CN112034350A - Battery pack health state prediction method and terminal equipment - Google Patents

Battery pack health state prediction method and terminal equipment Download PDF

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CN112034350A
CN112034350A CN202010886420.XA CN202010886420A CN112034350A CN 112034350 A CN112034350 A CN 112034350A CN 202010886420 A CN202010886420 A CN 202010886420A CN 112034350 A CN112034350 A CN 112034350A
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battery
health
battery pack
discharge
internal resistance
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CN112034350B (en
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陈四雄
傅克文
李镇
林汉伟
廖镕祥
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Xiamen Kecan Information Technology Co ltd
Xiamen Kehua Hengsheng Co Ltd
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Xiamen Kecan Information Technology Co ltd
Xiamen Kehua Hengsheng Co Ltd
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    • 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]
    • G01R31/371—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with remote indication, e.g. on external chargers
    • 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]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • 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]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • 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]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

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Abstract

The invention is suitable for the technical field of batteries, and discloses a battery pack health state prediction method and terminal equipment, wherein the method comprises the following steps: acquiring parameters of each battery in the battery pack, and calculating the discharge attenuation, the residual dischargeable capacity ratio and the internal resistance deviation rate corresponding to each battery according to the parameters of each battery; calculating the health degree of each battery according to the discharge attenuation degree, the residual dischargeable capacity ratio and the internal resistance deviation rate which respectively correspond to each battery; and calculating the average value of the health degree of the battery pack and the standard deviation of the health degree of the battery pack according to the health degree of each battery, and predicting the health state of the battery pack according to the average value of the health degree of the battery pack and the standard deviation of the health degree of the battery pack. The invention can not only calculate the health degree of a single battery, but also predict the health state of the battery pack.

Description

Battery pack health state prediction method and terminal equipment
Technical Field
The invention belongs to the technical field of batteries, and particularly relates to a battery pack health state prediction method and terminal equipment.
Background
The health state of the battery pack can be used for measuring the health degree of the battery pack and is an important index for measuring the service life of the battery pack, and therefore, the health state of the battery pack is necessary to be predicted.
At present, a plurality of methods can predict the health state of the battery, but most of the existing methods can only predict the health state of a single battery, but cannot predict the health state of the battery pack.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method for predicting a health state of a battery pack and a terminal device, so as to solve a problem that a health state of a battery pack cannot be predicted in the prior art.
A first aspect of an embodiment of the present invention provides a method for predicting a state of health of a battery pack, including:
acquiring parameters of each battery in the battery pack, and calculating the discharge attenuation, the residual dischargeable capacity ratio and the internal resistance deviation rate corresponding to each battery according to the parameters of each battery;
calculating the health degree of each battery according to the discharge attenuation degree, the residual dischargeable capacity ratio and the internal resistance deviation rate which respectively correspond to each battery;
and calculating the average value of the health degree of the battery pack and the standard deviation of the health degree of the battery pack according to the health degree of each battery, and predicting the health state of the battery pack according to the average value of the health degree of the battery pack and the standard deviation of the health degree of the battery pack.
A second aspect of the embodiments of the present invention provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the battery pack state of health prediction method according to the first aspect when executing the computer program.
A third aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program, which when executed by one or more processors implements the steps of the battery pack state of health prediction method according to the first aspect.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: according to the embodiment of the invention, the parameters of each battery in the battery pack are firstly obtained, the discharge attenuation degree, the residual dischargeable capacity occupation ratio and the internal resistance deviation rate which respectively correspond to each battery are calculated according to the parameters of each battery, then the health degree of each battery is calculated according to the discharge attenuation degree, the residual dischargeable capacity occupation ratio and the internal resistance deviation rate which respectively correspond to each battery, finally the average value of the health degree of each battery and the standard deviation of the health degree of each battery are calculated according to the health degree of each battery, and the health state of the battery pack is predicted according to the average value of the health degree of each battery and the standard deviation of the health degree of each battery, so that the health degree of each battery can be calculated, and.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart illustrating an implementation of a method for predicting a state of health of a battery pack according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating the relationship between the remaining life capacity and the internal resistance of a battery according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating the state of health of a battery pack according to an embodiment of the present invention;
FIG. 4 is a schematic block diagram of a battery pack state of health prediction system provided by an embodiment of the present invention;
fig. 5 is a schematic block diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 is a schematic flow chart of a method for predicting a state of health of a battery pack according to an embodiment of the present invention, and for convenience of description, only a part related to the embodiment of the present invention is shown. The execution main body of the embodiment of the invention can be terminal equipment. As shown in fig. 1, the method may include the steps of:
s101: and acquiring parameters of each battery in the battery pack, and calculating the discharge attenuation, the residual dischargeable capacity ratio and the internal resistance deviation rate corresponding to each battery according to the parameters of each battery.
In the embodiment of the present invention, by acquiring the parameters of each battery in the battery pack, the discharge attenuation of each battery, the remaining dischargeable capacity ratio of each battery, and the internal resistance deviation rate of each battery can be calculated respectively according to the parameters of each battery in the battery pack.
In one embodiment of the invention, the parameters of the battery comprise discharge current in unit time in a discharge process when the last discharge capacity of the battery reaches a preset percentage, unit time in a discharge process when the last discharge capacity of the battery reaches the preset percentage, rated capacity of the battery, full discharge capacity of a life cycle of the battery, residual discharge capacity of the life cycle of the battery, a failure internal resistance threshold value of the battery, nominal internal resistance of the battery and current internal resistance of the battery;
in the above S101, calculating the discharge attenuation, the percentage of remaining dischargeable capacity, and the internal resistance deviation rate corresponding to each battery according to the parameter of each battery may include:
according to
Figure BDA0002655708100000031
Calculating the discharge attenuation degree D of the first batteryN(ii) a Wherein, the first battery is any one battery in the battery pack, IiIs the discharge current, delta t, in the unit time of the discharge process when the last discharge amount of the first battery reaches the preset percentageiThe unit time of the discharging process when the last discharge amount of the first battery reaches a preset percentage, CSIs the rated capacity of the first battery;
according to CN＝cR÷cACalculating the ratio of the remaining dischargeable capacity of the first battery to the remaining dischargeable capacity of the first batteryN(ii) a Wherein, cRResidual discharge capacity for the first battery life cycle, cAThe first battery life cycle full discharge capacity;
if it is
Figure BDA0002655708100000032
The internal resistance deviation ratio γ of the first battery is 0;
if it is
Figure BDA0002655708100000041
Then according to
Figure BDA0002655708100000042
Calculating the internal resistance deviation rate gamma of the first battery; wherein R isNIs the current internal resistance, R, of the first batterySNominal internal resistance, R, for the first batteryOIs a first battery failure internal resistance threshold.
Wherein the preset percentage may be 80%. The discharge process in which the last discharge amount of the first battery reaches the preset percentage may mean that the last discharge amount of the first battery is greater than or equal to 80% of the discharge amount of the first battery in complete discharge. Preferably, the discharging process of the first battery when the last discharging amount reaches the preset percentage can be a discharging process of the first battery being completely discharged, namely a discharging process of the first battery from full charge to full discharge.
Optionally according to
Figure BDA0002655708100000043
Calculating the discharge attenuation degree D of the first batteryNBefore, the following steps can be further included:
judging whether the discharge capacity of the first battery, the last discharge capacity of which reaches the preset percentage, is greater than or equal to 75% of the rated capacity of the battery;
if the last discharge capacity of the first battery reaches 75% or more of the rated capacity of the battery when the last discharge capacity of the first battery reaches the preset percentageN＝1；
If the last discharge capacity of the first battery reaches 75% of the rated capacity of the battery, executing the method according to the preset percentage
Figure BDA0002655708100000044
Calculating the discharge attenuation degree D of the first batteryNThe step (2).
CNMay represent the percentage of the remaining dischargeable capacity of the first battery to the full life-cycle dischargeable capacity of the first battery. For example, when the total discharge capacity of a 100Ah battery is 100Ah × 80 to 8000Ah and the current discharged capacity is 800Ah, C isNThe value is (8000) -800 ÷ 8000 ═ 0.9.
cRIndicating the remaining dischargeable battery capacity of the first battery during the life cycle, cAIndicating the total dischargeable battery capacity of the first battery during the life cycle.
In one embodiment of the invention, the first battery life cycle residual discharge capacity cRThe calculation formula of (2) is as follows:
Figure BDA0002655708100000045
wherein, cjThe discharge capacity of the first battery in the j discharge is a preset coefficient, and n is the discharge frequency of the first battery.
In the embodiment of the present invention, it is,
Figure BDA0002655708100000051
the accumulated value of the number of discharge ampere hours for each current time is shown. c. CjThe discharge capacity of the first battery at one time is shown, the influence of different discharge capacities on the whole residual life of the battery is different, and in order to improve the accuracy of the value, the discharge capacity at each time is multiplied by a coefficient. The setting can be made according to actual requirements, and for example, the setting can be 1. According to the characteristic that the large-current discharge of the battery has larger battery loss, the current is larger along with the change of the discharge current, so that the discharge value of the whole life cycle is more accurately counted.
Alternatively, RNThe average value of the first battery internal resistance values collected within one month. Namely, it is
Figure BDA0002655708100000052
Figure BDA0002655708100000053
RkThe k-th collected internal resistance value of the first battery is obtained, and m is the number of times of collecting the internal resistance value of the first battery.
The internal resistance deviation rate of the battery may also be referred to as an internal resistance degradation coefficient of the battery. The nominal internal resistance of the battery may be a standard (reference) internal resistance of the battery at the point of departure.
RO-RSIndicating the internal resistance variation range, R, of the current first battery in the effective life cycleN-RSThe first battery internal resistance aging degree.
TABLE 1 corresponding relationship between rated capacity and nominal internal resistance of battery
Rated capacity (Ah) of 12V battery Nominal internal resistance (m omega)
24 8.5
38 6.0
65 4.0
85 5.4
100 4.5
110 4.5
120 4.0
155 3.5
165 3.5
200 2.5
210 2.5
230 2.0
As shown in fig. 2, the internal resistance of the battery increases as the performance of the battery decreases, and when the capacity of the battery decreases to 75% of the rated capacity, the life of the battery enters a steep decline period (Δ t). Similarly, when the internal resistance is greater than 125% of the nominal internal resistance, 75% of the corresponding capacity of the battery enters a rapid degradation period of the battery life, and the degradation period is very short. In summary, we refer to R within 125% of the nominal internal resistanceNSet as 100% RSI.e. gamma is 0; if the content exceeds 125%, the formula is followed
Figure BDA0002655708100000061
And calculating gamma.
The corresponding relationship between the battery rated capacity and the nominal internal resistance is shown in table 1.
In an embodiment of the present invention, the method for predicting the state of health of the battery pack may further include the following steps:
calculating the ratio of the current internal resistance of the first battery to the nominal internal resistance of the first battery;
if the ratio is less than or equal to a first preset ratio, determining that the first battery state is excellent;
if the ratio is greater than a first preset ratio and is less than or equal to a second preset ratio, determining that the first battery state is available;
if the ratio is greater than the second preset ratio and is less than or equal to a third preset ratio, determining that the state of the first battery is early-warning;
and if the ratio is larger than a third preset ratio, determining that the first battery state is replacement.
Alternatively, the first preset ratio may be 1.25, the second preset ratio may be 1.60, and the third preset ratio may be 2.2.
Alternatively, different states of the battery may be indicated by different colored indicator lights. For example, when the battery status is excellent, the indicator lamp color may be set to green; when the battery status is available, the color of the indicator light can be set to light green; when the battery state is early warning, the color of the indicator light can be set to be orange, so that a user is reminded of the poor battery state; when the battery state is replacement, the color of the indicator light can be set to be red, and the indicator light is used for reminding a user to replace the battery.
S102: and calculating the health degree of each battery according to the discharge attenuation degree, the residual dischargeable capacity occupation ratio and the internal resistance deviation rate which respectively correspond to each battery.
In an embodiment of the present invention, the above S102 may include the following steps:
according to SOH ═ α DN+βCN) X (1- γ), calculating the SOH of the first battery; wherein the first battery is any one of the batteries in the battery pack, DNIs the degree of discharge decay of the first cell, CNγ is an internal resistance deviation rate of the first battery, α is a first weight coefficient, β is a second weight coefficient, and α + β is 1.
Here, α and β may be set according to actual requirements, and for example, α may be set to 80% and β may be set to 20%.
In an embodiment of the present invention, the method for predicting the state of health of the battery pack may further include the following steps:
determining the interval time when the last discharge amount from the first battery reaches a preset percentage;
if the interval time is less than or equal to the first preset time, alpha is equal to alpha1，β＝β1；
If the interval time is greater than the first preset time and the interval time is less than or equal to the second preset time, alpha is equal to alpha2，β＝β2；
If the interval time is greater than the second preset time and is less than or equal to the third preset time, alpha is equal to alpha3，β＝β3；
If the interval time is greater than the third preset time and is less than or equal to the fourth preset time, α ═ α4，β＝β4；
If the interval time is greater than the fourth preset time and is less than or equal to the fifth preset time, alpha is equal to alpha5，β＝β5；
If the interval time is greater than the fifth preset time and is less than or equal to the sixth preset time, alpha is equal to alpha6，β＝β6；
Wherein alpha is1>α2>α3>α4>α5>α6，β1<β2<β3<β4<β5<β6。
Alternatively, the first preset time may be 3 months, the second preset time may be 6 months, the third preset time may be 9 months, the fourth preset time may be 12 months, the fifth preset time may be 15 months, and the sixth preset time may be 18 months.
Wherein alpha is1、α2、α3、α4、α5、α6And beta1、β2、β3、β4、β5、β6Are all constants between 0 and 1, and the specific numerical value can be set according to the actual situation.
Alternatively, α1＝80％，α2＝70％，α3＝60％，α4＝50％，α5＝40％，α6＝30％，β1＝20％，β2＝30％，β3＝40％，β4＝50％，β5＝60％，β6＝70％。
Degree of discharge decay DNThe battery is fully charged (or discharged by more than 80%) to update, and if the default values (80%, 20%) of the two coefficients alpha, beta are kept for a long time, the SOH prediction is deviated. The two coefficients α and β need to be adjusted in proportion to the time t until the battery is fully discharged (or discharged by more than 80%) last time. During this period, if a full-full discharge (or discharge of 80% or more) event occurs once, the time t is cleared and accumulated again from this event.
S103: and calculating the average value of the health degree of the battery pack and the standard deviation of the health degree of the battery pack according to the health degree of each battery, and predicting the health state of the battery pack according to the average value of the health degree of the battery pack and the standard deviation of the health degree of the battery pack.
In the embodiment of the invention, the average value of the health degree of the battery pack
Figure BDA0002655708100000081
The calculation formula of (2) is as follows:
Figure BDA0002655708100000082
Figure BDA0002655708100000083
wherein, SOHlIs the health of the l-th cell in the battery, and w is the number of cells in the battery.
Standard deviation of health (SOH) of battery packStandard deviation ofThe calculation formula of (2) is as follows:
Figure BDA0002655708100000084
the purpose of the standard deviation calculation analysis is to obtain the degree of dispersion of the health degree of the whole battery pack, and if the calculated battery standard is larger, the degree of dispersion is higher. A standard deviation value of a healthy battery pack, namely a first preset standard deviation S, can be calculated according to the battery pack related data0。
In an embodiment of the present invention, the "predicting the health status of the battery pack according to the average value of the health status of the battery pack and the standard deviation of the health status of the battery pack" in S103 may include the following steps:
establishing a coordinate system by taking the average health degree as a horizontal coordinate and taking the standard deviation of the health degree as a vertical coordinate; in a coordinate system, the abscissa of the origin is 100%, and the abscissa gradually decreases along the direction of the abscissa;
let line x be A and line y be S0And a region surrounded by the abscissa axis and the ordinate axis is defined as a first region, and the straight line x is B and the straight line y is S1The area enclosed by the abscissa axis and the ordinate axis is marked as a second area, and the straight line is arrangedLine x is C and line y is S2And a region surrounded by the abscissa axis and the ordinate axis is denoted as a third region, and the straight line x is D and the straight line y is S3The area enclosed by the abscissa axis and the ordinate axis is marked as a fourth area; wherein A > B > C > D, S3>S2>S1>S0A is a first preset mean value, B is a second preset mean value, C is a third preset mean value, D is a fourth preset mean value, S0Is a first predetermined standard deviation, S1Is the second predetermined standard deviation, S2Is a third predetermined standard deviation, S3Is a fourth preset standard deviation; x is an abscissa value in the coordinate system, and y is an ordinate value in the coordinate system;
recording the first area as a first target area, recording the area of the second area from which the first area is removed as a second target area, recording the area of the third area from which the second area is removed as a third target area, and recording the area of the fourth area from which the third area is removed as a fourth target area;
and predicting the health state of the battery pack according to the position relationship between the target point and the first target area, the second target area, the third target area and the fourth target area, wherein the abscissa value of the target point is the average value of the health degree of the battery pack, and the ordinate value of the target point is the standard deviation of the health degree of the battery pack.
In an embodiment of the present invention, the predicting the state of health of the battery pack according to the position relationship between the target point and the first target area, the second target area, the third target area, and the fourth target area may include:
if the target point is located in the first target area, the health state of the battery pack is predicted to be healthy;
if the target point is located in the second target area, the health state of the battery pack is predicted to be good;
if the target point is located in the third target area, the health state of the battery pack is predicted to be general;
and if the target point is located in the fourth target area, predicting that the health state of the battery pack is poor.
In one embodiment of the present invention, S1＝1.3S0，S2＝1.5S0，S3＝1.7S0，A＝70％，B＝60％，C＝45％，D＝30％。
Specifically, referring to fig. 3, the abscissa is the average value of the health degree, the ordinate is the standard deviation of the health degree, the ordinate is from the origin to the top, the numerical value gradually increases, and the abscissa is from the origin to the right, the numerical value gradually decreases. The four target regions can be determined by the four values on the abscissa axis and the four values on the ordinate axis. And determining the health state of the battery pack according to which target area the target point is located in. And if the target point is located outside the first target area, the second target area, the third target area and the fourth target area, predicting the health state of the battery pack to be extremely poor.
Wherein the first target area may include a segment x ═ a, (0 ≦ y ≦ S0) And the line segment y is S0(x is more than or equal to A and less than or equal to 100%); the second region does not include the segment x ═ A, (0 ≦ y ≦ S0) And the line segment y is S0(A ≦ x ≦ 100%), including line segment x ≦ B, (0 ≦ y ≦ S1) And the line segment y is S1(x is more than or equal to B and less than or equal to 100%); the third area does not include the segment x ═ B, (0 ≦ y ≦ S1) And the line segment y is S1(B.ltoreq.x.ltoreq.100%), including the line segment x.ltoreq.C, (0. ltoreq.y.ltoreq.S2) And the line segment y is S2(x is more than or equal to C and less than or equal to 100%); the fourth region does not include the segment x ═ C, (0 ≦ y ≦ S2) And the line segment y is S2(C ≦ x ≦ 100%), including line segment x ═ D, (0 ≦ y ≦ S3) And the line segment y is S3，(D≤x≤100％)。
A line segment x (y is more than or equal to 0 and less than or equal to S)0) Denotes 0. ltoreq. y. ltoreq.S taken on a straight line x. ltoreq.A0The other sections are similar and are not described in detail.
As can be seen from the above description, in the embodiment of the present invention, the parameters of each battery in the battery pack are obtained, the discharge attenuation, the remaining dischargeable capacity occupation ratio, and the internal resistance deviation rate respectively corresponding to each battery are calculated according to the parameters of each battery, then the health degree of each battery is calculated according to the discharge attenuation, the remaining dischargeable capacity occupation ratio, and the internal resistance deviation rate respectively corresponding to each battery, finally, the average value of the health degrees of the battery packs and the standard deviation of the health degrees of the battery packs are calculated according to the health degrees of each battery, and the health state of the battery packs is predicted according to the average value of the health degrees of the battery packs and the standard deviation of the health degrees of the battery packs.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 4 is a schematic block diagram of a battery pack state of health prediction system according to an embodiment of the present invention, and for convenience of description, only the parts related to the embodiment of the present invention are shown.
In an embodiment of the present invention, the battery pack state of health prediction system 40 may include a parameter acquisition module 401, a health calculation module 402, and a state of health prediction module 403.
The parameter obtaining module 401 is configured to obtain parameters of each battery in the battery pack, and calculate a discharge attenuation, a remaining dischargeable capacity fraction, and an internal resistance deviation rate, which correspond to each battery, according to the parameters of each battery;
a health degree calculation module 402, configured to calculate a health degree of each battery according to the discharge attenuation degree, the remaining dischargeable capacity fraction, and the internal resistance deviation rate respectively corresponding to each battery;
the health status prediction module 403 is configured to calculate a mean value of the health degree of the battery pack and a standard deviation of the health degree of the battery pack according to the health degree of each battery, and predict the health status of the battery pack according to the mean value of the health degree of the battery pack and the standard deviation of the health degree of the battery pack.
Optionally, the health status prediction module 403 may be further configured to:
establishing a coordinate system by taking the average health degree as a horizontal coordinate and taking the standard deviation of the health degree as a vertical coordinate; in a coordinate system, the abscissa of the origin is 100%, and the abscissa gradually decreases along the direction of the abscissa;
let line x be A and line y be S0The axis of abscissa and the axis of ordinateThe region is denoted as a first region, and the straight line x is B and the straight line y is S1And the area enclosed by the abscissa axis and the ordinate axis is marked as a second area, and the straight line x is equal to C, and the straight line y is equal to S2And a region surrounded by the abscissa axis and the ordinate axis is denoted as a third region, and the straight line x is D and the straight line y is S3The area enclosed by the abscissa axis and the ordinate axis is marked as a fourth area; wherein A > B > C > D, S3>S2>S1>S0A is a first preset mean value, B is a second preset mean value, C is a third preset mean value, D is a fourth preset mean value, S0Is a first predetermined standard deviation, S1Is the second predetermined standard deviation, S2Is a third predetermined standard deviation, S3Is a fourth preset standard deviation; x is an abscissa value in the coordinate system, and y is an ordinate value in the coordinate system;
recording the first area as a first target area, recording the area of the second area from which the first area is removed as a second target area, recording the area of the third area from which the second area is removed as a third target area, and recording the area of the fourth area from which the third area is removed as a fourth target area;
and predicting the health state of the battery pack according to the position relationship between the target point and the first target area, the second target area, the third target area and the fourth target area, wherein the abscissa value of the target point is the average value of the health degree of the battery pack, and the ordinate value of the target point is the standard deviation of the health degree of the battery pack.
Optionally, the health status prediction module 403 may be further configured to:
if the target point is located in the first target area, the health state of the battery pack is predicted to be healthy;
if the target point is located in the second target area, the health state of the battery pack is predicted to be good;
if the target point is located in the third target area, the health state of the battery pack is predicted to be general;
and if the target point is located in the fourth target area, predicting that the health state of the battery pack is poor.
Alternatively, S1＝1.3S0，S2＝1.5S0，S3＝1.7S0，A＝70％，B＝60％，C＝45％，D＝30％。
Optionally, in the parameter obtaining module 401, the parameters of the battery include a discharge current per unit time in a discharge process in which the last discharge amount of the battery reaches a preset percentage, a unit time in a discharge process in which the last discharge amount of the battery reaches the preset percentage, a rated capacity of the battery, a full discharge capacity of a life cycle of the battery, a remaining discharge capacity of the life cycle of the battery, a failure internal resistance threshold of the battery, a nominal internal resistance of the battery, and a current internal resistance of the battery;
the parameter obtaining module 401 may further be configured to:
according to
Figure BDA0002655708100000121
Calculating the discharge attenuation degree D of the first batteryN(ii) a Wherein, the first battery is any one battery in the battery pack, IiIs the discharge current, delta t, in the unit time of the discharge process when the last discharge amount of the first battery reaches the preset percentageiThe unit time of the discharging process when the last discharge amount of the first battery reaches a preset percentage, CSIs the rated capacity of the first battery;
according to CN＝R÷ACalculating the ratio of the remaining dischargeable capacity of the first battery to the remaining dischargeable capacity of the first batteryN(ii) a Wherein, cRResidual discharge capacity for the first battery life cycle, cAThe first battery life cycle full discharge capacity;
if it is
Figure BDA0002655708100000122
The internal resistance deviation ratio γ of the first battery is 0;
if it is
Figure BDA0002655708100000123
Then according to
Figure BDA0002655708100000124
Calculating the internal resistance deviation rate gamma of the first battery; wherein R isNIs the current internal resistance, R, of the first batterySNominal internal resistance, R, for the first batteryOIs a first battery failure internal resistance threshold.
Optionally, the first battery life cycle residual discharge capacity cRThe calculation formula of (2) is as follows:
Figure BDA0002655708100000125
wherein, cjThe discharge capacity of the first battery in the j discharge is a preset coefficient, and n is the discharge frequency of the first battery.
Optionally, the health degree calculation module 402 is specifically configured to:
according to SOH ═ α DN+βCN) X (1- γ), calculating the SOH of the first battery; wherein the first battery is any one of the batteries in the battery pack, DNIs the degree of discharge decay of the first cell, CNγ is an internal resistance deviation rate of the first battery, α is a first weight coefficient, β is a second weight coefficient, and α + β is 1.
Optionally, the health degree calculation module 402 may be further configured to:
determining the interval time when the last discharge amount from the first battery reaches a preset percentage;
if the interval time is less than or equal to the first preset time, alpha is equal to alpha1，β＝β1；
If the interval time is greater than the first preset time and the interval time is less than or equal to the second preset time, alpha is equal to alpha2，β＝β2；
If the interval time is greater than the second preset time and is less than or equal to the third preset time, alpha is equal to alpha3，β＝β3；
If the interval time is greater than the third preset time and is less than or equal to the fourth preset time, α ═ α4，β＝β4；
If the interval time is greater than the fourth preset time and the interval time is smallAt or equal to a fifth preset time, then alpha is alpha5，β＝β5；
If the interval time is greater than the fifth preset time and is less than or equal to the sixth preset time, alpha is equal to alpha6，β＝β6；
Wherein alpha is1>α2>α3>α4>α5>α6，β1<β2<β3<β4<β5<β6。
It will be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is merely used as an example, and in practical applications, the above function allocation may be performed by different functional units and modules as needed, that is, the internal structure of the battery pack health status prediction system is divided into different functional units or modules to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the above-mentioned apparatus may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Fig. 5 is a schematic block diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 5, the terminal device 50 of this embodiment includes: one or more processors 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processors 501. The processor 501, when executing the computer program 503, implements the steps in the various battery pack state of health prediction method embodiments described above, such as steps S101 to S103 shown in fig. 1. Alternatively, the processor 501, when executing the computer program 503, implements the functions of the modules/units in the above-described battery pack state of health prediction system embodiments, such as the functions of the modules 401 to 403 shown in fig. 4.
Illustratively, the computer program 503 may be partitioned into one or more modules/units that are stored in the memory 502 and executed by the processor 501 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 503 in the terminal device 50. For example, the computer program 503 may be divided into a parameter acquisition module, a health degree calculation module and a health state prediction module, and each module has the following specific functions:
the parameter acquisition module is used for acquiring parameters of each battery in the battery pack and calculating the discharge attenuation, the residual dischargeable capacity ratio and the internal resistance deviation rate corresponding to each battery according to the parameters of each battery;
the health degree calculation module is used for calculating the health degree of each battery according to the discharge attenuation degree, the residual dischargeable capacity occupation ratio and the internal resistance deviation rate which respectively correspond to each battery;
and the health state prediction module is used for calculating the average value of the health degree of the battery pack and the standard deviation of the health degree of the battery pack according to the health degree of each battery and predicting the health state of the battery pack according to the average value of the health degree of the battery pack and the standard deviation of the health degree of the battery pack.
Other modules or units can refer to the description of the embodiment shown in fig. 4, and are not described again here.
The terminal device 50 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device 50 includes, but is not limited to, a processor 501 and a memory 502. Those skilled in the art will appreciate that fig. 5 is only one example of a terminal device 50 and does not constitute a limitation to terminal device 50 and may include more or less components than those shown, or combine certain components, or different components, for example, terminal device 50 may also include an input device, an output device, a network access device, a bus, etc.
The Processor 501 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 502 may be an internal storage unit of the terminal device 50, such as a hard disk or a memory of the terminal device 50. The memory 502 may also be an external storage device of the terminal device 50, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 50. Further, the memory 502 may also include both an internal storage unit of the terminal device 50 and an external storage device. The memory 502 is used for storing the computer program 503 and other programs and data required by the terminal device 50. The memory 502 may also be used to temporarily store data that has been output or is to be output.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed system and method for predicting the state of health of a battery pack may be implemented in other ways. For example, the above-described embodiments of the battery pack state of health prediction system are merely illustrative, and for example, the division of the modules or units is only one logical function division, and other division manners may be implemented in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims (10)

1. A battery pack state of health prediction method, comprising:
acquiring parameters of each battery in the battery pack, and calculating the discharge attenuation, the residual dischargeable capacity ratio and the internal resistance deviation rate corresponding to each battery according to the parameters of each battery;
calculating the health degree of each battery according to the discharge attenuation degree, the residual dischargeable capacity ratio and the internal resistance deviation rate which respectively correspond to each battery;
and calculating the average value of the health degree of the battery pack and the standard deviation of the health degree of the battery pack according to the health degree of each battery, and predicting the health state of the battery pack according to the average value of the health degree of the battery pack and the standard deviation of the health degree of the battery pack.
2. The battery pack state of health prediction method of claim 1, wherein predicting the battery pack state of health based on the battery pack mean health and the battery pack standard deviation comprises:
establishing a coordinate system by taking the average health degree as a horizontal coordinate and taking the standard deviation of the health degree as a vertical coordinate; in the coordinate system, the abscissa of the origin is 100%, and the abscissa gradually decreases along the direction of the abscissa;
let line x be A and line y be S0And a region surrounded by the abscissa axis and the ordinate axis is defined as a first region, and the straight line x is B and the straight line y is S1And the area enclosed by the abscissa axis and the ordinate axis is marked as a second area, and the straight line x is equal to C, and the straight line y is equal to S2And a region surrounded by the abscissa axis and the ordinate axis is denoted as a third region, and the straight line x is D and the straight line y is S3The area enclosed by the abscissa axis and the ordinate axis is marked as a fourth area; wherein A > B > C > D, S3＞S2＞S1＞S0A is a first preset mean value, B is a second preset mean value, C is a third preset mean value, D is a fourth preset mean value, S0Is a first predetermined standard deviation, S1Is the second predetermined standard deviation, S2Is a third predetermined standard deviation, S3Is a fourth preset standard deviation; x is an abscissa value in the coordinate system, and y is an ordinate value in the coordinate system;
recording the first region as a first target region, recording a region of the second region from which the first region is removed as a second target region, recording a region of the third region from which the second region is removed as a third target region, and recording a region of the fourth region from which the third region is removed as a fourth target region;
and predicting the health state of the battery pack according to the position relationship between a target point and the first target area, the second target area, the third target area and the fourth target area, wherein the abscissa value of the target point is the average value of the health degree of the battery pack, and the ordinate value of the target point is the standard deviation of the health degree of the battery pack.
3. The battery pack state of health prediction method according to claim 2, wherein the predicting the battery pack state of health from the positional relationship of a target point with the first target region, the second target region, the third target region, and the fourth target region includes:
if the target point is located in the first target area, predicting the health state of the battery pack to be healthy;
if the target point is located in the second target area, the health state of the battery pack is predicted to be good;
if the target point is located in the third target area, predicting the health state of the battery pack to be general;
and if the target point is located in the fourth target area, predicting that the health state of the battery pack is poor.
4. The battery pack state of health prediction method of claim 2, wherein S1＝1.3S0，S2＝1.5S0，S3＝1.7S0，A＝70％，B＝60％，C＝45％，D＝30％。
5. The battery pack health status prediction method according to claim 1, wherein the parameters of the battery include a discharge current per unit time in a discharge process in which a last discharge amount of the battery reaches a preset percentage, a battery rated capacity, a battery life cycle full discharge capacity, a battery life cycle remaining discharge capacity, a battery failure internal resistance threshold, a battery nominal internal resistance, and a battery current internal resistance;
the calculating of the discharge attenuation degree, the residual dischargeable capacity ratio and the internal resistance deviation rate respectively corresponding to each battery according to the parameters of each battery comprises the following steps:
according to
Figure FDA0002655708090000021
Calculating the discharge attenuation degree D of the first batteryN(ii) a Wherein the first battery is any one battery in the battery pack, IiIs the discharge current, delta t, in the unit time of the discharge process when the last discharge amount of the first battery reaches the preset percentageiThe unit time of the discharging process when the last discharge amount of the first battery reaches a preset percentage, CSIs the rated capacity of the first battery;
according to CN＝cR÷cAAnd calculating the ratio C of the residual dischargeable capacity of the first batteryN(ii) a Wherein, cRResidual discharge capacity for the first battery life cycle, cAThe first battery life cycle full discharge capacity;
if it is
Figure FDA0002655708090000022
The internal resistance deviation ratio γ of the first battery is 0;
if it is
Figure FDA0002655708090000031
Then according to
Figure FDA0002655708090000032
Calculating an internal resistance deviation ratio gamma of the first battery; wherein R isNIs the current internal resistance, R, of the first batterySNominal internal resistance, R, for the first batteryOIs a first battery failure internal resistance threshold.
6. The battery pack state of health prediction method of claim 5, wherein the first battery life cycle residual discharge capacity cRThe calculation formula of (2) is as follows:
Figure FDA0002655708090000033
wherein, cjThe discharge capacity of the first battery at the j discharge time is a preset coefficient, and n is a firstThe number of times the battery was discharged.
7. The method for predicting the state of health of a battery pack according to any one of claims 1 to 6, wherein the calculating the state of health of each battery based on the discharge degradation, the remaining dischargeable capacity fraction, and the internal resistance deviation rate corresponding to each battery includes:
according to SOH ═ α DN+βCN) X (1- γ), calculating the SOH of the first battery; wherein the first battery is any one battery in the battery pack, DNIs the degree of discharge decay of the first cell, CNγ is an internal resistance deviation rate of the first battery, α is a first weight coefficient, β is a second weight coefficient, and α + β is 1.
8. The battery pack state of health prediction method of claim 7, further comprising:
determining the interval time when the last discharge amount from the first battery reaches a preset percentage;
if the interval time is less than or equal to a first preset time, alpha is equal to alpha1，β＝β1；
If the interval time is greater than the first preset time and the interval time is less than or equal to a second preset time, alpha is equal to alpha2，β＝β2；
If the interval time is greater than the second preset time and is less than or equal to a third preset time, alpha is equal to alpha3，β＝β3；
If the interval time is greater than the third preset time and is less than or equal to a fourth preset time, then alpha is equal to alpha4，β＝β4；
If the interval time is greater than the fourth preset time and is less than or equal to a fifth preset time, then alpha is equal to alpha5，β＝β5；
If the interval time is greater than the fifth preset time and the interval time is less than or equal to a sixth preset time, alpha is equal to alpha6，β＝β6；
Wherein alpha is1＞α2＞α3＞α4＞α5＞α6，β1＜β2＜β3＜β4＜β5＜β6。
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the battery state of health prediction method according to any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium, storing a computer program which, when executed by one or more processors, performs the steps of the battery state of health prediction method according to any one of claims 1 to 8.
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