CN110850296A - Method and device for evaluating health degree of battery - Google Patents
Method and device for evaluating health degree of battery Download PDFInfo
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- CN110850296A CN110850296A CN201810862400.1A CN201810862400A CN110850296A CN 110850296 A CN110850296 A CN 110850296A CN 201810862400 A CN201810862400 A CN 201810862400A CN 110850296 A CN110850296 A CN 110850296A
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Abstract
The invention discloses a method and a device for evaluating the health degree of a battery, and relates to the technical field of computers. One embodiment of the method comprises: acquiring the electric quantity percentage, charging voltage, charging current and temperature of a battery to be tested from the beginning of charging to the end of charging, and determining the actual change value of the electric quantity percentage of the battery to be tested, wherein the actual change value of the electric quantity percentage is the difference value between the electric quantity percentage at the end of charging and the electric quantity percentage at the beginning of charging; determining an evaluation change value of the electric quantity percentage according to the acquired electric quantity percentage of the battery to be tested, the charging voltage, the charging current and the battery temperature by using an evaluation model; and determining the health degree of the battery to be tested according to the rated capacity of the battery to be tested, the evaluation change value of the electric quantity percentage and the actual change value of the electric quantity percentage. This embodiment provides a basis for the measurement of the health of the battery.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for evaluating battery health, an electronic device, and a computer-readable medium.
Background
The battery health index is used for directly reflecting the current working state of a battery in electrical equipment, and is one of important indexes in many electrical equipment, for example, in the production application of an electrical robot warehouse, the battery health index is used for formulating a strategy for an Automatic Guided Vehicle (AGV) to execute a task, and under the condition of low battery health, the task assigned to the AGV cannot be smoothly completed due to inaccurate measurement and calculation of battery capacity, and even a production accident is caused.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
due to different parameter indexes and calculation formulas provided by battery manufacturers, batteries of different types need to be evaluated in multiple ways, and results cannot be measured uniformly, so that evaluation basis is lacked.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for evaluating battery health, which can utilize time series data generated during charging of a large number of new batteries to model and learn correlation relationships between different temperatures, currents, voltages, charging durations and changes of electric quantities, input the time series data generated during charging of a battery to be tested into a model to estimate the charging electric quantity when evaluating the electric quantity of the battery to be tested, and calculate an evaluation capacity in combination with an actual change value of the percentage of the electric quantity of the battery to be tested, evaluate the percentage of the capacity in a rated capacity, that is, the health of the battery to be tested, and use the evaluation capacity of the battery as a basis for evaluating the health of the battery.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method of evaluating health of a battery. The method comprises the following steps: acquiring the electric quantity percentage, charging voltage, charging current and temperature of a battery to be tested from the beginning of charging to the end of charging, and determining the actual change value of the electric quantity percentage of the battery to be tested, wherein the actual change value of the electric quantity percentage is the difference value between the electric quantity percentage at the end of charging and the electric quantity percentage at the beginning of charging; determining an evaluation change value of the electric quantity percentage according to the acquired electric quantity percentage of the battery to be tested, the charging voltage, the charging current and the battery temperature by using an evaluation model; and determining the health degree of the battery to be tested according to the rated capacity of the battery to be tested, the evaluation change value of the electric quantity percentage and the actual change value of the electric quantity percentage.
Optionally, the evaluation model is constructed according to the percentage of charge, the charging voltage, the charging current and the temperature of the battery with the charging times less than a first preset value and/or the service time less than a second preset value.
Optionally, acquiring the percentage of the electric quantity, the charging voltage, the charging current and the temperature of the battery to be tested from the beginning of charging to the end of charging in a sampling manner, including: and acquiring the electric quantity percentage, the charging voltage, the charging current and the temperature of the battery to be tested from the beginning to the end of charging according to a preset period.
Optionally, the evaluation model is based on a long-short term memory network.
Optionally, the health degree of the battery to be tested is determined according to the rated capacity of the battery to be tested, the evaluation change value of the power percentage and the actual change value of the power percentage by using the following formula:
the SOH is the health degree of the battery to be detected, the ER is the estimated capacity of the battery to be detected, the EF is the rated capacity of the battery to be detected, the CEP is the estimated change value of the electric quantity percentage of the battery to be detected, and the DV is the actual change value of the electric quantity percentage of the battery to be detected.
To achieve the above object, according to another aspect of embodiments of the present invention, there is provided an apparatus for evaluating health of a battery, including: the device comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring the electric quantity percentage, the charging voltage, the charging current and the temperature of a battery to be tested from the beginning of charging to the end of charging and determining the actual change value of the electric quantity percentage of the battery to be tested, and the actual change value of the electric quantity percentage is the difference value between the electric quantity percentage at the end of charging and the electric quantity percentage at the beginning of charging; the evaluation module is used for determining an evaluation change value of the electric quantity percentage according to the acquired electric quantity percentage of the battery to be tested, the charging voltage, the charging current and the battery temperature by using an evaluation model; and the calculation module is used for determining the health degree of the battery to be tested according to the rated capacity of the battery to be tested, the evaluation change value of the electric quantity percentage and the actual change value of the electric quantity percentage.
Optionally, the apparatus further comprises: and the modeling module is used for constructing the evaluation model according to the electric quantity percentage, the charging voltage, the charging current and the temperature of the battery with the charging times less than a first preset value and/or the service time less than a second preset value.
Optionally, the obtaining module is further configured to collect, according to a preset period, an electric quantity percentage, a charging voltage, a charging current, and a temperature of the battery to be tested from a time when charging starts to a time when charging ends.
Optionally, the evaluation model is based on a long-short term memory network.
Optionally, the calculation module is further configured to determine the health degree of the battery to be tested according to the rated capacity of the battery to be tested, the estimated change value of the power percentage, and the actual change value of the power percentage, using the following formulas:
the SOH is the health degree of the battery to be detected, the ER is the estimated capacity of the battery to be detected, the EF is the rated capacity of the battery to be detected, the CEP is the estimated change value of the electric quantity percentage of the battery to be detected, and the DV is the actual change value of the electric quantity percentage of the battery to be detected.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic apparatus including: one or more processors; a storage device to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement any of a method of assessing battery health.
To achieve the above object, according to still another aspect of embodiments of the present invention, there is provided a computer-readable medium having stored thereon a computer program which, when executed by one or more processors, implements any one of the methods of evaluating battery health.
One embodiment of the above invention has the following advantages or benefits: because the time sequence data generated in the charging process of the battery to be tested is input into the model when the electric quantity of the battery to be tested is evaluated, the evaluation change value of the electric quantity percentage is calculated, and the technical means of evaluating the health degree of the battery to be tested by combining the actual change value of the electric quantity percentage of the battery to be tested and the rated capacity is adopted, the technical problems of variable evaluation modes and complex parameters in the traditional method are solved, and the technical effect of unifying the judgment basis of the health degree of the battery is achieved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
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The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main steps of a method of assessing battery health in accordance with an embodiment of the present invention;
fig. 2 is a schematic diagram of a main part of an apparatus for evaluating the health of a battery according to an embodiment of the present invention;
FIG. 3 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 4 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of main steps of a method for evaluating the health of a battery according to an embodiment of the present invention, as shown in fig. 1:
step S101 represents obtaining the power percentage, the charging voltage, the charging current, and the temperature of the battery to be tested from the beginning of charging to the end of charging, and determining an actual change value of the power percentage of the battery to be tested, where the actual change value of the power percentage is a difference between the power percentage at the end of charging and the power percentage at the beginning of charging. The charge percentage refers to the ratio of the current charge of the battery to the total capacity of the battery. As shown in table 1, the percentage of charge, the charging voltage, the charging current and the temperature of the battery to be tested from the beginning of charging to the end of charging (17:10:23 to 18:51: 24) can be collected according to a preset period (e.g. 1 second), and the actual variation value of the percentage of charge of the battery to be tested is 37%. Further, if the time of the preset period of the acquired data is not accurate, the data can be complemented by an interpolation method or an average value method. Another embodiment of the present invention is to obtain the percentage of electric quantity, the charging voltage, the charging current and the temperature of the battery to be tested during a certain period of time during the charging process, i.e. the charging start time in this step is not necessarily 0, and the charging end time is not necessarily full of the battery.
TABLE 1
| Charging current | Electric quantity of battery | Charging voltage | Temperature of battery | Time stamp |
| 1.8 | 38％ | 46.8 | 40 | 17:10:23 |
| 1.9 | 38％ | 46.8 | 40 | 17:10:24 |
| 1.85 | 38％ | 46.8 | 40 | 17:10:25 |
| 1.85 | 38％ | 46.8 | 40 | 17:10:26 |
| … | … | .. | .. | … |
| 1.8 | 75％ | 41.5 | 42 | 18:51:24 |
Step S102 represents determining an evaluation change value of the power percentage according to the acquired power percentage of the battery to be tested, the charging voltage, the charging current, and the battery temperature, using the evaluation model. Wherein the evaluation model is constructed according to the percentage of charge, the charging voltage, the charging current and the temperature of the battery with the charging times less than a first preset value and/or the service time less than a second preset value. If a battery with charging times less than 10 times or a battery with service time less than 1 month is selected, the modeling data is ensured to come from a new and unaged battery, and the model accuracy is higher.
The evaluation model is based on a long-short term memory network (LSTM). LSTM (Long Short-term memory) is a Long Short-term memory network, a time recurrent neural network, suitable for processing and predicting important events with relatively Long intervals and delays in time series. And inputting a plurality of groups of data into a model for modeling, wherein each piece of data in each group of data is a parameter of the time sequence model at one moment, and the model output is an evaluation change value of the electric quantity percentage before and after charging. Because the charging time lengths are different every time and the change of the number of input data items is large, the LSTM model is adopted to model the data, and therefore the data with any charging time length can have good compatibility. For example, the 1 st data in Table 1 is Input to the model as the time t-1 data, Inputt-11.8, 0.38, 46.8, 40, the 2 nd data is Input into the model as the data at time t, Inputt1.9, 0.38, 46.8, 40, and so on, the last data is the last data in the data table, Inputt+n1.8, 0.75, 41.5, 42, namely index data such as charging current, percentage of charge, charging voltage, charging temperature and the like at the end of charging. And the model output is an electric quantity percentage evaluation change value before and after the battery is charged. As calculated from the data in table 1, the percentage capacity evaluation change value is 37%, i.e., Output is 0.37. In the modeling process, the number of the hidden layers of the whole LSTM network is equal to the number of the data entries acquired in each charging, and the number of neurons in each layer is equal to the input parameter quantity.
After the evaluation model is built, the acquired power percentage, charging voltage, charging current and battery temperature of the battery to be tested are brought into the evaluation model, so that an evaluation change value of the power percentage can be obtained, and the time of the evaluation change value of the power percentage of the battery to be tested corresponds to the time of the acquired power percentage, charging voltage, charging current and battery temperature of the battery to be tested.
Step S103 represents determining the health degree of the battery to be tested according to the rated capacity of the battery to be tested, the evaluation change value of the percentage, and the actual change value of the percentage of electric quantity.
Determining the health degree of the battery to be tested according to the rated capacity of the battery to be tested, the evaluation change value of the electric quantity percentage and the actual change value of the electric quantity percentage by using the following formula:
the SOH is the health degree of the battery to be detected, the ER is the estimated capacity of the battery to be detected, the EF is the rated capacity of the battery to be detected, the CEP is the estimated change value of the electric quantity percentage of the battery to be detected, and the DV is the actual change value of the electric quantity percentage of the battery to be detected. According to the definition of IEEE battery maintenance standard [ IEEE14], when the battery capacity is lower than 80% of rated capacity, the battery is replaced, namely when the calculated health degree of the battery to be tested is lower than 80%, the battery can be replaced.
Taking the data in table 1 as an example, if the percentage change value of the electric quantity obtained by inputting the battery to be tested into the evaluation model is 37%, and the actual change value of the percentage of the electric quantity is also 37%, the calculated evaluation capacity is equal to the rated capacity, and the health degree of the battery to be tested is 100%.
Example code for embodiments of the invention is as follows:
fig. 2 is a schematic diagram of the main parts of an apparatus 200 for evaluating the health of a battery according to an embodiment of the present invention, as shown in fig. 2:
the obtaining module 201 is configured to obtain an electric quantity percentage, a charging voltage, a charging current, and a temperature of the battery to be tested from a charging start time to a charging end time, and determine an actual change value of the electric quantity percentage of the battery to be tested, where the actual change value of the electric quantity percentage is a difference value between the electric quantity percentage at the charging end time and the electric quantity percentage at the charging start time. The charge percentage refers to the ratio of the current charge of the battery to the total capacity of the battery. As shown in table 1, the obtaining module 201 may further be configured to collect, according to a preset period (e.g., 1 second), the percentage of electric quantity, the charging voltage, the charging current, and the temperature of the battery to be tested from the charging start time to the charging end time (17:10:23 charging start time to 18:51:24 charging end time), where an actual variation value of the percentage of electric quantity of the battery to be tested is 37%. Further, if the time of the preset period of the acquired data is not accurate, the data can be complemented by an interpolation method or an average value method. Another embodiment of the present invention is to obtain the percentage of electric quantity, the charging voltage, the charging current and the temperature of the battery to be tested during a certain period of time during the charging process, i.e. the charging start time is not necessarily 0, and the charging end time is not necessarily full of the battery.
And the evaluation module 202 is used for determining an evaluation change value of the electric quantity percentage according to the acquired electric quantity percentage of the battery to be tested, the charging voltage, the charging current and the battery temperature by using an evaluation model. The device 200 further comprises a modeling module for constructing the evaluation model according to the charge percentage, the charge voltage, the charge current and the temperature of the battery with the charge times less than the first preset value and/or the service time less than the second preset value. If a battery with charging times less than 10 times or a battery with service time less than 1 month is selected, the modeling data is ensured to come from a new and unaged battery, and the model accuracy is higher.
The evaluation model is based on a long-short term memory network (LSTM). LSTM (Long Short-term memory) is a Long Short-term memory network, a time recurrent neural network, suitable for processing and predicting important events with relatively Long intervals and delays in time series. And inputting a plurality of groups of data into a model for modeling, wherein each piece of data in each group of data is a parameter of the time sequence model at one moment, and the model output is an evaluation change value of the electric quantity percentage before and after charging. Because the charging time lengths are different every time and the change of the number of input data items is large, the LSTM model is adopted to model the data, and therefore the data with any charging time length can have good compatibility. For example, the 1 st data in Table 1 is Input to the model as the time t-1 data, Inputt-11.8, 0.38, 46.8, 40, the 2 nd data is Input into the model as the data at time t, Inputt1.9, 0.38, 46.8, 40, and so on, the last data is the last data in the data table, Inputt+n1.8, 0.75, 41.5, 42, namely index data such as charging current, percentage of charge, charging voltage, charging temperature and the like at the end of charging. And the model output is an electric quantity percentage evaluation change value before and after the battery is charged. As calculated from the data in table 1, the percentage capacity evaluation change value is 37%, i.e., Output is 0.37. In the modeling process, the number of the hidden layers of the whole LSTM network is equal to the number of the data entries acquired in each charging, and the number of neurons in each layer is equal to the input parameter quantity.
After the evaluation model is built, the acquired power percentage, charging voltage, charging current and battery temperature of the battery to be tested are brought into the evaluation model, so that an evaluation change value of the power percentage can be obtained, and the time of the evaluation change value of the power percentage of the battery to be tested corresponds to the time of the acquired power percentage, charging voltage, charging current and battery temperature of the battery to be tested.
The calculating module 203 is configured to determine the health degree of the battery to be tested according to the rated capacity of the battery to be tested, the evaluation change value of the power percentage, and the actual change value of the power percentage.
The calculation module 203 is further configured to determine the health degree of the battery to be tested according to the rated capacity of the battery to be tested, the estimated change value of the power percentage, and the actual change value of the power percentage by using the following formulas:
the SOH is the health degree of the battery to be detected, the ER is the estimated capacity of the battery to be detected, the EF is the rated capacity of the battery to be detected, the CEP is the estimated change value of the electric quantity percentage of the battery to be detected, and the DV is the actual change value of the electric quantity percentage of the battery to be detected. According to the definition of IEEE battery maintenance standard [ IEEE14], when the battery capacity is lower than 80% of rated capacity, the battery is replaced, namely when the calculated health degree of the battery to be tested is lower than 80%, the battery can be replaced.
Taking the data in table 1 as an example, if the percentage change value of the electric quantity obtained by inputting the battery to be tested into the evaluation model is 37%, and the actual change value of the percentage of the electric quantity is also 37%, the calculated evaluation capacity is equal to the rated capacity, and the health degree of the battery to be tested is 100%.
Fig. 3 illustrates an exemplary system architecture 300 of a method of evaluating battery health or an apparatus for evaluating battery health to which embodiments of the present invention may be applied.
As shown in fig. 3, the system architecture 300 may include terminal devices 301, 302, 303, a network 304, and a server 305. The network 304 serves as a medium for providing communication links between the terminal devices 301, 302, 303 and the server 305. Network 304 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal device 301, 302, 303 to interact with the server 305 via the network 304 to receive or send messages or the like. The terminal devices 301, 302, 303 may have various client applications installed thereon, such as applications for acquiring battery parameters, data transfer software, and the like.
The terminal devices 301, 302, 303 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 305 may be a server providing various services, such as a background management server providing support for users using the terminal devices 301, 302, 303. The background management server may analyze and perform other processing on the received data such as the battery parameters, and feed back a processing result (for example, battery health degree) to the terminal device.
It should be noted that, the method for evaluating the battery health degree provided by the embodiment of the present invention is generally executed by the server 305, and accordingly, an apparatus for evaluating the battery health degree is generally disposed in the server 305.
It should be understood that the number of terminal devices, networks, and servers in fig. 3 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
FIG. 4 is a block diagram of a computer system 400 suitable for implementing a terminal device of an embodiment of the present invention. The terminal device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 4, the computer system 400 includes a Central Processing Unit (CPU)401 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the system 400 are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
In particular, the processes described in the above step diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, the disclosed embodiments of the invention include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the step diagrams. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program performs the above-described functions defined in the system of the present invention when executed by a Central Processing Unit (CPU) 401.
It should be noted that the computer readable media shown in the present invention include computer readable signal media or computer readable storage media, or any combination of the two. A computer readable storage medium includes, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, semiconductor system, apparatus, or device, or any combination of the foregoing. Computer-readable storage media specifically include, but are not limited to: an electrical connection having 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 combination of the foregoing. In the present invention, a computer readable storage medium includes any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device; a computer readable signal medium includes a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave, which may take many forms, including, but not limited to, electromagnetic signals, optical signals, or any combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF (radio frequency), etc., or any combination of the preceding.
The block diagrams or step diagrams in the figures, which illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention, may each represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should 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 or step diagrams, and combinations of blocks in the block diagrams or step diagrams, 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 modules or units described in the embodiments of the present invention may be implemented by software, or may be implemented by hardware. The described modules or units may also be provided in a processor, and may be described as: a processor includes an acquisition module, an evaluation module, and a computation module. The names of these modules or units do not in some cases constitute a limitation to the modules or units themselves, and for example, the evaluation module may also be described as "a module for determining an evaluation change value of the charge percentage from the acquired charge percentage of the battery to be tested, the charge voltage, the charge current, and the battery temperature using the evaluation model".
On the other hand, the embodiment of the present invention also provides a computer-readable medium, which may be included in the apparatus described in the above embodiment; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: acquiring the electric quantity percentage, charging voltage, charging current and temperature of a battery to be tested from the beginning of charging to the end of charging, and determining the actual change value of the electric quantity percentage of the battery to be tested, wherein the actual change value of the electric quantity percentage is the difference value between the electric quantity percentage at the end of charging and the electric quantity percentage at the beginning of charging; determining an evaluation change value of the electric quantity percentage according to the acquired electric quantity percentage of the battery to be tested, the charging voltage, the charging current and the battery temperature by using an evaluation model; and determining the health degree of the battery to be tested according to the rated capacity of the battery to be tested, the evaluation change value of the electric quantity percentage and the actual change value of the electric quantity percentage.
According to the technical scheme of the embodiment of the invention, the correlation relationship between different temperatures, currents, voltages and charging durations and the change of the electric quantity can be modeled and learned by utilizing time sequence data generated in the charging of a large number of new batteries, the time sequence data generated in the charging of the batteries to be tested is input into a model to estimate the charging electric quantity when the electric quantity of the batteries to be tested is estimated, the estimated capacity is calculated by combining the actual change value of the electric quantity percentage of the batteries to be tested, the percentage of the estimated capacity in the rated capacity, namely the health degree of the batteries to be tested, and the estimated capacity of the batteries is used as the basis for estimating the health degree.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. A method of assessing battery health, comprising:
acquiring the electric quantity percentage, charging voltage, charging current and temperature of a battery to be tested from the beginning of charging to the end of charging, and determining the actual change value of the electric quantity percentage of the battery to be tested, wherein the actual change value of the electric quantity percentage is the difference value between the electric quantity percentage at the end of charging and the electric quantity percentage at the beginning of charging;
determining an evaluation change value of the electric quantity percentage according to the acquired electric quantity percentage of the battery to be tested, the charging voltage, the charging current and the battery temperature by using an evaluation model;
and determining the health degree of the battery to be tested according to the rated capacity of the battery to be tested, the evaluation change value of the electric quantity percentage and the actual change value of the electric quantity percentage.
2. The method according to claim 1, characterized in that the evaluation model is constructed from the percentage of charge, the charging voltage, the charging current and the temperature of the battery charged less than a first preset value and/or used for a time less than a second preset value.
3. The method of claim 1, wherein obtaining the percentage of charge, the charging voltage, the charging current and the temperature of the battery under test from the beginning of charging to the end of charging in a sampled manner comprises: and acquiring the electric quantity percentage, the charging voltage, the charging current and the temperature of the battery to be tested from the beginning to the end of charging according to a preset period.
4. The method of claim 1, wherein the evaluation model is a long-short term memory network-based evaluation model.
5. The method of claim 1, wherein the health of the battery under test is determined from the rated capacity of the battery under test, the estimated change value of the percentage of charge, and the actual change value of the percentage of charge using the following formulas:
the SOH is the health degree of the battery to be detected, the ER is the estimated capacity of the battery to be detected, the EF is the rated capacity of the battery to be detected, the CEP is the estimated change value of the electric quantity percentage of the battery to be detected, and the DV is the actual change value of the electric quantity percentage of the battery to be detected.
6. An apparatus for assessing the health of a battery, comprising:
the device comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring the electric quantity percentage, the charging voltage, the charging current and the temperature of a battery to be tested from the beginning of charging to the end of charging and determining the actual change value of the electric quantity percentage of the battery to be tested, and the actual change value of the electric quantity percentage is the difference value between the electric quantity percentage at the end of charging and the electric quantity percentage at the beginning of charging;
the evaluation module is used for determining an evaluation change value of the electric quantity percentage according to the acquired electric quantity percentage of the battery to be tested, the charging voltage, the charging current and the battery temperature by using an evaluation model;
and the calculation module is used for determining the health degree of the battery to be tested according to the rated capacity of the battery to be tested, the evaluation change value of the electric quantity percentage and the actual change value of the electric quantity percentage.
7. The apparatus of claim 6, further comprising:
and the modeling module is used for constructing the evaluation model according to the electric quantity percentage, the charging voltage, the charging current and the temperature of the battery with the charging times less than a first preset value and/or the service time less than a second preset value.
8. The device of claim 6, wherein the obtaining module is further configured to collect the percentage of the electric quantity, the charging voltage, the charging current and the temperature of the battery to be tested from the beginning of charging to the end of charging according to a preset period.
9. The apparatus of claim 6, wherein the evaluation model is a long-short term memory network-based evaluation model.
10. The apparatus of claim 6, wherein the computing module is further configured to determine the health of the battery under test according to the rated capacity of the battery under test, the estimated variation value of the percentage of charge, and the actual variation value of the percentage of charge using the following formulas:
the SOH is the health degree of the battery to be detected, the ER is the estimated capacity of the battery to be detected, the EF is the rated capacity of the battery to be detected, the CEP is the estimated change value of the electric quantity percentage of the battery to be detected, and the DV is the actual change value of the electric quantity percentage of the battery to be detected.
11. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
12. A computer-readable medium, on which a computer program is stored, which, when being executed by one or more processors, carries out the method according to any one of claims 1-5.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111521945A (en) * | 2020-05-06 | 2020-08-11 | 江苏美的清洁电器股份有限公司 | Battery health state detection method and device, electronic equipment and storage medium |
| CN113093040A (en) * | 2021-03-22 | 2021-07-09 | 领翌技术(横琴)有限公司 | Method, device and system for evaluating health degree of battery of electric vehicle |
| CN115389958A (en) * | 2022-10-28 | 2022-11-25 | 江苏海铂德能源科技有限公司 | Lithium ion battery operation safety evaluation method and system |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090128097A1 (en) * | 2007-11-15 | 2009-05-21 | Broadcom Corporation | Method and system for tracking battery state-of-health |
| CN103163480A (en) * | 2013-03-29 | 2013-06-19 | 长城汽车股份有限公司 | Method for estimating health state of lithium battery |
| JP2013122450A (en) * | 2011-11-25 | 2013-06-20 | Honeywell Internatl Inc | Method and apparatus for online determination of battery state of charge and state of health |
| CN103197257A (en) * | 2013-04-03 | 2013-07-10 | 华为技术有限公司 | Method and device for detecting state of health (SOH) of battery |
| CN103823188A (en) * | 2014-02-25 | 2014-05-28 | 宁德时代新能源科技有限公司 | Lithium-ion battery pack health state assessment method |
| US20150293183A1 (en) * | 2012-11-30 | 2015-10-15 | Sanyo Electric Co., Ltd. | Battery management device, power supply, and soc estimation method |
| US20170205469A1 (en) * | 2016-01-14 | 2017-07-20 | Samsung Electronics Co., Ltd. | Apparatus and method for estimating state of battery |
| CN107037368A (en) * | 2016-02-04 | 2017-08-11 | 联发科技股份有限公司 | Estimate the method and electric power controller of the active volume of battery |
| CN107664751A (en) * | 2016-07-28 | 2018-02-06 | 中兴通讯股份有限公司 | The measuring method and measuring and calculating device of a kind of real-time state-of-charge of battery |
| CN107677962A (en) * | 2016-08-01 | 2018-02-09 | 现代自动车株式会社 | The system and method for battery are managed the time required to based on charging |
| CN107831444A (en) * | 2017-10-26 | 2018-03-23 | 哈尔滨工业大学 | A kind of health state of lithium ion battery method of estimation |
| CN108279383A (en) * | 2017-11-30 | 2018-07-13 | 深圳市科列技术股份有限公司 | battery life predicting method, battery data server and battery data processing system |
-
2018
- 2018-08-01 CN CN201810862400.1A patent/CN110850296A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090128097A1 (en) * | 2007-11-15 | 2009-05-21 | Broadcom Corporation | Method and system for tracking battery state-of-health |
| JP2013122450A (en) * | 2011-11-25 | 2013-06-20 | Honeywell Internatl Inc | Method and apparatus for online determination of battery state of charge and state of health |
| US20150293183A1 (en) * | 2012-11-30 | 2015-10-15 | Sanyo Electric Co., Ltd. | Battery management device, power supply, and soc estimation method |
| CN103163480A (en) * | 2013-03-29 | 2013-06-19 | 长城汽车股份有限公司 | Method for estimating health state of lithium battery |
| CN103197257A (en) * | 2013-04-03 | 2013-07-10 | 华为技术有限公司 | Method and device for detecting state of health (SOH) of battery |
| CN103823188A (en) * | 2014-02-25 | 2014-05-28 | 宁德时代新能源科技有限公司 | Lithium-ion battery pack health state assessment method |
| US20170205469A1 (en) * | 2016-01-14 | 2017-07-20 | Samsung Electronics Co., Ltd. | Apparatus and method for estimating state of battery |
| CN107037368A (en) * | 2016-02-04 | 2017-08-11 | 联发科技股份有限公司 | Estimate the method and electric power controller of the active volume of battery |
| CN107664751A (en) * | 2016-07-28 | 2018-02-06 | 中兴通讯股份有限公司 | The measuring method and measuring and calculating device of a kind of real-time state-of-charge of battery |
| CN107677962A (en) * | 2016-08-01 | 2018-02-09 | 现代自动车株式会社 | The system and method for battery are managed the time required to based on charging |
| CN107831444A (en) * | 2017-10-26 | 2018-03-23 | 哈尔滨工业大学 | A kind of health state of lithium ion battery method of estimation |
| CN108279383A (en) * | 2017-11-30 | 2018-07-13 | 深圳市科列技术股份有限公司 | battery life predicting method, battery data server and battery data processing system |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111521945A (en) * | 2020-05-06 | 2020-08-11 | 江苏美的清洁电器股份有限公司 | Battery health state detection method and device, electronic equipment and storage medium |
| CN111521945B (en) * | 2020-05-06 | 2022-04-08 | 美智纵横科技有限责任公司 | Battery health state detection method and device, electronic equipment and storage medium |
| CN113093040A (en) * | 2021-03-22 | 2021-07-09 | 领翌技术(横琴)有限公司 | Method, device and system for evaluating health degree of battery of electric vehicle |
| CN115389958A (en) * | 2022-10-28 | 2022-11-25 | 江苏海铂德能源科技有限公司 | Lithium ion battery operation safety evaluation method and system |
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