Driving energy consumption prediction system and method, storage medium and equipment

Abstract

The invention discloses a driving energy consumption prediction system and method, a storage medium and equipment. The prediction method comprises the steps of obtaining historical working condition data of a planned driving route; constructing a training sample data set based on the historical working condition data; performing data training on the training sample data set, and establishing a vehicle speed feature BP neural network model and a driving energy consumption BP neural network model; acquiring real-time working condition information on the planned driving route; and inputting the real-time working condition information into a vehicle speed characteristic BP neural network model for prediction to obtain vehicle speed characteristic data of future driving, and then inputting thevehicle speed characteristic data into a driving energy consumption BP neural network model for prediction to obtain future driving energy consumption data so as to realize online prediction of driving energy consumption. According to the invention, online effective prediction of the driving energy consumption under the driving working conditions of different road environments and traffic states can be realized, and the efficiency of intelligent energy management of the vehicle is improved.

Description

technical field [0001] The present invention relates to the technical field of vehicle-mounted intelligent energy management in an intelligent transportation system and an intelligent network environment, and in particular to a system, method, storage medium and device for predicting energy consumption of vehicles on any planned route for intelligent vehicles. Background technique [0002] IEMS (Intelligent Energy Management System, Intelligent Energy Management System) is an inevitable requirement for the development of intelligent networked vehicles and ITS (Intelligent Transport System, Intelligent Transportation System), and its goal is to enable online self-adaptation of vehicles in different driving scenarios Realize the efficient energy saving and optimal utilization of on-board energy, especially for the current new energy vehicles such as electric vehicles and hybrid vehicles. The vehicle energy consumption is mainly affected by the driving conditions in a certain r...

AI Technical Summary

Problems solved by technology

[0003] Since the traditional vehicle energy management system cannot know the driving conditions of the future vehicle, it is mainly based on the transient power point optimization control of the real-time operating point of the power system

However, the current intelligent energy management system can use the intelligent learning algorithm to realize the vehicle's driving speed prediction, driving power demand prediction or identification of driving conditions in a short period of time (usually within 3-5 minutes). Based on this, it can be further realized Adaptive operating condition control of vehicle energy, but these are only local optimal controls in the prediction time domain

Therefore, the current management of on-board energy is mainly focused on instantaneous optimization and local optimal control, and there is still little global optimal control of on-board energy.

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