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Development of Computational Tools for Modeling Engine Fuel Economy and Emissions Christopher L. Marlowe The Integrated Bus Information System (IBIS) is a vehicle fleet emission and fuel economy prediction software. IBIS is under development by faculty and students of West Virginia University (WVU). The overall goal of IBIS is to provide an approachable and reliable method for users, primarily transit agencies, to evaluate overall fleet emissions and fuel consumption. This approach differs from current modeling packages as IBIS is an online tool and allows for a customizable, user-defined vehicle fleet. The modeling strategy for IBIS involves creating models using data obtained from the WVU Center for Alternative Fuels, Engines, and Emissions (CAFEE) testing database. These models are multiple variable polynomials created through regression analysis. Additionally, multiplicative and additive correction factors are computed and applied to backbone models to account for variances in vehicle configurations and technologies. This modeling strategy includes the necessary development of tools to aid in the creation of continuous models. The first to be implemented is a polynomial regression tool. This methodology utilizes data gleaned from the WVU Center for Alternative Fuels, Engines and Emission database. The tool is designed to perform multivariable regression for standard driving cycles: where second-by-second data is available. The accuracy of these models is reliant upon large sets of data. Furthermore, in cases where limited a dataset is available, additional information may be computed by concatenating experimental data isolated from within existing testing cycles for which testing has been preformed. This data is extracted from a driving cycle by defining periods of non-idle. These periods, or microtrips, are rearranged into new cycles of varying length by a second computational tool. This second tool is a driving cycle generator which utilizes a genetic algorithm to reorder and concatenate microtrips such that the resulting cycle fulfills criteria supplied by the user. These parameters align with input parameters defining a driving cycle for both IBIS and the polynomial tool: parameters include average speed with idle, standard deviation of speed with idle, kinetic intensity, percentage idle, and number of stops per mile. In addition to providing additional data, the cycle generator yields insight as to acceptable limits on the user inputs defining a driving cycle. Once the data set has been expanded by the cycle generator, the new data is reintroduced to the polynomial regression tool. Expansion of the data set allows the polynomial tool to generate a much more realistic trend for a domain of average speed than was previously obtained with limited data. With the integration of the cycle generator into the polynomial tool, adverse effects caused by interpolation are significantly minimized in the polynomial model. The use of the polynomial tool has improved and accelerated the design process for models for IBIS. Additionally, the integration of the newly generated cycles through the use of a GA allows for accurate expansion of experimental data without necessitating supplementary dynamometer testing.

Development of computational tools for modeling engine fuel economy and emissions