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The Environmental Protection Agency’s (EPA’s) Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) tool was created to estimate greenhouse gas (GHG) emissions from light-duty vehicles. ALPHA is a physics-based, forward-looking, full vehicle computer simulation capable of analyzing various vehicle types with different powertrain technologies, showing realistic vehicle behavior, and auditing of internal energy flows in the model. In preparation for the midterm evaluation (MTE) of the 2017-2025 light-duty GHG emissions rule, ALPHA has been updated utilizing newly acquired data from model year 2013-2016 engines and vehicles. Simulations conducted with ALPHA provide data on the effectiveness of various GHG reduction technologies, and reveal synergies that exist between technologies. The ALPHA model has been validated against a variety of vehicles with different powertrain configurations and GHG reduction technologies.

The Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) tool was created by EPA to evaluate the Greenhouse Gas (GHG) emissions of Light-Duty (LD) vehicles [1]. ALPHA is a physics-based, forward-looking, full vehicle computer simulation capable of analyzing various vehicle types combined with different powertrain technologies. The software tool is a MATLAB/Simulink based desktop application. The ALPHA model has been updated from the previous version to include more realistic vehicle behavior and now includes internal auditing of all energy flows in the model [2]. As a result of the model refinements and in preparation for the mid-term evaluation (MTE) of the 2022-2025 LD GHG emissions standards, the model is being revalidated with newly acquired vehicle data.

The United States Environmental Protection Agency (EPA) has initiated Phase I of a regulatory program to control exhaust emissions of nonroad diesel engines over 37 kW. Central to any emissions regulation is the test procedure, which must include an appropriate test cycle. Based on actual in-use speed and estimated torque data collected from an agricultural tractor, a backhoe-loader, and a crawler tractor, three duty cycles were developed. Using an iterative process, comparison of chi-square statistical data was used to identify representative microtrips, segments of engine operation gathered during performance of selected activities. Representative microtrips for specific activities for a particular nonroad application were “strung” together to make up a test cycle. Before accepting the test cycle, data for the cycle was compared to statistical data used to characterize the raw data in an effort to validate that the cycle was representative of the raw data.