Battery pack remaining capacity estimation method and device, electronic equipment and storage medium

Abstract

The application provides a battery pack residual capacity estimation method and device, electronic equipment and storage medium. The method comprises: obtaining first charging data of a first electric vehicle battery pack; extracting feature data of the first charging data, the feature data comprising charging data when entering a feature interval, charging data within the feature interval and charging data when leaving the feature interval, wherein the feature interval is from when the lowest single battery voltage of the battery pack reaches a first voltage value to when the highest single battery voltage reaches a second voltage value during the charging process of the first electric vehicle, and the first voltage value is less than the second voltage value; and inputting the feature data into a capacity estimation model to enable the capacity estimation model to estimate the residual capacity of the battery pack. In the method of the application, the charging data under any working condition can be used to estimate the residual capacity of the battery pack, thereby improving the estimation accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy vehicles, and in particular to a battery pack residual capacity estimation method and device, an electronic device and a storage medium. BACKGROUND

[0002] Battery capacity refers to the total number of charges generated by a battery during complete discharge under given conditions and time. Battery residual capacity refers to the battery capacity measured after the battery has been used for a period of time, and is an important indicator for evaluating the performance of a battery after long-term use. As the energy source of an electric vehicle, the residual capacity of the battery will have a direct impact on the performance of the electric vehicle.

[0003] Currently, the residual capacity of a battery is estimated by allowing the battery to stand for at least one hour after discharge, then charging and recording charging data under a calibration condition, drawing an open circuit voltage (OCV) curve, training a model according to the charging data and OCV data, and using the model to estimate the residual capacity of the battery under other conditions.

[0004] However, in practice, the working condition of a vehicle often differs greatly from the calibration condition, and the vehicle often does not stand for a long enough time before charging. Using the above method to estimate the residual capacity of a battery under a working condition that differs greatly from the calibration condition has low estimation accuracy, and in some cases, estimation cannot be performed. SUMMARY

[0005] The present application provides a battery pack residual capacity estimation method and device, an electronic device and a storage medium to solve the problem of low estimation accuracy of the residual capacity of a battery pack in the prior art.

[0006] In a first aspect, the present application provides a battery pack residual capacity estimation method, comprising:

[0007] obtaining first charging data of a first electric vehicle battery pack;

[0008] extracting feature data of the first charging data, the feature data including charging data when entering a feature interval, charging data within the feature interval, and charging data when leaving the feature interval, wherein the feature interval is an interval corresponding to the battery pack reaching a first voltage value to a second voltage value during the charging process of the first electric vehicle, and the first voltage value is less than the second voltage value;

[0009] inputting the feature data into a capacity estimation model to cause the capacity estimation model to estimate the residual capacity of the battery pack.

[0010] Optionally, the charging data when entering the feature interval includes the amount of electricity, the current and the pressure difference of the battery pack when entering the feature interval.

[0011] The charging data entering the feature interval includes the average maximum temperature of the battery pack in the feature interval, the average minimum temperature, the charging capacity, the average charging current, the standard deviation of the current, the maximum current, and the minimum current;

[0012] The charging data leaving the feature interval includes the current when leaving the feature interval and the pressure difference of the battery pack.

[0013] Optionally, before obtaining the charging data of the first electric vehicle battery pack, the method further comprises:

[0014] Obtaining second charging data of a second electric vehicle, the charging and discharging working condition category of the second electric vehicle being greater than a first preset threshold;

[0015] According to the second charging data, calculating the remaining capacity of the battery pack of the second electric vehicle;

[0016] Extracting feature data of the second charging data;

[0017] According to the feature data of the second charging data and the remaining capacity of the battery pack of the second electric vehicle, training a capacity estimation model.

[0018] Optionally, if the plurality of second electric vehicles are of the same model, before obtaining the second charging data of the second electric vehicle, the method further comprises:

[0019] Determining that the usage mileage of the second electric vehicle within a first preset time period is within a preset range.

[0020] Optionally, if the second charging data is a plurality of second charging data of the same electric vehicle collected at a plurality of collection times, the time difference between the plurality of collection times is greater than a second preset time period and less than a third preset time period, and the second preset time period is less than the third preset time period.

[0021] Optionally, according to the second charging data, calculating the remaining capacity of the battery pack of the second electric vehicle comprises:

[0022] According to the full discharge and full charge charging data in the second charging data, calculating the remaining capacity of the battery pack.

[0023] Optionally, obtaining the second charging data of the second electric vehicle further comprises:

[0024] Fully discharging the battery pack of the second electric vehicle at a preset temperature and at a preset rate, and standing for a fourth preset time period;

[0025] Charging the battery pack at a preset rate to the highest cut-off voltage, and then charging at a constant voltage until the charging current is less than a second preset threshold;

[0026] Recording the second charging data.

[0027] In a second aspect, the present application provides a battery pack remaining capacity estimation device, comprising:

[0028] An acquisition module is configured to acquire first charging data of a first electric vehicle battery pack.

[0029] An extraction module is configured to extract feature data of the first charging data, the feature data comprising charging data when entering a feature interval, charging data within the feature interval, and charging data when leaving the feature interval, wherein the feature interval corresponds to an interval in which the lowest single-cell voltage of the battery pack reaches a first voltage value to the highest single-cell voltage reaching a second voltage value during the first electric vehicle charging process, and the first voltage value is less than the second voltage value.

[0030] A processing module is configured to input the feature data into a capacity estimation model, so that the capacity estimation model estimates the remaining capacity of the battery pack.

[0031] In a third aspect, the present application provides an electronic device, comprising a memory and a processor.

[0032] The memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory to implement the battery pack remaining capacity estimation method in the first aspect and any one of the examples of the first aspect.

[0033] In a fourth aspect, the present application provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the battery pack remaining capacity estimation method in the first aspect and any one of the examples of the first aspect.

[0034] The battery pack remaining capacity estimation method, device, electronic device, and storage medium provided by the present application can acquire the first charging data of the first electric vehicle, input the feature data of the first charging data into a capacity estimation model, and make the capacity estimation model estimate the remaining capacity of the battery pack. In addition, the charging data under any working condition can be selected to obtain the feature data, so that the charging data under any working condition can be used to estimate the remaining capacity of the battery pack, thereby improving the estimation efficiency and accuracy of the remaining capacity of the battery pack. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without any creative effort.

[0036] Figure 1 A scene schematic diagram of battery pack remaining capacity estimation provided by an embodiment of the present application;

[0037] Figure 2 A flow chart of a battery pack remaining capacity estimation method provided by an embodiment of the present application is shown in FIG. 1.

[0038] Figure 3 A schematic diagram of a feature interval provided by an embodiment of the present application is shown in FIG. 2.

[0039] Figure 4 A flow chart of a remaining capacity estimation model training method provided by an embodiment of the present application is shown in FIG. 3.

[0040] Figure 5 A structural schematic diagram of a battery pack remaining capacity estimation device provided by an embodiment of the present application is shown in FIG. 4.

[0041] Figure 6 A hardware structural schematic diagram of an electronic device provided by an embodiment of the present application is shown in FIG. 5. DETAILED DESCRIPTION

[0042] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described in detail below with reference to the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, but not all embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0043] The terms “first”, “second”, “third”, and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances. For example, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information, without departing from the scope of the present text.

[0044] As the power source of an electric vehicle, a battery has a direct impact on the performance of the electric vehicle. The remaining capacity of the battery is an important indicator of the battery life, and is directly related to the maximum output current and working time of the electric vehicle. Therefore, it is extremely important to estimate the remaining capacity of the battery pack.

[0045] Currently, the remaining capacity of the battery pack is estimated by placing the battery for at least one hour after discharging, then charging and recording the charging data under the calibration condition, drawing the OCV curve of the battery, training the model according to the charging data and OCV data, and using the model to estimate the remaining capacity of the battery under other conditions.

[0046] But in practice, it is usually charged immediately after parking, it is difficult to meet the static condition, and the charging rate, temperature and other data may be different from the calibration condition, so the above method is not suitable for all electric vehicle battery pack remaining capacity estimation under any charging condition.

[0047] To solve the above problems, the application provides a battery pack remaining capacity estimation method, device, electronic equipment and storage medium. The application obtains the first charging data of the first electric vehicle, extracts the feature data of the first charging data, and inputs the capacity estimation model, so that the model outputs the estimated battery pack remaining capacity. Wherein, the first charging data can be the charging data of the first electric vehicle under any condition, not limited to calibration condition, so the method of the application is suitable for more battery pack capacity estimation of electric vehicles, and improves the estimation efficiency.

[0048] The technical solutions of the application will be described in detail in the following specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in some embodiments.

[0049] Figure 1 A scene diagram of a battery pack remaining capacity estimation method provided by an embodiment of the application is shown. As shown in Figure 1 The electronic equipment inputs the battery pack charging data of the electric vehicle into the capacity estimation model, and the model can output the remaining capacity of the battery pack. Wherein, the charging data can be the data generated by charging the electric vehicle to full discharge, or the data generated by charging without complete discharge, and the charging time is also random, for example, it can only be charged for half an hour.

[0050] In the application, the electronic equipment is the execution subject, and the battery pack remaining capacity estimation method of the following embodiments is executed. Specifically, the execution subject can be a hardware device of the electronic equipment, or a software application for realizing the following embodiments in the electronic equipment, or a computer readable storage medium installed with the software application for realizing the following embodiments, or a code for realizing the following embodiments.

[0051] Figure 2 A flow chart of a battery pack remaining capacity estimation method provided by an embodiment of the application is shown. As shown in Figure 2 The method of the embodiment can include the following steps:

[0052] S201, obtaining the first charging data of the first electric vehicle battery pack.

[0053] In this embodiment, the first electric vehicle can be any electric vehicle, for example, the mileage is 20,000 kilometers or 100,000 kilometers, and the use time is 1 year or 5 years. The first charging data can be charging data generated by full discharge and full charge, or charging data generated by full charge without full discharge, or charging data generated by short charging, for example, only charging for half an hour or one hour. Among them, full discharge and full charge means charging to full power after complete discharge.

[0054] S202, extracting feature data of the first charging data, the feature data including charging data when entering the feature interval, charging data in the feature interval, and charging data when leaving the feature interval.

[0055] In this embodiment, the feature interval is an interval corresponding to the lowest single cell voltage of the battery pack reaching the first voltage value to the highest single cell voltage reaching the second voltage value during the charging process of the first electric vehicle, and the first voltage value is less than the second voltage value.

[0056] Among them, the selection of the feature interval is related to the charging interval of the first electric vehicle, and the difference between the first voltage value and the second voltage value can be set according to needs, for example, it can be 80mV, 100mV, 150mV, etc.

[0057] For example, assuming that during the charging process of the first electric vehicle, the lowest single cell voltage increases from 3.2V to 3.8V, and the highest single cell voltage increases from 3.3V to 3.9V, and the difference between the first voltage value and the second voltage value is 100mV, then the first voltage value can be 3300mV, 3400mV, 3500mV, 3600mV or 3700mV, and the feature interval can be [3300mV, 3400mV], [3400mV, 3500mV], [3500mV, 3600mV], [3600mV, 3700mV] or [3700mV, 3800mV].

[0058] When the lowest single cell voltage reaches the first voltage value, the charging data when entering the feature interval is collected, the charging data in the feature interval is continuously collected until the highest single cell voltage reaches the second voltage value, and when the highest single cell voltage reaches the second voltage value, the charging data when leaving the feature interval is collected.

[0059] For example, if the minimum cell voltage increases from 3.7V to 3.8V and the maximum cell voltage increases from 3.8V to 3.9V during the first charging process of the first electric vehicle, and the difference between the first voltage value and the second voltage value is 100mV, then the first voltage value can be 3800mV, and the feature interval can be [3800mV, 3900mV]. When the first voltage value is 3.8V and the second voltage value is 3.9V, that is, the data collection start time is the end time, the difference can be adjusted, for example, the difference is set to 80mV, and the charging data can still be used to estimate the remaining capacity of the battery pack.

[0060] As shown in FIG. 6, in some embodiments, the charging data when entering the feature interval includes the power, the current, and the voltage difference of the battery pack when entering the feature interval. The charging data when entering the feature interval includes the average maximum temperature, the average minimum temperature, the charging power, the average charging current, the standard deviation of the current, the maximum current, and the minimum current of the battery pack in the feature interval. The charging data when leaving the feature interval includes the current and the voltage difference of the battery pack when leaving the feature interval. Figure 3

[0061] The voltage difference of the battery pack is the difference between the cell voltages in the battery pack. The average temperature can be understood as that a plurality of temperature collection points are arranged in the battery pack, and the temperatures of the collection points are periodically collected during the charging process. After the charging is completed, the average maximum temperature is obtained by averaging a plurality of maximum temperatures, and the average minimum temperature is obtained by averaging a plurality of minimum temperatures.

[0062] Optionally, the feature data can further include a feature interval number, so that the model can estimate the remaining capacity of the battery pack according to different charging voltages after the model is input.

[0063] The feature interval number can be the first voltage value / 100. For example, the first voltage value is 3300mV or 3400mV, and the feature interval number is 33 or 34.

[0064] Optionally, the feature data can further include a feature interval corresponding to a mileage, an electric vehicle announcement model, and the like. These feature data can make the model estimate the remaining capacity of the battery pack more accurately.

[0065] S203, input the feature data into the capacity estimation model, so that the capacity estimation model estimates the remaining capacity of the battery pack.

[0066] ​The battery pack residual capacity estimation method provided by the embodiment can obtain first charging data of a first electric vehicle, extract feature data of the first charging data, and input the feature data into a capacity estimation model, so that the model can output estimated battery pack residual capacity, thereby achieving the effect of more accurately obtaining the battery pack residual capacity, and the feature interval can be selected from the charging data under any working condition, that is, the feature data can be extracted from the charging data under any working condition, and is used to estimate the battery pack residual capacity. The method of the embodiment is more universal.

[0067] Figure 4 A flowchart of a residual capacity estimation model training method provided by an embodiment of the application is shown. Figure 2 On the basis of the embodiment, the residual capacity estimation model training method can further train the capacity estimation model to improve the estimation accuracy of the capacity estimation model. As shown in Figure 4 The residual capacity estimation model training method can include the following steps with the electronic device as the execution subject.

[0068] S401, obtain second charging data of a second electric vehicle, and the charging and discharging working condition type of the second electric vehicle is greater than a first preset threshold.

[0069] In the embodiment, the second charging data is used to train the capacity estimation model, and therefore the richer the charging and discharging working conditions of the second electric vehicle, the better the effect of the trained model. The charging and discharging working conditions include, for example, high-speed working conditions and urban working conditions, and the proportion of using the on-board battery to charge and using the charging pile to charge is comparable.

[0070] In some embodiments, if the second electric vehicles are of the same model, before obtaining the second charging data of the second electric vehicle, the method further includes determining that the use mileage of the second electric vehicle within a first preset time period is within a preset range.

[0071] The preset range is that the use mileage of the second electric vehicle within the first preset time period is the median section of the same model electric vehicle. For example, the first preset time period is three months, and the preset range can be set to 10,000 to 20,000 kilometers. The preset range is set to avoid obtaining discrete data. For example, a certain electric vehicle is used for durability test, and the use mileage within three months reaches 100,000 kilometers. If the charging data of the electric vehicle is used to train the capacity estimation model, the effect of the model will be poor.

[0072] In some embodiments, if the second charging data is a plurality of second charging data of the same electric vehicle collected at a plurality of collection times, the time difference between the plurality of collection times is greater than a second preset time period but less than a third preset time period, and the second preset time period is less than the third preset time period.

[0073] The second preset time period cannot be set too short, for example, one day. The change of the usage mileage of the electric vehicle in one day has little effect on the remaining capacity of the battery pack. The third preset time period cannot be set too long, for example, six months. The usage mileage of the electric vehicle may change greatly before and after six months, and the change of the remaining capacity of the battery pack is also large, which is not conducive to model training.

[0074] Part of the feature data of the same electric vehicle, for example, the charging data generated by the charging habit of the vehicle owner, can be fixed. If the difference in the calculated remaining capacity of the battery pack is too large according to the charging data collected for multiple times, the model training is not conducive.

[0075] S402, calculating the remaining capacity of the battery pack of the second electric vehicle according to the second charging data.

[0076] In some embodiments, calculating the remaining capacity of the battery pack of the second electric vehicle according to the second charging data comprises: calculating the remaining capacity of the battery pack according to the full discharge and full charge charging data in the second charging data.

[0077] Different battery capacities correspond to different battery voltages. Calculating the remaining capacity of the battery pack according to the full discharge and full charge charging data can avoid errors caused by calculating according to other working condition charging data.

[0078] S403, extracting feature data of the second charging data.

[0079] In this embodiment, the feature data of the second charging data is similar to the feature data of the first charging data. For details, refer to the embodiment shown in Figure 2 The embodiment will not be described here.

[0080] It should be understood that the more feature data of the second charging data, the better the effect of the trained model.

[0081] S404, training the capacity estimation model according to the feature data of the second charging data and the remaining capacity of the battery pack of the second electric vehicle.

[0082] In this embodiment, the trained capacity estimation model can be a multi-layer perceptron, and the number of neurons in each layer can be set as needed, for example, the number of neurons in five layers from the input layer to the output layer is 15, 31, 17, 7 and 1 respectively. The training process is as follows: input the feature data of the second charging data into the model, make the model estimate the remaining capacity of the battery pack, then calculate the model loss function according to the calculated remaining capacity of the battery pack and the estimated remaining capacity of the battery pack, and adjust the model parameters according to the loss function, so as to complete the training model.

[0083] The battery pack remaining capacity estimation method provided by the embodiment is used for obtaining second charging data of a second electric vehicle, extracting feature data of the second charging data, calculating the battery pack remaining capacity according to the second charging data, and finally training a capacity estimation model according to the calculated battery pack remaining capacity and the feature data of the second charging data. Since the charging and discharging working conditions of the second electric vehicle are diversified, the model trained by the second charging data is better, and the battery pack remaining capacity estimated by the model is more accurate, and the battery pack remaining capacity estimation method is suitable for the battery pack of the electric vehicle under various working conditions.

[0084] On the basis of the above embodiments, the second charging data of the second electric vehicle is obtained, and the method further includes: completely discharging the battery pack of the second electric vehicle at a preset temperature and at a preset rate for a fourth preset time length; charging the battery pack to the highest cut-off voltage at the preset rate, and then charging the battery pack at a constant voltage until the charging current is less than a second preset threshold; and recording the second charging data.

[0085] In the method of the embodiment, in addition to the charging data obtained in the actual use process, part of the test charging data can be introduced for model training. The temperature and the rate can be effectively controlled during the test, and the obtained data is more accurate than the data obtained in the actual use process, which is beneficial to improve the model training effect and make the model estimate the battery pack remaining capacity more accurately.

[0086] Figure 5 A structural schematic diagram of a battery pack remaining capacity estimation device provided by an embodiment of the application is shown. As shown in Figure 5 The battery pack remaining capacity estimation device 50 of the embodiment is used to implement the operations corresponding to the electronic device in any of the above method embodiments. The battery pack remaining capacity estimation device 50 of the embodiment includes:

[0087] The obtaining module 501 is configured to obtain first charging data of a battery pack of a first electric vehicle.

[0088] The extracting module 502 is configured to extract feature data of the first charging data. The feature data includes charging data when entering a feature interval, charging data when being in the feature interval, and charging data when leaving the feature interval. The feature interval is from when the lowest single cell voltage of the battery pack reaches a first voltage value to when the highest single cell voltage reaches a second voltage value in the charging process of the first electric vehicle, and the first voltage value is less than the second voltage value.

[0089] The processing module 503 is configured to input the feature data into a capacity estimation model, so that the capacity estimation model estimates the remaining capacity of the battery pack.

[0090] The battery pack residual capacity estimation device 50 provided by the embodiments of the present application can execute the method embodiments described above, and the specific implementation principles and technical effects can be referred to the method embodiments described above, which will not be repeated here.

[0091] Figure 6 A hardware structure schematic diagram of an electronic device provided by the embodiments of the present application is shown. As shown in the figure, Figure 6 The electronic device 60 is used to implement the operations corresponding to the electronic device in any of the method embodiments described above. The electronic device 60 of the embodiments can include a memory 601, a processor 602 and a communication interface (not shown in the figure).

[0092] The memory 601 is used to store computer programs. The memory 601 can include a random access memory (RAM), and can also include a non-volatile memory (NVM), such as at least one disk memory, and can also be a U disk, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0093] The processor 602 is used to execute the computer programs stored in the memory to implement the battery pack residual capacity estimation method in the embodiments described above. Details can be referred to the related description in the method embodiments described above. The processor 602 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC) and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like. The steps of the method disclosed in the present application can be directly embodied as hardware processor execution or executed by a combination of hardware and software modules in the processor.

[0094] Optionally, the memory 601 can be independent or integrated with the processor 602.

[0095] When the memory 601 is a device independent of the processor 602, the electronic device 60 can further include a bus. The bus is used to connect the memory 601 and the processor 602. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0096] The communication interface can be connected with the processor 602 through the bus. The processor 602 can control the communication interface to realize the functions of receiving and sending signals.

[0097] The electronic device 60 provided by the embodiment can be used to execute the battery pack residual capacity estimation method described above, and the implementation manner and technical effects are similar, which will not be described here again.

[0098] The present application further provides a computer readable storage medium, and the computer readable storage medium stores computer programs / instructions. The computer programs / instructions are executed by the processor to realize the method provided by the various embodiments.

[0099] The computer readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transfer of computer programs from one place to another. The computer storage medium can be any available medium that can be accessed by a general or special purpose computer. For example, the computer readable storage medium is coupled to the processor, so that the processor can read information from the computer readable storage medium and write information to the computer readable storage medium. Of course, the computer readable storage medium can also be a component of the processor. The processor and the computer readable storage medium can be located in an application specific integrated circuit (ASIC). In addition, the ASIC can be located in the user equipment. Of course, the processor and the computer readable storage medium can also exist as discrete components in the communication device.

[0100] In particular, the computer readable storage media can be realized by any type of volatile or non-volatile storage devices or a combination thereof, such as a Static Random-Access Memory (SRAM), an Electrically-Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read Only Memory (EPROM), a Programmable read-only memory (PROM), a Read-Only Memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk. The storage media can be any available media that can be accessed by a general or special purpose computer.

[0101] The present application also provides a computer program product, which includes computer programs / instructions stored in a computer readable storage medium. At least one processor of the device can read the computer programs / instructions from the computer readable storage medium, and the at least one processor executes the computer programs / instructions to enable the device to implement the method provided by the various embodiments described above.

[0102] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the described apparatus embodiments are merely schematic. The division of the modules is merely a logical function division. There can be another division manner for the actual implementation. For example, a plurality of modules or a functional apparatus can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, apparatuses or modules, and can be in electrical, mechanical or other forms.

[0103] Each module can be physically separated, for example, installed in different positions of one device, or installed in different devices, or distributed to a plurality of network units, or distributed to a plurality of processors. Each module can also be integrated together, for example, installed in the same device, or integrated in a set of codes. Each module can exist in the form of hardware, or can exist in the form of software, or can be realized in the form of software plus hardware. According to actual needs, some or all of the modules can be selected to achieve the purpose of the embodiments of the present application.

[0104] It should be understood that, although each step in the flowchart in the above embodiments is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other sequences. Moreover, at least part of the steps in the figure can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence is not necessarily sequential, but can be alternately executed with at least part of other steps or sub-steps or stages of other steps.

[0105] It should be noted that the user vehicle charging data (including but not limited to data for analysis, stored data, etc.) involved in the present application is all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal.

[0106] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of estimating a remaining capacity of a battery pack, characterized by, The method comprises: obtaining first charging data of a first electric vehicle battery pack; extracting feature data of the first charging data, the feature data comprising charging data when entering a feature interval, charging data within the feature interval, and charging data when leaving the feature interval, wherein the feature interval corresponds to an interval in which the lowest single cell voltage of the battery pack reaches a first voltage value to the highest single cell voltage reaching a second voltage value during the first electric vehicle charging process, and the first voltage value is less than the second voltage value; wherein the feature data comprises a voltage difference of the battery pack when entering the feature interval and a voltage difference of the battery pack when leaving the feature interval; inputting the feature data into a capacity estimation model, so that the capacity estimation model estimates the remaining capacity of the battery pack.

2. The method of claim 1, wherein: the charging data when entering the feature interval comprises the electric quantity, the current, and the voltage difference of the battery pack when entering the feature interval; the charging data within the feature interval comprises the average maximum temperature, the average minimum temperature, the charging electric quantity, the average charging current, the current standard deviation, the maximum current, and the minimum current of the battery pack within the feature interval; the charging data when leaving the feature interval comprises the current and the voltage difference of the battery pack when leaving the feature interval.

3. The method of claim 1, wherein, Before the obtaining the first charging data of the first electric vehicle battery pack, the method further comprises: obtaining second charging data of a second electric vehicle, wherein the charging and discharging working condition category of the second electric vehicle is greater than a first preset threshold; calculating the remaining capacity of the battery pack of the second electric vehicle according to the second charging data; extracting feature data of the second charging data; training the capacity estimation model according to the feature data of the second charging data and the remaining capacity of the battery pack of the second electric vehicle.

4. The method of claim 3, wherein, If a plurality of second electric vehicles are of the same model, before the obtaining the second charging data of the second electric vehicle, the method further comprises: determining that the usage mileage of the second electric vehicle within a first preset time period is within a preset range.

5. The method of claim 3, wherein, If the second charging data is a plurality of second charging data of the same electric vehicle collected at a plurality of collection time points, the time difference between the plurality of collection time points is greater than a second preset time period and less than a third preset time period, and the second preset time period is less than the third preset time period.

6. The method according to any one of claims 3-5, characterized in that, The calculating the remaining capacity of the battery pack of the second electric vehicle according to the second charging data comprises: calculating the remaining capacity of the battery pack according to the full discharge and full charge charging data in the second charging data.

7. The method of claim 6, wherein, The obtaining the second charging data of the second electric vehicle further comprises: completely discharging the battery pack of the second electric vehicle at a preset temperature and at a preset rate, and standing for a fourth preset time period; charging the battery pack to the highest cut-off voltage at the preset rate, and then charging at a constant voltage until the charging current is less than a second preset threshold; recording the second charging data.

8. A battery pack remaining capacity estimating device characterized by comprising: The device comprises: an obtaining module configured to obtain first charging data of a first electric vehicle battery pack; The extraction module is configured to extract feature data of the first charging data, the feature data comprising charging data when entering a feature interval, charging data within the feature interval, and charging data when leaving the feature interval, wherein the feature interval corresponds to an interval during the first electric vehicle charging process in which a minimum single cell voltage of the battery pack reaches a first voltage value to a maximum single cell voltage reaching a second voltage value, the first voltage value being less than the second voltage value; wherein the feature data comprises a voltage difference of the battery pack when entering the feature interval and a voltage difference of the battery pack when leaving the feature interval. The processing module is configured to input the feature data into a capacity estimation model, and cause the capacity estimation model to estimate the remaining capacity of the battery pack.

9. An electronic device, comprising: The device comprises a memory and a processor; The memory is configured to store a computer program; The processor is configured to execute the computer program stored in the memory, and implement the battery pack remaining capacity estimation method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is configured to be executed by the processor to implement the battery pack remaining capacity estimation method according to any one of claims 1-7.

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