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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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A Robust Energy and Emissions Conscious Speed Control Framework for Connected Vehicles with Privacy Considerations
While perturbation schemes for vehicle-to-vehicle (V2V) communications can address data privacy concerns, they can significantly compromise the performance of the speed controllers of connected automated vehicles (CAVs) if such controllers rely on the preview information available through V2V in car-following scenarios. This paper presents a robust predictive speed controller for a CAV when preview information is provided through a privacy-guaranteed V2V communication network. This is the first such controller that considers energy and emissions concurrently. The impact of privacy assurance in the communication data is studied, while inter-vehicular distance constraint is guaranteed to be satisfied through a robust design of the predictive controller using a robust control invariant set. The robust optimal speed controller is shown to reduce fuel consumption and emissions successfully while satisfying the constraints even in the presence of perturbations in the V2V communication. Results suggest a need for an integrated design procedure to achieve the best performance under a given level of privacy guarantee and emissions requirements.
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- Award ID(s):
- 1646019
- PAR ID:
- 10195807
- Date Published:
- Journal Name:
- Proceedings of the American Control Conference
- ISSN:
- 0743-1619
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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