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Mr. John Smith

Job title



Modern vehicles have the possibility to collect several information from Intelligent Transportation Systems (ITSs) with a relevant potential for improvement of efficiency and safety of the vehicles at different levels. Among the possible vehicular and transportation applications, such information can be exploited for the vehicle speed prediction, considering many real-life factors such as traffic condition, driving path, and driver behavior, in either a deterministic or stochastic way. This potential allows for novel designs of Energy Management Systems (EMSs) able to optimally operate the components of hybrid vehicles. In such context, this paper proposes an innovative EMS based on Stochastic Model Predictive Control with Learning (SMPCL) to optimize the hydrogen consumption of a Fuel Cell Electric Vehicle (FCEV), while guaranteeing constraints on the battery state of charge (SOC) and the available power ranges as well as maximizing the lifetime of fuel cell and battery. In details, the proposed strategy is a scenario-based Stochastic Model Predictive Control that exploits the short-term vehicle speed predictions given by novel fuzzy Markov Chains (MCs). A number of simulations have been performed to evaluate the effectiveness of the presented approach. The SUMO traffic simulator has been used to generate different vehicle speed profiles for a real city bus route in Turin (Italy), where the traffic conditions have been also varied according to historical data. Then, a high-fidelity simulation plant of the FCEV has been developed in GT-SUITE to evaluate the vehicle behavior on the generated speed profiles, where the driving style has been also changed from a smooth to an aggressive behavior. Results on the considered scenarios have shown that a hydrogen consumption reduction of about 15% can be obtained on average compared to a classic rule-based approach, while getting also benefits in terms of fuel cell and battery lifetimes (about 2% and 38% on average, respectively, based on preliminary lifetime models). Finally, the next steps will include the EMS software integration in a Vehicle/Hybrid Control Unit (VCU/HCU) and the experimental validation of the proposed approach on a real propulsion system in a test bench located at PUNCH facilities in Turin (Italy).

Dr. Giulio Binetti, Advanced Control Technology Specialist, PUNCH Hydrocells

Advanced Control for Energy Management System of Fuel Cell Electric Vehicles

FWC2023-PPE-011 • Propulsion, power & energy efficiency


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