Environmental conditions have a significant effect on the performance of fuel cell systems. This paper studies the vehicle hydrogen consumption, the thermal management system, and the thermal loads of an automotive fuel cell system. A predictive control framework for thermal management is investigated to minimize the overall hydrogen consumption. Initially, a numerical modeling approach for the automotive fuel cell system is presented from electrochemical and thermal perspectives. Then, the problem formulation related to the thermal management strategy is presented and solved with an optimization method based on dynamic programming (DP). The implemented DP exploits the a priori knowledge of the driving mission to appropriately control the fuel cell system gross power and the operation of the radiator fan, the coolant pump, and the compressor. Optimization constraints involve maintaining the fuel cell stack temperature below the operational limit and avoiding the thermal system from being activated when the vehicle is at rest. The fuel cell system is tested while the vehicle performs different numbers of repetitions of the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) at high ambient temperature. Using the proposed predictive control framework for thermal management, results demonstrate that an average 62.5% to 63.0% efficiency can be attained by the fuel cell stack in extreme ambient conditions both in short distance and long distance driving missions.

Predictive Control Framework for Thermal Management of Automotive Fuel Cell Systems at High Ambient Temperatures / Anselma, PIER GIUSEPPE; Luciani, Sara; Tonoli, Andrea. - 55:(2022), pp. 298-303. ((Intervento presentato al convegno 10th IFAC Symposium on Advances in Automotive Control AAC 2022 tenutosi a Columbus, OH, USA nel 29-31 August 2022 [10.1016/j.ifacol.2022.10.300].

Predictive Control Framework for Thermal Management of Automotive Fuel Cell Systems at High Ambient Temperatures

Pier Giuseppe Anselma;Sara Luciani;Andrea Tonoli
2022

Abstract

Environmental conditions have a significant effect on the performance of fuel cell systems. This paper studies the vehicle hydrogen consumption, the thermal management system, and the thermal loads of an automotive fuel cell system. A predictive control framework for thermal management is investigated to minimize the overall hydrogen consumption. Initially, a numerical modeling approach for the automotive fuel cell system is presented from electrochemical and thermal perspectives. Then, the problem formulation related to the thermal management strategy is presented and solved with an optimization method based on dynamic programming (DP). The implemented DP exploits the a priori knowledge of the driving mission to appropriately control the fuel cell system gross power and the operation of the radiator fan, the coolant pump, and the compressor. Optimization constraints involve maintaining the fuel cell stack temperature below the operational limit and avoiding the thermal system from being activated when the vehicle is at rest. The fuel cell system is tested while the vehicle performs different numbers of repetitions of the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) at high ambient temperature. Using the proposed predictive control framework for thermal management, results demonstrate that an average 62.5% to 63.0% efficiency can be attained by the fuel cell stack in extreme ambient conditions both in short distance and long distance driving missions.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2972794
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo