Robust Model Predictive Control for Enhanced Fast Charging on Electric Vehicles through Integrated Power and Thermal Management

TL;DR

This paper presents a robust multi-objective model predictive control framework that optimizes fast charging in electric vehicles by integrating power and thermal management, improving robustness under uncertainty.

Contribution

It introduces a novel MPC approach with a time-varying weighting strategy to enhance fast charging performance considering thermal and power constraints.

Findings

Improved charging time robustness with the proposed strategy.

Effective management of power and thermal constraints during fast charging.

Performance degradation occurs without accurate preview information.

Abstract

This paper explores the synergies between integrated power and thermal management (iPTM) and battery charging in an electric vehicle (EV). A multi-objective model predictive control (MPC) framework is developed to optimize the fast charging performance while enforcing the constraints in the power and thermal loops. The approach takes into account the coupling of the battery and cabin thermal management. The case study of a commercial EV demonstrates that the proposed method can effectively meet the requirements of fast charging and thermal management when accurate preview information is available. However, failure to predict the charging event can result in performance degradation with longer charging time. A time-varying weighting strategy is proposed to enhance charging performance in the presence of uncertainty. This strategy leverages the battery state-of-charge (SOC) and adjusts the priority of the multi-objective MPC at different phases during charging. Simulated results using a commercial EV use case show improved robustness in charging time using the proposed strategy.