A battery consistency evaluation method, device and equipment

Abstract

An embodiment of the present application provides a battery consistency assessment method, comprising: obtaining multiple charging event data of the same battery within a preset time period; determining characteristic data of the charging event based on the charging event data, the characteristic data of the charging event including a temperature difference characteristic value and a current characteristic value of the charging event, the temperature difference characteristic value of the charging event characterizing the temperature difference characteristics of multiple single cells in the battery during the charging event; and determining a consistency assessment result of the battery based on the multiple characteristic data of the charging events.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the technical field of battery performance testing, and more specifically, to a battery consistency evaluation method and device. Background Art

[0002] Battery consistency refers to the fact that when single cells of uniform specifications and models are used to form a battery pack, there are certain differences in parameters such as voltage, state of charge, maximum remaining capacity and its decay rate, internal resistance and its change rate, lifespan, temperature influence, and self-discharge rate.

[0003] The difference in initial state consistency of battery cells in the battery pack due to the production process and the difference in consistency during subsequent use due to the structural design will significantly affect the overall performance of the battery. Specifically, it manifests in hazards such as large capacity attenuation of the battery pack, life attenuation, and thermal runaway of the battery caused by local overheating of the battery pack. This will cause hazards such as capacity loss, life loss, and increased internal resistance.

[0004] Therefore, it is necessary to evaluate the consistency of the battery to provide a reference for evaluating the performance and safety status of the battery. Summary of the invention

[0005] An object of the present disclosure is to provide a new technical solution for a battery consistency evaluation method, device and equipment.

[0006] According to a first aspect of the present disclosure, an embodiment of a method for evaluating battery consistency is provided, comprising:

[0007] Acquire multiple charging event data of the same battery within a preset time period;

[0008] Determining characteristic data of a charging event according to the charging event data, wherein the characteristic data of the charging event includes a temperature difference characteristic value and a current characteristic value of the charging event, and the temperature difference characteristic value of the charging event represents a temperature difference characteristic of a plurality of single cells in the battery during the charging event;

[0009] A consistency evaluation result of the battery is determined according to the characteristic data of the plurality of charging events.

[0010] Optionally, the charging event data includes a plurality of charging data acquired from the charging event, and the charging data includes a sampling time and a temperature of each temperature sampling point corresponding to the sampling time;

[0011] The determining characteristic data of the charging event according to the charging event data includes:

[0012] determining, based on the charging data, a maximum temperature difference between a plurality of temperature sampling points in the battery at the sampling time;

[0013] An average value or a median value of the maximum temperature difference corresponding to a plurality of sampling moments in the charging event is determined as a temperature characteristic value of the charging event.

[0014] Optionally, the charging data includes a charging current corresponding to the sampling moment;

[0015] The determining characteristic data of the charging event according to the charging event data further includes:

[0016] An average value or a median value of the charging current corresponding to a plurality of sampling moments in the charging event is determined as a current characteristic value of the charging event.

[0017] Optionally, determining the consistency evaluation result of the battery according to the characteristic data of the plurality of charging events includes:

[0018] The plurality of feature data are divided into N categories by using a maximum expectation algorithm to obtain N feature data sets, where N is an integer and N≥2;

[0019] Determine whether the target feature data is feature data of temperature difference abnormality according to the expectation and covariance of the nth feature data set and the temperature difference feature value of the target feature data; the nth feature data set is any feature data set among the N feature data sets, and the target feature data is any feature data among the nth feature data set;

[0020] The consistency evaluation result of the battery is determined according to the number of characteristic data of the temperature difference abnormality occurring in the battery within the preset time period.

[0021] Optionally, N is 3; before classifying the plurality of feature data into N categories by using a maximum expectation algorithm, the method further includes:

[0022] According to the distribution of the charging data, the expected initial value and initial covariance value of the first characteristic data set, the expected initial value and initial covariance value corresponding to the second characteristic data set, and the expected initial value and initial covariance value corresponding to the third characteristic data set are estimated.

[0023] Optionally, the method further comprises:

[0024] The expectation and covariance corresponding to each classification of the same type of battery in the plurality of preset time periods are obtained to determine the consistency change trend of the same type of battery.

[0025] According to a second aspect of the present disclosure, there is provided an embodiment of a battery consistency assessment device, comprising:

[0026] A first acquisition module is used to acquire multiple charging event data of the same type of battery within a preset time period;

[0027] A first determining module is used to determine characteristic data of a charging event according to the charging event data, wherein the characteristic data of the charging event includes a temperature difference characteristic value and a current characteristic value of the charging event, and the temperature difference characteristic value of the charging event represents the temperature difference characteristics of multiple single cells in the battery during the charging event;

[0028] The second determination module is used to determine the consistency evaluation result of the battery according to the characteristic data of the plurality of charging events.

[0029] Optionally, the charging event data includes a plurality of charging data acquired from the charging event, and the charging data includes a sampling time and a temperature of each temperature sampling point corresponding to the sampling time;

[0030] The first determination module includes a temperature difference determination submodule, and the temperature difference determination submodule is specifically used to:

[0031] determining, based on the charging data, a maximum temperature difference between a plurality of temperature sampling points in the battery at the sampling time;

[0032] An average value or a median value of the maximum temperature difference corresponding to a plurality of sampling moments in the charging event is determined as a temperature characteristic value of the charging event.

[0033] Optionally, the charging event data includes a charging current corresponding to the sampling moment;

[0034] The first determination module includes a current determination submodule, and the current determination submodule is used to:

[0035] An average value or a median value of the charging current corresponding to a plurality of sampling moments in the charging event is determined as a current characteristic value of the charging event.

[0036] According to a third aspect of the present disclosure, an embodiment of an electronic device is provided, comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein when the program or instruction is executed by the processor, the steps of the battery consistency assessment method as described in any one of 1 to 6 of the first aspect of this specification are implemented.

[0037] One beneficial effect of the disclosed embodiment is that by acquiring multiple charging event data of the same battery within a preset time period, the temperature difference characteristic value and current characteristic value of the charging event are determined according to the charging event data, and the battery consistency evaluation result is determined according to the characteristic data of the multiple charging events. In this way, batteries with poor consistency in the same time period can be found. Compared with using instantaneous data, the median or average statistical results used in this embodiment have higher credibility. Thus, a more accurate battery consistency evaluation result can be provided.

[0038] Other features and advantages of the present specification will become apparent from the following detailed description of exemplary embodiments of the present specification with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the specification and, together with the description, serve to explain the principles of the specification.

[0040] Figure 1 is a block diagram of an exemplary hardware configuration of a communication system;

[0041] Figure 2 is a flow chart of a method for evaluating the consistency of a battery according to an embodiment of the present disclosure;

[0042] Figure 3 is a schematic structural diagram of a battery consistency evaluation device according to an embodiment of the present disclosure;

[0043] Figure 4 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0044] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated.

[0045] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0046] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0047] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0048] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0049] <Hardware Configuration>

[0050] like Figure 1 As shown, the communication system 100 includes a server 1000 , a terminal device 2000 , and a vehicle 3000 .

[0051] The server 1000 may be a monolithic server or a distributed server across multiple computers or computer data centers. The server may be of various types, such as, but not limited to, a web server, a news server, a mail server, a message server, an advertising server, a file server, an application server, an interactive server, a database server, or a proxy server. In some embodiments, each server may include hardware, software, or an embedded logic component or a combination of two or more such components for executing the appropriate functions supported or implemented by the server. For example, the server may be a blade server, a cloud server, or a server group consisting of multiple servers, which may include one or more of the above-mentioned types of servers, etc.

[0052] In one embodiment, the server 1000 may be Figure 1 As shown, it includes a processor 1100 , a memory 1200 , an interface device 1300 , a communication device 1400 , a display device 1500 , and an input device 1600 .

[0053] In another embodiment, the server 1000 may also include a speaker, a microphone, etc., which is not limited here.

[0054] The processor 1100 is used to execute a computer program. The computer program can be written in an instruction set such as an x86, Arm, RISC, MIPS, SSE, etc. The memory 1200 includes, for example, a ROM (read-only memory), a RAM (random access memory), a non-volatile memory such as a hard disk, etc. The interface device 1300 includes, for example, various bus interfaces, such as a serial bus interface (including a USB interface), a parallel bus interface, etc. The communication device 1400 is capable of wired or wireless communication, for example. The display device 1500 is, for example, a liquid crystal display, an LED display, a touch display, etc. The input device 1600 may include, for example, a touch screen, a keyboard, etc.

[0055] Despite Figure 1 , multiple devices of the server 1000 are shown; however, the present disclosure may only involve some of the devices, for example, the server 1000 only involves the memory 1200 and the processor 1100 .

[0056] The terminal device 2000 may be, for example, an electronic device, such as a mobile phone, a tablet computer, a computer, or the like.

[0057] like Figure 1 As shown, the terminal device 2000 may include a processor 2100, a memory 2200, an interface device 2300, a communication device 2400, a display device 2500, an input device 2600, a speaker 2700, a microphone 2800, and the like.

[0058] The processor 2100 may be a mobile version processor. The memory 2200 may include, for example, a ROM (read-only memory), a RAM (random access memory), a non-volatile memory such as a hard disk, etc. The interface device 2300 may include, for example, a USB interface, a headphone interface, etc. The communication device 2400 may, for example, be capable of wired or wireless communication. The communication device 2400 may include a short-range communication device, for example, any device for short-range wireless communication based on short-range wireless communication protocols such as Hilink protocol, WiFi (IEEE 802.11 protocol), Mesh, Bluetooth, ZigBee, Thread, Z-Wave, NFC, UWB, LiFi, etc. The communication device 2400 may also include a remote communication device, for example, any device for WLAN, GPRS, 2G / 3G / 4G / 5G remote communication. The display device 2500 may be, for example, a liquid crystal display, a touch display, etc. The input device 2600 may include, for example, a touch screen, a keyboard, etc. The mobile terminal 2000 may output audio information through a speaker 2700, and may collect audio information through a microphone 2800.

[0059] In this embodiment, the memory 2200 of the terminal device 2000 is used to store instructions, and the instructions are used to control the processor 2100 to operate to achieve communication of the terminal device 2000. The technicians can design instructions according to the scheme disclosed in the present disclosure. How the instructions control the processor to operate is well known in the art, so it will not be described in detail here.

[0060] Despite Figure 1 Multiple devices of the terminal device 2000 are shown in the figure, but the present disclosure may only involve some of the devices, for example, the terminal device 2000 only involves the memory 2200 and the processor 2100, the communication device 2400 and the display device 2500.

[0061] The vehicle 3000 may be a shared power-assisted vehicle, an electric vehicle, or the like.

[0062] like Figure 1As shown, the vehicle 3000 may include a processor 3100, a memory 3200, an interface device 3300, a communication device 3400, an output device 3500, an input device 3600, and the like. The processor 3100 may be a microprocessor MCU, etc. The memory 3200 may include, for example, a ROM (read-only memory), a RAM (random access memory), a non-volatile memory such as a hard disk, etc. The interface device 3300 may include, for example, a USB interface, a headphone interface, etc. The communication device 3400 may include a short-range communication device, for example, any device for short-range wireless communication based on short-range wireless communication protocols such as Hilink protocol, WiFi (IEEE 802.11 protocol), Mesh, Bluetooth, ZigBee, Thread, Z-Wave, NFC, UWB, LiFi, etc. The communication device 3400 may also include a remote communication device, for example, any device for WLAN, GPRS, 2G / 3G / 4G / 5G remote communication. The output device 3500 may be a device that outputs signals, a display device such as a liquid crystal display, a touch screen display, etc., or a speaker that outputs voice information, etc. The input device 3600 may include a touch screen, a keyboard, etc., or a microphone that inputs voice information.

[0063] Despite Figure 1 Multiple devices of the vehicle 3000 are shown in the figure, but the present disclosure may only involve some of the devices, for example, the vehicle 3000 only involves the communication device 3400, the memory 3200 and the processor 3100.

[0064] The network 4000 may be a wireless communication network or a wired communication network, and may be a local area network or a wide area network. Figure 1 In the communication system 100 shown, the vehicle 3000 and the server 1000, and the terminal device 2000 and the server 1000 can communicate via the network 4000. In addition, the network 4000 based on which the vehicle 3000 and the server 1000, and the terminal device 2000 and the server 1000 communicate can be the same or different.

[0065] It should be understood that although Figure 1 Only one server 1000 , terminal device 2000 , and vehicle 3000 are shown, but this does not mean that their respective numbers are limited, and the communication system 100 may include multiple servers 1000 , multiple terminal devices 2000 , and multiple vehicles 3000 .

[0066] <Method>

[0067] Figure 21 is a flow chart of a battery consistency evaluation method according to an embodiment of the present disclosure. The battery consistency evaluation method according to this embodiment can be executed by a battery consistency evaluation device, which can be set in an electronic device, for example.

[0068] like Figure 2 As shown, the battery consistency evaluation method of this embodiment may specifically include the following steps 2100 to 2300:

[0069] Step 2100, obtaining multiple charging event data of the same type of battery within a preset time period.

[0070] In this embodiment, the same type of battery may be any type of battery of the same model, and this embodiment does not limit the type of battery.

[0071] In this embodiment, the preset time period may be one week or one month. The preset time period may also be the Nth week or the Nth month after the battery of the same type is used. Or other preset time periods for obtaining multiple charging events.

[0072] The vehicle records vehicle operation data during operation, which may include the battery state of charge (StateOfCharge, SOC), the start and end time of each charging event, the sampling time in each charging event, the charging current, voltage, temperature, etc. corresponding to the sampling time.

[0073] Acquiring multiple charging event data of the same battery within a preset time period includes acquiring data related to charging events from vehicle operation data, that is, charging event data. The charging event data can be obtained by sampling the battery management chip, and the sampling time interval can be flexibly set according to actual conditions, which is not limited here. In one example, the charging event data includes multiple charging data obtained in the charging event, and the charging data includes the sampling time and the temperature of each temperature sampling point corresponding to the sampling time, and the charging current corresponding to the sampling time.

[0074] In actual situations, those skilled in the art may set a temperature sampling point on each battery cell, or may set temperature sampling points at intervals in the battery cells. For example, for each of several adjacent battery cells, only one temperature sampling point is set. It should be noted that those skilled in the art may flexibly set this according to actual situations.

[0075] The multiple charging event data may include charging event data of all charging events of all electric vehicles using the same battery within a preset time period, or may include charging event data of some charging events of some vehicles using the same battery within a preset time period. In actual situations, those skilled in the art may flexibly set the multiple charging event data as needed.

[0076] Step 2200, determining characteristic data of a charging event based on the charging event data, wherein the characteristic data of the charging event includes a temperature difference characteristic value and a current characteristic value of the charging event, and the temperature difference characteristic value of the charging event characterizes the temperature difference characteristics of multiple single cells in the battery during the charging event.

[0077] In this step, after the charging event data is acquired, the charging event data is analyzed to determine the characteristic data of the charging event. Specifically, for any charging data in the charging event, the sampling time corresponding to the charging data and the temperature of each temperature sampling point corresponding to the sampling time are determined, and the maximum temperature difference between the multiple temperature sampling points corresponding to the sampling time is calculated. After obtaining the maximum temperature difference corresponding to the multiple sampling times in the charging event, the average value or median of the maximum temperature difference corresponding to the multiple sampling times in the charging event is determined as the temperature characteristic value of the charging event.

[0078] Specifically, at one of the sampling moments, the temperature of each temperature sampling point is read from the corresponding charging data, and the highest temperature value T is determined. max and the minimum temperature T min , the difference between the two is calculated as the maximum temperature difference T between multiple temperature sampling points in the battery at the sampling time, that is, T = T max -T min .

[0079] According to the maximum temperature difference at multiple sampling moments of the charging event, the average value T of the maximum temperature difference is determined mean Or the median T med , as the temperature characteristic value of the charging event. In an example of this embodiment, there are 5 sampling moments in a certain charging event, and the maximum temperature differences corresponding to these 5 sampling moments are recorded as T1=1°C, T2=2°C, T3=3°C, T4=4°C, and T5=5°C. The maximum temperature differences corresponding to these 5 sampling moments are arranged from small to large to determine the median of the maximum temperature difference T med =3°C, as the temperature characteristic value. In another example of this patent, the maximum temperature difference average value T can be calculated based on the maximum temperature difference corresponding to the five sampling moments. mean as the temperature characteristic value.

[0080]

[0081] In another example of this embodiment, the charging current in the battery at the sampling time can be determined according to the charging data, and the average value or median of the charging current corresponding to multiple sampling times in the charging event can be determined as the current characteristic value of the charging event.

[0082] According to the charging current at each sampling moment, determine the average value of the charging current I mean Or median I med , as the current characteristic value of the charging event. In an example of this embodiment, there are 5 sampling moments in a certain charging event, and the charging currents corresponding to these 5 sampling moments are recorded as I1=1A, I=2A, I3=3A, I4=4A, and I5=5A respectively. The charging current values ​​corresponding to these 5 sampling moments are arranged from small to large to determine the median of the charging current I med =3A, as the characteristic value of charging current.

[0083] In another example of this patent, the average value of the charging current I can be calculated based on the charging current corresponding to the five sampling moments. mean As the charging current characteristic value.

[0084]

[0085] In this embodiment, the feature data may be a binary array, for example, it may be expressed as [T med , I med ] or [T mean ,I mean ].

[0086] Step 2300: Determine a consistency evaluation result of the battery according to the characteristic data of the plurality of charging events.

[0087] Determining the consistency evaluation result of the battery according to the characteristic data of the plurality of charging events includes classifying the characteristic data by a maximum expectation algorithm to obtain N characteristic data sets, where N is an integer and N≥2. Determining the characteristic data of the temperature difference abnormality in the characteristic data set according to the distribution of the characteristic data in each characteristic data set. This is further described below.

[0088] Generally speaking, there are two charging modes for charging events: fast charging mode and slow charging mode, and the charging currents of the two are different. In this example, the starting charging current of the fast charge of the battery pack is 150A, while the starting charging current of the slow charge is 15A. Correspondingly, in one example, N is pre-set to 2, that is, the types of pre-set characteristic data include 2 categories in total. Then, the characteristic data is classified by the maximum expectation algorithm. Based on the current characteristics and temperature characteristics during the battery charging process, the first characteristic data set finally classified can be regarded as corresponding to the slow charging mode, and the second characteristic data set can be regarded as corresponding to the fast charging mode.

[0089] In actual situations, in fast charging mode, if the battery SOC is low at the start of charging, the charging current will usually be larger, which is called deep fast charging. If the battery SOC is high at the start of charging, the charging current will usually be smaller, which is called shallow fast charging. In one example, N is pre-set to 3, that is, the pre-set characteristic data types include a total of 3 categories. Then, the characteristic data is classified by the maximum expectation algorithm, based on the current characteristics and temperature characteristics of the battery charging process, the first characteristic data set finally classified can be regarded as corresponding to the slow charging mode, the second characteristic data set can be regarded as corresponding to the shallow fast charging mode, and the third characteristic data set can be regarded as corresponding to the deep fast charging mode.

[0090] Before dividing the multiple feature data into N categories through the maximum expectation algorithm, the method of this embodiment also includes: based on the distribution of the charging data, estimating the expected initial value and initial covariance value of the first feature data set, the expected initial value and initial covariance value corresponding to the second feature data set, and the expected initial value and initial covariance value corresponding to the third feature data set.

[0091] In this example, taking N as 3 as an example, before the plurality of feature data are classified into three categories by the maximum expectation algorithm, the initial parameter value of the feature data set may be set to:

[0092] θ 0 ={μ1=[1 0], u2=[80 0], μ3=[100 0], ε=[0.3 0.4 0.3]}

[0093] Where μ1 represents the expected initial value of the first feature data set, μ2 represents the expected initial value of the second feature data set, and μ3 represents the expected initial value of the third feature data set. σ1 represents the initial value of the covariance of the first feature data set, σ2 represents the initial value of the covariance of the second feature data set, and σ3 represents the initial value of the covariance of the third feature data set. The numbers in ε represent the probabilities of the distribution of the three feature data sets.

[0094] The maximum expectation algorithm is used for m iterations until the parameters converge, and the following is obtained:

[0095]

[0096] in Represents the expectation of the first feature data set after the maximum expectation algorithm iteration ends. Represents the expectation of the current eigenvalue of the first characteristic data set after the maximum expectation algorithm iteration ends, Represents the expectation of the temperature difference eigenvalue of the first feature data set after the maximum expectation algorithm iteration ends. Represents the covariance of the first feature data set after the maximum expectation algorithm iteration ends, Represents the upper limit of the covariance of the current eigenvalue of the first feature data set after the maximum expectation algorithm iteration is completed. Represents the lower limit of the covariance of the current eigenvalues ​​of the first feature data set after the maximum expectation algorithm iteration is completed, Represents the upper limit of the covariance of the temperature difference eigenvalue of the first feature data set after the maximum expectation algorithm iteration ends. Represents the lower limit of the covariance of the temperature difference eigenvalue of the first feature data set after the maximum expectation algorithm iteration ends. Represents the probability of the first feature data set distribution after the maximum expectation algorithm iteration is completed. Similarly, the values ​​of the second feature data set and the third feature data set after the maximum expectation algorithm iteration is completed are no longer repeated.

[0097] According to the expectation and covariance of the nth feature data set and the temperature difference characteristic value of the target feature data, it is determined whether the target feature data is feature data of temperature difference abnormality; the nth feature data set is any feature data set among the N feature data sets, and the target feature data is any feature data among the nth feature data set.

[0098] Specifically, whether the temperature difference characteristic value of the target characteristic data in the nth characteristic data set is characteristic data of temperature difference abnormality can be determined according to the expectation of the temperature difference characteristic value of the nth characteristic data set and the upper limit of the covariance of the temperature difference characteristic value.

[0099] In an example of this embodiment, the target characteristic data is the data in the first characteristic data set, and the temperature difference characteristic value of the target characteristic data can be T med , the expectation of the temperature difference eigenvalue of the first characteristic data set The upper limit of the covariance of the temperature difference eigenvalue of the first feature data set Determine whether it is characteristic data of temperature difference abnormality. When , the target characteristic data is determined as the characteristic data of temperature difference abnormality. In this example, q can be set to 3. When the target characteristic data is determined as the characteristic data of temperature difference abnormality. It should be noted that those skilled in the art can flexibly set the judgment range of the temperature difference characteristic value of the target characteristic data according to the actual situation.

[0100] The consistency evaluation result of the battery is determined according to the number of characteristic data of temperature difference abnormality that occurs in the battery within a preset time period. The consistency evaluation result of the battery can be determined by the number of characteristic data of temperature difference abnormality of the battery within a preset time period, or by the ratio of the number of characteristic data of temperature difference abnormality of the battery within a preset time period to the total number of all characteristic data of the battery, which is not limited here.

[0101] In another example of this embodiment, the method further includes obtaining the expectation and covariance corresponding to each classification of the same type of battery within a plurality of the preset time periods, and determining the consistency change trend of the same type of battery.

[0102] Specifically, the preset time period is 1 month, and the multiple preset time periods can be 6 consecutive months. The expected temperature difference characteristic value of the first characteristic data set in each month is obtained by iterative calculation. The upper limit of the covariance of the temperature difference eigenvalue of the first feature data set The expectation of the temperature difference eigenvalue of the second characteristic data set The upper limit of the covariance of the temperature difference eigenvalues ​​of the second feature data set The expectation of the temperature difference eigenvalue of the third characteristic data set The upper limit of the covariance of the temperature difference eigenvalue of the third characteristic data set The changing trend of the battery temperature difference characteristic value over time can be determined.

[0103] For example, for a certain type of battery, by calculating the expected temperature difference characteristic value in the sixth month and comparing it with the expected temperature difference characteristic value in the first five months, it is found that the expected temperature difference characteristic value in the sixth month is and A sharp increase indicates that the consistency trend of this battery includes a sharp drop in the consistency of the battery in the 6th month.

[0104] The above has been combined with the accompanying drawings to illustrate the consistency evaluation method of the battery of this embodiment. By acquiring multiple charging event data of the same battery within a preset time period, the temperature difference characteristic value and current characteristic value of the charging event are determined according to the charging event data, and the battery consistency evaluation result is determined according to the characteristic data of multiple charging events. In this way, batteries with poor consistency in the same time period can be found. Compared with using instantaneous data, the median or average statistical results used in this embodiment have higher credibility. Therefore, a more accurate battery consistency evaluation result can be provided.

[0105] <Device>

[0106] Figure 3 Schematic diagram of the structure of the battery consistency evaluation device of the embodiment of the present disclosure. Figure 3 As shown, the battery consistency evaluation device 300 of this embodiment may include:

[0107] The first acquisition module 310 is used to acquire multiple charging event data of the same battery within a preset time period. The first determination module 320 is used to determine the characteristic data of the charging event based on the charging event data, wherein the characteristic data of the charging event includes the temperature difference characteristic value and the current characteristic value of the charging event, and the temperature difference characteristic value of the charging event represents the temperature difference characteristics of multiple single cells in the battery during the charging event. The second determination module 330 is used to determine the consistency evaluation result of the battery based on the characteristic data of the multiple charging events.

[0108] In one embodiment, the charging event data includes multiple charging data obtained from the charging event, and the charging data includes a sampling time and the temperature of each temperature sampling point corresponding to the sampling time. The first determination module 310 includes a temperature difference determination module, which is specifically used to determine the maximum temperature difference between multiple temperature sampling points in the battery at the sampling time according to the charging data. The average value or median of the maximum temperature difference corresponding to multiple sampling times in the charging event is determined as the temperature characteristic value of the charging event.

[0109] In one embodiment, the charging event data includes multiple charging data obtained from the charging event, and the charging data includes a sampling time and a charging current corresponding to the sampling time. The first determination module 310 includes a current determination module, which is used to determine the average value or median of the charging current corresponding to multiple sampling times in the charging event as the current characteristic value of the charging event.

[0110] In one embodiment, the second determining module 330 includes:

[0111] The maximum expectation module is used to classify the plurality of feature data into N categories by using a maximum expectation algorithm to obtain N feature data sets, where N is an integer and N≥2.

[0112] an abnormality determination module, used to determine whether the target characteristic data is characteristic data of temperature difference abnormality according to the expectation and covariance of the nth characteristic data set and the temperature difference characteristic value of the target characteristic data; the nth characteristic data set is any one of the N characteristic data sets, and the target characteristic data is any one of the characteristic data in the nth characteristic data set;

[0113] The consistency determination module is used to determine the consistency evaluation result of the battery according to the number of characteristic data of the temperature difference abnormality occurring in the battery within the preset time period.

[0114] In one embodiment, N is 3, and the maximum expectation module includes an initial setting submodule, which is used to estimate the expected initial value and initial covariance value of the first feature data set, the expected initial value and initial covariance value corresponding to the second feature data set, and the expected initial value and initial covariance value corresponding to the third feature data set based on the distribution of the charging data.

[0115] In one embodiment, the battery consistency evaluation device 300 also includes a third determination module, which is used to obtain the expectation and covariance corresponding to each classification of the same type of battery within multiple preset time periods to determine the consistency change trend of the same type of battery.

[0116] The battery consistency evaluation device of this embodiment can be used to implement the technical solution of the above method embodiment. Its implementation principle and technical effect are similar and will not be repeated here.

[0117] <Electronic equipment>

[0118] Figure 4 is a schematic diagram of the hardware structure of an electronic device according to another embodiment.

[0119] like Figure 4 As shown, an embodiment of the present application also provides an electronic device 400, including a processor 410, a memory 420, and a program or instruction stored in the memory 420 and executable on the processor 410. When the program or instruction is executed by the processor 410, each process of the above-mentioned battery consistency evaluation method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0120] <Computer Readable Storage Medium>

[0121] This embodiment provides a computer-readable storage medium, in which executable commands are stored. When the executable commands are executed by a processor, the method described in any method embodiment of this specification is executed.

[0122] One or more embodiments of the present specification may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present specification.

[0123] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0124] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0125] The computer program instructions for performing the operation of the embodiments of this specification may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, executed as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In certain embodiments, by utilizing the state information of a computer-readable program instruction to customize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA) or a programmable logic array (PLA), the electronic circuit may execute a computer-readable program instruction, thereby realizing various aspects of this specification.

[0126] Various aspects of this specification are described herein with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of this specification. It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0127] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0128] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0129] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the multiple embodiments of this specification. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of an instruction, and a part of a module, a program segment or an instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0130] The embodiments of the present specification have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the marketplace, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of this application is defined by the appended claims.

Claims

1. A battery consistency evaluation method, characterized in that: include: Acquire multiple charging event data of the same battery within a preset time period; Determining characteristic data of a charging event according to the charging event data, wherein the characteristic data of the charging event includes a temperature difference characteristic value and a current characteristic value of the charging event, and the temperature difference characteristic value of the charging event represents a temperature difference characteristic of a plurality of single cells in the battery during the charging event; Determining a consistency evaluation result of the battery according to the characteristic data of the plurality of charging events; Wherein, determining the consistency evaluation result of the battery according to the characteristic data of the plurality of charging events includes: The plurality of feature data are divided into N categories by using a maximum expectation algorithm to obtain N feature data sets, where N is an integer and N≥2; Determine whether the target feature data is feature data of temperature difference abnormality according to the expectation and covariance of the nth feature data set and the temperature difference feature value of the target feature data; the nth feature data set is any feature data set among the N feature data sets, and the target feature data is any feature data among the nth feature data set; Determining a consistency evaluation result of the battery according to the number of characteristic data of the temperature difference abnormality occurring in the battery within the preset time period; Wherein, according to the expectation of the temperature difference characteristic value of the nth characteristic data set and the upper limit of the covariance of the temperature difference characteristic value, it is determined whether the temperature difference characteristic value of the target characteristic data in the nth characteristic data set is characteristic data of temperature difference abnormality; When the temperature difference characteristic value of the target characteristic data When , the target characteristic data is determined as the characteristic data of temperature difference abnormality, wherein the expectation of the temperature difference characteristic value is The upper limit of the covariance of the temperature difference eigenvalue is q is the coefficient.

2. The method according to claim 1, characterized in that The charging event data includes a plurality of charging data acquired from the charging event, and the charging data includes a sampling time and a temperature of each temperature sampling point corresponding to the sampling time; The determining characteristic data of the charging event according to the charging event data includes: determining, based on the charging data, a maximum temperature difference between a plurality of temperature sampling points in the battery at the sampling time; An average value or a median value of the maximum temperature difference corresponding to a plurality of sampling moments in the charging event is determined as a temperature characteristic value of the charging event.

3. The method according to claim 2, characterized in that The charging data includes a charging current corresponding to the sampling time; The determining characteristic data of the charging event according to the charging event data further includes: An average value or a median value of the charging current corresponding to a plurality of sampling moments in the charging event is determined as a current characteristic value of the charging event.

4. The method according to claim 2, characterized in that: The N is 3; before the plurality of feature data are classified into N categories by the maximum expectation algorithm, the method further comprises: According to the distribution of the charging data, the expected initial value and initial covariance value of the first characteristic data set, the expected initial value and initial covariance value corresponding to the second characteristic data set, and the expected initial value and initial covariance value corresponding to the third characteristic data set are estimated.

5. The method according to claim 1, characterized in that: The method further comprises: The expectation and covariance corresponding to each classification of the same type of battery in the plurality of preset time periods are obtained to determine the consistency change trend of the same type of battery.

6. A battery consistency evaluation device, characterized in that: include: A first acquisition module is used to acquire multiple charging event data of the same type of battery within a preset time period; A first determining module is used to determine characteristic data of a charging event according to the charging event data, wherein the characteristic data of the charging event includes a temperature difference characteristic value and a current characteristic value of the charging event, and the temperature difference characteristic value of the charging event represents the temperature difference characteristics of multiple single cells in the battery during the charging event; A second determination module, configured to determine a consistency evaluation result of the battery according to the characteristic data of the plurality of charging events; Wherein, determining the consistency evaluation result of the battery according to the characteristic data of the plurality of charging events includes: The plurality of feature data are divided into N categories by using a maximum expectation algorithm to obtain N feature data sets, where N is an integer and N≥2; Determine whether the target feature data is feature data of temperature difference abnormality according to the expectation and covariance of the nth feature data set and the temperature difference feature value of the target feature data; the nth feature data set is any feature data set among the N feature data sets, and the target feature data is any feature data among the nth feature data set; Determining a consistency evaluation result of the battery according to the number of characteristic data of the temperature difference abnormality occurring in the battery within the preset time period; Wherein, according to the expectation of the temperature difference characteristic value of the nth characteristic data set and the upper limit of the covariance of the temperature difference characteristic value, it is determined whether the temperature difference characteristic value of the target characteristic data in the nth characteristic data set is characteristic data of temperature difference abnormality; When the temperature difference characteristic value of the target characteristic data When , the target characteristic data is determined as the characteristic data of temperature difference abnormality, wherein the expectation of the temperature difference characteristic value is The upper limit of the covariance of the temperature difference eigenvalue is q is the coefficient.

7. The device according to claim 6, characterized in that The charging event data includes a plurality of charging data acquired from the charging event, and the charging data includes a sampling time and a temperature of each temperature sampling point corresponding to the sampling time; The first determination module includes a temperature difference determination submodule, and the temperature difference determination submodule is specifically used to: determining, based on the charging data, a maximum temperature difference between a plurality of temperature sampling points in the battery at the sampling time; An average value or a median value of the maximum temperature difference corresponding to a plurality of sampling moments in the charging event is determined as a temperature characteristic value of the charging event.

8. The device according to claim 7, characterized in that The charging event data includes a charging current corresponding to the sampling time; The first determination module includes a current determination submodule, and the current determination submodule is used to: An average value or a median value of the charging current corresponding to a plurality of sampling moments in the charging event is determined as a current characteristic value of the charging event.

9. An electronic device, characterized in that: The invention comprises a processor, a memory and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the battery consistency evaluation method as claimed in any one of claims 1 to 5.

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