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

Core Concepts

An integrated control strategy is proposed for fast charging and active thermal management of lithium-ion batteries in extreme ambient temperatures, using a control-oriented thermal-NDC battery model.

Abstract

The paper presents an integrated control strategy for fast charging and active thermal management of lithium-ion batteries (LiBs) in extreme ambient temperatures. A control-oriented thermal-NDC (nonlinear double-capacitor) battery model is proposed to describe the electrical and thermal dynamics, accounting for the impact from both an active thermal source and ambient temperature.
A state-feedback model predictive control (MPC) algorithm is developed for the integrated fast charging and active thermal management. Numerical experiments validate the algorithm under extreme temperatures, showing that it can energy-efficiently adjust the battery temperature to enhance fast charging.
Additionally, an output-feedback MPC algorithm with an extended Kalman filter (EKF) is proposed for battery charging when states are partially measurable. Numerical experiments validate the effectiveness of the output-feedback MPC under extreme temperatures.
The key highlights and insights from the numerical experiments are:

The proposed strategies (P and P1) outperform the others in charging time, energy consumption, and efficiency under different ambient temperatures (mild, high, and low).

Strategies A-E are not applicable at run-time when the ambient temperature is extreme, as they may not find a feasible solution.

The advantage of the proposed thermal-NDC model is that it can jointly determine the control of battery electrical and thermal dynamics, leading to mutually beneficial outcomes.

Heating the battery core to an optimal temperature can improve the charging speed, but it may not be energy-efficient. The MPC with a long enough horizon can implicitly obtain the optimal battery temperature without explicitly regulating it.

For the output-feedback MPC, the EKF-based strategy can effectively estimate the battery states and achieve fast charging under extreme temperatures when the states are partially measurable.

Stats

Tcore(t) ≤ 55 °C
Tsurf(t) ≤ 55 °C
I ≤ I(t) ≤ I
V ≤ V(t) ≤ V
Vs(t) - Vb(t) ≤ β1SoC(t) + β2
P act ≤ Pact(t) ≤ P act

Quotes

None

Key Insights Distilled From

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

by Zehui Lu,Hao... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04358.pdf

Deeper Inquiries

How can the proposed integrated control strategy be extended to battery packs or modules instead of a single cell

To extend the proposed integrated control strategy to battery packs or modules, the same principles can be applied but on a larger scale. Instead of focusing on a single cell, the control strategy would need to consider the interactions and dynamics between multiple cells within the pack or module. This would involve developing a model that accounts for the interconnected behavior of the cells, including how they affect each other's temperature, state of charge, and overall performance. The control algorithm would need to optimize the charging and thermal management of the entire pack or module, taking into consideration the varying characteristics of each individual cell.

What are the potential challenges and considerations in implementing the proposed strategies in real-world battery systems

Implementing the proposed strategies in real-world battery systems may face several challenges and considerations. One challenge is the complexity of scaling up the control strategy from a single cell to a larger battery pack or module. Ensuring the accuracy and efficiency of the control algorithm across multiple cells with different characteristics and behaviors can be a significant challenge. Additionally, integrating the control strategy into existing battery management systems and hardware may require modifications and adaptations to accommodate the new algorithms and models.
Other considerations include the need for robust and reliable sensors to provide real-time data on battery performance, temperature, and state of charge. Calibration and validation of the models and algorithms in real-world conditions are essential to ensure their effectiveness and safety. Furthermore, the impact of external factors such as environmental conditions, usage patterns, and aging effects on the battery system must be carefully analyzed and accounted for in the control strategy.

How can the thermal-NDC model be further improved to capture additional battery dynamics and aging effects for more comprehensive battery management

The thermal-NDC model can be further improved to capture additional battery dynamics and aging effects by incorporating more detailed and accurate representations of the battery's behavior. One way to enhance the model is to include more sophisticated thermal dynamics that consider heat generation and dissipation mechanisms within the battery cells. This could involve modeling the thermal conductivity of different components, heat transfer between cells, and thermal runaway effects.
Additionally, incorporating aging effects into the model can provide insights into how the battery's performance changes over time. This could involve modeling degradation mechanisms such as capacity fade, impedance growth, and internal resistance increase. By integrating aging effects into the model, the control strategy can proactively adjust charging and thermal management to prolong the battery's lifespan and optimize its performance.
Furthermore, considering non-linear behaviors and uncertainties in the battery system can improve the model's accuracy and robustness. Advanced techniques such as adaptive control, machine learning, and data-driven modeling can be utilized to enhance the thermal-NDC model's predictive capabilities and enable more effective battery management strategies.