用神经网络建模电池衰减，实现用户偏好下的充放电优化。
User-centric Vehicle-to-Grid Optimization with an Input Convex Neural Network-based Battery Degradation Model
- 基于输入凸神经网络构建数据驱动的电池衰减模型，保持充电速率的凸性。
- 通过多目标优化生成智能充电策略，平衡电网收益与电池损耗。
- 支持个性化设置，适合关注电池寿命的电动车用户和能源管理方。
我们提出一种以用户为中心的数据驱动型车网互动（V2G）方法，通过多目标优化，在电池衰减与电网收益之间实现平衡，满足用户偏好。针对现有电池衰减模型缺乏准确性与普适性的问题，采用输入凸神经网络（ICNN）基于大规模实验数据训练，能够捕捉电池温度与时间的非凸依赖关系，同时保持对充电速率的凸性。这一部分凸性保障了方法第二阶段的计算效率。在第二阶段，将数据驱动的衰减模型融入多目标优化框架，为每辆电动车生成最优智能充电方案，有效权衡电网收益与电池健康损失，由反映用户偏好的超参数调控。数值仿真显示，该ICNN模型在未见数据上的预测精度高。最后，我们展示基于用户偏好的收益-衰减权衡曲线，并呈现真实场景下的智能充电策略。
原文摘要 · Abstract (English)
We propose a data-driven, user-centric vehicle-to-grid (V2G) methodology based on multi-objective optimization to balance battery degradation and V2G revenue according to EV user preference. Given the lack of accurate and generalizable battery degradation models, we leverage input convex neural networks (ICNNs) to develop a data-driven degradation model trained on extensive experimental datasets. This approach enables our model to capture nonconvex dependencies on battery temperature and time while maintaining convexity with respect to the charging rate. Such a partial convexity property ensures that the second stage of our methodology remains computationally efficient. In the second stage, we integrate our data-driven degradation model into a multi-objective optimization framework to generate an optimal smart charging profile for each EV. This profile effectively balances the trade-off between financial benefits from V2G participation and battery degradation, controlled by a hyperparameter reflecting the user prioritization of battery health. Numerical simulations show the high accuracy of the ICNN model in predicting battery degradation for unseen data. Finally, we present a trade-off curve illustrating financial benefits from V2G versus losses from battery health degradation based on user preferences and showcase smart charging strategies under realistic scenarios.
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