CN112986848A - Method for estimating SOH of power battery - Google Patents

Method for estimating SOH of power battery Download PDF

Info

Publication number
CN112986848A
CN112986848A CN202110108382.XA CN202110108382A CN112986848A CN 112986848 A CN112986848 A CN 112986848A CN 202110108382 A CN202110108382 A CN 202110108382A CN 112986848 A CN112986848 A CN 112986848A
Authority
CN
China
Prior art keywords
battery
soc
ocv
soh
capacity
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.)
Pending
Application number
CN202110108382.XA
Other languages
Chinese (zh)
Inventor
沈永柏
王翰超
王云
姜明军
孙艳
刘欢
江梓贤
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ligo Shandong New Energy Technology Co ltd
Original Assignee
Ligo Shandong New Energy Technology Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Ligo Shandong New Energy Technology Co ltd filed Critical Ligo Shandong New Energy Technology Co ltd
Priority to CN202110108382.XA priority Critical patent/CN112986848A/en
Publication of CN112986848A publication Critical patent/CN112986848A/en
Pending legal-status Critical Current

Links

Images

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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • 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]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • 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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Secondary Cells (AREA)
  • Tests Of Electric Status Of Batteries (AREA)

Abstract

The invention relates to a method for estimating SOH of a power battery. The method for estimating the SOH of the power battery comprises the following steps: s1, establishing an equivalent circuit model of the lithium battery, identifying and acquiring the open-circuit voltage OCV of the lithium battery on line based on the model, and acquiring the SOC in the battery management system; s2, establishing an SOC-OCV relation table based on the collected OCV and SOC, and calculating the factory capacity of the battery; s3, continuously identifying the OCV of the battery in the battery running process, and calculating and recording the actual SOC of the battery based on the OCV and the SOC-OCV relation table; s4, calibrating two time points in the operation process, and obtaining the difference between the accumulated charge and discharge capacity and the actual SOC in the two time points based on the factory capacity of the battery and the actual SOC of the battery so as to obtain the current capacity of the battery; s5, calculating the SOH of the battery based on the current capacity of the battery; the method for estimating the SOH of the power battery can greatly improve the calculation accuracy of the SOH, is beneficial to saving manpower and material resources, has low use cost, and is convenient for popularization and use of the SOH in the industry.

Description

Method for estimating SOH of power battery
Technical Field
The invention belongs to the technical field of power batteries of electric vehicles, and particularly relates to a method for estimating SOH of a power battery.
Background
With the rapid development of new energy automobiles in China, a power lithium battery which is one of three major components is concerned more and more, and estimation methods of SOC, SOH, SOP and the like of the power lithium battery are always one of important research fields. In scientific research institutions and commercial institutions, much attention is paid to the performance index of the battery on the SOC, however, the SOH and the SOP are also very important performance indexes. Taking the SOH of the power battery to be discussed in the present invention as an example, the present invention not only relates to the accurate estimation of SOC, but also relates to the estimation of the remaining service life of the battery and the safe use of the full life cycle of the electric vehicle, and relates to the after-sale maintenance, echelon utilization, residual value evaluation, etc. of the battery.
Current SOH estimation methods can be broadly classified into three types, an experiment-based method, a model-based method, and a data-driven method. The methods based on the experiment include a table look-up method of accumulated charge, an empirical formula method, an Electrochemical Impedance Spectroscopy (EIS) method, and a capacity increase analysis (ICA) or a Differential Voltage Analysis (DVA) method. The accumulated electric quantity table look-up method obtains the relation between the accumulated charge/discharge data and the SOH through a previous experiment, and then look-up the accumulated charge/discharge data of the current battery to obtain the SOH of the battery. The empirical formula method is to fit experimental data by an empirical formula to obtain a battery SOH attenuation formula and then calculate the SOH of the current battery by using the formula, and the empirical formula method has the defects similar to the table look-up method of the accumulated electric quantity, and an improper fitting formula can bring extra errors. The EIS method determines impedance according to the frequency spectrum of the battery, thereby obtaining the degree of aging of the battery, but the method only stays in the stage of detecting SOH off-line. ICA and DVA are similar, and both utilize the characteristic that constant current charging/discharging curves with different aging degrees are different, and the defect of the characteristic is that errors caused by sensors cannot be avoided. The model-based method mainly comprises the steps of utilizing an equivalent circuit model of the battery to carry out parameter identification to obtain the internal resistance of the battery, and then utilizing a formula
Figure BDA0002918387360000021
And calculating the SOH, wherein the method has high requirement on the estimation accuracy of the internal resistance of the battery as can be seen from a formula. The method based on data is to adopt machine learning methods such as statistical learning, neural network and the like to estimate the SOH of the battery, wherein one step of the method is to establish a model by utilizing sensor measurement (such as voltage, current, temperature and the like) and the SOH, then train model parameters according to battery charging and discharging data, and finally estimate the SOH of the battery by using the trained model.
In view of the limitations of the above methods, in order to improve the SOH estimation accuracy and avoid the waste of time caused by the prior experiment, the SOH of the battery can be calculated by using the idea of estimating SOC and then estimating the capacity online.
Disclosure of Invention
The invention aims to solve the problems and provide a method for estimating the SOH of the power battery, which has a simple structure and is reasonably designed.
The invention realizes the purpose through the following technical scheme:
a method of estimating the SOH of a power cell, comprising the steps of:
s1, establishing an equivalent circuit model of the lithium battery, identifying and acquiring the open-circuit voltage OCV of the lithium battery on line based on the model, and acquiring the SOC in the battery management system;
s2, establishing an SOC-OCV relation table based on the collected OCV and SOC, and calculating the factory capacity of the battery;
s3, continuously identifying the OCV of the battery in the battery running process, and calculating and recording the actual SOC of the battery based on the OCV and the SOC-OCV relation table;
s4, calibrating two time points in the operation process, obtaining the accumulated charge-discharge capacity difference and the actual SOC difference in the two time points based on the factory capacity of the battery and the actual SOC of the battery, and calculating the current capacity of the battery based on the charge-discharge capacity difference and the actual SOC difference;
and S5, calculating the SOH of the battery based on the current capacity of the battery.
As a further optimization scheme of the present invention, in step S1, the equivalent current model sequentially includes resistors R connected in series0By a polarization resistor RP1And a polarization capacitor C connected in parallelP1First R of compositionCNetwork element comprising a polarization resistor RP2And a polarization capacitor C connected in parallelP2Second RC network unit and open-circuit voltage VOCThe terminal voltage of the lithium battery is equal to the open-circuit voltage VOCAnd the voltages at the two ends of the circuit are summed after the first RC network unit and the second RC network unit are connected in series.
As a further optimization scheme of the present invention, in step S1, the specific method for calculating the OCV based on the equivalent circuit model includes the following steps:
step S11, establishing a state equation and an observation equation of the system based on the equivalent current model of the battery;
step S12, transforming the system equation into least square form;
step S13, calculating the minimum two-times parameter by using the RLS algorithm based on UD decomposition;
in step S14, the OCV is solved by the least squares parameter.
As a further optimization scheme of the present invention, in step S11, the state equation and observation equation of the system are:
Figure BDA0002918387360000031
Figure BDA0002918387360000041
wherein the content of the first and second substances,
Figure BDA0002918387360000042
Figure BDA0002918387360000043
C＝[1 1]；
D＝R0；
v in the above formulaP1(K) And VP2(k) Respectively, polarization voltage at k moment of 2 RC circuits, V (k) is terminal voltage of a battery at k moment, delta t is a sampling period, I (k) is current on the lithium battery at k moment, and V is considered in the inventionocConstant for a short period of time.
As a further optimization of the present invention, in step S12, the equation in the form of least squares is:
Figure BDA0002918387360000044
wherein the content of the first and second substances,
Figure BDA0002918387360000045
θ＝[θ1,θ2,θ3,θ4,θ5,θ6]T；
and the number of the first and second electrodes,
θ1＝a1+a2；
θ2＝-a1a2；
θ3＝R0+b1+b2；
θ4＝-b1a2-b2a1-R0(a1+a2)；
θ5＝a1a2R0；
θ6＝[1-(a1+a2)+a1a2]Voc。
as a further optimization scheme of the present invention, in step S13, the RLS algorithm based on UD decomposition has the following steps:
s131, setting k to be 1 or 2, reading V (k) and I (k), determining an initial value of theta, setting a unit upper triangular matrix U and a unit diagonal matrix D to initialize covariance P, and defining a forgetting factor 0 to be more than or equal to lambda to be less than or equal to 1;
step S132, reading new V (k) and I (k), and updating
Figure BDA0002918387360000054
Step S133, let a0Defining vectors f and g:
Figure BDA0002918387360000051
1, 2.. 6, the following steps are performed:
in the step of S1331, the step of the method,
aj＝aj-1+fjgj；
D(k)jj＝(aj-1D(k-1)jj)/(ajλ)；
bj＝gj；
cj＝-fj/aj-1；
in the step of S1332, the step of the method,
when j is not equal to 1, the pair i 1, 2.
U(k)ij＝U(k-1)ij+bicj；
bi＝bi+U(k-1)ijbj；
Step S134, calculating l (k) ═ b1…bn]T/an；
Step S135, calculating an estimation error
Figure BDA0002918387360000052
In step S136, θ (k) ═ θ (k-1) + l (k) β (k).
As a further optimization scheme of the present invention, in step S14, the formula for solving OCV by the least squares parameter is:
Figure BDA0002918387360000053
as a further optimization scheme of the present invention, the method for establishing the SOC-OCV relationship table in step S2 is:
collecting N-cycle operation data before the lithium battery leaves a factory to identify the open-circuit voltage of the battery, and if a certain SOC value in the battery management system appears N times in total, calculating the OCV to be Voc，1，Voc，2，…，Voc，NThen the OCV corresponding to the SOC is the average value of the N OCV values; delivery capacity Q0The calculation method of (1) is that two time points are selected in each circulation process, the SOC difference of the two time points is required to be higher than 40%, and the accumulated charging capacity of the battery is assumed to be Q respectively at the two time pointsi c，1And Qi c，2The cumulative discharge point capacity is Qi d，1And Qi d，2The SOC in the battery management system is SOCi 1And SOCi 2Then, the battery capacity Q of the current cyclei 0The calculation formula of (2) is as follows:
Figure BDA0002918387360000061
assuming that n cycles are used to calculate M capacity values, the factory capacity Q0Is the average of the M capacity values, n ∈ [1, 5 ]]。
As a further preferable aspect of the present invention, in step S3, the method of calculating the SOC of the battery is a linear interpolation method based on the OCV and the SOC-OCV relationship table.
As a further optimization of the present invention, in step S4, the battery is calculatedThe current capacity method is that two time points in the current operation process are selected, the SOC difference of the two time points is required to be higher than 40%, and the accumulated charging capacity of the battery is assumed to be Q respectively at the two time pointsc，1And Qc，2The cumulative discharge point capacity is Qd，1And Qd，2The SOC in the battery management system is SOC1And SOC2Then, the battery capacity Q of the current cyclei 0The calculation formula of (2) is as follows:
Figure BDA0002918387360000062
in the step S5, in step S5,
Figure BDA0002918387360000063
the invention has the beneficial effects that: the invention avoids the consumption of time, manpower and material resources caused by battery experiments before leaving the factory, and saves resources; the SOC-OCV data is independently calculated for each battery, so that the matching between the data and the batteries is ensured, and the influence caused by the inconsistency of the batteries is eliminated; the scheme is easy to realize in engineering, high in estimation precision and convenient to popularize and use. The method has the advantages of ingenious design, convenient calculation, low use cost and convenience for popularization and use of SOH in the industry, and can greatly improve the calculation accuracy of SOH, and is beneficial to saving manpower and material resources.
Drawings
FIG. 1 is a model diagram of a second-order RC equivalent circuit of the lithium battery of the present invention.
Fig. 2 is a flow chart of the implementation of the present invention.
Fig. 3 is a current curve for a certain cycle of a battery in one embodiment of the present invention.
FIG. 4 is a comparison of estimated battery SOH and true SOH for practicing the present invention.
Detailed Description
The present application will now be described in further detail with reference to the drawings, it should be noted that the following detailed description is given for illustrative purposes only and is not to be construed as limiting the scope of the present application, as those skilled in the art will be able to make numerous insubstantial modifications and adaptations to the present application based on the above disclosure.
Example 1
As shown in fig. 1, a method for estimating SOH of a power battery includes the following steps:
s1, establishing an equivalent circuit model of the lithium battery, identifying and acquiring the open-circuit voltage OCV of the lithium battery on line based on the model, and acquiring the SOC in the battery management system; in the process, the running data of n cycles before the lithium battery leaves a factory is collected to identify the open-circuit voltage of the battery and the SOC value reported in the battery management system, wherein the SOC value is an initial precalculated value in the application; specifically, the equivalent current model sequentially comprises resistors R connected in series0By a polarization resistor RP1And a polarization capacitor C connected in parallelP1First R of compositionCNetwork element comprising a polarization resistor RP2And a polarization capacitor C connected in parallelP2Second RC network unit and open-circuit voltage VOCThe terminal voltage of the lithium battery is equal to the open-circuit voltage VOCAnd the voltages at the two ends of the circuit are summed after the first RC network unit and the second RC network unit are connected in series.
S2, establishing an SOC-OCV relation table based on the collected OCV and SOC, and calculating the factory capacity of the battery;
specifically, the method for establishing the SOC-OCV relationship table includes:
collecting N-cycle operation data before the lithium battery leaves a factory to identify the open-circuit voltage of the battery, and if a certain SOC value in the battery management system appears N times in total, calculating the OCV to be Voc，1，Voc，2，…，Voc，NThen the OCV corresponding to the SOC is the average value of the N OCV values;
delivery capacity Q0The calculation method of (1) is that two time points are selected in each circulation process, the SOC difference of the two time points is required to be higher than 40%, and the accumulated charging capacity of the battery is assumed to be Q respectively at the two time pointsi c，1And Qi c，2The cumulative discharge point capacity is Qi d，1And Qi d，2Management system of batteryThe SOC in the system is SOCi 1And SOCi 2Then, the battery capacity Q of the current cyclei 0The calculation formula of (2) is as follows:
Figure BDA0002918387360000081
assuming that n cycles are used to calculate M capacity values, the factory capacity Q0Is the average of the M capacity values, n ∈ [1, 5 ]]
S3, continuously identifying the OCV of the battery in the battery running process, and calculating and recording the actual SOC of the battery based on the OCV and the SOC-OCV relation table; the SOC value calculated from this SOC-OCV relationship table is referred to as an actual SOC value in the present embodiment. Specifically, the method for calculating the SOC of the battery is a linear interpolation method based on the OCV and the SOC-OCV relation table;
s4, calibrating two time points in the operation process, and calculating the current capacity of the battery based on the accumulated charge-discharge capacity difference and the actual SOC difference in the two time points; specifically, in step S4, the method for calculating the current capacity of the battery is to select two time points in the current operation process, and require that the SOC difference between the two time points is higher than 40%, assuming that the accumulated charging capacities of the battery at the two time points are Qc，1And Qc，2The cumulative discharge point capacity is Qd，1And Qd，2The SOC in the battery management system is SOC1And SOC2Then, the battery capacity Q of the current cyclei 0The calculation formula of (2) is as follows:
Figure BDA0002918387360000091
s5, calculating the SOH of the battery based on the current capacity of the battery, specifically,
Figure BDA0002918387360000092
it should be further noted that, in step S1, the specific method for calculating the OCV based on the equivalent circuit model includes the following steps:
step S11, establishing a state equation and an observation equation of the system based on the equivalent current model of the battery;
step S12, transforming the system equation into least square form;
step S13, calculating the minimum two-times parameter by using the RLS algorithm based on UD decomposition;
in step S14, the OCV is solved by the least squares parameter.
In step S11, the state equation and observation equation of the system are:
Figure BDA0002918387360000093
Figure BDA0002918387360000094
wherein the content of the first and second substances,
Figure BDA0002918387360000095
Figure BDA0002918387360000096
C＝[1 1]；
D＝R0；
v in the above formulaP1(K) And VP2(k) Respectively, polarization voltage at k moment of 2 RC circuits, V (k) is terminal voltage of a battery at k moment, delta t is a sampling period, I (k) is current on the lithium battery at k moment, and V is considered in the inventionocConstant for a short period of time;
in step S12, the equation in the form of least squares is:
Figure BDA0002918387360000101
wherein the content of the first and second substances,
Figure BDA0002918387360000102
θ＝[θ1,θ2,θ3,θ4,θ5,θ6]T；
and the number of the first and second electrodes,
θ1＝a1+a2；
θ2＝-a1a2；
θ3＝R0+b1+b2；
θ4＝-b1a2-b2a1-R0(a1+a2)；
θ5＝a1a2R0；
θ6＝[1-(a1+a2)+a1a2]Voc
in step S13, the RLS algorithm based on UD decomposition has the following steps:
s131, setting k to be 1 or 2, reading V (k) and I (k), determining an initial value of theta, setting a unit upper triangular matrix U and a unit diagonal matrix D to initialize covariance P, and defining a forgetting factor 0 to be more than or equal to lambda to be less than or equal to 1;
step S132, reading new V (k) and I (k), and updating
Figure BDA0002918387360000103
Step S133, let a0Defining vectors f and g:
Figure BDA0002918387360000104
1, 2.. 6, the following steps are performed:
in the step of S1331, the step of the method,
aj＝aj-1+fjgj；
D(k)jj＝(aj-1D(k-1)jj)/(ajλ)；
bj＝gj；；
cj＝-fj/aj-1；
in the step of S1332, the step of the method,
when j is not equal to 1, the pair i 1, 2.
U(k)ij＝U(k-1)ij+bicj；
bi＝bi+U(k-1)ijbj；
Step S134, calculating l (k) ═ b1…bn]T/an；
Step S135, calculating an estimation error
Figure BDA0002918387360000111
Step S136, θ (k) ═ θ (k-1) + l (k) β (k);
in step S14, the formula for solving the OCV from the least squares parameter is:
Figure BDA0002918387360000112
in order to verify the estimation accuracy of the method for the SOH of the lithium battery, a battery pack is used for carrying out a discharge cycle experiment. The SOC of the battery is estimated and recorded by using a battery management system, and the battery management system can record the accumulated charge-discharge ampere hours of the battery at the same time. The battery pack is subjected to 200 charge-discharge cycles in total, and the capacity of the battery pack is tested once every 20 cycles for estimating the true SOC. The current for one cycle is shown in figure 3. The initial capacity and the SOC-OCV curve of the battery are estimated by using the data of the first cycle, the SOH of all the cycles is estimated, the estimation result is shown in FIG. 4, the curve of the real SOH is drawn in FIG. 4 at the same time, and as can be seen from the graph, the maximum error between the estimated SOH and the real SOH is lower, so that the method has higher estimation precision and certain superiority.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.

Claims (10)

1. A method of estimating the SOH of a power cell, comprising the steps of:
s1, establishing an equivalent circuit model of the lithium battery, identifying and acquiring the open-circuit voltage OCV of the lithium battery on line based on the model, and acquiring the SOC in the battery management system;
s2, establishing an SOC-OCV relation table based on the collected OCV and SOC, and calculating the factory capacity of the battery;
s3, continuously identifying the OCV of the battery in the battery running process, and calculating and recording the actual SOC of the battery based on the OCV and the SOC-OCV relation table;
s4, calibrating two time points in the operation process, obtaining the accumulated charge-discharge capacity difference and the actual SOC difference in the two time points based on the factory capacity of the battery and the actual SOC of the battery, and calculating the current capacity of the battery based on the charge-discharge capacity difference and the actual SOC difference;
and S5, calculating the SOH of the battery based on the current capacity of the battery.
2. The method of claim 1, wherein the step of estimating the SOH of the power cell comprises: in step S1, the equivalent current model includes resistors R connected in series0By a polarization resistor RP1And a polarization capacitor C connected in parallelP1First R of compositionCNetwork element comprising a polarization resistor RP2And a polarization capacitor C connected in parallelP2Second RC network unit and open-circuit voltage VOCThe terminal voltage of the lithium battery is equal to the open-circuit voltage VOCAnd the voltages at the two ends of the circuit are summed after the first RC network unit and the second RC network unit are connected in series.
3. A method of estimating SOH of a power cell according to claim 2, wherein: in step S1, the specific method of calculating the OCV based on the equivalent circuit model includes the steps of:
step S11, establishing a state equation and an observation equation of the system based on the equivalent current model of the battery;
step S12, transforming the system equation into least square form;
step S13, calculating the minimum two-times parameter by using the RLS algorithm based on UD decomposition;
in step S14, the OCV is solved by the least squares parameter.
4. A method of estimating SOH of a power cell according to claim 3, wherein:
in step S11, the state equation and observation equation of the system are:
Figure FDA0002918387350000021
Figure FDA0002918387350000022
wherein the content of the first and second substances,
Figure FDA0002918387350000023
Figure FDA0002918387350000024
C＝[1 1]；
D＝R0；
v in the above formulaP1(K) And VP2(k) The polarization voltage of 2 RC circuits at the k moment, V (k) is the terminal voltage of the battery at the k moment, delta t is the sampling period, I (k) is the current of the lithium battery at the k moment, V (k) is the current of the lithium battery at the k momentocIs a constant.
5. The method of claim 4, wherein the step of estimating the SOH of the power cell comprises: in step S12, the equation in the form of least squares is:
Figure FDA0002918387350000025
wherein the content of the first and second substances,
Figure FDA0002918387350000026
θ＝[θ1,θ2,θ3,θ4,θ5,θ6]T；
and the number of the first and second electrodes,
θ1＝a1+a2；
θ2＝-a1a2；
θ3＝R0+b1+b2；
θ4＝-b1a2-b2a1-R0(a1+a2)；
θ5＝a1a2R0；
θ6＝[1-(a1+a2)+a1a2]Voc。
6. the method of claim 5, wherein the step of estimating the SOH of the power cell comprises: in step S13, the RLS algorithm based on UD decomposition has the following steps:
s131, setting k to be 1 or 2, reading V (k) and I (k), determining an initial value of theta, setting a unit upper triangular matrix U and a unit diagonal matrix D to initialize covariance P, and defining a forgetting factor 0 to be more than or equal to lambda to be less than or equal to 1;
step S132, reading new V (k) and I (k), and updating
Figure FDA0002918387350000031
Step S133, let a0Defining vectors f and g:
Figure FDA0002918387350000032
1, 2.. 6, the following steps are performed:
in the step of S1331, the step of the method,
aj＝aj-1+fjgj；
D(k)jj＝(aj-1D(k-1)jj)/(ajλ)；
bj＝gj；
cj＝-fj/aj-1；
in the step of S1332, the step of the method,
when j is not equal to 1, the pair i 1, 2.
U(k)ij＝U(k-1)ij+bicj；
bi＝bi+U(k-1)ijbj；
Step S134, calculating l (k) ═ b1…bn]T/an；
Step S135, calculating an estimation error
Figure FDA0002918387350000041
In step S136, θ (k) ═ θ (k-1) + l (k) β (k).
7. The method of claim 6, wherein the step of estimating the SOH of the power cell comprises: in step S14, the formula for solving the OCV from the least squares parameter is:
Figure FDA0002918387350000042
8. the method of claim 7, wherein the step of estimating the SOH of the power cell comprises: the method of establishing the SOC-OCV relationship table in step S2 is:
collecting N-cycle operation data before the lithium battery leaves a factory to identify the open-circuit voltage of the battery, and if a certain SOC value in the battery management system appears N times in total, calculating the OCV to be Voc，1，Voc，2，…，Voc，NThen the OCV corresponding to the SOC is the average value of the N OCV values;
delivery capacity Q0The calculation method of (1) is that two time points are selected in each circulation process, the SOC difference of the two time points is required to be higher than 40%, and the accumulated charging capacity of the battery is assumed to be Q respectively at the two time pointsi c，1And Qi c，2The cumulative discharge point capacity is Qi d，1And Qi d，2The SOC in the battery management system is SOCi 1And SOCi 2Then, the battery capacity Q of the current cyclei 0The calculation formula of (2) is as follows:
Figure FDA0002918387350000043
assuming that n cycles are used to calculate M capacity values, the factory capacity Q0Is the average of the M capacity values, n ∈ [1, 5 ]]。
9. The method of claim 8, wherein the step of estimating the SOH of the power cell comprises: in step S3, the method of calculating the battery SOC is a linear interpolation method based on the OCV and the SOC-OCV relationship table.
10. The method of claim 9, wherein the step of estimating the SOH of the power cell comprises: in step S4, the method for calculating the current capacity of the battery is to select two time points in the current operation process, and require the SOC difference between the two time points to be higher than 40%, assuming that the accumulated charging capacities of the battery at the two time points are Qc，1And Qc，2Tired ofThe capacity of the measuring and discharging point is respectively Qd，1And Qd，2The SOC in the battery management system is SOC1And SOC2Then, the battery capacity Q of the current cyclei 0The calculation formula of (2) is as follows:
Figure FDA0002918387350000051
in the step S5, in step S5,
Figure FDA0002918387350000052
CN202110108382.XA 2021-01-27 2021-01-27 Method for estimating SOH of power battery Pending CN112986848A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110108382.XA CN112986848A (en) 2021-01-27 2021-01-27 Method for estimating SOH of power battery

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110108382.XA CN112986848A (en) 2021-01-27 2021-01-27 Method for estimating SOH of power battery

Publications (1)

Publication Number Publication Date
CN112986848A true CN112986848A (en) 2021-06-18

Family

ID=76345573

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110108382.XA Pending CN112986848A (en) 2021-01-27 2021-01-27 Method for estimating SOH of power battery

Country Status (1)

Country Link
CN (1) CN112986848A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113740752A (en) * 2021-08-27 2021-12-03 济南大学 Lithium battery life prediction method based on battery model parameters
CN114035049A (en) * 2021-11-08 2022-02-11 东软睿驰汽车技术（沈阳）有限公司 SOH precision calculation method and device and electronic equipment
US11416899B2 (en) * 2018-07-11 2022-08-16 State Grid Jiangxi Electric Power Company Limited Research Institute Estimation method for whole life cycle cost of power battery in echelon utilization

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105093121A (en) * 2015-07-10 2015-11-25 桂林电子科技大学 Likelihood-function-particle-filter-based power battery state-of-charge estimation method and system
CN105301509A (en) * 2015-11-12 2016-02-03 清华大学 Combined estimation method for lithium ion battery state of charge, state of health and state of function
CN109061520A (en) * 2018-10-25 2018-12-21 杭州神驹科技有限公司 A kind of power battery health and power rating estimation on line method and system
CN109164391A (en) * 2018-07-12 2019-01-08 杭州神驹科技有限公司 A kind of power battery charged state estimation on line method and system
CN109991548A (en) * 2019-04-19 2019-07-09 中国计量大学 A kind of OCV-SOC method of calibration experiment, battery equivalent model parameter identification method and SOC estimation method
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
CN111208433A (en) * 2020-01-16 2020-05-29 北方工业大学 Method and device for identifying parameters of second-order RC equivalent circuit model of battery

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105093121A (en) * 2015-07-10 2015-11-25 桂林电子科技大学 Likelihood-function-particle-filter-based power battery state-of-charge estimation method and system
CN105301509A (en) * 2015-11-12 2016-02-03 清华大学 Combined estimation method for lithium ion battery state of charge, state of health and state of function
CN109164391A (en) * 2018-07-12 2019-01-08 杭州神驹科技有限公司 A kind of power battery charged state estimation on line method and system
CN109061520A (en) * 2018-10-25 2018-12-21 杭州神驹科技有限公司 A kind of power battery health and power rating estimation on line method and system
CN109991548A (en) * 2019-04-19 2019-07-09 中国计量大学 A kind of OCV-SOC method of calibration experiment, battery equivalent model parameter identification method and SOC estimation method
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
CN111208433A (en) * 2020-01-16 2020-05-29 北方工业大学 Method and device for identifying parameters of second-order RC equivalent circuit model of battery

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11416899B2 (en) * 2018-07-11 2022-08-16 State Grid Jiangxi Electric Power Company Limited Research Institute Estimation method for whole life cycle cost of power battery in echelon utilization
CN113740752A (en) * 2021-08-27 2021-12-03 济南大学 Lithium battery life prediction method based on battery model parameters
CN114035049A (en) * 2021-11-08 2022-02-11 东软睿驰汽车技术（沈阳）有限公司 SOH precision calculation method and device and electronic equipment

Similar Documents

Publication Publication Date Title
CN110501652B (en) Rapid assessment method and device for available capacity of retired lithium battery
CN110031770B (en) Method for rapidly obtaining capacity of all single batteries in battery pack
CN106716158B (en) Battery charge state evaluation method and device
CN112986848A (en) Method for estimating SOH of power battery
CN109358293B (en) Lithium ion battery SOC estimation method based on IPF
CN109078871B (en) Rejection method of retired battery parallel module for echelon utilization
CN109669132A (en) A kind of battery charge state estimation method based on variation Bayesian filter
CN112526352B (en) SOH estimation method for retired lithium ion battery
CN113253140A (en) Battery health state online estimation method
CN112526353B (en) Method and device for rapidly detecting SOC of retired lithium ion power battery
CN108169687A (en) A kind of accumulator SOC estimation method based on cloud platform
CN112710955B (en) Algorithm for improving battery capacity estimation precision
CN109901072B (en) Retired battery parameter detection method based on historical data and laboratory test data
CN110673037B (en) Battery SOC estimation method and system based on improved simulated annealing algorithm
CN112462282B (en) Method for determining real-time state of charge of battery pack based on mechanism model
CN109752660B (en) Battery state of charge estimation method without current sensor
CN112989690A (en) Multi-time scale state of charge estimation method for lithium battery of hybrid electric vehicle
CN110554321A (en) method for detecting SOC (state of charge) of retired power battery in real time
CN113484771A (en) Method for estimating wide-temperature full-life SOC and capacity of lithium ion battery
CN111216595B (en) SOC calibration method of severe hybrid electric vehicle based on lithium battery equivalent circuit model
CN113567861A (en) Estimation method for health state of power battery
CN112946481A (en) Based on federation H∞Filtering sliding-mode observer lithium ion battery SOC estimation method and battery management system
CN115327415A (en) Lithium battery SOC estimation method based on limited memory recursive least square algorithm
CN116203428A (en) Self-discharge detection method for calculating equivalent model parameters of lithium battery based on constant-voltage charging and discharging
CN115754724A (en) Power battery state of health estimation method suitable for future uncertainty dynamic working condition discharge

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
RJ01 Rejection of invention patent application after publication

Application publication date: 20210618

RJ01 Rejection of invention patent application after publication