A method for detecting the state of health of a retired power battery

Abstract

The application discloses a kind of retired power battery health state detection methods.The method includes: the voltage-time curve is formed by collecting the voltage of the battery to be measured after being converted from discharge state to discharge stop state;Equivalent circuit model of the battery to be measured is established based on equivalent circuit method, and initial parameters of equivalent circuit model are calculated according to voltage-time curve;Open-circuit voltage estimation value is calculated according to initial parameters, and open-circuit voltage estimation-time curve is obtained;Open-circuit voltage estimation-time curve is matched with open-circuit voltage-time curve of different kinds of batteries in SOC-OCV database, and the state of charge-time curve of the battery to be measured is obtained with the open-circuit voltage-time curve most suitable for the battery to be measured in SOC-OCV database;The battery health degree of the battery to be measured is determined according to the state of charge-time curve of the battery to be measured.The technical scheme of the embodiment of the application can quickly detect the battery health degree of retired power battery, with low cost and wide application range.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to power battery detection technical field, and particularly relates to a retired power battery health state detection method. BACKGROUND

[0002] The retired power battery gradient utilization technology refers to necessary detection, classification, splitting, battery repair or recombination of waste power storage batteries as gradient products, so that they can be applied to other fields. Since the energy and power characteristics of the retired power battery attenuate and the performance parameters of the battery monomers are quite different, direct recombination and utilization of the battery will cause a certain degree of safety problem, therefore, the battery monomer needs to be screened and recombined and the electrical performance is detected.

[0003] In the detection of the electrical performance of the retired power battery monomer, a key parameter reflecting the residual energy of the battery, i.e. the battery health degree (SOH), is generally used for gradient utilization screening and recombination according to the health degree of the retired battery monomer. However, the detection of the health degree of the retired power battery in the prior art is time-consuming, and different types of batteries will affect the accuracy of the detection. SUMMARY

[0004] The present application provides a retired power battery health state detection method to realize the rapid detection of the health degree of the retired power battery, and is suitable for different types of batteries, and has high detection accuracy.

[0005] According to an aspect of the present application, a retired power battery health state detection method is provided, which comprises:

[0006] Collecting the voltage of the battery to be tested after the battery to be tested is converted from the discharging state to the discharging stop state to form a voltage-time curve;

[0007] Establishing an equivalent circuit model of the battery to be tested based on the equivalent circuit method, and calculating the initial parameters of the equivalent circuit model according to the voltage-time curve;

[0008] Calculating the open circuit voltage estimation value according to the initial parameters to obtain an open circuit voltage estimation value-time curve;

[0009] Matching the open circuit voltage estimation value-time curve with the open circuit voltage-time curves of different types of batteries in the SOC-OCV database, obtaining the open circuit voltage-time curve in the SOC-OCV database that is most suitable for the battery to be tested, and taking the battery state of charge-time curve having a mapping relationship with the most suitable open circuit voltage-time curve as the state of charge-time curve of the battery to be tested;

[0010] Determining the battery health degree of the battery to be tested according to the state of charge-time curve of the battery to be tested.

[0011] Optionally, the equivalent circuit model of the battery to be measured comprises: an ideal battery, an ohmic internal resistance, a polarization resistance and a polarization capacitance; a positive electrode of the ideal battery is electrically connected with a first end of the ohmic internal resistance, and the polarization resistance and the polarization capacitance are electrically connected with a second end of the ohmic internal resistance in parallel.

[0012] The initial parameters comprise: an ohmic internal resistance value, a polarization resistance value and a polarization capacitance value of the battery to be measured.

[0013] Optionally, the voltage-time curve comprises: a constant current discharge phase and a static phase; the equivalent circuit model of the battery to be measured is established based on the equivalent circuit method, and initial parameters of the equivalent circuit model are calculated according to the voltage-time curve, comprising:

[0014] The ohmic internal resistance value of the battery to be measured is calculated according to formula 1, and formula 1 is expressed as:

[0015]

[0016] Wherein, R0 is the ohmic internal resistance value of the battery to be measured, I is the current value in the constant current discharge phase, U1 is the starting voltage value of the voltage-time curve between the two ends of the battery to be measured, and U2 is the voltage value after the voltage between the two ends of the battery to be measured instantaneously decreases at the beginning of the constant current discharge;

[0017] The curve in the static phase of the voltage-time curve is fitted, and the time constant is calculated according to formula 2, and formula 2 is expressed as:

[0018]

[0019] Wherein, U is the voltage between the two ends of the battery to be measured in the static phase, t is time, U3 is the voltage before the voltage between the two ends of the battery to be measured instantaneously rises after the constant current discharge ends, and τ is the time constant;

[0020] The constant current discharge phase in the voltage-time curve is fitted, and an equation is established according to formula 2 and formula 3, and the polarization resistance value of the battery to be measured is solved; formula 3 is expressed as:

[0021]

[0022] Wherein, R1 is the polarization resistance value of the battery to be measured;

[0023] The polarization capacitance value of the battery to be measured is calculated according to the polarization resistance value and the time constant of the battery to be measured.

[0024] Optionally, the open circuit voltage estimated value is calculated according to the initial parameters, comprising:

[0025] The initial parameters are substituted into an iterative parameter equation set to obtain an iterative parameter of an iteration process of the open circuit voltage estimation value; the iterative parameter includes a state parameter vector, an input vector and a system output; wherein the iterative parameter equation set is expressed as:

[0026] U t,k +U 1,k +I k R 0,k =OCV k

[0027] y k =U t,k +U 1,k +I k R 0,k

[0028]

[0029] θ k =OCV k

[0030]

[0031] wherein U t,k is an end voltage of the equivalent circuit model obtained by the kth measurement, U 1,k is a voltage value of a polarization resistance R1 and a polarization capacitance C1 parallel part in the equivalent circuit model obtained by the k-1th calculation, as a voltage value of the polarization resistance R1 and the polarization capacitance C1 parallel part in the kth calculation, I k is a current in a constant current discharge stage obtained by the kth measurement, R 0,k is an ohmic internal resistance value of the kth, OCV k is the open circuit voltage estimation value obtained by the kth, y k is the system output obtained by the kth calculation, is the input vector of the kth, θ k is the state parameter vector of the kth, U 1,k+1 is the voltage value of the polarization resistance R1 and the polarization capacitance C1 parallel part in the k+1th calculation, ΔT is a time interval between two measurements of the end voltage, τ 1,k is the time constant of the kth;

[0032] According to the iterative parameter, multiple iterations are performed until the state parameter vector converges to obtain the open circuit voltage estimation value.

[0033] Optionally, the multiple iterations are performed according to the iterative parameter until the state parameter vector converges to obtain the open circuit voltage estimation value, including:

[0034] Based on the recursive least square method with forgetting factor, the iteration parameter is taken as an initial iteration parameter for calculating the open circuit voltage estimation value, the initial iteration parameter is substituted into an iteration formula, and the state parameter vector tends to converge after multiple iterations, and the state parameter vector tends to converge as the open circuit voltage estimation value;

[0035] The iteration formula is represented as:

[0036]

[0037]

[0038]

[0039]

[0040] Wherein, the parameter with subscript n represents the parameter of the n th iteration process; The system output estimation value updated for the n th iteration process, The state parameter vector estimation value used for the n th iteration process, The state parameter vector estimation value updated for the n th iteration process, The transpose of the input vector used for the n th iteration process, e n+1 The difference between the system output prediction value updated for the n th iteration process and the measured input value of the system for the n th iteration process, P n The covariance matrix used for the n th iteration process, P n+1 The covariance matrix updated for the n th iteration process, K n+1 The gain matrix updated for the n th iteration process, and λ is the forgetting factor.

[0041] Optionally, the open circuit voltage estimation value-time curve is matched with the open circuit voltage-time curves of different types of batteries in the SOC-OCV database to obtain the open circuit voltage-time curve in the SOC-OCV database that best fits the battery to be measured, comprising:

[0042] Based on the clustering algorithm, the open circuit voltage estimation value-time curve is matched with the open circuit voltage-time curves of different types of batteries in the SOC-OCV database in a parallel computing manner to obtain the open circuit voltage-time curve in the SOC-OCV database that best fits the battery to be measured.

[0043] Optionally, the battery health degree of the battery to be measured is estimated according to the state of charge-time curve of the battery to be measured, comprising:

[0044] The to-be-tested battery state-of-charge-time curve comprises a constant-current discharge phase and a static phase; a plurality of points on the constant-current discharge phase of the to-be-tested battery state-of-charge-time curve are selected, and based on the selected plurality of points, the actual nominal capacity and random error of the to-be-tested battery in the constant-current discharge phase are calculated based on a classical least square method;

[0045] Based on an iterative reweighted least square method, the random error is optimized to obtain an optimized actual nominal capacity of the to-be-tested battery;

[0046] According to the optimized actual nominal capacity of the to-be-tested battery and the nominal capacity of the to-be-tested battery, the battery health degree of the to-be-tested battery is calculated.

[0047] Optionally, the actual nominal capacity of the to-be-tested battery is calculated based on the selected plurality of points and the classical least square method, comprising:

[0048] For the change amount of the battery state-of-charge in the constant-current discharge process of the to-be-tested battery, the change amount of the battery state-of-charge in the constant-current discharge phase is represented by formula 4 as:

[0049] y i =β1+β2x i +v i

[0050] Wherein, y i is the change amount of the battery state-of-charge in the constant-current discharge phase, x i is the remaining capacity of the to-be-tested battery, β2 is the reciprocal of the actual nominal capacity of the to-be-tested battery, v i is a random error vector, and β1 is a constant intercept;

[0051] According to the selected plurality of points, the relationship between y i and x i is represented in a matrix form by formula 5, and formula 5 is represented as:

[0052] Y=X·H+V

[0053] Wherein, H is a column matrix of β1 and β2, Y is X is V is a random error matrix, and is represented as

[0054] Formula 5 is converted into formula 6, and the actual nominal capacity of the to-be-tested battery and the random error are calculated by taking the extreme value of the residual sum of squares; wherein, formula 6 is represented as: The actual nominal capacity of the to-be-tested battery and the random error are calculated by ; wherein, formula 6 is represented as:

[0055]

[0056] Where S0 is the sum of squared residuals between matrices Y and X. This is the estimation matrix for H.

[0057] Optionally, the step of optimizing the random error based on the iterative reweighted least squares method to obtain the optimized actual nominal capacity of the battery under test includes:

[0058] The optimization function for optimizing the random error is expressed by Equation 7:

[0059]

[0060] Among them, y i x represents the change in the battery's state of charge during the constant current discharge phase. i β2 is the remaining capacity of the battery under test, and v is the reciprocal of the actual nominal capacity of the battery under test. i Let β1 be a random error vector, and let β1 be a constant intercept.

[0061] By using an influence function to standardize the random error, Equation 7 is transformed into Equation 8, which is expressed as:

[0062]

[0063] in, To affect the derivative of the function, u i The standardized residual function is expressed as follows: S is the robustness estimation parameter;

[0064] Multiple sets of points are selected during the constant current discharge stage of the state-of-charge-time curve of the battery under test. Based on the selected multiple sets of points, Equation 8 is transformed into solving for the extreme values ​​in matrix form, resulting in Equation 9; Equation 9 is expressed as:

[0065] X′WY=X′WXH

[0066]

[0067] Where W is

[0068] Calculated according to Formula 9 according to The actual nominal capacity of the optimized battery under test is calculated.

[0069] Optionally, the step of calculating the battery health of the battery under test based on the optimized actual nominal capacity and the nominal capacity of the battery under test includes:

[0070] The battery health degree of the to-be-tested battery is obtained by dividing the actual optimized nominal capacity of the to-be-tested battery by the nominal capacity of the to-be-tested battery.

[0071] The technical solution of the embodiment of the present application obtains the voltage-time curve between the positive and negative electrodes of the to-be-tested battery during the process of the to-be-tested battery being converted from the discharging state to the discharging stop state and being static for a period of time, and calculates the initial parameters of the equivalent circuit model based on the equivalent circuit method according to the voltage-time curve. The open-circuit voltage estimation-time curve is calculated according to the initial parameters, the open-circuit voltage-time curve is matched with the pre-stored open-circuit voltage-time curve in the SOC-OCV database, the most fitted open-circuit voltage-time curve is obtained, and the corresponding battery state of charge-time curve is taken as the state of charge-time curve of the to-be-tested battery, so as to determine the battery health degree of the to-be-tested battery. The detection method provided by the embodiment of the present application can quickly detect the battery health degree of the to-be-tested battery, and can be applied to different types of batteries, and has high detection accuracy.

[0072] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0073] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0074] Figure 1 is a flowchart of a method for detecting the health state of a retired power battery according to an embodiment of the present application;

[0075] Figure 2 is a structural schematic diagram of an equivalent circuit model according to an embodiment of the present application;

[0076] Figure 3 is a voltage-time curve across the to-be-tested battery according to an embodiment of the present application;

[0077] Figure 4 is a specific flowchart of step S120 in the method for detecting the health state of a retired power battery according to an embodiment of the present application;

[0078] Figure 5 is a specific flowchart of step S130 in the method for detecting the health state of a retired power battery according to an embodiment of the present application;

[0079] Figure 6 is a specific flowchart of step S150 in a method for detecting the state of health of a retired power battery according to an embodiment of the present application. DETAILED DESCRIPTION

[0080] In order to make the technical personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0081] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0082] As described in the background, the retired power battery cells are generally screened and reorganized according to the health degree. When measuring the remaining power of the retired power battery, five constant current charge-discharge tests are usually performed. When the difference between the results of three consecutive tests is less than 3% of the rated capacity, the test can be ended in advance. The average of the last three discharge capacities is taken as the remaining actual use capacity of the retired power battery. However, one constant current charge-discharge test takes about 10 hours, and even if the first three measurements meet the requirements, it takes about 30 hours to detect the health degree of the retired power battery, which is extremely time-consuming.

[0083] Therefore, the method of single constant current charge-discharge cycle is currently used to detect the remaining capacity of the retired power battery. The fully charged battery is discharged to the terminal voltage at a constant current rate at room temperature, and the capacity released during the discharge process is taken as the remaining actual use capacity of the retired battery. This method greatly shortens the detection time, but the detection process still takes about 8 hours, which cannot meet the demand of rapid detection of large-scale retired power batteries.

[0084] The existing residual capacity estimation algorithms can be divided into two kinds, namely indirectly using the measurement signal and directly using the measurement signal. Among them, the indirect use of the measurement signal is to use the measurement signal to establish a battery capacity estimation mathematical model, and then realize the real-time estimation of the battery parameters through the model. For example: the indirect use of the measurement signal can include the establishment of an electrochemical model. The electrochemical model is mathematically modeled according to a typical capacity loss mechanism. The most common loss mechanism used for modeling is the growth of the solid electrolyte interface (SEI) film, which consumes lithium ions and causes battery capacity loss. However, the electrochemical model is relatively complex, involves many battery internal electrochemical performance parameters, and the parameter extraction and identification are difficult, which has great difficulty in practical measurement. Direct use of the measurement signal refers to directly using the measurement signal (i.e. voltage, current and temperature signal on the surface of the battery, etc.) of the battery in the charging and discharging process as the input, and taking the capacity as the output. According to the different processing methods of the measurement signal, the direct use of the measurement signal can be divided into two kinds: without mathematical change of the test data and with mathematical change of the test data. For the method without mathematical change of the test data, since the capacity loss mechanism is not considered in the modeling, the model accuracy will decrease when the battery type is different.

[0085] The capacity increment analysis method and the differential voltage analysis method are used to represent the corresponding relationship between the change of the open circuit voltage of the battery and the capacity loss mechanism of the battery, the test accuracy is high, but the battery still needs to be charged and discharged once, and the time consumption is long.

[0086] Based on the above technical problems, the embodiments of the present application propose the following technical solutions.

[0087] The embodiments of the present application provide a method for detecting the health state of a retired power battery. Figure 1 A flowchart of a method for detecting the health state of a retired power battery is provided in the embodiments of the present application. The embodiments can be applied to quickly detect the health degree of the retired power battery. The method can be executed by software and / or hardware, and specifically includes the following steps:

[0088] S110, collect the voltage between the positive and negative electrodes of the battery to be tested after the battery to be tested is converted from the discharging state to the discharging stop state, and form a voltage-time curve.

[0089] Specifically, the detection process of the detection method provided by the embodiments of the present application starts from the normal discharging state of the retired power battery connected to the load, is converted from the normal discharging state to the instantaneous power-off state of the retired power battery disconnected from the load, and is static for a period of time. The detection process ends. The voltage between the positive and negative electrodes of the battery to be tested during the above detection process is measured by using a voltmeter, and a voltage-time curve is obtained.

[0090] S120, an equivalent circuit model of the to-be-tested battery is established based on an equivalent circuit method, and initial parameters of the equivalent circuit model are calculated according to the voltage-time curve.

[0091] Specifically, the equivalent circuit method belongs to a method of indirectly estimating the remaining power of the to-be-tested battery by using a measured signal. The equivalent circuit model calculates the remaining capacity by the state of charge of the to-be-tested battery, and calculates the health degree of the to-be-tested battery by the remaining capacity of the to-be-tested battery. When the model is established, a proper filter and an observer algorithm are used to minimize the difference between the simulated output voltage of the equivalent circuit model and the test voltage, so as to realize the identification of the model parameters and the joint estimation of the state of charge and the capacity of the to-be-tested battery. According to the voltage-time curve, the initial parameters of the to-be-tested battery in the equivalent circuit model are calculated.

[0092] S130, an open-circuit voltage estimation value is calculated according to the initial parameters, and an open-circuit voltage estimation value-time curve is obtained.

[0093] Specifically, the open-circuit voltage estimation value between the positive and negative electrodes of the ideal battery in the equivalent circuit model is calculated by using the calculated initial parameters of the to-be-tested battery. According to the initial parameters, a plurality of open-circuit voltage estimation values are calculated at different time points, so as to obtain the open-circuit voltage estimation value-time curve.

[0094] S140, the open-circuit voltage estimation value-time curve is matched with the open-circuit voltage-time curves of different types of batteries in the SOC-OCV database, the open-circuit voltage-time curve in the SOC-OCV database that is most suitable for the to-be-tested battery is obtained, and the battery state of charge-time curve that has a mapping relationship with the most suitable open-circuit voltage-time curve is taken as the state of charge-time curve of the to-be-tested battery.

[0095] Specifically, a plurality of open-circuit voltage-time curves of different types of power batteries are pre-stored in the SOC-OCV database, and each open-circuit voltage-time curve corresponds to a battery state of charge-time curve. The calculated open-circuit voltage estimation value-time curve is matched with each open-circuit voltage-time curve in the SOC-OCV database, and the most suitable open-circuit voltage-time curve is matched. The battery state of charge-time curve corresponding to the open-circuit voltage-time curve is taken as the battery state of charge-time curve of the to-be-tested battery. The SOC-OCV database is used for matching, and the type of the to-be-tested battery does not need to be determined in advance, and the range of applicable batteries is wide.

[0096] S150, the battery health degree of the to-be-tested battery is determined according to the state of charge-time curve of the to-be-tested battery.

[0097] The battery health degree of the to-be-tested battery can be calculated by the state of charge of the to-be-tested battery and the standard capacity of the battery.

[0098] The technical scheme of the embodiment is to collect the voltage-time curve between the positive and negative electrodes of the battery to be tested during the process of the battery to be tested being converted from a discharging state to a discharging stop state and being left for a period of time, calculate the initial parameters of the equivalent circuit model based on the equivalent circuit method according to the voltage-time curve, calculate the open circuit voltage estimation-time curve according to the initial parameters, match the open circuit voltage estimation-time curve with the pre-stored open circuit voltage-time curve in the SOC-OCV database, obtain the most fitted open circuit voltage-time curve, and take the corresponding battery state of charge-time curve as the state of charge-time curve of the battery to be tested, so as to determine the health degree of the battery to be tested. The detection method provided in the embodiment can quickly detect the battery health degree of the battery to be tested, and can be applied to different types of batteries, and has high detection accuracy.

[0099] Optionally, Figure 2 is a structure diagram of an equivalent circuit model provided by an embodiment of the application. Based on the above embodiments, as shown in Figure 2 , the equivalent circuit model of the battery to be tested includes an ideal battery 10, an ohmic internal resistance R0, a polarization resistance R1 and a polarization capacitance C1; the positive electrode of the ideal battery 10 is electrically connected with a first end of the ohmic internal resistance R0, and the polarization resistance R1 and the polarization capacitance C1 are connected in parallel and electrically connected with a second end of the ohmic internal resistance R0.

[0100] The initial parameters include the ohmic internal resistance value, the polarization resistance value and the polarization capacitance value of the battery to be tested.

[0101] Specifically, since the equivalent circuit method uses a circuit network composed of traditional circuit elements such as resistors, capacitors and voltage sources to describe the external characteristics of the retired power battery, and has good applicability to various working states of the retired power battery, the state equation of the equivalent circuit model can be derived, which is convenient for analysis and application. Therefore, the equivalent circuit method is selected to calculate the initial parameters required for detecting the health degree of the battery to be tested.

[0102] A typical equivalent circuit model of a power battery is composed of multiple resistor-capacitor network structures, which is referred to as an n-RC model. By comprehensively considering the model accuracy and identification difficulty, the Thevenin model is selected as the equivalent circuit model of the battery to be tested. The voltage between the positive and negative electrodes of the battery to be tested is measured by using a voltmeter, and the terminal voltage Ut between the polarization resistance R1 and the polarization capacitance C1 connected in parallel in the equivalent circuit model and the negative electrode of the ideal battery 10 is obtained.

[0103] Optionally, Figure 3 is a voltage-time curve of the battery to be tested provided by an embodiment of the application, Figure 4 is a specific flowchart of step S120 in the health state detection method of the retired power battery provided by an embodiment of the application. Based on the above embodiments, referring toFigure 3 and Figure 4 The voltage-time curve includes: a constant current discharge phase and a static phase. Among them, the t1-t2 phase is the constant current discharge phase of the battery to be measured, and the t2-t3 phase is the static phase of the battery to be measured. The voltage at the t1 and t2 time points changes instantaneously due to the voltage drop of the battery to be measured on the ohmic internal resistance, and the slow change of the voltage in the t1-t2 phase and the t2-t3 phase is caused by the polarization effect of the battery to be measured.

[0104] An equivalent circuit model of the battery to be measured is established based on the equivalent circuit method, and initial parameters of the equivalent circuit model are calculated according to the voltage-time curve, including:

[0105] S1201, the ohmic internal resistance value of the battery to be measured is calculated according to formula 1, and formula 1 is expressed as:

[0106]

[0107] Wherein, R0 is the ohmic internal resistance value of the battery to be measured, I is the current value in the constant current discharge phase, U1 is the starting voltage value of the voltage-time curve between the two ends of the battery to be measured, and U2 is the voltage value after the voltage between the two ends of the battery to be measured instantaneously decreases at the beginning of the constant current discharge.

[0108] Specifically, according to the voltage-time curve, the initial voltage value U1 of the battery to be measured before starting constant current discharge and the voltage value U2 at the beginning of constant current discharge can be obtained, and the ohmic internal resistance value of the battery to be measured can be calculated according to formula 1.

[0109] S1202, the curve of the static phase in the voltage-time curve is fitted, and the time constant is calculated by formula 2, and formula 2 is expressed as:

[0110]

[0111] Wherein, U is the voltage between the two ends of the battery to be measured in the static phase, t is the time, U3 is the voltage before the voltage between the two ends of the battery to be measured instantaneously rises at the end of the constant current discharge, and τ is the time constant.

[0112] Specifically, the curve of the static phase in the t2-t3 time period of the voltage-time curve is fitted, according to formula 2, the point (U, t) in the t2-t3 time period of the voltage-time curve is selected, the abscissa t and the ordinate U of the selected point are substituted into formula 2, and U3 can be obtained from the voltage-time curve, so that the time constant τ can be calculated.

[0113] S1203, the constant current discharge phase in the voltage-time curve is fitted, and the polarization resistance value of the battery to be measured is calculated according to formula 2 and formula 3.

[0114] Equation 3 is established to solve the polarization resistance value of the battery to be measured; formula 3 is expressed as:

[0115]

[0116] Wherein, E is the electromotive force of the battery to be measured, and R1 is the polarization resistance value of the battery to be measured.

[0117] Specifically, since formula 2 and formula 3 are both equations representing the voltage across the battery to be measured, formula 2 is established as an equation with formula 3. Only R1 is an unknown parameter in the equation, so the polarization resistance value R1 can be calculated.

[0118] S1204, according to the polarization resistance value and the time constant of the battery to be measured, the polarization capacitance value of the battery to be measured is calculated.

[0119] Specifically, by using the formula τ = RC, the calculated polarization resistance value R1 and the time constant τ are substituted into the formula, and the polarization capacitance value C1 can be calculated.

[0120] Optionally, Figure 5 is a specific flowchart of step S130 in a health state detection method of a retired power battery provided by the embodiment of the application. Based on the above embodiments, as shown in Figure 5 , the open circuit voltage estimated value is calculated according to the initial parameters, including:

[0121] S1301, the initial parameters are substituted into the iterative parameter equation set to obtain the iterative parameters of the iterative process of the open circuit voltage estimated value; the iterative parameters include: state parameter vector, input vector and system output; wherein the iterative parameter equation set is represented as:

[0122] U t,k +U 1,k +I k R 0,k =OCV k

[0123] y k =U t,k +U 1,k +I k R 0,k

[0124]

[0125] θ k =OCV k

[0126]

[0127] Wherein, U t,k is the terminal voltage of the equivalent circuit model obtained by the kth measurement, and U 1,kThe voltage value of the polarization resistance R1 and the polarization capacitance C1 parallel part in the equivalent circuit model obtained in the k-1th calculation is taken as the voltage value of the polarization resistance R1 and the polarization capacitance C1 parallel part in the kth calculation, I k The current in the constant current discharge stage obtained in the kth measurement, R 0,k The ohmic resistance value in the kth, OCV k The open circuit voltage estimation value obtained in the kth, y k The system output obtained in the kth calculation, The input vector in the kth, θ k The state parameter vector in the kth, U 1,k+1 The voltage value of the polarization resistance R1 and the polarization capacitance C1 parallel part in the k+1th calculation, ΔT is the time interval between the measurement of the two terminal voltages, τ 1,k The time constant in the kth.

[0128] Specifically, when calculating the open circuit voltage estimation value, the iterative initial parameter is needed, and the iterative initial parameter is iterated multiple times to obtain the open circuit voltage estimation value. When calculating the iterative initial parameter, the iterative parameter equation set can be used to calculate the iterative initial parameter. For example, when calculating the first open circuit voltage estimation value, i.e., k=0, the voltage U4 in the voltage-time curve can be taken as the voltage value U 1,0 of the polarization resistance R1 and the polarization capacitance C1 parallel part in the equivalent circuit model when calculating the first open circuit voltage estimation value. According to the initial parameters R0 and R1, OCV0 and y0 can be calculated, so that the state parameter vector θ0, the input vector and the system output y0 can be obtained. In summary, according to the iterative parameter equation set, the iterative initial parameter can be calculated, which includes the state parameter vector θ0, the input vector and the system output y0. When calculating the iterative initial parameter, the ohmic resistance value R 0,k is constant, which is the ohmic resistance value R0 of the battery to be measured calculated according to the equivalent circuit model.

[0129] Before calculating each new open circuit voltage estimation value, the iterative initial parameter used to calculate the open circuit voltage estimation value is obtained by using the above iterative parameter equation set.

[0130] S1302, according to the iterative parameter, multiple iterations are performed until the state parameter vector converges, and the open circuit voltage estimation value is obtained.

[0131] Specifically, the iteration initial parameters calculated according to the iteration parameter equation set are substituted into the iteration formula for calculating the open circuit voltage estimation value. After multiple iterations, when the state parameter vector θ converges to a numerical value, the converged numerical value is taken as an open circuit voltage estimation value. When another open circuit voltage estimation value is calculated, a set of iteration initial parameters is recalculated using the iteration parameter equation set. The newly obtained iteration initial parameters are substituted into the iteration formula for calculating the open circuit voltage estimation value, and multiple iterations are performed again until the state parameter vector converges, and the converged value is taken as another open circuit voltage estimation value. Based on the above calculation process, an open circuit voltage estimation value-time curve can be obtained.

[0132] Optionally, based on the above embodiment, according to the iteration parameters, multiple iterations are performed until the state parameter vector converges to obtain the open circuit voltage estimation value, comprising:

[0133] Based on the forgetting factor recursive least square method, the iteration parameters are taken as the initial iteration parameters for calculating the open circuit voltage estimation value, the initial iteration parameters are substituted into the iteration formula, and multiple iterations are performed until the state parameter vector converges, and the state parameter vector that tends to converge is taken as the open circuit voltage estimation value.

[0134] The iteration formula is represented as:

[0135]

[0136]

[0137]

[0138]

[0139] wherein the parameters with subscript n represent the parameters in the n th iteration process; is the system output estimation value updated in the n th iteration process, is the state parameter vector estimation value used in the n th iteration process, is the state parameter vector estimation value updated in the n th iteration process, is the transpose of the input vector used in the n th iteration process, n+1 is the difference between the system output prediction value updated in the n th iteration process and the measured input value of the system in the n th iteration process, n is the covariance matrix used in the n th iteration process, n+1 is the covariance matrix updated in the n th iteration process, n+1 is the gain matrix updated in the n th iteration process, and λ is the forgetting factor.

[0140] Specifically, the recursive least square method with forgetting factor (FFRLS) can effectively overcome the "data saturation" problem existing in the classical least square method. According to the iterative formula of the recursive least square method with forgetting factor, the state parameter vector θ n , the input vector and the system output y n are substituted into the iterative formula, and after multiple iterations, the state parameter vector θ n+1 tending to convergence can be obtained. θ n+1 is taken as the calculated open circuit voltage estimation value. The specific iterative calculation process is as follows:

[0141] According to the state parameter vector θ n updated in the n-1th iteration process and the input vector , the predicted value of the system output updated in the nth iteration process is estimated , wherein n≧1 and n is an integer;

[0142] According to the predicted value of the system output updated in the nth iteration process and the measured input value y n of the system in the nth iteration process, the system output difference value e n+1 updated in the nth iteration process is obtained by subtraction;

[0143] According to the forgetting factor λ and the covariance matrix P n updated in the n-1th iteration process, the gain matrix K n+1 updated in the nth iteration process is obtained; wherein the initial covariance matrix K1 is a symmetric non-positive definite matrix;

[0144] According to the forgetting factor λ, the input vector , the gain matrix K n+1 updated in the nth iteration process and the covariance matrix P n updated in the n-1th iteration process, the covariance matrix P n+1 updated in the nth iteration process is obtained;

[0145] According to the gain matrix K n+1 updated in the nth iteration process and the system output difference value e n+1 updated in the nth iteration process, the state parameter vector estimation value θ updated in the nth iteration process is obtained

[0146] According to the state parameter vector estimation value θ updated in the nth iteration process and the input vector , the predicted value of the system output updated in the n+1th iteration process is estimated

[0147] state parameter vector through multiple iteration processes converges to obtain an open circuit voltage estimation value, i.e. taking the value of the state parameter vector that tends to converge as the open circuit voltage estimation value.

[0148] It should be noted that the result of the calculated open circuit voltage estimation value is related to the value of the forgetting factor. If the value of the forgetting factor is too small, the result of the iteration will fluctuate or diverge, i.e. does not tend to converge, and thus the open circuit voltage estimation value cannot be obtained. If the value of the forgetting factor is too large, the iteration result will not be able to track the changing parameters in time, and the convergence speed will be slowed down. Preferably, when the forgetting factor is selected as 0.98, the iteration result has good stability and fast convergence speed.

[0149] Optionally, on the basis of each of the above embodiments, the open circuit voltage estimation value-time curve is matched with the open circuit voltage-time curves of different types of batteries in the SOC-OCV database to obtain the open circuit voltage-time curve in the SOC-OCV database that best fits the battery to be measured, including:

[0150] Based on the clustering algorithm, the open circuit voltage estimation value-time curve is matched with the open circuit voltage-time curves of different types of batteries in the SOC-OCV database in a parallel computing manner to obtain the open circuit voltage-time curve in the SOC-OCV database that best fits the battery to be measured.

[0151] Specifically, clustering is to divide a data set into different classes or clusters according to a certain specific standard, so that the similarity of data objects in the same cluster is as large as possible, and the difference of data objects in different clusters is as large as possible. Parallel computing refers to the process of simultaneously using multiple computing resources to solve a computing problem, and is an effective means to improve the computing speed and processing capacity of a computer system. The SOC-OCV database pre-stores the open circuit voltage-time curves of multiple different types of power batteries, and the open circuit voltage-time curve of each type of power battery corresponds to a battery state of charge-time curve, i.e. the open circuit voltage-time curve in the SOC-OCV database has a mapping relationship with the battery state of charge-time curve. In this embodiment, multiple groups of points on the open circuit voltage estimation value-time curve are selected, and based on the idea of the clustering algorithm, the multiple groups of points on the open circuit voltage-time curves of different types of batteries in the SOC-OCV database are matched in a parallel computing manner, so that the open circuit voltage-time curve of the most fitting type of power battery can be quickly matched.

[0152] Optionally, Figure 6 ​is a specific flowchart of step S150 in a method for detecting a health state of a retired power battery provided by the embodiment of the present application. Based on the above embodiment, as shown in Figure 6 , the battery health degree of the battery to be measured is estimated according to the battery state of charge-time curve of the battery to be measured, which includes:

[0153] S1501, the battery state of charge-time curve of the battery to be measured includes a constant current discharge phase and a standing phase; a plurality of points on the constant current discharge phase of the battery state of charge-time curve of the battery to be measured are selected, and the actual nominal capacity and random error of the battery to be measured in the constant current discharge phase are calculated based on the selected plurality of points and the classical least squares method.

[0154] Specifically, the battery state of charge can be calculated by formula , and thus the actual nominal capacity C norm can be obtained from the battery state of charge-time curve. The least squares method is also called the least square method, which is a mathematical optimization technique. The least squares method finds the best function match of data by minimizing the sum of squares of errors.

[0155] Based on the classical least squares method, the change amount of the battery state of charge in the constant current discharge process of the battery to be measured can be obtained according to the plurality of points selected on the constant current discharge phase of the battery state of charge-time curve of the battery to be measured, which is expressed by formula 4 as follows:

[0156] y i = β1+ β2x i + v i

[0157] wherein y i is the change amount of the battery state of charge in the constant current discharge phase, x i is the remaining capacity of the battery to be measured, β2 is the reciprocal of the actual nominal capacity of the battery to be measured, v i is a random error vector, and β1 is a constant intercept. y i = SOC(t2)-SOC(t1), β2=1 / C norm , x i is expressed as η is the charge-discharge efficiency of the battery to be measured. Since the value of the charge-discharge efficiency fluctuates, there is a random error vector v i .

[0158] The plurality of points on the battery state of charge-time curve of the battery to be measured are selected, and the relationship between y i and x i is expressed in matrix form by formula 5, which is expressed as:

[0159] Y=X·H+V

[0160] wherein H is a column matrix of β1 and β2, Y is X is V is a random error matrix, expressed as

[0161] By taking the extreme value of the residual sum of squares, formula 5 can be converted into formula 6, which is expressed as:

[0162]

[0163] wherein S0 is the residual sum of squares of matrix Y and X, is the estimated matrix of H.

[0164] Solving the extreme value problem of formula 6 can obtain the following formula:

[0165] H0=(X′X) -1 X′Y

[0166] The H0 matrix can be calculated by the above formula, since β2 can be calculated accordingly, and C norm =1 / β2, thus the actual nominal capacity C norm is obtained. The H0 matrix calculated is substituted into formula 5, and the random error matrix V can be derived.

[0167] S1502, based on the iterative reweighted least squares method, the random error is optimized to obtain the optimized actual nominal capacity of the battery to be measured.

[0168] Specifically, since the classical least squares method is equivalent to assigning the same weight to each y i value, the accuracy is low, therefore, the influence of the random error v i needs to be considered. The iterative reweighted least squares method (IRLS) is used to assign different weights to each y i value, to optimize the random error, thereby improving the accuracy of the calculation.

[0169] The optimization function for optimizing the random error is expressed by formula 7:

[0170]

[0171] wherein y i is the change of the battery state of charge in the constant current discharge stage, x i is the remaining capacity of the battery to be measured, β2 is the reciprocal of the actual nominal capacity of the battery to be measured, v i is the random error vector, and β1 is the constant intercept. y i , x iThe expressions of β1 and β2 are described above and will not be repeated here.

[0172] The random error is normalized by using an influence function, and formula 7 is regarded as a function of β2 and extremum analysis is performed. When formula 7 has an extremum, formula 8 is satisfied:

[0173]

[0174] wherein, is the derivative of the influence function, u i is a normalized residual function, and is expressed as S is a robust estimation parameter.

[0175] Specifically, a robust loss function (Huber error function) is used as the influence function instead of the residual sum of squares. Exemplarily, the robust loss function can be expressed as:

[0176]

[0177] wherein, ρ(x) is the influence function, the derivative of the influence function ρ(x) is expressed as , k is usually 1.345, and x is the independent variable in the expression, and x is the residual. Therefore,

[0178] In addition, the robust estimation parameter can be expressed by the following formula:

[0179]

[0180] According to the expression of the robust estimation parameter, the normalized residual function can be calculated by using the following formula. The calculation formula of the normalized residual function can be expressed as:

[0181]

[0182] The derivative of β2 in formula 7 is taken to obtain

[0183] A plurality of points are selected on the constant-current discharge stage of the to-be-measured battery state-of-charge-time curve. According to the selected plurality of points, formula 8 can be converted into a matrix form, that is, formula X′WY=X′WXH is obtained.

[0184] The extremum of the above matrix formula is solved to obtain formula: wherein, W is By solving the matrix, β2 can be obtained, and thus β2=1 / C norm, the value of the optimized actual nominal capacity C norm .

[0185] S1503, according to the optimized actual nominal capacity of the battery to be measured and the nominal capacity of the battery to be measured, the battery health of the battery to be measured is calculated.

[0186] Specifically, the optimized actual nominal capacity of the battery to be measured is multiplied by the nominal capacity of the battery to be measured to obtain the battery health of the battery to be measured.

[0187] The formula for calculating the battery health can be expressed as:

[0188]

[0189] Wherein, SOH is the battery health, C norm is the actual nominal capacity of the battery, C standard is the nominal capacity of the battery.

[0190] For calculating the battery health of the battery to be measured, the actual nominal capacity of the battery to be measured is calculated by the above calculation process, and the nominal capacity of the battery to be measured can be obtained from the label of the battery to be measured. Therefore, according to the battery health formula, the battery health of the battery to be measured can be calculated simply.

[0191] The above specific embodiments do not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for detecting the health status of retired power batteries, characterized in that, include: The voltage across the battery under test is collected after it transitions from a discharging state to a state where discharging has stopped, forming a voltage-time curve. An equivalent circuit model of the battery under test is established based on the equivalent circuit method, and the initial parameters of the equivalent circuit model are calculated based on the voltage-time curve. Calculate the estimated open-circuit voltage based on the initial parameters to obtain the estimated open-circuit voltage-time curve; The estimated open-circuit voltage-time curve is matched with the open-circuit voltage-time curves of different types of batteries in the SOC-OCV database to obtain the open-circuit voltage-time curve that best matches the battery under test in the SOC-OCV database. The state-of-charge-time curve of the battery that has a mapping relationship with the best-matching open-circuit voltage-time curve is used as the state-of-charge-time curve of the battery under test. Based on the clustering algorithm, the open-circuit voltage estimate-time curve is matched with the open-circuit voltage-time curves of different types of batteries in the SOC-OCV database using parallel computing, so as to obtain the open-circuit voltage-time curve in the SOC-OCV database that best matches the battery under test. The battery health of the battery under test is determined based on the state-of-charge-time curve of the battery under test. The state-of-charge-time curve of the battery under test includes a constant current discharge stage and a resting stage. Multiple sets of points are selected on the constant current discharge stage of the state-of-charge-time curve of the battery under test. Based on the selected multiple sets of points, the actual nominal capacity and random error of the battery under test in the constant current discharge stage are calculated using the classical least squares method. Based on the iterative reweighted least squares method, the random error is optimized to obtain the optimized actual nominal capacity of the battery under test. The battery health of the battery under test is calculated based on the optimized actual nominal capacity and the nominal capacity of the battery under test.

2. The detection method according to claim 1, characterized in that, The equivalent circuit model of the battery under test includes: an ideal battery, an ohmic internal resistance, a polarization resistor, and a polarization capacitor; the positive terminal of the ideal battery is electrically connected to the first end of the ohmic internal resistance, and the polarization resistor and the polarization capacitor are connected in parallel and then electrically connected to the second end of the ohmic internal resistance. The initial parameters include: the ohmic internal resistance, polarization resistance, and polarization capacitance of the battery under test.

3. The detection method according to claim 2, characterized in that, The voltage-time curve includes a constant current discharge stage and a resting stage; the equivalent circuit model of the battery under test is established based on the equivalent circuit method, and the initial parameters of the equivalent circuit model are calculated according to the voltage-time curve, including: The ohmic internal resistance of the battery under test is calculated according to Formula 1, which is expressed as: ； in, The value of the internal resistance of the battery under test is the ohmic resistance. This represents the current value during the constant current discharge phase. This represents the initial voltage value of the voltage-time curve across the battery under test. The voltage value is the instantaneous decrease in voltage across the battery after the constant current discharge begins; The voltage-time curve during the resting phase is fitted, and the time constant is calculated using Formula 2, which is expressed as: ； Where U is the voltage across the battery under test during the resting phase. For time, τ is the voltage across the battery under test before the voltage rises instantaneously after the constant current discharge ends, and τ is the time constant. The constant current discharge stage of the voltage-time curve is fitted, and an equation is established according to Equations 2 and 3 to obtain the polarization resistance value of the battery under test; Equation 3 is expressed as: ； Where R1 is the polarization resistance value of the battery under test; The polarization capacitance of the battery under test is calculated based on the polarization resistance and time constant of the battery under test.

4. The detection method according to claim 2, characterized in that, The step of calculating the estimated open-circuit voltage based on the initial parameters includes: The iterative parameters for the iterative process of substituting the initial parameters into the iterative parameter equations to obtain the estimated open-circuit voltage; the iterative parameters include: a state parameter vector, an input vector, and a system output; wherein, the iterative parameter equations are expressed as: ； ； ； ； ； in, The terminal voltage of the equivalent circuit model obtained from the k-th measurement is... The voltage value of the parallel connection between polarization resistor R1 and polarization capacitor C1 in the equivalent circuit model obtained in the (k-1)th calculation is used as the voltage value of the parallel connection between polarization resistor R1 and polarization capacitor C1 in the kth calculation. Let be the current during the constant current discharge phase obtained from the k-th measurement. Let be the ohmic internal resistance value for the kth iteration. This is the estimated open-circuit voltage obtained in the k-th iteration. This is the system output obtained from the k-th calculation. Let be the input vector for the k-th iteration. Let k be the state parameter vector. This represents the voltage value of the parallel connection between the polarization resistor R1 and the polarization capacitor C1 in the (k+1)th calculation. To measure the time interval between two terminal voltage measurements, Let k be the time constant. Based on the iteration parameters, multiple iterations are performed until the state parameter vector converges to obtain the estimated open-circuit voltage.

5. The detection method according to claim 4, characterized in that, The step of performing multiple iterations based on the iteration parameters until the state parameter vector converges to obtain the estimated open-circuit voltage includes: Based on the forgetting factor recursive least squares method, the iteration parameters are used as the initial iteration parameters for calculating the estimated open-circuit voltage. The initial iteration parameters are substituted into the iteration formula and iterated multiple times until the state parameter vector tends to converge. The converged state parameter vector is used as the estimated open-circuit voltage. The iterative formula is expressed as: ； ； ； ； Among them, the parameters with the subscript n represent the parameters of the nth iteration process; The system output estimate is updated for the nth iteration. The estimated values ​​of the state parameter vector used in the nth iteration process. The estimated values ​​of the state parameter vector updated in the nth iteration process. This is the transpose of the input vector used in the nth iteration. The difference between the predicted system output value updated in the nth iteration and the measured system input value in the nth iteration is given by the given value. Let be the covariance matrix used in the nth iteration. The covariance matrix updated during the nth iteration. The gain matrix updated during the nth iteration. It is a forgetting factor.

6. The detection method according to claim 1, characterized in that, Multiple sets of points are selected during the constant current discharge phase of the state-of-charge-time curve of the battery under test. Based on the selected multiple sets of points, the actual nominal capacity of the battery under test is calculated using the classical least squares method, including: The change in the state of charge of the battery during the constant current discharge process is expressed by Equation 4 as follows: ； in, This refers to the change in the battery's state of charge during the constant current discharge phase. The remaining capacity of the battery under test. The value is the reciprocal of the actual nominal capacity of the battery under test. Let be a random error vector. The intercept is a constant. Based on the selected multiple sets of points, and The relationship is expressed in matrix form by Formula 5, which is as follows: ； in, for and column matrix, for , for ; Let be the random error matrix, denoted as ; Equation 5 is transformed into Equation 6, and the result is obtained by finding the extreme value of the sum of squared residuals. ,pass Calculate the actual nominal capacity and the random error of the battery under test; wherein, Formula 6 is expressed as: ； in, For matrix and The sum of squared residuals, for The estimation matrix.

7. The detection method according to claim 6, characterized in that, The method based on iterative reweighted least squares optimizes the random error to obtain the optimized actual nominal capacity of the battery under test, including: The optimization function for optimizing the random error is expressed by Equation 7: ； in, This refers to the change in the battery's state of charge during the constant current discharge phase. The remaining capacity of the battery under test. The value is the reciprocal of the actual nominal capacity of the battery under test. Let be a random error vector. The intercept is a constant. By using an influence function to standardize the random error, Equation 7 is transformed into Equation 8, which is expressed as: ； Where φ is the derivative of the influence function. = ; The standardized residual function is expressed as follows: , For robust parameter estimation; Multiple sets of points are selected during the constant current discharge stage of the state-of-charge-time curve of the battery under test. Based on the selected multiple sets of points, Equation 8 is transformed into solving for the extreme values ​​in matrix form, resulting in Equation 9; Equation 9 is expressed as: ； ； in, for ; Calculated according to Formula 9 ,according to The actual nominal capacity of the optimized battery under test is calculated.

8. The detection method according to claim 1, characterized in that, The calculation of the battery health status of the battery under test based on the optimized actual nominal capacity and the nominal capacity of the battery under test includes: The battery health is obtained by dividing the optimized actual nominal capacity of the battery under test by the nominal capacity of the battery under test.

Images