Speed planning in a vehicle-following scenario can reduce vehicle fuel consumption even under limited traffic preview and in moderate penetration of connected autonomous vehicles (CAVs), but could also lead to colder exhaust temperature, and consequently, less efficient aftertreatment conversion. To investigate this potential trade-off, this paper presents a model predictive controller (MPC) to optimally plan in an energy-conscious way the optimal speed trajectory for a diesel car following a hypothetical lead vehicle that drives through the velocity trace of a federal test procedure. Using this energy-conscious optimal speed plan we investigate different horizons for three objective functions, including minimum acceleration, minimum fuel consumption and minimum power. Then, MPC results are compared to the trajectories obtained by dynamic programming with full knowledge of the drive cycle. As expected, longer previews lead to smoother velocity trajectories that reduce the fuel consumption by 11% when power is the objective function, if the preview is accurate. When the minimum fuel is set as the objective in the MPC, the controller coordinates to operate the engine at more efficient conditions, which increases the fuel saving to 25%. However, the extra fuel saving is shown to be achieved at the expense of high vehicle NOx emissions, since the engine operates at low speeds and high loads, where the output NOx emissions are high, when the aftertreatment catalyst is not hot enough. Finally, it is shown that the minimum power formulation leads to a better trade-off, where fuel economy can be increased without a large penalty on NOx emissions.
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Intelligent Cruise Control of Diesel Powered Vehicles Addressing the Fuel Consumption versus Emissions Trade-off
Intelligent cruise control with traffic preview introduces a potential to adjust the vehicle velocity and improve fuel consumption and emissions. This paper presents trade-offs observed during velocity trajectory optimizations when the objective function varies from fuel-based targets to emissions-based. The scenarios studied consider velocity optimization while following a hypothetical leader executing the federal test procedure (FTP) velocity profile with distance constraint, instead of the classical legislated velocity constraint, to enable the flexibility in optimizing the velocity trajectory. The vehicle model including longitudinal dynamics, fuel consumption and tailpipe NOx emissions is developed for a medium-duty truck with a diesel engine and verified over the FTP. Then, dynamic programming is applied on a reduced-order model to solve the constraint trajectory optimization problem and calculate an optimal vehicle velocity profile over the temperature stabilized phase (Bag 2) of the FTP. Results show 59% less tailpipe NOx emissions with an emission-optimized drive cycle but with 17% more fuel consumption compared to a non-optimized baseline. Whereas, a fuel-optimized cycle improves the fuel efficiency by 18% but with doubled tailpipe NOx emissions. Moreover, it is shown that for a diesel powertrain, including the aftertreatment system efficiency associated with the thermal dynamics is crucial to optimize the tailpipe NOx emissions and can not be ignored for problem simplification.
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- Award ID(s):
- 1646019
- NSF-PAR ID:
- 10076118
- Date Published:
- Journal Name:
- American Control Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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