CN113918889A - Lithium battery online aging diagnosis method based on charging data spatial distribution characteristics - Google Patents
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Abstract
The invention discloses an online lithium battery aging diagnosis method based on charging data spatial distribution characteristics. The method comprises the following steps: 1) collecting charging voltage and current data of the lithium battery in each charging and discharging cycle and the total capacity of the lithium battery; 2) calculating to obtain the accumulated spatial distribution characteristics of the current lithium battery in each charge-discharge cycle; 3) repeating the steps 1) -2), obtaining the total capacity and the corresponding cumulative spatial distribution characteristics of each lithium battery in each charge-discharge cycle, and forming a training set; 4) obtaining a trained regression model for the aging diagnosis of the lithium battery; 5) and during online diagnosis, acquiring charging voltage and current data of the lithium battery to be diagnosed, calculating to obtain corresponding accumulated spatial distribution characteristics, inputting the accumulated spatial distribution characteristics into a trained lithium battery aging diagnosis regression model for prediction, and outputting the total capacity of the current lithium battery to be diagnosed, so that the aging state of the lithium battery is diagnosed. The invention realizes the accurate diagnosis of the lithium battery aging and improves the reliability and the safety of the lithium battery.
Description
Technical Field
The invention belongs to an online lithium battery aging diagnosis method in the field of lithium battery application, and particularly relates to an online lithium battery aging diagnosis method based on charging data spatial distribution characteristics.
Background
Lithium batteries are commercially used in various fields on a large scale due to their numerous advantages of high energy density, low cost, fast response to power demand, long cycle life, etc. The aging diagnosis technology plays an important role in the safe and reliable operation of the lithium battery. However, since lithium batteries have a complex aging mechanism and the aging path is influenced by many factors in the design, production and application processes, it is a challenge to realize simple, fast and accurate aging diagnosis of lithium batteries under complex dynamic operating conditions. In addition, for a large-sized battery pack consisting of thousands of unit lithium batteries, there are inevitable intrinsic and extrinsic differences between each unit battery due to differences in manufacturing and operating conditions, and thus the entire battery pack cannot be regarded as one battery, and aging diagnosis needs to be performed separately for each unit battery therein, which causes a huge data storage burden, calculation burden, and cost burden. Effective solutions to the above problems include improvements in aging diagnostic algorithms and improvements in aging diagnostic features. However, much of the related research is currently focused on developing better algorithms and little attention is paid to developing better features. At present, most of practical applications adopt the total capacity parameter to represent the aging state of the lithium battery, and when the aging diagnosis characteristics of the lithium battery are good enough, the accurate lithium battery total capacity diagnosis can be realized by using a simple regression model. Meanwhile, as the discharge modes of the lithium battery in most practical application scenes are random, the characteristics cannot be extracted for aging diagnosis based on specific discharge test data. In contrast, the charging mode of a lithium battery is fixed in most scenarios. Taking a lithium battery for a vehicle as an example, there are only two charging modes, namely, a normal charging mode and a fast charging mode. Therefore, the important significance is achieved in designing and developing better aging diagnosis characteristics directly based on the charging sensing data of the lithium battery.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an online lithium battery aging diagnosis method based on the spatial distribution characteristics of charging data.
The scheme adopted by the invention is as follows:
the invention comprises the following steps:
1) collecting charging voltage and current data of a brand new lithium battery at the same constant current charging mode stage in each charging and discharging cycle, and simultaneously collecting the total capacity of the lithium battery in each charging and discharging cycle;
2) calculating to obtain the accumulated spatial distribution characteristics of the current lithium battery in each charge-discharge cycle according to the charge voltage and current data of the current lithium battery in each charge-discharge cycle;
3) repeating the steps 1) -2) to collect and calculate each lithium battery, obtaining the total capacity and the corresponding cumulative spatial distribution characteristics of each lithium battery in each charge-discharge cycle, and forming a training set;
4) training the lithium battery aging diagnosis regression model based on a training set to obtain a trained lithium battery aging diagnosis regression model;
5) during online diagnosis, collecting charging voltage and current data of a brand-new lithium battery to be diagnosed in the same constant current charging mode stage in each charging and discharging cycle starting from the first charging and discharging cycle, storing the charging voltage and current data as historical charging data, calculating to obtain the accumulated spatial distribution characteristics of the latest charging and discharging cycle of the lithium battery to be diagnosed, inputting the accumulated spatial distribution characteristics into a trained lithium battery aging diagnosis regression model for prediction, and outputting the total capacity of the current lithium battery to be diagnosed so as to diagnose the aging state of the lithium battery; in the subsequent aging diagnosis of the lithium battery to be diagnosed currently, if the lithium battery to be diagnosed currently has the next charge-discharge cycle, the charging voltage and current data of the next charge-discharge cycle calculate the accumulated spatial distribution characteristics on the basis of the historical charging data, and meanwhile, the charging voltage and current data of the next charge-discharge cycle are stored as the historical charging data.
The step 2) is specifically as follows:
according to charging voltage and current data of the current lithium battery in each charging and discharging cycle, drawing a two-dimensional coordinate axis space by taking the charging voltage as a horizontal axis and the charging current as a vertical axis, drawing the charging voltage and current data of the current lithium battery in each charging and discharging cycle in the two-dimensional coordinate axis space, and dividing the horizontal axis and the vertical axis into m equidistant voltage sub-intervals and n equidistant current sub-intervals according to the range of the charging voltage and the current to obtain m multiplied by n two-dimensional sub-spaces; counting the number of points of all charging voltage and current data collected from the 1 st to the kth charging and discharging cycle interval of the current lithium battery in each two-dimensional subspace, taking the points as the cumulative spatial distribution characteristics of the kth charging and discharging cycle of the current lithium battery, and repeatedly counting to obtain the cumulative spatial distribution characteristics of the current lithium battery in each charging and discharging cycle.
The same constant current charging mode stage in the step 5) is the same as the same constant current charging mode stage in the step 1).
And selecting a machine learning regression model from the lithium battery life prediction regression model.
The invention has the beneficial effects that:
the invention solves the problem that the online aging diagnosis of the lithium battery depends on accurate capacity measurement in practical application. The method is characterized in that the spatial distribution characteristics of charging voltage and current data are applied to the online aging diagnosis of the lithium battery, the charging voltage and current data of the lithium battery at the same constant current charging mode stage in each charging and discharging cycle are only required to be collected and stored when the method is applied online, then all the charging data collected in the charging and discharging cycle interval from 1 st time to k th time are counted, the accumulated number of the charging data distributed in each two-dimensional subspace is obtained and used as the accumulated spatial distribution characteristics, the accurate aging diagnosis of the lithium battery is further realized, the method is not required to depend on inconvenient accurate capacity measurement in the actual application of the lithium battery, the online aging diagnosis of the lithium battery in the actual application scene is more suitable, and the safe and reliable operation of the lithium battery is facilitated.
Drawings
Fig. 1 is a flow chart of an online lithium battery aging diagnosis method based on charging data spatial distribution characteristics according to the present invention.
Fig. 2 is a schematic diagram of the same constant current charging mode stage of the lithium battery selected in the embodiment of the present invention.
Fig. 3 is a schematic diagram of the cumulative distribution of charging voltage and current data in each two-dimensional subspace of the lithium battery in the same constant current charging mode stage in the 10 th, 20 th and 50 th charging and discharging cycles in the embodiment of the present invention.
Fig. 4 is a difference diagram of the total capacity of the lithium battery estimated from the accumulated spatial distribution characteristics in each charge-discharge cycle of 40 lithium batteries collected and calculated in the embodiment of the present invention and the actual total capacity of the lithium battery.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
As shown in fig. 1, the present invention comprises the steps of:
1) collecting charging voltage and current data of a brand new lithium battery at the same constant current charging mode stage in each charging and discharging cycle, taking the charging voltage and current data as charging data in each charging and discharging cycle, and collecting the total capacity of the lithium battery in each charging and discharging cycle; in specific implementation, the same constant current charging mode stage is as shown in fig. 2, where (a) in fig. 2 is a voltage variation curve of the same constant current charging mode stage, and (b) in fig. 2 is a current variation curve of the same constant current charging mode stage. The total number of charge and discharge cycles is the total number of cycles when the total capacity of the lithium battery decays to 80% of the initial total capacity of the lithium battery.
2) Calculating to obtain the accumulated spatial distribution characteristics of the current lithium battery in each charge-discharge cycle according to the charge voltage and current data of the current lithium battery in each charge-discharge cycle;
the step 2) is specifically as follows:
according to charging voltage and current data of the current lithium battery in each charging and discharging cycle, drawing a two-dimensional coordinate axis space by taking the charging voltage as a horizontal axis and the charging current as a vertical axis, drawing the charging voltage and current data of the current lithium battery in each charging and discharging cycle in the two-dimensional coordinate axis space, and dividing the horizontal axis and the vertical axis into m equidistant voltage sub-intervals and n equidistant current sub-intervals according to the range of the charging voltage and the current to obtain m multiplied by n two-dimensional sub-spaces; counting the number of points of all charging voltage and current data collected from the 1 st to the kth charging and discharging cycle interval of the current lithium battery in each two-dimensional subspace, and taking the points as the cumulative spatial distribution characteristic of the kth charging and discharging cycle of the current lithium battery. Wherein the point falling on the lower current and voltage boundary belongs to the current two-dimensional subspace and the point falling on the upper current and voltage boundary does not belong to the current two-dimensional subspace. And repeating statistics to obtain the accumulated spatial distribution characteristics of the current lithium battery in each charge-discharge cycle.
In specific implementation, as shown in tables 1 and 2, the horizontal axis is divided into 19 equidistant voltage sub-intervals, the vertical axis is divided into 12 equidistant current sub-intervals, and 228 two-dimensional sub-spaces are obtained, as shown in fig. 3, (a), (b), and (c) of fig. 3 are schematic cumulative distribution diagrams of charging voltage and current data of the lithium battery in the same constant current charging mode stage in the 10 th, 20 th, and 50 th charging and discharging cycles of the lithium battery in the embodiment of the present invention in each two-dimensional sub-space, respectively.
TABLE 1 Voltage subinterval partitioning (V) of the constant-current charging phase
| 3.40-3.41 | 3.41-3.42 | 3.42-3.43 | 3.43-3.44 | 3.44-3.45 | 3.45-3.46 | 3.46-3.47 | 3.47-3.48 | 3.48-3.49 | 3.49-3.50 |
| 3.50-3.51 | 3.51-3.52 | 3.52-3.53 | 3.53-3.54 | 3.54-3.55 | 3.55-3.56 | 3.56-3.57 | 3.57-3.58 | 3.58-3.59 |
TABLE 2 Current subinterval partitioning (A) of the constant-current charging phase
| 0.9970-0.9975 | 0.9975-0.9980 | 0.9980-0.9985 | 0.9985-0.9990 | 0.9990-0.9995 | 0.9995-1.0000 |
| 1.0000-1.0005 | 1.0005-1.0010 | 1.0010-1.0015 | 1.0015-1.0020 | 1.0020-1.0025 | 1.0025-1.0030 |
3) Repeating the steps 1) -2) to collect and calculate each lithium battery, obtaining the total capacity and the corresponding accumulated spatial distribution characteristics of each lithium battery in each charge-discharge cycle, and forming a training set, wherein the total capacity of each lithium battery is used as a label corresponding to the accumulated spatial distribution characteristics;
4) training the lithium battery aging diagnosis regression model based on a training set to obtain a trained lithium battery aging diagnosis regression model; the lithium battery aging diagnosis regression model selects a machine learning regression model. In specific implementation, an XGboost model is adopted.
5) During online diagnosis, collecting charging voltage and current data of a brand-new lithium battery to be diagnosed in the same constant current charging mode stage in each charging and discharging cycle starting from the first charging and discharging cycle, storing the charging voltage and current data as historical charging data, calculating to obtain the accumulated spatial distribution characteristics of the latest charging and discharging cycle of the lithium battery to be diagnosed, inputting the accumulated spatial distribution characteristics into a trained lithium battery aging diagnosis regression model for prediction, and outputting the total capacity of the current lithium battery to be diagnosed so as to diagnose the aging state of the lithium battery; in the subsequent aging diagnosis of the lithium battery to be diagnosed currently, if the lithium battery to be diagnosed currently has the next charge-discharge cycle, the charging voltage and current data of the next charge-discharge cycle calculate the accumulated spatial distribution characteristics on the basis of the historical charging data, and meanwhile, the charging voltage and current data of the next charge-discharge cycle are stored as the historical charging data. The same constant current charging mode stage in the step 5) is the same as the same constant current charging mode stage in the step 1).
Fig. 4 is a drawing of the total capacity of the lithium battery predicted by the cumulative spatial distribution characteristics in each charge-discharge cycle of 40 lithium batteries, the total number of the cumulative spatial distribution characteristics of 40 lithium batteries is 40000, and the direction along the horizontal axis in the drawing is all the charge-discharge cycles of one lithium battery after another lithium battery, so that a waveform in fig. 4 represents the difference between the actual total capacity and the predicted total capacity of all the charge-discharge cycles of one lithium battery, and as can be seen from fig. 4, the present invention can accurately predict the total capacity of the lithium battery, thereby realizing accurate aging diagnosis of the lithium battery.
Claims (5)
1. A lithium battery online aging diagnosis method based on charging data spatial distribution characteristics is characterized by comprising the following steps:
1) collecting charging voltage and current data of a brand new lithium battery at the same constant current charging mode stage in each charging and discharging cycle, and simultaneously collecting the total capacity of the lithium battery in each charging and discharging cycle;
2) calculating to obtain the accumulated spatial distribution characteristics of the current lithium battery in each charge-discharge cycle according to the charge voltage and current data of the current lithium battery in each charge-discharge cycle;
3) repeating the steps 1) -2) to collect and calculate each lithium battery, obtaining the total capacity and the corresponding cumulative spatial distribution characteristics of each lithium battery in each charge-discharge cycle, and forming a training set;
4) training the lithium battery aging diagnosis regression model based on a training set to obtain a trained lithium battery aging diagnosis regression model;
5) during online diagnosis, charging voltage and current data of the lithium battery to be diagnosed in the same constant current charging mode stage in each charging and discharging cycle starting from the first charging and discharging cycle are collected and stored as historical charging data, the accumulated spatial distribution characteristics of the latest charging and discharging cycle of the lithium battery to be diagnosed are obtained through calculation and input into a trained lithium battery aging diagnosis regression model for prediction, and the total capacity of the current lithium battery to be diagnosed is output, so that the aging state of the lithium battery is diagnosed.
2. The lithium battery online aging diagnosis method based on the charging data spatial distribution characteristics according to claim 1, wherein the step 2) is specifically:
according to charging voltage and current data of the current lithium battery in each charging and discharging cycle, drawing a two-dimensional coordinate axis space by taking the charging voltage as a horizontal axis and the charging current as a vertical axis, drawing the charging voltage and current data of the current lithium battery in each charging and discharging cycle in the two-dimensional coordinate axis space, and dividing the horizontal axis and the vertical axis into m equidistant voltage sub-intervals and n equidistant current sub-intervals according to the range of the charging voltage and the current to obtain m multiplied by n two-dimensional sub-spaces; counting the number of points of all charging voltage and current data collected from the 1 st to the kth charging and discharging cycle interval of the current lithium battery in each two-dimensional subspace, taking the points as the cumulative spatial distribution characteristics of the kth charging and discharging cycle of the current lithium battery, and repeatedly counting to obtain the cumulative spatial distribution characteristics of the current lithium battery in each charging and discharging cycle.
3. The lithium battery online aging diagnosis method based on the charging data spatial distribution characteristics as claimed in claim 1, wherein the same constant current charging mode stage in the step 5) is the same as the same constant current charging mode stage in the step 1).
4. The lithium battery online aging diagnosis method based on the charging data spatial distribution characteristics as claimed in claim 1, wherein the lithium battery life prediction regression model is a machine learning regression model.
5. The lithium battery online aging diagnosis method based on the charging data spatial distribution characteristics as claimed in claim 1, wherein the lithium battery to be diagnosed is a brand new lithium battery.
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