2020/03/10 by Mohammad Reza Amini, Ilya Kolmanovsky, Jing Sun
Engineering · #Advanced Battery Technologies Research #Electric and Hybrid Vehicle Technologies #Fuel Cells and Related Materials
paper · doi:10.1109/tcst.2020.2975464
openalex publication_date 2020/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.