CN115166566A - Method for identifying battery self-discharge rate abnormity on line - Google Patents
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- 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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- 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
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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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
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- 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
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- G—PHYSICS
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
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- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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- 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
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/52—Testing for short-circuits, leakage current or ground faults
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Abstract
The invention provides a method for identifying battery self-discharge rate abnormity on line, and relates to the technical field of new energy batteries. In particular by collecting a certain date D using discharge data i Inputting the battery model to obtain open-circuit voltage at each time by using identification algorithm, inputting the open-circuit voltage to MQ solving module to obtain corresponding MQ i Value when [ D ] i MQ i ] n×2 When the number of matrix lines is equal to 2, the SDR is obtained by adopting formula calculation, and when the matrix line is equal to 2, the momentAnd when the array dimension is larger than 2, deleting abnormal points in the matrix by adopting a DBSCAN algorithm, and performing linear fitting on the remaining points to obtain the SDR. The invention improves the accuracy of internal short circuit diagnosis and the detection efficiency of micro internal short circuit, the parameters needed to be stored in advance only relate to the SOC-OCV data of a new battery, no other off-line test and calibration are needed, the invention is not limited by working conditions, the invention does not depend on charging data, the battery does not need to be discharged to a certain depth or meet the requirement of a certain standing condition, the algorithm improves the calculated vehicle coverage rate, and the universality is strong.
Description
Technical Field
The invention relates to the technical field of new energy battery application, in particular to a method for identifying the abnormality of the self-discharge rate of a battery on line.
Background
After the power battery system is assembled and used, internal short circuit can occur to partial battery cells due to abuse or manufacturing defects, and the short circuit is one of main reasons for causing thermal runaway of the battery. In order to ensure the safety of the battery, the short circuit of the battery needs to be monitored, and early warning is timely given out when the battery is in a micro short circuit. At present, the common detection method of the short circuit in the battery is to lay and monitor the voltage change condition of the battery for a long time, and the laying time is as long as one week or more. Due to the limitation of the shelf time, the method cannot be used for monitoring the short circuit condition of the battery after loading.
In order to realize on-line monitoring of the vehicle-mounted battery, a cloud remote diagnosis technology appears in the related field, and analysis of a battery short circuit state is carried out based on battery data uploaded by a vehicle to the cloud. Up to now, there have been various methods for monitoring short circuits in batteries, such as: application publication No. CN 111929602A discloses a single battery leakage or micro short circuit quantitative diagnosis method based on capacity estimation, which comprises the following steps: s1, acquiring charge and discharge data of a single battery; s2, respectively estimating the battery charging capacities by adopting a traditional capacity estimation methodQuantity C C And discharge capacity C D (ii) a And S3, calculating the ratio of the discharge capacity to the charge capacity, and judging that the electric leakage fault occurs when the ratio is smaller than a threshold value. However, since the charging capacity of the real vehicle is greatly influenced by temperature and current multiplying power, and the discharging capacity of the real vehicle is also greatly influenced by environmental temperature and vehicle operating conditions, the method cannot be applied to the real vehicle obviously.
For another example: chinese patent application publication No. CN111208439A discloses a method for quantitatively detecting micro short circuit faults of a series lithium ion battery pack, which estimates 0CV of a battery based on an improved double kalman filter (DEKF); calculating the SOC of the batteries by an interpolation method, thereby calculating the SOC difference between the batteries; for lithium iron phosphate batteries, the SOC obtained according to the OCV has a large error, so that the algorithm accuracy is not high, and the method is difficult to identify the micro short circuit.
The following steps are repeated: chinese patent application publication No. CN 113848495A discloses a charging curve-based internal micro short-circuit fault diagnosis method, which mainly extracts aging characteristics according to an IC curve obtained from a battery charging curve to grasp the current aging state of a battery. However, in the practical use of the new energy automobile, constant current charging is difficult to realize for charging more vehicles, the charging current of more vehicles is greater than 1C, and the charging characteristics of the two platforms cannot be represented for charging more than 1C, so that the algorithm universality is low.
Therefore, a method for identifying the self-discharge rate abnormality of the battery on line is provided.
Disclosure of Invention
The invention provides a method for identifying battery self-discharge rate abnormity on line, which overcomes the defects of low accuracy, low universality and the like of the existing battery self-discharge rate abnormity identification.
The invention adopts the following technical scheme:
a method for identifying battery self-discharge rate abnormality on line comprises the following steps:
(1) Collecting relevant parameters of a battery in the running process of a vehicle Di at a certain date, wherein the relevant parameters comprise voltage V, current I and time t;
(2) Calculating the current I and time t array collected in the step (1) to obtainObtaining a capacity value Q (k) at every moment, and obtaining D through establishing a model and identifying parameters i Open-circuit voltage data uoc (k) corresponding to each voltage data of the current day;
(3) Inputting the open-circuit voltage data uoc (k) in the step (2) into an MQ solving module to obtain a corresponding MQ i A value;
(4) Selecting another date, repeating steps (1) to (3) to obtain [ D ] i MQ i ] n×2 A matrix, wherein n is the number of repetitions;
(5) When [ D ] i MQ i ] n×2 When n =2 in the matrix, adopting a formula SDR = [ MQ ] 2 -MQ 1 -(MQ 2 0 -MQ 1 0 )]/(D 2 -D 1 )/Q 0 Calculating to obtain SDR, wherein MQ 2 0 With MQ 1 0 Repeating the corresponding MQ values obtained by the calculation in the steps (1) to (3) on the days of D2 and D1 by using a standard voltage curve respectively; when n is greater than 2, firstly, the MQ is compared i Correcting, deleting abnormal points in the matrix by adopting a DBSCAN algorithm, performing linear fitting on the remaining points to obtain a slope K, and performing linear fitting on the slope K according to a formula SDR = K/Q 0 And calculating to obtain the self-discharge rate of the battery.
In a preferred embodiment, the time when the collection of the parameter data related to the battery in the step (1) is started requires that the battery system is in a full state, i.e. the SOC is greater than or equal to 99%, and the time when the collection of the data is finished is the time before the vehicle runs and the charging is started.
In a preferred embodiment, the capacity value Q (k) at each moment in the step (2) is obtained by processing according to the following two formulas: q (1) = Q 0 -(1)，Q(k)＝Q(k-1)-I(k)×[t(k)-t(k-1)]/3600- (2); wherein k is a serial number from 1 to N, N is the total number of the time t array, and Q0 is the rated capacity of the battery system.
In a preferred embodiment, the specific steps of the MQ solving module in step (3) above include:
s1, initializing three parameters of Mov _ Q, mov _ V and Comp, and substituting the parameters into the following formulas (3) to (5) to obtain an objective function J:
x(k)＝(Q(k)-Mov_Q)×Comp/Q 0 - (3)；
y(k)＝fi(x(k))- (4)；
wherein Mov _ Q is a design parameter the same as the dimension of the capacity Q, and the minimum boundary is 0 and the maximum boundary is Q 0 (ii) a Mov _ V is a design parameter the same as the uoc dimension, and the minimum boundary is 0 and the maximum boundary is 0.1; comp is a dimensionless design parameter, and the minimum boundary is 1 and the maximum boundary is 10; x (k) is the corresponding state of charge transition value; f (x) is a relation function of the SOC-open circuit voltage uoc data of the battery; y (k) is an open-circuit voltage conversion value obtained by converting x (k) through a relation function;
s2, solving by using a parameter optimization method to obtain the optimal Mov _ Q, mov _ V and Comp so that J is minimized;
and S3, calculating to obtain MQ according to a formula (6) by using the Mov _ Q obtained in the S2:
MQ＝f 1 (y 0 )*Q 0 /Comp+Mov_Q (6)
wherein f is 1 (y) is the inverse function of fi (x), y is taken to be y 0 ，y 0 At a certain fixed open circuit voltage value, D i The correspondingly obtained MQ is marked as MQ i 。
In a preferred embodiment, y in step S3 of the MQ solving module 0 Taking a corresponding open-circuit voltage value when the state of charge has obvious characteristics, such as a corresponding SOC value when an SOC = I, an SOC =0 or an SOC-uoc curve has an obvious inflection point; when taking the open circuit voltage value at SOC =0, f 1 (y 0 )＝0，MQ＝Mov_Q。
In a preferred embodiment, the above MQ solution can also be solved by a sequence number searching method according to the correspondence between Q (k) and y (k).
In a preferred embodiment, the optimization solving method in the specific step S2 of the MQ solving module is any one of a traversal algorithm, a genetic algorithm, a particle swarm algorithm, and an intelligent machine learning optimization algorithm.
In a preferred embodiment, f (x) in the specific step S3 of the MQ solving module is a relational function of the battery state of charge SOC-open circuit voltage uoc data, specifically a function obtained by fitting a formula, or a smooth function, or an interpolation function, or two arrays; when the array is a two-column array, x (k) obtained by the formula (4) is corresponding to the SOC array in a rounding, rounding and interpolation mode.
In a preferred embodiment, the voltage data in the step (1) is a lowest cell voltage value at every moment in the battery system, or a voltage value corresponding to a cell with a lowest state of charge, or a voltage of a specific cell electric core.
In a preferred embodiment, [ D ] of the above step (5) i MQ i ] n×2 When n =2 in the matrix and the battery discharge SOC is too high to meet the requirement that the discharge SOC is too low to y0, MQ 2 0 And MQ 1 0 The ellipses are 0.
In a preferred embodiment, MQ of the above step (5) i The correction method comprises calculating corresponding MQ obtained by calculating standard voltage curve i 0 The variation of MQ value caused by temperature, normal aging, charging rate and SOC error factors is obtained according to the variation of date, and the corrected MQ value is obtained after the interference generated by the variation factors is deducted i The value is obtained.
As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
1. the method for identifying the battery self-discharge rate abnormity on line utilizes the discharge working condition data of vehicle running, adopts the point with obvious open-circuit voltage characteristics as the basis of MQ calculation, and is in [ D ] i MQ i ] n×2 When the number of the matrix lines is equal to 2, calculating by adopting a formula to obtain SDR; and when the dimension of the matrix is more than 2, deleting abnormal points in the matrix by adopting a DBSCAN algorithm, and performing linear fitting on the remaining points to obtain the SDR. The method improves the accuracy of internal short circuit diagnosis and the detection efficiency of micro internal short circuits, and solves the problems of low accuracy and low universality of the existing battery self-discharge rate abnormity identification.
2. The parameters required to be stored in advance only relate to SOC-OCV data of a new battery, other offline tests and calibration are not required, the method is not limited by working conditions, charging data are not relied on, the battery does not need to be discharged to a certain depth or meet the requirements of certain standing conditions, a battery box body does not need to be disassembled, long-term standing is not required, and the algorithm in the whole method enables the calculated vehicle coverage rate to be improved and has strong universality.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
An embodiment of the present invention will be described with reference to fig. 1. Numerous details are set forth below in order to provide a thorough understanding of the present invention, but it will be apparent to those skilled in the art that the present invention may be practiced without these details. Well-known components, methods and processes are not described in detail below.
Example one
The method for identifying the battery self-discharge rate abnormity on line provided by the embodiment comprises the following steps:
step one, selecting a certain date D i Collection of D i The relevant parameters of the battery in the running process of the vehicle in the day comprise the highest cell voltage Vmax, the lowest cell voltage Vmin, the current I, the time t and the like. The time of starting data collection requires that the battery system is in a full-charge state, namely SOC is more than or equal to 99%, and the time of finishing data collection is the time before the vehicle runs and starts to charge.
Step two, processing the current I and time t array in the data collected in the step one according to formulas (1) - (2) to obtain a capacity value Q (k) at each moment, wherein: k is the sequence number from 1 to N, N is the total number held by the time t array, Q 0 Is the battery system rated capacity.
Q(1)＝Q 0 (1)
Q(k)＝Q(k-1)-I(k)×[t(k)-t(k-1)]/3600 (2)
Step three, obtaining D through building a battery model and identifying parameters according to the data obtained in the step one and the step two i Open circuit power corresponding to each Vmin of the dayThe pressure data are denoted as uoc (k).
And step four, solving the MQ. In an MQ solving module, three parameters of Mov _ Q, mov _ V and Comp are initialized and are substituted into formulas (3) - (5) to obtain an objective function J;
x(k)＝(Q(k)-Mov_Q)×Comp/Q 0 (3)
y(k)＝fi(x(k)) (4)
wherein Mov _ Q is a design parameter with the same dimension as the capacity Q, and the minimum boundary of the design parameter is 0; maximum Q 0 (ii) a Mov _ V is a design parameter same with the uoc dimension, and the minimum and maximum boundary of the Mov _ V is 0.1; comp is a dimensionless design parameter, and the minimum boundary is 1 and the maximum boundary is 10; x (k) is the corresponding state of charge transition value; f (x) is a relation function of the SOC-open circuit voltage uoc data of the battery; and y (k) is an open-circuit voltage conversion value obtained by converting x (k) through a relation function.
And step five, solving by using a parameter optimization method to obtain the optimal Mov _ Q, mov _ V and Comp so as to enable J to reach the minimum.
And step six, calculating MQ according to a formula (6) by using the Mov _ Q obtained in the step five:
MQ＝f 1 (y 0 )*Q 0 /Comp+Mov_Q (6)
wherein f is 1 (y) is the inverse function of f (x), y being y 0 ，y 0 For a certain fixed open-circuit voltage value, a corresponding open-circuit voltage value when the state of charge has obvious characteristics can be taken, such as a corresponding SOC value when an SOC =1, an SOC =0 or an SOC-uoc curve has an obvious inflection point; when taking the open circuit voltage value at SOC =0, f 1 (y 0 )＝0，MQ＝Mov_Q；D i The correspondingly obtained MQ is marked as MQ i 。
Step seven, selecting another date, and repeating the step one to the step six to obtain [ D ] i MQ i ] n×2 A matrix, wherein n is the number of repetitions;
step eight, whenWhen n =2, calculating the self-discharge rate SDR of the battery according to the formula (7), wherein MQ 2 0 And MQ 1 0 Respectively repeating the first step to the sixth step on the D2 and D1 days by using a standard voltage curve to obtain corresponding MQ values, and when the battery discharge SOC is too high and can not meet the requirement that y0 appears, MQ 2 0 And MQ 1 0 May be omitted as 0; when n > 2, first for MQ i Performing correction, and then adopting DBSCAN algorithm to pair [ D ] i MQ i ] n×2 And (3) eliminating abnormal points in the matrix, performing linear fitting on the remaining points to obtain a slope K, and calculating by using a formula (8) to obtain the self-discharge rate of the battery.
SDR＝[MQ 2 -MQ 1 -(MQ 2 0 -MQ 1 0 )]/(D 2 -D 1 )/Q 0 (n＝2) (7)
SDR＝K/Q 0 (n＞2) (8)
And step nine, setting a corresponding short circuit early warning grade according to the self-discharge rate, and realizing short circuit early warning.
The battery system related by the invention can be a battery system of a new energy vehicle and also can be an energy storage system.
The voltage data in the first step may be a lowest cell voltage value at every moment in the battery system, or a voltage value corresponding to a lowest cell in the state of charge, or a voltage of a specific cell core. The current value I in the step one specifies that charging is a negative value and discharging is a positive value; of course, if the current value I is defined as a positive value when charging and a negative value when discharging, the equation (1) may be adjusted to Q (1) = -Q 0 。
The initial value of Q in the second step is set as the rated capacity value, and may also be set as 0 or other values, and the formula associated with it is changed accordingly. If Q is 0, all Q in the above formula are used (Q) 0 -Q) substitution.
The battery model in the third step can be a battery equivalent circuit model or an electrochemical model. The identification algorithm can be a least square identification algorithm, a Kalman filtering algorithm, an H infinite algorithm, an intelligent machine learning optimization algorithm and other algorithms capable of identifying and obtaining the OCV.
F (x) in the fourth step is a relational function of the battery state of charge SOC-open circuit voltage uoc data, which can be a function obtained by fitting a formula, a smooth function, an interpolation function or two arrays; when the SOC array is an array, x (k) obtained by the formula (4) may be associated with the SOC array by rounding, interpolation, or the like.
The parameter optimization solving method in the fifth step can be all methods capable of solving such as a traversal algorithm, a genetic algorithm, a particle swarm algorithm, an intelligent machine learning optimization algorithm and the like;
the standard voltage curve in the step eight may be an average voltage of the battery system, or may be a highest cell voltage of the battery system or a voltage of a certain cell that is marked as normal. MQ in step eight i The correction method comprises calculating corresponding MQ obtained by calculating standard voltage curve i 0 The variation of MQ value caused by the influence of temp, aging of normal life, charge multiplying power and SOC error, and the interference of said variation factors are deducted to obtain corrected MQ value i The value is obtained.
Example two
The method for identifying the battery self-discharge rate abnormality on line in the present embodiment is substantially the same as the steps in the first embodiment, and the main difference is step six.
In the sixth step of this embodiment, MQ is calculated according to the formula by using the Mov _ Q obtained in the fifth step:
MQ＝Q(i)，i＝find(y(k)＝y 0 )
wherein, y 0 Taking a corresponding open-circuit voltage value when the state of charge has obvious characteristics, such as the corresponding open-circuit voltage value when the SOC = 0.62; d i The correspondingly obtained MQ is marked as MQ i 。
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.
Claims (11)
1. A method for identifying the self-discharge rate abnormality of a battery on line is characterized by comprising the following steps:
(1) Collecting relevant parameters of a battery in the running process of a vehicle Di at a certain date, wherein the relevant parameters comprise voltage V, current I and time t;
(2) Calculating the current I and time t arrays collected in the step (1) to obtain a capacity value Q (k) at every moment, and then obtaining D through building a model and identifying parameters i Open-circuit voltage data uoc (k) corresponding to each voltage data of the day;
(3) Inputting the open-circuit voltage data uoc (k) in the step (2) into an MQ solving module to obtain a corresponding MQ i A value;
(4) Selecting another date, repeating steps (1) to (3) to obtain [ D ] i MQ i ] n×2 A matrix, wherein n is the number of repetitions;
(5) When [ D ] i MQ i ] n×2 When n =2 in the matrix, the formula SDR = [ MQ = ] is adopted 2 -MQ 1 -(MQ 2 0 -MQ 1 0 )]/(D 2 -D 1 )/Q 0 Calculating to obtain SDR, wherein MQ 2 0 And MQ 1 0 Repeating the corresponding MQ values obtained by the calculation in the steps (1) to (3) on the days of D2 and D1 by using a standard voltage curve respectively; when n > 2, first pair MQ i Correcting, deleting abnormal points in the matrix by adopting a DBSCAN algorithm, linearly fitting the rest points to obtain a slope K, and obtaining the slope K according to a formula SDR = K/Q 0 And calculating to obtain the self-discharge rate of the battery.
2. The method for on-line identification of battery self-discharge rate anomalies according to claim 1, characterized in that: the moment when the collection of the relevant parameter data of the battery in the step (1) is started requires that the battery system is in a full-charge state, namely the SOC is more than or equal to 99%, and the moment when the collection of the data is finished is the moment before the vehicle runs and starts to charge.
3. As claimed in claim 1The method for identifying the battery self-discharge rate abnormity on line is characterized in that the capacity value Q (k) at each moment in the step (2) is obtained by processing according to the following two formulas: q (1) = Q 0 - (1)，Q(k)＝Q(k-1)-I(k)×[t(k)-t(k-1)]/3600- (2); wherein k is a sequence number from 1 to N, N is the total number of the time t array, Q 0 Is the battery system rated capacity.
4. The method for on-line identification of battery self-discharge rate abnormality as claimed in claim 3, wherein the specific step of the step (3) MQ solving module comprises:
s1, initializing three parameters of Mov _ Q, mov _ V and Comp, and substituting the parameters into the following formulas (3) to (5) to obtain an objective function J:
x(k)＝(Q(k)-Mov_Q)×Comp/Q 0 - (3)；
y(k)＝fi(x(k))- (4)；
wherein Mov _ Q is a design parameter the same as the dimension of the capacity Q, and the minimum boundary is 0 and the maximum boundary is Q 0 (ii) a Mov _ V is a design parameter same as the uoc dimension, and the minimum boundary is 0 and the maximum boundary is 0.1; comp is a dimensionless design parameter, and the minimum boundary is 1 and the maximum boundary is 10; x (k) is the corresponding state of charge transition value; f (x) is a relation function of the SOC-open circuit voltage uoc data of the battery; y (k) is an open-circuit voltage conversion value obtained by converting x (k) through a relation function;
s2, solving by using a parameter optimization method to obtain the optimal Mov _ Q, mov _ V and Comp so that J is minimized;
and S3, calculating to obtain MQ according to a formula (6) by using Mov _ Q obtained in the S2:
MQ＝f 1 (y0)*Q 0 /Comp+Mov_Q (6)
wherein, f 1 (y) is the inverse function of f (x), y being y 0 ，y 0 At a certain fixed open circuit voltage value, D i Correspondingly obtained MQ labelIs recorded as MQ i 。
5. The method for identifying the battery self-discharge rate abnormality on line as claimed in claim 4, wherein: y in the specific step S3 of the MQ solving module 0 Taking a corresponding open-circuit voltage value when the state of charge has obvious characteristics, such as a corresponding SOC value when an obvious inflection point appears on an SOC =1, SOC =0 or an SOC-uoc curve; when an open circuit voltage value at SOC =0 is taken, f 1 (y 0 )＝0，MQ＝Mov_Q。
6. The method for identifying the battery self-discharge rate abnormality on line as claimed in claim 4, wherein: the solution of the MQ can also be solved by a sequence number searching method according to the corresponding relation between Q (k) and y (k).
7. The method for identifying the battery self-discharge rate abnormality on line as claimed in claim 4, wherein: the optimization solving method in the specific step S2 of the MQ solving module is any one of a traversal algorithm, a genetic algorithm, a particle swarm algorithm, and an intelligent machine learning optimization algorithm.
8. The method for identifying the battery self-discharge rate abnormality on line as claimed in claim 4, wherein: in the specific step S3 of the MQ solving module, f (x) is a relational function of SOC-open circuit voltage uoc data of the battery, specifically a function obtained by formula fitting, or a smooth function, or an interpolation function, or two arrays; when the array is a two-column array, x (k) obtained by the formula (4) is corresponding to the SOC array in a rounding, rounding and interpolation mode.
9. The method for identifying the battery self-discharge rate abnormality on line as claimed in claim 1, wherein: the voltage data in the step (1) is the lowest cell voltage value at every moment in the battery system, or the voltage value corresponding to the cell with the lowest state of charge, or the voltage of a specific cell.
10. The method for identifying the battery self-discharge rate abnormality on line as claimed in claim 1, wherein: [ D ] of the step (5) i MQ i ] n×2 When n =2 in the matrix and the battery discharge SOC is too high to meet the requirement that the discharge SOC is too low to y0, MQ 2 0 And MQ 1 0 The ellipses are 0.
11. The method for identifying the battery self-discharge rate abnormality on line as claimed in claim 1, wherein: MQ of the step (5) i The correction method comprises calculating corresponding MQ obtained by calculating standard voltage curve i 0 The variation of MQ value caused by temperature, normal aging, charging rate and SOC error factors is obtained according to the variation of date, and the corrected MQ value is obtained after the interference generated by the variation factors is deducted i The value is obtained.
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| WO2024078850A1 (en) * | 2022-10-14 | 2024-04-18 | Volkswagen Ag | Method and device for estimating a self-discharge rate of a battery cell in battery cell production |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024078850A1 (en) * | 2022-10-14 | 2024-04-18 | Volkswagen Ag | Method and device for estimating a self-discharge rate of a battery cell in battery cell production |
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