CN116819341A - Lithium battery acceleration parameter identification method and system and electronic equipment - Google Patents
Lithium battery acceleration parameter identification method and system and electronic equipment Download PDFInfo
- Publication number
- CN116819341A CN116819341A CN202310814146.9A CN202310814146A CN116819341A CN 116819341 A CN116819341 A CN 116819341A CN 202310814146 A CN202310814146 A CN 202310814146A CN 116819341 A CN116819341 A CN 116819341A
- Authority
- CN
- China
- Prior art keywords
- parameter
- identified
- parameters
- candidate
- battery
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 119
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 119
- 238000000034 method Methods 0.000 title claims abstract description 34
- 230000001133 acceleration Effects 0.000 title claims abstract description 31
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 claims description 25
- 229910001416 lithium ion Inorganic materials 0.000 claims description 25
- 238000004590 computer program Methods 0.000 claims description 9
- 239000007790 solid phase Substances 0.000 claims description 6
- 239000007791 liquid phase Substances 0.000 claims description 5
- 238000012216 screening Methods 0.000 abstract description 8
- 238000012545 processing Methods 0.000 description 5
- 238000004146 energy storage Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 241000544061 Cuculus canorus Species 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000001311 chemical methods and process Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 239000003792 electrolyte Substances 0.000 description 1
- 239000002803 fossil fuel Substances 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The application provides a lithium battery acceleration parameter identification method, which comprises the following steps: establishing a P2D battery model for the lithium battery, and determining a parameter range of parameters to be identified in the P2D battery model according to known battery parameters; randomly generating a parameter set of parameters to be identified in a parameter range; obtaining a mean square error by using the analog voltage and the actual voltage; determining a parameter with the minimum mean square error as a candidate parameter set of parameters to be identified; reserving a candidate parameter set conforming to the nominal capacity; reducing the parameter range of the parameters to be identified according to the reserved candidate parameter set; when the parameter range of the parameter to be identified is reduced to the parameter range threshold value, calculating the optimal solution of the parameter to be identified by using a heuristic algorithm. The method and the device of the application sequentially reduce the parameter range of the parameter to be identified, and then rapidly determine the optimal solution in the reduced range, thereby being capable of eliminating the parameter with the capacity which does not meet the requirement in advance, improving the efficiency of screening the optimal solution and reducing the iteration times of screening the optimal solution.
Description
Technical Field
The application relates to the technical field of parameter identification, in particular to a lithium battery acceleration parameter identification method, a lithium battery acceleration parameter identification system and electronic equipment.
Background
The lithium ion battery technology is a mainstream battery technology of an energy storage power station in China, and has the advantages of high stability, large capacity, long service life and the like. If the battery is improperly used, overcharge, overdischarge, overheat and the like occur, the performance of the lithium battery is degraded. In order to ensure safe operation and effective management of the lithium battery, a P2D model is required to be established, but the P2D model is complex, the identification of high-dimensional parameters is difficult to converge, and the same result can be obtained by different parameters; the conventional identification method is difficult to effectively and rapidly identify, so that the accuracy of the P2D model for identifying the parameters of the lithium battery is low.
Disclosure of Invention
In order to solve the existing technical problems, the embodiment of the application provides an acceleration parameter identification method, an acceleration parameter identification system and electronic equipment.
In a first aspect, an embodiment of the present application provides a method for identifying acceleration parameters of a lithium battery, including:
establishing a P2D battery model for a lithium battery, acquiring known battery parameters of the lithium battery in the P2D battery model, and determining a parameter range of parameters to be identified in the P2D battery model according to the known battery parameters;
randomly generating a parameter set of the parameters to be identified in the parameter range of the parameters to be identified;
acquiring the actual voltage of a lithium battery, determining the analog voltage of the lithium battery according to the parameter set of the parameter to be identified and the P2D battery model, and obtaining the mean square error of the analog voltage and the actual voltage of the lithium battery according to the analog voltage of the lithium battery and the actual voltage of the lithium battery;
calculating the parameter set of the parameters to be identified to obtain the parameter with the minimum mean square error between the analog voltage and the actual voltage of the lithium battery as a candidate parameter set of the parameters to be identified;
when the number of the candidate parameter sets of the parameters to be identified reaches a parameter set number threshold, sequentially bringing the plurality of candidate parameter sets of the parameters to be identified into the P2D battery model, calculating to obtain the lithium battery capacity corresponding to each candidate parameter set in the plurality of candidate parameter sets, and reserving the candidate parameter sets with the calculated capacity conforming to the nominal capacity in each candidate parameter set;
reducing the parameter range of the parameters to be identified according to the reserved candidate parameter set;
and when the parameter range of the parameter to be identified is reduced to a parameter range threshold value, calculating an optimal solution of the parameter to be identified by using a heuristic algorithm.
In a second aspect, an embodiment of the present application further provides an acceleration parameter identification system, which is characterized in that the system includes:
the modeling module is used for establishing a P2D battery model for the lithium battery, acquiring known battery parameters of the lithium battery in the P2D battery model, and determining a parameter range of parameters to be identified in the P2D battery model according to the known battery parameters;
the generation module is used for randomly generating a parameter set of the parameter to be identified in the parameter range of the parameter to be identified;
the acquisition module is used for acquiring the actual voltage of the lithium battery, determining the analog voltage of the lithium battery according to the parameter set of the parameter to be identified and the P2D battery model, and obtaining the mean square error of the analog voltage and the actual voltage of the lithium battery according to the analog voltage of the lithium battery and the actual voltage of the lithium battery;
the determining module is used for calculating the parameter set of the parameter to be identified to obtain the parameter with the minimum mean square error between the analog voltage and the actual voltage of the lithium battery as a candidate parameter set of the parameter to be identified;
the selection module is used for sequentially bringing a plurality of candidate parameter sets of the parameters to be identified into the P2D battery model when the number of the candidate parameter sets of the parameters to be identified reaches a parameter set number threshold, calculating to obtain the lithium battery capacity corresponding to each candidate parameter set in the plurality of candidate parameter sets, and reserving the candidate parameter sets with the calculated capacity conforming to the nominal capacity in each candidate parameter set;
the reduction module is used for reducing the parameter range of the parameter to be identified according to the reserved candidate parameter set;
and the solving module is used for calculating the optimal solution of the parameter to be identified by using a heuristic algorithm when the parameter range of the parameter to be identified is reduced to a parameter range threshold value.
In a third aspect, an embodiment of the present application provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the transceiver, the memory, and the processor are connected through the bus, and the computer program when executed by the processor implements the steps in the method for identifying a lithium battery acceleration parameter according to the first aspect.
In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements the steps in the method for identifying acceleration parameters of a lithium battery according to the first aspect.
In summary, in the method for identifying acceleration parameters of a lithium battery provided by the embodiment of the application, a P2D model is built for the lithium battery, a parameter range of a parameter to be identified is determined according to known parameters of the lithium battery, a parameter set is randomly generated, an actual voltage of the lithium battery is obtained, an analog voltage is obtained by using the parameter set and the P2D model, a mean square error is obtained by the analog voltage and the actual voltage, a minimum mean square error in the parameter set is determined as a candidate parameter set, a capacity of the lithium battery corresponding to each parameter is obtained by the candidate parameter set, the candidate parameter set meeting a nominal capacity is reserved, the parameter range is narrowed, and when the parameter range meets a parameter range threshold, an optimal solution of the parameter to be identified is determined by a heuristic algorithm; compared with the prior art that the optimal solution search is directly performed in a large range, the method has the advantages that the nominal capacity of the lithium battery is utilized to reduce the parameter range of the parameters to be identified, then the optimal solution is determined in the reduced range, the parameters with the capacity which does not meet the requirements can be removed in advance, the efficiency of screening the optimal solution is effectively improved, and the iteration times of screening the optimal solution are reduced.
Drawings
In order to more clearly describe the embodiments of the present application or the technical solutions in the background art, the following description will describe the drawings that are required to be used in the embodiments of the present application or the background art.
Fig. 1 shows a flowchart of a method for identifying acceleration parameters of a lithium battery according to an embodiment of the present application;
fig. 2 shows a flowchart of a lithium battery acceleration parameter identification system according to an embodiment of the present application;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application. The present application will be further described in detail below with reference to the drawings and detailed description for the purpose of enabling those skilled in the art to better understand the aspects of the present application.
In recent years, new energy has been rapidly developed due to exhaustion of fossil fuel and the increasing environmental problems. Limited by the time dependence of new energy sources (wind and solar), energy storage systems are needed in electrical power systems to maintain a stable power output. Among them, an energy storage power station for storing electric power by using an ultra-large battery pack is used as an important supporting technology, and has been rapidly developed in recent years. The lithium ion battery has become the mainstream battery technology of the energy storage power station in China at present by virtue of the remarkable advantages of higher stability, higher capacity, longer service life, environmental protection and the like. However, due to material and structural problems, the lithium ion battery is prone to overdischarge, overcharge, overheat and degradation during practical application, and finally, the performance of the lithium ion battery is reduced and deeply fails. In order to ensure safe operation and effective energy management of the lithium ion battery, internal parameters of the lithium ion battery need to be identified, so that physical and chemical changes in the lithium ion battery can be effectively and accurately monitored. The Pseudo two-dimensional (P2D) model of the lithium ion battery is a practical and accurate electrochemical model in scientific research and practical engineering, and can effectively reflect the internal physical and chemical processes in the use process of the lithium ion battery.
The P2D model encompasses all basic components of a lithium ion battery including, but not limited to, electrodes (positive and negative), separator, electrolyte and current collector. The P2D model is complex in form, most of the traditional optimization methods are difficult to realize, and secondly, the high-dimensional parameters of the P2D model enable identification to be difficult to converge, and different parameters can obtain the same result. Conventional heuristic algorithms (such as genetic algorithm and cuckoo algorithm) often need to search in a larger range to avoid sinking into local optimum, but the probability of searching the global optimum solution is lower.
The application can obviously reduce the number of loop iteration times, enhance the repeatability and quickly find the global optimal solution by narrowing the search range of the parameters.
Example 1
The execution subject of the lithium battery acceleration parameter identification method provided in the embodiment is a server.
The present embodiment provides a method for identifying acceleration parameters of a lithium battery, referring to a first flowchart of the method for identifying acceleration parameters of a lithium battery shown in fig. 1, the method for identifying acceleration parameters of a lithium battery includes:
step 100: and establishing a P2D battery model for the lithium battery, acquiring known battery parameters of the lithium battery in the P2D battery model, and determining a parameter range of the parameters to be identified in the P2D battery model according to the known battery parameters.
In the step 100, the known battery parameter may be a known parameter (for example, a capacity or a current described in the specification) in a specification attached to a manufacturer of the lithium battery at the time of shipment, or may be battery capacity information described in a lithium battery label. Because the capacity of the lithium battery is fixed, the parameter information such as current and voltage output by the lithium battery is necessarily limited by the capacity of the battery, and the battery needs to be kept in a reasonable interval. Therefore, the parameter range of the parameter to be identified can be reasonably deduced through the known battery parameters. It should be noted that in order to facilitate the implementation of the subsequent steps, the parameter ranges of the parameters to be identified should be selected as large as possible when performing the derivation determination.
In one embodiment, the P2D battery model mentioned in the above step 100 needs to satisfy:
wherein U is t For lithium battery output terminal voltage, OCP p Open Circuit Potential (OCP) of positive electrode of lithium ion battery n Open circuit potential of lithium ion battery cathode, eta p Is positive electrode solid phase overpotential, eta n Is the solid phase overpotential of the negative electrode,is the liquid phase potential of the lithium ion battery.
Here, the unknown lithium battery output terminal voltage is obtained by inputting parameters to be identified into the P2D battery model.
The open circuit potential of the positive electrode of the lithium ion battery, the open circuit potential of the negative electrode of the lithium ion battery, the positive electrode of the fixed potential, the negative electrode of the fixed potential and the liquid phase potential of the lithium ion battery are all known battery parameters which are cached in the server in advance, the specific parameter values of the parameters are obtained through the known parameters, and meanwhile, the parameter values cached in the server can be modified manually.
Step 101: and randomly generating a parameter set of the parameters to be identified in the parameter range of the parameters to be identified.
In step 101, the parameters to be identified are obtained by the known parameters in step 100, so that a reasonable range of various parameters of the battery can be deduced by a known battery parameter. The parameters to be identified refer to a plurality of parameters and should not be understood as a single parameter. The parameters to be identified randomly generate a parameter in the respective parameter range, and the parameter sets are formed by combining the randomly generated parameters.
Step 102: and obtaining the actual voltage of the lithium battery, determining the analog voltage of the lithium battery according to the parameter set of the parameter to be identified and the P2D battery model, and obtaining the mean square error of the analog voltage and the actual voltage of the lithium battery according to the analog voltage and the actual voltage of the lithium battery.
In step 102 described above, the analog voltage mentioned is actually the lithium battery output terminal voltage of the P2D battery model in step 100. Since the P2D battery model is a lithium battery modeling process simulated in the server, the lithium battery output voltage obtained from the P2D battery model is actually a simulated voltage.
The actual voltage of the lithium battery is obtained by using a battery charging and discharging device which is connected with a server and used for collecting the voltage.
The parameter set of the parameters to be identified is input into the P2D battery model, and the analog voltage of the lithium battery can be obtained through the processing of the P2D battery model, wherein the specific processing process is the prior art and is not repeated here.
In step 102, if the mean square error between the simulated voltage and the actual voltage of the lithium battery is to be obtained according to the simulated voltage and the actual voltage of the lithium battery, the following needs to be satisfied:
wherein, vol real Actual voltage of lithium battery, vol sim The MSE is the mean square error of the simulated voltage and the actual voltage of the lithium battery.
Step 103: and calculating the parameter set of the parameters to be identified to obtain the parameter with the minimum mean square error between the analog voltage and the actual voltage of the lithium battery as the candidate parameter set of the parameters to be identified.
After the step 103 is performed, counting the number of candidate parameter sets, and returning to the step 101 when the number of candidate parameter sets of the parameters to be identified does not reach the threshold value of the number of parameter sets; otherwise, the process continues to step 104.
The parameter group quantity threshold is cached in the server.
In one embodiment, the parameter set number threshold may be set to 1000.
Step 104: when the number of candidate parameter sets of parameters to be identified reaches a parameter set number threshold, sequentially bringing the plurality of candidate parameter sets of the parameters to be identified into a P2D battery model, calculating to obtain the lithium battery capacity corresponding to each candidate parameter set in the plurality of candidate parameter sets, and reserving the candidate parameter sets with the calculated capacity conforming to the nominal capacity in each candidate parameter set.
In step 104, the mean square error obtained in steps 100-103 is a complete cycle, and the steps are repeated for a plurality of times to obtain a plurality of mean square errors, then a parameter set corresponding to the mean square error with the smallest value is selected as a candidate parameter set (one of required parameter sets) of parameters to be identified, the steps are repeated to obtain a plurality of candidate parameter sets, each candidate parameter set corresponds to a lithium battery capacity, and finally the candidate parameter sets are screened by taking the nominal capacity as a reference set. In particular, the candidate parameter set is retained in step 104, except that the nominal capacity is required to be met, including but not limited to the parameter range in which the data in the candidate parameter set meets the parameters to be identified.
For example, if the threshold value of the number of parameter sets is preferably 1000, 1000 candidate parameter sets may be obtained, the 1000 candidate parameter sets are brought into the P2D battery model, the current discharge of 60A in the full power state is simulated until the battery capacity is determined after the discharge is completed, if the battery capacity calculated by the candidate parameter sets is less than 0.9 times of the nominal capacity or greater than 1.1 times of the nominal capacity, each candidate parameter set fails parameters, and the server discards the candidate parameter sets. Otherwise, parameters in the candidate parameter set are reserved. In particular, the nominal capacity is obtained for human measurement and preset in the server. In order to prevent capacity fade of the lithium battery, the actual factory capacity of the lithium battery may be more than the nominal capacity.
Step 105: and reducing the parameter range of the parameters to be identified according to the reserved candidate parameter set.
In step 105, when the candidate parameter set is determined by using the nominal capacity of the battery in step 104, the parameters in the candidate parameter set belong to the values of the interval where the parameter to be identified is located, and the following steps (1) to (2) may be performed according to the reduced parameter range of the values:
(1) Calculating the mean value and standard deviation of each candidate parameter in the candidate parameter set;
(2) The parameter range of each candidate parameter in the parameter set to be identified after the candidate parameters are reduced satisfies the following formula:
Mean-sigma≤pr≤Mean+sigma
wherein Mean is the Mean value of the candidate parameters, sigma is the standard deviation of the candidate parameters, and pr is the reduced parameter range of the candidate parameters.
The average value in the step (1) satisfies:
wherein j is the number of candidate parameters, param i Is the i candidate parameter.
The standard deviation in the step (1) satisfies the following conditions:
after the step 105 is performed, it is determined whether the parameter range of the parameter to be identified is reduced to the parameter range threshold, if yes, step 106 is performed, and if not, step 101 is performed.
Since the parameters to be identified basically conform to the normal distribution, each modification reduces the parameter range of the parameters to be identified to 68.27% of the previous parameter range. So as to gradually narrow the parameter range of the parameter to be identified.
Step 106: when the parameter range of the parameter to be identified is reduced to the parameter range threshold value, calculating the optimal solution of the parameter to be identified by using a heuristic algorithm.
In step 106, the parameter range threshold indicates that the parameter range of the parameter to be identified has been narrowed to a reasonable range, and no further narrowing is required, so that the optimal solution can be quickly found in this interval. In addition, the parameter range threshold value refers to 10% of the parameter range given in the step 100, "reasonably estimating the parameter to be identified from the known battery parameters". Meanwhile, the heuristic algorithm is preferably a cuckoo algorithm.
The steps 100 to 106 may also be used for parameter identification of the same kind of cells in the same power station.
In summary, in the method for identifying acceleration parameters of a lithium battery provided by the embodiment of the application, a P2D model is built for the lithium battery, a parameter range of a parameter to be identified is determined according to known parameters of the lithium battery, a parameter set is randomly generated, an actual voltage of the lithium battery is obtained, an analog voltage is obtained by using the parameter set and the P2D model, a mean square error is obtained by the analog voltage and the actual voltage, a minimum mean square error in the parameter set is determined as a candidate parameter set, a capacity of the lithium battery corresponding to each parameter is obtained by the candidate parameter set, the candidate parameter set meeting a nominal capacity is reserved, the parameter range is narrowed, and when the parameter range meets a parameter range threshold, an optimal solution of the parameter to be identified is determined by a heuristic algorithm; compared with the prior art that the optimal solution search is directly performed in a large range, the method has the advantages that the nominal capacity of the lithium battery is utilized to reduce the parameter range of the parameters to be identified, then the optimal solution is determined in the reduced range, the parameters with the capacity which does not meet the requirements can be removed in advance, the efficiency of screening the optimal solution is effectively improved, and the iteration times of screening the optimal solution are reduced.
Example 2
The present embodiment proposes an acceleration parameter identification system, referring to a flowchart of the acceleration parameter identification system shown in fig. 2, the system includes:
the modeling module 200 establishes a P2D battery model for the lithium battery, acquires known battery parameters of the lithium battery in the P2D battery model, and determines a parameter range of parameters to be identified in the P2D battery model according to the known battery parameters;
the generation module 201 randomly generates a parameter set of the parameter to be identified in a parameter range of the parameter to be identified;
the acquisition module 202 acquires the actual voltage of the lithium battery, determines the analog voltage of the lithium battery according to the parameter set of the parameter to be identified and the P2D battery model, and obtains the mean square error of the analog voltage and the actual voltage of the lithium battery according to the analog voltage and the actual voltage of the lithium battery;
the determining module 203 calculates a parameter set of parameters to be identified to obtain a parameter with the minimum mean square error between the analog voltage and the actual voltage of the lithium battery as a candidate parameter set of the parameters to be identified;
the selecting module 204 sequentially brings a plurality of candidate parameter sets of the parameters to be identified into the P2D battery model when the number of the candidate parameter sets of the parameters to be identified reaches a parameter set number threshold, calculates to obtain the lithium battery capacity corresponding to each candidate parameter set in the plurality of candidate parameter sets, and reserves the candidate parameter set with the calculated capacity conforming to the nominal capacity in each candidate parameter set;
the shrinking module 205 shrinks the parameter range of the parameter to be identified according to the reserved candidate parameter set;
the solving module 206 calculates an optimal solution of the parameter to be identified by using a heuristic algorithm when the parameter range of the parameter to be identified is reduced to a parameter range threshold.
Further, the shrinking module includes:
calculating the mean value and standard deviation of each candidate parameter in the candidate parameter set;
the parameter range of each candidate parameter in the parameter set to be identified after the candidate parameter is reduced satisfies the following formula:
Mean-sigma≤pr≤Mean+sigma
wherein Mean is the Mean value of the candidate parameters, sigma is the standard deviation of the candidate parameters, and pr is the reduced parameter range of the candidate parameters.
In a further aspect, the modeling module includes:
wherein U is t OCP for output voltage of lithium battery p Open Circuit Potential (OCP) of positive electrode of lithium ion battery n Open circuit potential of lithium ion battery cathode, eta p Positive electrode with fixed potential, eta n As a negative electrode of a fixed potential,is the liquid phase potential of the lithium ion battery.
In summary, in the method for identifying acceleration parameters of a lithium battery provided by the embodiment of the application, a P2D model is built for the lithium battery, a parameter range of a parameter to be identified is determined according to known parameters of the lithium battery, a parameter set is randomly generated, an actual voltage of the lithium battery is obtained, an analog voltage is obtained by using the parameter set and the P2D model, a mean square error is obtained by the analog voltage and the actual voltage, a minimum mean square error in the parameter set is determined as a candidate parameter set, a capacity of the lithium battery corresponding to each parameter is obtained by the candidate parameter set, the candidate parameter set meeting a nominal capacity is reserved, the parameter range is narrowed, and when the parameter range meets a parameter range threshold, an optimal solution of the parameter to be identified is determined by a heuristic algorithm; compared with the prior art that the optimal solution search is directly performed in a large range, the method has the advantages that the nominal capacity of the lithium battery is utilized to reduce the parameter range of the parameters to be identified, then the optimal solution is determined in the reduced range, the parameters with the capacity which does not meet the requirements can be removed in advance, the efficiency of screening the optimal solution is effectively improved, and the iteration times of screening the optimal solution are reduced.
Example 3
The present embodiment proposes a computer-readable storage medium, on which a computer program is stored, which when executed by a processor performs the steps of the acceleration parameter identification method described in the above embodiment 1. The specific implementation can be referred to method embodiment 1, and will not be described herein.
In addition, referring to the schematic structural diagram of the electronic device shown in fig. 3, the present embodiment further proposes an electronic device, which includes a bus 300, a processor 301, a transceiver 302, a bus interface 303, a memory 304, and a user interface 305. The electronic device includes a memory 304.
In this embodiment, the electronic device further includes: one or more programs stored on memory 304 and executable on processor 301, configured to be executed by the processor for performing steps (1) through (7) below:
(1) And establishing a P2D battery model for the lithium battery, acquiring known battery parameters of the lithium battery in the P2D battery model, and determining a parameter range of the parameters to be identified in the P2D battery model according to the known battery parameters.
(2) And randomly generating a parameter set of the parameters to be identified in the parameter range of the parameters to be identified.
(3) And obtaining the actual voltage of the lithium battery, determining the analog voltage of the lithium battery according to the parameter set of the parameter to be identified and the P2D battery model, and obtaining the mean square error of the analog voltage and the actual voltage of the lithium battery according to the analog voltage and the actual voltage of the lithium battery.
(4) And calculating the parameter set of the parameters to be identified to obtain the parameter with the minimum mean square error between the analog voltage and the actual voltage of the lithium battery as the candidate parameter set of the parameters to be identified.
(5) When the number of candidate parameter sets of parameters to be identified reaches a parameter set number threshold, sequentially bringing the plurality of candidate parameter sets of the parameters to be identified into a P2D battery model, calculating to obtain the lithium battery capacity corresponding to each candidate parameter set in the plurality of candidate parameter sets, and reserving the candidate parameter sets with the calculated capacity conforming to the nominal capacity in each candidate parameter set.
(6) And reducing the parameter range of the parameters to be identified according to the reserved candidate parameter set.
(7) When the parameter range of the parameter to be identified is reduced to the parameter range threshold value, calculating the optimal solution of the parameter to be identified by using a heuristic algorithm.
A transceiver 302 for receiving and transmitting data under the control of the processor 301.
Where bus architecture (represented by bus 300), bus 300 may comprise any number of interconnected buses and bridges, with bus 300 linking together various circuits, including one or more processors, as represented by processor 301, and memory, as represented by memory 304. Bus 300 may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., as are well known in the art and, therefore, will not be described further herein. Bus interface 303 provides an interface between bus 300 and transceiver 302. The transceiver 302 may be one element or may be a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 302 receives external data from other devices. The transceiver 302 is used to transmit the data processed by the processor 301 to other devices. Depending on the nature of the computing system, a user interface 305 may also be provided, such as a keypad, display, speaker, microphone, joystick.
The processor 301 is responsible for managing the bus 300 and general processing as described above for running the general operating system 3041. And memory 304 may be used to store data used by processor 301 in performing operations.
Alternatively, the processor 301 may be, but is not limited to: a central processing unit, a single chip microcomputer, a microprocessor or a programmable logic device.
It will be appreciated that the memory 304 in embodiments of the application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile Memory may be Read-Only Memory (ROM), programmable Read-Only memory+programmable ROM, PROM, erasable Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (Double Data Rate SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DRRAM). The memory 304 of the system and method described in this embodiment is intended to comprise, without being limited to, these and any other suitable types of memory.
In some implementations, the memory 304 stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system 3041 and application programs 3042.
The operating system 3041 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application 3042 includes various application programs such as a Media Player (Media Player), a Browser (Browser), and the like for realizing various application services. A program for implementing the method of the embodiment of the present application may be included in the application program 3042.
The foregoing is merely a specific implementation of the embodiment of the present application, but the protection scope of the embodiment of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the embodiment of the present application, and the changes or substitutions are covered by the protection scope of the embodiment of the present application. Therefore, the protection scope of the embodiments of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. The method for identifying the acceleration parameters of the lithium battery is characterized by comprising the following steps of:
establishing a P2D battery model for a lithium battery, acquiring known battery parameters of the lithium battery in the P2D battery model, and determining a parameter range of parameters to be identified in the P2D battery model according to the known battery parameters;
randomly generating a parameter set of the parameters to be identified in the parameter range of the parameters to be identified;
acquiring the actual voltage of a lithium battery, determining the analog voltage of the lithium battery according to the parameter set of the parameter to be identified and the P2D battery model, and obtaining the mean square error of the analog voltage and the actual voltage of the lithium battery according to the analog voltage of the lithium battery and the actual voltage of the lithium battery;
calculating the parameter set of the parameters to be identified to obtain the parameter with the minimum mean square error between the analog voltage and the actual voltage of the lithium battery as a candidate parameter set of the parameters to be identified;
when the number of the candidate parameter sets of the parameters to be identified reaches a parameter set number threshold, sequentially bringing the plurality of candidate parameter sets of the parameters to be identified into the P2D battery model, calculating to obtain the lithium battery capacity corresponding to each candidate parameter set in the plurality of candidate parameter sets, and reserving the candidate parameter sets with the calculated capacity conforming to the nominal capacity in each candidate parameter set;
reducing the parameter range of the parameters to be identified according to the reserved candidate parameter set;
and when the parameter range of the parameter to be identified is reduced to a parameter range threshold value, calculating an optimal solution of the parameter to be identified by using a heuristic algorithm.
2. The acceleration parameter identification method according to claim 1, wherein the narrowing down of the parameter range of the parameter to be identified according to the reserved candidate parameter set comprises:
calculating the mean value and standard deviation of each candidate parameter in the candidate parameter set;
the parameter range of each candidate parameter in the parameter set to be identified after the candidate parameter is reduced satisfies the following formula:
Mean-sigma≤pr≤Mean+sigma
wherein Mean is the Mean value of the candidate parameters, sigma is the standard deviation of the candidate parameters, and pr is the reduced parameter range of the candidate parameters.
3. The acceleration parameter identification system of claim 1, wherein the modeling of the lithium battery in P2D battery comprises:
wherein U is t OCP for output voltage of lithium battery p Open Circuit Potential (OCP) of positive electrode of lithium ion battery n Open circuit potential of lithium ion battery cathode, eta p Is positive electrode solid phase overpotential, eta n Is the solid phase overpotential of the negative electrode,is the liquid phase potential of the lithium ion battery.
4. An acceleration parameter identification system, the system comprising:
the modeling module is used for establishing a P2D battery model for the lithium battery, acquiring known battery parameters of the lithium battery in the P2D battery model, and determining a parameter range of parameters to be identified in the P2D battery model according to the known battery parameters;
the generation module is used for randomly generating a parameter set of the parameter to be identified in the parameter range of the parameter to be identified;
the acquisition module is used for acquiring the actual voltage of the lithium battery, determining the analog voltage of the lithium battery according to the parameter set of the parameter to be identified and the P2D battery model, and obtaining the mean square error of the analog voltage and the actual voltage of the lithium battery according to the analog voltage of the lithium battery and the actual voltage of the lithium battery;
the determining module is used for calculating the parameter set of the parameter to be identified to obtain the parameter with the minimum mean square error between the analog voltage and the actual voltage of the lithium battery as a candidate parameter set of the parameter to be identified;
the selection module is used for sequentially bringing a plurality of candidate parameter sets of the parameters to be identified into the P2D battery model when the number of the candidate parameter sets of the parameters to be identified reaches a parameter set number threshold, calculating to obtain the lithium battery capacity corresponding to each candidate parameter set in the plurality of candidate parameter sets, and reserving the candidate parameter sets with the calculated capacity conforming to the nominal capacity in each candidate parameter set;
the reduction module is used for reducing the parameter range of the parameter to be identified according to the reserved candidate parameter set;
and the solving module is used for calculating the optimal solution of the parameter to be identified by using a heuristic algorithm when the parameter range of the parameter to be identified is reduced to a parameter range threshold value.
5. The acceleration parameter identification system of claim 4, wherein the scaling down module comprises:
calculating the mean value and standard deviation of each candidate parameter in the candidate parameter set;
the parameter range of each candidate parameter in the parameter set to be identified after the candidate parameter is reduced satisfies the following formula:
Mean-sigma≤pr≤Mean+sigma
wherein Mean is the Mean value of the candidate parameters, sigma is the standard deviation of the candidate parameters, and pr is the reduced parameter range of the candidate parameters.
6. The acceleration parameter identification system of claim 4, wherein the modeling module comprises:
wherein U is t OCP for output voltage of lithium battery p Open Circuit Potential (OCP) of positive electrode of lithium ion battery n Open circuit potential of lithium ion battery cathode, eta p Is positive electrode solid phase overpotential, eta n Is the solid phase overpotential of the negative electrode,is the liquid phase potential of the lithium ion battery.
7. An electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program when executed by the processor implements the steps in the acceleration parameter identification method according to any one of claims 1 to 3.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps in the acceleration parameter identification method according to any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310814146.9A CN116819341B (en) | 2023-07-04 | 2023-07-04 | Lithium battery acceleration parameter identification method and system and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310814146.9A CN116819341B (en) | 2023-07-04 | 2023-07-04 | Lithium battery acceleration parameter identification method and system and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116819341A true CN116819341A (en) | 2023-09-29 |
| CN116819341B CN116819341B (en) | 2024-11-01 |
Family
ID=88114388
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310814146.9A Active CN116819341B (en) | 2023-07-04 | 2023-07-04 | Lithium battery acceleration parameter identification method and system and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116819341B (en) |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110060539A1 (en) * | 2008-03-28 | 2011-03-10 | Antonio Sciarretta | Method of estimating the non-measurable characteristics of an electrochemical system |
| CN108445408A (en) * | 2018-03-20 | 2018-08-24 | 重庆大学 | A kind of total temperature SOC methods of estimation based on parameter Estimation OCV |
| US20190041464A1 (en) * | 2017-08-02 | 2019-02-07 | Total Solar International | Method and apparatus for identifying a battery model |
| CN109884550A (en) * | 2019-04-01 | 2019-06-14 | 北京理工大学 | A kind of identification of electrokinetic cell system on-line parameter and retrogressive method |
| CN112883632A (en) * | 2021-01-13 | 2021-06-01 | 合肥工业大学 | Lithium battery equivalent circuit model parameter identification method based on improved ant colony algorithm |
| US20220091655A1 (en) * | 2020-09-22 | 2022-03-24 | Dell Products L.P. | System and method of estimating time remaining for an information handling system to operate on electrical power provided by one or more batteries |
| CN114280493A (en) * | 2021-12-14 | 2022-04-05 | 哈尔滨工业大学 | Battery internal health state diagnosis method and system based on simplified P2D model |
| US20220123377A1 (en) * | 2020-10-19 | 2022-04-21 | Massachusetts Institute Of Technology | Convection-enhanced electrochemical energy storage devices and related methods |
| CN114609526A (en) * | 2022-05-11 | 2022-06-10 | 长城汽车股份有限公司 | Battery SOC estimation method and device, electronic equipment and vehicle |
| CN115392139A (en) * | 2022-10-27 | 2022-11-25 | 西北工业大学 | Fuel cell parameter identification method |
| CN115598539A (en) * | 2022-10-24 | 2023-01-13 | 中国科学院电工研究所（Cn） | Battery parameter identification method and system, electronic equipment and storage medium |
| US20230100216A1 (en) * | 2021-09-29 | 2023-03-30 | Samsung Electronics Co., Ltd. | Method and device with battery model optimization |
| CN116148680A (en) * | 2023-04-20 | 2023-05-23 | 上海玫克生储能科技有限公司 | Battery parameter identification method, device, computer equipment and storage medium |
| CN116224382A (en) * | 2023-04-19 | 2023-06-06 | 北京航空航天大学 | Post-processing method for weakening GPS (Global positioning System) related LOD (LOD) system errors |
| CN116359735A (en) * | 2021-12-28 | 2023-06-30 | 北京罗克维尔斯科技有限公司 | Battery parameter identification method, device, equipment and storage medium |
-
2023
- 2023-07-04 CN CN202310814146.9A patent/CN116819341B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110060539A1 (en) * | 2008-03-28 | 2011-03-10 | Antonio Sciarretta | Method of estimating the non-measurable characteristics of an electrochemical system |
| US20190041464A1 (en) * | 2017-08-02 | 2019-02-07 | Total Solar International | Method and apparatus for identifying a battery model |
| CN108445408A (en) * | 2018-03-20 | 2018-08-24 | 重庆大学 | A kind of total temperature SOC methods of estimation based on parameter Estimation OCV |
| CN109884550A (en) * | 2019-04-01 | 2019-06-14 | 北京理工大学 | A kind of identification of electrokinetic cell system on-line parameter and retrogressive method |
| US20220091655A1 (en) * | 2020-09-22 | 2022-03-24 | Dell Products L.P. | System and method of estimating time remaining for an information handling system to operate on electrical power provided by one or more batteries |
| US20220123377A1 (en) * | 2020-10-19 | 2022-04-21 | Massachusetts Institute Of Technology | Convection-enhanced electrochemical energy storage devices and related methods |
| CN112883632A (en) * | 2021-01-13 | 2021-06-01 | 合肥工业大学 | Lithium battery equivalent circuit model parameter identification method based on improved ant colony algorithm |
| US20230100216A1 (en) * | 2021-09-29 | 2023-03-30 | Samsung Electronics Co., Ltd. | Method and device with battery model optimization |
| CN114280493A (en) * | 2021-12-14 | 2022-04-05 | 哈尔滨工业大学 | Battery internal health state diagnosis method and system based on simplified P2D model |
| CN116359735A (en) * | 2021-12-28 | 2023-06-30 | 北京罗克维尔斯科技有限公司 | Battery parameter identification method, device, equipment and storage medium |
| CN114609526A (en) * | 2022-05-11 | 2022-06-10 | 长城汽车股份有限公司 | Battery SOC estimation method and device, electronic equipment and vehicle |
| CN115598539A (en) * | 2022-10-24 | 2023-01-13 | 中国科学院电工研究所（Cn） | Battery parameter identification method and system, electronic equipment and storage medium |
| CN115392139A (en) * | 2022-10-27 | 2022-11-25 | 西北工业大学 | Fuel cell parameter identification method |
| CN116224382A (en) * | 2023-04-19 | 2023-06-06 | 北京航空航天大学 | Post-processing method for weakening GPS (Global positioning System) related LOD (LOD) system errors |
| CN116148680A (en) * | 2023-04-20 | 2023-05-23 | 上海玫克生储能科技有限公司 | Battery parameter identification method, device, computer equipment and storage medium |
Non-Patent Citations (4)
| Title |
|---|
| JIA YANBO 等: "Parameter Identification Method for a Fractional-Order Model of Lithium-Ion Batteries Considering Electrolyte-Phase Diffusion", BATTERIES, no. 8, 14 August 2022 (2022-08-14), pages 90 * |
| 司伟 等: "储能蓄电池参数辨识研究进展", 飞航导弹, no. 07, 15 July 2018 (2018-07-15), pages 71 - 75 * |
| 康鑫 等: "基于锂离子电池简化电化学模型的参数辨识", 储能科学与技术, no. 03, 31 May 2020 (2020-05-31), pages 969 - 978 * |
| 葛亚明 等: "锂离子电池模型参数辨识方法和放电模拟", 兵器装备工程学报, no. 06, 25 June 2018 (2018-06-25), pages 188 - 191 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116819341B (en) | 2024-11-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2022253038A1 (en) | Method and system for predicting state of health of lithium battery on basis of elastic network, and device and medium | |
| CN115114878B (en) | Method and device for online prediction of battery life of energy storage power station and storage medium | |
| CN106129508B (en) | A kind of charging method and device | |
| CN107132490B (en) | Method for estimating state of charge of lithium battery pack | |
| CN211702078U (en) | Battery manager based on cloud-end mixed state estimation | |
| CN112883531A (en) | Lithium ion battery data processing method, computer device and storage medium | |
| CN114624604A (en) | Battery life prediction model generation method, battery life prediction model prediction method, battery life prediction device, and electronic equipment | |
| CN115395613A (en) | Battery charging management method and device, electronic equipment and storage medium | |
| CN112332493B (en) | Lithium battery charging current determination method, device, equipment and storage medium | |
| CN116819341B (en) | Lithium battery acceleration parameter identification method and system and electronic equipment | |
| Wang et al. | SOC Estimation of Lithium-ion Batteries Based on Extended Kalman Filter | |
| CN116559685A (en) | Method for characterizing reactivity of chemical system, detection device and readable storage medium | |
| CN114238424B (en) | Low-energy-consumption rapid screening method for high-capacity energy storage battery pack | |
| CN114089204B (en) | Battery capacity diving inflection point prediction method and device | |
| CN115986881A (en) | Constant-voltage control method and device for lithium battery series-connection component-capacity detection equipment | |
| CN115951225A (en) | Battery equalization optimization capacity estimation method and device | |
| Romero et al. | Fast charge of Li-ion batteries using a two-layer distributed MPC with electro-chemical and thermal constraints | |
| CN112240987A (en) | Power testing method, system, computer device and storage medium | |
| US20240353497A1 (en) | Charge Protocol Inspection Apparatus and Operating Method Thereof | |
| CN116840700B (en) | Method, device, equipment and storage medium for monitoring battery state in real time | |
| JP7521154B1 (en) | Secondary battery system and method for predicting abnormal deterioration of secondary battery | |
| CN113030750B (en) | Method and device for detecting residual use parameters of lithium battery | |
| Blaifi | Energy Storage and Photovoltaic Systems | |
| Zhang et al. | A Physics-Informed Hybrid Multitask Learning for Lithium-Ion Battery Full-Life Aging Estimation at Early Lifetime | |
| CN115524613A (en) | Internal short circuit fault diagnosis method, device and system of energy storage battery and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Country or region after: China Address after: Room 1101, No. 2, Lane 288, Qianfan Road, Xinqiao Town, Songjiang District, Shanghai 201612 Applicant after: Shanghai Meikesheng Energy Technology Co.,Ltd. Address before: Room 1101, No. 2, Lane 288, Qianfan Road, Xinqiao Town, Songjiang District, Shanghai Applicant before: Shanghai Meike Energy Storage Technology Co.,Ltd. Country or region before: China |
|
| GR01 | Patent grant |