Online identification method, system and medium for short-board single battery based on capacity characteristics

Abstract

The present application discloses a method, system and medium for online identification of short-board single-cell batteries based on capacity characteristics. The method includes the following steps: periodically collecting the terminal voltage, total current and vehicle speed data of all single-cells, and marking the maximum voltage and minimum voltage in the terminal voltage sequence of all single-cells collected each time; identifying the charge and discharge cycle conditions and calculating the extreme difference voltage of each single-cell; normalizing the extreme difference voltage to obtain the discreteness of the single-cell extreme difference voltage; counting the frequency of high discreteness of single-cells in a period of time, and marking the first several single-cell batteries with larger size in the time period as suspected short-board single-cell batteries. The present application can identify the short-board or suspected short-board single-cells in the power battery pack online, thereby identifying the single-cell batteries that are about to be retired, which is conducive to accurately replacing the single-cell batteries that are about to be retired, and can effectively improve the cruising range of electric vehicles, extend the retirement time of battery packs, and improve the utilization rate of battery packs.

Description

Technical Field

[0001] The present application relates to the technical field of power batteries, and in particular to an online identification method, system and medium for short-board single-cell batteries based on capacity characteristics. Background Art

[0002] For automotive power batteries, as the number of charge and discharge cycles increases, the ohmic internal resistance of the single cell will slowly increase, and the nominal capacity of the single cell will gradually decay. Accurately replacing the single cell that is about to be retired can effectively increase the range of electric vehicles, extend the retirement time of the battery pack, and improve the utilization rate of the battery pack, thereby truly achieving the goal of reducing costs and increasing efficiency for customers, which has important theoretical research significance and engineering application value.

[0003] However, how to online identify the single cells that are about to be retired in the in-service battery pack is a very challenging task. Summary of the invention

[0004] The present application provides a method, system and medium for online identification of short-board single-cell batteries based on capacity characteristics. The advantage is that it can online identify short-board or suspected short-board single-cell batteries in a power battery pack, thereby identifying single-cell batteries that are about to be retired, which is conducive to accurately replacing single-cell batteries that are about to be retired, and can effectively improve the cruising range of electric vehicles, extend the retirement time of battery packs, and improve the utilization rate of battery packs.

[0005] The above-mentioned object of the present application is achieved through the following technical solutions:

[0006] On the one hand, the present application provides a method for online identification of short-board single-cell batteries based on capacity characteristics, comprising the following steps:

[0007] Step 1: Periodically collect the terminal voltage, total current and vehicle speed data of all monomers, and mark the maximum voltage and minimum voltage in the terminal voltage sequence of all monomers collected each time;

[0008] Step 2: Identify the charge and discharge cycle conditions according to the data obtained in step 1 and calculate the extreme voltage difference of each cell;

[0009] Step 3: Normalize the extreme difference voltage to obtain the discreteness of the single extreme difference voltage;

[0010] Step 4: Count the frequency of high discreteness of cells within a period of time, and mark the first several cells with larger cells within the period as suspected short-board cells.

[0011] Furthermore, in step 1, the terminal voltage U of all monomers is periodically collected. o ={U1(k),U2(k),…,U i (k),…,UL (k)}, total current I(k), vehicle speed v(k), where L is the total number of monomers, k is the sampling time, i is the monomer number and i∈[1,L], and the terminal voltage sequence U of all monomers collected each time o In the figure, mark the maximum voltage U P (k) and minimum voltage U V (k) as an extreme feature.

[0012] Furthermore, the charge-discharge cycle condition identification in step 2 includes:

[0013] According to the changes in vehicle speed and total current, the vehicle can be distinguished as being in charging or non-charging condition:

[0014] If the total current jumps from 0 to a negative value, the vehicle is considered to be in a charging state; if the total current is always 0, the vehicle is considered to be in a non-charging state;

[0015] The period from the start of one charge to the start of the next charge is marked as a charge and discharge cycle.

[0016] Furthermore, the calculation of the extreme voltage difference of each monomer in step 2 includes:

[0017] Step 21: Traverse the maximum voltage sequence {U P (k)}, find the maximum voltage max{U P (k)} at sampling time k max , and the end moment k of the first charge and discharge cycle over ;

[0018] Step 22: Record the k in the first charge and discharge cycle respectively max , k over The maximum voltage of each monomer at the moment and the operation end voltage;

[0019] Step 23: According to step 21 and step 22, the maximum voltage and the end-of-operation voltage of each monomer in other charge-discharge cycles in one day are recorded, and then the maximum voltage set and the end-of-operation voltage set of each monomer in one day are obtained;

[0020] Step 24: Obtain the maximum value of the maximum voltage set and the minimum value of the end-of-run voltage set, respectively, and record them as Where d represents the dth natural day, and i is the monomer number;

[0021] Step 25: Calculate the voltage difference of each cell in one day

[0022] Furthermore, in step 3, the extreme difference voltage is normalized

[0023]

[0024] In the formula, δ i (d) is the normalized value of the extreme voltage difference of cell i on the dth day, which is used to represent the discreteness of the extreme voltage difference of cell i. max{·} and min{·} represent the maximum and minimum values ​​in the sequence, respectively.

[0025] Furthermore, in step 4, the frequency of high dispersion of monomer i in a period of time is counted

[0026]

[0027] In the formula, δ ref is the design parameter, F δ (i) is the number of times the dispersion of monomer i appears high within a period of time;

[0028] Mark the time period F δ (i) The first m larger cells are suspected to be short-board cells.

[0029] Furthermore, the method further comprises the steps of:

[0030] Draw a bar graph of the maximum voltage and end-of-operation voltage of each cell on a certain day, and Sort by size in descending order;

[0031] Plotting F over a period of time δ (i) Column distribution diagram of monomer i, and according to F δ (i) sorting by size in descending order to obtain a bar graph of the relative changes in the capacity characteristic parameters at the monomer level;

[0032] Count the total number of monomers with high dispersion on the dth day

[0033]

[0034] Where, N δ (d) is the total number of monomers with high dispersion on the dth day, and N δ (d) ≥1;

[0035] According to the statistical results of each natural day δ (d), fitting N δ (d) The relationship curve of natural day, and the situation diagram of vehicle-level capacity characteristic parameters

[0036] Furthermore, step 1 also includes the step of: balancing the terminal voltages or SOCs of all the single cells, so that the terminal voltages or SOCs of all the single cells remain consistent or the difference is within a set range.

[0037] On the other hand, the present application provides an online identification system for short-board single-cell batteries based on capacity characteristics, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is called and executed by the processor, the online identification method for short-board single-cell batteries based on capacity characteristics as described above is implemented.

[0038] On the other hand, the present application provides a computer-readable medium storing a computer program, and when the computer program is called and executed by a computer, the online identification method of a short-board single battery based on capacity characteristics as described above is implemented.

[0039] To sum up, the beneficial effects of the present application are as follows: the present application can online identify the short board or suspected short board cells in the power battery pack, thereby identifying the single cells that are about to be retired, which is conducive to the accurate replacement of the single cells that are about to be retired, and can effectively improve the cruising range of electric vehicles, extend the retirement time of battery packs, and improve the utilization rate of battery packs. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the principle of this application;

[0041] Figure 2 This is the operating condition diagram of an electric bus on a certain day;

[0042] Figure 3 It is a charge and discharge cycle operating diagram of an electric bus;

[0043] Figure 4 It is a bar distribution diagram of the maximum voltage, operation end voltage, and extreme difference voltage of each cell of an electric bus on a certain day. DETAILED DESCRIPTION

[0044] The specific implementation of the present application is described in detail below with reference to the accompanying drawings.

[0045] Embodiment: A specific implementation method of the present application is an online identification method of a short-board single cell based on capacity characteristics, which is used to online identify suspected "short-board" single cells in a power battery pack.

[0046] Before identification, the terminal voltage or SOC of all single cells is balanced to keep the terminal voltage or SOC of all single cells consistent or the difference is within a set range, so as to improve the accuracy and reliability of the identification of suspected "short board" single cells. Specifically, the method of balancing the terminal voltage or SOC of single cells can be implemented by using the method in invention patent 202410157345.1 or 202410157341.3, or by installing a system in invention patent 202410157345.1 or 202410157341.3 on the electric vehicle.

[0047] In this embodiment, if Figure 1 As shown, the method is implemented in the edge main control module and the power battery Internet of Things platform. The edge main control module is connected to the cloud platform for communication, and the identification object is the power battery pack in the electric bus. The edge main control module includes the following steps:

[0048] Step 1: Data Collection

[0049] Periodically collect the terminal voltage U of all monomers o ={U1(k),U2(k),…,U i (k),…,U L (k)}, total current I(k), vehicle speed v(k), where L is the total number of monomers, k is the sampling time, i is the monomer number and i∈[1,L].

[0050] If U o If the voltage at one end exceeds the threshold range, the re-collection logic is started to ensure that U o All are normal data. Note: When the vehicle is in the discharging condition, the total current is greater than 0; when the vehicle is in the charging condition, the total current is less than 0.

[0051] Extreme value feature extraction

[0052] The terminal voltage sequence U of all monomers collected each time o In the figure, mark the maximum voltage U P (k) Minimum voltage U V (k), and calculate the average voltage, denoted as U AVG (k).

[0053] Coding and Framing

[0054] Considering factors such as the message length limit, the byte overhead of the string, the cost of wireless network traffic, and data security, the terminal voltage is first encoded, that is, U offset is the offset, f is the resolution, and the terminal voltage Data such as total current I(k), vehicle speed v(k), and extreme characteristic voltage are encoded in hexadecimal and decimal combinations as needed, and then the encoded data are framed according to the MQTT transmission protocol standard.

[0055] Task monitoring processing

[0056] The task monitoring mechanism is enabled on the edge side, and the encoded data acquisition messages are periodically uploaded to the platform through the wireless network. When the edge master receives the message sent by the platform, the serial port data reception interrupt is triggered, and the data is first saved to the buffer, and then the parsing task of the sent message is executed when idle.

[0057] The platform side includes the following steps:

[0058] Based on the reported data acquisition message, draw the operating condition curve of a certain electric bus on a certain day, such as Figure 2 As shown. Among them, Figure 2 In the figure, the four curves from top to bottom are green, blue, orange and red. Figure 2 In the figure, the horizontal axis represents the sampling time, the vertical axis represents the extreme characteristic voltage and vehicle speed, the green curve represents the highest single cell voltage, the orange curve represents the lowest single cell voltage, the blue curve represents the average single cell voltage, and the red curve represents the vehicle speed.

[0059] Step 2: According to the data obtained in step 1, the charge and discharge cycle conditions are identified and the extreme voltage difference of each cell is calculated.

[0060] Identification of charge and discharge cycle conditions

[0061] The changes in vehicle speed and total current in the data acquisition message are used to distinguish whether the vehicle is in charging or non-charging condition.

[0062] (1) If the vehicle speed continues for a period of time is 0, and the total current continues for a period of time If it is 0, the vehicle is considered to have reached the terminal and turned off, i.e. stopped operating;

[0063] (2) During the vehicle shutdown process, if the total current jumps from 0 to a negative value, the vehicle is considered to be in a charging state; if the total current remains at 0, the vehicle is considered to be in a non-charging state;

[0064] (3) The period from the start of one charge to the start of the next charge is marked as a charge-discharge cycle;

[0065] (4) Repeat (1)(2)(3) to effectively identify the various charge and discharge cycle conditions during the day.

[0066] Curve fitting (one charge and discharge cycle condition)

[0067] Select and fit the curve of a certain charging and discharging cycle condition of an electric bus, such as Figure 3 shown.

[0068] Calculation of body voltage difference

[0069] (1) Traverse the maximum voltage sequence {U P (k)}, find the maximum voltage max{U P (k)} at sampling time k max , and the end moment of the first charge-discharge cycle (i.e. the start time of the next charge) k over ;

[0070] (2) Record the k in the first shift respectively max , k over The maximum voltage of each monomer at the moment and the operation end voltage;

[0071] (3) Similarly, the maximum voltage and the end-of-operation voltage of each monomer in other charge-discharge cycles within one day are recorded, and then the maximum voltage set and the end-of-operation voltage set of each monomer within one day are obtained;

[0072] (4) Then, the maximum value of the maximum voltage set and the minimum value of the end-of-operation voltage set are obtained respectively, which are recorded as Where d represents the dth natural day, and i is the monomer number;

[0073] (5) Finally, calculate the voltage difference of each cell within one day

[0074] Extreme value characteristic histogram drawing

[0075] Draw a bar graph of the maximum voltage and end-of-operation voltage of each cell of an electric bus on a certain day, and Sort by size in descending order, such as Figure 4 As shown. The horizontal axis represents the monomer serial number, and the vertical axis represents the extreme voltage of the monomer. The light blue (upper part of the bar graph) represents the maximum voltage of the monomer, the khaki (lower part of the bar graph) represents the end voltage of the monomer operation, and the remaining light blue part covered by the khaki represents the extreme voltage.

[0076] Step 3: Normalize the extreme difference voltage to obtain the discreteness of the single extreme difference voltage;

[0077] Normalize the extreme voltage difference

[0078]

[0079] In the formula, δ i (d) is the normalized value of the extreme voltage difference of cell i on the dth day, which is used to represent the discreteness of the extreme voltage difference of cell i. max{·} and min{·} represent the maximum and minimum values ​​in the sequence, respectively.

[0080] Step 4: Statistical feature extraction

[0081] (1) Count the frequency of high discreteness of monomer i within a period of time

[0082]

[0083] In the formula, δ ref is a design parameter close to 1, F δ(i) is the number of times the dispersion of monomer i appears high within a period of time, which is used to indirectly quantify the aging of monomer i.

[0084] (2) Count the total number of monomers with high dispersion on day d

[0085]

[0086] Where, N δ (d) is the total number of monomers with high dispersion on the dth day, and N δ (d)≥1. When the monomer consistency is high, δ i (d) The total number of monomers close to or equal to 1 is very small, i.e., N δ (d) Very small; otherwise, more.

[0087] Cell-level capacity characteristic parameter evaluation

[0088] Plotting F over a period of time δ (i) Column distribution diagram of monomer i, and according to F δ (i) are sorted in descending order to obtain a bar graph of the relative changes in the cell-level capacity characteristic parameters. As the time span continues to increase, the bar graph can intuitively reflect the relative aging degree of cell i, thereby providing a quantitative reference for the evaluation of cell-level capacity characteristic parameters.

[0089] In actual application, we found that the serial number of the "short board" battery is not always fixed, so we marked the F in this time period. δ (i) The first m larger cells, i.e., the m cells closest to the leftmost side in the columnar distribution diagram, are marked as suspected “short board” cells.

[0090] Vehicle-level capacity characteristic parameter evaluation

[0091] According to the statistical results of each natural day δ (d), fitting N δ (d) The relationship curve of natural days is used to obtain the trend diagram of vehicle-level capacity characteristic parameters. As time goes by, this curve diagram can intuitively reflect the evolution trend of the health status of the vehicle's power battery.

[0092] Another specific embodiment of the present application provides an online identification system for short-board single-cell batteries based on capacity characteristics, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is called and executed by the processor, the online identification method for short-board single-cell batteries based on capacity characteristics as described above is implemented.

[0093] Another specific embodiment of the present application provides a computer-readable medium, wherein the computer-readable medium stores a computer program, and when the computer program is called and executed by a computer, the online identification method of a short-board single cell based on capacity characteristics as described above is implemented.

[0094] The above is only a preferred implementation of the present application. It should be pointed out that a person skilled in the art can make several modifications and improvements without departing from the inventive concept of the present application, and these all fall within the scope of protection of the present application.

Claims

1. A method for online identification of short-board single-cell batteries based on capacity characteristics, characterized in that: The following steps are involved: Step 1: Periodically collect the terminal voltage, total current and vehicle speed data of all monomers, and mark the maximum voltage and minimum voltage in the terminal voltage sequence of all monomers collected each time; Step 2: Identify the charge and discharge cycle conditions according to the data obtained in step 1 and calculate the extreme voltage difference of each cell; Step 3: Normalize the extreme difference voltage to obtain the discreteness of the single extreme difference voltage; Step 4: Count the frequency of high discreteness of cells within a period of time, and mark the first several cells with larger discreteness within the period of time as suspected short-board cells; In step 1, the terminal voltage U of all monomers is collected periodically. o ={U1(k),U2(k),…,U i (k),…,U L (k)}, total current I(k), vehicle speed v(k), where L is the total number of monomers, k is the sampling time, i is the monomer number and i∈[1,L], and the terminal voltage sequence U of all monomers collected each time o In the figure, mark the maximum voltage U P (k) and minimum voltage U V (k) as an extreme feature; The charge and discharge cycle condition identification in step 2 includes: According to the changes in vehicle speed and total current, the vehicle can be distinguished as being in charging or non-charging condition: If the total current jumps from 0 to a negative value, the vehicle is considered to be in a charging state; if the total current is always 0, the vehicle is considered to be in a non-charging state; The time from the start of one charge to the start of the next charge is marked as a charge-discharge cycle; The calculation of the extreme voltage difference of each cell in step 2 includes: Step 21: Traverse the maximum voltage sequence {U P (k)}, find the maximum voltage max{U P (k)} at sampling time k max , and the end moment k of the first charge and discharge cycle over ; Step 22: Record the k in the first charge and discharge cycle respectively max , k over The maximum voltage of each monomer at the moment and the operation end voltage; Step 23: According to step 21 and step 22, the maximum voltage and the end-of-operation voltage of each monomer in other charge-discharge cycles in one day are recorded, and then the maximum voltage set and the end-of-operation voltage set of each monomer in one day are obtained; Step 24: Obtain the maximum value of the maximum voltage set and the minimum value of the end-of-operation voltage set, respectively, and record them as U i P (d) U i O (d), where d represents the dth natural day and i is the monomer number; Step 25: Calculate the voltage difference of each cell in one day Also includes the steps: Draw a bar graph of the maximum voltage and end-of-operation voltage of each cell on a certain day, and Sort by size in descending order; Plotting F over a period of time δ (i) Column distribution diagram of monomer i, F δ (i) is the number of times the dispersion of monomer i appears high within a period of time, and is calculated according to F δ (i) sorting by size in descending order to obtain a bar graph of the relative changes in the capacity characteristic parameters at the monomer level; Count the total number of monomers with high dispersion on the dth day Where, N δ (d) is the total number of monomers with high dispersion on the dth day, and N δ (d) ≥1; According to the statistical results of each natural day δ (d), fitting N δ (d) The relationship curve with respect to natural days is used to obtain the situation diagram of vehicle-level capacity characteristic parameters.

2. The method for online identification of short-board single-cell batteries based on capacity characteristics according to claim 1 is characterized in that: In step 3, the extreme difference voltage is normalized In the formula, δ i (d) is the normalized value of the extreme voltage difference of cell i on the dth day, which is used to represent the discreteness of the extreme voltage difference of cell i. max{·} and min{·} represent the maximum and minimum values ​​in the sequence, respectively.

3. The method for online identification of short-board single-cell batteries based on capacity characteristics according to claim 2 is characterized in that: In step 4, the frequency of high dispersion of monomer i in a period of time is counted In the formula, δ ref is the design parameter, F δ (i) is the number of times the dispersion of monomer i appears high within a period of time; Mark the time period F δ (i) The first m larger cells are suspected to be short-board cells.

4. The method for online identification of short-board single-cell batteries based on capacity characteristics according to claim 1, characterized in that: Step 1 also includes the step of: balancing the terminal voltages or SOCs of all the single cells, so that the terminal voltages or SOCs of all the single cells remain consistent or the difference is within a set range.

5. An online identification system for short-board single-cell batteries based on capacity characteristics, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program, and when the computer program is called and executed by the processor, the online identification method of a short-board single battery based on capacity characteristics as described in any one of claims 1 to 4 is implemented.

6. A computer-readable medium, characterized in that The computer-readable medium stores a computer program, and when the computer program is called and executed by a computer, the online identification method of a short-board single battery based on capacity characteristics as described in any one of claims 1 to 4 is implemented.

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