CN117110891A - Calculation method and calculation device for lithium ion battery state of charge estimation value - Google Patents
Calculation method and calculation device for lithium ion battery state of charge estimation value Download PDFInfo
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Abstract
The disclosure provides a calculation method and a calculation device for a lithium ion battery state of charge estimation value, and relates to the field of state detection of power supply equipment. The specific implementation scheme is as follows: firstly, an initial battery model is established based on charge-discharge current-terminal voltage response of a lithium ion battery, secondly, modeling noise acting on a state of charge (SOC) estimated value output by the initial battery model, current measurement noise and voltage measurement noise are added to the initial battery model, an SOC estimated model is established, and finally, center differential Kalman filtering (CDFK) based on matrix spectrum decomposition is carried out on the SOC estimated model, and a state of charge estimated value is calculated.
Description
Technical Field
The disclosure relates to the field of state detection of power supply equipment, in particular to a method and a device for calculating a state of charge estimation value of a lithium ion battery, electronic equipment and a storage medium.
Background
The lithium ion battery has wide application due to the advantages of high charge and discharge efficiency, high power density, small self-discharge rate, good cycle life and the like. From the power supply of portable electronic products to the power source of new energy electric vehicles, even on the energy storage equipment of large-scale energy storage systems, lithium ion batteries are applied.
In the field of military storage batteries, as a typical power supply for portable devices, the duty cycle of lithium ion batteries is also continuously increasing. However, the lithium ion battery is a complex electrochemical system, and has the characteristics of nonlinearity, time variability and the like, so that the state detection difficulty is increased, and great potential safety hazards are brought to power supply guarantee. In social production practice, a state of health (SOH) and a state of charge (SOC) of a lithium ion battery are of great concern. The SOC is similar to an automobile fuel gauge and directly reflects the cruising ability of an electric automobile or various types of equipment. Automotive fuel gauges have accurate gasoline level sensors, but currently there are no sensors available for measuring SOC.
Therefore, an accurate battery model must be established, and an estimated value of SOC is calculated using a certain algorithm in combination with measurement results of battery current, voltage, temperature, and the like. The lithium ion battery state of charge (SOC) estimation technology is also an important component of a Battery Management System (BMS), and has very important significance for military power supply guarantee.
At present, a Kalman filtering algorithm based on a Thevenin equivalent circuit model is widely applied to SOC estimation of a lithium ion battery, but in practice, due to the fact that the calculated amount of the algorithm is large (a large amount of matrix operation is involved) and pathological data (a large number of reduction numbers, a small number of division numbers and the like) are easy to appear, the instantaneity, the stability and the battery state estimation accuracy of the algorithm are affected, and the method is strictThe battery management system may also be halted when heavy. As a result, in the conventional modeling mode using the load current measurement error as the sole process noise of the battery model, process noise and measurement noise with huge variance orders of magnitude are introduced in the same time step (the current measurement error is divided by the maximum capacity when being converted into the SOC state error variance, and is divided by 3600 when being unified from the current unit mA to the capacity unit mAh), so that the orders of magnitude differences among covariance matrix elements occurring in the model calculation process are huge (the differences among the estimated covariance of the diffusion current and the SOC, the estimated variance of the SOC and the measured noise variance are 10) 10 -10 14 Multiple times), worsening the numerical computing environment. The load current measurement error is used as the battery model process noise, and errors caused by inaccuracy of the model per se to the SOC estimation result are also ignored. Since the Kalman filtering method uses the difference between the battery terminal voltage measurement value and the terminal voltage predicted value as a feedback correction SOC estimation result, when the inaccuracy of the model affects the terminal voltage predicted value, the error brought by the feedback correction mechanism to the SOC estimation cannot be ignored. In addition, in the covariance matrix decomposition step of the central differential kalman filter (central difference Kalman filter, CDFK), the common georgette decomposition method requires covariance matrix normalization, is not suitable for a scenario where the battery is left for a long time, and a user can initialize the SOC with confidence according to the open circuit voltage method, or the battery has completed standard charging or checkup discharging, and the SOC can be calibrated to be 100% or 0% of the scenario. In such a scenario, the covariance matrix of the battery state is semi-positive.
Disclosure of Invention
The disclosure provides a calculation method and device of a lithium ion battery state of charge estimation value, electronic equipment and a readable storage medium.
According to an aspect of the present disclosure, there is provided a method for calculating a state of charge estimation value of a lithium ion battery, including:
based on the charge-discharge current-terminal voltage response of the lithium ion battery, an initial battery model is established;
adding modeling noise, current measurement noise and voltage measurement noise which act on a State Of Charge (SOC) estimation value output by the initial battery model to the initial battery model, and establishing an SOC estimation model;
the SOC estimation model is subjected to a matrix spectral decomposition based central differential kalman filter (central difference Kalman filter, CDFK) to calculate a state of charge estimate.
Optionally, obtaining end voltage average values of small current charge and discharge of the lithium ion battery under different charge states, and establishing a corresponding relation between the charge state of the battery and open-circuit voltage;
and carrying out parameter identification of the battery model based on the corresponding relation between the charge state of the battery and the open-circuit voltage, and establishing the initial battery model.
Optionally, preprocessing the system state of the SOC estimation model to enable the amplified system state to contain noise;
based on the SOC estimation model with the system state being amplified, respectively calculating a system state predicted value and a system output predicted value of the SOC estimation model at the current sampling moment;
calculating a Kalman gain matrix corresponding to the SOC estimation model based on the system state predicted value and the system output predicted value, and updating covariance matrices corresponding to the system state predicted value and the system state predicted value based on the Kalman gain matrix and the difference between the system output predicted value and the system output measured value to obtain covariance matrices corresponding to the system state estimated value and the system state estimated value, wherein the state of charge estimated value is contained in the system state estimated value; and iteratively calculating the state of charge estimation value at the next moment based on the updated system state estimation value and a covariance matrix corresponding to the system state estimation value.
Optionally, the method further comprises:
and performing central differential Kalman filtering based on matrix spectrum decomposition on the amplified covariance matrix related to the system state estimation value, and calculating the system state prediction value at the next moment.
Optionally, the method further comprises:
and performing matrix spectrum decomposition-based center differential Kalman filtering on a covariance matrix related to the system state predicted value, and calculating the system output predicted value at the next moment.
According to still another aspect of the present disclosure, there is provided a calculation apparatus of a state of charge estimation value of a lithium ion battery, including:
the first model building unit is used for building an initial battery model based on the charge-discharge current-terminal voltage response of the lithium ion battery;
a second model building unit, configured to add modeling noise, current measurement noise and voltage measurement noise, which act on a State Of Charge (SOC) estimation value output by the initial battery model, to the initial battery model, and build an SOC estimation model;
a calculation unit for performing a central differential kalman filter (central difference Kalman filter, CDFK) based on matrix spectral decomposition on the SOC estimation model, calculating a state of charge estimation value.
Optionally, the first model building unit is further configured to:
acquiring end voltage average values of small-current charge and discharge of the lithium ion battery under different charge states, and establishing a corresponding relation between the charge state and open-circuit voltage of the battery;
and carrying out parameter identification of the battery model based on the corresponding relation between the charge state of the battery and the open-circuit voltage, and establishing the initial battery model.
According to still another aspect of the present disclosure, there is provided an electronic apparatus including:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the aspects and methods of any one of the possible implementations as described above. According to yet another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method of the aspects and any possible implementation described above.
According to a further aspect of the present disclosure there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method of the aspects and any one of the possible implementations described above.
According to the technical scheme, modeling noise is directly acted on the SOC estimated value to replace the thought of current measurement noise possibly causing deterioration of a numerical computing environment, and meanwhile, in covariance matrix decomposition operation, spectrum decomposition is used to replace a Georll decomposition method requiring strict positive covariance matrix determination, so that the possibility that the algorithm is stopped due to the fact that calculation conditions are not met in matrix decomposition is eliminated, the stability of the algorithm is improved, and meanwhile, the accuracy of the SOC estimated value is also improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram according to a first embodiment of the present disclosure;
fig. 2 is a schematic diagram of creating a model of a dyvalnan equivalent circuit in an initial battery model in a first embodiment according to the present disclosure;
FIG. 3a is a schematic illustration of experimental data for battery pack dynamic condition fitting in accordance with a first embodiment of the present disclosure;
FIG. 3b is a schematic illustration of experimental data fitted to the dynamic conditions of a cell in a first embodiment of the present disclosure;
fig. 4a to 4d are schematic views showing the effect of on-line estimation of the SOC of the battery pack according to the SOC estimation model in the first embodiment of the present disclosure;
fig. 5a to 5d are schematic views showing the effect of on-line estimation of SOC of a battery cell according to the SOC estimation model in the first embodiment of the present disclosure;
FIG. 6 is a schematic diagram according to a second embodiment of the present disclosure;
fig. 7 is a block diagram of an electronic device for implementing a method of calculating a state of charge estimate for a lithium-ion battery in accordance with an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It will be apparent that the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to be within the scope of this disclosure.
It should be noted that, the terminal device in the embodiments of the present disclosure may include, but is not limited to, smart devices such as a mobile phone, a personal digital assistant (Personal Digital Assistant, PDA), a wireless handheld device, and a Tablet Computer (Tablet Computer); the display device may include, but is not limited to, a personal computer, a television, or the like having a display function.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Aiming at the problems of inaccuracy and large calculated amount of the state of charge estimation value of the lithium ion battery in the prior art, the present disclosure provides a real-time, efficient and stable lithium ion battery SOC online estimation algorithm. Firstly, carrying out Thevenin equivalent circuit model parameter identification on a lithium ion battery by adopting battery slow charge and slow discharge experiment and dynamic working condition experiment data, and establishing an initial battery model; then, modeling noise directly acting on the SOC estimation value is applied to the initial battery model, the modeling noise and the current measurement noise are simultaneously used as a state equation of the process noise acting on the initial battery model, voltage measurement noise is added to an output (terminal voltage) equation, and a noisy SOC estimation model is established; finally, real-time online estimation of SOC is performed using a matrix spectral decomposition based central differential kalman filter (central difference Kalman filter, CDFK).
Fig. 1 is a schematic diagram according to a first embodiment of the present disclosure, as shown in fig. 1.
Step 101, an initial battery model is established based on the charge-discharge current-terminal voltage response of the lithium ion battery.
Step 102, adding modeling noise, current measurement noise and voltage measurement noise which act on the SOC estimation value output by the initial battery model to the initial battery model, and establishing an SOC estimation model.
And step 103, performing a central differential Kalman filter CDFK based on matrix spectrum decomposition on the SOC estimation model, and calculating a state of charge estimation value.
Specifically, the step 101 of establishing an initial battery model based on the charge-discharge current-terminal voltage response of the lithium ion battery specifically includes:
step 1011, obtaining the average value of the terminal voltage of the lithium ion battery charged and discharged with small current under different charge states, and establishing the corresponding relation between the charge state and the open circuit voltage of the battery;
and establishing an initial battery model by using the Thevenin equivalent circuit model and a corresponding second-order equivalent circuit model system discrete equation.
Fig. 2 is a schematic diagram of creating a model of the equivalent circuit of the dyvenin in the initial battery model in accordance with the first embodiment of the present disclosure. Specifically, the Thevenin equivalent circuit model is a lithium ion battery model with wider application, and combines an internal resistance model and an impedance spectrum model. U in FIG. 2 OC R is the open circuit voltage of the battery 0 Is the ohmic internal resistance of the battery, n%Where n=2) RC parallel circuits simulate the dynamic characteristics of the cell, including electrochemical polarization effects, concentration polarization effects. The second-order equivalent circuit model consists of an ohmic internal resistance and two RC loops for representing the internal polarization reaction of the battery, so that the reduction of the actual electrical characteristics of the battery is enhanced, the accuracy is improved, and meanwhile, the calculation simplicity is kept. R1 and C1 are respectively the electrochemical polarized internal resistance and the electrochemical polarized capacitance of the battery; r2 and C2 are respectively a battery concentration polarization resistance and a concentration polarization capacitance.
And (3) carrying out slow charge and slow discharge (current is 25-50 hours) experiments on the lithium ion battery under different temperature conditions, taking terminal voltage average values (polarization effects during charge and discharge are mutually offset) during corresponding SOC, and establishing a corresponding relationship OCV (z, T) between the SOC and the OCV (open circuit voltage).
Step 1012, performing parameter identification of the battery model based on the corresponding relationship between the state of charge and the open circuit voltage of the battery, and establishing an initial battery model.
Identifying a second-order equivalent circuit model parameter R by utilizing dynamic working condition experimental data and a least square method at different temperatures 0 、R n (n=1, 2), time constant τ n (n＝1,2,τ n ＝R n C n ) The maximum capacity Q can be approximately taken as the rated capacity of the battery, and the error of the SOC estimated value brought by the maximum capacity Q can be corrected by a feedback mechanism of a Kalman filtering algorithm.
The second-order equivalent circuit model system discrete equations include a state equation as in equation 1 and an output equation as in equation 2:
k is the sampling time of the system, and R in a discrete equation of a second-order equivalent circuit model system is identified by utilizing experimental data of dynamic working conditions of lithium ion single batteries at different temperatures and a least square method 0 、R n (n=1, 2), time constant τ n (n＝1,2,τ n ＝R n C n ) The maximum capacity Q can be approximately taken as the rated capacity of the battery, and the error of the SOC estimated value brought by the maximum capacity Q can be corrected by a feedback mechanism of a Kalman filtering algorithm.
And constructing an initial battery model by the discrete equation of the second-order equivalent circuit model system.
Further, in order to overcome the conventional modeling manner Of taking the load current measurement error as the sole process noise Of the battery model, in the same time step, the error Of the SOC estimation value caused by the process noise and the measurement noise with large variance orders Of magnitude difference is introduced, and in one possible implementation manner Of this embodiment, as in step 102, modeling noise acting on the State Of Charge (SOC) estimation value output by the initial battery model, current measurement noise and voltage measurement noise are added to the initial battery model, so as to build the SOC estimation model.
Specifically, first, a modeling noise directly acting on the SOC estimation value result, a current measurement noise affecting the diffusion current, and a voltage measurement noise acting on the terminal voltage measurement result may be applied to the initial battery model, and a noisy SOC estimation model may be established. Wherein, the SOC estimation model composed of the equation 3 and the equation 4 is constructed on the basis of the state equation of the equation 1 and the output equation of the equation 2:
wherein w is z Representing modeling noise directly acting on SOC estimation, w i And v is the current measurement noise and the voltage measurement noise of the system respectively, which are assumed to be Gaussian white noise, the average value is 0, and w z 、w i And v are independent of each other.
Further, to overcome the problem that the common cholesky decomposition method requires covariance matrix normalization in the covariance matrix decomposition step of the central differential kalman filter (central difference Kalman filter, CDFK), which is not applicable to the scene with long battery standing time, in one possible implementation of this embodiment, as in step 103, the SOC estimation model is subjected to central differential kalman filter (central difference Kalman filter, CDFK) based on matrix spectrum decomposition, and a state of charge estimation value is calculated.
Optionally, step 1031, performing system state preprocessing on the SOC estimation model;
specifically, the system state of the SOC estimation model is first augmented, as in (5).
Here, w [ k ]]＝[w z [k]w i [k]] T Is a process noise vector.
Based on this, the SOC estimation model can be rewritten as expressions such as equation (6) and equation (7):
x[k]＝f(x a [k-1],i[k-1]) Formula (6)
y[k]＝g(x a [k],i[k]) Formula (7)
Step 1032, calculating the system state predicted value and the system output predicted value of the SOC estimation model at the current sampling time based on the SOC estimation model after the system state preprocessing;
step 1033, calculating a Kalman gain matrix corresponding to the SOC estimation model based on the system state predicted value and the system output predicted value, and updating the covariance matrix corresponding to the system state predicted value and the system state predicted value based on the Kalman gain matrix and the difference between the system output predicted value and the system output measured value to obtain a covariance matrix corresponding to the system state estimated value and the system state estimated value, wherein the state of charge estimated value is contained in the system state estimated value;
optionally, a central differential kalman filter based on matrix spectrum decomposition is performed on the covariance matrix related to the system state estimation value, and the system state prediction value of the next moment is calculated.
Optionally, a central differential kalman filter based on matrix spectrum decomposition is performed on the covariance matrix related to the system state predicted value, and the system output predicted value at the next moment is calculated.
Optionally, step 1033-1 predicts the system state.
Let the last sampling time k-1 be known, the system state x [ k-1 ]]The estimated value (mean) of (a) isEstimating covariance matrix as +.>Due to w z 、w i And v are independent of each other, thus augmenting state x a [k-1]The estimated values (mean) and the estimated covariance matrix of (a) are respectively:
the covariance matrix of the process noise is as follows:
while
Wherein, formula 9 is matrix spectrum decomposition of covariance matrix, lambda 1,n And alpha n Respectively, a feature value and a corresponding standard feature vector. Construction of discrete random variablesThe distribution is as follows:
the discrete random vector variableAnd x a [k-1]Having the same first and second order numerical features (mean, covariance matrix) that can be used to replace x approximately a [k-1]Reasoning forward (next sampling instant k). Therefore, according to equation 6, the system state prediction value at the next sampling instant is:
optionally, step 1033-2, a covariance matrix of the system state prediction values is calculated.
Specifically, the covariance matrix of the system state prediction value at the sampling time k is:
optionally, step 1033-3 predicts the output of the system.
Among them, according to the current information, there are:
meanwhile, an ohmic internal resistance self-adaptive mechanism is introduced to track the R of battery aging and external environment change 0 The effect of the generation. And when the change of the current is larger than a threshold value, starting an ohmic internal resistance updating algorithm.
Filtered withWhere α=0.999 is generally taken. When the change of the current does not exceed the threshold value, R is as follows 0 No update is made.
In the same manner as in equation 9, the covariance matrix in equation 12 is subjected to matrix spectral decomposition:
constructing a discrete random variable, similar to step 301And x a [k]Having the same first and second order numerical features (mean, covariance matrix) that can be used to replace x approximately a [k]Reasoning forward (next discrete time k).
Further, the prediction system outputs according to equation 7:
optionally, at step 1033-4, a Kalman gain matrix L is calculated k
Wherein,is->Is the first 3 rows of (c).
Step 1033-5 to 1033-6, based on the Kalman gain matrix, the difference between the output measured value and the predicted value, updating the system state predicted value and the corresponding covariance matrix, obtaining the system state estimated value (including the state of charge estimated value) and the covariance matrix corresponding to the system state estimated value, and iteratively calculating the state of charge estimated value at the next moment.
Step 1033-5 updates the system state prediction value to obtain the system state estimation value:
optionally, step 1033-6 updates the covariance matrix of the system state prediction value to obtain the covariance matrix corresponding to the system state estimation value
Let k=k+1, go back to step 1033-1, and repeatedly execute the foregoing steps, and the SOC estimation model iteratively calculates the state of charge estimation value at the next moment.
According to the embodiment of the disclosure, based on the scheme, modeling noise describing inaccuracy of a model is introduced into an SOC estimation model as a part of process noise, load current measurement noise which generates negative influence on numerical calculation is replaced to directly act on an SOC estimation value, the change can decouple the SOC and diffusion current in a state vector, the state covariance matrix decomposition operation complexity is reduced, meanwhile, the situation that the magnitude order of covariance matrix elements is too different is avoided, the numerical calculation environment of a model algorithm is improved, and BMS real-time and efficient operation is facilitated. In covariance matrix decomposition operation, matrix spectrum decomposition is adopted, so that the requirement on a covariance matrix (from positive definite relaxation to semi-positive definite) can be relaxed, and the stability of a system algorithm is improved.
Further, in one possible implementation manner of this embodiment, the above calculation method of the state of charge estimation value of the lithium ion battery is experimentally verified, as follows:
as shown in fig. 3a, the model fitting effect of the experimental data of the dynamic working condition of the battery pack in the first embodiment of the present disclosure is described, and as shown in fig. 3b, the model fitting effect of the experimental data of the dynamic working condition of the single battery in the first embodiment of the present disclosure is described. The test is carried out at room temperature by using a certain automobile power battery pack (8 parallel strings of 144), wherein the single battery is a 18650 ternary lithium ion battery, the rated voltage is 3.6V, and the rated capacity is 2000mAh. And the least square method is used for carrying out parameter identification on the single battery and the battery pack under the dynamic working condition, and the effect diagram is shown in fig. 3a and 3b. The average absolute error of the battery pack data fitting is 2.0909V, and the average absolute error of the single battery data fitting is 11.0324mV.
Applying modeled white gaussian noiseThat is, because of inaccurate modeling, a disturbance with a standard deviation of 0.1% is brought to the SOC estimation value in each sampling interval. For ease of calculation, the effect of current measurement noise is ignored in the experiment (i.e). The on-line estimation of the power battery pack SOC is performed by using CDKF based on matrix spectrum decomposition, and the estimation effect is shown in fig. 4 a-4 d, wherein fig. 4a is a battery pack verification working condition (load current), fig. 4b is a battery pack verification working condition (terminal voltage), fig. 4c is a battery pack SOC estimation effect diagram, and fig. 4d is a battery pack SOC estimation effect diagram (dynamic phase). The average absolute error of the SOC estimation value was 0.6722%.
The SOC estimation effect of the 41 st-50 th cycle charge-discharge experiment after the single battery leaves the factory is shown in fig. 5 a-5 d, wherein fig. 5a is a single battery verification working condition (load current), fig. 5b is a single battery verification working condition (terminal voltage), fig. 5c is a single battery SOC estimation effect diagram, and fig. 5d is a single battery SOC estimation error. The maximum error of the SOC estimation value is 4.32%, and the average absolute error of the SOC estimation value is 1.0968%.
It should be noted that, in the present disclosure, part or all of the execution subject of the foregoing steps may be an application located at a local terminal, or may also be a functional unit such as a plug-in unit or a software development kit (Software Development Kit, SDK) disposed in the application located at the local terminal, or may also be a processing engine located in a server on a network side, or may also be a distributed system located on the network side, for example, a processing engine or a distributed system in a video processing platform on the network side, which is not limited in this embodiment.
It will be appreciated that the application may be a native program (native app) installed on the native terminal, or may also be a web page program (webApp) of a browser on the native terminal, which is not limited in this embodiment.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all of the preferred embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
Fig. 6 is a schematic diagram according to a second embodiment of the present disclosure, as shown in fig. 6. The calculation device 600 of the lithium ion battery state of charge estimation value of the present embodiment may include a first model creation unit 601, a first model creation unit 602, and a calculation unit 603. The first model building unit is used for building an initial battery model based on charge-discharge current-terminal voltage response of the lithium ion battery; a second model building unit for adding modeling noise, current measurement noise and voltage measurement noise, which act on a State Of Charge (SOC) estimation value output by the initial battery model, to the initial battery model, and building an SOC estimation model; a calculation unit for performing a matrix spectral decomposition based central differential kalman filter (central difference Kalman filter, CDFK) on the SOC estimation model to calculate a state of charge estimate.
Note that, part or all of the calculation device for the lithium ion battery state of charge estimation value in the present embodiment may be an application located in the local terminal, or may be a functional unit such as a plug-in unit or a software development kit (Software Development Kit, SDK) provided in the application located in the local terminal, which is not particularly limited in this embodiment.
It will be appreciated that the application may be a native program (native app) installed on the native terminal, or may also be a web page program (webApp) of a browser on the native terminal, which is not limited in this embodiment.
Optionally, in one possible implementation manner of this embodiment, the first model building unit is further configured to:
acquiring end voltage average values of small-current charge and discharge of the lithium ion battery under different charge states, and establishing a corresponding relation between the charge state and open-circuit voltage of the battery;
and carrying out parameter identification of the battery model based on the corresponding relation between the charge state of the battery and the open-circuit voltage, and establishing an initial battery model.
In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 7 illustrates a schematic block diagram of an example electronic device 700 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 7, the electronic device 700 includes a computing unit 701 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 may also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
Various components in the electronic device 700 are connected to the I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, etc.; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, an optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the electronic device 700 to exchange information/data with other devices through a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 701 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 701 performs the respective methods and processes described above, for example, a calculation method of a web page similarity model, a prediction method of web page similarity. For example, in some embodiments, the method of computing the web page similarity model, the method of predicting web page similarity may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 700 via the ROM 702 and/or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the calculation unit 701, one or more steps of the calculation method of the web page similarity model, the prediction method of web page similarity described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the method of computing the web page similarity model, the method of predicting web page similarity, in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
Claims (10)
1. A calculation method of a lithium ion battery state of charge estimation value comprises the following steps:
based on the charge-discharge current-terminal voltage response of the lithium ion battery, an initial battery model is established;
adding modeling noise, current measurement noise and voltage measurement noise which act on the SOC estimation value of the state of charge output by the initial battery model to the initial battery model, and establishing an SOC estimation model;
and performing central differential Kalman filtering CDFK based on matrix spectrum decomposition on the SOC estimation model, and calculating a state of charge estimation value.
2. The computing method of claim 1, wherein the step of establishing an initial battery model based on a charge-discharge current-terminal voltage response of the lithium-ion battery comprises:
acquiring end voltage average values of small-current charge and discharge of the lithium ion battery under different charge states, and establishing a corresponding relation between the charge state and open-circuit voltage of the battery;
and carrying out parameter identification of the battery model based on the corresponding relation between the charge state of the battery and the open-circuit voltage, and establishing the initial battery model.
3. The calculation method according to claim 1, wherein the step of calculating the state of charge estimation value by performing a matrix-spectrum-decomposition-based center differential kalman filter CDFK on the SOC estimation model includes:
preprocessing the system state of the SOC estimation model to enable the amplified system state to contain noise;
based on the SOC estimation model with the system state being amplified, respectively calculating a system state predicted value and a system output predicted value of the SOC estimation model at the current sampling moment;
calculating a Kalman gain matrix corresponding to the SOC estimation model based on the system state predicted value and the system output predicted value, and updating covariance matrices corresponding to the system state predicted value and the system state predicted value based on the Kalman gain matrix and the difference between the system output predicted value and the system output measured value to obtain covariance matrices corresponding to the system state estimated value and the system state estimated value, wherein the state of charge estimated value is contained in the system state estimated value;
and iteratively calculating the state of charge estimation value at the next moment based on the updated system state estimation value and a covariance matrix corresponding to the system state estimation value.
4. The calculation method according to claim 3, wherein the step of calculating a state of charge estimation value by performing a matrix-spectrum-decomposition-based center differential kalman filter CDFK on the SOC estimation model, further comprises:
and performing central differential Kalman filtering based on matrix spectrum decomposition on the covariance matrix related to the system state estimation value, and calculating the system state prediction value at the next moment.
5. The calculation method according to claim 3, wherein the step of calculating a state of charge estimation value by performing a matrix-spectrum-decomposition-based center differential kalman filter CDFK on the SOC estimation model, further comprises:
and performing matrix spectrum decomposition-based center differential Kalman filtering on a covariance matrix related to the system state predicted value, and calculating the system output predicted value at the next moment.
6. A computing device for a state of charge estimate of a lithium ion battery, comprising:
the first model building unit is used for building an initial battery model based on the charge-discharge current-terminal voltage response of the lithium ion battery;
a second model building unit, configured to add modeling noise, current measurement noise and voltage measurement noise, which act on the SOC estimation value of the state of charge output by the initial battery model, to the initial battery model, and build an SOC estimation model;
and the calculating unit is used for carrying out central differential Kalman filtering CDFK based on matrix spectrum decomposition on the SOC estimation model and calculating a charge state estimation value.
7. The computing device of claim 6, wherein the first model building unit is further to:
acquiring end voltage average values of small-current charge and discharge of the lithium ion battery under different charge states, and establishing a corresponding relation between the charge state and open-circuit voltage of the battery;
and carrying out parameter identification of the battery model based on the corresponding relation between the charge state of the battery and the open-circuit voltage, and establishing the initial battery model.
8. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1-5.
9. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-5.
10. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any of claims 1-5.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117310521A (en) * | 2023-11-29 | 2023-12-29 | 深圳市普裕时代新能源科技有限公司 | Method, system, equipment and storage medium for calibrating charging state of lithium ion battery |
| CN117420447A (en) * | 2023-12-18 | 2024-01-19 | 四川华泰电气股份有限公司 | Lithium battery SOC estimation method and system considering noise deviation compensation and electronic device |
| CN118549822A (en) * | 2024-07-29 | 2024-08-27 | 锦浪科技股份有限公司 | Battery state of charge evaluation method, electronic equipment and storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117310521A (en) * | 2023-11-29 | 2023-12-29 | 深圳市普裕时代新能源科技有限公司 | Method, system, equipment and storage medium for calibrating charging state of lithium ion battery |
| CN117310521B (en) * | 2023-11-29 | 2024-02-20 | 深圳市普裕时代新能源科技有限公司 | Method, system, equipment and storage medium for calibrating charging state of lithium ion battery |
| CN117420447A (en) * | 2023-12-18 | 2024-01-19 | 四川华泰电气股份有限公司 | Lithium battery SOC estimation method and system considering noise deviation compensation and electronic device |
| CN118549822A (en) * | 2024-07-29 | 2024-08-27 | 锦浪科技股份有限公司 | Battery state of charge evaluation method, electronic equipment and storage medium |
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