苹果淫院

Abstract:

To quickly reduce CO2 in the heavy-duty trucking sector, it is proposed to retrofit diesel trucks with hydrogen fueling resulting in a hydrogen diesel dual fuel engine. Steady state experimental results from engine testing to maximize the hydrogen usage while investigating the effect on exhaust emissions and combustion metrics are investigated and promising hydrogen energy replacement rates of up to 85% at one third engine load are achieved. Then, a control methodology of machine learning control which is a combination of model predictive control and machine learning is briefly described. This control is then implemented on the experimental engine using low-cost hardware and allows for transient engine load tracking under the imposed constraints on emissions and maximum pressure rise rates. The outlook for the control methodology, which seems to hold promise for constrained engineering systems, is described as it is also being applied to different applications.

Bio:

C. R. (Bob) Koch received his B.S. degree in mechanical engineering from the University of Alberta, Edmonton, Canada in 1985, and his M.S. and Ph.D. degrees from Stanford University, Palo Alto, CA, in 1986 and 1991, respectively. From 1991 to 1992 and from 1994 to 2001 he worked at Daimler-Benz/Daimler-Chrysler in Stuttgart, Germany in powertrain and internal combustion engine control. During 1992 to 1994 he worked for General Motors at the powertrain control center. In 2001, he joined the Mechanical Engineering Department of the University of Alberta in Edmonton, Canada, where he is a Professor.

His research interests include vehicle propulsion systems, controls, machine learning control, sensors for mobile emission measurement and air quality, mechatronics, and control of distributed parameter systems. Prof. Koch is currently an Associate Editor for the Mechatronics Journal (Elsevier) and Control Engineering Practice.