CN117805621A - Battery capacity estimation method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a battery capacity estimation method, a device, equipment and a storage medium. The method comprises the steps of obtaining a first aging characteristic data set of a first type of sample battery at a target temperature; the first aging characteristic data set comprises a specified number of battery capacity actual values and charging characteristics corresponding to early-cycle aging tests; inputting charging characteristics in the first aging characteristic data set into a pre-established standard battery capacity estimation model to obtain a battery capacity estimation value of a first type of sample battery; establishing a battery capacity correction model at a target temperature according to the battery capacity actual value and the battery capacity estimated value of the first-class sample battery; and estimating the battery capacity of the battery to be estimated at the target temperature according to the battery capacity estimation model and the battery capacity correction model. According to the technical scheme, modeling cost is reduced, computing resources and time are saved, and battery capacity estimation accuracy under other temperature conditions is improved.
Description
Technical Field
The present invention relates to the field of battery management technologies, and in particular, to a method, an apparatus, a device, and a storage medium for estimating battery capacity.
Background
The lithium ion battery is widely applied to the fields of electric automobiles, energy storage stations, base station standby power and the like. Capacity fade of the battery inevitably occurs during use. This strong time-varying, nonlinear decay is not directly measurable by the sensor and can only be estimated from measurable data (voltage, current, temperature, etc.). Accurate, robust battery online capacity estimation is critical to battery performance. Variable temperature conditions in real-world scenarios significantly impact capacity estimation performance, still a current significant challenge.
Because of the significant effect of temperature on battery capacity, establishing a battery capacity estimation model under fixed temperature conditions can present significant estimation errors when applied directly to other temperature conditions. In the prior art, the capacity attenuation values of charge and discharge under each temperature condition are counted, and capacity estimation is carried out through an ampere-hour integral accumulation technology; or based on the wiener process, the Power Rule stress model and the Arrhenius temperature stress model, constructing a lithium battery performance degradation model under a time-varying temperature working condition to realize battery capacity estimation under a temperature varying condition.
The inventors have found that the following drawbacks exist in the prior art in the process of implementing the present invention:
the existing method for estimating the battery capacity under the variable temperature condition has the problems of high modeling cost, large calculated amount, long experimental time and inaccurate estimation result.
Disclosure of Invention
The invention provides a battery capacity estimation method, a device, equipment and a storage medium, which are used for reducing modeling cost, saving computing resources and time and improving battery capacity estimation accuracy under other temperature conditions.
According to an aspect of the present invention, there is provided a battery capacity estimation method including:
acquiring a first aging characteristic data set of a first type of sample battery at a target temperature; the first aging characteristic data set comprises a specified number of battery capacity actual values and charging characteristics corresponding to early-cycle aging tests;
inputting the charging characteristics in the first aging characteristic data set into a pre-established standard battery capacity estimation model to obtain a battery capacity estimation value of the first type of sample battery;
establishing a battery capacity correction model at the target temperature according to the battery capacity actual value and the battery capacity estimated value of the first type of sample battery;
and estimating the battery capacity of the battery to be estimated at the target temperature according to the battery capacity estimation model and the battery capacity correction model.
According to another aspect of the present invention, there is provided a battery capacity estimating apparatus including:
the first aging characteristic data set acquisition module is used for acquiring a first aging characteristic data set of the first type of sample battery at the target temperature; the first aging characteristic data set comprises a specified number of battery capacity actual values and charging characteristics corresponding to early-cycle aging tests;
the first estimated value acquisition module is used for inputting the charging characteristics in the first aging characteristic data set into a pre-established standard battery capacity estimation model to obtain a battery capacity estimated value of the first type of sample battery;
the battery capacity correction model building module is used for building a battery capacity correction model at the target temperature according to the battery capacity actual value and the battery capacity estimated value of the first type of sample battery;
and the battery capacity estimation module is used for estimating the battery capacity of the battery to be estimated at the target temperature according to the battery capacity estimation model and the battery capacity correction model.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the battery capacity estimation method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the battery capacity estimation method according to any one of the embodiments of the present invention when executed.
According to the technical scheme, a first aging characteristic data set of a first type of sample battery at a target temperature is obtained; the first aging characteristic data set comprises a specified number of battery capacity actual values and charging characteristics corresponding to early-cycle aging tests; inputting charging characteristics in the first aging characteristic data set into a pre-established standard battery capacity estimation model to obtain a battery capacity estimation value of a first type of sample battery; establishing a battery capacity correction model at a target temperature according to the battery capacity actual value and the battery capacity estimated value of the first-class sample battery; according to the battery capacity estimation model and the battery capacity correction model, battery capacity estimation is carried out on a battery with to-be-estimated capacity at a target temperature, so that the problems of high modeling cost, large calculated amount, long experimental time and inaccurate estimation result existing in the existing battery capacity estimation method under the variable temperature condition are solved, the modeling cost is reduced, the calculation resources and time are saved, and the battery capacity estimation accuracy under other temperature conditions is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a battery capacity estimation method according to a first embodiment of the present invention;
FIG. 2a is a schematic diagram showing the capacity fade of a battery at 25deg.C according to an embodiment of the present invention;
FIG. 2b is a schematic diagram showing the capacity fade of a battery at 45℃according to an embodiment of the present invention;
FIG. 2c is a schematic diagram showing the relationship between the battery capacity and the charge duration at 25℃and 45℃according to the first embodiment of the present invention;
FIG. 2d is a schematic diagram showing a relationship between an estimated battery capacity and an actual battery capacity at 45 ℃ according to an embodiment of the present invention;
FIG. 2e is a diagram showing a comparison of estimated capacity values and actual capacity values estimated by different models according to an embodiment of the present invention;
FIG. 2f is a schematic diagram of an estimation error of a capacity estimation value and a capacity reality value estimated by different models according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a battery capacity estimating apparatus according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a battery capacity estimation method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a battery capacity estimation method according to an embodiment of the present invention, where the method may be performed by a battery capacity estimation device, and the battery capacity estimation device may be implemented in hardware and/or software, and the battery capacity estimation device may be configured in a server. As shown in fig. 1, the method includes:
s110, acquiring a first aging characteristic data set of a first type of sample battery at a target temperature; the first aging characteristic data set includes a specified number of battery capacity actual values and charging characteristics corresponding to early cycle aging tests.
The target temperature may be an ambient temperature at which the capacity of the battery to be estimated currently needs to be estimated, and may be different from a standard temperature. The standard temperature may be a normal temperature in general. The sample cell may be a lithium cell. The cyclic aging test may refer to a cyclic test of full charge and discharge of the battery. The specified number of early cycle aging tests may refer to, for example, the first 15 full charge discharge cycles of the first type of sample cell. The specified number is not limited in the present embodiment, and may be several, ten or more, and several tens, which are all within the required range, and the actual value of the battery capacity may be flexibly determined according to the actual situation, and may be the actual battery capacity obtained based on the complete discharging process of the first type sample battery.
The charging characteristics may include a charging duration of the target charging voltage segment, a voltage acquisition time point average value, and a voltage average value. The target charging voltage segment may refer to a fixed voltage segment of the first type of sample cell during charging, for example, 3.6V-3.8V. The target charging voltage segment may be a typical segment reflecting a change in battery capacity. The charging duration may refer to a duration of reaching a target charging voltage segment during a charging process of a current cycle of the first type of sample battery. The voltage acquisition time point average value may refer to an average value of time points of acquiring the charging voltage in real time corresponding to the target charging voltage segment. The voltage average value may refer to an average value of the real-time collected charging voltage corresponding to the target charging voltage segment.
In this embodiment, a cyclic aging test may be performed on a first type of sample battery at a target temperature, and charging characteristics corresponding to a specific number of early cyclic aging tests and to a target charging voltage segment are obtained, so as to obtain a first aging characteristic data set.
And S120, inputting the charging characteristics in the first aging characteristic data set into a pre-established standard battery capacity estimation model to obtain a battery capacity estimation value of the first type of sample battery.
The standard battery capacity estimation model may be a battery capacity estimation model established at a standard temperature.
In this embodiment, charging characteristics corresponding to a target charging voltage segment in the first aging characteristic data set are input into a pre-established standard battery capacity estimation model, so as to obtain estimated values of battery capacities corresponding to charging characteristics of the first type of sample battery.
The standard battery capacity estimation model may be established by: performing full charge and full discharge cyclic aging test on the second type of sample battery at the standard temperature to obtain a second aging characteristic data set of the second type of sample battery; and training the initial machine learning model for at least one round according to the second aging characteristic data set to obtain a standard battery capacity estimation model.
The second type of sample battery and the first type of sample battery can be the same type of battery which leaves the factory in the same batch. The initial machine learning model may be selected according to the computational demand of the application scenario, such as gaussian process regression (GRP) model, support Vector Regression (SVR), random Forest (RF), neural network, and the like.
Optionally, performing a full charge and full discharge cyclic aging test on the second type of sample battery at the standard temperature to obtain a second aging characteristic data set of the second type of sample battery may include: performing full charge and full discharge cyclic aging test on the second type of sample battery at standard temperature, and recording the discharge characteristic and the charge characteristic of each cycle of the second type of sample battery; determining the discharge capacity of each cycle of the second type of sample battery according to the discharge characteristics of each cycle of the second type of sample battery; and combining the discharge capacity and the charge characteristic of each cycle of the second type of sample battery to obtain a second aging characteristic data set.
Optionally, the discharge characteristic includes a real-time voltage value, a real-time current value, and a discharge characteristic recording time point; accordingly, determining the discharge capacity of the second type of sample cell per cycle based on the discharge characteristics of the second type of sample cell per cycle may include: and recording time points according to the real-time voltage value, the real-time current value and the discharge characteristics of each cycle of the second type sample battery, and calculating the discharge capacity of each cycle of the second type sample battery by an ampere-hour integration method.
S130, establishing a battery capacity correction model at the target temperature according to the battery capacity actual value and the battery capacity estimated value of the first-class sample battery.
The battery capacity correction model may be a model in which battery capacity is estimated at a target temperature using a standard battery capacity estimation model and then corrected.
Optionally, establishing the battery capacity correction model at the target temperature according to the battery capacity actual value and the battery capacity estimated value of the first type of sample battery may include: fitting the actual value of the battery capacity and the estimated value of the battery capacity of each cycle of the first-class sample battery, and determining the offset relation between the actual value of the battery capacity and the estimated value of the battery capacity; and establishing a battery capacity correction model at the target temperature according to the offset relation.
In this embodiment, a linear model may be selected to fit the actual battery capacity value and the estimated battery capacity value of each cycle of the first-class sample battery, where the linear model may be selected from linear regression, ridge regression, elastic network, and the like, and optionally, a nonlinear machine learning model with parameter adjustment may be selected to achieve a fitting effect.
And S140, estimating the battery capacity of the battery to be estimated at the target temperature according to the battery capacity estimation model and the battery capacity correction model.
Optionally, performing battery capacity estimation on the to-be-estimated capacity battery at the target temperature according to the battery capacity estimation model and the battery capacity correction model may include: charging the capacity battery to be estimated at the target temperature, and acquiring the charging characteristics of the capacity battery to be estimated; inputting the charging characteristics of the to-be-estimated capacity battery into a standard battery capacity estimation model to obtain a battery capacity estimation value of the to-be-estimated capacity battery; and inputting the battery capacity estimated value of the battery with the capacity to be estimated into a battery capacity correction model to obtain a battery capacity correction value of the battery with the capacity to be estimated.
According to the technical scheme, a first aging characteristic data set of a first type of sample battery at a target temperature is obtained; the first aging characteristic data set comprises a specified number of battery capacity actual values and charging characteristics corresponding to early-cycle aging tests; inputting charging characteristics in the first aging characteristic data set into a pre-established standard battery capacity estimation model to obtain a battery capacity estimation value of a first type of sample battery; establishing a battery capacity correction model at a target temperature according to the battery capacity actual value and the battery capacity estimated value of the first-class sample battery; according to the battery capacity estimation model and the battery capacity correction model, battery capacity estimation is carried out on a battery with to-be-estimated capacity at a target temperature, so that the problems of high modeling cost, large calculated amount, long experimental time and inaccurate estimation result existing in the existing battery capacity estimation method under the variable temperature condition are solved, the modeling cost is reduced, the calculation resources and time are saved, and the battery capacity estimation accuracy under other temperature conditions is improved.
In order to better understand the battery capacity estimation method of the present invention, the following provides a battery capacity estimation procedure for migrating a battery capacity model established under the condition of 25 ℃ (corresponding to a standard temperature) to the condition of 45 ℃ (corresponding to a target temperature) for the present embodiment. The invention is not limited to 25 ℃ and 45 ℃ temperature conditions, and other temperature conditions may be applicable. The following examples select NCM batteries as an illustration, and the invention is not limited to NCM batteries, as all lithium ion batteries of different material systems may also be applicable.
Step one:
4 NCM batteries were selected for battery aging testing at 25℃and the battery capacity decay pattern is shown in FIG. 2 a. Meanwhile, 1 NCM battery is selected to carry out battery aging test in a 45 ℃ environment, and a battery capacity attenuation diagram is shown in fig. 2b and is used for testing the temperature change estimation effect of the battery capacity estimation method in the embodiment. The charging characteristics are extracted from the charging process data of the battery to reflect the battery capacity fade, and the present example extracts the charging duration of the battery 3.6V-3.8V (i.e., the target charging voltage segment) as a characteristic (i.e., charging characteristic), the relationship between which and the battery capacity is as shown in fig. 2 c. The charging time characteristic at 25 ℃ is combined with the corresponding battery capacity to form a second aging characteristic data set, and the charging time characteristic at 45 ℃ is combined with the corresponding battery capacity to form a first aging characteristic data set. Although the correlation between the charging time length and the battery capacity under the same temperature condition is high, the characteristics of different temperature conditions are obviously different, so that a model constructed at 25 ℃ is directly used for 45 ℃ and a large capacity estimation error can occur.
Step two:
a Gaussian Process Regression (GPR) model is selected for training on the second aging characteristic dataset in step one to obtain a trained GPR (i.e., standard battery capacity estimation model).
Step three:
for a battery working in a 45 ℃ environment, selecting the first 15 cycles (namely a specified number of early cycle aging tests), extracting the actual battery capacity value and charging characteristics of each cycle, and estimating by using the trained GPR to obtain the estimated battery capacity value. The relationship between the estimated battery capacity and the actual battery capacity is shown in fig. 2 d. A Linear Regression (LR) model was chosen to fit these data to form a temperature migration model (gpr+tl). The temperature migration model may be a two-layer structure model in which a standard battery capacity estimation model and a battery capacity correction model are connected.
Step four:
and (3) extracting charging characteristics of a battery with unknown battery capacity (namely a battery with capacity to be estimated) at 45 ℃, obtaining a final battery capacity correction value by using a temperature migration model in the third step, namely obtaining a battery capacity estimated value of the battery with capacity to be estimated by using the trained GPR, and inputting the battery capacity estimated value into the LR in the third step to obtain the battery capacity correction value so as to realize battery capacity temperature change estimation. To demonstrate the temperature migration estimation effect, the direct estimation of the trained GPR and the (gpr+tl) migration estimation method are compared, the capacity estimation value and the capacity true value of the two methods are compared as shown in fig. 2e, and the estimation error diagram is shown in fig. 2 f. The average absolute percentage error of the direct estimate of GPR after training was 2.51%, while the average absolute percentage error of the gpr+tl shift estimation method was 1.03%. As can be seen from fig. 2e, when the trained GPR is directly used for a battery operating at 45 ℃, the capacity estimation deviates from the actual capacity trajectory, the error is larger, and the offset can be corrected by the (gpr+tl) method, so that the accuracy of the capacity estimation of the variable-temperature battery is improved.
The above example is that the charging characteristic of the charging duration of the target charging voltage segment and the discharging capacity form an aging characteristic data set, and for other charging characteristics such as the average value of the voltage acquisition time point and the average value of the voltage, the aging characteristic data set may also be formed by the discharging capacity, which is not described herein in detail.
Example two
Fig. 3 is a schematic structural diagram of a battery capacity estimating apparatus according to a second embodiment of the present invention. As shown in fig. 3, the apparatus includes: the first aging characteristic data set acquisition module 310, the first estimate acquisition module 320, the battery capacity correction model creation module 330, and the battery capacity estimation module 340.
Wherein:
a first aging characteristic data set obtaining module 310, configured to obtain a first aging characteristic data set of the first type of sample battery at the target temperature; the first aging characteristic data set comprises a specified number of battery capacity actual values and charging characteristics corresponding to early-cycle aging tests;
a first estimated value obtaining module 320, configured to input the charging characteristics in the first aging characteristic data set into a pre-established standard battery capacity estimation model, so as to obtain a battery capacity estimated value of the first type of sample battery;
a battery capacity correction model building module 330, configured to build a battery capacity correction model at the target temperature according to the actual battery capacity value and the estimated battery capacity value of the first type of sample battery;
the battery capacity estimation module 340 is configured to perform battery capacity estimation on the to-be-estimated-capacity battery at the target temperature according to the battery capacity estimation model and the battery capacity correction model.
According to the technical scheme, a first aging characteristic data set of a first type of sample battery at a target temperature is obtained; the first aging characteristic data set comprises a specified number of battery capacity actual values and charging characteristics corresponding to early-cycle aging tests; inputting charging characteristics in the first aging characteristic data set into a pre-established standard battery capacity estimation model to obtain a battery capacity estimation value of a first type of sample battery; establishing a battery capacity correction model at a target temperature according to the battery capacity actual value and the battery capacity estimated value of the first-class sample battery; according to the battery capacity estimation model and the battery capacity correction model, battery capacity estimation is carried out on a battery with to-be-estimated capacity at a target temperature, so that the problems of high modeling cost, large calculated amount, long experimental time and inaccurate estimation result existing in the existing battery capacity estimation method under the variable temperature condition are solved, the modeling cost is reduced, the calculation resources and time are saved, and the battery capacity estimation accuracy under other temperature conditions is improved.
Optionally, the battery capacity estimation device further includes a standard battery capacity estimation model building module, including:
the second aging characteristic data set acquisition unit is used for performing full charge and full discharge cyclic aging test on the second type of sample battery at the standard temperature to acquire a second aging characteristic data set of the second type of sample battery;
and the standard battery capacity estimation model acquisition unit is used for training the initial machine learning model at least one round according to the second aging characteristic data set to obtain the standard battery capacity estimation model.
Optionally, the second aging characteristic data set acquisition unit includes:
the charge-discharge characteristic recording subunit is used for carrying out full charge and full discharge cyclic aging test on the second type of sample battery at the standard temperature and recording the discharge characteristic and the charge characteristic of each cycle of the second type of sample battery;
the discharge capacity acquisition subunit is used for determining the discharge capacity of each cycle of the second type of sample battery according to the discharge characteristics of each cycle of the second type of sample battery;
and the second aging characteristic data set acquisition subunit is used for combining the discharge capacity and the charging characteristic of each cycle of the second type of sample battery to obtain the second aging characteristic data set.
Optionally, the discharging characteristic comprises a real-time voltage value, a real-time current value and a discharging characteristic recording time point;
correspondingly, the discharge capacity obtaining subunit may be specifically configured to:
and calculating the discharge capacity of each cycle of the second type sample battery by an ampere-hour integration method according to the real-time voltage value, the real-time current value and the discharge characteristic record time point of each cycle of the second type sample battery.
Optionally, the battery capacity correction model building module 330 may specifically be configured to:
fitting the actual battery capacity value and the estimated battery capacity value of each cycle of the first-class sample battery, and determining the offset relation between the actual battery capacity value and the estimated battery capacity value;
and establishing a battery capacity correction model at the target temperature according to the offset relation.
Optionally, the battery capacity estimation module 340 may be specifically configured to:
charging the to-be-estimated capacity battery at the target temperature, and acquiring charging characteristics of the to-be-estimated capacity battery;
inputting the charging characteristics of the to-be-estimated capacity battery into the standard battery capacity estimation model to obtain a battery capacity estimation value of the to-be-estimated capacity battery;
and inputting the battery capacity estimated value of the battery with the capacity to be estimated into the battery capacity correction model to obtain the battery capacity correction value of the battery with the capacity to be estimated.
Optionally, the charging characteristics include a charging duration of the target charging voltage segment, a voltage acquisition time point average value, and a voltage average value.
The battery capacity estimation device provided by the embodiment of the invention can execute the battery capacity estimation method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example III
Fig. 4 shows a schematic diagram of an electronic device 400 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers or various forms of mobile devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 400 includes at least one processor 401, and a memory communicatively connected to the at least one processor 401, such as a Read Only Memory (ROM) 402, a Random Access Memory (RAM) 403, etc., in which the memory stores a computer program executable by the at least one processor, and the processor 401 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 402 or the computer program loaded from the storage unit 408 into the Random Access Memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 may also be stored. The processor 401, the ROM 402, and the RAM 403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Various components in electronic device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, etc.; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408, such as a magnetic disk, optical disk, etc.; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
Processor 401 may be a variety of general purpose and/or special purpose processing components with processing and computing capabilities. Some examples of processor 401 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 401 performs the various methods and processes described above, such as a battery capacity estimation method.
In some embodiments, the battery capacity estimation method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 408. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 400 via the ROM 402 and/or the communication unit 409. When the computer program is loaded into RAM 403 and executed by processor 401, one or more steps of the battery capacity estimation method described above may be performed. Alternatively, in other embodiments, the processor 401 may be configured to perform the battery capacity estimation method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A battery capacity estimation method, characterized by comprising:
acquiring a first aging characteristic data set of a first type of sample battery at a target temperature; the first aging characteristic data set comprises a specified number of battery capacity actual values and charging characteristics corresponding to early-cycle aging tests;
inputting the charging characteristics in the first aging characteristic data set into a pre-established standard battery capacity estimation model to obtain a battery capacity estimation value of the first type of sample battery;
establishing a battery capacity correction model at the target temperature according to the battery capacity actual value and the battery capacity estimated value of the first type of sample battery;
and estimating the battery capacity of the battery to be estimated at the target temperature according to the battery capacity estimation model and the battery capacity correction model.
2. The method of claim 1, wherein the standard battery capacity estimation model is established by:
performing full charge and full discharge cyclic aging test on a second type of sample battery at a standard temperature to obtain a second aging characteristic data set of the second type of sample battery;
and training the initial machine learning model for at least one round according to the second aging characteristic data set to obtain the standard battery capacity estimation model.
3. The method of claim 2, wherein performing a full charge cyclic aging test on a second type of sample cell at a standard temperature to obtain a second aging characteristic dataset of the second type of sample cell comprises:
performing full charge and full discharge cyclic aging test on the second type of sample battery at the standard temperature, and recording the discharge characteristic and the charge characteristic of each cycle of the second type of sample battery;
determining the discharge capacity of each cycle of the second type sample battery according to the discharge characteristics of each cycle of the second type sample battery;
and combining the discharge capacity and the charge characteristic of each cycle of the second type of sample battery to obtain the second aging characteristic data set.
4. A method according to claim 3, wherein the discharge characteristics comprise a real-time voltage value, a real-time current value and a discharge characteristic recording time point;
determining the discharge capacity of the second type sample battery in each cycle according to the discharge characteristics of the second type sample battery in each cycle, wherein the method comprises the following steps:
and calculating the discharge capacity of each cycle of the second type sample battery by an ampere-hour integration method according to the real-time voltage value, the real-time current value and the discharge characteristic record time point of each cycle of the second type sample battery.
5. The method of claim 1, wherein establishing a battery capacity correction model at the target temperature based on the actual battery capacity value and the estimated battery capacity value of the first type of sample battery comprises:
fitting the actual battery capacity value and the estimated battery capacity value of each cycle of the first-class sample battery, and determining the offset relation between the actual battery capacity value and the estimated battery capacity value;
and establishing a battery capacity correction model at the target temperature according to the offset relation.
6. The method of claim 1, wherein estimating the battery capacity of the battery to be estimated at the target temperature based on the battery capacity estimation model and the battery capacity correction model comprises:
charging the to-be-estimated capacity battery at the target temperature, and acquiring charging characteristics of the to-be-estimated capacity battery;
inputting the charging characteristics of the to-be-estimated capacity battery into the standard battery capacity estimation model to obtain a battery capacity estimation value of the to-be-estimated capacity battery;
and inputting the battery capacity estimated value of the battery with the capacity to be estimated into the battery capacity correction model to obtain the battery capacity correction value of the battery with the capacity to be estimated.
7. The method of any of claims 1-6, wherein the charging characteristics include a charging duration of a target charging voltage segment, a voltage acquisition time point average, and a voltage average.
8. A battery capacity estimation device, characterized by comprising:
the first aging characteristic data set acquisition module is used for acquiring a first aging characteristic data set of the first type of sample battery at the target temperature; the first aging characteristic data set comprises a specified number of battery capacity actual values and charging characteristics corresponding to early-cycle aging tests;
the first estimated value acquisition module is used for inputting the charging characteristics in the first aging characteristic data set into a pre-established standard battery capacity estimation model to obtain a battery capacity estimated value of the first type of sample battery;
the battery capacity correction model building module is used for building a battery capacity correction model at the target temperature according to the battery capacity actual value and the battery capacity estimated value of the first type of sample battery;
and the battery capacity estimation module is used for estimating the battery capacity of the battery to be estimated at the target temperature according to the battery capacity estimation model and the battery capacity correction model.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the battery capacity estimation method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the battery capacity estimation method of any one of claims 1-7 when executed.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118011225A (en) * | 2024-04-08 | 2024-05-10 | 瑞浦兰钧能源股份有限公司 | Method and device for correcting chargeable and dischargeable capacity, storage medium and electronic device |
| CN118068199A (en) * | 2024-04-18 | 2024-05-24 | 无锡锂云科技有限公司 | Battery charge-discharge curve prediction method and device, electronic equipment and storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118011225A (en) * | 2024-04-08 | 2024-05-10 | 瑞浦兰钧能源股份有限公司 | Method and device for correcting chargeable and dischargeable capacity, storage medium and electronic device |
| CN118068199A (en) * | 2024-04-18 | 2024-05-24 | 无锡锂云科技有限公司 | Battery charge-discharge curve prediction method and device, electronic equipment and storage medium |
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