Vehicle-mounted networking energy-saving auxiliary driving control method and system

Abstract

The invention relates to a vehicle-mounted networking energy-saving auxiliary driving control method and system, and belongs to the technical field of vehicle energy saving. Different from other energy-saving driving technologies, the method is characterized in that a V2I technology-based vehicle queue length estimation method is constructed to predict an effective green light passable time interval when a networked vehicle arrives at an intersection. Secondly, a layered energy-saving optimization strategy system is developed, the first layer provides a global recommendation optimal speed with minimum energy consumption as a target, and the second layer considers a driver tracking error and re-plans to obtain a corrected recommendation speed. Implementation proves that the improved EADS considering the traffic flow queuing effect and the driving error can help the driver to execute the suggested energy-saving speed, and then the energy-saving performance of the vehicle is improved.

Description

technical field [0001] The invention belongs to the technical field of vehicle energy saving, and in particular relates to a control method and system for vehicle networking energy-saving auxiliary driving. Background technique [0002] Connected smart car technology is revolutionizing the automotive industry. In particular, connected vehicles have path / speed planning, vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication capabilities. In this way, the vehicle can be connected to the intelligent transportation system and share a large amount of traffic condition data and vehicle operation data with the intelligent transportation system. This data can be used to improve the overall performance of the vehicle, including maneuverability, safety and fuel economy. [0003] It is an important research content of current urban road ecological driving to reduce the congestion or unnecessary stop-and-go behavior of vehicles at signalized intersections of urban ...

AI Technical Summary

Problems solved by technology

However, traditional energy-saving assisted driving systems only consider the phase and timing of traffic light signals

However, the other two important factors, that is, the queuing effect of the traffic flow and the driver's actual operation error, are rarely considered, thus reducing the practical application performance of EADS

The queuing effect of the traffic flow makes the networked vehicles unable to pass the intersection smoothly when they arrive at the intersection, and the time window of the green light signal that can pass is compressed; the driver's operation error causes the driver to deviate more and more from the theoretical suggested speed when implementing the suggested speed , causing vehicles to fail to pass through the intersection smoothly as planned

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