A power management strategy for a parallel through-the-road plug-in hybrid electric vehicle using genetic algorithm
thesisposted on 07.05.2020 by Akshay Amarendra Kasture
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
With the upsurge of greenhouse gas emissions and rapid depletion of fossil fuels, the pressure on the transportation industry to develop new vehicles with improved fuel economy without sacriﬁcing performance is on the rise. Hybrid Electric Vehicles (HEVs), which employ an internal combustion engine as well as an electric motor as power sources, are becoming increasingly popular alternatives to traditional engine only vehicles. However, the presence of multiple power sources makes HEVs more complex. A signiﬁcant task in developing an HEV is designing a power management strategy, deﬁned as a control system tasked with the responsibility of eﬃciently splitting the power/torque demand from the separate energy sources. Five diﬀerent types of power management strategies, which were developed previously, are reviewed in this work, including dynamic programming, equivalent consumption minimization strategy, proportional state-of-charge algorithm, regression modeling and long short term memory modeling. The eﬀects of these power management strategies on the vehicle performance are studied using a simpliﬁed model of the vehicle. This work also proposes an original power management strategy development using a genetic algorithm. This power management strategy is compared to dynamic programming and several similarities and diﬀerences are observed in the results of dynamic programming and genetic algorithm. For a particular drive cycle, the implementation of the genetic algorithm strategy on the vehicle model leads to a vehicle speed proﬁle that almost matches the original speed proﬁle of that drive cycle.