CN111929585A - Battery state of charge calculation apparatus, battery state of charge calculation method, battery state of charge calculation server, and battery state of charge calculation medium - Google Patents
Battery state of charge calculation apparatus, battery state of charge calculation method, battery state of charge calculation server, and battery state of charge calculation medium Download PDFInfo
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Abstract
The application discloses a battery charge state calculating device, a method, a server and a medium, wherein the device comprises: the acquisition module is used for acquiring the current value and the current voltage value; the updating module is used for updating parameters of the calculation model based on the current value, the voltage value and the battery charge state value at the current moment, and the parameters at least comprise a forgetting factor of a least square method; and the calculation module is used for calculating the battery charge state value at the next moment based on the updated parameters of the calculation model. According to the method and the device, after the current value and the current voltage value are acquired, the current battery charge state value is used for updating the model parameters of the estimation model, including the forgetting factor of the least square method, so that the calculation model calculates the battery charge state value at the next moment by using the updated parameters, the real-time updating of the estimation model parameters in the circulation process is realized, the defect that the gain correction capability is weakened due to the preset empirical parameters is overcome, and the accuracy of the battery charge state calculation is improved.
Description
Technology neighborhood
The present application relates generally to the field of battery management technologies, and in particular, to a battery state of charge calculation apparatus, method, server, and medium.
Background
The estimation of the battery State of Charge (SOC) of the battery management system is mainly applied to the field of electric vehicles, the battery management system of the unmanned aerial vehicle still adopts simple calculation, and the application environment of the battery management system of the unmanned aerial vehicle is complex.
At present, an ampere-hour integration algorithm and a static state correction algorithm, including various filtering algorithms, such as a kalman filter, are generally adopted when calculating the battery state of charge value.
For the various algorithms adopted, after multiple iterative cycles, the current error caused by the sensor precision is increased due to the fact that the ampere-hour integral method algorithm has large dependence on the current measurement precision, and the accuracy of the SOC value is reduced; for recursive two-multiplication, the prediction of the SOC value is distorted because the prediction result of the algorithm is not sensible to the measured value.
Disclosure of Invention
In view of the above-mentioned drawbacks and deficiencies of the prior art, it is desirable to provide a battery soc calculating apparatus, method, server, and medium to improve the accuracy of battery soc calculation.
In a first aspect, an embodiment of the present application provides a battery state of charge calculation apparatus, including:
the acquisition module is used for acquiring the current value and the current voltage value;
the updating module is used for updating parameters of the calculation model based on the current value, the voltage value and the battery charge state value at the current moment, and the parameters at least comprise forgetting factors of a recursive least square method;
and the calculation module is used for calculating the battery charge state value at the next moment based on the updated parameters of the calculation model.
In a second aspect, an embodiment of the present application provides a battery state of charge calculation method, including:
acquiring a current value and a current voltage value;
updating parameters of the calculation model based on the current value, the voltage value and the battery charge state value at the current moment, wherein the parameters at least comprise forgetting factors of a recursive least square method;
and calculating the battery state of charge value at the next moment based on the updated parameters of the calculation model.
In a third aspect, an embodiment of the present application provides a server, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the battery state of charge calculation method according to the first aspect when executing the program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, the computer program being used for implementing the battery charge state calculation method according to the first aspect.
To sum up, the battery state of charge calculation apparatus, method, server, and medium provided in the embodiments of the present application update the model parameters including the forgetting factor of the recursive least square method in the estimation model by using the current battery state of charge value after acquiring the current value and voltage value, so that the calculation model calculates the battery state of charge value at the next time by using the updated parameters, thereby implementing real-time update of the estimated model parameters in the cycle process, avoiding the defect of reduced correction capability of gain in the algorithm due to the preset empirical parameters, and improving the accuracy of the battery state of charge calculation.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a schematic diagram of a battery SOC calculation apparatus according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram illustrating a battery SOC calculation method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a battery SOC calculation method according to another embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer system of a server according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant application and are not limiting of the application. It should be noted that, for the convenience of description, only the portions relevant to the application are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It can be understood that, in order to improve the accuracy of calculating the State of Charge (SOC) of the battery and avoid the problem of inaccurate calculation caused by the rigid parameters of the estimation model, the battery SOC estimation model according to the embodiment of the present invention includes a combination of a least square method based on a variable forgetting factor and an optimized H ∞ filtering algorithm.
It is understood that the calculation model involved in the embodiment of the present application is a combination of a recursive least square method and an H ∞ filtering algorithm, both of which are obtained by performing a modified discretization on a first-order RC model, and are expressed by the following equations:
for the RC model, the following can be expressed:
ut＝OCV-up-ir0
wherein, cpRepresenting the polarization capacitance, tau, of the cellp＝rpcpRepresents a time constant of the battery, whereinrpIs the polarization internal resistance. u. ofpRepresenting the cell polarization voltage, r0Is the ohmic internal resistance of the battery, OCV is the open circuit voltage, utFor the battery terminal voltage, i represents a current.
The equation of state for the equivalent circuit described above yields the following transfer function:
the transfer function of the first-order RC model can be subjected to bilinear transformation for discretization, and the s function is converted into the z functionI.e., converted to the standard Recursive Least Squares (RLS) to solve for the model parameter θ values. Thus, the model parameter r can be obtained0，rp，τpAnd the relation with theta in the least square method can identify real-time model parameters according to the voltage and current values acquired in real time.
Specifically, it can be expressed by the following formula:
yr，k-1＝Φr，k-1θr，k-1+er，k-1 (1)
Pr，k＝[I-Kr，kΦr，k-1]Pr，k-1/λk-1 (3)
the above equation represents the iteration of time k-1 and time k, where yr，k-1Representing the output of the RLS algorithm, i.e. the measured voltage value of the output, ek-1Representing the discrete error of the RLS algorithm, thetar，k-1Representing the model parameter to be estimated, phir，k-1Denotes an observation matrix, Kr，kRepresents the gain, Pr，kRepresents the error covariance, λk-1The addition of the forgetting factor can improve the authenticity of the estimated parameters for the forgetting factor at the current moment.
In the embodiment of the application, in order to improve the accuracy of the prediction, the situation that the gain calculated by the measurement value is basically a small value in the circulation process, which causes the calculation distortion, is avoided. The forgetting factor needs to be updated in real time to improve the accuracy of the prediction. Specifically, it can be calculated by the following formula:
according to the above formula, the method is advantageousUsing recursive least squares method based on real-time collected voltages utAnd i can identify r0，rp，τp。
For the first-order RC equivalent circuit model deformation discretization, the following state equation and observation equation are expressed. u. ofkRepresenting the input current i, x at time kh，kRepresenting the estimated state value, x, at time kh，k＝[up，k sock]。ykIndicating the output voltage value u at time kt。Lh，k＝[0 1]This means that in SOC value estimation, the accuracy of the output of the pair with respect to the SOC value portion is required to be higher.
xh，k+1＝f(xh，k，uk)+ωh，k
yk＝g(xh，k，uk)+vh，k
zh，k＝Lh，kxh，k
Further, after the battery model parameter θ is identified on line by the least square method, and the coefficients a and C are obtained, the battery state of charge (SOC) can be calculated by using an optimized H ∞ filtering algorithm. For the H ∞ filtering algorithm, the standard expression is specifically as follows:
wherein the content of the first and second substances,
is the final estimate of the system, se，k-1Weight matrix, Ke，kRepresenting the gain matrix at time k, Pe，kError covariance at time k, Ae，k-1And
respectively representing matrices associated with the system at different times. Qe，k-1And Re，k-1The k-1 time system noise variance and the measurement noise variance are respectively expressed, generally, the k-1 time system noise variance and the measurement noise variance can be determined as a certain fixed value according to experience, mu represents an H-infinity filtering performance boundary value, and proper mu is selected according to actual engineering requirements, so that the H-infinity filtering algorithm can be guaranteed to have solution and not scatter at each time.
Denotes the estimated value of k-1 time, ykRepresenting the system output measurement at time k, ukRepresenting the system input at time k,
representing the system measurement equation.
In order to better understand the battery state of charge calculation method provided by the embodiment of the present application, the calculation process is described in detail below with reference to fig. 1 to 4.
Fig. 1 is a schematic structural diagram of a battery soc calculation apparatus according to an embodiment of the present application, and as shown in fig. 1, the apparatus 100 includes:
the obtaining module 110 is configured to obtain a current value and a current voltage value.
Specifically, the battery state of charge calculation apparatus provided in the embodiment of the present application may first acquire a current value and a current voltage value by using the acquisition module, for example, by using a sensor, and acquire the current voltage value and the current voltage value in real time according to a preset acquisition frequency.
An updating module 120, configured to update parameters of the calculation model based on the current value, the voltage value, and the battery charge state value at the current time, where the parameters at least include a forgetting factor of a recursive least square method.
Specifically, in the cyclic process of calculating the SOC value, each time one SOC value is obtained by calculation, the parameters of the calculation model may be updated in real time by using the SOC calculated at the current time, so as to calculate the SOC value at the next time. The calculation model is a combination of a recursive least square method and an H filtering algorithm, and specifically, parameters of each calculation model including a forgetting factor in the recursive least square method, a boundary value in the H filtering algorithm and the like can be updated.
For example, when updating the model parameters in the recursive least squares method, the updating module 120 may specifically include:
a first calculation unit 121, configured to calculate an observation matrix of the recursive least square method based on the SOC value, the current value, and the voltage value at the current time.
A second calculation unit 122 for calculating and updating a recursive least squares gain and covariance matrix based on the observation matrix
The first updating unit 123 is configured to calculate and update the model parameters and the forgetting factor based on the updated gain and covariance matrix.
Specifically, in the cyclic process, after the SOC value at the previous time (i.e., the current time) is calculated by the calculation module 130, that is, the prior value, the first calculation unit may be used to calculate the observation matrix Φ for recursive two-multiplication based on the prior value, the collected current value, and the collected voltage value.
Further, the second calculation unit may be used to substitute the calculated observation matrix into the formula (2) and the formula (3) to obtain the gain K at the current timer，kAnd covariance matrix Pr，kAnd updating the gain K of the previous moment by using the obtained parameter of the current momentk-1And covariance matrix Pk-1。
Further, gain K at the current time is obtained through calculationr，kAnd covariance matrix Pr，kThen, the gain K of the current time can be obtainedr，kAnd covariance matrix Pr，kSubstituting into equation (4) to calculate the model parameter θ at the current timer，kThen, the gain, the observation matrix and the model parameter at the current moment are substituted into the formula (5), the forgetting factor at the current moment can be calculated, and the forgetting factor lambda of the current moment is utilizedkUpdating the forgetting factor lambda of the last momentk-1。
For another example, in another embodiment, when the parameters of the least square method are updated, and the parameters of the model in the H ∞ filtering algorithm can also be updated, the updating module 120 further includes:
a second updating unit 124 for determining and updating the boundary value of the H ∞ filtering algorithm based on the battery soc value at the current time and a preset value;
and a third updating unit 125, configured to determine and update the weight matrix of the H ∞ filtering algorithm based on the boundary value and the covariance matrix.
Specifically, in the cyclic process, when the calculation module calculates the SOC value of the current time, the boundary value of the current time may be determined according to the size of the SOC value and the preset value of the SOC.
For example, when the battery operating environment is severe or the SOC value is small, the SOC value estimation error becomes large because the OCV ═ f (SOC) curve error is large. Therefore, the boundary value mu is determined based on the SOC range, and the value ranges of the cost functions of the H filter in different SOC ranges are changed, so that the sensitivity of the predicted value to the measured value is improved.
One or more preset values may be preset, and the boundary value at the current time is determined by comparing the current SOC value with the preset value. It is understood that there is a correspondence between preset values and boundary values.
If provided with SOC1And SOC2And under the above severe environment, define 1 > SOC1＞SOC2> 0, and defineThe boundary values have a corresponding relation of SOC value ranges:
from the above formula, when the SOC value at the current time is in different ranges, the corresponding boundary values have different values.
Further, after the boundary value at the current time is determined by the relational expression, the boundary value at the previous time may be updated by using the boundary value. Furthermore, the weight matrix of the H ∞ filter algorithm can be determined and updated using the boundary value, covariance matrix, and coefficient matrix according to the following inequality expression.
Namely, in order to ensure the accuracy and convergence of the H filtering algorithm, the judgment is carried out through the formula, and the muS is adjustedh，kWhen the inequality is satisfied, a weight matrix s of the H infinity filtering algorithm is determinedh，k。
It is understood that the parameters in the updated H-filter algorithm, in addition to the boundary values and the weight matrix, may also update the gain, the covariance matrix, etc. at the previous time according to equations (8) and (9).
And a calculating module 130, configured to calculate a battery soc value at the next time based on the updated parameters of the calculation model.
Specifically, after the parameters of the calculation model are updated by using the SOC value at the current time, the SOC value at the next time may be calculated by using the updated parameters.
For example, the updated model parameter θ value is used to calculate the identification parameter, and then the coefficients a and C of the H-filter algorithm are calculated, and finally the battery state of charge estimated value (SOC) can be calculated based on the formula (10).
It can be understood that the battery state of charge calculation device provided by the embodiment of the application can be operated initiallyAn initial value of the calculation model is set. For example, the initial value θ in the recursive minimum multiplication is set for a calculation model in which the least square method based on the variable forgetting factor and the optimized H ∞ filter algorithm are combined0、λ0And P0Initial value x in H infinity filtering algorithmh，0、Sh，0And Ph，0。
After the initial value setting is completed, the current value and the current voltage value are collected and input into the calculation model so as to drive the calculation model to start to enter a cycle. That is, the priori estimation value may be calculated by using the initial value, and then each initial parameter of the calculation model may be updated by using the current value, the voltage value, and the priori estimation value, so as to calculate the SOC value at the next time. Further, after the SOC value at the next time is obtained through calculation, the current voltage value and the current value are collected again by the sensor. In this case, the above-mentioned SOC value at the next time is used as the current SOC value, and the above-mentioned updating module may be repeated to update the parameters of the calculation model using the current SOC value, the current voltage value, and the current value, so as to calculate the SOC value at the next time with respect to the current time, that is, the calculation is continuously cycled, and the prediction of the SOC value and the update of the calculation model parameters are performed in real time.
It can be understood that, according to the battery state of charge calculation apparatus provided in the embodiment of the present application, since the current value and the voltage value are continuously collected according to the preset frequency, the estimation model is continuously driven at the preset frequency, so that the battery state of charge calculation at the next time can be performed in real time, and the calculation model parameters are updated, thereby improving the calculation accuracy and the sensitivity of the measurement value to the SOC prediction value.
It can be further understood that the battery charge state calculation method provided by the embodiment of the application can be applied to the field of electric automobiles and can also be applied to the field of battery management systems of unmanned aerial vehicles with complex environments.
Fig. 2 is a schematic flow chart of a battery state of charge calculation method according to an embodiment of the present application, and as shown in fig. 2, the method may include:
s210, acquiring a current value and a current voltage value;
and S220, updating the parameters of the calculation model based on the current value, the voltage value and the current battery charge state value.
And S230, calculating the battery charge state value at the next moment based on the updated parameters of the calculation model.
Specifically, according to the battery state of charge calculation method provided by the embodiment of the application, by acquiring the current value and the current voltage value, the parameters of the calculation model, such as forgetting factor of minimum two-multiplication in the calculation model, can be calculated and updated by using the acquired voltage value, current value and SOC value at the current moment, so that when the SOC value at the next moment is calculated by using the updated calculation model, the accuracy of SOC value prediction is improved, and the sensitivity of the measured value to the predicted value is improved.
For better understanding of the present application, the calculation process of the battery soc value is explained in detail below with reference to fig. 3, using a combination of the recursive least square method of the variable forgetting factor and the H ∞ filtering algorithm as a calculation model. Fig. 3 illustrates a battery charge state calculating method according to another embodiment of the present application, where as shown in the figure, the method includes:
s310, setting an initial value of the calculation model.
And S320, collecting the current value and the voltage value at the current moment.
Specifically, the method provided by the embodiment of the present application may first initialize the calculation model, that is, set an initial value related to the calculation model and a frequency of the current signal and the voltage signal collected by the sensor. And then the current value and the current voltage value can be acquired by utilizing the sensor so as to start the estimation model and enter the cycle of battery charge state value calculation.
S330, model parameters of the initial calculation model are calculated based on the initial values.
Specifically, after the method is driven by the initial value, the initial model parameter can be obtained by calculation by the initial value, and the initial model parameter is input into an H-infinity filtering algorithm to calculate the SOC value.
For example, for the RLS algorithm, θ is utilized0Calculating to obtain initial r0，rp，τp. For the H ∞ filtering algorithm, x can be utilizedd，0Calculating to obtain initial r0，rp，τp。
S340, calculating a priori estimation value based on the initial model parameters and an H-infinity filtering algorithm.
Specifically, after the initial model parameters are obtained in the previous step, the initial model parameters can be substituted into the formula of the H ∞ filtering algorithm to calculate the prior estimation value
(i.e., initial value of SOC), upAnd first checking the covariance matrix P. In detail, the coefficients A, C of each estimation model can be calculated by substituting the coefficients into equation (6), and then the coefficients and the initial value x are obtainedd，0Calculating to obtain an initial prior estimated value in the Kalman algorithm by using the formula (10)
upAnd a prior covariance matrix P.
Similarly, it will be appreciated that in estimating the model-driven and stable loop, the coefficients obtained and x at the previous time may be used in this stepd，k-1And Pd，k-1Calculating to obtain the current prior estimation value
upAnd a prior covariance matrix P.
And S350, calculating and updating the gain, the covariance matrix and the forgetting factor of the recursive minimum two-multiplication based on the current value, the voltage value and the initial SOC value.
And S360, calculating and updating the gain, the boundary value and the weight matrix in the H infinity filtering algorithm based on the current value, the voltage value and the SOC initial value.
Specifically, after the priori estimated value (SOC initial value) is obtained through calculation, each parameter in the recursive least square method and each parameter in the H ∞ filtering algorithm may be updated according to the priori estimated value, the current value, and the voltage value, and the specific process is not described here again.
And S370, calculating the battery charge state value at the next moment based on the calculation module after the parameters are updated.
Specifically, after calculating and updating parameters in the recursive least square method and the H ∞ filtering algorithm in the calculation model, the SOC value at the next time can be calculated by using the formula (10).
It can be understood that, after the SOC value is obtained by calculation, the steps of collecting the current value and the voltage value and updating the parameters of the calculation model by using the SOC value, the current value and the voltage value may be returned.
On the other hand, embodiments of the present application further provide a server, where the server includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the battery state of charge calculation method as described above.
Referring now to FIG. 4, a block diagram of a computer system 400 suitable for use in implementing a server according to embodiments of the present application is shown.
As shown in fig. 4, the computer system 400 includes a Central Processing Unit (CPU)401 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage section 403 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the system 400 are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 405.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
In particular, according to embodiments of the battery state of charge calculation disclosed herein, the process described above with reference to fig. 1 may be implemented as a computer software program. For example, embodiments of battery state of charge calculation disclosed herein include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for performing the method of fig. 1. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various battery state of charge calculation embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present application may be implemented by software or hardware. The described units or modules may also be provided in a processor, and may be described as: a processor includes an acquisition module, an update module, and a calculation module. The names of the units or modules do not limit the units or modules in some cases, and for example, the updating module may be further described as "a parameter for updating the calculation model based on the current value, the voltage value, and the battery state of charge value at the current time, the parameter including at least a forgetting factor of the least square method".
As another aspect, the present application also provides a computer-readable storage medium, which may be a computer-readable storage medium contained in the foregoing device in the above-described embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the method of determining battery state of charge calculation described herein, and in particular:
acquiring a current value and a current voltage value;
updating parameters of the calculation model based on the current value, the voltage value and the battery charge state value at the current moment, wherein the parameters at least comprise a forgetting factor of a least square method;
and calculating the battery state of charge value at the next moment based on the updated parameters of the calculation model.
To sum up, the battery state of charge calculation apparatus, method, server and medium provided in the embodiments of the present application update the model parameters of the forgetting factor including the least square method in the estimation model by using the current battery state of charge value after acquiring the current value and voltage value, so that the calculation model calculates the battery state of charge value at the next moment by using the updated parameters, thereby implementing real-time update of the estimation model parameters in the cycle process, avoiding the defect of weakening the correction capability of the gain in the algorithm due to the preset checked parameters, and improving the accuracy of the battery state of charge calculation.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the application referred to in the present application is not limited to the particular combination of features described above, but also covers other arrangements formed by any combination of the above features or their equivalents without departing from the concept of the application. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A battery state of charge calculation apparatus, the apparatus comprising:
the acquisition module is used for acquiring the current value and the current voltage value;
the updating module is used for updating parameters of the calculation model based on the current value, the voltage value and the battery charge state value at the current moment, wherein the parameters at least comprise forgetting factors of a recursive least square method;
and the calculation module is used for calculating the battery charge state value at the next moment based on the updated parameters of the calculation model.
2. The battery soc calculating device according to claim 1, wherein the updating module specifically comprises:
the first calculation unit is used for calculating an observation matrix of the recursive least square method based on the battery charge state value, the current value and the voltage value at the current moment;
a second calculation unit for calculating and updating a gain and covariance matrix of the recursive least square method based on the observation matrix;
and the first updating unit is used for calculating and updating the model parameters and the forgetting factor based on the updated gain and covariance matrix.
3. The battery state of charge calculation apparatus of claim 1 or 2, wherein the update module is further configured to update the boundary values of the H ∞ filtering algorithm in the calculation model.
4. The battery state of charge calculation apparatus of claim 3, wherein the update module further comprises:
the second updating unit is used for determining and updating the boundary value of the H-infinity filtering algorithm based on the battery state of charge value and the preset value of the battery state of charge at the current moment;
and the third updating unit is used for determining and updating the weight matrix of the H-infinity filtering algorithm based on the boundary value and the covariance matrix.
5. A battery state of charge calculation method, the method comprising:
acquiring a current value and a current voltage value;
updating parameters of a calculation model based on the current value, the voltage value and the battery charge state value at the current moment, wherein the parameters at least comprise forgetting factors of a recursive least square method;
and calculating the battery state of charge value at the next moment based on the updated parameters of the calculation model.
6. The battery soc computation method of claim 5, wherein the updating the parameters of the computation model based on the current value, the voltage value, and the battery soc value at the current time comprises:
calculating an observation matrix of the recursive least square method based on the battery state of charge value, the current value and the voltage value at the current moment;
calculating and updating a gain and covariance matrix of the recursive least square method based on the observation matrix;
and calculating and updating the model parameters and the forgetting factor based on the updated gain and covariance matrix.
7. The battery soc value calculation method according to claim 5 or 6, wherein the updating the parameters of the calculation model based on the current value, the voltage value, and the battery soc value at the current time further comprises:
and updating the boundary values of the H-infinity filtering algorithm in the calculation model.
8. The battery soc value calculation method according to claim 7, wherein the updating of the parameters of the calculation model based on the current value, the voltage value, and the battery soc value at the current time comprises:
determining and updating a boundary value of the H-infinity filtering algorithm according to the battery charge state value and the preset value at the current moment;
and determining and updating a weight matrix of the H-infinity filtering algorithm based on the boundary value, the covariance matrix and the coefficient matrix of the H-infinity filtering algorithm.
9. A server comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the battery state of charge calculation method according to any one of claims 5-8 when executing the program.
10. A computer-readable storage medium, having stored thereon a computer program for implementing the battery state of charge calculation method according to any one of claims 5 to 8.
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