cloud-based remote engine and transmission recalibration; cloud-based engine and transmission control; and efficient truck platooning. The most promising strategies will be evaluated and refined using a phased approach relying on a combination of simulations, development and real-world testing.  Purdue contributors also include Professors Dan DeLaurentis, Darcy Bullock, and  Neera Jain.  The effort is funded by ARPA-E, with cost share provided by all project partners.   More.

Current research projects

Greg Shaver, Phd

Additional information for Several of the Current Research projects


3514 Old Romney Rd, L

US-China Clean Energy Research Center (CERC) Medium- and Heavy-Duty Trucks Consortium     (sponsor: DOE) 

Medium- and heavy-duty trucks are the backbone of freight transportation systems and vital to economic growth in the United States and China.  To accelerate the development and deployment of technologies that will increase the fuel efficiency of medium- and heavy- 

duty trucks, the Presidents of the United States and China announced a fifth CERC consortium in September 2015.  This new consortium focuses on developing cost-effective measures to improve the on-road freight efficiency of these trucks by more than 50% compared to the 2016 baseline.  Argonne National Labs is leading the United States partners that include Purdue, Cummins, Freightliner, Oak Ridge National Lab, Ohio State, and University of Michigan.  Our team is working with Cummins to develop advanced control systems for high BMEP SI engines used in medium-duty PHEVs   More.

Enabling High-Efficiency Operation through Next-Generation Control Systems Development for Connected and Automated Class 8 Trucks 

(sponsor: ARPA-E NEXTCAR Program) 

Purdue University, together with its partners Cummins, Peloton Technologies, Peterbilt, and NREL, has a multi-pronged approach for the implementation of their heavy-duty diesel truck project, focusing on concepts including: transmission and engine optimization; more efficient maintenance of exhaust after-treatment systems using look-ahead information;