CN111458649A - Rapid detection method for health degree of battery module - Google Patents
Rapid detection method for health degree of battery module Download PDFInfo
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- CN111458649A CN111458649A CN202010329057.1A CN202010329057A CN111458649A CN 111458649 A CN111458649 A CN 111458649A CN 202010329057 A CN202010329057 A CN 202010329057A CN 111458649 A CN111458649 A CN 111458649A
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- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
本发明公开了一种电池模组健康度快速检测方法，通过获取已知可用容量的电池模组样本充放电过程中的平台电压数据，根据平台电压数据建立已知可用容量电池模组样本的概率密度函数PDF曲线计算设定电压区间的峰面积，拟合建立电池模组健康度SOH‑概率密度函数PDF曲线，得到了电池模组充放电过程中电压数据与电池老化程度的关系，检测过程只需要检测待检测电池模组充放电过程中的平台电压数据，建立电池模组健康度SOH‑概率密度函数PDF曲线中的峰面积与检测峰面积一致所对应的电池模组样本的电池模组健康度SOH值即为待检测电池模组的电池模组健康度SOH值，方法简单，检测速度快，不需要对电池模组进行单独建模检测，检测结果准确。
The invention discloses a method for rapidly detecting the health of a battery module. By acquiring the platform voltage data during the charging and discharging process of a battery module sample with a known available capacity, the probability of a battery module sample with a known available capacity is established according to the platform voltage data. The density function PDF curve calculates the peak area of the set voltage range, and establishes the SOH-probability density function PDF curve for the health of the battery module. The relationship between the voltage data and the battery aging degree during the charging and discharging process of the battery module is obtained. It is necessary to detect the platform voltage data during the charging and discharging process of the battery module to be tested, and establish the battery module health of the battery module sample corresponding to the peak area in the SOH‑probability density function PDF curve of the battery module health degree that is consistent with the detected peak area The degree of SOH value is the SOH value of the battery module health degree of the battery module to be tested. The method is simple and the detection speed is fast. There is no need for separate modeling and detection of the battery module, and the detection results are accurate.
Description
Technical Field
The invention relates to a battery operation and maintenance technology, in particular to a method for rapidly detecting the health degree of a battery module.
Background
The energy storage provides important technical support for ubiquitous power internet of things including clean energy consumption, comprehensive energy services and the like, and more lithium battery energy storage power stations are becoming important components of links such as power grid power generation and transmission transformation and distribution. After the energy storage power station operates for a period of time, the performance of the lithium battery with good consistency gradually becomes uneven, and potential risks are brought to safe and efficient operation of the energy storage power station. The low-cost and quick online detection of the health degree of the battery is a core technology which is very concerned by operation and maintenance personnel of the energy storage power station and the power battery system.
The method includes that a large number of studies are conducted on a method for estimating battery SOH domestically, Bian et al propose an equivalent circuit model based on incremental capacity analysis to represent a constant current portion of a charging/discharging curve, the model can effectively and reliably estimate SOH of a lithium ion battery, L ai et al research characteristics of a large-scale retired battery series charging curve, establish a neural network model, and can estimate capacity of battery cells in batches through the established model, ZHENG et al extract three characteristic points which are likely to be easily recognized by a battery management system from an SOC-based IC/DV curve, quantify a relationship between the characteristic points and SOC/capacity and apply to vehicle-mounted battery capacity estimation, and the research results can achieve a relative error of 2.0% for battery capacity estimation, Shen et al propose a deep learning method, which uses a Deep Convolutional Neural Network (DCNN) to perform cell-level capacity estimation based on voltage, current and charge capacity measurement within a partial charging cycle, which is based on a battery state estimation model, and which is based on a high-load cycle test, and a cell-based on a correlation between a high-load-based on a battery-cycle-based on a cell-based on a correlation between a battery-based on a high-load-cycle-test, and a battery-cycle-model, and a battery-cycle-model, and-cycle-evaluation method, which is based on a-cycle-model, and-cycle-model, which are based on a-cycle-model, which is based on a high-cycle-.
Disclosure of Invention
The invention aims to provide a method for rapidly detecting the health degree of a battery module so as to overcome the defects of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for rapidly detecting the health degree of a battery module comprises the following steps:
step 1), charging and discharging a battery module sample with known available capacity to obtain platform voltage data in the charging and discharging process of the battery module sample with known available capacity, establishing a probability density function PDF curve of the battery module sample with known available capacity according to the platform voltage data, and calculating peak areas of set voltage intervals on two sides of a peak point of the probability density function PDF curve;
step 2), fitting and establishing a battery module health degree SOH-probability density function PDF curve according to a battery module health degree SOH value of a battery module sample with known available capacity and the probability density function PDF curve with the known available capacity;
step 3), repeating the step 1) and the step 2) to obtain a battery module health degree SOH-probability density function PDF curve of battery modules with different available capacities;
step 4), collecting platform voltage data in the charging and discharging process of the battery module to be detected in real time, and converting the platform voltage data in the charging and discharging process of the battery module to be detected into a probability density function PDF curve; calculating a detection peak area of a set voltage interval in a probability density function PDF curve of the battery module to be detected, wherein the SOH value of the battery module health degree of the battery module sample corresponding to the peak area in the battery module health degree-probability density function PDF curve of the battery module obtained in the step 3) and the detection peak area are consistent is the SOH value of the battery module to be detected.
Further, in step 1), the absolute value of the difference between the voltage values at the two ends of the set voltage interval and the voltage value corresponding to the peak point is equal.
Furthermore, the absolute value of the difference between the voltage values at the two ends of the set voltage interval and the voltage value corresponding to the peak point is less than or equal to 0.5% of the voltage value corresponding to the peak point.
Further, specifically, peak area integration is performed on peak points in a probability density function PDF curve, a perpendicular line of a horizontal axis is drawn to the peak points of the peak points, and voltage values of maximum peak top voltage not greater than 0.5% are respectively taken as set voltage intervals on two sides of intersection points of the perpendicular line and the horizontal axis.
Further, the SOH value of the battery module health of the battery module sample of the known available capacity is:
further, the set voltage intervals on two sides of the peak point of the probability density function PDF curve of the battery module with the known available capacity calculated in the step 1) are consistent with the set voltage intervals in the probability density function PDF curve of the battery module to be detected calculated in the step 4).
Further, the platform voltage data of a battery module sample with known available capacity is converted into a probability density function PDF curve by using a ksDensity function in a Matlab statistical toolbox.
Furthermore, the battery module sample is formed by connecting n battery units in series, or is an electric core, or a group of electric cores connected in parallel.
Furthermore, in the charging and discharging process of the battery module, the charging and discharging voltage data of the battery module at different moments are collected in real time through a battery management system.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention discloses a method for rapidly detecting the health degree of a battery module, which comprises the steps of acquiring platform voltage data in the charging and discharging process of a battery module sample with known available capacity, establishing a probability density function PDF curve of the battery module sample with the known available capacity according to the platform voltage data, and calculating peak areas of set voltage intervals on two sides of a peak point of the probability density function PDF curve; fitting and establishing a battery module health degree SOH-probability density function PDF curve according to the SOH value of the battery module and the probability density function PDF curve of the known available capacity, reflecting that the battery aging process and a voltage platform have correlation by counting the occurrence times of a voltage constant, obtaining the relation between voltage data and the battery aging degree in the charging and discharging processes of the battery module, wherein the detection process only needs to detect the platform voltage data in the charging and discharging processes of the battery module to be detected, and establishing the probability density function PDF curve, and the SOH value of the battery module health degree of a battery module sample corresponding to the consistency of the peak area in the battery module health degree SOH-probability density function PDF curve and the detection peak area is the SOH value of the battery module to be detected, the method is simple, the detection speed is high, and the battery module does not need to be subjected to independent modeling detection, the detection result is accurate, the health degree of the battery module can be rapidly evaluated, and the safe operation and maintenance level of the energy storage power station or the power battery system is improved.
Furthermore, the absolute value of the difference between the voltage values at the two ends of the set voltage interval and the voltage value corresponding to the peak point is equal, so that the peak area result can be ensured to be accurate.
Furthermore, the absolute value of the difference between the voltage values at the two ends of the set voltage interval and the voltage value corresponding to the peak point is less than or equal to 0.5% of the voltage value corresponding to the peak point, and the result is accurate and stable.
Drawings
Fig. 1 is a charging/discharging curve of a battery module with different available capacities according to an embodiment of the present invention.
Fig. 2 is a PDF curve of the probability density function converted from platform voltage data during charging and discharging of battery modules with different available capacities according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of peak area integration of the maximum peak in the PDF curve of the probability density function according to the embodiment of the present invention.
FIG. 4 is a fitting curve relationship of the maximum peak area A-SOH of the probability density function PDF in the charging process according to the embodiment of the present invention.
FIG. 5 is a fitting curve relationship of the maximum peak area A-SOH of the probability density function PDF in the discharging process in the embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
as shown in fig. 1, a method for rapidly detecting the health degree of a battery module includes the following steps:
step 1), charging and discharging a battery module sample with known available capacity to obtain platform voltage data in the charging and discharging process of the battery module sample with known available capacity; establishing a Probability Density Function (PDF) curve of a battery module sample with known available capacity according to the acquired platform voltage data with known available capacity, calculating a peak area A of a voltage interval at two sides of a peak point of the PDF curve of the Probability Density Function, and setting the absolute value of the difference between the voltage value at two ends of the voltage interval and the corresponding voltage value of the peak point to be less than or equal to 0.5% of the corresponding voltage value of the peak point; specifically, the method comprises the following steps: performing peak area integration on a maximum peak (peak point) in a probability density function PDF curve (in a charging process or a discharging process), drawing a vertical line of a transverse axis passing through the maximum peak, respectively taking a voltage value (V) which is not more than 0.5% of the voltage of the maximum peak on two sides of an intersection point of the vertical line and the transverse axis, wherein the two voltage values are set voltage intervals, and solving a peak area A of the set voltage interval;
specifically, a ksDensity function in a Matlab statistical tool box is used for converting platform voltage data of a battery module sample with known available capacity into a probability density function PDF curve, and in the constructed probability density function PDF curve, the abscissa is working voltage and the ordinate is probability density; specific Matlab was used as follows:
x [ ]; introducing platform voltage data of a battery module sample with known available capacity in the charging and discharging process;
[f,xi]＝ksdensity(x)；
[f,xi]is MAT L AB calculation shows the result form, where f corresponds to the numerical value of probability density, xiIs the corresponding abscissa (voltage value);
step 2), fitting and establishing a battery module health degree SOH-probability density function PDF curve, namely an A-SOH fitting curve, according to a battery module health degree SOH value of a battery module sample with known available capacity and the probability density function PDF curve with the known available capacity;
the SOH value of the battery module health of the battery module sample of known available capacity is:
step 3), repeating the step 1) and the step 2) to obtain an A-SOH fitting curve of the battery modules with different available capacities;
step 4), collecting platform voltage data in the charging and discharging process of the battery module to be detected in real time, and converting the platform voltage data in the charging and discharging process of the battery module to be detected into a probability density function PDF curve; performing peak area integration on a maximum peak in a probability density function PDF curve (in a charging process or a discharging process) obtained by a battery module to be detected, drawing a vertical line of a horizontal axis after passing through the maximum peak, respectively taking a voltage value (V) of maximum peak top voltage not greater than 0.5% on two sides of an intersection point of the vertical line and the horizontal axis, obtaining a peak area A1 of the voltage interval, and searching an SOH value of the battery module corresponding to the peak area A value which is the same as the peak area A1 in an A-SOH fitting curve in the step 3) according to the obtained peak area A1 of the battery module to be detected, namely the SOH value of the battery module to be detected.
The battery module sample is formed by connecting n battery units in series, or is formed by one battery cell, or can be formed by a group of battery cells connected in parallel; in the process of charging and discharging the battery module, the charging and discharging voltage data of the battery module at different moments are collected in real time through a battery management system.
Example (b): firstly, the available capacity of the battery module is calibrated, the SOH of the battery module is calculated,
available capacity measuring step: (1) charging the battery module to the specified upper limit stopping condition by using a C/5 constant current and constant voltage; (2) standing for 30 min; (3) discharging to the specified lower limit of the battery module by using a C/5 constant current to cut off the condition; (4) standing for 30 min; the discharge capacity is used as the available capacity. 7 battery modules with different capacities are tested, and are respectively marked as 1#, 2#, 3#, 4#, 5#, 6#, and 7#, and the available capacities are 37.59Ah, 33.77Ah, 30.69Ah, 28.58Ah, 27.2Ah, 25.81Ah, and 24.83Ah, and the specific results are shown in fig. 1.
As shown in fig. 2, the working voltage data of the battery module with known available capacity on the voltage platform during charging and discharging is collected by the battery management system, the ksdense function in the Matlab statistical tool box is used to convert the platform voltage data during charging and discharging of the battery module with known available capacity into a probability density function PDF curve, peak area integration is performed on the maximum peak during charging or the maximum peak during discharging in the probability density function PDF curve, a vertical line is drawn through the top of the maximum peak, voltage values of 0.02V are respectively taken on two sides of the intersection point of the vertical line and the horizontal coordinate, and the peak area a of the 0.04V voltage interval is obtained. The peak areas a of the 1#, 2#, 3#, 4#, 5#, 6#, and 7# modules are 0.8216, 0.8172, 0.8054, 0.7869, 0.7545, 0.7393, and 0.7200, and the specific results are shown in fig. 3. The lithium iron phosphate battery module (15P4S, 15 in parallel 4 series) of the Chery S18B electric automobile is adopted, has the nominal capacity of 40Ah and is formed by connecting 4 15P1S battery units in series; the 15P1S cell nominal voltage was 3.2V.
And finding that the peak area A of the maximum peak in the PDF curve of the probability density function has a positive correlation with the SOH of the 15P4S battery module. Therefore, the maximum peak area A value in the probability density function PDF curve can be used as the quick evaluation index of the SOH of the battery, the SOH value of the battery to be detected can be quickly detected through the positive correlation relationship of the maximum peak area A value and the maximum peak area A value, and the safety operation and maintenance level of a large energy storage power station or a power battery system is improved.
Or detecting the available capacity of the battery module by adopting a Bitrode FTV 1-300-type 100-module battery testing system, discharging by using a 1I 5(I5 is 1/5C rate current, 8A) constant current until the cut-off voltage is 10.8V (2.7V 4) under the condition of 25C +/-2C at the testing temperature, standing for 0.5 hour, then performing constant voltage charging after charging by using the 1I 5 constant current until the cut-off voltage is 14.6V (3.65V 4), stopping charging when the current is reduced to I20(I20 is 1/20C rate current, 2A), standing for 0.5 hour, then discharging by using the 1I 5 until the discharge cut-off voltage reaches 10.8V, finishing standing for 0.5 hour, and finally calculating the available capacity (measured by Ah) of the battery and the SOH value of the available capacity of the battery according to the current value and the discharge time data of the 1I 5 (A).
The calculation process of the peak area A value of the maximum peak in the PDF curve of the probability density function is as follows:
working voltage data of the battery module on a voltage platform in the charging and discharging process is collected through a battery management system, and the platform voltage data of the battery module in the charging and discharging process is converted into a probability density function PDF curve through a ksDensity function in a Matlab statistical tool box. And (3) performing peak area integration on the maximum peak in the charging process or the maximum peak in the discharging process in the probability density function PDF curve, drawing a vertical line of a horizontal axis passing through the peak of the maximum peak, respectively taking a voltage value of 0.02V at two sides of the intersection point of the vertical line and the horizontal axis, and obtaining a peak area A of the 0.04V voltage interval.
Probability density function PDF maximum peak area A-SOH fitting curve
The fitting curve relation of the maximum peak area A-SOH of the probability density function PDF in the charging process is shown in figure 4. As can be seen from fig. 4, the maximum peak area a of the probability density function PDF during charging is positively correlated with SOH: a ═ -0.3335+0.02615 SOH-1.47410-4 SOH2, R2 ═ 0.9883.
The fitting curve relation of the maximum peak area A-SOH of the probability density function PDF in the discharging process is shown in figure 5. As can be seen from fig. 5, the maximum peak area a of the probability density function PDF during discharging is positively correlated with SOH: a ═ -0.4592+0.029 SOH-1.65110-4 SOH2, R2 ═ 0.9671.
After the maximum peak area A-SOH fitting curve of the probability density function PDF in the charging (discharging) process is determined, the available capacity of the battery module to be tested is not needed to be calibrated to determine the SOH value of the battery module, the maximum peak area A of the probability density function PDF in the charging (discharging) process in the same voltage interval is obtained only by acquiring the working voltage data of a charging and discharging platform according to a battery management system and performing probability density function PDF conversion, and the SOH value of the battery module to be tested is found out according to the fitting curve, so that the health degree of the battery module can be quickly evaluated. The probability density function PDF is the statistics of a voltage constant in the using process of the battery, the phase change process of lithium release/insertion in the charging and discharging point process of an electrode material is reflected on a voltage curve to be a voltage platform, and the process is considered as a main factor causing battery aging along with the loss of the electrode material and the loss of recyclable lithium, so that the charging and discharging platform is shorter along with the increase of the aging degree, the probability density function PDF method reflects that the battery aging process and the voltage platform have correlation through the statistics of the occurrence times of the voltage constant.
Claims (9)
1. A method for rapidly detecting the health degree of a battery module is characterized by comprising the following steps:
step 1), charging and discharging a battery module sample with known available capacity to obtain platform voltage data in the charging and discharging process of the battery module sample with known available capacity, establishing a probability density function PDF curve of the battery module sample with known available capacity according to the platform voltage data, and calculating peak areas of set voltage intervals on two sides of a peak point of the probability density function PDF curve;
step 2), fitting and establishing a battery module health degree SOH-probability density function PDF curve according to a battery module health degree SOH value of a battery module sample with known available capacity and the probability density function PDF curve with the known available capacity;
step 3), repeating the step 1) and the step 2) to obtain a battery module health degree SOH-probability density function PDF curve of battery modules with different available capacities;
step 4), collecting platform voltage data in the charging and discharging process of the battery module to be detected in real time, and converting the platform voltage data in the charging and discharging process of the battery module to be detected into a probability density function PDF curve; calculating a detection peak area of a set voltage interval in a probability density function PDF curve of the battery module to be detected, wherein the SOH value of the battery module health degree of the battery module sample corresponding to the peak area in the battery module health degree-probability density function PDF curve of the battery module obtained in the step 3) and the detection peak area are consistent is the SOH value of the battery module to be detected.
2. The method as claimed in claim 1, wherein in step 1), the absolute difference between the voltage values at the two ends of the voltage interval and the voltage value corresponding to the peak point is equal.
3. The method as claimed in claim 2, wherein the absolute value of the difference between the voltage values at the two ends of the voltage interval and the voltage value corresponding to the peak point is less than or equal to 0.5% of the voltage value corresponding to the peak point.
4. The method as claimed in claim 4, wherein peak area integration is performed on peak points in a PDF curve, a vertical line is drawn across the peak points of the peak points, and a voltage value of the maximum peak top voltage of not more than 0.5% is respectively taken at two sides of the intersection point of the vertical line and the horizontal axis as a set voltage interval.
5. The method as claimed in claim 1, wherein the SOH value of the battery module health of the battery module sample with known available capacity is:
6. the method for rapidly detecting the health degree of the battery module as claimed in claim 1, wherein the set voltage intervals at two sides of the peak point of the probability density function PDF curve of the battery module sample with the known available capacity calculated in the step 1) are consistent with the set voltage intervals in the probability density function PDF curve of the battery module to be detected calculated in the step 4).
7. The method of claim 1, wherein a ksDensity function in a Matlab statistical tool kit is used to convert platform voltage data of battery module samples with known available capacity into a PDF curve.
8. The method as claimed in claim 1, wherein the battery module sample is formed by connecting n battery units in series, and is one battery cell or a group of battery cells connected in parallel.
9. The method as claimed in claim 1, wherein the battery management system collects the charging and discharging voltage data of the battery module at different times in real time during the charging and discharging process of the battery module.
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