Computer-implemented method for optimizing at least one parameter of at least one function for estimating an open-circuit voltage, control unit and vehicle

Abstract

The invention relates to a computer-implemented method (100) for optimizing at least one parameter of at least one function for estimating an open-circuit voltage (Vocv) of at least one electrical energy storage device (12), wherein the function has at least one parameter vector, wherein at least one component of the parameter vector represents the open-circuit voltage (Vocv), and wherein at least one recursive least-squares algorithm is used (104) for estimating the at least one parameter vector, characterized in that a forgetting factor of the recursive algorithm is set to the value 1 (114) if at least one component of the parameter vector is at least temporarily unobservable. The method (100) increases the robustness of the recursive algorithm against situations in which no statements can be made about modeled parameters.

Description

[0001] The invention relates to a computer-implemented method for optimizing at least one parameter of at least one function for estimating an open-circuit voltage, a control unit and a vehicle.

[0002] When using electrical energy storage devices, the state of charge can be monitored. In particular, if the energy storage device is used for an electric vehicle, the state of charge can be displayed to the driver. To determine the state of charge, the open-circuit voltage of the energy storage device can be estimated.

[0003] In der Publikation HOSSAIN, M. [et al.]: Modeling and SoC estimation of Li-ion batteries with an improved variable forgetting factor RLS method augmented with extended kalman filter. In: Proceedings / 2022 IEEE industry applications society annual meeting, 09-14 October 2022, Art.-Nr. 0231, 9 S. - ISBN 978-1-6654-7816-8, der Publikation LIU, Shiqi [et al.]: Deep-discharging Li-ion battery state of charge estimation using a partial adaptive forgetting factors least square method. In: IEEE access, Vol. 7, 2019, S. 47339-47352. - ISSN 2169-3536, der Publikation SUN, Xiangdong [et al.]: Adaptive forgetting factor recursive least square algorithm for online identification of equivalent circuit model parameters of a lithium-ion battery. In: Energies, Vol. 12, 2019, No. 12, Art.-Nr. 2242, 15 S. - ISSN 1996-1073. URL: https: / / doi.org / 10.3390 / en12122242 und US 2023 / 0152380 A1 werden RLS-Algorithmen mit Vergessensfaktor beschrieben, um einen Ladezustand einer Batterie zu ermitteln.

[0004] From LU 504545 B1, it is known to use a recursive least squares algorithm (RLS) for estimating the open-circuit voltage. A forgetting factor of the recursive algorithm can be adaptively adjusted to the properties of the energy storage device used.

[0005] However, the recursive algorithm can become unstable when situations arise in which no statement can be made about, in particular, the open-circuit voltage and / or other modeled parameters.

[0006] From LI, Ming [et al.]: A battery SOC estimation method based on AFFRLS-EKF. In: Sensors, Vol. 21, 2019, No. 17, Art.-Nr. 5698, 12 pp. - ISSN 1424-8220. URL: https: / / doi.org / 10.3390 / s21175698, it is known to estimate the state of charge of a battery via an open-circuit voltage using an RLS algorithm and a forgetting factor. The forgetting factor can be set to 1 in certain situations.

[0007] The object of the invention is to provide a computer-implemented method for optimizing at least one parameter of at least one function for estimating an open-circuit voltage, which increases the robustness of the recursive algorithm against situations in which no statements can be made about modeled parameters.

[0008] The problem is solved by the features of the independent claims. Advantageous further developments are the subject of the dependent claims and the following description.

[0009] According to a first aspect, a computer-implemented method for optimizing at least one parameter of at least one function for estimating an open-circuit voltage of at least one electrical energy storage device is described, wherein the function has at least one parameter vector, wherein at least one component of the parameter vector represents the open-circuit voltage, and wherein at least one recursive algorithm of the method of least squares is used at least for estimating the at least one parameter vector, wherein, according to the invention, it is provided that a forgetting factor of the recursive algorithm is set to the value 1 if at least one component of the parameter vector is at least temporarily unobservable.

[0010] This method gives greater weight to historical values ​​of the recursive algorithm than to current values ​​or values ​​determined outside of that situation, in cases where at least one component of the parameter vector is at least temporarily unobservable. For example, during the charging process of an energy storage device, the parameter vector might include the open-circuit voltage and the resistances of the energy storage system as vector components. Furthermore, a voltage change can occur during the charging process. This voltage change could be due to a change in the open-circuit voltage of the energy storage device or a change in the resistances. The contributions of the voltage change cannot be uniquely identified. In such a situation, the components of the parameter vector can become unobservable, resulting in a divergence of variance among the components of the parameter vector.Setting the forgetting factor to 1 minimizes the result of the recursive algorithm in the situation described above, without amplifying historical values ​​of the algorithm. This reduces or completely prevents divergence in the recursive algorithm. Estimating the parameter vector using the recursive algorithm can be based on a computation. In some implementations, the forgetting factor can be the output of a closed-loop control system for the properties, particularly the variance, of parameters of the recursive algorithm. By giving greater consideration to the historical values ​​of the recursive algorithm in the situation described above, the robustness of the recursive algorithm can be increased.

[0011] According to the invention, it is further provided that the forgetting factor can be determined by means of at least one metric based on at least one variance of at least one component of the parameter vector, in particular the estimated open-circuit voltage.

[0012] In the following, the term "open-circuit voltage" refers to the estimated open-circuit voltage. The metric can, for example, represent the sum of all variances, where each term can be weighted. If, for instance, only one of the terms, specifically only the variance of the open-circuit voltage, is to be used, the weights of the other terms can be set to zero. The weight of the variance of the open-circuit voltage can then, for example, be set to 1. Using this metric, the variance of the open-circuit voltage can be easily selected from a covariance matrix of the parameter vector.

[0013] According to some embodiments, it is conceivable that the metric can be used as an input value of a control logic, with the control logic providing the forgetting factor as an output value.

[0014] The control logic can form a closed-loop control system for the variance of at least one component of the parameter vector. For example, the control logic can include a conversion table, preferably one-dimensional saturated, in which at least the variance of the at least one component of the parameter vector can serve as an input value. The output value can then be read from the conversion table. This allows a closed-loop control system for the variance of the at least one component to be formed using simple means.

[0015] According to some embodiments, it is conceivable that the control logic can have a maximum value for the input value which, in particular if the metric only considers the variance of the open-circuit voltage, preferably limits a variance of the open-circuit voltage.

[0016] Furthermore, it is conceivable that input values ​​exceeding the maximum value could be limited to the maximum value. In this way, for example, the variance of the open-circuit voltage could be limited. If the maximum value is entered, the control logic could output the value 1 for the forgetting factor. This allows for the limitation of the variance of an open-circuit voltage to be implemented without significant effort.

[0017] According to some embodiments, it is conceivable that at least one feedforward term can be added to at least one component of the parameter vector in at least one correction term of the recursive algorithm.

[0018] According to some embodiments, it is conceivable that the feedforward term can be added at least to the component that indicates the open-circuit voltage.

[0019] The feedforward term can thus provide the largest contribution to calculating a current value of the recursive algorithm in situations where at least one component of the parameter rector, especially the open-circuit voltage, is unobservable. For this purpose, the feedforward term can be added retrospectively to an earlier value determined by the recursive algorithm. Together with limiting the variance or setting the forgetting factor to 1, this can keep the recursive algorithm stable.

[0020] According to some embodiments, it is conceivable that the feedforward coupling term can specify at least one value for a change in the open-circuit voltage when the state of charge of the energy storage device changes.

[0021] This means that in situations where at least one component of the parameter vector can be observed, an estimated change in the open-circuit voltage can form the basis for estimating the current open-circuit voltage using the recursive algorithm.

[0022] According to a second aspect, a computer program product is described, comprising instructions that, when the program is executed by a computer, cause it to perform the steps of the procedure according to the preceding description.

[0023] The advantages, effects, and further developments of the computer program product result from the advantages, effects, and further developments of the method described above. Therefore, reference is made to the preceding description in this regard. A computer program product can be understood, for example, as a data carrier on which a computer program element is stored, containing instructions executable by a computer. Alternatively or additionally, a computer program product can also be understood, for example, as a permanent or volatile data storage medium, such as flash memory or main memory, that contains the computer program element. However, this does not exclude other types of data storage media that contain the computer program element.

[0024] According to a third aspect, a control unit for a vehicle is described, wherein the vehicle has at least one electrical energy storage device, wherein the control unit is configured to perform a function for estimating an open-circuit voltage of the electrical energy storage device and is configured to perform the steps of the procedure according to the preceding description for optimizing at least one parameter of at least the function.

[0025] The advantages, effects, and further developments of the control unit result from the advantages, effects, and further developments of the procedure described above. To avoid repetition, reference is therefore made to the preceding description in this regard.

[0026] According to a fourth aspect, a vehicle is described comprising at least one electrical energy storage device and at least one control unit as described above, which is designed to estimate an open-circuit voltage of the electrical energy storage device.

[0027] The advantages, effects, and further developments of the vehicle result from the advantages, effects, and further developments of the procedure and control unit described above. To avoid repetition, reference is therefore made to the preceding description in this regard.

[0028] The invention is described below with reference to an exemplary embodiment and the accompanying drawing. The drawing shows: Fig. 1. A flowchart of the process; Fig. 2. A schematic representation of an energy storage system; and Fig. 3 a schematic representation of a vehicle.

[0029] The computer-implemented method for optimizing at least one parameter of at least one function for estimating an open-circuit voltage V OCV At least one electrical energy storage device 12 is referred to below by the reference symbol 100.

[0030] In step 102, measured values ​​can first be recorded. These measured values ​​can include, for example, resistances, capacitances, electrical currents and / or temporal changes of currents in an energy storage system 12.

[0031] In some embodiments, the system can generally be represented as a function for estimating the open-circuit voltage Vocv as follows: y(t)=θ(t)Tφ(t).

[0032] The vector y(t) represents a system parameterized over t. The parameter t can, for example, stand for a specific relative time or indicate a recursive iteration. The term θ(t) comprises the parameters to be estimated, particularly in real time, and can be called a parameter vector. The term φ(t) can describe a linear model for the parameters to be estimated.

[0033] At least one component of the parameter vector can exhibit the open-circuit voltage Vocv.

[0034] For a system according to Fig. 2 with an energy storage device 12, which has an open-circuit voltage Vocv, the resistors R1 and R2 and the capacitance C2, the following vectors can be used for the estimation: φ(t)=[I˙IR2C21R2C2]θ(t)=[R1R1+R2VOCV]y(t)=V˙+VR2C2

[0035] The value I can represent the electric current in the system, and the parameter İ the rate of change of the electric current. The parameter V can represent the total voltage of the system, and the parameter V the rate of change of the total voltage.

[0036] In a further step, the function can be approximated using a recursive algorithm of the method of least squares to determine the at least one parameter vector, in particular the open-circuit voltage V. OCV to appreciate.

[0037] In substep 108 of step 104, the parameters of θ(t) and the values ​​of φ(t), which are not yet available, can first be initialized to obtain a starting point for the recursion. If this has already been done, an earlier determined value, for example φ(t - 1) or θ(t - 1), can alternatively be used as the starting value.

[0038] In step 110, it can be checked whether a situation has occurred in which the parameters of the vector θ(t) to be estimated, in particular the open-circuit voltage Vocv, are unobservable. This can happen, for example, if the energy storage device 12 is charged with a constant current. In that case, a contribution from the open-circuit voltage Vocv may be indistinguishable from an increase in the system's resistance.

[0039] To prevent the covariance matrix V(t) of the estimated components of θ(t) from diverging, step 114 can then be performed. In this step, a forgetting factor µ used for the recursive algorithm can be set to 1. This can be done, in particular, if the variance of the open-circuit voltage Vocv exceeds a predefined threshold.

[0040] The covariance matrix V(t) can, for example, be subjected to a closed-loop control system. In some embodiments, control can be achieved using a metric. The metric can take as its input the variance of at least one component of the parameter vector θ(t). Advantageously, the input can be simply the variance of the open-circuit voltage Vocv.

[0041] The output value can be the forgetting factor µ.

[0042] The metric can be used as an input value for control logic. The control logic can further include a saturated one-dimensional conversion table from which the output value or the forgetting factor µ can be determined.

[0043] Furthermore, the metric can be defined as follows: Vctrl=∑i=1NαiVii where V ctrlThis can be used as an input value for the conversion table. It calculates the sum of the traces of the covariance matrix, where the summands are weighted by the parameter α. 11 The variance of resistance R1 can be V 22 the variance of the sum of R1 and R2, and V 33 kann the variance of the open-circuit voltage V OCV be.

[0044] If α1 = 0, α2 = 0, α3 = 1 is chosen, then V ctrl equal to Vocv. Furthermore, the input values ​​x LUT and the output values ​​y LUT be limited as follows: xLUT=[0;20] yLUT=[0.98;1], around V ctrl to limit to a maximum of 20, whereby the forgetting factor µ is set to the value 1 if V ctrl has a value of 20 or greater. The value 20 is purely an example here and can be set to any value that avoids divergence.

[0045] By setting a forgetting factor µ to 1, historical values ​​or previous recursions of the recursive algorithm are given greater weight. This prevents divergence in the variance or the covariance matrix V(t).

[0046] The forgetting factor µ can be used in a further substep 112 to determine an amplification term of the recursive algorithm.

[0047] The forgetting factor µ appears in the denominator of a fraction from which the amplification term can be derived. Therefore, the larger the forgetting factor, the smaller the amplification term becomes.

[0048] According to a further substep 116, a feedforward linkage term ∂VOCV∂SOC|SOC=SOC^ICbattTs a correction term is added to a recursive algorithm, where ∂VOCV∂SOC|SOC=SOC^ It can describe an antiderivative of the change in the open-circuit voltage Vocv when the state of charge (SOC) of the energy storage device changes from a given state of charge. The value I can represent the current in the system, the value C batt the battery capacity and the value T s describe the temperature of the storage unit or the system.

[0049] The value ∂VOCV∂SOC|SOC=SOC^ICbattTs For example, the third state of the parameter rector θ(t - 1) can be added to the correction term of the recursive algorithm. The third state could, for example, be the open-circuit voltage Vocv.

[0050] In a further step 106, the optimized parameter determined with the recursive algorithm can be output.

[0051] In Fig. Figure 3 shows a vehicle 10 which may have an energy storage device 12 and a control unit 14. The vehicle 10 may also have a display unit 16.

[0052] The control unit 14 can be connected to the energy storage device 12 via a signal connection 18, which can be wireless or wired. The control unit 14 can further be configured to receive measurement data about the state of the energy storage device 12 and to perform the procedure 100 as described above.

[0053] The control unit 14 can provide the estimated value for the open-circuit voltage Vocv to the display unit 16, for example, via another signal connection 20, which can also be wired or wireless. The display unit 16 can be read, for example, by a driver of the vehicle 10.

[0054] The example described above does not in any way limit the invention. Rather, the invention can be modified in many different ways.

[0055] All of the features of the invention described above can be essential to the invention, either alone or in combination with one another. Reference symbol list 10 vehicles 12 Energy storage devices 14 Control unit 16 Display unit 18 Signal connection 20 Signal connection

Claims

[1] Computer-implemented method (100) for optimizing at least one parameter of at least one function for estimating an open-circuit voltage (Vocv) of at least one electrical energy storage device (12), wherein the function has at least one parameter vector, wherein at least one component of the parameter vector represents the open-circuit voltage (Vocv), and at least one recursive least squares algorithm is used at least for estimating the at least one parameter vector (104). characterized by , that a forgetting factor of the recursive algorithm is set to the value 1 (114) if at least one component of the parameter vector is at least temporarily unobservable, wherein the forgetting factor is determined by means of at least one metric based on at least one variance of at least one component of the parameter vector. [2] Computer-implemented method (100) according to claim 1, characterized by, that the metric is based on at least one variance of at least the open-circuit voltage (Vocv). [3] Computer-implemented method (100) according to claim 2, characterized by , that the metric is used as the input value of a control logic, where the control logic provides the forgetting factor as the output value. [4] Computer-implemented method (100) according to claim 2 or 3, characterized by , that the control logic has a maximum value for the input value. [5] Computer-implemented method (100) according to any one of the preceding claims, characterized by , that in at least one correction term of the recursive algorithm at least one feedforward term is added to at least one component of the parameter vector (116). [6] Computer-implemented method (100) according to claim 5, characterized by , that the feedforward term is added at least to the component that indicates the open-circuit voltage (Vocv). [7] Computer-implemented method (100) according to claim 5 or 6, characterized by , that the forward coupling term specifies at least one value for a change in the open-circuit voltage (Vocv) when there is a change in the state of charge of the energy storage device (12). [8] Computer program product comprising instructions which, when the program is executed by a computer, cause it to perform the steps of the method (100) according to any one of claims 1 to 7. [9] Control unit (14) for a vehicle (10) having at least one electrical energy storage device (12), wherein the control unit (14) is configured to perform a function for estimating an open-circuit voltage (Vocv) of the electrical energy storage device (12) and is configured to perform the steps of the method (100) according to any one of claims 1 to 7 for optimizing at least one parameter of at least the function. [10] Vehicle (10) comprising at least one electrical energy storage device (12) and at least one control unit (14) according to claim 9, which is configured to estimate an open-circuit voltage (Vocv) of the electrical energy storage device (12).

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