A driver-in-the-loop vehicle fuel economy control method

Abstract

The invention discloses a driver-in-the-loop vehicle fuel economy control method. The method is characterized in that a vehicle is an intelligent network connection hybrid power vehicle, and the control method is a hierarchal control method in which driver random errors are considered; a superior control method in the hierarchal control method calculates an optimal target vehicle speed based on the driver random errors and state information obtained based on an intelligent network connection environment, wherein the state information comprises surrounding vehicle state information and trafficlight state information; a substratum control method in the hierarchal control method calculates the optimal energy distribution of the hybrid power vehicle based on a self-adaption equivalent fuel consumption minimum principle. According to the control method, a preset vehicle speed which is very approximate to an ideal condition can be obtained, and better fuel economy is obtained.

Description

Technical field[0001]The invention belongs to the field of control of intelligent networked vehicles and new energy vehicles, and particularly relates to a fuel economy control method of an intelligent networked hybrid vehicle with a driver in the loop.Background technique[0002]Intelligent networked vehicles are equipped with advanced sensors, controllers, actuators and modern communication equipment, which can realize information interaction with surrounding vehicles and transportation facilities, and provide energy-saving, safe, environmentally friendly, comfortable and fast transportation modes, which are recognized globally One of the future development directions of the automotive industry.[0003]In the intelligent networked environment, the position information of the vehicle can be obtained through the global positioning system, the relative position information between the vehicles can be obtained through radar or lidar, the absolute speed information of the vehicle can be me...

AI Technical Summary

Problems solved by technology

However, none of the above studies considered the influence of the driver’s random error on the vehicle’s predictive control, because the driver’s execution has errors, and the vehicle’s driving state will deviate from the predicted optimal value.

[0004] Existing IoV predictive control literature generally studies highly automated unmanned vehicles, regardless of driver intervention

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