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

A prediction method and energy consumption technology, which is applied in the direction of prediction, neural learning methods, data processing applications, etc., can solve the problems of less global optimization control of vehicle energy and the inability to know the future driving conditions of vehicles, and achieve strong adaptability to working conditions and Practicality, reduce input, and ensure the effect of prediction accuracy

Active Publication Date: 2019-12-24
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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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.

Method used

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

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Embodiment 1

[0024] This embodiment provides an intelligent prediction system for driving energy consumption, figure 1 It is a schematic diagram of an intelligent prediction system for driving energy consumption in this embodiment, referring to figure 1 , the system includes data acquisition subsystem, offline training subsystem and online prediction subsystem.

[0025] The data acquisition subsystem is used to collect and record road environment parameter data, traffic state parameter data and vehicle operation data on the planned driving route;

[0026] The offline training subsystem is used to divide the planned driving route into multiple road sections, extract and calculate the road environment parameters, traffic state parameters, vehicle speed characteristic parameters and energy consumption values ​​of the road sections, and analyze the vehicle speed characteristic parameters , establish a sample data set; build a BP neural network model, train and verify the data set through the ...

Embodiment 2

[0037] Such as figure 2 As shown, a driving energy consumption prediction method, including:

[0038] Step 1. Obtain the historical working condition data of the planned driving route, including road environment parameters, traffic state parameters and vehicle operation data;

[0039] Use vehicle-mounted GPS positioning devices and GIS information receiving devices to collect road environment parameters such as road types, road slopes, road speed limits, and traffic status parameters such as traffic congestion levels; use CAN bus and speed sensors to collect vehicle operating data such as driving distance and speed Wait.

[0040] Step 2. Construct a training sample data set based on the acquired raw data, specifically including the following steps:

[0041] Since the traffic status of different road sections on the planned driving route is different and time-varying, and the energy consumption of the vehicle operation is different under different traffic conditions, the dri...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/30G06N3/04G06N3/08
CPCG06Q10/04G06Q50/30G06N3/08G06N3/044
Inventor 李玉芳张俊任陈卢小丁倪铭
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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