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

TL;DR

This paper presents Deep-MPC, an imitation learning approach using DAGGER to optimize constrained battery charging, improving safety, efficiency, and computational performance under uncertain conditions.

Contribution

It adapts the DAGGER algorithm for battery charging with uncertain parameters and unobservable states, offering a novel imitation learning strategy for this domain.

Findings

Enhanced safety and efficiency in battery charging

Outperforms traditional strategies in computational speed

Effective under uncertain battery parameters

Abstract

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.