Technologies for optimal vehicle platooning control over steep terrain

1 . A method for platooned vehicle control, the method comprising:

controlling, by a first computing device of a leader vehicle, velocity of the leader vehicle within a predetermined route based on a grade profile of the predetermined route; and

controlling, by a second computing device of a follower vehicle, velocity of the follower vehicle within the predetermined route based on a headway distance between the follower vehicle and the leader vehicle and the grade profile of the predetermined route;

wherein controlling the velocity of the following vehicle comprises determining a system input over a control horizon using nonlinear model predictive control with a cost function, wherein the system input comprises a torque trajectory and a velocity trajectory, and wherein the cost function is evaluated based on predicted headway error, predicted headway rate of change, and predicted fuel consumption; and

wherein evaluating the cost function comprises:

determining wheel speed of the follower vehicle using a nonlinear longitudinal vehicle model;

determining engine speed based on the wheel speed;

determining engine torque based on the engine speed and a torque curve associated with the follower vehicle; and

determining the predicted fuel consumption based on the engine torque and the engine speed.

2 . The method of claim 1 , further comprising:

communicating, by the first computing device, vehicle state data for the leader vehicle to the follower vehicle;

wherein controlling the velocity of the follower vehicle further comprises controlling the velocity of the follower vehicle based on the vehicle state data for the leader vehicle.

3 . The method of claim 1 , wherein determining the system input comprising the torque trajectory and the velocity trajectory further comprises:

simulating headway, leader velocity, and follower velocity using a nonlinear longitudinal vehicle model with the grade profile of the predetermined route;

evaluating the cost function based on simulating the headway, the leader velocity, and the follower velocity; and

determining the torque trajectory and the velocity trajectory by minimizing the cost function.

4 . The method of claim 3 , wherein simulating the headway, the leader velocity, and the follower velocity further comprises calculating a derivative of the headway comprising a change in headway, calculating a derivative of the leader velocity comprising a damped leader acceleration, and calculating a derivative of the follower velocity comprising a follower acceleration.

5 . The method of claim 4 , wherein:

simulating the headway, the leader velocity, and the follower velocity further comprises receiving leader velocity data from the leader vehicle via a vehicle-to-vehicle communication link; and

calculating the derivative of the headway comprising the change in headway comprises calculating a difference between the leader velocity data and follower velocity data.

6 . The method of claim 4 , wherein:

simulating the headway, the leader velocity, and the follower velocity further comprises receiving leader acceleration data from the leader vehicle via a vehicle-to-vehicle communication link; and

calculating the derivative of the leader velocity comprising the damped leader acceleration comprises applying a first order decay to the acceleration data.

7 . The method of claim 1 , wherein controlling the velocity of the leader vehicle comprises determining a second system input over a second control horizon using a second nonlinear model predictive control with a second cost function, wherein the second system input comprises a second torque trajectory and a second velocity trajectory, and wherein the second cost function is evaluated based on predicted velocity error and a second predicted fuel consumption.

8 . A system for platooned vehicle control, the system comprising:

a first computing device coupled to a leader vehicle, the first computing device comprising a lookahead cruise controller to control velocity of the leader vehicle within a predetermined route based on a grade profile of the predetermined route; and

a second computing device coupled to a follower vehicle, the second computing device comprising a headway controller to control velocity of the follower vehicle within the predetermined route based on a headway distance between the follower vehicle and the leader vehicle and the grade profile of the predetermined route;

wherein to control the velocity of the following vehicle comprises to determine a system input over a control horizon using a nonlinear model predictive control with a cost function, wherein the system input comprises a torque trajectory and a velocity trajectory, and wherein the cost function is evaluated based on predicted headway error, predicted headway rate of change, and predicted fuel consumption; and

wherein to evaluate the cost function comprises to:

determine wheel speed of the follower vehicle with a nonlinear longitudinal vehicle model;

determine engine speed based on the wheel speed;

determine engine torque based on the engine speed and a torque curve associated with the follower vehicle; and

determine the predicted fuel consumption based on the engine torque and the engine speed.

9 . The system of claim 8 , wherein:

the lookahead cruise controller of the leader vehicle is further to communicate vehicle state data for the leader vehicle to the follower vehicle; and

to control the velocity of the follower vehicle further comprises to control the velocity of the follower vehicle based on the vehicle state data for the leader vehicle.

10 . The system of claim 8 , wherein to determine the system input comprising the torque trajectory and the velocity trajectory further comprises to:

simulate headway, leader velocity, and follower velocity with a nonlinear longitudinal vehicle model with the grade profile of the predetermined route;

evaluate the cost function based on simulating the headway, the leader velocity, and the follower velocity; and

determine the torque trajectory and the velocity trajectory by minimization of the cost function.

11 . The system of claim 8 , wherein to control the velocity of the leader vehicle comprises to determine a second system input over a second control horizon using a second nonlinear model predictive control with a second cost function, wherein the second system input comprises a second torque trajectory and a second velocity trajectory, and wherein the second cost function is evaluated based on predicted velocity error and a second predicted fuel consumption.

12 . A computing device for platooned vehicle control, the computing device comprising:

a lookahead headway controller to control velocity of a follower vehicle within a predetermined route based on a headway distance between the follower vehicle and a leader vehicle and a grade profile of the predetermined route; and

a nonlinear model predictive controller to determine a system input over a control horizon for the lookahead headway controller using a cost function, wherein the system input comprises a torque trajectory and a velocity trajectory, and wherein the cost function is evaluated based on predicted headway error, predicted headway rate of change, and predicted fuel consumption;

wherein to evaluate the cost function comprises to:

determine wheel speed of the follower vehicle with a nonlinear longitudinal vehicle model;

determine engine speed based on the wheel speed;

determine engine torque based on the engine speed and a torque curve associated with the follower vehicle; and

determine the predicted fuel consumption based on the engine torque and the engine speed.

13 . The computing device of claim 12 , wherein:

the headway controller is further to receive vehicle state data for the leader vehicle from the leader vehicle; and

to control the velocity of the follower vehicle further comprises to control the velocity of the follower vehicle based on the vehicle state data for the leader vehicle.

14 . The computing device of claim 12 , wherein to determine the system input comprising the torque trajectory and the velocity trajectory further comprises to:

simulate headway, leader velocity, and follower velocity with a nonlinear longitudinal vehicle model with the grade profile of the predetermined route;

evaluate the cost function based on simulating the headway, the leader velocity, and the follower velocity; and

determine the torque trajectory and the velocity trajectory by minimization of the cost function.

15 . The computing device of claim 14 , wherein to simulate the headway, the leader velocity, and the follower velocity further comprises to calculate a derivative of the headway comprising a change in headway, to calculate a derivative of the leader velocity comprising a damped leader acceleration, and to calculate a derivative of the follower velocity comprising a follower acceleration.