Giorgio Romano
Energy Management Strategy of Fuel Cell Hybrid Electric Vehicle based on Dynamic Programming and Neural Networks.
Rel. Andrea Tonoli, Sara Luciani. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2021
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Abstract
Increasing concern about climate change, air pollution and petroleum resource depletion has led regulators to impose more stringent standards in the automotive industry, which accounts for 22% of global CO2 emissions. Battery Electric Vehicles (BEVs) are the most popular among the different alternatives proposed, but Fuel Cell Hybrid Electric Vehicles (FCHEVs) are regaining attention after a setback during the last decade. HEVs have two or more power sources that propel the vehicle. Consequently, Energy Management Strategies (EMS) play a key role in the performance of such vehicles because they seek to optimize power split between those sources to minimize fuel consumption.
Modern EMSs consider additional criteria, such as increasing lifecycle of components to minimize Well-to-Wheel (WTW) emissions, thus leading to multi-objective optimization problems
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