Method, device and electronic device for determining battery nominal capacity

Abstract

The present invention provides a method, device and electronic device for determining the nominal capacity of a battery, comprising: first determining the battery capacity of a battery to be tested in each charging stroke, the temperature characteristics and current characteristics of the battery to be tested in each charging stroke based on target charging detailed data within a pre-set interval, and then constructing a nominal capacity prediction model based on the above-determined information; then, in all charging strokes, statistically analyzing the target temperature characteristics and target current characteristics with the most concentrated distribution; finally, inputting the target temperature characteristics and target current characteristics with the most concentrated distribution into the nominal capacity prediction model, and thus obtaining the nominal capacity of the battery to be tested. The method is to first construct a nominal capacity prediction model, and then input the target temperature characteristics and target current characteristics with the most concentrated distribution into the nominal capacity prediction model, and output the nominal capacity of the battery to be tested, so that the nominal capacity of the battery to be tested obtained in this way is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and in particular to a method, device and electronic equipment for determining the nominal capacity of a battery. Background Art

[0002] In recent years, as the energy crisis has become increasingly serious, electric vehicles, with their excellent energy-saving and environmentally friendly features, have become a key development focus of the future vehicle industry. Accurately assessing the SOH (State of Health) of batteries installed in electric vehicles is extremely important. In actual use, the battery SOH is generally calculated as a percentage of the ratio of battery capacity to the battery's nominal capacity. Generally speaking, the initial state of the battery's SOH is 100%. During battery use, the battery's SOH gradually decays. When the battery's SOH reaches 80%, it indicates the end of its life.

[0003] However, in the above method of calculating battery SOH, the battery's nominal capacity is sometimes unavailable or inaccurately obtained. Different manufacturers may have different definitions of battery nominal capacity. Some manufacturers use the minimum capacity of a batch of batteries as the battery's nominal capacity to ensure that all batteries meet the standard. If the battery's SOH is calculated using the battery nominal capacity defined in this way, the initial SOH of many batteries will be greater than 100%, which clearly does not conform to the normal meaning of battery SOH.

[0004] In summary, how to accurately determine the nominal capacity of a battery has become a technical problem that urgently needs to be solved. Summary of the Invention

[0005] In view of this, an object of the present invention is to provide a method, device and electronic device for determining the nominal capacity of a battery, so as to alleviate the technical problem that the prior art cannot accurately determine the nominal capacity of a battery.

[0006] In a first aspect, an embodiment of the present invention provides a method for determining a nominal capacity of a battery, the method comprising:

[0007] Obtaining target charging detailed data of the battery under test within a previously preset interval, and determining the battery capacity of the battery under test in each charging stroke, and the temperature characteristics and current characteristics of the battery under test in each charging stroke based on the target charging detailed data, wherein one charging stroke includes multiple consecutive target charging detailed data;

[0008] Building a nominal capacity prediction model according to the battery capacity of the battery under test in each charging stroke, and the temperature characteristics and current characteristics of the battery under test in each charging stroke;

[0009] The target temperature characteristics and target current characteristics with the most concentrated statistical distribution in all the charging trips;

[0010] The nominal capacity prediction model is used to perform nominal capacity prediction on the target temperature characteristic and the target current characteristic to obtain the nominal capacity of the battery to be tested.

[0011] Furthermore, detailed target charging data of the battery under test within the preset interval is obtained, including:

[0012] Acquiring real-time monitoring data of the electric vehicle to which the battery to be tested belongs, and cleaning the real-time monitoring data to obtain cleaned real-time monitoring data;

[0013] Performing conversion processing on the cleaned real-time monitoring data to obtain converted real-time monitoring data;

[0014] classifying the converted real-time monitoring data according to data status information contained in the converted real-time monitoring data to obtain charging detailed data and discharging detailed data;

[0015] When the preset interval is a preset time interval, filtering out the charging detailed data of the previous preset time interval from the charging detailed data according to the time information in the charging detailed data, and using the charging detailed data of the previous preset time interval as the target charging detailed data;

[0016] When the preset interval is a preset mileage interval, the charging detailed data of the previous preset mileage interval is filtered out from the charging detailed data according to the mileage information in the charging detailed data, and the charging detailed data of the previous preset mileage interval is used as the target charging detailed data.

[0017] Furthermore, determining the battery capacity of the battery under test in each charging stroke, and the temperature characteristics and current characteristics of the battery under test in each charging stroke according to the target charging detailed data includes:

[0018] taking the time-continuous target charging detailed data in the target charging detailed data as target charging detailed data within a charging trip;

[0019] Calculating the battery capacity of the battery under test in each charging stroke according to the current information and time information in the target charging detailed data in each charging stroke;

[0020] Determining the temperature characteristics of the battery under test in each charging stroke according to the temperature information in the target charging detailed data in each charging stroke;

[0021] The current characteristics of the battery to be tested in each charging stroke are determined according to the current information in the target charging detailed data in each charging stroke.

[0022] Furthermore, the nominal capacity prediction model includes a neural network model, which is constructed based on the battery capacity of the battery under test in each charging stroke, the temperature characteristics and the current characteristics of the battery under test in each charging stroke, including:

[0023] The battery capacity of the battery under test in each charging stroke, and the temperature characteristics and current characteristics of the battery under test in each charging stroke are used as training samples;

[0024] The initial nominal capacity prediction model is trained using the training samples to obtain the nominal capacity prediction model.

[0025] Furthermore, the nominal capacity prediction model includes a nominal capacity function model, which is constructed according to the battery capacity of the battery under test in each charging stroke, the temperature characteristics and the current characteristics of the battery under test in each charging stroke, including:

[0026] Obtaining a preset nominal capacity function model, wherein the preset nominal capacity function model is a function of nominal capacity, temperature, and current, and the preset nominal capacity function model includes unknown parameters;

[0027] Solving unknown parameters in the preset nominal capacity function model according to the battery capacity of the battery under test in each charging stroke, and the temperature characteristics and current characteristics of the battery under test in each charging stroke to obtain values of the unknown parameters, thereby obtaining a preset nominal capacity function model with known unknown parameters;

[0028] The preset nominal capacity function model with known unknown parameters is used as the nominal capacity prediction model.

[0029] Furthermore, in all the charging trips, the target temperature characteristics and target current characteristics with the most concentrated statistical distribution include:

[0030] Calculating the most concentrated target temperature feature based on the temperature information in the target charging detailed data within all the charging trips;

[0031] According to the current information in the target charging detailed data within all the charging trips, the target current characteristics with the most concentrated distribution are statistically analyzed.

[0032] Furthermore, after obtaining the nominal capacity of the battery to be tested, the method further includes:

[0033] The SOH of the battery to be tested is calculated based on the nominal capacity of the battery to be tested.

[0034] In a second aspect, an embodiment of the present invention further provides a device for determining a nominal capacity of a battery, the device comprising:

[0035] an acquisition and determination unit, configured to acquire target charging detailed data of the battery under test within a previously preset interval, and determine the battery capacity of the battery under test in each charging stroke, and the temperature characteristics and current characteristics of the battery under test in each charging stroke based on the target charging detailed data, wherein one charging stroke includes multiple consecutive target charging detailed data;

[0036] a model building unit, configured to build a nominal capacity prediction model according to the battery capacity of the battery to be tested in each charging stroke, and the temperature characteristics and current characteristics of the battery to be tested in each charging stroke;

[0037] a statistical unit, configured to collect the most concentrated target temperature characteristics and target current characteristics in all the charging trips;

[0038] A nominal capacity prediction unit is configured to perform nominal capacity prediction on the target temperature characteristic and the target current characteristic using the nominal capacity prediction model to obtain the nominal capacity of the battery to be tested.

[0039] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0040] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to execute any method described in the first aspect above.

[0041] In an embodiment of the present invention, a method for determining a nominal capacity of a battery is provided, the method comprising: obtaining target charging detailed data of a battery to be tested within a previously preset interval, and determining the battery capacity of the battery to be tested in each charging stroke, the temperature characteristics, and the current characteristics of the battery to be tested in each charging stroke based on the target charging detailed data, wherein one charging stroke contains multiple continuous target charging detailed data; constructing a nominal capacity prediction model based on the battery capacity of the battery to be tested in each charging stroke, the temperature characteristics, and the current characteristics of the battery to be tested in each charging stroke; statistically determining the target temperature characteristics and target current characteristics with the most concentrated distribution in all charging strokes; and using the nominal capacity prediction model to perform nominal capacity prediction on the target temperature characteristics and target current characteristics to obtain the nominal capacity of the battery to be tested. From the above description, it can be seen that the method for determining the nominal capacity of the battery of the present invention first determines the battery capacity of the battery to be tested in each charging stroke, the temperature characteristics and current characteristics of the battery to be tested in each charging stroke according to the target charging detailed data in the pre-set interval, and then constructs a nominal capacity prediction model based on the above-mentioned information determined. Then, in all charging strokes, the target temperature characteristics and target current characteristics with the most concentrated distribution are statistically analyzed. Finally, the target temperature characteristics and target current characteristics with the most concentrated distribution are input into the nominal capacity prediction model, and the nominal capacity of the battery to be tested can be obtained. This method is to first construct a nominal capacity prediction model, and then input the target temperature characteristics and target current characteristics with the most concentrated distribution into the nominal capacity prediction model, and output the nominal capacity of the battery to be tested. The nominal capacity of the battery to be tested obtained in this way is more accurate, which alleviates the technical problem that the existing technology cannot accurately determine the nominal capacity of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 A flowchart of a method for determining a battery nominal capacity provided by an embodiment of the present invention;

[0044] Figure 2 A flow chart of a method for obtaining target charging detailed data of a battery under test within a preset interval provided by an embodiment of the present invention;

[0045] Figure 3 A flow chart of a method for determining the battery capacity, temperature characteristics, and current characteristics of a battery under test in each charging stroke according to target charging detailed data provided by an embodiment of the present invention;

[0046] Figure 4 A schematic diagram of a device for determining a battery nominal capacity according to an embodiment of the present invention;

[0047] Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0049] The existing method for determining the nominal capacity generally uses the minimum capacity of a batch of batteries as the nominal capacity of the battery. When the battery SOH is calculated based on the battery nominal capacity determined by the above method, the initial state of SOH of many batteries is greater than 100%, which obviously does not conform to the usual meaning of battery SOH. In other words, the nominal capacity of the battery determined by the above method has a large deviation.

[0050] Based on this, the method for determining the nominal capacity of the battery of the present invention first determines the battery capacity of the battery to be tested in each charging stroke, the temperature characteristics and current characteristics of the battery to be tested in each charging stroke according to the target charging detailed data in the pre-set interval, and then constructs a nominal capacity prediction model based on the above-mentioned information. Then, in all charging strokes, the target temperature characteristics and target current characteristics with the most concentrated distribution are statistically analyzed. Finally, the target temperature characteristics and target current characteristics with the most concentrated distribution are input into the nominal capacity prediction model, and the nominal capacity of the battery to be tested can be obtained. This method is to first construct a nominal capacity prediction model, and then input the target temperature characteristics and target current characteristics with the most concentrated distribution into the nominal capacity prediction model, and output the nominal capacity of the battery to be tested. The nominal capacity of the battery to be tested obtained in this way is more accurate.

[0051] To facilitate understanding of this embodiment, a method for determining the nominal capacity of a battery disclosed in an embodiment of the present invention is first introduced in detail.

[0052] Example 1:

[0053] According to an embodiment of the present invention, an embodiment of a method for determining the nominal capacity of a battery is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0054] Figure 1 FIG. 1 is a flow chart of a method for determining a battery nominal capacity according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0055] Step S102, obtaining target charging detailed data of the battery under test within a previously preset interval, and determining the battery capacity of the battery under test in each charging stroke, and the temperature characteristics and current characteristics of the battery under test in each charging stroke based on the target charging detailed data, wherein a charging stroke may contain multiple consecutive target charging detailed data;

[0056] In an embodiment of the present invention, the above-mentioned battery to be tested is for multiple batteries of the same model, and the previous preset interval can be within the previous preset time interval, for example, within the previous X days, or within the previous preset mileage interval, for example, within the previous Y mileage, or a combination of the two methods, for example, within the previous X days and within the previous Y mileage.

[0057] Specifically, the present invention's solution assumes that the battery capacity of an electric vehicle exhibits minimal degradation within the first X days / Y kilometers, approximating the factory capacity (i.e., nominal capacity). Furthermore, capacity varies under different conditions, and the present invention primarily uses temperature and current as factors influencing capacity. Therefore, the battery capacity, temperature characteristics, and current characteristics of the battery under test during each charging trip must be determined based on the target charging data.

[0058] Step S104, building a nominal capacity prediction model based on the battery capacity of the battery under test in each charging stroke, and the temperature characteristics and current characteristics of the battery under test in each charging stroke;

[0059] The above-mentioned nominal capacity prediction model can be a neural network model or a nominal capacity function model. The process will be described in detail below and will not be repeated here.

[0060] Step S106, in all charging trips, statistically analyzing the most concentrated target temperature characteristics and target current characteristics;

[0061] The target temperature characteristics and target current characteristics with the most concentrated statistical distribution are used to obtain the most frequently operating temperature and the most frequently operating current of the battery to be tested, so as to serve as a basis for determining the accurate nominal capacity of the battery to be tested.

[0062] Step S108 : Using the nominal capacity prediction model to perform nominal capacity prediction on the target temperature characteristics and the target current characteristics, to obtain the nominal capacity of the battery to be tested.

[0063] In an embodiment of the present invention, a method for determining a nominal capacity of a battery is provided, the method comprising: obtaining target charging detailed data of a battery to be tested within a previously preset interval, and determining the battery capacity of the battery to be tested in each charging stroke, the temperature characteristics, and the current characteristics of the battery to be tested in each charging stroke based on the target charging detailed data, wherein one charging stroke contains multiple continuous target charging detailed data; constructing a nominal capacity prediction model based on the battery capacity of the battery to be tested in each charging stroke, the temperature characteristics, and the current characteristics of the battery to be tested in each charging stroke; statistically determining the target temperature characteristics and target current characteristics with the most concentrated distribution in all charging strokes; and using the nominal capacity prediction model to perform nominal capacity prediction on the target temperature characteristics and target current characteristics to obtain the nominal capacity of the battery to be tested. From the above description, it can be seen that the method for determining the nominal capacity of the battery of the present invention first determines the battery capacity of the battery to be tested in each charging stroke, the temperature characteristics and current characteristics of the battery to be tested in each charging stroke according to the target charging detailed data in the pre-set interval, and then constructs a nominal capacity prediction model based on the above-mentioned information determined. Then, in all charging strokes, the target temperature characteristics and target current characteristics with the most concentrated distribution are statistically analyzed. Finally, the target temperature characteristics and target current characteristics with the most concentrated distribution are input into the nominal capacity prediction model, and the nominal capacity of the battery to be tested can be obtained. This method is to first construct a nominal capacity prediction model, and then input the target temperature characteristics and target current characteristics with the most concentrated distribution into the nominal capacity prediction model, and output the nominal capacity of the battery to be tested. The nominal capacity of the battery to be tested obtained in this way is more accurate, which alleviates the technical problem that the existing technology cannot accurately determine the nominal capacity of the battery.

[0064] The above content briefly introduces the method for determining the nominal capacity of a battery according to the present invention. The specific contents involved are described in detail below.

[0065] In an alternative embodiment of the present invention, reference Figure 2 The above step S102, obtaining target charging detailed data of the battery under test within the previous preset interval, specifically includes the following steps:

[0066] Step S201, obtaining real-time monitoring data of the electric vehicle to which the battery to be tested belongs, and pre-processing and classifying the real-time monitoring data to obtain detailed charging data and detailed discharging data;

[0067] Since the batteries under test are multiple batteries of the same model, the electric vehicle to which the batteries under test belong is also an electric vehicle with batteries of the same model. The national monitoring platform can monitor the relevant data of all electric vehicles, so it can obtain real-time monitoring data (RTM data) of the electric vehicle to which the batteries under test belong.

[0068] The above-mentioned pre-processing classification includes: cleaning, conversion and classification, which specifically includes the following processes:

[0069] (1) Cleaning the real-time monitoring data to obtain cleaned real-time monitoring data;

[0070] Specifically, the real-time monitoring data includes: current, voltage, time, mileage, SOC, temperature and status, etc., which are monitored every 10 seconds. Each item has a unified unit. For example, for the current item, the unit is ampere.

[0071] The above-mentioned cleaning refers to filtering and deleting data that is obviously erroneous. For example, in the first four pieces of real-time monitoring data, the mileage items are: 1, 2, 10,000, and 4. Obviously, the data corresponding to mileage 10,000 has obvious errors, and it is filtered and deleted through cleaning.

[0072] (2) performing conversion processing on the cleaned real-time monitoring data to obtain converted real-time monitoring data;

[0073] The above conversion process may be a data format conversion process, for example, converting time data into the format of year, month, day, hour, minute, and second, etc.

[0074] (3) Classifying the converted real-time monitoring data according to the data status information contained in the converted real-time monitoring data to obtain charging detailed data and discharging detailed data.

[0075] Specifically, the data status information may indicate whether the corresponding data is charging data or discharging data. In this way, the converted real-time monitoring data may be classified into charging detailed data and discharging detailed data.

[0076] Step S202 : Filter out charging detailed data of a previous preset interval from the charging detailed data, and use the charging detailed data of the previous preset interval as target charging detailed data.

[0077] Optionally, when the preset interval is a preset time interval, filtering out the charging detailed data of the previous preset interval from the charging detailed data includes: filtering out the charging detailed data of the previous preset time interval from the charging detailed data according to time information in the charging detailed data;

[0078] Specifically, from the time information in the charging detailed data of the electric vehicle to which the battery to be tested belongs, the minimum time (or the most recent time) is first determined, and the minimum time is used as the starting usage time. By subtracting the other time information in the charging detailed data of the electric vehicle to which the battery to be tested belongs from the minimum time, the usage days corresponding to the various charging detailed data of the electric vehicle to which the battery to be tested belongs can be obtained. From the usage days, the charging detailed data corresponding to the target usage days that are less than a preset day threshold are filtered out, that is, the charging detailed data of the battery to be tested in the previous preset time interval.

[0079] Optionally, when the preset interval is a preset mileage interval, filtering out the charging detailed data of the previous preset interval from the charging detailed data includes: filtering out the charging detailed data of the previous preset mileage interval from the charging detailed data according to mileage information in the charging detailed data.

[0080] Specifically, the mileage starts from 0, so the mileage information in the charging details data is the mileage data. The charging details data corresponding to the target mileage less than the preset mileage threshold is filtered out from the mileage data, which is the charging details data of the previous preset mileage interval of the battery to be tested.

[0081] In an alternative embodiment of the present invention, reference Figure 3 The above step S102, determining the battery capacity of the battery under test in each charging stroke, the temperature characteristics and the current characteristics of the battery under test in each charging stroke according to the target charging detailed data, specifically includes the following steps:

[0082] Step S301, taking the time-continuous target charging detailed data in the target charging detailed data as target charging detailed data within a charging trip;

[0083] Step S302 , calculating the battery capacity of the battery under test in each charging stroke according to the current information and time information in the target charging detailed data in each charging stroke;

[0084] Specifically, the present invention calculates the battery capacity of the battery to be tested in each charging stroke based on the ampere-hour integration method. Other existing methods can also be used to calculate the battery capacity.

[0085] Step S303, determining the temperature characteristics of the battery under test in each charging stroke according to the temperature information in the target charging detailed data in each charging stroke;

[0086] Specifically, the temperature characteristics include at least one of the following: average temperature, maximum temperature, and minimum temperature.

[0087] It should be noted that the above temperature characteristics may also be other temperature characteristics, and the embodiment of the present invention does not specifically limit the above temperature characteristics.

[0088] Step S304 : determining the current characteristics of the battery under test in each charging stroke according to the current information in the target charging detailed data in each charging stroke.

[0089] Specifically, the current characteristics include at least one of the following: average current, maximum current, and minimum current.

[0090] It should be noted that the above-mentioned current characteristics may also be other current characteristics, and the embodiment of the present invention does not specifically limit the above-mentioned current characteristics.

[0091] In an optional embodiment of the present invention, the nominal capacity prediction model includes a neural network model. Step S104, which constructs the nominal capacity prediction model based on the battery capacity of the battery under test at each charging stroke, and the temperature and current characteristics of the battery under test at each charging stroke, specifically includes the following steps:

[0092] (1) The battery capacity, temperature characteristics, and current characteristics of the battery under test at each charging stroke are used as training samples;

[0093] (2) The initial nominal capacity prediction model is trained using training samples to obtain the nominal capacity prediction model.

[0094] In an optional embodiment of the present invention, the nominal capacity prediction model includes a nominal capacity function model. Step S104, constructing the nominal capacity prediction model based on the battery capacity of the battery under test at each charging stroke, and the temperature characteristics and current characteristics of the battery under test at each charging stroke, specifically includes the following steps:

[0095] (1) obtaining a preset nominal capacity function model, wherein the preset nominal capacity function model is a function of nominal capacity, temperature, and current, and the preset nominal capacity function model includes unknown parameters;

[0096] (2) solving the unknown parameters in the preset nominal capacity function model according to the battery capacity of the battery under test at each charging stroke, the temperature characteristics and the current characteristics of the battery under test at each charging stroke, obtaining the values of the unknown parameters, and then obtaining the preset nominal capacity function model with known unknown parameters;

[0097] Specifically, the battery capacity of the battery under test in each charging stroke, the temperature characteristics and the current characteristics of the battery under test in each charging stroke are substituted into the preset nominal capacity function model, and the simultaneous equations are solved to obtain the values of the unknown parameters.

[0098] (3) The preset nominal capacity function model with known unknown parameters is used as the nominal capacity prediction model.

[0099] In an optional embodiment of the present invention, the target temperature characteristics and target current characteristics with the most concentrated statistical distribution in all charging trips specifically include:

[0100] (1) Based on the temperature information in the target charging detailed data of all charging trips, the target temperature characteristics with the most concentrated distribution are statistically analyzed;

[0101] (2) Based on the current information in the target charging detailed data within all charging trips, the target current characteristics with the most concentrated distribution are statistically analyzed.

[0102] It should be noted that if the temperature characteristic is the average temperature, the target temperature characteristic is also the average temperature with the most concentrated distribution; if the temperature characteristic is the maximum temperature, the target temperature characteristic is also the maximum temperature with the most concentrated distribution; if the current characteristic is the average current, the target current characteristic is also the average current with the most concentrated distribution; if the current characteristic is the maximum current, the target current characteristic is also the maximum current with the most concentrated distribution, that is, the target temperature characteristic corresponds to the parameters of the above-mentioned temperature characteristics, and the target current characteristic corresponds to the parameters of the above-mentioned current characteristics.

[0103] The target temperature characteristics and target current characteristics obtained by the above statistics represent the temperature and current at which the battery to be tested most often operates. In this way, the nominal capacity of the battery to be tested subsequently determined based on the target temperature characteristics and target current characteristics is more accurate.

[0104] In an optional embodiment of the present invention, after obtaining the nominal capacity of the battery to be tested, the method further includes: calculating the SOH of the battery to be tested based on the nominal capacity of the battery to be tested.

[0105] Big data testing shows that when the nominal capacity obtained by this method is used to calculate the SOH, the initial SOH of different vehicles is concentrated at 100%. It can be seen that the nominal capacity of the battery to be tested determined by the method of the present invention is more accurate.

[0106] Example 2:

[0107] An embodiment of the present invention further provides a device for determining the nominal capacity of a battery. The device for determining the nominal capacity of a battery is mainly used to execute the method for determining the nominal capacity of a battery provided in the first embodiment of the present invention. The device for determining the nominal capacity of a battery provided in the embodiment of the present invention is specifically introduced below.

[0108] Figure 4 is a schematic diagram of a device for determining a battery nominal capacity according to an embodiment of the present invention. Figure 4 As shown, the device mainly includes: an acquisition and determination unit 10, a model building unit 20, a statistics unit 30 and a nominal capacity prediction unit 40, wherein:

[0109] an acquisition and determination unit, configured to acquire target charging detailed data of the battery under test within a previously preset interval, and determine the battery capacity of the battery under test in each charging stroke, and the temperature characteristics and current characteristics of the battery under test in each charging stroke based on the target charging detailed data, wherein a charging stroke includes multiple consecutive target charging detailed data;

[0110] a model building unit, configured to build a nominal capacity prediction model based on the battery capacity of the battery under test at each charging stroke, and the temperature characteristics and current characteristics of the battery under test at each charging stroke;

[0111] A statistical unit is used to collect the most concentrated target temperature characteristics and target current characteristics in all charging trips;

[0112] The nominal capacity prediction unit is used to perform nominal capacity prediction on the target temperature characteristics and the target current characteristics using a nominal capacity prediction model to obtain the nominal capacity of the battery to be tested.

[0113] In an embodiment of the present invention, a device for determining the nominal capacity of a battery is provided, the device comprising: obtaining target charging detailed data of a battery to be tested within a previously preset interval, and determining the battery capacity of the battery to be tested in each charging stroke, the temperature characteristics, and the current characteristics of the battery to be tested in each charging stroke based on the target charging detailed data, wherein one charging stroke contains multiple continuous target charging detailed data; constructing a nominal capacity prediction model based on the battery capacity of the battery to be tested in each charging stroke, the temperature characteristics, and the current characteristics of the battery to be tested in each charging stroke; statistically determining the target temperature characteristics and target current characteristics with the most concentrated distribution in all charging strokes; and using the nominal capacity prediction model to perform nominal capacity prediction on the target temperature characteristics and target current characteristics to obtain the nominal capacity of the battery to be tested. From the above description, it can be seen that the device for determining the nominal capacity of the battery of the present invention first determines the battery capacity of the battery to be tested in each charging stroke, the temperature characteristics and current characteristics of the battery to be tested in each charging stroke according to the target charging detailed data in the pre-set interval, and then constructs a nominal capacity prediction model based on the above-determined information. Then, in all charging strokes, the target temperature characteristics and target current characteristics with the most concentrated distribution are statistically analyzed. Finally, the target temperature characteristics and target current characteristics with the most concentrated distribution are input into the nominal capacity prediction model, and the nominal capacity of the battery to be tested can be obtained. The method is to first construct a nominal capacity prediction model, and then input the target temperature characteristics and target current characteristics with the most concentrated distribution into the nominal capacity prediction model, and output the nominal capacity of the battery to be tested. The nominal capacity of the battery to be tested obtained in this way is more accurate, which alleviates the technical problem that the existing technology cannot accurately determine the nominal capacity of the battery.

[0114] Optionally, the acquisition and determination unit is also used to: acquire real-time monitoring data of the electric vehicle to which the battery to be tested belongs, and pre-process and classify the real-time monitoring data to obtain charging detail data and discharge detail data; filter out the charging detail data of the previous preset interval from the charging detail data, and use the charging detail data of the previous preset interval as the target charging detail data.

[0115] Optionally, the acquisition and determination unit is also used to: clean the real-time monitoring data to obtain cleaned real-time monitoring data; convert the cleaned real-time monitoring data to obtain converted real-time monitoring data; classify the converted real-time monitoring data according to the data status information contained in the converted real-time monitoring data to obtain charging detail data and discharging detail data.

[0116] Optionally, when the preset interval is a preset time interval, the acquisition and determination unit is further used to: filter out the charging details data of the previous preset time interval from the charging details data based on the time information in the charging details data; when the preset interval is a preset mileage interval, the acquisition and determination unit is further used to: filter out the charging details data of the previous preset mileage interval from the charging details data based on the mileage information in the charging details data.

[0117] Optionally, the acquisition and determination unit is further used to: take the time-continuous target charging detail data in the target charging detail data as the target charging detail data within a charging stroke; calculate the battery capacity of the battery to be tested in each charging stroke based on the current information and time information in the target charging detail data within each charging stroke; determine the temperature characteristics of the battery to be tested in each charging stroke based on the temperature information in the target charging detail data within each charging stroke; and determine the current characteristics of the battery to be tested in each charging stroke based on the current information in the target charging detail data within each charging stroke.

[0118] Optionally, the nominal capacity prediction model includes a neural network model, and the model construction unit is further used to: use the battery capacity of the battery to be tested in each charging stroke, and the temperature characteristics and current characteristics of the battery to be tested in each charging stroke as training samples; and use the training samples to train the initial nominal capacity prediction model to obtain the nominal capacity prediction model.

[0119] Optionally, the nominal capacity prediction model includes a nominal capacity function model, and the model construction unit is further used to: obtain a preset nominal capacity function model, wherein the preset nominal capacity function model is a function between the nominal capacity and the temperature and current, and the preset nominal capacity function model contains unknown parameters; solve the unknown parameters in the preset nominal capacity function model according to the battery capacity of the battery to be tested in each charging stroke, the temperature characteristics and the current characteristics of the battery to be tested in each charging stroke, obtain the values of the unknown parameters, and then obtain the preset nominal capacity function model with known unknown parameters; use the preset nominal capacity function model with known unknown parameters as the nominal capacity prediction model.

[0120] Optionally, the temperature characteristic includes at least one of the following: average temperature, maximum temperature, minimum temperature; the current characteristic includes at least one of the following: average current, maximum current, minimum current.

[0121] Optionally, the statistical unit is further used to: calculate the most concentrated target temperature characteristics based on the temperature information in the target charging detailed data within all charging trips; and calculate the most concentrated target current characteristics based on the current information in the target charging detailed data within all charging trips.

[0122] Optionally, the device is further configured to calculate the SOH of the battery to be tested based on the nominal capacity of the battery to be tested.

[0123] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding content in the aforementioned method embodiment.

[0124] like Figure 5 As shown, an electronic device 600 provided in an embodiment of the present application includes: a processor 601, a memory 602 and a bus, wherein the memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 through the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the method for determining the nominal capacity of the battery as described above.

[0125] Specifically, the memory 602 and the processor 601 can be general-purpose memories and processors, which are not specifically limited here. When the processor 601 runs the computer program stored in the memory 602, it can execute the above-mentioned method for determining the nominal capacity of the battery.

[0126] The processor 601 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 601 or by instructions in the form of software. The above-mentioned processor 601 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 602, and processor 601 reads the information in memory 602 and performs the steps of the above method in conjunction with its hardware.

[0127] Corresponding to the above-mentioned method for determining the nominal capacity of the battery, an embodiment of the present application also provides a computer-readable storage medium, which stores machine-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to execute the steps of the above-mentioned method for determining the nominal capacity of the battery.

[0128] The device for determining the nominal capacity of the battery provided in the embodiment of the present application can be specific hardware on the device or software or firmware installed on the device. The device provided in the embodiment of the present application, its implementation principle and the technical effect produced are the same as those in the aforementioned method embodiment. For the sake of brief description, where the device embodiment is not mentioned, reference can be made to the corresponding content in the aforementioned method embodiment. Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0129] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, 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 communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0130] For another example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that 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 flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0131] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0132] In addition, each functional unit in the embodiments provided in the present 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.

[0133] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling an electronic device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the vehicle marking method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program codes.

[0134] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0135] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-mentioned embodiments within the technical scope disclosed in the present application, or perform equivalent replacements for some of the technical features thereof. However, these modifications, changes, or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application. They should all be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for determining the nominal capacity of a battery, characterized in that: The method comprises: Obtaining target charging detailed data of the battery under test within a previous preset interval, and determining the battery capacity of the battery under test in each charging trip, the temperature characteristics, and the current characteristics of the battery under test in each charging trip based on the target charging detailed data, wherein one charging trip contains multiple consecutive target charging detailed data, wherein the batteries under test are for multiple batteries of the same model, and the preset interval includes: a preset time interval, or a preset mileage interval; Building a nominal capacity prediction model according to the battery capacity of the battery under test in each charging stroke, and the temperature characteristics and current characteristics of the battery under test in each charging stroke; The target temperature characteristic and the target current characteristic with the most concentrated statistical distribution in all the charging trips, wherein the target temperature characteristic and the target current characteristic are the temperature and the current at which the battery under test operates most frequently; The nominal capacity prediction model is used to perform nominal capacity prediction on the target temperature characteristic and the target current characteristic to obtain the nominal capacity of the battery to be tested, wherein the nominal capacity is the factory capacity.

2. The method according to claim 1, characterized in that Obtain detailed target charging data for the battery under test within the preset range, including: Acquiring real-time monitoring data of the electric vehicle to which the battery to be tested belongs, and cleaning the real-time monitoring data to obtain cleaned real-time monitoring data; Performing conversion processing on the cleaned real-time monitoring data to obtain converted real-time monitoring data; classifying the converted real-time monitoring data according to data status information contained in the converted real-time monitoring data to obtain charging detailed data and discharging detailed data; When the preset interval is a preset time interval, filtering out the charging detailed data of the previous preset time interval from the charging detailed data according to the time information in the charging detailed data, and using the charging detailed data of the previous preset time interval as the target charging detailed data; When the preset interval is a preset mileage interval, the charging detailed data of the previous preset mileage interval is filtered out from the charging detailed data according to the mileage information in the charging detailed data, and the charging detailed data of the previous preset mileage interval is used as the target charging detailed data.

3. The method according to claim 1, characterized in that Determining the battery capacity of the battery under test in each charging stroke, and the temperature characteristics and current characteristics of the battery under test in each charging stroke according to the target charging detailed data includes: taking the time-continuous target charging detailed data in the target charging detailed data as target charging detailed data within a charging trip; Calculating the battery capacity of the battery under test in each charging stroke according to the current information and time information in the target charging detailed data in each charging stroke; Determining the temperature characteristics of the battery under test in each charging stroke according to the temperature information in the target charging detailed data in each charging stroke; The current characteristics of the battery to be tested in each charging stroke are determined according to the current information in the target charging detailed data in each charging stroke.

4. The method according to claim 1, wherein The nominal capacity prediction model includes a neural network model, and the nominal capacity prediction model is constructed according to the battery capacity of the battery under test in each charging stroke, the temperature characteristics and the current characteristics of the battery under test in each charging stroke, including: The battery capacity of the battery under test in each charging stroke, and the temperature characteristics and current characteristics of the battery under test in each charging stroke are used as training samples; The initial nominal capacity prediction model is trained using the training samples to obtain the nominal capacity prediction model.

5. The method according to claim 1, wherein The nominal capacity prediction model includes a nominal capacity function model, which is constructed according to the battery capacity of the battery under test in each charging stroke, the temperature characteristics and the current characteristics of the battery under test in each charging stroke, including: Obtaining a preset nominal capacity function model, wherein the preset nominal capacity function model is a function of nominal capacity, temperature, and current, and the preset nominal capacity function model includes unknown parameters; Solving unknown parameters in the preset nominal capacity function model according to the battery capacity of the battery under test in each charging stroke, and the temperature characteristics and current characteristics of the battery under test in each charging stroke to obtain values of the unknown parameters, thereby obtaining a preset nominal capacity function model with known unknown parameters; The preset nominal capacity function model with known unknown parameters is used as the nominal capacity prediction model.

6. The method according to claim 1, characterized in that Among all the charging trips, the target temperature characteristics and target current characteristics with the most concentrated statistical distribution include: Calculating the most concentrated target temperature feature based on the temperature information in the target charging detailed data within all the charging trips; According to the current information in the target charging detailed data within all the charging trips, the target current characteristics with the most concentrated distribution are statistically analyzed.

7. The method according to claim 1, characterized in that After obtaining the nominal capacity of the battery to be tested, the method further includes: The SOH of the battery to be tested is calculated based on the nominal capacity of the battery to be tested.

8. A device for determining the nominal capacity of a battery, characterized in that: The device comprises: an acquisition and determination unit, configured to acquire target charging detailed data of the battery under test within a previous preset interval, and determine the battery capacity of the battery under test in each charging stroke, the temperature characteristics, and the current characteristics of the battery under test in each charging stroke based on the target charging detailed data, wherein one charging stroke includes multiple consecutive target charging detailed data, wherein the batteries under test are for multiple batteries of the same model, and the preset interval includes: a preset time interval, or a preset mileage interval; a model building unit, configured to build a nominal capacity prediction model according to the battery capacity of the battery to be tested in each charging stroke, and the temperature characteristics and current characteristics of the battery to be tested in each charging stroke; a statistical unit, configured to collect statistics of the most concentrated target temperature characteristics and target current characteristics in all the charging trips, wherein the target temperature characteristics and the target current characteristics are the temperature and current at which the battery to be tested operates most frequently; A nominal capacity prediction unit is configured to perform nominal capacity prediction on the target temperature characteristic and the target current characteristic using the nominal capacity prediction model to obtain the nominal capacity of the battery to be tested, wherein the nominal capacity is the factory capacity.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to execute the method according to any one of claims 1 to 7.

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