CN118011247A - Method and system for estimating state of charge of lead-carbon battery - Google Patents
Method and system for estimating state of charge of lead-carbon battery Download PDFInfo
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Abstract
The application relates to a method and a system for estimating the state of charge of a lead-carbon battery, wherein the method comprises the following steps: acquiring a preset working condition, and constructing a prediction model corresponding to different working conditions according to the system state, the working condition parameters and the system state at the current moment at the previous moment; acquiring a transition probability matrix between working conditions and the probability of each working condition at the previous moment, and acquiring system parameters at the previous moment, wherein the system parameters comprise discharge parameters and working condition mixing parameters, and the working condition mixing parameters comprise the system state and state covariance of each working condition after the working conditions are mixed; predicting and updating the state parameters of each working condition at the current moment according to the prediction model and the system parameters at the last moment through an extended Kalman filter; for any working condition, the probability of each working condition at the current moment is determined according to the normal distribution probability density, the transition probability matrix and the probability of the working condition at the last moment, and the overall system state at the current moment is determined according to the probability and the state parameters of each working condition at the current moment.
Description
Technical Field
The application relates to the technical field of SOC estimation of an energy storage system, in particular to a method and a system for estimating the state of charge of a lead-carbon battery.
Background
The State of Charge (SOC) of the battery is the most important and basic parameter in the battery management system, and is the control logic basis of the battery management system. If accurate estimation of the SOC cannot be ensured, the battery management system cannot be additionally provided with more protection functions to ensure stable operation of the battery. Therefore, an accurate and stable SOC estimation method is required to ensure the daily maintenance of the battery.
However, as the service time of the battery increases, the capacity of the battery decreases, and a complex nonlinear relationship exists between the state of charge and the charge-discharge current of the battery, so that it is difficult to establish an accurate mathematical model to describe the charge-discharge characteristics of the battery, and the internal reaction condition of the energy storage battery cannot be well reflected, thereby causing inaccurate estimation of the SOC of the energy storage system.
The estimation accuracy of the SOC estimation method of the energy storage system in the prior art is low, and the improvement still remains.
Disclosure of Invention
The embodiment of the application provides a method and a system for estimating the state of charge of a lead-carbon battery, which are used for at least solving the problem of low estimation accuracy of an energy storage system SOC estimation method in the related art.
In a first aspect, an embodiment of the present application provides a method for estimating a state of charge of a lead-carbon battery, including:
Acquiring a preset working condition, and constructing a prediction model corresponding to different working conditions according to the system state, the working condition parameters and the system state at the current moment at the previous moment;
acquiring a transition probability matrix between working conditions and the probability of each working condition at the previous moment, and acquiring system parameters at the previous moment, wherein the system parameters comprise discharge parameters and working condition mixing parameters, and the working condition mixing parameters comprise the system state and state covariance of each working condition after the working conditions are mixed;
Predicting and updating the state parameters of each working condition at the current moment according to the prediction model and the system parameters at the last moment through an extended Kalman filter;
For any working condition, determining the probability of each working condition at the current moment according to the normal distribution probability density of the working condition, the transition probability matrix and the probability of the working condition at the last moment; and determining the overall system state at the current moment according to the probability and state parameters of each working condition at the current moment.
In an embodiment, the preset working conditions include: discharging loading, discharging unloading, charging loading and charging unloading, wherein the working condition parameters corresponding to each working condition are different.
In an embodiment, the working condition parameters include a discharging current, white noise, an open circuit voltage function and a voltage, and the obtaining the preset working condition, and constructing a prediction model corresponding to different working conditions according to the system state at the previous moment, the working condition parameters and the system state at the current moment includes:
Constructing a first prediction equation according to the system state at the last moment, the discharge current, the first white noise and the system state at the current moment;
Constructing a second prediction equation according to the open-circuit voltage function, the system state at the last moment, the discharge current, the second white noise and the voltage;
and constructing a prediction model according to the first prediction equation and the second prediction equation.
In an embodiment, the acquiring the system parameter at the previous time includes:
determining the overall probability of the mixed working conditions according to the transition probability matrix and the probability of each working condition at the previous moment;
determining the system state of each working condition after the working conditions are mixed according to the overall probability and the system state of each working condition;
And determining the state covariance of each working condition after mixing according to the overall probability, the first covariance matrix and the system state of each working condition after mixing.
In an embodiment, the predicting and updating, by using an extended kalman filter, the state parameter of each working condition at the current moment according to the prediction model and the system parameter at the previous moment includes:
predicting the state parameters of each working condition at the current moment through the first prediction equation and the system parameters at the last moment;
And updating the state parameters of the current moment through the second prediction equation, the state parameters of each working condition predicted at the current moment and the system parameters.
In an embodiment, the predicting, by the first prediction equation and the system parameter at the previous time, the state parameter of each working condition at the current time includes:
determining a first partial derivative matrix according to the first prediction equation;
Predicting the predicted system state of each working condition at the current moment according to the first prediction equation and the discharge current at the previous moment;
And determining the prediction covariance of each working condition at the current moment according to the first partial derivative matrix, the first covariance matrix and the state covariance of each working condition at the last moment after the working conditions are mixed.
In an embodiment, the updating the state parameter of the current moment by the second prediction equation, the state parameter of each working condition predicted at the current moment and the system parameter includes:
determining a second partial derivative matrix according to the second prediction equation;
Determining the predicted voltage of each working condition at the current moment according to the second prediction equation, the predicted system state and the voltage at the current moment;
the state parameters at the current moment are updated according to the following specific formula:
Wherein i represents the working condition, k represents the moment, y (k) is the measured voltage at moment k, Voltage value at time k, which is predicted from system state at time k-1, is expressed as/>Representing the error value of the real voltage and the predicted voltage at the time k,
Wherein S i (k) isIs H i (k) is a second partial derivative matrix, P i is a predicted covariance, R is a second covariance matrix,
Wherein K i (K) is the optimal Kalman gain,
Wherein,Is the predicted system state, I is the identity matrix,/>To predict the system state at k time after update,/>Is the predicted covariance for k time instant after updating.
In one embodiment, determining the probability of each condition at the current time includes:
wherein i, j, l represent working conditions, k represents time, The probability of the working condition i at the moment k is represented, N is a normal distribution probability density function, nr represents the number of the working conditions, pi represents a transition probability matrix between the working conditions, and/>Represents the condition probability of the working condition i at the next moment if the working condition j is applied at the current moment, mu represents the probability of the working condition,/>The probability of the operating condition j at the previous time is represented.
In one embodiment, the predictive model is an equivalent physical model comprising a fast subsystem with second order dynamics, a slow RC branch, and a state of charge integrator, the system states including a first parameter, a second parameter, a third parameter, and a fourth parameter,
The first parameter characterizes the pressure drop of the rapid subsystem with second-order dynamics in an equivalent physical model of the lead-carbon battery;
the second parameter characterizes the internal state of the rapid subsystem with second-order dynamics in an equivalent physical model of the lead-carbon battery;
The third parameter characterizes the state of charge of the lead-carbon battery;
the fourth parameter characterizes the pressure drop of one slow RC leg in the equivalent physical model.
In a second aspect, an embodiment of the present application provides a system for estimating a state of charge of a lead-carbon battery, including:
the construction module comprises: the method comprises the steps of acquiring preset working conditions, and constructing prediction models corresponding to different working conditions according to the system state at the last moment, working condition parameters and the system state at the current moment;
mixing module: the method comprises the steps of obtaining a transition probability matrix between working conditions and the probability of each working condition at the previous moment, and obtaining system parameters at the previous moment, wherein the system parameters comprise discharge parameters and working condition mixing parameters, and the working condition mixing parameters comprise the system state and state covariance of each working condition after the working conditions are mixed;
and a prediction module: the system parameter prediction module is used for predicting and updating the state parameter of each working condition at the current moment according to the prediction model and the system parameter at the last moment through an extended Kalman filter;
And a determination module: for any working condition, determining the probability of each working condition at the current moment according to the normal distribution probability density of the working condition, the transition probability matrix and the probability of the working condition at the last moment; and determining the overall system state at the current moment according to the probability and state parameters of each working condition at the current moment.
The method and the system for estimating the state of charge of the lead-carbon battery provided by the embodiment of the application have at least the following technical effects.
According to the application, different prediction models are constructed according to different working conditions through different working condition parameters, and the prediction model under each working condition accords with the characteristics of different working conditions of the lead-carbon battery. Interaction among multiple models is realized by mixing working conditions, and prediction of the overall system state is realized by combining an interaction model with an extended Kalman filter. In this way, the prediction accuracy of the battery state of charge is improved.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart illustrating a method of estimating a state of charge of a lead-carbon battery, according to an exemplary embodiment;
fig. 2 is a block diagram illustrating a lead-carbon battery state of charge estimation system according to an exemplary embodiment.
Detailed Description
The present application will be described and illustrated with reference to the accompanying drawings and examples in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. All other embodiments, which can be made by a person of ordinary skill in the art based on the embodiments provided by the present application without making any inventive effort, are intended to fall within the scope of the present application.
It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the described embodiments of the application can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," and similar referents in the context of the application are not to be construed as limiting the quantity, but rather as singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in connection with the present application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.
In a first aspect, an embodiment of the present application provides a method for estimating a state of charge of a lead-carbon battery, and fig. 1 is a flowchart of a method for estimating a state of charge of a lead-carbon battery according to an exemplary embodiment, where the method includes:
Step S101, obtaining preset working conditions, and constructing prediction models corresponding to different working conditions according to the system state, the working condition parameters and the system state at the current moment at the last moment. Optionally, the operating parameters of each operating mode are different, and the corresponding predictive model is also different. In this way, multiple working conditions are considered, so that the accuracy of the subsequent SOC prediction is improved conveniently.
In one example, the working condition parameters in step S101 include a discharge current, white noise, an open circuit voltage function, and a voltage, and a preset working condition is obtained, and a prediction model corresponding to different working conditions is constructed according to the system state at the previous moment, the working condition parameters, and the system state at the current moment, including:
In step S1011, a first predictive equation is constructed based on the system state at the previous time, the discharge current, the first white noise, and the system state at the current time.
Alternatively, assume thatThe system state at the moment is/>The first predictive equation may be expressed as:
Wherein k represents discretization time, and the value range is a natural number; a 0、a1、h1、h2 is a function of x 3; k SOC 、K4、g4 is a constant parameter; w (k) is white noise with a mean value of 0 and a covariance matrix of Q (k); u (k) is the discharge current.
Step S1012, constructing a second prediction equation according to the open circuit voltage function, the system state at the previous time, the discharge current, the second white noise and the voltage. Alternatively, the second predictive equation may be expressed as:
Wherein y is a voltage; k represents discretization time, and the value range is a natural number; OCV is an open circuit voltage function; b 2 is a function of x 3; u (k) is the discharge current; v (k) is white noise with a mean value of 0 and a covariance matrix of R (k).
In step S1013, a prediction model is constructed according to the first prediction equation and the second prediction equation.
Alternatively, the prediction model is constructed according to the first prediction equation and the second prediction equation, and the prediction model may be abbreviated as:
Wherein A (x 3)、B(x3) and C are matrix symbols.
In one example, the preset conditions of step S101 include: discharging loading, discharging unloading, charging loading and charging unloading, wherein the working condition parameters corresponding to each working condition are different.
Optionally, the discharging loading is a process from standing to discharging, the discharging unloading is a process from discharging to standing, the charging loading is a process from standing to charging, and the charging unloading is a process from charging to standing. The function a 0、a1、h1、h2、b2 in the predictive model is different and known for each operating condition. The OCV function and constant K SOC 、K4、g4 are not affected by the operating conditions and are known. Covariance matrices Q (k) and R (k) of the white noise signal are unaffected by the operating conditions and are known.
In this way, all working conditions in the running process of the lead-carbon battery are comprehensively considered, and the accuracy of SOC estimation of the energy storage system of the lead-carbon battery is improved.
In one example, the prediction model in step S101 is an equivalent physical model, which includes a fast subsystem with second order dynamics, a slow RC branch, and a state of charge integrator, where the system state includes a first parameter, a second parameter, a third parameter, and a fourth parameter.
The first parameter characterizes the pressure drop of a rapid subsystem with second-order dynamics in an equivalent physical model of the lead-carbon battery; the second parameter characterizes the internal state of a rapid subsystem with second-order dynamics in an equivalent physical model of the lead-carbon battery; the third parameter characterizes the state of charge of the lead-carbon battery; the fourth parameter characterizes the pressure drop of one slow RC leg in the equivalent physical model.
Alternatively, the system state may be expressed asWherein x 1 (k) is a first parameter, x 2 (k) is a second parameter, x 3 (k) is a third parameter, and x 4 (k) is a fourth parameter. The equivalent physical model in the embodiment can be set according to specific requirements, and can be a second-order equivalent model, a third-order equivalent model and a fourth-order equivalent model. Preferably, the equivalent model in the application is a fourth-order equivalent model, and the SOC dynamic is increased on the basis of the third-order model, so that the internal reaction condition of the lead-carbon battery can be more accurately reflected.
In this way, the SOC estimation problem is converted into an equivalent physical model, and the equivalent physical model can well reflect the internal reaction condition of the energy storage battery, so that the accuracy of the SOC estimation of the energy storage battery is improved.
Step S102, a transition probability matrix between working conditions and the probability of each working condition at the previous moment are obtained, and system parameters at the previous moment are obtained, wherein the system parameters comprise discharge parameters and working condition mixing parameters, and the working condition mixing parameters comprise the system state and state covariance of each working condition after the working conditions are mixed.
Alternatively, the transition probability matrix between operating conditions is represented by pi, e.g.,And the condition probability of the working condition i at the next moment is represented if the working condition j is applied at the current moment. The specific value is set according to the actual operation strategy of the battery, and generally, the value set when i=j is larger, and the value set when i is not equal to j is smaller. Wherein, i, j and l represent working conditions, and represent "the value of a variable under a certain working condition". The initial probability of the working condition is represented by mu, mu i (0) represents the probability of the working condition i at the moment 0, the specific value is set according to the actual running condition of the battery, the probability of the working condition conforming to the actual is set as 1, and the probability of the other working conditions is set as 0.
In the set start-stop time, step S102-step S104 are executed to estimate the value of the SOC in the preset time range.
Optionally, before performing step S102, the method further includes: an initial value is set. For example, the overall system state initial value may be denoted as X (0). The value method comprises the following steps: x 1(0)、x2(0)、x4 (0) is taken as 0, x 3 (0) is taken as an estimated value of the actual SOC of the battery at time 0, and any existing SOC estimation method (for example, an open circuit voltage method, an ampere-hour integration method, an electrochemical model-based method, etc.) can be selected as the estimation method. The initial state of each condition is expressed as: x i (0) is equal to the value of X (0). The initial state covariance for each condition is denoted as P i (0). And taking the value according to the first covariance matrix Q, and taking Q (0).
In this way, interaction among multiple working condition models is realized by mixing working conditions, so that the overall system state can be conveniently determined, and the subsequent accurate estimation of the SOC of the lead-carbon battery system is facilitated.
In one example, the step S102 of acquiring the system parameter at the previous time includes:
step S1021, determining the overall probability after the working conditions are mixed according to the transition probability matrix and the probability of each working condition at the previous moment.
Optionally, the calculation formula of the overall probability μ ji is as follows:
Wherein, The condition probability of the working condition i applied at the next moment if the working condition j is applied at the current moment is represented, and pi li represents the condition probability of the working condition i applied at the next moment if the working condition l is applied at the current moment. Mu with only one subscript represents the probability of each condition,/>The probability of operating condition j at time k-1 is represented. Nr is the total number of working conditions used, nr can be taken as the total number 4 of all working conditions, a plurality of working conditions can be screened out from all working condition types, and the rest working conditions are abandoned, wherein the number is smaller than 4.
Step S1022, determining the system state of each working condition after the working condition is mixed according to the overall probability and the system state of each working condition.
Optionally, the system state of each working condition after mixingThe calculation mode of (2) is as follows, so as to calculate the system state/>, of the mixed working condition iThe following are examples:
Where mu ji is the overall probability, nr is the total number of conditions used, Is an estimate of the system state for condition j.
Step S1023, determining the state covariance of each working condition after mixing according to the overall probability, the first covariance matrix and the system state of each working condition after mixing.
Optionally, the state covariance P 0 of each working condition after mixing is calculated as follows, taking the state covariance P 0i of the working condition i after mixing as an example:
Where mu ji is the overall probability, nr is the total number of conditions used, Is the estimated value of the system state of the working condition j,/>For the system state of the mixed working condition i, P j is the state covariance of the working condition j before mixing, and the value of P j is determined according to the first covariance matrix Q.
In this way, interaction among multiple working condition models is realized by mixing working conditions, so that the overall system state is determined, and the subsequent accurate estimation of the SOC of the lead-carbon battery system is facilitated.
Step S103, predicting and updating the state parameters of each working condition at the current moment according to the prediction model and the system parameters at the last moment through an extended Kalman filter. Optionally, the extended kalman filter is interactively combined with the multi-task model to accurately predict the state parameters under each condition.
In one example, step S103 includes:
In step S301, the state parameters of each working condition at the current moment are predicted by the first prediction equation and the system parameters at the previous moment. Optionally, the state parameters include a predicted system state and a predicted covariance.
In one example, step S301 includes:
Step S3011, determining a first partial derivative matrix according to the first prediction equation. The first partial derivative matrix F i is calculated as follows:
wherein X i is the system state of the working condition i, Is the system state of the working condition i after mixing,/>Is a discharge voltage.
Step S3012, predicting the predicted system state of each working condition at the current moment according to the first prediction equation and the discharge current at the previous moment. Predicting system stateThe calculation formula is as follows:
wherein, A i and B i are matrix marks in a simple prediction model under the working condition i, Is the system state of the working condition i after mixing,/>And u is a discharge current, which is a third parameter of the system state of the mixed working condition i. k and k-1 are the discretized moments.
Step S3013, determining the prediction covariance of each working condition at the current moment according to the first partial derivative matrix, the first covariance matrix and the state covariance of each working condition at the last moment after the working conditions are mixed.
Wherein F i (k) is a first partial derivative matrix, P 0i is a state covariance of the mixed working condition i, and Q is a first covariance matrix.
In step S302, the state parameter at the current moment is updated by the second prediction equation, the state parameter of each working condition predicted at the current moment, and the system parameter.
In one example, step S302 includes:
in step S3021, a second partial derivative matrix is determined according to the second predictive equation.
Wherein X i is the system state of the working condition i, y i is the voltage value of the working condition i, and u is the discharge voltage.
In step S3022, the predicted voltage for each operating mode at the current time is determined according to the second prediction equation, the predicted system state, and the voltage at the current time.
Wherein OCV is an open circuit voltage function, C is a matrix sign in a shorthand predictive model, and u is a discharge voltage.
Step S3023, updating the state parameter at the current time, specifically the steps are as follows:
Wherein i represents the working condition, k represents the moment, y (k) is the measured voltage at moment k, Voltage value at time k, which is predicted from system state at time k-1, is expressed as/>And the error value of the real voltage and the predicted voltage at the moment k is represented.
Wherein S i (k) isIs H i (k) is the second partial derivative matrix, P i is the predicted covariance, and R is the second covariance matrix. /(I)
Where K i (K) is the optimal Kalman gain.
Wherein,Is the predicted system state, I is the identity matrix,/>To predict the system state at k time after update,/>Is the predicted covariance for k time instant after updating.
Through step S103, the extended Kalman filter is combined with the multi-working-condition interaction model, and the models constructed for different working conditions are integrated interactively under the condition of considering all working conditions of the lead-carbon battery, so that a more accurate overall system state is obtained. And predicting the state parameters through an extended Kalman filter, and updating the state parameters through the measured value and the predicted value. In this way, the update is iterated over a set period of time to obtain more accurate state parameters.
Step S104, for any working condition, determining the probability of each working condition at the current moment according to the normal distribution probability density, the transition probability matrix and the probability of the working condition at the last moment; and determining the overall system state at the current moment according to the probability and state parameters of each working condition at the current moment. Optionally, the probability and state parameters of each working condition after the working conditions are mixed are obtained, so that a more accurate overall system state is obtained.
In one example, determining the probability of each operating condition at the current time in step S104 includes:
wherein i, j, l represent working conditions, k represents time, The probability of the working condition i at the moment k is represented, N is a normal distribution probability density function, nr represents the number of the working conditions, pi represents a transition probability matrix between the working conditions, and/>Represents the condition probability of the working condition i at the next moment if the working condition j is applied at the current moment, mu represents the probability of the working condition,/>The probability of the operating condition j at the previous time is represented.
In step S104, the system state of the whole current timeThe calculation formula of (2) is as follows:
where Nr represents the number of operating conditions, Representing the probability of the working condition i at the moment k/(Is the predicted system state at time k. From/>Extracted/>And the final estimated value of the SOC of the lead-carbon battery at the moment k is obtained. The probability and state parameters of each working condition after the working conditions are mixed are obtained, so that a more accurate overall system state is obtained.
In summary, according to the application, different prediction models are constructed for different working conditions through different working condition parameters, and the prediction model under each working condition accords with the characteristics of different working conditions of the lead-carbon battery. Combining the extended Kalman filter with the multi-working condition interaction model, and under the condition of considering all working conditions of the lead-carbon battery, interactively fusing the models constructed aiming at different working conditions to obtain a more accurate overall system state. And predicting the state parameters through an extended Kalman filter, and updating the state parameters through the measured value and the predicted value. In this way, the update is iterated over a set period of time to obtain more accurate state parameters. According to the method, the prediction model conforming to the working condition characteristics of the lead-carbon battery is constructed, and the prediction accuracy of the state of charge of the battery is effectively improved.
In a second aspect, an embodiment of the present application provides a system for estimating a state of charge of a lead-carbon battery, and fig. 2 is a block diagram of a structure of the system for estimating a state of charge of a lead-carbon battery according to an exemplary embodiment, and as shown in fig. 2, the system includes:
building module 100: the method is used for acquiring preset working conditions and constructing prediction models corresponding to different working conditions according to the system state, the working condition parameters and the system state at the current moment at the last moment.
Mixing module 200: the method is used for acquiring a transition probability matrix between working conditions and the probability of each working condition at the previous moment, acquiring system parameters at the previous moment, wherein the system parameters comprise discharge parameters and working condition mixing parameters, and the working condition mixing parameters comprise the system state and state covariance of each working condition after the working conditions are mixed.
Prediction module 300: the method is used for predicting and updating the state parameters of each working condition at the current moment according to the prediction model and the system parameters at the last moment through the extended Kalman filter.
Determination module 400: for any working condition, the probability of each working condition at the current moment is determined according to the normal distribution probability density, the transition probability matrix and the probability of the working condition at the last moment, and the overall system state at the current moment is determined according to the probability of each working condition at the current moment and the state parameters.
In one example, the preset conditions in the building block 100 include: discharging loading, discharging unloading, charging loading and charging unloading, wherein the working condition parameters corresponding to each working condition are different.
In one example, the predictive model in building block 100 is an equivalent physical model that includes a fast subsystem with second order dynamics, a slow RC branch, and a state of charge integrator, the system state including a first parameter, a second parameter, a third parameter, and a fourth parameter,
The first parameter characterizes the pressure drop of a fast subsystem with second order dynamics in an equivalent physical model of the lead carbon battery.
The second parameter characterizes the internal state of the fast subsystem with second order dynamics in the equivalent physical model of the lead-carbon battery,
The third parameter characterizes the state of charge of the lead-carbon battery.
The fourth parameter characterizes the pressure drop of one slow RC leg in the equivalent physical model.
In one example, the operating parameters in the building block 100 include discharge current, white noise, open circuit voltage function, and voltage, the building block 100 includes:
A first construction unit: for constructing a first predictive equation based on the system state at the previous time, the discharge current, the first white noise, and the system state at the current time.
A second construction unit: and the method is used for constructing a second prediction equation according to the open circuit voltage function, the system state at the last moment, the discharge current, the second white noise and the voltage.
Prediction unit: for constructing a prediction model from the first prediction equation and the second prediction equation.
In an example, the system parameters in the mixing module 200 that obtain the last time include:
probability unit: and the method is used for determining the overall probability after the working conditions are mixed according to the transition probability matrix and the probability of each working condition at the last moment.
State unit: and the system state of each working condition after the working condition is mixed is determined according to the overall probability and the system state of each working condition.
Covariance unit: and the system is used for determining the state covariance of each working condition after mixing according to the overall probability, the first covariance matrix and the system state of each working condition after mixing.
In one example, the prediction module 300 includes:
Prediction unit: and the system parameter prediction module is used for predicting the state parameter of each working condition at the current moment through the first prediction equation and the system parameter at the last moment.
An updating unit: and the system parameter is used for updating the state parameter of the current moment through the second prediction equation, the state parameter of each working condition predicted at the current moment and the system parameter.
In one example, the prediction units in prediction module 300 include:
partial derivative subunit: for determining a first partial derivative matrix from the first predictive equation.
Prediction state subunit: and the prediction system state is used for predicting each working condition at the current moment according to the first prediction equation and the discharge current at the last moment.
Prediction covariance subunit: the method is used for determining the prediction covariance of each working condition at the current moment according to the first partial derivative matrix, the first covariance matrix and the state covariance of each working condition at the last moment after the working conditions are mixed.
In one example, the update unit in the prediction module 300 includes:
partial derivative subunit: a second partial derivative matrix is determined from the second predictive equation.
A predicted voltage subunit: and determining the predicted voltage of each working condition at the current moment according to the second prediction equation, the predicted system state and the voltage at the current moment.
Updating the subunit: the specific formula for updating the state parameter at the current moment is as follows:
Wherein i represents the working condition, k represents the moment, y (k) is the measured voltage at moment k, Voltage value at time k, which is predicted from system state at time k-1, is expressed as/>And the error value of the real voltage and the predicted voltage at the moment k is represented. /(I)
Wherein S i (k) isIs H i (k) is the second partial derivative matrix, P i is the predicted covariance, and R is the second covariance matrix. /(I)
Wherein K i (K) is the optimal Kalman gain,
Wherein,Is the predicted system state, I is the identity matrix,/>To predict the system state at k time after update,/>Is the predicted covariance for k time instant after updating.
In an example, determining the probability of each condition at the current time in the determining module 400 includes:
wherein i, j, l represent working conditions, k represents time, The probability of the working condition i at the moment k is represented, N is a normal distribution probability density function, nr represents the number of the working conditions, pi represents a transition probability matrix between the working conditions, and/>Represents the condition probability of the working condition i at the next moment if the working condition j is applied at the current moment, mu represents the probability of the working condition,/>The probability of the operating condition j at the previous time is represented.
In summary, the present application constructs different prediction models for different working conditions by using different working condition parameters by the construction module 100, and the prediction model under each working condition accords with the characteristics of different working conditions of the lead-carbon battery. The models constructed for different working conditions are interactively fused through the mixing module 200 to obtain a more accurate overall system state. The state parameters are predicted by the extended Kalman filter under the condition of considering all working conditions of the lead-carbon battery by combining the extended Kalman filter with the multi-working-condition interaction model through the prediction module 300, and are updated through the measured value and the predicted value, so that the state parameters are updated iteratively in a set period time to obtain more accurate state parameters. The overall system state is determined by the determination module 400. According to the method, the prediction model conforming to the working condition characteristics of the lead-carbon battery is constructed, and the prediction accuracy of the state of charge of the battery is effectively improved.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method for estimating the state of charge of a lead-carbon battery, comprising:
Acquiring a preset working condition, and constructing a prediction model corresponding to different working conditions according to the system state, the working condition parameters and the system state at the current moment at the previous moment;
acquiring a transition probability matrix between working conditions and the probability of each working condition at the previous moment, and acquiring system parameters at the previous moment, wherein the system parameters comprise discharge parameters and working condition mixing parameters, and the working condition mixing parameters comprise the system state and state covariance of each working condition after the working conditions are mixed;
Predicting and updating the state parameters of each working condition at the current moment according to the prediction model and the system parameters at the last moment through an extended Kalman filter;
for any working condition, determining the probability of each working condition at the current moment according to the normal distribution probability density of the working condition, the transition probability matrix and the probability of the working condition at the last moment, and determining the integral system state at the current moment according to the probability and the state parameter of each working condition at the current moment.
2. The method for estimating a state of charge of a lead-carbon battery according to claim 1, wherein the preset conditions include: discharging loading, discharging unloading, charging loading and charging unloading, wherein the working condition parameters corresponding to each working condition are different.
3. The method for estimating a state of charge of a lead-carbon battery according to claim 1, wherein the working condition parameters include a discharge current, white noise, an open-circuit voltage function and a voltage, the obtaining a preset working condition, and constructing a prediction model corresponding to different working conditions according to a system state at a previous moment, the working condition parameters and the system state at a current moment, the method comprises:
Constructing a first prediction equation according to the system state at the last moment, the discharge current, the first white noise and the system state at the current moment;
Constructing a second prediction equation according to the open-circuit voltage function, the system state at the last moment, the discharge current, the second white noise and the voltage;
and constructing a prediction model according to the first prediction equation and the second prediction equation.
4. A method for estimating a state of charge of a lead-carbon battery according to claim 3, wherein the obtaining the system parameter at the previous time comprises:
determining the overall probability of the mixed working conditions according to the transition probability matrix and the probability of each working condition at the previous moment;
determining the system state of each working condition after the working conditions are mixed according to the overall probability and the system state of each working condition;
And determining the state covariance of each working condition after mixing according to the overall probability, the first covariance matrix and the system state of each working condition after mixing.
5. The method for estimating a state of charge of a lead-carbon battery according to claim 4, wherein predicting and updating the state parameters of each working condition at the current time by the extended kalman filter according to the prediction model and the system parameters at the previous time comprises:
predicting the state parameters of each working condition at the current moment through the first prediction equation and the system parameters at the last moment;
And updating the state parameters of the current moment through the second prediction equation, the state parameters of each working condition predicted at the current moment and the system parameters.
6. The method for estimating a state of charge of a lead-carbon battery according to claim 5, wherein predicting the state parameter of each working condition at the current time from the first prediction equation and the system parameter at the previous time comprises:
determining a first partial derivative matrix according to the first prediction equation;
Predicting the predicted system state of each working condition at the current moment according to the first prediction equation and the discharge current at the previous moment;
And determining the prediction covariance of each working condition at the current moment according to the first partial derivative matrix, the first covariance matrix and the state covariance of each working condition at the last moment after the working conditions are mixed.
7. The method according to claim 5, wherein updating the state parameter of the current time by the second predictive equation, the state parameter of each working condition predicted at the current time, and the system parameter comprises:
determining a second partial derivative matrix according to the second prediction equation;
Determining the predicted voltage of each working condition at the current moment according to the second prediction equation, the predicted system state and the voltage at the current moment;
the state parameters at the current moment are updated according to the following specific formula:
Wherein i represents the operating condition, k represents the moment, y (k) is the measured voltage at moment k,/> Voltage value at time k, which is predicted from system state at time k-1, is expressed as/>Representing the error value of the real voltage and the predicted voltage at the time k,
Wherein S i (k) is/>Is H i (k) is a second partial derivative matrix, P i is a predicted covariance, R is a second covariance matrix,
Wherein K i (K) is the optimal Kalman gain,
Wherein/>Is the predicted system state, I is the identity matrix,/>To predict the system state at k time after update,/>Is the predicted covariance for k time instant after updating.
8. The method of estimating a state of charge of a lead-carbon battery of claim 7, wherein determining the probability of each condition at the current time comprises:
Wherein i, j and l represent working conditions, k represents time,/> The probability of the working condition i at the moment k is represented, N is a normal distribution probability density function, nr represents the number of the working conditions, pi represents a transition probability matrix between the working conditions, and/>Represents the condition probability of the working condition i at the next moment if the working condition j is applied at the current moment, mu represents the probability of the working condition,/>The probability of the operating condition j at the previous time is represented.
9. The method of claim 1, wherein the predictive model is an equivalent physical model comprising a fast subsystem with second order dynamics, a slow RC branch, and a state of charge integrator, the system state comprising a first parameter, a second parameter, a third parameter, and a fourth parameter,
The first parameter characterizes the pressure drop of the rapid subsystem with second-order dynamics in an equivalent physical model of the lead-carbon battery;
the second parameter characterizes the internal state of the rapid subsystem with second-order dynamics in an equivalent physical model of the lead-carbon battery;
The third parameter characterizes the state of charge of the lead-carbon battery;
the fourth parameter characterizes the pressure drop of one slow RC leg in the equivalent physical model.
10. A lead-carbon battery state of charge estimation system, comprising:
the construction module comprises: the method comprises the steps of acquiring preset working conditions, and constructing prediction models corresponding to different working conditions according to the system state at the last moment, working condition parameters and the system state at the current moment;
mixing module: the method comprises the steps of obtaining a transition probability matrix between working conditions and the probability of each working condition at the previous moment, and obtaining system parameters at the previous moment, wherein the system parameters comprise discharge parameters and working condition mixing parameters, and the working condition mixing parameters comprise the system state and state covariance of each working condition after the working conditions are mixed;
and a prediction module: the system parameter prediction module is used for predicting and updating the state parameter of each working condition at the current moment according to the prediction model and the system parameter at the last moment through an extended Kalman filter;
and a determination module: for any working condition, the probability of each working condition at the current moment is determined according to the normal distribution probability density of the working condition, the transition probability matrix and the probability of the working condition at the last moment, and the overall system state at the current moment is determined according to the probability and state parameters of each working condition at the current moment.
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