Battery Model Parameter Identification Method, Device, Equipment and Vehicle

Abstract

The present application relates to the technical field of batteries, and provides a method, device, equipment and vehicle for identifying battery model parameters. The method includes: establishing a battery equivalent circuit model, obtaining measurement values of a target battery, and constructing the measurement values into an input vector; determining a current forgetting factor according to the measured temperature in the measurement values and the error change rate corresponding to the current moment; determining an estimated parameter vector at the current moment through a parameter optimal solution algorithm according to the identification algorithm data at the previous moment, the current forgetting factor and the input vector; and determining model parameters in the battery equivalent circuit model according to the estimated parameter vector at the current moment and the measured temperature. Through the technical solution of the present application, the forgetting factor is adjusted in a timely manner in combination with the measured temperature, and the parameter accuracy when identifying battery model parameters based on the forgetting factor is improved.

Description

Technical Field

[0001] The present application relates to the technical field of batteries, and in particular, to a method, device, equipment and vehicle for identifying battery model parameters. Background Art

[0002] The equivalent circuit model is often used to study the internal electrochemical process of the battery, and obtaining the accurate parameters of the model is the key to constructing an effective model. Accurate parameter identification can be carried out in two ways: offline and online. The offline method determines the model parameters through experiments, but may not accurately reflect the time-varying characteristics of the battery; the online method uses the terminal voltage and output current data during the operation of the battery to identify the model parameters through algorithms. The identification results obtained by this method are more accurate and can well capture the dynamic characteristics of the battery.

[0003] Currently, the methods for online identifying battery model parameters include: the least squares method, the genetic algorithm, and the swarm particle algorithm. For the widely used least squares method, a forgetting factor can be introduced to overcome the "data saturation" phenomenon, and a battery model parameter identification method of the forgetting factor recursive least square (FFRLS) can be obtained. However, the forgetting factor in FFRLS is a fixed value selected artificially, which will affect the real-time performance and stability of the algorithm. Summary of the Invention

[0004] In view of this, the purpose of the present application is to propose a method, device, equipment and vehicle for identifying battery model parameters to solve the problem of poor battery model parameter identification effect caused by temperature change.

[0005] Based on the above purpose, the present application provides a method for identifying battery model parameters, and the method includes:

[0006] Establish a battery equivalent circuit model, obtain the measured values of the target battery, and construct the measured values into an input vector;

[0007] Determine the current forgetting factor according to the measured temperature in the measured values and the error change rate corresponding to the current moment;

[0008] Determine the estimated parameter vector at the current moment through a parameter optimal solution algorithm according to the identification algorithm data at the previous moment, the current forgetting factor, and the input vector;

[0009] Determine the model parameters in the battery equivalent circuit model according to the estimated parameter vector at the current moment and the measured temperature.

[0010] Through the above method, the forgetting factor is adjusted in a timely manner by combining the measured temperature, improving the parameter accuracy when identifying battery model parameters based on the forgetting factor.

[0011] Based on the above method, determining the current forgetting factor according to the measured temperature in the measured value and the error change rate corresponding to the current moment includes:

[0012] Determine the temperature influence factor according to the measured temperature in the measured value;

[0013] Determine the current forgetting factor according to the temperature influence factor and the error change rate corresponding to the current moment.

[0014] Through the above method, the variable current forgetting factor is adjusted by introducing the temperature influence factor, improving the accuracy of determining the current forgetting factor.

[0015] Based on the above method, determining the temperature influence factor according to the measured temperature in the measured value includes:

[0016] In response to the measured temperature being less than the lower limit of the battery temperature, determine the temperature influence factor according to the measured temperature, the lower limit of the battery temperature, and the first temperature parameter;

[0017] In response to the measured temperature being greater than or equal to the lower limit of the battery temperature and less than or equal to the upper limit of the battery temperature, determine the temperature influence factor to be 1;

[0018] In response to the measured temperature being greater than the upper limit of the battery temperature, determine the temperature influence factor according to the measured temperature, the upper limit of the battery temperature, and the second temperature parameter.

[0019] Through the above method, three different ways of determining the temperature influence factor are realized according to the lower limit of the battery temperature and the upper limit of the battery temperature, improving the correlation between the measured temperature and the temperature influence factor.

[0020] Based on the above method, determining the temperature influence factor according to the measured temperature, the lower limit of the battery temperature, and the first temperature parameter includes:

[0021] Take the difference between the lower limit of the battery temperature and the measured temperature as the first difference, and take the opposite of the product of the first difference and the first temperature parameter as the first exponent;

[0022] Determine the temperature influence factor according to the first exponent and the natural constant;

[0023] Correspondingly, determining the temperature influence factor according to the measured temperature, the upper limit of the battery temperature, and the second temperature parameter includes:

[0024] Take the difference between the measured temperature and the upper limit of the battery temperature as the second difference, and take the opposite of the product of the second difference and the second temperature parameter as the second exponent;

[0025] Determine the temperature influence factor according to the second exponent and the natural constant.

[0026] By the above method, the effect of improving the accuracy of calculating the temperature influence factor when the measured temperature is less than the lower limit of the battery temperature or greater than the upper limit of the battery temperature is achieved.

[0027] Based on the above method, the determining the current forgetting factor according to the temperature influence factor and the error change rate corresponding to the current moment includes:

[0028] Determine a third exponent according to the error change rate corresponding to the current moment, the calibrated error change rate, and the temperature influence factor; wherein, the error change rate corresponding to the current moment is the ratio of the difference between the error at the current moment and the error at the previous moment to the error at the previous moment;

[0029] Determine the current forgetting factor according to the initial forgetting factor, the natural constant, and the third exponent.

[0030] By the above method, the effect of accurately calculating the current forgetting factor based on the temperature influence factor and the error change rate corresponding to the current moment is achieved.

[0031] Based on the above method, the determining the third exponent according to the error change rate corresponding to the current moment, the calibrated error change rate, and the temperature influence factor includes:

[0032] Take the difference between the error change rate corresponding to the current moment and the minimum value of the calibrated error change rate as the third difference, and take the difference between the maximum value of the calibrated error change rate and the minimum value of the calibrated error change rate as the fourth difference;

[0033] Take the ratio of the third difference to the fourth difference as the first addend;

[0034] Take the product of the preset second coefficient and the square of the temperature influence factor as the second addend;

[0035] Determine the third exponent according to the sum value of the first addend and the second addend and the preset first coefficient.

[0036] By the above method, the effect of improving the accuracy of the third exponent used when determining the current forgetting factor is achieved.

[0037] Based on the above method, the battery equivalent circuit model includes: an ohmic resistance, an electrochemical polarization circuit, and a concentration polarization circuit connected in series in sequence. The electrochemical polarization circuit includes a first polarization internal resistance and a first polarization capacitor connected in parallel, and the concentration polarization circuit includes a second polarization internal resistance and a second polarization capacitor connected in parallel;

[0038] Correspondingly, determining the model parameters in the battery equivalent circuit model according to the estimated parameter vector at the current moment and the measured temperature includes:

[0039] Determining an intermediate variable and a time constant according to the estimated parameter vector at the current moment and the measured temperature;

[0040] Determining each resistance value and each capacitance value in the battery equivalent circuit model according to the intermediate variable and the time constant, and using each resistance value and each capacitance value as the model parameters in the battery equivalent circuit model.

[0041] Through the above method, the construction of the battery equivalent circuit model and the accurate identification of the battery model parameters are achieved.

[0042] Based on the above purpose, the present application further provides a battery model parameter identification device, which includes:

[0043] An input vector determination module, configured to establish a battery equivalent circuit model, obtain the measured values of the target battery, and construct the measured values into an input vector;

[0044] A current forgetting factor determination module, configured to determine a current forgetting factor according to the measured temperature in the measured values and the error change rate corresponding to the current moment;

[0045] An estimated parameter vector determination module, configured to determine an estimated parameter vector at the current moment through a parameter optimal solution algorithm according to the identification algorithm data at the previous moment, the current forgetting factor, and the input vector;

[0046] A model parameter determination module, configured to determine the model parameters in the battery equivalent circuit model according to the estimated parameter vector at the current moment and the measured temperature.

[0047] Based on the above purpose, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the battery model parameter identification method provided in any embodiment of the present application.

[0048] Based on the above purpose, the present application further provides a vehicle, and the vehicle includes the electronic device provided in any embodiment of the present application.

[0049] For the above purpose, the present application also provides a computer-readable storage medium storing computer instructions for causing a computer to execute the battery model parameter identification method provided in any embodiment of the present application.

[0050] As can be seen from the above, in the battery model parameter identification method provided by the present application, by establishing a battery equivalent circuit model, obtaining the measured values of the target battery, and constructing the measured values into an input vector to facilitate the calculation of the least squares method with a forgetting factor for the measured values. Furthermore, according to the measured temperature in the measured values and the error change rate corresponding to the current moment, the current forgetting factor is determined, and the current forgetting factor is adjusted in combination with the measured temperature. Further, according to the identification algorithm data at the previous moment, the current forgetting factor, and the input vector, the estimated parameter vector at the current moment is determined through a parameter optimal solution algorithm to complete the calculation of the least squares method with a forgetting factor. According to the estimated parameter vector at the current moment and the measured temperature, the model parameters in the battery equivalent circuit model are determined. Since the temperature of the battery has a particularly significant impact on the battery performance, in order to break through the limitation that the forgetting factor is a fixed value in the prior art, the present application timely adjusts the forgetting factor in combination with the measured temperature, improves the adaptability of the forgetting factor to the current state of the battery, and further improves the parameter accuracy when identifying the battery model parameters based on the forgetting factor. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0052] Figure 1 It is a flowchart of a battery model parameter identification method provided by an embodiment of the present application;

[0053] Figure 2 It is a schematic diagram of a battery second-order equivalent circuit model provided by an embodiment of the present application;

[0054] Figure 3 It is a flowchart of another battery model parameter identification method provided by an embodiment of the present application;

[0055] Figure 4 It is a schematic structural diagram of a battery model parameter identification device provided by an embodiment of the present application;

[0056] Figure 5 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] To make the objectives, technical solutions, and advantages of this application more clear and understandable, the following further elaborates on this application in detail with reference to specific embodiments and the accompanying drawings.

[0058] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the embodiments of this application should have the ordinary meanings understood by those with ordinary skills in the field to which this application belongs. The "first", "second", and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0059] The forgetting factor can control the degree of forgetting of old data and the weight decay rate of historical data, thereby affecting the real-time performance and stability of the FFRLS algorithm. During the charging and discharging process of the battery, with the dynamic changes in voltage and current, the forgetting factor should not be fixed and should be adjusted in real time. In practical applications, the influence of temperature on battery performance is particularly significant, and there are significant differences in the changes of the battery at different temperatures. In the existing methods, the influence of temperature on battery performance has not been fully considered. Therefore, there are limitations in practical applications because it cannot accurately reflect the real changes in battery model parameters under different temperature conditions.

[0060] Figure 1 It is a flowchart of a method for identifying battery model parameters provided by an embodiment of this application, which is applicable to identifying the model parameters of the battery in a vehicle to facilitate the subsequent accurate calculation of the corresponding state of charge. This method can be configured in an electronic device. As Figure 1 shown, this method may specifically include the following steps:

[0061] S110. Establish a battery equivalent circuit model, obtain the measurement values of the target battery, and construct the measurement values into an input vector.

[0062] Among them, the battery equivalent circuit model may refer to the battery second-order equivalent circuit model in this example. The battery second-order equivalent circuit model is a topological structure obtained by adding a parallel RC circuit to the first-order RC (resistance-capacitance) model. This structure represents the consideration of the charge diffusion impedance of the battery. The target battery is the battery for which model parameter identification is to be performed. The measured values may include the measured voltage, measured current, and measured temperature, which are the real-time data of the target battery measured by a voltmeter, ammeter, and temperature sensor respectively. The input vector is a vector composed of the measured voltage, measured current, and measured temperature at the current moment and the measured voltage, measured current, and measured temperature at the previous moment, and is used for model parameter identification.

[0063] Specifically, a battery equivalent circuit model is established. Through measurement, the measured values of the target battery can be obtained, that is, it can include the measured voltage, measured current, and measured temperature. The measured values at each required moment are combined to construct an input vector.

[0064] Exemplarily, the measured voltage of the target battery is taken as Y(k), the measured current is taken as I(k), and the measured temperature is taken as T(k), and the input vector φ(k) = [Y(k - 1), Y(k - 2), I(k), I(k - 1), I(k - 2), T(k), T(k - 1), T(k - 2)] is formed. T , where k is the current moment.

[0065] Based on the above example, the battery equivalent circuit model includes: an ohmic resistance, an electrochemical polarization circuit, and a concentration polarization circuit connected in series in sequence. The electrochemical polarization circuit includes a first polarization internal resistance and a first polarization capacitance connected in parallel, and the concentration polarization circuit includes a second polarization internal resistance and a second polarization capacitance connected in parallel. The electrochemical polarization circuit and the concentration polarization circuit respectively reflect the electrochemical polarization and concentration polarization inside the target battery.

[0066] Battery second-order equivalent circuit model Figure 2 As shown, the battery second-order equivalent circuit model can be represented by the following formula:

[0067]

[0068] Among them, U OCVLet \(U(t)\) denote the ideal voltage (voltage of the ideal voltage source) at the current moment, \(U_1(t)\) denote the voltage across the first polarization internal resistance and the first polarization capacitor at the current moment, \(U_2(t)\) denote the voltage across the second polarization internal resistance and the second polarization capacitor at the current moment, \(U(t)\) denote the terminal voltage, \(I(t)\) denote the current at the current moment, \(R_0\) be the resistance value of the ohmic resistance, \(R_1\) be the resistance value of the first polarization internal resistance, \(R_2\) be the resistance value of the second polarization internal resistance, \(C_1\) be the capacitance value of the first polarization capacitor, and \(C_2\) be the capacitance value of the second polarization capacitor. \(R_0\), \(R_1\), \(R_2\), \(C_1\) and \(C_2\) change with the operation of the target battery, thereby affecting the change of \(U\). OCV of \(U\).

[0069] S120. Determine the current forgetting factor according to the measured temperature in the measurement values and the error change rate corresponding to the current moment.

[0070] Among them, the error change rate corresponding to the current moment is the ratio of the difference between the error at the current moment and the error at the previous moment to the error at the previous moment. The error is the difference between the parameter estimation value and the true value. The current forgetting factor is the forgetting factor determined at the current moment. The forgetting factor affects the accuracy and stability of the FFRLS algorithm. The larger the forgetting factor, the smaller the forgetting of historical data, and the more stable the result of parameter identification. However, the algorithm is not sensitive to the current measurement value and cannot track the change of model parameters quickly. On the contrary, it means that the forgetting of history is larger and it is more sensitive to the current measurement value. Then the real-time performance of the algorithm is good, the response speed is fast, and the model accuracy is high. However, the parameter identification is unstable, may fluctuate all the time, and even be difficult to converge.

[0071] Specifically, a corresponding relationship or functional calculation relationship, etc., between the current forgetting factor, the measured temperature, and the error change rate corresponding to the current moment is pre-constructed. By looking up or calculating according to the measured temperature and the error change rate corresponding to the current moment, the current forgetting factor can be obtained.

[0072] S130. Determine the estimated parameter vector at the current moment through the parameter optimal solution algorithm according to the identification algorithm data at the previous moment, the current forgetting factor, and the input vector.

[0073] Among them, the identification algorithm data includes the identification algorithm gain matrix, the estimated parameter vector, and the covariance matrix. The parameter optimal solution algorithm can be the FFRLS algorithm. The estimated parameter vector is the output value to be solved.

[0074] Specifically, the estimated parameter vector at the current moment can be calculated by the FFRLS algorithm. Specifically:

[0075]

[0076] where k is the current moment, k - 1 is the previous moment, K is the identification algorithm gain matrix, P is the covariance matrix, E is the identity matrix, λ is the current forgetting factor, φ is the input vector, is the estimated parameter vector, = [a0, a1, a2, a3, a4, a5, a6, a7], where a0, a1, a2, a3, and a4 are used for subsequent parameter identification, and a5, a6, a7 are only used in the calculation process of the FFRLS algorithm, , and v is the system white noise.

[0077] S140. Determine the model parameters in the battery equivalent circuit model according to the estimated parameter vector at the current moment and the measured temperature.

[0078] Among them, the model parameters are the resistance values of each resistor and the capacitance values of each capacitor in the battery equivalent circuit model.

[0079] Specifically, substitute the estimated parameter vector at the current moment and the measured temperature into the calculation formula between each value in the pre - constructed estimated parameter vector and the model parameters, and the model parameters in the battery equivalent circuit model can be calculated.

[0080] Based on the above example, the model parameters in the battery equivalent circuit model can be determined according to the estimated parameter vector at the current moment and the measured temperature in the following way:

[0081] Determine the intermediate variable and the time constant according to the estimated parameter vector at the current moment and the measured temperature;

[0082] Determine the resistance values of each resistor and the capacitance values of each capacitor in the battery equivalent circuit model according to the intermediate variable and the time constant, and use the resistance values and capacitance values as the model parameters in the battery equivalent circuit model.

[0083] Among them, the resistance values of each resistor in the battery equivalent circuit model include the resistance value of the ohmic resistor, the resistance value of the first polarization internal resistor, and the resistance value of the second polarization internal resistor, and the capacitance values of each capacitor include the capacitance value of the first polarization capacitor and the capacitance value of the second polarization capacitor. The intermediate variable is a variable introduced to simplify the calculation in the model parameter identification process. The time constant is a value related to time.

[0084] Specifically, by substituting the estimated parameter vector and the measured temperature at the current moment into the calculation formulas for each intermediate variable and each time constant, each intermediate variable and each time constant can be obtained. The above calculation formulas are determined through circuit analysis and conform to the principle of the battery model. By substituting each intermediate variable and each time constant into the calculation formula related to the model parameters pre-constructed, the resistance value of the ohmic resistance, the resistance value of the first polarization internal resistance, the resistance value of the second polarization internal resistance, the capacitance value of the first polarization capacitor, and the capacitance value of the second polarization capacitor can be obtained. Thus, the resistance value of the ohmic resistance, the resistance value of the first polarization internal resistance, the resistance value of the second polarization internal resistance, the capacitance value of the first polarization capacitor, and the capacitance value of the second polarization capacitor can be used as the model parameters in the battery equivalent circuit model.

[0085] Based on the above example, further describe how to determine each intermediate variable and each time constant used when calculating the model parameters in the battery equivalent circuit model, that is, describe the calculation formulas for each intermediate variable and each time constant. To simplify the calculation, the intermediate variables include the first intermediate variable, the second intermediate variable, and the third intermediate variable, and the time constants include the first time constant and the second time constant. Then, the first intermediate variable, the second intermediate variable, the third intermediate variable, the first time constant, and the second time constant can be determined according to the following formulas based on the estimated parameter vector and the measured temperature at the current moment:

[0086] ;

[0087] Furthermore, the resistance value of the ohmic resistance, the resistance value of the first polarization internal resistance, the resistance value of the second polarization internal resistance, the capacitance value of the first polarization capacitor, and the capacitance value of the second polarization capacitor can be determined according to the following formulas based on the first intermediate variable, the second intermediate variable, the third intermediate variable, the first time constant, and the second time constant:

[0088] 。

[0089] Wherein, a0, a1, a2, a3, and a4 are the components in the estimated parameter vector at the current moment, T is the measured temperature, k1 is the first intermediate variable, k2 is the second intermediate variable, k3 is the third intermediate variable, τ1 is the first time constant, τ2 is the second time constant, R0 is the resistance value of the ohmic resistance, R1 is the resistance value of the first polarization internal resistance, R2 is the resistance value of the second polarization internal resistance, C1 is the capacitance value of the first polarization capacitor, and C2 is the capacitance value of the second polarization capacitor.

[0090] The battery model parameter identification method provided in this embodiment establishes a battery equivalent circuit model, obtains the measured values of the target battery, constructs the measured values into an input vector to facilitate the calculation of the least squares method with a forgetting factor. Furthermore, according to the measured temperature in the measured values and the error change rate corresponding to the current moment, the current forgetting factor is determined, and the current forgetting factor is adjusted in combination with the measured temperature. Further, according to the identification algorithm data at the previous moment, the current forgetting factor, and the input vector, the estimated parameter vector at the current moment is determined through a parameter optimal solution algorithm to complete the calculation of the least squares method with a forgetting factor. According to the estimated parameter vector at the current moment and the measured temperature, the model parameters in the battery equivalent circuit model are determined. Since the temperature of the battery has a particularly significant impact on the battery performance, in order to break through the limitation that the forgetting factor is a fixed value in the prior art, this embodiment adjusts the forgetting factor in a timely manner in combination with the measured temperature, improves the adaptability of the forgetting factor to the current state of the battery, and further improves the parameter accuracy when identifying the battery model parameters based on the forgetting factor.

[0091] The above embodiment describes the method for identifying battery model parameters. In addition to the establishment of the battery equivalent circuit model, the determination of the estimated parameter vector, and the identification of the model parameters in the battery equivalent circuit model, in this embodiment, the process of changing the current forgetting factor is further described in combination with the measured temperature and the error change rate corresponding to the current moment, as Figure 3 shown. Figure 3 It is a flowchart of another battery model parameter identification method provided by an embodiment of the present application. Among them, the explanations of the same or corresponding terms as those in the above embodiments are not repeated here. As Figure 3 shown, the method may specifically include the following steps:

[0092] S210. Establish a battery equivalent circuit model, obtain the measured values of the target battery, and construct the measured values into an input vector.

[0093] S220. Determine the temperature influence factor according to the measured temperature in the measured values.

[0094] Among them, the temperature influence factor is a parameter used to measure the influence of the measured temperature of the target battery on the current forgetting factor.

[0095] Specifically, according to the magnitude of the measured temperature, different calculation methods for the temperature influence factor can be obtained. Furthermore, in combination with the measured temperature and the corresponding calculation method, the temperature influence factor corresponding to the measured temperature can be calculated.

[0096] Based on the above example, the temperature influence factor can be determined according to the measured temperature in the following manner:

[0097] In response to the measured temperature being less than the lower limit of the battery temperature, the temperature influence factor is determined according to the measured temperature, the lower limit of the battery temperature, and the first temperature parameter;

[0098] In response to the measured temperature being greater than or equal to the lower limit of the battery temperature and less than or equal to the upper limit of the battery temperature, the temperature influence factor is determined to be 1;

[0099] In response to the measured temperature being greater than the upper limit of the battery temperature, the temperature influence factor is determined according to the measured temperature, the upper limit of the battery temperature, and the second temperature parameter.

[0100] Among them, the lower limit of the battery temperature is the pre-calibrated lower limit value of the battery operating temperature, and the upper limit of the battery temperature is the pre-calibrated upper limit value of the battery operating temperature. The first temperature parameter and the second temperature parameter are pre-set parameters used to adjust the temperature influence factor.

[0101] Specifically, if the measured temperature is less than the lower limit of the battery temperature, the temperature influence factor needs to be corrected by the measured temperature. Combining the measured temperature, the lower limit of the battery temperature, and the first temperature parameter, the temperature influence factor is determined according to the pre-constructed calculation method or corresponding relationship. If the measured temperature is greater than or equal to the lower limit of the battery temperature and less than or equal to the upper limit of the battery temperature, it can be considered that the battery is in a normal operating state, and the temperature influence factor is directly determined to be 1. If the measured temperature is greater than the upper limit of the battery temperature, the temperature influence factor needs to be corrected by the measured temperature. Combining the measured temperature, the upper limit of the battery temperature, and the second temperature parameter, the temperature influence factor is determined according to the pre-constructed calculation method or corresponding relationship.

[0102] Based on the above example, when the measured temperature is less than the lower limit of the battery temperature, the temperature influence factor can be determined according to the measured temperature, the lower limit of the battery temperature, and the first temperature parameter in the following way:

[0103] Take the difference between the lower limit of the battery temperature and the measured temperature as the first difference, and take the opposite of the product of the first difference and the first temperature parameter as the first exponent;

[0104] Determine the temperature influence factor according to the first exponent and the natural constant.

[0105] Among them, the first difference is the difference between the lower limit of the battery temperature and the measured temperature. The first exponent is the opposite of the product of the first difference and the first temperature parameter.

[0106] Specifically, take the difference between the lower limit of the battery temperature and the measured temperature as the first difference, and take the opposite of the product of the first difference and the first temperature parameter as the first exponent. Taking the natural constant as the base and the first exponent as the exponent, the power value can be solved, and the reciprocal of the sum of the power value and 1 is taken as the temperature influence factor.

[0107] Correspondingly, when the measured temperature is greater than the upper limit of the battery temperature, the temperature influence factor can be determined according to the measured temperature, the upper limit of the battery temperature, and the second temperature parameter in the following manner, including:

[0108] Take the difference between the measured temperature and the upper limit of the battery temperature as the second difference, and take the opposite of the product of the second difference and the second temperature parameter as the second exponent;

[0109] Determine the temperature influence factor according to the second exponent and the natural constant.

[0110] Among them, the second difference is the difference between the measured temperature and the upper limit of the battery temperature. The second exponent is the opposite of the product of the second difference and the second temperature parameter.

[0111] Specifically, take the difference between the measured temperature and the upper limit of the battery temperature as the second difference, and take the opposite of the product of the second difference and the second temperature parameter as the second exponent. Taking the natural constant as the base and the second exponent as the exponent, the power value can be solved, and the reciprocal of the sum value of the power value and 1 is taken as the temperature influence factor.

[0112] Exemplarily, the temperature influence factor can be determined by the following formula:

[0113]

[0114] Among them, k is the current moment, T is the measured temperature, T s is the temperature influence factor, e is the natural constant, k0 is the first temperature parameter, k1 is the second temperature parameter, T min is the lower limit of the battery temperature, T max is the upper limit of the battery temperature.

[0115] S230. Determine the current forgetting factor according to the temperature influence factor and the error change rate corresponding to the current moment.

[0116] Specifically, a corresponding relationship or functional calculation relationship, etc., between the current forgetting factor, the temperature influence factor, and the error change rate corresponding to the current moment is pre-constructed, and the current forgetting factor can be obtained by looking up or calculating according to the temperature influence factor and the error change rate corresponding to the current moment.

[0117] Based on the above example, the current forgetting factor can be determined according to the temperature influence factor and the error change rate corresponding to the current moment in the following manner:

[0118] Determine the third exponent according to the error change rate corresponding to the current moment, the calibrated error change rate, and the temperature influence factor;

[0119] Determine the current forgetting factor according to the initial forgetting factor, the natural constant, and the third exponent.

[0120] Among them, the calibration error change rate is a value obtained by analyzing the historical usage data of other batteries of the same type as the target battery, and it is a range of change rates. For example, it can include the minimum value of the calibration error change rate and the maximum value of the calibration error change rate. If the target battery is a lithium battery, calibration needs to be carried out separately for different types of lithium batteries. For example, the minimum value of the calibration error change rate and the maximum value of the calibration error change rate of common lithium iron phosphate batteries and ternary lithium batteries need to be statistically analyzed separately. The third exponent is the exponent corresponding to the natural constant when solving the current forgetting factor, and it is calculated through the error change rate corresponding to the current moment, the minimum value of the calibration error change rate, the maximum value of the calibration error change rate, the temperature influence factor, and the calibration coefficient. The initial forgetting factor is the initially set forgetting factor, which can be obtained through statistical analysis of historical data or can be set to a value of 1.

[0121] Specifically, according to the calculation formula corresponding to the pre-constructed third exponent, the error change rate corresponding to the current moment, the calibration error change rate, and the temperature influence factor can be input to calculate the third exponent. Furthermore, substituting the calculated third exponent into the current forgetting factor calculation formula constructed by the initial forgetting factor, the natural constant, and the third exponent, the current forgetting factor is obtained for updating the forgetting factor according to the measured temperature.

[0122] Based on the above example, the third exponent can be determined according to the error change rate corresponding to the current moment, the calibration error change rate, and the temperature influence factor in the following manner:

[0123] Taking the difference between the error change rate corresponding to the current moment and the minimum value of the calibration error change rate as the third difference, and taking the difference between the maximum value of the calibration error change rate and the minimum value of the calibration error change rate as the fourth difference;

[0124] Taking the ratio of the third difference to the fourth difference as the first addend;

[0125] Taking the product of the preset second coefficient and the square of the temperature influence factor as the second addend;

[0126] Determining the third exponent according to the sum value of the first addend and the second addend and the preset first coefficient.

[0127] Among them, the first coefficient and the second coefficient are pre-calibrated parameters used to adjust the error change rate and the temperature influence factor. The third difference is the difference between the error change rate corresponding to the current moment and the minimum value of the calibration error change rate. The fourth difference is the difference between the maximum value of the calibration error change rate and the minimum value of the calibration error change rate. The first addend is the ratio of the third difference to the fourth difference. The second addend is the product of the second coefficient in the calibration coefficient and the square of the temperature influence factor.

[0128] Specifically, the opposite of the product of the sum of the first addend and the second addend and the first coefficient in the calibration coefficient is the third exponent.

[0129] Exemplarily, the error change rate corresponding to the current moment is calculated by the following formula:

[0130]

[0131] where k is the current moment, k - 1 is the previous moment, err is the error, and r is the error change rate.

[0132] Further, the current forgetting factor is determined by the following formula:

[0133]

[0134] where k is the current moment, λ is the current forgetting factor, λ0 is the initial forgetting factor, e is the natural constant, T s is the temperature influence factor, r is the error change rate, r min is the minimum value of the calibrated error change rate, r max is the maximum value of the calibrated error change rate, α is a preset first coefficient, and β is a preset second coefficient.

[0135] S240. Determine the estimated parameter vector at the current moment through a parameter optimal solution algorithm based on the identification algorithm data at the previous moment, the current forgetting factor, and the input vector.

[0136] S250. Determine the model parameters in the battery equivalent circuit model based on the estimated parameter vector at the current moment and the measured temperature.

[0137] Under experimental conditions, the relationship between the voltage and SOC (State of Charge) of the target battery used in the specified vehicle is fitted to obtain the relationship between Uocv and SOC, i.e., Uocv = f(SOC).

[0138] Optionally, after determining the model parameters in the battery equivalent circuit model, it is also possible to combine the U OCV = f(SOC) obtained in the laboratory. Based on this, methods such as UKF (Unscented Kalman Filter) can be used to predict SOC.

[0139] The battery model parameter identification method provided by this embodiment determines the temperature influence factor according to the measured temperature in the measurement values, so as to determine different adjustment methods of the current forgetting factor according to different measured temperatures. Furthermore, according to the temperature influence factor and the error change rate corresponding to the current moment, the current forgetting factor is determined to more accurately reflect the changes of model parameters under different temperature conditions, enabling the current forgetting factor to better adapt to the target battery, achieving the effect of using lower computing resources and improving the accuracy of combining the current forgetting factor.

[0140] It should be noted that the method of this embodiment of the present application can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In this case of a distributed scenario, one of the multiple devices can only execute one or more steps of the method of this embodiment of the present application, and these multiple devices will interact with each other to complete the described method.

[0141] It should be noted that some embodiments of the present application have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order than in the above embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0142] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides a battery model parameter identification device. Figure 4 It is a structural schematic diagram of a battery model parameter identification device provided by an embodiment of the present application. Refer to Figure 4 The battery model parameter identification device includes: an input vector determination module 310, a current forgetting factor determination module 320, an estimated parameter vector determination module 330, and a model parameter determination module 340.

[0143] Among them, the input vector determination module 310 is used to establish a battery equivalent circuit model, obtain the measurement values of the target battery, and construct the measurement values into an input vector; the current forgetting factor determination module 320 is used to determine the current forgetting factor according to the measured temperature in the measurement values and the error change rate corresponding to the current moment; the estimated parameter vector determination module 330 is used to determine the estimated parameter vector at the current moment through a parameter optimal solution algorithm according to the identification algorithm data at the previous moment, the current forgetting factor, and the input vector; the model parameter determination module 340 is used to determine the model parameters in the battery equivalent circuit model according to the estimated parameter vector at the current moment and the measured temperature.

[0144] Based on the above example, optionally, the current forgetting factor determination module 320 is further configured to determine a temperature influence factor according to the measured temperature in the measurement values; and determine the current forgetting factor according to the temperature influence factor and the error change rate corresponding to the current moment.

[0145] Based on the above example, optionally, the current forgetting factor determination module 320 is further configured to, in response to the measured temperature being less than the lower limit of the battery temperature, determine the temperature influence factor according to the measured temperature, the lower limit of the battery temperature, and the first temperature parameter; in response to the measured temperature being greater than or equal to the lower limit of the battery temperature and less than or equal to the upper limit of the battery temperature, determine the temperature influence factor to be 1; in response to the measured temperature being greater than the upper limit of the battery temperature, determine the temperature influence factor according to the measured temperature, the upper limit of the battery temperature, and the second temperature parameter.

[0146] Based on the above example, optionally, the current forgetting factor determination module 320 is further configured to use the difference between the lower limit of the battery temperature and the measured temperature as the first difference, and use the opposite of the product of the first difference and the first temperature parameter as the first exponent; and determine the temperature influence factor according to the first exponent and the natural constant.

[0147] Correspondingly, the current forgetting factor determination module 320 is further configured to use the difference between the measured temperature and the upper limit of the battery temperature as the second difference, and use the opposite of the product of the second difference and the second temperature parameter as the second exponent; and determine the temperature influence factor according to the second exponent and the natural constant.

[0148] Based on the above example, optionally, the current forgetting factor determination module 320 is further configured to determine a third exponent according to the error change rate corresponding to the current moment, the calibrated error change rate, and the temperature influence factor; wherein, the error change rate corresponding to the current moment is the ratio of the difference between the error at the current moment and the error at the previous moment to the error at the previous moment; and determine the current forgetting factor according to the initial forgetting factor, the natural constant, and the third exponent.

[0149] Based on the above example, optionally, the current forgetting factor determination module 320 is further configured to use the difference between the error change rate corresponding to the current moment and the minimum value of the calibrated error change rate as the third difference, and use the difference between the maximum value of the calibrated error change rate and the minimum value of the calibrated error change rate as the fourth difference; use the ratio of the third difference to the fourth difference as the first addend; use the product of the preset second coefficient and the square of the temperature influence factor as the second addend; and determine the third exponent according to the sum value of the first addend and the second addend and the preset first coefficient.

[0150] Based on the above example, optionally, the battery equivalent circuit model includes: an ohmic resistance, an electrochemical polarization circuit, and a concentration polarization circuit connected in series in sequence, the electrochemical polarization circuit includes a first polarization internal resistance and a first polarization capacitance connected in parallel, and the concentration polarization circuit includes a second polarization internal resistance and a second polarization capacitance connected in parallel;

[0151] Correspondingly, the model parameter determination module 340 is further configured to determine an intermediate variable and a time constant according to the estimated parameter vector at the current moment and the measured temperature; determine the resistance values and capacitance values in the battery equivalent circuit model according to the intermediate variable and the time constant, and use the resistance values and capacitance values as the model parameters in the battery equivalent circuit model.

[0152] For the convenience of description, the above device is described by dividing it into various modules according to functions. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0153] The device in the above embodiment is used to implement the corresponding battery model parameter identification method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0154] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, it implements the battery model parameter identification method described in any of the above embodiments.

[0155] Figure 5 FIG. 1 shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0156] The processor 1010 may be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0157] The memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0158] The input / output interface 1030 is used to connect to an input / output module to implement information input and output. The input / output module may be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0159] The communication interface 1040 is used to connect to a communication module (not shown in the figure) to implement communication interaction between this device and other devices. Among them, the communication module may implement communication in a wired manner (such as USB, network cable, etc.) or in a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0160] The bus 1050 includes a path for transmitting information between various components of the device (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0161] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solution of the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0162] The electronic device in the above embodiment is used to implement the corresponding battery model parameter identification method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0163] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present application also provides a vehicle, which includes the electronic device in any of the foregoing embodiments.

[0164] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present application also provides a computer-readable storage medium, which stores computer instructions for causing the computer to execute the battery model parameter identification method in any of the foregoing embodiments.

[0165] The computer-readable medium in this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0166] The computer instructions stored in the storage medium in the above embodiment are used to cause the computer to execute the battery model parameter identification method in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0167] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the present application (including the claims) is limited to these examples; under the concept of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present application as described above, and they are not provided in detail for the sake of brevity.

[0168] In addition, for simplicity of explanation and discussion, and in order not to make the embodiments of the present application difficult to understand, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Further, the devices may be shown in block diagram form in order to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (i.e., these details should be entirely within the understanding of those skilled in the art). In cases where specific details (such as circuits) are set forth to describe exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0169] Although the present application has been described in connection with specific embodiments of the present application, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0170] The embodiments of the present application are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application shall be included within the protection scope of the present application.

Claims

1. A battery model parameter identification method, characterized in that: include: Establishing a battery equivalent circuit model, obtaining measurement values ​​of a target battery, and constructing the measurement values ​​into an input vector; Determining a temperature influence factor according to a measured temperature in the measured value; Determine a third index according to the error change rate corresponding to the current moment, the calibration error change rate and the temperature influence factor; wherein the error change rate corresponding to the current moment is the ratio of the difference between the error at the current moment and the error at the previous moment to the error at the previous moment; Determine a current forgetting factor according to the initial forgetting factor, the natural constant and the third index; Determine the estimated parameter vector at the current moment through a parameter optimal solution algorithm according to the identification algorithm data at the previous moment, the current forgetting factor and the input vector; Model parameters in the battery equivalent circuit model are determined according to the estimated parameter vector at the current moment and the measured temperature.

2. The method according to claim 1, characterized in that The step of determining the temperature influence factor according to the measured temperature in the measured value includes: In response to the measured temperature being less than a lower limit of the battery temperature, determining a temperature influence factor according to the measured temperature, the lower limit of the battery temperature and a first temperature parameter; In response to the measured temperature being greater than or equal to the battery temperature lower limit and the measured temperature being less than or equal to the battery temperature upper limit, determining the temperature influence factor to be 1; In response to the measured temperature being greater than the battery temperature upper limit, a temperature influence factor is determined according to the measured temperature, the battery temperature upper limit and a second temperature parameter.

3. The method according to claim 2, characterized in that The determining of the temperature influence factor according to the measured temperature, the battery temperature lower limit and the first temperature parameter includes: taking the difference between the lower limit of the battery temperature and the measured temperature as a first difference, and taking the inverse of the product of the first difference and the first temperature parameter as a first index; Determine the temperature influence factor based on the first exponent and the natural constant; Correspondingly, determining the temperature influence factor according to the measured temperature, the battery temperature upper limit and the second temperature parameter includes: taking the difference between the measured temperature and the upper limit of the battery temperature as a second difference, and taking the inverse of the product of the second difference and the second temperature parameter as a second index; The temperature influence factor is determined based on the second exponent and the natural constant.

4. The method according to claim 1, characterized in that Determining the third index according to the error change rate, the calibration error change rate and the temperature influence factor corresponding to the current moment includes: The difference between the error change rate corresponding to the current moment and the minimum value of the calibration error change rate is taken as the third difference, and the difference between the maximum value of the calibration error change rate and the minimum value of the calibration error change rate is taken as the fourth difference; Using the ratio of the third difference to the fourth difference as a first addend; The product of the preset second coefficient and the square of the temperature influence factor is used as the second addend; A third index is determined according to a sum of the first addend and the second addend and a preset first coefficient.

5. The method according to claim 1, characterized in that The battery equivalent circuit model comprises: an ohmic resistor, an electrochemical polarization circuit and a concentration polarization circuit connected in series in sequence, wherein the electrochemical polarization circuit comprises a first polarization internal resistor and a first polarization capacitor connected in parallel, and the concentration polarization circuit comprises a second polarization internal resistor and a second polarization capacitor connected in parallel; Correspondingly, determining the model parameters in the battery equivalent circuit model according to the estimated parameter vector at the current moment and the measured temperature includes: Determining an intermediate variable and a time constant according to the estimated parameter vector at the current moment and the measured temperature; According to the intermediate variable and the time constant, each resistance value and each capacitance value in the battery equivalent circuit model are determined, and each resistance value and each capacitance value are used as model parameters in the battery equivalent circuit model.

6. A battery model parameter identification device, characterized in that: include: An input vector determination module is used to establish a battery equivalent circuit model, obtain measurement values ​​of a target battery, and construct the measurement values ​​into an input vector; A current forgetting factor determination module is used to determine the temperature influence factor according to the measured temperature in the measured value; determine the third index according to the error change rate corresponding to the current moment, the calibration error change rate and the temperature influence factor; determine the current forgetting factor according to the initial forgetting factor, the natural constant and the third index; wherein the error change rate corresponding to the current moment is the ratio of the difference between the error at the current moment and the error at the previous moment to the error at the previous moment; An estimated parameter vector determination module, used to determine the estimated parameter vector at the current moment through a parameter optimal solution algorithm according to the identification algorithm data at the previous moment, the current forgetting factor and the input vector; The model parameter determination module is used to determine the model parameters in the battery equivalent circuit model according to the estimated parameter vector at the current moment and the measured temperature.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the battery model parameter identification method according to any one of claims 1 to 5 is implemented.

8. A vehicle, characterized in that: The vehicle comprises the electronic device as claimed in claim 7.

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