Battery remaining life prediction method and system integrating test and Internet of Vehicles

Abstract

The invention provides a battery remaining life prediction method integrating test and Internet of Vehicles. The battery remaining life prediction method comprises the following steps: acquiring vehicle operation data; according to the new energy bus networking operation data, vehicle characteristic value extraction and battery capacity target value calculation are carried out; and obtaining a prediction result by using an LSTM model according to the vehicle characteristic value and the battery capacity target value. The method solves the problem of predicting the residual life of the power battery of the new energy automobile, masters the stable operation capability of the power battery of the automobile through an accurate battery residual life prediction result, and guarantees the stable operation of a public transportation system.

Description

technical field

[0001] The invention relates to the technical field of bus batteries, in particular to a method and system for predicting the remaining life of a battery that integrates testing and Internet of Vehicles. Background technique

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] New energy vehicles are an important means of industrial transformation, scientific and technological innovation, as well as solving the energy crisis and reducing pollution emissions in various countries, and attach great importance to the development of this industry, especially in the field of public transportation. However, with the large-scale use of new energy buses, the mileage decline and safety risks of vehicles have gradually become prominent, which has caused drivers' anxiety about the mileage of new energy vehicles and increased the difficulty of dispatching the bu...

Examples

Embodiment 1

[0036] Such as figure 1 As shown, a method for predicting the remaining battery life of the fusion test and Internet of Vehicles, including:

[0037] Obtain vehicle operation data;

[0038] According to the operation data of the new energy bus network, the vehicle characteristic value is extracted and the battery capacity target value is calculated;

[0039] According to the characteristic value of the vehicle and the target value of the battery capacity, the prediction result is obtained by using the LSTM model.

[0040] The acquisition of networked operation data of new energy buses includes obtaining the original continuous operation data of the vehicle and performing data preprocessing.

[0041] The original continuous running data of the vehicle is divided into driving with charging segment, driving without charging segment, parking with charging segment and parking without charging segment.

[0042] The calculation of the battery capacity target value includes fusing ...

Embodiment 2

[0084] A battery remaining life prediction system that integrates testing and Internet of Vehicles, including:

[0085] The data acquisition module is configured to acquire vehicle operation data;

[0086] The calculation module is configured to extract the vehicle characteristic value and calculate the battery capacity target value according to the network operation data of the new energy bus;

[0087] The prediction module is configured to use the LSTM model to obtain a prediction result according to the vehicle characteristic value and the battery capacity target value.

Embodiment 3

[0089] A computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor of a terminal device and executing the method for predicting the remaining battery life of a fusion test and Internet of Vehicles provided in Embodiment 1.

technical field

[0001] The invention relates to the technical field of bus batteries, in particular to a method and system for predicting the remaining life of a battery that integrates testing and Internet of Vehicles. Background technique

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] New energy vehicles are an important means of industrial transformation, scientific and technological innovation, as well as solving the energy crisis and reducing pollution emissions in various countries, and attach great importance to the development of this industry, especially in the field of public transportation. However, with the large-scale use of new energy buses, the mileage decline and safety risks of vehicles have gradually become prominent, which has caused drivers' anxiety about the mileage of new energy vehicles and increased the difficulty of dispatching the bu...

Examples

Embodiment 1

[0036] Such as figure 1 As shown, a method for predicting the remaining battery life of the fusion test and Internet of Vehicles, including:

[0037] Obtain vehicle operation data;

[0038] According to the operation data of the new energy bus network, the vehicle characteristic value is extracted and the battery capacity target value is calculated;

[0039] According to the characteristic value of the vehicle and the target value of the battery capacity, the prediction result is obtained by using the LSTM model.

[0040] The acquisition of networked operation data of new energy buses includes obtaining the original continuous operation data of the vehicle and performing data preprocessing.

[0041] The original continuous running data of the vehicle is divided into driving with charging segment, driving without charging segment, parking with charging segment and parking without charging segment.

[0042] The calculation of the battery capacity target value includes fusing ...

Embodiment 2

[0084] A battery remaining life prediction system that integrates testing and Internet of Vehicles, including:

[0085] The data acquisition module is configured to acquire vehicle operation data;

[0086] The calculation module is configured to extract the vehicle characteristic value and calculate the battery capacity target value according to the network operation data of the new energy bus;

[0087] The prediction module is configured to use the LSTM model to obtain a prediction result according to the vehicle characteristic value and the battery capacity target value.

Embodiment 3

[0089] A computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor of a terminal device and executing the method for predicting the remaining battery life of a fusion test and Internet of Vehicles provided in Embodiment 1.

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