Vehicle Platooning Control for Steep Terrain Fuel Savings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Class 8 trucks and other large vehicles face challenges in maintaining constant speed over hills and steep terrain, which hampers fuel economy improvements when platooning due to vehicle power limitations and excessive braking, leading to inefficiencies in fuel consumption.
Innovation Solution
Implementing a method and system for platooned vehicle control that uses nonlinear model predictive control and vehicle-to-vehicle communication to adjust the velocity of leader and follower vehicles based on grade profiles, headway distances, and vehicle state data, determining torque and velocity trajectories to minimize fuel consumption and maintain optimal following distances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If constant following distance platooning is used on level terrain, then fuel economy is significantly improved, but on steep terrain excessive braking occurs and fuel economy improvement is lost
Solution Approach 1:
The system dynamically adjusts the following distance between platooning vehicles based on real-time terrain grade information. Instead of maintaining a fixed following distance, the control system modifies the headway gap according to the steepness of the terrain, allowing vehicles to adapt their spacing to current driving conditions and avoid excessive braking on steep descents.
Solution Approach 2:
The system obtains grade profile information of the upcoming terrain in advance and uses this predictive data to adjust vehicle velocity and following distance before reaching steep sections. By anticipating upcoming grades, the platooning vehicles can prepare appropriate speed reductions or accelerations, avoiding sudden braking actions that waste energy and compromise fuel economy on steep terrain.
2Use of energy by moving object
If velocity is adjusted based on grade profile and headway distance, then fuel consumption is minimized, but control system complexity increases
Solution Approach 1:
The control system continuously monitors multiple parameters including actual following distance, leader vehicle velocity, terrain grade profile, and vehicle dynamics states. This multi-parameter feedback is fed into the nonlinear model predictive control algorithm, which dynamically computes optimal velocity and torque trajectories. The feedback mechanism enables the system to adapt to real-time conditions while maintaining fuel efficiency, balancing the increased computational complexity with tangible energy savings.
Solution Approach 2:
The system changes key operational parameters such as following distance, vehicle velocity, and engine torque based on the computed optimal trajectories. By dynamically adjusting these parameters according to terrain grade and headway conditions, the system minimizes fuel consumption while managing the complexity through structured parameter optimization rather than uncontrolled system complexity.
3Ease of operation
If class 8 trucks maintain constant speed over hills, then operational simplicity is maintained, but vehicle power limitations cause inability to maintain constant speed
Solution Approach 1:
The system transitions from rigid constant speed control to dynamic velocity control that adapts to terrain grade. The velocity profiles are continuously adjusted based on upcoming grades, allowing vehicles to maintain optimal speeds that respect power limitations while ensuring reliable performance on steep terrain. This dynamic approach replaces the unrealistic constant speed requirement with a more adaptable and reliable velocity management strategy.
Data Source
AI summary
Technologies for platooned include a leader vehicle and one or more follower vehicles each including a computing device. The leader vehicle computing device controls velocity of the leader vehicle within a predetermined route based on a grade profile of the predetermined route. The leader vehicle may perform nonlinear model predictive control using a cost function based on predicted velocity error and predicted fuel consumption. The follower vehicle computing device controls velocity of the follower vehicle within the predetermined route based on headway distance to the leader vehicle and the grade profile. The follower vehicle may perform nonlinear model predictive control using a cost function based on predicted headway error, predicted headway rate of change, and predicted fuel consumption. Other embodiments are described and claimed.


