Battery model parameter identification method, device and equipment and vehicle

Abstract

The invention relates to the technical field of batteries, and provides a battery model parameter identification method, device and equipment and a vehicle, and the method comprises the steps: building a battery equivalent circuit model, obtaining a measurement value of a target battery, and constructing the measurement value into an input vector; determining a current forgetting factor according to the measured temperature in the measured value and the error change rate corresponding to the current moment; determining an estimation 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 scheme of the invention, the forgetting factor is adjusted in time in combination with the measured temperature, and the parameter accuracy when the battery model parameters are identified based on the forgetting factor is improved.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a battery model parameter identification method, device, equipment and vehicle. Background Art

[0002] Equivalent circuit models are often used to study the internal electrochemical processes of batteries, and obtaining accurate model parameters is the key to building an effective model. Accurate parameter identification can be performed in both offline and online methods. 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 of the battery during operation to identify the model parameters through algorithms. This method obtains more accurate identification results and can well capture the dynamic characteristics of the battery.

[0003] At present, the methods for online identification of battery model parameters include: least squares method, genetic algorithm, and swarm particle algorithm. For the widely used least squares method, the forgetting factor can be introduced to overcome the "data saturation" phenomenon, and the least squares method with forgetting factor (Forgetting Factor Recursive Least Square, FFRLS) battery model parameter identification method is obtained. However, the forgetting factor in FFRLS is a fixed value selected by humans, 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 battery model parameter identification method, device, equipment and vehicle to solve the problem of poor battery model parameter identification effect caused by temperature changes.

[0005] Based on the above purpose, the present application provides a battery model parameter identification method, the method comprising: Establishing a battery equivalent circuit model, obtaining measurement values ​​of a target battery, and constructing the measurement values ​​into an input vector; Determine a current forgetting factor according to the measured temperature in the measured value and the error change rate corresponding to the current moment; 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.

[0006] Through the above method, the forgetting factor is adjusted in time in combination with the measured temperature, thereby improving the parameter accuracy when identifying the battery model parameters based on the forgetting factor.

[0007] Based on the above method, the current forgetting factor is determined according to the measured temperature in the measured value and the error change rate corresponding to the current moment, including: Determining a temperature influence factor according to a measured temperature in the measured value; The current forgetting factor is determined according to the temperature influence factor and the error change rate corresponding to the current moment.

[0008] Through the above method, the temperature influence factor is introduced to adjust the variable current forgetting factor, thereby improving the accuracy of determining the current forgetting factor.

[0009] Based on the above method, 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.

[0010] 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, thereby improving the correlation between the measured temperature and the temperature influence factor.

[0011] Based on the above method, determining 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.

[0012] The above method can improve the accuracy of calculating the temperature influence factor when the measured temperature is less than the lower limit of the battery temperature or the measured temperature is greater than the upper limit of the battery temperature.

[0013] On the basis of the above method, the current forgetting factor is determined according to the temperature influence factor and the error change rate corresponding to the current moment, including: 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; The current forgetting factor is determined according to the initial forgetting factor, the natural constant and the third index.

[0014] Through 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.

[0015] Based on the above method, 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.

[0016] Through the above method, the effect of improving the accuracy of determining the third index used in calculating the current forgetting factor is achieved.

[0017] On the basis of the above method, the battery equivalent circuit model includes: an ohmic resistor, an electrochemical polarization circuit and a concentration polarization circuit connected in series in sequence, the electrochemical polarization circuit includes a first polarization internal resistor and a first polarization capacitor connected in parallel, and the concentration polarization circuit includes 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.

[0018] Through the above method, the construction of the battery equivalent circuit model and the accurate identification of the battery model parameters are achieved.

[0019] Based on the above purpose, the present application also provides a battery model parameter identification device, which includes: 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, used to determine the current forgetting factor according to the measured temperature in the measured value and the error change rate corresponding to the current 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.

[0020] Based on the above purpose, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a battery model parameter identification method as provided in any embodiment of the present application is implemented.

[0021] Based on the above purpose, the present application also provides a vehicle, which includes an electronic device provided in any embodiment of the present application.

[0022] Based on the above purpose, the present application also provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute a battery model parameter identification method as provided in any embodiment of the present application.

[0023] From the above, it can be seen that the battery model parameter identification method provided by the present application establishes a battery equivalent circuit model, obtains the measurement value of the target battery, and constructs the measurement value into an input vector to obtain the measurement value for the least squares calculation with a forgetting factor. Then, according to the measured temperature in the measurement value and the error change rate corresponding to the current moment, the current forgetting factor is determined to adjust the current forgetting factor in combination with the measured temperature. Further, according to the identification algorithm data of the previous moment, the current forgetting factor and the input vector, the estimated parameter vector at the current moment is determined by the parameter optimal solution algorithm to complete the least squares calculation with the 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 of the forgetting factor as a fixed value in the prior art, the present application timely adjusts the forgetting factor in combination with the measured temperature, thereby improving the adaptability of the forgetting factor to the current state of the battery, and thereby improving the parameter accuracy when identifying the battery model parameters based on the forgetting factor. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A flow chart of a battery model parameter identification method provided in an embodiment of the present application; Figure 2 A schematic diagram of a second-order equivalent circuit model of a battery provided in an embodiment of the present application; Figure 3 A flowchart of another battery model parameter identification method provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of a battery model parameter identification device provided in an embodiment of the present application; Figure 5 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0027] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0028] The forgetting factor can control the degree of forgetting of old data and the weight decay speed of historical data, thus affecting the real-time performance and stability of the FFRLS algorithm. In the dynamic changes of voltage and current during battery charging and discharging, the forgetting factor should not be static and should be adjusted in real time. In practical applications, the impact of temperature on battery performance is particularly significant, and there are significant differences in the changes of batteries at different temperatures. The existing method fails to fully consider the impact of temperature on battery performance. Therefore, it has limitations in practical applications because it cannot accurately reflect the actual changes in battery model parameters under different temperature conditions.

[0029] Figure 1 The flowchart of a battery model parameter identification method provided in an embodiment of the present application is suitable for identifying the model parameters of the battery in the vehicle to facilitate the subsequent accurate calculation of the corresponding state of charge. The method can be configured in an electronic device. Figure 1 As shown, the method may specifically include the following steps: S110, establishing a battery equivalent circuit model, obtaining measurement values ​​of the target battery, and constructing the measurement values ​​into an input vector.

[0030] Among them, the battery equivalent circuit model in this example can refer to the second-order equivalent circuit model of the battery. The second-order equivalent circuit model of the battery is a topological structure obtained by adding a parallel RC loop to the first-order RC (resistance and capacitance) model. This structure indicates that the charge diffusion impedance of the battery is taken into account. The target battery is the battery for which model parameter identification is to be performed. The measured values ​​may include measured voltage, measured current, and measured temperature. The measured voltage, measured current, and measured temperature are real-time data of the target battery measured by a voltmeter, an ammeter, and a 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, which is used for model parameter identification.

[0031] Specifically, a battery equivalent circuit model is established, and the measurement value of the target battery can be obtained through measurement, that is, it can include measuring voltage, measuring current and measuring temperature, and the measurement values ​​at each required time are combined to construct an input vector.

[0032] 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), forming an 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)] T , where k is the current moment.

[0033] Based on the above example, the battery equivalent circuit model includes: an ohmic resistor, 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. The electrochemical polarization circuit and the concentration polarization circuit reflect the electrochemical polarization and concentration polarization inside the target battery, respectively.

[0034] Battery second-order equivalent circuit model Figure 2 As shown, the second-order equivalent circuit model of the battery can be expressed by the following formula:

[0035] Among them, U OCV (t) represents the ideal voltage at the current moment (ideal voltage source voltage), U 1 (t) represents the voltage across the first polarization internal resistance and the first polarization capacitor at the current moment, U 2 (t) represents the voltage across the second polarization internal resistance and the second polarization capacitor at the current moment, U(t) represents the terminal voltage, I(t) represents the current at the current moment, R 0 is the resistance value of the ohmic resistor, R 1 is the resistance value of the first polarization internal resistance, R2 is the resistance value of the second polarization internal resistance, C 1 is the capacitance value of the first polarization capacitor, C 2 is the capacitance value of the second polarization capacitor. 0 , R 1 , R 2 , C 1 and C 2 Changes with the operation of the target battery, thus affecting U OCV changes.

[0036] S120 , determining a current forgetting factor according to the measured temperature in the measured value and the error change rate corresponding to the current moment.

[0037] 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 estimate 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 less the historical data is forgotten, and the more stable the parameter identification result. However, the algorithm is not sensitive to the current measurement value and cannot track the changes in the model parameters quickly. On the contrary, it means that the historical data is forgotten more and the current measurement value is more sensitive. In this case, the algorithm has good real-time performance, fast response speed, and high model accuracy, but the parameter identification is unstable and may fluctuate all the time, or even difficult to converge.

[0038] Specifically, a corresponding relationship or function calculation relationship between the current forgetting factor and the measured temperature and the error change rate corresponding to the current moment is pre-built, and the current forgetting factor can be obtained by searching or calculating according to the measured temperature and the error change rate corresponding to the current moment.

[0039] S130, determining 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.

[0040] The identification algorithm data includes the identification algorithm gain matrix, the estimated parameter vector, and the covariance matrix. The optimal parameter solution algorithm may be the FFRLS algorithm. The estimated parameter vector is the output value to be solved.

[0041] Specifically, the estimated parameter vector at the current moment can be calculated by the FFRLS algorithm, specifically:

[0042] Among them, k is the current moment, k-1 is the previous moment, K is the recognition algorithm gain matrix, P is the covariance matrix, E is the identity matrix, λ is the current forgetting factor, φ is the input vector, To estimate the parameter vector, =[a 0 , a 1 , a 2 , a 3 , a 4 , a 5 , a 6 , a 7 ], where a 0 , a 1 , a 2 , a 3 and a 4 For subsequent parameter identification, a 5 , a 6 , a 7 Only used in the FFRLS algorithm calculation process, , v is the system white noise.

[0043] S140: Determine model parameters in the battery equivalent circuit model according to the estimated parameter vector at the current moment and the measured temperature.

[0044] The model parameters are the resistance values ​​of each resistor and the capacitance values ​​of each capacitor in the battery equivalent circuit model.

[0045] Specifically, the estimated parameter vector and the measured temperature at the current moment are substituted into the calculation formula between each numerical 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.

[0046] Based on the above example, the model parameters in the battery equivalent circuit model can be determined according to the estimated parameter vector and the measured temperature at the current moment in the following manner: Determine the intermediate variables and the time constant according to the estimated parameter vector and the measured temperature at the current moment; According to the intermediate variables 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.

[0047] Among them, each resistance value in the battery equivalent circuit model includes the resistance value of the ohmic resistor, the resistance value of the first polarization internal resistance and the resistance value of the second polarization internal resistance, and each capacitance value includes the capacitance value of the first polarization capacitor and the capacitance value of the second polarization capacitor. The intermediate variable is a variable introduced in the process of model parameter identification for the convenience of calculation. The time constant is a value related to time.

[0048] Specifically, the estimated parameter vector and the measured temperature at the current moment are substituted into the calculation formulas of the intermediate variables and the time constants, and the intermediate variables and the time constants can be obtained. The above calculation formula is determined by circuit analysis and conforms to the principle of the battery model. Substituting the intermediate variables and the time constants into the pre-constructed model parameter related calculation formula, the resistance value of the ohmic resistor, 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 resistor, 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 model parameters in the battery equivalent circuit model.

[0049] Based on the above example, how to determine the intermediate variables and time constants used when calculating the model parameters in the battery equivalent circuit model is further described, that is, the calculation formulas for describing the intermediate variables and time constants. In order to simplify the calculation, the intermediate variables include the first intermediate variable, the second intermediate variable and the third intermediate variable, and the time constant includes 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 estimated parameter vector and the measured temperature at the current moment by the following formula: ; Furthermore, the resistance value of the ohmic resistor, 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 first intermediate variable, the second intermediate variable, the third intermediate variable, the first time constant, and the second time constant by the following formula: .

[0050] Among them, a 0 、a 1 、a 2 、a 3 and a 4 is the component in the estimated parameter vector at the current moment, T is the measured temperature, k 1 is the first intermediate variable, k 2 is the second intermediate variable, k 3 is the third intermediate variable, τ 1 is the first time constant, τ 2 is the second time constant, R 0 is the resistance value of the ohmic resistor, R 1 is the resistance value of the first polarization internal resistance, R 2 is the resistance value of the second polarization internal resistance, C 1is the capacitance value of the first polarization capacitor, C 2 is the capacitance value of the second polarization capacitor.

[0051] The battery model parameter identification method provided in the present embodiment establishes a battery equivalent circuit model, obtains the measurement value of the target battery, constructs the measurement value into an input vector, so as to obtain the measurement value for the least squares calculation with a forgetting factor, and then determines the current forgetting factor according to the measured temperature in the measurement value and the error change rate corresponding to the current moment, so as to adjust the current forgetting factor in combination with the measured temperature, and further determines 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, so as to complete the least squares calculation with the forgetting factor, and determines the model parameters in the battery equivalent circuit model according to the estimated parameter vector at the current moment and the measured temperature, because the temperature of the battery has a particularly significant impact on the battery performance, in order to break through the limitation of the forgetting factor as a fixed value in the prior art, the present embodiment timely adjusts the forgetting factor in combination with the measured temperature, thereby improving the adaptability of the forgetting factor to the current state of the battery, and further improving the parameter accuracy when identifying the battery model parameters based on the forgetting factor.

[0052] The above embodiment describes the method of identifying the 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 the current forgetting factor change is further described in combination with the measured temperature and the error change rate corresponding to the current moment, such as Figure 3 shown. Figure 3 This is a flow chart of another battery model parameter identification method provided in an embodiment of the present application, wherein the explanations of the terms that are the same or corresponding to the above-mentioned embodiments are not repeated here. Figure 3 As shown, the method may specifically include the following steps: S210, establishing a battery equivalent circuit model, obtaining measurement values ​​of the target battery, and constructing the measurement values ​​into an input vector.

[0053] S220. Determine a temperature influence factor according to the measured temperature in the measured value.

[0054] The temperature impact factor is a parameter used to measure the impact of the measured temperature of the target battery on the current forgetting factor.

[0055] Specifically, according to the magnitude of the measured temperature, calculation methods of different temperature influence factors can be obtained. Furthermore, by combining the measured temperature and the corresponding calculation method, the temperature influence factor corresponding to the measured temperature can be calculated.

[0056] Based on the above example, the temperature influence factor can be determined according to the measured temperature in the following way: In response to the measured temperature being less than the lower limit of the battery temperature, determining a 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 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 upper battery temperature limit, a temperature influence factor is determined according to the measured temperature, the upper battery temperature limit and a second temperature parameter.

[0057] The battery temperature lower limit is a pre-calibrated lower limit of the battery operating temperature, and the battery temperature upper limit is a pre-calibrated upper limit of the battery operating temperature. The first temperature parameter and the second temperature parameter are pre-set parameters for adjusting the temperature influence factor.

[0058] Specifically, if the measured temperature is less than the lower limit of the battery temperature, it is necessary to correct the temperature influence factor by measuring the temperature, and determine the temperature influence factor according to a pre-constructed calculation method or corresponding relationship in combination with the measured temperature, the lower limit of the battery temperature and the first temperature parameter. If the measured temperature is greater than or equal to the lower limit of the battery temperature and the measured temperature is less than or equal to the upper limit of the battery temperature, it can be considered that the battery is in a normal working 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, it is necessary to correct the temperature influence factor by measuring the temperature, and determine the temperature influence factor according to a pre-constructed calculation method or corresponding relationship in combination with the measured temperature, the upper limit of the battery temperature and the second temperature parameter.

[0059] Based on the above example, when the measured temperature is less than the lower limit of the battery temperature, the temperature influence factor may be determined according to the measured temperature, the lower limit of the battery temperature and the first temperature parameter in the following manner: 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; The temperature influence factor is determined based on the first exponent and the natural constant.

[0060] The first difference is the difference between the lower limit of the battery temperature and the measured temperature. The first index is the inverse of the product of the first difference and the first temperature parameter.

[0061] Specifically, the difference between the lower limit of the battery temperature and the measured temperature is taken as the first difference, and the inverse of the product of the first difference and the first temperature parameter is taken as the first exponent. With 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.

[0062] Accordingly, when the measured temperature is greater than the upper limit of the battery temperature, the temperature influence factor may be determined according to the measured temperature, the upper limit of the battery temperature and the second temperature parameter in the following manner, including: 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.

[0063] The second difference is the difference between the measured temperature and the upper limit of the battery temperature. The second index is the inverse of the product of the second difference and the second temperature parameter.

[0064] Specifically, the difference between the measured temperature and the upper limit of the battery temperature is taken as the second difference, and the inverse of the product of the second difference and the second temperature parameter is taken as the second exponent. With 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 of the power value and 1 is taken as the temperature influence factor.

[0065] Exemplarily, the temperature influence factor can be determined by the following formula:

[0066] Where k is the current time, T is the measured temperature, T s is the temperature influence factor, e is the natural constant, k 0 is the first temperature parameter, k 1 is the second temperature parameter, T min is the lower limit of battery temperature, T max The upper limit of battery temperature.

[0067] S230: Determine a current forgetting factor according to the temperature influence factor and the error change rate corresponding to the current moment.

[0068] Specifically, a corresponding relationship or function calculation relationship between the current forgetting factor, the temperature influence factor, and the error change rate corresponding to the current moment is pre-built, and the current forgetting factor can be obtained by searching or calculating according to the temperature influence factor and the error change rate corresponding to the current moment.

[0069] 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: 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; The current forgetting factor is determined according to the initial forgetting factor, the natural constant and the third exponent.

[0070] 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. It is a range of change rates, for example, it may include the minimum calibration error change rate and the maximum calibration error change rate. If the target battery is a lithium battery, different types of lithium batteries need to be calibrated separately. For example, the minimum calibration error change rate and the maximum calibration error change rate of common lithium iron phosphate batteries and ternary lithium batteries need to be counted separately. The third exponent is the exponent corresponding to the natural constant when solving the current forgetting factor. It is calculated by the error change rate corresponding to the current moment, the minimum calibration error change rate, the maximum calibration error change rate, the temperature influence factor and the calibration coefficient. The initial forgetting factor is the forgetting factor initially set, which can be obtained through historical data statistics, or it can be set to a value of 1.

[0071] Specifically, according to the calculation formula corresponding to the pre-constructed third index, the error change rate, the calibration error change rate and the temperature influence factor corresponding to the current moment can be input to calculate the third index. Then, the calculated third index is substituted into the current forgetting factor calculation formula constructed by the initial forgetting factor, the natural constant and the third index to obtain the current forgetting factor, which is used to update the forgetting factor according to the measured temperature.

[0072] Based on the above example, the third index can be determined according to the error change rate, calibration error change rate and temperature influence factor corresponding to the current moment in the following manner: 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 a 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 the sum of the first addend and the second addend and a preset first coefficient.

[0073] 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 calibrated error change rate. The fourth difference is the difference between the maximum value of the calibrated error change rate and the minimum value of the calibrated 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.

[0074] Specifically, the inverse 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.

[0075] Exemplarily, the error change rate corresponding to the current moment is calculated by the following formula:

[0076] Among them, k is the current moment, k-1 is the previous moment, err is the error, and r is the error change rate.

[0077] Furthermore, the current forgetting factor is determined by the following formula:

[0078] Among them, k is the current moment, λ is the current forgetting factor, and λ 0 is the initial forgetting factor, e is a natural constant, T s is the temperature influence factor, r is the error change rate, r min is the minimum value of the calibration error change rate, r max is the maximum value of the calibration error change rate, α is the preset first coefficient, and β is the preset second coefficient.

[0079] S240, determining 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.

[0080] S250: Determine model parameters in the battery equivalent circuit model according to the estimated parameter vector at the current moment and the measured temperature.

[0081] In the experimental environment, the relationship between 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, that is, Uocv=f(SOC).

[0082] Optionally, after determining the model parameters in the battery equivalent circuit model, the U OCV =f(SOC), the SOC can be predicted by methods such as UKF (Unscented Kalman Filter).

[0083] The battery model parameter identification method provided in this embodiment determines the temperature influence factor according to the measured temperature in the measured value, so as to determine different adjustment methods of the current forgetting factor according to different measured temperatures, and then determines the current forgetting factor according to the temperature influence factor and the error change rate corresponding to the current moment, so as to more accurately reflect the changes in model parameters under different temperature conditions, so that the current forgetting factor can better adapt to the target battery, thereby achieving the effect of using lower computing resources and improving the accuracy of the current forgetting factor.

[0084] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0085] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0086] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a battery model parameter identification device. Figure 4 A schematic diagram of a battery model parameter identification device provided in an embodiment of the present application, referring 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.

[0087] Among them, the input vector determination module 310 is used to establish a battery equivalent circuit model, obtain the measurement value of the target battery, and construct the measurement value 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 value 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 the 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.

[0088] Based on the above example, optionally, the current forgetting factor determination module 320 is further used to determine the temperature influence factor according to the measured temperature in the measured value; and determine the current forgetting factor according to the temperature influence factor and the error change rate corresponding to the current moment.

[0089] Based on the above example, optionally, the current forgetting factor determination module 320 is further used to determine the temperature influence factor according to the measured temperature, the battery temperature lower limit and the first temperature parameter in response to the measured temperature being less than the battery temperature lower limit; determine the temperature influence factor to be 1 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; and determine the temperature influence factor according to the measured temperature, the battery temperature upper limit and the second temperature parameter in response to the measured temperature being greater than the battery temperature upper limit.

[0090] Based on the above example, optionally, the current forgetting factor determination module 320 is further configured to take the difference between the lower limit of the battery temperature and the measured temperature as a first difference, and take the inverse of the product of the first difference and the first temperature parameter as a first index; determine the temperature influence factor according to the first index and the natural constant; Correspondingly, the current forgetting factor determination module 320 is further used to take the difference between the measured temperature and the upper limit of the battery temperature as the second difference, and take the inverse 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.

[0091] Based on the above example, optionally, the current forgetting factor determination module 320 is further used to 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; the current forgetting factor is determined according to the initial forgetting factor, the natural constant and the third index.

[0092] Based on the above example, optionally, the current forgetting factor determination module 320 is further used to 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 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; take the ratio of the third difference to the fourth difference as the first addend; take 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 of the first addend and the second addend and the preset first coefficient.

[0093] Based on the above example, optionally, the battery equivalent circuit model includes: an ohmic resistor, an electrochemical polarization circuit and a concentration polarization circuit connected in series in sequence, the electrochemical polarization circuit includes a first polarization internal resistor and a first polarization capacitor connected in parallel, and the concentration polarization circuit includes a second polarization internal resistor and a second polarization capacitor connected in parallel; Correspondingly, the model parameter determination module 340 is also used to determine the intermediate variables and the time constant according to the estimated parameter vector at the current moment and the measured temperature; determine the resistance values ​​and the capacitance values ​​in the battery equivalent circuit model according to the intermediate variables and the time constant, and use the resistance values ​​and the capacitance values ​​as model parameters in the battery equivalent circuit model.

[0094] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0095] The device of the above embodiment is used to implement the corresponding battery model parameter identification method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0096] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the battery model parameter identification method described in any of the above embodiments is implemented.

[0097] Figure 5 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0098] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0099] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0100] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. 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.

[0101] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0102] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0103] 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, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0104] The electronic device of the above embodiment is used to implement the corresponding battery model parameter identification method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0105] Based on the same inventive concept, corresponding to any of the above embodiments and methods, the present application also provides a vehicle, which includes the electronic device as described in any of the above embodiments.

[0106] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the battery model parameter identification method described in any of the above embodiments.

[0107] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0108] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the battery model parameter identification method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0109] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0110] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device may be shown in the form of a block diagram 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 (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0111] Although the present application has been described in conjunction with specific embodiments of the present application, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the discussed embodiments.

[0112] The embodiments of the present application are intended to cover all such substitutions, 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 should be included in the scope of protection 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; Determine a current forgetting factor according to the measured temperature in the measured value and the error change rate corresponding to the current moment; 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 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: Determining a temperature influence factor according to a measured temperature in the measured value; The current forgetting factor is determined according to the temperature influence factor and the error change rate corresponding to the current moment.

3. The method according to claim 2, 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.

4. The method according to claim 3, 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.

5. The method according to claim 2, characterized in that: The determining of the current forgetting factor according to the temperature influence factor and the error change rate corresponding to the current moment includes: 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; The current forgetting factor is determined according to the initial forgetting factor, the natural constant and the third index.

6. The method according to claim 5, 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.

7. 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: Determine 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.

8. 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, used to determine the current forgetting factor according to the measured temperature in the measured value and the error change rate corresponding to the current 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.

9. 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 7 is implemented.

10. A vehicle, characterized in that: The vehicle comprises the electronic device as claimed in claim 9.

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