Diagnosis method for liquid leakage battery based on second-order equivalent circuit model

Abstract

A kind of leakage battery diagnosis method based on second-order equivalent circuit model, by based on lithium battery second-order equivalent circuit model, HPPC test is carried out to leakage battery and normal battery, the model parameters of battery are identified by least square method, then the model parameter differences of normal battery and leakage battery under different SOC are compared, obtain the feature parameter set related to battery leakage fault, then each parameter in feature parameter set is uniformly represented in the form of time variation rate, the feature parameter threshold for distinguishing leakage battery and normal battery is given, the steps of test, obtaining parameter set and distinguishing diagnosis are used to realize battery leakage fault diagnosis, effectively identify whether battery has leakage.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of lithium ion battery fault diagnosis, and relates to a liquid leakage battery diagnosis method based on a second-order equivalent circuit model. BACKGROUND

[0002] With the rapid development of battery technology, lithium ion batteries have become an indispensable part in electric vehicles and energy storage power stations. Due to factors such as unqualified external packaging and harsh use environment, the problem of excessive internal pressure may cause the battery to leak, which may further cause the battery to have an external short circuit, and in severe cases, may cause thermal runaway. This seriously limits the safe operation of electric vehicles and the reliable output of energy storage power stations. Therefore, it is necessary to diagnose the faults of lithium ion batteries.

[0003] When a lithium ion battery leaks, the change of the battery parameters is not clear, and the existing technology cannot directly detect battery leakage. Therefore, how to obtain a set of characteristic parameters related to battery leakage faults and use the characteristic parameters to diagnose the liquid leakage battery is a key problem to be solved at present. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a liquid leakage battery diagnosis method based on a second-order equivalent circuit model. The second-order equivalent circuit model of the lithium battery is used to test the HPPC of the liquid leakage battery and the normal battery, the model parameters of the battery are identified by the least square method, the differences in the model parameters of the normal battery and the liquid leakage battery under different SOC are compared, a set of characteristic parameters related to the battery leakage fault is obtained, and each parameter in the set of characteristic parameters is uniformly represented in the form of time change rate. The characteristic parameter threshold for distinguishing the liquid leakage battery and the normal battery is given, and the steps of testing, obtaining the parameter set and distinguishing diagnosis are used to realize the battery leakage fault diagnosis, and the battery leakage is effectively identified.

[0005] To solve the above technical problems, the technical solution adopted by the present application is: a liquid leakage battery diagnosis method based on a second-order equivalent circuit model, which comprises the following steps:

[0006] Step 1, testing, based on the second-order equivalent circuit model of the lithium battery, the HPPC of the liquid leakage battery and the normal battery is tested, and the model parameters of the battery are identified by the least square method;

[0007] Step 2, obtaining the parameter set, comparing the differences in the model parameters of the normal battery and the liquid leakage battery under different SOC, and obtaining a set of characteristic parameters related to the battery leakage fault;

[0008] Step 3, distinguishing diagnosis, uniformly representing each parameter in the set of characteristic parameters in the form of time change rate, giving the characteristic parameter threshold for distinguishing the liquid leakage battery and the normal battery, and realizing the battery leakage fault diagnosis.

[0009] In step 1, the lithium-ion battery second-order equivalent circuit model is composed of two RC network structures and a resistance in series; the mathematical expression of the model is

[0010] where U oc represents the open-circuit voltage of the battery, R o represents the ohmic internal resistance of the battery, R p1 and R p2 represent the polarization internal resistance of the battery, C p1 and C p2 represent the polarization capacitance of the battery; I represents the load current; U o represents the terminal voltage of the battery which can be directly measured.

[0011] The calculation formula of the model terminal voltage V o (t) is obtained from equation (1):

[0012]

[0013] where R o is the 1s ohmic internal resistance, and the calculation formula is:

[0014]

[0015] Based on the nonlinear characteristics of the battery, the identification problem of R p1 , R p2 , C p1 , and C p2 is converted into a nonlinear optimization problem; as can be seen from equation (2), for any θ Id =[R p1 , R p2 , C p1 , C p2 ] parameter, there is a unique U o (t) corresponding to it at any time t,

[0016]

[0017] A nonlinear least squares optimization model is constructed,

[0018]

[0019] The global optimal solution θ Id of the nonlinear least squares optimization model is solved, that is, the parameter identification of the second-order equivalent circuit model is completed.

[0020] In step 2, the model parameters of the battery have a strong correlation with the SOC state in which the battery is located; when the battery is at different SOC points, the lithium intercalation states of the positive and negative electrode materials of the battery are different, the internal electrochemical reaction process is also different, and therefore the model parameters are also different.

[0021] The parameter set of the second-order model θ Model = [U oc , R o , R p1 , R p2, C p1 , C p2, τ1, τ2]; θ at different SOC states is selected to represent the leakage failure of the lithium ion battery; the characteristic parameter set θ = [R o , R p1 , C p2 , τ2] representing the leakage failure of the battery is extracted, wherein τ1, τ2 are time constants.

[0022] In step 3, the aged leakage battery and the normal battery have different health states, and each parameter in the characteristic parameter set is uniformly represented in the form of a time change rate, thereby enhancing the adaptability of the characteristic parameter threshold to different batteries.

[0023] Each parameter in the characteristic parameter set is uniformly represented in the form of a time change rate, θ is the characteristic parameter of the battery after aging, θ0 is the characteristic parameter of the battery at the initial moment, T is the interval time between two tests, and θ CR = [R o(CR) , R p1(CR) , C p2(CR) , τ 2(CR) ]; according to the parameter representation result, the characteristic parameter threshold for distinguishing the leakage battery and the normal battery is obtained, and the leakage failure diagnosis of the lithium ion battery is realized.

[0024] The main beneficial effects of the present application are as follows:

[0025] The present application is based on the second-order equivalent circuit model of the lithium ion battery, and the differences in the model parameters of the normal battery and the leakage battery at different SOC are compared to obtain a characteristic parameter set that can reflect the leakage failure of the lithium ion battery.

[0026] The characteristic parameters are uniformly represented in the form of a time change rate, the characteristic parameter threshold for distinguishing the leakage battery and the normal battery is obtained, and whether the battery has a leakage failure can be effectively identified. BRIEF DESCRIPTION OF DRAWINGS

[0027] The present application will be further described below in combination with the drawings and examples.

[0028] Figure 1 The flowchart of the present application.

[0029] Figure 2 Figure for the second order equivalent circuit model of the lithium ion battery of the present application.

[0030] Figure 3 Figure for the variation of the model parameters of the normal battery and the leaking battery with SOC.

[0031] Figure 4 Figure for the variation of the model parameters of the normal battery and the leaking battery with SOC.

[0032] Figure 5 Figure for the characteristic parameter representation of the battery leakage fault and the threshold value selection. DETAILED DESCRIPTION

[0033] In the preferred embodiment, a method for diagnosing a leaking battery based on a second order equivalent circuit model comprises the following steps: Figures 1-5 Step 1, testing, based on the second order equivalent circuit model of the lithium ion battery, HPPC testing is performed on the leaking battery and the normal battery, and the model parameters of the battery are identified by the least square method;

[0034] Step 2, obtaining the parameter set, comparing the differences in the model parameters of the normal battery and the leaking battery at different SOCs, and obtaining the characteristic parameter set related to the battery leakage fault;

[0035] Step 3, distinguishing diagnosis, uniformly representing each parameter in the characteristic parameter set in the form of time change rate, giving the characteristic parameter threshold value for distinguishing the leaking battery and the normal battery, and realizing the battery leakage fault diagnosis.

[0036] In the preferred embodiment, in step 1, the second order equivalent circuit model of the lithium ion battery is composed of two RC network structures and a resistor in series; the mathematical expression of the model is

[0037] where U oc represents the open circuit voltage of the battery, R o represents the ohmic internal resistance of the battery, R p1 and R p2 represent the polarization internal resistance of the battery, C p1 and C p2 represent the polarization capacitance of the battery; I represents the load current; U o represents the terminal voltage of the battery which can be directly measured.

[0038] In the preferred embodiment, the calculation formula of the model terminal voltage V o (t) is obtained from formula (1):

[0039]

[0040] ​

[0041] Among them, R o The internal resistance is 1 ohm, and the calculation formula is:

[0042]

[0043] Based on the nonlinear characteristics of the circuit, R p1 ,R p2 C p1 C p2 The identification problem is transformed into a nonlinear optimization problem; from equation (2), it can be seen that for any θ Id =[R p1 ,R p2 C p1 C p2 The parameter U is uniquely determined at any time t. o (t) corresponds to this,

[0044]

[0045] Construct a nonlinear least squares optimization model.

[0046]

[0047] Find the global optimal solution θ of this nonlinear least squares optimization model. Id That is, to complete the parameter identification of the second-order equivalent circuit model.

[0048] In a preferred embodiment, in step 2, the model parameters of the battery are strongly correlated with its SOC state; when the battery is at different SOC points, the lithium intercalation state of the positive and negative electrode materials of the battery is different, and the internal electrochemical reaction process is also different, so the model parameters are also different.

[0049] In the preferred scheme, the parameter set θ of the second-order model Model =[U oc ,R o ,R p1 ,R p2, C p1 C p2, τ1,τ2]; θ at different SOC states is used to characterize leakage faults in lithium-ion batteries; the extracted feature parameter set θ = [R] is used to characterize battery leakage faults. o ,R p1 C p2 ,τ2], where τ1,τ2 are time constants.

[0050] Preferably, HPPC tests are performed on normal batteries and leaking batteries at the initial moment and after a period of time to obtain θ. ModelAfter a period of time, the θ of the leaking battery and the normal battery model Compared to the initial time, the trend of change is consistent, but some parameters of the leaking battery differ significantly from those of the battery after normal aging over the same time interval. Therefore, θ at different SOC states is used to characterize the leakage fault in lithium-ion batteries.

[0051] In a preferred embodiment, in step 3, the aged leaking battery and the normal battery have different health states. The parameters in the feature parameter set are uniformly characterized in the form of time change rate, thereby enhancing the adaptability of the feature parameter threshold to different batteries.

[0052] In a preferred embodiment, each parameter in the feature parameter set is uniformly characterized in the form of a rate of change over time. θ represents the characteristic parameters of the battery after aging, θ0 represents the characteristic parameters of the battery at the initial moment, and T represents the time interval between the two tests. CR =[R o(CR) ,R p1(CR) C p2(CR) ,τ 2(CR) Based on the parameter characterization results, characteristic parameter thresholds that distinguish between leaking batteries and normal batteries are obtained, thereby realizing the diagnosis of lithium-ion battery leakage faults.

[0053] Specifically,

[0054] First, HPPC testing was performed on normal and leaking batteries to obtain their terminal voltage and current, based on a second-order equivalent circuit model, such as... Figure 2 The least squares method is used to identify the model parameters.

[0055] The model parameters of lithium-ion batteries are highly dependent on their state of charge (SOC). When the battery is at different SOC points, the lithium intercalation state of the positive and negative electrode materials is different, the internal electrochemical reaction process is different, and the model parameters are therefore different.

[0056] The model parameters θ were obtained for normal and leaking batteries after aging under different SOCs. Model By comparison, it can be seen that some parameters differ significantly, such as τ2; while the differences in some parameters are not significant, such as τ1. Figure 3 and Figure 4 .

[0057] Since the health status of an aged, leaking battery differs from that of a normal battery, the parameters in the characteristic parameter set are uniformly characterized in the form of time-varying rates.

[0058] Battery leakage fault characterization results based on second-order equivalent circuit model parameters and the selection of thresholds for diagnosing whether battery leakage has occurred, such as using τ 2(CR) For example, see Figure 5 As shown.

[0059] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The embodiments and features described in these embodiments can be arbitrarily combined without conflict. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A method for diagnosing leaking batteries based on a second-order equivalent circuit model, characterized in that, It includes the following steps: Step 1, Testing: Based on the second-order equivalent circuit model of lithium battery, HPPC testing is performed on leaking batteries and normal batteries to identify the battery model parameters using the least squares method. Step 2: Obtain the parameter set, compare the differences in model parameters between normal batteries and leaking batteries under different SOCs, and obtain the feature parameter set related to battery leakage faults; Step 3, differentiate diagnosis: uniformly represent each parameter in the feature parameter set in the form of time change rate, give the feature parameter threshold to distinguish between leaking batteries and normal batteries, and realize battery leakage fault diagnosis. In step 2, the battery model parameters are strongly correlated with its SOC state; when the battery is at different SOC points, the lithium insertion state of the positive and negative electrode materials is different, and the internal electrochemical reaction process is also different, so the model parameters are also different. Parameter set of the second-order model Characteristic parameter sets under different SOC states are selected to characterize leakage faults in lithium-ion batteries; among them This indicates the open-circuit voltage of the battery. This indicates the ohmic internal resistance of the battery. and This indicates the polarization internal resistance of the battery. and Indicates the polarization capacitance of the battery. , It is a time constant; HPPC tests were performed on normal and leaking batteries at the initial moment and after a certain period of time, and the results were obtained. After a period of time, the leaking battery and the normal battery... Compared with the initial time, the trend of change is consistent, but some parameters of the leaking battery are very different from those of the battery after normal aging at the same time interval; therefore, characteristic parameter sets under different SOC states are selected to characterize the leakage fault of lithium-ion batteries. In step 3, the aged leaking battery and the normal battery have different health states. The parameters in the feature parameter set are uniformly characterized in the form of time change rate, which enhances the adaptability of the feature parameter threshold to different batteries.

2. The method for diagnosing leaking batteries based on a second-order equivalent circuit model according to claim 1, characterized in that: in In step 1, the second-order equivalent circuit model of the lithium-ion battery consists of two RC network structures and a resistor connected in series; the mathematical expression of this model is: (1); I represents the load current; This indicates the battery's terminal voltage, which can be measured directly. .

3. The method for diagnosing leaking batteries based on a second-order equivalent circuit model according to claim 2, characterized in that: The model terminal voltage is obtained from equation (1). The calculation formula is as follows: （2）； in, The internal resistance is 1 ohm, and the calculation formula is: （3）； Based on the nonlinear characteristics of batteries, , , , The identification problem is transformed into a nonlinear optimization problem; from equation (2), it can be seen that for any Parameters, arbitrary There is always one unique certainty. Correspondingly, (4); Construct a nonlinear least squares optimization model. （5）； Find the global optimal solution of this nonlinear least squares optimization model. That is, to complete the parameter identification of the second-order equivalent circuit model.

4. The method for diagnosing leaking batteries based on a second-order equivalent circuit model according to claim 1, characterized in that: Each parameter in the feature parameter set is uniformly represented in the form of a rate of change over time. , This is a set of characteristic parameters after battery aging. This is the set of characteristic parameters of the battery at its initial moment. The interval between two tests; Based on the parameter characterization results, characteristic parameter thresholds that distinguish between leaking batteries and normal batteries are obtained, thereby enabling fault diagnosis of lithium-ion batteries leaking.

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