(363q) Model Predictive Control Considering Stochastic Heat Generation for Thermal Management of Electric Vehicle
AIChE Annual Meeting
Tuesday, November 15, 2022 - 3:30pm to 5:00pm
In the batteryâs 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 . 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 . 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 .
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.
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