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Artificial neural network-based highest surface temperature prediction method of secondary battery

An artificial neural network, the highest temperature technology, applied in the direction of radiation pyrometry, biological neural network model, measuring device, etc., can solve the problem of secondary battery thermal runaway, etc., and achieve the effect of easy parameters

Inactive Publication Date: 2012-06-13
BEIJING INSTITUTE OF TECHNOLOGYGY
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the potential safety hazard of thermal runaway in the secondary battery, and propose a method for predicting the maximum surface temperature of the secondary battery based on the artificial neural network model

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  • Artificial neural network-based highest surface temperature prediction method of secondary battery
  • Artificial neural network-based highest surface temperature prediction method of secondary battery

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Embodiment

[0024] The method for predicting the maximum surface temperature of secondary battery based on artificial neural network, the specific operation steps are as follows:

[0025] 1) Put the 8Ah cylindrical Ni-MH power battery in the high and low temperature test box, and connect it to the charge and discharge test machine;

[0026] 2) To charge the battery at ambient temperature of -10℃, 0, 10, 20, 30, 40℃, the battery should be discharged to SOC=0 before charging;

[0027] 3) Under the same ambient temperature, charge the battery at the rate of 1C, 3C and 5C respectively, and stop when the battery SOC = 1.1;

[0028] 4) Use an infrared thermal imager to monitor the change of the maximum surface temperature of the battery during charging, such as figure 1 shown;

[0029] 5) Establish a Back-Propagation neural network model, set the input of the model as the ambient temperature (for the ambient temperature data in step 2) and charging time (for the time during the charging proce...

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Abstract

The invention, which belongs to the battery thermal management system technology field, relates to an artificial neural network-based highest surface temperature prediction method of a secondary battery. A secondary battery is placed in a high and low temperature test box and a charge and discharge testing machine is connected; the battery is discharged and then charging is carried out; a changing situation of highest surface temperatures of the battery during the charging process is monitored; input, output, the neuron number, the number of layers, a transfer function, and a training algorithm of a Back-Propagation neural network model are set so as to complete construction of the model; those data are used for model training, so that the model can be applied to prediction; and highest surface temperatures of the battery during charging processes under other environmental temperatures can be predicted by the model. According to the invention, the above-mentioned model can be applied simply; parameters are easy to control; and results have practical values; because highest surface temperatures of the battery during working processes under different environmental temperatures can be predicted, guarantees are provided for effective work of a battery thermal management system and safety of the battery.

Description

technical field [0001] The invention relates to a method for predicting the maximum surface temperature of a secondary battery based on an artificial neural network, and belongs to the technical field of battery thermal management systems. Background technique [0002] With the rapid development of world economy and society, environmental issues and energy issues are getting more and more attention. The huge fuel consumption of automobiles and automobile exhaust emissions have caused a series of problems such as the depletion of oil resources and the global greenhouse effect, which has prompted people to continue to explore Green transport. In recent years, the emergence of HEV (hybrid electric vehicle) and EV (pure electric vehicle) using green secondary batteries as power has made great contributions to reducing carbon dioxide emissions, suppressing the greenhouse effect and saving petroleum resources. There are two main types of power batteries currently in use: nickel-m...

Claims

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Application Information

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IPC IPC(8): G01J5/00G06N3/02
Inventor 穆道斌方凯正吴锋陈实吴伯荣宋亮林静
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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