CN113466728B - Method and system for online identification of two-stage battery model parameters - Google Patents
Method and system for online identification of two-stage battery model parameters Download PDFInfo
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Abstract
The invention provides a two-stage battery model parameter online identification method, which comprises the steps of firstly dividing the accumulated charge/discharge volume percentage at each moment in sequence according to the charge/discharge time of a battery to be detected to obtain the accumulated charge/discharge volume percentage at any moment in each time period; then, obtaining the open-circuit voltage at any time in the current time period and a system state equation in the current time period according to the open-circuit voltage at the initial time; and finally, analyzing a system state equation in the current time period to obtain battery model parameters in the current time period. According to the invention, the open-circuit voltage at the ending moment in the current time period is used as the open-circuit voltage at the initial moment in the next time period, the model parameters of each time period are identified by using a recurrence method, so that the model parameter change caused by battery aging can be corrected, and the accuracy of the whole period calculation is ensured. The invention also provides a two-stage battery model parameter online identification system.
Description
Technical Field
The invention belongs to the technical field of battery model parameter identification, and particularly relates to a method and a system for two-stage battery model parameter online identification.
Background
In recent years, with the development of new national energy and the improvement of power supply reliability, an energy storage power station for storing electric power by using an ultra-large battery pack is used as a supporting technology of intelligent energy of the intelligent power grid and the Internet, so that the rapid development is brought forward. The lithium ion battery has the remarkable advantages of high stability, large capacity, long service life, environmental protection and the like, and is widely applied to energy storage power stations. The internal resistance, the polarized capacitance and the polarized network voltage of the lithium ion battery are key parameters for representing the health state of the battery, and in order to ensure the safe operation of the battery in an energy storage power station and perform effective energy management and state evaluation, the on-line parameter identification of the battery of the energy storage power station is necessary.
The idea of battery model parameter identification is essentially to estimate terminal voltage based on a circuit equation by utilizing data such as current, voltage, temperature and the like measured by a battery in real time, correct model parameters according to errors of the estimated battery terminal voltage and the actually measured terminal voltage, and gradually reduce estimated deviation so that the estimated model parameters are converged to a true value.
The current method for testing the internal resistance of the battery mainly comprises the following steps: open circuit voltage method, direct current discharge method, and alternating current test method. The open-circuit voltage method estimates the internal resistance through the voltage of the battery, but the accuracy is obviously reduced in the state of battery power shortage; the direct current discharging method is to inject a relatively large constant direct current into the battery, measure the voltage at two ends of the battery, and calculate the current internal resistance by using the voltage and current at the moment; the ac test method calculates the internal resistance by obtaining the voltage across the battery using alternating current of low frequency.
The internal resistance is used as one of important indexes of the use state of the battery, the ohmic internal resistance and the polarization internal resistance are accurately identified and calculated, the degradation degree of the battery of the energy storage station can be timely found, and the accident potential is reduced to the minimum. The direct current discharging method is used for measuring the internal resistance when the battery is static or offline, and the battery is charged immediately after the test is finished, so that the on-line test cannot be performed. Although the AC test method can perform online test and does not generate potential safety hazard, the charging state of the battery is required to be changed for the energy storage power station, and the AC test cannot be finished well obviously; in addition, the alternating current test method is designed to denoise ripple current and other interferences on a circuit, so that the execution difficulty is greatly increased.
The conventional RLS method generally performs simplification processing on the assumption that the open circuit voltage is not changed in a unit sampling interval, affects the identification accuracy, and needs to enhance the drawing of the open circuit voltage variation. The prior method for determining the open circuit voltage is to obtain the SOC through an ampere-hour integration method, and then obtain the corresponding ocv through the query of an SOC-ocv table. Because of the nonlinearity of the SOC-ocv curve, if the analysis expression needs up to 6 times of polynomials, the number of identification parameters is greatly increased, and the difficulty of online identification is increased. And the SOC-ocv curves of different batteries are different and change with the aging of the batteries, so that the complexity and difficulty of the problem are increased.
Disclosure of Invention
The invention aims to provide a method and a system for on-line identification of two-stage battery model parameters, which aim to solve the problems of low accuracy and complex calculation process of battery model parameters calculated by the existing battery parameter identification method.
In order to achieve the above purpose, the invention adopts the following technical scheme: a two-stage battery model parameter online identification method comprises the following steps:
step 1: acquiring continuous charge/discharge data of a battery to be tested;
step 2: obtaining the accumulated charge/discharge quantity percentage of each moment according to the continuous charge/discharge data of the battery to be tested;
step 3: dividing the accumulated charge/discharge volume percentage of each moment in sequence according to the charge/discharge time of the battery to be detected to obtain the accumulated charge/discharge volume percentage of any moment in each time period;
step 4: acquiring an open-circuit voltage at an initial moment in a current time period;
step 5: obtaining the open-circuit voltage at any time in the current time period according to the open-circuit voltage at the initial time;
step 6: obtaining a system state equation in the current time period according to the open-circuit voltage at any time in the current time period and the accumulated charge/discharge quantity percentage at each time;
step 7: analyzing a system state equation in the current time period to obtain battery model parameters in the current time period;
step 8: and (4) taking the open-circuit voltage at the ending moment in the current time period as the open-circuit voltage at the initial moment in the next time period, and returning to the step (4).
Preferably, the step 2: obtaining the accumulated charge/discharge amount percentage of each moment according to the continuous charge/discharge data of the battery to be tested, wherein the method comprises the following steps:
integrating current data in the continuous charge/discharge data of the battery to be tested by using an ampere-hour integration method to obtain the accumulated charge/discharge quantity percentage at each moment; wherein the cumulative charge/discharge amount percentage at each time is:
wherein C is k Representing the cumulative charge/discharge amount percentage at time k, Δt is the data sampling step size, I L,k For current at time k, C rated Is the rated capacity of the battery.
Preferably, the open circuit voltage at any time in the current time period is:
wherein U is ocv Represents the open-circuit voltage of the battery, P represents the proportionality coefficient, SOC represents the charge state of the battery, U ocv，k Represents the open cell voltage at time k,representing the open circuit voltage at the initial time during the current time period.
Preferably, the step 6: obtaining a system state equation in the current time period according to the open-circuit voltage at any time in the current time period and the accumulated charge/discharge quantity percentages at all times, wherein the system state equation comprises the following components:
step 6.1: constructing a transfer function of a first-order RC circuit model of the battery by using the first-order circuit model; wherein the transfer function is:
wherein I is L (s) represents current, R 0 Represents ohmic internal resistance, R 1 Representing internal resistance of polarization, C 1 Representing polarization capacitance, s representing the mapping of time variable t in the frequency domain;
step 6.2: obtaining a battery terminal voltage formula in the current time period according to the transfer function and the open-circuit voltage at any moment in the current time period;
step 6.3: and obtaining a system state equation in the current time period according to the battery terminal voltage formula in the current time period.
Preferably, the battery terminal voltage formula in the current time period is:
wherein the method comprises the steps of，U t,k Representing the battery terminal voltage at time k in the current period of time c 1 Representing the first coefficient to be solved, c 2 Representing the second coefficient to be solved, c 3 Representing the third coefficient to be solved, U t,k-1 Represents the battery terminal voltage at time k-1 in the current period of time, I L,k Indicating the current at time k in the current period, I L,k-1 Representing the current at time k-1 in the present time period.
Preferably, the system state equation in the current time period is:
y k ＝Φ k Θ k
wherein phi is k Representing a data matrix, Θ k Representing a parameter matrix.
Preferably, the step 7: analyzing the system state equation in the current time period to obtain battery model parameters in the current time period, wherein the method comprises the following steps:
step 7.1: identifying a parameter matrix by utilizing a least square recursion method according to a system state equation in the current time period to obtain an identification result;
step 7.2: and adopting a formula according to the identification result:
and obtaining the battery model parameters in the current time period.
The invention also provides a two-stage battery model parameter online identification system, which comprises:
the charging/discharging data acquisition module is used for acquiring continuous charging/discharging data of the battery to be tested;
the accumulated charge/discharge quantity percentage calculation module is used for obtaining the accumulated charge/discharge quantity percentage at each moment according to the continuous charge/discharge data of the battery to be tested;
the charge/discharge data dividing module is used for sequentially dividing the accumulated charge/discharge volume percentage at each moment according to the charge/discharge time of the battery to be detected to obtain the accumulated charge/discharge volume percentage at any moment in each time period;
the initial time open circuit voltage acquisition module is used for acquiring the open circuit voltage at the initial time in the current time period;
the open circuit voltage calculation module is used for obtaining the open circuit voltage at any time in the current time period according to the open circuit voltage at the initial time;
the system state equation construction module is used for obtaining a system state equation in the current time period according to the open-circuit voltage at any time in the current time period and the accumulated charge/discharge quantity percentage at each time;
the battery model parameter calculation module is used for analyzing the system state equation in the current time period to obtain battery model parameters in the current time period;
and the return module is used for taking the open-circuit voltage at the ending moment in the current time period as the open-circuit voltage at the initial moment in the next time period, and returning to the initial moment open-circuit voltage acquisition module.
The method and the system for on-line identification of the two-stage battery model parameters have the beneficial effects that: compared with the prior art, the method for on-line identification of the two-stage battery model parameters comprises the steps of firstly obtaining the accumulated charge/discharge quantity percentage at each moment according to continuous charge/discharge data of a battery to be detected; dividing the accumulated charge/discharge volume percentage of each moment in sequence according to the charge/discharge time of the battery to be detected to obtain the accumulated charge/discharge volume percentage of any moment in each time period; then, obtaining the open-circuit voltage at any time in the current time period and a system state equation in the current time period according to the open-circuit voltage at the initial time; and finally, analyzing a system state equation in the current time period to obtain battery model parameters in the current time period. According to the invention, the open-circuit voltage at the ending moment in the current time period is used as the open-circuit voltage at the initial moment in the next time period, the model parameters of each time period are identified by using a recurrence method, so that the model parameter change caused by battery aging can be corrected, and the accuracy of the whole period calculation is ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can 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 identification of two-stage battery model parameters according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a method for online identifying parameters of a two-stage battery model according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a first-order circuit model according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention aims to provide a method and a system for on-line identification of two-stage battery model parameters, which aim to solve the problems of low accuracy and complex calculation process of battery model parameters calculated by the existing battery parameter identification method.
Referring to fig. 1-2, in order to achieve the above objective, the present invention adopts the following technical scheme: a two-stage battery model parameter online identification method comprises the following steps:
s1: acquiring continuous charge/discharge data of a battery to be tested;
the method for calculating the internal resistance of the battery comprises the steps of constructing a data vector according to data such as the voltage, the current and the temperature of the battery, and considering that a ocv-SOC curve can be approximately a straight line in a shorter SOC interval, wherein the change amount of ocv is in direct proportion to the change amount of the SOC; based on the assumption, a battery model parameter identification method is provided.
S2: obtaining the accumulated charge/discharge quantity percentage of each moment according to the continuous charge/discharge data of the battery to be tested;
s2 comprises the following steps:
integrating current data in continuous charge/discharge data of the battery to be tested by using an ampere-hour integration method to obtain accumulated charge/discharge quantity percentages at all moments; wherein the cumulative charge/discharge amount percentage at each time is:
wherein C is k Representing the cumulative charge/discharge amount percentage at time k, Δt is the data sampling step size, I L,k For current at time k, C rated Is the rated capacity of the battery.
In practical application, firstly, continuous charge/discharge data of a battery is selected, and current data is integrated by utilizing an ampere-hour integration method to obtain a cumulative charge/discharge quantity percentage C at each time k from an initial time k For characterizing the amount of change in SOC.
S3: dividing the accumulated charge/discharge volume percentage of each moment in sequence according to the charge/discharge time of the battery to be detected to obtain the accumulated charge/discharge volume percentage of any moment in each time period;
in the invention, a plurality of data fragments can be obtained by dividing the fragments based on the percentage of charge and discharge amount, the capacity of a single data fragment is 5% -10% of the nominal capacity of the battery, and the interval length is slightly shorter (such as 5% of the nominal capacity) in a low SOC interval due to larger variation of battery model parameters; the interval parameter variation of the high SOC is not large, and the interval length can be slightly longer. And carrying out parameter identification by using a least square recursion method for each data segment (the cumulative charge/discharge amount percentage at any time in each time period).
S4: acquiring an open-circuit voltage at an initial moment in a current time period;
s5: obtaining the open-circuit voltage at any time in the current time period according to the open-circuit voltage at the initial time;
the open circuit voltage at any time in the current time period is as follows:
wherein U is ocv Represents the open-circuit voltage of the battery, P represents the proportionality coefficient, SOC represents the charge state of the battery, U ocv，k Represents the open cell voltage at time k,representing the open circuit voltage at the initial time during the current time period.
Specifically, given an initial value of open circuit voltage at time k=0Since within the data segment can be approximated as(where P is a constant within the data segment), the open circuit voltage at any time is +.>
S6: obtaining a system state equation in the current time period according to the open-circuit voltage at any time in the current time period and the accumulated charge/discharge quantity percentage at each time;
s6 comprises the following steps:
s6.1: constructing a transfer function of a first-order RC circuit model of the battery by using the first-order circuit model;
in the present invention, as shown in fig. 3, the first-order circuit model may be described as: ohmic internal resistance R 0 Polarization impedance R 1 ,C 1 Current I L Open circuit voltage U of battery ocv Battery terminal voltage U t Polarized network voltage U 1 The ohmic internal resistance is composed of electrode material, diaphragm and contact part resistance, and the polarized resistance is formed by polarization during positive and negative electrochemical reaction. The differential equation of the model is:
discretizing the differential equation of the model to obtain the following steps:
U 1,k+1 ＝D·U 1,k +(1-D)·I L,k ·R 1
U ocv,k+1 ＝U t,k+1 +U 1,k+1 +I L,k+1 ·R 0
wherein d=exp (- Δt/R) 1 ·C 1 ) For data of a fixed step size, it can be considered as a constant.
Definition E L (s)＝U t (s)-U ocv (s) obtaining a transfer function of the system in a frequency domain by carrying out Law transformation, wherein the transfer function of the first-order RC circuit model is as follows:
wherein I is L(s) Represents current, R 0 Represents ohmic internal resistance, R 1 Representing internal resistance of polarization, C 1 Representing the polarization capacitance, s representing the mapping of the time variable t in the frequency domain.
S6.2: obtaining a battery terminal voltage formula in the current time period according to the transfer function and the open-circuit voltage at any time in the current time period;
specifically, transfer functions are firstly converted into discrete time sequences through bilinear variation, and the discrete time sequences are obtained:
E L,k ＝c 1 E L,k-1 +c 2 I L,k +c 3 I L,k-1
further processing to obtain the following components:
U t,k ＝U ocv,k -c 1 U ocv,k-1 +c 1 U t,k-1 +c 2 I L,k +c 3 I L,k-1
finally, combining the open-circuit voltage value mapped by the ampere-hour integral to obtain a battery terminal voltage formula in the current time period, wherein the battery terminal voltage formula is as follows:
wherein U is t,k Representing the battery terminal voltage at time k in the current period of time c 1 Representation, c 2 Representation, c 3 Representation, U t,k-1 Represents the battery terminal voltage at time k-1 in the current period of time, I L,k Indicating the current at time k in the current period, I L,k-1 Representing the current at time k-1 in the present time period.
S6.3: taking the sampling time interval of the voltage and current data under the actual working condition into consideration, and obtaining a discretization processing system state equation in the current time period according to the battery terminal voltage formula in the current time period.
The system state equation in the current time period is:
y k ＝Φ k Θ k
wherein phi is k Representing a data matrix, Θ k Representing a parameter matrix.
S7: analyzing a system state equation in the current time period to obtain battery model parameters in the current time period;
s7 comprises the following steps:
s7.1: identifying the parameter matrix by using a least square recursion method according to a system state equation in the current time period to obtain an identification result;
s7.2: the formula is adopted according to the identification result:
and obtaining the battery model parameters in the current time period.
In the invention, S1-S7 are recursions of the first stage of the invention.
S8: and taking the open circuit voltage at the ending moment in the current time period as the open circuit voltage at the initial moment in the next time period, and returning to S4.
The second stage recursion in the invention is as follows: according to the method (S4-S7), the model parameters of each data segment are identified by using a recursive algorithm, wherein the initial open circuit voltage value of each segment is the open circuit voltage value of the last data segment at the end of the segment obtained according to the identified model parameters, namely:
open circuit voltages at the beginning and end of the nth segment, respectively, the open circuit voltages of adjacent two segments have the following relationship: />According to this relationship, the open circuit voltage recurrence result of the previous segment can be taken as the open circuit voltage initial value of the next segment.
By the method, the identification result of the last segment can be continuously inherited, the deviation can be corrected step by step, and the accurate battery model parameters in each time period can be obtained.
Compared with the prior art, the method for optimizing the battery parameters reduces the iterative times of calculation, improves the operation instantaneity, can ensure the accuracy of model calculation, considers the influence of the change of the model parameters on the algorithm in the battery aging process, and ensures the accuracy of the algorithm in the whole life cycle operation of the battery.
The invention also provides a two-stage battery model parameter online identification system, which comprises:
the charging/discharging data acquisition module is used for acquiring continuous charging/discharging data of the battery to be tested;
the accumulated charge/discharge quantity percentage calculation module is used for obtaining the accumulated charge/discharge quantity percentage at each moment according to the continuous charge/discharge data of the battery to be detected;
the charge/discharge data dividing module is used for sequentially dividing the accumulated charge/discharge quantity percentages at all times according to the charge/discharge time of the battery to be detected to obtain the accumulated charge/discharge quantity percentages at any time in all time periods;
the initial time open circuit voltage acquisition module is used for acquiring the open circuit voltage at the initial time in the current time period;
the open circuit voltage calculation module is used for obtaining the open circuit voltage at any time in the current time period according to the open circuit voltage at the initial time;
the system state equation construction module is used for obtaining a system state equation in the current time period according to the open-circuit voltage at any time in the current time period and the accumulated charge/discharge quantity percentage at each time;
the battery model parameter calculation module is used for analyzing a system state equation in the current time period to obtain battery model parameters in the current time period;
and the return module is used for taking the open-circuit voltage at the ending moment in the current time period as the open-circuit voltage at the initial moment in the next time period, and returning to the open-circuit voltage acquisition module at the initial moment.
The invention discloses a method and a system for on-line identification of two-stage battery model parameters, wherein the method for on-line identification of the two-stage battery model parameters comprises the steps of firstly obtaining accumulated charge/discharge quantity percentages at all moments according to continuous charge/discharge data of a battery to be detected; dividing the accumulated charge/discharge volume percentage of each moment in sequence according to the charge/discharge time of the battery to be detected to obtain the accumulated charge/discharge volume percentage of any moment in each time period; then, obtaining the open-circuit voltage at any time in the current time period and a system state equation in the current time period according to the open-circuit voltage at the initial time; and finally, analyzing a system state equation in the current time period to obtain battery model parameters in the current time period. According to the invention, the open-circuit voltage at the ending moment in the current time period is used as the open-circuit voltage at the initial moment in the next time period, the model parameters of each time period are identified by using a recurrence method, so that the model parameter change caused by battery aging can be corrected, and the accuracy of the whole period calculation is ensured.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (2)
1. The method for on-line identification of the two-stage battery model parameters is characterized by comprising the following steps:
step 1: acquiring continuous charge/discharge data of a battery to be tested;
step 2: obtaining the accumulated charge/discharge quantity percentage of each moment according to the continuous charge/discharge data of the battery to be tested;
the step 2: obtaining the accumulated charge/discharge amount percentage of each moment according to the continuous charge/discharge data of the battery to be tested, wherein the method comprises the following steps:
integrating current data in the continuous charge/discharge data of the battery to be tested by using an ampere-hour integration method to obtain the accumulated charge/discharge quantity percentage at each moment; wherein the cumulative charge/discharge amount percentage at each time is:
wherein C is k Representing the cumulative charge/discharge amount percentage at time k, Δt is the data sampling step size, I L,k For current at time k, C rated Is the rated capacity of the battery;
step 3: dividing the accumulated charge/discharge volume percentage of each moment in sequence according to the charge/discharge time of the battery to be detected to obtain the accumulated charge/discharge volume percentage of any moment in each time period;
step 4: acquiring an open-circuit voltage at an initial moment in a current time period;
step 5: obtaining the open-circuit voltage at any time in the current time period according to the open-circuit voltage at the initial time;
the open circuit voltage at any time in the current time period is as follows:
wherein U is ocv Represents the open-circuit voltage of the battery, P represents the proportionality coefficient, SOC represents the charge state of the battery, U ocv，k Represents the open cell voltage at time k,an open circuit voltage representing an initial time within a current time period;
step 6: obtaining a system state equation in the current time period according to the open-circuit voltage at any time in the current time period and the accumulated charge/discharge quantity percentage at each time;
the step 6: obtaining a system state equation in the current time period according to the open-circuit voltage at any time in the current time period and the accumulated charge/discharge quantity percentages at all times, wherein the system state equation comprises the following components:
step 6.1: constructing a transfer function of a first-order RC circuit model of the battery by using the first-order circuit model; wherein the transfer function is:
wherein I is L(s) Represents current, R 0 Represents ohmic internal resistance, R 1 Representing internal resistance of polarization, C 1 Representing the polarization capacitance, s representing the time variation t in the frequency domainIs mapped to;
step 6.2: obtaining a battery terminal voltage formula in the current time period according to the transfer function and the open-circuit voltage at any moment in the current time period;
step 6.3: obtaining a system state equation in the current time period according to the battery terminal voltage formula in the current time period;
the battery terminal voltage formula in the current time period is as follows:
wherein U is t,k Representing the battery terminal voltage at time k in the current period of time c 1 Representing the first coefficient to be solved, c 2 Representing the second coefficient to be solved, c 3 Representing the third coefficient to be solved, U t,k-1 Represents the battery terminal voltage at time k-1 in the current period of time, I L,k Indicating the current at time k in the current period, I L,k-1 Representing the current at time k-1 in the current time period;
step 7: analyzing a system state equation in the current time period to obtain battery model parameters in the current time period; the system state equation in the current time period is as follows:
y k ＝Φ k Θ k
wherein phi is k Representing a data matrix, Θ k Representing a parameter matrix;
the step 7: analyzing the system state equation in the current time period to obtain battery model parameters in the current time period, wherein the method comprises the following steps:
step 7.1: identifying a parameter matrix by utilizing a least square recursion method according to a system state equation in the current time period to obtain an identification result;
step 7.2: and adopting a formula according to the identification result:
obtaining battery model parameters in the current time period;
step 8: and (4) taking the open-circuit voltage at the ending moment in the current time period as the open-circuit voltage at the initial moment in the next time period, returning to the step (4) to continuously inherit the identification result of the last segment, and gradually correcting the deviation to obtain the battery model parameters in each time period.
2. A two-stage battery model parameter on-line identification system, comprising:
the charging/discharging data acquisition module is used for acquiring continuous charging/discharging data of the battery to be tested;
the accumulated charge/discharge quantity percentage calculation module is used for obtaining the accumulated charge/discharge quantity percentage at each moment according to the continuous charge/discharge data of the battery to be tested;
the method for obtaining the accumulated charge/discharge amount percentage at each moment according to the continuous charge/discharge data of the battery to be tested comprises the following steps:
integrating current data in the continuous charge/discharge data of the battery to be tested by using an ampere-hour integration method to obtain the accumulated charge/discharge quantity percentage at each moment; wherein the cumulative charge/discharge amount percentage at each time is:
wherein C is k Representing the cumulative charge/discharge amount percentage at time k, Δt is the data sampling step size, I L,k For current at time k, C rated Is the rated capacity of the battery;
the charge/discharge data dividing module is used for sequentially dividing the accumulated charge/discharge volume percentage at each moment according to the charge/discharge time of the battery to be detected to obtain the accumulated charge/discharge volume percentage at any moment in each time period;
the initial time open circuit voltage acquisition module is used for acquiring the open circuit voltage at the initial time in the current time period;
the open circuit voltage calculation module is used for obtaining the open circuit voltage at any time in the current time period according to the open circuit voltage at the initial time;
the open circuit voltage at any time in the current time period is as follows:
wherein U is ocv Represents the open-circuit voltage of the battery, P represents the proportionality coefficient, SOC represents the charge state of the battery, U ocv，k Represents the open cell voltage at time k,an open circuit voltage representing an initial time within a current time period;
the system state equation construction module is used for obtaining a system state equation in the current time period according to the open-circuit voltage at any time in the current time period and the accumulated charge/discharge quantity percentage at each time;
the step of obtaining a system state equation in the current time period according to the open-circuit voltage at any time in the current time period and the accumulated charge/discharge quantity percentages at all times, comprises the following steps:
step 6.1: constructing a transfer function of a first-order RC circuit model of the battery by using the first-order circuit model; wherein the transfer function is:
wherein I is L(s) Represents current, R 0 Represents ohmic internal resistance, R 1 Representing internal resistance of polarization, C 1 Representing polarization capacitance, s representing the mapping of time variable t in the frequency domain;
step 6.2: obtaining a battery terminal voltage formula in the current time period according to the transfer function and the open-circuit voltage at any moment in the current time period;
step 6.3: obtaining a system state equation in the current time period according to the battery terminal voltage formula in the current time period;
the battery terminal voltage formula in the current time period is as follows:
wherein U is t,k Representing the battery terminal voltage at time k in the current period of time c 1 Representing the first coefficient to be solved, c 2 Representing the second coefficient to be solved, c 3 Representing the third coefficient to be solved, U t,k-1 Represents the battery terminal voltage at time k-1 in the current period of time, I L,k Indicating the current at time k in the current period, I L,k-1 Representing the current at time k-1 in the current time period;
the battery model parameter calculation module is used for analyzing the system state equation in the current time period to obtain battery model parameters in the current time period;
the system state equation in the current time period is as follows:
y k ＝Φ k Θ k
wherein phi is k Representing a data matrix, Θ k Representing a parameter matrix;
step 7: analyzing the system state equation in the current time period to obtain battery model parameters in the current time period, wherein the method comprises the following steps:
step 7.1: identifying a parameter matrix by utilizing a least square recursion method according to a system state equation in the current time period to obtain an identification result;
step 7.2: and adopting a formula according to the identification result:
obtaining battery model parameters in the current time period;
and the return module is used for taking the open-circuit voltage at the ending moment in the current time period as the open-circuit voltage at the initial moment in the next time period, and returning to the open-circuit voltage acquisition module at the initial moment so as to continuously inherit the identification result of the last segment, and gradually correcting the deviation to obtain the battery model parameters in each time period.
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Citations (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6388447B1 (en) * | 2000-11-07 | 2002-05-14 | Moltech Power Systems, Inc. | Method and apparatus for battery fuel gauging |
| KR20120082965A (en) * | 2011-01-16 | 2012-07-25 | 김득수 | The measurment method of battery soc |
| JP2013108919A (en) * | 2011-11-24 | 2013-06-06 | Toyota Industries Corp | Soc estimator |
| CN103529396A (en) * | 2013-10-25 | 2014-01-22 | 重庆长安汽车股份有限公司 | Estimating method for initial value of stage of charge of high-accuracy lithium ion battery |
| CN105319515A (en) * | 2015-11-18 | 2016-02-10 | 吉林大学 | A combined estimation method for the state of charge and the state of health of lithium ion batteries |
| CN106842060A (en) * | 2017-03-08 | 2017-06-13 | 深圳市海云图新能源有限公司 | A kind of electrokinetic cell SOC estimation method and system based on dynamic parameter |
| CN107368619A (en) * | 2017-06-02 | 2017-11-21 | 华南理工大学 | EKF SOC estimation method based on battery hysteresis voltage characteristic and resilience voltage characteristic |
| 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 |
| CN107957562A (en) * | 2018-01-05 | 2018-04-24 | 广西大学 | A kind of lithium ion battery residual life on-line prediction method |
| CN107991623A (en) * | 2017-11-27 | 2018-05-04 | 山东大学 | It is a kind of to consider temperature and the battery ampere-hour integration SOC methods of estimation of degree of aging |
| CN108776308A (en) * | 2018-04-26 | 2018-11-09 | 北京长城华冠汽车科技股份有限公司 | Estimate the method and device of battery remaining power |
| CN110187282A (en) * | 2019-06-03 | 2019-08-30 | 珠海东帆科技有限公司 | Battery charge state evaluation method and estimation device |
| CN110208703A (en) * | 2019-04-24 | 2019-09-06 | 南京航空航天大学 | The method that compound equivalent-circuit model based on temperature adjustmemt estimates state-of-charge |
| CN110244236A (en) * | 2019-05-16 | 2019-09-17 | 深圳猛犸电动科技有限公司 | A kind of lithium ion battery packet SOC estimation method, device and terminal device |
| CN110531274A (en) * | 2019-08-26 | 2019-12-03 | 江西优特汽车技术有限公司 | A kind of power battery SOC Prediction System and method |
| CN110673045A (en) * | 2019-10-30 | 2020-01-10 | 无锡凌博电子技术有限公司 | Battery charging and discharging data processing method |
| CN110687468A (en) * | 2018-06-19 | 2020-01-14 | 华为技术有限公司 | Method and device for estimating state of charge of battery |
| CN110967647A (en) * | 2019-06-24 | 2020-04-07 | 宁德时代新能源科技股份有限公司 | Charge state correction method and device |
| CN111048857A (en) * | 2019-12-20 | 2020-04-21 | 宁德时代新能源科技股份有限公司 | OCV-SOC curve updating method of battery pack, battery management system and vehicle |
| CN111289911A (en) * | 2020-04-03 | 2020-06-16 | 深圳天邦达新能源技术有限公司 | SOC estimation method and device based on battery and electronic equipment |
| CN111781504A (en) * | 2020-08-03 | 2020-10-16 | 北京理工大学 | Method for identifying aging state and reconstructing open-circuit voltage of lithium ion power battery |
| CN112180281A (en) * | 2020-09-02 | 2021-01-05 | 傲普(上海)新能源有限公司 | Estimation method of SOC of battery pack |
| CN112433155A (en) * | 2019-08-26 | 2021-03-02 | 南京理工大学 | Lithium ion battery SOC estimation algorithm based on parameter online estimation |
| CN113009360A (en) * | 2019-12-20 | 2021-06-22 | 恒大新能源技术(深圳)有限公司 | Lithium battery SOC-OCV testing method and device and terminal equipment |
| CN113009361A (en) * | 2021-03-13 | 2021-06-22 | 福州大学 | Battery state of charge estimation method based on open circuit voltage calibration |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11035902B2 (en) * | 2017-10-10 | 2021-06-15 | Integrated Device Technology, Inc. | Advanced fuel gauge |
-
2021
- 2021-07-13 CN CN202110788623.XA patent/CN113466728B/en active Active
Patent Citations (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6388447B1 (en) * | 2000-11-07 | 2002-05-14 | Moltech Power Systems, Inc. | Method and apparatus for battery fuel gauging |
| KR20120082965A (en) * | 2011-01-16 | 2012-07-25 | 김득수 | The measurment method of battery soc |
| JP2013108919A (en) * | 2011-11-24 | 2013-06-06 | Toyota Industries Corp | Soc estimator |
| CN103529396A (en) * | 2013-10-25 | 2014-01-22 | 重庆长安汽车股份有限公司 | Estimating method for initial value of stage of charge of high-accuracy lithium ion battery |
| CN105319515A (en) * | 2015-11-18 | 2016-02-10 | 吉林大学 | A combined estimation method for the state of charge and the state of health of lithium ion batteries |
| CN106842060A (en) * | 2017-03-08 | 2017-06-13 | 深圳市海云图新能源有限公司 | A kind of electrokinetic cell SOC estimation method and system based on dynamic parameter |
| CN107368619A (en) * | 2017-06-02 | 2017-11-21 | 华南理工大学 | EKF SOC estimation method based on battery hysteresis voltage characteristic and resilience voltage characteristic |
| 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 |
| CN107991623A (en) * | 2017-11-27 | 2018-05-04 | 山东大学 | It is a kind of to consider temperature and the battery ampere-hour integration SOC methods of estimation of degree of aging |
| CN107957562A (en) * | 2018-01-05 | 2018-04-24 | 广西大学 | A kind of lithium ion battery residual life on-line prediction method |
| CN108776308A (en) * | 2018-04-26 | 2018-11-09 | 北京长城华冠汽车科技股份有限公司 | Estimate the method and device of battery remaining power |
| CN110687468A (en) * | 2018-06-19 | 2020-01-14 | 华为技术有限公司 | Method and device for estimating state of charge of battery |
| CN110208703A (en) * | 2019-04-24 | 2019-09-06 | 南京航空航天大学 | The method that compound equivalent-circuit model based on temperature adjustmemt estimates state-of-charge |
| CN110244236A (en) * | 2019-05-16 | 2019-09-17 | 深圳猛犸电动科技有限公司 | A kind of lithium ion battery packet SOC estimation method, device and terminal device |
| CN110187282A (en) * | 2019-06-03 | 2019-08-30 | 珠海东帆科技有限公司 | Battery charge state evaluation method and estimation device |
| CN110967647A (en) * | 2019-06-24 | 2020-04-07 | 宁德时代新能源科技股份有限公司 | Charge state correction method and device |
| CN110531274A (en) * | 2019-08-26 | 2019-12-03 | 江西优特汽车技术有限公司 | A kind of power battery SOC Prediction System and method |
| CN112433155A (en) * | 2019-08-26 | 2021-03-02 | 南京理工大学 | Lithium ion battery SOC estimation algorithm based on parameter online estimation |
| CN110673045A (en) * | 2019-10-30 | 2020-01-10 | 无锡凌博电子技术有限公司 | Battery charging and discharging data processing method |
| CN111048857A (en) * | 2019-12-20 | 2020-04-21 | 宁德时代新能源科技股份有限公司 | OCV-SOC curve updating method of battery pack, battery management system and vehicle |
| CN113009360A (en) * | 2019-12-20 | 2021-06-22 | 恒大新能源技术(深圳)有限公司 | Lithium battery SOC-OCV testing method and device and terminal equipment |
| CN111289911A (en) * | 2020-04-03 | 2020-06-16 | 深圳天邦达新能源技术有限公司 | SOC estimation method and device based on battery and electronic equipment |
| CN111781504A (en) * | 2020-08-03 | 2020-10-16 | 北京理工大学 | Method for identifying aging state and reconstructing open-circuit voltage of lithium ion power battery |
| CN112180281A (en) * | 2020-09-02 | 2021-01-05 | 傲普(上海)新能源有限公司 | Estimation method of SOC of battery pack |
| CN113009361A (en) * | 2021-03-13 | 2021-06-22 | 福州大学 | Battery state of charge estimation method based on open circuit voltage calibration |
Non-Patent Citations (5)
| Title |
|---|
| Estimation of the SOC of Energy-Storage Lithium Batteries Based on the Voltage Increment;Bo Zhao;IEEE Access;20201015;全文 * |
| He, W ; .A Physics-Based Electrochemical Model for Lithium-Ion Battery State-of-Charge Estimation Solved by an Optimised Projection-Based Method and Moving-Window Filtering.ENERGIES.2018,全文. * |
| 一种基于预测开路电压的SOC估算方法;徐欣歌;电子设计工程;20110720;全文 * |
| 刘鹏飞.电池荷电状态估算方法研究.汽车文摘.2019,全文. * |
| 龙宇舟 ; .一种基于预测开路电压估算SOC初值的方法.通信电源技术.2014,全文. * |
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