Shahbakhti, Mahdi | Search NASA

DOE OSTI2022

Development of a medium-duty stoichiometric diesel micro-pilot natural gas engine

Fueling a compression-ignition engine with premixed natural gas offers the potential to combine a clean-burning, low-carbon fuel with a high compression ratio, high-efficiency engine. This work describes the development of a multi-cylinder 6.7 L diesel engine converted to run stoichiometric diesel micro-pilot/ natural gas premix combustion with a maximum diesel contribution target of 5% of the total fuel energy and a three-way catalyst aftertreatment system. Results are given by comparing the stoichiometric combustion to the diesel baseline operation, showing combustion characteristics differences, including the rapid two stage heat release. A high load output of 23 bar brake mean effective pressure was obtained with diesel-like brake thermal efficiency of 41%. This operating condition enabled a brake specific CO 2 emissions reduction of up to 25% when compared to diesel. It was observed that the low load output is limited by combustion stability when operated at stoichiometric condition. The three-way catalyst is observed to run at peak efficiency with an equivalence ratio of 1.01. Injector fouling was observed through the inspection of the nozzle and its internal parts, indicating carbon build-up similar to that seen in injector coking mechanisms. A comparison of the developed engine to other engine technologies is given, showing that the diesel micro-pilot natural gas engine performance is in good standing among other diesel and gas engines in the market.

Engineering↗

DOE OSTI2022

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.

Engineering↗