CN115659180A - Method, device and system for evaluating health level of capacity grading channel of lithium battery capacity grading cabinet and medium thereof - Google Patents
Method, device and system for evaluating health level of capacity grading channel of lithium battery capacity grading cabinet and medium thereof Download PDFInfo
- Publication number
- CN115659180A CN115659180A CN202211359137.7A CN202211359137A CN115659180A CN 115659180 A CN115659180 A CN 115659180A CN 202211359137 A CN202211359137 A CN 202211359137A CN 115659180 A CN115659180 A CN 115659180A
- Authority
- CN
- China
- Prior art keywords
- grading
- capacity
- cell
- channel
- data set
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Secondary Cells (AREA)
Abstract
The invention belongs to the field of lithium battery detection, and relates to a method, a device, a system and a medium for evaluating the health level of a capacity grading channel of a lithium battery capacity grading cabinet, wherein a first data set of the capacity grading result of a cell of the same type in the capacity grading channel to be evaluated in a preset time range is obtained, and the first data set of the capacity grading result of the cell is a set of calibrated capacity values of all cells in the capacity grading channel to be evaluated; calculating dispersion distribution of the cell capacity grading result according to a first data set of the cell capacity grading result; inputting the dispersion distribution of the cell capacity grading result into a dispersion model trained in advance, and determining the deviation degree of the cell capacity grading result distribution; determining the health degree score of the grading channel to be evaluated according to the deviation degree of the distribution of the cell grading result; the capacity grading device has the advantages that the working personnel can timely find the abnormal capacity grading channel and timely maintain the abnormal channel, so that the conditions that the grade of the battery cell capacity is mismarked and the normal battery cell capacity is misjudged to be unqualified can be greatly reduced.
Description
Technical Field
The invention belongs to the field of lithium battery detection, and relates to a method, a device and a system for evaluating the health level of a capacity grading channel of a capacity grading cabinet of a lithium battery and a medium thereof.
Background
The capacity grading is to charge and discharge the battery cell of the lithium battery in the production process, and the discharge capacity when the capacity grading is full is detected to determine the capacity of the battery cell. Only cells with a capacity of the test that meets or exceeds the design capacity are acceptable. The equipment used for capacity grading is a capacity grading cabinet which has a multilayer structure, each layer is provided with a certain number of channels, and each channel can carry out capacity grading calibration on one power saving core.
When a section of electric core is judged to be insufficient in capacity after capacity grading, two possibilities exist, one is that the electric core is not qualified, the other is that the electric core is qualified, but because some abnormity or damage occurs in a channel of the capacity grading cabinet, the condition that the electric core capacity rating is reduced due to misjudgment occurs, and no matter whether misjudgment is not qualified or the rating is influenced, the loss of a large amount of profits can be caused finally. The research on the channel health monitoring of the grading cabinet is almost blank at present, and the grading channel detection scheme adopted by a factory at present is as follows: when a capacity grading result which is far lower than a normal level (out of 6 standard deviations) appears in one channel for multiple times, the channel is overhauled, although the scheme can reduce the situation of reduction of the cell capacity grading to a certain extent, the scheme can only find the problem when the channel problem is serious, does not have the capability of finding in advance, and ignores the abnormality which can cause a higher capacity grading result because the factory focuses on judging the abnormality as a low capacity. Therefore, health monitoring of the capacity grading channel is an urgent problem to be solved.
Disclosure of Invention
The invention aims to provide a method, a device, a system and a medium for evaluating the health level of a capacity grading channel of a lithium battery capacity grading cabinet, so that a worker can find the abnormal capacity grading channel in time and can maintain the abnormal capacity grading channel in time.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a method for evaluating the health level of a capacity grading channel of a lithium battery capacity grading cabinet, which comprises the following steps of:
acquiring a first data set of the grading result of the same type of battery cells in the grading channel to be evaluated within a preset time range, wherein the first data set of the grading result of the battery cells is a set of calibrated capacity values of all the battery cells in the grading channel to be evaluated;
calculating dispersion distribution of the cell capacity grading result according to the first data set of the cell capacity grading result;
inputting the dispersion distribution of the cell capacity grading result into a dispersion model trained in advance, and determining the deviation degree of the cell capacity grading result distribution;
and determining the health degree score of the grading channel to be evaluated according to the deviation degree of the distribution of the cell grading result.
Further, the calculating the dispersion distribution of the cell capacity grading result according to the first data set of the cell capacity grading result includes:
calculating a first current mean value and a first current standard deviation of the capacity of the battery cell in the first data set according to the first data set of the battery cell capacity grading result;
and calculating the dispersion distribution of the cell capacity grading result according to the first current mean value and the first current standard deviation of the cell capacity in the first data set.
Further, the obtaining of the first data set of the grading result of the electric core of the same model in the grading channel to be evaluated within the preset time range further includes:
acquiring historical data sets of grading results of different types of battery cells in a grading channel to be evaluated;
calculating a first historical mean value and a first historical standard deviation of the capacity of each type of battery cell according to a first historical data set of the grading result of the battery cells of different types;
removing discrete data in the first historical data set by adopting an iterative algorithm according to the first historical mean value and the first historical standard deviation of the capacity of each type of battery cell to obtain a second historical data set;
calculating a second historical mean value and a second historical standard deviation of the capacity of each type of battery cell in the second historical data set according to the second historical data set;
and calculating a dispersion distribution set of grading results of all the model battery cores according to the second historical mean value and the second historical standard deviation to obtain a dispersion model.
Further, after determining the health degree score of the grading channel to be evaluated according to the deviation degree of the distribution of the cell grading result, the method further includes:
determining the health degree of a grading channel to be evaluated according to the health degree score and a preset health degree score model;
and generating corresponding early warning information according to the health degree of the grading channel to be evaluated.
The second aspect of the present invention provides a device for evaluating the health level of a capacity grading channel of a capacity grading cabinet of a lithium battery, including:
the system comprises an acquisition module, a capacity evaluation module and a capacity evaluation module, wherein the acquisition module is used for acquiring a first data set of the capacity evaluation result of the same type of battery cells in a capacity evaluation channel to be evaluated within a preset time range, and the first data set of the capacity evaluation result of the battery cells is a set of calibrated capacity values of all the battery cells in the capacity evaluation channel to be evaluated;
the calculation module is used for calculating dispersion distribution of the cell capacity grading result according to the first data set of the cell capacity grading result;
the first determining module is used for inputting the dispersion distribution of the cell capacity grading result into a dispersion model trained in advance and determining the deviation degree of the cell capacity grading result distribution;
and the second determining module is used for determining the health degree score of the grading channel to be evaluated according to the deviation degree of the distribution of the cell grading result.
A third inventive aspect of the present invention provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the method when executing the computer program.
A fourth inventive aspect of the present invention is to provide a storage medium storing a computer program which, when executed by a processor, implements the method.
The invention has the beneficial effects that:
obtaining a first data set of the grading result of the same type of battery cells in the grading channel to be evaluated within a preset time range, wherein the first data set of the grading result of the battery cells is a set of calibrated capacity values of all the battery cells in the grading channel to be evaluated; calculating dispersion distribution of the cell capacity grading result according to the first data set of the cell capacity grading result; inputting the dispersion distribution of the cell capacity grading result into a dispersion model trained in advance, and determining the deviation degree of the cell capacity grading result distribution; determining the health degree score of the grading channel to be evaluated according to the deviation degree of the distribution of the cell grading result; the capacity grading device has the advantages that the working personnel can timely find the abnormal capacity grading channel and timely maintain the abnormal channel, so that the conditions that the grade of the battery cell capacity is mismarked and the normal battery cell capacity is misjudged to be unqualified can be greatly reduced.
Drawings
FIG. 1 is a general flow chart of a method of health level assessment in the present invention;
FIG. 2 is a flow chart illustrating the substeps of step S200 of the present invention;
FIG. 3 is a schematic flow chart of the method for constructing the dispersion model;
FIG. 4 is a schematic structural diagram of a health level evaluating apparatus according to the present invention;
fig. 5 is a schematic structural diagram of a computer device in the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
Referring to fig. 1, a first aspect of the present invention provides a method for evaluating a health level of a capacity grading channel of a lithium battery capacity grading cabinet, including the following steps:
s100: the method comprises the steps of obtaining a first data set of the cell capacity grading result of the same model in a capacity grading channel to be evaluated within a preset time range, wherein the first data set of the cell capacity grading result is a set of calibrated capacity values of all cells in the capacity grading channel to be evaluated.
The first data set is obtained by obtaining the capacity grading result of the cells of the same model, namely the capacity value of each cell calibrated at last, which is called cap, from the database, and thus the capacity grading results of the cells of the same model form a set C, namely the capacity grading result of the cells of the same model is called cap
. The preset time range in this embodiment may be the time for evaluating the compatibility channel on the same day, and in other embodiments, a certain time period may also be used as a unit.
S200: and calculating the dispersion distribution of the cell capacity grading result according to the first data set of the cell capacity grading result.
According to the central limit theorem, under appropriate conditions, the mean values of a large number of mutually independent random variables are properly normalized and then are converged to the standard normal distribution according to the distribution. In the same way, in an ideal state, the volume division result of a single channel and the volume division results of all channels are in normal distribution and equal in mean and variance, and the shape of the distribution has a certain deviation and still resembles normal distribution in consideration of the influence of temperature fluctuation and the like. Therefore, the dispersion distribution of the cell capacity grading result can be calculated according to the first data set of the cell capacity grading result.
Referring to fig. 2, further, the calculating a dispersion distribution of the cell capacity grading result according to the first data set of the cell capacity grading result includes:
s210: and calculating a first current mean value and a first current standard deviation of the battery cell capacity in the first data set according to the first data set of the battery cell capacity grading result.
In step S100, a first data set of capacity grading results of cells of the same model in a capacity grading channel to be evaluated within a preset time range is obtained, so that a mean avgA and a standard deviation stdA of capacities of cells of the same model can be obtained through the sets.
S220: and calculating the dispersion distribution of the cell capacity grading result according to the first current mean value and the first current standard deviation of the cell capacity in the first data set.
S300: and inputting the dispersion distribution of the cell capacity grading result into a dispersion model trained in advance, and determining the deviation degree of the cell capacity grading result distribution.
It should be noted that, because one capacity grading channel is only used for grading a single-model battery core, and can also be used for grading multiple-model battery cores, and one lithium battery capacity grading cabinet includes multiple capacity grading channels, the pre-trained dispersion model can be the dispersion of a single-model battery core in the capacity grading channel to be tested, can also be the dispersion set of multiple-model battery cores in a single channel, and can also be the dispersion set of all model battery cores of all capacity grading channels in one battery cabinet.
S400: and determining the health degree score of the grading channel to be evaluated according to the deviation degree of the distribution of the cell grading result.
Obtaining a first data set of the grading result of the same type of battery cells in the grading channel to be evaluated within a preset time range, wherein the first data set of the grading result of the battery cells is a set of calibrated capacity values of all the battery cells in the grading channel to be evaluated; calculating dispersion distribution of the cell capacity grading result according to the first data set of the cell capacity grading result; inputting the dispersion distribution of the cell capacity grading result into a dispersion model trained in advance, and determining the deviation degree of the cell capacity grading result distribution; determining a health degree score of a grading channel to be evaluated according to the deviation degree of the distribution of the cell grading result; make the staff can be according to in time discovering to have unusual partial capacity passageway on the one hand to carry out timely maintenance to having unusual passageway, thereby can reduce by a wide margin the wrong mark of grade and the normal electric core capacity of electric core capacity and be judged the unqualified condition by mistake to electric core capacity, on the other hand guarantees that the staff can in time discover to have the partial capacity passageway of certain deviation.
Referring to fig. 3, in an embodiment, before obtaining the first data set of the capacity grading result of the battery cells of the same model in the capacity grading channel to be evaluated within the preset time range, the method further includes:
s500, establishing a dispersion model, specifically, establishing a dispersion model includes the following steps:
s510: and acquiring historical data sets of the capacity grading results of different types of battery cores in the capacity grading channel to be evaluated.
S520: and calculating a first historical mean value and a first historical standard deviation of the capacity of each type of battery cell according to the first historical data set of the grading result of the battery cells of different types.
S530: and removing discrete data in the first historical data set by adopting an iterative algorithm according to the first historical mean value and the first historical standard deviation of the capacity of each type of battery cell to obtain a second historical data set.
Wherein the calculated mean and standard deviation are meaningful only after a certain amount of data. Therefore, a large number of battery cell grading results of the same model are selected in a random selection mode, and therefore the accuracy of the mean value and the standard deviation can be guaranteed. In this embodiment, the number of capacity grading results of the cells of the same model is greater than 100000 and less than 500000.
S540: and calculating a second historical mean value and a second historical standard deviation of the capacity of each type of battery cell in the second historical data set according to the second historical data set.
S550: and calculating a dispersion distribution set of grading results of all the model battery cores according to the second historical mean value and the second historical standard deviation to obtain a dispersion model.
Although the first historical mean and the first historical standard deviation are obtained in step S520, to exclude the influence of discrete data, we will be less than in the set
And is greater than
To obtain a new set
In this embodiment, the number of iterations is five, and the mean and the variance converge to a stable value by such a data cleaning and iteration method.
It should be noted that, due to different cell modelsThe capacity distribution is different, and different types of battery cores can be classified in one channel within a period of time, and the capacity classification results of the channels need to be unified into one standard. By subtracting the mean value of the cell of the model from the capacity of one cell and dividing the mean value by the standard deviation, we can obtain the position of the capacity relative to the cell of the model at the first standard deviation, which is called as the dispersion, for example, the dispersion is 1, which indicates that the position of the capacity data is at the standard deviation. If the model of the battery cell in certain data is model A and the capacity is cap, the dispersion
That is, the data is distant from the mean value
The method comprises the steps that a positive number represents more than or equal to a mean value and more than or equal to a negative number represents more than or equal to a mean value, for all normal distributions, the normal distributions with the mean value of 0 and the standard deviation of 1 can be converted by the method, no matter how many the mean value and the standard deviation are, for all normal distributions, the data sets conforming to the normal distribution with the mean value of 0 and the standard deviation of 1 are mixed, the obtained data sets still conform to the normal distribution with the mean value of 0 and the standard deviation of 1, through calculation in the mode, the dispersion of all the battery cells of different models is obtained, and therefore a dispersion model is built, namely, the normal distribution with the mean value of 0 and the standard deviation of 1 is obtained, and the health degree of a channel can be judged according to the degree of whether the distribution of input data conforms to the normal distribution or not.
In one embodiment, after determining the health degree score of the grading channel to be evaluated according to the deviation degree of the distribution of the electric core grading result, the method further includes:
s600: and determining the health degree of the grading channel to be evaluated according to the health degree score and a preset health degree score model.
In this embodiment, it is set that when the health degree score is lower than 60, the early warning level is considered to be serious, when the health degree score is higher than 60 and lower than 80 minutes, the early warning level is considered to be normal, and when the health degree score is higher than 80 minutes, the early warning is not performed.
S700: and generating corresponding early warning information according to the health degree of the grading channel to be evaluated.
Referring to fig. 4, a second aspect of the present invention provides a health level evaluation device for a capacity grading channel of a lithium battery capacity grading cabinet, including:
the system comprises an acquisition module, a capacity evaluation module and a capacity evaluation module, wherein the acquisition module is used for acquiring a first data set of the capacity evaluation result of the same type of battery cells in a capacity evaluation channel to be evaluated within a preset time range, and the first data set of the capacity evaluation result of the battery cells is a set of calibrated capacity values of all the battery cells in the capacity evaluation channel to be evaluated;
the calculation module is used for calculating dispersion distribution of the cell capacity grading result according to the first data set of the cell capacity grading result;
the first determining module is used for inputting the dispersion distribution of the cell capacity grading result into a dispersion model trained in advance and determining the deviation degree of the cell capacity grading result distribution;
and the second determining module is used for determining the health degree score of the grading channel to be evaluated according to the deviation degree of the distribution of the cell grading result.
The method comprises the steps that an acquisition module, a calculation module, a first determination module and a second determination module are arranged; the acquisition module is used for acquiring a first data set of the grading result of the same type of battery cell in the grading channel to be evaluated within a preset time range, wherein the first data set of the grading result of the battery cell is a set of calibrated capacity values of all the battery cells in the grading channel to be evaluated; the calculation module is used for calculating the dispersion distribution of the cell capacity grading result according to the first data set of the cell capacity grading result; the first determining module is used for inputting the dispersion distribution of the cell capacity grading result into a dispersion model trained in advance and determining the deviation degree of the distribution of the cell capacity grading result; the second determining module is used for determining the health degree score of the grading channel to be evaluated according to the deviation degree of the distribution of the cell grading result; make the staff can be according to in time discovering to have unusual partial capacity passageway on the one hand to carry out timely maintenance to having unusual passageway, thereby can reduce by a wide margin the wrong mark of grade and the normal electric core capacity of electric core capacity and be judged the unqualified condition by mistake to electric core capacity, on the other hand guarantees that the staff can in time discover to have the partial capacity passageway of certain deviation.
Referring to fig. 5, a third aspect of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements a method for evaluating a health level of a grading channel of a lithium battery grading cabinet when executing the computer program.
The memory may be used to store software programs and modules, and the processor may execute various functional applications and data processing by operating the software programs and modules stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs needed by functions and the like; the storage data area may store data created according to use of the apparatus, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory may also include a memory controller to provide the processor access to the memory.
The internal construction of the computer device may include, but is not limited to: the system comprises a processor, a network interface and a memory, wherein the processor, the network interface and the memory in the vehicle interior personnel health monitoring terminal can be connected through a bus or in other manners, and the processor, the network interface and the memory are connected through the bus in fig. 5 in the embodiment of the description.
The processor (or CPU) is a computing core and a control core of the in-vehicle personnel health monitoring terminal. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI, mobile communication interface, etc.). Memory (Memory) is the Memory device in a computer device used to store programs and data. It is understood that the memory herein may be a high-speed RAM storage device, or may be a non-volatile storage device (non-volatile memory), such as at least one magnetic disk storage device; optionally, at least one memory device located remotely from the processor. The memory provides storage space that stores the operating system of the computer device, which may include, but is not limited to: windows system (an operating system), linux (an operating system), etc., which are not limited thereto; also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. In an embodiment of the present specification, the processor loads and executes one or more instructions stored in the memory to implement the method for evaluating the health level of the capacity grading channel of the lithium battery capacity grading cabinet provided in the embodiment of the method.
The embodiment of the invention also provides a computer-readable storage medium, which may be set in an in-vehicle personnel health monitoring terminal to store at least one instruction, at least one section of program, a code set, or an instruction set related to a method for evaluating the health level of a capacity grading channel of a lithium battery capacity grading cabinet in an implementation method embodiment, where the at least one instruction, the at least one section of program, the code set, or the instruction set may be loaded and executed by a processor of an electronic device to implement the method for evaluating the health level of the capacity grading channel of the lithium battery capacity grading cabinet provided in the implementation method embodiment.
Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The above-described embodiments are only one of the preferred embodiments of the present invention, and general changes and substitutions by those skilled in the art within the technical scope of the present invention are included in the protection scope of the present invention.
Claims (7)
1. A method for evaluating the health level of a capacity grading channel of a lithium battery capacity grading cabinet is characterized by comprising the following steps:
acquiring a first data set of the grading result of the same type of battery cells in the grading channel to be evaluated within a preset time range, wherein the first data set of the grading result of the battery cells is a set of calibrated capacity values of all the battery cells in the grading channel to be evaluated;
calculating dispersion distribution of the cell capacity grading result according to the first data set of the cell capacity grading result;
inputting the dispersion distribution of the cell capacity grading result into a dispersion model trained in advance, and determining the deviation degree of the cell capacity grading result distribution;
and determining the health degree score of the grading channel to be evaluated according to the deviation degree of the distribution of the cell grading result.
2. The method for evaluating the health level of the grading channel of the lithium battery grading cabinet according to claim 1, wherein the calculating the dispersion distribution of the cell grading result according to the first data set of the cell grading result includes:
calculating a first current mean value and a first current standard deviation of the capacity of the battery cell in the first data set according to the first data set of the battery cell capacity grading result;
and calculating the dispersion distribution of the cell capacity grading result according to the first current mean value and the first current standard deviation of the cell capacity in the first data set.
3. The method for evaluating the health level of the capacity grading channel of the lithium battery capacity grading cabinet according to claim 1, wherein before obtaining the first data set of the capacity grading result of the battery cell of the same model in the capacity grading channel to be evaluated within a preset time range, the method further comprises:
acquiring historical data sets of grading results of different types of battery cells in a grading channel to be evaluated;
calculating a first historical mean value and a first historical standard deviation of the capacity of each type of battery cell according to a first historical data set of the grading result of the battery cells of different types;
removing discrete data in the first historical data set by adopting an iterative algorithm according to the first historical mean value and the first historical standard deviation of the capacity of each type of battery cell to obtain a second historical data set;
calculating a second historical mean value and a second historical standard deviation of the capacity of each type of battery cell in the second historical data set according to the second historical data set;
and calculating a dispersion distribution set of grading results of all the model battery cores according to the second historical mean value and the second historical standard deviation to obtain a dispersion model.
4. The method for evaluating the health level of the grading channel of the lithium battery grading cabinet according to claim 1, wherein the step of determining the health level score of the grading channel to be evaluated according to the deviation degree of the distribution of the cell grading result further comprises the following steps:
determining the health degree of the grading channel to be evaluated according to the health degree score and a preset health degree score model;
and generating corresponding early warning information according to the health degree of the grading channel to be evaluated.
5. The utility model provides a device is evaluated to lithium cell partial volume cabinet's partial volume passageway health level which characterized in that includes:
the system comprises an acquisition module, a capacity grading module and a capacity grading module, wherein the acquisition module is used for acquiring a first data set of a capacity grading result of a battery cell of the same model in a capacity grading channel to be evaluated within a preset time range, and the first data set of the capacity grading result of the battery cell is a set of calibrated capacity values of all battery cells in the capacity grading channel to be evaluated;
the calculation module is used for calculating dispersion distribution of the cell capacity grading result according to the first data set of the cell capacity grading result;
the first determining module is used for inputting the dispersion distribution of the cell capacity grading result into a dispersion model trained in advance and determining the deviation degree of the cell capacity grading result distribution;
and the second determining module is used for determining the health degree score of the grading channel to be evaluated according to the deviation degree of the distribution of the cell grading result.
6. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method according to any of claims 1-4.
7. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211359137.7A CN115659180A (en) | 2022-11-02 | 2022-11-02 | Method, device and system for evaluating health level of capacity grading channel of lithium battery capacity grading cabinet and medium thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211359137.7A CN115659180A (en) | 2022-11-02 | 2022-11-02 | Method, device and system for evaluating health level of capacity grading channel of lithium battery capacity grading cabinet and medium thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115659180A true CN115659180A (en) | 2023-01-31 |
Family
ID=84995263
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211359137.7A Pending CN115659180A (en) | 2022-11-02 | 2022-11-02 | Method, device and system for evaluating health level of capacity grading channel of lithium battery capacity grading cabinet and medium thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115659180A (en) |
-
2022
- 2022-11-02 CN CN202211359137.7A patent/CN115659180A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6313502B2 (en) | Storage battery evaluation device, power storage system, storage battery evaluation method, and computer program | |
| CN107870306A (en) | A kind of lithium battery charge state prediction algorithm based under deep neural network | |
| US11965935B2 (en) | Method and apparatus for operating a system for providing predicted states of health of electrical energy stores for a device using machine learning methods | |
| CN113064089B (en) | Internal resistance detection method, device, medium and system of power battery | |
| CN112597263B (en) | Pipe network detection data abnormity judgment method and system | |
| CN113791350B (en) | Battery fault prediction method | |
| CN115951230B (en) | Abnormality detection method and system for lithium battery energy storage box | |
| EP4031886A1 (en) | Condition value for rechargeable batteries | |
| CN116424096B (en) | New energy automobile battery acquisition assembly method and system for dynamic resource optimization configuration | |
| CN114371408A (en) | Estimation method of battery charge state, and extraction method and device of charging curve | |
| CN114497770A (en) | Method, system and terminal for analyzing state of battery box in battery cluster | |
| US11938838B2 (en) | Method and device for the robust prediction of the aging behavior of an energy storage unit in a battery-operated machine | |
| CN113540589B (en) | Battery temperature difference self-adaptive threshold value determination method and system | |
| CN114879070A (en) | Battery state evaluation method and related equipment | |
| CN115542176A (en) | Method and system for monitoring voltage consistency in battery module, storage medium and terminal | |
| CN117148166A (en) | Battery safety level prediction method, device, computer equipment and storage medium | |
| CN115659180A (en) | Method, device and system for evaluating health level of capacity grading channel of lithium battery capacity grading cabinet and medium thereof | |
| CN116736166A (en) | Battery cell abnormality detection method and device of battery pack and battery pack | |
| US20230213587A1 (en) | Method and System for Efficiently Monitoring Battery Cells of a Device Battery in an External Central Processing Unit Using a Digital Twin | |
| CN116593915A (en) | Method, system, storage medium and terminal for monitoring voltage in battery module | |
| CN105759217A (en) | Lead-acid battery pack online fault diagnosis method based on measurable data | |
| CN114003175B (en) | Air conditioner and control system thereof | |
| CN115219932A (en) | Method and device for evaluating the relative aging state of a battery of a device | |
| CN111751732B (en) | Electric quantity calculation method based on self-adaptive Gaussian convolution integral method | |
| CN114879054A (en) | Battery safety monitoring method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |