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Lithium-ion battery remaining life prediction method with active model update strategy

A lithium-ion battery, life prediction technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problems of model mismatch, unable to adjust adaptively, poor prediction accuracy, etc., to overcome the difficulty of energy regeneration prediction, flexible The effect of nonparametric inference

Inactive Publication Date: 2016-07-06
SHANGHAI JIAOTONG UNIV
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Problems solved by technology

[0009] In view of the problems existing in the prior art, the object of the present invention is to provide a method for predicting the remaining life of a lithium-ion battery, which solves the problem of relying on an empirical model to establish a state transition equation in the existing method for predicting the remaining life of a lithium-ion battery. The problem that the mismatch of the model cannot be adjusted adaptively

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  • Lithium-ion battery remaining life prediction method with active model update strategy
  • Lithium-ion battery remaining life prediction method with active model update strategy
  • Lithium-ion battery remaining life prediction method with active model update strategy

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specific Embodiment approach 2

[0083] Embodiment 2. The difference between this embodiment and the method for predicting the remaining life of a lithium-ion battery with an active model update strategy described in Embodiment 1 is that the training data of the voltage and capacity of the lithium-ion battery are selected in step 1, and The specific process of constructing the initial SOH index from this data set is as follows:

[0084] Select the k-cycle data sequence of lithium-ion battery capacity and discharge voltage data from the beginning of battery use to the current charge-discharge cycle as the initial data sequence X(0)={x(0),x(1),...,x( k-1)}, where X(0) represents the data sequence obtained from the initial construction.

specific Embodiment approach 3

[0085] Specific Embodiment 3. The difference between this embodiment and the method for predicting the remaining life of a lithium-ion battery with an active model update strategy described in Embodiment 2 is that the sampling entropy feature extraction of the original data described in step 2, and the Gaussian process The specific process of regression to establish SOH index is as follows:

[0086] Step 31. Select the lithium-ion discharge voltage data sequence X of the charge-discharge cycle before the current moment obtained in step 1. 2 , construct N-m+1-dimensional vector x m (i)=[x(i), x(i+1),..., x(i+m-1)], i=[1, 2,..., N-m+1]. m is a value related to the battery degradation model. In this example, m=2, and the current moment is the corresponding moment of the latest data predicted by the rolling time window.

[0087] Step 32. Calculate the distance between two different discharge cycles according to the following formula:

[0088] d m [x m (i), x m (j)]=max[x m ...

specific Embodiment approach 4

[0110] Embodiment 4. The difference between this embodiment and the method for predicting the remaining life of lithium-ion batteries based on the model active update strategy described in Embodiment 1 or Embodiment 3 is that the specific method for judging whether the SOH prediction described in step 4 is completed The process is:

[0111] Use Gaussian process regression for multi-step forecasting, initially set n=5, and get the variance confidence range s of each step forecast i , according to s i Whether it is greater than twice the mean value of the confidence range of the previous i-1 step, that is, the following discriminant formula

[0112] s i ≥ 2 · Σ n = 0 i - 1 s ...

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Abstract

The invention relates to a method for remain useful life prognostic of a lithium ion battery with a model active updating strategy. According to a time series obtained through a voltage range of a discharge curve, conversion is conducted so that an equivalent discharge difference series obtained by discharge circulation at each time can be obtained, and therefore a health index time series of the ion battery is obtained; according to correspondence of a discharge voltage series and a time series, prognostic is conducted on the health index series to determine the remain useful life of the battery. Sampling entropy characteristic extraction and modeling are conducted on a charge voltage curve so that a relationship between a complete and accurate charge / discharge process and a battery performance index can be provided. On the basis of a performance index model, a short-term time series prognostic result is continuously updated to a known performance index data series and correlation analysis is conducted. According to the difference of the correlation degrees, retraining is conducted in the mode of training set expansion. The method is different from an existing iteration updating draining method, the prognostic model is updated dynamically, and therefore the prognostic precision is improved.

Description

technical field [0001] The invention relates to a method for predicting the remaining life of a lithium-ion battery with an active model update strategy, which is used for reliability monitoring of electronic equipment and belongs to the field of storage batteries. Background technique [0002] Due to its light weight, high energy density and long service life, lithium batteries have been widely used in mobile communication devices, electric vehicles, military electronic equipment, and aerospace electronic systems. However, during the use of lithium batteries, as the charge-discharge cycle proceeds, the internal resistance of lithium-ion batteries will increase, and the performance will gradually decline. Its failure will not only bring huge economic losses due to downtime, replacement or maintenance. It can also lead to catastrophic accidents. Therefore, the prediction and health management (PHM, Prognostics and Health Management) technology of lithium batteries can predic...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/50G01R31/36
Inventor 魏岩张峰华王毓杨煜普
Owner SHANGHAI JIAOTONG UNIV
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