Vehicle starting optimization control method and device based on fused traffic information

Abstract

The invention relates to a fusion traffic information-based vehicle starting optimization control method, which comprises the following steps of: when a vehicle stops or idles, acquiring a current traffic flow, a state vector of a controlled vehicle, a state of a traffic signal lamp and a distance between the traffic signal lamp and the controlled vehicle as state information of the controlled vehicle; and when the vehicle stops, whether the controlled vehicle is started at the next moment or not is judged through the vehicle starting optimization control model based on the state information. The vehicle starting optimization control model establishes a corresponding relation between the vehicle state information and whether the vehicle is started or not. After the vehicle stops, whether the vehicle is started or not can be judged according to the current state of the vehicle, so that starting and stopping of the vehicle are reduced on the basis of not additionally increasing time cost, and starting energy consumption is reduced. The corresponding device is realized according to the method.

Description

technical field [0001] The present disclosure relates to vehicle startup optimization control, and in particular, to a vehicle startup optimization control method and device based on fusion traffic information. Background technique [0002] During the driving of the vehicle, complex working conditions will lead to frequent start and stop. For example, when the vehicle is in a traffic jam, the vehicle will start and stop intermittently while waiting for the vehicle in front to move, and the vehicle will drive at low speed for a short time and a short distance. Starting and stopping will lead to additional energy waste; for buses, when the bus departs from the bus station, it often stops again due to problems such as traffic congestion, frequent starting and stopping will reduce energy efficiency, and so on. The working condition information on the road will have an important impact on the energy management effect. Therefore, integrating the traffic information and optimizing ...

AI Technical Summary

Problems solved by technology

However, it is currently impossible to directly use multi-source high-dimensional information during driving for optimal control

The main reason is that, on the one hand, directly using the obtained multi-source high-dimensional information will greatly increase the calculation amount of the optimization algorithm, resulting in too long or impossible to solve

On the other hand, these high-source and multi-dimensional driving condition information has different characteristics in different aspects, and the information association is relatively obscure, so it is difficult to directly use

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