Advancement and Validation of a Plug-In Hybrid Electric Vehicle Plant Model

The objective of the research into modeling and simulation was to provide an improvement to the Wayne State EcoCAR 2 team’s math-based modeling and simulation tools for hybrid electric vehicle powertrain analysis, with a goal of improving the simulation results to be less than 10% error to experimental data.

The team used the modeling and simulation tools for evaluating different outcomes based on hybrid powertrain architecture changes (hardware), and controls code development and testing (software). The first step was model validation to experimental data, as the plant models had not yet been validated.

This paper includes the results of the team’s work in the U.S. Department of Energy’s EcoCAR 2 Advanced vehicle Technical Competition for university student teams to create and test a plug-in hybrid electric vehicle for reducing petroleum oil consumption, pollutant emissions, and Green House Gas (GHG) emissions.

Plant model validations and advancements brought the vehicle plant model directionally closer to the actual vehicle’s experimental data and achieved error reduction in 10 of 11 metrics detailed in the research. Experimental data was used from 5 sources to validate and advance the vehicle plant model:

The advancement of the electric motor powertrain and the vehicle chassis portions of the vehicle plant model brought 4 of the 11 metrics to under 5% error:

The 6 of the 7 remaining metrics (ones still over 10% error) did have their error reduced by an average of 45%.

Thorough validation and advancement of the internal combustion engine powertrain fell outside of the research scope, so only some validation and advancement was performed. Not adding a torque converter to the plant model was a main item for future work.

The vehicle plant model now provides higher confidence and higher accuracy (in most of the cases) for the simulation results, making the vehicle plant model significantly more useful for evaluating fuel economy, dynamic performance, and emissions improvement results when testing the team’s controls code changes for optimization.