CN112415392B - Method for determining forgetting factor, electronic equipment, storage medium and device - Google Patents
Method for determining forgetting factor, electronic equipment, storage medium and device Download PDFInfo
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- CN112415392B CN112415392B CN202011212540.8A CN202011212540A CN112415392B CN 112415392 B CN112415392 B CN 112415392B CN 202011212540 A CN202011212540 A CN 202011212540A CN 112415392 B CN112415392 B CN 112415392B
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The application discloses a method, electronic equipment, a storage medium and a device for determining a forgetting factor, wherein the method comprises the steps of obtaining a value of the forgetting factor at a moment k; obtaining a first online parameter value of a first group of parameters of the battery model at the time k by utilizing the value of the forgetting factor at the time k; determining the residual capacity of the battery corresponding to the online parameter value; determining an offline parameter value corresponding to the residual capacity of the battery; determining a target identification error value by using the first online parameter value and the offline parameter value; and determining a target value of the forgetting factor corresponding to the target identification error value, and taking the target value as the value of the forgetting factor at the moment of k + 1. The technical scheme of the application can adjust the value of the genetic factor according to the online identification result in the online identification process, so that the convergence speed of parameters can be guaranteed, and the stability in the convergence process can be guaranteed.
Description
Technical Field
The present application relates to the field of batteries, and in particular, to a method, an electronic device, a storage medium, and an apparatus for determining a forgetting factor.
Background
The battery state estimation is the key point of the battery management system, the accurate state estimation cannot be separated from the parameter identification of the battery, and the identification of the battery parameter cannot be separated from the determination of the genetic factor. In the prior art, when the battery parameters are identified, the genetic factors are fixed, so that the stability of the identification process of the battery parameters cannot be ensured.
Disclosure of Invention
In order to ensure the stability of the identification process of the battery parameters in the identification process of the battery parameters, the application provides a method, an electronic device, a storage medium and a device for determining a forgetting factor, which specifically comprise the following steps:
in a first aspect, a method for determining a forgetting factor is provided, including:
obtaining the value of a forgetting factor at the time k;
obtaining a first online parameter value of a first group of parameters of the battery model at the time k by utilizing the value of the forgetting factor at the time k;
determining the residual capacity of the battery corresponding to the first online parameter value;
determining an off-line parameter value corresponding to the residual electric quantity of the battery by using a mapping relation between a preset battery electric quantity and an off-line parameter value of the first group of parameters;
determining a target identification error value using the first online parameter value and the offline parameter value;
and determining a target value of the forgetting factor corresponding to the target identification error value by using a preset mapping relation between the forgetting factor and the identification error, and taking the target value as the value of the forgetting factor at the moment of k + 1.
Optionally, obtaining a first online parameter value of a first group of parameters of the battery model at the time k by using the value of the forgetting factor at the time k, including:
collecting second on-line parameter values of a second set of parameters from the battery; the second online parameter value is a parameter value of the second group of parameters at the time k and each time before the time k, and each time is determined by using a preset sampling period and the time k;
and determining a first online parameter value of the first group of parameters at the time k by using the value of the forgetting factor at the time k and the second online parameter value.
Optionally, determining a first online parameter value of the first group of parameters at the time k by using the value of the forgetting factor at the time k and the second online parameter value, includes:
determining a measurement vector matrix at the time k, a gain matrix at the time k, a covariance matrix at the time k and a vector matrix to be identified at the time k, wherein the measurement vector matrix, the gain matrix, the covariance matrix and the vector matrix to be identified all correspond to the second group of parameters;
determining the value of the measurement vector matrix at the k moment according to the second online parameter value;
and recursion is carried out on the vector matrix to be identified at the moment k by utilizing the value of the forgetting factor at the moment k, the value of the measurement vector matrix at the moment k, the gain matrix at the moment k and the covariance matrix at the moment k, so as to obtain the first online parameter value.
Optionally, determining the measurement vector matrix at the time k, the gain matrix at the time k, the covariance matrix at the time k, and the vector matrix to be identified at the time k includes:
determining a time domain discrete equation expression corresponding to the battery model;
and constructing a measurement vector matrix at the time k, a gain matrix at the time k, a covariance matrix at the time k and a vector matrix to be identified at the time k by using the time domain discrete equation expression.
Optionally, constructing the measurement vector matrix at the time k, the gain matrix at the time k, the covariance matrix at the time k, and the vector matrix to be identified at the time k by using the time-domain discrete equation expression, including:
constructing a measurement vector matrix at the k moment and a state equation of the battery at the k moment by using the time domain discrete equation expression;
and obtaining a gain matrix at the moment k, a covariance matrix at the moment k and a vector matrix to be identified at the moment k by using the measurement vector matrix at the moment k and the state equation of the battery at the moment k.
Optionally, determining a time-domain discrete equation expression corresponding to the battery model includes:
generating an electrical expression corresponding to the battery model;
performing laplace transform on the electrical expression to obtain a first expression, wherein the first expression comprises the first group of parameters;
performing z transformation on the first expression to obtain a second expression, wherein the second expression comprises an identification vector in the matrix to be identified at the moment k;
and performing time domain discrete conversion on the second expression to obtain the time domain discrete equation expression.
Optionally, the step of recursion of the vector matrix to be identified at the time k by using the value of the forgetting factor at the time k, the value of the measurement vector matrix at the time k, the gain matrix at the time k and the covariance matrix at the time k to obtain the first online parameter value includes:
recursion is carried out by utilizing the value of the forgetting factor at the time k, the value of the measurement vector matrix at the time k, the gain matrix at the time k and the covariance matrix at the time k to obtain the value of the vector matrix to be identified at the time k;
determining a conversion relation between a vector to be identified in the matrix to be identified and the first set of parameters according to the z transformation;
and taking the values of the first group of parameters corresponding to the values of the vector matrix to be identified at the k moment as the online parameter values at the k moment according to the conversion relation.
In a second aspect, a storage medium is provided, in which a computer program is stored, which, when executed by a processor, implements the method of any one of the above first aspects.
In a third aspect, an electronic device is provided, which includes a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface, and the memory complete communication with each other through the communication bus; wherein:
a memory for storing a computer program;
a processor configured to execute the program stored in the memory to implement the method of any of the first aspects.
In a fourth aspect, an apparatus for determining a forgetting factor is provided, including:
the acquisition unit is used for acquiring the value of the forgetting factor at the moment k;
the obtaining unit is used for obtaining a first online parameter value of a first group of parameters of the battery model at the time k by utilizing the value of the forgetting factor at the time k;
a first determination unit, configured to determine a remaining capacity of the battery corresponding to the first online parameter value;
the second determining unit is used for determining an offline parameter value corresponding to the residual electric quantity of the battery by utilizing a mapping relation between preset battery electric quantity and the offline parameter value of the first group of parameters;
a third determining unit, configured to determine a target identification error value using the first online parameter value and the offline parameter value;
and the fourth determining unit is used for determining a target value of the forgetting factor corresponding to the target identification error value by using a preset mapping relation between the forgetting factor and the identification error, and taking the target value as the value of the forgetting factor at the moment of k + 1.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
according to the scheme provided by the embodiment of the application, the first online parameter value of the first group of parameters of the battery model at the time k is obtained according to the value of the forgetting factor at the time k; and determining an off-line parameter value corresponding to the residual capacity of the battery by using a mapping relation between the preset battery capacity and the off-line parameter value of the first group of parameters, so that the value of the forgetting factor at the moment of k +1 is determined by using the first on-line parameter value and the off-line parameter value. Namely, the scheme of the application can adjust the value of the genetic factor according to the online identification result in the online identification process, so that the convergence rate of parameters can be ensured, and the stability in the convergence process can also be ensured.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a method for determining a forgetting factor according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a second-order RC equivalent circuit model according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of another method for determining a forgetting factor according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an apparatus for determining a forgetting factor according to an embodiment of the present application.
Detailed Description
In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments, and the illustrative embodiments and descriptions thereof of the present application are used for explaining the present application and do not constitute a limitation to the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a method for determining a forgetting factor, which is applied to an electronic device, wherein the electronic device can be connected with a collection device, and the collection device can collect a second group of parameters of a battery at different times, as shown in fig. 1, the method can include the following steps:
and 101, obtaining the value of a forgetting factor at the moment k.
Optionally, when the time k is an initial time, a value of the forgetting factor may be a default value; and when the moment k is not the initial moment, determining the value of the forgetting factor at the moment k according to the value of the forgetting factor at the moment k-1.
And 102, obtaining a first online parameter value of a first group of parameters of the battery model at the time k by utilizing the value of the forgetting factor at the time k.
In the embodiment of the present application, the battery model includes, but is not limited to, a second-order RC equivalent circuit model. Referring to FIG. 2, in the second order RC equivalent circuit model of FIG. 2, the second order RC equivalent circuit model has a second order RC equivalent circuit model with a second order RC equivalent circuit model consisting of R0,R1,R2,C1,C2,UocA first set of parameters consisting of, and consisting oft、I0A second set of parameters.
Optionally, as an alternative implementation, as shown in fig. 3, step 102 may include the following steps:
and 301, generating an electrical expression corresponding to the battery model.
Based on the second-order RC equivalent circuit model shown in fig. 2, the generated electrical expression corresponding to the battery model may be:
Ut＝Uoc-I0*R0-U1-U2 (1)
U1＝exp(-Δt/R1C1)+R1(1-exp(-Δt/R1C1))I0 (2)
U2＝exp(-Δt/R2C2)+R2(1-exp(-Δt/R2C2))I0 (3)
wherein, UtIs terminal voltage, UocIs an open circuit voltage, I0Is a current, R0Is the internal resistance of the battery, U1For RC1 loop voltage, U2For the RC2 loop voltage, Δ t is the preset sampling period.
Performing laplace transform on (1), (2) and (3) to obtain the following first expression:
converting equation (4) into z-transformed form, and introducing
Linear transformation is carried out, and the formula (4) can be converted into:
and step 304, performing time domain discrete conversion on the second expression to obtain a time domain discrete equation expression.
Converting equation (5) to a time-domain discrete equation:
Ut(k)＝(1-θ1-θ2)Uoc(k)+θ1Ut(k-1)+θ2Ut(k-2)+θ3I0(k)+θ4I0(k-1)+θ5I0(k-2) (6)
and 305, constructing a measurement vector matrix at the time k and a state equation of the battery at the time k by using a time domain discrete equation expression.
Obtaining a measurement vector matrix at the k time by using the formula (6)
And equation of state of the battery at time k (y (k)):
wherein, Ut(k-1) terminal voltage at time k-1, Ut(k-2) terminal voltage at time k-2, I0(k) Current value at time k, I0(k-1) is the current value at the time k-1, I0And (k-2) is the current value at the moment k-2, and theta (k) is the vector matrix to be identified at the moment k.
And step 306, obtaining a gain matrix at the time k, a covariance matrix at the time k and a vector matrix to be identified at the time k by using the measurement vector matrix at the time k and the state equation of the battery at the time k.
θ(k)＝θ(k-1)+K(k)(Ut(k)-y(k)) (10)
Wherein, KkIs a gain matrix at the time k, theta (k) is a vector matrix to be identified at the time k, P (k) is a covariance matrix at the time k, P (k-1) is a covariance matrix at the time k-1, theta (k-1) is a vector matrix to be identified at the time k-1, Ut(k) And lambda is a genetic factor for the actual terminal voltage value at the moment k.
In the embodiment of the present application, the initial value of θ (k) may be set to θ (0) ═ zero (6), that is, a zero vector of 6 elements, and the initial value of P (k) may be set to P (0) ═ 106*I6*6I.e., P (0) is the identity matrix.
And 307, determining the value of the measurement vector matrix at the time k according to the second online parameter value.
And 308, recurrently obtaining the value of the vector matrix to be identified at the moment k by utilizing the value of the forgetting factor at the moment k, the value of the measurement vector matrix at the moment k, the gain matrix at the moment k and the covariance matrix at the moment k.
The value of the vector matrix to be identified at the moment k can be obtained by recursion according to the formulas (7), (8), (9), (10) and (11), and the value is obtained by recursionθ (k) being [ θ ]0、θ1、θ2、θ3、θ4、θ5]。
The conversion relationship between the determined vector to be identified in the matrix to be identified and the first set of parameters may be:
and 310, taking the values of the first group of parameters corresponding to the values of the vector matrix to be identified at the moment k as the online parameter values at the moment k according to the conversion relation.
According to the formulas (12), (13), (14), (15), (16) and (17), the k time R can be determined0,R1,R2,C1,C2,UocThe online parameter value of (a).
And 103, determining the residual capacity of the battery corresponding to the first online parameter value.
Alternatively, extended kalman filtering may be employed to determine the remaining charge of the battery corresponding to the online parameter value.
And 104, determining an off-line parameter value corresponding to the residual electric quantity of the battery by using a mapping relation between the preset battery electric quantity and the off-line parameter value of the first group of parameters.
The offline parameter values of the first set of parameters of the battery model are all mapped with the battery capacity in the offline state (see formula 19):
from SOC (k), a set of offline parameters can be calculated, denoted
Comprises the following steps:
and 105, determining a target identification error value by using the first online parameter value and the offline parameter value.
Defining a target identification error e (k):
wherein, the larger e (k) is, the larger the fluctuation of the first group of parameters is, the smaller the forgetting factor is required to be, the tracking speed is increased, and the convergence is accelerated;
the smaller the e (k), the smaller the fluctuation of the first group of parameters, the slow convergence is already achieved, the forgetting factor needs to be increased, and the stability is improved.
In practical application, in order to enable the first group of parameters to be rapidly converged at the initial use stage of the battery, optionally, in this embodiment of the application, the initial value at the time k may be greater than a preset value, for example, the initial value of k is greater than 100, that is, in the previous 100 times, the forgetting factor takes a fixed value (for example, 0.99), so that the battery is rapidly converged, and when k is greater than 100, the value of the forgetting factor at the time k +1 may be determined by using the method steps corresponding to fig. 1.
And 106, determining a target value of the forgetting factor corresponding to the target identification error value by using a preset mapping relation between the forgetting factor and the identification error, and taking the target value as the value of the forgetting factor at the moment of k + 1.
The preset mapping relationship between the forgetting factor and the identification error may be:
in the embodiment of the application, the recursion times can be manually set, after online identification is performed each time to obtain online parameter values of a first group of parameters, different forgetting factors are selected according to the mapping relation between the preset forgetting factors and identification errors, new forgetting factors are provided for next online identification, and then a group of new online parameters is obtained until all recursion times are completed.
According to the scheme provided by the embodiment of the application, the first online parameter value of the first group of parameters of the battery model at the time k is obtained according to the value of the forgetting factor at the time k; and determining an off-line parameter value corresponding to the residual capacity of the battery by using a mapping relation between the preset battery capacity and the off-line parameter value of the first group of parameters, so that the value of the forgetting factor at the moment of k +1 is determined by using the first on-line parameter value and the off-line parameter value. Namely, the scheme of the application can adjust the value of the genetic factor according to the online identification result in the online identification process, so that the convergence rate of parameters can be ensured, and the stability in the convergence process can also be ensured.
Based on the same concept, an embodiment of the present application further provides an electronic device, as shown in fig. 4, the electronic device mainly includes: a processor 401, a communication interface 402, a memory 403 and a communication bus 404, wherein the processor 401, the communication interface 402 and the memory 403 communicate with each other via the communication bus 404. Wherein, the memory 403 stores programs executable by the processor 401, and the processor 401 executes the programs stored in the memory 403, implementing the following steps:
obtaining the value of a forgetting factor at the time k;
obtaining a first online parameter value of a first group of parameters of the battery model at the time k by utilizing the value of the forgetting factor at the time k;
determining the residual capacity of the battery corresponding to the first online parameter value;
determining an off-line parameter value corresponding to the residual electric quantity of the battery by using a mapping relation between a preset battery electric quantity and an off-line parameter value of the first group of parameters;
determining a target identification error value by using the first online parameter value and the offline parameter value;
and determining a target value of the forgetting factor corresponding to the target identification error value by using a preset mapping relation between the forgetting factor and the identification error, and taking the target value as the value of the forgetting factor at the moment of k + 1.
The communication bus 404 mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus 404 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.
The communication interface 402 is used for communication between the above-described electronic apparatus and other apparatuses.
The Memory 403 may include a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Alternatively, the memory may be at least one memory device located remotely from the aforementioned processor 401.
The Processor 401 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc., and may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components.
In a further embodiment of the present application, there is also provided a computer-readable storage medium having stored therein a computer program which, when run on a computer, causes the computer to execute the method of determining a forgetting factor described in the above embodiments.
Based on the same inventive concept, an embodiment of the present application further provides an apparatus for determining a forgetting factor, as shown in fig. 5, including:
an obtaining unit 501, configured to obtain a value of a forgetting factor at time k;
an obtaining unit 502, configured to obtain a first online parameter value of a first group of parameters of the battery model at the time K by using a value of the forgetting factor at the time K;
a first determining unit 503, configured to determine a remaining capacity of the battery corresponding to the first online parameter value;
a second determining unit 504, configured to determine an offline parameter value corresponding to the remaining battery capacity of the battery by using a mapping relationship between a preset battery capacity and an offline parameter value of the first set of parameters;
a third determining unit 505, configured to determine a target recognition error value using the first online parameter value and the offline parameter value;
a fourth determining unit 506, configured to determine a target value of the forgetting factor corresponding to the target identification error value by using a mapping relationship between a preset forgetting factor and the identification error, and use the target value as the value of the forgetting factor at the time k + 1.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The available media may be magnetic media (e.g., floppy disks, hard disks, tapes, etc.), optical media (e.g., DVDs), or semiconductor media (e.g., solid state drives), among others.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of determining a forgetting factor, comprising:
obtaining the value of a forgetting factor at the time k;
obtaining a first online parameter value of a first group of parameters of the battery model at the time k by utilizing the value of the forgetting factor at the time k;
determining the residual capacity of the battery corresponding to the first online parameter value;
determining an off-line parameter value corresponding to the residual electric quantity of the battery by using a mapping relation between a preset battery electric quantity and an off-line parameter value of the first group of parameters;
determining a target identification error value using the first online parameter value and the offline parameter value;
determining a target value of the forgetting factor corresponding to the target identification error value by using a preset mapping relation between the forgetting factor and the identification error, and taking the target value as the value of the forgetting factor at the moment of k +1, wherein in the mapping relation, the forgetting factor is inversely proportional to the identification error.
2. The method of claim 1, wherein obtaining a first online parameter value of a first set of parameters of the battery model at the time k by using a value of the forgetting factor at the time k comprises:
collecting second on-line parameter values of a second set of parameters from the battery; the second online parameter value is a parameter value of the second group of parameters at the time k and each time before the time k, and each time is determined by using a preset sampling period and the time k;
and determining a first online parameter value of the first group of parameters at the time k by using the value of the forgetting factor at the time k and the second online parameter value.
3. The method of claim 2, wherein determining the first online parameter value of the first set of parameters at the time k by using the value of the forgetting factor at the time k and the second online parameter value comprises:
determining a measurement vector matrix at the time k, a gain matrix at the time k, a covariance matrix at the time k and a vector matrix to be identified at the time k, wherein the measurement vector matrix, the gain matrix, the covariance matrix and the vector matrix to be identified all correspond to the second group of parameters;
determining the value of the measurement vector matrix at the k moment according to the second online parameter value;
and recursion is carried out on the vector matrix to be identified at the moment k by utilizing the value of the forgetting factor at the moment k, the value of the measurement vector matrix at the moment k, the gain matrix at the moment k and the covariance matrix at the moment k, so as to obtain the first online parameter value.
4. The method of claim 3, wherein determining the k-time metrology vector matrix, the k-time gain matrix, the k-time covariance matrix, and the k-time to-be-identified vector matrix comprises:
determining a time domain discrete equation expression corresponding to the battery model;
and constructing a measurement vector matrix at the time k, a gain matrix at the time k, a covariance matrix at the time k and a vector matrix to be identified at the time k by using the time domain discrete equation expression.
5. The method of claim 4, wherein constructing the k-time measurement vector matrix, the k-time gain matrix, the k-time covariance matrix, and the k-time vector matrix to be identified by using the time-domain discrete equation expression comprises:
constructing a measurement vector matrix at the k moment and a state equation of the battery at the k moment by using the time domain discrete equation expression;
and obtaining a gain matrix at the moment k, a covariance matrix at the moment k and a vector matrix to be identified at the moment k by using the measurement vector matrix at the moment k and the state equation of the battery at the moment k.
6. The method of claim 4, wherein determining a time-domain discrete equation expression corresponding to the battery model comprises:
generating an electrical expression corresponding to the battery model;
performing laplace transform on the electrical expression to obtain a first expression, wherein the first expression comprises the first group of parameters;
performing z transformation on the first expression to obtain a second expression, wherein the second expression comprises an identification vector in the matrix to be identified at the moment k;
and performing time domain discrete conversion on the second expression to obtain the time domain discrete equation expression.
7. The method according to claim 6, wherein the step of recursion of the vector matrix to be identified at the time k by using the value of the forgetting factor at the time k, the value of the measurement vector matrix at the time k, the gain matrix at the time k, and the covariance matrix at the time k to obtain the first online parameter value comprises:
recursion is carried out by utilizing the value of the forgetting factor at the time k, the value of the measurement vector matrix at the time k, the gain matrix at the time k and the covariance matrix at the time k to obtain the value of the vector matrix to be identified at the time k;
determining a conversion relation between a vector to be identified in the matrix to be identified and the first set of parameters according to the z transformation;
and taking the values of the first group of parameters corresponding to the values of the vector matrix to be identified at the k moment as the online parameter values at the k moment according to the conversion relation.
8. A storage medium, in which a computer program is stored, wherein the computer program, when executed by a processor, implements the method of any of claims 1 to 7.
9. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus; wherein:
a memory for storing a computer program;
a processor for executing a program stored in the memory to implement the method of any one of claims 1-7.
10. An apparatus for determining a forgetting factor, comprising:
the acquisition unit is used for acquiring the value of the forgetting factor at the moment k;
the obtaining unit is used for obtaining a first online parameter value of a first group of parameters of the battery model at the time k by utilizing the value of the forgetting factor at the time k;
a first determination unit, configured to determine a remaining capacity of the battery corresponding to the first online parameter value;
the second determining unit is used for determining an offline parameter value corresponding to the residual electric quantity of the battery by utilizing a mapping relation between preset battery electric quantity and the offline parameter value of the first group of parameters;
a third determining unit, configured to determine a target identification error value using the first online parameter value and the offline parameter value;
a fourth determining unit, configured to determine, by using a preset mapping relationship between a forgetting factor and an identification error, a target value of the forgetting factor corresponding to the target identification error value, and use the target value as a value of the forgetting factor at a time k +1, where in the mapping relationship, the forgetting factor is inversely proportional to the identification error.
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