A method and device for detecting the state of health of a power battery

Abstract

The application provides a kind of detection method and device of power battery health state, it is related to power battery technical field, the detection method includes: obtaining the battery state parameter of power battery;Based on the battery state parameter, obtain the parameter characteristic related to the health index of power battery, and calculate the contribution degree of the parameter characteristic and the historical statistical value of the contribution degree;According to the contribution degree, the first view and the second view of power battery health state are constructed;According to preset first generation rule, the parameter characteristic and the contribution degree are filled into the first view to generate life attenuation abnormal view;And according to preset second generation rule, the parameter characteristic, the contribution degree and the historical statistical value are filled into the second view to generate single charge-discharge cycle visual analysis view.The application can greatly improve the efficiency of analysis and detection of power battery.

Description

Technical Field

[0001] This invention relates to the field of power battery technology, and in particular to a method and apparatus for detecting the health status of a power battery. Background Technology

[0002] Battery state of health (SOH) is a comprehensive assessment of current battery performance. Using SOH indicators and battery anomaly alarms, battery health can be monitored, allowing for maintenance or process improvements. However, current battery state analysis and visual analysis systems lack effective visualization of electric vehicle battery health data. The final analysis of battery anomalies must be performed by battery experts or engineers. Traditional data-driven detection and analysis methods require combining multiple data points and visual analyses, resulting in a massive amount of diverse data, posing a significant challenge for manual analysis. Observers cannot intuitively and quickly grasp the relationships, comparisons, and trends between data points. The lack of a means to detect and analyze anomalies significantly reduces the efficiency of manual analysis. Summary of the Invention

[0003] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method and apparatus for detecting the health status of power batteries, in order to solve the problems of large amounts of data on the health status of electric vehicle power batteries, lack of interaction, and low efficiency of data detection and analysis in the prior art.

[0004] To achieve the above and other related objectives, the present invention provides a method for detecting the health status of a power battery, comprising:

[0005] Obtain the battery state parameters of the power battery;

[0006] Based on the battery state parameters, obtain parameter features related to the health indicators of the power battery, and calculate the contribution of the parameter features and the historical statistical values ​​of the contribution.

[0007] Based on the contribution level, construct a first type view and a second type view of the power battery health status;

[0008] According to a preset first generation rule, the parameter features and the contribution degree are filled into the first type of view to generate a lifetime decay anomaly view; and

[0009] According to the preset second generation rule, the parameter features, the contribution degree, and the historical statistical values ​​are filled into the second type of view to generate a single charge-discharge cycle visual analysis view.

[0010] In one embodiment of the present invention, the step of filling the parameter features and the contribution degree into the first type of view according to a preset first generation rule to generate a lifetime decay anomaly view includes:

[0011] Establish a data mapping relationship between the health status data of the power battery and the first type of view, wherein the health status data represents data reflecting the changing trend of the battery's health status; and

[0012] Based on the data mapping relationship and the first generation rule, the features and the contribution are filled into the first type of view to generate a lifespan decay anomaly view.

[0013] In one embodiment of the present invention, the generation step of the first generation rule includes:

[0014] Construct a feature block diagram for each charge-discharge cycle, where the width of each block represents one charge-discharge cycle, and the height of the block represents the absolute value of the contribution.

[0015] Within a selected time period of charge-discharge cycles, the parameter features that make an average positive contribution are marked with a first color;

[0016] When a parameter feature marked with the first color makes a negative contribution in the current charge-discharge cycle, the square corresponding to that parameter feature is placed at the position furthest from the return-to-health state line; and

[0017] The higher the height of the block, the closer it is to the return to a healthy state line; the lower the height of the block, the farther it is from the return to a healthy state line.

[0018] In one embodiment of the present invention, after the step of placing the block corresponding to the parameter feature, which is marked with the first color, at the position furthest from the return-to-health state line when the parameter feature makes a negative contribution in the current charge-discharge cycle, the method includes:

[0019] Within a selected time period of charge-discharge cycles, the parameter features that make an average negative contribution are marked with a second color;

[0020] When the parameter feature marked with the second color makes a positive contribution in the current charge-discharge cycle, the block corresponding to the parameter feature is placed at the position furthest from the return to healthy state line.

[0021] In one embodiment of the present invention, the first type of view displays changes in battery health status, charge-discharge cycles in which abnormal alarms occur, changes in battery health indicators, and / or the contribution of parameter characteristics in each cycle to changes in battery health status.

[0022] In one embodiment of the present invention, the step of filling the parameter features, the contribution degree, and the historical statistical values ​​into the second type of view according to a preset second generation rule to generate a single charge-discharge cycle visual analysis view includes:

[0023] Establish a data mapping relationship between the data from a single charge-discharge cycle and the second type of view; and

[0024] Based on the data mapping relationship and the second generation rule, the parameter features, the contribution degree, and the historical statistical values ​​are filled into the second type of view to generate a single charge-discharge cycle visual analysis view.

[0025] In one embodiment of the present invention, the generation step of the second generation rule includes:

[0026] Construct a multi-ring sector diagram for each charge-discharge cycle, where each sector represents one of the aforementioned parameter characteristics affecting changes in health status, and the radius represents the absolute value of the contribution of that parameter characteristic in the current charge-discharge cycle; and

[0027] The shortest distance from the inner side of each annular sector to the edge of the midline annular sector represents the average contribution of the parameter characteristics over a specified number of charge-discharge cycles.

[0028] In one embodiment of the present invention, the state parameters include the total voltage of the power battery, the total current, the standard state of charge, the individual cell voltage, the cell number with the highest voltage, the cell voltage with the highest voltage, the cell number with the lowest voltage, the cell voltage with the lowest voltage, the individual cell temperature, the cell number with the highest temperature, the cell temperature with the highest temperature, the cell number with the lowest temperature, the cell temperature with the lowest temperature, the insulation resistance, the battery alarm type, the driving speed and / or the mileage traveled.

[0029] In one embodiment of the present invention, within a preset time period, the features include cumulative mileage, maximum maximum temperature, average maximum temperature, minimum minimum temperature, average minimum temperature, average driving time per trip, average state of charge per charge, and / or average driving speed.

[0030] The present invention also provides a device for detecting the health status of a power battery, comprising:

[0031] The data acquisition module is used to acquire the battery state parameters of the power battery;

[0032] The parameter feature extraction module is used to obtain parameter features related to the health indicators of the power battery based on the battery state parameters, and to calculate the contribution of the parameter features and the historical statistical values ​​of the contribution.

[0033] A view construction module is used to construct a first type of view and a second type of view of the health status of the power battery based on the contribution level.

[0034] The first type of view module is used to fill the parameter features and the contribution degree into the first type of view according to a preset first generation rule, so as to generate a lifetime decay anomaly view; and

[0035] The second type of view module is used to fill the parameter features, the contribution degree and the historical statistical values ​​into the second type of view according to the preset second generation rule, so as to generate a single charge-discharge cycle visual analysis view.

[0036] As described above, the method and apparatus for detecting the health status of a power battery according to the present invention have the following beneficial effects: the present invention can provide the trend of battery health status changes, comparison with historical statistical values, suggestions for abnormal characteristics, and display of charge and discharge data, which greatly improves the efficiency of analyzing and detecting power batteries. Attached Figure Description

[0037] Figure 1 The diagram shown is a flowchart illustrating a method for detecting the health status of a power battery provided by this invention.

[0038] Figure 2 The diagram shows a specific implementation of step S40.

[0039] Figure 3 The diagram shown is a schematic representation of a first type of view in an embodiment of the present invention.

[0040] Figure 4 This is a schematic diagram showing the increase in health status of a first type of view in an embodiment of the present invention.

[0041] Figure 5 The diagram shown illustrates a decline in health status in a first-type view according to an embodiment of the present invention.

[0042] Figure 6 The diagram shown is a flowchart illustrating a specific implementation of step S50.

[0043] Figure 7 The diagram shown is a schematic representation of a second type of view in one embodiment of the present invention.

[0044] Figure 8 The diagram shown is a schematic representation of a multi-ring sector diagram in a second type of view according to an embodiment of the present invention.

[0045] Figure 9 The diagram shown is a structural block diagram of a power battery health status detection device according to an embodiment of the present invention.

[0046] Figure 10The diagram shown is a structural schematic of a computer device according to an embodiment of the present invention. Detailed Implementation

[0047] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features can be combined with each other. It should also be understood that the terminology used in the embodiments of the present invention is for describing specific implementation schemes and not for limiting the scope of protection of the present invention. Test methods in the following embodiments that do not specify specific conditions are generally performed under conventional conditions or according to the conditions recommended by the respective manufacturers.

[0048] Please see Figures 1-10 It should be understood that the structures, proportions, sizes, etc., illustrated in the accompanying drawings of this specification are merely for illustrative purposes to aid those skilled in the art and are not intended to limit the scope of the invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, without affecting the effectiveness and purpose of the invention, should still fall within the scope of the technical content disclosed in this invention. Furthermore, the terms such as "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity and are not intended to limit the scope of the invention. Changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention's implementation.

[0049] This invention provides a method and apparatus for detecting the health status of a power battery, applicable to the field of power battery technology. This invention can detect the health status, battery anomalies, and abnormal factors of power batteries in electric vehicles. This invention can generate a set of interactive, rule-based visual analysis views that integrate power battery health status data and anomaly factor analysis, and integrate a large amount of information within the views, improving detection and analysis efficiency.

[0050] Please see Figure 1 As shown, in one embodiment of the present invention, Figure 1 The diagram shown is a flowchart of a method for detecting the health status of a power battery provided by the present invention, which may include the following steps:

[0051] Step S10: Obtain the battery state parameters of the power battery;

[0052] Step S20: Based on the battery state parameters, obtain the parameter features related to the health indicators of the power battery, and calculate the contribution of the parameter features and the historical statistical values ​​of the contribution.

[0053] Step S30: Based on the contribution level, construct a first-class view and a second-class view of the power battery health status;

[0054] Step S40: According to the preset first generation rule, fill the parameter features and contribution into the first type of view to generate a lifetime decay anomaly view; and

[0055] Step S50: According to the preset second generation rule, fill the parameter features, contribution degree and historical statistical values ​​into the second type of view to generate a single charge-discharge cycle visual analysis view.

[0056] In one embodiment of the present invention, when step S10 is executed, the battery state parameters of the power battery are obtained. Specifically, the battery management system data of the power battery in the electric vehicle can be obtained first, and battery state parameters and health indicators such as battery SOH (state of health) can be extracted from the battery management system data. The battery state parameters can be measured in charge-discharge cycles. In this embodiment, the battery management system data may include: power battery number, battery model, number of batteries in the battery pack; charging record time, charging mode, total voltage, total current, standard SOC (state of charge), individual battery voltage, maximum voltage individual battery number, maximum individual battery voltage, minimum voltage individual battery number, minimum individual battery voltage, individual battery temperature, highest temperature individual battery number, highest individual battery temperature, lowest temperature individual battery number, lowest individual battery temperature, insulation resistance, battery alarm type; new energy vehicle model, number, driving status, driving speed and / or mileage, etc.

[0057] In one embodiment of the present invention, battery status parameters are extracted from battery management system data. The battery status parameters may include: total battery voltage, total current, standard SOC (state of charge), individual cell voltage, maximum voltage individual cell number, maximum individual cell voltage, minimum voltage individual cell number, minimum individual cell voltage, individual cell temperature, highest temperature individual cell number, highest individual cell temperature, lowest temperature individual cell number, lowest individual cell temperature, insulation resistance, battery alarm type, driving speed and / or mileage traveled.

[0058] In one embodiment of the present invention, when step S20 is executed, that is, based on the battery state parameters, parameter features related to the health indicators of the power battery are obtained, and the contribution of the parameter features and the historical statistical values ​​of the contribution are calculated. Specifically, the parameter features refer to the features related to the health indicators of the power battery. Within a preset time period, the parameter features may include: cumulative mileage, maximum maximum temperature, average maximum temperature, minimum minimum temperature, average minimum temperature, average driving time per charge, average SOC (state of charge) per charge, and / or average driving speed. In this embodiment, the preset time period can be set to the most recent 7 days. The parameter features can be obtained by first extracting charging records from the battery management system data and dividing them by the number of charging cycles; then by... The charge-discharge cycle is divided in a certain way, that is, the accumulation of SOC to 100 is recorded as one charge-discharge cycle; finally, based on all the charging records in each charge-discharge cycle, the parameter characteristics of each charge-discharge cycle are calculated.

[0059] In one embodiment of the present invention, the contribution of each parameter characteristic is calculated, and historical statistical values ​​of the contribution are calculated based on historical data. First, the maximum charge capacity for each charge-discharge cycle is calculated using the ampere-hour integration method; then, the maximum charge capacity is divided by the initial maximum charge capacity to obtain the battery's SOH (state of health).

[0060] Furthermore, in one embodiment of the present invention, a machine learning model can be used to regress the input parameter features into SOH (state of health), and the contribution of each parameter feature can be obtained by analyzing the obtained model.

[0061] In one embodiment of the present invention, when step S30 is executed, a first type of view and a second type of view of the power battery health status are constructed based on the contribution degree. Specifically, based on the parameter type of the contribution degree calculated in step S20, a first type of view and a second type of view integrating the characteristic contribution degree of the power battery health status can be constructed. The first type of view can display changes in battery health status, charge / discharge cycles with abnormalities and alarms, changes in battery health indicators, and the contribution of parameter characteristics to changes in battery health status in each cycle. The second type of view can display the average voltage, maximum voltage, average temperature, maximum temperature, average current, and maximum current of all batteries in the current cycle, charging time, fast charging time, and slow charging time, average cruising speed, power consumption per kilometer, and / or total discharge time during discharge. The second type of view can also be used to reflect the contribution of different parameter characteristics to changes in battery health status in the current cycle, and to compare with historical patterns.

[0062] In one embodiment of the present invention, the detection of the health status of the power battery may include data display and anomaly judgment. The overall data display requires analysis and display based on the overall trend of the power battery's health status and the contribution of local features; while the anomaly judgment is based on the patterns in the data and the degree to which specific data deviates from these patterns. To obtain and display these two types of information, firstly, the data can be sorted by charge-discharge cycle time; then, the parameter feature contribution of each charge-discharge cycle is standardized, that is, positive and negative contributions are marked and separated, the average contribution of each parameter feature up to this charge-discharge cycle is calculated, and its positive or negative value is marked as a historical statistical value for comparison with the current cycle; finally, among the parameter features of the current charge-discharge cycle, the dominant contribution, and its maximum and minimum values ​​are determined to determine the standard parameters for view generation.

[0063] Please see Figure 2 As shown, in one embodiment of the present invention, when step S40 is executed, parameter features and contribution levels are filled into a first type of view according to a preset first generation rule to generate a lifetime decay anomaly view. Specifically, step S40 may include steps S41 to S42, which are described in detail below:

[0064] Step S41: Establish a data mapping relationship between the health status data of the power battery and the first type of view; the health status data represents data reflecting the changing trend of the battery's health status; and

[0065] Step S42: Based on the data mapping relationship and the first generation rule, fill the features and contribution into the first type of view to generate a lifespan decay anomaly view.

[0066] In one embodiment of the present invention, when step S41 is executed, a data mapping relationship is established between the health status data of the power battery and the first type of view. The health status data represents data reflecting the changing trend of the battery's health status. Specifically, the health status data represents data reflecting the changing trend of the battery's health status, and the data mapping relationship between the health status data and the first type of view may include: the mapping relationship between the alarm records of each charge-discharge cycle of the power battery and the alarm layer in the first type of view; the mapping relationship between the health status change sequence of the power battery and the step curve in the first type of view; and the mapping relationship between the parameter feature contribution of each charge-discharge cycle of the power battery and the parameter feature block in the first type of view.

[0067] In one embodiment of the present invention, the first type of view can integrate the contribution of parameter features of the power battery's state of health. First, the SOH (state of health) value of each charge-discharge cycle and the current charge-discharge cycle is visually analyzed and transformed into screen pixel xy coordinates; then, the parameter feature contribution values, which are visually analyzed as rectangles, are drawn on both the top and bottom sides of the image node of each charge-discharge cycle.

[0068] Please see Figure 3 As shown, in one embodiment of the present invention, the first type of view may include an alarm data display view, i.e. Figure 3 Area a. The first type of view displays the mapping relationship between alarm records of each charge / discharge cycle of the power battery and the alarm layers in the view. Within the alarm data display view, the horizontal axis represents the number of charge / discharge cycles, and the vertical axis represents discrete alarm types; each layer represents one type of alarm. Following a top-down order, the first layer represents the overall health status of the power battery, which can be represented by different colors to indicate healthy, critical, and scrapped states. The second to last layers can represent voltage anomalies, impedance anomalies, and temperature anomalies, respectively. Abnormal charge / discharge cycles are indicated by a different color for each layer. This invention supports the custom implementation of anomaly thresholds and displays related issues according to the set thresholds.

[0069] Please see Figure 3 As shown, in one embodiment of the present invention, the main body of the first type of view is a stepped curve diagram, i.e. Figure 3 Region b. The first type of view displays the sequence of SOH (state of health) changes in the power battery and the mapping relationship between it and the stepped curve in the view. The X-axis represents the number of cycles, the Y-axis represents the remaining SOH percentage or abnormal indicators of the monitored battery, and the stepped curve represents the SOH change, with each step corresponding to one charge-discharge cycle. The first type of view includes an abnormal indicator curve, which can mark abnormal cycles exceeding a user-defined abnormality threshold.

[0070] Please see Figure 4 , Figure 5 As shown, in one embodiment of the present invention, it is stipulated that when the SOH (state of health) value increases compared to the previous data, the parameter feature that drives the current regression SOH towards a large change in value will make a positive contribution, and the parameter feature that drives the current regression SOH towards a small change in value will make a negative contribution; when the SOH data decreases compared to the previous data, the parameter feature that drives the current regression SOH towards a small change in value will make a positive contribution, and the parameter feature that drives the current regression SOH towards a large change in value will make a negative contribution.

[0071] Please see Figure 6 As shown, in one embodiment of the present invention, on the regression SOH (state of health) curve, the present invention provides a discretized module corresponding to the mapping relationship between the parameter characteristics of each charge-discharge cycle of the power battery and the parameter feature blocks in the first type of view, so as to represent the contribution of each parameter feature and its absolute value. The generation step of the first generation rule may include:

[0072] Construct a feature block diagram for each charge-discharge cycle, where the width of each block represents one charge-discharge cycle, the height of each block represents the absolute value of the contribution, and the scale of the contribution remains constant across different charge-discharge cycles.

[0073] Within a selected time period of charge-discharge cycles, the parameter features that make an average positive contribution are marked with a first color, and the parameter features that make an average negative contribution are marked with a second color, in order to represent the historical statistical patterns of the parameter features.

[0074] When a parameter feature marked with the first color makes a negative contribution in the current charge-discharge cycle, the block corresponding to that parameter feature is placed at the position furthest from the return-to-health state line; when a parameter feature marked with the second color makes a positive contribution in the current charge-discharge cycle, the block corresponding to that parameter feature is placed at the position furthest from the return-to-health state line; and

[0075] The higher the height of the square, the closer it is to the regression line of SOH (state of health), and the lower the height of the square, the further it is from the regression line of SOH. In other words, the greater the contribution, the closer it is to the regression line of SOH, and the smaller the contribution, the further it is from the regression line of SOH.

[0076] In one embodiment of the present invention, when a square is marked with a first color, that is, a parameter feature that makes a positive contribution according to historical statistical patterns, but makes a negative contribution in the current cycle, then the square is placed at the position farthest from the regression line; when a square is marked with a second color, that is, a parameter feature that makes a negative contribution according to historical statistical patterns, but makes a positive contribution in the current cycle, then the square is placed at the position farthest from the regression line.

[0077] Please see Figures 3 to 5As shown, in one embodiment of the present invention, when step S42 is executed, features and contribution levels are filled into the first type of view according to the data mapping relationship and the first generation rule to generate a lifetime decay anomaly view. Specifically, when the charge-discharge cycle represented by the marked square bar shows a regression increase compared to the charge-discharge cycle on the left, the parameter features below the regression line visually represent that they "push" the regression line upward, that is, parameter features making a positive contribution; the square bars on the regression line represent that they "push" the regression line downward, that is, parameter features making a negative contribution. In the square bars making a positive contribution, they can be divided into a first color area, marked as a, and a second color area, marked as b, both of which are parameter features arranged from largest to smallest from top to bottom. In addition, if the first color square makes a positive contribution in the current charge-discharge cycle, the second color square needs to be placed below the first color square. Similarly, in the negative contribution area above, the first color square making a positive contribution should be placed at the position farthest from the regression line.

[0078] Please see Figure 3 As shown, in one embodiment of the present invention, the first type of view may include an abnormal loop area, i.e. Figure 3 In the middle (c) area, in the first view, when the parameter feature box of one of the loops is selected, the abnormal loop area can display a thumbnail of the abnormal information and provide interactivity. Furthermore, clicking on the current loop will generate a second view, displaying detailed information about the contribution of this loop's parameter features.

[0079] Please see Figure 6 As shown, in one embodiment of the present invention, when step S50 is executed, parameter features, contribution levels, and historical statistical values ​​are filled into the second type of view according to a preset second generation rule to generate a single charge-discharge cycle visual analysis view. Specifically, step S50 may include steps S51 to S52, which are described in detail below:

[0080] Step S51: Establish a data mapping relationship between the data from a single charge-discharge cycle and the second type of view; and

[0081] Step S52: Based on the data mapping relationship and the second generation rule, fill the parameter characteristics, contribution degree and historical statistical values ​​into the second type of view to generate a visual analysis view of a single charge-discharge cycle.

[0082] In one embodiment of the present invention, when step S51 is executed, a data mapping relationship between the data of a single charge-discharge cycle and the second type of view is established. Specifically, the data mapping relationship between the data of a single charge-discharge cycle and the second type of view may include: the mapping between the real-time state parameters of the power battery in the current charge-discharge cycle and their corresponding display bars; the contribution of the parameter characteristics of the current charge-discharge cycle to the change of SOH (state of health) and its comparison with historical statistical patterns, and the mapping with the multi-ring sector diagram; and the mapping between the charging or discharging SOC (state of charge) change process in the current charge-discharge cycle and the SOC display view.

[0083] In one embodiment of the present invention, the step of generating the second generation rule may include:

[0084] Construct a multi-ring sector diagram for each charge-discharge cycle, where each sector represents a parameter characteristic affecting changes in health status, and the radius represents the absolute value of the parameter characteristic's contribution in the current charge-discharge cycle. A white line can be placed in the middle of the sector to indicate that the current parameter characteristic makes a negative contribution; and

[0085] The shortest distance from the inner side of each annular sector to the center annular edge represents the average contribution of the parameter characteristics over a specified number of charge-discharge cycles. A white line can be set in the annular sector to indicate that the average contribution of the parameter characteristics is negative.

[0086] In one embodiment of the present invention, if the absolute value of the average contribution of a certain parameter feature in the current loop is too large, exceeding the radius of the largest parameter feature, the average value of the parameter feature will always be fixed in the outer retention layer, regardless of how much the maximum leaf radius represents the value that the average value exceeds, because the occurrence of this situation indicates that a parameter feature anomaly may have occurred. Similarly, if the average value is too small, it will be placed in the inner retention layer, no matter how small it is.

[0087] In one embodiment of the present invention, when step S52 is executed, parameter features, contribution rates, and historical statistical values ​​are filled into the second type of view according to the data mapping relationship and the second generation rule to generate a visual analysis view of a single charge-discharge cycle. Specifically, after obtaining the data distribution of parameter feature values ​​for each charge-discharge cycle and the current charge-discharge cycle, visual analysis encoding is performed on the entirety of several consecutive charge-discharge cycles selected by the user to generate pixel images; visual analysis encoding is performed based on the values ​​of the original data in different charge-discharge cycles and compared with the threshold manually set by the user.

[0088] Please see Figure 7As shown, in one embodiment of the present invention, the contribution of each factor within the cycle to SOH (state of health) is displayed according to a preset second generation rule and filled into a second type of view. The main body a of the second type of view is a multi-ring sector diagram, and the view may include various types of data.

[0089] Please see Figure 8 As shown, in one embodiment of the present invention, in the multi-ring sector diagram a, the left semicircular ring c represents the remaining SOC (state of charge) of the current charge-discharge cycle, and the right semicircular ring d represents the SOH (state of health) of the current cycle. Depending on the number of percentage-type data to be displayed within the cycle, multiple outer rings can be stacked. Each semicircular ring can be represented by a different color; in this embodiment, the left semicircular ring c can be brown, and the right semicircular ring d can be green.

[0090] Please see Figure 8 As shown in one embodiment of the present invention, in the multi-ring sector diagram a, the area between the left semicircular ring c and the right semicircular ring d is a sector region displaying the contribution of parameter features. Each sector region has a different color, and each color can represent a parameter feature. The radius of the sector region can represent the absolute value of the contribution of this parameter feature in the current charge-discharge cycle. Adding a white line to the sector region indicates that the current parameter feature is making a negative contribution. Each parameter feature's sector region has a corresponding very narrow annular sector. The annular sector represents the average contribution of the parameter feature in the number of cycles specified by the user. The absolute value of the average contribution is represented by the shortest distance from the inner side of the annular sector to the edge of the central annular ring. Similarly, adding a white line indicates that the average contribution is negative.

[0091] In one embodiment of the present invention, in order to make the graph more intuitively reflect the characteristics of the data parameters, the present invention specifies that the maximum and minimum radii of the parameter characteristics of different charge and discharge cycles are uniform values, so as to ensure that there is always one sector region with the largest visual effect and one sector region with the smallest visual effect in the multi-ring sector graph a. The largest and smallest sector regions represent the magnitude of the absolute value of the contribution of the parameter characteristics of the current charge and discharge cycle, and the remaining sector regions are arranged proportionally according to the parameter characteristics.

[0092] In one embodiment of the invention, considering extreme cases where the absolute value of the average contribution value of a certain parameter feature in the current charge-discharge cycle is too large, exceeding the radius of the maximum parameter feature, the invention adds a retaining layer to the maximum sector region and the two outer semi-rings. If the above situation exists, regardless of how much the average value of a certain parameter feature exceeds the value represented by the maximum leaf radius, it is always fixed in the outer retaining layer. Similarly, if the average value is too small, it is placed in the outermost layer of the inner ring, no matter how small.

[0093] Please see Figure 8 As shown, in one embodiment of the present invention, the contribution of parameter feature A is displayed in sector region a. The radius of sector region a1 represents the contribution of parameter feature A in the current loop; a white line passing through the sector indicates a negative contribution in the current loop. The radius of annular sector a2 represents the average contribution of parameter feature A to date. In the illustration, annular sector a2 is located at the outermost edge of the circle, indicating that it is greater than the maximum contribution value of the parameter feature in the current loop. The white line does not pass through annular sector a2, indicating an average positive contribution. The contribution of parameter feature B is displayed in sector region b. Annular sector b2 is located at the innermost edge of the circle and is smaller than sector region b1, indicating that the average contribution of parameter feature B to date is less than the minimum contribution value in the current loop. Both sector region b1 and annular sector b2 have white lines in the middle, indicating that both are negative contributions.

[0094] Please see Figure 7 As shown, in one embodiment of the present invention, the second type of view may further include a cycle information display box b. The cycle information display box b can display a mapping of the real-time status parameters of the battery in the current cycle, and the information displayed within it varies depending on the charging and discharging process. The battery status boxes will display data such as the average voltage, maximum voltage, average temperature, maximum temperature, average current, and / or maximum current of all batteries. If the battery is currently in a charging cycle, the upper right box will display charging-related information including total charging time, fast charging time, and slow charging time; if it is in a discharging cycle, it will display information such as the electric vehicle's average cruising speed, power consumption per kilometer, and total discharging time. Additionally, in the warning bar of the cycle information display box b, if the data of one or more batteries exceeds the abnormal threshold, the corresponding abnormality for that battery will be displayed according to the abnormality type. This invention allows users to find detailed charging and discharging information for the corresponding battery and cycle through the corresponding warning information.

[0095] Please see Figure 8As shown, in one embodiment of the present invention, the second type of view may further include a SOC (state of charge) history histogram c. The SOC history histogram c can show the mapping of the SOC change process during the current charge or discharge cycle. One charge-discharge cycle may contain multiple charge-discharge processes. In the SOC history histogram c, the horizontal axis represents the number of charge-discharge cycles, and the vertical axis represents the SOC percentage. Taking discharge as an example, the lower edge of each square in the SOC history histogram c represents the SOC when the battery starts charging, the upper edge represents the SOC at the end, and the height represents how much SOC was consumed in this discharge. In this embodiment, the squares can use different color zones to distinguish between slow charging and fast charging, such as yellow for slow charging and green for fast charging. The height of all squares should be 100% SOC. If it is a discharge cycle, there will be no color distinction for each discharge.

[0096] In one embodiment of the present invention, a practical application is described. When a power battery is put into use and has reached its full maintenance period, the BMS (Battery Management System) stores sufficient data. The BMS data is processed using the power battery health status detection method provided by the present invention: First, the entire process of battery life degradation in units of charge-discharge cycles is obtained, along with all abnormal and alarm cycles. Each cycle displays the contribution of various parameter characteristics to the SOH (state of health) change, as well as historical comparisons. Based on this parameter display, the cycle in which the problem to be analyzed occurs, or the cycle in which the contribution of parameter characteristics is abnormal, can be quickly located. Then, by selecting, a single cycle thumbnail information can be viewed. Interactive access to the single cycle view is achieved through interface clicks. After opening the single cycle view, the state parameters of the power battery, the charge-discharge behavior within the cycle, and the parameter characteristic contribution analysis view can be intuitively seen. The above views can assist in analyzing the main factors in the SOH change of this cycle, as well as factors that differ from other cycles, thereby helping to locate the factors causing the abnormality. For example, mileage is a major factor in declining battery health. Before a certain abnormal cycle, the highest temperature became the primary factor, suggesting that abnormally high temperatures may have caused the anomaly. Based on this, repairs can be carried out, or targeted improvements can be made to the battery manufacturing process.

[0097] Please see Figure 9 , Figure 9 This is a schematic diagram of a power battery health status detection device in one embodiment of the present invention. In some embodiments, the power battery health status detection device may include a data acquisition module 100, a parameter feature extraction module 200, a view construction module 300, a first type of view module 400, and a second type of view module 500. The functional modules are described in detail below.

[0098] The data acquisition module 100 is used to acquire the battery state parameters of the power battery;

[0099] The parameter feature extraction module 200 is used to obtain parameter features related to the health indicators of the power battery based on the battery state parameters, and to calculate the contribution of the parameter features and the historical statistical values ​​of the contribution.

[0100] The view construction module 300 is used to construct a first-class view and a second-class view of the health status of the power battery based on the contribution level.

[0101] The first type of view module 400 is used to fill parameter features and contribution levels into the first type of view according to a preset first generation rule to generate a lifetime decay anomaly view; and

[0102] The second type of view module 500 is used to fill parameter characteristics, contribution degree and historical statistical values ​​into the second type of view according to the preset second generation rule, so as to generate a visual analysis view of a single charge-discharge cycle.

[0103] In one embodiment of the present invention, the data acquisition module 100 can be used to acquire battery state parameters of the power battery. Specifically, it can first acquire battery management system data of the power battery in the electric vehicle, and extract battery state parameters, battery SOH (state of health), and other health indicators from the battery management system data. The battery state parameters can be measured in charge-discharge cycles.

[0104] In one embodiment of the present invention, the parameter feature extraction module 200 can be used to obtain parameter features related to the health indicators of the power battery based on battery state parameters, and calculate the contribution of the parameter features and the historical statistical values ​​of the contribution. Specifically, the parameter features refer to the features related to the health indicators of the power battery. Within a preset time period, the parameter features may include: cumulative mileage, maximum maximum temperature, average maximum temperature, minimum minimum temperature, average minimum temperature, average driving time per charge, average SOC (state of charge) per charge, and / or average driving speed. In this embodiment, the preset time period can be set to the most recent 7 days. The parameter features can be obtained by first extracting charging records from the battery management system data and dividing them by the number of charging cycles; then by... The charge-discharge cycle is divided in a certain way, that is, the accumulation of SOC to 100 is recorded as one charge-discharge cycle; finally, based on all the charging records in each charge-discharge cycle, the parameter characteristics of each charge-discharge cycle are calculated.

[0105] In one embodiment of the present invention, the view construction module 300 can be used to construct a first type of view and a second type of view of the power battery health status based on the contribution degree. Specifically, based on the parameter type of the calculated contribution degree, a first type of view and a second type of view integrating the contribution degree of parameter features of the power battery health status can be constructed. The first type of view can display changes in battery health status, charge / discharge cycles with abnormalities and alarms, changes in battery health indicators, and the contribution degree of parameter features to changes in battery health status in each cycle. The second type of view can display the average voltage, maximum voltage, average temperature, maximum temperature, average current, and maximum current of all batteries in the current cycle, charging time, fast charging time, and slow charging time, average cruising speed, power consumption per kilometer, and / or total discharge time during discharge. The second type of view can also be used to reflect the contribution of different parameter features of the current cycle to changes in battery health status, and to compare with historical patterns.

[0106] In one embodiment of the present invention, the first type of view module 400 can be used to fill parameter features and contribution levels into a first type of view according to a preset first generation rule to generate a lifespan degradation anomaly view. Specifically, firstly, a data mapping relationship is established between the health status data of the power battery and the first type of view; then, a preset first generation rule is constructed; finally, according to the data mapping relationship and the first generation rule, parameter features and contribution levels are filled into the first type of view to generate a lifespan degradation anomaly view.

[0107] In one embodiment of the present invention, the second type of view module 500 can be used to fill parameter features, contribution degree, and historical statistical values ​​into the second type of view according to a preset second generation rule, so as to generate a single charge-discharge cycle visual analysis view. Specifically, firstly, a data mapping relationship is established between the data of a single charge-discharge cycle and the second type of view; then, a preset second generation rule is constructed; finally, according to the data mapping relationship and the first generation rule, parameter features, contribution degree, and historical statistical values ​​are filled into the second type of view to generate a single charge-discharge cycle visual analysis view.

[0108] Please see Figure 10 As shown, Figure 10A computer device 1000 is provided, which can be a server. The computer device 1000 includes a processor 1001, a memory 1002, a network interface, and a database connected via a system bus. The processor 1001 of the computer device 1000 provides computing and control capabilities. The memory 1002 of the computer device 1000 includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device 1000 is used for communication with an external user terminal via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a power battery health status detection method on the server side.

[0109] In one embodiment, a computer device 1000 is provided, including a memory 1002, a processor 1001, and a computer program stored in the memory and executable on the processor. When the processor 1001 executes the computer program, it performs the following steps:

[0110] Obtain the battery state parameters of the power battery;

[0111] Based on battery state parameters, obtain parameter features related to the health indicators of the power battery, and calculate the contribution of the parameter features and the historical statistical values ​​of the contribution.

[0112] Based on the contribution level, construct a first-class view and a second-class view of the power battery health status;

[0113] Based on the preset first generation rule, parameter characteristics and contribution levels are filled into the first type of view to generate a lifetime decay anomaly view; and

[0114] According to the preset second generation rule, the parameter characteristics, contribution degree and historical statistical values ​​are filled into the second type of view to generate a visual analysis view of a single charge-discharge cycle.

[0115] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0116] Obtain the battery state parameters of the power battery;

[0117] Based on battery state parameters, obtain parameter features related to the health indicators of the power battery, and calculate the contribution of the parameter features and the historical statistical values ​​of the contribution.

[0118] Based on the contribution level, construct a first-class view and a second-class view of the power battery health status;

[0119] Based on the preset first generation rule, parameter characteristics and contribution levels are filled into the first type of view to generate a lifetime decay anomaly view; and

[0120] According to the preset second generation rule, the parameter characteristics, contribution degree and historical statistical values ​​are filled into the second type of view to generate a visual analysis view of a single charge-discharge cycle.

[0121] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0122] In summary, this invention provides a method and apparatus for detecting the health status of a power battery. Based on data from the electric vehicle's BMS (Battery Management System), this invention obtains the health status and abnormal charge-discharge cycles, derives the contribution of each parameter characteristic in each charge-discharge cycle, performs comprehensive statistical analysis on this data, extracts key data for analyzing and diagnosing battery anomalies, and ultimately generates a combined view system integrating parameter characteristics, parameter characteristic contribution, and lifespan degradation information. By using this system, users can quickly identify abnormal charge-discharge cycles and, with the assistance of the system's visual analysis view, quickly and conveniently analyze and diagnose battery anomalies, perform battery fault repair, and specifically improve battery and electric vehicle manufacturing processes, significantly enhancing driving range. Therefore, this invention effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0123] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for detecting the health status of a power battery, characterized in that: include Obtain the battery state parameters of the power battery; Based on the battery state parameters, obtain parameter features related to the health indicators of the power battery, and calculate the contribution of the parameter features and the historical statistical values ​​of the contribution. Based on the contribution level, construct a first type view and a second type view of the power battery health status; According to the preset first generation rule, the parameter features and the contribution degree are filled into the first type of view to generate a lifespan decay anomaly view; as well as According to the preset second generation rule, the parameter features, the contribution degree and the historical statistical values ​​are filled into the second type of view to generate a single charge-discharge cycle visual analysis view; The generation step of the first generation rule includes: Construct a feature block diagram for each charge-discharge cycle, where the width of each block represents one charge-discharge cycle, and the height of the block represents the absolute value of the contribution. Within a selected time period of charge-discharge cycles, the parameter features that make an average positive contribution are marked with a first color; when a parameter feature marked with the first color makes a negative contribution in the current charge-discharge cycle, the block corresponding to the parameter feature is placed at the position furthest from the return to a healthy state line. Within a selected time period of charge-discharge cycles, the parameter features that make an average negative contribution are marked with a second color; when the parameter feature marked with the second color makes a positive contribution in the current charge-discharge cycle, the square corresponding to the parameter feature is placed at the position furthest from the return to health state line. The higher the height of the square, the closer it is to the regression health state line; the lower the height of the square, the farther it is from the regression health state line. When the charge-discharge cycle represented by the marked square bar shows a regression increase compared to the charge-discharge cycle on the left, it is a parameter feature that makes a positive contribution; the square bar on the regression line is a parameter feature that makes a negative contribution. The generation steps of the second generation rule include: Construct a multi-ring sector diagram for each charge-discharge cycle, where each sector represents one of the aforementioned parameter characteristics affecting changes in health status, and the radius represents the absolute value of the contribution of that parameter characteristic in the current charge-discharge cycle; and Each parameter feature has a sector area with a narrow annular sector. The shortest distance from the inner side of each annular sector to the center annular edge represents the average contribution of the parameter feature over a specified number of charge-discharge cycles.

2. The method for detecting the health status of a power battery according to claim 1, characterized in that, The step of filling the parameter features and the contribution degree into the first type of view according to the preset first generation rule to generate a lifetime decay anomaly view includes: Establish a data mapping relationship between the health status data of the power battery and the first type of view, wherein the health status data represents data reflecting the changing trend of the battery's health status; and Based on the data mapping relationship and the first generation rule, the features and the contribution are filled into the first type of view to generate a lifespan decay anomaly view.

3. The method for detecting the health status of a power battery according to claim 1, characterized in that, The first type of view displays changes in battery health status, charge / discharge cycles that trigger abnormal alarms, changes in battery health indicators, and / or the contribution of parameter characteristics to changes in battery health status in each cycle.

4. The method for detecting the health status of a power battery according to claim 1, characterized in that, The step of filling the parameter features, the contribution degree, and the historical statistical values ​​into the second type of view according to the preset second generation rule to generate a single charge-discharge cycle visual analysis view includes: Establish a data mapping relationship between the data from a single charge-discharge cycle and the second type of view; and Based on the data mapping relationship and the second generation rule, the parameter features, the contribution degree, and the historical statistical values ​​are filled into the second type of view to generate a single charge-discharge cycle visual analysis view.

5. The method for detecting the health status of a power battery according to claim 1, characterized in that, The status parameters include the total voltage and total current of the power battery, standard state of charge, individual cell voltage, maximum voltage individual cell number, maximum individual cell voltage, minimum voltage individual cell number, minimum individual cell voltage, individual cell temperature, highest temperature individual cell number, highest individual cell temperature, lowest temperature individual cell number, lowest individual cell temperature, insulation resistance, battery alarm type, driving speed and / or mileage traveled.

6. The method for detecting the health status of a power battery according to claim 1, characterized in that, Within a preset time period, the features include cumulative mileage, maximum maximum temperature, average maximum temperature, minimum minimum temperature, average minimum temperature, average driving time per trip, average state of charge per charge, and / or average driving speed.

7. A device for detecting the health status of a power battery, characterized in that, include: The data acquisition module is used to acquire the battery state parameters of the power battery; The parameter feature extraction module is used to obtain parameter features related to the health indicators of the power battery based on the battery state parameters, and to calculate the contribution of the parameter features and the historical statistical values ​​of the contribution. A view construction module is used to construct a first type of view and a second type of view of the health status of the power battery based on the contribution level. The first type of view module is used to fill the parameter features and the contribution degree into the first type of view according to the preset first generation rule, so as to generate a lifespan decay anomaly view; as well as The second type of view module is used to fill the parameter features, the contribution degree and the historical statistical values ​​into the second type of view according to the preset second generation rule, so as to generate a single charge-discharge cycle visual analysis view. The generation step of the first generation rule includes: Construct a feature block diagram for each charge-discharge cycle, where the width of each block represents one charge-discharge cycle, and the height of the block represents the absolute value of the contribution. Within a selected time period of charge-discharge cycles, the parameter features that make an average positive contribution are marked with a first color; when a parameter feature marked with the first color makes a negative contribution in the current charge-discharge cycle, the block corresponding to the parameter feature is placed at the position furthest from the return to a healthy state line. Within a selected time period of charge-discharge cycles, the parameter features that make an average negative contribution are marked with a second color; when the parameter feature marked with the second color makes a positive contribution in the current charge-discharge cycle, the square corresponding to the parameter feature is placed at the position furthest from the return to health state line. The higher the height of the square, the closer it is to the regression health state line; the lower the height of the square, the farther it is from the regression health state line. When the charge-discharge cycle represented by the marked square bar shows a regression increase compared to the charge-discharge cycle on the left, it is a parameter feature that makes a positive contribution; the square bar on the regression line is a parameter feature that makes a negative contribution. The generation steps of the second generation rule include: Construct a multi-ring sector diagram for each charge-discharge cycle, where each sector represents one of the aforementioned parameter characteristics affecting changes in health status, and the radius represents the absolute value of the contribution of that parameter characteristic in the current charge-discharge cycle; and Each parameter feature has a sector area with a narrow annular sector. The shortest distance from the inner side of each annular sector to the center annular edge represents the average contribution of the parameter feature over a specified number of charge-discharge cycles.

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