Multihorizon Model Predictive Control: An Application to Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

• Published in: IEEE transactions on control systems technology Vol. 30; no. 3; pp. 1052 - 1064
• Main Authors: Hu, Qiuhao, Amini, Mohammad Reza, Kolmanovsky, Ilya, Sun, Jing, Wiese, Ashley, Seeds, Julia Buckland
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automation & Control Systems; Batteries; Coolants; Dynamic programming; Electric vehicles; Engineering; Engines; Heating systems; Horizon; Hybrid electric vehicles; Model accuracy; Model predictive control (MPC); multitimescale optimization; Optimization; power and thermal management (PTM); Predictive control; Sensitivity analysis; Thermal management; Vehicle dynamics
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2021.3091887

In this article, we propose a multihorizon model predictive control (MH-MPC) approach with applications to integrated power and thermal management (iPTM) of connected hybrid electric vehicles (HEVs). The proposed MH-MPC leverages preview and optimization over a short receding and a long shrinking horizon, where the accuracy of preview, model, and integration can be different over different horizons. Compared with a conventional MPC-based approach with a short prediction horizon and terminal cost, the MH-MPC improves fuel consumption to a level comparable to dynamic programming (DP) while still being computationally affordable. A statistical sensitivity analysis over real-world city driving cycles is conducted to demonstrate the robustness of MH-MPC to moderate levels of uncertainty in the long-term preview.