Multi-horizon MPC and Its Application to the Integrated Power and Thermal Management of Electrified Vehicles

Qiuhao Hu (), Mohammad Reza Amini (), Ilya Kolmanovsky () and Jing Sun ()
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Qiuhao Hu: University of Michigan
Mohammad Reza Amini: University of Michigan
Ilya Kolmanovsky: University of Michigan
Jing Sun: University of Michigan

Chapter Chapter 1 in Model Predictive Control, 2025, pp 1-28 from Springer

Abstract: Abstract The chapter provides an overview of several recent applications of model predictive control (MPC) to coordinated power and thermal management in connected and automated electrified vehicles. Such applications of MPC face significant challenges due to the need to accommodate different timescales of faster power and slower thermal dynamics, interactive constraints, and the need to generate and accommodate long-term vehicle speed and traction power forecasts in the optimization. It will be shown that a multi-horizon formulation of MPC consisting of a shorter receding horizon phase and a longer shrinking horizon phase can help address these challenges. Perspectives on creating forecasts and exploiting multi-horizon MPC for energy efficiency optimization based on several case studies are summarized.

Keywords: Model predictive control; Multi-horizon model predictive control; Real-time optimization; Energy management; Thermal management; Integrated power and thermal management; Hybrid electric vehicles; Electric vehicles; Battery charging; Fast charging; Connected vehicles; Long term forecasting; Long term preview; Long prediction horizon; Uncertainty mitigation; Computational complexity reduction (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:dymchp:978-3-031-85256-5_1

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DOI: 10.1007/978-3-031-85256-5_1

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