Method for predicating life of aerospace Ni-Cd storage battery based on improved dynamic wavelet neural network

A dynamic wavelet and life prediction technology, applied in neural learning methods, biological neural network models, measurement of electricity, etc., can solve engineering applications that limit battery life prediction, low battery life prediction accuracy, and can not well reflect the single battery to be predicted. body characteristics, etc.

Inactive Publication Date: 2013-04-03
BEIHANG UNIV +2
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Since the core of this patent lies in the prediction accuracy of the primary and secondary MPNN, and the MPNN network is improved from the PNN network with statistical characteristics, the MPNN network retains the advantages of the PNN network and also introduces a problem that cannot be well reflected. Insufficient prediction of battery cell characteristics; in addition, in this patent, the

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  • Method for predicating life of aerospace Ni-Cd storage battery based on improved dynamic wavelet neural network
  • Method for predicating life of aerospace Ni-Cd storage battery based on improved dynamic wavelet neural network
  • Method for predicating life of aerospace Ni-Cd storage battery based on improved dynamic wavelet neural network

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[0075] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0076] The present invention is a life prediction method for aerospace Ni-Cd storage battery based on improved dynamic wavelet neural network. The life prediction method is a non-parametric method, and the method improves the existing DWNN network to obtain M-DWNN Network, after the analysis and preprocessing of the Ni-Cd battery data, use the processed data to form the training set and test set of the 1M-DWNN network, after training and learning, use the 1M-DWNN network to predict and supplement historical data samples; After completing these tasks, the 2M-DWNN network can be used to carry out iterative life prediction work, and finally the life value of the Ni-Cd battery can be determined according to the end-of-life criterion. figure 1 Shown is the overall flowchart of the life prediction method of the present invention, image 3 It is the logic ...

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Abstract

A method for predicating life of an aerospace Ni-Cd storage battery based on an improved dynamic wavelet neural network is achieved by the steps: collecting life predication relevant data of all aerospace Ni-Cd storage batteries, pre-processing the life predication relevant data, analyzing data correlation, mapping data and obtaining equivalent data of discharge final voltage of the Ni-Cd storage batteries, improving the DWNN (dynamic wavelet neural network), building primary M-DWNN (1M-DWNN), training and predicating, building self-adaption iteration predicating model on the basis of a secondary M-DWNN (2M-DWNN), training and predicating, and adjusting a dynamic time window. According to the invention, the whole DWNN is adjusted dynamically in the life predication process, thereby ensuring that the predication precision in the whole life predication process is improved continually along with the prolonging of the time and the increment of the data volume.

Description

technical field [0001] The invention belongs to the technical field of life prediction of aerospace Ni-Cd accumulators, in particular to a method for predicting the life of aerospace Ni-Cd accumulators based on an improved dynamic wavelet neural network. Background technique [0002] Life prediction technology involves a wide range of fields and fields, from the fatigue life of raw materials to the life of complex molded products, from the civil field to the national defense field. At present, the main life prediction technologies for aerospace Ni-Cd batteries can be summarized as follows: [0003] a, Life prediction based on physical model: This method analyzes the physical and chemical process inside the Ni-Cd battery, so as to establish a physical model reflecting the evolution process of the object, adjust the model parameters through relevant data, and finally obtain the required life prediction model; [0004] b. Life prediction based on statistical model assumptions...

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Application Information

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IPC IPC(8): G01R31/36G06N3/08
Inventor 吕琛陶来发刘红梅刘元默刘一薇杨生胜
Owner BEIHANG UNIV
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