CN112798962B - Battery hysteresis model training method, method and device for estimating battery SOC - Google Patents
Battery hysteresis model training method, method and device for estimating battery SOC Download PDFInfo
- Publication number
- CN112798962B CN112798962B CN202110277793.1A CN202110277793A CN112798962B CN 112798962 B CN112798962 B CN 112798962B CN 202110277793 A CN202110277793 A CN 202110277793A CN 112798962 B CN112798962 B CN 112798962B
- Authority
- CN
- China
- Prior art keywords
- battery
- time
- hysteresis model
- circuit voltage
- sample
- 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.)
- Active
Links
- 238000012549 training Methods 0.000 title claims abstract description 90
- 238000000034 method Methods 0.000 title claims abstract description 74
- 230000010354 integration Effects 0.000 claims abstract description 47
- 238000012360 testing method Methods 0.000 claims description 18
- 230000008569 process Effects 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 8
- 238000004146 energy storage Methods 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 description 7
- 238000007599 discharging Methods 0.000 description 6
- 238000002474 experimental method Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000002790 cross-validation Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 239000002253 acid Substances 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- OJIJEKBXJYRIBZ-UHFFFAOYSA-N cadmium nickel Chemical compound [Ni].[Cd] OJIJEKBXJYRIBZ-UHFFFAOYSA-N 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011038 discontinuous diafiltration by volume reduction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- 229910001416 lithium ion Inorganic materials 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
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 229920000642 polymer Polymers 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
- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
Abstract
The application provides a battery hysteresis model training method, which comprises the following steps: collecting working current I k of the battery from the time t 0 to the time t k on line; a current integration quantity Q is calculated from the operating current (t k), wherein,If the battery meets the open-circuit voltage acquisition condition at the time t k according to the working current, acquiring the open-circuit voltage OCV (t k) of the battery at the time t k; collecting the temperature T bat(tk of the battery at the time T k); constructing a sample set from the current integration quantity Q (T k), the temperature T bat(tk), and the open circuit voltage OCV (T k); and training the battery hysteresis model according to the sample set. The application also provides a method and a device for estimating the SOC of the battery. The application can obtain the open-circuit voltage of the battery on line according to the current and the temperature acquired in real time.
Description
Technical Field
The application relates to the technical field of batteries, in particular to a battery hysteresis model training method, a battery SOC estimation method and a battery hysteresis model training device.
Background
The State of Charge (SOC) of the battery is estimated as one of the core functions of the battery management system. The accurate SOC estimation can ensure the safe and reliable operation of the battery system, optimize the battery system, and provide basis for energy management and safety management of electric devices (such as electric automobiles) and the like. The conventional SOC estimation method is often obtained by using a correspondence relationship between the SOC and an open circuit voltage (Open Circuit Voltage, OCV). However, the open circuit voltage and OCV in the battery do not correspond exactly one to one, but there is a hysteresis relationship. Therefore, in estimating the SOC of the battery using the open circuit voltage, it is necessary to consider the influence of the hysteresis characteristic on the OC of the battery. The existing modeling method of the battery open-circuit voltage hysteresis characteristic introduces more simplification, so that the modeling precision of a hysteresis model is low, and the SOC estimation is influenced. Therefore, meeting the precision requirement and obtaining the battery SOC in real time is always a problem to be solved in the industry.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a battery hysteresis model training method, a method and a device for estimating the SOC of a battery, which can obtain the open circuit voltage of the battery on line according to the current and the temperature acquired in real time.
The application provides a battery hysteresis model training method, which is used for collecting working current I k of a battery from the time t 0 to the time t k on line, wherein k is more than 0; calculating a current integration quantity Q (t k) from the time t 0 to the time t k according to the operating current,If the battery meets the open-circuit voltage acquisition condition at the time t k according to the working current, acquiring the open-circuit voltage OCV (t k) of the battery at the time t k; collecting the temperature T bat(tk of the battery at the time T k); constructing a sample set from the current integration quantity Q (T k), the temperature T bat(tk), and the open circuit voltage OCV (T k); and training the battery hysteresis model according to the sample set.
According to some embodiments of the application, the sample set comprises a positive sample set and a negative sample set, constructing a sample set from the current integration quantity Q (T k), the temperature T bat(tk), and the open circuit voltage OCV (T k) comprises: acquiring a current integration quantity Q (T k), a temperature T bat(tk) and an open circuit voltage OCV (T k) of positive samples in the positive sample set, and a current integration quantity Q (T k), a temperature T bat(tk) and an open circuit voltage OCV (T k) of negative samples in the negative sample set; the class data is marked with the current integration quantity Q (T k), the temperature T bat(tk) and the open circuit voltage OCV (T k) of the positive sample, so that the class label is carried by the current integration quantity Q (T k), the temperature T bat(tk) and the open circuit voltage OCV (T k) of the positive sample.
According to some embodiments of the application, the training the battery hysteresis model from the sample set comprises: generating a sample training set and a sample testing set according to the sample set; training the battery hysteresis model according to the sample training set, and verifying the accuracy of the trained battery hysteresis model according to the sample testing set; and if the accuracy is greater than or equal to the preset accuracy, ending the training process of the battery hysteresis model.
According to some embodiments of the application, the training the battery hysteresis model from the sample set includes further comprising: and if the accuracy is smaller than the preset accuracy, increasing the number of the sample training sets to retrain the battery hysteresis model until the accuracy is greater than or equal to the preset accuracy.
According to some embodiments of the application, the generating a sample training set and a sample testing set from the sample set comprises: randomly selecting a first preset number of sample training sets from the generated sample training sets for training; a second preset number of sample test sets are randomly selected among the generated sample test sets for verification.
According to some embodiments of the application, if the operating current remains less than a first preset value for a first preset period of time, it is determined that the battery satisfies an open circuit voltage collection condition at time t k, where a starting time point of the first preset period of time is t k-i, and an ending time point is t k, where 0 < i < k.
According to some embodiments of the application, the method further comprises: if the working current is greater than or equal to a second preset value in a second preset time period, ending the collection of the open-circuit voltage of the battery; and continuously collecting the working current of the battery, and updating historical data according to the collected working current.
According to some embodiments of the application, the first preset value is related to at least one of battery capacity or battery temperature.
According to some embodiments of the application, the battery hysteresis model includes an input layer, an hidden layer, and an output layer.
The application provides a method for estimating battery SOC by using a hysteresis model trained by the battery hysteresis model training method, which comprises the following steps: collecting working current I k of the battery from the time t 0 to the time t k on line, wherein k is more than 0; calculating a current integration amount Q (t k) from the time t 0 to the time t k from the operating current; acquiring the temperature T bat(tk of the battery at the time T k); inputting the current integration quantity Q (T k) and the temperature T bat(tk) to the battery hysteresis model to obtain the open circuit voltage of the battery at the time T k; and inquiring the corresponding relation of the SOC-OCV according to the open-circuit voltage to obtain the charge state of the battery.
According to some embodiments of the application, the SOC-OCV correspondence is obtained by combining an ampere-hour integration method and an open-circuit voltage method.
An embodiment of the present application provides an electric device, which includes a memory and a processor, where the processor is configured to implement a battery hysteresis model training method as described above or implement a method for estimating a battery SOC as described above when executing a computer program stored in the memory.
According to some embodiments of the application, the power utilization device comprises an energy storage device, or more than two electric vehicles, or an unmanned aerial vehicle, or an electric tool.
According to the embodiment of the application, the working current data of the battery is collected on line, and the working current data is abstracted into a current integration quantity. And training the current integral quantity, the battery temperature and the open-circuit voltage acquired at the current moment as sample data to obtain the battery hysteresis model. In the use process, the current integral quantity can be calculated according to the current collected in real time, the current integral quantity and the collected battery temperature are taken as input quantities together, the corresponding open-circuit voltage is obtained through the trained battery hysteresis model, and the nuclear power state of the battery is obtained according to the open-circuit voltage. The method does not carry out off-line experiments in the modeling process, and the input quantity of the battery hysteresis model is simple, so that the requirements on the computing capacity and the storage capacity of the power utilization device are low. The method has the advantages of low experimental quantity, low calculation quantity and low storage quantity while the accuracy is acceptable.
Drawings
Fig. 1 is a schematic structural view of an electric device according to an embodiment of the present application.
FIG. 2 is a flow chart of a battery hysteresis model training method according to one embodiment of the application.
FIG. 3 is a schematic diagram of a battery hysteresis model according to one embodiment of the application.
Fig. 4 is a flowchart of a method of estimating a battery SOC according to an embodiment of the present application.
Description of the main reference signs
Electric device 1
Memory 11
Processor 12
Battery 13
Acquisition device 14
Timer 15
The present application will be described in further detail with reference to the following detailed description and the accompanying drawings.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the present application.
Referring to fig. 1, fig. 1 is a schematic diagram of an electric device according to an embodiment of the application. Referring to fig. 1, the power utilization device 1 includes, but is not limited to, a memory 11, at least one processor 12, a battery 13, a collecting device 14, and a timer 15, and these components may be connected by a bus or may be directly connected.
In one embodiment, the battery 13 is a rechargeable battery for providing electrical energy to the power consumer 1. For example, the battery 13 may be a lead-acid battery, a nickel-cadmium battery, a nickel-hydrogen battery, a lithium ion battery, a lithium polymer battery, a lithium iron phosphate battery, or the like. The Battery 13 is logically connected to the processor 12 through a Battery management system (Battery MANAGEMENT SYSTEM, BMS), so that functions of charging, discharging, etc. are realized through the Battery management system. The battery management system CAN be in communication connection with the energy storage inverter (Power Conversion System, PCS) through CAN or RS 485. The battery 13 includes a cell (not shown) that can be recharged repeatedly in a recyclable manner.
In this embodiment, the acquisition device 14 includes an analog-to-digital converter for acquiring the voltage of the battery 13 and the current of the battery 13 and a thermometer for acquiring the temperature of the battery 13. It is understood that the collector 14 may be other voltage collectors and current collectors. The timer 15 is used for recording the operating time of the battery 13. It will be appreciated that the power utilization device 1 may also comprise other devices, such as pressure sensors, light sensors, gyroscopes, hygrometers, infrared sensors, etc.
Fig. 1 is only an illustration of the power utilization device 1. In other embodiments, the power consuming device 1 may also include more or fewer elements, or have a different arrangement of elements. The power utilization device 1 may be an energy storage product, or an electric tool, or a cleaning tool, a single-or more than 2-wheel electric vehicle, an unmanned aerial vehicle, or any other suitable rechargeable device.
Although not shown, the power utilization apparatus 1 may further include a wireless fidelity (WIRELESS FIDELITY, WIFI) unit, a bluetooth unit, a speaker, and other components, which are not described in detail herein.
Referring to fig. 2, fig. 2 is a flowchart of a battery hysteresis model training method according to an embodiment of the application. The battery hysteresis model training method is used for establishing a battery hysteresis model, and the state of charge of the battery 13 can be estimated on line through the battery hysteresis model, and the battery hysteresis model training method can comprise the following steps:
Step S21: the working current I k of the battery 13 from the time t 0 to the time t k is collected online, wherein k is more than 0.
In this embodiment, the operating current of the battery 13 may be collected by the collecting device 14. For example, the acquisition device 14 is a hall current sensor.
For example, the operation current at time t 0 is collected to be I 0,t1, I 1,t2, I 2,tk, and I k. A current information sequence may be generated based on the collected operating current and time information, the current information sequence being { I 0,I1,I2...Ik }.
Step S22: and calculating a current integration quantity Q (t k) from the time t 0 to the time t k according to the working current.
In order to solve the problems that in the existing method, the time consumption of experiments is high due to the fact that experimental data are required to be acquired offline, or the calculation capability requirement is high due to the fact that a large amount of recent historical current data train the battery hysteresis model online. The battery hysteresis model training method provided by the application can abstract the working current data acquired in real time into a parameter (such as a current integral quantity) and use the parameter and the battery temperature acquired at the current moment together as the data for constructing a training sample so as to train the battery hysteresis model. The technical effects of on-line calculation and data volume reduction are realized.
In this embodiment, the calculation formula of the current integration value Q (t k) is: I.e. by calculating the amount of power of the battery from the time t 0 to the time t k as sample data. The discharge direction of the operating current I k is assumed to be positive.
Step S23: it is determined whether the battery 13 satisfies an open circuit voltage collection condition at time t k. If it is determined that the battery meets the open circuit voltage acquisition condition at time t k, the flow proceeds to step S24; if it is determined that the battery does not meet the open circuit voltage collection condition at time t k, the flow returns to step S21.
In this embodiment, it is determined whether the battery 13 satisfies the open circuit voltage acquisition condition at time t k by determining whether the operating current of the battery remains less than a first preset value for a first preset period of time Δt1. Wherein, the starting time point of Deltat 1 is t i, the ending time point is t k, and 0< i < k. If the working current of the battery 13 is kept smaller than the first preset value in the first preset time period deltat 1, it is determined that the battery meets the open circuit voltage acquisition condition at time t k, and the flow proceeds to step S24; if the working current of the battery 13 is not kept smaller than the first preset value in the first preset time period, it is determined that the battery does not meet the open circuit voltage acquisition condition at time t k, and the flow returns to step S21.
In this embodiment, whether the battery 13 is in a stationary state is determined by determining whether the operating current of the battery 13 is kept less than a first preset value for a first preset period of time, so that the open circuit voltage collection condition is satisfied. Normally, after the battery 13 is charged and discharged, the battery 13 is left to stand, and the open circuit voltage of the battery 13 can be collected during the process of leaving the battery 13 to stand. While the electrical flow of the battery 13 is maintained at a small value (e.g., zero) during the rest of the battery 13. In this embodiment, when the operating current of the battery 13 is set to be smaller than the first preset value in the first preset period, the battery 13 enters a rest state, and the collection condition of the open circuit voltage of the battery 13 is satisfied. In this embodiment, the first preset value is related to at least one of a battery capacity or a battery temperature.
Step S24: the open circuit voltage OCV of the battery at the time t k is collected (t k).
In this embodiment, if the operating current of the battery 13 is kept smaller than the first preset value in the first preset period, it is determined that the battery 13 is in the rest state, and the open circuit voltage of the battery 13 at the time t k may be collected.
Step S25: the temperature T bat(tk of the battery at time T k is acquired).
In the present embodiment, the temperature T bat(tk of the battery at time T k is detected by the detection device 14. Because the temperature of the battery has a great influence on the performance of the battery, the temperature of the battery is used as sample data for training the battery hysteresis model, and a more accurate open-circuit voltage value can be obtained.
Step S26: a sample set is constructed from the current integration amount Q (T k), the temperature T bat(tk), and the open circuit voltage OCV (T k), wherein the sample set includes a positive sample set and a negative sample set.
In this embodiment, in order to train the battery hysteresis model, training data required by the battery hysteresis model needs to be constructed first, and then a sample set needs to be constructed according to the training data. The training bit includes the current integration amount Q (T k) of the battery 13, the temperature T bat(tk), and the open circuit voltage OCV (T k).
Specifically, the constructing a sample set from the current integration amount Q (T k) of the battery 13, the temperature T bat(tk), and the open circuit voltage OCV (T k) includes: acquiring a current integration quantity Q (T k) of a positive sample in the positive sample set, the temperature T bat(tk), and the open circuit voltage OCV (T k), and a current integration quantity Q (T k) of a negative sample in the negative sample set, the temperature T bat(tk), and the open circuit voltage OCV (T k); class data is annotated with the current integration quantity Q (T k), the temperature T bat(tk), and the open circuit voltage OCV (T k) of the positive sample such that the current integration quantity Q (T k), the temperature T bat(tk), and the open circuit voltage OCV (T k) of the positive sample carry class labels. For example, open-circuit voltage data corresponding to the current integration amounts and temperatures at 500 different times are selected, and class data is labeled for each open-circuit voltage data. The open circuit voltage data tag corresponding to the current integration amount and the temperature at time t 0 may be "1", the open circuit voltage data tag corresponding to the current integration amount and the temperature at time t 1 may be "2", and the open circuit voltage data tag corresponding to the current integration amount and the temperature at time t 2 may be "3".
As an alternative embodiment, to make the battery hysteresis model more intelligent, the negative samples input to the battery hysteresis model can be identified after the current integral Q (T k) of the positive samples in the positive sample set, the temperature T bat(tk), the open circuit voltage OCV (T k), and the current integral Q (T k), the temperature T bat(tk), and the open circuit voltage OCV (T k) of the negative samples in the negative sample set are obtained; the class data is marked with the current integration quantity Q (T k), the temperature T bat(tk) and the open circuit voltage OCV (T k) of the negative sample, so that the class label is carried by the current integration quantity Q (T k), the temperature T bat(tk) and the open circuit voltage OCV (T k) of the negative sample. It can be understood that, for positive samples of the sample set, a class label is carried, and the positive samples can be identified according to the class label through the battery hysteresis model; and carrying class labels on the negative samples of the sample set, and identifying the negative samples according to the class labels through the battery hysteresis model. But generally, only the class label corresponding to the positive sample input into the battery hysteresis model needs to be identified, so that an accurate output result is obtained according to the battery hysteresis model.
Step S27: and training the battery hysteresis model according to the sample set.
In this embodiment, after acquiring training data and constructing the sample set according to the training data, the battery hysteresis model is trained according to the sample set.
Specifically, the training the battery hysteresis model according to the sample set includes:
(1) And generating a sample training set and a sample testing set according to the sample set.
In this embodiment, the idea of cross-validation (CrossValidation) may be used to train the battery hysteresis model, and the constructed sample set may be divided into a sample training set and a sample testing set according to a suitable ratio. For example, a suitable division ratio is 6:4.
Further, if the total number of the divided sample training sets is still larger, that is, all the sample training sets are used for training the battery hysteresis model, the cost of searching the parameters corresponding to the battery hysteresis model is larger. Thus, the generating of the sample training set may further comprise: a first preset number of sample training sets are randomly selected from the generated sample training sets for training.
In the preferred embodiment, in order to increase the randomness of the training sample training set, a random number generation algorithm may be used for random selection.
In the preferred embodiment, the first preset number may be a preset fixed value, for example, 500, that is, 500 samples are randomly selected from the generated sample training set for training of the battery hysteresis model. The first preset number may also be a preset scale value, for example, 1/10, i.e. 1/10 of the samples in the generated sample training set are randomly selected for training of the battery hysteresis model.
(2) And training the battery hysteresis model according to the sample training set, and verifying the accuracy of the trained battery hysteresis model according to the sample testing set.
In this embodiment, the sample training set is used to train the battery hysteresis model, and the sample test set is used to test the performance of the trained battery hysteresis model.
In this embodiment, the training samples in the sample training set are first distributed into different folders. For example, the training samples of the current integration amount and the temperature at time t 0 are distributed to the first folder, the training samples of the current integration amount and the temperature at time t 1 are distributed to the second folder, and the training samples of the current integration amount and the temperature at time t 2 are distributed to the third folder. And then respectively extracting training samples with a first preset proportion (for example, 70%) from different folders as total training samples to train the battery hysteresis model, respectively taking the remaining training samples with a second preset proportion (for example, 30%) from different folders as total testing samples to carry out accuracy verification on the trained battery hysteresis model.
In this embodiment, as shown in fig. 3, the battery hysteresis model includes an input layer, an hidden layer, and an output layer. During the charge-discharge cycle of the subsequent battery 13, the integrated current values and temperatures at different moments are input into the battery hysteresis model, and the corresponding open circuit voltage can be output.
(3) And confirming whether the accuracy is larger than or equal to a preset accuracy.
In this embodiment, if the accuracy of the test is higher, the performance of the trained battery hysteresis model is better; if the accuracy of the test is lower, the trained battery hysteresis model is poorer in performance. And confirming whether to train a battery hysteresis model with good performance by confirming whether the accuracy is larger than or equal to the preset accuracy. If the accuracy is greater than or equal to the preset accuracy, confirming that the trained battery hysteresis model has good performance, and entering a step (4) in the flow; and (5) if the accuracy is smaller than the preset accuracy, confirming that the trained battery hysteresis model is poor in performance, and entering a step (5) in the flow.
(4) And ending the training process of the battery hysteresis model.
In this embodiment, if the accuracy is greater than or equal to the preset accuracy, it is determined that the battery hysteresis model trained is good in performance, and the training process of the battery hysteresis model is finished.
(5) And increasing the number of the sample training sets to retrain the battery hysteresis model until the accuracy is greater than or equal to the preset accuracy.
In this embodiment, if the accuracy is less than the preset accuracy, it is determined that the trained battery hysteresis model has poor performance and does not meet the requirement, and the number of the sample training sets needs to be increased to retrain the battery hysteresis model until the accuracy is greater than or equal to the preset accuracy, so as to obtain the battery hysteresis model meeting the requirement.
In this embodiment, the battery hysteresis model training method further includes:
Determining whether the working current of the battery 13 is greater than or equal to a second preset value in a second preset time period delta t2, and if the working current of the battery 13 is greater than or equal to the second preset value in the second preset time period delta t2, ending collecting the open-circuit voltage of the battery 13; the operation current of the battery 13 is continuously collected, and the history data is updated according to the collected operation current. If the operating current of the battery 13 is kept smaller than the second preset value within the second preset time period Δt2, continuously judging whether the current of the battery 13 is greater than or equal to the second preset value within the third preset time period Δt3.
It should be noted that the second preset time period is a time after the first preset time period, the third preset time period is a time after the second preset time period, and so on. The starting time point of delta t2 is t j, the ending time point is t n, and k is less than j and less than n; the starting time point of Deltat 3 is t l, the ending time point is t m, and n < l < m.
In the present embodiment, by determining whether the operating current of the battery 13 is greater than or equal to the second preset value within the second preset period of time, it is possible to determine whether the open circuit voltage acquisition exit condition of the battery 13 is satisfied. If the working current of the battery 13 is greater than or equal to a second preset value in a second preset time period, determining that the battery 13 enters a charging and discharging process, and ending collecting the open-circuit voltage of the battery 13; the operation current of the battery 13 is continuously collected, and the update history data is calculated according to the collected operation current.
The second preset value is a larger current value, and may be used to determine that the battery 13 enters the charging and discharging process.
Referring to fig. 4, fig. 4 is a flowchart of a method for estimating a state of charge of a battery 13 according to an embodiment of the application. The method for estimating the state of charge of the battery 13 specifically includes the following steps, the order of the steps in the flowchart may be changed according to different needs, and some steps may be omitted.
Step S31: the working current I k of the battery 13 from the time t 0 to the time t k is collected online, wherein k is more than 0.
In the present embodiment, the working current of the battery 13 is collected in real time by the collecting device 14 during the cyclic charge and discharge of the battery 13.
Step S32: and calculating a current integration quantity Q (t k) from the time t 0 to the time t k according to the working current. In this embodiment, the calculation formula of the current integration value Q (t k) is: I.e. by calculating the amount of power of the battery from the time t 0 to the time t k as sample data. The discharge direction of the operating current I k is assumed to be positive.
Step S33: the temperature T bat(tk of the battery at time T k is acquired).
Step S34: and inputting the current integration quantity Q (T k) and the temperature T bat(tk) to the battery hysteresis model to obtain the open circuit voltage of the battery at the time T k.
In the present embodiment, the current integration amount Q (T k) and the temperature T bat(tk are input to the trained battery hysteresis model according to the open circuit voltage OCV (T k) at the time of the current output T k.
Step S35: and inquiring the corresponding relation of the SOC-OCV according to the open-circuit voltage to obtain the charge state of the battery 13.
In the present embodiment, the power consumption device 1 stores therein the SOC-OCV correspondence relation in advance. It should be noted that, after the battery system is determined, the SOC-OCV correspondence relationship of the battery is generally fixed. Even if the battery is subjected to a plurality of cycles of charge and discharge, the corresponding relationship between SOC and OCV will not change.
Specifically, the state of charge (SOC) -Open Circuit Voltage (OCV) correspondence of the battery may be obtained by:
1) Taking a battery, charging the battery to a full charge state, and discharging the battery to a discharge state by using a first preset current; in this embodiment, the first preset current is a small-rate current, for example, 0.01C, and may be other currents.
2) And recording the voltage and capacity change of the battery in the charge and discharge process.
3) And acquiring the charge state of the battery in the discharging process. For example, the discharge maximum capacity of the battery is taken as the full capacity of the battery, and the capacity value of the battery, which changes with time during the discharge process, is divided by the full capacity to obtain the charge state of the battery during the discharge process.
4) And respectively establishing the corresponding relation of the battery voltages of the batteries in different charge states in the discharging process to obtain the corresponding relation of the SOC-OCV of the batteries.
In another embodiment, the SOC-OCV correspondence may be obtained by combining an ampere-hour integration method and an open-circuit voltage method.
The application provides a battery hysteresis model training method with low experimental quantity and low calculation quantity on the premise of acceptable precision. The method is characterized in that working current data of a battery are collected on line, and the working current data are abstracted into current integration values. And training the current integral quantity, the battery temperature and the open-circuit voltage acquired at the current moment as sample data to obtain the battery hysteresis model. In the use process, the current integral quantity can be calculated according to the current collected in real time, the current integral quantity and the collected battery temperature are taken as input quantities together, the corresponding open-circuit voltage is obtained through the trained battery hysteresis model, and the nuclear power state of the battery is obtained according to the open-circuit voltage. The method does not carry out off-line experiments in the modeling process, and the input quantity of the battery hysteresis model is simple, so that the requirements on the computing capacity and the storage capacity of the power utilization device are low. The method has the advantages of low experimental quantity, low calculation quantity and low storage quantity while the accuracy is acceptable.
With continued reference to fig. 1, in this embodiment, the memory 11 may be an internal memory of the power consumption device, that is, a memory built in the power consumption device. In other embodiments, the memory 11 may also be an external memory of the electric device, i.e. a memory external to the electric device.
In some embodiments, the memory 11 is used to store program codes and various data, and to implement high-speed, automatic access to programs or data during operation of the power-consuming device.
The memory 11 may include random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card (SMART MEDIA CARD, SMC), secure Digital (SD) card, flash memory card (FLASH CARD), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
In one embodiment, the Processor 12 may be a Central processing unit (Central ProcessingUnit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application specific integrated circuits (Application Specific IntegratedCircuit, ASIC), field-Programmable gate arrays (Field-Programmable GATEARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any other conventional processor or the like.
The program code and various data in said memory 11 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a separate product. Based on such understanding, the present application may implement all or part of the procedures in the method of the above embodiments, for example, implement the steps in the method of detecting a short circuit in a battery, or may be implemented by instructing relevant hardware through a computer program, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of the method embodiments described above when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), or the like.
It will be appreciated that the above-described division of modules into a logical function division may be implemented in other ways. In addition, each functional module in the embodiments of the present application may be integrated in the same processing unit, or each module may exist alone physically, or two or more modules may be integrated in the same unit. The integrated modules may be implemented in hardware or in hardware plus software functional modules.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present application without departing from the spirit and scope of the technical solution of the present application.
Claims (11)
1. A battery hysteresis model training method, characterized in that the method comprises:
Collecting working current I k of the battery from time t 0 to time t k on line, wherein k is more than 0;
Calculating a current integration quantity Q (t k) from the time t 0 to the time t k according to the operating current,
If the battery meets the open-circuit voltage acquisition condition at the time t k according to the working current, acquiring the open-circuit voltage OCV (t k) of the battery at the time t k;
Acquiring the temperature T bat(tk of the battery at the time T k);
Constructing a sample set from the current integration quantity Q (T k), the temperature T bat(tk), and the open circuit voltage OCV (T k);
training the battery hysteresis model according to the sample set;
If the working current is kept smaller than a first preset value in a first preset time period, determining that the battery meets an open-circuit voltage acquisition condition at the time t k, wherein the starting time point of the first preset time period is t k-i, and the ending time point is t k, 0< i < k;
if the working current is greater than or equal to a second preset value in a second preset time period, ending the collection of the open-circuit voltage of the battery; and
And continuously collecting the working current of the battery, and updating historical data according to the collected working current.
2. The battery hysteresis model training method of claim 1, wherein the sample set comprises a positive sample set and a negative sample set, constructing a sample set from the current integration quantity Q (T k), the temperature T bat(tk), and the open circuit voltage OCV (T k) comprises:
Acquiring a current integration quantity Q (T k), a temperature T bat(tk) and an open circuit voltage OCV (T k) of positive samples in the positive sample set, and a current integration quantity Q (T k), a temperature T bat(tk) and an open circuit voltage OCV (T k) of negative samples in the negative sample set;
The class data is marked with the current integration quantity Q (T k), the temperature T bat(tk) and the open circuit voltage OCV (T k) of the positive sample, so that the class label is carried by the current integration quantity Q (T k), the temperature T bat(tk) and the open circuit voltage OCV (T k) of the positive sample.
3. The battery hysteresis model training method of claim 2, wherein said training said battery hysteresis model from said sample set comprises:
Generating a sample training set and a sample testing set according to the sample set;
training the battery hysteresis model according to the sample training set, and verifying the accuracy of the trained battery hysteresis model according to the sample testing set; and
And if the accuracy is greater than or equal to the preset accuracy, ending the training process of the battery hysteresis model.
4. The battery hysteresis model training method of claim 3, wherein said training said battery hysteresis model from said sample set further comprises:
And if the accuracy is smaller than the preset accuracy, increasing the number of the sample training sets to retrain the battery hysteresis model until the accuracy is greater than or equal to the preset accuracy.
5. The battery hysteresis model training method of claim 3, wherein said generating a sample training set and a sample testing set from said sample set comprises:
Randomly selecting a first preset number of sample training sets from the generated sample training sets for training;
a second preset number of sample test sets are randomly selected among the generated sample test sets for verification.
6. The battery hysteresis model training method of claim 1, wherein the first preset value is related to at least one of battery capacity or battery temperature.
7. The battery hysteresis model training method of claim 1, wherein the battery hysteresis model comprises an input layer, an hidden layer, and an output layer.
8. A method of estimating battery SOC from a hysteresis model trained in accordance with the method of any of claims 1-7, the method comprising:
Collecting working current I k of a battery from the time t 0 to the time t k on line, wherein k is more than 0;
calculating a current integration amount Q (t k) from the time t 0 to the time t k from the operating current;
Acquiring the temperature T bat(tk of the battery at the time T k);
inputting the current integration quantity Q (T k) and the temperature T bat(tk) to the battery hysteresis model to obtain the open circuit voltage of the battery at the time T k; and
And inquiring the corresponding relation of the SOC-OCV according to the open-circuit voltage to obtain the charge state of the battery.
9. The method of estimating a battery SOC of claim 8, wherein the SOC-OCV correspondence is obtained by combining an ampere-hour integration method and an open circuit voltage method.
10. An electrical device, the electrical device comprising:
A memory; and
A processor for implementing the battery hysteresis model training method according to any one of claims 1 to 7 or the method of estimating battery SOC according to any one of claims 8 to 9 when executing the computer program stored in the memory.
11. The electrical device of claim 10, wherein the electrical device comprises an energy storage device, or more than two electric vehicles, or an unmanned aerial vehicle, or an electric tool.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110277793.1A CN112798962B (en) | 2021-03-15 | 2021-03-15 | Battery hysteresis model training method, method and device for estimating battery SOC |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110277793.1A CN112798962B (en) | 2021-03-15 | 2021-03-15 | Battery hysteresis model training method, method and device for estimating battery SOC |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112798962A CN112798962A (en) | 2021-05-14 |
| CN112798962B true CN112798962B (en) | 2024-04-30 |
Family
ID=75817063
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110277793.1A Active CN112798962B (en) | 2021-03-15 | 2021-03-15 | Battery hysteresis model training method, method and device for estimating battery SOC |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112798962B (en) |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201293826Y (en) * | 2008-11-24 | 2009-08-19 | 南开大学 | Energy detector for wireless sensor network node |
| CN203037829U (en) * | 2013-01-23 | 2013-07-03 | 天津雅迪实业有限公司 | Electric car continuous mileage obtaining apparatus |
| CN103616647A (en) * | 2013-12-09 | 2014-03-05 | 天津大学 | Battery remaining capacity estimation method for electric car battery management system |
| CN103675701A (en) * | 2013-11-29 | 2014-03-26 | 宇龙计算机通信科技(深圳)有限公司 | Battery state-of-charge correction method and device |
| CN105203963A (en) * | 2015-09-11 | 2015-12-30 | 同济大学 | Charge state estimation method based on open-circuit voltage hysteretic characteristics |
| CN105699907A (en) * | 2016-01-28 | 2016-06-22 | 广州市香港科大霍英东研究院 | A battery SOC estimation method and system based on dynamic impedance correction |
| CN106646265A (en) * | 2017-01-22 | 2017-05-10 | 华南理工大学 | Method for estimating SOC of lithium battery |
| CN106918787A (en) * | 2017-03-20 | 2017-07-04 | 国网重庆市电力公司电力科学研究院 | A kind of electric automobile lithium battery residual charge evaluation method and device |
| CN107861073A (en) * | 2017-11-06 | 2018-03-30 | 合肥工业大学 | A kind of vehicle-mounted electric quantity of lead-acid storage battery On-line Estimation method based on CHVT models |
| CN109884528A (en) * | 2019-02-25 | 2019-06-14 | 江苏大学 | A kind of lithium ion battery open-circuit voltage evaluation method and system with temperature input |
| CN110045286A (en) * | 2019-03-21 | 2019-07-23 | 江苏大学 | A kind of battery open circuit voltage evaluation method and device based on air-or liquid-operated mechanical model |
| CN110333450A (en) * | 2019-04-30 | 2019-10-15 | 蜂巢能源科技有限公司 | Battery open circuit voltage predictor method and system |
| CN110609232A (en) * | 2019-03-29 | 2019-12-24 | 深圳猛犸电动科技有限公司 | Lithium ion battery SOC estimation method and device and terminal equipment |
| CN110850315A (en) * | 2019-11-29 | 2020-02-28 | 北京邮电大学 | Method and device for estimating state of charge of battery |
| CN111458646A (en) * | 2020-05-25 | 2020-07-28 | 安徽理工大学 | Lithium battery SOC estimation method based on PSO-RBF neural network |
| CN111537887A (en) * | 2020-04-27 | 2020-08-14 | 南京航空航天大学 | Hybrid power system battery open-circuit voltage model optimization method considering hysteresis characteristic |
-
2021
- 2021-03-15 CN CN202110277793.1A patent/CN112798962B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201293826Y (en) * | 2008-11-24 | 2009-08-19 | 南开大学 | Energy detector for wireless sensor network node |
| CN203037829U (en) * | 2013-01-23 | 2013-07-03 | 天津雅迪实业有限公司 | Electric car continuous mileage obtaining apparatus |
| CN103675701A (en) * | 2013-11-29 | 2014-03-26 | 宇龙计算机通信科技(深圳)有限公司 | Battery state-of-charge correction method and device |
| CN103616647A (en) * | 2013-12-09 | 2014-03-05 | 天津大学 | Battery remaining capacity estimation method for electric car battery management system |
| CN105203963A (en) * | 2015-09-11 | 2015-12-30 | 同济大学 | Charge state estimation method based on open-circuit voltage hysteretic characteristics |
| CN105699907A (en) * | 2016-01-28 | 2016-06-22 | 广州市香港科大霍英东研究院 | A battery SOC estimation method and system based on dynamic impedance correction |
| CN106646265A (en) * | 2017-01-22 | 2017-05-10 | 华南理工大学 | Method for estimating SOC of lithium battery |
| CN106918787A (en) * | 2017-03-20 | 2017-07-04 | 国网重庆市电力公司电力科学研究院 | A kind of electric automobile lithium battery residual charge evaluation method and device |
| CN107861073A (en) * | 2017-11-06 | 2018-03-30 | 合肥工业大学 | A kind of vehicle-mounted electric quantity of lead-acid storage battery On-line Estimation method based on CHVT models |
| CN109884528A (en) * | 2019-02-25 | 2019-06-14 | 江苏大学 | A kind of lithium ion battery open-circuit voltage evaluation method and system with temperature input |
| CN110045286A (en) * | 2019-03-21 | 2019-07-23 | 江苏大学 | A kind of battery open circuit voltage evaluation method and device based on air-or liquid-operated mechanical model |
| CN110609232A (en) * | 2019-03-29 | 2019-12-24 | 深圳猛犸电动科技有限公司 | Lithium ion battery SOC estimation method and device and terminal equipment |
| CN110333450A (en) * | 2019-04-30 | 2019-10-15 | 蜂巢能源科技有限公司 | Battery open circuit voltage predictor method and system |
| CN110850315A (en) * | 2019-11-29 | 2020-02-28 | 北京邮电大学 | Method and device for estimating state of charge of battery |
| CN111537887A (en) * | 2020-04-27 | 2020-08-14 | 南京航空航天大学 | Hybrid power system battery open-circuit voltage model optimization method considering hysteresis characteristic |
| CN111458646A (en) * | 2020-05-25 | 2020-07-28 | 安徽理工大学 | Lithium battery SOC estimation method based on PSO-RBF neural network |
Non-Patent Citations (1)
| Title |
|---|
| 锂电池开路电压的预估及SOC估算;邓晔;胡越黎;滕华强;;仪表技术;20150215(02);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112798962A (en) | 2021-05-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lipu et al. | A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations | |
| CN106383316B (en) | A kind of echelon utilizes lithium battery method of evaluating performance | |
| CN109991554B (en) | Battery electric quantity detection method and device and terminal equipment | |
| CN109100655B (en) | Data processing method and device for power battery | |
| KR20160067510A (en) | Method and apparatus for estimating state of battery | |
| Song et al. | Model-based health diagnosis for lithium-ion battery pack in space applications | |
| He et al. | An improved coulomb counting approach based on numerical iteration for SOC estimation with real-time error correction ability | |
| CN111913113A (en) | Method and device for identifying short circuit in electric core, storage medium and electronic equipment | |
| CN106998086A (en) | MW class energy-accumulating power station battery management method and its system | |
| CN111628535A (en) | Battery module balance control method and device | |
| Zhao et al. | The li-ion battery state of charge prediction of electric vehicle using deep neural network | |
| Liu et al. | Comparative study of incremental capacity curve determination methods for lithium-ion batteries considering the real-world situation | |
| US11360148B2 (en) | Method for updating capacity of battery, device for updating capacity of battery, electronic device, and storage unit | |
| Zhang et al. | A novel battery state-of-health estimation method for hybrid electric vehicles | |
| Vennam et al. | A dynamic soh-coupled lithium-ion cell model for state and parameter estimation | |
| Chen et al. | An approach for state of charge estimation of Li-ion battery based on Thevenin equivalent circuit model | |
| CN116047300A (en) | Controller for predicting characteristic parameters of battery and method thereof | |
| Alsabari et al. | Modeling and validation of lithium-ion battery with initial state of charge estimation | |
| Devi et al. | Lithium-ion battery management system: a review | |
| Xu et al. | State-of-charge estimation and health prognosis for lithium-ion batteries based on temperature-compensated Bi-LSTM network and integrated attention mechanism | |
| CN112731160A (en) | Battery hysteresis model training method, and method and device for estimating battery SOC | |
| Song et al. | FPGA-based implementation of lithium-ion battery soh estimator using particle filter | |
| CN112798962B (en) | Battery hysteresis model training method, method and device for estimating battery SOC | |
| CN115461635A (en) | Battery capacity estimation method, electronic device, and storage medium | |
| Luan et al. | Charging/discharging monitoring and simulation platform for Li-ion batteries |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |