CN115166521A - Battery capacity evaluation method and device and electronic equipment - Google Patents
Battery capacity evaluation method and device and electronic equipment Download PDFInfo
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- CN115166521A CN115166521A CN202210817034.4A CN202210817034A CN115166521A CN 115166521 A CN115166521 A CN 115166521A CN 202210817034 A CN202210817034 A CN 202210817034A CN 115166521 A CN115166521 A CN 115166521A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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Abstract
The invention provides a method and a device for evaluating battery capacity and electronic equipment, wherein the method comprises the following steps: when a battery of a vehicle is charged in a direct current mode, acquiring charging data of the current preset charging duration of the battery; standardizing the charging data to obtain the standardized charging data; and evaluating the battery capacity of the charge data after the standardization treatment by adopting a battery capacity evaluation model to obtain the evaluation capacity of the battery, wherein the battery capacity evaluation model is obtained by training historical charge data samples of different batteries. The battery capacity evaluation model is obtained by training historical charging data samples of different batteries, a large amount of historical data of the battery to be evaluated is not needed, and only the charging data of the current preset charging duration of the battery to be evaluated is needed, so that the evaluation capacity of the battery can be accurately evaluated, and the real-time performance and the accuracy of capacity evaluation are improved.
Description
Technical Field
The present invention relates to the field of battery technologies, and in particular, to a method and an apparatus for evaluating battery capacity, and an electronic device.
Background
Known battery capacity evaluation technologies are divided into two types, namely laboratory data and industrial big data, from the viewpoint of sample data sources; from the research method, the method is divided into a model-driven method and a data-driven method. The laboratory data refers to standard charge and discharge tests of the battery under strict test conditions, and then various index parameters of the battery are observed; the industrial big data refers to the real charge and discharge data (including charge and discharge data of different regions, different time, different temperature, different vehicle use habits and the like) of the battery on the vehicle, and is index data under the comprehensive action of various factors; the model driving refers to the research of battery charging and discharging data based on an electrochemical model, an equivalent circuit model, an empirical model and the like; the data driving means that measures such as machine learning, signal processing, statistical analysis and the like are adopted based on the charging and discharging data, and the aging rule of the battery is learned from each index of the charging and discharging data.
Due to the complex electrochemical behavior of lithium ion batteries, different material characteristics, different battery structures, different attenuation mechanisms, different vehicle use habits and different use environments all affect the attenuation process of the batteries. Even among vehicles in the same batch of the same vehicle type, the attenuation characteristics of the batteries are different. Therefore, whether laboratory data or industrial big data, it is necessary to evaluate the current capacity of the battery of the respective vehicle based on a large amount of charging history data of the battery of the vehicle. Moreover, due to the influence of seasonal temperature, the estimated capacity under industrial big data shows a periodic fluctuation trend, in this case, if the current capacity of the battery is accurately estimated, historical data (at least 1 year) of a complete period of the battery is required as a sample, and the periodic influence can be determined and eliminated. In summary, in the prior art, if the battery capacity is to be accurately evaluated, a large amount of historical data of the battery needs to be accumulated as a sample, and the battery capacity cannot be evaluated only by means of a few charging records in the near future.
In summary, the conventional battery capacity evaluation method needs to rely on a large amount of historical data of the battery as a sample, and the real-time performance of the battery capacity evaluation is poor.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method and an apparatus for evaluating battery capacity, and an electronic device, so as to solve the technical problem that the existing method for evaluating battery capacity needs to rely on a large amount of historical data of the battery as a sample, and the real-time performance of battery capacity evaluation is poor.
In a first aspect, an embodiment of the present invention provides a method for evaluating battery capacity, including:
when a battery of a vehicle is charged in a direct current mode, acquiring charging data of the current preset charging duration of the battery;
standardizing the charging data to obtain standardized charging data;
and performing battery capacity evaluation on the standardized charging data by adopting a battery capacity evaluation model to obtain the evaluation capacity of the battery, wherein the battery capacity evaluation model is obtained by training historical charging data samples of different batteries.
Further, the method further comprises:
acquiring historical charging data samples of different batteries and capacity labels corresponding to the charging data samples, wherein the charging data samples are determined based on initial charging data of a historical current charging process of the batteries;
and training an initial battery capacity evaluation model through the charging data samples and the capacity labels corresponding to the charging data samples to obtain the battery capacity evaluation model.
Further, obtaining historical charging data samples of different batteries and capacity labels corresponding to the charging data samples includes:
acquiring initial charging data of a historical current charging process of a target battery, wherein the target battery is any one of the different batteries;
cleaning the initial charging data to obtain a historical charging data sample of the target battery in the current charging process, and taking the historical charging data sample of the target battery in the current charging process as the historical charging data sample of the target battery;
calculating a capacity label corresponding to the charging data sample of the historical current charging process of the target battery based on the charging data sample of the previous preset charging process of the historical current charging process of the target battery, the charging data sample of the historical current charging process of the target battery and the charging data sample of the later preset charging process of the historical current charging process of the target battery.
Further, calculating a capacity label corresponding to the charging data sample of the historical current charging process of the target battery based on the charging data sample of the previous preset charging process of the historical current charging process of the target battery, the charging data sample of the historical current charging process of the target battery and the charging data sample of the later preset charging process of the historical current charging process of the target battery, includes:
capacity label corresponding to charging data sample of historical current charging process of target battery
Wherein the charging amount is a charging amount in the charging data sample, Δ SOC = battery SOC at the end of one charging process-battery SOC at the start of the charging process.
Further, the charging data includes: SOC, actual voltage, actual current, required voltage, required current, maximum temperature, minimum temperature, cell maximum voltage, cell minimum voltage, and charge amount.
Further, if the charging duration of the current charging process is N minutes, the estimated capacity of the battery is:
in a second aspect, an embodiment of the present invention further provides an apparatus for evaluating battery capacity, including:
the device comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring the charging data of the current preset charging time of a battery when the battery of a vehicle is subjected to direct current charging;
the standardization processing unit is used for carrying out standardization processing on the charging data to obtain the charging data after the standardization processing;
and the battery capacity evaluation unit is used for evaluating the battery capacity of the charge data after the standardization processing by adopting a battery capacity evaluation model to obtain the evaluation capacity of the battery, wherein the battery capacity evaluation model is obtained by training historical charge data samples of different batteries.
Further, the apparatus is further configured to:
acquiring historical charging data samples of different batteries and capacity labels corresponding to the charging data samples, wherein the charging data samples are determined based on initial charging data of a historical current charging process of the batteries;
and training an initial battery capacity evaluation model through the charging data samples and the capacity labels corresponding to the charging data samples to obtain the battery capacity evaluation model.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method according to any one of the first aspect.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium storing machine executable instructions, which when invoked and executed by a processor, cause the processor to perform the method of any of the first aspect.
In an embodiment of the present invention, a method for evaluating battery capacity is provided, including: when a battery of a vehicle is subjected to direct current charging, acquiring charging data of the current preset charging duration of the battery; standardizing the charging data to obtain the standardized charging data; and performing battery capacity evaluation on the charging data after the standardization processing by adopting a battery capacity evaluation model to obtain the evaluation capacity of the battery, wherein the battery capacity evaluation model is obtained by training historical charging data samples of different batteries. According to the description, the battery capacity evaluation model is obtained by training historical charging data samples of different batteries, a large amount of historical data of the battery to be evaluated is not needed, and only the charging data of the current preset charging time of the battery to be evaluated is needed, so that the evaluation capacity of the battery can be accurately evaluated, the real-time performance and the accuracy of capacity evaluation are improved, and the technical problem that the existing battery capacity evaluation method needs to rely on a large amount of historical data of the battery as samples and the real-time performance of battery capacity evaluation is poor is solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a method for evaluating battery capacity according to an embodiment of the present invention;
fig. 2 is a schematic network structure diagram of a battery capacity evaluation model according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an apparatus for evaluating battery capacity according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In the prior art, if the battery capacity is required to be accurately evaluated, a large amount of historical data of the battery needs to be accumulated as a sample, and the battery capacity cannot be evaluated only by relying on a few charging records in the near term.
Based on this, in the method for evaluating the battery capacity, the battery capacity evaluation model is obtained by training the historical charging data samples of different batteries, a large amount of historical data of the battery to be evaluated is not needed, and only the charging data of the current preset charging time of the battery to be evaluated is needed, so that the evaluation capacity of the battery can be accurately evaluated, and the real-time performance and the accuracy of capacity evaluation are improved.
For the understanding of the present embodiment, a method for evaluating battery capacity disclosed in the embodiment of the present invention will be described in detail first.
The first embodiment is as follows:
in accordance with an embodiment of the present invention, there is provided an embodiment of a method for evaluating battery capacity, it should be noted that the steps illustrated in the flowchart of the accompanying drawings may be executed in a computer system such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be executed in an order different than that herein.
Fig. 1 is a flowchart of a method for evaluating battery capacity according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:
step S102, when the battery of the vehicle is charged by direct current, the charging data of the current preset charging time of the battery is obtained;
in the embodiment of the invention, the method for evaluating the battery capacity can be applied to a cloud platform, and specifically, when a charging pile charges a battery of a vehicle in a direct current manner, the charging pile can interact with a battery management system of the vehicle, so that charging data of the current preset charging duration of the battery is obtained, and then the charging pile sends the obtained charging data to the cloud platform, so that the cloud platform evaluates the battery capacity.
The above charging data includes: SOC, actual voltage, actual current, required voltage, required current, maximum temperature, minimum temperature, cell maximum voltage, cell minimum voltage, and charge amount.
The required voltage and the required current refer to the required voltage and current when the battery is charged; the actual voltage and the actual current refer to the actual voltage and the actual current when the charging pile charges the battery.
The preset charging time period may be 10 continuous minutes, and the first preset time period is not particularly limited in the embodiment of the present invention.
Step S104, carrying out standardization processing on the charging data to obtain the standardized charging data;
specifically, the normalization process may be a Z-score normalization process, so as to eliminate the dimension. The charge data after the standardization process conforms to the standard normal distribution, namely the mean value is 0 and the standard deviation is 1.
And S106, performing battery capacity evaluation on the standardized charging data by adopting a battery capacity evaluation model to obtain the evaluation capacity of the battery, wherein the battery capacity evaluation model is obtained by training historical charging data samples of different batteries.
The battery capacity evaluation model is obtained by training historical charging data samples of different batteries, different material characteristics, different battery structures, different attenuation mechanisms, different vehicle use habits and use environments can be obtained by learning a large number of historical charging data samples of different batteries through the battery capacity evaluation model, the influence on battery attenuation is further obtained, and the accuracy of the evaluation capacity of the battery is good when the battery capacity evaluation is subsequently carried out according to the charging data after standardized processing.
In an embodiment of the present invention, a method for evaluating battery capacity is provided, including: when a battery of a vehicle is charged in a direct current mode, acquiring charging data of the current preset charging duration of the battery; standardizing the charging data to obtain the standardized charging data; and performing battery capacity evaluation on the charging data after the standardization processing by adopting a battery capacity evaluation model to obtain the evaluation capacity of the battery, wherein the battery capacity evaluation model is obtained by training historical charging data samples of different batteries. According to the above description, the battery capacity evaluation model is obtained by training the historical charging data samples of different batteries, a large amount of historical data of the battery to be evaluated is not needed, and the evaluation capacity of the battery can be accurately evaluated only by the charging data of the current preset charging time of the battery to be evaluated, so that the real-time performance and the accuracy of capacity evaluation are improved, and the technical problem that the existing battery capacity evaluation method needs to rely on a large amount of historical data of the battery as samples and the real-time performance of battery capacity evaluation is poor is solved.
The above description briefly introduces the method for estimating the battery capacity of the present invention, and the details thereof will be described below.
In an alternative embodiment of the present invention, the training process for the battery capacity estimation model includes:
(1) Acquiring historical charging data samples of different batteries and capacity labels corresponding to the charging data samples, wherein the charging data samples are determined based on initial charging data of a historical current charging process of the batteries;
specifically, acquiring historical charging data samples of different batteries and capacity labels corresponding to the charging data samples specifically includes the following steps:
(11) Acquiring initial charging data of a historical current charging process of a target battery, wherein the target battery is any one of different batteries;
specifically, an effective charging process is calculated only when the charging duration of one charging process reaches 10 minutes, and the charging process in the embodiment of the present invention refers to a charging process with a charging duration of 10 minutes.
The above process is exemplified below: initial charging data for the historical 20 charging processes of the a battery is obtained, and then, the current charging process may be the 11 th charging process of the 20 charging processes.
(12) Cleaning the initial charging data to obtain a historical charging data sample of the current charging process of the target battery, and taking the historical charging data sample of the current charging process of the target battery as the historical charging data sample of the target battery;
in the embodiment of the invention, the cleaning means to eliminate the abnormal value of the voltage-related data, and the abnormal value refers to a value exceeding the normal range of the index, which is considered as invalid data and needs to be eliminated to avoid influencing the model precision.
For the actual voltage, if a piece of charging data is acquired, wherein the actual voltage is less than or equal to 0V, or the actual voltage is greater than or equal to 1000V, the acquired piece of charging data is regarded as invalid data, and the invalid data is deleted;
for the required voltage, if the required voltage of the acquired charging data is less than or equal to 0V, or the required voltage is greater than or equal to 1000V, the acquired charging data is considered as invalid data and is deleted;
for the maximum voltage of the single body, if the maximum voltage of the single body is less than or equal to 0V or more than or equal to 10V, the acquired charging data is regarded as invalid data and deleted;
for the minimum voltage of the single body, if the minimum voltage of the single body is less than or equal to 0V or the minimum voltage of the single body is greater than or equal to 10V, the acquired charging data is regarded as invalid data, and the invalid data is deleted.
The following is illustrated by way of example: if the current charging process is the 11 th charging process of the 20 charging processes, the charging duration of the 11 th charging process is 60 minutes, and one piece of initial charging data (including 10-dimensional charging data) is acquired every minute, so that 60 pieces of initial charging data of the 11 th charging process are acquired, the initial charging data are cleaned, 5 pieces of initial charging data are deleted, 55 charging data samples of the 11 th charging process are acquired, and the charging data samples are used as historical charging data samples of the target battery.
(13) And calculating a capacity label corresponding to the charging data sample of the historical current charging process of the target battery based on the charging data sample of the previous preset charging process of the historical current charging process of the target battery, the charging data sample of the historical current charging process of the target battery and the charging data sample of the later preset charging process of the historical current charging process of the target battery.
Specifically, calculating a capacity label corresponding to a charging data sample of a historical current charging process of the target battery based on a charging data sample of a previous preset charging process of the historical current charging process of the target battery, a charging data sample of a historical current charging process of the target battery, and a charging data sample of a later preset charging process of the historical current charging process of the target battery specifically includes:
The previous preset-time charging process may be a previous 10-time charging process, and the next preset-time charging process may be a next 9-time charging process. As exemplified above, the capacity labels corresponding to the 55 charging data samples of the 11 th historical charging process of the target battery are: the sum of the charge quantities of the historical 20 charging sessions is divided by the sum of the Δ SOC of the 20 charging sessions. Therefore, when the capacity label is determined, the embodiment of the invention does not only consider the charging amount in the current charging process and the Δ SOC in the current charging process, but performs the sliding average processing on the capacity label in the current charging process according to the data of the charging processes before and after the current charging process, thereby eliminating the influence of various factors on the capacity label in a single charging process.
(2) And training the initial battery capacity evaluation model through the charging data samples and the capacity labels corresponding to the charging data samples to obtain the battery capacity evaluation model.
After obtaining the historical charging data samples and the capacity labels corresponding to the charging data samples, the data may be normalized, and then the initial battery capacity evaluation model may be trained through the normalized data, so as to obtain the battery capacity evaluation model.
As shown in fig. 2, the network of the battery capacity estimation model is composed of 2 bidirectional Lstm layers, 1 dropout layer, and 1 full connection layer, where Lstm time step is 10, number of neurons is selected 64, activation function is tanh, dropout ratio is selected 0.5, full connection layer activation function is selected sigmoid, loss function is selected MAE, optimizer is selected RMSprop, and when the model is trained, number of batch is selected 128, and number of epoch is selected 190.
In the model training process, firstly, 2 bidirectional LSTM layers are utilized to extract 64-dimensional characteristic data from a 10-dimensional charging data sample, then the characteristic data is regularized by a Dropout layer, and finally the characteristic data is input into a full-connection layer to obtain a predicted value of the estimated capacity of the battery.
The loss function MAE in the training process is the average absolute error, and the calculation formula of the MAE is as follows:
wherein n represents the number of samples,
a predicted value, y, representing the estimated capacity of the battery k A capacity label indicating a battery.
In an alternative embodiment of the present invention, if the charging duration of the current charging process is N minutes, the estimated capacity of the battery is:
for example, the charging time of the current charging process is 60 minutes, the preset time is 10 consecutive minutes, and the estimated capacity of the battery is:
namely, the estimated capacity of the battery obtained every 10 minutes in the charging time of 60 minutes is summed, and then the average is obtained, so that the estimated capacity of the battery in the charging process is obtained.
The method for evaluating the battery capacity can accurately evaluate the battery capacity according to the charging data of the battery in one-time charging process (reaching the preset charging time), and improves the real-time performance and the accuracy of capacity evaluation.
The second embodiment:
the embodiment of the present invention further provides a device for evaluating battery capacity, which is mainly used for executing the method for evaluating battery capacity provided in the first embodiment of the present invention, and the following describes the device for evaluating battery capacity provided in the first embodiment of the present invention in detail.
Fig. 3 is a schematic diagram of a battery capacity evaluation apparatus according to an embodiment of the present invention, as shown in fig. 3, the apparatus mainly includes: an acquisition unit 10, a normalization processing unit 20, and a battery capacity evaluation unit 30, wherein:
the system comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring charging data of the current preset charging time of a battery when the battery of a vehicle is subjected to direct current charging;
the standardization processing unit is used for carrying out standardization processing on the charging data to obtain the standardized charging data;
and the battery capacity evaluation unit is used for evaluating the battery capacity of the standardized charging data by adopting a battery capacity evaluation model to obtain the evaluation capacity of the battery, wherein the battery capacity evaluation model is obtained by training historical charging data samples of different batteries.
In an embodiment of the present invention, there is provided a battery capacity evaluation apparatus including: when a battery of a vehicle is subjected to direct current charging, acquiring charging data of the current preset charging duration of the battery; standardizing the charging data to obtain the standardized charging data; and evaluating the battery capacity of the charge data after the standardization treatment by adopting a battery capacity evaluation model to obtain the evaluation capacity of the battery, wherein the battery capacity evaluation model is obtained by training historical charge data samples of different batteries. According to the description, the battery capacity evaluation model is obtained by training historical charging data samples of different batteries, a large amount of historical data of the battery to be evaluated is not needed, and only the charging data of the current preset charging time of the battery to be evaluated is needed, so that the evaluation capacity of the battery can be accurately evaluated, the real-time performance and the accuracy of capacity evaluation are improved, and the technical problem that the existing battery capacity evaluation method needs to rely on a large amount of historical data of the battery as samples and the real-time performance of battery capacity evaluation is poor is solved.
Optionally, the apparatus is further configured to: acquiring historical charging data samples of different batteries and capacity labels corresponding to the charging data samples, wherein the charging data samples are determined based on initial charging data of a historical current charging process of the batteries; and training the initial battery capacity evaluation model through the charging data samples and the capacity labels corresponding to the charging data samples to obtain the battery capacity evaluation model.
Optionally, acquiring initial charging data of a historical current charging process of a target battery, wherein the target battery is any one of different batteries; cleaning the initial charging data to obtain a historical charging data sample of the current charging process of the target battery, and taking the historical charging data sample of the current charging process of the target battery as the historical charging data sample of the target battery; and calculating a capacity label corresponding to the charging data sample of the historical current charging process of the target battery based on the charging data sample of the previous preset charging process of the historical current charging process of the target battery, the charging data sample of the historical current charging process of the target battery and the charging data sample of the later preset charging process of the historical current charging process of the target battery.
Alternatively,
where the charge is the charge in the charge data sample, Δ SOC = the battery SOC at the end of a charge session-the battery SOC at the beginning of the charge session.
Optionally, the charging data includes: SOC, actual voltage, actual current, required voltage, required current, maximum temperature, minimum temperature, cell maximum voltage, cell minimum voltage, and charge amount.
Optionally, if the charging duration of the current charging process is N minutes, the estimated capacity of the battery is:
the device provided by the embodiment of the present invention has the same implementation principle and the same technical effects as those of the foregoing method embodiments, and for the sake of brief description, reference may be made to corresponding contents in the foregoing method embodiments for the parts of the device embodiments that are not mentioned.
As shown in fig. 4, 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 operated, the processor 601 and the memory 602 communicate with each other through the bus, and the processor 601 executes the machine-readable instructions to execute the steps of the above-mentioned method for estimating battery capacity.
Specifically, the memory 602 and the processor 601 can be general-purpose memories and processors, which are not specifically limited herein, and the battery capacity evaluation method can be performed when the processor 601 runs a computer program stored in the memory 602.
The processor 601 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 601. The Processor 601 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device 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 Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 602, and the processor 601 reads the information in the memory 602 and completes the steps of the method in combination with the hardware thereof.
In response to the above method for evaluating battery capacity, the present application further provides a computer-readable storage medium storing machine executable instructions, which, when invoked and executed by a processor, cause the processor to execute the steps of the above method for evaluating battery capacity.
The battery capacity evaluation device provided by the embodiment of the present application may be specific hardware on the device, or software or firmware installed on the device, or the like. The device provided by the embodiment of the present application has the same implementation principle and technical effect as the foregoing method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiments where no part of the device embodiments is mentioned. It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the system, the apparatus and the unit described above may all refer to the corresponding processes in the method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
For another example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, or portions of the technical solutions that substantially contribute to the prior art, may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the vehicle marking method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the scope of the embodiments of the present application. Are intended to be covered by the scope of this application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for evaluating a capacity of a battery, comprising:
when a battery of a vehicle is charged in a direct current mode, acquiring charging data of the current preset charging duration of the battery;
standardizing the charging data to obtain standardized charging data;
and performing battery capacity evaluation on the standardized charging data by adopting a battery capacity evaluation model to obtain the evaluation capacity of the battery, wherein the battery capacity evaluation model is obtained by training historical charging data samples of different batteries.
2. The method of claim 1, further comprising:
acquiring historical charging data samples of different batteries and capacity labels corresponding to the charging data samples, wherein the charging data samples are determined based on initial charging data of a historical current charging process of the batteries;
and training an initial battery capacity evaluation model through the charging data sample and a capacity label corresponding to the charging data sample to obtain the battery capacity evaluation model.
3. The method of claim 2, wherein obtaining historical charging data samples of different batteries and capacity labels corresponding to the charging data samples comprises:
acquiring initial charging data of a historical current charging process of a target battery, wherein the target battery is any one of the different batteries;
cleaning the initial charging data to obtain a historical charging data sample of the current charging process of the target battery, and taking the historical charging data sample of the current charging process of the target battery as the historical charging data sample of the target battery;
calculating a capacity label corresponding to the charging data sample of the historical current charging process of the target battery based on the charging data sample of the previous preset charging process of the historical current charging process of the target battery, the charging data sample of the historical current charging process of the target battery and the charging data sample of the next preset charging process of the historical current charging process of the target battery.
4. The method of claim 3, wherein calculating the capacity label corresponding to the charging data sample of the historical current charging process of the target battery based on the charging data sample of the previous preset secondary charging process of the historical current charging process of the target battery, the charging data sample of the historical current charging process of the target battery and the charging data sample of the later preset secondary charging process of the historical current charging process of the target battery comprises:
the above-mentioned
Wherein the charging amount is a charging amount in the charging data sample, Δ SOC = battery SOC at the end of one charging process-battery SOC at the start of the charging process.
5. The method of claim 1, wherein the charging data comprises: SOC, actual voltage, actual current, required voltage, required current, maximum temperature, minimum temperature, cell maximum voltage, cell minimum voltage, and charge amount.
6. The method of claim 1, wherein if the charging duration of the current charging process is N minutes, the estimated capacity of the battery is:
7. an evaluation device of battery capacity, characterized by comprising:
the device comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring charging data of the current preset charging time of a battery when the battery of a vehicle is subjected to direct current charging;
the standardization processing unit is used for carrying out standardization processing on the charging data to obtain the standardized charging data;
and the battery capacity evaluation unit is used for evaluating the battery capacity of the charge data after the standardization processing by adopting a battery capacity evaluation model to obtain the evaluation capacity of the battery, wherein the battery capacity evaluation model is obtained by training historical charge data samples of different batteries.
8. The apparatus of claim 7, wherein the apparatus is further configured to:
acquiring historical charging data samples of different batteries and capacity labels corresponding to the charging data samples, wherein the charging data samples are determined based on initial charging data of a historical current charging process of the batteries;
and training an initial battery capacity evaluation model through the charging data sample and a capacity label corresponding to the charging data sample to obtain the battery capacity evaluation model.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of the preceding claims 1 to 6 are implemented when the computer program is executed by the processor.
10. A computer readable storage medium having stored thereon machine executable instructions which, when invoked and executed by a processor, cause the processor to perform the method of any of claims 1 to 6.
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| CN117390016A (en) * | 2023-09-27 | 2024-01-12 | 希维科技(广州)有限公司 | Method, apparatus and storage medium for generating battery passport |
| CN117390016B (en) * | 2023-09-27 | 2024-05-31 | 希维科技(广州)有限公司 | Method, apparatus and storage medium for generating battery passport |
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