CN114252771B - Battery parameter online identification method and system - Google Patents
Battery parameter online identification method and system Download PDFInfo
- Publication number
- CN114252771B CN114252771B CN202111514699.XA CN202111514699A CN114252771B CN 114252771 B CN114252771 B CN 114252771B CN 202111514699 A CN202111514699 A CN 202111514699A CN 114252771 B CN114252771 B CN 114252771B
- Authority
- CN
- China
- Prior art keywords
- parameter
- parameters
- parameter set
- time
- internal resistance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 96
- 238000004364 calculation method Methods 0.000 claims abstract description 55
- 230000008569 process Effects 0.000 claims abstract description 44
- 238000005457 optimization Methods 0.000 claims description 24
- 230000004927 fusion Effects 0.000 claims description 22
- 230000010287 polarization Effects 0.000 claims description 20
- 238000005070 sampling Methods 0.000 claims description 14
- 238000012360 testing method Methods 0.000 claims description 14
- 230000008859 change Effects 0.000 claims description 13
- 238000004422 calculation algorithm Methods 0.000 claims description 11
- 239000011159 matrix material Substances 0.000 claims description 6
- 230000014509 gene expression Effects 0.000 claims description 2
- 238000001914 filtration Methods 0.000 description 15
- 238000004590 computer program Methods 0.000 description 14
- 230000002159 abnormal effect Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 239000003990 capacitor Substances 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000007500 overflow downdraw method Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
Abstract
The embodiment of the invention provides a battery parameter online identification method and a system, wherein the method comprises the following steps: establishing an equivalent circuit model of the battery, and acquiring related parameters of the equivalent circuit model offline; dividing model parameters needing online identification update into a first parameter set and a second parameter set; judging whether the current working condition meets the calculation updating condition of the parameter identification process in the first parameter set or meets the calculation updating condition of the parameter identification process in the second parameter set on line; if the calculation updating condition of the parameter identification process in the first parameter set is met, carrying out online parameter identification and updating on the parameters in the first parameter set; if the calculation updating condition of the parameter identification process in the second parameter set is met, carrying out online parameter identification on the parameters in the second parameter set; and judging whether the current working condition meets the updating condition of the parameters in the second parameter set on line, and if so, updating the parameters in the second parameter set. 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 increasing severity of global energy crisis and environmental pollution problems, the trend of the motor industry has been overwhelmed. The power battery system is an energy storage and power supply device of the electric automobile, and accurate estimation of the battery state directly influences the output capacity, service life characteristic and safety performance of the battery system, so that the important states such as available electric quantity, service life, available energy and power of the battery are influenced, and the power performance and safety of the motor car are influenced, wherein the reasonable safety of the power battery system is influenced. In the battery State estimation, state of Charge (SOC), state of Health (SOH), energy State of Energy (SOE), power State of Power (SOP), etc. are important indicators reflecting the battery Energy usage, and the accurate estimation of the above states is based on obtaining accurate parameters of the battery cells in all conditions and all temperature ranges. 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 the off-line parameter identification, so that the on-line parameter identification is necessary.
In the prior art, aiming at the problem of on-line 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 a filtering method or an optimization algorithm by combining information such as the voltage, the current and the like of a battery cell terminal 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, polarized capacitance and the like. In addition, because the use of online identification is limited by the computational power and memory of a Battery Management System (BMS) controller, a more complex optimization algorithm cannot be used, and more historical data is not suitable to be stored.
At present, most online parameter identification methods establish a group of filtering or optimizing equations according to a battery model, and all parameters to be estimated are calculated through one-time filtering or optimizing calculation. However, in practical use of the battery pack, fewer parameters such as voltage and current can be measured, and more parameters to be estimated, for example, four parameters for the first-order RC model need to be estimated. Therefore, in the method of identifying all parameters by one calculation, the stability of the calculation result is poor, and abnormal fluctuation of the parameter identification result is liable to occur.
Disclosure of Invention
The specification provides a method and a system for on-line identification of battery parameters, which are used for overcoming at least one technical problem existing in the prior art.
According to a first aspect, according to an embodiment of the present disclosure, there is provided a battery parameter online identification method, including:
Establishing an equivalent circuit model of a battery, and acquiring related parameters of the equivalent circuit model offline;
grouping model parameters according to different roles of parameters in the equivalent circuit model, and dividing the model parameters needing to be identified and updated online into a first parameter set and a second parameter set;
Judging whether the current working condition meets the calculation updating condition of the parameter identification process in the first parameter set or meets the calculation updating condition of the parameter identification process in the second parameter set on line;
If the calculation updating condition of the parameter identification process in the first parameter set is met, carrying out online parameter identification on the parameters in the first parameter set;
Fusion optimization is carried out on the obtained identification results of the parameters in the first parameter set, battery parameter updating coefficients are obtained, and the parameters in the first parameter set are updated;
if the calculation updating condition of the parameter identification process in the second parameter set is met, carrying out online parameter identification on the parameters in the second parameter set;
judging whether the current working condition meets the updating condition of the parameters in the second parameter set on line;
And when the updating condition of the parameters in the second parameter set is met, fusion optimization is carried out on the obtained identification result of the parameters in the second parameter set, a battery parameter updating coefficient is obtained, and the parameters in the second parameter set are updated.
Optionally, the equivalent circuit model is a first-order RC equivalent circuit model, and the establishing an equivalent circuit model of the battery includes offline obtaining relevant parameters of the equivalent circuit model:
Establishing a first-order RC equivalent circuit model of a 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 formulas (1) and (2), U 1 is the voltage at two ends of the polarized internal resistance, U t is the terminal voltage output by the model, I is the current passing through the ohmic internal resistance R 0, R 1 is the polarized internal resistance, τ 1 is the time constant, τ 1＝R1C1,C1 is the polarized capacitance, U OCV is the ideal voltage source, and R 0 is the ohmic internal resistance;
Testing the characteristic working condition of the battery offline to obtain the related parameters of the equivalent circuit model; the test comprises a battery HPPC test and an open circuit voltage test, and the related parameters comprise an open circuit voltage U OCV, an ohmic internal resistance R 0, a polarization internal resistance R 1 and a time constant tau 1.
Further optionally, the parameters in the first parameter set include an open circuit voltage U OCV and an ohmic internal resistance R 0, and the parameters in the second parameter set include a polarization internal resistance R 1 and a time constant τ 1.
Still further optionally, the calculating and updating conditions of the parameter identification process in the first parameter set include:
the variance of the current sampling value in the historical period of time is larger than a first threshold value;
The calculation update condition of the parameter identification process in the second parameter set comprises:
the variance of the current sampling value in the historic period of time is larger than a second threshold value; the second threshold is not less than the first threshold;
The updating conditions of the parameters in the second parameter set include:
The current generates step change with a change value larger than a third threshold value in a historical period of time, and the variance of the current sampling value in a preset period of time after the step change is smaller than a fourth threshold value.
Still further optionally, the performing online parameter identification by the parameter includes:
Discretizing the battery state equation, and establishing a parameter identification iterative calculation equation;
Taking the parameters in the reference parameter set as fixed values, and utilizing the parameter identification iterative calculation equation to identify the parameters in the parameter set to be detected on line; when the reference parameter set is a first parameter set, the second parameter set is a parameter set to be measured, and when the reference parameter set is a second parameter set, the first parameter set is a parameter set to be measured.
Still further optionally, the iterative calculation method for identifying parameters in the parameter set to be measured includes one of a kalman filter method and a recursive least square method.
Still further optionally, the iterative calculation method for on-line identifying parameters in the parameter set to be detected is a recursive least square method with forgetting factors, and the on-line parameter identification for parameters in the first parameter set specifically includes:
initializing an algorithm, and collecting voltage and current data of a battery at the moment k; the recursive equation of the recursive least squares method is:
In the above formulas (3) - (7), k-1 is the time immediately preceding the k time, y k is the system output vector at the k time, For an observation vector formed by observation values at the moment K, theta k is a vector to be estimated, which contains parameters to be estimated at the moment K, theta k-1 is a vector to be estimated, which contains parameters to be estimated at the moment K-1, e k is an estimation error output by a system at the moment K, P k is a covariance matrix at the moment K, P k-1 is a covariance matrix at the moment K-1, K k is a gain at the moment K, lambda is a forgetting factor, and the value range is 0-1;
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 formulas (8) - (12), k+1 is the next time of k time, U t,k is the terminal voltage output by the k time model, U 1,k is the terminal voltage at both ends of the k time polarized internal resistance, U OCV,k is the open circuit voltage of the k time battery, R 0,k is the ohmic internal resistance of k time, I k is the current passing through the ohmic internal resistance of k time, U 1,k+1 is the terminal voltage at both ends of the k+1 polarized internal resistance, Δt is the time interval of the discretized sampling point, τ 1,k is the time constant of k time, and R 1,k is the polarized internal resistance of k time;
Measuring and obtaining terminal voltage U t,k output by a k moment model and current I k passing through ohmic internal resistance, obtaining polarized internal resistance R 1,k at the k moment and a time constant tau 1,k according to a Map of current SOC and temperature checking model parameters, and obtaining terminal voltage U 1,k at two ends of the polarized internal resistance at the k moment through iterative calculation of the formula (12);
Substituting the terminal voltage U t,k output by the k moment model, the terminal voltage U 1,k at two ends of the polarized internal resistance and the current I k passing through the ohmic internal resistance into the formulas (8) - (11), and calculating to obtain a system output vector y k and an observation vector The vector θ k is to be estimated.
Still further optionally, the performing fusion optimization on the identification 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 fusing and optimizing the identification result of the open circuit voltage U OCV specifically includes:
Calculating an open circuit voltage U OCV in the identification result in a period of time b1 to obtain an updated value of the current open circuit voltage U OCV;
The process of fusion optimization of the identification result of the ohmic internal resistance R 0 specifically comprises the following steps:
And calculating the ratio R 1 of the ohmic internal resistance R 0 and the pre-update stored value in a period b2 in the time period a1 of the history, calculating the average value R a1 of R 1 in the time period a1, and taking the product of R a1 and the pre-update ohmic internal resistance stored value as the update value of the ohmic internal resistance R 0.
In a second aspect, according to an embodiment of the present disclosure, there is provided a battery parameter online identification system, including:
the model building module is used for building an equivalent circuit model of the battery;
the off-line parameter acquisition module is used for acquiring related parameters of the equivalent circuit model;
The parameter grouping module is used for dividing the parameters needing to be identified and updated online into a first parameter set and a second parameter set;
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 set and the second parameter set;
the identification module is used for carrying out online parameter identification on the parameters in the first parameter set or the second parameter set based on parameter decoupling;
And the updating module is used for carrying out fusion optimization according to the on-line parameter identification result and updating the battery parameters.
In a third aspect, according to an embodiment of the present specification, there is provided an on-line identification device for battery parameters, including: the battery parameter online identification method according to the first aspect is executed by a processor, a memory, and a computer program stored in the memory.
In a fourth aspect, according to an embodiment of the present specification, there is provided a computer readable storage medium storing a computer program executable by a processor to perform the battery parameter online identification method according to the first aspect.
The beneficial effects of the embodiment of the specification are as follows:
And decoupling parameters to be estimated, carrying out grouping identification, determining whether to enable related identification results according to the current working condition, and finally carrying out fusion optimization output, thereby ensuring the reliability of the parameter identification process and the stability of the results, and solving the problems that the stability of the calculation results in the method for calculating and identifying all the parameters at one time in the prior art is poor and the abnormal fluctuation of the parameter identification results is easy to occur.
The innovation points of the embodiment of the specification comprise:
1. In this 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, and the calculation result is stable, so that the problem that the method for identifying all parameters by one calculation in the prior art is easy to cause abnormal fluctuation of the parameter identification result is solved, which is one of the innovation points of the embodiments of the present specification.
2. In this embodiment, according to different roles of parameters in a battery model, a plurality of parameters to be estimated are divided into a plurality of parameter sets, each parameter set is identified for one time, all parameter sets are estimated respectively, in actual use, by judging the current working condition, whether to identify and update parameters in the relevant parameter sets is selected, reliability of an identification process is ensured, and meanwhile, by fusion optimization of parameter identification results, stability of the parameter identification results is ensured, which is one of innovation points of the embodiments of the present specification.
Drawings
In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for online identifying battery parameters according to an embodiment of the present disclosure;
Fig. 2 is a schematic diagram of an equivalent circuit of a first-order RC model in the battery parameter online identification method according to the embodiment of the present disclosure.
Detailed Description
The technical solutions of the embodiments of the present specification will be clearly and completely described below with reference to the drawings of the embodiments of the present specification, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without any inventive effort, are intended to be within the scope of the invention.
It should be noted that the terms "comprising" and "having" and any variations thereof in the embodiments and figures herein are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may 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 parameters to be identified, respectively identifying different parameters, selecting whether to identify and update related parameters according to the judgment of the current working condition, ensuring the reliability of an identification process, and ensuring the stability of the parameter identification result through fusion optimization of the parameter identification result. The following is a detailed description.
Fig. 1 illustrates a battery parameter online identification method according to an embodiment of the present disclosure. As shown in fig. 1, the battery parameter online identification method comprises the following steps:
And 100, establishing an equivalent circuit model of the battery, and acquiring related parameters of the equivalent circuit model offline.
Specifically, the battery model in the embodiment of the present disclosure uses an RC equivalent circuit model. The equivalent circuit model is a commonly used battery voltage model, and based on the battery principle, basic elements such as capacitance and resistance are used for forming a circuit, so that the external characteristics of the battery are described, and further, the RC equivalent circuit model is selected to accurately simulate the characteristics of the lithium ion battery. In the application process, a 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 equivalent circuit model is a first-order RC equivalent circuit model. A first-order RC equivalent circuit model of the battery is established, as shown in fig. 2, and is composed of an ideal voltage source (OCV), an ohmic internal resistance R 0 and a resistor R 1 -capacitor C 1 in parallel connection link, wherein the ideal voltage source is U OCV,R1 and is called polarized internal resistance, C 1 is called polarized capacitor, the RC link is used for describing concentration polarization and electrochemical polarization characteristics of the battery, the current passing through the ohmic internal resistance R 0 is recorded as I, the voltages at two ends of the polarized internal resistance R 1 are recorded as U 1, the terminal voltage output by the model is recorded as U t, and the time constant tau 1＝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)
And testing the characteristic working condition of the battery offline, and acquiring relevant parameters of an equivalent circuit model, wherein the acquired relevant parameters (namely battery parameters) can include, but are not limited to, open circuit voltage, ohmic internal resistance, polarized internal resistance, RC time constant and the like, and the testing method can include, but is not limited to, open circuit voltage testing, HPPC testing and the like. In the external characteristic equation of the first-order RC equivalent circuit model, the relevant parameters of the equivalent circuit model include an open circuit voltage U OCV, an ohmic internal resistance R 0, a polarized internal resistance R 1, and a time constant τ 1, in a specific embodiment, the open circuit voltage U OCV of the battery may be obtained through an open circuit voltage test, and the ohmic internal resistance R 0, the polarized internal resistance R 1, and the time constant τ 1 of the battery may be obtained through a battery HPPC test.
And 200, grouping model parameters according to different roles of the parameters in the equivalent circuit model, and dividing the model parameters needing to be identified and updated on line into a first parameter set and a second parameter set.
Specifically, the model parameters are grouped according to different roles of the parameters in the battery model, that is, the model parameters to be identified and updated online are divided into two or more groups according to different influences of the different parameters of the battery on the external characteristics of the battery, but the algorithm difficulty is correspondingly increased due to the increase of the number of the parameter groups, so that in the embodiment of the specification, the model parameters to be identified and updated online are divided into two groups of the first parameter group and the second parameter group.
In a specific embodiment, according to the external characteristic equation of the first-order RC equivalent circuit model in step 100, the relevant parameters of the equivalent circuit model include the open circuit voltage U OCV, the ohmic internal resistance R 0, the polarization internal resistance R 1, and the time constant τ 1, and according to the effects of the four parameters in the battery model, for example, the ohmic internal resistance affects the terminal voltage change when the instantaneous current changes, and the polarization internal resistance and the time constant affect the terminal voltage change law within a period of time after the current changes, the ohmic internal resistance and the polarization parameters can be identified according to the external characteristic equation, so the following groupings can be made: the first parameter group comprises an open circuit voltage U OCV and an ohmic internal resistance R 0, and the second parameter group comprises a polarization internal resistance R 1 and a time constant tau 1.
Step 300, it is determined whether the current working condition meets the calculation update condition of the parameter identification process in the first parameter set or meets the calculation update condition of the parameter identification process in the second parameter set.
When the current working condition is judged to meet the calculation updating condition of the parameter identification process in the first parameter set on line, step 300 is carried out to step 400; when it is determined that the current working condition meets the calculation update condition of the parameter identification process in the second parameter set on line, step 300 proceeds to step 600.
Specifically, the calculation update conditions of the parameter identification process in the first parameter set include, but are not limited to: the variance of the current sample values over the history period is greater than the first threshold Trd 1, that is, the current volatility over any history period exceeds a certain condition. The first threshold Trd 1 is selected with the best accuracy and stability of the identification result as the target, and is adjusted 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 the second threshold Trd 2, namely, whether the current fluctuation in any history period exceeds a certain condition is judged. The second threshold Trd 2 is not smaller than the first threshold Trd 1, and similarly, the second threshold Trd 2 is selected for best accuracy and stability of the identification result, and is adjusted according to the actual working condition.
When it is determined that the current working condition meets the calculation update condition of the parameter identification process in the first parameter set, step 300 proceeds to step 400:
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 parameters in the first parameter set.
When the current fluctuation in any period of time exceeds a certain condition, namely the variance of the current sampling value in the period of time is larger than a first threshold Trd 1, the calculation updating condition of the parameter identification process in the first parameter set is met, the parameter in the second parameter set is treated as a fixed value, and only the parameter in the first parameter set is identified.
Specifically, by discretizing the battery state equation, a parameter identification iterative calculation equation is established, the second parameter set is set as a reference parameter set to be treated as a fixed value, the first parameter set is set as a parameter set to be measured, and only the parameters in the first parameter set are identified on line. The iterative calculation method of 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, double Kalman filtering, combined Kalman filtering and the like.
In a specific embodiment, the iterative calculation method for identifying parameters in the parameter set to be tested online selects a Recursive Least Squares (RLS) method with forgetting factors. After the algorithm is initialized, continuously collecting voltage and current data of the battery, and recording the current moment as k, wherein the previous moment is k-1, the next moment is k+1, and a recursive equation of the RLS algorithm is as follows:
In the above formulas (3) - (7), y k is the system output vector at time k, For the observation vector composed of observation values at the moment K, theta k is the to-be-estimated vector containing the to-be-estimated parameter at the moment K, theta k-1 is the to-be-estimated vector containing the to-be-estimated parameter at the moment K-1, e k is the estimation error output by the system at the moment K, P k is the covariance matrix at the moment K, P k-1 is the covariance matrix at the moment K-1, K k is the gain at the moment K, lambda is the forgetting factor, and the value range is 0-1.
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 formulas (8) - (12), U t,k is the terminal voltage output by the k-time model, U 1,k is the terminal voltage across the k-time polarized internal resistance, U OCV,k is the open circuit voltage of the k-time battery, R 0,k is the ohmic internal resistance at k-time, I k is the current passing through the ohmic internal resistance at k-time, U 1,k+1 is the terminal voltage across the k+1-time polarized internal resistance, Δt is the time interval of the discretized sampling point, τ 1,k is the time constant at k-time, and R 1,k is the polarized internal resistance at k-time.
The parameters to be identified in the RLS algorithm are parameters in a first parameter group, namely an open-circuit voltage U OCV and an ohmic internal resistance R 0, wherein the terminal voltage U t,k output by a k-moment model can be obtained through actual measurement, the terminal voltages U 1,k at two ends of the k-moment polarized internal resistance can be obtained through iterative calculation of the formula (12), the polarized internal resistance R 1,k and the time constant tau 1,k which are treated as fixed values can be obtained through current SOC and temperature checking model parameter Map images, and therefore a system output vector y k and an observation vector can be obtained through the formula (8)The vector θ k to be estimated is specifically as shown in the above formulas (9) to (11).
And 500, fusion optimization is carried out on the identification results of the parameters in the obtained first parameter set, battery parameter updating coefficients are obtained, and the parameters in the first parameter set are updated.
And (3) performing fusion optimization on the parameters in the first parameter set 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 in a period of time.
In a specific embodiment, the open circuit voltage U OCV in the identification result in the step 400 is calculated for a period of time b1 to obtain an updated value of the current open circuit voltage U OCV, where b1 is a shorter period of time, and 1s may be selected. Calculating the ratio R 1 of the ohmic internal resistance R 0 and the stored value before updating in a period b2 in a time period of the history a1, calculating the average value R a1 of R 1 in the period a1, and taking the product of R a1 and the stored value of the ohmic internal resistance before updating as the updated value of the ohmic internal resistance R 0, wherein b2 is a longer period and can be selected for 1min. And updating the parameters in the first parameter set in the model by using the obtained updated values of the parameters in the first parameter set, namely the battery parameter updating coefficient, so as to finish the parameter identification and updating of the first parameter set of the battery.
When the current working condition is judged to meet the calculation update condition of the parameter identification process in the second parameter set on line, step 300 is performed to enter step 600:
Step 600, if the calculation update condition of the parameter identification process in the second parameter set is satisfied, performing online parameter identification on the parameters in the second parameter set.
When the current fluctuation in any period of time exceeds a certain condition, more specifically, when the variance of the current sampling value in the period of time is larger than the second threshold Trd 2, that is, the calculation update condition of the parameter identification process in the second parameter set is satisfied, the parameters in the first parameter set are treated as known values, and only the parameters in the second parameter set are identified.
Specifically, by discretizing a battery state equation, a parameter identification iterative calculation equation is established, a first parameter set is set as a reference parameter set to be treated as a known value, a second parameter set is set as a parameter set to be measured, and only parameters in the second parameter set are identified online. The iterative calculation method of 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, double Kalman filtering, combined Kalman filtering and the like.
In a specific embodiment, the iterative calculation method for on-line identifying parameters in the parameter set to be tested also selects a Recursive Least Squares (RLS) method with forgetting factors.
yk＝Ut,k+IkR0,k-UOCV,k (13)
θk＝[a1 a2]T (15)
In the formulas (13) - (17), y k is the system output vector at the time of k, U t,k is the terminal voltage output by the model at the time of k, I k is the current passing through the ohmic internal resistance at the time of k, R 0,k is the ohmic internal resistance at the time of k, U OCV,k is the open circuit voltage of the battery at the time of k,For the observation vector composed of the observation values at the k moment, I k-1 is the current passing through the ohmic internal resistance at the k-1 moment, y k-1 is the system output vector at the k-1 moment, θ k is the vector to be estimated containing the parameters to be estimated at the k moment, a 1、a2 is the parameters in the vector to be estimated, the open-circuit voltage U OCV and the ohmic internal resistance R 0,τ1 obtained from the step 400 are respectively time constants, Δt is the time interval of the discretization sampling point, and R 1 is the polarization internal resistance.
In step 600, the parameters to be identified in the RLS algorithm are parameters in the second parameter set, that is, the internal polarization resistance R 1, the time constant τ 1, and other parameters are treated as known values. And U OCV,k、R0,k uses the optimized updating result in the step 500, and the terminal voltage U t,k output by the k moment model and the current I k passing through the ohmic internal resistance at the k moment are obtained through actual measurement, so that the calculation result of the parameters in the second parameter set can be obtained according to the output vector, the observation vector, the expressions (13) - (15) of the vector to be estimated and the relational formulas (16) - (17) between the parameters to be identified and the vector to be estimated in the RLS algorithm.
Step 700, judging whether the current working condition meets the updating condition of the parameters in the second parameter set on line.
When it is determined on line 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 determining whether the parameters in the second parameter set meet the updated conditions includes, but is not limited to: the current generates step change in a period of time, and the current fluctuation after the step change is smaller than a certain condition. More specifically, the current produces a step change with a change value greater than the third threshold Trd 3 over a period of time, and the variance of the current sample value is less than the fourth threshold Trd 4 over a predetermined period of time after the step. The third threshold Trd 3 and the fourth threshold Trd 4 are selected with the best accuracy and stability of the identification result as targets, and the third threshold Trd 3 and the fourth threshold Trd 4 are adjusted according to the actual working conditions.
When the update condition of the parameters in the second parameter set is satisfied, step 700 proceeds to step 800:
step 800, when the update condition of the parameters in the second parameter set is satisfied, fusion optimization is performed on the obtained identification result of the parameters in the second parameter set to obtain a battery parameter update coefficient, and the parameters in the second parameter set are updated.
And when the current working condition is judged to meet the updating condition of the parameters in the second parameter set, carrying out fusion optimization on the parameters in the second parameter set calculated in the step 700, and calculating the updating coefficient 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 in a period of time.
In a specific embodiment, the ratio R 2 of the polarization internal resistance R 1 to the stored value before updating in a long period of time is calculated, the average value R a2 of R 2 in the period of time a2 is calculated, and the product of R a2 and the stored value of the polarization internal resistance before updating is used as the updated value of the polarization internal resistance R 1. Similarly, the ratio r 3 of the time constant τ 1 to the stored value before updating in a long period of time is calculated, the average value r a3 of r 3 in the period of time a3 is calculated, and the product of r a3 and the stored value of the time constant before updating is used as the updated value of the time constant τ 1, so that the online identification updated value of the parameters in the second parameter set of the battery is obtained.
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, a group of parameter groups needs to be identified once, and the battery parameter online identification method does not limit the identification sequence of the parameters in the parameter groups.
Corresponding to the above embodiment of the method, the embodiment of the present invention further provides a battery parameter online identification system, which is configured to execute the battery parameter online identification method steps in the above embodiment. The battery parameter online identification system comprises a model building module, an offline parameter acquisition module, a parameter grouping module, a working condition judging module, an identification module and an updating module.
Specifically, the model building module is used for building an equivalent circuit model of the battery.
The off-line parameter acquisition module is used for acquiring related parameters of the equivalent circuit model.
The parameter grouping module is used for dividing the parameters needing to be identified and updated online into a first parameter set and a second parameter set.
The working condition judging module is used for judging whether the current working condition meets the calculation updating condition of the parameter identification process in the first parameter set and the second parameter set.
The identification module is used for carrying out on-line parameter identification on the parameters in the first parameter set or the second parameter set based on parameter decoupling.
And the updating module is used for carrying out fusion optimization according to the on-line 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 effects brought by the system are the same as the battery parameter online identification method embodiment of the present invention, and specific content can be referred to the description in the battery parameter online identification method embodiment of the present invention, which is not repeated here.
Therefore, the battery parameter online identification system provided in the embodiment of the present disclosure can also obtain 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 conditions, respectively identify and update each group of parameters, 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 an on-line identification device for the battery parameters, which comprises the following steps: 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 realized.
In a specific embodiment, a computer program may be split into one or more modules, which are stored in memory and executed by a processor to perform embodiments of the invention. One or more of the modules may be a series of computer program instruction segments capable of performing a specific function for describing the execution of a computer program in the battery parameter online identification apparatus. 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, where each module specifically functions as follows:
The model building module is used for building an equivalent circuit model of the battery;
the off-line parameter acquisition module is used for acquiring related parameters of the equivalent circuit model;
the parameter grouping module is used for dividing the parameters needing to be identified and updated online 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 set and the second parameter set;
the identification module is used for carrying out online parameter identification on the parameters in the first parameter set or the second parameter set based on parameter decoupling;
And the updating module is used for carrying out fusion optimization according to the on-line parameter identification result and updating the battery parameters.
The battery parameter online identification device can be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud management server and the like. Those skilled in the art will appreciate that the battery parameter online identification apparatus may include, but is not limited to, a processor, a memory, more or fewer components, or some combination of components, or different components, for example, the battery parameter online identification apparatus may also include an input-output device, a network access device, a bus, etc.
The Processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor, 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 apparatus, for example, a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure digital card (Secure DIGITAL CARD, abbreviated as SD card), a flash memory card (FLASH CARD), and the like, which are provided on the battery parameter online identification apparatus. Furthermore, the memory can also comprise an internal storage unit and an external storage device of the battery parameter on-line identification device. The memory is used for storing computer programs and other programs or data required by the battery parameter on-line identification device. The memory may also be used to temporarily store data that has been output or is to be output.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, that is not described or illustrated in any embodiment, reference is made to the related descriptions of other embodiments.
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 solution. Those skilled in the art may implement the described functionality using different approaches 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, the embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program which can be executed by a processor to complete the battery parameter online 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, executable files or in some intermediate form, etc. The computer readable storage medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.
In summary, the specification discloses a method, a system and a device for on-line identification of battery parameters, which are used for decoupling parameters to be estimated, identifying the parameters in groups, determining whether to start related identification results according to current working conditions, and finally performing fusion optimization output, so that reliability of a parameter identification process and stability of results are ensured, and the problems that the stability of calculation results in a method for calculating and identifying all parameters at one time in the prior art is poor and abnormal fluctuation of the parameter identification results is easy to occur are solved.
Those of ordinary skill in the art will appreciate that: the drawing is a schematic diagram of one embodiment and the modules or flows in the drawing are not necessarily required to practice the invention.
Those of ordinary skill in the art will appreciate that: the modules in the apparatus of the embodiments may be distributed in the apparatus of the embodiments according to the description of the embodiments, or may be located in one or more apparatuses different from the present embodiments with corresponding changes. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the 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 scheme described in the foregoing embodiments can be modified or some of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. The battery parameter on-line identification method is characterized by comprising the following steps of:
Establishing a first-order RC equivalent circuit model of a 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:
（1）
（2）
In the above formulas (1), (2), For the voltage across the polarized internal resistance,/>Terminal voltage output for model,/>To pass through ohmic internal resistance/>Current of/>For polarization internal resistance,/>Is a time constant,/>，/>For polarized capacitance,/>Is an ideal voltage source,/>Is ohmic internal resistance;
Testing the characteristic working condition of the battery offline to obtain the related parameters of the equivalent circuit model; wherein the test comprises a battery HPPC test and an open circuit voltage test, and the related parameters comprise the open circuit voltage Ohmic internal resistance/>Polarization internal resistance/>Time constant/>；
Grouping model parameters according to different roles of the parameters in the first-order RC equivalent circuit model, and dividing the model parameters needing to be identified and updated online into a first parameter set and a second parameter set; wherein the parameters in the first parameter set include an open circuit voltageOhmic internal resistance/>The parameters in the second parameter group comprise polarization internal resistance/>Time constant/>；
Judging whether the current working condition meets the calculation updating condition of the parameter identification process in the first parameter set or meets the calculation updating condition of the parameter identification process in the second parameter set on line;
If the calculation updating condition of the parameter identification process in the first parameter set is met, carrying out online parameter identification on the parameters in the first parameter set;
Fusion optimization is carried out on the obtained identification results of the parameters in the first parameter set, battery parameter updating coefficients are obtained, and the parameters in the first parameter set are updated;
if the calculation updating condition of the parameter identification process in the second parameter set is met, carrying out online parameter identification on the parameters in the second parameter set;
judging whether the current working condition meets the updating condition of the parameters in the second parameter set on line;
when the updating condition of the parameters in the second parameter set is met, fusion optimization is carried out on the obtained identification result of the parameters in the second parameter set, a battery parameter updating coefficient is obtained, and the parameters in the second parameter set are updated;
Wherein, the parameter on-line parameter identification comprises:
Discretizing the battery state equation, and establishing a parameter identification iterative calculation equation;
Taking the parameters in the reference parameter set as fixed values, and utilizing the parameter identification iterative calculation equation to identify the parameters in the parameter set to be detected on line; when the reference parameter set is a first parameter set, the second parameter set is a parameter set to be measured, and when the reference parameter set is a second parameter set, the first parameter set is a parameter set to be measured;
The iterative calculation method for identifying parameters in the parameter set to be detected on line is a recursive least square method with forgetting factors, and the on-line parameter identification for the parameters in the first parameter set specifically comprises the following steps:
Algorithm initialization, acquisition Voltage and current data of the battery at the moment; the recursive equation of the recursive least squares method is:
（3）
（4）
（5）
（6）
（7）
In the above formulas (3) to (7), For/>Time immediately preceding time,/>For/>Time system output vector,/>For/>Observation vector consisting of observations at time,/>For/>Time includes to-be-estimated vector of to-be-estimated parameter,/>For/>Time includes to-be-estimated vector of to-be-estimated parameter,/>For/>Estimation error of time system output,/>For/>The covariance matrix of the time of day,For/>Covariance matrix of time,/>For/>Time gain,/>The value range of the forgetting factor is 0-1;
（8）
（9）
（10）
（11）
（12）
In the above formulas (8) - (12), For/>Next to the moment,/>For/>The terminal voltage output by the time model,For/>Terminal voltage at both ends of internal resistance of moment polarization,/>For/>Open circuit voltage of battery at time,/>For/>Ohmic internal resistance at time,/>For/>Current passing through ohmic internal resistance at any time,/>For/>Terminal voltage at both ends of internal resistance of moment polarization,/>For the time interval of discretized sampling points,/>For/>Time constant of moment,/>For/>Polarization internal resistance at moment;
Measuring and obtaining Terminal voltage output by time model/>Current through ohmic internal resistance/>Obtaining/> according to Map images of current SOC and temperature check model parametersPolarization internal resistance at time/>Time constant/>And is calculated iteratively by the formula (12) to obtain/>Terminal voltage across internal resistance of moment polarization/>；
Will beTerminal voltage output by time model/>Terminal voltage across polarized internal resistance/>Current through ohmic internal resistance/>Substituting the above formulas (8) - (11), and calculating to obtain the system output vector/>Observation vector/>Vector to be estimated/>；
The performing on-line parameter identification on the parameters in the second parameter set specifically includes:
（13）
（14）
（15）
（16）
（17）
in the above formulas (13) to (17), For/>Time system output vector,/>For/>The terminal voltage output by the time model,For/>Current passing through ohmic internal resistance at any time,/>For/>Ohmic internal resistance at time,/>For/>Open circuit voltage of battery at time,/>For/>Observation vector consisting of observations at time,/>For/>Current passing through ohmic internal resistance at any time,/>Is thatTime system output vector,/>For/>Time includes to-be-estimated vector of to-be-estimated parameter,/>、/>As parameters in the vector to be estimated (open circuit voltage/>, respectively)Ohmic internal resistance/>），/>Is a time constant,/>For the time interval of the discretized sampling points,Is polarized internal resistance;
Measuring and obtaining Terminal voltage output by time model/>And/>Current passing through ohmic internal resistance at all times/>And calculating according to the output vector, the observation vector, the expressions (13) - (15) of the vector to be estimated and the relation formulas (16) - (17) between the parameter to be identified and the vector to be estimated in the recursive least square method to obtain the calculation result of the parameters in the second parameter set.
2. The method according to claim 1, wherein the calculating and updating conditions of the parameter identification process in the first parameter set include:
the variance of the current sampling value in the historical period of time is larger than a first threshold value;
The calculation update condition of the parameter identification process in the second parameter set comprises:
the variance of the current sampling value in the historic period of time is larger than a second threshold value; the second threshold is not less than the first threshold;
The updating conditions of the parameters in the second parameter set include:
The current generates step change with a change value larger than a third threshold value in a historical period of time, and the variance of the current sampling value in a preset period of time after the step change is smaller than a fourth threshold value.
3. The battery parameter online identification method according to claim 1, 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.
4. The method for on-line identification of battery parameters according to claim 3, wherein the voltage across the open circuitThe fusion optimization of the identification result specifically comprises the following steps:
Calculate the history for a period of time Open circuit voltage/>, in internal recognition resultsObtaining the current open circuit voltage/>Is a new value of (1);
to ohmic internal resistance The fusion optimization of the identification result specifically comprises the following steps:
Computation history Period of time within a period of time/>Internal ohmic resistance/>Ratio to pre-update stored value/>And calculateWithin a period of time/>Average value/>Will/>The product of the stored value of ohmic internal resistance before updating is used as ohmic internal resistance/>Is a new value of (c).
5. A battery parameter online identification system for implementing the battery parameter online identification method of any one of claims 1 to 4, comprising:
the model building module is used for building an equivalent circuit model of the battery;
the off-line parameter acquisition module is used for acquiring related parameters of the equivalent circuit model;
The parameter grouping module is used for dividing the parameters needing to be identified and updated online into a first parameter set and a second parameter set;
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 set and the second parameter set;
the identification module is used for carrying out online parameter identification on the parameters in the first parameter set or the second parameter set based on parameter decoupling;
And the updating module is used for carrying out fusion optimization according to the on-line parameter identification result and updating the battery parameters.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111514699.XA CN114252771B (en) | 2021-12-13 | 2021-12-13 | Battery parameter online identification method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111514699.XA CN114252771B (en) | 2021-12-13 | 2021-12-13 | Battery parameter online identification method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114252771A CN114252771A (en) | 2022-03-29 |
| CN114252771B true CN114252771B (en) | 2024-05-07 |
Family
ID=80791925
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111514699.XA Active CN114252771B (en) | 2021-12-13 | 2021-12-13 | Battery parameter online identification method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114252771B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116203435A (en) * | 2023-05-06 | 2023-06-02 | 广汽埃安新能源汽车股份有限公司 | Battery parameter acquisition method and device, electronic equipment and storage medium |
Citations (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05341807A (en) * | 1992-06-05 | 1993-12-24 | Hitachi Ltd | Parameter dimension variable type identifying method and adaptive control method |
| DE19959019A1 (en) * | 1999-12-08 | 2001-06-13 | Bosch Gmbh Robert | Method for status detection of an energy store |
| DE102012210866A1 (en) * | 2011-06-28 | 2013-01-03 | Ford Global Technologies, Llc | NONLINEAR OBSERVERS FOR BATTERY LEVEL STATEMENT OF ESTIMATION |
| CN103208815A (en) * | 2013-04-02 | 2013-07-17 | 清华大学 | d-q axis parameter identification method for grid-connected inverter of photovoltaic power generation system |
| CN103700871A (en) * | 2013-12-07 | 2014-04-02 | 西南交通大学 | Control method for optimal efficiency of proton exchange membrane fuel cell system of locomotive |
| JP2014086313A (en) * | 2012-10-24 | 2014-05-12 | Calsonic Kansei Corp | Parameter identification device of continuous-time system and identification method thereof |
| WO2015188610A1 (en) * | 2014-06-11 | 2015-12-17 | 北京交通大学 | Method and device for estimating state of charge of battery |
| WO2016134496A1 (en) * | 2015-02-28 | 2016-09-01 | 北京交通大学 | Method and apparatus for estimating state of charge of lithium ion battery |
| JP2016166857A (en) * | 2015-03-06 | 2016-09-15 | 株式会社デンソー | Battery state estimation device |
| CN107390127A (en) * | 2017-07-11 | 2017-11-24 | 欣旺达电动汽车电池有限公司 | A kind of SOC estimation method |
| CN107561445A (en) * | 2016-07-01 | 2018-01-09 | 深圳市沃特玛电池有限公司 | Battery parameter on-line identification method and system |
| CN108896912A (en) * | 2017-05-15 | 2018-11-27 | 半导体组件工业公司 | Method and apparatus for determining the health status of battery |
| WO2019051956A1 (en) * | 2017-09-13 | 2019-03-21 | 山东大学 | New iterative identification method for power battery equivalent circuit model parameter |
| CN109633453A (en) * | 2018-12-28 | 2019-04-16 | 东莞钜威动力技术有限公司 | Battery parameter on-line identification method, apparatus and computer readable storage medium |
| CN109728369A (en) * | 2018-12-21 | 2019-05-07 | 上海交通大学 | The self-adaptation control method and system of system for chain type energy storage based on power battery |
| CN109781693A (en) * | 2013-12-19 | 2019-05-21 | 原子能及能源替代委员会 | Monitor the method and system of the quality of photovoltaic cell |
| WO2019156377A1 (en) * | 2018-02-07 | 2019-08-15 | 주식회사 엘지화학 | Method for estimating parameter of equivalent circuit model for battery, and battery management system |
| WO2019208924A1 (en) * | 2018-04-23 | 2019-10-31 | 삼성에스디아이주식회사 | Battery state estimation method |
| CN110456274A (en) * | 2019-08-29 | 2019-11-15 | 清华大学 | Battery impulse heats temperature rise rate estimation method |
| CN111366855A (en) * | 2020-03-19 | 2020-07-03 | 北京理工大学 | Battery equivalent circuit model disturbance-resistant parameterization method |
| AU2020103886A4 (en) * | 2020-12-04 | 2021-02-11 | Nanjing Forestry University | A Method for Estimating SOC of a Fractional-Order Kinetic Battery Considering Temperature and Hysteresis Effect |
| DE102019127384A1 (en) * | 2019-10-10 | 2021-04-15 | Bayerische Motoren Werke Aktiengesellschaft | Method for parameter estimation in an impedance model of a lithium ion cell |
| WO2021073462A1 (en) * | 2019-10-15 | 2021-04-22 | 国网浙江省电力有限公司台州供电公司 | 10 kv static load model parameter identification method based on similar daily load curves |
| WO2021099102A1 (en) * | 2019-11-20 | 2021-05-27 | Dekra Automobil Gmbh | Method for determining a state value of a traction battery |
| WO2021143592A1 (en) * | 2020-01-17 | 2021-07-22 | 华为技术有限公司 | Battery equivalent circuit model establishing method, and health state estimation method and apparatus |
| CA3169651A1 (en) * | 2020-03-12 | 2021-09-16 | Wisk Aero Llc | Real-time battery fault detection and state-of-health monitoring |
| WO2021197038A1 (en) * | 2020-03-31 | 2021-10-07 | 比亚迪股份有限公司 | Method and device for determining state of charge of battery, and battery management system |
| CN113761726A (en) * | 2021-08-19 | 2021-12-07 | 国网江苏省电力有限公司电力科学研究院 | Lithium battery parameter identification method and system |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8880253B2 (en) * | 2011-06-28 | 2014-11-04 | Ford Global Technologies, Llc | Nonlinear adaptive observation approach to battery state of charge estimation |
| US10664562B2 (en) * | 2013-02-24 | 2020-05-26 | Fairchild Semiconductor Corporation and University of Connecticut | Battery state of charge tracking, equivalent circuit selection and benchmarking |
| US20180246173A1 (en) * | 2017-02-28 | 2018-08-30 | Honeywell International Inc. | Online determination of model parameters of lead acid batteries and computation of soc and soh |
| CN109917298A (en) * | 2017-12-13 | 2019-06-21 | 北京创昱科技有限公司 | A kind of cell charge state prediction method and system |
-
2021
- 2021-12-13 CN CN202111514699.XA patent/CN114252771B/en active Active
Patent Citations (29)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05341807A (en) * | 1992-06-05 | 1993-12-24 | Hitachi Ltd | Parameter dimension variable type identifying method and adaptive control method |
| DE19959019A1 (en) * | 1999-12-08 | 2001-06-13 | Bosch Gmbh Robert | Method for status detection of an energy store |
| US6668233B1 (en) * | 1999-12-08 | 2003-12-23 | Robert Bosch Gmbh | Method for identifying the condition of an energy accumulator |
| DE102012210866A1 (en) * | 2011-06-28 | 2013-01-03 | Ford Global Technologies, Llc | NONLINEAR OBSERVERS FOR BATTERY LEVEL STATEMENT OF ESTIMATION |
| JP2014086313A (en) * | 2012-10-24 | 2014-05-12 | Calsonic Kansei Corp | Parameter identification device of continuous-time system and identification method thereof |
| CN103208815A (en) * | 2013-04-02 | 2013-07-17 | 清华大学 | d-q axis parameter identification method for grid-connected inverter of photovoltaic power generation system |
| CN103700871A (en) * | 2013-12-07 | 2014-04-02 | 西南交通大学 | Control method for optimal efficiency of proton exchange membrane fuel cell system of locomotive |
| CN109781693A (en) * | 2013-12-19 | 2019-05-21 | 原子能及能源替代委员会 | Monitor the method and system of the quality of photovoltaic cell |
| WO2015188610A1 (en) * | 2014-06-11 | 2015-12-17 | 北京交通大学 | Method and device for estimating state of charge of battery |
| WO2016134496A1 (en) * | 2015-02-28 | 2016-09-01 | 北京交通大学 | Method and apparatus for estimating state of charge of lithium ion battery |
| JP2016166857A (en) * | 2015-03-06 | 2016-09-15 | 株式会社デンソー | Battery state estimation device |
| CN107561445A (en) * | 2016-07-01 | 2018-01-09 | 深圳市沃特玛电池有限公司 | Battery parameter on-line identification method and system |
| CN108896912A (en) * | 2017-05-15 | 2018-11-27 | 半导体组件工业公司 | Method and apparatus for determining the health status of battery |
| CN107390127A (en) * | 2017-07-11 | 2017-11-24 | 欣旺达电动汽车电池有限公司 | A kind of SOC estimation method |
| WO2019051956A1 (en) * | 2017-09-13 | 2019-03-21 | 山东大学 | New iterative identification method for power battery equivalent circuit model parameter |
| WO2019156377A1 (en) * | 2018-02-07 | 2019-08-15 | 주식회사 엘지화학 | Method for estimating parameter of equivalent circuit model for battery, and battery management system |
| WO2019208924A1 (en) * | 2018-04-23 | 2019-10-31 | 삼성에스디아이주식회사 | Battery state estimation method |
| CN109728369A (en) * | 2018-12-21 | 2019-05-07 | 上海交通大学 | The self-adaptation control method and system of system for chain type energy storage based on power battery |
| CN109633453A (en) * | 2018-12-28 | 2019-04-16 | 东莞钜威动力技术有限公司 | Battery parameter on-line identification method, apparatus and computer readable storage medium |
| CN110456274A (en) * | 2019-08-29 | 2019-11-15 | 清华大学 | Battery impulse heats temperature rise rate estimation method |
| DE102019127384A1 (en) * | 2019-10-10 | 2021-04-15 | Bayerische Motoren Werke Aktiengesellschaft | Method for parameter estimation in an impedance model of a lithium ion cell |
| WO2021073462A1 (en) * | 2019-10-15 | 2021-04-22 | 国网浙江省电力有限公司台州供电公司 | 10 kv static load model parameter identification method based on similar daily load curves |
| WO2021099102A1 (en) * | 2019-11-20 | 2021-05-27 | Dekra Automobil Gmbh | Method for determining a state value of a traction battery |
| WO2021143592A1 (en) * | 2020-01-17 | 2021-07-22 | 华为技术有限公司 | Battery equivalent circuit model establishing method, and health state estimation method and apparatus |
| CA3169651A1 (en) * | 2020-03-12 | 2021-09-16 | Wisk Aero Llc | Real-time battery fault detection and state-of-health monitoring |
| CN111366855A (en) * | 2020-03-19 | 2020-07-03 | 北京理工大学 | Battery equivalent circuit model disturbance-resistant parameterization method |
| WO2021197038A1 (en) * | 2020-03-31 | 2021-10-07 | 比亚迪股份有限公司 | Method and device for determining state of charge of battery, and battery management system |
| AU2020103886A4 (en) * | 2020-12-04 | 2021-02-11 | Nanjing Forestry University | A Method for Estimating SOC of a Fractional-Order Kinetic Battery Considering Temperature and Hysteresis Effect |
| CN113761726A (en) * | 2021-08-19 | 2021-12-07 | 国网江苏省电力有限公司电力科学研究院 | Lithium battery parameter identification method and system |
Non-Patent Citations (19)
| Title |
|---|
| 一种改进的动力电池阻抗参数和荷电状态分层在线联合估计方法;黄冉军;周维;王旭;;汽车工程(08);第1000-1007页 * |
| 可重构卫星锂离子电池剩余寿命预测系统研究;周建宝;王少军;马丽萍;杨思远;彭宇;彭喜元;;仪器仪表学报;20130915(第09期);全文 * |
| 在线参数辨识和扩展卡尔曼算法的锂离子电池SOC估算研究;李博文;王顺利;于春梅;李建超;谢伟;;自动化仪表(第03期);全文 * |
| 基于RBF网络监督的电池用双向DC/DC变换器控制策略;张融悉;张春;;电气自动化;20180730(第04期);全文 * |
| 基于RLS法的锂离子电池离线参数辨识;曹铭;张越;黄菊花;;电池;20200625(03);全文 * |
| 基于Simulink的等效电路参数辨识研究;林文发;陈德旺;林松青;;现代信息科技;20191225(24);全文 * |
| 基于向量式多遗忘因子最小二乘法的城轨列车储能元件充放电参数辨识;唐佳;刘士齐;刘静雯;刘启胜;赵诣;连张翔;;武汉大学学报(工学版);20200615(06);全文 * |
| 基于改进型Thevenin模型的锂电池SOC估算研究;刘;;现代机械;20180628(03);全文 * |
| 基于模型与双卡尔曼滤波的锂电池参数辨识;刘政;黄和悦;赵振华;;桂林航天工业学院学报;20200915(第03期);全文 * |
| 基于自适应CKF的老化锂电池SOC估计;郑涛;张里;侯杨成;陈薇;;储能科学与技术;20201231(04);全文 * |
| 大容量锂电池在线参数辨识及SOC联合估计;袁赛;邓志刚;帅孟超;;电气开关(第02期);全文 * |
| 无迹卡尔曼滤波的电池荷电状态估计试验研究;赵凯;朱黎明;;汽车工程学报;20130920(05);全文 * |
| 电动汽车用动力电池组建模和参数辨识方法;熊瑞;何洪文;许永莉;何银;;吉林大学学报(工学版);20120715(04);全文 * |
| 适用于双馈风电场联络线的距离保护方案;杨增力;孔祥平;王力军;张哲;周虎兵;;电工技术学报;20161225(第24期);全文 * |
| 采用等效电路的参数自适应电池模型及电池荷电状态估计方法;宁博;徐俊;曹秉刚;杨晴霞;王斌;许广灿;;西安交通大学学报;20151231(10);全文 * |
| 采用非线性最小二乘法的超级电容等效电路模型参数辨识;王知雨;王斌;王朝晖;;西安交通大学学报;20201231(04);全文 * |
| 铅酸电池模型及参数辨识研究;张彦琴;郭凯;刘汉雨;;蓄电池;20130620(03);全文 * |
| 锂电池等效模型建立与参数辨识方法研究;皇甫海文;韩艾呈;;电气开关;20200615(03);全文 * |
| 锂离子电池等效电路模型及参数辨识方法研究;曹丽鹏;谢阳;李玲玲;李玲玲;;电气时代;20170210(02);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114252771A (en) | 2022-03-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Huang et al. | Robustness evaluation of extended and unscented Kalman filter for battery state of charge estimation | |
| Yang et al. | State-of-health estimation for the lithium-ion battery based on support vector regression | |
| CN111581904B (en) | Lithium battery SOC and SOH collaborative estimation method considering cycle number influence | |
| CN106842060A (en) | A kind of electrokinetic cell SOC estimation method and system based on dynamic parameter | |
| CN111679199B (en) | Lithium ion battery SOC estimation method and device | |
| CN111505506A (en) | Battery SOC estimation method integrating multi-scale Kalman filtering and unscented Kalman filtering | |
| JP7157909B2 (en) | Battery capacity estimation method and battery capacity estimation device | |
| CN113156321B (en) | Estimation method of lithium ion battery state of charge (SOC) | |
| Solomon et al. | State of charge estimation of Lithium-ion battery using an improved fractional-order extended Kalman filter | |
| Zhou et al. | Peak power prediction for series-connected LiNCM battery pack based on representative cells | |
| Ouyang et al. | A novel state of charge estimation method for lithium-ion batteries based on bias compensation | |
| CN114729967A (en) | Method for estimating the state of an energy store | |
| CN113466725B (en) | Method and device for determining state of charge of battery, storage medium and electronic equipment | |
| CN114252771B (en) | Battery parameter online identification method and system | |
| Havangi | Adaptive robust unscented Kalman filter with recursive least square for state of charge estimation of batteries | |
| Ren et al. | Novel strategy based on improved Kalman filter algorithm for state of health evaluation of hybrid electric vehicles Li-ion batteries during short-and longer term operating conditions | |
| CN116203490A (en) | Sensor fault diagnosis method, device, equipment and storage medium | |
| CN115407205A (en) | Multi-time scale SOC and SOH collaborative estimation method considering temperature influence | |
| Liu et al. | An improved adaptive cubature H‑infinity filter for state of charge estimation of lithium‑ion battery | |
| Xu et al. | A dual‐rate sampled multiple innovation adaptive extended Kalman filter algorithm for state of charge estimation | |
| Venugopal et al. | State-of-charge estimation methods for Li-ion batteries in electric vehicles | |
| Sugashini et al. | Hybrid Deep Learning Algorithm for the State of Charge Prediction of the Lithium-Ion Battery for Electric Vehicles | |
| CN116449219A (en) | Lithium battery model parameter identification method for improving VFFRLS | |
| CN113933725B (en) | Method for determining state of charge of power battery based on data driving | |
| CN112213644B (en) | Battery state of charge estimation method and battery management system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |