학술논문
Multihorizon Model Predictive Control: An Application to Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles
Document Type
Periodical
Author
Source
IEEE Transactions on Control Systems Technology IEEE Trans. Contr. Syst. Technol. Control Systems Technology, IEEE Transactions on. 30(3):1052-1064 May, 2022
Subject
Language
ISSN
1063-6536
1558-0865
2374-0159
1558-0865
2374-0159
Abstract
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.