CN114252771A - Battery parameter online identification method and system - Google Patents
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Abstract
The embodiment of the invention provides a method and a system for identifying battery parameters on line, wherein the method comprises the following steps: establishing an equivalent circuit model of the battery, and obtaining relevant parameters of the equivalent circuit model in an off-line manner; dividing model parameters needing online identification updating into a first parameter group and a second parameter group; judging whether the current working condition meets the calculation updating condition of the parameter identification process in the first parameter group or meets the calculation updating condition of the parameter identification process in the second parameter group on line; if the calculation updating condition of the parameter identification process in the first parameter group is met, performing online parameter identification and updating on the parameter in the first parameter group; if the calculation updating condition of the parameter identification process in the second parameter group is met, performing online parameter identification on the parameters in the second parameter group; and judging whether the current working condition meets the updating condition of the parameters in the second parameter group on line, and if so, updating the parameters in the second parameter group. And decoupling the parameters to be estimated, and respectively identifying different parameters.
Description
Technical Field
The invention relates to the technical field of batteries, in particular to a battery parameter online identification method and system.
Background
With the global energy crisis and the environmental pollution becoming more serious, the trend of the automobile industry towards electric driving has become overwhelming. The power battery system is a device for storing and providing power for the electric automobile, and accurate estimation of the battery state directly influences the output capacity, service life characteristics and safety performance of the battery system, further influences important states of available electric quantity, service life, available energy, power and the like of the battery, and influences the dynamic property and safety of the power battery system for reasonably and safely driving the motor car. In the estimation of the battery State, the State of Charge (SOC), the State of Health (SOH), the State of Energy (SOE), the Power State (SOP), and the like are important indexes reflecting the use condition of the battery Energy, and the accurate estimation of the states is established on the basis of obtaining accurate parameters of the battery cell in the full-working-condition and full-temperature range. However, in the actual use process, the parameters of the battery also change along with the aging of the battery, and the accuracy of the actual state estimation cannot be ensured by simply relying on offline parameter identification, so that online parameter identification is necessary.
In the prior art, aiming at the problem of online identification of battery parameters, a state equation and an output equation of a battery are constructed by establishing a battery model, and the battery parameters are estimated by using a filtering method or an optimization algorithm in combination with information such as terminal voltage and current of a battery core acquired in real time. The representative battery model is an RC equivalent circuit model, and in the model, a plurality of parameters influencing the calculation accuracy of the model are provided, including open-circuit voltage, ohmic internal resistance, polarization capacitance and the like. In addition, since the use of online identification is limited by the computing power and the memory of a Battery Management System (BMS) controller, a more complicated optimization algorithm cannot be used, and it is not suitable to store more historical data.
At present, most of online parameter identification methods establish a group of filtering or optimization equations according to a battery model, and all parameters to be estimated are calculated through one-time filtering or optimization calculation. However, when the battery pack is actually used, the battery pack can measure a small number of parameters, such as voltage, current and the like, and the number of parameters to be estimated is large, for example, four parameters need to be estimated for a first-order RC model. Therefore, in the method for identifying all parameters through one-time calculation, the stability of the calculation result is poor, and the phenomenon that the parameter identification result is abnormally fluctuated is easy to occur.
Disclosure of Invention
The present specification provides a method and system for online identification of battery parameters, which overcome at least one of the technical problems in the prior art.
In a first aspect, according to an embodiment of the present disclosure, there is provided a method for online identifying battery parameters, including:
establishing an equivalent circuit model of the battery, and obtaining relevant parameters of the equivalent circuit model in an off-line manner;
grouping the model parameters according to different functions of the parameters in the equivalent circuit model, and dividing the model parameters needing online identification updating into a first parameter group and a second parameter group;
judging whether the current working condition meets the calculation updating condition of the parameter identification process in the first parameter group or not on line, or whether the current working condition meets the calculation updating condition of the parameter identification process in the second parameter group or not;
if the calculation updating condition of the parameter identification process in the first parameter group is met, performing online parameter identification on the parameters in the first parameter group;
performing fusion optimization on the obtained identification result of the parameters in the first parameter group to obtain a battery parameter updating coefficient, and updating the parameters in the first parameter group;
if the calculation updating condition of the parameter identification process in the second parameter group is met, performing online parameter identification on the parameters in the second parameter group;
judging whether the current working condition meets the updating condition of the parameters in the second parameter group on line;
and when the update condition of the parameters in the second parameter group is met, performing fusion optimization on the obtained identification result of the parameters in the second parameter group to obtain a battery parameter update coefficient, and updating the parameters in the second parameter group.
Optionally, the equivalent circuit model is a first-order RC equivalent circuit model, the establishing an equivalent circuit model of the battery, and the obtaining relevant parameters of the equivalent circuit model offline includes:
establishing a first-order RC equivalent circuit model of the battery, constructing a battery state equation according to the first-order RC equivalent circuit model, and obtaining an external characteristic equation of the first-order RC equivalent circuit model according to the first-order RC equivalent circuit model and a basic circuit principle; the external characteristic equation of the first-order RC equivalent circuit model is as follows:
U1＝IR1×[1-exp(-t/τ1)] (1)
Ut＝UOCV-IR0-U1 (2)
in the above formulas (1) and (2), U1For polarizing the voltage across the internal resistance, UtTerminal voltage for model output, I is ohmic internal resistance R0Current of R1For polarizing internal resistance, tau1Is a time constant, τ1＝R1C1，C1To polarize the capacitance, UOCVIs an ideal voltage source, R0Ohmic internal resistance;
testing the characteristic working condition of the battery off line to obtain relevant parameters of the equivalent circuit model; wherein the tests comprise a battery HPPC test and an open-circuit voltage test, and the related parameters comprise an open-circuit voltage UOCVOhmic internal resistance R0Internal polarization resistance R1Time constant τ1。
Further optionally, the parameter in the first parameter group comprises an open circuit voltage UOCVOhmic internal resistance R0The parameters in the second parameter group comprise polarization internal resistance R1Time constant τ1。
Still further optionally, the calculation updating condition of the parameter identification process in the first parameter group includes:
the variance of the current sampling values in a historical period of time is greater than a first threshold value;
the calculation updating condition of the parameter identification process in the second parameter group comprises the following steps:
the variance of the current sampling values in a historical period of time is greater than a second threshold value; the second threshold is not less than the first threshold;
the update condition of the parameters in the second parameter group comprises:
and the current generates step change with the change value larger than a third threshold value within a historical period of time, and the variance of the current sampling value within the preset time after the step change is smaller than a fourth threshold value.
Still further optionally, the performing online parameter identification on the parameter includes:
discretizing the battery state equation, and establishing a parameter identification iterative calculation equation;
taking parameters in the reference parameter group as fixed values, and identifying the parameters in the parameter group to be detected on line by using the parameter identification iterative computation equation; when the reference parameter group is a first parameter group, the second parameter group is a parameter group to be measured, and when the reference parameter group is a second parameter group, the first parameter group is the parameter group to be measured.
Still further optionally, the iterative computation method for online identifying parameters in the parameter set to be measured includes one of a kalman filtering method and a recursive least squares method.
Still further optionally, the iterative computation method for identifying parameters in the parameter set to be measured on line is a recursive least square method with a forgetting factor, and the identifying parameters in the first parameter set on line specifically includes:
initializing an algorithm, and collecting voltage and current data of the battery at the moment k; the recursive equation of the recursive least squares method is:
in the above formulas (3) to (7)K-1 is the time immediately preceding time k, ykFor the system output vector at time k,
for an observation vector consisting of observations at time k, θkFor the vector to be estimated, theta, containing the parameter to be estimated at time kk-1For the vector to be estimated containing the parameter to be estimated at the time k-1, ekEstimation error, P, of system output at time kkIs a covariance matrix at time k, Pk-1Is the covariance matrix at time K-1, KkThe gain is at the moment k, the lambda is a forgetting factor, and the value range is between 0 and 1;
Ut,k+U1,k＝UOCV,k-IkR0,k (8)
yk＝Ut,k+U1,k (9)
θk＝[UOCV,k R0,k]T (11)
U1,k+1＝U1,kexp(-Δt/τ1,k)+R1,k(1-exp(-Δt/τ1,k))Ik (12)
in the above equations (8) to (12), k +1 is the next time of k, Ut,kTerminal voltage, U, output for the time k model1,kPolarising terminal voltage, U, across internal resistance at time kOCV,kOpen circuit voltage of the cell at time k, R0,kOhmic internal resistance at time k, IkCurrent passing ohmic internal resistance at time k, U1,k+1The terminal voltages at two ends of the polarized internal resistance at the moment of k +1, delta t is the time interval of the discretization sampling point, tau1,kIs the time constant at time k, R1,kThe polarization internal resistance at the time k;
measuring and obtaining terminal voltage U output by k moment modelt,kCurrent I through ohmic internal resistancekRoot of Chinese characterObtaining polarization internal resistance R at the moment k according to the current SOC and the temperature lookup model parameter Map1,kTime constant τ1,kAnd obtaining the terminal voltage U at two ends of the polarized internal resistance at the k moment by iterative calculation of the formula (12)1,k；
Terminal voltage U output by k time modelt,kPolarized internal resistance terminal voltage U1,kCurrent I through ohmic internal resistancekSubstituting the above equations (8) - (11) to calculate the system output vector ykObservation vector
The vector theta to be estimatedk。
Still further optionally, the performing fusion optimization on the recognition result includes:
and calculating the average value of the ratio of the parameter identification result to the original parameter in a period of time.
Still further optionally, the split open circuit voltage UOCVThe fusion optimization of the recognition result specifically comprises the following steps:
calculating the open-circuit voltage U in the identification result in the historical period of time b1OCVTo obtain the current open-circuit voltage UOCVAn updated value of (d);
the resistance to ohm R0The fusion optimization of the recognition result specifically comprises the following steps:
calculating ohmic internal resistance R in a period of time b2 in the historical a1 period of time0Ratio r to the pre-update stored value1And find r in a1 time period1Average value of ra1R is toa1The product of the resistance and the stored value of the ohmic resistance before updating is used as the ohmic resistance R0The update value of (2).
In a second aspect, according to an embodiment of the present specification, there is provided an online battery parameter identification system, including:
the model establishing module is used for establishing an equivalent circuit model of the battery;
the off-line parameter acquisition module is used for acquiring relevant parameters of the equivalent circuit model;
the parameter grouping module is used for dividing the parameters needing online identification updating into a first parameter group and a second parameter group;
the working condition judging module is used for judging whether the current working condition meets the calculation updating conditions of the parameter identification process in the first parameter group and the second parameter group;
the identification module is used for carrying out online parameter identification on the parameters in the first parameter group or the second parameter group based on parameter decoupling;
and the updating module is used for performing fusion optimization according to the online parameter identification result and updating the battery parameters.
In a third aspect, according to an embodiment of the present specification, there is provided an online battery parameter identification device, including: the battery parameter online identification method comprises a processor, a memory and a computer program stored in the memory, wherein when the processor executes the computer program, the online identification method for the battery parameter according to the first aspect is executed.
In a fourth aspect, according to an embodiment of the present specification, there is provided a computer-readable storage medium storing a computer program, which is executable by a processor to perform the online battery parameter identification method according to the first aspect.
The beneficial effects of the embodiment of the specification are as follows:
the parameters to be estimated are decoupled and identified in groups, whether relevant identification results are started or not is determined according to the current working condition, and finally fusion optimization output is performed, so that the reliability of the parameter identification process and the stability of the results are ensured, and the problems that the calculation results in the method for calculating and identifying all the parameters once in the prior art are poor in stability and abnormal fluctuation of the parameter identification results is easy to occur are solved.
The innovation points of the embodiment of the specification comprise:
1. in the embodiment, by decoupling the parameters to be estimated and further identifying different parameters respectively, the reliability and stability of the parameter identification process can be ensured, the calculation result is stable, the problem that the abnormal fluctuation of the parameter identification result is easy to occur in the method for identifying all parameters by calculating once in the prior art is solved, and the method is one of the innovation points of the embodiment of the specification.
2. In this embodiment, according to different functions of parameters in a battery model, a plurality of parameters to be estimated are divided into a plurality of parameter groups, each group of parameters is identified once, all the parameter groups are estimated separately, and in actual use, whether to identify and update parameters in related parameter groups is selected by judging the current working condition, so as to ensure the reliability of the identification process, and meanwhile, the stability of the parameter identification result is ensured by fusing and optimizing the parameter identification result, which is one of the innovative points in the embodiments of the present specification.
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In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart illustrating a method for online identification of battery parameters according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of an equivalent circuit using a first-order RC model in the battery parameter online identification method provided in the embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
It should be noted that the terms "including" and "having" and any variations thereof in the embodiments of the present specification and the drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
The embodiment of the specification discloses an online battery parameter identification method, which is used for decoupling a parameter to be identified, identifying different parameters respectively, selecting whether to identify and update related parameters according to judgment of current working conditions, ensuring reliability of an identification process, and ensuring stability of a parameter identification result by fusing and optimizing parameter identification results. The details will be described below.
Fig. 1 illustrates an online battery parameter identification method provided in an embodiment of the present disclosure. As shown in fig. 1, the online battery parameter identification method includes the following steps:
and step 100, establishing an equivalent circuit model of the battery, and obtaining relevant parameters of the equivalent circuit model in an off-line manner.
Specifically, the battery model in the embodiment of the present specification selects an RC equivalent circuit model. The equivalent circuit model is a commonly used battery voltage model, and is based on the battery principle, and a circuit is formed by basic elements such as capacitors and resistors, so that the external characteristics of the battery are described, and further, the characteristics of the lithium ion battery can be more accurately simulated by selecting the RC equivalent circuit model. In the application process, the corresponding n-order RC model can be selected according to the number of RC links.
In a specific embodiment, the number of RC links is 1, and correspondingly, the first-order RC equivalent circuit model is selected as the equivalent circuit model. Establishing a first-order RC equivalent circuit model of the battery, wherein the first-order RC equivalent circuit model consists of an ideal voltage source (OCV) and an ohmic internal resistance R as shown in figure 20And a resistor R1-a capacitance C1A parallel connection link, wherein the ideal voltage source is UOCV，R1Called internal resistance to polarization, C1Called polarization capacitance, the RC link is used for describing concentration polarization and electrochemical polarization characteristics of the battery and passes through ohmic internal resistance R0Is marked as I, polarization internal resistance R1The voltage across is denoted as U1And the terminal voltage of the model output is recorded as UtTime of dayConstant τ1＝R1C1. Accordingly, according to the first-order RC equivalent circuit model and the basic circuit principle, the external characteristic equation of the first-order RC equivalent circuit model can be obtained as follows:
U1＝IR1×[1-exp(-t/τ1)] (1)
Ut＝UOCV-IR0-U1 (2)
the off-line test of the characteristic working condition of the battery is carried out, and relevant parameters of the equivalent circuit model are obtained, wherein the obtained relevant parameters (namely the battery parameters) can include but are not limited to open-circuit voltage, ohmic internal resistance, polarization internal resistance, RC time constant and the like, and the test method can include but is not limited to open-circuit voltage test, HPPC test and the like. In the external characteristic equation of the first-order RC equivalent circuit model, the relevant parameters of the equivalent circuit model comprise the open-circuit voltage UOCVOhmic internal resistance R0Internal polarization resistance R1Time constant τ1In one specific embodiment, the open circuit voltage U of the battery can be obtained by an open circuit voltage testOCVObtaining the ohmic internal resistance R of the battery through the HPPC test of the battery0Internal polarization resistance R1Time constant τ1。
Specifically, the model parameters are grouped according to different functions of the parameters in the battery model, that is, the model parameters which need to be identified and updated online are divided into two or more groups according to different influences of different parameters of the battery on the external characteristics of the battery according to actual conditions, but the algorithm difficulty is correspondingly increased by increasing the grouping number of the parameters, so in the embodiment of the present specification, the model parameters which need to be identified and updated online are divided into two groups, namely, the first parameter group and the second parameter group.
In a specific embodiment, the external characteristic equation of the first-order RC equivalent circuit model in step 100 is used as the external characteristic equation of the equivalent circuit modelThe relevant parameters include open circuit voltage UOCVOhmic internal resistance R0Internal polarization resistance R1Time constant τ1According to the effects of four parameters in the battery model, for example, the ohmic internal resistance affects the terminal voltage change during the instantaneous current change, and the polarization internal resistance and the time constant affect the terminal voltage change rule in a period of time after the current change, the ohmic internal resistance and the polarization parameters can be respectively identified, so that the following grouping can be performed: the parameter in the first parameter group includes an open circuit voltage UOCVOhmic internal resistance R0The parameters in the second parameter group comprise polarization internal resistance R1Time constant τ1。
When the current working condition is judged to meet the calculation updating condition of the parameter identification process in the first parameter group on line, the step 300 is executed to step 400; when it is determined on-line that the current operating condition satisfies the calculation update condition of the parameter identification process in the second parameter set, step 300 proceeds to step 600.
Specifically, the calculation updating conditions of the parameter identification process in the first parameter group include, but are not limited to: the variance of the current sampling values in the history period is larger than the first threshold value Trd1That is, the current fluctuation in any period of time in the history exceeds a certain condition. Wherein, the first threshold Trd is selected with the goal of making the accuracy and stability of the identification result best1And adjusting according to the actual working condition.
The calculation update conditions of the parameter identification process in the second parameter set include, but are not limited to: the variance of the current sampling value in the history period is larger than a second threshold value Trd2That is, it is determined whether the current fluctuation exceeds a certain condition within any period of time in history. Wherein the second threshold Trd2Not less than the first threshold value Trd1Similarly, the second threshold Trd is selected with a view to optimizing the accuracy and stability of the recognition result2And adjusting according to the actual working condition.
When the current working condition is judged to meet the calculation updating condition of the parameter identification process in the first parameter group on line, the step 300 is entered into the step 400:
in step 400, if the calculation update condition of the parameter identification process in the first parameter set is satisfied, performing online parameter identification on the parameter in the first parameter set.
When the current fluctuation in any period of history exceeds a certain condition, namely the variance of the current sampling values in the period of history is greater than the first threshold value Trd1And if so, the calculation updating condition of the parameter identification process in the first parameter group is met, the parameters in the second parameter group are treated as fixed values, and only the parameters in the first parameter group are identified.
Specifically, a parameter identification iterative calculation equation is established by discretizing a battery state equation, the second parameter group is set as a reference parameter group and treated as a fixed value, the first parameter group is set as a parameter group to be measured, and only the parameters in the first parameter group are identified on line. The iterative calculation method for parameter identification includes, but is not limited to, a kalman filtering method, a recursive least square method, and the like, wherein the kalman filtering method includes unscented kalman filtering, extended kalman filtering, dual kalman filtering, joint kalman filtering, and the like.
In a specific embodiment, the iterative computation method for online identifying parameters in the parameter set to be measured adopts a Recursive Least Squares (RLS) with forgetting factor. Initializing an algorithm, continuously acquiring voltage and current data of the battery, recording the current time as k, the previous time as k-1 and the next time as k +1, wherein the recursive equation of the RLS algorithm is as follows:
in the above formulas (3) to (7), ykFor the system output vector at time k,
for an observation vector consisting of observations at time k, θkFor the vector to be estimated, theta, containing the parameter to be estimated at time kk-1For the vector to be estimated containing the parameter to be estimated at the time k-1, ekEstimation error, P, of system output at time kkIs a covariance matrix at time k, Pk-1Is the covariance matrix at time K-1, KkAnd the gain at the moment k is obtained, and the lambda is a forgetting factor, and the value range is between 0 and 1.
Ut,k+U1,k＝UOCV,k-IkR0,k (8)
yk＝Ut,k+U1,k (9)
θk＝[UOCV,k R0,k]T (11)
U1,k+1＝U1,kexp(-Δt/τ1,k)+R1,k(1-exp(-Δt/τ1,k))Ik (12)
In the above formulas (8) to (12), Ut,kTerminal voltage, U, output for the time k model1,kPolarising terminal voltage, U, across internal resistance at time kOCV,kIs the open circuit voltage of the battery at time k，R0,kOhmic internal resistance at time k, IkCurrent passing ohmic internal resistance at time k, U1,k+1The terminal voltages at two ends of the polarized internal resistance at the moment of k +1, delta t is the time interval of the discretization sampling point, tau1,kIs the time constant at time k, R1,kIs the polarization internal resistance at time k.
The parameter to be identified in the RLS algorithm is a parameter in the first parameter group, namely the open-circuit voltage UOCVOhmic internal resistance R0Wherein the terminal voltage U outputted by the k-time model can be obtained by actual measurementt,kK time polarizes terminal voltage U at both ends of internal resistance1,kPolarization internal resistance R which can be obtained by iterative calculation of the above formula (12) and treated as a fixed value1,kTime constant τ1,kCan be obtained by checking the model parameter Map of the current SOC and the temperature, so that the system output vector y can be obtained by using the formula (8)kObservation vector
The vector theta to be estimatedkSpecifically, as shown in the above formulas (9) to (11).
And 500, performing fusion optimization on the obtained identification result of the parameters in the first parameter group to obtain a battery parameter updating coefficient, and updating the parameters in the first parameter group.
And performing fusion optimization on the parameters in the first parameter group calculated in the step 400, and calculating the update coefficients of the related parameters. The fusion method includes, but is not limited to, calculating an average value of the ratio of the parameter identification result to the original parameter over a period of time.
In one embodiment, the open-circuit voltage U identified in step 400 above is calculated over a historical period of time b1OCVTo obtain the current open-circuit voltage UOCVWherein b1 is a short period of time, 1s may be selected. Calculating ohmic internal resistance R in a period of time b2 in the historical a1 period of time0Ratio r to the pre-update stored value1And find r in the period of time a11Average value of ra1R is toa1The product of the resistance and the stored value of the ohmic resistance before updating is used as the ohmic resistance R0Wherein b2 is a longer period of time, which may be selected to be 1 min. And updating the parameters in the first parameter group in the model by using the obtained updating value of the parameters in the first parameter group, namely the battery parameter updating coefficient, so as to complete the parameter identification and updating of the first parameter group of the battery.
When the current working condition is judged to meet the calculation updating condition of the parameter identification process in the second parameter group on line, the step 300 proceeds to step 600:
When the current fluctuation exceeds a certain condition within any period of history, more specifically, when the variance of the current sampling value within a period of history is greater than the second threshold Trd2If the calculation updating condition of the parameter identification process in the second parameter group is satisfied, the parameters in the first parameter group are treated as known values, and only the parameters in the second parameter group are identified.
Specifically, a parameter identification iterative calculation equation is established by discretizing a battery state equation, a first parameter group is set as a reference parameter group and treated as a known value, a second parameter group is set as a parameter group to be measured, and only the parameters in the second parameter group are identified on line. The iterative calculation method for parameter identification includes, but is not limited to, a kalman filtering method, a recursive least square method, and the like, wherein the kalman filtering method includes unscented kalman filtering, extended kalman filtering, dual kalman filtering, joint kalman filtering, and the like.
In a specific embodiment, the iterative calculation method for online identifying the parameters in the parameter set to be measured also adopts a Recursive Least Squares (RLS) with forgetting factor.
yk＝Ut,k+IkR0,k-UOCV,k (13)
θk＝[a1 a2]T (15)
In the above formulas (13) to (17), ykFor the system output vector at time k, Ut,kTerminal voltage, I, output for the time-k modelkCurrent passing ohmic internal resistance at time k, R0,kOhmic internal resistance at time k, UOCV,kThe open circuit voltage of the cell at time k,
an observation vector consisting of observations for time k, Ik-1Current through ohmic internal resistance at time k-1, yk-1For the system output vector at time k-1, θkFor the vector to be estimated containing the parameter to be estimated at time k, a1、a2The parameters in the vector to be estimated are the open circuit voltage U obtained from the above step 400OCVOhmic internal resistance R0，τ1For the time constant, Δ t is the time interval of the discretized sample point, R1Is the polarization internal resistance.
In step 600, the parameter to be identified in the RLS algorithm is a parameter in the second parameter set, i.e. the polarization internal resistance R1Time constant τ1The other parameters are treated as known values. Wherein, UOCV,k、R0,kTaking the optimized updating result in the step 500, the terminal voltage U output by the model at the moment kt,kAnd the current I passing through ohmic internal resistance at the time of kkAnd obtaining through actual measurement, and obtaining a calculation result of the parameters in the second parameter group according to the output vector, the observation vector, expressions (13) - (15) of the vector to be estimated and relational expressions (16) - (17) between the parameters to be identified and the vector to be estimated in the RLS algorithm.
When it is determined that the current operating condition satisfies the update condition of the parameters in the second parameter set, step 700 proceeds to step 800.
Specifically, the judgment condition of whether the parameters in the second parameter group satisfy the update includes, but is not limited to: step change is generated in the current within a period of history, and the fluctuation of the current after the step change is smaller than a certain condition. More specifically, the current changes by a value greater than the third threshold Trd during the historical period3And the variance of the current sampling value within the preset time after the step is smaller than the fourth threshold value Trd4. Wherein, the third threshold Trd is selected by taking the identification result with the best precision and stability as the target3And a fourth threshold Trd4And according to the actual working condition, the third threshold value Trd is adjusted3A fourth threshold value Trd4And (6) adjusting.
When the update condition of the parameters in the second parameter group is satisfied, step 700 proceeds to step 800:
and step 800, when the update condition of the parameters in the second parameter group is met, performing fusion optimization on the obtained identification result of the parameters in the second parameter group to obtain a battery parameter update coefficient, and updating the parameters in the second parameter group.
And when the current working condition is judged to meet the updating condition of the parameters in the second parameter group, performing fusion optimization on the parameters in the second parameter group obtained by calculation in the step 700, and calculating the updating coefficients of the related parameters. The fusion method includes, but is not limited to, calculating an average value of the ratio of the parameter identification result to the original parameter over a period of time.
In a specific embodiment, the polarization internal resistance R is calculated over a long period of time in history1Ratio r to the pre-update stored value2And find r in the period of time a22Average value of ra2R is toa2The product of the value of the stored polarization internal resistance before updating is used as the polarization internal resistance R1The update value of (2). Similarly, the time constant tau in a long period of history is calculated1Ratio r to the pre-update stored value3And find r in the period of time a33Average value of ra3R is toa3The product of the time constant and the stored value of the time constant before update is taken as the time constant tau1Thereby obtaining an online identification update value of the parameter in the second parameter group of the battery.
It should be noted and understood that the number of the identification processes of the battery parameter online identification method corresponds to the number of parameter groups, one group of parameter groups needs to be identified at a time, and the battery parameter online identification method does not limit the identification sequence of the parameters in the parameter groups.
Corresponding to the above method embodiment, the embodiment of the present invention further provides an online battery parameter identification system, which is used for performing the steps of the online battery parameter identification method in the above embodiment. The battery parameter online identification system comprises a model establishing module, an offline parameter obtaining module, a parameter grouping module, a working condition judging module, an identification module and an updating module.
Specifically, the model establishing module is used for establishing an equivalent circuit model of the battery.
The off-line parameter obtaining module is used for obtaining relevant parameters of the equivalent circuit model.
The parameter grouping module is used for dividing the parameters needing online identification updating into a first parameter group and a second parameter group.
The working condition judging module is used for judging whether the current working condition meets the calculation updating conditions of the parameter identification process in the first parameter group and the second parameter group.
The identification module is used for carrying out online parameter identification on the parameters in the first parameter group or the second parameter group based on parameter decoupling.
And the updating module is used for performing fusion optimization according to the online parameter identification result and updating the battery parameters.
It should be noted that, since the battery parameter online identification system provided in the embodiment of the present invention is based on the same concept as the battery parameter online identification method embodiment of the present invention, the technical effect brought by the battery parameter online identification system is the same as the battery parameter online identification method embodiment of the present invention, and specific contents may be referred to the description in the battery parameter online identification method embodiment of the present invention, and are not described herein again.
Therefore, the battery parameter online identification system provided in the embodiments of the present specification can also acquire the relevant parameters of the equivalent circuit model offline by establishing the equivalent circuit model of the battery, group the parameters to be identified and updated online, decouple the parameters to be identified, determine whether to identify and update the relevant parameter groups according to the current working condition, identify and update each group of parameter groups respectively, and finally perform fusion optimization output, thereby ensuring the reliability of the parameter identification process and the stability of the result.
The embodiment of the invention also provides a device for identifying the battery parameters on line, which comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the embodiment of the battery parameter online identification method described in the above embodiment are implemented. Or, when the processor executes the computer program, the functions of the modules in the embodiment of the battery parameter online identification system described in the above embodiment are implemented.
In a particular embodiment, the computer program may be partitioned into one or more modules, which are stored in the memory and executed by the processor to implement embodiments of the present invention. One or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the battery parameter online identification device. For example, the computer program may be divided into a model building module, an offline parameter obtaining module, a parameter grouping module, a working condition judging module, an identifying module, and an updating module, and the specific functions of each module are as follows:
the model establishing module is used for establishing an equivalent circuit model of the battery;
the off-line parameter acquisition module is used for acquiring relevant parameters of the equivalent circuit model;
the parameter grouping module is used for dividing the parameters needing online identification updating into a first parameter group and a second parameter group;
the working condition judging module is used for judging whether the current working condition meets the calculation updating conditions of the parameter identification process in the first parameter group and the second parameter group;
the identification module is used for carrying out online parameter identification on the parameters in the first parameter group or the second parameter group based on parameter decoupling;
and the updating module is used for performing fusion optimization according to the online parameter identification result and updating the battery parameters.
The battery parameter online identification device can be computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud management server. It will be understood by those skilled in the art that the battery parameter online identification device may include, but is not limited to, a processor, a memory, and may include more or less components, or some components in combination, or different components, for example, the battery parameter online identification device may further include an input-output device, a network access device, a bus, and the like.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor, among other things, or the processor may be any conventional processor or the like.
The memory may be an internal storage unit of the battery parameter online identification device, for example, a hard disk or a memory of the battery parameter online identification device. The memory may also be an external storage device of the battery parameter online identification device, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital Card (SD Card), a Flash memory Card (Flash Card), and the like, which are provided on the battery parameter online identification device. Further, the memory may also include both an internal storage unit of the battery parameter online identification apparatus and an external storage device. The memory is used for storing computer programs and other programs or data required by the battery parameter online identification device. The memory may also be used to temporarily store data that has been output or is to be output.
In the above embodiments, the descriptions of the respective embodiments have different emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the modules and algorithm steps of the embodiments described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and the computer program can be executed by a processor to implement the online battery parameter identification method according to the embodiment. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), etc.
In summary, the present specification discloses a method, a system, and a device for online identification of battery parameters, which decouple parameters to be estimated, identify the parameters in groups, determine whether to use related identification results according to current working conditions, and finally perform fusion optimization output, thereby ensuring the reliability of the parameter identification process and the stability of the results, and solving the problems of poor stability of the calculation results and easy abnormal fluctuation of the parameter identification results in the method for identifying all parameters by one-time calculation in the prior art.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
Those of ordinary skill in the art will understand that: modules in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, or may be located in one or more devices different from the embodiments with corresponding changes. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A battery parameter online identification method is characterized by comprising the following steps:
establishing an equivalent circuit model of the battery, and obtaining relevant parameters of the equivalent circuit model in an off-line manner;
grouping the model parameters according to different functions of the parameters in the equivalent circuit model, and dividing the model parameters needing online identification updating into a first parameter group and a second parameter group;
judging whether the current working condition meets the calculation updating condition of the parameter identification process in the first parameter group or not on line, or whether the current working condition meets the calculation updating condition of the parameter identification process in the second parameter group or not;
if the calculation updating condition of the parameter identification process in the first parameter group is met, performing online parameter identification on the parameters in the first parameter group;
performing fusion optimization on the obtained identification result of the parameters in the first parameter group to obtain a battery parameter updating coefficient, and updating the parameters in the first parameter group;
if the calculation updating condition of the parameter identification process in the second parameter group is met, performing online parameter identification on the parameters in the second parameter group;
judging whether the current working condition meets the updating condition of the parameters in the second parameter group on line;
and when the update condition of the parameters in the second parameter group is met, performing fusion optimization on the obtained identification result of the parameters in the second parameter group to obtain a battery parameter update coefficient, and updating the parameters in the second parameter group.
2. The method according to claim 1, wherein the equivalent circuit model is a first-order RC equivalent circuit model, the establishing of the equivalent circuit model of the battery, and the obtaining of the relevant parameters of the equivalent circuit model offline includes:
establishing a first-order RC equivalent circuit model of the battery, constructing a battery state equation according to the first-order RC equivalent circuit model, and obtaining an external characteristic equation of the first-order RC equivalent circuit model according to the first-order RC equivalent circuit model and a basic circuit principle; the external characteristic equation of the first-order RC equivalent circuit model is as follows:
U1＝IR1×[1-exp(-t/τ1)] (1)
Ut＝UOCV-IR0-U1 (2)
in the above formulas (1) and (2), U1For polarizing the voltage across the internal resistance, UtTerminal voltage for model output, I is ohmic internal resistance R0Current of R1For polarizing internal resistance, tau1Is a time constant, τ1＝R1C1，C1To polarize the capacitance, UOCVIs an ideal voltage source, R0Ohmic internal resistance;
testing the characteristic working condition of the battery off line to obtain relevant parameters of the equivalent circuit model; wherein the tests comprise a cell HPPC test, an open circuit voltage test, a battery power supply controller (HPPC)The related parameters comprise open-circuit voltage UOCVOhmic internal resistance R0Internal polarization resistance R1Time constant τ1。
3. The method of claim 2, wherein the parameter of the first parameter group comprises an open circuit voltage UOCVOhmic internal resistance R0The parameters in the second parameter group comprise polarization internal resistance R1Time constant τ1。
4. The method of claim 3, wherein the calculating and updating conditions of the parameter identification process in the first parameter group comprise:
the variance of the current sampling values in a historical period of time is greater than a first threshold value;
the calculation updating condition of the parameter identification process in the second parameter group comprises the following steps:
the variance of the current sampling values in a historical period of time is greater than a second threshold value; the second threshold is not less than the first threshold;
the update condition of the parameters in the second parameter group comprises:
and the current generates step change with the change value larger than a third threshold value within a historical period of time, and the variance of the current sampling value within the preset time after the step change is smaller than a fourth threshold value.
5. The method for online identification of battery parameters according to claim 3, wherein the online parameter identification of the parameters comprises:
discretizing the battery state equation, and establishing a parameter identification iterative calculation equation;
taking parameters in the reference parameter group as fixed values, and identifying the parameters in the parameter group to be detected on line by using the parameter identification iterative computation equation; when the reference parameter group is a first parameter group, the second parameter group is a parameter group to be measured, and when the reference parameter group is a second parameter group, the first parameter group is the parameter group to be measured.
6. The method for identifying battery parameters on line as claimed in claim 5, wherein the iterative calculation method for identifying parameters in the parameter set to be measured on line includes one of Kalman filtering method and recursive least squares method.
7. The method of claim 6, wherein the iterative calculation method for online identifying parameters in the parameter set to be measured is a recursive least square method with a forgetting factor, and the online parameter identification of the parameters in the first parameter set specifically comprises:
initializing an algorithm, and collecting voltage and current data of the battery at the moment k; the recursive equation of the recursive least squares method is:
in the above equations (3) to (7), k-1 is the time immediately preceding the time k, ykFor the system output vector at time k,
for an observation vector consisting of observations at time k, θkFor the vector to be estimated, theta, containing the parameter to be estimated at time kk-1For the vector to be estimated containing the parameter to be estimated at the time k-1, ekEstimation error, P, of system output at time kkIs a covariance matrix at time k, Pk-1Is the covariance matrix at time K-1, KkThe gain is at the moment k, the lambda is a forgetting factor, and the value range is between 0 and 1;
Ut,k+U1,k＝UOCV,k-IkR0,k (8)
yk＝Ut,k+U1,k (9)
θk＝[UOCV,k R0,k]T (11)
U1,k+1＝U1,kexp(-Δt/τ1,k)+R1,k(1-exp(-Δt/τ1,k))Ik (12)
in the above equations (8) to (12), k +1 is the next time of k, Ut,kTerminal voltage, U, output for the time k model1,kPolarising terminal voltage, U, across internal resistance at time kOCV,kOpen circuit voltage of the cell at time k, R0,kOhmic internal resistance at time k, IkCurrent passing ohmic internal resistance at time k, U1,k+1The terminal voltages at two ends of the polarized internal resistance at the moment of k +1, delta t is the time interval of the discretization sampling point, tau1,kIs the time constant at time k, R1,kThe polarization internal resistance at the time k;
measuring and obtaining terminal voltage U output by k moment modelt,kCurrent I through ohmic internal resistancekObtaining the polarization internal resistance R at the k moment according to the current SOC and the temperature lookup model parameter Map1,kTime constant τ1,kAnd obtaining the polarization internal resistance at the k moment by iterative calculation of the formula (12)Terminal voltage U at both ends1,k；
Terminal voltage U output by k time modelt,kPolarized internal resistance terminal voltage U1,kCurrent I through ohmic internal resistancekSubstituting the above equations (8) - (11) to calculate the system output vector ykObservation vector
The vector theta to be estimatedk。
8. The online battery parameter identification method according to claim 7, wherein the fusion optimization of the identification result comprises:
and calculating the average value of the ratio of the parameter identification result to the original parameter in a period of time.
9. The method according to claim 8, wherein the pair of open-circuit voltages U is determined by a comparison of the open-circuit voltage U and the reference voltage UOCVThe fusion optimization of the recognition result specifically comprises the following steps:
calculating the open-circuit voltage U in the identification result in the historical period of time b1OCVTo obtain the current open-circuit voltage UOCVAn updated value of (d);
the resistance to ohm R0The fusion optimization of the recognition result specifically comprises the following steps:
calculating ohmic internal resistance R in a period of time b2 in the historical a1 period of time0Ratio r to the pre-update stored value1And find r in a1 time period1Average value of ra1R is toa1The product of the resistance and the stored value of the ohmic resistance before updating is used as the ohmic resistance R0The update value of (2).
10. An online battery parameter identification system, comprising:
the model establishing module is used for establishing an equivalent circuit model of the battery;
the off-line parameter acquisition module is used for acquiring relevant parameters of the equivalent circuit model;
the parameter grouping module is used for dividing the parameters needing online identification updating into a first parameter group and a second parameter group;
the working condition judging module is used for judging whether the current working condition meets the calculation updating conditions of the parameter identification process in the first parameter group and the second parameter group;
the identification module is used for carrying out online parameter identification on the parameters in the first parameter group or the second parameter group based on parameter decoupling;
and the updating module is used for performing fusion optimization according to the online parameter identification result and updating the battery parameters.
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