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Lifelong Reinforcement Learning for Health-Aware Fast Charging of Lithium-ion Batteries

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Title: Lifelong Reinforcement Learning for Health-Aware Fast Charging of Lithium-ion Batteries
Authors: Yuan, Meng, 1991, Zou, Changfu, 1987
Source: Integrering av förstärkningsinlärning och prediktiv styrning för energihantering i smarta hem (SmartHEM) IEEE Transactions on Transportation Electrification. In press
Subject Terms: battery degradation., Lithium-ion battery, reinforcement learning, fast charging
Description: Fast charging of lithium-ion batteries remains a critical bottleneck for widespread adoption of electric vehicles and stationary energy storage systems, as improperly designed fast charging can accelerate battery degradation and shorten lifespan. In this work, we address this challenge by proposing a health-aware fast charging strategy that explicitly balances charging speed and battery longevity across the entire service life. The key innovation lies in establishing a mapping between side-reaction overpotential and the state of health (SoH) of battery, which is then used to constrain the terminal charging voltage in a twin delayed deep deterministic policy gradient (TD3) framework. By incorporating this SoH-dependent voltage constraint, our designed deep learning method mitigates side reactions and effectively extends battery life. To validate the proposed approach, a high-fidelity single particle model with electrolyte is implemented in the widely adopted PyBaMM simulation platform, capturing degradation phenomena at realistic scales. Comparative life-cycle simulations against conventional CC-CV, its variants, and constant current–constant overpotential methods show that the TD3-based controller reduces overall degradation while maintaining competitively fast charge times. These results demonstrate the practical viability of deep reinforcement learning for advanced battery management systems and pave the way for future explorations of health-aware, performance-optimized charging strategies.
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  Data: Lifelong Reinforcement Learning for Health-Aware Fast Charging of Lithium-ion Batteries
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Yuan%2C+Meng%22">Yuan, Meng</searchLink>, 1991<br /><searchLink fieldCode="AR" term="%22Zou%2C+Changfu%22">Zou, Changfu</searchLink>, 1987
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  Data: <i>Integrering av förstärkningsinlärning och prediktiv styrning för energihantering i smarta hem (SmartHEM) IEEE Transactions on Transportation Electrification</i>. In press
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  Data: <searchLink fieldCode="DE" term="%22battery+degradation%2E%22">battery degradation.</searchLink><br /><searchLink fieldCode="DE" term="%22Lithium-ion+battery%22">Lithium-ion battery</searchLink><br /><searchLink fieldCode="DE" term="%22reinforcement+learning%22">reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22fast+charging%22">fast charging</searchLink>
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  Label: Description
  Group: Ab
  Data: Fast charging of lithium-ion batteries remains a critical bottleneck for widespread adoption of electric vehicles and stationary energy storage systems, as improperly designed fast charging can accelerate battery degradation and shorten lifespan. In this work, we address this challenge by proposing a health-aware fast charging strategy that explicitly balances charging speed and battery longevity across the entire service life. The key innovation lies in establishing a mapping between side-reaction overpotential and the state of health (SoH) of battery, which is then used to constrain the terminal charging voltage in a twin delayed deep deterministic policy gradient (TD3) framework. By incorporating this SoH-dependent voltage constraint, our designed deep learning method mitigates side reactions and effectively extends battery life. To validate the proposed approach, a high-fidelity single particle model with electrolyte is implemented in the widely adopted PyBaMM simulation platform, capturing degradation phenomena at realistic scales. Comparative life-cycle simulations against conventional CC-CV, its variants, and constant current–constant overpotential methods show that the TD3-based controller reduces overall degradation while maintaining competitively fast charge times. These results demonstrate the practical viability of deep reinforcement learning for advanced battery management systems and pave the way for future explorations of health-aware, performance-optimized charging strategies.
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