CN112034350B - Battery pack health state prediction method and terminal equipment - Google Patents
Battery pack health state prediction method and terminal equipment Download PDFInfo
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- 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
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- 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
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- G—PHYSICS
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- 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
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- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- 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
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- G—PHYSICS
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- 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
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Abstract
The invention is applicable to the technical field of batteries, and discloses a battery pack health state prediction method and terminal equipment, wherein the method comprises the following steps: parameters of each battery in the battery pack are obtained, and the discharge attenuation degree, the residual discharge capacity occupancy ratio and the internal resistance deviation rate corresponding to each battery are calculated according to the parameters of each battery; calculating the health degree of each battery according to the discharge attenuation degree, the residual discharge capacity duty ratio and the internal resistance deviation rate corresponding to each battery respectively; and calculating a battery health average value and a battery health standard deviation according to the health degrees of the batteries, and predicting the health state of the battery according to the battery health average value and the battery health standard deviation. The invention can calculate the health degree of the single battery and predict the health state of the battery pack.
Description
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 status of the battery pack can be used to measure the health of the battery pack, which is an important indicator for measuring the life of the battery pack, so it is necessary to predict the health status of the battery pack.
Currently, there are many methods for predicting the health status of a battery, but most of the existing methods only can predict the health status of a single battery, but cannot predict the health status of a battery pack.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a method for predicting the health state of a battery pack and a terminal device, so as to solve the problem that the health state of the 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 health state of a battery pack, including:
parameters of each battery in the battery pack are obtained, and the discharge attenuation degree, the residual discharge capacity occupancy ratio and the internal resistance deviation rate corresponding to each battery are calculated according to the parameters of each battery;
calculating the health degree of each battery according to the discharge attenuation degree, the residual discharge capacity duty ratio and the internal resistance deviation rate corresponding to each battery respectively;
and calculating a battery health average value and a battery health standard deviation according to the health degrees of the batteries, and predicting the health state of the battery according to the battery health average value and the battery health standard deviation.
A second aspect of an embodiment of the present invention provides a terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the battery health status 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 state of health prediction method of the first aspect.
Compared with the prior art, the embodiment of the invention has the beneficial effects that: according to the embodiment of the invention, parameters of each battery in the battery pack are firstly obtained, the corresponding discharge attenuation degree, the residual discharge capacity ratio and the internal resistance deviation rate of each battery are calculated according to the parameters of each battery, then the health degree of each battery is calculated according to the corresponding discharge attenuation degree, the residual discharge capacity ratio and the internal resistance deviation rate of each battery, finally the health degree average value and the health degree standard deviation of the battery pack are calculated according to the health degree of each battery, and the health state of the battery pack is predicted according to the health degree average value and the health degree standard deviation of the battery pack, so that the health degree of an individual battery can be calculated, and the health state of the battery pack can be predicted.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an implementation of a method for predicting a health status of a battery pack according to an embodiment of the present invention;
FIG. 2 is a schematic diagram showing a relationship between remaining lifetime capacity and internal resistance of a battery according to an embodiment of the present invention;
FIG. 3 is a schematic diagram 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 configurations, 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 illustrate the technical scheme of the invention, the following description is made by specific examples.
Fig. 1 is a schematic flow chart of an implementation of a method for predicting a health state of a battery pack according to an embodiment of the present invention, and for convenience of explanation, only a portion related to the embodiment of the present invention is shown. The execution body of the embodiment of the invention can be a terminal device. As shown in fig. 1, the method may include the steps of:
s101: parameters of each battery in the battery pack are obtained, and the discharge attenuation degree, the residual discharge capacity ratio and the internal resistance deviation rate corresponding to each battery are calculated according to the parameters of each battery.
In the embodiment of the invention, the discharge attenuation degree of each battery, the residual discharge capacity ratio of each battery and the internal resistance deviation rate of each battery can be respectively calculated according to the parameters of each battery in the battery pack by acquiring the parameters of each battery in the battery pack.
In one embodiment of the invention, the parameters of the battery include a discharge current per unit time during which the 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 residual discharge capacity, a battery failure internal resistance threshold, a battery nominal internal resistance, and a battery current internal resistance;
in S101, calculating, according to the parameters of each battery, the discharge attenuation degree, the remaining dischargeable capacity ratio, and the internal resistance deviation ratio corresponding to each battery, may include:
according to
Calculating the discharge attenuation degree D of the first battery N The method comprises the steps of carrying out a first treatment on the surface of the Wherein the first battery is any one battery in the battery pack, I i For the discharge current in unit time in the discharge process of the last discharge capacity of the first battery reaching a preset percentage, deltat i C is the unit time in the discharging process of the last discharging quantity of the first battery reaching a preset percentage S Is the rated capacity of the first battery;
according to C N ＝c R ÷c A Calculating the remaining dischargeable capacity ratio C of the first battery N The method comprises the steps of carrying out a first treatment on the surface of the Wherein c R Residual discharge capacity for the first cell life cycle, c A Full discharge capacity for the first battery life cycle;
if it is
The internal resistance deviation rate gamma of the first battery is 0;
if it is
Then according to->
Calculating an internal resistance deviation rate gamma of the first battery; wherein R is N For the current internal resistance of the first battery, R S Nominal internal resistance of first battery, R O Is the first battery failure internal resistance threshold.
Wherein the preset percentage may be 80%. The discharging 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 that is completely discharged. Preferably, the last discharging process of the first battery when the discharging amount reaches the preset percentage may be a discharging process of the first battery when the first battery is completely discharged, that is, a discharging process from full charge to complete discharge of the first battery.
Alternatively, according to
Calculating the discharge attenuation degree D of the first battery N Previously, the method may further comprise the steps of:
judging whether the last discharge capacity of the first battery reaches a preset percentage, and judging whether the discharge capacity of the first battery is greater than or equal to 75% of the rated capacity of the battery;
d, if the last discharge capacity of the first battery reaches 75% or more of the rated capacity of the battery N ＝1；
If the last discharge capacity of the first battery reaches that the discharge capacity of the preset percentage is less than 75 percent of the rated capacity of the battery, the method is executed according to
Calculating the discharge attenuation degree D of the first battery N Is carried out by a method comprising the steps of.
C N The remaining dischargeable capacity of the first battery may be expressed as a percentage of the total life cycle dischargeable capacity of the first battery. For example, when the total discharge capacity of a 100Ah battery is 100ah×80=8000 Ah and the current discharge capacity is 800Ah, C N The value is (8000-800)/(8000=0.9).
c R Representing the remaining dischargeable battery capacity of the first battery during a life cycle, c A Indicating 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 c R The calculation formula of (2) is as follows:
wherein c j The discharging capacity of the first battery in the j-th discharging is shown as delta as a preset coefficient, and n is the discharging frequency of the first battery.
In an embodiment of the present invention, in the present invention,
the accumulated value of the discharge ampere-hour number at each time is shown up to now. c j The single discharge capacity of the first battery is shown, and the influence of different discharge capacities on the whole residual life of the battery is different, so that the accuracy of the value is improved, and the discharge capacity of each time is multiplied by a coefficient delta. Delta may be set according to actual requirements, for example, may be set to 1. According to the characteristic that the battery is more lost due to the large-current discharge of the battery, delta changes along with the magnitude of the discharge current, and the larger the current is, the larger the delta is, so that the discharge value of the full life cycle is counted more accurately.
Alternatively, R N Is an average value of the first battery internal resistance value acquired within one month. I.e.
R k And m is the number of times of acquiring the first battery internal resistance value.
The internal resistance deviation rate of the battery may also be referred to as an internal resistance aging coefficient of the battery. The nominal internal resistance of the battery may be the battery's off-standard (reference) internal resistance.
R O -R S Represents the internal resistance change range in the effective life cycle of the current first battery, R N -R S Is the degree of aging of the internal resistance of the first battery.
TABLE 1 correspondence between rated capacity and nominal internal resistance of battery
| 12V Battery rated capacity (Ah) | Nominal internal resistance (mΩ) |
| 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 battery performance decreases, and when the battery capacity decreases to 75% of the rated capacity, the life of the battery enters a steep decline period (Δt). Also, when the internal resistance is greater than 125% of the nominal internal resistanceAt this time, 75% of the corresponding capacity of the battery is put into the steep decline period of the service life of the battery, and the decline period is very short. In summary, we will have R within 125% of the nominal internal resistance N Set to 100% R S I.e., γ is 0; if the content exceeds 125%, the formula is adopted
Gamma is calculated.
The correspondence between the rated capacity and the nominal internal resistance of the battery is shown in table 1.
In an embodiment of the present invention, the method for predicting a health status of a battery pack may further include the steps of:
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 smaller than or equal to a first preset ratio, determining that the first battery state is excellent;
if the ratio is greater than the first preset ratio and the ratio is less than or equal to the second preset ratio, determining that the first battery state is available;
if the ratio is greater than the second preset ratio and the ratio is less than or equal to the third preset ratio, determining that the first battery state is early warning;
if the ratio is greater than a third predetermined ratio, the first battery state is determined to be changed.
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 status of the battery may be indicated by different colored indicator lights. For example, when the battery state is excellent, the indicator light color may be set to green; when the battery status is available, the indicator light color may be set to light green; when the battery state is early-warning, the color of the indicator lamp can be set to be orange, so that a user is reminded of bad battery state; when the battery status is replacement, the indicator light may be set to red for alerting the user to replace the battery.
S102: and calculating the health degree of each battery according to the discharge attenuation degree, the residual dischargeable capacity ratio and the internal resistance deviation rate corresponding to each battery.
In one embodiment of the present invention, the step S102 may include the following steps:
according to soh= (αd) N +βC N ) X (1- γ), calculating the state of health SOH of the first battery; wherein the first battery is any one battery in the battery pack, D N C is the discharge attenuation degree of the first battery N The remaining dischargeable capacity of the first battery is the duty ratio, γ is the internal resistance deviation rate of the first battery, α is the first weight coefficient, β is the second weight coefficient, and α+β=1.
Here, α and β may be set according to actual requirements, and for example, may be set to α=80%, and β=20%.
In an embodiment of the present invention, the method for predicting a health status of a battery pack may further include the steps of:
determining the interval time when the last discharge capacity of the first battery reaches a preset percentage;
if the interval time is less than or equal to the first preset time, α=α 1 ，β＝β 1 ；
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, α=α 2 ，β＝β 2 ；
If the interval time is greater than the second preset time and the interval time is less than or equal to the third preset time, α=α 3 ，β＝β 3 ；
If the interval is greater than the third preset time and the interval is less than or equal to the fourth preset time, α=α 4 ，β＝β 4 ；
If the interval is greater than the fourth preset time and the interval is less than or equal to the fifth preset time, α=α 5 ，β＝β 5 ；
If the interval is greater than the fifth preset time and the interval is less than or equal to the sixth preset time, α=α 6 ，β＝β 6 ；
Wherein alpha is 1 >α 2 >α 3 >α 4 >α 5 >α 6 ，β 1 <β 2 <β 3 <β 4 <β 5 <β 6 。
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 is 1 、α 2 、α 3 、α 4 、α 5 、α 6 Beta 1 、β 2 、β 3 、β 4 、β 5 、β 6 And the constant values are all between 0 and 1, and specific values can be set according to actual conditions.
Alternatively, alpha 1 ＝80％，α 2 ＝70％，α 3 ＝60％，α 4 ＝50％，α 5 ＝40％，α 6 ＝30％，β 1 ＝20％，β 2 ＝30％，β 3 ＝40％，β 4 ＝50％，β 5 ＝60％，β 6 ＝70％。
Degree of discharge decay D N The battery needs to be fully charged (or discharged by more than 80%) to be updated, and if the default values (80% and 20%) of the two coefficients alpha and beta are kept for a long time, the SOH prediction is deviated. The two coefficients α, β need to be scaled in proportion to the time t from the last full charge discharge (or more than 80% discharge) of the battery. During this period, if a full charge discharge (or discharge of 80% or more) event occurs once, the time t is cleared, and accumulation is restarted from the current event.
S103: and calculating a battery health average value and a battery health standard deviation according to the health degrees of the batteries, and predicting the health state of the battery according to the battery health average value and the battery health standard deviation.
In an embodiment of the invention, the health average of the battery pack
The calculation formula of (2) is as follows: />
Wherein SOH l The degree of health of the first cell in the battery pack is defined as w, which is the number of cells in the battery pack.
Standard deviation of battery health SOH Standard deviation of The calculation formula of (2) is as follows:
the purpose of standard deviation calculation analysis is to obtain the degree of dispersion of the health of the whole battery, and if the calculated battery standard is larger, the degree of dispersion is higher. The standard deviation value of a healthy battery pack, namely a first preset standard deviation S, can be calculated according to the related data of the battery pack 0 。
In one embodiment of the present invention, the "predicting the health status of the battery according to the average value of the health of the battery and the standard deviation of the health of the battery" in S103 may include the following steps:
establishing a coordinate system by taking the health mean value as an abscissa and the health standard deviation as an ordinate; in the coordinate system, the abscissa of the origin is 100%, and the abscissa value gradually decreases along the abscissa direction;
let straight line x=a, straight line y=s 0 The area enclosed by the abscissa axis and the ordinate axis is denoted as a first area, and a straight line x=b, a straight line y=s 1 The area enclosed by the abscissa axis and the ordinate axis is denoted as a second area, and a straight line x=c, a straight line y=s 2 The area enclosed by the abscissa axis and the ordinate axis is denoted as a third area, and a straight line x=d, a straight line y=s 3 The area surrounded by the abscissa axis and the ordinate axis is denoted as a fourth area; wherein A > B > C > D, S 3 >S 2 >S 1 >S 0 A is a first preset average value, B is a second preset average value, C is a third preset average value, D is a fourth preset average value, S 0 Is the firstA preset standard deviation S 1 Is the second preset standard deviation, S 2 Is a third preset standard deviation S 3 A fourth preset standard deviation; x is an abscissa value in a coordinate system, and y is an ordinate value in the coordinate system;
the first area is marked as a first target area, the area of the second area after the first area is removed is marked as a second target area, the area of the third area after the second area is removed is marked as a third target area, and the area of the fourth area after the third area is removed is marked as a fourth target area;
and predicting the health state of the battery pack according to the position relation between the target point and the first, second, third and fourth target areas, 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 health status of the battery pack according to the positional relationship between the target point and the first, second, third and fourth target areas may include:
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 positioned in the second target area, predicting the health state of the battery pack to be good;
if the target point is positioned in the third target area, predicting the health state of the battery pack as normal;
if the target point is located in the fourth target area, the battery pack health state is predicted to be poor.
In one embodiment of the invention, S 1 ＝1.3S 0 ，S 2 ＝1.5S 0 ，S 3 ＝1.7S 0 ，A＝70％，B＝60％，C＝45％，D＝30％。
Specifically, referring to fig. 3, the abscissa is the average value of the health degrees, the ordinate is the standard deviation of the health degrees, the ordinate is from the origin point upward, the value gradually increases, and the abscissa is from the origin point to the right, and the value gradually decreases. The four target areas 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. If the target point is located outside the first target area, the second target area, the third target area and the fourth target area, the state of health of the battery pack is predicted to be extremely poor.
Wherein the first target region may include a line segment x=a, (0.ltoreq.y.ltoreq.s 0 ) And line segment y=s 0 (A is less than or equal to x is less than or equal to 100 percent); the second region does not include line segment x=a, (0.ltoreq.y.ltoreq.s 0 ) And line segment y=s 0 (A is not less than x is not more than 100%), including line segment x=B, (0 is not less than y is not less than S) 1 ) And line segment y=s 1 (B is less than or equal to x is less than or equal to 100 percent); the third region does not include line segment x=b, (0.ltoreq.y.ltoreq.s 1 ) And line segment y=s 1 (B.ltoreq.x.ltoreq.100%) including the line segment x=C, (0.ltoreq.y.ltoreq.S 2 ) And line segment y=s 2 (C is less than or equal to x is less than or equal to 100 percent); the fourth region does not include line segment x=c, (0.ltoreq.y.ltoreq.s 2 ) And line segment y=s 2 (C is not less than x is not more than 100%), including line segment x=D, (0 is not less than y is not less than S) 3 ) And line segment y=s 3 ，(D≤x≤100％)。
Line segment x=a, (0.ltoreq.y.ltoreq.S 0 ) Represents 0.ltoreq.y.ltoreq.S taken on the straight line x=A 0 And the like, and will not be described in detail.
As can be seen from the foregoing description, according to the embodiment of the present invention, by obtaining the parameters of each battery in the battery pack, calculating the discharge attenuation degree, the remaining dischargeable capacity ratio, and the internal resistance deviation ratio corresponding to each battery according to the parameters of each battery, then calculating the health degree of each battery according to the discharge attenuation degree, the remaining dischargeable capacity ratio, and the internal resistance deviation ratio corresponding to each battery, and finally calculating the health degree average value and the health degree standard deviation of the battery according to the health degree of each battery, and predicting the health state of the battery according to the health degree average value and the health degree standard deviation of the battery, not only can the health degree of an individual battery be calculated, but also the health state of the battery can be predicted, and the accuracy is higher.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment 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 only a portion related to the embodiment of the present invention is shown for convenience of explanation.
In an embodiment of the present invention, the battery pack state of health prediction system 40 may include a parameter acquisition module 401, a state of 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 degree, a remaining dischargeable capacity ratio and an internal resistance deviation rate corresponding to each battery according to the parameters of each battery;
a health degree calculating module 402, configured to calculate health degrees of the respective batteries according to the discharge attenuation degrees, the remaining dischargeable capacity ratios, and the internal resistance deviation rates of the respective batteries;
the health status prediction module 403 is configured to calculate a battery health average and a battery health standard deviation according to the health degrees of the batteries, and predict the health status of the battery according to the battery health average and the battery health standard deviation.
Alternatively, the health status prediction module 403 may be further configured to:
establishing a coordinate system by taking the health mean value as an abscissa and the health standard deviation as an ordinate; in the coordinate system, the abscissa of the origin is 100%, and the abscissa value gradually decreases along the abscissa direction;
let straight line x=a, straight line y=s 0 The area enclosed by the abscissa axis and the ordinate axis is denoted as a first area, and a straight line x=b, a straight line y=s 1 The area enclosed by the abscissa axis and the ordinate axis is denoted as a second area, and a straight line x=c, a straight line y=s 2 The area enclosed by the abscissa axis and the ordinate axis is denoted as a third area, and a straight line x=d, a straight line y=s 3 The area surrounded by the abscissa axis and the ordinate axis is denoted as a fourth area; wherein A > B > C > D, S 3 >S 2 >S 1 >S 0 A is a first preset average value, B is a second preset average value, C is a third preset average value, D is a fourth preset average value, S 0 For the first preset standard deviation, S 1 Is the second preset standard deviation, S 2 Is a third preset standard deviation S 3 A fourth preset standard deviation; x is an abscissa value in a coordinate system, and y is an ordinate value in the coordinate system;
the first area is marked as a first target area, the area of the second area after the first area is removed is marked as a second target area, the area of the third area after the second area is removed is marked as a third target area, and the area of the fourth area after the third area is removed is marked as a fourth target area;
and predicting the health state of the battery pack according to the position relation between the target point and the first, second, third and fourth target areas, 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.
Alternatively, the health status prediction module 403 may be further configured to:
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 positioned in the second target area, predicting the health state of the battery pack to be good;
if the target point is positioned in the third target area, predicting the health state of the battery pack as normal;
if the target point is located in the fourth target area, the battery pack health state is predicted to be poor.
Alternatively S 1 ＝1.3S 0 ，S 2 ＝1.5S 0 ，S 3 ＝1.7S 0 ，A＝70％，B＝60％，C＝45％，D＝30％。
Optionally, in the parameter obtaining module 401, the parameters of the battery include a discharge current in a unit time during a discharge process in which a last discharge amount of the battery reaches a 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 acquisition module 401 may also be used to:
according to
Calculating the discharge attenuation degree D of the first battery N The method comprises the steps of carrying out a first treatment on the surface of the Wherein the first battery is any one battery in the battery pack, I i For the discharge current in unit time in the discharge process of the last discharge capacity of the first battery reaching a preset percentage, deltat i C is the unit time in the discharging process of the last discharging quantity of the first battery reaching a preset percentage S Is the rated capacity of the first battery;
according to C N ＝ R ÷ A Calculating the remaining dischargeable capacity ratio C of the first battery N The method comprises the steps of carrying out a first treatment on the surface of the Wherein c R Residual discharge capacity for the first cell life cycle, c A Full discharge capacity for the first battery life cycle;
if it is
The internal resistance deviation rate gamma of the first battery is 0;
if it is
Then according to->
Calculating an internal resistance deviation rate gamma of the first battery; wherein R is N For the current internal resistance of the first battery, R S Nominal internal resistance of first battery, R O Is the first battery failure internal resistance threshold.
Optionally, the first battery life cycle residual discharge capacity c R The calculation formula of (2) is as follows:
wherein c j The discharging capacity of the first battery in the j-th discharging is shown as delta as a preset coefficient, and n is the discharging frequency of the first battery.
Optionally, the health calculation module 402 is specifically configured to:
according to soh= (αd) N +βC N ) X (1- γ), calculating the state of health SOH of the first battery; wherein the first battery is any one battery in the battery pack, D N C is the discharge attenuation degree of the first battery N The remaining dischargeable capacity of the first battery is the duty ratio, γ is the internal resistance deviation rate of the first battery, α is the first weight coefficient, β is the second weight coefficient, and α+β=1.
Alternatively, the health calculation module 402 may also be configured to:
determining the interval time when the last discharge capacity of the first battery reaches a preset percentage;
if the interval time is less than or equal to the first preset time, α=α 1 ，β＝β 1 ；
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, α=α 2 ，β＝β 2 ；
If the interval time is greater than the second preset time and the interval time is less than or equal to the third preset time, α=α 3 ，β＝β 3 ；
If the interval is greater than the third preset time and the interval is less than or equal to the fourth preset time, α=α 4 ，β＝β 4 ；
If the interval is greater than the fourth preset time and the interval is less than or equal to the fifth preset time, α=α 5 ，β＝β 5 ；
If the interval is greater than the fifth preset time and the interval is less than or equal to the sixth preset time, α=α 6 ，β＝β 6 ；
Wherein alpha is 1 >α 2 >α 3 >α 4 >α 5 >α 6 ，β 1 <β 2 <β 3 <β 4 <β 5 <β 6 。
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in practical application, the above-mentioned functional allocation may be performed by different functional units and modules according to needs, i.e. the internal structure of the battery pack health status prediction system is divided into different functional units or modules, so as to perform all or part of the above-mentioned functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a 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 process of the units and modules in the above device may refer to the corresponding process in the foregoing method embodiment, which is 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 processor 501. The processor 501, when executing the computer program 503, implements the steps in the above-described embodiments of the battery pack state of health prediction method, such as steps S101 to S103 shown in fig. 1. Alternatively, the processor 501, when executing the computer program 503, performs the functions of the modules/units in the embodiment of the battery pack state of health prediction system described above, such as the functions of the modules 401 to 403 shown in fig. 4.
Illustratively, the computer program 503 may be split into one or more modules/units that are stored in the memory 502 and executed by the processor 501 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution 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 calculation module, and a health status prediction module, where each module specifically functions as follows:
the parameter acquisition module is used for acquiring parameters of each battery in the battery pack and calculating the corresponding discharge attenuation degree, the residual dischargeable capacity ratio and the internal resistance deviation rate of 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 ratio and the internal resistance deviation rate which are respectively corresponding to each battery;
the health state prediction module is used for calculating a battery pack health average value and a battery pack health standard deviation according to the health degrees of the batteries, and predicting the health state of the battery pack according to the battery pack health average value and the battery pack health standard deviation.
Other modules or units may be described with reference to the embodiment shown in fig. 4, and will not be described here again.
The terminal device 50 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The terminal device 50 includes, but is not limited to, a processor 501, a memory 502. It will be appreciated by those skilled in the art that fig. 5 is merely an example of the terminal device 50 and is not meant to be limiting as to the terminal device 50, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the terminal device 50 may also include input devices, output devices, network access devices, buses, etc.
The processor 501 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, 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) or the like, which are provided on the terminal device 50. Further, the memory 502 may also include both internal storage units and external storage devices of the terminal device 50. 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 foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
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 solution. 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 herein, it should be understood that the disclosed battery pack state of health prediction system and method may be implemented in other ways. For example, the above-described embodiments of the battery pack state of health prediction system are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each method embodiment described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (9)
1. A method of predicting a state of health of a battery, comprising:
parameters of each battery in the battery pack are obtained, and the discharge attenuation degree, the residual discharge capacity occupancy ratio and the internal resistance deviation rate corresponding to each battery are calculated according to the parameters of each battery;
calculating the health degree of each battery according to the discharge attenuation degree, the residual discharge capacity duty ratio and the internal resistance deviation rate corresponding to each battery respectively;
calculating a battery pack health average value and a battery pack health standard deviation according to the health degrees of the batteries, and predicting the health state of the battery pack according to the battery pack health average value and the battery pack health standard deviation;
wherein, the predicting the health status of the battery pack according to the health average value and the health standard deviation of the battery pack comprises:
establishing a coordinate system by taking the health mean value as an abscissa and the health standard deviation as an ordinate; in the coordinate system, the abscissa of the origin is 100%, and the abscissa value gradually decreases along the abscissa direction;
determining four target areas through four values on the abscissa axis and four values on the ordinate axis, and predicting the health state of the battery pack according to the position relation between the target points and the four target areas; 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;
the calculating the health degree of each battery according to the discharge attenuation degree, the residual dischargeable capacity ratio and the internal resistance deviation rate corresponding to each battery comprises the following steps:
according to soh= (αd) N +C N ) X (1-), calculating the health degree SOH of the first battery; wherein the first battery is any one battery in the battery pack, D N C is the discharge attenuation degree of the first battery N And gamma is the internal resistance deviation rate of the first battery, alpha is a first weight coefficient, beta is a second weight coefficient, and alpha+beta=1.
2. The battery pack state of health prediction method as set forth in claim 1, wherein the determining four target areas by four values on the abscissa axis and four values on the ordinate axis and predicting the battery pack state of health according to the positional relationship of the target points and the four target areas comprises:
let straight line x=a, straight line y=s 0 The area enclosed by the abscissa axis and the ordinate axis is denoted as a first area, and a straight line x=b, a straight line y=s 1 The area enclosed by the abscissa axis and the ordinate axis is denoted as a second area, and a straight line x=c, a straight line y=s 2 The area enclosed by the abscissa axis and the ordinate axis is denoted as a third area, and a straight line x=d, a straight line y=s 3 The area surrounded by the abscissa axis and the ordinate axis is denoted as a fourth area; wherein A > B > C > D, S 3 ＞S 2 ＞S 1 ＞S 0 A is a first preset average value, B is a second preset average value, C is a third preset average value, D is a fourth preset average value, S 0 For the first preset standard deviation, S 1 Is the second preset standard deviation, S 2 Is a third preset standard deviation S 3 To the fourth preset standard deviationThe method comprises the steps of carrying out a first treatment on the surface of the x is an abscissa value in the coordinate system, and y is an ordinate value in the coordinate system;
the first area is marked as a first target area, the area of the second area after the first area is removed is marked as a second target area, the area of the third area after the second area is removed is marked as a third target area, and the area of the fourth area after the third area is removed is marked as a fourth target area;
and predicting the health state of the battery pack according to the position relation between the target point and the first target area, the second target area, the third target area and the fourth target area.
3. The battery pack state of health prediction method of claim 2, wherein predicting the battery pack state of health based on the positional relationship of the target point with the first target area, the second target area, the third target area, and the fourth target area comprises:
if the target point is located in the first target area, predicting that the health state of the battery pack is healthy;
if the target point is located in the second target area, predicting that the health state of the battery pack is good;
if the target point is located in the third target area, predicting the health state of the battery pack as normal;
and if the target point is positioned in the fourth target area, predicting that the health state of the battery pack is poor.
4. The battery health prediction method according to claim 2, wherein S 1 ＝1.3S 0 ，S 2 ＝1.5S o ，S 3 ＝1.7S 0 ，A＝70％，B＝60％，C＝45％，D＝30％。
5. The method according to claim 1, wherein the parameters of the battery include a discharge current per unit time during which a last discharge amount of the battery reaches a preset percentage, a rated capacity of the battery, a full discharge capacity of the battery life cycle, a remaining discharge capacity of the battery life cycle, a failure internal resistance threshold of the battery, a nominal internal resistance of the battery, and a current internal resistance of the battery;
the calculating of the discharge attenuation degree, the remaining discharge capacity duty ratio and the internal resistance deviation rate corresponding to each battery according to the parameters of each battery comprises the following steps:
according to
Calculating the discharge attenuation degree D of the first battery N The method comprises the steps of carrying out a first treatment on the surface of the Wherein the first battery is any one battery in the battery pack, I i For the discharge current in unit time in the discharge process of the last discharge capacity of the first battery reaching a preset percentage, deltat i C is the unit time in the discharging process of the last discharging quantity of the first battery reaching a preset percentage S Is the rated capacity of the first battery;
according to C N ＝c R ÷c A Calculating the remaining dischargeable capacity ratio C of the first battery N The method comprises the steps of carrying out a first treatment on the surface of the Wherein c R Residual discharge capacity for the first cell life cycle, c A Full discharge capacity for the first battery life cycle;
if it is
The internal resistance deviation rate gamma of the first battery is 0;
if it is
Then according to->
Calculating an internal resistance deviation rate gamma of the first battery; wherein R is N For the current internal resistance of the first battery, R S Is the firstNominal internal resistance of a battery, R O Is the first battery failure internal resistance threshold.
6. The method of claim 5, wherein the first battery life cycle residual discharge capacity c R The calculation formula of (2) is as follows:
wherein c j The discharging capacity of the first battery in the j-th discharging is shown as delta as a preset coefficient, and n is the discharging frequency of the first battery.
7. The battery pack state of health prediction method of claim 1, further comprising:
determining an interval time when the last discharge capacity of the first battery reaches a preset percentage;
if the interval time is less than or equal to the first preset time, α=α 1 ，β＝β 1 ；
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, α=α 2 ，β＝β 2 ；
If the interval time is greater than the second preset time and the interval time is less than or equal to a third preset time, α=α 3 ，β＝β 3 ；
If the interval time is greater than the third preset time and the interval time is less than or equal to a fourth preset time, α=α 4 ，β＝β 4 ；
If the interval time is greater than the fourth preset time and the interval time is less than or equal to a fifth preset time, α=α 5 ，β＝β 5 ；
If the interval time is greater than the fifth preset time, and the interval timeLess than or equal to a sixth preset time, then α=α 6 ，β＝β 6 ；
Wherein alpha is 1 ＞α 2 ＞α 3 ＞α 4 ＞α 5 ＞α 6 ，β 1 ＜β 2 ＜β 3 ＜β 4 ＜β 5 ＜β 6 。
8. 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 health status prediction method according to any one of claims 1 to 7 when the computer program is executed.
9. A computer readable storage medium, characterized in that it stores a computer program which, when executed by one or more processors, implements the steps of the battery state of health prediction method of any of claims 1 to 7.
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| CN113085558A (en) * | 2021-05-20 | 2021-07-09 | 宝能(广州)汽车研究院有限公司 | Battery electric connection state determination method and device, new energy automobile and medium |
| CN113306449B (en) * | 2021-06-15 | 2023-11-03 | 安徽信息工程学院 | Battery health reminding method and system for new energy automobile |
| CN113433458B (en) * | 2021-07-16 | 2023-06-27 | 北京现代汽车有限公司 | Battery health state determining method and device |
| CN114460470A (en) * | 2022-01-26 | 2022-05-10 | 上海玫克生智能科技有限公司 | Battery pack state analysis method and system based on voltage and terminal |
| CN117949824A (en) * | 2022-10-31 | 2024-04-30 | 比亚迪股份有限公司 | Battery health state prediction method, electronic equipment and readable storage medium |
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