Storage battery aging prediction method, and target voltage drop information acquisition method and device

Abstract

The embodiment of the invention discloses a storage battery aging prediction method and device and a target voltage drop information acquisition method and device. The method comprises the steps of obtaining target voltage drop information in a first period, and obtaining a voltage drop distribution condition of a vehicle storage battery in the first period based on the target voltage drop information in the first period; and judging whether the vehicle storage battery is aged or not based on the voltage drop distribution condition. Through the above mode, since the target voltage drop information represents the difference value between the first storage battery voltages at the two adjacent acquisition moments, the change condition of the internal resistance of the storage battery can be effectively reflected, and the method does not need to depend on a complex special sensor to acquire the related parameters of the storage battery. The aging state of the storage battery can be quickly and accurately judged only by collecting and analyzing the voltage drop distribution condition of the vehicle storage battery, so that the hardware cost and the maintenance cost of the vehicle are greatly reduced, and the management efficiency and reliability of the vehicle storage battery are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and more specifically, to a battery aging prediction method, a target voltage drop information acquisition method, and a device. Background Art

[0002] With the continuous development of vehicle technology and the growing market demand, the requirements for vehicle intelligence and electrification are becoming increasingly higher. As a key power supply unit for the vehicle's electronic system, the stability of low-voltage batteries directly affects the vehicle's starting, communication, safety and other functions. Therefore, the problem of battery aging, degradation and power loss leading to vehicle failure to start has gradually become a pain point for various automobile manufacturers and users.

[0003] In the related method, the battery's aging status is determined by relying on the battery's own charging and discharging current, voltage, power and other information, and collecting voltage and current data through dedicated sensors to estimate the battery's internal resistance. However, the related method has some limitations: on the one hand, the use of dedicated sensors increases the cost of the vehicle; on the other hand, it mainly focuses on the parameter changes of the battery itself, and does not fully consider the impact of the vehicle's actual usage scenarios on battery aging, resulting in low accuracy of the prediction results. Summary of the Invention

[0004] In view of the above problems, the present application proposes a battery aging prediction method, a target voltage drop information acquisition method and a device to improve the above problems.

[0005] In a first aspect, the present application provides a battery aging prediction method, the method comprising: Acquire target voltage drop information within a first cycle, where the target voltage drop information represents a difference between first battery voltages at two adjacent acquisition moments; obtaining a voltage drop distribution of the vehicle battery within the first cycle based on the target voltage drop information within the first cycle; Whether the vehicle battery is aged is determined based on a voltage drop distribution of the vehicle battery during the first cycle.

[0006] Optionally, the target voltage drop information includes at least a plurality of abnormal voltage drop levels, and judging whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery in the first cycle includes: determining a state of the vehicle battery based on target voltage drop information within the first cycle; When the state of the vehicle battery is abnormal, obtaining a voltage drop distribution of the vehicle battery during the first cycle; When the voltage drop distribution indicates that a first abnormal level has a first proportion within the plurality of abnormal voltage drop levels that is included in a first preset proportion range, it is determined that the vehicle battery has aged.

[0007] Optionally, the target voltage drop information includes at least a plurality of abnormal voltage drop levels, and judging whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery in the first cycle includes: When a first proportion of the first abnormal level represented by the voltage drop distribution condition among the multiple abnormal voltage drop levels is included in a second preset proportion range, obtaining a duration of the voltage drop distribution condition; If the duration of the voltage drop profile is greater than a duration threshold, it is determined that the vehicle battery has aged.

[0008] Optionally, the target voltage drop information further includes a plurality of normal voltage drop levels. Based on the voltage drop distribution of the vehicle battery in the first cycle, determining whether the vehicle battery is aged includes: When the voltage drop distribution indicates that the abnormal voltage drop level accounts for a proportion of the target voltage drop information that is lower than a first threshold, determining that the vehicle battery is not aged; When the voltage drop distribution indicates that the proportion of the abnormal voltage drop level in the target voltage drop information is higher than a first threshold and lower than a second threshold, it is determined that the vehicle battery is aged to a first degree; When the voltage drop distribution indicates that a proportion of abnormal voltage drop levels in the target voltage drop information is higher than a second threshold, it is determined that the vehicle battery is aged to a second degree.

[0009] Optionally, determining the state of the vehicle battery based on target voltage drop information within the first cycle includes: determining, based on a plurality of abnormal voltage drop levels in the target voltage drop information, a second proportion of the second abnormal level within the plurality of abnormal voltage drop levels; When the second proportion is higher than a second proportion threshold, it is determined that the state of the vehicle battery is abnormal.

[0010] Optionally, determining the state of the vehicle battery further includes: Acquire multiple target low voltage states within the first cycle; Based on a plurality of target low voltage states in the first cycle, obtaining a cumulative number of target low voltage states in the first cycle; When the cumulative number of target low voltage states in the first cycle is greater than a number threshold, it is determined that the state of the vehicle battery is abnormal.

[0011] Optionally, after determining whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery in the first cycle, the method further includes: acquiring target voltage drop information within a second period, where the second period includes a plurality of the first periods; determining, based on the target voltage drop information within the second period, an output trend of the voltage drop distribution within the second period, the output trend of the voltage drop distribution being obtained by chronologically sorting a plurality of voltage drop distributions within the first period; When the output trend of the voltage drop distribution indicates that the vehicle battery has aged, a battery aging prompt message is sent to the user terminal, and an alarm message is sent to the vehicle terminal.

[0012] In a second aspect, the present application provides a method for obtaining target voltage drop information, the method comprising: collecting a first battery voltage of a vehicle battery multiple times; determining a plurality of voltage drop levels based on a difference between the first battery voltages at two adjacent acquisition moments, and adding time stamps to the plurality of voltage drop levels to obtain target voltage drop information; The target voltage drop information is sent to the cloud, so that the cloud executes the above-mentioned battery aging prediction method based on the target voltage drop information.

[0013] Optionally, before acquiring the first battery voltage of the vehicle battery multiple times, the method further includes: Continuously collect vehicle battery voltage multiple times; When monitoring that the vehicle battery voltage collected multiple times continuously is lower than the first voltage threshold, collecting the first battery voltage of the vehicle battery multiple times; Based on the difference between the first battery voltages at two adjacent acquisition moments, multiple voltage drop levels are determined, including: When the difference is lower than the first voltage difference threshold, determining that the voltage drop level at the current moment is a normal voltage drop level; When the difference is lower than the second voltage difference threshold and higher than the first voltage difference threshold, determining that the voltage drop level at the current moment is the first level among the abnormal voltage drop levels; When the difference is lower than the third voltage difference threshold and higher than the second voltage difference threshold, determining that the voltage drop level at the current moment is the second level among the abnormal voltage drop levels; When the difference is lower than the third voltage difference threshold and higher than the second voltage difference threshold, the voltage drop level at the current moment is determined to be the third level among the abnormal voltage drop levels; wherein the combination of the first level and the second level is the first abnormal level, and the combination of the second level and the third level is the second abnormal level.

[0014] Optionally, the method further includes: After the vehicle is detected to be awake, the vehicle battery voltage is collected multiple times continuously; When the voltages of multiple vehicle batteries collected continuously are all lower than the second voltage threshold, a low voltage state is obtained, a timestamp is added to the low voltage state, a target low voltage state is obtained, and the target low voltage state is sent to the cloud, so that the cloud determines the state of the vehicle battery based on the target low voltage state.

[0015] In a third aspect, the present application provides a battery aging prediction method and device, the device comprising: a vehicle cloud platform, a battery aging prediction module; The vehicle cloud platform is used to store the target voltage drop information sent by the vehicle; The battery aging prediction module is configured to obtain target voltage drop information within a first cycle, the target voltage drop information representing a difference between first battery voltages at two adjacent acquisition moments; obtain a voltage drop distribution of the vehicle battery within the first cycle based on the target voltage drop information within the first cycle; and determine whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery within the first cycle.

[0016] In a fourth aspect, the present application provides a device for obtaining target voltage drop information, the device comprising: a vehicle battery, a power monitoring module, and a remote communication terminal; a vehicle battery, connected to the power monitoring module; The power supply monitoring module is configured to, upon monitoring that the vehicle battery voltages collected multiple times are all below a first voltage threshold, collect a first battery voltage of the vehicle battery multiple times, and determine a plurality of voltage drop levels based on a difference between two adjacent collected first battery voltages; after monitoring that the vehicle is awake, collect a plurality of second battery voltages continuously, and determine a low voltage state if the plurality of second battery voltages collected continuously are all below a second voltage threshold; and transmit the plurality of voltage drop levels and the low voltage state to a remote communication terminal; The remote communication terminal is used to receive the voltage drop level and the low voltage status sent from the power supply monitoring module, add a timestamp to the voltage drop level and the low voltage status, obtain target voltage drop information and target low voltage status, and send the target voltage drop information and target low voltage status to the cloud.

[0017] In a fifth aspect, the present application provides a vehicle comprising one or more processors and a memory; one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the above-mentioned target voltage drop information acquisition method.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium, in which program code is stored, wherein the above-mentioned battery aging prediction method or target voltage drop information acquisition method is executed when the program code is run.

[0019] The present application provides a battery aging prediction method and device, which obtain target voltage drop information within a first cycle, wherein the target voltage drop information represents the difference between the first battery voltages at two adjacent acquisition moments; based on the target voltage drop information within the first cycle, obtain the voltage drop distribution of the vehicle battery within the first cycle; and based on the voltage drop distribution of the vehicle battery within the first cycle, determine whether the vehicle battery is aged.

[0020] In the present application, the above-mentioned method enables the voltage drop distribution of the vehicle battery in the first cycle to be obtained based on the target voltage drop information in the first cycle, so as to determine whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery in the first cycle; because the target voltage drop information represents the difference between the first battery voltages at two adjacent acquisition moments, this difference can effectively reflect the change in the battery's internal resistance, thereby providing a more accurate basis for aging determination. Therefore, the present application does not need to rely on complex dedicated sensors to collect parameters such as the battery's charge and discharge current, voltage, and internal resistance. By simply collecting and analyzing the voltage drop distribution of the vehicle battery, a rapid and accurate judgment of the battery's aging status can be achieved, which not only greatly reduces the vehicle's hardware cost and maintenance cost, but also significantly improves the efficiency and reliability of vehicle battery management. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A schematic diagram of a battery aging prediction system proposed in an embodiment of the present application is shown; Figure 2A flowchart of a battery aging prediction method proposed in an embodiment of the present application is shown; Figure 3 A schematic diagram showing voltage drop curves of a vehicle battery under the same load at different aging levels according to an embodiment of the present application is shown; Figure 4 A schematic diagram showing the proportion of voltage dip levels 3, 4, 5, and 6 in a single day over the past seven days in an embodiment of the present application; Figure 5 A schematic diagram showing the cumulative number of low voltage status flags in the past seven days according to an embodiment of the present application; Figure 6 A schematic diagram showing the voltage drop distribution of a non-aged battery according to an embodiment of the present application; Figure 7 A schematic diagram showing a voltage drop distribution of a first-degree aged battery according to an embodiment of the present application; Figure 8 A schematic diagram showing a voltage drop distribution of a second-degree aged battery according to an embodiment of the present application; Figure 9 An output trend chart showing the percentage of single-cycle voltage sag levels 3, 4, 5, and 6 over the past 25 weeks in an embodiment of the present application is shown; Figure 10 An output trend chart of the cumulative number of target low voltage states in a single week for nearly 25 weeks is shown in an embodiment of the present application; Figure 11 A flow chart of a method for obtaining target voltage drop information proposed in an embodiment of the present application is shown; Figure 12 A flowchart of a preferred battery aging prediction method and a method for obtaining target voltage drop information proposed in an embodiment of the present application is shown; Figure 13 The following is a structural block diagram of a battery aging prediction device proposed in an embodiment of the present application; Figure 14 The following is a structural block diagram of a device for obtaining target voltage drop information proposed in an embodiment of the present application; Figure 15 Shown is a structural block diagram of a vehicle proposed in this application. DETAILED DESCRIPTION

[0023] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0024] In the drawings, the sizes of components, layer thicknesses, or regions may be exaggerated for clarity. Therefore, any implementation of the present disclosure is not necessarily limited to the dimensions shown in the drawings, and the shapes and sizes of components in the drawings do not reflect true proportions. Furthermore, the drawings schematically illustrate idealized examples, and any implementation of the present disclosure is not limited to the shapes or values ​​shown in the drawings.

[0025] In an embodiment of the present application, a battery aging prediction method provided herein obtains target voltage drop information within a first cycle, wherein the target voltage drop information represents the difference between the first battery voltages at two adjacent acquisition moments; based on the target voltage drop information within the first cycle, a voltage drop distribution of the vehicle battery within the first cycle is obtained; and based on the voltage drop distribution of the vehicle battery within the first cycle, whether the vehicle battery is aged is determined. Through the above-described method, the voltage drop distribution of the vehicle battery within the first cycle can be obtained based on the target voltage drop information within the first cycle, so as to determine whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery within the first cycle; because the target voltage drop information represents the difference between the first battery voltages at two adjacent acquisition moments, this difference can effectively reflect the change in the battery's internal resistance, thereby providing a more accurate basis for aging determination. Therefore, this application does not need to rely on complex dedicated sensors to collect battery parameters such as charging and discharging current, voltage, and internal resistance. It can quickly and accurately judge the battery aging status by simply collecting and analyzing the voltage drop distribution of the vehicle battery. This not only greatly reduces the vehicle's hardware cost and maintenance cost, but also significantly improves the efficiency and reliability of vehicle battery management.

[0026] Before further describing the embodiments of the present application in detail, the nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations.

[0027] The embodiments of this application will be described below with reference to the accompanying drawings.

[0028] In order to better understand the solution of the embodiment of the present application, a battery aging prediction system of the present application is introduced below.

[0029] See also Figure 1The system may include a vehicle-side and a cloud-side. The cloud-side may include a vehicle-to-cloud platform and a battery aging prediction module. The vehicle-to-cloud platform is communicatively connected to the vehicle-side and can receive target voltage drop information from the vehicle-side and store it for at least six months. In this application, the vehicle-to-cloud platform can also be used to display an output trend of voltage drop distribution. The battery aging prediction module can determine the voltage drop distribution of the vehicle battery based on the target voltage drop information, and determine whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery.

[0030] On the vehicle side, it can include a vehicle battery, a power monitoring module, and a remote communication terminal. The vehicle battery can be used to power vehicle components, and the power monitoring module is connected to the vehicle battery. The power monitoring module can be used to monitor and collect the voltage of the vehicle battery, thereby obtaining multiple voltage drop levels and low voltage states, and sending the multiple voltage drop levels and low voltage states to the remote communication terminal. The remote communication terminal can be used to receive the voltage drop levels and low voltage states sent by the power monitoring module, add timestamps to the voltage drop levels and low voltage states, obtain target voltage drop information and target low voltage state, and send the target voltage drop information and target low voltage state to the vehicle cloud platform in the cloud.

[0031] See also Figure 2 , an embodiment of the present application provides a battery aging prediction method, the method comprising: S110: Acquire target voltage drop information within a first cycle, where the target voltage drop information represents a difference between first battery voltages at two adjacent acquisition moments.

[0032] Among them, the first cycle can be 7 days a week. In this application, the first cycle can specifically refer to the past 7 days, or it can refer to the past week, or it can be a specified 7 days. The specific time is set by the R&D personnel based on multiple tests; the first battery voltage can be the output voltage of the battery when the vehicle is in a power-off state.

[0033] In this application, based on the chemical and electrical characteristics of lead-acid batteries, multiple tests can be conducted using vehicle batteries with different degrees of aging, so that the voltage levels of vehicle batteries at different degrees of aging and the differences in voltage drops under the same load conditions can be obtained; for example, Figure 3 As shown, Figure 3 The voltage drop curve of the vehicle battery under different aging degrees and the same load can be shown in this figure. Figure 3In the figure, the horizontal axis can represent time and the vertical axis can represent voltage. The multiple curves in the figure show how the voltage of the battery changes over time at different degrees of aging (such as a healthy fully charged battery, a healthy undercharged battery, a slightly aged battery, a moderately aged battery, a severely aged battery, and a completely aged battery). Through the multiple curves in the figure, it can be found that the voltage curve of a healthy battery under the same load decreases gently and the voltage drop is small; and as the degree of battery aging increases, the voltage curve shows a steeper downward trend. The above-mentioned differentiated voltage response characteristics are the important basis for dividing the voltage drop level in the present application scheme. Therefore, through the above-mentioned experimental verification, the present application has established a complete voltage drop level evaluation system. In actual applications, the cloud can obtain the target voltage drop information for each day in the first cycle, and classify and organize this information, thereby providing data support for the subsequent judgment of whether the battery is aged.

[0034] S120: Based on the target voltage drop information in the first cycle, obtain voltage drop distribution of the vehicle battery in the first cycle.

[0035] The target voltage drop information may include multiple abnormal voltage drop levels and multiple normal voltage drop levels. The normal voltage drop level may indicate that the difference between the first battery voltage at two adjacent acquisition moments is lower than a first voltage difference threshold. The abnormal voltage drop level may indicate that the difference between the first battery voltage at two adjacent acquisition moments is higher than the first voltage difference threshold. The first voltage difference threshold may be 500mV.

[0036] In an optional embodiment, normal voltage drop levels and abnormal voltage drop levels may be further divided into level 1, level 2, level 3, level 4, level 5, and level 6. A difference of less than 200 mV between the first battery voltages at two adjacent collection moments may be classified as level 1 (normal voltage drop level), a difference of greater than 200 mV but less than 500 mV between the first battery voltages at two adjacent collection moments may be classified as level 2 (normal voltage drop level); a difference of greater than 500 mV but less than 1000 mV between the first battery voltages at two adjacent collection moments may be classified as level 3 (abnormal voltage drop level); a difference of greater than 1000 mV but less than 1500 mV between the first battery voltages at two adjacent collection moments may be classified as level 4 (abnormal voltage drop level); a difference of greater than 1500 mV but less than 2000 mV between the first battery voltages at two adjacent collection moments may be classified as level 5 (abnormal voltage drop level); and a difference of greater than 2000 mV between the first battery voltages at two adjacent collection moments may be classified as level 6 (abnormal voltage drop level).

[0037] In the present application, the target voltage drop information of each day in the first cycle can be sorted and classified, and the abnormal voltage drop level and the normal voltage drop level can be clearly distinguished to obtain the voltage drop distribution of the vehicle battery in the first cycle (which can be the distribution of abnormal voltage drop level and normal voltage drop level), which can provide data support for the subsequent judgment of whether the battery is aging.

[0038] S130: Determine whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery during the first cycle.

[0039] In the present application, based on the voltage drop distribution of the vehicle battery in the first cycle (which can be the distribution of abnormal voltage drop levels and normal voltage drop levels), by analyzing the abnormal voltage drop levels and the changes in their proportion to the normal levels, data support can be provided for the subsequent judgment of whether the battery is aged, and the health status of the battery can be evaluated more accurately. Since the target voltage drop information represents the difference between the first battery voltages at two adjacent acquisition moments, this difference can effectively reflect the change in the battery's internal resistance, thereby providing a more accurate basis for aging judgment. Therefore, the present application does not need to rely on complex dedicated sensors to collect parameters such as the battery's charge and discharge current, voltage, and internal resistance. By only collecting and analyzing the voltage drop distribution of the vehicle battery, a rapid and accurate judgment of the battery's aging status can be achieved, which not only greatly reduces the vehicle's hardware cost and maintenance cost, but also significantly improves the efficiency and reliability of vehicle battery management.

[0040] Based on this, an embodiment of the present application further provides a battery aging prediction method. In this method, the target voltage drop information includes at least multiple abnormal voltage drop levels. The above step S130: "Determining whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery during the first cycle" may also include the following steps S131 to S133: Step S131: Determine the state of the vehicle battery based on the target voltage drop information in the first cycle.

[0041] The state of the vehicle battery may be normal or abnormal. In the present application, the abnormal state of the vehicle battery is usually a voltage drop (a long-term and stable abnormality) caused by battery aging.

[0042] In this application, there are two ways to determine the status of the vehicle battery: In an optional embodiment, based on multiple abnormal voltage drop levels in the target voltage drop information, a second proportion of the second abnormal level within the multiple abnormal voltage drop levels can be determined; when the second proportion is higher than a second proportion threshold, the state of the vehicle battery is determined to be abnormal.

[0043] The second abnormality level may include level 4, level 5, and level 6. The second proportion of the second abnormality level within the multiple abnormal voltage drop levels may be the proportion of levels 4, 5, and 6 within the multiple abnormal voltage drop levels including levels 3, 4, 5, and 6. The second proportion threshold may be a preset threshold used to determine whether the vehicle battery status is abnormal. In this application, the second proportion threshold may be 20%. In this application, when the second proportion is greater than 20%, it can be determined that the vehicle battery status is abnormal.

[0044] In the present application, when it is determined that the second abnormal level in the first cycle is higher than the second proportion threshold in the plurality of abnormal voltage drop levels, the state of the vehicle battery is determined to be abnormal. Figure 4 As shown, Figure 4 The percentage of voltage drops of levels 3, 4, 5, and 6 in a single day in the past 7 days. Figure 4 It can be easily seen that in this first cycle, the second percentage on the first day is 0 (less than 20%), the second percentage on the second day is 50% (greater than 20%), the second percentage on the third day is 30% (greater than 20%), the second percentage on the fourth day is 80% (greater than 20%), the second percentage on the fifth day is 60% (greater than 20%), and the second percentage on the sixth day is 30% (greater than 20%). In this first cycle, only the second percentage on the first day is less than 20%. Therefore, it can be determined that the status of the vehicle battery in this first cycle is abnormal.

[0045] In another optional embodiment, multiple target low voltage states within the first cycle can be obtained, and based on the multiple target low voltage states within the first cycle, the cumulative number of target low voltage states within the first cycle can be obtained. When the cumulative number of target low voltage states within the first cycle is greater than a number threshold, the state of the vehicle battery is determined to be abnormal.

[0046] Among them, the target low voltage state can indicate that the vehicle battery voltage is still lower than the preset value within a certain period of time after the vehicle is awakened. The target low voltage state can be a binary flag bit, which can be 0 or 1. In this application, since the target low voltage state can directly reflect the battery capacity attenuation (unable to maintain the instantaneous load during awakening), when the target low voltage state is 1, it can be characterized as a low voltage abnormality of the vehicle battery (there is a continuous voltage of ≤11V for 500ms within 10 seconds after the vehicle is awakened). When the target low voltage state is 0, it can be characterized as a normal voltage of the vehicle battery (the voltage is continuously >11V within 10 seconds after the vehicle is awakened).

[0047] Among them, the number threshold can be a preset threshold used to determine whether the state of the vehicle battery is abnormal. In this application, the number threshold can be 5. In this application, when the cumulative number of target low voltage states in the first cycle is greater than 5, it can be determined that the state of the vehicle battery is abnormal.

[0048] In this application, if the cumulative number of target low voltage states in the first cycle is greater than the number threshold, the state of the vehicle battery is determined to be abnormal. Figure 5 As shown, Figure 5 This is a diagram showing the cumulative number of low voltage status flags in the past 7 days. Figure 5 It can be easily seen that in the first cycle, the cumulative number of low voltage status flags is 10. Therefore, it can be determined that the state of the vehicle battery in the first cycle is abnormal.

[0049] Step S132: When the state of the vehicle battery is abnormal, obtain the voltage drop distribution of the vehicle battery in the first cycle.

[0050] Step S133: When a first proportion of the voltage drop distribution indicating a first abnormal level among the plurality of abnormal voltage drop levels is included in a first preset proportion range, determining that the vehicle battery has aged.

[0051] The first abnormality level may include level 3 and level 4. The first proportion of the first abnormality level within the multiple abnormal voltage drop levels may be the proportion of levels 3 and 4 in the multiple abnormal voltage drop levels including levels 3, 4, 5, and 6. The first preset proportion range may be a preset range used to determine whether the vehicle battery is aged. In this application, the first preset proportion range may be [40%, 60%].

[0052] In the present application, within the first cycle, if the vehicle battery status is abnormal based on the voltage drop distribution, and the first percentage of the first abnormal level within the multiple abnormal voltage drop levels is within a first preset percentage range, then the vehicle battery can be determined to have aged. Therefore, in the embodiment of the present application, by combining the dual verification of the voltage drop distribution and the target low voltage state monitoring, an accurate judgment of the vehicle battery status is achieved. Furthermore, when the following two conditions are simultaneously met within the first cycle: the vehicle battery status is abnormal, and the percentage of the first abnormal level (e.g., level 3 or level 4) within all abnormal drop levels is within a first preset range (e.g., 40%-60%), the vehicle battery can be determined to have aged. This allows for an accurate determination of vehicle battery aging, and can provide timely warnings and maintenance recommendations to vehicle manufacturers and users, effectively extending the battery's service life and reducing the risk of vehicle failure.

[0053] Based on this, an embodiment of the present application further provides a battery aging prediction method. In this method, the target voltage drop information includes at least multiple abnormal voltage drop levels. The above step S130: "Determining whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery during the first cycle" may also include the following steps S134-S135: Step S134: when a first proportion of the voltage drop distribution indicating a first abnormal level within the plurality of abnormal voltage drop levels is included in a second preset proportion range, obtaining a duration of the voltage drop distribution.

[0054] Step S135: When the duration of the voltage drop distribution condition is greater than a duration threshold, it is determined that the vehicle battery has aged.

[0055] The second preset percentage range can be a preset range used to determine whether the vehicle battery is aged. In the present application, the second preset percentage range can be [0, 40%]. The duration of the voltage drop distribution can represent the duration of a situation in which a first percentage of the first abnormal level within the plurality of abnormal voltage drop levels is within the second preset percentage range. The duration threshold can be a preset range used to determine whether the vehicle battery is aged. In the present application, the duration threshold can be 4 weeks.

[0056] In the present application, if the first proportion of the first abnormal level within the plurality of abnormal voltage drop levels obtained based on the voltage drop distribution is within a second preset proportion range, the duration of the voltage drop distribution can be determined. If the duration of the voltage drop distribution is greater than four weeks, it can be determined that the vehicle battery has aged. Therefore, in the embodiments of the present application, by introducing verification in the time dimension and combining the proportion of the voltage drop distribution, the aging state of the vehicle battery can be more comprehensively and accurately determined, thereby further improving the accuracy and reliability of battery aging determination.

[0057] Based on this, an embodiment of the present application further provides a battery aging prediction method. In this method, the target voltage drop information includes multiple normal voltage drop levels and multiple abnormal voltage drop levels. The above step S130: "Determining whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery during the first cycle" may also include the following steps S136 to S138: Step S136: The voltage drop distribution indicates that the proportion of the abnormal voltage drop level in the target voltage drop information is lower than a first threshold, and it is determined that the vehicle battery is not aged.

[0058] The proportion of abnormal voltage drop levels in the target voltage drop information may be the proportion of levels 3, 4, 5, and 6 in a plurality of abnormal voltage drop levels including levels 1, 2, 3, 4, 5, and 6. The first threshold may be 5%. In this application, the first threshold may be set based on experiments by researchers and is not further expanded upon.

[0059] In this application, after obtaining the voltage drop distribution of the vehicle battery in the first cycle, it can be determined that the vehicle battery is not aged when the proportion of abnormal voltage drop level in target voltage drop information is lower than a first threshold. For example, Figure 6 As shown, Figure 6 It may be a voltage drop distribution in a non-aged vehicle battery under a predetermined load during a first cycle.

[0060] Step S137: The voltage drop distribution characterizing the proportion of abnormal voltage drop levels in the target voltage drop information is higher than a first threshold and lower than a second threshold, and it is determined that the vehicle battery is aged to a first degree.

[0061] Among them, the second threshold can be 15%. In this application, the second threshold must be greater than the first threshold, and the second threshold can be set based on experiments by R&D personnel, and will not be further expanded here.

[0062] In the present application, after obtaining the voltage drop distribution of the vehicle battery in the first cycle, it can be determined that the vehicle battery is aged to the first degree when the proportion of abnormal voltage drop level in the target voltage drop information is higher than the first threshold and lower than the second threshold. For example, Figure 7 As shown, Figure 7 It may be a voltage drop distribution in a vehicle battery of a first degree of aging under a predetermined load within a first cycle.

[0063] Step S138: If the voltage drop distribution indicates that the abnormal voltage drop level accounts for a larger proportion than a second threshold in the target voltage drop information, it is determined that the vehicle battery is aged to a second degree.

[0064] In the present application, after obtaining the voltage drop distribution of the vehicle battery in the first cycle, it can be determined that the vehicle battery is aged to the second degree when the proportion of abnormal voltage drop level in the target voltage drop information is higher than the second threshold. For example, Figure 8 As shown, Figure 8 It may be a voltage drop distribution in a vehicle battery of a second degree of aging under a predetermined load within a first cycle.

[0065] In the implementation of this application, the degree of aging of the vehicle battery (non-aging, first degree aging, second degree aging) can be accurately determined based on the proportion of voltage drop distribution. This method not only improves the reliability and safety of the vehicle, but also reduces maintenance costs and effectively extends the service life of the battery.

[0066] Based on this, an embodiment of the present application further provides a battery aging prediction method. In this method, after the above step S130: "Determining whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery during the first cycle", the following steps S140 to S160 may be further included: Step S140: acquiring target voltage drop information in a second cycle, where the second cycle includes a plurality of the first cycles.

[0067] Among them, the second cycle can be 25 weeks. In this application, the second cycle can specifically refer to the past 25 weeks, or the past 6 months, or a specified 25 weeks. The specific time is set by the R&D personnel based on multiple experiments.

[0068] Step S150: Based on the target voltage drop information in the second cycle, determining an output trend of the voltage drop distribution in the second cycle, wherein the output trend of the voltage drop distribution is obtained by chronologically sorting a plurality of voltage drop distributions in the first cycle.

[0069] Step S160: When the output trend of the voltage drop distribution indicates that the vehicle battery has aged, a battery aging prompt message is sent to the user terminal, and an alarm message is sent to the vehicle terminal.

[0070] In the present application, after determining whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery in the first cycle, it is also possible to verify whether the vehicle battery is aged based on the voltage drop distribution corresponding to the target voltage drop information in the second cycle: the output trend of the voltage drop distribution in the second cycle can be determined based on the target voltage drop information in the second cycle, and when the proportion of level 3 in the abnormal voltage drop level in all abnormal voltage drop levels gradually decreases over time, it can be determined that the vehicle battery is aged, and then a battery aging prompt message can be sent to the user terminal, and an alarm message can be sent to the vehicle end. For example, Figure 9 As shown, Figure 9 It can output the trend graph of the percentage of single-week voltage drop levels 3, 4, 5, and 6 in the past 25 weeks. Figure 9 It can be easily seen that the proportion of level 3 in all abnormal voltage drop levels gradually decreases over time. Therefore, it can be determined that the vehicle battery of the vehicle has aged. After that, a battery aging prompt message can be sent to the user terminal and an alarm message can be sent to the vehicle terminal.

[0071] In another optional embodiment, after determining whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery in the first cycle, the aging of the vehicle battery can be verified based on the target low voltage state in the second cycle: based on the target low voltage state in the second cycle, the output trend of the target low voltage state in the second cycle can be determined, and when the target low voltage state gradually increases over time, it can be determined that the vehicle battery has aged, and then a battery aging prompt message can be sent to the user terminal, and an alarm message can be sent to the vehicle end. For example, Figure 10 As shown, Figure 10 It can output the trend graph of the cumulative number of low voltage states of the target in a single week for the past 25 weeks. Figure 10 In the figure, it can be easily seen that the target low voltage state gradually increases over time, so it can be determined that the vehicle battery of the vehicle has aged. After that, a battery aging prompt message can be sent to the user terminal and an alarm message can be sent to the vehicle terminal.

[0072] This embodiment provides a battery aging prediction method. This application provides a battery aging prediction method that obtains target voltage drop information within a first cycle, wherein the target voltage drop information represents the difference between the first battery voltages at two adjacent acquisition moments; based on the target voltage drop information within the first cycle, obtains the voltage drop distribution of the vehicle battery within the first cycle; and based on the voltage drop distribution of the vehicle battery within the first cycle, determines whether the vehicle battery is aged. Through the above method, the voltage drop distribution of the vehicle battery within the first cycle can be obtained based on the target voltage drop information within the first cycle, so as to determine whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery within the first cycle; because the target voltage drop information represents the difference between the first battery voltages at two adjacent acquisition moments, this difference can effectively reflect the change in the battery's internal resistance, thereby providing a more accurate basis for aging determination. Therefore, this application does not need to rely on complex dedicated sensors to collect battery parameters such as charging and discharging current, voltage, and internal resistance. It can quickly and accurately judge the battery aging status by simply collecting and analyzing the voltage drop distribution of the vehicle battery. This not only greatly reduces the vehicle's hardware cost and maintenance cost, but also significantly improves the efficiency and reliability of vehicle battery management.

[0073] See also Figure 11 , an embodiment of the present application provides a method for obtaining target voltage drop information, the method comprising: S210: Collecting a first battery voltage of the vehicle battery multiple times.

[0074] S220: Determine multiple voltage drop levels based on the difference between the first battery voltages at two adjacent acquisition moments, and add time stamps to the multiple voltage drop levels to obtain target voltage drop information.

[0075] S230: Sending the target voltage drop information to the cloud, so that the cloud executes the above-mentioned battery aging prediction method based on the target voltage drop information.

[0076] The first battery voltage may be the output voltage of the battery when the vehicle is in a power-off state.

[0077] In the present application, before collecting the first battery voltage of the vehicle battery multiple times, the vehicle battery voltage can be collected multiple times continuously. When it is monitored that the vehicle battery voltage collected multiple times continuously is lower than the first voltage threshold, the first battery voltage of the vehicle battery is collected multiple times.

[0078] Among them, the first voltage threshold can be 13V. In this application, when the battery voltage collected for 3 consecutive seconds is lower than 13V, it can be determined that the vehicle is in a power-off state.

[0079] In this application, since the low-voltage load of the vehicle is within 150w (13A) in more than 90% of scenarios within 60s after the vehicle is used normally and powered off (that is, the battery voltage jumps to below 13V), the voltage drop monitoring can be triggered when the power monitoring module monitors that the collected battery voltage is lower than 13V for 3 consecutive seconds. After the voltage drop monitoring is triggered, the first battery voltage of the vehicle battery is collected every 10s.

[0080] In an optional implementation, multiple voltage drop levels may be determined based on the difference between the first battery voltages at two adjacent acquisition moments, which may specifically include: If the difference is lower than the first voltage difference threshold, the voltage dip level at the current moment is determined to be a normal voltage dip level. If the difference is lower than the second voltage difference threshold and higher than the first voltage difference threshold, the voltage dip level at the current moment is determined to be the first level among abnormal voltage dip levels. If the difference is lower than the third voltage difference threshold and higher than the second voltage difference threshold, the voltage dip level at the current moment is determined to be the second level among abnormal voltage dip levels. If the difference is lower than the third voltage difference threshold and higher than the second voltage difference threshold, the voltage dip level at the current moment is determined to be the third level among abnormal voltage dip levels.

[0081] The first voltage difference threshold may be 500 mV, the second voltage difference threshold may be 1000 mV, and the third voltage difference threshold may be 1500 mV.

[0082] In this application, the first voltage difference threshold can be used as the demarcation point. If the difference is lower than the first voltage difference threshold, the current voltage drop level can be determined to be a normal voltage drop level; if the difference is higher than the first voltage difference threshold, the current voltage drop level can be determined to be an abnormal voltage drop level. Furthermore, within the normal voltage drop level, a difference between the first battery voltages at two adjacent acquisition times of less than 200mV can be considered Level 1 (normal voltage drop level), while a difference between the first battery voltages at two adjacent acquisition times of greater than 200mV and less than 500mV can be considered Level 2 (normal voltage drop level).

[0083] In the abnormal voltage drop level, when the difference is lower than the second voltage difference threshold and higher than the first voltage difference threshold, the voltage drop level at the current moment is determined to be the first level among the abnormal voltage drop levels, that is, level 3 (abnormal voltage drop level); when the difference is lower than the third voltage difference threshold and higher than the second voltage difference threshold, the voltage drop level at the current moment is determined to be the second level among the abnormal voltage drop levels, that is, level 4 (abnormal voltage drop level); when the difference is lower than the third voltage difference threshold and higher than the second voltage difference threshold, the voltage drop level at the current moment is determined to be the third level among the abnormal voltage drop levels, that is, it can be level 5 or level 6 (abnormal voltage drop level).

[0084] In summary, normal voltage drop levels and abnormal voltage drop levels can be further divided into: Level 1, Level 2, Level 3, Level 4, Level 5, and Level 6. Among them, if the difference between the first battery voltages at two adjacent collection moments is less than 200mV, it can be classified as Level 1 (normal voltage drop level); if the difference between the first battery voltages at two adjacent collection moments is greater than 200mV and less than 500mV, it can be classified as Level 2 (normal voltage drop level); if the difference between the first battery voltages at two adjacent collection moments is greater than 500mV and less than 1000mV, it can be classified as Level 3 (abnormal voltage drop level); if the difference between the first battery voltages at two adjacent collection moments is greater than 1000mV and less than 1500mV, it can be classified as Level 4 (abnormal voltage drop level); if the difference between the first battery voltages at two adjacent collection moments is greater than 1500mV and less than 2000mV, it can be classified as Level 5 (abnormal voltage drop level); and if the difference between the first battery voltages at two adjacent collection moments is greater than 2000mV, it can be classified as Level 6 (abnormal voltage drop level). The combination of the first level (level 3) and the second level (level 4) is the first abnormality level, and the combination of the second level (level 4) and the third level (level 5, level 6) is the second abnormality level.

[0085] Based on this, an embodiment of the present application further provides a method for obtaining target voltage drop information. In this method, after the above step S230 of "sending the target voltage drop information to the cloud, so that the cloud performs the above-mentioned battery aging prediction method based on the target voltage drop information", the following steps S240 to S250 may be further included: S240: After monitoring the vehicle to wake up, continuously collect the vehicle battery voltage multiple times.

[0086] S250: When the voltages of multiple vehicle batteries collected continuously are all lower than the second voltage threshold, a low voltage state is obtained, a timestamp is added to the low voltage state, a target low voltage state is obtained, and the target low voltage state is sent to the cloud, so that the cloud determines the state of the vehicle battery based on the target low voltage state.

[0087] The second voltage threshold can be 11V. In this application, the power monitoring module can sample the vehicle battery voltage every 10ms within 10s after monitoring the vehicle wake-up. If 50 consecutive sampling points are all below 11V, the low voltage flag is set to 1, otherwise it is set to 0. Afterwards, the low voltage status can be timestamped based on the remote communication terminal to obtain the target low voltage status, and the target low voltage status can be sent to the cloud, so that the cloud can determine the status of the vehicle battery based on the target low voltage status.

[0088] This embodiment provides a method for obtaining target voltage drop information. After repeatedly collecting the first battery voltage of a vehicle battery, the method determines multiple voltage drop levels based on the difference between the first battery voltages at two adjacent collection moments, adds timestamps to the multiple voltage drop levels, and obtains target voltage drop information. The target voltage drop information is then sent to a cloud server, so that the cloud server executes the aforementioned battery aging prediction method based on the target voltage drop information. This method allows the continuous acquisition of voltage drop differences to directly reflect changes in battery internal resistance, and combines timestamp information to achieve accurate trend analysis, thus avoiding the hardware costs associated with traditional current sensors. Furthermore, the acquired target voltage drop information and target low voltage status are sent to the cloud server, so that the cloud server collects and analyzes the voltage drop distribution of the vehicle battery based on the target voltage drop information and the target low voltage status. This allows for rapid and accurate judgment of the battery aging status, significantly reducing vehicle hardware and maintenance costs while significantly improving the efficiency and reliability of vehicle battery management.

[0089] In order to better understand the solutions of all embodiments of the present application, the basic business process of the battery aging prediction method and the target voltage drop information acquisition method of the present application is introduced below.

[0090] See also Figure 12The power supply monitoring module on the vehicle side can determine multiple voltage drop levels based on the difference between the first battery voltages at two adjacent collection moments after collecting the first battery voltage of the vehicle battery multiple times based on step S1, and can collect the vehicle battery voltage multiple times based on step S2. When the multiple continuously collected vehicle battery voltages are all lower than the second voltage threshold, a low voltage state is obtained. Thereafter, the remote communication terminal on the vehicle side can obtain target voltage drop information and target low voltage state after adding a timestamp to the voltage drop level and low voltage state based on the voltage drop level and low voltage state received from the power supply monitoring module, and send the target voltage drop information and target low voltage state to the cloud.

[0091] Afterwards, the battery aging prediction module in the cloud can obtain the target voltage drop information within the first cycle based on step S4, wherein the target voltage drop information represents the difference between the first battery voltages at two adjacent acquisition moments, and can obtain the voltage drop distribution of the vehicle battery within the first cycle based on the target voltage drop information within the first cycle in step S5. Finally, based on the voltage drop distribution of the vehicle battery within the first cycle in step S6, it can be determined whether the vehicle battery is aged.

[0092] See also Figure 13 The present application provides a battery aging prediction device 600, which includes: The vehicle cloud platform 610 is used to store the target voltage drop information sent by the vehicle end.

[0093] The battery aging prediction module 620 is configured to obtain target voltage drop information within a first cycle, where the target voltage drop information represents the difference between the first battery voltages at two adjacent acquisition moments; obtain a voltage drop distribution of the vehicle battery within the first cycle based on the target voltage drop information within the first cycle; and determine whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery within the first cycle.

[0094] See also Figure 14 The present application provides a device 800 for obtaining target voltage drop information, the device 800 comprising: The vehicle battery 810 is used to connect to the power monitoring module.

[0095] The power supply monitoring module 820 is used to collect the first battery voltage of the vehicle battery multiple times when it is monitored that the vehicle battery voltage collected multiple times in succession is lower than the first voltage threshold, and determine multiple voltage drop levels based on the difference between the first battery voltages collected twice adjacently; is used to collect multiple second battery voltages continuously after monitoring the vehicle being awakened, and obtain a low voltage state when the multiple second battery voltages collected continuously are lower than the second voltage threshold; and is used to send the multiple voltage drop levels and low voltage states to the remote communication terminal.

[0096] The remote communication terminal 830 is used to receive the voltage drop level and the low voltage status sent from the power supply monitoring module, add a timestamp to the voltage drop level and the low voltage status, obtain the target voltage drop information and the target low voltage status, and send the target voltage drop information and the target low voltage status to the cloud.

[0097] The following will be combined Figure 15 A vehicle provided in this application is described.

[0098] See also Figure 15 Based on the aforementioned battery aging prediction method, apparatus, and target voltage drop information acquisition method, apparatus, and apparatus, embodiments of the present application further provide another vehicle 100 capable of executing the aforementioned battery aging prediction method and target voltage drop information acquisition method. Vehicle 100 includes a processor 102, a memory 104, and a communication module 106. The memory 104 stores a program capable of executing the aforementioned embodiments, and the processor 102 can execute the program stored in the memory 104.

[0099] The processor 102 may include one or more processing cores. The processor 102 utilizes various interfaces and circuits to connect various components within the vehicle 100. It executes instructions, programs, code sets, or instruction sets stored in the memory 104 and accesses data stored in the memory 104 to perform various functions and process data for the vehicle 100. Optionally, the processor 102 may be implemented in the form of at least one of a network processor (NPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 102 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; the NPU is responsible for processing multimedia data such as video and images; and the modem is responsible for wireless communication. It is understandable that the above-mentioned modem may not be integrated into the processor 102, but may be implemented separately through a communication chip.

[0100] The memory 104 may include random access memory (RAM), read-only memory (ROM), and double data rate synchronous dynamic random access memory (DDR). The memory 104 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 104 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the various method embodiments described below, etc. The data storage area may also store data created by the vehicle 100 during use (such as a phone book, audio and video data, chat history data, etc.).

[0101] The communication module 106 can be used to implement information exchange between the vehicle 100 and other devices, for example, transmitting device control instructions, operation request instructions, and status information acquisition instructions, etc. When the other devices are different devices, the corresponding communication modules 106 may be different.

[0102] An embodiment of the present application provides a computer-readable storage medium having program code stored therein, wherein the program code can be invoked by a processor to execute the method described in the above method embodiment.

[0103] The computer-readable storage medium can be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer-readable storage medium has storage space for program codes for executing any of the method steps described above. These program codes can be read from or written to one or more computer program products. The program codes can be compressed, for example, in an appropriate form.

[0104] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1A step that specifies a function in one or more boxes.

[0107] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0108] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0109] The above is a detailed introduction to the battery aging prediction method, device and vehicle provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core ideas of the present application. At the same time, for general technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A battery aging prediction method, characterized in that: include: Acquire target voltage drop information within a first cycle, where the target voltage drop information represents a difference between first battery voltages at two adjacent acquisition moments; obtaining a voltage drop distribution of the vehicle battery within the first cycle based on the target voltage drop information within the first cycle; Whether the vehicle battery is aged is determined based on a voltage drop distribution of the vehicle battery during the first cycle.

2. The method according to claim 1, characterized in that The target voltage drop information includes at least a plurality of abnormal voltage drop levels. Based on the voltage drop distribution of the vehicle battery in the first cycle, determining whether the vehicle battery is aged includes: determining a state of the vehicle battery based on target voltage drop information within the first cycle; When the state of the vehicle battery is abnormal, obtaining a voltage drop distribution of the vehicle battery during the first cycle; When the voltage drop distribution indicates that a first abnormal level has a first proportion within the plurality of abnormal voltage drop levels that is included in a first preset proportion range, it is determined that the vehicle battery has aged.

3. The method according to claim 1, characterized in that The target voltage drop information includes at least a plurality of abnormal voltage drop levels. Based on the voltage drop distribution of the vehicle battery in the first cycle, determining whether the vehicle battery is aged includes: When a first proportion of the first abnormal level represented by the voltage drop distribution condition among the multiple abnormal voltage drop levels is included in a second preset proportion range, obtaining a duration of the voltage drop distribution condition; If the duration of the voltage drop profile is greater than a duration threshold, it is determined that the vehicle battery has aged.

4. The method according to any one of claims 1 to 3, characterized in that: The target voltage drop information further includes a plurality of normal voltage drop levels. Based on the voltage drop distribution of the vehicle battery during the first cycle, determining whether the vehicle battery is aged includes: When the voltage drop distribution indicates that the abnormal voltage drop level accounts for a proportion of the target voltage drop information that is lower than a first threshold, determining that the vehicle battery is not aged; When the voltage drop distribution indicates that the proportion of the abnormal voltage drop level in the target voltage drop information is higher than a first threshold and lower than a second threshold, it is determined that the vehicle battery is aged to a first degree; When the voltage drop distribution indicates that a proportion of abnormal voltage drop levels in the target voltage drop information is higher than a second threshold, it is determined that the vehicle battery is aged to a second degree.

5. The method according to claim 2, characterized in that Determining a state of the vehicle battery based on target voltage drop information within the first cycle includes: determining, based on a plurality of abnormal voltage drop levels in the target voltage drop information, a second proportion of the second abnormal level within the plurality of abnormal voltage drop levels; When the second proportion is higher than a second proportion threshold, it is determined that the state of the vehicle battery is abnormal.

6. The method according to claim 2, characterized in that Determining the state of the vehicle battery further includes: Acquire multiple target low voltage states within the first cycle; Based on a plurality of target low voltage states in the first cycle, obtaining a cumulative number of target low voltage states in the first cycle; When the cumulative number of target low voltage states in the first cycle is greater than a number threshold, it is determined that the state of the vehicle battery is abnormal.

7. The method according to claim 1, characterized in that After determining whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery in the first cycle, the method further includes: acquiring target voltage drop information within a second period, where the second period includes a plurality of the first periods; determining, based on the target voltage drop information within the second period, an output trend of the voltage drop distribution within the second period, the output trend of the voltage drop distribution being obtained by chronologically sorting a plurality of voltage drop distributions within the first period; When the output trend of the voltage drop distribution indicates that the vehicle battery has aged, a battery aging prompt message is sent to the user terminal, and an alarm message is sent to the vehicle terminal.

8. A method for obtaining target voltage drop information, characterized in that: include: collecting a first battery voltage of a vehicle battery multiple times; determining a plurality of voltage drop levels based on a difference between the first battery voltages at two adjacent acquisition moments, and adding time stamps to the plurality of voltage drop levels to obtain target voltage drop information; The target voltage drop information is sent to the cloud, so that the cloud executes the battery aging prediction method according to any one of claims 1 to 7 based on the target voltage drop information.

9. The method according to claim 8, characterized in that Before acquiring the first battery voltage of the vehicle battery multiple times, the method further includes: Continuously collect vehicle battery voltage multiple times; When monitoring that the vehicle battery voltage collected multiple times continuously is lower than the first voltage threshold, collecting the first battery voltage of the vehicle battery multiple times; Based on the difference between the first battery voltages at two adjacent acquisition moments, multiple voltage drop levels are determined, including: When the difference is lower than the first voltage difference threshold, determining that the voltage drop level at the current moment is a normal voltage drop level; When the difference is lower than the second voltage difference threshold and higher than the first voltage difference threshold, determining that the voltage drop level at the current moment is the first level among the abnormal voltage drop levels; When the difference is lower than the third voltage difference threshold and higher than the second voltage difference threshold, determining that the voltage drop level at the current moment is the second level among the abnormal voltage drop levels; When the difference is lower than the third voltage difference threshold and higher than the second voltage difference threshold, the voltage drop level at the current moment is determined to be the third level among the abnormal voltage drop levels; wherein the combination of the first level and the second level is the first abnormal level, and the combination of the second level and the third level is the second abnormal level.

10. The method according to claim 8, characterized in that Also includes: After the vehicle is detected to be awake, the vehicle battery voltage is collected multiple times continuously; When the voltages of multiple vehicle batteries collected continuously are all lower than the second voltage threshold, a low voltage state is obtained, a timestamp is added to the low voltage state, a target low voltage state is obtained, and the target low voltage state is sent to the cloud, so that the cloud determines the state of the vehicle battery based on the target low voltage state.

11. A battery aging prediction device, characterized in that: The device includes: a vehicle cloud platform and a battery aging prediction module; The vehicle cloud platform is used to store the target voltage drop information sent by the vehicle; The battery aging prediction module is configured to obtain target voltage drop information within a first cycle, the target voltage drop information representing a difference between first battery voltages at two adjacent acquisition moments; obtain a voltage drop distribution of the vehicle battery within the first cycle based on the target voltage drop information within the first cycle; and determine whether the vehicle battery is aged based on the voltage drop distribution of the vehicle battery within the first cycle.

12. A device for acquiring target voltage drop information, characterized in that: The device includes: a vehicle battery, a power monitoring module, and a remote communication terminal; a vehicle battery, connected to the power monitoring module; The power supply monitoring module is configured to, upon monitoring that the vehicle battery voltages collected multiple times are all below a first voltage threshold, collect a first battery voltage of the vehicle battery multiple times, and determine a plurality of voltage drop levels based on a difference between two adjacent collected first battery voltages; after monitoring that the vehicle is awake, collect a plurality of second battery voltages continuously, and determine a low voltage state if the plurality of second battery voltages collected continuously are all below a second voltage threshold; and transmit the plurality of voltage drop levels and the low voltage state to a remote communication terminal; The remote communication terminal is used to receive the voltage drop level and the low voltage status sent from the power supply monitoring module, add a timestamp to the voltage drop level and the low voltage status, obtain target voltage drop information and target low voltage status, and send the target voltage drop information and target low voltage status to the cloud.

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