CN112147512A - Diagnosis and separation method for short-circuit and abuse faults of lithium ion battery - Google Patents

Diagnosis and separation method for short-circuit and abuse faults of lithium ion battery Download PDF

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CN112147512A
CN112147512A CN202010978479.1A CN202010978479A CN112147512A CN 112147512 A CN112147512 A CN 112147512A CN 202010978479 A CN202010978479 A CN 202010978479A CN 112147512 A CN112147512 A CN 112147512A
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battery
model
circuit
short circuit
abuse
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CN112147512B (en
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李军求
孙超
孙逢春
郭婷婷
杨子传
江海赋
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Beijing Shouke Energy Technology Co ltd
Beijing Institute of Technology BIT
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Beijing Institute of Technology BIT
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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]
    • G01R31/367—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]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • 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]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • 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
    • G01R31/50—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 diagnosing and separating short-circuit and abuse faults of a lithium ion battery, which provides an online fault diagnosis framework based on battery electric and thermal models and adopting multi-model estimation and temperature estimation by fully considering temperature factors aiming at two general typical faults of short-circuit and abuse damage. The method can acquire key fault information on line and effectively detect and separate various faults. The invention considers the influence of temperature on the internal resistance of the battery, and effectively improves the fault diagnosis and separation precision. Through introducing the thermal model, the extra heat production action of battery can be effectively detected, the emergence of suggestion trouble provides the basis for the differentiation of inside and outside short circuit simultaneously.

Description

Diagnosis and separation method for short-circuit and abuse faults of lithium ion battery
Technical Field
The invention relates to the technical field of battery fault diagnosis and separation, in particular to a method for diagnosing and separating short-circuit and abuse faults of a lithium ion battery.
Background
Due to various reasons such as manufacturing defects or poor use of the lithium ion battery, battery operation failures cannot be completely avoided. If the fault can not be detected in time, a huge risk is brought to the safe operation of the battery system. Short circuits, which are a typical battery failure, whether internal or external, can cause heat build up from the exothermic reaction that can induce thermal runaway and damage other surrounding cells. Due to manufacturing inconsistencies and management strategy deficiencies, batteries are subject to abusive conditions such as overcharging, overdischarging, and the like to varying degrees. Although the battery can still continue to operate after abuse, irreversible damage to the battery caused by the abuse can cause a number of adverse risks. Therefore, in order to ensure safe use of the lithium ion battery, it is necessary and valuable to develop an accurate and efficient lithium ion battery fault diagnosis method.
The methods for diagnosing battery faults in the prior art are mainly divided into two categories: modeless methods and model-based methods. The model-free method captures a fault signal by monitoring variables such as current and voltage of the battery and utilizing a data analysis means according to the change of the battery characteristic during the fault. Although the detection speed of the method is high, the fault separation is difficult to realize due to the lack of description of the working mechanism of the battery. The model-based method needs to construct an analytic model for accurately describing the electrical and thermal behaviors of the battery, and usually realizes fault diagnosis by combining parameter identification or state estimation means. Meanwhile, the existing battery fault diagnosis technology is limited to only aiming at single type of faults, the consideration of battery characteristic differences under different types of faults is lacked, and a relatively complete comprehensive fault diagnosis framework is not formed so as to realize the diagnosis and separation of different types of faults.
Disclosure of Invention
In view of this, the present invention aims to fully consider temperature factors for two types of typical faults of short circuit and abuse damage of a lithium battery, to realize online acquisition of key fault information, and to effectively detect and separate multiple faults.
The invention provides a method for diagnosing and separating short-circuit and abuse faults of a lithium ion battery, which is mainly divided into an offline preparation stage from a first step to a second step and an online fault diagnosis and separation stage from a third step to a fourth step, and specifically comprises the following steps:
the method comprises the following steps of firstly, carrying out a lithium ion battery characteristic test, and acquiring test data such as current, voltage, temperature and the like; carrying out a lithium ion battery abuse test to obtain a battery sample after abuse damage; and also carrying out characteristic experiments on the abused damaged battery;
selecting a proper battery equivalent circuit model, and identifying by using the test data obtained in the step one to obtain a plurality of equivalent circuit models respectively corresponding to normal conditions and different abuse damage conditions; acquiring thermal physical parameters of the battery, and constructing a control-oriented thermal model of the battery;
thirdly, obtaining equivalent circuit models corresponding to normal conditions and different abuse damage conditions based on identification, estimating the open-circuit voltage of the battery and calculating the matching probability of the models corresponding to different faults; calculating temperature residual error information of models corresponding to different faults by combining the thermal model of the battery;
and step four, based on the fault diagnosis information, judging the fault condition of the system according to the difference characteristics of different faults and separating.
Further, the lithium ion battery characteristic experiment described in the step one mainly includes necessary experiments required for modeling, such as a capacity test, an Open Circuit Voltage (OCV) test, a constant current discharge test, a mixed pulse test, and the like.
Further, the appropriate battery equivalent circuit model is selected to be any one of a Rint model, a first-order RC model, a second-order RC model and the like in the second step; the control-oriented thermal model of the battery can be one of a concentrated mass thermal model, a double-layer lumped parameter model and the like, and the acquired thermal physical parameters of the battery comprise heat capacity, heat resistance and the like.
Further, the third step specifically includes:
1) according to the current and the terminal voltage of the battery obtained by measurement, estimating the open-circuit voltage of the battery by using a filtering estimation algorithm based on the equivalent circuit model in the second step, and simultaneously calculating the residual error between the terminal voltage measurement value and each model estimation value;
2) calculating the normalized matching probability of the model under normal or fault conditions by a residual error probability evaluation method, and outputting the battery open-circuit voltage estimated value and the matching probability of each fault model;
3) calculating the heat generation rate of the battery according to the estimated value of the open-circuit voltage of the battery, the measured value of the terminal voltage and the measured value of the current obtained by estimation;
4) based on the battery thermal model, obtaining a temperature estimation value according to the measured battery temperature, the ambient temperature and the battery heat generation rate obtained by the calculation and combining a filtering algorithm, and feeding back and updating the model parameters of the equivalent circuit model;
5) and calculating temperature residual error information output by the thermal model according to the temperature estimation value and the temperature measurement value.
Further, the fourth step of determining and separating the system fault condition includes distinguishing between a short circuit and an abusive fault, and further distinguishing between the occurrence of a short circuit inside or outside the battery.
Further, said distinguishing between short-circuit or abuse faults specifically comprises the steps of:
1) SOC obtained from ampere-hour integrationAHSOC obtained by looking up table with reference open-circuit voltage estimated valueOCVAbsolute value of difference (| SOC)Ah-SOCOCVA large threshold and a small threshold are set for indicating serious short circuit and slight short circuit, the serious short circuit threshold can be set to be 0.2, the slight short circuit threshold can be set to be 0.1, and more reasonable calibration can be carried out according to a battery short circuit experiment;
2) when the absolute value of the difference is | SOCAh-SOCOCV| greater than severe short circuit thresholdJudging that serious short circuit occurs;
3) the normal model probability is about 1, and the absolute value of the difference | SOCAh-SOCOCVWhen | is between the slight short circuit threshold and the serious short circuit threshold, judging that a slight short circuit fault occurs;
4) and the abuse damage model probability is about 1, and the absolute value of the difference is smaller than a slight short circuit threshold value, determining that the abuse damage occurs.
Further, the distinguishing between the inside and the outside of the battery specifically comprises the following steps:
1) presetting a threshold value of the sum of squares of temperature estimation residuals within a period of time;
2) when the sum of squares of the residual errors of the temperature estimation is obviously increased and exceeds the threshold value within a period of time, judging that a slight internal short circuit occurs;
3) and judging that a slight external short circuit occurs when the sum of squares of the temperature estimation residuals does not obviously increase within a period of time and does not exceed the threshold value.
Compared with the prior art, the method provided by the invention at least has the following beneficial effects:
1. the invention provides an online fault diagnosis framework based on battery electric and thermal models and adopting multi-model estimation and temperature estimation, aiming at two types of typical faults of short circuit and abuse damage, and fully considering temperature factors. Key fault information can be acquired online, and various faults can be effectively detected and separated;
2. the invention considers the influence of temperature on the internal resistance of the battery, and the fault diagnosis and the separation precision are effectively improved;
3. by introducing the thermal model, the invention can effectively detect the extra heat production behavior of the battery, prompt the occurrence of faults and provide a basis for distinguishing internal and external short circuits.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic diagram of an equivalent circuit model circuit constructed by an embodiment of the present invention;
FIG. 3 is a flow chart of the online diagnosis framework for short circuit and abuse damage in step three and the fault separation in step four of the present invention;
fig. 4 is a result of simulation diagnosis of a critical short-circuit fault according to an embodiment of the present invention: the SOC difference value;
FIG. 5 shows the simulation and diagnosis results of the slight external short circuit fault according to the embodiment of the present invention: (a) the probability of each model; (b) the SOC difference value; (c) the sum of squares of temperature residuals J;
FIG. 6 is the result of simulation diagnosis of a minor internal short fault according to an embodiment of the present invention: (a) the probability of each model; (b) the SOC difference value; (c) the sum of squares of temperature residuals J;
FIG. 7 is the results of simulation diagnosis of abuse damage faults according to the embodiment of the present invention: (a) the probability of each model; (b) the SOC difference value; (c) temperature residual sum of squares J.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a method for diagnosing and separating short-circuit and abuse faults of a lithium ion battery, which comprises the following steps of:
the method comprises the following steps of firstly, carrying out a lithium ion battery characteristic test, and acquiring test data such as current, voltage, temperature and the like; carrying out a lithium ion battery abuse test to obtain a battery sample after abuse damage; and also carrying out characteristic experiments on the abused damaged battery;
selecting a proper battery equivalent circuit model, and identifying by using the test data obtained in the step one to obtain a plurality of equivalent circuit models corresponding to normal conditions and different abuse damage conditions; acquiring thermal physical parameters of the battery, and constructing a control-oriented thermal model of the battery; (ii) a
Thirdly, obtaining equivalent circuit models corresponding to normal conditions and different abuse damage conditions based on identification, estimating the open-circuit voltage of the battery and calculating the matching probability of the models corresponding to different faults; calculating temperature residual error information of models corresponding to different faults by combining the thermal model of the battery;
and step four, based on the fault diagnosis information, judging the fault condition of the system according to the difference characteristics of different faults and separating.
In a preferred embodiment of the present invention, the equivalent circuit model of the battery under normal conditions and different abuse damage conditions is established by using a second-order RC equivalent circuit model, as shown in fig. 2:
wherein the open circuit voltage UOCVTaken as a function of SOC, which is defined by:
Figure BDA0002686647960000041
where I is the battery current (positive for discharge), C is the battery capacity, η is the coulombic efficiency, and is time, where η is 1, SOCtFor the battery SOC at time t, SOC0Is the initial battery SOC.
Based on kirchhoff's law, the mathematical model of the second-order RC model is as follows:
Figure BDA0002686647960000042
the equivalent circuit model of the battery under normal conditions and after abuse is subjected to parameter identification through the experimental data in the first step, and second-order RC model parameters are obtained: ohmic internal resistance R0(ii) a Polarization resistance R1，R2(ii) a Polarization capacitance C1，C2；U1，U2In order to be the polarization voltage,
Figure BDA0002686647960000043
its derivative with time UTAnd representing the terminal voltage to finish the construction of each equivalent circuit model.
The steps of constructing the control-oriented thermal model are as follows, and in the embodiment, the concentrated quality thermal model is constructed as an example:
1) the mass concentration thermal model takes the battery as a mass point, the temperature distribution is uniform, and the simplified calculation of the whole temperature is realized. The neglecting of thermal radiation thermal model is defined by:
Figure BDA0002686647960000044
wherein. T isbAnd TfBattery temperature and ambient temperature, respectively;
Figure BDA0002686647960000045
is the derivative of the battery temperature with respect to time; m, CpAnd A are the mass, specific heat capacity and surface area of the battery respectively; h is the convective heat transfer coefficient;
Figure BDA0002686647960000046
the heat generation rate when the battery is in normal operation;
Figure BDA0002686647960000047
the heat generation rate of the battery in the case of internal short circuit is normal operation
Figure BDA0002686647960000048
2) The heat generation rate was defined using Bernardi heat generation rate as:
Figure BDA0002686647960000051
wherein the content of the first and second substances,
Figure BDA0002686647960000052
is the entropy thermal coefficient. The second term on the right of the equation is entropy heat, which generally accounts for a small proportion of the rate of heat generation. If the entropy heat is neglected, the normal heat generation rate of the battery can be further simplified into
Figure BDA0002686647960000053
3) According to characteristic experiment data at different temperatures, a parameter identification method is utilized to construct two sets of model parameter tables (model parameters are related to SOC and temperature) of the battery under normal conditions and after abuse, and therefore two second-order RC equivalent circuit models under normal conditions and after abuse are obtained; and completing the construction of the concentrated mass thermal model by utilizing the acquired thermal physical parameters of the battery, such as heat capacity, convective heat transfer coefficient and the like. It should be noted that, in the process of rapid temperature rise of the battery, since there is hysteresis in the measurement of the external temperature of the battery, the internal resistance matched according to the temperature may be slightly greater than the true value, so a low impedance model (i.e. the internal resistance of the battery model is reduced proportionally) is also established in the example, so as to ensure that the accuracy of the open-circuit voltage estimated by the multiple models can be ensured under the condition. The construction of this low impedance model is not essential, but is optional.
In a preferred embodiment of the present invention, as shown in fig. 3, the specific steps are as follows:
1) and inputting the measured current and voltage data into the models in parallel based on the equivalent circuit models under the normal condition, the low impedance condition and the abuse damage condition which are constructed in the step two, and realizing state (open-circuit voltage) estimation by applying a Kalman filter based on the models. :
2) for the following systems:
Figure BDA0002686647960000054
the residual information passing through each model-filter can be calculated according to the following formula:
Figure BDA0002686647960000055
wherein x, u and y are respectively the state, input and output of the system; w and v represent process noise and measurement noise, respectively; k represents a certain state time; z is the measured value of the system output;
Figure BDA0002686647960000056
outputting an estimated value for the system obtained by the filter;f and H respectively represent a process equation and an output equation, and the coefficient matrixes A, B, H and D can be directly used for describing a linear system.
3) When the model is matched with the output of the actual system, and the mean value of the residual signal is considered to be 0, the covariance of the residual signal is defined by the following formula:
ψn,k＝HnPn,k|kHn T+Rn
in the formula, Pn,k|kA state error covariance matrix as a posteriori; rnMeasuring a noise covariance matrix; hnFor coefficient matrices of the preceding systems, #n,kThe index n denotes the nth model as the covariance of the residual signal.
4) Assuming that a residual signal follows zero-mean Gaussian distribution and meets Z in a historical measurement sequencek-1＝[zT(t1),...,zT(tk-1)]Under the condition (1), the conditional probability density function corresponding to the nth model is defined by the following relation:
Figure BDA0002686647960000068
Figure BDA0002686647960000061
l is the dimension of the measured quantity, i is 1 in this example, rnIs the residual signal corresponding to the nth model, fz(k)|a,Z(k-1)(zk|an,Zk-1) Is the aforementioned conditional probability density function, anAnd representing the model parameters corresponding to the nth model.
5) N-th model conditional probability pnAn iterative normalized evaluation is performed, defined by the following relationship:
Figure BDA0002686647960000062
6) model with highest conditional probabilityThe state estimation value can be used as the state estimation value finally obtained by multi-model estimation, namely the estimated value of the open-circuit voltage
Figure BDA0002686647960000063
The step of calculating the temperature residual error information by combining the control-oriented thermal model comprises the following steps:
1) and calculating a battery heat generation rate using the current, the terminal voltage measurement value, and the open circuit voltage estimation value, defined by:
Figure BDA0002686647960000064
2) and utilizing the battery heat generation rate to carry out filter estimation on the battery temperature under Kalman filtering, wherein the approximate discretized concentrated mass calorimetric model is defined by the following formula:
Figure BDA0002686647960000065
3) feeding the temperature estimation value back to the multi-model estimation for updating the model parameters and calculating the temperature residual error information, which is defined by the following formula:
Figure BDA0002686647960000066
wherein, TbmIs a measurement of the temperature of the battery,
Figure BDA0002686647960000067
is an estimate of the battery temperature.
The step four short circuit and abuse differentiation comprises the following steps:
1) setting two-gear threshold value according to the absolute value of the SOC difference value to indicate serious short circuitSOC,1To slight short circuitSOC,2；
2) The absolute value of the SOC difference value is larger than a serious short circuit threshold (| SOC)Ah-SOCOCV|≥SOC,1) When it is, directly determiningA severe short circuit occurs;
3) the normal model probability is about 1 (p)normal1) and the absolute value of the SOC difference is greater than the light short threshold and less than the severe short threshold: (SOC,2＜|SOCAh-SOCOCV|＜SOC,1) Judging that a slight short circuit fault occurs;
4) any failure model probability is about 1 (p)damage1) and the absolute value of the SOC difference is less than the threshold (| SOC)Ah-SOCOCV|≤SOC,2) And judging that abuse damage occurs.
The distinguishing of the slight internal short circuit and the slight external short circuit comprises the following steps:
1) setting a threshold value according to the square sum of the residual errors of the temperature residual errors in a period of time as a judgment indexJIndicating whether a thermal fault occurs, wherein the evaluation indexes are as follows:
Figure BDA0002686647960000071
2) slight short circuit fault occurs and thermal model residual information significantly increases and exceeds a threshold (J)T≥J) Judging that slight internal short circuit occurs;
3) slight short circuit fault occurs and thermal model temperature residual information does not significantly increase without exceeding a threshold (J)T＜J) And judging that a slight external short circuit occurs.
In order to verify the effect, a battery simulation model is constructed in a computer, computer simulation of 4 fault conditions is performed, and simulated battery data is used as input of a diagnosis and separation method to verify the effectiveness of the method. The following are simulation settings and results descriptions:
1. severe short circuit failure: and arranging a short-circuit resistor with the resistance value of 60m omega between the positive electrode and the negative electrode of the battery model to simulate serious short-circuit faults. The result is shown in fig. 4, which indicates that the SOC difference value is significantly increased and exceeds the threshold value after the occurrence of the severe short circuit, and the serious short circuit fault can be determined;
2. slight external short-circuit fault: and arranging a short-circuit resistor with the resistance value of 600m omega between the positive electrode and the negative electrode of the battery model to simulate slight external short-circuit fault. The results are shown in fig. 5(a-c), which shows that the system can accurately and quickly judge the occurrence of the fault after the fault occurs, and the specific fault can be judged to be a slight external short circuit according to the SOC difference value and the temperature residual error index;
3. minor internal short circuit fault: adding additional heat generation rate caused by short-circuit resistance in thermal model of battery based on slight external short-circuit fault setting
Figure BDA0002686647960000072
To simulate a minor internal short fault. Wherein, ISCIs a short circuit current; rSCIs a short circuit resistor. The results are shown in fig. 6(a-c), which shows that the system can quickly judge the occurrence of the fault after the fault occurs, and can judge that the specific fault is a slight internal short circuit according to the SOC difference value and the temperature residual error index;
4. abuse damage failure: taking overcharge as an example, after the battery is subjected to severe overcharge, the ohmic internal resistance and the polarization internal resistance of the battery both increase greatly. Here, we set the ohmic and polarization internal resistances to 1.5 times the normal values to simulate the increase in impedance due to abuse damage. The results are shown in fig. 7(a-c), which indicate that the system can quickly determine that a fault occurs after the fault occurs, and determine that the fault is an abuse damage fault according to the model probability.
It should be understood that, the sequence numbers of the steps in the embodiments of the present invention do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic of the process, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (7)

1. A method for diagnosing and separating short-circuit and abuse faults of a lithium ion battery is characterized by comprising the following steps: the method specifically comprises the following steps:
carrying out a lithium ion battery characteristic test to obtain current, voltage and temperature test data; carrying out a lithium ion battery abuse test to obtain a battery sample after abuse damage; and also carrying out characteristic experiments on the abused damaged battery;
selecting a proper battery equivalent circuit model, and identifying by using the test data obtained in the step one to obtain a plurality of equivalent circuit models respectively corresponding to normal conditions and different abuse damage conditions; acquiring thermal physical parameters of the battery, and constructing a control-oriented thermal model of the battery;
thirdly, obtaining equivalent circuit models corresponding to normal conditions and different abuse damage conditions based on identification, estimating the open-circuit voltage of the battery and calculating the matching probability of the models corresponding to different faults; calculating temperature residual error information of models corresponding to different faults by combining the thermal model of the battery;
and step four, based on the fault diagnosis information, judging the fault condition of the system according to the difference characteristics of different faults and separating.
2. The method of claim 1, wherein: the lithium ion battery characteristic experiment in the step one comprises a capacity test, an open-circuit voltage test, a constant current discharge test and a mixed pulse test.
3. The method of claim 1, wherein: selecting a proper battery equivalent circuit model as any one of a Rint model, a first-order RC model and a second-order RC model; the battery thermal model facing the control can be one of a concentrated mass thermal model and a double-layer lumped parameter model, and the collected battery thermophysical parameters comprise heat capacity and heat resistance.
4. The method of claim 1, wherein: the third step specifically comprises:
1) according to the current and the terminal voltage of the battery obtained by measurement, estimating the open-circuit voltage of the battery by using a filtering estimation algorithm based on the equivalent circuit model in the second step, and simultaneously calculating the residual error between the terminal voltage measurement value and each model estimation value;
2) calculating the normalized matching probability of the model under normal or fault conditions by a residual error probability evaluation method, and outputting the battery open-circuit voltage estimated value and the matching probability of each fault model;
3) calculating the heat generation rate of the battery according to the estimated value of the open-circuit voltage of the battery, the measured value of the terminal voltage and the measured value of the current obtained by estimation;
4) based on the battery thermal model, obtaining a temperature estimation value according to the measured battery temperature, the ambient temperature and the battery heat generation rate obtained by the calculation and combining a filtering algorithm, and feeding back and updating the model parameters of the equivalent circuit model;
5) and calculating temperature residual error information output by the thermal model according to the temperature estimation value and the temperature measurement value.
5. The method of claim 1, wherein: and step four, judging the system fault condition and separating, including distinguishing short circuit or abuse fault, and further distinguishing whether the short circuit occurs inside or outside the battery.
6. The method of claim 5, wherein: the distinguishing between short-circuit and abuse faults specifically comprises the following steps:
1) SOC obtained from ampere-hour integrationAHSOC obtained by looking up table with reference open-circuit voltage estimated valueOCVAbsolute value of difference | SOCAh-SOCOCVSetting a large threshold and a small threshold to indicate severe short circuit and slight short circuit, setting the severe short circuit threshold to be 0.2 and the slight short circuit threshold to be 0.1, and carrying out more reasonable calibration according to a battery short circuit experiment;
2) when the absolute value of the difference is larger than a serious short circuit threshold value, judging that serious short circuit occurs;
3) the normal model probability is about 1, and the absolute value of the difference | SOCAh-SOCOCVWhen | is between the slight short circuit threshold and the serious short circuit threshold, judging that a slight short circuit fault occurs;
4) the abuse damage model probability is about 1 and the absolute value of the difference | SOCAh-SOCOCVAnd if the | is smaller than the slight short circuit threshold, judging that abuse damage occurs.
7. The method of claim 5, wherein: the distinguishing between the occurrence inside or outside the battery specifically comprises the following steps:
1) presetting a threshold value of the sum of squares of temperature estimation residuals within a period of time;
2) when the sum of squares of the residual errors of the temperature estimation is obviously increased and exceeds the threshold value within a period of time, judging that a slight internal short circuit occurs;
3) and judging that a slight external short circuit occurs when the sum of squares of the temperature estimation residuals does not obviously increase within a period of time and does not exceed the threshold value.
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CN114252772A (en) * 2021-12-22 2022-03-29 中国科学院电工研究所 Lithium ion battery internal short circuit diagnosis method and system
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CN114252772A (en) * 2021-12-22 2022-03-29 中国科学院电工研究所 Lithium ion battery internal short circuit diagnosis method and system
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