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

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.

💡 Research Summary

The paper addresses the challenge of operating electric‑vehicle (EV) charging stations in cold climates, where low ambient temperatures degrade battery performance and increase the need for thermal management. Existing works either assume perfect temperature conditions, treat heating as a pre‑charging step, or rely on offline optimization that requires accurate forecasts of future uncertainties (EV arrival times, ambient temperature, photovoltaic generation, electricity prices, etc.). To overcome these limitations, the authors propose a fully online control framework that jointly manages charging power and battery heating without any prior knowledge of stochastic inputs.

First, the authors model the electrical and thermal dynamics of each EV. The battery energy evolves according to a simple linear update (charging power multiplied by efficiency and time step). Battery temperature dynamics are captured by a heat‑balance equation that includes heat loss to the environment, heat generated by charging, and heat supplied by an onboard heater. Temperature‑dependent limits on both charging and heating rates are introduced, reflecting the empirical observation that colder batteries charge more slowly and require more heating power.

The key methodological contribution is the transformation of these coupled dynamics into two queueing models. The “deadline‑aware charging demand queue” groups vehicles with the same remaining parking time, ensuring that vehicles with tighter deadlines receive higher service priority. The “thermal queue” treats the battery temperature as an energy‑storage queue: heat loss acts as a discharge, while heating and charging‑induced heat act as arrivals. By expressing the temperature evolution as T_{t+1}=T_t−ΔT_d+ΔT_c, the temperature constraint