In this paper, we present an advanced control framework for power management applications, named Neural Adaptive Model Predictive Control (NA-MPC), designed to provide an optimal power allocation among multiple energy sources, perform a multi-objective online adaptation of the optimal control policy, and ensure a fast real-time execution with low computational demand. NA-MPC augments general MPC problems with three key features: 1) an online metaheuristic tuning strategy adapts the MPC cost function weights, to attain multiple concurrent control objectives at once; 2) through neural emulation, the MPC control policy is replaced by an equivalent neural MPC controller, exhibiting universal approximation guarantees and ensuring real-time feasibility; 3) a neural black-box MPC prediction model is employed, identified only via noise-corrupted input-output measurements from the plant, which is assumed to be unknown. The general formulation and versatility of NA-MPC make it potentially applicable to several power management scenarios; in this work, we apply NA-MPC to the case study of power management in fuel cell hybrid electric vehicles (FCHEVs), a topic of growing interest within the frame of sustainable transportation, for which novel and efficient strategies are still lacking. The effectiveness of NA-MPC is thoroughly assessed via numerical simulations, demonstrating its capability to optimally attain multiple control objectives concurrently in real time; moreover, NA-MPC consistently outperforms the most prominent state-of-the-art HEV power management strategies.

Neural adaptive MPC with online metaheuristic tuning for power management in fuel cell hybrid electric vehicles / Calogero, Lorenzo; Pagone, Michele; Cianflone, Francesco; Gandino, Edoardo; Karam, Carlo; Rizzo, Alessandro. - In: IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING. - ISSN 1545-5955. - ELETTRONICO. - (2025). [10.1109/TASE.2025.3534402]

Neural adaptive MPC with online metaheuristic tuning for power management in fuel cell hybrid electric vehicles

Calogero, Lorenzo;Pagone, Michele;Rizzo Alessandro
2025

Abstract

In this paper, we present an advanced control framework for power management applications, named Neural Adaptive Model Predictive Control (NA-MPC), designed to provide an optimal power allocation among multiple energy sources, perform a multi-objective online adaptation of the optimal control policy, and ensure a fast real-time execution with low computational demand. NA-MPC augments general MPC problems with three key features: 1) an online metaheuristic tuning strategy adapts the MPC cost function weights, to attain multiple concurrent control objectives at once; 2) through neural emulation, the MPC control policy is replaced by an equivalent neural MPC controller, exhibiting universal approximation guarantees and ensuring real-time feasibility; 3) a neural black-box MPC prediction model is employed, identified only via noise-corrupted input-output measurements from the plant, which is assumed to be unknown. The general formulation and versatility of NA-MPC make it potentially applicable to several power management scenarios; in this work, we apply NA-MPC to the case study of power management in fuel cell hybrid electric vehicles (FCHEVs), a topic of growing interest within the frame of sustainable transportation, for which novel and efficient strategies are still lacking. The effectiveness of NA-MPC is thoroughly assessed via numerical simulations, demonstrating its capability to optimally attain multiple control objectives concurrently in real time; moreover, NA-MPC consistently outperforms the most prominent state-of-the-art HEV power management strategies.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2997008