User-centric Vehicle-to-Grid Optimization with an Input Convex Neural Network-based Battery Degradation Model

TL;DR

This paper introduces a user-centric V2G optimization approach using an input convex neural network-based battery degradation model, enabling efficient and accurate balancing of V2G revenue and battery health based on user preferences.

Contribution

It develops a novel data-driven degradation model with ICNNs and integrates it into a multi-objective optimization framework for personalized EV charging strategies.

Findings

ICNN model accurately predicts battery degradation on unseen data

The framework effectively balances V2G revenue and battery health

Trade-off curves demonstrate customizable user preferences

Abstract

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.