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 control field of intelligent networked vehicles and new energy vehicles, and in particular relates to a fuel economy control method of a driver-in-the-loop intelligent networked hybrid vehicle. Background technique [0002] Intelligent connected 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 network environment, the location information of the vehicle can be obtained through the global positioning system, the relative position information between vehicles can be obtained through radar or lidar, the absolute speed information of the vehicle can be...

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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