Search for: All records

The proliferation of electric vehicles (EVs) is resulting in a big transition in the automotive industry, with the goal of reducing greenhouse gas emissions and improving energy efficiency. There are a variety of different architectural configurations and power distribution strategies that can be optimized for drivability performance, all-electric range, and overall efficiency. This paper describes the efforts of the research team in exploring different EV architectures to better understand their impacts on system performance in terms of energy efficiency and vehicle drivability. In search for an ideal powertrain architecture for a shared-use EV, the research team conducted a comprehensive analysis of a various EV architectures (including RWD and AWD) with different motor parameters, considering a spectrum of targeted vehicle technology specifications such as acceleration and braking performance, and fuel economy. To quantify these performance indices, a model-based design approach was utilized, leveraging the EV development tools developed by MATLAB/Simulink and Simscape. Standard driving cycles, e.g., Highway Fuel Economy Driving Schedule (HWFET) and Urban Dynamometer Driving Schedule (UDDS) were utilized to evaluate different EV powertrain architectures and rear/front wheel power splits. The simulation results showed that for the architectures (with respective parameters) investigated in this study, the AWD architectures have higher energy efficiency than the RWD architecture in the range of 5.4 – 37.9%. To further scrutinize performance across a wide spectrum of driving scenarios, we introduced a specialized modal driving profile. This comprehensive profile encompasses a diverse array of modal events, including varying acceleration rates and steady-state speeds, among others. In our analysis, we found that a standard torque split of 50/50 keeps a good balance between energy efficiency and drivability for our target AWD architecture. 

This study evaluates the beneficial effects of discharging nanosecond pulse transient plasma (NPTP) in a coaxial electrostatic precipitator for capturing nanoscale soot particles (∼50 nm) produced by an ethylene flame. 

As the importance of non-tailpipe particles (NTP) over tailpipe emissions from urban traffic has been increasing, there is a need to evaluate NTP contributions to ambient particulate matter (PM) using representative source profiles. The Brake and Tire Wear Study conducted in Los Angeles, California in the winter of 2020 collected 64 PM2.5 and 64 PM10 samples from 32 pairs of downwind-upwind measurements at two near-road locations (I-5 in Anaheim and I-710 in Long Beach). These samples were characterized for inorganic and organic markers and, along with locally-developed brake wear, tire wear, and road dust source profiles, subject to source apportionment using the effective-variance chemical mass balance (EV-CMB) model. Model results highlighted the dominance of resuspended dust in both PM2.5 (23–33%) and PM10 (32–53%). Brake and tire wear contributed more to PM2.5 than tailpipe exhausts (diesel + gasoline) for I-5 (29–30% vs. 19–21%) while they were comparable for I-710 (15–17% vs. 15–19%). For PM10, the brake and tire wear contributions were 2–3 times the exhaust contributions. Different fleet compositions on and near I-5 and I-710 appeared to influence the relative importance of NTP and exhaust sources. The downwind-upwind differences in source contributions were often insignificant, consistent with small and/or nearly equal impacts of adjacent highway traffic emissions on the downwind and upwind sites. The utility of sole markers, such as barium and zinc, to predict brake and tire wear abundances in ambient PM is evaluated.