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

arXiv: https://arxiv.org/abs/2505.11047 · HTML: https://mlsanity.com/paper/2505.11047/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 6 of 6)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Input Convex Neural Networks](https://mlsanity.com/paper/1609.07152/) (2016)
- [Input Convex Neural Networks for Building MPC](https://mlsanity.com/paper/2011.13227/) (2020)
- [On the probabilistic feasibility of solutions in multi-agent optimization problems under uncertainty](https://mlsanity.com/paper/2009.02706/) (2020)
- [Price of anarchy in electric vehicle charging control games: When Nash equilibria achieve social welfare](https://mlsanity.com/paper/1612.01342/) (2016)
- [A posteriori probabilistic feasibility guarantees for Nash equilibria in uncertain multi-agent games](https://mlsanity.com/paper/2003.11307/) (2020)

## Abstract (arXiv)

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.