Battery health diagnosis and prediction method and related device

Abstract

The embodiment of the invention relates to the technical field of power batteries, and provides a battery health diagnosis and prediction method and related device.The method comprises the steps that state evaluation parameters of a to-be-detected battery are obtained, the state evaluation parameters comprise a temperature parameter, a current parameter and a voltage parameter, and user use information of the to-be-detected battery is obtained, the battery state information is determined by adopting the temperature parameter, the current parameter and the voltage parameter, and prediction processing is performed by adopting the battery state information and the user use information, so that the health prediction result of the to-be-detected battery is obtained, and the accuracy of performing health diagnosis on the to-be-detected battery is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power batteries, and in particular to a battery health diagnosis and prediction method and related devices. Background Art

[0002] The power battery is the power source that provides power for the tool. It is mainly different from the starting battery used to start the car engine. It mostly adopts valve-sealed lead-acid batteries, open tubular lead-acid batteries and lithium iron phosphate batteries. With the rapid development of the electric vehicle industry, the use of power batteries is getting higher and higher. When using the power battery, it may cause itself to enter the battery thermal failure state. Once the battery enters thermal failure, the entire battery will be scrapped, and the number of battery charge and discharge times will be seriously affected.

[0003] In existing solutions, the working principle of the power battery is usually analyzed to achieve health diagnosis and prediction of the battery to be tested. However, when the power battery is working, it will be affected by external environment or human factors, which will cause the battery life to end prematurely, resulting in low accuracy in health diagnosis and prediction of the battery to be tested. Summary of the invention

[0004] The embodiments of the present application provide a battery health diagnosis prediction method and related devices, which can automatically predict the health of a battery to be detected based on the state evaluation parameters of the battery to be detected, thereby obtaining a health prediction result of the battery to be detected, thereby improving the accuracy of health diagnosis of the battery to be detected.

[0005] A first aspect of an embodiment of the present application provides a battery health diagnosis and prediction method, the method comprising:

[0006] Acquire state evaluation parameters of the battery to be detected, the state evaluation parameters including temperature parameters, current parameters and voltage parameters, and acquire user usage information of the battery to be detected;

[0007] Determine battery status information using the temperature parameter, current parameter and voltage parameter;

[0008] The battery status information and the user usage information are used to perform prediction processing to obtain a health prediction result of the battery to be tested.

[0009] In this example, by obtaining status evaluation parameters of the battery to be detected, which include temperature parameters, current parameters and voltage parameters, and obtaining user usage information of the battery to be detected, the temperature parameters, current parameters and voltage parameters are used to determine the battery status information, and the battery status information and the user usage information are used to perform predictive processing, so as to obtain a health prediction result of the battery to be detected, thereby improving the accuracy of health diagnosis of the battery to be inspected.

[0010] In a possible implementation, a method for determining battery status information using the temperature parameter, current parameter, and voltage parameter includes:

[0011] Determining temperature change information of the battery to be detected according to the temperature parameter;

[0012] Determine the current and voltage change information of the battery to be detected according to the current parameter and the voltage parameter;

[0013] The battery status information is determined according to the temperature change information and the current and voltage change information.

[0014] In a possible implementation manner, a method for determining temperature change information of the battery to be detected according to the temperature parameter includes:

[0015] Performing clustering processing on the temperature parameters to obtain k cluster centers;

[0016] Extract the temperature value corresponding to each of the k cluster centers to obtain a set of k temperature values;

[0017] Obtaining the average temperature value of each temperature value set in the k temperature value sets to obtain k first average temperature values;

[0018] Determine temperature fluctuation information according to k average temperature values ​​and corresponding temperature value sets, and obtain k target temperature fluctuation information;

[0019] The temperature change information is determined according to k target temperature fluctuation information.

[0020] In a possible implementation, the determining the battery status information according to the temperature change information and the current and voltage change information includes:

[0021] Determine the temperature change concentration point according to the temperature change information to obtain a temperature change concentration point set, and determine the current and voltage change concentration point according to the current and voltage change information to obtain a current and voltage change concentration point set;

[0022] Determine a temperature state transition diagram according to the temperature change concentration point set, and determine a current and voltage change transition diagram according to the current and voltage change concentration point set;

[0023] Determine a battery state transition diagram according to the temperature state transition diagram and the current and voltage change transition diagram;

[0024] The battery status information is determined according to the battery status transition diagram.

[0025] In a possible implementation, a method for performing prediction processing using the battery status information and the user usage information to obtain a health prediction result of the battery to be tested includes:

[0026] Extracting a state evaluation factor corresponding to the user usage information;

[0027] Determine a first reference health status prediction result according to the status evaluation factor and the user usage information;

[0028] Using the battery status information to make a prediction according to a preset battery status prediction model to obtain a second reference health status prediction result;

[0029] The first reference health state prediction result and the second reference health state prediction result are fused to obtain the health state prediction result of the battery to be tested.

[0030] A second aspect of an embodiment of the present application provides a battery health diagnosis and prediction device, the device comprising:

[0031] An acquisition unit, used to acquire state evaluation parameters of the battery to be detected, the state evaluation parameters including temperature parameters, current parameters and voltage parameters, and to acquire user usage information of the battery to be detected;

[0032] a determination unit, configured to determine battery status information using the temperature parameter, current parameter, and voltage parameter;

[0033] The processing unit is used to perform prediction processing using the battery status information and the user usage information to obtain a health prediction result of the battery to be tested.

[0034] In a possible implementation manner, in the aspect of determining the battery status information by using the temperature parameter, the current parameter and the voltage parameter, the determining unit is specifically configured to:

[0035] Determining temperature change information of the battery to be detected according to the temperature parameter;

[0036] Determine the current and voltage change information of the battery to be detected according to the current parameter and the voltage parameter;

[0037] The battery status information is determined according to the temperature change information and the current and voltage change information.

[0038] In a possible implementation manner, in determining the temperature change information of the battery to be detected according to the temperature parameter, the determining unit is specifically configured to:

[0039] Performing clustering processing on the temperature parameters to obtain k cluster centers;

[0040] Extract the temperature value corresponding to each of the k cluster centers to obtain a set of k temperature values;

[0041] Obtaining the average temperature value of each temperature value set in the k temperature value sets to obtain k first average temperature values;

[0042] Determine temperature fluctuation information according to k average temperature values ​​and corresponding temperature value sets, and obtain k target temperature fluctuation information;

[0043] The temperature change information is determined according to k target temperature fluctuation information.

[0044] In a possible implementation manner, in determining the battery status information according to the temperature change information and the current and voltage change information, the determining unit is specifically configured to:

[0045] Determine the temperature change concentration point according to the temperature change information to obtain a temperature change concentration point set, and determine the current and voltage change concentration point according to the current and voltage change information to obtain a current and voltage change concentration point set;

[0046] Determine a temperature state transition diagram according to the temperature change concentration point set, and determine a current and voltage change transition diagram according to the current and voltage change concentration point set;

[0047] Determine a battery state transition diagram according to the temperature state transition diagram and the current and voltage change transition diagram;

[0048] The battery status information is determined according to the battery status transition diagram.

[0049] In a possible implementation manner, in performing prediction processing using the battery status information and the user usage information to obtain a health prediction result of the battery to be tested, the processing unit is specifically configured to:

[0050] Extracting a state evaluation factor corresponding to the user usage information;

[0051] Determine a first reference health status prediction result according to the status evaluation factor and the user usage information;

[0052] Using the battery status information to make a prediction according to a preset battery status prediction model to obtain a second reference health status prediction result;

[0053] The first reference health state prediction result and the second reference health state prediction result are fused to obtain the health state prediction result of the battery to be tested.

[0054] A third aspect of an embodiment of the present application provides a terminal, comprising a processor, an input device, an output device and a memory, wherein the processor, input device, output device and memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the step instructions in the first aspect of the embodiment of the present application.

[0055] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiments of the present application.

[0056] A fifth aspect of the embodiments of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 A flowchart of a battery health diagnosis and prediction method is provided for an embodiment of the present application;

[0059] Figure 2 A schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0060] Figure 3 A structural schematic diagram of a battery health diagnosis and prediction device is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0062] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0063] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0064] In order to better understand a battery health diagnosis prediction method provided by an embodiment of the present application, the following first briefly introduces the health diagnosis prediction method for power batteries in existing solutions. In existing solutions, the health diagnosis prediction of the battery to be tested is usually achieved by analyzing the working principle of the power battery. However, when the power battery is working, it will be affected by external environment or human factors, which will cause the battery life to end prematurely, resulting in low accuracy in the health diagnosis prediction of the battery to be tested.

[0065] In order to solve the above-mentioned technical problems, an embodiment of the present application provides a battery health diagnosis and prediction method, which can automatically predict the health of a battery to be detected based on the state evaluation parameters of the battery to be detected, thereby obtaining a health prediction result of the battery to be detected, thereby improving the accuracy of health diagnosis of the battery to be detected.

[0066] See also Figure 1 , Figure 1 A flowchart of a battery health diagnosis and prediction method is provided for an embodiment of the present application. Figure 1 As shown, the method includes:

[0067] 101. Obtain state evaluation parameters of a battery to be detected, where the state evaluation parameters include temperature parameters, current parameters, and voltage parameters, and obtain user usage information of the battery to be detected.

[0068] Among them, the server may obtain the reading of the battery monitoring sensor carried on the vehicle through the wireless network, so as to obtain the state evaluation parameters of the battery to be tested. Specifically, the server may obtain the reading of the battery monitoring sensor carried on the vehicle through the TBOX (Telematics Box, Internet of Vehicles terminal) carried on the vehicle using the 4G / 5G wireless transmission network to obtain the state evaluation parameters of the battery to be tested. Among them, the battery monitoring sensors carried on the vehicle include temperature sensors, voltage sensors and current sensors, and the temperature sensors, voltage sensors and current sensors may be universal sensors; the state evaluation parameters include temperature parameters, current parameters and voltage parameters.

[0069] The user usage information of the battery to be detected may be obtained by acquiring the user usage time, user charging time, user usage environment temperature, etc. of the battery to be detected.

[0070] 102. Determine battery status information using the temperature parameter, current parameter, and voltage parameter.

[0071] Among them, the temperature change information corresponding to the battery to be detected can be obtained by clustering the temperature parameters, the voltage and current change information can be determined according to the current parameters and the voltage parameters, the temperature state transfer change diagram and the voltage and current state transfer change diagram corresponding to the battery to be detected can be determined according to the temperature change information and the voltage and current change information, a battery state transfer change diagram can be determined according to the temperature state transfer change diagram and the voltage and current state transfer change diagram, and then the battery state information of the battery to be detected can be obtained according to the battery state transfer change diagram.

[0072] 103. Perform prediction processing using the battery status information and the user usage information to obtain a health prediction result of the battery to be tested.

[0073] Among them, it can be that by extracting the status evaluation factor corresponding to the user usage information, the health status of the battery to be detected is predicted according to the status evaluation factor and the user usage information to obtain a first reference health status prediction result, a preset battery status prediction model is used to predict the health status of the battery to be detected using the battery status information to obtain a second reference health status prediction result, and the first reference health status prediction result and the second reference health status prediction result are fused to obtain the health prediction result of the battery to be detected.

[0074] In this example, by obtaining status evaluation parameters of the battery to be detected, which include temperature parameters, current parameters and voltage parameters, and obtaining user usage information of the battery to be detected, the temperature parameters, current parameters and voltage parameters are used to determine the battery status information, and the battery status information and the user usage information are used to perform predictive processing, so as to obtain a health prediction result of the battery to be detected, thereby improving the accuracy of health diagnosis of the battery to be inspected.

[0075] In a possible implementation, a method for determining battery status information using the temperature parameter, current parameter, and voltage parameter includes:

[0076] A1. Determine the temperature change information of the battery to be tested according to the temperature parameter;

[0077] A2. Determine the current and voltage change information of the battery to be tested according to the current parameter and the voltage parameter;

[0078] A3. Determine the battery status information according to the temperature change information and the current and voltage change information.

[0079] Specifically, the temperature parameters may be clustered by using a clustering method to obtain k target temperature fluctuation information, and then the temperature change information of the battery to be detected is determined by the k target temperature fluctuation information.

[0080] While obtaining the temperature change information of the battery to be detected, the current change information corresponding to the battery to be detected can be determined according to the current parameter information by adopting the same method as that for obtaining the temperature change information of the battery to be detected, and the voltage change information corresponding to the battery to be detected can be determined according to the voltage parameter information, and then the current change information and the voltage change information can be combined according to the time change rules corresponding to the current parameters and the voltage parameters to obtain the current and voltage change information.

[0081] After obtaining the temperature change information of the battery to be detected and the current and voltage change information of the battery to be detected, it is possible to obtain a temperature state transition diagram corresponding to the battery to be detected based on the temperature change information of the battery to be detected, and obtain a current and voltage state transition diagram corresponding to the battery to be detected based on the current and voltage change information of the battery to be detected, and then determine the battery status information based on the temperature state transition diagram and the current and voltage state transition diagram.

[0082] In a possible implementation, a method for determining temperature change information of the battery to be detected according to the temperature parameter includes:

[0083] B1. Clustering the temperature parameters to obtain k cluster centers;

[0084] B2. Extract the temperature value corresponding to each of the k cluster centers to obtain a set of k temperature values;

[0085] B3, obtaining the average temperature value of each temperature value set in the k temperature value sets, and obtaining k first average temperature values;

[0086] B4. Determine temperature fluctuation information according to the k average temperature values ​​and the corresponding temperature value set to obtain k target temperature fluctuation information;

[0087] B5. Determine the temperature change information according to the k target temperature fluctuation information.

[0088] Among them, it can be obtained by calculating the similarity between each temperature value in the temperature parameter, and the similarity set is obtained. The temperature parameters are clustered according to the similarity set and the preset similarity threshold, and the temperature values ​​corresponding to the similarity set greater than or equal to the preset similarity threshold are determined as elements in the same cluster, and the average value corresponding to the temperature value in each cluster is calculated to obtain k cluster centers; wherein the similarity between each temperature value in the temperature parameter can be characterized by the external environment temperature. Specifically, the similarity between each temperature value in the temperature parameter can be calculated according to the method shown in the following formula to obtain the similarity set:

[0089]

[0090] β in the formula i Indicates the similarity between each temperature value in the temperature parameter; δ i represents the difference factor; Q i Indicates the number of temperature values ​​in the temperature parameter; a i Indicates the i-th temperature value in the temperature parameter; b i Indicates the external environment temperature corresponding to the i-th temperature value in the temperature parameter; |a i |′ represents the average value of all temperature values ​​in the temperature parameter; |b i |′ represents the average value corresponding to the external temperature value.

[0091] After k cluster centers are obtained, k first average temperature values ​​may be obtained by averaging the temperature value of each temperature value set in each cluster corresponding to the k cluster centers.

[0092] After obtaining k first average temperature values, k target temperature fluctuation information may be obtained by acquiring the absolute value of the difference between the k average temperature values ​​and the temperature values ​​in the corresponding temperature value set.

[0093] After obtaining k target temperature fluctuation information, the k target temperature fluctuation information may be normalized using a common normalization method to obtain temperature change information corresponding to the battery to be detected.

[0094] In this example, k cluster centers are obtained by clustering the temperature values ​​in the temperature parameters, k temperature value sets are determined based on the k cluster centers, and k target temperature fluctuation information are obtained based on the k temperature value sets and the temperature average values ​​corresponding to each temperature value set in the k temperature value sets. Then, the temperature change information corresponding to the battery to be inspected is determined based on the k target temperature fluctuation information, thereby improving the accuracy of health diagnosis of the battery to be inspected.

[0095] In a possible implementation, a method for determining the battery status information according to the temperature change information and the current and voltage change information includes:

[0096] C1. determining a temperature change concentration point according to the temperature change information to obtain a temperature change concentration point set, and determining a current and voltage change concentration point according to the current and voltage change information to obtain a current and voltage change concentration point set;

[0097] C2. Determine a temperature state transition diagram based on the temperature change concentration point set, and determine a current and voltage change transfer diagram based on the current and voltage change concentration point set;

[0098] C3. Determine a battery state transition diagram according to the temperature state transition diagram and the current and voltage change transition diagram;

[0099] C4. Determine the battery status information according to the battery status transition diagram.

[0100] Among them, the temperature change concentration point set can be obtained by obtaining the temperature value corresponding to the concentrated temperature change in the temperature change information, and the current and voltage values ​​corresponding to the concentrated current and voltage changes in the current and voltage change information can be obtained to obtain the current and voltage change concentration point set.

[0101] After obtaining the current and voltage change concentration point set and the temperature change concentration point set, the probability of occurrence of the temperature corresponding to each temperature change concentration point in the temperature change concentration point set and the transfer condition can be obtained, and the temperature state transition diagram can be determined using a state transition diagram generation model according to the probability of occurrence of the temperature corresponding to each temperature change concentration point in the temperature change concentration point set and the transfer condition, and then the current and voltage change transfer diagram can be obtained by using the same method as that for obtaining the temperature state transition diagram.

[0102] After obtaining the current and voltage change transfer diagram and the temperature state transfer diagram, a reference battery state transfer node set can be obtained by arranging and combining each current and voltage change transfer node in the current and voltage change transfer diagram with each temperature state transfer node in the temperature state transfer diagram, and using a preset data clearing model to clear unreasonable reference battery state transfer nodes in the reference battery state transfer node set to obtain a target battery state transfer node set, and using a state transfer diagram generation model to generate a battery state transfer diagram corresponding to the battery to be tested according to the target battery state transfer node set.

[0103] After obtaining the battery state transition diagram corresponding to the battery to be detected, the current state information corresponding to the battery to be detected can be obtained in the battery state transition diagram according to the current temperature value, current value and voltage value of the battery to be detected to obtain the battery state information.

[0104] In this example, a temperature state transfer diagram and a current and voltage change transfer diagram of the battery to be tested are constructed by using a preset state transition diagram, and then a battery state transfer diagram corresponding to the battery to be tested is generated based on the temperature state transfer diagram and the current and voltage change transfer diagram, and the battery state information is obtained based on the battery state transfer diagram, thereby improving the accuracy of health diagnosis of the battery to be inspected.

[0105] In a possible implementation, a method for performing prediction processing using the battery status information and the user usage information to obtain a health prediction result of the battery to be tested includes:

[0106] D1. extracting a status evaluation factor corresponding to the user usage information;

[0107] D2. determining a first reference health status prediction result according to the status evaluation factor and the user usage information;

[0108] D3. Using the battery status information to make a prediction according to a preset battery status prediction model to obtain a second reference health status prediction result;

[0109] D4. Fusing the first reference health state prediction result and the second reference health state prediction result to obtain a health prediction result of the battery to be tested.

[0110] The influence weights of various factors in the user usage information on the battery status information may be extracted from a preset database to obtain the status evaluation factor. The various factors in the user usage information may be user usage time, user charging time, user usage environment temperature, etc.

[0111] After obtaining the state evaluation factor corresponding to the user usage information, the health state corresponding to the battery to be tested may be predicted according to the state evaluation factor and the user usage information by using mechanism analysis to obtain a first reference health state prediction result.

[0112] While obtaining the first reference health state prediction result, the battery state information of the battery to be detected may be predicted using the battery state information as an input parameter of a preset battery state prediction model to obtain a second reference health state prediction result.

[0113] After obtaining the first reference health state prediction result and the second reference health state prediction result, the first reference health state prediction result and the second reference health state prediction result may be fused by the method shown in the following formula to obtain the health prediction result of the battery to be tested:

[0114]

[0115] In the formula, ω2 represents the weight information corresponding to the second reference health status prediction result; sin represents the sine function in the trigonometric function; η represents the number of iterations of the preset battery status prediction model when predicting the health status of the battery to be tested; π represents the pi; ω1 represents the weight information corresponding to the first reference health status prediction result; S3 represents the health prediction result of the battery to be tested; S1 represents the first reference health status prediction result; S2 represents the second reference health status prediction result.

[0116] In this example, the health status of the battery to be tested is predicted based on the user usage information and the status assessment factor corresponding to the user usage information to obtain a first reference health status prediction result, and then the health status of the battery to be tested is predicted using a preset battery status prediction model to obtain a second reference health status prediction result, and the first reference health status prediction result and the second reference health status prediction result are fused to obtain a health prediction result of the battery to be tested, thereby improving the accuracy of health diagnosis of the battery to be tested.

[0117] For the above embodiments, please refer to Figure 2 , Figure 2 A schematic diagram of the structure of a terminal provided in an embodiment of the present application, such as Figure 2 As shown, it includes a processor, an input device, an output device and a memory, which are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the program includes instructions for executing the following steps;

[0118] Acquire state evaluation parameters of the battery to be detected, the state evaluation parameters including temperature parameters, current parameters and voltage parameters, and acquire user usage information of the battery to be detected;

[0119] Determine battery status information using the temperature parameter, current parameter and voltage parameter;

[0120] The battery status information and the user usage information are used to perform prediction processing to obtain a health prediction result of the battery to be tested.

[0121] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that in order to realize the above functions, the terminal includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0122] The embodiment of the present application can divide the terminal into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0123] In line with the above, see Figure 3 , Figure 3 The present invention provides a schematic diagram of a battery health diagnosis and prediction device. Figure 3 As shown, the device comprises:

[0124] An acquisition unit 301 is used to acquire state evaluation parameters of a battery to be detected, wherein the state evaluation parameters include temperature parameters, current parameters and voltage parameters, and to acquire user usage information of the battery to be detected;

[0125] A determination unit 302, configured to determine battery status information using the temperature parameter, current parameter, and voltage parameter;

[0126] The processing unit 303 is used to perform prediction processing using the battery status information and the user usage information to obtain a health prediction result of the battery to be tested.

[0127] In a possible implementation, the determining unit 302 is specifically configured to:

[0128] Determining temperature change information of the battery to be detected according to the temperature parameter;

[0129] Determine the current and voltage change information of the battery to be detected according to the current parameter and the voltage parameter;

[0130] The battery status information is determined according to the temperature change information and the current and voltage change information.

[0131] In a possible implementation, in determining the temperature change information of the battery to be detected according to the temperature parameter, the determining unit 302 is specifically configured to:

[0132] Performing clustering processing on the temperature parameters to obtain k cluster centers;

[0133] Extract the temperature value corresponding to each of the k cluster centers to obtain a set of k temperature values;

[0134] Obtaining the average temperature value of each temperature value set in the k temperature value sets to obtain k first average temperature values;

[0135] Determine temperature fluctuation information according to k average temperature values ​​and corresponding temperature value sets, and obtain k target temperature fluctuation information;

[0136] The temperature change information is determined according to k target temperature fluctuation information.

[0137] In a possible implementation, in determining the battery status information according to the temperature change information and the current and voltage change information, the determining unit 302 is specifically configured to:

[0138] Determine the temperature change concentration point according to the temperature change information to obtain a temperature change concentration point set, and determine the current and voltage change concentration point according to the current and voltage change information to obtain a current and voltage change concentration point set;

[0139] Determine a temperature state transition diagram according to the temperature change concentration point set, and determine a current and voltage change transition diagram according to the current and voltage change concentration point set;

[0140] Determine a battery state transition diagram according to the temperature state transition diagram and the current and voltage change transition diagram;

[0141] The battery status information is determined according to the battery status transition diagram.

[0142] In a possible implementation, the processing unit 303 is specifically configured to:

[0143] Extracting a state evaluation factor corresponding to the user usage information;

[0144] Determine a first reference health status prediction result according to the status evaluation factor and the user usage information;

[0145] Using the battery status information to make a prediction according to a preset battery status prediction model to obtain a second reference health status prediction result;

[0146] The first reference health state prediction result and the second reference health state prediction result are fused to obtain the health state prediction result of the battery to be tested.

[0147] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any battery health diagnosis and prediction method recorded in the above method embodiments.

[0148] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program enables a computer to execute part or all of the steps of any battery health diagnosis and prediction method recorded in the above method embodiments.

[0149] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0150] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0151] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0152] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] In addition, the functional units in the various embodiments of the application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software program modules.

[0154] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0155] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which can include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0156] The embodiments of the present application are introduced in detail above. 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 idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method 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 health diagnosis and prediction method, characterized in that: The method comprises: Acquire state evaluation parameters of the battery to be detected, the state evaluation parameters including temperature parameters, current parameters and voltage parameters, and acquire user usage information of the battery to be detected; Determine battery status information using the temperature parameter, current parameter and voltage parameter; The battery status information and the user usage information are used to perform prediction processing to obtain a health prediction result of the battery to be tested.

2. The battery health diagnosis and prediction method according to claim 1, characterized in that: The method of using the temperature parameter, the current parameter and the voltage parameter to determine the battery status information includes: Determining temperature change information of the battery to be detected according to the temperature parameter; Determine the current and voltage change information of the battery to be detected according to the current parameter and the voltage parameter; The battery status information is determined according to the temperature change information and the current and voltage change information.

3. The battery health diagnosis and prediction method according to claim 2, characterized in that: The step of determining the temperature change information of the battery to be detected according to the temperature parameter includes: Performing clustering processing on the temperature parameters to obtain k cluster centers; Extract the temperature value corresponding to each of the k cluster centers to obtain a set of k temperature values; Obtaining the average temperature value of each temperature value set in the k temperature value sets to obtain k first average temperature values; Determine temperature fluctuation information according to k average temperature values ​​and corresponding temperature value sets, and obtain k target temperature fluctuation information; The temperature change information is determined according to k target temperature fluctuation information.

4. The battery health diagnosis and prediction method according to claim 3, characterized in that: The determining the battery status information according to the temperature change information and the current and voltage change information includes: Determine the temperature change concentration point according to the temperature change information to obtain a temperature change concentration point set, and determine the current and voltage change concentration point according to the current and voltage change information to obtain a current and voltage change concentration point set; Determine a temperature state transition diagram according to the temperature change concentration point set, and determine a current and voltage change transition diagram according to the current and voltage change concentration point set; Determine a battery state transition diagram according to the temperature state transition diagram and the current and voltage change transition diagram; The battery status information is determined according to the battery status transition diagram.

5. The battery health diagnosis and prediction method according to any one of claims 1 to 4, characterized in that: The battery status information and the user usage information are used to perform prediction processing to obtain a health prediction result of the battery to be tested, including: Extracting a state evaluation factor corresponding to the user usage information; Determine a first reference health status prediction result according to the status evaluation factor and the user usage information; Using the battery status information to make a prediction according to a preset battery status prediction model to obtain a second reference health status prediction result; The first reference health state prediction result and the second reference health state prediction result are fused to obtain the health state prediction result of the battery to be tested.

6. A battery health diagnosis and prediction device, characterized in that: The device comprises: An acquisition unit, used to acquire state evaluation parameters of the battery to be detected, the state evaluation parameters including temperature parameters, current parameters and voltage parameters, and to acquire user usage information of the battery to be detected; a determination unit, configured to determine battery status information using the temperature parameter, current parameter, and voltage parameter; A processing unit is used to perform prediction processing using the battery status information and the user usage information to obtain a health prediction result of the battery to be tested.

7. The battery health diagnosis and prediction method according to claim 6, characterized in that: In the aspect of determining the battery status information by using the temperature parameter, the current parameter and the voltage parameter, the determining unit is specifically used for: Determining temperature change information of the battery to be detected according to the temperature parameter; Determine the current and voltage change information of the battery to be detected according to the current parameter and the voltage parameter; The battery status information is determined according to the temperature change information and the current and voltage change information.

8. The battery health diagnosis and prediction method according to claim 7, characterized in that: In determining the temperature change information of the battery to be detected according to the temperature parameter, the determining unit is specifically used for: Performing clustering processing on the temperature parameters to obtain k cluster centers; Extract the temperature value corresponding to each of the k cluster centers to obtain a set of k temperature values; Obtaining the average temperature value of each temperature value set in the k temperature value sets to obtain k first average temperature values; Determine temperature fluctuation information according to k average temperature values ​​and corresponding temperature value sets, and obtain k target temperature fluctuation information; The temperature change information is determined according to k target temperature fluctuation information.

9. A terminal, characterized in that: The method comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 5.

Images