Model-predictive control for improved battery thermal management in connected and automated vehicles

Connected and autonomous vehicles (CAVs) have situational awareness that can be exploited for optimal power and thermal management. In this article, we develop a hierarchical model predictive control (H-MPC) strategy for eco-cooling of CAVs, which reduces energy consumption through real-time prediction and multi-timescale and multi-layer optimization. The application of the proposed H-MPC is studied for battery thermal and energy management of an electric vehicle (EV). Our H-MPC approach addresses the uncertainty in the long-term preview of the vehicle speed through robust constraint handling to prevent constraint violation. The simulation results show that compared with a conventional battery thermal management (BTM) strategy, the proposed robust H-MPC saves the battery energy by up to 5.4% under the uncertainties in the long-term vehicle speed predictions in an urban CAV operation scenario.

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Amini, M.R., I. Kolmanovsky and J. Sun. Hierarchical MPC for Robust Eco-Cooling of Connected and Automated Vehicles and Its Application to Electric Vehicle Battery Thermal Management. IEEE Transactions on Control Systems Technology, March 2020.