Power battery remaining life prediction method based on data driving

A power battery and life prediction technology, which is applied in special data processing applications, measurement electronics, and measurement devices, can solve problems such as difficult measurement of internal parameters and power battery attenuation, and achieve prediction results, accuracy improvements, and improved prediction accuracy. Effect

Active Publication Date: 2021-05-07
WUHAN UNIV OF TECH
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AI Technical Summary

Problems solved by technology

With the increase of the number of charge and discharge cycles of the power battery, the capacity of the power battery will gradually decline. This is an irreversibl

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  • Power battery remaining life prediction method based on data driving
  • Power battery remaining life prediction method based on data driving
  • Power battery remaining life prediction method based on data driving

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Embodiment Construction

[0058] The principles and features of the present invention are described below, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0059] A method for predicting the remaining life of a power battery based on data, the specific steps are as follows:

[0060] In the data preparation stage, the pure electric vehicle driving data is obtained;

[0061] The pure electric vehicle driving data is the historical data of five operating vehicles in Beijing and Shanghai respectively, including vehicle state data, vehicle operation data, power battery data and vehicle abnormality alarm data; wherein the vehicle state data includes vehicle operation state and charging status data; the vehicle operation data includes vehicle operation mode, vehicle speed and accumulated mileage; the power battery data itself includes total current, total voltage, battery SOC, battery history SOH, the highest value of ba...

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Abstract

The invention relates to a power battery remaining life prediction method based on data driving. The method comprises the steps of obtaining pure electric vehicle driving data; preprocessing the driving data of the pure electric vehicle; performing feature extraction on the preprocessed data; constructing an algorithm model by using a fusion model of an LSTM model and BP-GM (1, N) according to related feature data obtained by feature extraction; and predicting the residual life of the power battery through the algorithm model. According to driving big data of an electric vehicle, battery health state information and a change rule thereof are fully mined, related characteristics such as historical SOH, SOH characterization parameters, driving characteristics and vehicle category characteristics of the battery are constructed, and the hidden battery health state information and the change rule thereof are fully mined from vehicle data and battery data, therefore, the RUL is predicted on the basis of the SOH of the battery.

Description

technical field [0001] The invention relates to the fields of prediction of decommissioning time and value evaluation of a power battery of an electric vehicle, in particular to a data-driven method for predicting the remaining life of a power battery. Background technique [0002] The automobile industry is taking the new "four modernizations" represented by electrification, networking, intelligence, and sharing as the development direction. More and more electric vehicles have entered the consumer market, and power batteries are the main components of electric vehicles. power source. As the number of charge and discharge cycles of the power battery increases, the capacity of the power battery will gradually decay. This is an irreversible aging reaction that occurs inside the power battery. Its internal parameters are difficult to measure, and there are still huge challenges in estimating its state. [0003] The remaining life of the power battery (RUL) refers to the remai...

Claims

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Application Information

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IPC IPC(8): G06F30/20G06F30/27G06N3/04G01R31/392G01R31/367G01R31/396G06F119/04
CPCG06F30/20G06F30/27G06N3/049G01R31/392G01R31/367G01R31/396G06F2119/04G06N3/044Y02T10/70
Inventor 胡杰杨博闻翁灵隆高志文
Owner WUHAN UNIV OF TECH
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