CN114152826B - Method for detecting short circuit in lithium ion battery unit - Google Patents
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Abstract
Compared with the prior art, the method for detecting the internal short circuit of the lithium ion battery cell does not need a model-based SOC estimation process, avoids the adverse effect of the defects of the selected model on the precision, and has small calculation amount. In the method, the current dOCV/dQ of the battery to be detected is updated in real time by adopting the LMRLS, so that the calculated amount and the data storage amount are greatly reduced. The whole detection process of the internal short circuit does not depend on the temperature rise phenomenon, so that the limitation of the specification index, the installation position and the like of the temperature sensor can be avoided. Because the method does not depend on data or parameter comparison among the battery cells, the method is suitable for both single battery cells and grouped scenes, and particularly has higher applicability to the lithium iron phosphate battery with a flat open-circuit voltage curve.
Description
Technical Field
The invention belongs to the technical field of battery fault detection, and particularly relates to an internal short circuit detection method suitable for a lithium ion battery monomer.
Background
With the popularization of lithium ion batteries and electric vehicles, the electric vehicle fire accident caused by battery thermal runaway often occurs, and an internal short circuit is one of important inducers of thermal runaway and needs to be rapidly and accurately detected when necessary. In the prior art, methods for detecting short circuits in lithium ion batteries are roughly classified into the following types: 1) Threshold value method: the internal short circuit detection is realized by judging whether the voltage, the temperature, the voltage change rate or the temperature change rate of the battery exceeds a preset threshold value; 2) Electric quantity consumption method: the actual consumed electric quantity of the battery is obtained through an accurate SOC estimation process, and is compared with the used electric quantity (output end current integral) to obtain the battery leakage condition so as to realize detection; 3) Remaining chargeable capacity method: judging the leakage condition of the battery according to the difference of the residual chargeable capacities in the two adjacent charging processes; 4) Comparative analysis method: for the series battery pack, internal short circuit detection is realized by comparing consistency and correlation of voltage or other parameters among single batteries; 5) Sensor measurement: for the parallel battery pack, the detection of the internal short circuit can be realized through a ring circuit topological structure and a circuit sensor.
However, these methods have some drawbacks that limit their applicability or effectiveness, such as thresholding, which tends to be difficult to identify in the early stages of a relatively small degree, and limited by the placement of the temperature sensors; the electric quantity consumption method is greatly influenced by SOC estimation accuracy and is limited by a flat open-circuit voltage curve of the lithium iron phosphate battery, and the method is often difficult to be applied to the lithium iron phosphate battery; the residual chargeable capacity rule needs to be carried out in a charging scene, and cannot be used for sudden internal short circuit faults under the condition of battery discharging operation; the contrastive analysis method requires mutual contrast among the battery cells and cannot be used for scenes of single battery cells; sensor measurements rely on the topology of the circuit and additional current sensors, and are limited in applicability. Therefore, how to provide a more accurate internal short circuit detection method with less self-limitation and wide applicability is a technical problem to be solved in the art.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a method for detecting short circuit in a lithium ion battery cell, which specifically comprises the following steps:
s1, carrying out a small-rate discharge test on a single battery, and respectively obtaining the relationship among the open-circuit voltage OCV, the discharge capacity Q and the state of charge SOC of the single battery;
s2, calculating a standard value of dOCV/dQ according to the relationship between the OCV and the Q obtained in the step S1;
s3, measuring voltage and current data of the target lithium ion battery in real time;
s4, establishing an equivalent circuit model considering the internal short circuit condition for the target lithium ion battery, utilizing the voltage and current data obtained by measurement in the step S3, identifying model parameters on line based on a recursive least square method with forgetting factors, and calculating open-circuit voltage;
s5, aiming at the linear relation among the open-circuit voltage, the discharge capacity and the dOCV/dQ, updating and calculating the dOCV/dQ value in real time based on a recursive least square method and a limited memory recursive least square method by utilizing the open-circuit voltage identified in the step S4 and the measured accumulated discharge capacity;
and S6, comparing the dOCV/dQ value obtained by calculation in S5 with the OCV/dQ standard value obtained in the step S2, and determining that an internal short-circuit fault occurs when the difference value of the two exceeds a preset threshold value, otherwise, returning to S3 to continue to perform detection at the next moment.
Further, the specific process of the small-rate discharge test performed in step S1 includes: firstly, charging a battery monomer to full charge electric quantity in a constant current and constant voltage mode, standing for more than 3 hours, and then discharging to lower cut-off voltage in a constant current mode at a multiplying power less than or equal to 1/20C; the open circuit voltage is equivalent by the terminal voltage in the discharging process; the discharge test also comprises the step of repeatedly carrying out the same battery monomer at different temperatures respectively so as to obtain the open-circuit voltage of the battery at different temperatures; the relation between the equation capacity Q and the state of charge SOC is calculated by using an ampere-hour integration method.
Further, the calculation method of calculating the standard value of dcv/dQ in step S2 includes the following steps:
1) Taking the discharge capacity Q as an independent variable and the open-circuit voltage OCV as a dependent variable, and solving corresponding derivatives under different independent variables to obtain corresponding dOCV/dQ standard values;
2) According to the total capacity of the battery and the relation between the open-circuit voltage OCV and the SOC, spline sampling and derivation are adopted, and the result is converted into a dOCV/dQ standard value result;
the calculation of the standard value of the dOCV/dQ comprises the calculation aiming at different temperature conditions respectively.
Further, step S4 specifically includes the following steps:
1) Selecting one of a Rint model, a first-order RC model, a second-order RC model and the like to establish an equivalent circuit model considering the internal short circuit condition;
2) And identifying the model parameters of the equivalent circuit model according to the voltage and current data measured in the step S3 and based on a recursive least square method with forgetting factors, wherein the process is as follows:
in the formula, mu is a forgetting factor, and is usually 0.95 & lt mu & lt 1;
in order to input the vector of data,
for the parameter vector to be identified, the superscript Λ represents the estimated value of the corresponding parameter,
is an output; k is LS,k For RLS gain, P LS,k Is a covariance matrix, I is an identity matrix, and k represents the current time;
3) Calculating and identifying equivalent circuit model open-circuit voltage
Further, step S5 specifically includes the following processes:
1) Setting the length L of a memory window;
2) Using linear equations
Describes the linear relationship among the open-circuit voltage, the discharge capacity and dOCV/dQ, where y k ＝U OCV,k ，
Q Ah,k For the accumulated discharge capacity obtained by ampere-hour integration, the parameter vector theta = [ alpha beta ]] T Where the term α is dOCV/dQ, and subscript k denotes the current time;
3) By calculation
And Q Ah,k When k is less than or equal to L, updating the parameter vector theta by adopting a recursive least square method, as shown in the following formula:
when k is larger than L, updating the parameter vector theta by using a finite memory recursive least square method, wherein the method comprises the following two processes:
1) New data was introduced as shown by the following equation:
in the formula, P LS,k-L,k-1 And
respectively representing covariance matrix and parameter vector estimated value formed by L groups of measurement data from k-L to k-1; p is LS,k-L,k And
respectively representing a covariance matrix and a parameter vector estimation value formed by L +1 groups of measurement data from k-L to k; k LS,k-L,k Is the gain;
2) Removing old data as shown in the following formula:
in the formula, P LS,k-L+1,k And
respectively representing a covariance matrix and a parameter vector estimation value formed by L groups of measurement data from k-L +1 to k moments; k LS,k-L+1,k In order to obtain the gain of the gain,
i.e. the identification value that needs to be updated at the current moment.
Further, step S6 specifically includes:
1) Calculating the SOC of the target lithium ion battery by using the measured data based on an ampere-hour integration method, and determining the dOCV/dQ standard value at the current k moment by combining the relationship between the dOCV/dQ standard value obtained in the step S2 and the SOC;
2) And D, comparing the time k of dOCV/dQ calculated in the step S5 with the standard value of the time k-L/2 in a delayed manner, and determining whether the internal short circuit condition occurs.
Compared with the prior art, the method for detecting the short circuit in the lithium ion battery unit provided by the invention at least has the following beneficial effects:
1. the method does not need a model-based SOC estimation process, avoids the adverse effect of the defects of the selected model on the precision, has small calculated amount and has high practicability.
2. The current dOCV/dQ of the battery to be detected is updated in real time by adopting the LMRLS, so that the calculated amount and the data storage amount are greatly reduced.
3. The method does not depend on the temperature rise phenomenon in the detection process of the internal short circuit, so that the method is not limited by the specification indexes, the installation positions and the like of the temperature sensor.
4. Because data or parameter comparison among the cells is not relied on, the method is suitable for both single cells and grouped scenes.
5. The method has high applicability particularly to the lithium iron phosphate battery with a flat open-circuit voltage curve.
Drawings
FIG. 1 is a flow chart of the overall steps of the method provided by the present invention;
FIG. 2 is a graph of OCV versus SOC and a graph of dOCV/dQ versus SOC obtained in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a first-order RC model for considering internal short circuits according to an embodiment of the present invention;
FIG. 4 shows the results of a test under no short circuit condition in an embodiment of the present invention;
fig. 5 shows the result of the detection under the internal short circuit condition in the embodiment of the present invention.
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 detecting short circuit in a lithium ion battery cell, as shown in fig. 1, which specifically comprises the following steps:
s1, carrying out a small-rate discharge test on a single battery, and respectively obtaining the relationship among the open-circuit voltage OCV, the discharge capacity Q and the state of charge SOC of the single battery;
s2, calculating a standard value of dOCV/dQ according to the relationship between the OCV and the Q obtained in the step S1;
s3, measuring voltage and current data of the target lithium ion battery in real time;
s4, establishing an equivalent circuit model considering the internal short circuit condition for the target lithium ion battery, utilizing the voltage and current data obtained by measurement in the step S3, identifying model parameters on line based on a recursive least square method with forgetting factors, and calculating open-circuit voltage;
s5, aiming at the linear relation among the open-circuit voltage, the discharge capacity and the dOCV/dQ, updating and calculating the dOCV/dQ value in real time based on a recursive least square method and a limited memory recursive least square method by using the open-circuit voltage identified in the step S4 and the measured accumulated discharge capacity;
and S6, comparing the dOCV/dQ value obtained by calculation in S5 with the OCV/dQ standard value obtained in the step S2, and determining that an internal short-circuit fault occurs when the difference value of the two exceeds a preset threshold value, otherwise, returning to S3 to continue to perform detection at the next moment.
In a preferred embodiment of the present invention, the specific process of the small-rate discharge test performed in step S1 includes: firstly, charging a battery monomer to full charge capacity in a constant-current constant-voltage mode, standing for more than 3 hours, and then discharging to lower cut-off voltage of 2.5V in a constant-current manner at a rate of 1/20C; the collected voltage is regarded as the OCV of the battery because the discharge rate is lower; and the discharge capacity and the SOC are calculated by an ampere-hour integration method according to the acquired current data. The solid line in fig. 2 shows the OCV versus SOC.
In a preferred embodiment of the present invention, in step S2, the relationship between the dvcv/dQ standard value and the discharge capacity is first obtained by fitting and deriving a smooth spline curve, and further, the relationship between the dvcv/dQ and the discharge capacity is converted into a relationship between the dvcv/dQ and the SOC according to the total battery capacity, as shown by a dashed-dotted line in fig. 2.
In a preferred embodiment of the present invention, the target battery to be detected is tested in the online detection stage under Dynamic Stress Test (DST) and Federal Urban operation (FUDS). And acquiring current and voltage data of the battery under corresponding current working conditions in real time at a sampling frequency of 1Hz through a sensor.
In a preferred embodiment of the present invention, step S4 specifically includes the following steps:
1) A first order RC model (including the short circuit resistance) as shown in fig. 3 is selected to establish an equivalent circuit model for the case of the internal short circuit under consideration, as shown in the following equation:
wherein t represents time; u shape t ,U OCV ,U 1 Respectively representing a battery terminal voltage, an open circuit voltage and a polarization voltage; r is 0 Is the ohmic internal resistance of the battery, C 1 For polarization capacitance, polarization time constant tau 1 ＝R 1 C 1 ，R 1 Is the polarization internal resistance; i denotes the current actually passing through the battery, represented by the operating current (i.e. the measured current) I load And short-circuit current I SC Forming; r SC Then the equivalent internal short circuit resistance is represented;
at U OCV,k ≈U OCV,k-1 Under the assumption that the model is discretized into the following linear equation form by a bilinear transformation method:
in the formula: y is k ＝U t,k ，
θ k ＝[(1-a 1 )U OCV,k a 1 a 2 a 3 ] T ；
Δ t is the sampling interval, I = I when no internal short circuit occurs load 。
When considering the internal short circuit condition, the short circuit current I is not measured SC The existence of I cannot be accurately measured, and an input vector is required
I to I in load The above linear equation can be converted into the following form:
in the formula:
2) And identifying the model parameters of the equivalent circuit model according to the voltage and current data measured in the step S3 and based on a recursive least square method with forgetting factors, wherein the process is as follows:
in the formula, mu is a forgetting factor, and is usually 0.95 & lt mu & lt 1;
in order to input the vector of data,
for the parameter vector to be identified, the superscript Λ represents the estimated value of the corresponding parameter,
is an output; k LS,k For RLS gain, P LS,k Is a covariance matrix, I is an identity matrix, and k represents the current moment;
to improve the performance of the method, the forgetting factor μ is adaptively adjusted according to the following formula:
wherein C represents the battery capacity, | I k I/C indicates the current multiplying power condition of the battery, and zeta is a self-defined parameter for adjusting mu when the zeta is more than or equal to 0 k Approximate numerical ranges of (d);
3) Calculating and identifying equivalent circuit model open-circuit voltage
In the formula (I), the compound is shown in the specification,
and
respectively corresponding to the parameter vectors
A first element and a second element. Note that when no internal short circuit occurs, the short-circuit resistance R SC Approaching infinity, the OCV calculated by the above equation will be substantially equal to the battery actual OCV; and after an internal short circuit occurs, the OCV calculated according to the above equation will deviate to:
significantly lower than the actual OCV. This deviation phenomenon is a key element of the method to be able to detect internal short circuits.
In a preferred embodiment of the present invention, step S5 specifically includes the following processes:
1) Setting the length L of a memory window;
2) Using linear equations
Describes the linear relationship among the open-circuit voltage, the discharge capacity and dOCV/dQ, where y k ＝U OCV,k ，
Q Ah,k For the accumulated discharge capacity obtained by ampere-hour integration, the parameter vector theta = [ alpha beta ]] T Wherein the term α is dOCV/dQ, and subscript k represents the current time;
3) Using calculation
And Q Ah,k When k is less than or equal to L, updating the parameter vector theta by adopting a recursive least square method, as shown in the following formula:
when k is larger than L, updating the parameter vector theta by using a finite memory recursive least square method, wherein the method comprises the following two processes:
1) New data was introduced as shown by the following equation:
in the formula, P LS,k-L,k-1 And
respectively representing a covariance matrix and a parameter vector estimation value formed by L groups of measurement data from k-L to k-1; p LS,k-L,k And
respectively representing a covariance matrix and a parameter vector estimation value formed by L +1 groups of measurement data from k-L to k; k is LS,k-L,k Is the gain;
2) Removing old data as shown in the following formula:
in the formula, P LS,k-L+1,k And
respectively representing a covariance matrix and a parameter vector estimation value formed by L groups of measurement data from k-L +1 to k moments; k LS,k-L+1,k In order to achieve the gain,
i.e. the identification value that needs to be updated at the current moment.
Further, step S6 specifically includes:
1) Calculating the SOC of the target lithium ion battery by using the measured data based on an ampere-hour integration method, and determining the dOCV/dQ standard value at the current k moment by combining the relationship between the dOCV/dQ standard value obtained in the step S2 and the SOC;
2) And (4) time delay comparison is carried out on the dOCV/dQ calculated at the time k through the step S5 and the standard value of the time k-L/2 (in the embodiment, the time k-500), and whether the internal short circuit condition occurs or not is determined.
In the battery used in the preferred embodiment, the detection is performed only in the battery normal section of 10-93% soc, considering that the change in dcv/dQ in the 0-10% and 93-100% soc sections is too significant to be effective for the detection of internal short circuits.
Fig. 4 and fig. 5 show the verification results of the online detection stage of the method in this embodiment. In fig. 4, the battery normally operates under the DST and FUDS conditions, respectively, and it can be seen that the dcv/dQ tracking value calculated in step S5 is always closer to the reference value and is maintained within the threshold. In fig. 5, during the operation of the battery, the internal short circuit conditions of different degrees (i.e. different short circuit currents) are simulated in the form of external parallel equivalent loads; the results show that within a short time after the short circuit trigger, the dcv/dQ trace value calculated in step S5 deviates from the standard value and exceeds the threshold value to successfully detect an internal short circuit fault. Although to a lesser extent internal short-circuiting (I) SC The detection performance of = 5A) is degraded.
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 (4)
1. A method for detecting short circuit in a lithium ion battery unit is characterized by comprising the following steps: the method specifically comprises the following steps:
s1, carrying out a small-rate discharge test on a single battery, and respectively obtaining the relationship among the open-circuit voltage OCV, the discharge capacity Q and the state of charge SOC of the single battery;
s2, calculating a standard value of dOCV/dQ according to the relationship between the OCV and the Q obtained in the step S1;
s3, measuring voltage and current data of the target lithium ion battery in real time;
s4, 1) selecting one of a Rint model, a first-order RC model and a second-order RC model for the target lithium ion battery to establish an equivalent circuit model considering the internal short circuit condition;
2) And (3) utilizing the voltage and current data obtained by the measurement in the step (S3) and identifying model parameters on line based on a recursive least square method with forgetting factors:
in the formula, mu is a forgetting factor, and mu is more than 0.95 and less than 1;
in order to input the vector of data,
for the parameter vector to be identified, the superscript Λ represents the estimated value of the corresponding parameter,
is an output; k is LS,k For RLS gain, P LS,k Is a covariance matrix, I is an identity matrix, and k represents the current moment;
3) Calculating and identifying open-circuit voltage of equivalent circuit model
S5, aiming at the linear relation among the open-circuit voltage, the discharge capacity and the dOCV/dQ, the open-circuit voltage identified in the step S4 and the measured accumulated discharge capacity are utilized, and the dOCV/dQ value is updated and calculated in real time based on a recursive least square method and a limited memory recursive least square method, wherein the specific process comprises the following steps of:
1) Setting the length L of a memory window;
2) Using linear equations
Describes the linear relationship among the open-circuit voltage, the discharge capacity and dOCV/dQ, where y k ＝U OCV,k ，
Q Ah,k For the accumulated discharge capacity obtained by ampere-hour integration, the parameter vector theta = [ alpha beta ]] T Where the term α is dOCV/dQ, and subscript k denotes the current time;
3) Using calculation
And Q Ah,k When k is less than or equal to L, updating the parameter vector theta by adopting a recursive least square method, as shown in the following formula:
when k is larger than L, updating the parameter vector theta by using a finite memory recursive least square method, wherein the method comprises the following two processes:
1) New data was introduced as shown by the following formula:
in the formula, P LS,k-L,k-1 And
respectively representing a covariance matrix and a parameter vector estimation value formed by L groups of measurement data from k-L to k-1; p LS,k-L,k And
then respectively represent the total of L +1 sets of measurements from k-L to kMeasuring a covariance matrix and a parameter vector estimation value formed by data; k LS,k-L,k Is the gain;
2) Culling old data, as shown in the following equation:
in the formula, P LS,k-L+1,k And
respectively representing covariance matrix and parameter vector estimated value formed by L groups of measurement data from k-L +1 to k moments; k LS,k-L+1,k In order to achieve the gain,
namely the identification value which needs to be updated at the current moment;
and S6, comparing the dOCV/dQ value obtained by calculation in the S5 with the OCV/dQ standard value obtained in the step S2, determining that an internal short-circuit fault occurs when the difference value of the dOCV/dQ value and the OCV/dQ standard value exceeds a preset threshold value, and returning to the S3 to continue to perform detection at the next moment if the difference value of the dOCV/dQ value and the OCV/dQ standard value exceeds the preset threshold value.
2. The method of claim 1, wherein: the specific process of the small-rate discharge test developed in the step S1 includes: firstly, charging a battery monomer to full charge electric quantity in a constant current and constant voltage mode, standing for more than 3 hours, and then discharging to lower cut-off voltage in a constant current mode at a multiplying power less than or equal to 1/20C; the open circuit voltage is equivalent by the terminal voltage in the discharging process; the discharge test also comprises the step of repeatedly carrying out the same battery monomer at different temperatures respectively so as to obtain the open-circuit voltage of the battery at different temperatures; the relationship between the discharge capacity Q and the state of charge SOC is calculated by an ampere-hour integration method.
3. The method of claim 2, wherein: the calculation method for calculating the standard value of dcv/dQ in step S2 includes the following steps:
1) Taking the discharge capacity Q as an independent variable and the open-circuit voltage OCV as a dependent variable, and solving corresponding derivatives under different independent variables to obtain a corresponding dOCV/dQ standard value;
2) According to the total capacity of the battery and the relation between the open-circuit voltage OCV and the SOC, sampling and derivation are carried out by adopting a spline, and a dOCV/dQ standard value result is converted;
the calculation of the standard value of the dOCV/dQ comprises the calculation for different temperature conditions.
4. The method of claim 1, wherein: step S6 specifically includes:
1) Calculating the SOC of the target lithium ion battery by using the measured data based on an ampere-hour integration method, and determining the dOCV/dQ standard value at the current k moment by combining the relationship between the dOCV/dQ standard value obtained in the step S2 and the SOC;
2) And D, comparing the time k of dOCV/dQ calculated in the step S5 with the standard value of the time k-L/2 in a delayed manner, and determining whether the internal short circuit condition occurs.
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