CN116520194A - Diagnosis method for short-circuit fault and capacity loss in lithium ion battery - Google Patents
Diagnosis method for short-circuit fault and capacity loss in lithium ion battery Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 39
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 22
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 22
- 238000003745 diagnosis Methods 0.000 title claims abstract description 20
- 238000001914 filtration Methods 0.000 claims abstract description 31
- 239000000178 monomer Substances 0.000 claims abstract description 19
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 15
- 239000013598 vector Substances 0.000 claims description 52
- 239000011159 matrix material Substances 0.000 claims description 28
- 230000010287 polarization Effects 0.000 claims description 26
- 238000007600 charging Methods 0.000 claims description 15
- 238000005070 sampling Methods 0.000 claims description 14
- 238000007599 discharging Methods 0.000 claims description 11
- 238000010277 constant-current charging Methods 0.000 claims description 6
- 238000002474 experimental method Methods 0.000 claims description 6
- 230000004044 response Effects 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 239000003990 capacitor Substances 0.000 claims description 3
- 230000032683 aging Effects 0.000 abstract description 7
- 238000010586 diagram Methods 0.000 description 3
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 2
- 229910052744 lithium Inorganic materials 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000001453 impedance spectrum Methods 0.000 description 1
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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
- G01R31/50—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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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
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- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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Abstract
The invention discloses a method for diagnosing internal short circuit faults and capacity loss of a lithium ion battery, which is used for preliminarily judging whether the internal short circuit faults of all battery monomers possibly occur or not by utilizing a voltage change rule after the battery is charged. When the battery operates under the working condition, estimating the internal short circuit resistance value of the battery by using a double-expansion Kalman filtering algorithm of an internal short circuit fault model on the suspected internal short circuit fault battery, so as to realize quantitative diagnosis of the internal short circuit fault degree of the battery; and estimating the ohmic internal resistance value of the battery by using a double-expansion Kalman filtering algorithm of the Thevenin model for the non-internal short-circuit fault battery, and calculating the SOH of the battery by combining the ohmic internal resistance value of the brand-new battery with the ohmic internal resistance value of the aging battery of the same type, thereby realizing quantitative diagnosis of the capacity loss degree of the battery. Namely, the invention can distinguish the internal short circuit fault and the capacity loss of the battery, and realize the quantitative diagnosis of the internal short circuit fault degree or the quantitative diagnosis of the capacity loss degree of the battery.
Description
Technical Field
The invention relates to the technical field of battery management, in particular to a method for diagnosing short-circuit faults and capacity loss in a battery.
Background
Internal short circuit fault is one of the most common causes in thermal runaway accidents of lithium ion batteries, is also a common link of mechanical abuse, electric abuse and thermal abuse, is a potential safety threat, and prevents further application of lithium ion batteries. The battery often needs tens or hundreds of hours to develop from internal short circuit fault into thermal runaway accident, if the internal short circuit of the lithium ion battery can be diagnosed, the fault can be diagnosed in time at the early stage of the internal short circuit of the battery, and the safe operation of the battery system is ensured.
The existing battery internal short-circuit fault diagnosis method usually adopts an expert system method or a fault diagnosis method based on data driving, but the methods are complex in operation and difficult to realize the diagnosis of the internal short-circuit fault; the model parameters of the battery can be changed in the actual running process, noise interference exists in the sensor detection process, and the existing fault diagnosis method can be seriously influenced.
Disclosure of Invention
The invention aims to provide a method for diagnosing internal short-circuit faults and capacity loss of a lithium ion battery, which solves the problems that the internal short-circuit faults and the capacity loss of the lithium ion battery are difficult to distinguish, the internal short-circuit faults and the capacity loss are difficult to diagnose in the running process, and the like.
The technical solution for realizing the purpose of the invention is as follows: in a first aspect, the present invention provides a method for diagnosing a short-circuit fault and a capacity loss in a lithium ion battery, comprising the steps of:
step one: intermittent constant-current charging and hybrid power pulse capability characteristic test experiments are carried out on all new batteries with the same type as battery monomers used in battery packsObtaining parameters of a battery Thevenin equivalent circuit model, including ohmic internal resistance R, according to the voltage response condition of the battery terminal 0 Internal resistance of polarization R P And polarization capacitor C P ；
Step two: charging the battery pack, and primarily judging whether each battery cell is suspected to have an internal short circuit fault or not by utilizing the voltage change condition of a period of time after the charging is finished;
step three: under the operating condition, estimating the internal short-circuit current I of the battery cell by using a double-expansion Kalman filtering algorithm of an internal short-circuit fault model for the battery cell suspected of internal short-circuit fault s Obtaining an internal short circuit resistor R according to the voltage of the battery terminal and ohm law s Thereby realizing quantitative diagnosis of short-circuit faults in the lithium ion battery;
step four: under the operating condition, estimating the ohmic internal resistance R of the battery cell by using a double-expansion Kalman filtering algorithm of a Thevenin equivalent model for the battery cell without internal short circuit fault 0 Ohmic internal resistance R of brand-new battery according to model new And an ohmic internal resistance R of the aged battery old And calculating SOH of the battery cell, thereby realizing quantitative diagnosis of the capacity loss degree of the battery cell.
In a second aspect, the invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of the first aspect when the program is executed.
In a third aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that: (1) The invention provides a method for diagnosing internal short-circuit faults and capacity loss of a lithium ion battery. (2) The internal micro-short circuit fault diagnosis method for the lithium ion battery optimizes the fault diagnosis method based on the model, can reduce the operation cost, and improves the fault diagnosis speed and the sensor noise interference resistance. The invention is suitable for electric vehicles, energy storage systems, battery monomers such as electric tools and the like and grouping application occasions.
Drawings
Fig. 1 is a battery cell SOC-OCV plot.
Fig. 2 is a voltage drop diagram during rest after the battery charge is completed.
Fig. 3 is a schematic diagram of an intra-cell short equivalent circuit.
Fig. 4 is a diagram of a battery Thevenin equivalent circuit model.
Fig. 5 is a graph of the result of estimating the SOC of the internal short-circuit fault battery.
Fig. 6 is a graph of the result of estimating the short-circuit current of the internal short-circuit fault battery.
Fig. 7 is a graph showing the result of calculating the short-circuit resistance of the internal short-circuit fault battery.
Fig. 8 is a graph of the result of estimating the ohmic internal resistance of the non-internal short-circuit fault battery.
Fig. 9 is a flow chart for diagnosing short-circuit faults and capacity loss in a lithium ion battery.
Detailed Description
A diagnosis method for short-circuit faults and capacity losses in lithium ion batteries comprises the following steps:
step one, carrying out intermittent constant current charging and hybrid power pulse capability characteristic test experiments on an all-new battery with the same type as a battery monomer used by a battery pack, and obtaining battery Thevenin equivalent circuit model parameters including ohmic internal resistance R according to battery terminal voltage response conditions 0 Internal resistance of polarization R P And polarization capacitor C P ；
Step two, charging the battery pack, and primarily judging whether each battery monomer is suspected to have internal short circuit faults or not by utilizing the voltage change condition of a period of time after the charging is finished;
step three, under the operating condition, estimating the internal of the battery cell by using a double-expansion Kalman filtering algorithm of an internal short circuit fault model for the battery cell suspected of internal short circuit faultShort-circuit current I s Obtaining an internal short circuit resistor R according to the voltage of the battery terminal and ohm law s Thereby realizing quantitative diagnosis of short-circuit faults in the lithium ion battery;
step four, under the operation condition, estimating the ohmic internal resistance R of the battery monomer by utilizing a double-expansion Kalman filtering algorithm of a Thevenin equivalent model for the battery monomer without internal short circuit fault 0 Ohmic internal resistance R of brand-new battery according to model new And an ohmic internal resistance R of the aged battery old And calculating SOH of the battery cell, thereby realizing quantitative diagnosis of the capacity loss degree of the battery cell.
Further, the first step in this embodiment is specifically:
step 1-1, adopting a brand new battery monomer with the same model as a battery monomer used by the battery pack, discharging to the lower limit cutoff voltage of the battery at the rate of 0.5C, and standing for 1h;
step 1-2, carrying out intermittent charging-standing experiments, charging with 0.5C constant current, standing for 1 minute every 1% of electric quantity, regarding the voltage of the battery after standing as the open-circuit voltage of the battery, obtaining the SOC-OCV relation of the battery, and charging the battery to the upper limit cut-off voltage of the battery;
step 1-3, standing for 1h;
step 1-4, discharging the battery with a constant current of 1C for 10s;
step 1-5, standing the battery for 40s;
step 1-6, charging the battery pack with a constant current of 0.75 ℃ for 10 seconds;
step 1-7, discharging 10% SOC at a constant current of 0.5C;
step 1-8, standing the battery pack for 1h;
step 1-9, repeating steps 1-4 to 1-8, discharging the battery pack to a residual capacity of 10% SOC;
step 1-10, calculating the model parameter R of the battery by using the sampled voltage value 0 、R P And C P ；
When a 1C discharge current is applied, the voltage drop of the battery terminal is mainly caused by ohmic resistance, and the discharge current is I B ApplyingThe voltage drop value of the battery terminal at the moment of charging and discharging current is U BC The ohmic internal resistance can be expressed by the following formula:
voltage drop U during application of 1C discharge current CD Is caused by polarization resistance, and thus polarization resistance can be expressed by the following formula:
in zero state response, the polarization voltage corresponds to a time of 3τ when 95% of the maximum voltage drop is reached, where τ is R P And C P And therefore the polarization capacitance can be expressed by:
further, the second step in this embodiment specifically is:
step 2-1, carrying out constant-current charging on the battery pack until the battery reaches the upper limit cut-off voltage, and standing the battery for 1h;
step 2-2, calculating the voltage drop change trend f' in the process of standing the battery, wherein the calculation method comprises the following steps:
wherein k is the sampling point sequence number, T s For sampling period, u (kT) s ) The voltage sampling value of the battery terminal corresponding to the kth sampling point;
step 2-3, when the characteristic parameter f' of a certain battery cell is greater than the threshold valueWhen it is judged that the fault is suspected to be an internal short circuit faultAnd judging the other batteries as non-internal short-circuit fault batteries. Wherein->The coefficient a in this embodiment is 2.5, which is the average value of the characteristic parameters f' of each battery cell.
Further, the third step in this embodiment is specifically:
step 3-1, discharging the battery pack to 90% SOC at a constant current of 0.5C;
step 3-2, standing the battery pack for 30min;
step 3-3, initializing parameters of a double-expansion Kalman filtering algorithm by using the battery model parameters and experience measured in the step 1;
step 3-4, setting the voltage sampling time to be 1s under the working condition of the battery pack, and taking the real-time sampling voltage and current of the suspected internal short-circuit fault battery as the input of the double-expansion Kalman filtering of the internal short-circuit fault model;
and 3-5, performing on-line estimation of battery parameters by using a discrete state space system equation and an output observation equation of the battery parameters adopted by the double-expansion Kalman filtering of the battery internal short-circuit fault model, wherein the discrete state space system equation and the output observation equation are as follows:
wherein R is 0,k For ohmic internal resistance corresponding to k moment, R P,k For the polarization resistance corresponding to time k, C P,k For the polarization capacitance corresponding to the k moment, r k To estimate the error, U L,k For battery terminal voltage, U oc (SOC k-1 ) For battery open circuit voltage, I k-1 For load current, the current direction is positive in charging, I s,k-1 For short-circuit current, V P,k-1 For polarization voltage, n k-1 Is observation noise;
and 3-6, a discrete state space system equation and an output observation equation of the battery state on-line estimation adopted by the double-expansion Kalman filtering of the battery internal short-circuit fault model are as follows:
wherein x is k Is a state vector, A k 、B k As a coefficient matrix, u k ＝I k For input vector, Q n For battery capacity, v k Is observation noise;
step 3-7, forward calculating the parameter vector, wherein the parameter vector
Wherein,,for a priori estimation of the parameter vector at time k, θ k A posterior estimation of the parameter vector at the moment k;
step 3-8, forward estimating the parameter vector error covariance
Wherein,,for the parameter vector prior covariance matrix, P θ For the parameter vector posterior covariance matrix, Q θ Exciting a noise covariance matrix for a process of the parameter vector;
step 3-9, forward calculating the state vector,
step 3-10, forward calculating the state vector error covariance
Wherein,,P x a is a state vector prior covariance matrix and a posterior covariance matrix respectively x As a matrix of coefficients,Q x exciting a noise covariance matrix for a process of state vectors;
step 3-11, calculating Kalman gain of the state vector
Wherein K is x Kalman gain as state vector, R x An observed noise covariance matrix for the state vector, H x As a matrix of coefficients,
step 3-12, updating the state vector estimate from the observed variable
Step 3-13, updating the state vector error covariance
Wherein E is a 3-order identity matrix;
step 3-14, calculating Kalman gain of the parameter vector
Wherein K is θ Kalman gain as parameter vector, R θ An observed noise covariance matrix for the parameter vector, H θ As a matrix of coefficients,
step 3-15, updating the parameter vector estimate from the observed variable
Step 3-16, updating the parameter vector error covariance
Step 3-17, obtaining the SOC and the internal short-circuit current I of the suspected internal short-circuit fault battery monomer under the operating condition by the double-extended Kalman filtering algorithm s ；
Step 3-16, obtaining the internal short-circuit resistance of the battery according to ohm's law from the measured values of the internal short-circuit current and the battery terminal voltage
Wherein U is L,k Is the battery terminal voltage.
Further, the specific method in the fourth step of this embodiment is:
step 4-1, under the working condition of the battery pack, taking the real-time sampling voltage and current of the non-internal short-circuit fault battery as the input of the double-expansion Kalman filtering of the Thevenin equivalent model;
and 4-2, a discrete state space system equation and an output observation equation of the battery parameter on-line estimation adopted by the double-expansion Kalman filtering of the Thevenin model are as follows:
wherein R 'is' 0,k For ohmic internal resistance corresponding to k time, R' P,k For the polarization resistance corresponding to time k, C' P,k For the polarization capacitance corresponding to the k moment, r k To estimate the error, U' L,k For battery terminal voltage, U' oc (SOC k-1 ) For battery open circuit voltage, I' k-1 The current direction is positive when the load current is charged, n k-1 Is observation noise;
and 4-3, a discrete state space system equation and an output observation equation of the battery state on-line estimation adopted by the double-expansion Kalman filtering of the battery internal short-circuit fault model are as follows:
wherein x' k Is a state vector, A' k 、B' k As coefficient matrix, u' k ＝I' k For input vector, Q n For battery capacity, v k Is observation noise;
step 4-4, the iterative computation process of the double extended Kalman filtering is the same as that of steps 3-7 to 3-16;
step 4-5, obtaining an ohmic internal resistance estimated value R 'of the non-internal short circuit fault battery monomer under the operating condition by the double-extended Kalman filtering algorithm' 0 ；
Step 4-6, quantitatively calculating the capacity loss degree of the aged battery,
wherein R is old For the ohmic internal resistance value corresponding to the case that the capacity loss of the same type battery reaches 80%, R now I.e. the current ohmic internal resistance value of the battery,R new the ohmic internal resistance value corresponding to the brand new battery of the same type is obtained.
The present invention will be specifically described below by taking a certain ternary lithium battery as an example.
Examples
In the embodiment, an ISR18650-2.2Ah ternary lithium battery is used as an experimental object, six batteries are connected in series to form a battery pack, each battery cell is numbered by adopting #a, # b, …, # f, the same type of aging battery is adopted for the cell #b, and the rest batteries are all brand new batteries. The 50 omega power resistor is connected in parallel to two ends of the battery cell #a for simulating an internal short-circuit battery, and the method is carried out in a room temperature environment, and the specific process is as follows:
taking a brand new battery monomer with the same type, and performing constant-current discharge on the battery by adopting a discharge rate of 0.5C until the lower limit cutoff voltage of the battery reaches 2.75V; then, the battery was subjected to an intermittent charge-rest test, charged at a constant current of 0.5C, and kept for 1 minute for 1% of charge, and the battery terminal voltage after rest was regarded as the open circuit voltage of the battery, thereby obtaining the SOC-OCV relationship of the battery, as shown in fig. 1. And charge the battery from 0% to 100%. Standing for 1h, and performing a hybrid power pulse capability characteristic test to obtain the initial values of the model parameters of the battery, namely ohmic internal resistance R 0 =15mΩ, internal polarization resistance R P =30mΩ and polarization capacitance C P ＝1100F。
The battery pack is subjected to 1A constant current charging, and is kept stand for a period of time after full charge, the voltage change is shown in fig. 2, and the measured characteristic parameter f' (#a) = -2.48 is smaller than a threshold valueTherefore, the suspected internal short-circuit fault battery of #a is primarily judged, and the rest batteries are non-fault batteries. The internal short-circuit fault battery equivalent circuit model and the normal battery Thevenin equivalent circuit model are shown in fig. 3 and fig. 4 respectively. Then the battery pack is operated under the FUDS working condition, and the double-expansion Kalman filtering of an internal short circuit fault model is adopted for the suspected internal short circuit fault battery to estimate the battery SOC and the short circuit current I s The estimation results are shown in fig. 5 and 6, respectively. Can be calculated by using the cell terminal voltage and ohm lawShort-circuit resistor R s As shown in fig. 7, the estimated value of the short-circuit resistance converges after 2000s, the average estimated value of the short-circuit resistance after convergence is 51.7179 Ω, the true value is 50Ω, the estimated error is 3.44%, and the quantitative estimated error requirement is satisfied.
Under FUDS working condition, ohmic resistance R 'of non-internal short-circuit fault battery obtained by double-expansion Kalman filtering of Thevenin equivalent circuit model for non-internal short-circuit fault battery' 0 The estimated value is shown in FIG. 8, it can be observed that the ohmic internal resistance of the aging battery cell #b is significantly greater than that of other battery cells, and the average estimated value of the converged ohmic internal resistance is R' 0,b = 0.0942 Ω, and the average estimated value of the ohmic internal resistance of the remaining cells isThe experiment shows that the ohmic internal resistance corresponding to the capacity of the battery model is reduced to 80 percent is about R old =0.097Ω, and the degree of aging of the aged battery was quantitatively calculated as:
the actual capacity of the battery is 1.7764Ah, the nominal capacity is 2.2Ah, the SOH true value is 0.8075, and the estimated error is 4.3%.
Finally, the characteristic parameters f', R are synthesized s And the estimation result of SOH, the determination result of 6 single cells of the battery pack can be obtained as follows: the results of the internal short-circuit fault battery, # a, the aging battery, # c, # d, # e and #f are consistent with expectations, and indicate the effectiveness of the diagnostic method for the internal short-circuit fault and the capacity loss of the lithium ion battery.
In summary, the invention only uses the measured value of the battery terminal voltage and the battery capacity to realize the identification of the internal micro short circuit fault and the aging of the battery. The internal short-circuit resistance of the internal-circuit fault battery qualitatively distinguished by the electrochemical impedance spectrum is quantitatively diagnosed by adopting double-extended Kalman filtering, and the aging degree of the non-internal short-circuit fault battery is quantitatively diagnosed. The method does not depend on complex algorithms, and solves the problems of uncertainty of sensor noise, high calculation complexity and the like.
Claims (7)
1. A method for diagnosing a short circuit fault and a capacity loss in a lithium ion battery, comprising the steps of:
step 1, carrying out intermittent constant current charging and hybrid power pulse capability characteristic test experiments on an all-new battery with the same type as a battery monomer used by a battery pack, and obtaining battery Thevenin equivalent circuit model parameters including ohmic internal resistance R according to battery terminal voltage response conditions 0 Internal resistance of polarization R P And polarization capacitor C P ；
Step 2, charging the battery pack, and primarily judging whether each battery cell is suspected to have an internal short circuit fault or not by utilizing the voltage change condition of a period of time after the charging is finished;
step 3, under the operating condition, estimating the internal short-circuit current I of the battery cell by using a double-expansion Kalman filtering algorithm of an internal short-circuit fault model for the battery cell suspected of internal short-circuit fault s Obtaining an internal short circuit resistor R according to the voltage of the battery terminal and ohm law s Thereby realizing quantitative diagnosis of short-circuit faults in the lithium ion battery;
step 4, under the operating condition, estimating the ohmic internal resistance R of the battery monomer with the double-expansion Kalman filtering algorithm of the Thevenin equivalent model for the battery monomer without internal short circuit fault 0 Ohmic internal resistance R of brand-new battery according to model new And an ohmic internal resistance R of the aged battery old And calculating SOH of the battery cell, thereby realizing quantitative diagnosis of the capacity loss degree of the battery cell.
2. The method for diagnosing internal short-circuit failure and capacity loss of a lithium ion battery according to claim 1, wherein step 1 specifically comprises:
step 1-1, adopting a brand new battery monomer with the same model as a battery monomer used by the battery pack, discharging to the lower limit cutoff voltage of the battery at the rate of 0.5C, and standing for 1h;
step 1-2, carrying out intermittent charging-standing experiments, charging with 0.5C constant current, standing for 1 minute every 1% of electric quantity, regarding the voltage of the battery after standing as the open-circuit voltage of the battery, obtaining the SOC-OCV relation of the battery, and charging the battery to the upper limit cut-off voltage of the battery;
step 1-3, standing for 1h;
step 1-4, discharging the battery with a constant current of 1C for 10s;
step 1-5, standing the battery for 40s;
step 1-6, charging the battery pack with a constant current of 0.75 ℃ for 10 seconds;
step 1-7, discharging 10% SOC at a constant current of 0.5C;
step 1-8, standing the battery pack for 1h;
step 1-9, repeating steps 1-4 to 1-8, discharging the battery pack to a residual capacity of 10% SOC;
step 1-10, calculating the model parameter R of the battery by using the sampled voltage value 0 、R P And C P ；
When a 1C discharge current is applied, the voltage drop of the battery terminal is mainly caused by ohmic resistance, and the discharge current is I B The voltage drop value of the battery terminal at the moment of applying the discharge current is U BC The ohmic internal resistance can be expressed by the following formula:
voltage drop U during application of 1C discharge current CD Is caused by polarization resistance, and thus polarization resistance can be expressed by the following formula:
in zero state response, the polarization voltage corresponds to a time of 3τ when 95% of the maximum voltage drop is reached, where τ is R P And C P And therefore the polarization capacitance can be expressed by:
3. the method for diagnosing internal short-circuit failure and capacity loss of a lithium ion battery according to claim 2, wherein step 2 is specifically:
step 2-1, carrying out constant-current charging on the battery pack until the battery reaches the upper limit cut-off voltage, and standing the battery for a period of time;
step 2-2, calculating the voltage drop change trend f' in the process of standing the battery, wherein the calculation method comprises the following steps:
wherein k is the sampling point sequence number, T s For sampling period, u (kT) s ) The voltage sampling value of the battery terminal corresponding to the kth sampling point;
step 2-3, when the characteristic parameter f' of a certain battery cell is smaller than the threshold valueWhen the battery is judged to be an internal short-circuit fault battery, other batteries are judged to be non-internal short-circuit fault batteries, wherein +.>Is the average value of the characteristic parameters f' of each battery cell.
4. The method for diagnosing internal short-circuit failure and capacity loss of a lithium ion battery according to claim 3, wherein step 3 specifically comprises:
step 3-1, discharging the battery pack to 90% SOC at a constant current of 0.5C;
step 3-2, standing the battery pack for 30min;
step 3-3, initializing parameters of a double-expansion Kalman filtering algorithm by using the battery model parameters and experience measured in the step 1;
step 3-4, setting the voltage sampling time to be 1s under the working condition of the battery pack, and taking the real-time sampling voltage and current of the suspected internal short-circuit fault battery as the input of the double-expansion Kalman filtering of the internal short-circuit fault model;
and 3-5, performing on-line estimation of battery parameters by using a discrete state space system equation and an output observation equation of the battery parameters adopted by the double-expansion Kalman filtering of the battery internal short-circuit fault model, wherein the discrete state space system equation and the output observation equation are as follows:
wherein R is 0,k For ohmic internal resistance corresponding to k moment, R P,k For the polarization resistance corresponding to time k, C P,k For the polarization capacitance corresponding to the k moment, r k To estimate the error, U L,k For battery terminal voltage, U oc (SOC k-1 ) For battery open circuit voltage, I k-1 For load current, the current direction is positive in charging, I s,k-1 For short-circuit current, V P,k-1 For polarization voltage, n k-1 Is observation noise;
and 3-6, a discrete state space system equation and an output observation equation of the battery state on-line estimation adopted by the double-expansion Kalman filtering of the battery internal short-circuit fault model are as follows:
wherein x is k Is a state vector, A k 、B k As a coefficient matrix, u k ＝I k For input vector, Q n For battery capacity, v k Is observation noise;
step 3-7, forward calculating the parameter vector, wherein the parameter vector
Wherein,,for a priori estimation of the parameter vector at time k, θ k A posterior estimation of the parameter vector at the moment k;
step 3-8, forward estimating the parameter vector error covariance
Wherein,,for the parameter vector prior covariance matrix, P θ For the parameter vector posterior covariance matrix, Q θ Exciting a noise covariance matrix for a process of the parameter vector;
step 3-9, forward calculating the state vector,
step 3-10, forward calculating the state vector error covariance
Wherein,,P x a is a state vector prior covariance matrix and a posterior covariance matrix respectively x As a matrix of coefficients,Q x exciting a noise covariance matrix for a process of state vectors;
step 3-11, calculating Kalman gain of the state vector
Wherein K is x Kalman gain as state vector, R x An observed noise covariance matrix for the state vector, H x As a matrix of coefficients,
step 3-12, updating the state vector estimate from the observed variable
Step 3-13, updating the state vector error covariance
Wherein E is a 3-order identity matrix;
step 3-14, calculating Kalman gain of the parameter vector
Wherein K is θ Kalman gain as parameter vector, R θ An observed noise covariance matrix for the parameter vector, H θ As a matrix of coefficients,
step 3-15, updating the parameter vector estimate from the observed variable
Step 3-16, updating the parameter vector error covariance
Step 3-17, obtaining the SOC and the internal short-circuit current I of the suspected internal short-circuit fault battery monomer under the operating condition by the double-extended Kalman filtering algorithm s ；
Step 3-16, obtaining the internal short-circuit resistance of the battery according to ohm's law from the measured values of the internal short-circuit current and the battery terminal voltage
Wherein U is L,k Is the battery terminal voltage.
5. The method for diagnosing a short-circuit fault and a capacity loss in a lithium ion battery according to claim 4, wherein step 4 specifically comprises:
step 4-1, under the working condition of the battery pack, taking the real-time sampling voltage and current of the non-internal short-circuit fault battery as the input of the double-expansion Kalman filtering of the Thevenin equivalent model;
and 4-2, a discrete state space system equation and an output observation equation of the battery parameter on-line estimation adopted by the double-expansion Kalman filtering of the Thevenin model are as follows:
wherein R 'is' 0,k For ohmic internal resistance corresponding to k time, R' P,k For the polarization resistance corresponding to time k, C' P,k For the polarization capacitance corresponding to the k moment, r k To estimate the error, U' L,k For battery terminal voltage, U' oc (SOC k-1 ) For battery open circuit voltage, I' k-1 The current direction is positive when the load current is charged, n k-1 Is observation noise;
and 4-3, a discrete state space system equation and an output observation equation of the battery state on-line estimation adopted by the double-expansion Kalman filtering of the battery internal short-circuit fault model are as follows:
wherein x' k Is a state vector, A' k 、B' k As coefficient matrix, u' k ＝I' k For input vector, Q n For battery capacity, v k Is observation noise;
step 4-4, the iterative computation process of the double extended Kalman filtering is the same as that of steps 3-7 to 3-16;
step 4-5, obtaining an ohmic internal resistance estimated value R 'of the non-internal short circuit fault battery monomer under the operating condition by the double-extended Kalman filtering algorithm' 0 ；
Step 4-6, quantitatively calculating the capacity loss degree of the aged battery,
wherein R is old For the ohmic internal resistance value corresponding to the case that the capacity loss of the same type battery reaches 80%, R now I.e. the current ohmic internal resistance value of the battery, R new The ohmic internal resistance value corresponding to the brand new battery of the same type is obtained.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-5 when the program is executed.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of claims 1-5.
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