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Power battery system residual available life prediction method based on relevance vector machine and particle filter

A correlation vector machine and power battery technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problem that the RUL prediction value deviates from the real value, etc.

Active Publication Date: 2017-10-10
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

However, the exponent of the exponential function term of the three-parameter capacity fading model is empirically obtained by observing the capacity fading rate. If the exponent is not selected accurately, the predicted value of RUL may deviate far from the real value

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  • Power battery system residual available life prediction method based on relevance vector machine and particle filter
  • Power battery system residual available life prediction method based on relevance vector machine and particle filter
  • Power battery system residual available life prediction method based on relevance vector machine and particle filter

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Embodiment Construction

[0057] Combine below figure 1 To further explain the method provided by the present invention, it specifically includes the following steps:

[0058] Step 1, online acquisition of capacity decline data of the power battery system;

[0059] Step 2, using a correlation vector machine to extract the feature vector of the capacity decline data;

[0060] Step 3, using the nonlinear least squares regression method to fit the power battery aging model;

[0061] Step 4, constructing a state-space equation describing the aging of the power battery;

[0062] Step five, predict the remaining usable life of the power battery system based on the particle filter theory.

[0063] In a preferred embodiment of the present application, said step 1 specifically includes: online acquisition of power battery capacity measurement value y=(y 1 ,y 2 ,...,y N ) T And the corresponding number of charge and discharge cycles k=(1,2,...,N) T . Among them, y 1 ,y 2 ,...,y N It represents the me...

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Abstract

The invention provides a power battery system residual available life prediction method based on a relevance vector machine and particle filter; the method comprises the following steps: using the relevance vector machine to extract collected power battery capacity declining characteristic vector values; building a power battery system aging model; using a particle filter theory to predict the power battery system residual available life through the aging model. The method can effectively reduce the training data bulk, can improve the algorithmic prediction precision, can ensure the RUL estimator stability, and can be expected to obtain accurate and reliable prediction results in real applications.

Description

technical field [0001] The invention relates to the fields of forecasting and health state management of power batteries, in particular to establishing an aging model of the power battery based on a small amount of aging data, and predicting the usable life based on the model. Background technique [0002] The remaining useful life (Remaining useful life: RUL) of the power battery system is used to indicate its remaining life time under healthy state conditions. The RUL prediction of power batteries can help manufacturers accurately evaluate the life time of products, and then formulate an economical and competitive product warranty period. At the same time, it can help users repair and replace battery systems in advance to avoid unnecessary losses. The early residual life prediction algorithm was based on Relevancevector machine (RVM) to extract battery characteristic values ​​from Electrochemical impedance spectroscopy (EIS) test data to build a power battery aging model, ...

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Application Information

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IPC IPC(8): G01R31/36
CPCG01R31/3648G01R31/367G01R31/392
Inventor 熊瑞张永志何洪文田金鹏
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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