Economical driving control method for intelligent networked vehicles in dynamic traffic scenarios

Abstract

The invention relates to an economical driving control method of an intelligent networked vehicle in a dynamic traffic scene, which is suitable for urban road environments, considers dynamic signal light information and networked vehicle traffic information prediction capabilities, and optimizes energy-optimized vehicles based on optimal control. Driving speed; take the minimization of energy consumption as the control goal, construct an optimal control problem based on vehicle longitudinal dynamics, and use the minimum value principle to analyze and solve the optimal control rate, and obtain a general solution model for this problem; use double-layer rolling distance Domain vehicle speed optimization strategy, with the expected end goal as the control constraint, the upper-level economic vehicle speed planning is carried out, and the upper-level vehicle speed trajectory that meets the driving purpose is obtained, and the upper-level vehicle speed trajectory is used as the guiding layer, combined with the actually obtained information such as the phase of the signal light and the recommended speed at the intersection. The lower-level economic vehicle speed is planned, and the general solution model is used for analytical solution to optimize and calculate the intersection speed spectrum with the best energy consumption.

Description

technical field [0001] The invention relates to the technical field of vehicle speed optimization control, in particular to a control method for economical driving of an intelligent networked vehicle in a dynamic traffic scene. Background technique [0002] As an important traffic sign to control the traffic authority of vehicles in all directions, traffic lights have effectively reduced traffic accidents at urban intersections and ensured the safety of pedestrians. However, because human drivers cannot obtain the phase information of the front signal lights in real time, accurately avoid the phase of the red light, and repeatedly brake and stop at the intersection, unnecessary energy loss is caused. [0003] In the prior art, the rule-based economic vehicle speed control method relies too much on human driving experience, and the energy-saving effect is limited. In addition, due to the complexity of the vehicle model, the optimization control methods represented by dynamic...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the defects of the prior art, the present invention provides an economical driving control method of an intelligent networked vehicle in a dynamic traffic scene, which solves the technical problem that the economical vehicle speed control method relies too much on human driving experience, resulting in limited energy-saving effect

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