A Novel Approach to Plug-In Hybrid Electric Vehicle Coolant System Modeling

Paper #:
  • 2018-01-1189

Published:
  • 2018-04-03
Abstract:
EPA 2017-2025 regulations have increased the corporate average fuel economy (CAFE) requirement by 33% for 2025 model year vehicles. Similarly, the EU has set a target of reducing CO2 emission by 27% (with respect to 2015 targets) for vehicles launching after 2021. These constraints have diverted the attention of most of the OEMs towards hybrid electric vehicles, which give out lower emissions and have higher fuel economy as compared to the vehicles propelled by an IC engine. Hence, many automakers have started working on plug-in hybrid electric (PHEV) variants of the existing vehicle models. The vehicle architecture of a PHEV consists of additional parts like battery pack, battery charging module, power invertor module, electric coolant heaters, etc., apart from the conventional coolant consumers. The requirement of coolant flow to each component for its efficient operation and the system level flow balancing becomes challenging due to increased number of components. In addition, these parts must be packaged compactly in the vehicle due to limited space constraint. Moreover, separate cooling loops are required for battery and electronic components, as their operating temperature ranges are different from the engine operating temperature. The PHEV under consideration has three closed loops, viz. high temperature (HT) coolant loop, low temperature (LT) coolant loop and battery loop. HT and battery loops are linked together with an inter-loop heat exchanger, which is used to heat the coolant in battery loop in cold ambient conditions. The scope of current work is to simulate the PHEV coolant system to visualize flow distribution to all coolant consumers in the vehicle, in a one-dimensional (1D) environment using FloMASTER®. The 1D coolant network model is validated with available vehicle test data. Transient simulations are also performed to mimic the actual driving conditions by using standard test cycles. Good correlation is obtained with the test data with deviations well under 10% from the test values.
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