Purdue University Graduate School
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posted on 2020-01-16, 18:59 authored by Alexander H. TaylorAlexander H. Taylor
The United States Environmental Protection Agency (EPA) is charged with pro-tecting human health and the environment. Part of this mission involves regulating heavy-duty trucks that produce particulate matter (PM), unburned hydrocarbons (UHC), carbon dioxide (CO2), and nitrogen oxides (NOx). A byproduct of lean burn combustion in diesel engines is NOx. NOx output limits from commercial vehicles have been reduced significantly from 10 g/hp-hr in 1979 to 0.2 g/hp-hr in 2010. Ad-ditional reductions are expected in the near future.

One pathway to meet future NOx emissions regulations in a fuel efficient manner is with higher performing exhaust aftertreatment systems through improved engine air handling. As exhaust aftertreatment’s capability to convert harmful NOx into harmless N2 and H2O is a function of temperature, a key performance factor is how quickly does the exhaust aftertreatment system heat up (warm-up), and how well does the system stay at elevated temperatures (stay-warm).

When the warm-up strategy of iEGR was implemented over the heavy duty federal test procedure (HD-FTP) drive-cycle, it was able to get the SCR above the critical 250◦C peak NOx conversion threshold 100 seconds earlier than the TM baseline. While iEGR consumed 2.1% more fuel than the TM baseline, it reduced predicted tailpipe NOx by 7.9%.

CDA implemented as a stay-warm strategy over the idle portions of the HD-FTP successfully kept the SCR above the 250◦C threshold for as long as the TM baseline and consumed 3.0% less fuel. Implementing CDA both at idle and from 0 to 3 bar BMEP consumed an additional 0.4% less fuel, for a total fuel consumption reduction of 3.4%.

A method to predict and avoid compressor surge (which can destroy turbochargers and in fact did so during the HD-FTP experiments) instigated by CDA was devel-oped, as discussed later, and implemented with staged cylinder deactivation to avoid compressor surge.

The literature does not consider the fidelity of road grade data required to ad-equately predict vehicle fuel consumption and operational behavior. This work ad-dresses this issue for Class 8 trucks by comparing predicted fuel consumption and operation (shifting, engine torque/speed, and braking) of a single Class 8 truck simu-lated with grade data for the same corridor from different sources. The truth baseline road grade (best fidelity available with LiDAR) was obtained previously. This work compares road grade data to the truth baseline from four other typical methods i) utilizing GPS to record horizontal position and vertical elevation, ii) logging the pitch of a cost effective, commercially available IMU, iii) integrating the horizontal and ver-tical velocities of the same IMU, and iv) a commercially available dataset (Comm). Comm grade data (R2=0.992) best matches the LiDAR reference over a 5,432 m stretch of US 231 where high quality LiDAR data was available, followed in quality by the integrated IMU velocity road grade (R2=0.979). Limitations of the Comm dataset are shown, namely missing road grade (decreased point density) for up to 1 km spans on other sections of US 231, as well as for Interstate 69. Vehicle simulations show that both the Comm data (where available and accurate) and integrated IMU road grade data result in fuel consumption predictions within 2.5% of those simulated with the truth reference grade data.

The simulation framework described in Chapter 6 combines high fidelity vehicle and powertrain models (from Chapter 5) with a novel production-intent platooning controller. This controller commands propulsive engine torque, engine-braking, or friction-braking to a rear vehicle in a two-truck platoon to maintain a desired following distance. Additional unique features of the framework include high fidelity road grade and traffic speed data. A comparison to published experimental platooning results is performed through simulation with the platooning trucks traveling at a constant 28.6 m/s (64 MPH) on flat ground and separated by 11 m (36 ft). Simulations of platooning trucks separated by a 16.7 m (54.8 ft) gap are also performed in steady-state operation, at different speeds and on different grades (flat, uphill, and downhill), to demonstrate how platooning affects fuel consumption and torque demand (propulsive and braking) as speed and grade are varied. For instance, while platooning trucks with the same 16.7 m gap at 28.6 m/s save the same absolute quantity of fuel on a 1% grade as on flat ground (1.00 per-mile, normalized), the trucks consume more fuel overall as grade increases, such that relative savings for the platoon average decrease from 6.90% to 4.94% for flat vs. 1% grade, respectively. Furthermore, both absolute and relative fuel savings improve during platooning as speed increases, due to increase in aerodynamic drag force with speed. There are no fuel savings during the downhill operation, regardless of speed, as the trucks are engine braking to maintain reasonable speeds and thus not consuming fuel. Results for a two-truck platoon are also shown for moderately graded I-74 in Indiana, using traffic speed from INDOT for a typical Friday at 5PM. A 16.7 m (54.8 ft) gap two-truck platoon decreases fuel consumption by 6.18% over the baseline without degradation in trip time (average speed of 28.3 m/s (63.3 MPH)). The same platooning trucks operating on aggressively graded I-69 in Indiana shows a lower platoon-average 3.71% fuel savings over baseline at a slower average speed of 24.5 m/s (54.8 MPH). The impact of speed variation over, and grade difference between, these realistic routes (I-74 & I-69) on two-truck platooning is described in detail.


Degree Type

  • Doctor of Philosophy


  • Mechanical Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Dr. Gregory Shaver

Additional Committee Member 2

Dr. Peter Meckl

Additional Committee Member 3

Dr. Steven Son

Additional Committee Member 4

Dr. Monika Ivantysynova

Additional Committee Member 5

Dr. John Lumkes

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