(363q) Model Predictive Control Considering Stochastic Heat Generation for Thermal Management of Electric Vehicle

With growing environmental concerns about carbon emissions and climate change, electromobility is a vital step for the automotive industry to meet rigorous emissions regulations. The battery pack is a critical component of the electrified vehicle’s powertrain module, but it is also the most expensive. As a result, battery performance and lifetime are important factors for the success of electromobility products. When exposed to extremely low or high temperatures, the battery’s efficiency, durability, safety, thermal runaway, and long-term performance all deteriorate significantly [1]. To make the best use of currently available battery packs, researchers are focusing their efforts on designing effective battery management systems. This is particularly critical for increasing the driving range of electric vehicles, as the thermal management system of the battery consumes a significant amount of electric energy [2].

In the battery’s thermal management system, forecasting heat generation and state of charge (SOC) requires future driving demand information, which is impossible to predict a priori. As a result, the model predictive control (MPC) technique is appropriate, because it can accommodate a variety of probable future loads over a short horizon with periodic feedback updates [3]. There have been a growing number of studies utilizing an MPC technique for battery management, including a stochastic MPC approach that forecasts drive demand using a probabilistic model [4]. However, a review of thermal issues in batteries concluded that variations in cell internal resistance and the temperature gradient in the coolant generate thermal imbalance, resulting in deterioration of battery performance and health [5].

The purpose of this study is to build an MPC controller that takes thermal imbalances within a battery pack into account. The proposed controller utilizes experimental data to estimate a probability model for heat generation of battery cell. The SOC, voltage, and cell temperature are calculated using an equivalent circuit model (ECM). Additionally, the electrical power consumed by the battery thermal management system is calculated and included in the objective function, for example, power consumed by the pump in the coolant cycle. Thermal degradation of battery performance and lifetime is deduced from the penalty function when the desired temperature range is exceeded. Through the use of this performance-based objective function, the suggested control strategy was able to reduce power consumption while maintaining a more stable operating temperature range.

References

[1] Xia, Guodong, Lei Cao, and Guanglong Bi. "A review on battery thermal management in electric vehicle application." Journal of power sources 367 (2017): 90-105.

[2] Lipu, MS Hossain, M. A. Hannan, Tahia F. Karim, Aini Hussain, Mohamad Hanif Md Saad, Afida Ayob, Md Sazal Miah, and TM Indra Mahlia. "Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook." Journal of Cleaner Production 292 (2021): 126044.

[3] Altaf, Faisal, Bo Egardt, and Lars Johannesson Mårdh. "Load management of modular battery using model predictive control: Thermal and state-of-charge balancing." IEEE Transactions on Control Systems Technology 25, no. 1 (2016): 47-62.

[4] Park, Seho, and Changsun Ahn. "Computationally efficient stochastic model predictive controller for battery thermal management of electric vehicle." IEEE Transactions on Vehicular Technology 69, no. 8 (2020): 8407-8419.

[5] Bandhauer, Todd M., Srinivas Garimella, and Thomas F. Fuller. "A critical review of thermal issues in lithium-ion batteries." Journal of the Electrochemical Society 158, no. 3 (2011): R1.