CN114441968A

CN114441968A - Battery self-discharge monitoring method and device, computer equipment and storage medium

Info

Publication number: CN114441968A
Application number: CN202111459956.4A
Authority: CN
Inventors: 郑文婕; 郝一鸣; 王巍
Original assignee: Beijing Chehejia Automobile Technology Co Ltd
Current assignee: Beijing Chehejia Automobile Technology Co Ltd
Priority date: 2021-12-02
Filing date: 2021-12-02
Publication date: 2022-05-06

Abstract

The disclosure provides a battery self-discharge monitoring method and device, computer equipment and a storage medium, and relates to the technical field of battery management. The method comprises the following steps: acquiring voltage monitoring data and current monitoring data of each battery monomer; acquiring a first charge state and a second charge state of each battery cell according to the voltage monitoring data and the current monitoring data; determining a first state of charge (SOC) outlier trend of each battery cell according to each first SOC; determining a second state of charge outlier trend of each battery cell according to each second state of charge; and determining whether the self-discharge of each battery cell is abnormal or not according to the first charge state outlier trend and the second charge state outlier trend corresponding to each battery cell. Therefore, the self-discharge condition is monitored based on the outlier trend of the battery charge state, and the accuracy and the reliability of the battery self-discharge monitoring are improved.

Description

Battery self-discharge monitoring method and device, computer equipment and storage medium

Technical Field

The present disclosure relates to the field of battery management technologies, and in particular, to a method and an apparatus for monitoring battery self-discharge, a computer device, and a storage medium.

Background

Batteries are adopted as power sources of the electric automobiles, and the influence on the environment is smaller than that of the traditional automobiles, so more and more people select the electric automobiles as travel tools. The battery of the electric automobile is formed by connecting and superposing a plurality of battery monomers in series, and when a short-circuit point exists in the battery, the lap joint of a positive electrode and a negative electrode generates short-circuit current, so that the self-discharge problem of the battery can be caused. When the self-discharge of the battery is abnormal, the normal use of the electric automobile is influenced. Therefore, the method has important significance in researching how to accurately and reliably monitor the self-discharge condition of the battery.

Disclosure of Invention

The present disclosure is directed to solving, at least to some extent, one of the technical problems in the related art.

An embodiment of a first aspect of the present disclosure provides a method for monitoring self-discharge of a battery, including:

acquiring voltage monitoring data and current monitoring data of each battery monomer;

acquiring a first charge state and a second charge state of each battery cell according to the voltage monitoring data and the current monitoring data, wherein the first charge state is the charge state of each battery cell at the initial moment of a set period, and the second charge state is the charge state of each battery cell at the end moment of the set period;

determining a first state of charge (SOC) outlier trend of each battery cell according to each first SOC;

determining a second state of charge outlier trend of each battery cell according to each second state of charge;

and determining whether the self-discharge of each single battery is abnormal or not according to the first charge state outlier trend and the second charge state outlier trend corresponding to each single battery.

An embodiment of a second aspect of the present disclosure provides a monitoring apparatus for battery self-discharge, including:

the first acquisition module is used for acquiring voltage monitoring data and current monitoring data of each battery cell;

the second obtaining module is used for obtaining a first charge state and a second charge state of each single battery according to the voltage monitoring data and the current monitoring data, wherein the first charge state is the charge state of each single battery at the initial moment of a set period, and the second charge state is the charge state of each single battery at the end moment of the set period;

the first determining module is used for determining a first state of charge outlier trend of each battery cell according to each first state of charge;

the second determining module is used for determining a second charge state outlier trend of each battery cell according to each second charge state;

and the third determining module is used for determining whether the self-discharge of each single battery is abnormal or not according to the first charge state outlier trend and the second charge state outlier trend corresponding to each single battery.

An embodiment of a third aspect of the present disclosure provides a computer device, including: the monitoring method comprises the following steps of storing a memory, a processor and computer instructions stored on the memory and capable of running on the processor, wherein the processor executes the instructions to realize the monitoring method for the self-discharge of the battery according to the embodiment of the first aspect of the disclosure.

A fourth aspect embodiment of the present disclosure proposes a vehicle comprising a computer device as set forth in the third aspect embodiment of the present disclosure.

A fifth aspect of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, which when executed by a processor, implement the method for monitoring self-discharge of a battery as set forth in the first aspect of the present disclosure.

A sixth aspect of the present disclosure provides a computer program product, which when executed by an instruction processor in the computer program product performs the method for monitoring self-discharge of a battery set forth in the first aspect of the present disclosure.

The monitoring method, the monitoring device, the computer equipment and the storage medium for the self-discharge of the battery have the following beneficial effects:

firstly, acquiring voltage monitoring data and current monitoring data of each battery monomer; then, acquiring a first charge state and a second charge state of each battery cell according to the voltage monitoring data and the current monitoring data, wherein the first charge state is the charge state of each battery cell at the initial moment of a set period, and the second charge state is the charge state of each battery cell at the end moment of the set period; then, according to each first charge state, determining a first charge state outlier trend of each battery cell, and according to each second charge state, determining a second charge state outlier trend of each battery cell; and finally, determining whether the self-discharge of each battery cell is abnormal or not according to the first charge state outlier trend and the second charge state outlier trend corresponding to each battery cell. Therefore, the self-discharge condition of the battery is monitored based on the charge state outlier trend of the battery in a certain time, the false alarm rate caused by data fluctuation is reduced, and the accuracy and the reliability of self-discharge monitoring of the battery are improved.

Additional aspects and advantages of the disclosure will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the disclosure.

Drawings

The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

fig. 1 is a schematic flow chart illustrating a method for monitoring self-discharge of a battery according to an embodiment of the present disclosure;

fig. 2 is a schematic flow chart illustrating a method for monitoring self-discharge of a battery according to another embodiment of the present disclosure;

FIG. 3 is a schematic diagram of an empirical battery SOC _ OCV curve provided by an embodiment of the present disclosure;

fig. 4 is a schematic structural diagram of a monitoring apparatus for battery self-discharge according to an embodiment of the present disclosure;

FIG. 5 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present disclosure.

Detailed Description

Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present disclosure, and should not be construed as limiting the present disclosure.

A monitoring method, an apparatus, a computer device, and a storage medium for battery self-discharge according to embodiments of the present disclosure are described below with reference to the accompanying drawings.

Fig. 1 is a schematic flow chart of a monitoring method for battery self-discharge according to an embodiment of the disclosure.

The embodiment of the present disclosure is exemplified by the method for monitoring self-discharge of a battery being configured in a monitoring device for self-discharge of a battery, and the monitoring device for self-discharge of a battery can be applied to any hardware device, such as a vehicle-mounted device, a cloud device, and the like, having various operating systems, touch screens, and/or display screens, so that the device can perform a monitoring function of self-discharge of a battery.

As shown in fig. 1, the method for monitoring self-discharge of a battery may include the steps of:

step 101, acquiring voltage monitoring data and current monitoring data of each battery cell.

The power battery of the electric vehicle is formed by electrically connecting a plurality of battery cells. For example, a power battery may include 96 cells.

It can be understood that, in order to ensure reliable operation of the battery and prolong the service life of the battery, each battery cell in the battery pack should reach a balanced and consistent state. Therefore, the voltage and the current of each battery cell can be monitored so as to maintain and manage the battery.

In which the current monitoring data of each of the battery cells connected in series with each other is the same, and the voltage monitoring data of the battery cells may be different due to differences among individuals. The voltage monitoring data is the voltage value of each battery monomer at any moment, and the current monitoring data is the current value of each battery monomer at any moment.

Step 102, acquiring a first charge state and a second charge state of each battery cell according to the voltage monitoring data and the current monitoring data, wherein the first charge state is the charge state of each battery cell at the initial time of the set period, and the second charge state is the charge state of each battery cell at the end time of the set period.

The state of charge (SOC) of the battery is a remaining battery capacity. When a short-circuit point exists in the battery, the lap joint of the positive electrode and the negative electrode generates short-circuit current, the available electric quantity is gradually reduced along with the accumulation of time, namely the SOC is reduced, and meanwhile, the voltage of the battery is reduced.

It is understood that both the battery state of charge SOC and the battery voltage can be used as indicators for characterizing the self-discharge of the battery. The decrease of the SOC, i.e. the integral of the short-circuit current and the time, is the most accurate index for measuring the self-discharge of the battery at present.

Therefore, in the embodiment of the disclosure, the state of charge SOC of each battery cell at the initial time and the end time of the set period may be obtained, so as to perform the self-discharge condition evaluation based on the state of charge SOC of each battery cell at different times.

It should be noted that, the evaluation of the self-discharge condition of the battery may be repeated according to a set time, so as to continuously monitor the battery.

For example, the self-discharge condition of the battery may be evaluated once a day. Alternatively, the self-discharge condition of the battery was evaluated every two days. The present disclosure is not limited thereto.

It is understood that the self-discharge condition of the battery may gradually change with the accumulation of time, but the self-discharge condition of the battery may not change much in a short time.

Therefore, in the embodiment of the disclosure, a set period can be set, and the self-discharge condition of the battery can be evaluated based on the state of charge of the battery at the initial time and the end time of the set period.

The duration of the setting period can be set according to actual needs. For example, it may be 5 days, 7 days, or 10 days, etc., which is not limited by the disclosure.

And 103, determining a first charge state outlier trend of each battery cell according to each first charge state.

It should be noted that the power battery of the electric vehicle may include a plurality of battery cells, and each battery cell corresponds to a state of charge. The state of charge outlier trend can be any type of value that can characterize the difference in state of charge of the individual cells.

For example, the first states of charge of the individual cells are respectively SOC₁₁，SOC₁₂，……，SOC_1n. Wherein n is the number of the single batteries. Average value of first state of charge of each battery cell

Is (SOC)₁₁+SOC₁₂+……+SOC_1n) And/n, the first state of charge outlier trend for each cell may be:

it should be noted that the above examples are only illustrative and should not be taken as limiting the first state of charge outlier trend in the embodiments of the present disclosure.

And 104, determining a second charge state outlier trend of each battery cell according to each second charge state.

The second state of charge outlier trend and the first state of charge outlier trend represent the state of charge characteristics of the battery cell at different moments. Therefore, according to each second state of charge, a specific implementation manner of determining the second state of charge outlier trend of each battery cell may refer to an implementation manner of determining the first state of charge outlier trend of each battery cell according to each first state of charge, which is not described herein again.

And 105, determining whether the self-discharge of each battery cell is abnormal or not according to the first charge state outlier trend and the second charge state outlier trend corresponding to each battery cell.

When determining whether the self-discharge of each battery cell is abnormal, various judgment modes can be provided.

For example, a certain value range may be set in advance, and when the difference between the first state of charge outlier trend and the second state of charge outlier trend exceeds the value range, it may be determined that the self-discharge of the battery cell is abnormal. Otherwise, the self-discharge of the battery cell can be determined to be normal.

Or, corresponding weights may be respectively given to the first state of charge outlier trend and the second state of charge outlier trend, and then the first state of charge outlier trend and the second state of charge outlier trend are respectively multiplied by the corresponding weights, and then subtracted, and if the difference is greater than a certain set threshold, it may be determined that the self-discharge of the battery cell is abnormal. Otherwise, it may be determined that the battery cell is normally self-discharged.

It should be noted that the above examples are only illustrative, and cannot be taken as a limitation for determining whether the self-discharge of the battery cell is abnormal or not according to the first state of charge outlier tendency and the second state of charge outlier tendency in the embodiments of the present disclosure.

In the embodiment of the disclosure, firstly, voltage monitoring data and current monitoring data of each battery cell are obtained; then, acquiring a first charge state and a second charge state of each battery cell according to the voltage monitoring data and the current monitoring data, wherein the first charge state is the charge state of each battery cell at the initial moment of a set period, and the second charge state is the charge state of each battery cell at the end moment of the set period; then, according to each first charge state, determining a first charge state outlier trend of each battery cell, and according to each second charge state, determining a second charge state outlier trend of each battery cell; and finally, determining whether the self-discharge of each battery cell is abnormal or not according to the first charge state outlier trend and the second charge state outlier trend corresponding to each battery cell. Therefore, the self-discharge condition of the battery is monitored based on the charge state outlier trend of the battery in a certain time, the false alarm rate caused by data fluctuation is reduced, and the accuracy and the reliability of self-discharge monitoring of the battery are improved.

Fig. 2 is a schematic flow chart of a monitoring method for battery self-discharge according to another embodiment of the present disclosure. As shown in fig. 2, the method for monitoring self-discharge of a battery may include the steps of:

step 201, obtaining voltage monitoring data and current monitoring data of each battery cell.

The specific implementation manner of step 201 may refer to the detailed description of other embodiments of the present disclosure, and is not described herein again.

In step 202, a target time period within a set range of the current monitoring data in a set period is determined.

It can be understood that when the electric vehicle is in a driving state, the battery current is significantly larger than the current when the electric vehicle is in a standing state. On the premise that batteries with the same design are placed for more than a certain time after a large current and have similar change trends, the time period that the electric automobile is in the standing state can be determined according to the battery current monitoring data, and the corresponding state of charge is determined according to the battery voltage in the standing state.

In the embodiment of the disclosure, the setting range may be set according to the current value, and the time period in which the current monitoring data is within the setting range is screened as the target time period.

For example, the setting range may be 0 to 10 amperes, and the time period in which the current magnitude is between 0 and 10 amperes in the setting period is the target time period. Alternatively, the setting range may be 0 to 5 amperes, and the time period in which the current magnitude is between 0 and 5 amperes in the setting period is the target time period.

It should be noted that the above examples are merely illustrative, and are not intended to limit the scope, the target time period, and the like in the embodiments of the present disclosure.

Step 203, determining a first charge state of each battery cell according to the voltage monitoring data of each battery cell at the initial moment of the target time interval and the mapping relation between the battery charge state and the open-circuit voltage.

It can be understood that the current data and the voltage data of the battery cells at each moment are in one-to-one correspondence. That is, after the target period is determined from the current monitoring data, the voltage monitoring data of the target period may be acquired.

The terminal voltage of the battery in the open circuit state is referred to as an Open Circuit Voltage (OCV). The state of charge of the battery has a certain mapping relation with the open-circuit voltage, and can be characterized by an SOC _ OCV empirical curve, as shown in fig. 3.

Therefore, according to the voltage monitoring data of the battery cell at the initial moment of the target period, based on the SOC _ OCV empirical curve representing the mapping relation between the battery state of charge and the open-circuit voltage, the state of charge at the initial moment of the target period can be obtained.

Based on the battery SOC _ OCV empirical curve, SOC and OCV are similar to interval linear relationship, and the state of charge difference values corresponding to the same differential pressure in different voltage intervals are different. Therefore, the linear interpolation method can be adopted to acquire the battery state of charge at all the moments in the first target time period, and the down-sampling and data noise reduction are carried out by the moving average method.

It should be noted that, the target time period is determined according to the current magnitude of the battery. Within a set period, the target period may be composed of a plurality of spaced sub-periods.

For example, when the set period is 7 days, the target period may include 0 o 'clock to 8 o' clock, 12 o 'clock to 14 o' clock on the first day, 6 o 'clock to 22 o' clock on the second day, 0 o 'clock to 12 o' clock on the seventh day, etc. Correspondingly, the initial time of the target time interval is 0 o 'clock of the first day, and the end time of the target time interval is 12 o' clock of the seventh day.

Further, in some embodiments, the target period may be determined in conjunction with the duration in addition to the current of the battery. For example, a period in which the current of the battery lasts for 1 hour or more within a set range is a target period.

It should be noted that the above examples are only illustrative and should not be taken as limiting the target time period in the embodiments of the present disclosure.

And step 204, determining the first state of charge dispersion rate of all the battery cells according to each first state of charge.

It should be noted that, in theory, charge and discharge should be consistent between the cells, i.e., the state of charge difference between the cells should be as small as possible. When the state of charge difference of each battery cell is large, it can be determined that the consistency of each battery cell is poor. At this time, the result of self-discharge condition determination according to the state of charge of the battery cell may be inaccurate.

In the embodiment of the disclosure, in order to improve the accuracy of the determination result, the first state of charge dispersion rates of all the battery cells may be determined according to the first state of charge corresponding to each battery cell, and then the consistency of all the battery cells is evaluated according to the state of charge dispersion rates.

In one possible implementation manner, the first state of charge dispersion rates of all the battery cells may be determined according to the standard deviation and/or the variance of the first state of charge corresponding to each battery cell.

Step 205, in response to that the first state of charge dispersion rate is smaller than or equal to the second set threshold, determining a first reference state of charge according to the median of the first state of charge corresponding to each of all the battery cells.

The second set threshold may be any value set in advance, which is not limited in this disclosure. The first reference state of charge may be used as a reference for comparing the first state of charge corresponding to each battery cell. By comparing the first state of charge corresponding to each cell to a first reference state of charge, the difference between the two can be determined.

In the embodiment of the present disclosure, the median of the first states of charge corresponding to all the battery cells may be used as the first reference state of charge.

For example, the first states of charge of the individual cells are respectively the SOC₁₁，SOC₁₂，……，SOC_1n. Will SOC₁₁，SOC₁₂，……，SOC_1nThe numbers are arranged according to the numerical value, and the number at the middle position is the first reference state of charge. If the first state of charge has an even number, the average of the two most intermediate values may be taken as the first reference state of charge.

Step 206, determining a first state of charge outlier trend of each battery cell according to a difference value between the first state of charge corresponding to each battery cell and the first reference state of charge.

The state of charge outlier trend can represent the difference of the first states of charge corresponding to the battery cells. In the embodiment of the present disclosure, a difference between the first state of charge corresponding to each battery cell and the first reference state of charge may be used as the first state of charge outlier trend of each battery cell.

For example, the first states of charge of the individual cells are respectively the SOC₁₁，SOC₁₂，……，SOC_1n. The first state of charge outlier trend for each cell may be: SOC₁₁-SOC_1i，SOC₁₂-SOC_1i，……，SOC_1n-SOC_1i. Therein, SOC_1iIs a first reference state of charge.

And step 207, determining a second charge state of each battery cell according to the voltage monitoring data of each battery cell at the end time of the target time interval and the mapping relation between the battery charge state and the open-circuit voltage.

The specific implementation manner of determining the second state of charge of each battery cell may refer to the specific implementation manner of determining the first state of charge of each battery cell in the embodiments of the present disclosure, and is not described herein again.

And 208, determining second state of charge dispersion rates of all the battery cells according to each second state of charge.

For a specific implementation manner of determining the second state of charge discrete rate, reference may be made to the detailed description of determining the first state of charge discrete rate in the embodiments of the present disclosure, and details are not described here again.

Step 209, in response to that the second state of charge dispersion rate is less than or equal to a second set threshold, determining a second reference state of charge according to the median of the second state of charge corresponding to each battery cell.

For a specific implementation manner of determining the second reference state of charge, reference may be made to detailed description of determining the first reference state of charge in the embodiments of the present disclosure, and details are not described herein again.

Step 210, determining a second state of charge outlier trend of each battery cell according to a difference value between the second state of charge corresponding to each battery cell and a second reference state of charge.

For a specific implementation manner of determining the second soc outlier trend of each battery cell, reference may be made to the detailed description of determining the first soc outlier trend of each battery cell in the embodiments of the present disclosure, and details are not repeated herein.

Step 211, determining the variation of the outlier trend of each battery cell according to the difference between the first state of charge outlier trend and the second state of charge outlier trend corresponding to each battery cell.

Step 212, determining the variation rate of the outlier trend of each battery cell according to the ratio of the variation of the outlier trend of each battery cell to the set period.

The SOC outlier trend corresponding to each battery cell represents the difference of SOC among the battery cells. In theory, the charge and discharge of each cell should be consistent, i.e., the state of charge difference between the cells should be as small as possible.

It is understood that, as time goes up, when the difference between a certain battery cell and other battery cells becomes larger, it indicates that the self-discharge of the battery cell is abnormal.

Therefore, in the embodiment of the disclosure, the variation of the outlier trend of the battery cell can be obtained by comparing the difference between the first state of charge outlier trend and the second state of charge outlier trend of the battery cell.

Furthermore, according to the ratio of the variation of the outlier trend of the single battery to the set period, the change rate of the outlier trend of the single battery can be obtained.

For example, the first state of charge outlier trend of the battery cell is Δ SOC1, the second state of charge outlier trend is Δ SOC2, the set period is T, and the rate of change of the outlier trend of the battery cell is: (Δ SOC1- Δ SOC 2)/T.

In step 213, in response to the rate of change of the outlier trend of any battery cell being greater than the first set threshold, it is determined that the self-discharge of the battery cell is abnormal.

And 214, in response to the change rate of the outlier trend of each battery cell being less than or equal to the first set threshold, determining that the self-discharge of each battery cell is normal.

It can be understood that, when the rate of change of the outlier trend of a certain battery cell becomes larger, it indicates that the difference between the battery cell and other battery cells is larger and larger.

Therefore, a threshold value of the change rate of the outlier trend of the battery cell can be preset, and whether the self-discharge of the battery cell is abnormal or not can be determined by comparing the change rate of the outlier trend with the set threshold value.

Specifically, when the outlier trend change rate of any battery cell is greater than a first set threshold, it may be determined that the battery cell has abnormal self-discharge. Otherwise, the self-discharge of the battery cell is normal.

The first set threshold may be any value set in advance, which is not limited in this disclosure.

For example, if the first set threshold is 0.1, the rate of change of the outlier trend of a certain battery cell is 0.16, and the first set threshold is greater than the first set threshold, it may be determined that the self-discharge of the battery cell is abnormal. Or, if the change rate of the outlier trend of a certain battery cell is 0.07 and is smaller than the first set threshold, it can be determined that the self-discharge of the battery cell is normal.

According to the method and the device, firstly, voltage monitoring data and current monitoring data of each battery cell are obtained, then a target time interval in a set period is screened according to the current monitoring data of the battery cells, and a corresponding charge state is obtained based on the voltage monitoring data of the target time interval; then determining a first charge state outlier trend of the single battery at the initial time of the target time interval and a second charge state outlier trend at the end time of the target time interval according to the charge states; and finally, determining whether the self-discharge of the battery monomer is abnormal or not according to the first charge state outlier trend, the second charge state outlier trend and the set period duration. Therefore, the long-term charge state outlier trend of the single battery is adopted for judgment, the accuracy of self-discharge judgment of the battery is greatly improved, and the false alarm rate caused by data fluctuation is reduced.

In order to realize the above embodiment, the present disclosure further provides a monitoring device for battery self-discharge.

Fig. 4 is a schematic structural diagram of a monitoring device for battery self-discharge provided in the embodiment of the present disclosure.

As shown in fig. 4, the monitoring apparatus 100 for self-discharging of a battery may include: a first obtaining module 110, a second obtaining module 120, a first determining module 130, a second determining module 140, and a third determining module 150.

The first obtaining module 110 is configured to obtain voltage monitoring data and current monitoring data of each battery cell;

the second obtaining module 120 is configured to obtain a first state of charge and a second state of charge of each battery cell according to the voltage monitoring data and the current monitoring data, where the first state of charge is a state of charge of each battery cell at an initial time of a set period, and the second state of charge is a state of charge of each battery cell at an end time of the set period;

the first determining module 130 is configured to determine a first state of charge outlier trend of each battery cell according to each first state of charge;

the second determining module 140 is configured to determine a second state of charge outlier trend of each battery cell according to each second state of charge;

the third determining module 150 is configured to determine whether self-discharge of each battery cell is abnormal according to the first state of charge outlier trend and the second state of charge outlier trend corresponding to each battery cell.

The functions and specific implementation principles of the modules in the embodiments of the present disclosure may refer to the embodiments of the methods, and are not described herein again.

The monitoring device for the self-discharge of the battery of the embodiment of the disclosure firstly acquires voltage monitoring data and current monitoring data of each battery monomer; then, acquiring a first charge state and a second charge state of each battery cell according to the voltage monitoring data and the current monitoring data, wherein the first charge state is the charge state of each battery cell at the initial moment of a set period, and the second charge state is the charge state of each battery cell at the end moment of the set period; then, according to each first charge state, determining a first charge state outlier trend of each battery cell, and according to each second charge state, determining a second charge state outlier trend of each battery cell; and finally, determining whether the self-discharge of each battery cell is abnormal or not according to the first charge state outlier trend and the second charge state outlier trend corresponding to each battery cell. Therefore, the self-discharge condition of the battery is monitored based on the charge state outlier trend of the battery in a certain time, the false alarm rate caused by data fluctuation is reduced, and the accuracy and the reliability of the self-discharge monitoring of the battery are improved.

In a possible implementation manner of the embodiment of the present disclosure, the third determining module 150 includes:

the first determining unit is used for determining the variation of the outlier trend of each battery cell according to the difference value of the first charge state outlier trend and the second charge state outlier trend corresponding to each battery cell;

the second determining unit is used for determining the outlier trend change rate of each single battery according to the ratio of the outlier trend variation of each single battery to the set period;

the third determining unit is used for responding to the fact that the change rate of the outlier trend of any battery cell is larger than a first set threshold value, and determining self-discharge abnormity of the battery cells;

and the fourth determining unit is used for responding to the fact that the change rate of the outlier trend of each battery cell is smaller than or equal to the first set threshold value, and determining that the self-discharge of each battery cell is normal.

In a possible implementation manner of the embodiment of the present disclosure, the second obtaining module 120 includes:

a fifth determination unit configured to determine a target period during which the current monitoring data is within a set range in a set cycle;

and the sixth determining unit is used for determining the first charge state of each battery cell according to the voltage monitoring data of each battery cell at the initial moment of the target time interval and the mapping relation between the charge state of the battery and the open-circuit voltage.

And the seventh determining unit is used for determining the second charge state of each battery cell according to the voltage monitoring data of each battery cell at the ending moment of the target time interval and the mapping relation between the charge state of the battery and the open-circuit voltage.

In a possible implementation manner of the embodiment of the present disclosure, the first determining module 130 includes:

the eighth determining unit is used for determining the reference state of charge according to the median of the first state of charge corresponding to all the battery cells respectively;

and the ninth determining unit is used for determining the first charge state outlier trend of each battery cell according to the difference value between the first charge state corresponding to each battery cell and the reference charge state.

In a possible implementation manner of the embodiment of the present disclosure, the second determining module 140 includes:

a tenth determining unit, configured to determine a second reference state of charge according to the median of the second state of charge corresponding to each of the battery cells;

an eleventh determining unit, configured to determine a second state of charge outlier trend of each battery cell according to a difference between the second state of charge corresponding to each battery cell and the second reference state of charge.

In a possible implementation manner of the embodiment of the present disclosure, the apparatus further includes:

the fourth determining module is used for determining the first state of charge dispersion rate of all the battery cells according to each first state of charge;

the first determining module is used for responding to the fact that the first state of charge dispersion rate is smaller than or equal to a second set threshold value, and determining the first state of charge outlier trend of each battery cell according to each first state of charge.

the fifth determining module is used for determining the second state of charge dispersion rate of all the battery cells according to each second state of charge;

the second determining module is used for responding to the second state of charge dispersion rate being smaller than or equal to a second set threshold value, and determining a second state of charge outlier trend of each battery cell according to each second state of charge.

In a possible implementation manner of the embodiment of the present disclosure, the fourth determining module is configured to:

and determining the first state of charge dispersion rate according to the standard deviation and/or the variance of the first state of charge corresponding to each battery cell.

In a possible implementation manner of the embodiment of the present disclosure, the fifth determining module is configured to:

and determining a second state of charge dispersion rate according to the standard deviation and/or the variance of the second state of charge corresponding to each battery cell.

The monitoring device for the self-discharge of the battery of the embodiment of the disclosure firstly acquires voltage monitoring data and current monitoring data of each battery cell, then screens a target time period in a set period according to the current monitoring data of the battery cells, and acquires a corresponding state of charge based on the voltage monitoring data of the target time period; then determining a first charge state outlier trend of the single battery at the initial moment of the target time interval and a second charge state outlier trend at the initial moment of the target time interval according to the charge states; and finally, determining whether the self-discharge of the battery monomer is abnormal or not according to the first charge state outlier trend, the second charge state outlier trend and the set period duration. Therefore, the long-term charge state outlier trend of the single battery is adopted for judgment, the accuracy of self-discharge judgment of the battery is greatly improved, and the false alarm rate caused by data fluctuation is reduced.

In order to implement the foregoing embodiments, the present disclosure also provides a computer device, including: the monitoring method for the self-discharge of the battery is provided by the embodiment of the disclosure.

In order to achieve the above embodiments, the present disclosure also proposes a vehicle comprising a computer device as proposed in the previous embodiments of the present disclosure.

In order to implement the foregoing embodiments, the present disclosure also proposes a non-transitory computer-readable storage medium storing computer instructions, which when executed by a processor implement the monitoring method for self-discharge of a battery as proposed by the foregoing embodiments of the present disclosure.

In order to implement the foregoing embodiments, the present disclosure further provides a computer program product, wherein when instructions in the computer program product are executed by a processor, the monitoring method for battery self-discharge as proposed in the foregoing embodiments of the present disclosure is performed.

FIG. 5 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present disclosure. The computer device 12 shown in fig. 5 is only one example and should not bring any limitations to the functionality or scope of use of the embodiments of the present disclosure.

As shown in FIG. 5, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. These architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, to name a few.

Computer device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

Memory 28 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 30 and/or cache Memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, and commonly referred to as a "hard drive"). Although not shown in FIG. 5, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk Read Only Memory (CD-ROM), a Digital versatile disk Read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the disclosure.

A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described in this disclosure.

Computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with computer device 12, and/or with any devices (e.g., network card, modem, etc.) that enable computer device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet) via Network adapter 20. As shown, network adapter 20 communicates with the other modules of computer device 12 via bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processing unit 16 executes various functional applications and data processing, for example, implementing the methods mentioned in the foregoing embodiments, by running a program stored in the system memory 28.

According to the technical scheme, firstly, voltage monitoring data and current monitoring data of each battery monomer are obtained; then, acquiring a first charge state and a second charge state of each battery cell according to the voltage monitoring data and the current monitoring data, wherein the first charge state is the charge state of each battery cell at the initial moment of a set period, and the second charge state is the charge state of each battery cell at the end moment of the set period; then, according to each first charge state, determining a first charge state outlier trend of each battery cell, and according to each second charge state, determining a second charge state outlier trend of each battery cell; and finally, determining whether the self-discharge of each battery cell is abnormal or not according to the first charge state outlier trend and the second charge state outlier trend corresponding to each battery cell. Therefore, the self-discharge condition of the battery is monitored based on the charge state outlier trend of the battery in a certain time, the false alarm rate caused by data fluctuation is reduced, and the accuracy and the reliability of self-discharge monitoring of the battery are improved.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

It will be understood by those skilled in the art that all or part of the steps carried out in the method for implementing the above embodiment may be implemented by hardware that is related to instructions of a program, and the program may be stored in a computer readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiment.

In addition, functional units in the embodiments of the present disclosure may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure, and that changes, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present disclosure.

Claims

1. A method for monitoring self-discharge of a battery, comprising:

2. The method of claim 1, wherein the obtaining the first state of charge and the second state of charge of each of the battery cells according to the voltage monitoring data and the current monitoring data comprises:

determining a target time period within which the current monitoring data is within a set range within the set period;

determining a first charge state of each single battery according to voltage monitoring data of each single battery at the initial moment of the target time interval and a mapping relation between the charge state of the battery and the open-circuit voltage;

and determining a second charge state of each single battery according to the voltage monitoring data of each single battery at the end time of the target time interval and the mapping relation between the charge state of the battery and the open-circuit voltage.

3. The method of claim 1, wherein said determining a first state of charge outlier trend for each of said cells based on each of said first states of charge comprises:

determining a first reference state of charge according to the median of the first state of charge corresponding to all the battery cells respectively;

determining a first state of charge (SOC) outlier trend of each battery cell according to a difference value between the first SOC corresponding to each battery cell and the first reference SOC;

determining a second state of charge outlier trend for each of the cells based on each of the second states of charge, comprising:

determining a second reference state of charge according to the median of the second state of charge corresponding to all the battery cells respectively;

and determining a second charge state outlier trend of each battery cell according to the difference value between the second charge state corresponding to each battery cell and the second reference charge state.

4. The method of claim 1, wherein the determining whether the self-discharge of each of the battery cells is abnormal according to the first state of charge outlier trend and the second state of charge outlier trend corresponding to each of the battery cells comprises:

determining the variation of the outlier trend of each battery cell according to the difference value of the first charge state outlier trend and the second charge state outlier trend corresponding to each battery cell;

determining the outlier trend change rate of each single battery according to the ratio of the outlier trend change quantity of each single battery to the set period;

determining that the self-discharge of the battery monomer is abnormal in response to the fact that the outlier trend change rate of any battery monomer is larger than a first set threshold;

and determining that the self-discharge of each battery cell is normal in response to the outlier trend change rate of each battery cell being smaller than or equal to a first set threshold value.

5. The method of any of claims 1-4, further comprising:

determining first state of charge dispersion rates of all the battery cells according to each first state of charge;

determining second state of charge dispersion rates of all the battery cells according to each second state of charge;

determining a first state of charge outlier trend for each of the cells based on each of the first states of charge, comprising:

responding to the first state of charge dispersion rate being smaller than or equal to a second set threshold, and determining a first state of charge outlier trend of each battery cell according to each first state of charge;

and responding to the second state of charge dispersion rate being smaller than or equal to a second set threshold, and determining a second state of charge outlier trend of each battery cell according to each second state of charge.

6. The method of claim 5, wherein said determining a first state of charge dispersion rate for all of said cells from each of said first states of charge comprises:

determining the first state of charge dispersion rate according to the standard deviation and/or variance of the first state of charge corresponding to each battery cell;

determining a second state of charge dispersion rate of all the battery cells according to each second state of charge, including:

and determining the second state of charge discrete rate according to the standard deviation and/or the variance of the second state of charge corresponding to each single battery.

7. A device for monitoring self-discharge of a battery, comprising:

the first acquisition module is used for acquiring voltage monitoring data and current monitoring data of each battery monomer;

8. The apparatus of claim 7, wherein the third determining module comprises:

the second determining unit is used for determining the outlier trend change rate of each single battery according to the ratio of the outlier trend change quantity of each single battery to the set period;

the third determining unit is used for determining self-discharge abnormity of the battery cells in response to the fact that the change rate of the outlier trend of any battery cell is larger than a first set threshold value;

and the fourth determining unit is used for determining that the self-discharge of each battery cell is normal in response to the outlier trend change rate of each battery cell being smaller than or equal to a first set threshold value.

9. A computer device comprising a memory, a processor and computer instructions stored on the memory and executable on the processor, the processor implementing the method of monitoring self-discharge of a battery as claimed in any one of claims 1 to 6 when executing the instructions.

10. A vehicle, characterized in that it comprises a computer device according to claim 9.

11. A computer readable storage medium storing computer instructions, wherein the computer instructions, when executed by a processor, implement the method for monitoring self-discharge of a battery according to any one of claims 1-6.

12. A computer program product comprising computer instructions which, when executed by a processor, implement the method of monitoring self-discharge of a battery according to any one of claims 1-6.