Online Electric Vehicle Charging Control with Battery Thermal Management in Cold Environments

TL;DR

This paper introduces a prediction-free online control strategy for EV charging that integrates battery thermal management in cold environments, ensuring stable and efficient operation without relying on uncertainty models.

Contribution

It presents a novel Lyapunov optimization-based online control algorithm that coordinates EV charging and thermal management without predictions or uncertainty assumptions.

Findings

The method maintains queue stability for charging and thermal dynamics.

Numerical results show improved robustness and efficiency.

The approach is theoretically proven to be feasible and optimal.

Abstract

Electric vehicles (EVs) are expanding rapidly, driven by the proposal to comply with global emission reduction targets. However, EV adoption in cold regions is hindered by degraded battery performance at low temperatures, which necessitates effective battery thermal management. Hence, this work proposes a novel online EV charging control strategy, incorporating battery thermal management for cold environments. We first build queue models for both battery charging and thermal dynamics. Then, we formulate an optimization problem, which allows us to coordinate battery charging and heating through maintaining queue stability. To solve the problem, we develop an online control algorithm within the theoretical framework of Lyapunov optimization. Note that our online method is prediction-free and independent of any assumed modeling of uncertainty. We also characterize both the feasibility and optimality of the proposed control approach. Numerical results based on real-world data corroborate our theoretical findings and demonstrate the effectiveness and robustness of our control method through comparisons.