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EmiSense Technologies, LLC (www.emisense.com) is commercializing its electronic particulate matter (PM) sensor that is based on technology developed at the University of Texas at Austin (UT). To demonstrate the capability of this sensor for real-time PM measurements and on board diagnostics (OBD) for failure detection of diesel particle filters (DPF), independent measurements were performed to characterize the engine PM emissions and to compare with the PM sensor response. Computational fluid dynamics (CFD) modeling was performed to characterize the hydrodynamics of the sensor's housing and to develop an improved PM sensor housing with reproducible hydrodynamics and an internal baffle to minimize orientation effects. PM sensors with the improved housing were evaluated in the truck exhaust of a heavy duty (HD) diesel engine tested on-road and on a chassis dynamometer at the University of California, Riverside (UCR) using their Mobile Emissions Laboratory (MEL).

With funding from the California Energy Commission, the California Hybrid, Efficient and Advanced Truck Research Center, contracted with the University of California, Riverside's College of Engineering to evaluate the performance of a Class 5 battery electric urban delivery vehicle over two standardized driving cycles and a steady state range test on a chassis dynamometer. The test vehicle, a Smith Electric Newton Step Van, was equipped with a proprietary data acquisition system which was set to record a wide variety of vehicle parameters at a 1 Hz sampling period. In addition, the chassis dynamometer was set to measure and record additional parameters. Lastly, a portable J1772 EVSE recorded both grid energy and power at 15-minute intervals. This project provides a controlled test evaluation of the Smith Electric Newton Step Van. It recognizes the vehicle's potential for a successful delivery vehicle and identifies several important findings and areas that will need further research.