Method and system for predicting remaining driving mileage of electric automobile

Abstract

The invention discloses a method for predicting the remaining driving mileage of an electric automobile. The method includes the steps that information of an input destination of a driver is acquired; information of the electric automobile is acquired; information of a real world is acquired; historical driving behaviors are loaded; according to the information of the input destination of the driver, the information of the electric automobile, the information of the real world and the historical driving behaviors, the optimal path and the remaining driving mileage of the electric automobile and the remaining electric quantity, generated when the electric automobile reaches the destination, of the electric automobile are obtained through calculation. The invention further discloses a system for predicting the remaining driving mileage of the electric automobile. The system comprises a unit for acquiring the information of the input destination of the driver, a unit for acquiring the information of the real world, a unit for acquiring the information of the electric automobile, a cloud server and a display unit. The method and system for predicting the remaining driving mileage of the electric automobile guarantee that a user can reasonably judge the remaining driving mileage of the electric automobile when driving the electric automobile, and the running performance and the running reliability of the electric automobile are greatly improved.

Description

technical field [0001] The invention relates to a prediction technology for the remaining mileage of an electric vehicle, in particular to a method and system for predicting the remaining mileage of an electric vehicle based on a cloud computing framework. Background technique [0002] With the development and popularization of electric vehicles, users have put forward higher requirements for the batteries used in electric vehicles, not only for the materials and charging and discharging quality of the batteries themselves, but also for the rationality of battery use and information management. The accuracy of the battery puts forward higher requirements, that is, how to accurately evaluate the battery status, driving behavior, road conditions, traffic conditions, and environmental conditions, and integrate the above information to accurately predict the remaining mileage of the electric vehicle. [0003] At present, the prediction of the remaining mileage of electric vehicl...

AI Technical Summary

Problems solved by technology

The limitation of this method is that: (1) the future power consumption is predicted only by the statistics of power consumption per unit mileage, without comprehensive consideration of factors such as driving behavior and road conditions of the whole route; (2) only by the power consumption per unit mileage to evaluate Driving behavior without taking into account other factors that affect consumption, including traffic conditions, road conditions, and weather; (3) not analyzing the reasons for the change in the remaining mileage, and not providing the driver with suggestions for extending the remaining mileage

Therefore, the above method cannot judge the pros and cons of driving behavior, cannot analyze the reasons for the high and low energy consumption of different driving routes, cannot accurately predict the remaining mileage based on multi-source information, and cannot provide suggestions for improving the mobility of electric vehicles

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