CN115144779A - Battery state determination method, device, vehicle and storage medium - Google Patents

Battery state determination method, device, vehicle and storage medium Download PDF

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CN115144779A
CN115144779A CN202210655260.7A CN202210655260A CN115144779A CN 115144779 A CN115144779 A CN 115144779A CN 202210655260 A CN202210655260 A CN 202210655260A CN 115144779 A CN115144779 A CN 115144779A
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state
battery
probability
frequency
state data
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高长胜
刘相超
张弦
彭凯
王明月
邵天东
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FAW Group Corp
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FAW Group Corp
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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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health

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Abstract

The invention discloses a battery state determination method and device, a vehicle and a storage medium. The method comprises the following steps: respectively obtaining state data groups of the battery at a plurality of moments to obtain a plurality of state data groups, wherein each state data group comprises at least one state data of the battery, and the state data is used for representing the attribute of the battery; determining a first frequency corresponding to the state data in each state data group, and determining a first probability of each state data group based on the first frequency; determining a second frequency corresponding to the state data in each state data group, and determining a second probability of each state data group based on the second frequency; and determining the target state of the battery at the target moment based on the first probability of each state data group and the second probability of each state data group, wherein the target moment is the moment when the battery operates, and the target state is an aging state or a non-aging state. The invention solves the technical problem of low accuracy in judging the aging degree of the battery.

Description

Battery state determination method, device, vehicle and storage medium
Technical Field
The invention relates to the field of vehicles, in particular to a method and a device for determining a battery state, a vehicle and a storage medium.
Background
At present, aiming at aging judgment of a battery, in the related technology, a cloud real-time model is constructed, a vehicle networking signal related to the battery is monitored in real time, the signal is transmitted into the constructed model, and whether the battery is aged or not is judged.
Aiming at the problem of low accuracy rate of judging the aging degree of the battery in the prior art, an effective solution is not provided at present.
Disclosure of Invention
The embodiment of the invention provides a method and a device for determining a battery state, a vehicle and a storage medium, which are used for at least solving the technical problem of low accuracy in judging the aging degree of a battery.
According to an aspect of an embodiment of the present invention, there is provided a method for determining a battery state, the method including: respectively acquiring state data sets of the battery at a plurality of moments to obtain a plurality of state data sets, wherein each state data set comprises at least one state data of the battery, and the state data is used for representing the attribute of the battery; determining a first frequency corresponding to the state data in each state data group, and determining a first probability of each state data group based on the first frequency, wherein the first frequency is used for representing the occurrence number of the state data of the battery in a corresponding data interval when the state of the battery is in an aging state, and the first probability is used for representing the possibility that the state of the battery is in the aging state; determining a second frequency corresponding to the state data in each state data group, and determining a second probability of each state data group based on the second frequency, wherein the second frequency is used for representing the occurrence frequency of the state data of the battery in a corresponding data interval when the state of the battery is in a non-aging state, and the second probability is used for representing the possibility that the state of the battery is in the non-aging state; and determining a target state of the battery at a target moment based on the first probability of each state data group and the second probability of each state data group, wherein the target moment is the moment when the battery operates, and the target state is an aging state or a non-aging state.
Optionally, determining a first frequency corresponding to the status data in each status data group includes: determining a data type of each state data; and determining the frequency of the corresponding data interval of each state data in a first frequency distribution graph as a first frequency based on the data type, wherein the first frequency distribution graph is used for representing the distribution condition of the historical state data in the aging state.
Optionally, determining a second frequency corresponding to the status data in each status data group includes: determining a data type of each state data; and determining the frequency of each state data in the corresponding data interval in a second frequency distribution graph as a second frequency based on the data type, wherein the second frequency distribution graph is used for representing the distribution condition of the historical state data in the non-aging state.
Optionally, determining the first probability for each state data set based on the first frequency comprises: determining a first frequency group based on the first frequency to obtain a plurality of first frequency groups, wherein the plurality of first frequency groups correspond to the plurality of state data groups one to one; respectively determining the probability of each first frequency in a first frequency group to obtain a first probability group; and carrying out weight calculation on the probability of each first frequency in the first probability group to obtain a first probability.
Optionally, determining the second probability for each state data set based on the second frequency comprises: determining a second frequency group based on the second frequency to obtain a plurality of second frequency groups, wherein the plurality of second frequency groups correspond to the plurality of state data groups one to one; respectively determining the probability of each second frequency in a second frequency group to obtain a second probability group; and carrying out weight calculation on the probability of each second frequency in the second probability group to obtain a second probability.
Optionally, determining the target state of the battery at the target time based on the first probability of each state data set and the second probability of each state data set comprises: determining a first target probability from the average of the first probabilities and a second target probability from the average of the second probabilities; a target state is determined based on the first target probability and the second target probability.
Optionally, determining the target state of the battery based on the first target probability and the second target probability comprises: in response to the first target probability being greater than the second target probability, determining the target state to be an aging state; or in response to the first target probability not being greater than the second target probability, determining the target state to be a non-aging state.
According to another aspect of the embodiments of the present invention, there is also provided a battery state determining apparatus, including: the processing unit is used for respectively acquiring state data sets of the battery at multiple moments to obtain multiple state data sets, wherein each state data set comprises at least one state data of the battery, and the state data is used for representing the attribute of the battery; the first determining unit is used for determining a first frequency corresponding to the state data in each state data group and determining a first probability of each state data group based on the first frequency, wherein the first frequency is used for representing the occurrence number of the state data of the battery in a corresponding data interval when the state of the battery is in an aging state, and the first probability is used for representing the possibility that the state of the battery is in the aging state; a second determining unit, configured to determine a second frequency corresponding to the state data in each state data set, and determine a second probability of each state data set based on the second frequency, where the second frequency is used to represent the number of occurrences of the state data of the battery in a corresponding data interval when the state of the battery is in a non-aged state, and the second probability is used to represent the possibility that the state of the battery is in the non-aged state; and a third determining unit, configured to determine a target state of the battery at a target time based on the first probability of each state data set and the second probability of each state data set, where the target time is a time when the battery operates, and the target state is an aged state or a non-aged state.
According to another aspect of the embodiment of the invention, a vehicle is also provided. The vehicle is used for executing the battery state determination method of the embodiment of the invention.
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium. The computer-readable storage medium includes a stored program, wherein when the program runs, the apparatus in which the computer-readable storage medium is located is controlled to execute the method for determining a battery state according to the embodiment of the present invention.
In the embodiment of the invention, state data groups of a battery at a plurality of moments are obtained, a plurality of state data groups are obtained, a first frequency and a second frequency corresponding to state data in each state data group are determined, a first probability and a second probability of each state data group are determined based on the first frequency and the second frequency, and a target state of the battery at a target moment is determined based on the first probability and the second probability of each state data group. That is to say, in the embodiment of the present invention, the first frequency and the second frequency corresponding to the state data of the battery are obtained, the probability corresponding to each state data is determined, and the target state of the battery is determined by using the probability distribution condition of the state data, so that the technical effect of improving the accuracy of determining the aging degree of the battery is achieved, and the technical problem of low accuracy of determining the aging degree of the battery is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flow chart of a method of determining a battery condition according to an embodiment of the present invention;
FIG. 2 is a flow chart of another method of determining battery status according to an embodiment of the present invention;
FIG. 3 (a) is a schematic diagram of a cell aging versus non-aging profile with state data as a ratio of battery voltage to charge capacity, according to an embodiment of the present invention;
FIG. 3 (b) is a schematic illustration of a distribution of cell aging versus non-aging for status data as battery discharge rates in accordance with an embodiment of the present invention;
FIG. 3 (c) is a schematic diagram of a distribution of cell aging and non-aging for state data of battery charge capacity, in accordance with an embodiment of the present invention;
FIG. 3 (d) is a diagram of a voltage V with a state data of 60 and a battery discharge state according to an embodiment of the present invention 60 Schematic diagram of the distribution of aging and non-aging of the cell of (1);
FIG. 3 (e) shows the state data of 80 CCD and the battery discharge voltage V according to the embodiment of the present invention 80 Schematic diagram of the distribution of aging and non-aging of the cell of (1);
FIG. 3 (f) is a diagram of a voltage V with a state data of 70 CCD and a battery discharge state according to an embodiment of the present invention 70 A schematic diagram of the distribution of aging and non-aging of the battery;
FIG. 3 (g) is a diagram of voltage V with a state data of 90 and a battery discharge state according to an embodiment of the present invention 90 Schematic diagram of the distribution of aging and non-aging of the cell of (1);
FIG. 3 (h) shows a voltage V with a state data of 100 and a battery discharge state according to an embodiment of the present invention 100 Schematic diagram of the distribution of aging and non-aging of the cell of (1);
FIG. 3 (i) shows the voltage V when the state data is 70 and the battery is charged according to the embodiment of the present invention 7 Schematic diagram of the distribution of aging and non-aging of the cell of (1);
FIG. 3 (j) shows the voltage V when the state data is 80 and the battery charging/discharging state is charging according to the embodiment of the present invention 8 A schematic diagram of the distribution of aging and non-aging of the battery;
FIG. 3 (k) shows the voltage V when the state data is 90 and the battery charging/discharging state is charging according to the embodiment of the present invention 9 A schematic diagram of the distribution of aging and non-aging of the battery;
FIG. 3 (l) is a diagram of a current I when the state data is 70 and the battery is charged according to the embodiment of the present invention 7 Of the distribution of aging and non-aging of the cellA schematic diagram;
FIG. 3 (m) shows the state data of 90 CCD and the charging/discharging state of the battery is the voltage I during charging according to the embodiment of the present invention 9 Schematic diagram of the distribution of aging and non-aging of the cell of (1);
FIG. 3 (n) is a diagram illustrating the state data of 80 CCD and the charging/discharging state of the battery is the voltage I during charging according to the embodiment of the present invention 8 Schematic diagram of the distribution of aging and non-aging of the cell of (1);
fig. 4 is a schematic diagram of a battery state determining apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided an embodiment of a method for determining a battery state, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that presented herein.
Fig. 1 is a flowchart of a battery state determination method according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps.
Step S102, state data sets of the battery at multiple moments are respectively obtained to obtain multiple state data sets, wherein each state data set comprises at least one state data of the battery, and the state data are used for representing attributes of the battery.
In the technical solution provided by the above step S102 of the present invention, state data sets of the battery at multiple moments are obtained to obtain multiple state data sets, where each state data set may include all state data of the battery at a certain moment, may include at least one state data of the battery, and may pass through a vector
Figure BDA0003689221300000051
Is represented, for example, as a vector
Figure BDA0003689221300000052
Figure BDA0003689221300000053
The state data may be used to characterize the state of the battery, may be attributes of the battery, such as physical and chemical attributes of the battery, may be voltage data of the battery, state of charge of the battery, etc., and may be represented by d 1 ，d 2 ，d 3 ，…，d n Is shown by d 1 ，d 2 ，d 3 ，…，d n The state data of different dimensions can be respectively represented, wherein the battery can be a storage battery or the like.
Optionally, data uploaded to the cloud end by the vehicle may be acquired, state data sets of the battery at multiple times may be acquired, multiple state data sets may be acquired, the state data may be divided into data of different dimensions according to types of the state data, and a set of state data may include at least one dimension.
For example, the vehicle uploads the battery data of a certain period of time to the cloud end to obtain a plurality of state data sets (vectors)
Figure BDA0003689221300000054
As another example, the dimension of the state data may be a battery discharge rate (e.g., a decrease in charge capacity within 30 minutes), a ratio of battery voltage to charge capacity, a battery charge capacity of 60, 70, 80, 90, 100, respectively, and a battery charge-discharge state being a discharged voltage V 60 ，V 70 ，V 80 ，V 90 ，V 100 The charge capacity is 70, 80, 90 respectively, and the charge-discharge state is the voltage V during charging 7 ，V 8 ，V 9 And current I 7 ，I 8 ，I 9 And the 14 dimensions are used, and it should be noted that the dimensions are only for example and the dimensions of the state data are not specifically limited.
Step S104, determining a first frequency corresponding to the state data in each state data group, and determining a first probability of each state data group based on the first frequency, wherein the first frequency is used for representing the occurrence frequency of the state data of the battery in a corresponding data interval when the state of the battery is in an aging state, and the first probability is used for representing the possibility that the state of the battery is in the aging state.
In the technical solution provided by step S104 of the present invention, based on the obtained multiple state data sets of the battery, a first frequency corresponding to the state data in each state data set is determined, and based on the first frequency corresponding to each state data, a first probability of each state data set is determined, where the first frequency may be used to represent the number of occurrences of the state data of the battery in a corresponding data interval when the battery state is in an aging state, and may be X i By way of illustration, the first probability may be used to characterize the state of the battery as an aging stateCan be determined by
Figure BDA0003689221300000061
And (4) performing representation.
Optionally, the frequencies corresponding to the state data of different dimensions may be represented by an X distribution, where the X distribution may be used to characterize the distribution of the aged battery, and may also be a matrix
Figure BDA0003689221300000062
For example, may be X 1 ，X 2 ，X 3 ，…，X N 。
For example, when the charge capacity corresponding to the status data D is 70, the status data D is distributed in the [12, 13] data interval, and the number of times that the battery aging occurs in [12, 13] is 80, it can be determined that the first frequency corresponding to the status data is 80.
Step S106, determining a second frequency corresponding to the state data in each state data group, and determining a second probability of each state data group based on the second frequency, wherein the second frequency is used for representing the occurrence frequency of the state data of the battery in a corresponding data interval when the state of the battery is in a non-aging state, and the second probability is used for representing the possibility that the state of the battery is in the non-aging state.
In the technical solution provided by step S106 of the present invention, based on the obtained multiple state data sets of the battery, a second frequency corresponding to the state data in each state data set is determined, and based on the second frequency corresponding to each state data, a second probability of each state data set is determined, where the second frequency may be used to represent the number of occurrences of the state data of the battery in the corresponding data interval when the battery state is in the non-aging state, and may be Y i As shown, the second probability can be used to characterize the likelihood that the state of the battery is non-aging, and can be determined by
Figure BDA0003689221300000063
And (4) performing representation.
Alternatively, the distribution frequencies corresponding to the state data of different dimensions mayExpressed in terms of a Y distribution, wherein the Y distribution can be used to characterize the distribution of the non-aged cells, and can be a matrix
Figure BDA0003689221300000064
For example, may be Y 1 ，Y 2 ，Y 3 ，…，Y N 。
For example, when the charge capacity corresponding to the state data D is 70, the state data D is distributed in the [12, 13] data interval, the number of times that the non-aging of the battery occurs in [12, 13] is 175, and then the first frequency corresponding to the state data may be determined to be 175.
And step S108, determining a target state of the battery at a target time based on the first probability of each state data group and the second probability of each state data group, wherein the target time is the time when the battery operates, and the target state is an aging state or a non-aging state.
In the technical solution provided by step S108 of the present invention, the first probability and the second probability of each state data set of the battery are obtained, and the target state of the battery at the target time is determined based on the obtained first probability and the obtained second probability of the state data set, where the target time may be a time when the battery operates, and the target state may be used to represent that the battery is in an aging state or a non-aging state.
In the above steps S102 to S108 of the present application, a plurality of state data sets are obtained by obtaining state data sets of the battery at a plurality of times, a first frequency and a second frequency corresponding to the state data in each state data set are determined, a first probability and a second probability of each state data set are determined based on the first frequency and the second frequency, and a target state of the battery at a target time is determined based on the first probability of each state data set and the second probability of each state data set. That is to say, in the embodiment of the present invention, the first frequency and the second frequency corresponding to the state data of the battery are obtained, the probability corresponding to each state data is determined, and the target state of the battery is determined by using the probability distribution condition of the state data, so that the technical effect of improving the accuracy of determining the aging degree of the battery is achieved, and the technical problem of low accuracy of determining the aging degree of the battery is solved.
The above-described method of this embodiment is further described below.
As an alternative embodiment, determining the first frequency corresponding to the status data in each status data group includes: determining a data type of each state data; and determining the frequency of the corresponding data interval of each state data in a first frequency distribution graph as a first frequency based on the data type, wherein the first frequency distribution graph is used for representing the distribution condition of the historical state data in the aging state.
In the embodiment of the invention, the data type of each state data is determined, and the frequency of the corresponding data interval of each state data in the first frequency distribution map is determined as the first frequency based on the determined data type, so as to achieve the purpose of determining the first frequency corresponding to the state data in each state data group, wherein the data type can be the dimension of the state data of the battery; the first frequency distribution map may be obtained by processing a large amount of historical data, and may be used to represent a distribution situation of historical state data when the battery is in an aged state, for example, the number of occurrences of the aged battery may be when the data type is in a corresponding interval, and the first frequency distribution maps corresponding to data of different data types may also be different.
Alternatively, the distribution of different data types of the aged battery may be determined by analyzing the retained aged battery data in conjunction with battery expertise to determine the distribution of different data types of the aged battery to corresponding first frequency profiles, wherein a sample of the aged battery data needs to have a sufficient amount for the first frequency profile to be accurately acquired.
For example, suppose that a vehicle uploads N sets of state data sets to the cloud within a certain period of time, and the N sets of state data sets are analyzed to obtain vectors
Figure BDA0003689221300000071
Determining the data type of the state data set to obtain
Figure BDA0003689221300000081
Figure BDA0003689221300000082
And respectively determining the corresponding frequency of each state data in the first distribution graph to obtain a first frequency.
As an alternative embodiment, determining the second frequency corresponding to the status data in each status data group includes: determining a data type of each state data; and determining the frequency of each state data in the corresponding data interval in a second frequency distribution graph as a second frequency based on the data type, wherein the second frequency distribution graph is used for representing the distribution condition of the historical state data in the non-aging state.
In the embodiment of the invention, the data type of each state data is determined, and the frequency of the corresponding data interval of each state data in the second frequency distribution map is determined as the second frequency based on the determined data type, so as to achieve the purpose of determining the second frequency corresponding to the state data in each state data group, wherein the data type can be the dimension of the state data of the battery; the second histogram may be obtained by processing a large amount of historical data, and may be used to represent a distribution of historical state data when the battery is in a non-aged state, for example, when the data type is in a corresponding interval, the occurrence frequency of the non-aged battery may be different, and the second histograms corresponding to data of different data types may also be different.
As an alternative embodiment, determining the first probability of each state data group based on the first frequency includes: determining a first frequency group based on the first frequency to obtain a plurality of first frequency groups, wherein the plurality of first frequency groups correspond to the plurality of state data groups one to one; respectively determining the probability of each first frequency in a first frequency group to obtain a first probability group; and carrying out weight calculation on the probability of each first frequency in the first probability group to obtain a first probability.
In the embodiment of the invention, the first frequency corresponding to the state data in each state data group is obtained, the first frequency group is determined based on the first frequency, a plurality of first frequency groups are obtained, and the first frequency groups are respectivelyDetermining the probability of each first frequency in the first frequency group to obtain a first probability group, performing weight calculation on the probability of each first frequency in the first probability group, so that the calculated value can be the first probability, wherein the weight can be a value set according to experience or actual requirements, for example, a value set according to the influence degree of state data on battery aging, can be used for representing the importance degree of each data type, and can be obtained by a weight vector
Figure BDA0003689221300000083
Carrying out representation; the first frequency groups correspond to the state data groups one to one, and one probability group corresponds to one first probability.
Optionally, the different state data correspond to different first frequencies, the first frequencies corresponding to the state data in the state data group are determined, a first frequency group is obtained, and according to the distribution situation of each state data in different data types, the probability of each first frequency in the corresponding first frequency group is determined, and a first probability group is obtained, wherein the first probability group includes the probability corresponding to at least one first frequency.
For example, d may be based on different dimensions 1 ，d 2 ，d 3 ，…，d n Setting weight vector for influence degree of battery aging
Figure BDA0003689221300000091
The weight vector may be set manually, and it should be noted that w in the weight vector 1 +w 2 +w 3 +…+w n =1 and w 1 ，w 2 ，w 3 ，…，w n Are all non-negative numbers.
Alternatively, the first probability may be represented by a product of the transpose of the first probability group and the corresponding weight, which may be represented by the following formula:
Figure BDA0003689221300000092
as an alternative embodiment, determining the second probability of each state data group based on the second frequency includes: determining a second frequency group based on the second frequency to obtain a plurality of second frequency groups, wherein the plurality of second frequency groups correspond to the plurality of state data groups one to one; respectively determining the probability of each second frequency in a second frequency group to obtain a second probability group; and carrying out weight calculation on the probability of each second frequency in the second probability group to obtain a second probability.
In the embodiment of the present invention, a second frequency corresponding to state data in each state data group is obtained, the second frequency group is determined based on the second frequency, a plurality of second frequency groups are obtained, a probability of each second frequency in the second frequency group is respectively determined, a second probability group is obtained, and a weight calculation is performed on the probability of each second frequency in the second probability group, so that the calculated value may be the second probability, where the weight may be a value set according to experience or actual requirements, for example, a value set according to an influence degree of the state data on battery aging, and may be used to represent an importance degree of each data type, and a weight vector may be used to represent an importance degree of each data type
Figure BDA0003689221300000093
Carrying out representation; the second frequency groups correspond to the state data groups one to one, and one probability group corresponds to one second probability.
Optionally, the different state data correspond to different second frequencies, the second frequencies corresponding to the state data in the state data group are determined to obtain a second frequency group, and the probability of each second frequency in the corresponding second frequency group is determined according to the distribution condition of each state data in different data types to obtain a second probability group, where the second probability group includes the probability corresponding to at least one second frequency.
For example, d may be based on different dimensions 1 ，d 2 ，d 3 ，…，d n Setting weight vector for influence degree of battery aging
Figure BDA0003689221300000094
Wherein the weight vector can be artificially setIt is to be noted that w in the weight vector 1 +w 2 +w 3 +…+w n =1 and w 1 ，w 2 ，w 3 ，…，w n Are all non-negative numbers.
Alternatively, the second probability may be represented by a product of the transpose of the second probability group and the corresponding weight, which may be represented by the following formula:
Figure BDA0003689221300000095
for example, a vehicle uploads N pieces of data, the ith piece of data in the N pieces of data is taken to be analyzed from 14 dimensions, and the 14 dimensions can be analyzed through
Figure BDA0003689221300000101
Representing the dimension of the ith piece of data
Figure BDA0003689221300000102
(may be respectively d) 1 ，d 2 ，d 3 ，…，d n ) Is taken into X 1 ，X 2 ，X 3 ，…，X n And Y 1 ，Y 2 ，Y 3 ，…，Y n To obtain each probability group (which may be the first probability group)
Figure BDA0003689221300000103
Or may be a second set of probabilities
Figure BDA0003689221300000104
Figure BDA0003689221300000105
) According to
Figure BDA0003689221300000106
Or
Figure BDA0003689221300000107
Calculating a first probability that the ith piece of data belongs to agingOr a second probability of non-aging, wherein,
Figure BDA0003689221300000108
may be a transpose of the first set of probabilities,
Figure BDA0003689221300000109
may be a transpose of the second probability set.
As an alternative embodiment, determining the target state of the battery at the target time based on the first probability of each state data set and the second probability of each state data set includes: determining a first target probability from the average of the first probabilities and a second target probability from the average of the second probabilities; a target state is determined based on the first target probability and the second target probability.
In the embodiment of the invention, based on the obtained first probability of each state data group and the second probability of each state data group, the average value of a plurality of first probabilities is determined as a first target probability, the average value of a plurality of second probabilities is determined as a second target probability, and the target state of the battery at the target moment is determined based on the determined first target probability and the second target probability, wherein the first target probability can be represented by Ex and can be used for representing the average value of the plurality of first probabilities; the second target probability may be represented by Ey, and may be used to characterize the mean of the plurality of second probabilities.
Alternatively, according to the chevlov law, the first target probability may be obtained by weighted averaging the first probabilities, where N may be used to represent the number of the first probabilities, which may be expressed by the following formula:
Figure BDA00036892213000001010
the second target probability may be obtained by weighted average of the second probabilities, where N may be used to represent the number of the second probabilities, and may be represented by the following formula:
Figure BDA00036892213000001011
in the embodiment of the invention, the data mean value of the battery in a period of time is determined, different data mean values are compared, and different comparison results correspond to different states of the battery, so that the purpose of accurately judging the aging state of the battery is achieved on the basis of ensuring the real-time aging judgment of the battery, and the accuracy of judging the aging degree of the battery is improved.
As an alternative embodiment, determining the target state of the battery based on the first target probability and the second target probability includes: in response to the first target probability being greater than the second target probability, determining that the target state is an aging state; or in response to the first target probability not being greater than the second target probability, determining the target state to be a non-aging state.
In the embodiment of the invention, based on the first target probability and the second target probability, the target state of the battery can be determined by judging the magnitude of the first target probability and the second target probability, and when the first target probability is greater than the second target probability, the target state of the battery can be determined to be an aging state; when the first target probability is not greater than the second target probability, the target state of the battery may be determined to be a non-aging state.
Alternatively, when E X >E Y While, the battery is in an aged state; when E is X <E Y At this time, the battery is in a non-aged state.
Further, in the embodiment of the present invention, a difference between the first target probability and the second target probability of the battery may be determined, and when a difference obtained by subtracting the second target probability from the first target probability is greater than 0, it indicates that the first target probability is greater than the second target probability, and it may be determined that the state of the battery is in an aging state; when the difference between the first target probability and the second target probability is less than or equal to 0, which indicates that the first target probability is less than or equal to the second target probability, it may be determined that the state of the battery is in a non-aging state.
According to the embodiment, the probability corresponding to each state data is determined by acquiring the first frequency and the second frequency corresponding to the state data of the battery, and the target state of the battery is determined by utilizing the probability distribution condition of the state data, so that the technical effect of improving the accuracy of judging the aging degree of the battery is achieved, and the technical problem of low accuracy of judging the aging degree of the battery is solved.
Example 2
The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments.
In automatic driving, aging judgment of a battery is an important index for ensuring the driving safety of a vehicle, and in the related technology, the aging judgment of the battery is usually to detect a storage battery by using a special instrument and replace the aged battery; or monitoring the vehicle networking signals related to the battery in real time by constructing a cloud real-time model, and transmitting the signals into the constructed model so as to judge the aging of the battery; or by constructing and deploying an aging model at the edge end; or the battery health degree of the battery is calculated according to the ratio of the charging energy to the rated capacity, but due to the fact that ignition is difficult due to battery power shortage, the influence of the real-time model on the received signal quality is large, the false alarm rate of the constructed model is difficult to guarantee, charging energy is calculated by carrying out ampere-hour integration on a high-frequency signal, and the like, the aging judgment of the battery is important on the basis of big data on the basis of guaranteeing instantaneity.
In a related technology, a method for judging the health degree of a secondary battery is provided, the method judges whether a vehicle battery is aged or not by fixing voltages and energies (which can be the voltages and energies of a starting point, a starting point energy, an end point voltage and an end point energy) of a starting point and an end point of a charging interval.
In another related technology, a battery diagnosis monitoring method is provided, the method judges a threshold value in real time at a cloud according to a preset rule, and achieves the purpose of diagnosing the aging fault of the battery by acquiring voltage data of the vehicle in different states and comparing the voltage data, but the method does not judge based on big data and statistics and has the problem of low accuracy of data judgment.
In another related technology, a battery State detection method is provided, in which an alarm voltage threshold and a battery State Of Health (State Of Health, abbreviated as SOH) threshold are updated according to a real-time battery State parameter, so as to achieve the purpose Of solving the problem that a fixed threshold cannot accurately judge the aging Of a battery.
In order to solve the problems, the invention provides a battery aging judgment method based on big data, which classifies and counts a large amount of data, takes state data with different dimensionalities as classification standards, uses real vehicle data for modeling based on the big data and statistics, increases related dimensionalities at low cost, and enables the classification to be more accurate, thereby solving the problems of difficult modeling and high computing resource consumption of battery aging judgment on the basis of ensuring instantaneity, and being easier to realize on engineering.
The following further describes embodiments of the present invention.
Fig. 2 is a flowchart of another battery state determination method according to an embodiment of the present invention, which includes the following steps, as shown in fig. 2.
In step S202, a status data set is acquired.
In the embodiment of the present invention, state data sets of a battery at multiple times are obtained to obtain multiple state data sets, where each state data set may include all state data of the battery at a certain time, may include at least one state data of the battery, and may be represented by a vector D, for example, may be a vector D
Figure BDA0003689221300000121
The state data may be used to characterize the state of the battery, may be a property of the battery, and may be, for example, a physical property and a quantification of the batteryThe chemical property can be voltage data of the battery, the charge state of the battery and the like, and can be represented by d 1 ，d 2 ，d 3 ，…，d n Is shown by d 1 ，d 2 ，d 3 ，…，d n State data of different dimensions can be represented separately.
Optionally, data uploaded to the cloud end by the vehicle may be acquired, state data sets of the battery at multiple times may be acquired, multiple state data sets may be acquired, the state data may be divided into data of different dimensions according to types of the state data, and a set of state data may include at least one dimension.
For example, the vehicle uploads the battery data of a certain period of time to the cloud end to obtain a plurality of state data sets (vectors)
Figure BDA0003689221300000122
) The dimension of the state data may be a battery discharge rate (e.g., a decrease in charge capacity in 30 minutes), a ratio of battery voltage to charge capacity, a battery charge capacity of 60, 70, 80, 90, 100, respectively, and a battery charge-discharge state being a discharged voltage V 60 ，V 70 ，V 80 ，V 90 ，V 100 The charge capacity is 70, 80, 90 respectively, and the charge-discharge state is the voltage V during charging 7 ，V 8 ，V 9 And current I 7 ，I 8 ，I 9 And the 14 dimensions are used, and it should be noted that the dimensions are only for example and the dimensions of the state data are not specifically limited.
Step S204, determining the corresponding frequency.
In the embodiment of the invention, based on a plurality of acquired state data groups of the battery, a first frequency and a second frequency corresponding to the state data in each state data group are determined, wherein the first frequency can be used for representing the occurrence frequency of the state data of the battery in a corresponding data interval when the battery state is in an aging state, and can be X i The second frequency is used for representing the occurrence times of the state data of the battery in the corresponding data interval when the state of the battery is in the non-aging stateNumber, can be represented by Y i And (4) performing representation.
Optionally, the frequencies corresponding to the state data of different dimensions may be represented by an X distribution or a Y distribution, where the X distribution may be used to characterize the distribution of the aged battery, and may also be a matrix
Figure BDA0003689221300000131
For example, it may be X 1 ，X 2 ，X 3 ，…，X n (ii) a The Y distribution can be used for representing the distribution of the non-aging battery and can also be a matrix
Figure BDA0003689221300000132
For example, it may be Y 1 ，Y 2 ，Y 3 ，…，Y n 。
Optionally, a data type of each status data is determined, based on the determined data type, a frequency of a corresponding data interval of each status data in the first frequency distribution map is determined as a first frequency, and a frequency of a corresponding data interval of each status data in the second frequency distribution map is determined as a second frequency, so as to achieve the purpose of determining the first frequency and the second frequency corresponding to the status data in each status data set, wherein the data type may be a dimension of the status data of the battery; the first frequency distribution map and the second frequency distribution map can be obtained by processing a large amount of historical data, the first frequency distribution map can be used for representing the distribution condition of historical state data when the battery is in an aging state, and the second frequency distribution map can be used for representing the distribution condition of the historical state data when the battery is in a non-aging state, for example, the first frequency distribution map can represent the occurrence frequency of an aged battery when the data type is in a corresponding interval, and the first frequency distribution maps corresponding to data with different data types are also different; the second histogram may be the number of times of occurrence of the non-aged battery when the data type is in the corresponding interval, and the second histograms corresponding to data of different data types may also be different.
Alternatively, the distribution of different data types of the aged battery may be determined by analyzing the retained aged battery data in conjunction with battery expertise to determine the distribution of different data types of the aged battery to corresponding first frequency profiles, wherein a sample of the aged battery data needs to have a sufficient amount for the first frequency profile to be accurately acquired.
The embodiment of the present invention may characterize the state of the state data of the battery under aging and non-aging in 14 dimensions through the distribution of the state data of the battery, for example, fig. 3 (a) is a schematic diagram of the distribution of the aging and non-aging of the battery with the state data being the ratio of the voltage to the charge capacity of the battery according to the embodiment of the present invention, as shown in fig. 3 (a), the distribution of the aging and non-aging of the battery with the state data being the ratio of the voltage to the charge capacity of the battery may be included; FIG. 3 (b) is a diagram illustrating a distribution of aging and non-aging of the battery with the state data of the discharge rate of the battery according to the embodiment of the present invention, which can include a distribution of aging and non-aging of the battery with the state data of the discharge rate of the battery as shown in FIG. 3 (b); FIG. 3 (c) is a diagram illustrating a distribution of aging and non-aging of the battery with the state data being the charge capacity of the storage battery according to the embodiment of the present invention, as shown in FIG. 3 (c), which may include a distribution of aging and non-aging of the battery with the state data being the charge capacity of the storage battery; FIG. 3 (d) is a diagram of a voltage V with a state data of 60 and a battery discharge state according to an embodiment of the present invention 60 The distribution of aging and non-aging of the battery can include a voltage V with a state data of 60 charge capacity and a battery charge-discharge state of discharge, as shown in FIG. 3 (d) 60 Distribution of aging and non-aging of the battery; FIG. 3 (e) is a diagram of a voltage V with a state data of 80C and a battery discharge state according to an embodiment of the present invention 80 The distribution of aging and non-aging of the battery can include a voltage V with a state data of 80 charge capacity and a battery charging/discharging state of discharging as shown in FIG. 3 (e) 80 Distribution of aging and non-aging of the battery; FIG. 3 (f) shows the state data V with the charge capacity of 70 and the battery discharge state being the discharge voltage V according to the embodiment of the present invention 70 The distribution of aging and non-aging of the battery of (a) can include a voltage V with a state data of 70 charge capacity and a battery charge-discharge state of discharge, as shown in FIG. 3 (f) 70 Distribution of aging and non-aging of the battery; FIG. 3 (g) is a diagram of voltage V with a state data of 90 and a battery discharge state according to an embodiment of the present invention 90 The distribution of aging and non-aging of the battery of (1) can include a voltage V, as shown in FIG. 3 (g), in which the state data is a charge capacity of 90 and the charge-discharge state of the battery is a discharge state 90 Distribution of aging and non-aging of the battery; FIG. 3 (h) shows a state data of 100 and a battery discharge voltage V according to an embodiment of the present invention 100 The distribution of aging and non-aging of the battery can include a voltage V with a state data of 100 and a battery charging/discharging state of discharging, as shown in FIG. 3 (h) 100 Distribution of aging and non-aging of the battery; FIG. 3 (i) shows the voltage V when the state data is 70 and the battery is charged according to the embodiment of the present invention 7 The distribution of aging and non-aging of the battery can include, as shown in fig. 3 (i), a voltage V when the state data is a charge capacity of 70 and the charge-discharge state of the battery is charging 7 Distribution of aging and non-aging of the battery; FIG. 3 (j) shows the voltage V when the state data is 80 and the battery charging/discharging state is charging according to the embodiment of the present invention 8 The distribution of aging and non-aging of the battery can be schematically shown in fig. 3 (j), and the distribution can include a voltage V when the state data is a charge capacity of 80 and the charge-discharge state of the battery is charging 8 Distribution of aging and non-aging of the battery; FIG. 3 (k) is a diagram of a voltage V when the state data is a charge capacity of 90 and the battery is charged according to the embodiment of the present invention 9 The distribution of aging and non-aging of the battery of (1) can include, as shown in fig. 3 (k), a voltage V when the state data is a charge capacity of 90 and the charge-discharge state of the battery is charging 9 Distribution of aging and non-aging of battery(ii) a FIG. 3 (l) is a diagram of a current I when the state data is 70 and the battery is charged according to the embodiment of the present invention 7 The distribution of aging and non-aging of the battery of (1) can include, as shown in fig. 3 (l), a current I when the state data is a charge capacity of 70 and the charge-discharge state of the battery is charging 7 Distribution of aging and non-aging of the battery; FIG. 3 (m) shows the state data of 90 CCD and the charging/discharging state of the battery is the voltage I during charging according to the embodiment of the present invention 9 The distribution of aging and non-aging of the battery can include a voltage I when the state data is a charge capacity of 90 and the charge-discharge state of the battery is charging, as shown in FIG. 3 (m) 9 Distribution of aging and non-aging of the battery; FIG. 3 (n) shows the voltage I when the state data is 80 and the battery charging/discharging state is charging according to the embodiment of the present invention 8 The distribution of aging and non-aging of the battery can include a voltage I when the state data is a charge capacity of 80 and the charge/discharge state of the battery is charging, as shown in FIG. 3 (n) 8 And (4) distribution of aging condition and non-aging condition of the battery.
Optionally, the upper part of each graph in fig. 3 is an aging condition distribution of state data with different dimensions, and the lower part is an non-aging condition distribution of state data with different dimensions.
For example, when the state data corresponds to a charge capacity of 70 and the battery charge-discharge state is a discharged voltage V 70 When the distribution of (2) is in correspondence with the distribution of cells X [11, 12 ]]Within the data interval, the number of occurrences is 30, and the cells Y are distributed in the corresponding [11, 12 ]]In the data interval, the occurrence frequency is 100 times; when the state data corresponds to a charge capacity of 90 and the battery charge-discharge state is a discharged voltage V 90 When the distribution of (2) is in correspondence with the distribution of cells X [11, 12 ]]Within the data interval, the number of occurrences is 8, and the cells Y are distributed in correspondence [11, 12 ]]Within the data interval, the number of occurrences was 12.
Step S206, determining the corresponding probability.
In the embodiment of the invention, the first frequency and the second frequency corresponding to each state data are used for determiningDetermining a first probability and a second probability for each state data set, wherein the first probability can be used to characterize the possibility that the state of the battery is an aging state
Figure BDA0003689221300000151
Carrying out representation; the second probability may be used to characterize the likelihood that the state of the battery is non-aging, and may be determined by
Figure BDA0003689221300000152
And (4) performing representation.
Optionally, a first frequency and a second frequency corresponding to state data in each state data group are obtained, the first frequency group and the second frequency group are determined based on the first frequency and the second frequency, a plurality of first frequency groups and a plurality of second frequency groups are obtained, the probability of each first frequency in the first frequency group and the probability of each second frequency in the second frequency group are respectively determined, the first probability group and the second probability groups are obtained, and weight calculation is performed on the probability of each first frequency and each second frequency in the first probability group and the second probability group, so that the calculated values can be the first probability and the second probability, wherein the weight can be a value set according to experience or actual requirements, for example, a value set according to the degree of influence of the state data on battery aging, can be used for representing the importance degree of each data type, and can be represented by a weight vector w; the first and second frequency groups are in one-to-one correspondence with the state data groups, and one probability group corresponds to one first probability and one second probability.
Alternatively, according to Bernoulli's law of numbers, X can be distributed 1 ，X 2 ，X 3 ，…，X n Frequency versus different dimensions d of the status data 1 ，d 2 ，d 3 ，…，d n Is distributed at X 1 ，X 2 ，X 3 ，…，X n Is represented by the probability of (c); can be distributed by distribution Y 1 ，Y 2 ，Y 3 ，…，Y n Frequency versus different dimensions d of the status data 1 ，d 2 ，d 3 ，…，d n Distributed in Y 1 ，Y 2 ，Y 3 ，…，Y n Is represented by the probability of (c).
Optionally, different state data correspond to different first and second frequencies, the first and second frequencies corresponding to the state data in the state data group are determined to obtain first and second frequency groups, and the probability of each first and second frequency in the corresponding first and second frequency groups is determined according to the distribution condition of each state data in different data types to obtain first and second probability groups, wherein the first and second probability groups include at least one probability corresponding to the first and second frequencies.
Optionally, d can be varied according to different dimensions 1 ，d 2 ，d 3 ，…，d n Setting weight vector for influence degree of battery aging
Figure BDA0003689221300000161
Wherein, the weight vector is set artificially, and it should be noted that the vector satisfies w 1 +w 2 +w 3 +…+w n =1 and w 1 ，w 2 ，w 3 ，…，w n Are all non-negative numbers.
Alternatively, the first probability may be represented by a product of the transpose of the first probability group and the corresponding weight, which may be represented by the following formula:
Figure BDA0003689221300000162
the second probability may be represented by a product of the transpose of the second probability group and the corresponding weight, and may be represented by the following formula:
Figure BDA0003689221300000163
for example, a vehicle uploads N pieces of data, the ith piece of data in the N pieces of data is taken to be analyzed from 14 dimensions, and the 14 dimensions can be analyzed through
Figure BDA0003689221300000164
Representing the dimension of the ith piece of data
Figure BDA0003689221300000165
(may be d respectively 1 ，d 2 ，d 3 ，…，d n ) Is taken into X 1 ，X 2 ，X 3 ，…，X n And Y 1 ，Y 2 ，Y 3 ，…，Y n To obtain each probability group (which may be the first probability group)
Figure BDA0003689221300000166
Or may be a second set of probabilities
Figure BDA0003689221300000167
Figure BDA0003689221300000168
) According to
Figure BDA0003689221300000169
Or
Figure BDA00036892213000001610
Calculating a first probability that the ith piece of data belongs to aging, or a second probability that the ith piece of data belongs to non-aging, wherein,
Figure BDA00036892213000001611
may be a transpose of the first set of probabilities,
Figure BDA00036892213000001612
may be a transpose of the second probability set.
In step S208, the target state is determined.
In the embodiment of the invention, based on the obtained first probability of each state data group and the second probability of each state data group, the average value of a plurality of first probabilities is determined as a first target probability, the average value of a plurality of second probabilities is determined as a second target probability, and the target state of the battery at the target moment is determined based on the determined first target probability and the second target probability, wherein the first target probability can be represented by Ex and can be used for representing the average value of the plurality of first probabilities; the second target probability may be represented by Ey, and may be used to characterize the mean of the plurality of second probabilities.
Alternatively, according to the chevlev's law, the first target probability may be obtained by weighted averaging of the first probabilities, where N may be used to represent the number of the first probabilities, and may be expressed by the following formula:
Figure BDA0003689221300000171
the second target probability may be obtained by weighted average of the second probabilities, where N may be used to represent the number of the second probabilities, and may be represented by the following formula:
Figure BDA0003689221300000172
optionally, based on the first target probability and the second target probability, the target state of the battery may be determined by judging the magnitudes of the first target probability and the second target probability, and when the first target probability is greater than the second target probability, the target state may be determined to be an aging state; when the first target probability is not greater than the second target probability, the target state may be determined to be an unaged state.
Alternatively, when E X >E Y While, the battery is in an aged state; when E is X <E Y At this time, the battery is in a non-aged state.
Further, the difference value between the first target probability and the second target probability of the battery can be judged, when the difference value between the first target probability and the second target probability is larger than 0, the first target probability is larger than the second target probability, and the battery can be determined to be in an aging state; when the difference between the first target probability and the second target probability is less than or equal to 0, which indicates that the first target probability is less than or equal to the second target probability, it may be determined that the battery is in a non-aging state.
According to the embodiment, the probability corresponding to each state data is determined by acquiring the first frequency and the second frequency corresponding to the state data of the battery, and the target state of the battery is determined by utilizing the probability distribution condition of the state data, so that the technical effect of improving the accuracy of judging the aging degree of the battery is achieved, and the technical problem of low accuracy of judging the aging degree of the battery is solved.
Example 3
According to the embodiment of the invention, the device for determining the battery state is also provided. It is to be noted that the battery state determination device can be used to execute the battery state determination method in embodiment 1.
Fig. 4 is a schematic diagram of a battery state determining apparatus according to an embodiment of the present invention. As shown in fig. 4, the battery state determining apparatus 400 may include: a processing unit 402, a first determining unit 404, a second determining unit 406 and a third determining unit 408.
A processing unit 402, configured to obtain state data sets of the battery at multiple moments respectively, to obtain multiple state data sets, where each state data set includes at least one state data of the battery, and the state data is used to represent an attribute of the battery;
a first determining unit 404, configured to determine a first frequency corresponding to the state data in each state data set, and determine a first probability of each state data set based on the first frequency, where the first frequency is used to represent the number of occurrences of the state data of the battery in a corresponding data interval when the state of the battery is in an aging state, and the first probability is used to represent the possibility that the state of the battery is in the aging state;
a second determining unit 406, configured to determine a first frequency corresponding to the state data in each state data set, and determine a first probability of each state data set based on the first frequency, where the first frequency is used to represent the number of occurrences of the state data of the battery in a corresponding data interval when the state of the battery is in an aging state, and the first probability is used to represent the possibility that the state of the battery is in the aging state;
a third determining unit 408, configured to determine a target state of the battery at a target time based on the first probability of each state data set and the second probability of each state data set, where the target time is a time when the battery operates, and the target state is an aging state or a non-aging state.
Optionally, the first determining unit 404 includes: the first determining module is used for determining the data type of each state data; and determining the frequency of each state data in the corresponding data interval in the first frequency distribution graph as a first frequency based on the data type, wherein the first frequency distribution graph is used for representing the distribution condition of the historical state data in the aging state.
Optionally, the second determining unit 406 includes: a second determining module for determining a data type of each status data; and determining the frequency of each state data in the corresponding data interval in a second frequency distribution graph as a second frequency based on the data type, wherein the second frequency distribution graph is used for representing the distribution condition of the historical state data in the non-aging state.
Optionally, the first determining unit 404 includes: a third determining module, configured to determine a first frequency group based on the first frequency to obtain multiple first frequency groups, where the multiple first frequency groups correspond to multiple status data groups one to one; respectively determining the probability of each first frequency in a first frequency group to obtain a first probability group; and carrying out weight calculation on the probability of each first frequency in the first probability group to obtain a first probability.
Optionally, the second determining unit 406 includes: a fourth determining module, configured to determine a second frequency group based on the second frequency to obtain multiple second frequency groups, where the multiple second frequency groups correspond to the multiple status data groups one to one; respectively determining the probability of each second frequency in a second frequency group to obtain a second probability group; and carrying out weight calculation on the probability of each second frequency in the second probability group to obtain a second probability.
Optionally, the third determining unit 408 includes: the fifth determining module is used for determining the average value of the first probabilities into a first target probability and determining the average value of the second probabilities into a second target probability; a target state is determined based on the first target probability and the second target probability.
Optionally, the third determining unit 408 includes: a sixth determining module, configured to determine that the target state is an aging state in response to the first target probability being greater than the second target probability; or in response to the first target probability not being greater than the second target probability, determining the target state to be a non-aging state.
In the embodiment of the invention, state data sets of a battery at a plurality of moments are respectively obtained through an obtaining unit to obtain a plurality of state data sets, wherein each state data set comprises at least one state data of the battery, and the state data is used for representing the attribute of the battery; determining a detection frame in the detection result through a first determination unit, wherein the detection frame is used for determining the position of the detection object; determining, by a second determining unit, a first frequency corresponding to the state data in each state data group, and determining a first probability of each state data group based on the first frequency, wherein the first frequency is used for representing the occurrence number of the state data of the battery in a corresponding data interval when the state of the battery is in an aging state, and the first probability is used for representing the possibility that the state of the battery is in the aging state; and determining a second frequency corresponding to the state data in each state data group through a third determining unit, and determining a second probability of each state data group based on the second frequency, wherein the second frequency is used for representing the occurrence frequency of the state data of the battery in a corresponding data interval when the state of the battery is in the non-aging state, and the second probability is used for representing the possibility that the state of the battery is in the non-aging state. That is to say, the present invention determines the probability corresponding to each state data by obtaining the first frequency and the second frequency corresponding to the state data of the battery, and determines the target state of the battery by using the probability distribution condition of the state data, thereby achieving the technical effect of improving the accuracy of judging the aging degree of the battery, and solving the technical problem of low accuracy of judging the aging degree of the battery.
Example 4
According to an embodiment of the present invention, there is also provided a vehicle for executing the determination method of the battery state in embodiment 1.
Example 5
According to an embodiment of the present invention, there is also provided a computer-readable storage medium including a stored program, wherein the program executes the method of determining a battery state in embodiment 1.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple 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, units or modules, and may be in an electrical 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 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 invention 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 unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and amendments can be made without departing from the principle of the present invention, and these modifications and amendments should also be considered as the protection scope of the present invention.

Claims (10)

1. A method for determining a state of a battery, comprising:
respectively obtaining state data groups of a battery at a plurality of moments to obtain a plurality of state data groups, wherein each state data group comprises at least one state data of the battery, and the state data is used for representing the attribute of the battery;
determining a first frequency corresponding to the state data in each state data group, and determining a first probability of each state data group based on the first frequency, wherein the first frequency is used for representing the occurrence number of the state data of the battery in a corresponding data interval when the state of the battery is in an aging state, and the first probability is used for representing the possibility that the state of the battery is in the aging state;
determining a second frequency corresponding to the state data in each state data group, and determining a second probability of each state data group based on the second frequency, wherein the second frequency is used for representing the occurrence times of the state data of the battery in a corresponding data interval when the state of the battery is in a non-aging state, and the second probability is used for representing the possibility that the state of the battery is in the non-aging state;
and determining a target state of the battery at a target moment based on the first probability of each state data group and the second probability of each state data group, wherein the target moment is the moment when the battery runs, and the target state is the aging state or the non-aging state.
2. The method of claim 1, wherein determining the first frequency for the status data in each of the status data sets comprises:
determining a data type of each of the status data;
and determining the frequency of each state data in a corresponding data interval in a first frequency distribution graph as the first frequency based on the data type, wherein the first frequency distribution graph is used for representing the distribution condition of the historical state data in the aging state.
3. The method of claim 1, wherein determining the second frequency for the status data in each of the status data sets comprises:
determining a data type of each of the status data;
and determining the frequency of each state data in a corresponding data interval in a second frequency distribution graph as the second frequency based on the data type, wherein the second frequency distribution graph is used for representing the distribution condition of the historical state data in the non-aging state.
4. The method of claim 1, wherein determining a first probability for each of the state data sets based on the first frequency comprises:
determining a first frequency group based on the first frequency to obtain a plurality of first frequency groups, wherein the plurality of first frequency groups correspond to the plurality of state data groups one to one;
respectively determining the probability of each first frequency in the first frequency group to obtain a first probability group;
and carrying out weight calculation on the probability of each first frequency in the first probability group to obtain the first probability.
5. The method of claim 1, wherein determining a second probability for each of the state data sets based on the second frequency comprises:
determining a second frequency group based on the second frequency to obtain a plurality of second frequency groups, wherein the plurality of second frequency groups correspond to the plurality of state data groups one to one;
respectively determining the probability of each second frequency in the second frequency group to obtain a second probability group;
and performing weight calculation on the probability of each second frequency in the second probability group to obtain the second probability.
6. The method of claim 1, wherein determining the target state of the battery at the target time based on the first probability of each of the state data sets and the second probability of each of the state data sets comprises:
determining a first target probability from a mean of a plurality of the first probabilities and a second target probability from a mean of a plurality of the second probabilities;
determining the target state based on the first target probability and the second target probability.
7. The method of claim 6, wherein determining the target state of the battery based on the first target probability and the second target probability comprises:
in response to the first target probability being greater than the second target probability, determining that the target state is an aging state;
or in response to the first target probability not being greater than the second target probability, determining the target state to be a non-aging state.
8. An apparatus for determining a state of a battery, comprising:
the processing unit is used for respectively acquiring state data sets of the battery at multiple moments to obtain multiple state data sets, wherein each state data set comprises at least one state data of the battery, and the state data is used for representing the attribute of the battery;
a first determining unit, configured to determine a first frequency corresponding to state data in each of the state data sets, and determine a first probability of each of the state data sets based on the first frequency, where the first frequency is used to represent a number of occurrences of the state data of the battery in a corresponding data interval when the state of the battery is in an aging state, and the first probability is used to represent a possibility that the state of the battery is in the aging state;
a second determining unit, configured to determine a second frequency corresponding to the state data in each state data group, and determine a second probability of each state data group based on the second frequency, where the second frequency is used to represent the number of occurrences of the state data of the battery in a corresponding data interval when the state of the battery is in a non-aging state, and the second probability is used to represent the possibility that the state of the battery is in the non-aging state;
a third determining unit, configured to determine a target state of the battery at a target time based on the first probability of each state data set and the second probability of each state data set, where the target time is a time when the battery operates, and the target state is the aged state or the non-aged state.
9. A vehicle, characterized by being configured to perform the method of any one of claims 1 to 7.
10. A computer-readable storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method of any one of claims 1 to 7.
CN202210655260.7A 2022-06-10 2022-06-10 Battery state determination method, device, vehicle and storage medium Pending CN115144779A (en)

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