Method for predicting energy consumption in future driving process based on GRU network model
A driving process and neural network model technology, applied in the field of predicting future driving process energy consumption based on GRU network model, can solve problems such as battery improvement in prediction accuracy, improve prediction accuracy, reduce prediction errors, and save mathematical modeling. Effect
Active Publication Date: 2020-06-19
NANTONG UNIVERSITY
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[0005] Aiming at the problem that the above-mentioned prior art can only predict the state of charge of the battery from the improvement of the model and cannot improve the prediction accuracy from the actual situation, the present invention proposes a method for predicting energy consumption in the future driving process by a GRU network model
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[0045] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention.
[0046] refer to figure 1 , in an embodiment of the present invention, a method for predicting energy consumption during future driving by a GRU network model is provided, and the method specifically includes the following steps:
[0047]Step 1) Referring to the electric vehicle upload data specification of GB / T 32960 "Technical Specifications for Electric Vehicle Remote Service and Management System", select the electric passenger vehicle with the license plate number "Lu FAZ055" from April 16, 2019 to May 2019 Data between the 16th. The sampling data has a total of 159,788 pieces of data, including 192 features. Select effective features, including license plate number, me...
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The invention discloses a method for predicting energy consumption in a future driving process based on a GRU network model. The method comprises steps of inputting variables having stronger correlation with battery energy consumption change in the running process of an electric vehicle into a GRU network, calculating the weight and deviation of input time sequence data, and storing optimal modelparameters by continuously adjusting parameter values of a model; obtaining the training result of the GRU network based on weight and deviation calculation; inputting a test sample to a working condition driving SOC prediction model, and outputting the prediction result; by training historical driving data, accurately predicting change of the battery energy consumption value of the electric vehicle in the future driving process. The method is advantaged in that a prediction error of the battery energy consumption in the future driving process of the electric vehicle is small, and prediction accuracy is effectively improved; by accurately predicting an energy consumption value, an endurance mileage of the electric vehicle under the future driving working condition can be accurately estimated, and thereby a driver of the electric vehicle is helped to establish driving mileage confidence.
Description
technical field [0001] The invention belongs to the technical field of transportation, and is mainly applied to a method for estimating the cruising range of an electric vehicle by predicting the energy consumption of future driving conditions, and specifically relates to a method for predicting the energy consumption of the future driving process based on a GRU network model. Background technique [0002] Electric vehicles use electric energy as the power source. Electric energy has the characteristics of high efficiency, fast, wide sources, clean and pollution-free, making electric vehicles the latest trend in the development of the automotive industry. Since the energy of an electric vehicle is only provided by the battery, and the energy of the battery is not sufficient, it is easy for the driver to have mileage anxiety problems during the driving process of the vehicle, that is, the mental pain or anxiety caused by the fear of sudden power loss when driving an electric v...
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IPC IPC(8): B60W40/00B60L3/12G06K9/62G06N3/04G06N3/08
CPCB60W40/00B60L3/12G06N3/08B60W2510/244B60W2530/18B60W2520/10B60W2520/00G06N3/045G06F18/23
Inventor 施佺周唯昶徐慧邵叶秦曹阳荆彬彬
Owner NANTONG UNIVERSITY