CN117092539A - Dynamic calibration method, device and system for local SOC at tail end of lithium battery - Google Patents
Dynamic calibration method, device and system for local SOC at tail end of lithium battery Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 35
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 35
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- 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]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- 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]
- G01R31/3644—Constructional arrangements
- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
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- 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
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
Abstract
The embodiment of the application provides a method, a device and a system for dynamically calibrating local SOC at the tail end of a lithium battery, and belongs to the technical field of batteries. The method comprises the following steps: parameters of each single battery at different temperatures are obtained to obtain a battery model parameter table at different temperatures, and the battery model parameter table is used for estimating the battery SOC through a Kalman filtering algorithm; judging whether the single battery enters the tail end of the battery or not; and when the single battery enters the end of the battery, estimating the SOC of the battery through a Kalman filtering algorithm. The Kalman filtering algorithm can be applied to the tail end of the battery, which is favorable for reducing accumulated errors, so that the algorithm result is not easy to diverge, and the accuracy of the battery SOC estimated value is improved.
Description
Technical Field
The application relates to the technical field of batteries, in particular to a method, a device and a system for dynamically calibrating local SOC at the tail end of a lithium battery
Background
In recent years, electric automobiles and related technologies have been rapidly developed. The estimation (State-of-Charge, SOC) of the battery State of Charge is an important component of a battery management system, and accurate SOC estimation is beneficial to fully playing the power performance of the battery system, preventing the overcharge and overdischarge of the power battery, and guaranteeing the service life of the power battery and the safety in the use process.
The currently commonly used SOC estimation methods include an ampere-hour integration method, an open-circuit voltage method, a neural network method and a Kalman filtering method. The ampere-hour integrating method has lower cost and simple implementation method, but needs to determine the initial value of the SOC by other methods and can cause larger accumulated error due to the measurement error of the current during long-time operation; the open-circuit voltage method takes the terminal voltage of the electric automobile after long-time standing as the open-circuit voltage, determines a calibration value according to the corresponding relation between the open-circuit voltage and the SOC, and is difficult to realize dynamic estimation in the actual operation process; the neural network belongs to the field of artificial intelligence, the mapping relation between input and output is obtained through a large amount of training data, the power battery is used as a complex nonlinear system to estimate the SOC by adopting a neural network method, and higher precision can be realized, but the power battery is difficult to realize on a singlechip due to complex calculation and a large amount of data storage space; the Kalman filtering method is used for estimating the SOC, the SOC estimation value is calculated through a system state equation based on a battery equivalent circuit model, and then the estimation value is corrected according to the current voltage measurement value, so that the process of carrying out minimum variance estimation on the system state is realized. When the Kalman filtering method is used for estimating the SOC of the whole battery operation process, the situation of error accumulation can occur, so that the algorithm result diverges, and the estimation accuracy of the SOC can be insufficient.
Disclosure of Invention
The embodiment of the application aims to provide a method, a device and a system for dynamically calibrating the local SOC of the tail end of a lithium battery, wherein the method can apply a Kalman filtering algorithm to the tail end of the battery, is beneficial to reducing accumulated errors, ensures that the algorithm result is not easy to diverge, and improves the accuracy of the estimated value of the SOC of the battery.
In order to achieve the above object, in one aspect, an embodiment of the present application provides a method for dynamically calibrating a local SOC of a lithium battery terminal, the method including:
parameters of each single battery at different temperatures are obtained to obtain a battery model parameter table at different temperatures, and the battery model parameter table is used for estimating the battery SOC through a Kalman filtering algorithm;
judging whether the single battery enters the tail end of the battery or not;
and when the single battery enters the end of the battery, estimating the SOC of the battery through a Kalman filtering algorithm.
Optionally, the obtaining parameters of each single battery at different temperatures to obtain a battery model parameter table at different temperatures, and the estimating the battery SOC by using the kalman filter algorithm includes:
acquiring HPPC test data of each single battery at a preset temperature, and establishing a second-order RC equivalent circuit model of each single battery;
acquiring the temperature, current, voltage and time of HPPC pulse data;
identifying an SOC breakpoint according to the current trend of the single battery with time at a certain temperature;
dividing a data subset according to the SOC breakpoint, wherein the data subset comprises temperature, current, voltage and time corresponding to the SOC breakpoint;
performing data fitting on the battery response state curves in each data subset to obtain battery parameters corresponding to different battery SOCs;
taking the terminal voltage obtained by standing for a certain time as an open circuit voltage corresponding to the current battery SOC;
performing n-order polynomial fitting on the functional relation between the open-circuit voltage and the battery SOC;
and summarizing to obtain a parameter table and an OCV-SOC function relation of model parameters of each single battery at different temperatures along with the change of the battery SOC.
Optionally, the performing data fitting on the battery response state curve in each data subset to obtain battery parameters corresponding to different battery SOCs includes:
performing data fitting on the single battery response state curve;
acquiring two adjacent SOC break points of the single battery in the fitting curve;
acquiring voltage parameters corresponding to two adjacent SOC break points, and acquiring the ohmic internal resistance of the single battery according to the formula (1):
wherein R is 0 Representing ohmic internal resistance, V A Representing the high voltage corresponding to the first breakpoint, V B Representing a low voltage corresponding to the first breakpoint, V D Representing a high voltage corresponding to the second breakpoint, V C The low voltage corresponding to the second breakpoint is represented, and I represents the current of the single battery;
polarizing a first resistor, a second resistor, a first capacitor and a second capacitor in a second-order RC equivalent circuit model corresponding to the single battery;
obtaining a functional relation of a second-half curve in the fitted curve according to the parameters of the single battery and through a formula (2):
wherein U is L Is equivalent to voltage, U oc Is open circuit voltage, R 1 R is the first resistance 2 Is a second resistance, C 1 Is a first capacitance, C 2 And t is time.
Optionally, the determining whether the single battery enters the battery terminal includes:
collecting the current, the terminal voltage and the temperature of the single batteries according to the equal time interval;
performing cluster analysis on each single battery by using the terminal voltage as a data characteristic, so as to determine a single cluster representing the SOC of the power battery pack;
acquiring the minimum cluster voltage in the monomer clusters;
judging whether the cluster voltage is smaller than or equal to a preset threshold value;
and when the cluster voltage is smaller than or equal to the preset threshold value, acquiring a monomer number in a monomer cluster corresponding to the cluster voltage, and determining that the battery enters the tail end of the battery.
Optionally, the determining whether the single battery enters the battery terminal includes:
and when the cluster voltage is larger than the preset threshold value, returning to the step of collecting the current, the terminal voltage and the temperature of the single batteries according to the equal time interval.
Optionally, the step of performing cluster analysis on each single battery by using the terminal voltage as a data characteristic, so as to determine a single cluster representing the SOC of the power battery pack includes:
obtaining a single voltage maximum value, a single voltage minimum value and an average value of the single voltage maximum value and the single voltage minimum value of terminal voltages in the battery pack as initial values of 3 cluster centers;
traversing all the terminal voltages, and calculating the distance between the terminal voltage of each single battery and the center of each cluster;
distributing the single batteries to the cluster center closest to the terminal voltage;
judging whether the cluster center is changed or not;
when the center of the cluster is not changed, taking the average value of the terminal voltage of the single batteries in each cluster to represent the cluster voltage of the cluster, and recording the single number in the cluster.
Optionally, the step of performing cluster analysis on each single battery by using the terminal voltage as a data characteristic, so as to determine a single cluster representing the SOC of the power battery pack includes:
and when the cluster centers are changed, updating each cluster center to be an average value of the voltages of the distributed single batteries, and then returning to execute the step of traversing all the terminal voltages to calculate the distance between the terminal voltage of each single battery and each cluster center.
Optionally, when the single battery enters the battery end, estimating the battery SOC through a kalman filter algorithm includes:
obtaining a monomer number corresponding to the minimum cluster voltage to obtain a corresponding single battery and a corresponding parameter table;
setting an initial state estimation value according to the parameters of the single battery serving as an input state quantity and a second-order RC equivalent circuit equation corresponding to the single battery serving as an output equation, and obtaining an error covariance estimation matrix through a pre-test estimation;
obtaining a Kalman gain matrix;
updating state quantity and an error covariance matrix, estimating and obtaining the battery SOC of each single battery in the minimum cluster voltage through a Kalman filtering algorithm, and taking the minimum value as an estimated value of the Kalman filtering algorithm;
and comparing the current apparent SOC with the estimated value of the battery SOC and correcting.
In another aspect, the present application also provides a lithium battery terminal local SOC dynamic calibration apparatus, the apparatus comprising a processor configured to perform a lithium battery terminal local SOC dynamic calibration method as described above.
In one aspect, the present application also provides a system for dynamic calibration of local SOC at the end of a lithium battery, the system comprising:
a lithium battery pack;
the lithium battery terminal local SOC dynamic calibration device is as described above.
According to the technical scheme, the method, the device and the system for dynamically calibrating the local SOC of the tail end of the lithium battery are used for obtaining the battery model parameter table at different temperatures by obtaining the parameters of each single battery at different temperatures, and the battery model parameter table can be used for estimating the SOC of the battery by a Kalman filtering algorithm and judging whether the single battery enters the tail end of the battery. When the unit cell enters the battery end, the battery SOC of the unit cell may be estimated by a kalman filter algorithm. Because the Kalman filtering algorithm carries out the battery SOC estimation when the single battery enters the battery terminal, the accumulated error is reduced, the algorithm result is not easy to diverge, and the accuracy of the battery SOC estimation value is improved.
Additional features and advantages of embodiments of the application will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the embodiments of the application. In the drawings:
FIG. 1 is a flow chart of a method for dynamic calibration of lithium battery terminal local SOC according to one embodiment of the application;
fig. 2 is a first flowchart for acquiring parameters of a battery cell according to a method for dynamic calibration of a local SOC of a lithium battery terminal according to an embodiment of the present application;
FIG. 3 is a second flowchart of a method for obtaining parameters of a battery cell for dynamic calibration of a local SOC at a lithium battery terminal according to one embodiment of the application;
FIG. 4 is a flow chart of a method for dynamic calibration of local SOC at the end of a lithium battery, according to one embodiment of the present application, for determining whether to enter the end of the battery;
FIG. 5 is a flow chart of a method of determining a cell cluster for dynamic calibration of local SOC at a lithium battery terminal according to one embodiment of the application;
FIG. 6 is a flow chart of an estimated battery SOC of a lithium battery terminal local SOC dynamic calibration method according to one embodiment of the application;
FIG. 7 is a second order RC equivalent circuit model of a lithium battery terminal local SOC dynamic calibration method according to one embodiment of the present application;
FIG. 8 is HPPC experimental data and a partial sub-data set of a method for dynamic calibration of local SOC at the end of a lithium battery in accordance with one embodiment of the application;
fig. 9 is a result of estimating the SOC of a certain unit cell within a minimum cluster voltage unit cluster of a lithium battery terminal partial SOC dynamic calibration method according to an embodiment of the present application;
fig. 10 is an estimation result of the SOC of a certain unit cell within a minimum cluster voltage unit cluster of a lithium battery terminal partial SOC dynamic calibration method according to an embodiment of the present application, an initial error of 10%.
Detailed Description
The following describes the detailed implementation of the embodiments of the present application with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the application, are not intended to limit the application.
Fig. 1 is a flowchart of a method for dynamic calibration of a lithium battery terminal local SOC according to an embodiment of the present application. In the present application, the process of calibrating the battery SOC may include:
in step S1, parameters of each single battery at different temperatures are obtained to obtain a battery model parameter table at different temperatures, which is used for estimating the battery SOC by the kalman filter algorithm.
In step S2, it is determined whether or not the unit cell enters the cell end.
In step S3, when the unit cell enters the battery end, the battery SOC is estimated by a kalman filter algorithm.
In the application, when the battery plate SOC of the single battery is predicted, the method can be divided into an experimental stage and an online operation stage, various parameters of the single battery can be obtained through repeated experiments in the experimental stage, and then the battery SOC can be estimated through the parameters of the battery obtained in the experimental stage when the single battery is judged to enter the tail end of the battery. In the test stage, parameters of each single battery at different temperatures can be obtained, so that a battery model parameter table at different temperatures can be obtained, and the battery model parameter table can be used for estimating the battery SOC of the single battery by a Kalman filtering algorithm. It can then be determined whether the unit cell has entered the end of the cell. When the unit cell enters the battery end, the battery SOC of the unit cell may be estimated by a kalman filter algorithm at this time. Because the Kalman filtering algorithm is adopted to estimate the battery SOC when the single battery is judged to enter the battery terminal, the Kalman filtering algorithm is adopted to achieve better calibration effect, and is favorable for reducing accumulated errors, so that the algorithm result is not easy to diverge, and the accuracy of the battery SOC estimated value can be improved.
In one embodiment of the present application, as shown in fig. 2, the first procedure for obtaining parameters of the battery cell may include:
in step S4, HPPC test data of the battery of each single battery at a preset temperature is obtained, and a second-order RC equivalent circuit model of each single battery is established.
In step S5, the temperature, current, voltage, and time of the HPPC pulse data are acquired.
In step S6, the SOC break point is identified from the trend of the current of the unit cell over time at a certain temperature.
In step S7, the data subsets are divided according to the SOC break points, and the data subsets include temperature, current, voltage and time corresponding to the SOC break points.
In step S8, a data fitting is performed on the battery response state curve in each data subset to obtain battery parameters corresponding to different battery SOCs.
In step S9, the terminal voltage obtained by standing for a certain period of time is set as the open circuit voltage corresponding to the current battery SOC.
In step S10, an n-order polynomial fit is performed on the functional relationship between the open-circuit voltage and the battery SOC.
In step S11, a parameter table and an OCV-SOC functional relationship of model parameters of each unit battery at different temperatures according to the battery SOC change are obtained together.
In the application, parameters of each single battery can be obtained through various tests in the test stage, so that the parameters of the single battery can be brought into a subsequent battery SOC estimation method to estimate the battery SOC. When parameters of the single batteries are acquired, HPPC test data of the batteries of the single batteries at a preset temperature can be acquired through an HPPC test, and a second-order RC equivalent circuit model of each single battery can be established, as shown in fig. 7. The required parameters of each single battery can be obtained by establishing a second-order RC equivalent circuit model and then by using an equation of the second-order RC equivalent circuit model. After performing the HPPC test, the temperature, current, voltage and time of the HPPC pulse data can be acquired. The parameters of each single battery are counted into a time-varying table, so that the time-varying trend of each parameter of the single battery at a certain temperature can be obtained, the current of the single battery is not constant, and the current can be suddenly pulled up or suddenly reduced, and therefore, the SOC breakpoint can be identified according to the time-varying trend of the current of the single battery at a certain temperature. Between the two SOC break points may be a trend in which the current changes more strongly than the rest of the current changes, and thus peaks and valleys may occur at the SOC break points. After obtaining the SOC breakpoint, the data subsets may be partitioned according to the SOC breakpoint, and the partitioned data subsets may include a temperature, a current, a voltage, and a time corresponding to the SOC breakpoint. After obtaining the data subsets, a data fitting may be performed on the battery response state curve in each data subset, as shown in fig. 8, so as to obtain battery parameters corresponding to different battery SOCs. After obtaining the battery parameters, the terminal voltage of the single battery which is kept standing for a certain time can be used as the open circuit voltage corresponding to the current battery SOC, and then the function relationship between the open circuit and the battery SOC can be subjected to n-order polynomial fitting, so that the OCV-SOC function relationship can be obtained. After obtaining each parameter of each single battery, a parameter table and an OVC-SOC function relation of model parameters of each single battery at different temperatures along with the change of the battery SOC can be obtained in a summarizing mode, so that preparation can be made for subsequent battery SOC estimation.
In one embodiment of the present application, as shown in fig. 3, the second process of obtaining parameters of the battery cell may include:
in step S12, the cell response state curve is data-fitted.
In step S13, two SOC break points adjacent to the unit cells in the fitted curve are obtained.
In step S14, voltage parameters corresponding to the adjacent two SOC breakpoints are obtained, and the ohmic internal resistance of the unit cell is obtained according to formula (1):
wherein R is 0 Representing ohmic internal resistance, V A Representing a high voltage corresponding to a first breakpoint，V B Representing a low voltage corresponding to the first breakpoint, V D Representing a high voltage corresponding to the second breakpoint, V C The low voltage corresponding to the second break point is indicated, and I indicates the current of the single battery.
In step S15, the first resistor, the second resistor, the first capacitor and the second capacitor in the second-order RC equivalent circuit model corresponding to the unit cell are polarized.
In step S16, according to the parameters of the unit battery, the functional relationship of the second-half curve in the fitted curve is obtained through formula (2):
wherein U is L Is equivalent to voltage, U oc Is open circuit voltage, R 1 R is the first resistance 2 Is a second resistance, C 1 Is a first capacitance, C 2 And t is time.
In the application, after the data fitting of the single battery response state curve, two adjacent SOC break points of the single battery in the fitted curve can be obtained, as shown in fig. 8, voltage parameters corresponding to the two adjacent SOC break points can be obtained, and then the ohmic internal resistance of the single battery can be obtained according to the formula 1. V (V) A Representing the high voltage corresponding to the first breakpoint, V B Representing a low voltage corresponding to the first breakpoint, V D Representing a high voltage corresponding to the second breakpoint, V C Representing a low voltage corresponding to a second breakpoint, where V A 、V B 、V C 、V D The corresponding points can be found in fig. 8. After the ohmic internal resistance is obtained, the first resistor, the second resistor, the first capacitor and the second capacitor in the second-order RC equivalent circuit model corresponding to the single battery can be polarized. The parameters of the single battery can then be used to obtain the functional relationship of the second half curve in the fitted curve through formula (2), as the functional relationship of the DE section in FIG. 8 can be represented by formula (2). By the method, various parameters of the single battery can be obtained, so that the subsequent battery SOC estimation is facilitated.
In one embodiment of the present application, as shown in fig. 4, the process of determining whether to enter the battery terminal may include:
in step S17, the current, the terminal voltage and the temperature of the individual battery cells are collected at equal time intervals.
In step S18, a cluster analysis is performed on each unit cell with a terminal voltage as a data feature, thereby determining a cluster of units that characterizes the power battery pack SOC.
In step S19, the smallest cluster voltage among the single clusters is acquired.
In step S20, it is determined whether the cluster voltage is equal to or less than a preset threshold.
In step S21, when the cluster voltage is less than or equal to the preset threshold, the cell number in the cell cluster corresponding to the cluster voltage is obtained, and it is determined that the cell enters the end of the battery.
In the application, the battery SOC is estimated by adopting a Kalman filtering algorithm after judging that the single battery enters the battery end, and when judging whether the single battery enters the battery end, the current, the terminal voltage and the temperature of the single battery can be acquired according to the equal time interval. The terminal voltage can be used for carrying out cluster analysis on each single battery by using the terminal voltage as a data characteristic, so that a single cluster representing the SOC of the power battery pack can be determined, and the single cluster can better represent the variation trend of the battery pack relative to the single battery. After the single clusters are obtained, the minimum cluster voltage in all the single clusters and the single clusters corresponding to the minimum cluster voltage can be obtained, the cluster voltage can represent the voltage of the battery pack entering the battery end at the earliest time, and if the cluster voltage of the single cluster does not enter the battery end, the situation that the whole battery pack possibly does not enter the battery end is indicated. Therefore, after the minimum cluster voltage in the single cluster and the single cluster corresponding to the minimum cluster voltage are obtained, whether the cluster voltage is smaller than or equal to the preset threshold value can be judged, if the cluster voltage is smaller than or equal to the preset threshold value, the fact that the single cluster corresponding to the cluster voltage possibly enters the tail end of the battery can be indicated, and therefore the single number in the single cluster corresponding to the cluster voltage can be obtained, and various parameter information of the single batteries in the single cluster can be obtained.
In the present application, when the cluster voltage is greater than the preset threshold, the process returns to step S17.
If the minimum cluster voltage in the single clusters is greater than the preset threshold, it can be stated that the single cluster corresponding to the cluster voltage may not enter the end of the battery at this time, and because the cluster voltage is the lowest, the single clusters corresponding to the rest of the cluster voltages also cannot enter the end of the battery, so that current, terminal voltage and temperature can be continuously collected at equal intervals, and then whether the minimum cluster voltage in the single clusters is less than or equal to the preset threshold is judged again.
In one embodiment of the present application, as shown in fig. 5, the process of determining the monomer clusters may include:
in step S22, the cell voltage maximum value, the cell voltage minimum value, and the average of the cell voltage maximum value and the cell voltage minimum value of the terminal voltages in the battery pack are acquired as initial values of the 3 cluster centers.
In step S23, all the terminal voltages are traversed, and the distance between the terminal voltage of each unit cell and the center of each cluster is calculated.
In step S24, the unit cells are allocated to the cluster center closest to the terminal voltage thereof.
In step S25, it is determined whether the cluster center has changed.
In step S26, when the cluster center is unchanged, the average value of the terminal voltages of the unit cells in each cluster is used to represent the cluster voltage of the cluster, and the number of the unit cells in the cluster is recorded.
In the application, when the single batteries are clustered, 3 cluster centers of the single batteries can be taken. The single cells can be classified into 3 types, namely, a single cell with a higher terminal voltage, a single cell with a lower terminal voltage and a single cell with a normal terminal voltage. The cell voltage maximum value, the cell voltage minimum value, and the average of the cell voltage maximum value and the cell voltage minimum value of the terminal voltages in the battery pack may be obtained as initial values of the 3 cluster centers. After the initial value of the cluster center is obtained, all terminal voltages can be traversed, the distance between the terminal voltage of each single battery and each cluster center is calculated, and then the single battery is distributed to the cluster center closest to the terminal voltage, so that all the single batteries in the battery pack can be classified. After the single batteries are distributed, whether the distributed cluster center is changed or not can be judged, if the cluster center is not changed, the average value of the terminal taking voltages of the single batteries in each cluster can be used for representing the cluster voltage of the cluster, and the single number in the cluster can be recorded. After the cluster voltage of each cluster is obtained, the minimum cluster voltage can be taken to judge whether the single battery enters the tail end of the battery, and then the calibration of the battery SOC can be carried out.
In the present application, as shown in fig. 5, when the cluster center is changed, the steps may be performed including:
in step S27, when the cluster center is changed, each of the cluster centers is updated to an average value of the voltages of the allocated unit cells, and then step S23 is performed back.
When the allocated cluster center changes, a new cluster center can be updated again, each cluster center can be updated to be the low-voltage average value of the allocated single batteries, then the new cluster center is obtained, the terminal voltage of all the single batteries in the battery pack and the distance between the new cluster centers can be calculated again, and the subsequent steps are continued until the cluster center does not change any more.
In one embodiment of the present application, as shown in fig. 6, the process of estimating the battery SOC may include:
in step S28, a cell number corresponding to the minimum cluster voltage is acquired to acquire a corresponding cell and a corresponding parameter table.
In step S29, according to the parameters of the unit cell as the input state quantity and the second-order RC equivalent circuit equation corresponding to the unit cell as the output equation, an initial state estimation value is set, and an error covariance estimation matrix is obtained through a pre-test estimation.
In step S30, a kalman gain matrix is obtained.
In step S31, the state quantity and the error covariance matrix are updated, the battery SOC of each unit cell in the minimum cluster voltage is estimated by the kalman filter algorithm, and the minimum value is taken as the estimated value of the kalman filter algorithm.
In step S32, the estimated values of the current apparent SOC and the battery SOC are compared and corrected.
In the application, when the battery SOC estimation is carried out, the monomer number corresponding to the minimum cluster voltage can be obtained, and then the corresponding monomer battery and the corresponding parameter table can be obtained. The parameters of the obtained single battery can be used as input state quantity, the secondary RC equivalent circuit equation corresponding to the single battery can be used as output equation, so that the initial state estimation value of the battery SOC can be set, and the error covariance estimation matrix can be obtained through the anterior-posterior estimation, so that the Kalman gain evidence can be obtained. When the SOC of the battery is estimated, preliminary estimation can be performed by an ampere-hour integration method, and then correction is performed by a Kalman filtering algorithm. After obtaining the Kalman gain matrix and the error covariance estimation matrix, the state quantity and the error covariance can be updated for verification, then the battery SOC of each single battery with the minimum cluster voltage can be obtained through the algorithm, then the minimum value can be taken as the estimated value of the Kalman filtering algorithm, and the current apparent SOC and the obtained estimated value of the battery SOC can be compared and corrected, as shown in fig. 9. If the initial SOC to error is set to 10%, the error may converge to within 5% after estimation by the local kalman filter algorithm, as shown in fig. 10.
In another aspect, the present application may provide a lithium battery terminal local SOC dynamic calibration device, which may include a processor, which may be configured to perform a lithium battery terminal local SOC dynamic calibration method as described above.
In yet another aspect, the present application may also provide a lithium battery terminal local SOC dynamic calibration system, which may include: a lithium battery pack that can power a vehicle. The lithium battery terminal local SOC dynamic calibration device is as described above.
According to the technical scheme, the method, the device and the system for dynamically calibrating the local SOC of the tail end of the lithium battery are used for obtaining the battery model parameter table at different temperatures by obtaining the parameters of each single battery at different temperatures, and the battery model parameter table can be used for estimating the SOC of the battery by a Kalman filtering algorithm so as to judge whether the single battery enters the tail end of the battery. When the unit cell enters the battery end, the battery SOC of the unit cell may be estimated by a kalman filter algorithm. Because the Kalman filtering algorithm carries out the battery SOC estimation when the single battery enters the battery terminal, the accumulated error is reduced, the algorithm result is not easy to diverge, and the accuracy of the battery SOC estimation value is improved.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. A method for dynamically calibrating a local SOC at a lithium battery terminal, the method comprising:
parameters of each single battery at different temperatures are obtained to obtain a battery model parameter table at different temperatures, and the battery model parameter table is used for estimating the battery SOC through a Kalman filtering algorithm;
judging whether the single battery enters the tail end of the battery or not;
and when the single battery enters the end of the battery, estimating the SOC of the battery through a Kalman filtering algorithm.
2. The method of claim 1, wherein the obtaining parameters of each battery cell at different temperatures to obtain a table of battery model parameters at different temperatures, the estimating the battery SOC for the kalman filter algorithm comprises:
acquiring HPPC test data of each single battery at a preset temperature, and establishing a second-order RC equivalent circuit model of each single battery;
acquiring the temperature, current, voltage and time of HPPC pulse data;
identifying an SOC breakpoint according to the current trend of the single battery with time at a certain temperature;
dividing a data subset according to the SOC breakpoint, wherein the data subset comprises temperature, current, voltage and time corresponding to the SOC breakpoint;
performing data fitting on the battery response state curves in each data subset to obtain battery parameters corresponding to different battery SOCs;
taking the terminal voltage obtained by standing for a certain time as an open circuit voltage corresponding to the current battery SOC;
performing n-order polynomial fitting on the functional relation between the open-circuit voltage and the battery SOC;
and summarizing to obtain a parameter table and an OCV-SOC function relation of model parameters of each single battery at different temperatures along with the change of the battery SOC.
3. The method of claim 2, wherein said fitting data to the battery response status curve in each of the data subsets to obtain battery parameters corresponding to different battery SOCs comprises:
performing data fitting on the single battery response state curve;
acquiring two adjacent SOC break points of the single battery in the fitting curve;
acquiring voltage parameters corresponding to two adjacent SOC break points, and acquiring the ohmic internal resistance of the single battery according to the formula (1):
wherein R is 0 Representing ohmic internal resistance, V A Representing the high voltage corresponding to the first breakpoint, V B Representing a low voltage corresponding to the first breakpoint, V D Representing a high voltage corresponding to the second breakpoint, V C The low voltage corresponding to the second breakpoint is represented, and I represents the current of the single battery;
polarizing a first resistor, a second resistor, a first capacitor and a second capacitor in a second-order RC equivalent circuit model corresponding to the single battery;
obtaining a functional relation of a second-half curve in the fitted curve according to the parameters of the single battery and through a formula (2):
wherein U is L Is equivalent to voltage, U oc Is open circuit voltage, R 1 R is the first resistance 2 Is a second resistance, C 1 Is a first capacitance, C 2 And t is time.
4. The method of claim 2, wherein the determining whether the cell has entered a battery end comprises:
collecting the current, the terminal voltage and the temperature of the single batteries according to the equal time interval;
performing cluster analysis on each single battery by using the terminal voltage as a data characteristic, so as to determine a single cluster representing the SOC of the power battery pack;
acquiring the minimum cluster voltage in the monomer clusters;
judging whether the cluster voltage is smaller than or equal to a preset threshold value;
and when the cluster voltage is smaller than or equal to the preset threshold value, acquiring a monomer number in a monomer cluster corresponding to the cluster voltage, and determining that the battery enters the tail end of the battery.
5. The method of claim 4, wherein said determining whether said cell is entering a battery end comprises:
and when the cluster voltage is larger than the preset threshold value, returning to the step of collecting the current, the terminal voltage and the temperature of the single batteries according to the equal time interval.
6. The method of claim 4, wherein the clustering the individual cells with the terminal voltage as the data characteristic to determine a cell cluster that characterizes a power battery SOC comprises:
obtaining a single voltage maximum value, a single voltage minimum value and an average value of the single voltage maximum value and the single voltage minimum value of terminal voltages in the battery pack as initial values of 3 cluster centers;
traversing all the terminal voltages, and calculating the distance between the terminal voltage of each single battery and the center of each cluster;
distributing the single batteries to the cluster center closest to the terminal voltage;
judging whether the cluster center is changed or not;
when the center of the cluster is not changed, taking the average value of the terminal voltage of the single batteries in each cluster to represent the cluster voltage of the cluster, and recording the single number in the cluster.
7. The method of claim 6, wherein the clustering the individual cells with the terminal voltage as the data characteristic to determine a cell cluster that characterizes a power battery SOC comprises:
and when the cluster centers are changed, updating each cluster center to be an average value of the voltages of the distributed single batteries, and then returning to execute the step of traversing all the terminal voltages to calculate the distance between the terminal voltage of each single battery and each cluster center.
8. The method of claim 4, wherein estimating the battery SOC by a kalman filter algorithm when the battery cell enters a battery end comprises:
obtaining a monomer number corresponding to the minimum cluster voltage to obtain a corresponding single battery and a corresponding parameter table;
setting an initial state estimation value according to the parameters of the single battery serving as an input state quantity and a second-order RC equivalent circuit equation corresponding to the single battery serving as an output equation, and obtaining an error covariance estimation matrix through a pre-test estimation;
obtaining a Kalman gain matrix;
updating state quantity and an error covariance matrix, estimating and obtaining the battery SOC of each single battery in the minimum cluster voltage through a Kalman filtering algorithm, and taking the minimum value as an estimated value of the Kalman filtering algorithm;
and comparing the current apparent SOC with the estimated value of the battery SOC and correcting.
9. A lithium battery terminal local SOC dynamic calibration apparatus, characterized in that the apparatus comprises a processor configured to perform a lithium battery terminal local SOC dynamic calibration method as claimed in any of claims 1-8.
10. A lithium battery terminal local SOC dynamic calibration system, the system comprising:
a lithium battery pack;
the lithium battery terminal local SOC dynamic calibration device of claim 9.
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