Visual health state evaluation method based on charging data of battery pack

Abstract

The invention discloses a visual evaluation method for a health state based on charging data of a battery pack, and belongs to the technical field of batteries, the battery pack is formed by connecting a plurality of battery units in series and is provided with a plurality of temperature measuring points, and the visual evaluation method for the health state comprises the following steps: obtaining the charging data of the battery pack in a complete charging process, the charging data at least comprises the charge state, the total voltage of the battery pack, the voltage of each series unit and the temperature of each temperature measuring point; dividing the charging process into a plurality of data sections at fixed charge state intervals; for each data section, carrying out statistics on a state-of-charge mid-value, a battery pack total voltage mean value, a voltage statistical value combination and a temperature statistical value combination; and based on the statistical data, drawing a visual graph. The method solves the problems that an existing battery health state evaluation method is difficult in parameter acquisition, single in evaluation dimension and neglects the consistency difference of battery units.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and in particular to a method for visually evaluating the health status of a battery pack based on charging data. Background Technology

[0002] Battery technology is currently widely used in various fields. To ensure the required energy, power, and voltage output, multiple battery cells are typically connected in series to form a battery pack. Each battery cell can be a single cell or multiple cells connected in parallel. Due to the complex physicochemical changes that occur within batteries during their service life, performance degradation in various aspects is inevitable. Therefore, various technologies have been developed to assess their health status.

[0003] Current known technical solutions typically compare battery parameters such as internal resistance and charge / discharge capacity during service life with their factory condition to assess their health. However, this approach has three main problems: First, obtaining these parameters often requires specialized testing or complex inferences, making it inconvenient and difficult to implement. Second, it lacks comprehensiveness, often considering only single factors such as internal resistance or capacity, and lacking a holistic evaluation. Third, for battery packs, it lacks consideration of the consistency between individual cells. In fact, if the performance of individual cells in a battery pack differs significantly from the overall performance, even if the overall internal resistance and charge / discharge capacity parameters of the battery pack are superior, the "weakest link effect" caused by poor consistency means that the overall health of the battery pack is not good.

[0004] In view of this, the present invention provides a new solution to the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a method for visually evaluating the health status of battery packs based on charging data. This method solves the problems of existing battery health status assessment methods, such as difficulty in obtaining parameters, limited assessment dimensions, and neglect of differences in battery cell consistency.

[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution.

[0007] A method for visually evaluating the health status of a battery pack based on charging data, wherein the battery pack consists of multiple battery cells connected in series and has multiple temperature measuring points, includes the following steps: Acquire charging data of the battery pack during a complete charging process. The charging data includes at least the state of charge, the total voltage of the battery pack, the voltage of each series unit, and the temperature of each temperature measuring point. The charging process is divided into multiple data segments at fixed states of charge intervals; For each of the said data segments, count the median of the charge state, the average of the total voltage of the battery pack, the combination of voltage statistical values, and the combination of temperature statistical values; Based on the statistical data, draw a visualization graph, where the charge state and voltage are used as coordinate axes, and a graphic element is generated for each of the said data segments.

[0008] Further preferably: the combination of voltage statistical values includes the voltage mean U μ and the voltage standard deviation U σ ; The combination of temperature statistical values includes the temperature mean T μ and the temperature deviation T d .

[0009] Further preferably: the graphic element is a rectangle, where: The central coordinates of the rectangle are determined by the median of the charge state and the average of the total voltage of the battery pack corresponding to the said data segment; The length of the rectangle is used to characterize the voltage consistency of each series unit; The width of the rectangle is used to characterize the temperature consistency inside the battery pack; The color of the rectangle is used to characterize the overall temperature level of the battery pack.

[0010] Further preferably: for the i-th data segment, denote the corresponding central coordinate point as (SOC i , U i ), where SOC i and U i are respectively the median of the charge state and the average of the total voltage of the battery pack of the i-th data segment; Taking its central coordinate point as the center, draw a rectangle with the long side parallel to the vertical coordinate, and its length and width are respectively aU σ and bW0, where a is a coefficient greater than 0, U σ is the voltage standard deviation of the corresponding data segment, b is a coefficient determined based on the temperature deviation T d , and W0 is the reference width; According to the temperature mean T μ of each of the said data segments, color the rectangle: If T2 ≤ T μ ≤ T3, then color it green; If T1 ≤ T μ < T2 or T3 < T μ ≤ T4, then color it yellow; If T μ < T1 or T μ > T4, then color it red; Among them, T1, T2, T3, and T4 are all preset temperature thresholds, and T1 < T2 < T3 < T4. Further preferably: The value of the coefficient b is determined by the temperature deviation T within its section d as follows: If T d < TI, then b = 1; If TI ≤ T d ≤ TII, then b = 2; If T d > TII, then b = 3, where TI and TII are both temperature deviation thresholds, and TI < TII.

[0011] Further preferably: The deviation threshold TI is between 0.5 °C and 3 °C, and the deviation threshold TII is between 5 °C and 10 °C.

[0012] Further preferably: The temperature threshold T1 is between -20 °C and 5 °C, T2 is between 10 °C and 20 °C, T3 is between 30 °C and 40 °C, and T4 is between 50 °C and 70 °C.

[0013] Further preferably: After the visualization graph is drawn, outlier detection is performed on the set composed of the average voltages of all series units in each data section. If there are elements greater than U μ + kU σ or less than U μ - kU σ , the number of such elements is marked above or below the rectangle corresponding to the data section in the drawing, where k is a coefficient between 2 and 4.

[0014] Further preferably: The coefficient a of each data section is equal, and its value is 100 - 5000; The reference width W0 is 0.02 - 0.2 times the charge state interval.

[0015] Further preferably: The number of data sections is 5 - 100.

[0016] In summary, the present invention has the following beneficial effects: (1) The health state assessment of the present invention is realized by using the data passively collected during the battery pack charging process, without requiring a specific charge and discharge operation on the battery pack. The collection process only requires the existing software and hardware facilities such as sensors of the battery management system, without the need to add specific instruments, and does not affect the user's use. Therefore, the overall cost is low, the parameter acquisition is simple, it is easy to implement, and it has a wide applicability; (2) This invention applies statistical principles and comprehensively considers the changes in voltage consistency, temperature level and temperature consistency of each battery cell in different data segments during the charging process, which is more dynamic, systematic and comprehensive; (3) The present invention evaluates multiple state-of-charge intervals throughout the charging process, which can reveal the problem of cell consistency deterioration in specific state-of-charge intervals; (4) The present invention processes and analyzes charging data and finally visualizes and plots it. Users see a complete graphical interface, which is convenient for observation and comparison with historical plots. The interface is user-friendly and easy to find problems.

[0017] Therefore, this invention solves the problems of existing battery health status assessment methods, such as difficulty in obtaining parameters, single assessment dimensions, and neglect of battery cell consistency differences. Attached Figure Description

[0018] Figure 1 This is a flowchart of a preferred embodiment of the health status visualization evaluation method of the present invention; Figure 2 This is a visualization graphic drawn in a preferred embodiment of the present invention. Detailed Implementation

[0019] The invention will now be further described with reference to the accompanying drawings.

[0020] This invention provides a method for visually evaluating the health status of a battery pack based on charging data. The battery pack consists of multiple battery cells connected in series, and each battery cell consists of one or more cells connected in parallel. The battery pack has multiple temperature measurement points pre-set on it.

[0021] like Figure 1 As shown, the visual assessment method for health status includes the following steps: S1. Obtain charging data of the battery pack during a complete charging process. The charging data includes at least the state of charge (SOC), the total voltage of the battery pack, the voltage of each series unit, and the temperature of each temperature measuring point.

[0022] Specifically, during the battery pack charging process, the state of charge, total battery pack voltage, voltage of each series unit, and temperature information of each temperature measuring point are measured and obtained at regular intervals.

[0023] S2. The charging process is divided into multiple data segments at fixed states of charge intervals.

[0024] Specifically, after charging is completed, based on the measurement data in step S1, the charging process is divided into multiple data segments at fixed state-of-charge intervals.

[0025] The number of data segments is preferably 5-100.

[0026] S3. For each data segment, calculate its median state of charge, average total battery pack voltage, combination of voltage statistics, and combination of temperature statistics. Among these, The median state of charge is the average of the state of charge at the beginning and end of the data segment. The average total battery pack voltage is the average of the total battery pack voltage measurements for all battery packs in the data segment. The voltage statistics combination includes the average voltage U μ and voltage standard deviation U σ For each series unit, calculate its average voltage within the data segment; then, for the set of obtained average voltage values ​​of each series unit, calculate its average value as the voltage mean U. μ The standard deviation is calculated as the voltage standard deviation U. σ ; The combination of temperature statistics includes the average temperature T. μ and temperature deviation T d For each temperature measuring point, calculate the average temperature within the data segment; then, for the set of average temperature values ​​from each temperature measuring point, calculate the average value as the temperature mean T. μ The difference between its maximum and minimum values ​​is the temperature deviation T. d .

[0027] S4. Based on the statistical data in step S3, draw a visualization graph, where the state of charge and voltage are used as coordinate axes, and a graphic element is generated for each data segment.

[0028] Preferably, the graphic element is a rectangle, wherein: The center coordinates of the rectangle are determined by the median state of charge and the mean total voltage of the battery pack for the corresponding data segment; The length of the rectangle is related to the voltage standard deviation of the data segment and is used to characterize the voltage consistency of each series unit. The width of the rectangle is related to the temperature deviation of the data segment and is used to characterize the temperature consistency inside the battery pack. The color of the rectangle is related to the average temperature of that data segment, and is used to characterize the overall temperature level of the battery pack.

[0029] Specifically, the statistical data is plotted and displayed using the following steps: S4.1. Establish a rectangular coordinate system with the state of charge (SOC) as the x-axis and voltage as the y-axis. For each data segment, use the median SOC and the average total voltage of the battery pack as the x-axis and y-axis, respectively, to form its center coordinate point. That is, for the i-th data segment, denote its corresponding center coordinate point as (SOC). i U i ), where SOCi and U i are the median charge state and the average total voltage of the battery pack for the i-th data segment, respectively; S4.2. For each data segment, draw a rectangle with its long side parallel to the vertical axis centered at its central coordinate point, where the length and width are aU σ and bW0, where a and b are both coefficients greater than 0, U σ is the voltage standard deviation of this data segment, and W0 is the reference width; S4.3. According to the average temperature T μ of each data segment, color the rectangle corresponding to this data segment: If T2 ≤ T μ ≤ T3, color it green; If T1 ≤ T μ < T2 or T3 < T μ ≤ T4, color it yellow; If T μ < T1 or T μ > T4, color it red; where T1, T2, T3, and T4 are the first temperature threshold, the second temperature threshold, the third temperature threshold, and the fourth temperature threshold respectively, all of which are preset values, and T1 < T2 < T3 < T4.

[0030] Preferably, the reference width W0 is 0.02 - 0.2 times the charge state interval in step 2.

[0031] Preferably, the coefficient a of each data segment is equal, and its value is 100 - 5000.

[0032] Preferably, the value of the coefficient b of each data segment is determined by the temperature deviation T d within its segment, specifically: If T d < TI, then b = 1; If TI ≤ T d ≤ TII, then b = 2; If T d > TII, then b = 3; where TI and TII are the first temperature deviation threshold and the second temperature deviation threshold respectively, and TI < TII.

[0033] Further preferably, the first temperature deviation threshold TI is between 0.5°C and 3°C, and the second temperature deviation threshold TII is between 5°C and 10°C. [[ID=,61]]

[0034] Preferably, the first temperature threshold T1 is between -20°C and 5°C, the second temperature threshold T2 is between 10°C and 20°C, the third temperature threshold T3 is between 30°C and 40°C, and the fourth temperature threshold T4 is between 50°C and 70°C.

[0035] S5. Perform outlier detection on the set of average voltages of all series units in each data segment, and mark the number of such elements in the rectangle corresponding to the data segment in the drawing.

[0036] Specifically: If there exists a value greater than U μ +kU σ For elements that are not specified, the number of such elements will be marked above the rectangle corresponding to that data segment in the drawing; If there exists a value less than U μ -kU σ For elements that are not specified, the number of such elements will be written below the rectangle corresponding to that data segment in the drawing. Where k is a coefficient between 2 and 4.

[0037] It should be noted that the measurement and storage of the above data are performed by calling the existing battery management system (BMS) of the battery pack. The state of charge is obtained by the ampere-hour integration method: the battery pack is equipped with an ampere-hour meter to measure the change in charge and discharge capacity, and the battery management system has stored and calibrated the initial state of charge in advance. The current state of charge is obtained by dividing the current capacity change value during the charge and discharge process by the total capacity and then adding it to the initial state of charge.

[0038] The battery management system transmits the state of charge, total battery pack voltage, voltage of each series unit, and temperature information of each temperature measuring point measured during the charging process to the back-end data center via the network. The back-end performs a visual evaluation of the health status and pushes the graph to the battery pack operator and / or user.

[0039] The evaluation method of this invention is particularly applicable to pure electric vehicles or certain battery energy storage applications, where battery packs are often charged in a controlled manner according to a pre-set system within a certain time period, while their discharge is often uncontrollable. For example, the discharge of a pure electric vehicle is entirely determined by the owner's usage needs and driving habits, while its charging relies on charging stations and is carried out according to the current or power preset by the manufacturer. In this way, the evaluation results of historical battery pack charging data are comparable.

[0040] If any of the following situations occur during an evaluation drawing, it should raise concern and the battery pack should be professionally inspected and maintained as soon as possible: (1) A red rectangular block or multiple yellow rectangular blocks appear; (2) Numbers appear above or below certain rectangular blocks; (3) Some rectangular blocks reach 3 times the base width or multiple rectangular blocks reach 2 times the base rectangle width.

[0041] If any of the above situations occur in multiple consecutive evaluation drawings, the use of the battery pack should be taken very seriously and stopped immediately.

[0042] The health status assessment in this invention is achieved by passively collecting data during the battery pack charging process. It does not require additional specific instruments and does not affect user operation. Therefore, the overall cost is low, it is simple and easy to implement, and it has wide applicability.

[0043] This invention applies statistical principles, comprehensively considering the changes in voltage consistency, temperature variation, and temperature consistency of each battery cell across different data segments during the charging process. This approach is relatively dynamic, systematic, and comprehensive. For each data segment: the voltage consistency of each battery cell is represented by a rectangle of length aU. σ This indicates the voltage standard deviation U of this data segment. σ The larger the value, the worse the voltage consistency, and the longer the rectangle. This is due to the voltage standard deviation U. σ Since the values ​​are often small, they are multiplied by a fixed coefficient 'a' for easy plotting and observation. Battery pack temperatures are represented by colored rectangles: green, yellow, and red represent the suitable, warning, and danger zones, respectively. Battery pack temperature consistency is represented by a rectangle with a width of 'bW0', based on the baseline width 'W0' and adjusted according to the temperature deviation 'T'. d The value of coefficient b is determined by the magnitude of the temperature deviation T. d A smaller value results in better temperature uniformity of the battery pack. Setting b=1 results in a narrower rectangle, while the temperature deviation T... d The larger the value of b, the worse the temperature uniformity of the battery pack, and the wider or narrower the rectangle becomes.

[0044] In addition, outliers in the average voltage of all series units in each data segment were specifically statistically analyzed: if there were outliers greater than U... μ +kU σ If an element is found to be an anomaly, indicating an abnormally high average voltage, its number will be written above the rectangle corresponding to that data segment; if an element is less than U... μ -kU σ If any element indicates an anomaly where the average voltage is unusually low, its number will be written below the rectangle corresponding to that data segment. Furthermore, the charging data is processed, analyzed, and ultimately visualized into a graphical interface. Users see a complete graphical interface that facilitates observation and comparison with historical plots; the interface is user-friendly and makes it easy to identify problems.

[0045] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0046] Example: A method for visually evaluating the health status of a battery pack based on charging data. The battery pack of a pure electric vehicle consists of 120 series-connected ternary lithium-ion batteries (i.e., the battery pack contains 120 ternary lithium-ion cells connected in series). These 120 batteries are arranged in 6 columns, with 20 batteries in each column. One temperature measuring point is located at the center of the top surface of the batteries in the middle of each column. The battery pack has a rated capacity of 100Ah and a rated total voltage of 432V. Its battery management system can collect the total voltage of the battery pack, the voltage of each cell, the charging and discharging current, and the temperature values ​​of each of the 6 different temperature measuring points on the battery pack in real time. The system then uses the ampere-hour integral method and the initial state of charge (SOC) to determine the state of charge (SOC) during the battery pack's operation.

[0047] The battery pack is connected to a charging station for charging according to the charging schedule preset by the car manufacturer. Data is collected every 10 seconds during the charging process. At the start and end of a certain charging process, the battery pack's state of charge is 5% and 95%, respectively. The state of charge is sampled at 10% intervals. The charging process data is divided into 9 equal segments, and the data in each segment is statistically analyzed.

[0048] In this embodiment, the first temperature deviation threshold TI = 2℃, the second temperature deviation threshold TII = 6℃, the first temperature threshold T1 = -5℃, the second temperature threshold T2 = 15℃, the third temperature threshold T3 = 38℃, the fourth temperature threshold T4 = 55℃, the coefficient a = 1000, the reference width W0 is 0.1 times the charged state interval, i.e., 1%, and the coefficient k = 2.8.

[0049] Taking the first segment as an example, within this data segment: the state of charge at the starting point and the ending point are 5% and 15% respectively, so the median state of charge is the average of the two, which is 10%; the average total voltage of the battery pack is 336.20V; in the combination of voltage statistics, the average voltage U μ =2.802V, voltage standard deviation U σ =0.023V; In the combination of temperature statistical values, the average temperature T μ =23.58℃, temperature deviation T d =1.73℃.

[0050] The statistical data is presented in graphs below: First, a rectangular coordinate system is established with the state of charge (SOC) as the x-axis and voltage as the y-axis. For each data segment, the median SOC and the average total battery pack voltage are used as the x-axis and y-axis, respectively, to form its center coordinate point. For example, the center point coordinates of the first data segment are (10%, 336.20V).

[0051] Secondly, for each data segment, draw a rectangle with its long side parallel to the vertical axis, centered at its center coordinate point, with length and width aU respectively. σ And bW0. For example, the length aU of the first data segment σ =1000×0.023V=23V; due to the temperature deviation T in this section d =1.73℃ is less than the first temperature deviation threshold TI, so its coefficient b=1 and width bW0=1×1%=1%.

[0052] Then, based on the average temperature Tμ of each data segment, the rectangle corresponding to that data segment is colored. For example, the average temperature Tμ of the first data segment... μ =23.58℃, which is between the second temperature threshold T2 and the third temperature threshold T3, so the corresponding rectangle is colored green.

[0053] Finally, outlier detection is performed on the set of average voltages of all series units in each data segment, and values ​​less than U are excluded. μ -kU σ or greater than U μ +kU σ Elements that are outliers are considered outliers, and their number is marked above or below the rectangle corresponding to the data segment. For example, for the first data segment, U μ -kU σ =2.802 - 2.8 × 0.023V = 2.738V, U μ +kU σ =2.867V. Upon investigation, it was found that the set of average voltages of all series-connected units in this data segment does not contain any voltage less than U. μ -kU σ or greater than U μ +kU σ The elements are all valid, therefore there are no outliers.

[0054] The data segments 1 to 9 were numbered from 1 to 9 respectively, and the relevant statistical results are shown in Table 1. From this, the information for rectangle drawing and labeling was obtained, as shown in Table 2. The rectangle was then drawn based on the data in Tables 1 and 2. Figure 2 middle.

[0055] Table 1 Statistical results for each data segment Table 2. Rectangular plotting and marking information for each data segment. For the third data segment, U μ +kU σ=3.236 + 2.8 × 0.031 = 3.323V. However, in the set of average voltages of all series-connected units in this section, there is one element with a value of 3.325V, which is greater than U. μ +kU σ Therefore, a relatively large outlier has appeared, and the number 1 needs to be marked above the corresponding rectangle in this section.

[0056] For the fourth data segment, U μ -kU σ =3.390 - 2.8 × 0.033V = 3.298V. However, in the set of average voltages of all series-connected units in this section, there are two elements with values ​​of 3.295V and 3.296V, which are less than U. μ -kU σ Therefore, two outlier values ​​appeared, and the number 2 needs to be marked below the rectangle corresponding to this segment.

[0057] By observing the drawings used in this evaluation, we found that: (1) Four yellow rectangular blocks appear; (2) Numbers appear above the third rectangle and below the fourth rectangle; (3) The width of the four rectangular blocks is twice that of the base rectangle.

[0058] Therefore, the results were sent to electric vehicle owners and maintenance organizations to raise their awareness and advise them to conduct professional inspections and maintenance of the battery packs as soon as possible.

[0059] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for visually evaluating the health status of a battery pack based on charging data, wherein the battery pack is composed of multiple battery cells connected in series and has multiple temperature measuring points, characterized in that: Includes the following steps: Acquire charging data of the battery pack during a complete charging process. The charging data includes at least the state of charge, the total voltage of the battery pack, the voltage of each series unit, and the temperature of each temperature measuring point. The charging process is divided into multiple data segments at fixed states of charge intervals; For each data segment, the median state of charge, the average total voltage of the battery pack, the combination of voltage statistics, and the combination of temperature statistics are statistically analyzed. Based on statistical data, a visualization is generated, with the state of charge and voltage as coordinate axes, and a graphic element is generated for each data segment.

2. The method for visually evaluating the health status based on battery pack charging data according to claim 1, characterized in that: The voltage statistics combination includes the average voltage U μ and voltage standard deviation U σ ; The temperature statistical value combination includes the average temperature T. μ and temperature deviation T d .

3. The method for visually evaluating the health status based on battery pack charging data according to claim 2, characterized in that: The graphic element is a rectangle, wherein: The center coordinates of the rectangle are determined by the median state of charge and the average total voltage of the battery pack corresponding to the data segment; The length of the rectangle is used to characterize the voltage consistency of each series unit; The width of the rectangle is used to characterize the temperature uniformity inside the battery pack; The color of the rectangle is used to characterize the overall temperature level of the battery pack.

4. The method for visually evaluating the health status based on battery pack charging data according to claim 3, characterized in that: For the i-th data segment, let its corresponding center coordinate point be (SOC). i U i ), where SOC i and U i These are the median state of charge and the average total voltage of the battery pack for the i-th data segment, respectively. Using its center coordinate point as the center, draw a rectangle with its long side parallel to the vertical axis, and its length and width are aU and aU respectively. σ and bW0, where a is a coefficient greater than 0, U σ Let be the voltage standard deviation for the corresponding data segment, and b be the voltage standard deviation based on temperature deviation T. d The coefficients are defined, with W0 being the baseline width; Based on the average temperature T of each data segment μ Color the rectangle: If T2≤T μ If the value is less than or equal to T3, then the coloring should be green. If T1 ≤ T μ < T2 or T3 < T μ ≤ T4, then the coloring is yellow; If T μ <T1 or T μ > T4, then the coloring is red; Where T1, T2, T3, and T4 are all preset temperature thresholds, and T1 <T2<T3<T4。 5. The method for visually evaluating the health status based on battery pack charging data according to claim 4, characterized in that: The value of the coefficient b is determined by the temperature deviation T within its section. d Decide: If T d <TI, then b = 1; If TI≤T d If ≤TII, then b=2; If T d If >TII, then b=3. Where TI and TII are both temperature deviation thresholds, and TI <TII。 6. The method for visually evaluating the health status based on battery pack charging data according to claim 5, characterized in that: The deviation threshold TI is between 0.5°C and 3°C, and the deviation threshold TII is between 5°C and 10°C.

7. The method for visually evaluating the health status based on battery pack charging data according to claim 4, characterized in that: The temperature thresholds T1 are between -20℃ and 5℃, T2 are between 10℃ and 20℃, T3 are between 30℃ and 40℃, and T4 are between 50℃ and 70℃.

8. The method for visually evaluating the health status based on battery pack charging data according to claim 4, characterized in that: After the visualization is completed, outlier detection is performed on the set of average voltages of all series-connected units in each data segment. If an outlier is found, it is detected that the voltage value is greater than U. μ +kU σ or less than U μ -kU σ If the element is a certain type, then the number of such elements is marked above or below the rectangle corresponding to that data segment in the drawing, where k is a coefficient between 2 and 4.

9. The method for visually evaluating the health status based on battery pack charging data according to claim 4, characterized in that: The coefficient 'a' is the same in each data segment, and its value ranges from 100 to 5000. The reference width W0 is 0.02-0.2 times the charged state interval.

10. The method for visually evaluating the health status based on battery pack charging data according to claim 1, characterized in that: The number of data segments is 5-100.

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