CN109738811B - External short circuit fault diagnosis method of lithium ion battery pack based on two-stage model prediction - Google Patents
External short circuit fault diagnosis method of lithium ion battery pack based on two-stage model prediction Download PDFInfo
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Abstract
The invention provides a lithium ion battery pack external short circuit fault diagnosis method based on two-stage model prediction, and relates to the technical field of lithium ion power battery safety. Firstly, performing an external short circuit experiment on a lithium ion battery pack, constructing a battery pack external short circuit two-stage equivalent circuit model, and performing offline optimality identification on battery model parameters by using tested experimental data; then, when the battery pack is operated, the battery state in the battery pack is judged according to the battery measurement data, when part of battery voltage is found to be abnormal, adjacent battery units with the abnormal voltage are marked as a whole and recorded as an abnormal battery pack, a first-stage battery model is started, and if the error of the first-stage battery model is smaller than a critical threshold value, a second-stage battery model is triggered to calculate and obtain a model error; and finally, carrying out fault diagnosis on the abnormal battery through the measured data and the goodness of fit of the two-stage model. The method has simple steps, is easy to realize on line, has high reliability, and is suitable for on-line fault diagnosis and safety management of the power battery of the electric automobile.
Description
Technical Field
The invention relates to the technical field of lithium ion power battery safety, in particular to a lithium ion battery pack external short circuit fault diagnosis method based on two-stage model prediction.
Background
In recent years, electric automobiles develop rapidly, the development of the electric automobiles is regarded as an effective way for solving the environmental pollution, reducing the fuel consumption and building green and environment-friendly urban traffic, however, the safety accidents of fire and explosion often occur in the application process of battery automobiles, and the root cause of the safety accidents is thermal runaway caused by battery faults. The external short circuit is one of the common and serious faults in the battery faults, when the external short circuit fails, the battery pack generates large current, high temperature and high heat of the battery are easily caused, and the duration time of the external short circuit fault is only dozens of seconds, so that how to effectively, accurately and quickly perform online fault diagnosis on the external short circuit fault is an important technical problem.
Most of the existing battery management systems aim at the state estimation, service life prediction and the like of batteries, but methods for battery safety problem and fault diagnosis are not mature, and particularly, external short circuit fault diagnosis technologies of high-power battery packs are deficient.
Disclosure of Invention
The invention aims to solve the technical problem of providing a lithium ion battery pack external short circuit fault diagnosis method based on two-stage model prediction aiming at the defects of the prior art; the method has simple steps, is easy to realize on line, has high reliability, and is suitable for on-line fault diagnosis and safety management of the power battery of the electric automobile.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the invention provides a lithium ion battery pack external short circuit fault diagnosis method based on two-stage model prediction, which comprises the following steps of:
step 1: performing external short circuit experiment of battery pack, and recording experimental data including current measurement data Ic＝[Ic1,Ic2,…,IcN]TTerminal voltage measurement data Uc＝[Uc1,Uc2,…,UcN]TN is the number of data samples, the value of N depends on the duration time of current and sampling step length in an external short circuit test, and T represents the transposition of a matrix;
step 2: establishing a two-stage battery model of the external short circuit fault, and respectively carrying out offline optimality parameter identification on the two-stage battery model through the experimental data obtained in the step 1;
the first-stage battery model is an improved equivalent circuit model, and the improved method comprises the following steps: the battery state of charge SOC in the traditional equivalent circuit model is improved into the depth of discharge xi in the short circuit processEAnd the open circuit voltage is regarded as the depth of discharge xiEA polynomial function of (a);
the specific mathematical expression form of the first-stage battery model is as follows:
where k denotes the current sampling instant, τ ═ RpCp，Ut,UpAnd U isocRespectively representing a terminal voltage, a polarization voltage, and an open circuit voltage of the battery pack; rpAnd R0Then respectively represent the polarization internal resistance and the ohm internal resistance, CpRepresenting the polarization capacitance, iLRepresenting the battery current, ipRepresents RpThe current flowing upwards, delta t is the sampling step length, xiEIndicating the depth of discharge in an external short-circuit fault.
The second-stage battery model is a half-battery model, and the specific mathematical expression form is as follows:
wherein
Represents a constant voltage source;
and step 3: monitoring the voltage of each single battery of the battery pack in real time by using a battery management system, and entering a step 4 when the voltage of a part of single batteries is lower than a critical threshold Vn;
and 4, step 4: triggering a first-stage battery model, regarding adjacent abnormal single batteries as an abnormal battery pack, inputting the current of the battery pack as a model, and calculating the predicted voltage output by the model in real time;
and 5: calculating the goodness of fit sigma between the predicted voltage and the actually measured voltage of the first-stage battery model, and the duration T1At the moment, if the goodness of fit sigma is less than the critical threshold value chi1If not, preliminarily defining the external short-circuit fault, triggering a second-stage battery model, and entering the step 6;
step 6: taking the current of the battery pack as the input of the second-stage battery model and calculating the model in real timeThe output predicted voltage is used for calculating the goodness of fit sigma between the predicted voltage and the actually measured voltage of the second-stage battery model and the duration time T2At the moment, if the goodness of fit sigma is larger than the critical threshold value chi2If the abnormal condition is caused by the external short circuit fault, the position of the abnormal battery monomer is positioned and the step 8 is carried out; otherwise, increase the diagnostic duration to T3And go to step 7;
and 7: repeatedly judging the goodness of fit by adopting a second-stage battery model, and if the goodness of fit sigma is less than a critical threshold value chi2The possibility of external short-circuit faults is eliminated if the goodness of fit sigma > the critical threshold chi2If yes, confirming as an external short-circuit fault;
and 8: and storing and outputting the diagnosis result, returning to the step 3, and waiting for the next operation.
The two-stage battery circuit models in the step 2 are divided into two stages in total, wherein the first-stage battery model is a battery integral model, and the second-stage battery model is a half-battery model; the second-stage battery model is a half-battery model, the modeling method of the second stage is to regard the battery as a two-part equivalent circuit model, the two-part equivalent circuit model comprises a model 1 and a model 2, namely the sum of the model 1 and the model 2 is a battery integral model, and the second-stage battery model refers in particular to the model 2; in model 1, a variable voltage source is used
And internal resistance R of battery0Short-circuit resistor RSAre connected into a loop; in model 2, there is a constant voltage source
The RC link is connected with the RC link to generate an end voltage Ut, and the RC link is formed by connecting a capacitor C and a polarization internal resistance Rp in parallel; the open-circuit voltage of the whole battery is a variable voltage source
And a constant voltage source
And (3) the sum:
the off-line optimality parameter in step 2 is identified as the experimental current measurement value IcAs model input, terminal voltage output U ═ U1,U2,…,UN]TAnd (3) as model output, performing offline optimality identification on the model parameters in the step (2) by using a global optimization algorithm, wherein the parameters of the two-stage model are required to be respectively identified in the identification process of the model, and the parameters of the two-stage model are mutually independent.
The definition of the goodness of fit σ in the step 5 is as follows: the inverse of the root mean square error of the model predicted results and the actual test results over a certain duration, namely:
where ρ is the number of samples in the duration T, Ut,mFor model prediction of terminal voltage, UtFor on-line measurement of terminal voltage, thetanRepresenting a model parameter matrix.
The critical threshold value χ in the step 61The value of the critical threshold value is slightly lower than the calculation result of the goodness of fit of the model during the experiment;
the critical threshold value χ in the step 72And the value of the critical threshold value is a critical threshold value of the goodness of fit of the second-stage battery model, and the value of the critical threshold value needs to be slightly lower than a model goodness of fit calculation result in an experiment.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in: the invention provides a lithium ion battery pack external short circuit fault diagnosis method based on two-stage model prediction, which adopts a two-stage optimization equivalent circuit model, wherein the first-stage model is a battery integral model and contains more parameters to be identified, the model has good adaptability but slightly low precision, and the second-stage model is a half-battery model and contains less parameters to be identified and has higher model precision; and identifying parameters of the two-stage model by using external short circuit data of the battery pack, and performing online fault diagnosis of the external short circuit according to the goodness of fit between the measured data of the battery pack and the model prediction. The method has simple steps, is easy to realize on line, has high reliability, and is suitable for on-line fault diagnosis and safety management of the power battery of the electric automobile.
Drawings
Fig. 1 is a two-stage equivalent circuit model for external short-circuit fault diagnosis according to an embodiment of the present invention, where a is a first-stage battery model; b is model 1 in the second-stage battery model; c is model 2 in the second-stage battery model;
FIG. 2 is a flowchart of an external short-circuit online diagnosis estimation method based on bi-level model prediction according to an embodiment of the present invention;
fig. 3 is a graph of an identification error analysis result of an external short-circuit dual-stage model according to an embodiment of the present invention, where a is the graph of the identification error analysis result, and b is an error diagram of the identification error analysis result;
fig. 4 is a diagram illustrating external short circuit diagnosis of a battery pack according to an embodiment of the present invention, wherein a-1 is a voltage diagnosis diagram, a-2 is a partially enlarged view of H in fig. a-1, b-1 is a two-stage model error diagram, and b-2 is a partially enlarged view of Z in fig. b-1;
fig. 5 is a graph of the result of the external short circuit test provided by the embodiment of the invention, wherein a-1 is a graph of the result of the voltage test, a-2 is a partially enlarged view of AB in the graph a-1, and b is a graph of the result of the current test.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In the embodiment, a 18650NMC cylindrical lithium ion power battery is taken as an example, the rated voltage of the battery is 3.6V, the nominal capacity of the battery is 2.4Ah, and 6 battery monomers with the SOH value larger than 0.96 are adopted to form a battery pack; the experimental equipment adopted is as follows: the NEU _ ESCTEST02 test bed is matched with a marine instrument GD-2045D temperature control box,
the method of this example is as follows.
The invention provides a lithium ion battery pack external short circuit fault diagnosis method based on two-stage model prediction, which comprises the following steps as shown in figure 2:
step 1: performing external short circuit experiment of battery pack, and recording experimental data including current measurement data Ic＝[Ic1,Ic2,…,IcN]TTerminal voltage measurement data Uc＝[Uc1,Uc2,…,UcN]TN is the number of data samples, the value of N depends on the duration time of current and sampling step length in an external short circuit test, and T represents the transposition of a matrix;
step 2: establishing a two-stage battery model of the external short circuit fault, and respectively carrying out offline optimality parameter identification on the two-stage battery model through the experimental data obtained in the step 1; offline optimality parameter identification as experimental current measurements IcAs model input, terminal voltage output U ═ U1,U2,…,UN]TAnd (3) as model output, performing offline optimality identification on the model parameters in the step (2) by using a global optimization algorithm, wherein the parameters of the two-stage model are required to be respectively identified in the identification process of the model, and the parameters of the two-stage model are mutually independent.
The first-stage battery model is an improved equivalent circuit model, as shown in a diagram in fig. 1, and the improved method is as follows: the battery state of charge SOC in the traditional equivalent circuit model is improved into the depth of discharge xi in the short circuit processEAnd the open circuit voltage is regarded as the depth of discharge xiEA polynomial function of (a);
the specific mathematical expression form of the first-stage battery model is as follows:
where k denotes the current sampling instant, τ ═ RpCp，Ut,UpAnd U isocRespectively representing a terminal voltage, a polarization voltage, and an open circuit voltage of the battery pack; rpAnd R0Then respectively represent the polarization internal resistance and the ohm internal resistance, CpRepresenting the polarization capacitance, iLRepresenting the battery current, ipRepresents RpThe current flowing upwards, delta t is the sampling step length, xiEIndicating the depth of discharge in an external short-circuit fault.
The open circuit voltage is expressed by a polynomial expression as shown in the following formula
In the formula NpIs the degree of a polynomial, alphaiRepresenting polynomial coefficient, ξEThe depth of discharge in an external short-circuit fault is expressed by the following calculation method:
in the formula QRIs the nominal capacity.
The second-stage battery model is a half-battery model, the modeling method of the second stage is to regard the battery as a two-part equivalent circuit model, the two-part equivalent circuit model comprises a model 1 and a model 2, namely the sum of the model 1 and the model 2 is a battery integral model, and the second-stage battery model refers in particular to the model 2; in model 1, as shown in b of FIG. 1, a variable voltage source is used
And internal resistance R of battery0Short-circuit resistor RSAre connected into a loop; in model 2, there is a constant voltage source, as shown in c of FIG. 1
The RC link is connected with the RC link to generate an end voltage Ut, and the RC link is formed by connecting a capacitor C and a polarization internal resistance Rp in parallel; the open-circuit voltage of the whole battery is a variable voltage source
And a constant voltage source
And (3) the sum:
the second-stage battery model is a half-battery model, and the specific mathematical expression form is as follows:
wherein
Represents a constant voltage source;
for the first-stage battery model, the parameter theta to be identified1＝[α1,α2,…,α10,τ,Rp,R0]A total of 13 parameters, for the second-stage battery model, with an identification parameter theta2＝[U0,τ,Rp]A total of 3 parameters. The experimental data are used for respectively carrying out offline optimality parameter identification on the two-stage battery model, the identification method can adopt a global optimization method, the parameter identification is carried out by adopting a genetic algorithm in the embodiment, and the selection method does not limit the invention. The recognition error is shown in fig. 3, and it can be seen that the model constructed in this way, the second-stage battery model prediction accuracy is very high. After the identification is completed, the identification results are recorded as shown in tables 1-2:
TABLE 1 first-stage Battery model parameter identification results
TABLE 2 second-stage Battery model parameter identification results
| Parameter(s) | The result of the recognition |
| U0(mV) | 589.7 |
| τ(s) | 6.9 |
| Rp(mΩ)) | 6.5 |
And step 3: monitoring the voltage of each single battery of the battery pack in real time by using a battery management system, and entering a step 4 if the voltage of a part of single batteries is lower than a critical threshold Vn;
in this embodiment, a critical threshold Vn of the cell voltage is set to be 2.0V, and the critical threshold is set to be slightly lower than the normal discharge cutoff voltage of the battery; the battery management system is a battery management system of a new energy automobile and mainly has a current, voltage and temperature acquisition function, a battery state estimation function, an overvoltage protection function and a safety management system;
and 4, step 4: triggering a first-stage battery model, regarding adjacent abnormal single batteries as an abnormal battery pack, inputting the current of the battery pack as a model, and calculating the predicted voltage output by the model in real time;
and 5: calculating the goodness of fit sigma between the predicted voltage and the actually measured voltage of the first-stage battery model, and the duration T1At the moment, if the goodness of fit sigma is less than the critical threshold value chi1If not, the external short-circuit fault is preliminarily defined, a second-stage battery model is triggered, and the step is entered6；
The goodness of fit σ is defined as: the inverse of the root mean square error of the model predicted results and the actual test results over a certain duration, namely:
where ρ is the number of samples in the duration T, Ut,mFor model prediction of terminal voltage, UtFor on-line measurement of terminal voltage, thetanRepresenting a model parameter matrix.
The duration T is set in this embodiment1＝1.0s，T2＝3.0s，T310.0 s; setting a critical threshold χ13.5, critical threshold χ2＝30。
Critical threshold value χ1The value of the critical threshold value is slightly lower than the calculation result of the goodness of fit of the model during the experiment;
critical threshold value χ2And the value of the critical threshold value is a critical threshold value of the goodness of fit of the second-stage battery model, and the value of the critical threshold value needs to be slightly lower than a model goodness of fit calculation result in an experiment.
The critical threshold value of the model goodness of fit is determined according to the experimental result, and the value of the critical threshold value needs to be slightly lower than the model goodness of fit calculation result in the experiment, so that the condition that the missing judgment does not occur in the diagnosis process can be ensured; according to the experimental results of the external short circuit, the calculation results of the model goodness of fit are shown in table 3:
TABLE 3 model goodness of fit
| Number of experiments | Goodness of fit of first-stage model | Second level model goodness of |
| 1 | 3.9 | 33.8 |
| 2 | 4.1 | 32.4 |
| 3 | 4.4 | 31.7 |
| 4 | 3.7 | 35.5 |
| 5 | 4.6 | 37.1 |
Therefore, a critical threshold χ is set13.5, critical threshold χ2＝30。
Step 6: taking the current of the battery pack as the input of a second-stage battery model, calculating the predicted voltage output by the model in real time, and calculating the goodness of fit sigma between the predicted voltage and the actually measured voltage of the second-stage battery model and the duration T2At the moment, if the goodness of fit sigma is larger than the critical threshold value chi2If the abnormal condition is caused by the external short circuit fault, the position of the abnormal battery monomer is positioned and the step 8 is carried out; otherwise, increase the diagnostic duration to T3And go to step 7;
and 7: repeatedly judging the goodness of fit by adopting a second-stage battery model, and if the goodness of fit sigma is less than a critical threshold value chi2The possibility of external short-circuit faults is eliminated if the goodness of fit sigma > the critical threshold chi2If yes, confirming as an external short-circuit fault;
and 8: and storing and outputting the diagnosis result, returning to the step 3, and waiting for the next operation.
The method comprises the steps of running on line, monitoring the voltage of each single battery of a battery pack in real time by using a battery management system, in the embodiment, carrying out short circuit on the battery pack consisting of 6 batteries, enabling the voltage of the battery pack to rapidly drop below 0.5V and be lower than a critical threshold value, triggering a first-stage battery model, enabling abnormal battery monomers to form an abnormal battery pack according to adjacent individuals, inputting the current of the battery pack as a model, calculating the predicted voltage output by the model in real time, as shown by a solid line of a-1 in a graph in fig. 4, and simultaneously obtaining the measurement result of the terminal voltage of the battery pack on line, as shown by a dotted line of a-1 in a graph in fig. 4, and as shown by a double-stage model error.
Calculating the goodness of fit sigma between the predicted voltage and the actually measured voltage of the model, comparing the goodness of fit with a critical threshold value, and carrying out fault diagnosis according to the logic judgment process in the invention content, wherein in the embodiment, the goodness of fit sigma of the first-stage battery model is approximately equal to 6.34, the second-stage battery model is triggered, in the operation process of the second-stage battery model, the error between the predicted result of the model and the actually measured data is lower than 20mV, and the goodness of fit sigma of the second-stage battery model is approximately equal to 93.7 and is larger than the critical threshold value chi after calculation2And according to the judgment criteria, the external short-circuit fault is confirmed, the online diagnosis process is completed, and the diagnosis result is stored and output, as shown in fig. 5.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions and scope of the present invention as defined in the appended claims.
Claims (5)
1. A lithium ion battery pack external short circuit fault diagnosis method based on two-stage model prediction is characterized by comprising the following steps: the method comprises the following steps:
step 1, carrying out a short circuit experiment outside the battery pack, and recording experimental data including current measurement data Ic＝[Ic1,Ic2,…,IcN]TTerminal voltage measurement data Uc＝[Uc1,Uc2,…,UcN]TN is the number of data samples, the value of N depends on the duration time of current and sampling step length in an external short circuit test, and T represents the transposition of a matrix;
step 2, establishing a double-stage battery model of the external short circuit fault, and respectively carrying out off-line optimality parameter identification on the double-stage battery model through the experimental data obtained in the step 1;
the first-stage model is an improved equivalent circuit model, and the improved method comprises the following steps: the battery state of charge SOC in the traditional equivalent circuit model is improved into the depth of discharge xi in the short circuit processEAnd the open circuit voltage is regarded as the depth of discharge xiEA polynomial function of (a);
the specific mathematical expression form of the first-stage battery model is as follows:
where k denotes the current sampling instant, τ ═ RpCp，Ut、UpAnd UocRespectively representing the terminal voltage, the polarization voltage and the open-circuit voltage of the battery pack; rpAnd R0Then respectively represent the polarization internal resistance and the ohm internal resistance, CpRepresenting the polarization capacitance, iLRepresenting the battery current, ipRepresents RpThe current flowing upwards, delta t is the sampling step length, xiEIndicating a depth of discharge in an external short-circuit fault;
the second-stage battery model is a half-battery model, and the specific mathematical expression form is as follows:
in the formula
Represents a constant voltage source;
and step 3: monitoring the voltage of each single battery in real time by using a battery management system, and when the voltage of partial single batteries is lower than a critical threshold value VnEntering step 4;
and 4, step 4: triggering a first-stage battery model, regarding adjacent abnormal single batteries as an abnormal battery pack, inputting the current of the battery pack as a model, and calculating the predicted voltage output by the model in real time;
and 5: calculating the goodness of fit sigma between the predicted voltage and the actually measured voltage of the first-stage battery model, and the duration T1At the moment, if the goodness of fit sigma is less than the critical threshold value chi1If not, preliminarily defining the external short-circuit fault, triggering a second-stage battery model, and entering the step 6;
step 6: taking the current of the battery pack as the input of a second-stage battery model, calculating the predicted voltage output by the model in real time, and calculating the goodness of fit sigma between the predicted voltage and the actually measured voltage of the second-stage battery model and the duration T2At the moment, if the goodness of fit sigma is larger than the critical threshold value chi2If the abnormal condition is caused by the external short circuit fault, the position of the abnormal battery monomer is positioned and the step 8 is carried out; otherwise, increase the diagnostic duration to T3And go to step 7;
and 7: repeatedly judging the goodness of fit by adopting a second-stage battery model, and if the goodness of fit sigma is less than a critical threshold value chi2The possibility of external short-circuit faults is eliminated if the goodness of fit sigma > the critical threshold chi2If yes, confirming as an external short-circuit fault;
and 8: and storing and outputting the diagnosis result, returning to the step 3, and waiting for the next operation.
2. The lithium ion battery pack external short-circuit fault diagnosis method based on the two-stage model prediction as claimed in claim 1, wherein: what is needed isThe two-stage battery models in the step 2 are divided into two stages in total, wherein the first-stage battery model is a battery integral model, and the second-stage battery model is a half-battery model; the second-stage modeling method is to regard the battery as a two-part equivalent circuit model, and comprises a model 1 and a model 2, namely the sum of the model 1 and the model 2 is a battery integral model, and the second-stage battery model refers in particular to the model 2; in model 1, a variable voltage source is used
And internal resistance R of battery0Short-circuit resistor RSAre connected into a loop; in model 2, there is a constant voltage source
Connected with RC link and generating terminal voltage UtThe RC link consists of a capacitor C and a polarization internal resistance RpAre connected in parallel; the open-circuit voltage of the whole battery is a variable voltage source
And a constant voltage source
And (3) the sum:
3. the lithium ion battery pack external short-circuit fault diagnosis method based on the two-stage model prediction as claimed in claim 1, wherein: the off-line optimality parameter in step 2 is identified as the experimental current measurement value IcAs model input, terminal voltage output U ═ U1,U2,…,UN]TAs model output, performing offline optimality identification on the model parameters in the step 2 by using a global optimization algorithm, wherein the identification process of the model needs to respectively identify the parameters of the two-stage battery model, and the two-stage battery is powered onThe parameters of the pool model are independent of each other.
4. The lithium ion battery pack external short-circuit fault diagnosis method based on the two-stage model prediction as claimed in claim 1, wherein: the definition of the goodness of fit σ in the step 5 is as follows: the inverse of the root mean square error of the model predicted results and the actual test results over a certain duration, namely:
where ρ is the number of samples in the duration T, UtM is the model prediction result of terminal voltage, UtFor on-line measurement of terminal voltage, thetanRepresenting a model parameter matrix.
5. The lithium ion battery pack external short-circuit fault diagnosis method based on the two-stage model prediction as claimed in claim 1, wherein: the critical threshold value χ in the step 51The value of the critical threshold value is slightly lower than the calculation result of the goodness of fit of the model during the experiment;
the critical threshold value χ in the step 72And the value of the critical threshold value is a critical threshold value of the goodness of fit of the second-stage battery model, and the value of the critical threshold value needs to be slightly lower than a model goodness of fit calculation result in an experiment.
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| CN113711070B (en) * | 2020-12-15 | 2024-09-17 | 东莞新能德科技有限公司 | Method for detecting short circuit in battery, electronic device and storage medium |
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