Battery management system BMS data collection calibration method and system based on cloud platform

Abstract

The present invention discloses a method and system for calibrating data collected by a battery management system (BMS) based on a cloud platform. The method includes: providing a cloud platform that is connected to the BMS in communication, and a voltage calibration coefficient calculation model K(n) is set in the cloud platform; a coefficient query table is also set in the cloud platform; the BMS transmits the original voltage value and the current ambient temperature value of the collected battery to the cloud platform; the cloud platform queries the temperature influence coefficient table based on the ambient temperature value transmitted by the BMS to obtain the temperature influence coefficient corresponding to the current ambient temperature; the cloud platform processes the temperature influence coefficient and the original voltage value based on the calculation model K(n) to obtain the voltage calibration coefficient, and feeds the voltage calibration coefficient back to the BMS to update the voltage calibration coefficient recorded in the BMS. Through the above data calibration method, the ambient temperature can be prevented from affecting the accuracy of the BMS collected voltage, and the accuracy and consistency of the BMS calibration of the collected voltage can be effectively ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of BMS data processing, and in particular to a cloud platform-based battery management system BMS data collection calibration method and system. Background Art

[0002] As society's requirements for battery power systems continue to increase, on the one hand, the focus is on improving battery capacity, and on the other hand, the focus is on improving battery management efficiency. Therefore, current battery power systems are generally equipped with a battery management system BMS for the battery. During operation, the BMS collects the battery's voltage, current, capacity and other operating parameters in real time, and controls the battery's output state and charging state based on the collected operating parameters to optimize the battery's operating performance.

[0003] For the current BMS, affected by the acquisition accuracy, a calibration coefficient is generally set in the BMS. When the BMS acquires the original voltage of the battery, the original voltage is calibrated by the calibration coefficient to obtain the calibrated voltage, and the corresponding control command is executed according to the calibrated voltage.

[0004] However, the error in BMS's acquisition of battery voltage is not only affected by the system accuracy, but also by the ambient temperature, that is, the acquisition accuracy of BMS is different at different ambient temperatures. Therefore, if the original voltage is calibrated based on a fixed calibration coefficient, the accuracy of the obtained calibration voltage is not ideal, and it is easy to issue an erroneous control command. Summary of the invention

[0005] The object of the present invention is to provide a cloud platform-based battery management system (BMS) data collection calibration method and system, which can adjust the calibration coefficient for calibrating the collected battery voltage in the BMS in real time according to the ambient temperature.

[0006] In order to achieve the above object, the present invention discloses a battery management system BMS data collection calibration method based on a cloud platform, which includes:

[0007] A cloud platform is provided which is in communication with the BMS, wherein a voltage calibration coefficient calculation model K(n) is provided in the cloud platform, where K(n)=(m, Vc(n)), wherein m is a temperature influence coefficient corresponding to a corresponding temperature range, and Vc(n) is an original voltage value of a battery connected thereto and collected by the BMS;

[0008] The cloud platform is also provided with a coefficient query table, which records the temperature influence coefficients corresponding to different temperature ranges;

[0009] The BMS transmits the collected original voltage value and current ambient temperature value of the battery to the cloud platform;

[0010] The cloud platform queries the temperature influence coefficient table based on the ambient temperature value transmitted by the BMS to obtain the temperature influence coefficient corresponding to the current ambient temperature;

[0011] The cloud platform processes the temperature influence coefficient and the original voltage value based on the calculation model K(n) to obtain a voltage calibration coefficient, and feeds the voltage calibration coefficient back to the BMS to update the voltage calibration coefficient recorded in the BMS;

[0012] The BMS calibrates the collected original voltage value according to the recorded voltage calibration coefficient.

[0013] Preferably, the battery connected to the BMS includes a plurality of battery cells. In each collection cycle, the BMS simultaneously collects the original voltage value of each battery cell; the cloud platform calculates a corresponding voltage calibration coefficient according to the original voltage value of each battery cell, and feeds back the calculated voltage calibration coefficients to the BMS. The BMS calibrates the original voltage values ​​according to the voltage calibration coefficients corresponding to each battery cell.

[0014] Preferably, after the cloud platform receives several original voltage values ​​of the current acquisition cycle transmitted by the BMS, it further calculates the arithmetic mean of the several original voltage values;

[0015] and calculating the absolute value of the difference between each of the original voltage values ​​and the arithmetic mean value to obtain a voltage difference;

[0016] generating marking data corresponding to each of the original voltage values ​​according to the relationship between the voltage difference and a preset threshold range;

[0017] The method for generating the marked data comprises:

[0018] When the voltage difference is greater than the preset threshold range, generating a first mark corresponding to the original voltage value;

[0019] When the voltage difference is within a preset threshold range, generating a second mark corresponding to the original voltage value;

[0020] When the voltage difference is less than a preset threshold range, generating a third mark corresponding to the original voltage value;

[0021] The cloud platform feeds back the voltage calibration coefficient and the marking data corresponding to each battery cell to the BMS;

[0022] Based on the first mark, the BMS discards the original voltage value and the voltage calibration coefficient corresponding to the first mark;

[0023] Based on the second mark, if the BMS records the voltage calibration coefficient of the battery cell corresponding to the second mark, then abandoning updating the existing voltage calibration coefficient based on the new voltage calibration coefficient;

[0024] Based on the third mark, the BMS uses a new voltage calibration coefficient to update the voltage calibration coefficient of the battery cell corresponding to the third mark.

[0025] 4. The cloud-based battery management system BMS data collection calibration method according to claim 1, characterized in that the calculation model K(n) satisfies the following formula 1:

[0026] K(n)=1+m1*Vc(n)+m2*Vc(n)2(Formula 1);

[0027] Among them, m1 and m2 are temperature influence coefficients, m1 is a positive value, m2 is a negative value, and Vc(n) is the original voltage value.

[0028] Preferably, the BMS calibrates the original voltage value using the following formula 2 to obtain a calibrated voltage value V(n) corresponding to each original voltage value;

[0029] V(n)=Vc(n) / K(n) (Formula 2), where Vc(n) is the original voltage value.

[0030] Preferably, the cloud platform is communicatively connected with a plurality of the BMSs.

[0031] Preferably, the calibration system works based on the calibration method according to any one of claims 1 to 6.

[0032] The present invention also discloses a battery management system BMS data collection and calibration system, which includes:

[0033] one or more processors;

[0034] Memory;

[0035] and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include instructions for executing the battery management system BMS data collection calibration method as described above.

[0036] The present invention further discloses a computer-readable storage medium, which includes a computer program. The computer program can be executed by a processor to complete the battery management system BMS data collection and calibration method as described above.

[0037] Compared with the prior art, the data calibration method disclosed in the above technical solution of the present invention receives the original voltage value and ambient temperature value collected by the BMS in real time through the cloud platform, and then queries the coefficient query table based on the ambient temperature value to obtain the temperature influence coefficient corresponding to the current ambient temperature, thereby substituting the temperature influence coefficient and the original voltage value into the calculation model to obtain the voltage calibration coefficient related to the current ambient temperature, and transmitting the voltage calibration coefficient to the BMS, and the BMS calibrates the collected original voltage value according to the latest voltage calibration coefficient to ensure the accuracy of the calibrated battery voltage. It can be seen that through the above data calibration method, the influence of ambient temperature on the accuracy of the BMS collected voltage can be avoided, and the accuracy and consistency of the BMS calibration of the collected voltage can be effectively ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of the data calibration method in an embodiment of the present invention.

[0039] Figure 2 4 is a flow chart of a data calibration method in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to explain the technical content, structural features, achieved objectives and effects of the present invention in detail, the following is a detailed description in conjunction with the implementation methods and the accompanying drawings.

[0041] This embodiment discloses a method for calibrating data collected by a battery management system (BMS), which is used to calibrate the voltage data collected by the BMS. In order to ensure the accuracy and consistency of the calibration, the voltage calibration coefficient in this embodiment changes dynamically, that is, based on the change of the ambient temperature, a voltage calibration coefficient generated based on the current ambient temperature is provided to the BMS in each data collection cycle. Specifically, in this embodiment, a cloud platform is provided for the BMS to communicate with, and the original data collected by the BMS is received through the cloud platform, and data analysis operations are performed based on the original data.

[0042] As a cloud platform for data analysis and calculation, a voltage calibration coefficient calculation model K(n) is provided, K(n)=(m, Vc(n)), where m is the temperature influence coefficient corresponding to the corresponding temperature range, and Vc(n) is the original voltage value of the battery connected thereto collected by the BMS. Therefore, the calculation model K(n) in this embodiment calculates the voltage calibration coefficient using the temperature influence coefficient m and the original voltage value Vc(n) of the battery as parameters.

[0043] In addition, a coefficient query table is also provided in the cloud platform, which records the temperature influence coefficients corresponding to different temperature ranges.

[0044] Based on the above cloud platform, such as Figure 1 and Figure 2, the data calibration method in this embodiment includes the following steps:

[0045] S1: In the current data collection cycle, the BMS transmits the collected original voltage value of the battery and the current ambient temperature value to the cloud platform;

[0046] S2: The cloud platform queries the temperature influence coefficient table based on the ambient temperature value transmitted by the BMS to obtain the temperature influence coefficient m corresponding to the current ambient temperature;

[0047] S3: The cloud platform processes the temperature influence coefficient m and the original voltage value Vc(n) based on the calculation model K(n) to obtain a voltage calibration coefficient, and feeds the voltage calibration coefficient back to the BMS to update the voltage calibration coefficient recorded in the BMS;

[0048] S4: The BMS calibrates the collected original voltage value according to the recorded voltage calibration coefficient.

[0049] Furthermore, the cloud platform is connected to multiple BMSs in communication, so that data analysis services can be provided to multiple BMSs at the same time through the same cloud platform.

[0050] On the other hand, for current high-power power battery systems, output power is generally provided in the form of a battery pack, which is composed of a number of battery cells. During the operation of the battery pack, the voltage value of each battery cell may be different. Therefore, in each collection cycle, the BMS simultaneously collects the original voltage value of each battery cell. For example, a battery pack has ten battery cells, namely battery cell 1 to battery cell 10. The corresponding original voltage values ​​collected by the BMS are Vc(1), Vc(2)...Vc(10), etc., and Vc(1), Vc(2)...Vc(10) are transmitted to the cloud platform respectively.

[0051] The cloud platform calculates a corresponding voltage calibration coefficient according to the original voltage value of each battery cell, and feeds back the calculated voltage calibration coefficients to the BMS. The BMS calibrates the original voltage value according to the voltage calibration coefficient corresponding to each battery cell. Taking the battery pack including the No. 1 to No. 10 battery cells as an example, the cloud platform calculates ten corresponding voltage calibration coefficients, namely K(1), K(2)...K(10) according to Vc(1), Vc(2)...Vc(10), and feeds back K(1), K(2)...K(10) to the BMS. The BMS calibrates the original voltage value Vc(1) of the No. 1 battery cell according to K(1). Correspondingly, the BMS calibrates the original voltage value Vc(10) of the No. 10 battery cell according to K(10).

[0052] Furthermore, when the voltage value of a certain battery cell is significantly greater than the overall voltage value of the battery pack, it indicates that the voltage of the battery cell is abnormal and it is not suitable to make corresponding control commands based on it. In addition, when the voltage value of a certain battery cell is within the overall voltage range of the battery pack, it indicates that the error of the currently collected voltage value meets the accuracy requirement, and if calibration is performed according to the new calibration coefficient, the error may increase. Therefore, the calibration method in this embodiment also includes:

[0053] When the cloud platform receives several raw voltage values ​​of the current acquisition cycle transmitted by the BMS, it also calculates the arithmetic mean of the several raw voltage values;

[0054] and calculating the absolute value of the difference between each original voltage value and the arithmetic mean value to obtain the voltage difference;

[0055] The tag data corresponding to each original voltage value is generated according to the relationship between the voltage difference and the preset threshold range.

[0056] Specifically, the method for generating labeled data includes:

[0057] When the voltage difference is greater than a preset threshold range, generating a first mark corresponding to the original voltage value;

[0058] When the voltage difference is within a preset threshold range, generating a second mark corresponding to the original voltage value;

[0059] When the voltage difference is smaller than the preset threshold range, a third mark corresponding to the original voltage value is generated.

[0060] Taking the preset threshold range as [a, b] as an example, the voltage difference is δU. When δU>b, a first mark is generated. When a≤δU≤b, a second mark is generated, and when δU<a, a third mark is generated.

[0061] The cloud platform packages the voltage calibration coefficient and marking data corresponding to each battery cell and feeds them back to the BMS.

[0062] Based on the first mark, the BMS discards the original voltage value and the voltage calibration coefficient corresponding to the first mark.

[0063] Based on the second mark, if the BMS records the voltage calibration coefficient of the battery cell corresponding to the second mark, that is, the BMS has recorded the voltage calibration coefficient corresponding to the battery cell in the previous acquisition cycle, then the updating of the existing voltage calibration coefficient based on the new voltage calibration coefficient is abandoned, and the original voltage value of the battery cell is calibrated by the existing voltage calibration coefficient in the previous cycle.

[0064] Based on the third mark, the BMS updates the voltage calibration coefficient of the battery cell corresponding to the third mark with a new voltage calibration coefficient, and calibrates the original voltage of the battery cell based on the updated latest voltage calibration coefficient.

[0065] Furthermore, the calculation model K(n) satisfies the following formula 1:

[0066] K(n)=1+m1*Vc(n)+m2*Vc(n)2(Formula 1);

[0067] Among them, m1 and m2 are temperature influence coefficients, m1 is a positive value, m2 is a negative value, and Vc(n) is the original voltage value.

[0068] In this regard, the coefficient query table in this embodiment is shown in Table 1 below, so that the corresponding temperature influence coefficients m1 and m2 can be queried according to the current ambient temperature.

[0069]

[0070] Table 1

[0071] On the other hand, the BMS calibrates the original voltage value using the following formula 2 to obtain a calibrated voltage value V(n) corresponding to each original voltage value.

[0072] V(n)=Vc(n) / K(n) (Formula 2).

[0073] In this embodiment, 0<K(n)<1.

[0074] In another preferred embodiment of the present invention, a BMS data acquisition calibration system is also disclosed, and the calibration system works based on the calibration method disclosed in the above embodiment.

[0075] In summary, the above technical embodiment of the present invention discloses a data calibration method, which receives the original voltage value and ambient temperature value collected by the BMS in real time through the cloud platform, and then queries the coefficient query table based on the ambient temperature value to obtain the temperature influence coefficient corresponding to the current ambient temperature, thereby substituting the temperature influence coefficient and the original voltage value into the calculation model to obtain the voltage calibration coefficient related to the current ambient temperature, and transmits the voltage calibration coefficient to the BMS, and the BMS calibrates the collected original voltage value according to the latest voltage calibration coefficient to ensure the accuracy of the calibrated battery voltage. Through the above data calibration method, the ambient temperature can be prevented from affecting the accuracy of the BMS collected voltage, and the accuracy and consistency of the BMS calibration of the collected voltage can be effectively ensured.

[0076] The present invention also discloses another BMS data acquisition calibration system, which includes one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the program includes instructions for executing the BMS data acquisition calibration method as described above. The processor can adopt a general central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs to implement the functions required to be executed by the modules in the BMS data acquisition calibration system of the embodiment of the present application, or to execute the BMS data acquisition calibration method of the method embodiment of the present application.

[0077] The present invention also discloses a computer-readable storage medium, which includes a computer program, and the computer program can be executed by a processor to complete the BMS data collection calibration method as described above. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a read-only memory (ROM), or a random access memory (RAM), or a magnetic medium, such as a floppy disk, a hard disk, a tape, a magnetic disk, or an optical medium, such as a digital versatile disc (DVD), or a semiconductor medium, such as a solid state disk (SSD), etc.

[0078] The embodiment of the present application also discloses a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes the above-mentioned BMS data collection calibration method.

[0079] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of rights of the present invention. Therefore, equivalent changes made according to the scope of the patent application of the present invention are still within the scope covered by the present invention.

Claims

1. A battery management system BMS data collection calibration method based on a cloud platform, characterized in that: include: A cloud platform is provided which is in communication with the BMS, wherein a voltage calibration coefficient calculation model K(n) is provided in the cloud platform, where K(n)=(m, Vc(n)), wherein m is a temperature influence coefficient corresponding to a corresponding temperature range, and Vc(n) is an original voltage value of a battery connected thereto and collected by the BMS; The cloud platform is also provided with a coefficient query table, which records the temperature influence coefficients corresponding to different temperature ranges; The BMS transmits the collected original voltage value and current ambient temperature value of the battery to the cloud platform; The cloud platform queries the coefficient query table based on the ambient temperature value transmitted by the BMS to obtain a temperature influence coefficient corresponding to the current ambient temperature; The cloud platform processes the temperature influence coefficient and the original voltage value based on the calculation model K(n) to obtain a voltage calibration coefficient, and feeds the voltage calibration coefficient back to the BMS to update the voltage calibration coefficient recorded in the BMS; The BMS calibrates the collected original voltage value according to the recorded voltage calibration coefficient; The battery connected to the BMS includes a plurality of battery cells. In each collection cycle, the BMS simultaneously collects the original voltage value of each of the battery cells. The cloud platform calculates a corresponding voltage calibration coefficient according to the original voltage value of each battery cell and the corresponding temperature influence coefficient, and feeds back the calculated voltage calibration coefficients to the BMS, and the BMS calibrates the original voltage value according to the voltage calibration coefficient corresponding to each battery cell; when the cloud platform receives the original voltage values ​​of the current acquisition cycle transmitted by the BMS, it also calculates the arithmetic mean of the original voltage values; and calculating the absolute value of the difference between each of the original voltage values ​​and the arithmetic mean value to obtain a voltage difference; generating marking data corresponding to each of the original voltage values ​​according to the relationship between the voltage difference and a preset threshold range; The method for generating the label data comprises: When the voltage difference is greater than the preset threshold range, generating a first mark corresponding to the original voltage value; When the voltage difference is within a preset threshold range, generating a second mark corresponding to the original voltage value; When the voltage difference is less than a preset threshold range, generating a third mark corresponding to the original voltage value; The cloud platform feeds back the voltage calibration coefficient and the marking data corresponding to each battery cell to the BMS; Based on the first mark, the BMS discards the original voltage value and the voltage calibration coefficient corresponding to the first mark; Based on the second mark, if the BMS records the voltage calibration coefficient of the battery cell corresponding to the second mark, then abandoning updating the existing voltage calibration coefficient based on the new voltage calibration coefficient; Based on the third mark, the BMS uses a new voltage calibration coefficient to update the voltage calibration coefficient of the battery cell corresponding to the third mark.

2. The cloud platform-based battery management system BMS data collection calibration method according to claim 1, characterized in that: The calculation model K(n) satisfies the following formula 1: K(n)=1+m1*Vc(n)+m2*Vc(n)2(Formula 1); Among them, m1 and m2 are temperature influence coefficients, m1 is a positive value, m2 is a negative value, and Vc(n) is the original voltage value.

3. The cloud platform-based battery management system BMS data collection calibration method according to claim 1, characterized in that: The BMS calibrates the original voltage value using the following formula 2 to obtain a calibrated voltage value V(n) corresponding to each original voltage value; V(n)=Vc(n) / K(n) (Formula 2), where Vc(n) is the original voltage value.

4. The cloud platform-based battery management system BMS data collection calibration method according to claim 1, characterized in that: The cloud platform is communicatively connected with the plurality of BMSs.

5. A battery management system BMS data acquisition calibration system based on a cloud platform, characterized in that: The calibration system works based on the calibration method according to any one of claims 1 to 4.

6. A battery management system BMS data collection and calibration system, characterized in that: include: one or more processors; Memory; And one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include instructions for executing the battery management system BMS data collection calibration method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: It includes a computer program, which can be executed by a processor to complete the battery management system BMS data collection calibration method as described in any one of claims 1 to 4.

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