CN107843853B - Power battery pack series connection fault diagnosis method - Google Patents
Power battery pack series connection fault diagnosis method Download PDFInfo
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- CN107843853B CN107843853B CN201711331218.5A CN201711331218A CN107843853B CN 107843853 B CN107843853 B CN 107843853B CN 201711331218 A CN201711331218 A CN 201711331218A CN 107843853 B CN107843853 B CN 107843853B
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- G—PHYSICS
- 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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- 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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
Abstract
The invention provides a power battery pack series connection fault diagnosis method, wherein a battery pack is formed by connecting n battery monomers in series, and a cross voltage test method is adopted. The battery management system records the voltage of a single battery and the surface temperature of the single battery, uses MATLAB/simulink to simulate the power battery and records the simulation voltage. And calculating the mean square error of the experimental and simulated output voltage, preliminarily judging whether voltage is abnormal or not, and calculating Z-score as a voltage detection abnormal coefficient based on the mean square error if the voltage is abnormal. Judging whether the connection fault or the monomer internal resistance increasing fault exists or not according to the voltage detection abnormal coefficient, and if so, judging that the fault is a secondary fault; and calculating the surface temperature rise rate of the battery, judging whether the surface temperature rise rate is greater than a preset threshold value, and if so, judging as a primary fault. The method can realize reliable diagnosis of the connection fault of the power battery pack, distinguish the fault of contact resistance increase and internal resistance increase, has simple and convenient algorithm, and can also effectively reduce the operation burden of a battery management system.
Description
Technical Field
The invention relates to the technical field of power battery packs for vehicles, in particular to a method for diagnosing series connection faults of a power battery pack.
Background
Generally, a battery pack of an electric vehicle is formed by connecting a plurality of large-capacity battery cells in series so as to meet the performance and power requirements of the electric vehicle. However, equipment defects, as well as a range of factors such as road conditions and vehicle performance during the driving of the electric vehicle, may cause the loosening of the connection between the batteries. Once the connecting piece is not hard up, the contact resistance between two battery monomers will increase to directly influence battery management system to the measurement of battery monomer voltage, the joule heat that produces on the connecting piece between the battery also increases simultaneously, leads to the battery surface temperature rise.
The large-capacity battery has a very small internal resistance of the battery, generally in the order of m Ω, and the contact resistance in a normal connection state is generally in the order of μ Ω (or m Ω). If the connector loosens, the contact resistance will reach the level above m omega. Therefore, when the power battery is charged, the measured single battery voltage reaches the charge cut-off voltage first and the charging process is ended in advance; when the power battery discharges, the measured single battery voltage reaches the discharge cut-off voltage first and then the discharge process is ended. The power battery pack can be not fully charged or discharged. In addition, the contact resistance between the two battery monomers is increased due to the looseness of the connecting piece, the generated joule heat is also increased, and the serious potential safety hazard exists.
Once the power battery pack for the vehicle has the phenomenon, the normal running of the electric vehicle is directly influenced, and even a fire and explosion accident can be caused. Therefore, in the related art, a target abnormal cell group is generated by calculating a difference between the cell voltage and the average voltage and comparing the difference with a preset threshold, and a fault level of each cell in the target abnormal cell group is determined. The method can judge all the single batteries with abnormal voltage in the power battery pack, has strong universality, but has complicated threshold judgment method, is only judged from abnormal voltage threshold, has low reliability, and cannot distinguish the connection piece looseness and the battery internal resistance increasing faults.
Disclosure of Invention
The invention aims to solve the problems that the reliability of fault diagnosis only carried out from a threshold value is low, and the contact resistance and the internal resistance of a battery cannot be distinguished from each other, and provides a power battery pack series connection fault diagnosis method which adopts a cross voltage test method to distinguish the contact resistance and the internal resistance of the battery from each other and improves the diagnosis reliability based on a voltage detection abnormal coefficient and a temperature rise rate threshold value.
Therefore, the invention provides a power battery pack series connection fault diagnosis method, wherein the power battery pack is formed by connecting n battery monomers in series, a cross voltage test method is adopted at a position which is easy to corrode or virtually connect, and the method specifically comprises the following steps:
(1) the current flowing through each battery monomer is consistent in the use process of the series battery pack, and the battery management system records the voltage of the n battery monomers and the surface temperature of the cathode of each battery monomer;
(2) simulating the power battery monomer based on MATLAB/Simulink, and recording output voltage;
(3) calculating the mean square error between the voltage of the n battery monomers and the simulation output voltage, and if the mean square error values are all approximately zero, judging that no voltage abnormality exists; if the mean square error value has obvious fluctuation, judging that voltage is abnormal, and calculating voltage detection abnormal coefficients of all battery monomers at the detection point by adopting Z-score based on the mean square error;
(4) judging whether the voltage abnormal coefficients of the ith battery monomer and the i +1 battery monomer are larger than zero or not, and if the voltage abnormal coefficients of the ith battery monomer and the i +1 battery monomer are both larger than zero, judging that the connection part of the ith battery and the i +1 battery has a fault; if the voltage abnormality coefficient of the ith battery monomer is larger than zero and the voltage abnormality coefficient of the (i + 1) th battery monomer is not larger than zero, determining that the internal resistance of the ith battery is increased; outputting a result as a secondary fault;
(5) calculating the surface temperature rise rate of the n battery cells;
(6) judging whether the temperature rise rate in the ith and i +1 th battery monomer is greater than a preset threshold value, if so, judging that the fault is accurately judged and positioned in the step (4), and outputting a result as a first-stage fault; if not, the next sampling time calculation is carried out, and the output result is still a secondary fault.
In the step (1), the sampling frequency of the voltage of the n battery single cells and the surface temperature of the negative electrode of the battery single cell is recorded to be 1 Hz.
In the step (1), smoothing and denoising are performed on the cell voltage and temperature data recorded by the battery management system.
In the step (2), the simulation step length is a fixed step length 1 s.
In the step (3), the method for calculating the mean square error between the cell voltage and the simulation output voltage comprises the following steps:
therein, MSEiIs the mean square error of the ith cell, epsiloniThe residual error between the ith cell voltage and the simulated output voltage is m, which is the sampling time interval.
In the step (3), the voltage detection abnormality coefficient calculation method based on Z-score includes:
wherein A isiFor the i-th cell abnormal coefficient, MSEiMean square error, MSE, of the ith cellaveFor detecting mean value, sigma, of mean square error of said n cellsMAnd the standard deviation of the mean square error of the n battery monomers is detected.
In the step (6), the preset threshold is twice of the maximum temperature rise rate of the battery monomer under the normal working condition.
The smoothing denoising method is a Savitzky-Golay method.
The sampling time interval m is 180s, namely, the calculation is performed every three minutes, the power battery pack is formed by connecting n battery monomers in series and can be performed in a grouping mode, and therefore the operation burden of a battery management system is reduced.
The invention has the advantages that:
(1) according to the invention, a cross voltage test method is adopted at the position where connection looseness is easy to occur, so that the contact resistance increasing fault caused by looseness of the connecting piece and the internal resistance increasing fault of the single battery body can be distinguished;
(2) according to the invention, whether voltage abnormality exists or not is preliminarily judged by calculating the mean square error, and if the voltage abnormality exists, the voltage detection abnormality coefficient based on the mean square error and the Z-score is calculated and compared with zero, so that the occurrence and the position of the connection fault can be accurately judged, and the method is convenient and visual. The battery monomer is calculated in groups, so that the operation burden of a battery management system can be effectively reduced;
(3) according to the invention, the temperature rise rate of the single battery is used for auxiliary analysis to carry out fault classification, so that the reliability of fault diagnosis is increased.
Drawings
FIG. 1 is a method for diagnosing a series connection fault of a power battery pack according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of cell connection and cell voltage measurement according to an embodiment of the present invention.
Fig. 3 is a diagram of a circuit structure and a voltage measurement model of the battery cell shown in fig. 2 according to the present invention.
FIG. 4 shows the mean square error value of each cell in the first embodiment of the present invention.
Fig. 5 shows the cell voltage detection abnormality coefficient of each battery according to example (a) of the present invention.
Fig. 6 is a graph showing the rate of temperature rise of the surface of the negative electrode of each battery cell in example (a) of the present invention.
FIG. 7 shows the mean square error values of the cells according to example (two) of the present invention.
Fig. 8 shows the cell voltage detection abnormality coefficients of example (two) of the present invention.
Fig. 9 shows the surface temperature rise rate of the negative electrode of each battery cell of example (two) of the present invention.
Detailed Description
The following detailed description of the present invention will be made in conjunction with the accompanying drawings.
Fig. 1 is a flow chart of a series connection fault diagnosis method for a power battery pack according to an embodiment of the present invention, in which the power battery pack is formed by connecting n battery cells in series.
In the invention, a cross voltage test method is adopted at the position where corrosion is easy to occur or virtual connection is easy to occur, and as shown in fig. 2, current I passes through the whole battery pack from right to left during discharging.
The specific implementation mode is as follows:
(1) the battery management system records n battery cell voltages and the battery cell negative electrode surface temperature, and it should be noted that the embodiment of recording the battery cell negative electrode surface temperature is exemplary and not limited to the negative electrode surface. And the battery management system records the voltage of the battery monomer and the surface temperature of the battery monomer, and the recording time interval, namely the sampling time interval can be flexibly set. If the sampling time interval is longer, the storage space of the recorded data can be reduced; if the sampling time interval is short, for example 1s, it means that the recorded information is relatively comprehensive, including almost all the time points at which the fault may occur.
It should be noted that fig. 2 is a schematic diagram of a cross-over voltage testing method according to an embodiment of the present invention, and is combined with the battery cell circuit structure and the voltage measurement model diagram shown in fig. 3, EiRepresents the open circuit voltage, r, of cell iiRepresents the internal resistance of cell i, RiRepresents the contact resistance of the cell i with other cells, UiRepresenting the actually measured terminal voltage of the battery cell i. Thus if a connection failure occurs, RiIncreasing the measured terminal voltage U when charging the power battery packiThe charging process is ended in advance when the charging cut-off voltage is reached; when discharging the power battery, the measured terminal voltage will be UiThe discharge cutoff voltage is reached first and the discharge process is ended early. And, the difference of the measured terminal voltage increases with the increase of the current I and the contact resistance R, that is, the more serious the loosening phenomenon of the connection member, the larger the contact resistance R, the larger the difference of the measured terminal voltageThe more heat is generated at the same time on the connection, which results in a significant temperature increase.
The embodiment adopts a Savitzky-Golay method to smoothly de-noise the battery cell voltage and temperature data recorded by the battery management system. It should be noted that the power battery pack is affected by noise, vibration and other interference factors during operation, and therefore, a smoothing denoising process needs to be performed on the acquired data before the diagnostic algorithm.
(2) Simulating the power battery monomer adopted by the example based on MATLAB/Simulink, and recording the output voltage;
(3) calculating the mean square error between the voltage of the n battery monomers and the simulation output voltage, and if the mean square error values are all approximately zero, judging that no voltage abnormality exists; and if the mean square error value has obvious fluctuation, judging that voltage is abnormal, and calculating voltage detection abnormal coefficients of all the battery cells at the detection point by adopting Z-score based on the mean square error.
The mean square error calculation formula of the single battery voltage and the simulation output voltage is as follows:
therein, MSEiIs the mean square error of the ith cell, epsiloniIn the embodiment of the present invention, m is a sampling time interval, where m is a residual error between the ith cell voltage and the simulation output voltage, and m is 180s, it should be noted here that the sampling time interval may be flexibly set according to an actual situation, and is not limited to this embodiment. That is, the smaller the value of m is, the more timely the fault diagnosis is, but the operation burden of the battery management system is increased; the larger the value of m is, the hysteresis phenomenon may exist in the fault diagnosis.
If the voltage is judged to be abnormal preliminarily, calculating voltage detection abnormal coefficients of all the battery cells at the detection point by adopting Z-score:
wherein A isiFor the i-th cell abnormal coefficient, MSEiMean square error, MSE, of the ith cellaveFor detecting mean value, sigma, of mean square error of said n cellsMAnd the standard deviation of the mean square error of the n battery monomers is detected.
(4) Judging whether the voltage abnormal coefficients of the ith battery cell and the i +1 battery cell are larger than zero or not, and if the voltage abnormal coefficients of the ith battery cell and the i +1 battery cell are both larger than zero, judging that the fault is caused by loose connection of the ith battery cell and the i +1 battery cell; and if the voltage abnormality coefficient of the ith battery cell is larger than zero and the voltage abnormality coefficient of the (i + 1) th battery cell is not larger than zero, determining that the fault is caused by the increase of the internal resistance of the ith battery cell. And the occurrence of the fault is output as a secondary fault.
(5) Calculating the surface temperature rise rate of the n battery monomers, and if the temperature rise rate in the ith and i +1 th battery monomers is greater than a preset threshold value, further determining the occurrence and the position of a fault and outputting the fault as a primary fault; if the temperature rise rate of the ith battery monomer and the (i + 1) th battery monomer is not greater than the preset threshold value, the output is still a secondary fault.
According to the fault level, corresponding fault processing should be performed, as described in table 1:
TABLE 1
| Failure class | Fault handling |
| First stage | Red warning of fault to prompt user to need immediate maintenance |
| Second stage | Early warning of yellow fault to prompt user of slight fault |
It should be noted that, when the charge and discharge test is performed under the normal connection condition, the surface temperature of the battery cell directly connected to the cathode of the cycler is significantly higher than that of other battery cells, mainly because the current first flows through the battery cell during charge and discharge.
It should be noted that none of the above fault determination processes include a pending status of the battery test.
The analysis is carried out by two experimental examples below.
In the experiment, 4 NCM power batteries (namely n is 4, the numbers of the battery monomers are 1, 2, 3 and 4) with the rated capacity of 43Ah and the standard charging and discharging current of 1C are connected in series, the contact resistance is smaller than 1.5m omega under the normal condition, only one connection fault occurs in each group of experiments, 1C constant current discharging and FUDS working condition simulation tests are carried out on the battery pack, and the terminal voltage and the surface temperature of a negative electrode of each monomer are recorded. And (4) simulating the power battery by using MATLAB/Simulink, and recording the simulation voltage. And then carrying out diagnosis analysis on the experimental data result.
Example (a): the mean square error value of each battery cell is shown in fig. 4, wherein the mean square error values of 3 and 4 battery cells are constantly approximately zero, and the mean square error values of 1 and 2 battery cells are greater than zero and have obvious fluctuation, and the voltage abnormality is preliminarily determined. The voltage detection abnormal coefficient calculated by adopting Z-score based on the mean square error is shown in figure 5, except for the shelving state, the voltage abnormal coefficient of the 3 and 4 battery monomers is less than zero, and the voltage abnormal coefficient of the 1 and 2 battery monomers is more than zero, so that the connection fault between the 1 and 2 battery monomers can be judged, namely, the occurrence of the secondary fault. Referring to fig. 6, when the single battery 1 (directly connected to the negative electrode of the cycler) is removed, the temperature rise rate of the single battery 2 is significantly greater than twice the maximum temperature rise rate of 0.01 ℃/s under normal conditions, and it is further verified that a connection fault exists between the single batteries 1 and 2, which is a first-order fault.
Example (b): the mean square error value of each battery cell is shown in fig. 7, wherein the mean square error values of 1 and 4 battery cells are constantly approximately zero, and the mean square error values of 2 and 3 battery cells are greater than zero and have obvious fluctuation, and the voltage abnormality is preliminarily determined. The voltage detection abnormal coefficient calculated by adopting Z-score based on the mean square error is shown in figure 8, except for the shelving state, the voltage abnormal coefficient of 1 and 4 battery monomers is smaller than zero, and the voltage abnormal coefficient of 2 and 3 battery monomers is larger than zero, so that the connection fault between 2 and 3 battery monomers can be judged, namely a secondary fault occurs. Referring to fig. 9, when the single battery 1 (directly connected to the cathode of the cycler) is removed, the temperature rise rate of the single battery 3 is significantly greater than twice the maximum temperature rise rate of 0.01 ℃/s under normal conditions, and it is further verified that a connection fault exists between the single batteries 2 and 3, which is a first-order fault.
In summary, according to the power battery pack series connection fault diagnosis method provided by the embodiment of the invention, through a cross voltage test, the faults of contact resistance increase and internal resistance increase can be distinguished, the mean square error calculation can preliminarily determine whether voltage is abnormal, and if no voltage is abnormal, the calculation is not needed; if there is an abnormality, the voltage detection abnormality coefficient is calculated based on the Z-score. And the occurrence and the position of the fault can be further determined by combining the temperature rise rate of the surface of the battery. According to different fault levels, the user can make corresponding treatment.
In addition, the power battery pack series connection fault diagnosis method is not only suitable for pure electric vehicles, but also suitable for storage batteries of hybrid electric vehicles and non-electric vehicles.
Claims (9)
1. A power battery pack series connection fault diagnosis method is characterized in that: the power battery pack is formed by connecting n battery monomers in series, adopts a cross voltage test method at a position which is easy to corrode or virtually connect, and comprises the following steps:
the method comprises the following steps that (1) the currents flowing through each battery monomer of the series battery pack are consistent in the using process, and a battery management system records the voltage of the n battery monomers and the surface temperature of the negative electrode of each battery monomer;
simulating the power battery monomer based on MATLAB/Simulink, and recording output voltage;
calculating the mean square error of the voltage of the n battery monomers and the simulation output voltage, if the mean square error values are all approximate to zero, judging that no voltage is abnormal, and entering the next sampling time calculation; if the mean square error value has obvious fluctuation, judging that voltage is abnormal, and calculating voltage detection abnormal coefficients of all battery monomers at the detection point by adopting Z-score based on the mean square error;
step (4) judging whether the voltage abnormal coefficients of the ith and i +1 th battery cells are larger than zero, and if the voltage abnormal coefficients of the ith and i +1 th battery cells are both larger than zero, judging that the connection part of the ith and i +1 th batteries has a fault; if the voltage abnormality coefficient of the ith battery monomer is larger than zero and the voltage abnormality coefficient of the (i + 1) th battery monomer is not larger than zero, determining that the internal resistance of the ith battery is increased; outputting a result as a secondary fault;
step (5) calculating the surface temperature rise rate of the n battery units;
step (6) judging whether the temperature rise rate in the ith and i +1 th battery monomer is greater than a preset threshold value, if so, judging that the fault is accurately judged and positioned in the step (4), and outputting a result as a first-level fault; if not, the next sampling time calculation is carried out, and the output result is still a secondary fault.
2. The power battery pack series connection fault diagnosis method according to claim 1, characterized in that: in the step (1), the sampling frequency of the voltage of the n battery monomers and the surface temperature of the negative electrode of the battery monomer is recorded to be 1 Hz.
3. The power battery pack series connection fault diagnosis method according to claim 1, characterized in that: in the step (1), smoothing and denoising are performed on the single battery voltage and temperature data recorded by the battery management system.
4. The power battery pack series connection fault diagnosis method according to claim 1, characterized in that: in the step (2), the simulation step length is a fixed step length 1 s.
5. The power battery pack series connection fault diagnosis method according to claim 1, characterized in that: in the step (3), the method for calculating the mean square error of the single battery voltage and the simulation output voltage comprises the following steps:
therein, MSEiIs the mean square error of the ith cell, epsiloniThe residual error between the ith cell voltage and the simulated output voltage is m, which is the sampling time interval.
6. The power battery pack series connection fault diagnosis method according to claim 1, characterized in that: in the step (3), the voltage detection abnormal coefficients of all the battery cells at the detection point are calculated by adopting Z-score based on the mean square error, and the calculation method comprises the following steps:
wherein A isiFor the i-th cell abnormal coefficient, MSEiMean square error, MSE, of the ith cellaveFor detecting mean value, sigma, of mean square error of said n cellsMAnd the standard deviation of the mean square error of the n battery monomers is detected.
7. The power battery pack series connection fault diagnosis method according to claim 1, characterized in that: in the step (6), the preset threshold is twice of the maximum temperature rise rate of the battery monomer under the normal working condition.
8. The power battery pack series connection fault diagnosis method according to claim 3, characterized in that: the smooth denoising method is a Savitzky-Golay method.
9. The power battery pack series connection fault diagnosis method according to claim 5, characterized in that: and m is 180 s.
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