Hybrid power bus driving condition forecasting method based on internet of vehicles

Abstract

The invention discloses a hybrid power bus driving condition forecasting method based on an internet of vehicles, and belongs to the technical field of modern transportation. The hybrid power bus driving condition forecasting method is characterized by including steps that real-time vehicle position information and running data are matched and stored; the position information of a vehicle is transmitted in real time, the position information of vehicles around the vehicle is received, and front vehicles which run in the same direction and on the same road with the vehicle and are positioned in front of the vehicle by certain distances are selected; the front vehicles separated from the vehicle within a certain distance transmit historical data to the vehicle; forecasting weights of driving parameters of the front vehicles to the driving condition of the vehicle are determined according to the distances between the front vehicles and the vehicle, and forecast characteristic parameters of the driving condition of the vehicle are computed according to the forecasting weights and characteristic parameters of the front vehicles; the driving condition within a certain distance in front of the vehicle is identified and forecast according to the forecast characteristic parameters of the driving condition of the vehicle and a fuzzy identification model; and control parameters of the vehicle are adjusted by an HCU (hybrid control unit) according to a forecast result. The hybrid power bus driving condition forecasting method has the advantages that lagging of a traditional method is eliminated, forecast accuracy is improved, and accordingly fuel economy and emission performance of the vehicle are improved.

Description

Technical field [0001] The invention belongs to the field of modern transportation technology, and relates to a method for predicting driving conditions of a hybrid electric vehicle, and in particular to a method for predicting driving conditions of a hybrid electric bus based on the Internet of Vehicles. Background technique [0002] Driving conditions have a great influence on the power matching, emission level and fuel consumption of the vehicle, so it plays a vital role in the power matching and control strategy formulation of hybrid electric vehicles. In recent years, our country has established the driving conditions of large cities such as Beijing and Shanghai. However, the actual operating conditions of automobiles change with time, location, environment, climate and other factors, which is a random and uncertain process. The driving condition prediction method is to make the adaptive adjustment of the control strategy of the hybrid electric vehicle based on the driving c...

AI Technical Summary

Problems solved by technology

In recent years, my country has studied and constructed the driving conditions of big cities such as Beijing and Shanghai. However, the actual operating conditions of automobiles change with time, place, environment, climate and other factors, which is a random and uncertain process. The driving condition prediction method is to make an adaptive adjustment of the control strategy of the hybrid vehicle based on the driving condition data of a certain period, but this method is based on the data accumulation of the vehicle running for a certain period to identify and adjust the control strategy, so it has a lag accuracy, and the accuracy rate is low, so it is of little significance for the control reference of the future operating state of the vehicle

Therefore, there is currently a need for a method for predicting the driving conditions of hybrid electric buses to identify and predict the future driving conditions of the vehicle, so as to solve the problems of lagging recognition of vehicle driving conditions and low recognition accuracy

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