Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Xiaowei Wang, Yize Chen, Yue Chen

TL;DR

The paper tackles online coordination of EV charging and battery heating in cold environments to preserve charging performance and control costs. It introduces a queue-based reformulation of charging and thermal dynamics and applies Lyapunov drift-plus-penalty optimization to derive a prediction-free online policy that jointly controls charging and heating via backlog signals. Theoretical results guarantee temperature feasibility and bound the performance gap to the offline optimum under mild assumptions, with a cost bound that scales as $B/V$. Numerical experiments using real-world data show the proposed scheme reduces energy costs relative to baselines while maintaining high charging fulfillment and robust performance across varying ambient temperatures, including extreme cold. The approach offers a scalable, uncertainty-robust framework for integrating battery thermal management into online EV charging control, with practical implications for cold-region deployments.

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.

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

TL;DR

The paper tackles online coordination of EV charging and battery heating in cold environments to preserve charging performance and control costs. It introduces a queue-based reformulation of charging and thermal dynamics and applies Lyapunov drift-plus-penalty optimization to derive a prediction-free online policy that jointly controls charging and heating via backlog signals. Theoretical results guarantee temperature feasibility and bound the performance gap to the offline optimum under mild assumptions, with a cost bound that scales as . Numerical experiments using real-world data show the proposed scheme reduces energy costs relative to baselines while maintaining high charging fulfillment and robust performance across varying ambient temperatures, including extreme cold. The approach offers a scalable, uncertainty-robust framework for integrating battery thermal management into online EV charging control, with practical implications for cold-region deployments.

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.
Paper Structure (22 sections, 2 theorems, 52 equations, 7 figures, 3 tables)

This paper contains 22 sections, 2 theorems, 52 equations, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. System Framework
  4. System Modeling
  5. Online Algorithm Design
  6. Queue Modeling of Thermal Dynamics
  7. Queue System Construction
  8. Deadline-Aware EV Charging Demand Queue
  9. Debt Queue
  10. Virtual Temperature Queue
  11. Lyapunov Optimization
  12. Online EV Charging Control
  13. Theoretical Guarantee
  14. Feasibility Guarantee
  15. Optimality Guarantee
  16. ...and 7 more sections

Key Result

Theorem 1

Suppose that the energy prices $\lambda_t$ are upper bounded by $\bar{\lambda}$, and $T_i^l \leq T_{i,1} \leq \theta_i + \max_t\{ \Delta T_{i,t}^c - \Delta T_{i,t}^d\} \leq T_i^u$. If $\theta_i$ and V satisfy: where $V_{max}= \min\nolimits_i\frac{T_i^u - T_i^l- \max\nolimits_t\{ \Delta T_{i,t}^c - \Delta T_{i,t}^d\} -\max\nolimits_t\{ \Delta T_{i,t}^d - \Delta T_{i,t}^c\}}{q_i\bar{\lambda}\Delta

Figures (7)

  • Figure 1: System model.
  • Figure 2: Ambient temperature profile on a specific day.
  • Figure 3: EV battery temperature and charging power profiles under different methods.
  • Figure 4: Impact of parameter V and parameter $\gamma$.
  • Figure 5: Impact of temperature on different online charging schemes.
  • ...and 2 more figures

Theorems & Definitions (4)

  • Theorem 1
  • proof
  • Theorem 2
  • proof