# Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging

arXiv: https://arxiv.org/abs/2406.15985 · HTML: https://mlsanity.com/paper/2406.15985/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 23 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [Efficient representation and approximation of model predictive control laws via deep learning](https://mlsanity.com/paper/1806.10644/) (2018)
- [Optimal Charging of an Electric Vehicle Battery Pack: A Real-Time Sensitivity-Based MPC approach](https://mlsanity.com/paper/1909.12361/) (2019)

## Abstract (arXiv)

In the realm of battery charging, several complex aspects demand meticulous attention, including thermal management, capacity degradation, and the need for rapid charging while maintaining safety and battery lifespan. By employing the imitation learning paradigm, this manuscript introduces an innovative solution to confront the inherent challenges often associated with conventional predictive control strategies for constrained battery charging. A significant contribution of this study lies in the adaptation of the Dataset Aggregation (DAGGER) algorithm to address scenarios where battery parameters are uncertain, and internal states are unobservable. Results drawn from a practical battery simulator that incorporates an electrochemical model highlight substantial improvements in battery charging performance, particularly in meeting all safety constraints and outperforming traditional strategies in computational processing.