Model prediction method for remaining mileage of pure electric vehicles based on route information

Abstract

The invention discloses a pure electric automobile remaining mileage model predication method based on path information. The pure electric automobile remaining mileage model predication method based on path information comprises the following steps of analyzing driver history running data, extracting the path information and generating a state transferring probability matrix satisfying driver behavior characteristics; generating a predicated automobile speed on the basis of road information of a future path and the corresponding state transferring probability matrix; establishing a parameter estimation model to estimate running parameters affecting energy consumption and remaining mileage of an automobile; and establishing an RDR calculation model to predicate a vehicle remaining mileage,wherein an energy consumption predication model is used for calculating a vehicle energy consumption rate by using the predicated automobile speed obtained by an automobile speed predication model andthe running parameters estimated by the parameter estimation model as model input; a remaining energy predication model is used for estimating vehicle battery remaining energy; by integrating the vehicle energy consumption rate and the battery remaining energy, the vehicle remaining mileage can be predicated and can be displayed by using a remaining mileage display model.

Description

technical field [0001] The invention relates to a method for predicting the remaining mileage model of a pure electric vehicle based on path information, and belongs to the technical field of new energy vehicles. Background technique [0002] Pure electric vehicles (Battery Electric Vehicle, BEV) have obvious advantages over traditional internal combustion engine vehicles in terms of energy consumption and emissions, such as good power, low driving noise, energy saving and zero emissions. However, due to the limitations of battery technology development, the driving range of electric vehicles is still relatively short and the charging time is relatively long. Pure electric vehicle drivers worry about whether they can reach their destination with the current remaining energy, which is called "range anxiety", and range anxiety is one of the main factors currently limiting the acceptance of electric vehicles. Obviously, installing large-capacity batteries, fast charging and bu...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that when the future working conditions change greatly, the error of this prediction will become larger and even the prediction results cannot be trusted at all.

Among them, working conditions (vehicle speed) are one of the most important factors affecting energy consumption. Under different types of working conditions, such as urban, suburban and high-speed, there are huge differences in energy consumption of electric vehicles. Obviously, when the working conditions change , the RDR prediction based on historical data will inevitably fail

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