A vehicle battery system monitoring platform and a method for warning of poor battery system consistency

Abstract

The present invention relates to a monitoring platform for a vehicle battery system and a method for warning of poor battery system consistency, belonging to the technical field of battery system safety management. Based on the minimum single-cell battery voltage and battery system pressure difference data of a large number of battery systems received by the vehicle battery system monitoring platform as samples, the present invention obtains the battery system pressure difference threshold values of different minimum single-cell battery voltage magnitudes through cluster analysis, determines the relationship between the minimum single-cell battery voltage and the battery system pressure difference threshold values through fitting, and increases the recognition range of consistency through continuously variable battery system pressure difference threshold values. Regardless of the depth of vehicle discharge, there is an opportunity to identify vehicles with poor voltage consistency. The present invention can quickly and accurately give a warning of the consistency of the battery system based on the relevant data of the battery system received by the monitoring platform.

Description

Technical Field

[0001] The present invention relates to a vehicle battery system monitoring platform and a method for warning of poor battery system consistency, and belongs to the technical field of battery system safety management. Background Art

[0002] The purpose of identifying the voltage consistency of the battery system is to detect problems such as single-cell battery failures, abnormal attenuation of battery capacity, and untimely battery equalization maintenance in advance. If not dealt with in time, it may lead to vehicle breakdown, shortened driving range, battery thermal runaway, battery insulation failure, etc. The battery system consistency generally includes voltage consistency, resistance consistency, capacity consistency, state of charge (SOC) consistency, temperature consistency, etc. Among them, voltage consistency is the most commonly used and economical indicator for evaluating the performance of the battery system. Other indicators require data such as high-frequency voltage list data and high-frequency single-branch current data, which are costly. There are various expression formulas for voltage consistency. For example, the pressure difference of the battery system in different states (the difference between the highest single-cell voltage and the lowest single-cell voltage), the variance of the single-cell voltage of the battery system, the standard deviation of the single-cell voltage of the battery system, etc. Among them, the pressure difference of the battery system is the most economical indicator, which can be calculated only with voltage extreme value data; the other calculation methods require voltage list data, which are costly.

[0003] The domestic new energy vehicle market mainly includes passenger vehicles and commercial vehicles. The power system types used in passenger vehicles are mostly ternary batteries, and the batteries used in commercial vehicles are mostly lithium iron phosphate batteries. The charge and discharge curve of the ternary battery is linear, and there is a one-to-one relationship between voltage and SOC, which can be mapped one-to-one more accurately. While the lithium iron phosphate battery has a long plateau period. When the SOC changes within a large range, the corresponding voltage only changes slightly, resulting in the inability to accurately map the single-cell voltage and SOC value one-to-one. Due to factors such as the difference in the number of batteries carried and cost limitations, passenger vehicles usually upload the voltage and temperature information of all single cells, while commercial vehicles generally only upload the maximum and minimum values of the single-cell voltage and temperature. Through remote monitoring, passenger vehicles can monitor all single-cell information with high voltage discrimination, while commercial vehicles can only monitor the highest and lowest information with low voltage discrimination. Therefore, it is easy to identify the battery voltage consistency of passenger vehicles; it is not easy to identify the battery voltage consistency of commercial vehicles. Summary of the Invention

[0004] The purpose of the present invention is to provide a vehicle battery system monitoring platform and a method for warning of poor battery system consistency to solve the problem that the lithium iron phosphate battery cannot give a warning of poor consistency when only battery extreme value data is available.

[0005] The present invention provides a method for warning of poor battery system consistency to solve the above technical problems. The warning method includes the following steps:

[0006] 1) Sample and obtain the data of the battery system to be measured at the set sampling interval. The battery system data includes the lowest single-cell battery voltage and the battery system pressure difference, and preliminarily screen the obtained data to ensure that the data in the battery system obtained is the data when not charging;

[0007] 2) Re-screen the preliminarily screened data to ensure that the data of the battery system obtained is not abnormal voltage data and is the battery data with the voltage below the inflection point voltage of the plateau period;

[0008] 3) Perform a warning for poor consistency based on the screened battery system data. If the following conditions are met, it indicates that there is a problem of poor consistency in this battery system:

[0009] The conditions are: the battery system pressure difference is greater than or equal to the corresponding battery system pressure difference threshold and the single continuous duration is greater than the set time threshold;

[0010] The corresponding battery system pressure difference threshold is obtained by looking up the lowest single-cell battery voltage corresponding to the battery system pressure difference and using the fitting relationship between the lowest single voltage and the system pressure difference threshold;

[0011] The described fitting relationship is obtained by clustering analysis and fitting of a large number of lowest single-cell battery voltages and battery system pressure differences.

[0012] The present invention also provides a monitoring platform for a vehicle battery system. The monitoring platform is communicatively connected to each vehicle. It is characterized in that the monitoring platform includes a processor and a memory, and the processor executes a computer program stored by the memory to implement the method for warning of poor consistency of the battery system as in the present invention.

[0013] Based on a large number of lowest single-cell battery voltages and battery system pressure difference data of the battery system received by the vehicle battery system monitoring platform as samples, the present invention obtains the battery system pressure difference threshold of different lowest single-cell battery voltage magnitudes through clustering analysis, determines the relationship between the lowest single-cell battery voltage and the battery system pressure difference threshold through fitting, and increases the recognition range of consistency through the continuously variable battery system pressure difference threshold. Regardless of the discharge depth of the vehicle, there is a chance to identify the vehicle with poor voltage consistency. The present invention can quickly and accurately perform a warning of the consistency of the battery system based on the relevant data of the battery system received by the monitoring platform.

[0014] Further, to accurately describe the relationship between the lowest single-cell battery voltage and the battery system pressure difference threshold, the described fitting relationship is a linear fitting relationship:

[0015] Y = Vmin * A + B

[0016] Where Y represents the battery system pressure difference threshold; Vmin represents the lowest single voltage; A and B are the fitted coefficients.

[0017] Further, to improve the accuracy of the warning for poor consistency, the re-screening in step 2) also includes comparing the data upload time and storage time of the battery system to delete the re-transmitted battery system data.

[0018] Further, the warning method also includes classifying the level of poor consistency of the battery system. The more the pressure difference of the battery system exceeds the corresponding battery system pressure difference threshold, the longer the duration of a single exceedance, and the more times of exceedance within a set time period, the more serious the poor consistency of the battery system indicates.

[0019] Further, the method also includes determining the reason for the poor consistency of the battery system. When it is determined that the battery system has poor consistency, if the state of health (SOH) of the battery system capacity is lower than the normal attenuation curve, it indicates that the overall battery system has abnormal attenuation.

[0020] Further, the method also includes determining the reason for the poor consistency of the battery system. If the state of health (SOH) of the battery system capacity is higher than a certain threshold and the static pressure difference of the battery system is small at the high end, it indicates that there is a short board in the capacity of the single battery in the battery system; when the state of health (SOH) of the battery system capacity is higher than a certain threshold and the static pressure difference of the battery system is large at the high end, it indicates that the state of charge of the battery system is inconsistent.

[0021] Further, the method also includes determining the reason for the poor consistency of the battery system. If a certain single battery in the battery system has the highest terminal voltage during charging and the lowest terminal voltage during discharging, it indicates that the impedance of the single battery is large.

[0022] Further, the method also includes determining the reason for the poor consistency of the battery system. If the highest voltage increases and the lowest voltage decreases simultaneously, whether adjacent or non-adjacent, and when the specified single battery voltage value appears during a sampling interruption, it indicates that the voltage sampling function of the battery system is abnormal. Description of the Drawings

[0023] Figure 1 is a schematic diagram of the result of correlation analysis in an embodiment of the present invention;

[0024] Figure 2 is the result obtained by performing clustering analysis using the DBSCAN method in an embodiment of the present invention;

[0025] Figure 3 is a flowchart of the warning method for poor consistency of the battery system of the present invention. Detailed Embodiments

[0026] The following further describes the detailed embodiments of the present invention with reference to the drawings.

[0027] Method Embodiment

[0028] Through methods such as battery mechanism analysis and big data statistical correlation, the present invention discovers that there are two difficulties in using the pressure difference of the battery system to measure the consistency of the lithium iron phosphate battery system: First, the discharge curve of the lithium iron phosphate battery has an obvious plateau period, and within this plateau period, the system consistency cannot be accurately characterized through the voltage difference; Second, when the SOC is less than 40% or when the SOC is close to 100%, the pressure difference shows an increasing trend. Using a fixed pressure difference threshold to evaluate the system consistency is inaccurate, and a dynamic pressure difference threshold that changes with the voltage should be formulated to characterize the system consistency. Therefore, the present invention proposes a method for warning about poor battery system consistency. This method uses a continuously variable pressure difference threshold table for the battery system, increasing the recognition range of the algorithm. Regardless of the depth of vehicle discharge, there is an opportunity to identify vehicles with poor voltage consistency. Through indicators such as the single continuous duration, cumulative continuous duration, or cumulative number of times that the battery system pressure difference exceeds the threshold, the accuracy of warning about poor consistency recognition is improved. The implementation process of this method is as Figure 3 shown.

[0029] 1. According to the vehicle operation data, through correlation analysis, determine the indicators for evaluating the battery system consistency.

[0030] Using the monitoring platform, a large amount of operation data of tens of thousands of vehicles can be obtained. These operation data include the performance indicators of the battery. Since the present invention is aimed at commercial vehicles, its performance indicators are mainly extreme value data, including the lowest single-cell voltage, the highest single-cell voltage, the battery pressure difference, the lowest temperature, the highest temperature, the total voltage, the total current, the average voltage, the average temperature, etc. The present invention conducts pairwise correlation analysis on the above indicators, finds the two indicators with the strongest correlation, and uses these two coordinates as the evaluation indicators.

[0031] For this embodiment, pairwise pearson correlation analysis is used, and the results are as Figure 1 shown. It can be seen from this that there is a strong correlation between the lowest single-cell battery voltage and the battery system pressure difference, and the correlation coefficient reaches 0.99. Therefore, the present invention selects the lowest single-cell voltage and the battery system pressure difference as the evaluation indicators.

[0032] 2. Conduct cluster analysis on the selected evaluation indicators to determine the system pressure difference threshold corresponding to different lowest single-cell voltages in the battery consistency difference model.

[0033] Using the big data analysis algorithm (DBSCAN algorithm), conduct cluster analysis on the pressure difference and the lowest voltage of the vehicle, find the abnormal points and the coordinates (Vmin, Y) of these abnormal points on the "lowest voltage - pressure difference" graph, where Y represents the pressure difference and Vmin represents the lowest voltage, as Figure 2 shown; conduct linear fitting on these coordinate values, and the obtained fitting formula is:

[0034] Y = Vmin * A + B

[0035] Where Y represents the differential pressure threshold of the battery system; Vmin represents the lowest single-cell voltage; A and B are the fitted coefficients. For a certain type of lithium iron phosphate battery, the data of A is -0.827 and the data of B is 2.725.

[0036] Through the above fitting relationship, the differential pressure threshold of the system corresponding to different lowest single-cell voltages can be determined.

[0037] 3. Screen the battery systems to be identified, select the battery systems that meet the set conditions, and identify the consistency of the battery systems according to the differential pressure threshold of the system corresponding to different lowest single-cell voltages obtained.

[0038] To ensure the accuracy of the judgment of the battery system consistency, it is necessary to screen the data therein. The set conditions adopted during screening include:

[0039] 1) Battery system type

[0040] Since the present invention is directed to lithium iron phosphate batteries, it is necessary to screen the battery system type to find lithium iron phosphate batteries therefrom.

[0041] 2) Battery system state

[0042] Because charging at different rates will cause the voltage difference of the battery system to decrease to varying degrees, interfering with the accuracy of early warning, only the data in the non-charging state is selected.

[0043] 3) Battery system data upload data and storage time

[0044] If the difference between the monitoring data upload time and the data storage time ≥ X1 hours, it means that the battery system data stored in the database is supplementary transmission data. The quality of the supplementary transmission data is poor and interferes with the calculation results, so the supplementary transmission data cannot be used. Therefore, the present invention extracts the data with the difference △T between the monitoring data upload time and the data storage time less than X1 hours. According to the big data statistical law of the monitoring platform, X1 can be set to ±1.

[0045] 4) Lowest single-cell battery voltage

[0046] To exclude abnormal voltage data, the data below the inflection point voltage of the plateau period is selected. The present invention selects the data where the lowest single-cell voltage Vmin is greater than X2 and less than X3. Among them, X2 is the abnormal voltage threshold, and X2 can be set to 0V; X2 is the inflection point voltage of the OCV curve plateau period of lithium iron phosphate and can be set to 3.2V.

[0047] Based on the above four conditions, the data of the battery system to be pre-warned is screened. After the screening is completed, first find the lowest single-cell battery voltage data from the screened battery systems. According to the relationship between the lowest single-cell voltage and the battery system pressure difference threshold established in step 2, find the corresponding battery system pressure difference threshold, and compare the battery system pressure difference with the corresponding battery system pressure difference threshold. If the following conditions are met, it indicates that the battery system has poor consistency.

[0048] The conditions are: the battery system pressure difference △V ≥ the battery system pressure difference threshold Y and the single continuous duration T1 is greater than X4 seconds (Note: Abnormal data such as data jumps are excluded through the continuous duration. X4 can be set to 100 seconds according to experience).

[0049] Through this step, the present invention can accurately identify that the battery system has poor consistency and realize the early warning of poor battery system consistency.

[0050] 4. Determine the severity of poor consistency.

[0051] When it is detected that the battery system has poor consistency, it is also necessary to classify its severity. There are two parameters for the severity level classification:

[0052] Classify the battery system pressure difference threshold Y, which is achieved by adjusting the B value. The larger the B value, the more serious it is;

[0053] Numerically classify the single continuous duration T1; classify the number of times T1 appears within a period of time, such as within one day. The more times it appears, the more serious the problem is.

[0054] The more the battery system pressure difference exceeds the corresponding battery system pressure difference threshold, the longer the single continuous exceeding duration, and the more times it exceeds within the set time period, the more serious the poor consistency of the battery system is.

[0055] 5. Determine the reason for the poor consistency of the battery system.

[0056] When it is detected that the battery system has poor consistency, in order to avoid battery safety problems caused by poor consistency, the present invention also needs to analyze the reason for the poor consistency to better give treatment measures and improve the battery system life and battery safety. After analysis, the main reasons for the poor consistency are mainly the following five:

[0057] Abnormal voltage sampling function: The adjacent or non-adjacent highest voltage increases while the lowest voltage decreases, and specific single-cell voltage values appear; for example, when the BMS strategy stipulates that the voltage shows 5.118V during sampling interruption, it is recommended to repair the voltage sampling circuit at this time.

[0058] The overall abnormal attenuation of the battery system: The battery system capacity retention rate SOH is lower than a certain threshold. The SOH threshold of a certain type of battery is its normal attenuation curve. It is recommended to replace the battery pack with abnormal SOH attenuation.

[0059] Capacity shortcoming: If the battery system capacity retention rate SOH is higher than a certain threshold and the static pressure difference is small when the battery system is at the high end (the battery is close to fully charged), it is recommended to replace the single battery with capacity shortcoming.

[0060] Inconsistent battery charge: The battery system capacity retention rate SOH is higher than a certain threshold, and the static pressure difference of the battery system is large when it is at the high end (the battery is close to full charge). It is recommended to perform balanced maintenance on the battery system.

[0061] The impedance of the single cell is large: the battery has the phenomenon of high charging and low discharge (that is, a single cell has the highest voltage at the charging end and the lowest voltage at the discharging end); it is recommended to check whether the high-voltage connection is normal, or replace the single cell with the problem of high charging and low discharge.

[0062] Through the above process, for lithium iron phosphate batteries, the present invention can accurately realize early warning of poor consistency of lithium iron phosphate batteries through the battery system pressure difference through the relationship between the lowest single cell voltage and the battery system pressure difference threshold.

[0063] Platform Implementation

[0064] The vehicle battery system monitoring platform of the present invention adopts a cloud platform, including a processor and a memory, and the processor executes a computer program stored in the memory to implement the method of the present invention to implement the above method embodiment. That is to say, the method in the above method embodiment should be understood to be a process of the power system branch circuit breaker identification method that can be implemented by computer program instructions. These computer program instructions can be provided to the processor so that the processor executes these instructions to generate functions specified by the above method flow.

[0065] The processor referred to in this embodiment refers to a processing device such as a microprocessor MCU or a programmable logic device FPGA; the memory referred to in this embodiment includes a physical device for storing information, which usually digitizes the information and then stores it in a medium using electrical, magnetic or optical methods. For example: various memories that use electrical energy to store information, such as RAM, ROM, etc.; various memories that use magnetic energy to store information, such as hard disks, floppy disks, magnetic tapes, magnetic core memories, magnetic bubble memories, and U disks; various memories that use optical methods to store information, such as CDs or DVDs. Of course, there are other types of memories, such as quantum memories, graphene memories, and so on.

[0066] The device composed of the above-mentioned memory, processor, and computer program is implemented by the processor executing corresponding program instructions in a computer. The processor can run various operating systems, such as the Windows operating system, Linux system, Android, iOS system, etc.

[0067] As another implementation, the device may further include a display for presenting the diagnostic results for the reference of the staff.

Claims

1. A method for warning of poor consistency of a battery system, characterized in that, the warning method includes the following steps: 1) Sampling and obtaining battery system data to be measured at a set sampling interval, where the battery system data includes the lowest single-cell battery voltage and the battery system pressure difference, and preliminarily screening the obtained data to ensure that the data in the obtained battery system is data when not charging; 2) Re-screening the preliminarily screened data to ensure that the obtained battery system data is non-abnormal voltage data and battery data with a voltage below the inflection point voltage of the plateau period; 3) Conducting a warning of poor consistency based on the screened battery system data. If the following conditions are met, it indicates that there is a problem of poor consistency in the battery system: The condition is: the battery system pressure difference is greater than or equal to the corresponding battery system pressure difference threshold and the single continuous duration is greater than the set time threshold; The corresponding battery system pressure difference threshold is obtained by looking up the lowest single-cell battery voltage corresponding to the battery system pressure difference and using the fitting relationship between the lowest single voltage and the system pressure difference threshold; The described fitting relationship is obtained by clustering analysis and fitting of the lowest single-cell battery voltage and the battery system pressure difference.

2. The method for warning of poor consistency of a battery system according to claim 1, characterized in that, the described fitting relationship is a linear fitting relationship: Y = Vmin * A + B where Y represents the battery system pressure difference threshold; Vmin represents the lowest single voltage; A and B are the fitted coefficients.

3. The method for warning of poor consistency of a battery system according to claim 1, characterized in that, the re-screening in step 2) also includes comparing the upload time and storage time of the battery system data to delete the re-transmitted battery system data.

4. The method for warning of poor consistency of a battery system according to claim 1 or 2, characterized in that, the warning method also includes classifying the level of poor consistency of the battery system. The more the battery system pressure difference exceeds the corresponding battery system pressure difference threshold, the longer the single-time exceeding duration, and the more times exceeding within the set time period, the more serious the poor consistency of the battery system.

5. The method for warning of poor consistency of a battery system according to claim 1 or 2, characterized in that, the method also includes determining the reason for the poor consistency of the battery system. When it is determined that the battery system has poor consistency, if the state of health (SOH) of the battery system capacity retention rate is lower than the normal attenuation curve, it indicates that the battery system has an overall abnormal attenuation.

6. The method for warning of poor consistency of a battery system according to claim 1 or 2, characterized in that, the method also includes determining the reason for the poor consistency of the battery system. If the state of health (SOH) of the battery system capacity retention rate is higher than a certain threshold and the static pressure difference of the battery system is small at the high end, it indicates that there is a short board in the capacity of the single-cell battery in the battery system; when the state of health (SOH) of the battery system capacity retention rate is higher than a certain threshold and the static pressure difference of the battery system is large at the high end, it indicates that the state of charge of the battery system is inconsistent.

7. The method for warning of poor consistency of a battery system according to claim 1 or 2, characterized in that, The method further includes determining the reason for the poor consistency of the battery system. If a single battery in the battery system has the highest terminal voltage during charging and the lowest terminal voltage during discharging, it indicates that the impedance of this single battery is large.

8. The method for warning of poor consistency of the battery system according to claim 1 or 2, wherein, the method further includes determining the reason for the poor consistency of the battery system. If the highest voltage increases while the lowest voltage decreases, whether adjacent or non - adjacent, and the specified single - cell voltage value during sampling interruption appears, it indicates that the voltage sampling function of the battery system is abnormal.

9. A vehicle battery system monitoring platform, which is communicatively connected to each vehicle, wherein, the monitoring platform includes a processor and a memory. The processor executes a computer program stored in the memory to implement the method for warning of poor consistency of the battery system according to any one of claims 1 - 8 above.

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