CN117007976A - Battery simulation method and device, electronic equipment and storage medium - Google Patents
Battery simulation method and device, electronic equipment and storage medium Download PDFInfo
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- 238000004088 simulation Methods 0.000 title claims abstract description 175
- 238000000034 method Methods 0.000 title claims abstract description 83
- 238000012360 testing method Methods 0.000 claims abstract description 160
- 230000032683 aging Effects 0.000 claims abstract description 65
- 238000001453 impedance spectrum Methods 0.000 claims abstract description 30
- 238000004590 computer program Methods 0.000 claims description 12
- 125000004122 cyclic group Chemical group 0.000 claims description 9
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 8
- 229910052744 lithium Inorganic materials 0.000 claims description 8
- 238000001556 precipitation Methods 0.000 claims description 7
- 238000000157 electrochemical-induced impedance spectroscopy Methods 0.000 claims 1
- 230000008859 change Effects 0.000 abstract description 8
- 239000000463 material Substances 0.000 abstract description 8
- 230000008569 process Effects 0.000 description 32
- 238000004891 communication Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 238000009792 diffusion process Methods 0.000 description 6
- 239000003792 electrolyte Substances 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 4
- 238000003411 electrode reaction Methods 0.000 description 4
- 229910001416 lithium ion Inorganic materials 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 239000007790 solid phase Substances 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000002238 attenuated effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 239000007791 liquid phase Substances 0.000 description 1
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
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- 239000002356 single layer Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000004154 testing of material Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
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Abstract
The embodiment of the application provides a battery simulation method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: obtaining a simulation test signal; performing electrochemical impedance spectrum test, HPPC test and high-low temperature multiplying power charging test on the battery in different aging states according to the simulation test signals to obtain test data; parameter calibration is carried out on a pre-constructed simulation model according to the test data, and a calibrated simulation model is obtained; and simulating the battery according to the calibrated simulation model to obtain the charging strategies of the battery in different aging states. By implementing the embodiment of the application, the change of the potential of the negative electrode in the aging process of the battery can be considered, so that the simulation result is more accurate, the actual use condition is more fitted, and the condition of the battery when charged under different SOH can be accurately determined, so that the method is simpler and clearer. Further, the fast charge performance of the same battery material at different battery sizes can be predicted.
Description
Technical Field
The application relates to the technical field of battery analysis, in particular to a battery simulation method, a device, electronic equipment and a storage medium.
Background
Currently, three-electrode batteries are manufactured in the industry so as to directly measure the negative electrode potential of the battery in the charging process, and a fast charge simulation strategy of the battery in a BOL state is determined based on a method that the negative electrode potential is always higher than a lithium precipitation potential threshold value.
However, in practical applications, the effect of battery aging on the fast charge performance is also considered. The common method in the prior art is to directly test the battery cell in the BOL state to obtain the charging current of the quick charging strategy and multiply the attenuation coefficient, and the calculation process in the mode is simple, so that the charging current in different aging states can be obtained quickly. However, the method does not consider the change of the potential of the negative electrode of the lithium ion battery in the aging process, and can not accurately calibrate the quick charge performance of the lithium ion battery under different SOH.
Disclosure of Invention
The embodiment of the application aims to provide a battery simulation method, a device, electronic equipment and a storage medium, which can consider the change in the aging process of a battery, so that the simulation result is more accurate and is more suitable for the actual use condition, and the condition of the battery when charged under different SOH can be accurately determined, so that the battery simulation method, the device, the electronic equipment and the storage medium are simpler and more obvious. Further, the fast charge performance of the same battery material at different battery sizes can be predicted.
In a first aspect, an embodiment of the present application provides a battery simulation method, where the method includes:
obtaining a simulation test signal;
performing electrochemical impedance spectrum test, HPPC test and high-low temperature multiplying power charging test on the battery in different aging states according to the simulation test signals to obtain test data;
parameter calibration is carried out on a pre-constructed simulation model according to the test data, and a calibrated simulation model is obtained;
and simulating the battery according to the calibrated simulation model to obtain the charging strategies of the battery in different aging states.
In the implementation process, the battery is subjected to different tests and parameter calibration is performed on the simulation model, so that the simulation of the battery is realized, the change of the battery in the aging process can be considered, the simulation result is more accurate, the actual use condition is more attached, the condition of the battery when charged under different SOH can be accurately determined, and the simulation method is simpler and more clear. Further, the fast charge performance of the same battery material at different battery sizes can be predicted.
Further, before the step of acquiring the simulation test signal, the method further includes:
acquiring an aging state of the battery;
setting an attenuation threshold according to the aging state of the battery;
performing a cyclic test on the battery to obtain an actual attenuation value;
judging whether the actual attenuation value is larger than or equal to the attenuation threshold value or not;
if yes, obtaining the simulation test signal.
In the implementation process, the attenuation test is performed according to the capacity of the battery to obtain an attenuation actual value, and then the attenuation actual value is compared with the attenuation threshold value to obtain a simulation test signal, so that the battery can be simulated in a proper state, and the accuracy of a simulation result is improved.
Further, the step of performing an electrochemical impedance spectrum test, an HPPC test and a high-low temperature rate charging test on the battery in different aging states according to the simulation test signal to obtain test data includes:
and respectively carrying out electrochemical impedance spectrum test, HPPC test and high-low temperature multiplying power charging test on a plurality of electric cores in the battery according to the simulation test signals to obtain the test data.
In the implementation process, test data is obtained according to the plurality of battery cells, so that the test data contains more battery samples, and errors in the test process can be reduced.
Further, the step of calibrating parameters of the pre-constructed simulation model according to the test data to obtain a calibrated simulation model includes:
acquiring an electrochemical impedance spectrum, an HPPC voltage curve and different charging condition voltage curves in the test data;
and calibrating parameters of the pre-constructed simulation model according to the electrochemical impedance spectrum, the HPPC voltage curve and the voltage curves under different charging conditions to obtain the calibrated simulation model.
In the implementation process, parameter calibration is carried out on the simulation model according to the electrochemical impedance spectrum, the HPPC voltage curve and the voltage curves under different charging conditions, so that the simulation model is more close to the actual condition of the battery, and the accuracy of the simulation result of the battery is improved.
Further, the step of pre-constructing the simulation model includes:
respectively obtaining aging states of a plurality of electric cores in the battery;
and constructing the simulation model according to the aging states of the plurality of battery cells.
In the implementation process, the simulation model is built according to the aging states of the multiple battery cells, so that the robustness of the simulation model can be improved, and the simulation model is not easy to be interfered by the outside in the simulation process.
Further, the step of simulating the battery according to the calibrated simulation model to obtain the charging strategy of the battery in different aging states comprises the following steps:
respectively simulating the charging currents of the battery in different aging states at preset temperatures and preset charge states according to the calibrated simulation model, and obtaining simulation data when the negative electrode potential of the battery reaches a lithium precipitation threshold value;
and obtaining charging strategies of the battery in different aging states according to the simulation data.
In the implementation process, the charging strategy is obtained according to the simulation data, so that the charging strategy can refer to the simulation data to the greatest extent, and the usability of the charging strategy is improved.
In a second aspect, an embodiment of the present application further provides a battery simulation apparatus, where the apparatus includes:
the acquisition module is used for acquiring the simulation test signal;
the test module is used for carrying out electrochemical impedance spectrum test, HPPC test and high-low temperature multiplying power charging test on the battery in different aging states according to the simulation test signals to obtain test data;
the parameter calibration module is used for calibrating parameters of a pre-constructed simulation model according to the test data to obtain a calibrated simulation model;
and the simulation module is used for simulating the battery according to the calibrated simulation model to obtain the charging strategies of the battery in different aging states.
In the implementation process, the battery is subjected to different tests and parameter calibration is performed on the simulation model, so that the simulation of the battery is realized, the change of the battery in the aging process can be considered, the simulation result is more accurate, the actual use condition is more attached, the condition of the battery when charged under different SOH can be accurately determined, and the simulation method is simpler and more clear. Further, the fast charge performance of the same battery material at different battery sizes can be predicted.
Further, the device also comprises a data obtaining module for:
acquiring an aging state of the battery;
setting an attenuation threshold according to the aging state of the battery;
performing a cyclic test on the battery to obtain an actual attenuation value;
judging whether the actual attenuation value is larger than or equal to the attenuation threshold value or not;
if yes, obtaining the simulation test signal.
In the implementation process, the actual attenuation value is obtained through the cyclic test according to the capacity of the battery, and then the actual attenuation value is compared with the attenuation threshold value to obtain the simulation test signal, so that the battery can be simulated in a proper state, and the accuracy of the simulation result is improved.
In a third aspect, an electronic device provided in an embodiment of the present application includes: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to any one of the first aspects when the computer program is executed.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where instructions are stored, when the instructions are executed on a computer, to cause the computer to perform the method according to any one of the first aspects.
In a fifth aspect, embodiments of the present application provide a computer program product, which when run on a computer causes the computer to perform the method according to any of the first aspects.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part will be obvious from the description, or may be learned by practice of the techniques of the disclosure.
And can be implemented in accordance with the teachings of the specification, the following detailed description of the preferred embodiments of the application, taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be construed as limiting the scope values, and other related drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a battery simulation method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a battery simulation device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
The following describes in further detail the embodiments of the present application with reference to the drawings and examples. The following examples are illustrative of the application and are not intended to limit the scope of the application.
Example 1
Fig. 1 is a flow chart of a battery simulation method according to an embodiment of the present application, as shown in fig. 1, the method includes:
s1, acquiring a simulation test signal;
s2, performing electrochemical impedance spectrum test, HPPC test and high-low temperature multiplying power charging test on the battery in different aging states according to the simulation test signals to obtain test data;
s3, calibrating parameters of a pre-constructed simulation model according to the test data to obtain a calibrated simulation model;
and S4, simulating the battery according to the calibrated simulation model to obtain the charging strategies of the battery in different aging states.
In the implementation process, the battery is subjected to different tests and parameter calibration on the simulation model, so that the simulation of the battery is realized, the change of the potential of the negative electrode in the aging process of the battery can be considered, the simulation result is more accurate, the actual use condition is more attached, and the condition of the battery when the battery is charged under different battery Health (SOH) can be accurately determined, so that the battery is simpler and more obvious.
The embodiment of the application provides a method for simulating a lithium-ion battery-free quick-charge strategy under different aging states by using an electrochemical model of the battery. The simulation of the negative electrode potential of the same battery material under the charging working conditions of different battery size specifications, different aging states, different cut-off charge states and different temperatures can be realized.
Further, before the step of acquiring the simulation test signal, the method further includes:
acquiring an aging state of a battery;
setting an attenuation threshold according to the aging state of the battery;
performing a cyclic test on the battery to obtain an actual attenuation value;
judging whether the actual attenuation value is larger than or equal to an attenuation threshold value or not;
if so, obtaining the simulation test signal.
In the implementation process, the actual attenuation value is obtained according to the battery through the cyclic test, and then the actual attenuation value is compared with the attenuation threshold value to obtain the simulation test signal, so that the battery can be simulated in a proper state, and the accuracy of the simulation result is improved.
A plurality of three-electrode batteries with assembled reference electrodes are taken for cycle test (the copper wires wrapping lithium sheets can be used as the reference electrodes of the batteries for test), the test working conditions can refer to the conventional use working conditions of the batteries, and the purpose of the test is to obtain the batteries in different aging states.
When the SOH of the battery is attenuated to a first set value, namely an attenuation threshold (for example, the SOH can be divided into 100%,97%,94%, 91%, 88%, 85%, 82%, 80%, and the set value is 97%), 2 cells are arbitrarily taken from the cells with better cycle test consistency to perform the test required by the simulation model parameter calibration.
When the SOH of the cell decays to the next set point (e.g., SOH can be divided into 100%,97%,94%, 91%, 88%, 85%, 82%, 80%, where set point is 94%), the above simulation steps are repeated until the test required for simulation model parameter calibration is completed at set point 80%.
Further, S2 includes:
and respectively carrying out electrochemical impedance spectrum test, HPPC test and high-low temperature multiplying power charging test on a plurality of electric cores in the battery according to the simulation test signals to obtain test data.
Illustratively, performing an electrochemical impedance spectrum test, a hybrid power pulse characteristic (Hybrid PulsePower Characteristic, HPPC) test and a high-low temperature rate charging test on a first cell in the battery according to the simulation test signal to obtain first test data; performing electrochemical impedance spectrum test, HPPC test and high-low temperature multiplying power charging test on a second electric core in the battery according to the simulation test signal to obtain second test data; according to the simulation test signal, performing electrochemical impedance spectrum test, HPPC test and high-low temperature multiplying power charging test on an Nth electric core in the battery to obtain Nth test data; test data is obtained from the first test data, the second test data … …, and the nth test data.
In the implementation process, the plurality of battery cells are tested to obtain the test data, so that the test data contains more battery samples, and errors in the test process can be reduced.
EIS tests with different temperatures and different charge states are carried out on the first electric core and the second electric core … … Nth electric core to obtain test data, wherein the test data comprise positive electrode relative reference impedance spectrum and negative electrode relative reference impedance spectrum, and full-battery voltage and negative electrode relative reference voltage change curves with time are obtained by carrying out HPPC tests with different temperatures and different multiplying powers and charging tests with different temperatures and different multiplying powers.
Further, S3 includes:
acquiring electrochemical impedance spectrum, HPPC voltage curve and voltage curve of different charging conditions in test data;
and carrying out parameter calibration on a pre-constructed simulation model according to the electrochemical impedance spectrum, the HPPC voltage curve and the voltage curves under different charging conditions to obtain a calibrated simulation model.
In the implementation process, parameter calibration is carried out on the simulation model according to the electrochemical impedance spectrum, the HPPC test and the voltage curves under different charging conditions, so that the simulation model is more close to the actual condition of the battery, and the accuracy of the simulation result of the battery is improved.
Calibrating kinetic parameters of an electrochemical model (simulation model) of the battery in different aging states according to positive electrode relative reference resistance spectrums and negative electrode relative reference resistance spectrums under different aging states in test data, HPPC tests under different temperatures and different multiplying factors, and full battery voltage and negative electrode voltage curves under charging working conditions of different temperatures and different multiplying factors, wherein the calibration parameters comprise: positive electrode electron conductivity, negative electrode electron conductivity, electrolyte ionic conductivity, positive electrode diffusion coefficient, negative electrode diffusion coefficient, electrolyte diffusion coefficient, positive electrode reaction rate constant, negative electrode reaction rate constant.
According to the embodiment of the application, the dynamic parameters of different charge states at different temperatures are obtained by calibrating the positive electrode relative reference impedance spectrum and the negative electrode relative reference impedance spectrum, the dynamic parameters of different multiplying powers are obtained by calibrating the HPPC at different temperatures and different multiplying powers, and finally the accuracy of the model is verified by using full-cell voltage and negative electrode voltage curves under different-multiplying-power charging conditions at different temperatures. Finally obtained are multidimensional arrays of the respective kinetic parameters, the independent variables 4 being SOH, temperature, state of charge and charge rate, respectively.
The simulation model of the embodiment of the application can refer to a single-layer pole piece three-dimensional electrochemical model of a lithium ion battery, such as a homogeneous Newman model, and the geometric structure in the model at least comprises an anode and a cathode, a current collector, a diaphragm and an anode and cathode lug. The equations solved by homogeneous Newman model are: solid phase charge conservation equation, solid phase material conservation equation, liquid phase charge conservation equation, and solid-liquid interface charge transfer equation. The kinetic parameters related to equation solving include positive electrode electron conductivity, negative electrode electron conductivity, electrolyte ionic conductivity, positive electrode diffusion coefficient, negative electrode diffusion coefficient, electrolyte diffusion coefficient, positive electrode reaction rate constant and negative electrode reaction rate constant. The positive electrode electronic conductivity, the negative electrode electronic conductivity and the electrolyte ionic conductivity of the battery in the BOL state can be obtained through material testing, and other kinetic parameters are difficult to detect.
Further, the step of pre-constructing the simulation model includes:
respectively obtaining aging states of a plurality of battery cores in the battery;
and constructing a simulation model according to the aging states of the multiple battery cells.
In the implementation process, the simulation model is built according to the aging states of the multiple battery cells, so that the robustness of the simulation model can be improved, and the simulation model is not easy to be interfered by the outside in the simulation process.
Further, S4 includes:
respectively simulating the charging currents of the battery in different aging states at preset temperatures and in preset charge states according to the calibrated simulation model, and obtaining simulation data when the negative electrode potential of the battery reaches a lithium precipitation threshold value;
and obtaining the charging strategies of the battery in different aging states according to the simulation data.
In the implementation process, the charging strategy is obtained according to the simulation data, so that the charging strategy can refer to the simulation data to the greatest extent, and the usability of the charging strategy is improved.
And simulating charging currents under different SOH, different temperatures and different cut-off charge states by adopting the calibrated electrochemical model, obtaining a lithium-precipitation-free quick charging strategy under different aging states based on the fact that the potential of the negative electrode is always higher than a lithium precipitation threshold (for example, 0V), and expanding the battery to batteries with different sizes and types with the same material formula.
The present application is illustratively described in terms of a 25 ℃ fast charge strategy simulation procedure for lithium iron phosphate cells at 97% soh. The charge state interval is divided into 0-30%, 30-40%, 40-50%, 50-60%, 60-70%, 70-80%, 80-85%, 85-90%, 90-95%, 95-97%, 97-100%, and the charge state interval can be adjusted and set according to actual application requirements, and the electrochemical model is used for calculating the negative electrode potential of the battery in the charging process according to the initial charging strategy. And then optimizing the charging current of each section in turn, specifically, if the negative electrode potential of the 30% state of charge is lower than 0V, reducing the charging rate of the 0-30% state of charge section, calculating the negative electrode potential again, if the negative electrode potential of the 30% state of charge is higher than 0V, increasing the charging rate of the 0-30% state of charge section, calculating the negative electrode potential again, repeating the calculation until the negative electrode potential of the 30% state of charge is just equal to 0V (the lithium precipitation threshold can be adjusted and set according to actual requirements), and then optimizing the current of the 30% -40% state of charge section, wherein the method is the same as that of the 0-30% state of charge section until the charging current optimization of all the state of charge sections is completed. Finally, the simulation fast charging strategy of the battery cell at 25 ℃ of 97% SOH is obtained.
Example two
In order to perform a corresponding method of the above embodiment to achieve the corresponding functions and technical effects, a battery simulation apparatus is provided below, as shown in fig. 2, which includes:
the acquisition module 1 is used for acquiring a simulation test signal;
the test module 2 is used for carrying out electrochemical impedance spectrum test, HPPC test and high-low temperature multiplying power charging test on the battery in different aging states according to the simulation test signals to obtain test data;
the parameter calibration module 3 is used for carrying out parameter calibration on a pre-constructed simulation model according to the test data to obtain a calibrated simulation model;
and the simulation module 4 is used for simulating the battery according to the calibrated simulation model to obtain the charging strategies of the battery in different aging states.
In the implementation process, the battery is subjected to different tests and parameter calibration is performed on the simulation model, so that the simulation of the battery is realized, the change of the potential of the negative electrode in the aging process of the battery can be considered, the simulation result is more accurate, the actual use condition is more attached, and the condition of the battery when charged under different aging conditions can be accurately determined, so that the simulation method is simpler and more clear. Further, the fast charge performance of the same battery material at different battery sizes can be predicted.
Further, the device also comprises a judging module for:
acquiring an aging state of a battery;
setting an attenuation threshold according to the aging state of the battery;
performing a cyclic test on the battery to obtain an actual attenuation value;
judging whether the actual attenuation value is larger than or equal to an attenuation threshold value or not;
if so, obtaining the simulation test signal.
In the implementation process, the battery is subjected to cyclic test to obtain the attenuation actual value, and then the attenuation actual value is compared with the attenuation threshold value to obtain the simulation test signal, so that the battery can be simulated in a proper state, and the accuracy of a simulation result is improved.
Further, the test module 2 is further configured to:
and respectively carrying out electrochemical impedance spectrum test, HPPC test and high-low temperature multiplying power charging test on a plurality of electric cores in the battery according to the simulation test signals to obtain test data.
In the implementation process, test data are obtained for a plurality of battery cells, so that the test data contain more battery samples, and errors in the test process can be reduced.
Further, the parameter calibration module 3 is further configured to:
acquiring electrochemical impedance spectrum, HPPC voltage curve and voltage curve of different charging conditions in test data;
and carrying out parameter calibration on a pre-constructed simulation model according to the electrochemical impedance spectrum, the HPPC voltage curve and the voltage curves under different charging conditions to obtain a calibrated simulation model.
In the implementation process, parameter calibration is carried out on the simulation model according to the electrochemical impedance spectrum, the HPPC voltage curve and the voltage curves under different charging conditions, so that the simulation model is more close to the actual condition of the battery, and the accuracy of the simulation result of the battery is improved.
Further, the parameter calibration module 3 further comprises a construction unit for:
respectively obtaining aging states of a plurality of battery cores in the battery;
and constructing a simulation model according to the aging states of the multiple battery cells.
In the implementation process, the simulation model is built according to the aging states of the multiple battery cells, so that the robustness of the simulation model can be improved, and the simulation model is not easy to be interfered by the outside in the simulation process.
Further, the simulation module 4 is further configured to:
respectively simulating the charging currents of the battery in different aging states at preset temperatures and in preset charge states according to the calibrated simulation model, and obtaining simulation data when the negative electrode potential of the battery reaches a lithium precipitation threshold value;
and obtaining the charging strategies of the battery in different aging states according to the simulation data.
In the implementation process, the charging strategy is obtained according to the simulation data, so that the charging strategy can refer to the simulation data to the greatest extent, and the usability of the charging strategy is improved.
The battery simulation apparatus described above may implement the method of the first embodiment described above. The options in the first embodiment described above also apply to this embodiment, and are not described in detail here.
The rest of the embodiments of the present application may refer to the content of the first embodiment, and in this embodiment, no further description is given.
Example III
An embodiment of the present application provides an electronic device, including a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to execute the computer program to cause the electronic device to execute the battery simulation method of the first embodiment.
Alternatively, the electronic device may be a server.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the application. The electronic device may include a processor 31, a communication interface 32, a memory 33, and at least one communication bus 34. Wherein the communication bus 34 is used to enable direct connection communication of these components. The communication interface 32 of the device in the embodiment of the present application is used for performing signaling or data communication with other node devices. The processor 31 may be an integrated circuit chip with signal processing capabilities.
The processor 31 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. The general purpose processor may be a microprocessor or the processor 31 may be any conventional processor or the like.
The Memory 33 may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc. The memory 33 has stored therein computer readable instructions which, when executed by the processor 31, enable the apparatus to perform the various steps described above in relation to the embodiment of the method of fig. 1.
Optionally, the electronic device may further include a storage controller, an input-output unit. The memory 33, the memory controller, the processor 31, the peripheral interface, and the input/output unit are electrically connected directly or indirectly to each other, so as to realize data transmission or interaction. For example, the components may be electrically coupled to each other via one or more communication buses 34. The processor 31 is arranged to execute executable modules stored in the memory 33, such as software functional modules or computer programs comprised by the device.
The input-output unit is used for providing the user with the creation task and creating the starting selectable period or the preset execution time for the task so as to realize the interaction between the user and the server. The input/output unit may be, but is not limited to, a mouse, a keyboard, and the like.
It will be appreciated that the configuration shown in fig. 3 is merely illustrative, and that the electronic device may also include more or fewer components than shown in fig. 3, or have a different configuration than shown in fig. 3. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof.
In addition, an embodiment of the present application further provides a computer readable storage medium storing a computer program, which when executed by a processor, implements the method for predicting battery life according to the first embodiment.
The present application also provides a computer program product which, when run on a computer, causes the computer to perform the method described in the method embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based devices which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The above description is merely illustrative of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the scope of the present application, and the application is intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be defined by the protection scope of the claims.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A battery simulation method, the method comprising:
obtaining a simulation test signal;
performing electrochemical impedance spectrum test, HPPC test and high-low temperature multiplying power charging test on the battery in different aging states according to the simulation test signals to obtain test data;
parameter calibration is carried out on a pre-constructed simulation model according to the test data, and a calibrated simulation model is obtained;
and simulating the battery according to the calibrated simulation model to obtain the charging strategies of the battery in different aging states.
2. The battery simulation method according to claim 1, further comprising, prior to the step of acquiring the simulation test signal:
acquiring an aging state of the battery;
setting an attenuation threshold according to the aging state of the battery;
performing a cyclic test on the battery to obtain an actual attenuation value;
judging whether the actual attenuation value is larger than or equal to the attenuation threshold value or not;
if yes, obtaining the simulation test signal.
3. The battery simulation method according to claim 1, wherein the step of performing electrochemical impedance spectroscopy test, HPPC test and high-low temperature rate charging test on the battery under different aging states according to the simulation test signal to obtain test data comprises:
and respectively carrying out electrochemical impedance spectrum test, HPPC test and high-low temperature multiplying power charging test on a plurality of electric cores in the battery according to the simulation test signals to obtain the test data.
4. The battery simulation method according to claim 1, wherein the step of calibrating parameters of the pre-constructed simulation model according to the test data to obtain a calibrated simulation model comprises:
acquiring an electrochemical impedance spectrum, an HPPC voltage curve and different charging condition voltage curves in the test data;
and calibrating parameters of the pre-constructed simulation model according to the electrochemical impedance spectrum, the HPPC voltage curve and the voltage curves under different charging conditions to obtain the calibrated simulation model.
5. The battery simulation method according to claim 1, wherein the step of constructing a simulation model in advance includes:
respectively obtaining aging states of a plurality of electric cores in the battery;
and constructing the simulation model according to the aging states of the plurality of battery cells.
6. The battery simulation method according to claim 5, wherein the step of simulating the battery according to the calibrated simulation model to obtain the charging strategies of the battery in different aging states comprises the steps of:
respectively simulating the charging currents of the battery in different aging states at preset temperatures and preset charge states according to the calibrated simulation model, and obtaining simulation data when the negative electrode potential of the battery reaches a lithium precipitation threshold value;
and obtaining charging strategies of the battery in different aging states according to the simulation data.
7. A battery simulation apparatus, the apparatus comprising:
the acquisition module is used for acquiring the simulation test signal;
the test module is used for carrying out electrochemical impedance spectrum test, HPPC test and high-low temperature multiplying power charging test on the battery in different aging states according to the simulation test signals to obtain test data;
the parameter calibration module is used for calibrating parameters of a pre-constructed simulation model according to the test data to obtain a calibrated simulation model;
and the simulation module is used for simulating the battery according to the calibrated simulation model to obtain the charging strategies of the battery in different aging states.
8. The battery simulation apparatus of claim 7, further comprising a data acquisition module configured to:
acquiring an aging state of the battery;
setting an attenuation threshold according to the aging state of the battery;
performing a cyclic test on the battery to obtain an actual attenuation value;
judging whether the actual attenuation value is larger than or equal to the attenuation threshold value or not;
if yes, obtaining the simulation test signal.
9. An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the battery simulation method according to any one of claims 1 to 6.
10. A storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the battery simulation method according to any one of claims 1 to 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117289683A (en) * | 2023-11-21 | 2023-12-26 | 晶科储能科技有限公司 | Energy storage battery management system testing method and system, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117289683A (en) * | 2023-11-21 | 2023-12-26 | 晶科储能科技有限公司 | Energy storage battery management system testing method and system, electronic equipment and storage medium |
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