CN114509677A - Multi-factor evaluation method and system for residual capacity of battery and electronic equipment - Google Patents
Multi-factor evaluation method and system for residual capacity of battery and electronic equipment Download PDFInfo
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Abstract
The invention provides a multi-factor evaluation method and a multi-factor evaluation system for remaining battery capacity, relates to the technical field of battery capacity detection, and can adapt to the change of battery operation conditions based on model parameter identification, calculate high-precision model parameters and evaluate the remaining battery capacity parameters more accurately. The method comprises the following steps: constructing a first-order RC equivalent circuit model; discretizing the first-order RC equivalent circuit model to obtain a discretized first-order RC equivalent circuit model; according to the discretized first-order RC equivalent circuit model, identifying model parameters by adopting a recursive least square method with forgetting factors; and performing regression analysis on the identified model parameters by using a multiple linear regression method to estimate the residual capacity of the battery. The battery residual capacity multi-factor evaluation system is applied to a battery residual capacity multi-factor evaluation method.
Description
Technical Field
The invention relates to the technical field of battery capacity detection, in particular to a battery residual capacity multi-factor evaluation method, a battery residual capacity multi-factor evaluation system, electronic equipment and a computer-readable storage medium.
Background
The traditional fossil energy represented by coal is non-renewable, the phenomenon of energy shortage is increasingly aggravated in recent years, and the appearance and application of new energy can well relieve the shortage of traditional energy and the burden on the environment. The lithium ion battery has the advantages of high energy density, long service life, low self-discharge rate and the like, and is widely applied to a plurality of fields of traffic, household appliances, aerospace, energy storage power stations and the like. However, in practical applications, as the number of cycles of the lithium ion battery increases, the performance of the battery gradually decreases, and the remaining capacity of the battery is also lost, thereby affecting the remaining service life of the battery. In addition, lithium ion batteries are prone to thermal runaway under extreme conditions such as short circuit, overcharge and overdischarge, and serious safety accidents such as explosion can occur in severe cases, so that adverse social effects and property loss are caused. In order to realize efficient utilization of the lithium ion battery and ensure safe and stable operation of the lithium ion battery, it is necessary to accurately evaluate the residual capacity of the lithium ion battery, so as to enhance the battery management function and prolong the service life of the battery.
The remaining capacity of a battery is one of important indicators for evaluating the performance of the battery after a long period of cyclic use. The battery capacity refers to the total amount of charge generated during the discharge of the battery under certain conditions, and the remaining capacity of the battery refers to the battery capacity measured after the battery is used for a period of time. Due to the fact that the working condition of the battery is complex in the actual operation process, the difficulty in on-line evaluation of the residual capacity of the battery is high. Currently, researchers have proposed various methods for estimating and predicting the remaining capacity of a battery, mainly including a bayesian-based method and an empirical fitting method. The Bayes-based method is used for evaluating the state of the battery through a closed-loop filtering algorithm, and has strong robustness, but the modeling process is complex, and the calculation amount is large in the evaluation process. The experience fitting method is that the battery aging model is constructed by combining electrochemical knowledge and engineering experience, and the evaluation result is more accurate. However, a large amount of test data is needed in the modeling process, and in addition, the empirical fitting method has poor universality, and a specific model can only be applied to a specific type of battery or a battery under a specific aging condition.
Disclosure of Invention
In order to solve the technical problems, the invention provides a battery residual capacity multi-factor evaluation method, a system, an electronic device and a computer-readable storage medium.
The invention provides a multi-factor evaluation method for the remaining capacity of a battery, which comprises the following steps,
step 1: constructing a first-order RC equivalent circuit model;
step 2: discretizing the first-order RC equivalent circuit model to obtain a discretized first-order RC equivalent circuit model;
and step 3: according to the discretized first-order RC equivalent circuit model, identifying model parameters by adopting a recursive least square method with forgetting factors;
and 4, step 4: and performing regression analysis on the identified model parameters by using a multiple linear regression method to estimate the residual capacity of the battery.
Preferably, the circuit equation of the first-order RC equivalent circuit model is:
wherein, UocvIs the open circuit voltage of the battery, R0Is ohmic internal resistance, RdIs a polarization resistance, CdIs a polarized capacitor, I is a charging current, UtTerminal voltage of battery, UdIs the polarization voltage.
Preferably, in step 2, the discretizing the first-order RC equivalent circuit model to obtain a discretized first-order RC equivalent circuit model includes:
step 2.1: the transfer function that defines the first order RC equivalent circuit model is:
wherein G(s) is a transfer function of a first-order RC equivalent circuit model, UocvIs the open circuit voltage of the battery, UtIs the terminal voltage of the battery, I is the charging current, R0Is ohmic internal resistance, RdIs a polarization resistance, CdIs the polarization capacitance, s is the transfer function variable;
step 2.2: using bilinear transformation
Obtaining a discrete transfer function with a sampling time interval Δ t:
wherein, G (z)-1) In terms of discrete transfer function, z is a transformation parameter, Δ t is a sampling period, and c1, c2 and c3 are identification parameters;
step 3.3: discrete transfer function G (z)-1) The corresponding time domain expression is: e (k) ═ c1E(k-1)+c2I(k)+c3I (k-1), wherein E (k) is the difference between the open circuit voltage and the terminal voltage of the battery, c1,c2,c3Is an identification parameter;
step 2.4: defining a data matrix and a parameter matrix of a first-order RC equivalent circuit model as follows:
θ(k)＝[(1-c1) Uocv(k) c1 c2 c3]T
wherein the content of the first and second substances,
is a data matrix, theta (k) is a parameter matrix, c1,c2,c3To identify the parameter, UtTerminal voltage of battery, UocvIs the open circuit voltage of the battery, and I is the charging current;
step 2.5: simplifying the transfer function of a first-order RC equivalent circuit model into
Wherein, Ut(k) For the transfer function of the discretized first-order RC equivalent circuit model,
is a data matrix, and θ (k) is a parameter matrix.
Preferably, the recursive least square parameter estimation formula with forgetting factor is as follows:
wherein, λ is forgetting factor, K (k) is gain of algorithm, P (k) is error covariance matrix of state estimation value, k is k sampling time of data value k,
as variable parameters, Ut(k) For the transfer function of the discretized first-order RC equivalent circuit model,
i (k) is a data matrix and i (k) is an identity matrix. The algorithm can be simplified through the discretization processing in the step 2, so that the calculation efficiency of the algorithm is improved.
Preferably, in step 3, identifying the model parameters by using a recursive least square method with a forgetting factor includes:
and executing an online parameter identification algorithm of a recursive least square method with forgetting factors by adopting MATLAB software so as to perform online identification on the discretized first-order RC equivalent circuit model parameters.
Preferably, in step 4, performing regression analysis on the identified model parameters by using a multiple linear regression method to estimate the remaining battery capacity, including:
constructing a multiple linear regression equation: qremain＝α0+α1R0+α2Rd+α3CdWherein R is0Is ohmic internal resistance, RdFor polarizing internal resistance, CdTo polarize the capacitance, α0、α1、α2、α3Are constant coefficients of a linear regression equation.
And carrying out regression analysis on the model parameters obtained by the resolution by using a multiple linear regression equation, and estimating the residual capacity of the battery.
Preferably, the resolved model parameters include: ohmic internal resistance, polarization internal resistance, and polarization capacitance.
Compared with the prior art, the multi-factor evaluation method for the remaining capacity of the battery provided by the invention has the following beneficial effects: the method comprises the steps of firstly constructing a first-order RC equivalent circuit model, carrying out discretization processing on the first-order RC equivalent circuit model to obtain a discretized first-order RC equivalent circuit model, then identifying model parameters by adopting a recursive least square method with forgetting factors according to the discretized first-order RC equivalent circuit model, and finally carrying out regression analysis on the identified model parameters by utilizing a multiple linear regression method to estimate the residual capacity of the battery. The battery residual capacity evaluation method based on model parameter identification can adapt to the change of battery operation conditions, high-precision model parameters are calculated in a fitting mode, the universality is high, and the battery residual capacity parameters can be evaluated more accurately, so that the safe and stable operation of the lithium ion battery is better guaranteed, and the application and development of the lithium ion battery and a battery management system are facilitated.
The invention also provides a multi-factor evaluation system for the remaining capacity of the battery, which comprises:
the building module is used for building a first-order RC equivalent circuit model;
the discrete processing module is used for carrying out discretization processing on the first-order RC equivalent circuit model to obtain a discretized first-order RC equivalent circuit model;
the model identification module is used for identifying model parameters by adopting a recursive least square method with forgetting factors according to the discretized first-order RC equivalent circuit model;
and the evaluation module is used for carrying out regression analysis on the identified model parameters by utilizing a multiple linear regression method and estimating the residual capacity of the battery.
Compared with the prior art, the beneficial effect of the battery remaining capacity multi-factor evaluation system provided by the invention is the same as that of the battery remaining capacity multi-factor evaluation method in the technical scheme, and details are not repeated herein.
The invention further provides an electronic device, which comprises a bus, a transceiver, a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are connected through the bus, and the computer program realizes the steps in the battery residual capacity multi-factor evaluation method when being executed by the processor.
Compared with the prior art, the beneficial effects of the electronic device provided by the invention are the same as the beneficial effects of the battery residual capacity multi-factor evaluation method in the technical scheme, and are not repeated herein.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a battery remaining capacity multi-factor evaluation method as set forth in any one of the above.
Compared with the prior art, the beneficial effect of the computer-readable storage medium provided by the invention is the same as that of the battery residual capacity multi-factor evaluation method in the technical scheme, and the detailed description is omitted here.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart illustrating a method for multi-factor estimation of remaining battery capacity according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a first-order RC equivalent circuit model according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram illustrating a system for estimating remaining battery capacity by multiple factors according to an embodiment of the present invention.
Detailed Description
In the description of the present invention, it is to be understood that the terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
The "plurality" mentioned in the present embodiment means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a is present alone, A and B are present simultaneously, and B is present alone. The terms "exemplary" or "such as" are used herein to mean serving as an example, instance, or illustration, and are intended to present concepts in a concrete fashion, and should not be construed as preferred or advantageous over other embodiments or designs.
The embodiment of the invention provides a multi-factor evaluation method for remaining battery capacity, and fig. 1 shows a flow chart of the multi-factor evaluation method for remaining battery capacity provided by the embodiment of the invention. As shown in fig. 1, the method includes:
step 1: and constructing a first-order RC equivalent circuit model.
It should be noted that the accuracy of the battery model directly affects the accuracy of estimating the battery operating state, and a first-order RC equivalent circuit model is selected as the battery model for modeling in the embodiment of the present invention, and the structure of the first-order RC equivalent circuit model is shown in fig. 2.
The equivalent circuit model abstracts the complex electrochemical reaction in the battery and represents the complex electrochemical reaction by basic electrical elements such as capacitance, resistance, inductance and the like, so that the parameters of the equivalent circuit model are greatly reduced, and the solution is easy. The first-order RC equivalent circuit model constructed in the application is additionally provided with a nonlinear network formed by connecting a resistor and a capacitor in parallel on the basis of an ohmic internal resistance model, and is used for representing the dynamic change of voltage along with current mutation in the charging and discharging processes of the battery.
According to kirchhoff's voltage law, the circuit equation of the first-order RC equivalent circuit model is as follows:
Step 2: and discretizing the first-order RC equivalent circuit model to obtain a discretized first-order RC equivalent circuit model.
It should be noted that the first-order RC equivalent circuit model constructed in step 1 is discretized for better application in a Battery Management System (BMS).
Specifically, step 2 comprises:
step 2.1: the transfer function that defines the first order RC equivalent circuit model is:
wherein G(s) is a transfer function of a first-order RC equivalent circuit model, Uocv(s) is the open circuit voltage of the cell, UtIs the terminal voltage of the battery, I is the charging current, R0Is ohmic internal resistance, RdIs a polarization resistance, CdFor polarization capacitance, s is the transfer function variable.
Step 2.2: using bilinear transformation
Obtaining a discrete transfer function with a sampling time interval Δ t:
wherein, G (z)-1) For a discrete transfer function, z is the transformation parameter, Δ t is the sampling period, c1,c2,c3Are identification parameters.
Step 2.3: discrete transfer function G (z)-1) The corresponding time domain expression is: e (k) ═ c1E(k-1)+c2I(k)+c3I (k-1), wherein E (k) is the difference between the open circuit voltage and the terminal voltage of the battery, c1,c2,c3Are identification parameters.
Step 2.4: defining a data matrix and a parameter matrix of a first-order RC equivalent circuit model as follows:
θ(k)＝[(1-c1) Uocv(k) c1 c2 c3]T
in the formula (I), the compound is shown in the specification,
is a data matrix, theta (k) is a parameter matrix, c1,c2,c3To identify the parameters, UtTerminal voltage of battery, UocvIs the open circuit voltage of the battery, and I is the charging current.
Step 2.5: finally, the transfer function of the first-order RC equivalent circuit model can be simplified as follows:
wherein, Ut(k) For the transfer function of the discretized first-order RC equivalent circuit model,
θ (k) is a parameter matrix.
And step 3: and identifying the model parameters by adopting a recursive least square method with forgetting factors according to the discretized first-order RC equivalent circuit model.
It should be noted that the equivalent circuit model can use online data for parameter identification. The embodiment of the invention adopts a recursive least square method with forgetting factors to identify the model parameters of the discretized first-order RC equivalent circuit model, thereby realizing the online updating of the model parameters.
Specifically, step 3 includes:
step 3.1: the forgetting factor representation algorithm adds a time-varying weighting coefficient to historical data monitored by a Battery Management System (BMS), the latest monitored data is weighted by 1, and the data at the previous n moments adopts lambdanWeighting, and the least square method parameter estimation formula with forgetting factor is as follows:
wherein, λ is forgetting factor, when λ is equal to 1, the above formula is degenerated to traditional recursive least squares, k (k) is gain of algorithm, p (k) is error covariance matrix of state estimation value, k is kth sampling time of data value,
as variable parameters, Ut(k) For discretizationThe transfer function of the first order RC equivalent circuit model,
is a data matrix, and I (k) is an identity matrix.
And then, MATLAB software is adopted to execute an online parameter identification algorithm of a recursive least square method with forgetting factors so as to perform online identification on the discretized first-order RC equivalent circuit model parameters.
And 4, step 4: and performing regression analysis on the identified model parameters by using a multiple linear regression method to estimate the residual capacity of the battery.
It should be noted that, according to the result of identifying the model parameters, the corresponding relationship between the remaining battery capacity and the model parameters is obtained, and the regression analysis is performed on the identified model parameters by using the multiple linear regression method to estimate the remaining battery capacity.
Specifically, the identified model parameters such as ohmic internal resistance, polarization capacitance and the like are used for evaluating the residual capacity (Q) of the batteryremain) The multiple linear regression equation is constructed as follows:
Qremain＝α0+α1R0+α2Rd+α3Cdwherein R is0Is ohmic internal resistance, RdFor polarizing internal resistance, CdIs a polarization capacitance. Alpha is alpha0、α1、α2、α3Are constant coefficients of a linear regression equation.
In the research of estimating the remaining capacity of the battery, in the prior art, the ohmic internal resistance of the equivalent circuit model parameter is usually used as a characteristic quantity to represent the remaining capacity of the battery, and the ohmic internal resistance becomes an important parameter for estimating the remaining capacity of the battery according to an approximate linear relation between the remaining capacity of the battery and the ohmic internal resistance, but the estimation precision is lower by adopting a single characteristic quantity method. In order to improve the accuracy of a battery management system in evaluating the state of a lithium ion battery of an energy storage system, the embodiment of the invention provides a battery residual capacity evaluation method based on a multi-factor regression model of ohmic internal resistance, polarization internal resistance and polarization capacitance of the lithium ion battery. By establishing a first-order RC equivalent circuit model, in order to improve the accuracy of the evaluation of the remaining capacity of the battery, a recursive least square method with forgetting factors is combined to identify model parameters, and the identified circuit parameters are used for evaluating the remaining capacity of the battery on line. The battery residual capacity evaluation method based on model parameter identification can adapt to the change of battery operation conditions, high-precision model parameters are calculated in a fitting mode, the universality is high, and the battery residual capacity parameters can be evaluated more accurately, so that the safe and stable operation of the lithium ion battery is better guaranteed, and the application and development of the lithium ion battery and a battery management system are facilitated.
The embodiment of the invention provides a multi-factor evaluation system for remaining battery capacity, and fig. 3 shows a schematic structural diagram of the multi-factor evaluation system for remaining battery capacity provided by the embodiment of the invention. As shown in fig. 3, the system includes:
and the building module 1 is used for building a first-order RC equivalent circuit model.
And the discrete processing module 2 is used for carrying out discretization processing on the first-order RC equivalent circuit model to obtain a discretized first-order RC equivalent circuit model.
And the model identification module 3 is used for identifying model parameters by adopting a recursive least square method with forgetting factors according to the discretized first-order RC equivalent circuit model.
And the evaluation module 4 is used for performing regression analysis on the identified model parameters by using a multiple linear regression method and estimating the residual capacity of the battery.
Compared with the prior art, the beneficial effect of the battery remaining capacity multi-factor evaluation system provided by the embodiment of the invention is the same as that of the battery remaining capacity multi-factor evaluation method in the technical scheme, and details are not repeated herein.
In addition, an embodiment of the present invention further provides an electronic device, which includes a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the processes of the above-mentioned embodiment of the method for estimating remaining battery capacity by multiple factors are implemented, and the same technical effects can be achieved, and are not described herein again to avoid repetition.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements each process of the above-mentioned method for estimating remaining capacity by multiple factors, and can achieve the same technical effect, and in order to avoid repetition, the detailed description is omitted here.
The computer-readable storage medium includes: permanent and non-permanent, removable and non-removable media may be tangible devices that retain and store instructions for use by an instruction execution apparatus. The computer-readable storage medium includes: electronic memory devices, magnetic memory devices, optical memory devices, electromagnetic memory devices, semiconductor memory devices, and any suitable combination of the foregoing. The computer-readable storage medium includes: 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), non-volatile random access memory (NVRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic tape cartridge storage, magnetic tape disk storage or other magnetic storage devices, memory sticks, mechanically encoded devices (e.g., punched cards or raised structures in a groove having instructions recorded thereon), or any other non-transmission medium useful for storing information that may be accessed by a computing device. As defined in embodiments of the present invention, the computer-readable storage medium does not include transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses traveling through a fiber optic cable), or electrical signals transmitted through a wire.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus, electronic device and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electrical, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to solve the problem to be solved by the embodiment of the invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be substantially or partially contributed by the prior art, or all or part of the technical solutions may be embodied in a software product stored in a storage medium and including instructions for causing a computer device (including a personal computer, a server, a data center, or other network devices) to execute all or part of the steps of the methods of the embodiments of the present invention. And the storage medium includes various media that can store the program code as listed in the foregoing.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and the present invention shall be covered by the claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A multi-factor evaluation method for residual capacity of a battery is characterized by comprising the following steps:
step 1: constructing a first-order RC equivalent circuit model;
step 2: discretizing the first-order RC equivalent circuit model to obtain a discretized first-order RC equivalent circuit model;
and step 3: according to the discretized first-order RC equivalent circuit model, identifying model parameters by adopting a recursive least square method with forgetting factors;
and 4, step 4: and performing regression analysis on the identified model parameters by using a multiple linear regression method to estimate the residual capacity of the battery.
2. The multi-factor estimation method of remaining battery capacity according to claim 1,
the circuit equation of the first-order RC equivalent circuit model is as follows:
wherein, UocvIs the open circuit voltage of the battery, R0Is ohmic internal resistance, RdIs a polarization resistance, CdIs a polarized capacitor, I is a charging current, UtTerminal voltage of battery, UdIs the polarization voltage.
3. The multi-factor estimation method for the remaining battery capacity according to claim 1, wherein in the step 2, the discretization processing is performed on the first-order RC equivalent circuit model to obtain a discretized first-order RC equivalent circuit model, and the method comprises the following steps:
step 2.1: the transfer function that defines the first order RC equivalent circuit model is:
wherein G(s) is a transfer function of a first-order RC equivalent circuit model, UocvIs the open circuit voltage of the battery, UtIs the terminal voltage of the battery, I is the charging current, R0Is ohmic internal resistance, RdIs a polarization resistance, CdIs the polarization capacitance, s is the transfer function variable;
step 2.2: using bilinear transformation
Obtaining a discrete transfer function with a sampling time interval Δ t:
wherein, G (z)-1) In terms of discrete transfer function, z is a transformation parameter, Δ t is a sampling period, and c1, c2 and c3 are identification parameters;
step 2.3: discrete transfer function G (z)-1) The corresponding time domain expression is: e (k) ═ c1E(k-1)+c2I(k)+c3I (k-1), wherein E (k) is the difference between the open circuit voltage and the terminal voltage of the battery, c1，c2，c3Is an identification parameter;
step 2.4: defining a data matrix and a parameter matrix of a first-order RC equivalent circuit model as follows:
wherein the content of the first and second substances,
is a data matrix, theta (k) is a parameter matrix, c1，c2，c3To identify the parameter, UtTerminal voltage of battery, UocvIs the open circuit voltage of the battery, and I is the charging current;
step 2.5: simplifying the transfer function of a first-order RC equivalent circuit model into
Wherein, Ut(k) For the transfer function of the discretized first-order RC equivalent circuit model,
θ (k) is a parameter matrix.
4. The multi-factor estimation method for the remaining battery capacity according to claim 3, wherein the recursive least square parameter estimation formula with forgetting factor is as follows:
wherein, λ is forgetting factor, K (k) is gain of algorithm, P (k) is error covariance matrix of state estimation value, k is k sampling time of data value k,
as variable parameters, Ut(k) For the transfer function of the discretized first-order RC equivalent circuit model,
is a data matrix, and I (k) is an identity matrix.
5. The multi-factor estimation method for the remaining battery capacity according to claim 4, wherein in the step 3, the model parameters are identified by using a recursive least square method with a forgetting factor, and the method comprises the following steps:
and executing an online parameter identification algorithm of a recursive least square method with forgetting factors by adopting MATLAB software so as to perform online identification on the discretized first-order RC equivalent circuit model parameters.
6. The multi-factor estimation method for remaining battery capacity according to claim 1, wherein in step 4, performing regression analysis on the identified model parameters by using a multiple linear regression method to estimate the remaining battery capacity, comprises:
constructing a multiple linear regression equation:
Qremain＝α0+α1R0+α2Rd+α3Cdwherein R is0Is ohmic internal resistance, RdFor polarizing internal resistance, CdIs a polarization capacitance. Alpha is alpha0、α1、α2、α3Constant coefficients of a linear regression equation;
and carrying out regression analysis on the model parameters obtained by the resolution by using a multiple linear regression equation, and estimating the residual capacity of the battery.
7. The method according to claim 1, wherein the model parameters obtained by the analysis comprise: ohmic internal resistance, polarization internal resistance, and polarization capacitance.
8. A battery remaining capacity multi-factor evaluation system, comprising:
the building module is used for building a first-order RC equivalent circuit model;
the discrete processing module is used for carrying out discretization processing on the first-order RC equivalent circuit model to obtain a discretized first-order RC equivalent circuit model;
the model identification module is used for identifying model parameters by adopting a recursive least square method with forgetting factors according to the discretized first-order RC equivalent circuit model;
and the evaluation module is used for carrying out regression analysis on the identified model parameters by utilizing a multiple linear regression method and estimating the residual capacity of the battery.
9. An electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected via the bus, characterized in that the computer program, when executed by the processor, implements the steps of a method for multi-factor estimation of remaining battery capacity as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of a method for multifactor evaluation of remaining battery capacity according to one of claims 1 to 7.
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