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Abstract This paper investigates temporal correlations in human driving behavior using real-world driving to improve speed forecasting accuracy. These correlations can point to a measurement weighting function with two parameters: a forgetting factor for past speed measurements that the vehicle itself drove with, and a discount factor for the speeds of vehicles ahead based on information from vehicle-to-vehicle communication. The developed weighting approach is applied to a vehicle speed predictor using polynomial regression, a prediction method well-known in the literature. The performance of the developed approach is then assessed in both real-world and simulated traffic scenarios for accuracy and robustness. The new weighting method is applied to an ecological adaptive cruise control system, and its influence is analyzed on the prediction accuracy and the performance of the ecological adaptive cruise control in an electric vehicle powertrain model. The results show that the new prediction method improves energy saving from the eco-driving by up to 4.7% compared to a baseline least-square-based polynomial regression. This is a 10% improvement over the constant speed/acceleration model, a conventional speed predictor. 

Studies on eco-driving have mostly taken an energy-centric view and considered driving performance, while less attention has been paid on emissions behavior. This work extends in an experimentally verified way our understanding of the trade-offs among fuel economy, driving aggressiveness, and, especially, emissions in connected automated diesel-powered vehicles. Experiments are performed with a 6.7-L Ford Powerstroke diesel engine, a urea-SCR based NOx aftertreatment system, and a full model for a Ford F250 medium-duty truck in the loop to provide realistic assessment of fuel consumption, tailpipe emissions, and driving style performances. An energy and emissions conscious speed planner is leveraged to follow the traffic. This planner offers flexibility in prioritizing energy or emissions while satisfying user-defined headway constraints, and thus allows exploration of different calibrations in a unified way. Results show how various calibrations of the flexible leader following policy yield 8%–14% decrease in total fuel consumption and 64%–70% decrease in tailpipe emissions compared with a strictly constrained following policy.

 

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. 

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.