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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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Optimal Weight Adaptation for Model Predictive Control of Connected and Automated Vehicles in Mixed Traffic with Bayesian Optimization
In this paper, we develop an optimal weight adap- tation strategy of model predictive control (MPC) for connected and automated vehicles (CAVs) in mixed traffic. We model the interaction between a CAV and a human-driven vehicle (HDV) as a simultaneous game and formulate a game-theoretic MPC problem to find a Nash equilibrium of the game. In the MPC problem, the weights in the HDV’s objective function can be learned online using moving horizon inverse reinforcement learning. Using Bayesian optimization, we propose a strategy to optimally adapt the weights in the CAV’s objective function so that the expected true cost when using MPC in simulations can be minimized. We validate the effectiveness of the optimal strategy by numerical simulations of a vehicle crossing example at an unsignalized intersection.
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- PAR ID:
- 10421260
- Date Published:
- Journal Name:
- 2023 American Control Conference
- Page Range / eLocation ID:
- 1183-1188
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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