control outperforms conventional approaches, achieving a 42.2% reduction in charging time compared to the manufacturer's charging recommendation, without increasing degradation. MPC reduces the charging time by 5.2% compared to PID control, but at a significant computational cost. This improvement demonstrates the untapped potential of integrated thermal management in fast-charging protocols. |
| title | Degradation-Aware Fast-Charging of Li-Ion Batteries Using Joint Electrical and Thermal Model Predictive Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2605.24889 |