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<p>The paper propose an optimal charging and discharging strategy for electric vehicle (EV) batteries based on Vehicle-to-Grid (V2G) technology, aiming to minimize battery degradation while satisfying grid demand constraints. The method employs a Transformer-based model to predict the Battery Capacity Degradation (BCD), which is integrated into an optimization framework to balance battery lifespan and grid service efficiency. The optimization problem incorporates penalty terms to mitigate power fluctuations and introduces ramp rate constraints to limit abrupt changes in charging and discharging power. Additionally, it incorporates time-of-use (TOU) electricity pricing to maximize economic returns under dynamic pricing conditions. The objective function combines battery degradation cost, power fluctuation penalties, and revenue gains, leading to a theoretically derived and experimentally validated optimal scheduling algorithm suitable for engineering applications. Experimental results demonstrate the effectiveness of the proposed approach in achieving a trade-off between battery health and grid service performance, highlighting its applicability in intelligent energy systems.</p>

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An optimization framework for battery health management in vehicle-to-grid systems integrating transformer-based degradation prediction and grid service requirements

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月6日

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  • Chaochun Zhong,
  • Qingliang Ma,
  • Mingzhu Ren,
  • Lei Xiong

摘要

The paper propose an optimal charging and discharging strategy for electric vehicle (EV) batteries based on Vehicle-to-Grid (V2G) technology, aiming to minimize battery degradation while satisfying grid demand constraints. The method employs a Transformer-based model to predict the Battery Capacity Degradation (BCD), which is integrated into an optimization framework to balance battery lifespan and grid service efficiency. The optimization problem incorporates penalty terms to mitigate power fluctuations and introduces ramp rate constraints to limit abrupt changes in charging and discharging power. Additionally, it incorporates time-of-use (TOU) electricity pricing to maximize economic returns under dynamic pricing conditions. The objective function combines battery degradation cost, power fluctuation penalties, and revenue gains, leading to a theoretically derived and experimentally validated optimal scheduling algorithm suitable for engineering applications. Experimental results demonstrate the effectiveness of the proposed approach in achieving a trade-off between battery health and grid service performance, highlighting its applicability in intelligent energy systems.