Fuel cell electrified powertrains are currently a promising technology towards decarbonizing the heavy-duty transportation sector. In this context, extensive research is required to thoroughly assess the hydrogen economy potential of fuel cell heavy-duty electrification. This paper proposes a real-time capable energy management strategy (EMS) that can achieve improved hydrogen economy for a fuel cell electrified heavy-duty truck. The considered heavy-duty truck is modelled first in Simulink® environment. A baseline heuristic map-based controller is then retained that can instantaneously control the electrical power split between fuel cell system and the high-voltage battery pack of the heavy-duty truck. Particle swarm optimization (PSO) is consequently implemented to optimally tune the parameters of the considered EMS. For the aim of this study, the calibration optimization objective involves minimizing the hydrogen consumption estimated by simulating the heavy-duty truck in the Simulink® model. Simulations entail different driving missions, some of which have been generated by using the VECTO software, i.e. the tool used in Europe to certify the CO2 emissions of new heavy-duty vehicles. Furthermore, dynamic programming (DP) is implemented as an off-line reference EMS approach that can identify the global optimal control trajectory over time by knowing the entire driving mission in advance. The real-time EMS calibrated by means of PSO is demonstrated achieving remarkable hydrogen saving potential, which results being only around 5% worse compared with the global optimal benchmark provided by DP.

Calibrating a Real-time Energy Management for a Heavy-Duty Fuel Cell Electrified Truck towards Improved Hydrogen Economy / Anselma, Pier Giuseppe; Spano, Matteo; Capello, Marco; Misul, Daniela; Belingardi, Giovanni. - In: SAE TECHNICAL PAPER. - ISSN 0148-7191. - 1:(2022). ((Intervento presentato al convegno 2022 SAE CO2 Reduction for Transportation Systems Conference tenutosi a Torino, Italy nel 21-22 June 2022 [10.4271/2022-37-0014].

Calibrating a Real-time Energy Management for a Heavy-Duty Fuel Cell Electrified Truck towards Improved Hydrogen Economy

Anselma, Pier Giuseppe;Spano, Matteo;Misul, Daniela;Belingardi, Giovanni
2022

Abstract

Fuel cell electrified powertrains are currently a promising technology towards decarbonizing the heavy-duty transportation sector. In this context, extensive research is required to thoroughly assess the hydrogen economy potential of fuel cell heavy-duty electrification. This paper proposes a real-time capable energy management strategy (EMS) that can achieve improved hydrogen economy for a fuel cell electrified heavy-duty truck. The considered heavy-duty truck is modelled first in Simulink® environment. A baseline heuristic map-based controller is then retained that can instantaneously control the electrical power split between fuel cell system and the high-voltage battery pack of the heavy-duty truck. Particle swarm optimization (PSO) is consequently implemented to optimally tune the parameters of the considered EMS. For the aim of this study, the calibration optimization objective involves minimizing the hydrogen consumption estimated by simulating the heavy-duty truck in the Simulink® model. Simulations entail different driving missions, some of which have been generated by using the VECTO software, i.e. the tool used in Europe to certify the CO2 emissions of new heavy-duty vehicles. Furthermore, dynamic programming (DP) is implemented as an off-line reference EMS approach that can identify the global optimal control trajectory over time by knowing the entire driving mission in advance. The real-time EMS calibrated by means of PSO is demonstrated achieving remarkable hydrogen saving potential, which results being only around 5% worse compared with the global optimal benchmark provided by DP.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2968150