Integrated Optimal Fast Charging and Active Thermal Management of Lithium-Ion Batteries in Extreme Ambient Temperatures

TL;DR

The paper addresses fast charging of Li-ion batteries while ensuring safety under extreme ambient temperatures. It introduces a control-oriented thermal-NDC model that couples nonlinear electrochemical dynamics with a two-state lumped thermal model, enabling integrated optimization. A state-feedback MPC optimizes charging and active thermal management, and an EKF-based output-feedback MPC extends the approach to partially measurable states, with numerical validation showing feasible, energy-efficient operation across temperature extremes. The work offers a practical framework for energy-efficient, health-aware fast charging in harsh environments, while outlining limitations and avenues for modeling fidelity and higher-rate operation.

Abstract

This paper presents an integrated control strategy for optimal fast charging and active thermal management of Lithium-ion batteries in extreme ambient temperatures, striking a balance between charging speed and battery health. A control-oriented thermal-NDC (nonlinear double-capacitor) battery model is proposed to describe the electrical and thermal dynamics, incorporating the effects of both an active thermal source and ambient temperature. A state-feedback model predictive control algorithm is then developed for optimal fast charging and active thermal management. Numerical experiments validate the algorithm under extreme temperatures, showing that the proposed algorithm can energy-efficiently adjust the battery temperature, thereby balancing charging speed and battery health. Additionally, an output-feedback model predictive control algorithm with an extended Kalman filter is proposed for battery charging when states are partially measurable. Numerical experiments validate the effectiveness under extreme temperatures.

Figures (9)

• Figure 1: The thermal-NDC (nonlinear double-capacitor) model. $V_{\mathrm{b}}$ and $V_{\mathrm{s}}$ are the normalized voltage in $[0\text{ V},1 \text{ V}]$.
• Figure 2: Battery core and surface temperature and active thermal power by some strategies in different ambient temperatures. In the upper portion of each subfigure, the solid lines in blue and red represent $T_{\mathrm{core}}$ and $T_{\mathrm{surf}}$ by strategy P. In the lower portion of each subfigure, the solid lines in blue and red represent the active cooling and heating power by P. In subfigure (a), the dash-dot lines in blue and red represent $T_{\mathrm{core}}$ and $T_{\mathrm{surf}}$ by strategy A; the dash-dot line in black represents the zero active thermal power by A. Note that there is no constraint on $T_{\mathrm{surf}}$.
• Figure 3: A trajectory segment for $I$, $V_{\mathrm{b}}$, $V_{\mathrm{s}}$, $V$, and $V_{\mathrm{s}}(t) - V_{\mathrm{b}}(t)$ vs $\beta_1 \mathrm{SoC}(t) + \beta_2$ with $T_{\mathrm{amb}} = 70$$^{\circ}\text{C}$. The red and blue curves in the second subfigure indicate $V_{\mathrm{s}}$ and $V_{\mathrm{b}}$, respectively. The black dashed lines in the top three subfigures are the upper bounds. The black dashed line in the last subfigure indicates $\beta_1 \mathrm{SoC}(t) + \beta_2$ as $\mathrm{SoC}(t)$ increases over time.
• Figure 4: Battery core and surface temperature and active thermal power by strategies P1, P3, and P5 with larger thermal power bounds in the low ambient temperature. Note that there is no constraint on $T_{\mathrm{surf}}$.
• Figure 5: Actual and measured system outputs with $T_{\mathrm{amb}} = -25$$^{\circ}\text{C}$.

Theorems & Definitions (4)

• Remark 1
• Remark 2
• Remark 3
• Remark 4