CN117148166A - Battery safety level prediction method, device, computer equipment and storage medium - Google Patents
Battery safety level prediction method, device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN117148166A CN117148166A CN202311212144.9A CN202311212144A CN117148166A CN 117148166 A CN117148166 A CN 117148166A CN 202311212144 A CN202311212144 A CN 202311212144A CN 117148166 A CN117148166 A CN 117148166A
- Authority
- CN
- China
- Prior art keywords
- differential pressure
- battery
- charging period
- system range
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 230000008859 change Effects 0.000 claims abstract description 122
- 230000015654 memory Effects 0.000 claims description 22
- 230000008569 process Effects 0.000 description 8
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 5
- 210000004027 cell Anatomy 0.000 description 5
- 229910052744 lithium Inorganic materials 0.000 description 5
- 238000012502 risk assessment Methods 0.000 description 5
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 4
- 238000007599 discharging Methods 0.000 description 4
- 229910001416 lithium ion Inorganic materials 0.000 description 4
- 230000009467 reduction Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000007086 side reaction Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000015556 catabolic process Effects 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000006731 degradation reaction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000002923 metal particle Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000001787 dendrite Anatomy 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2123/00—Data types
- G06F2123/02—Data types in the time domain, e.g. time-series data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Pure & Applied Mathematics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Mathematical Physics (AREA)
- Evolutionary Biology (AREA)
- Mathematical Optimization (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Evolutionary Computation (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Artificial Intelligence (AREA)
- Algebra (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Secondary Cells (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to the technical field of battery safety, and discloses a battery safety level prediction method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: determining a plurality of first differential pressure average values of a battery to be predicted in a current charging period; acquiring a plurality of second differential pressure average values of the first charging period, and a plurality of third differential pressure average values of the second charging period; determining a first system range change rate of a corresponding charging state interval according to a first differential pressure average value and a second differential pressure average value of the current charging period and the first charging period in the same charging state interval; determining the second system range change rate of the corresponding charging state interval according to the first differential pressure average value and the third differential pressure average value of the current charging period and the second charging period in the same charging state interval; and predicting the safety level of the battery to be predicted according to the first system range change rate and the second system range change rate of the charging state intervals. The invention can accurately predict the safety risk level of the battery.
Description
Technical Field
The invention relates to the technical field of battery safety, in particular to a battery safety level prediction method, a device, computer equipment and a storage medium.
Background
The lithium ion battery is widely used for an energy system of an electric automobile due to the characteristics of no pollution, high specific energy, long cycle life and the like. The self-discharge of the lithium ion battery system is a scene that the open-circuit voltage continuously drops due to factors such as side reaction of a battery core, micro short circuit, external connection leakage and the like in the use or storage process of the battery. The existence of the power self-discharging phenomenon not only causes the energy loss of the battery, but also causes the service life reduction of the lithium battery pack due to the self-discharging inconsistency among the battery cores, the discharge capacity reduction is realized, the user experience is influenced, and the use safety of the battery is influenced by the serious failure scene.
Wherein, the battery performance is reduced due to the difference of the SOC of the battery after storage in the battery pack caused by the non-uniform self-discharge of the battery. The problem of performance degradation can be frequently found after a period of storage of the battery pack, and when the SOC difference reaches about 20%, the capacity of the battery pack only remains 60% -70%; in terms of safety, since lithium precipitation, metal particles, etc. cause micro-short circuits, which are important factors for thermal runaway, due to battery design and production problems, research and discrimination of self-discharge characteristics through big data application and evaluation of battery safety risk have a critical influence on safety of new energy automobiles.
At present, the safety risk assessment of the battery based on cloud data uploaded by a vehicle can cause the problems of misjudgment and missed judgment, and the safety risk level of the battery cannot be accurately predicted.
Therefore, there is a need for a battery safety level prediction method that can accurately predict the safety risk level of a battery.
Disclosure of Invention
In view of the above, the present invention provides a method, an apparatus, a computer device and a storage medium for predicting a battery safety level, so as to solve the problem that in the related art, erroneous judgment and missed judgment exist in the battery safety risk assessment, and thus the battery safety risk level cannot be accurately predicted.
In a first aspect, the present invention provides a battery safety level prediction method, which determines a plurality of first differential pressure averages of a battery to be predicted in a current charging period; the first differential pressure average values are in one-to-one correspondence with the charging state intervals;
acquiring a plurality of second differential pressure average values corresponding to a plurality of charging state intervals of the first charging period one by one, and acquiring a plurality of third differential pressure average values corresponding to a plurality of charging state intervals of the second charging period one by one; wherein the second charging period is earlier than the first charging period;
determining a first system range change rate of a corresponding charging state interval according to a first differential pressure mean value and a second differential pressure mean value of the current charging period and the first charging period in the same charging state interval;
Determining a second system range change rate of the corresponding charging state interval according to a first differential pressure average value and a third differential pressure average value of the current charging period and the second charging period in the same charging state interval;
and predicting the safety level of the battery to be predicted according to the first system range change rate and the second system range change rate of the charging state intervals.
According to the battery safety level prediction method provided by the invention, the first system range change rate of the corresponding charging state interval is determined according to the first pressure difference average value and the second pressure difference average value of the current charging period and the first charging period in the same charging state interval; determining the second system range change rate of the corresponding charging state interval according to the first differential pressure average value and the third differential pressure average value of the current charging period and the second charging period in the same charging state interval; according to the first system extremely poor change rate and the second system extremely poor change rate of the charging state intervals, the safety level of the battery to be predicted is predicted, the misjudgment rate and the missed judgment rate of the battery safety risk assessment can be reduced, and the prediction accuracy of the battery safety risk level is further improved.
In an alternative embodiment, before determining the first differential pressure averages of the battery to be predicted over the current charging cycle, the method further comprises:
Obtaining a differential pressure value of a battery to be predicted in each time frame in a current charging period;
and determining a first differential pressure average value of each charging state interval according to the differential pressure values of a plurality of time frames in each charging state interval.
In an alternative embodiment, the battery to be predicted includes a plurality of single cells; the method for obtaining the differential pressure value of the battery to be predicted in each time frame in the current charging period comprises the following steps:
acquiring a voltage value of each single battery in each time frame in a current charging period;
determining a voltage maximum value and a voltage minimum value from a plurality of voltage values of each time frame;
and determining the differential pressure value of the battery to be predicted in each time frame in the current charging period according to the maximum voltage value and the minimum voltage value.
In an alternative embodiment, determining the first differential pressure mean value for each state of charge interval based on the differential pressure values for the plurality of time frames within each state of charge interval includes:
wherein dV_avg|SOC i A first differential pressure average value representing an ith charge state interval, i representing a sequence number of the charge state interval,representing a plurality of time frames within an ith state of charge intervalThe sum of the differential pressures, deltav, represents the differential pressure and n represents the total number of time frames in the ith state of charge interval.
According to the battery safety level prediction method provided by the invention, the first differential pressure average value of each charging state interval is determined according to the differential pressure values of a plurality of time frames in each charging state interval, and the system state of the battery can be accurately represented, so that the accuracy of battery safety risk assessment is improved.
In an alternative embodiment, determining the first system range rate of change of the corresponding charging state interval according to the first differential pressure average value and the second differential pressure average value of the current charging period and the first charging period in the same charging state interval includes:
wherein k is i The system margin change rate indicating the i-th charge state section, i indicating the number of charge state sections, dV_avg|SOC2 i A second differential pressure average value, dV_avg|SOC1, representing the first charge period in the ith charge state interval i And (3) representing a first differential pressure average value of the current charging period in the ith charging state interval, wherein m represents a time difference value between the first charging period and the current charging period.
According to the battery safety level prediction method provided by the invention, the first system range change rate of the corresponding charging state interval is determined according to the first differential pressure average value and the second differential pressure average value of the current charging period and the first charging period in the same charging state interval, and the safety risk of the battery can be perceived innovatively through the system range change rate, so that the purpose of accurately predicting the battery safety risk level is achieved.
In an alternative embodiment, predicting the safety level of the battery to be predicted according to the first system range rate of change and the second system range rate of change of the plurality of charge state intervals includes:
if a first target time frame exists in a plurality of time frames in the current charging period or the extremely poor change rate of the first target system is smaller than a second threshold value, determining that the battery to be predicted is normal;
the target time frame is a time frame in which the differential pressure value is smaller than a first threshold value, and the first target system range change rate is the maximum value of a plurality of first system range change rates.
In an alternative embodiment, predicting the safety level of the battery to be predicted according to the first system range rate of change and the second system range rate of change of the plurality of charge state intervals includes:
if a second target time frame exists in the time frames in the current charging period, the first target system range change rate is larger than or equal to a second threshold value, the first target system range change rate is smaller than a third threshold value, and the second target system range change rate is smaller than a fourth threshold value, determining that the battery to be predicted is a first-level security risk level; wherein the second target time frame is a time frame with a differential pressure value greater than or equal to a first threshold value; the second target system range rate of change is the maximum of a plurality of second system range rates of change;
And if a second target time frame exists in the time frames in the current charging period, the first target system range change rate is larger than or equal to a second threshold value, the first target system range change rate is smaller than a third threshold value, and the second target system range change rate is larger than or equal to a fourth threshold value, determining that the battery to be predicted is a secondary safety risk level.
According to the battery safety level prediction method, the safety risk level of the battery and the self-discharge type of the battery can be accurately predicted through the first system range change rate and the second system range change rate of different charging state intervals.
In a second aspect, the present invention provides a battery safety level prediction apparatus, comprising:
the first differential pressure average value determining module is used for determining a plurality of first differential pressure average values of the battery to be predicted in the current charging period; the first differential pressure average values are in one-to-one correspondence with the charging state intervals;
the differential pressure average value acquisition module is used for acquiring a plurality of second differential pressure average values which are in one-to-one correspondence with a plurality of charging state intervals of the first charging period and acquiring a plurality of third differential pressure average values which are in one-to-one correspondence with a plurality of charging state intervals of the second charging period; wherein the second charging period is earlier than the first charging period;
The first system range change rate determining module is used for determining the first system range change rate of the corresponding charging state interval according to a first pressure difference average value and a second pressure difference average value of the current charging period and the first charging period in the same charging state interval;
the second system range change rate determining module is used for determining the second system range change rate of the corresponding charging state interval according to the first pressure difference average value and the third pressure difference average value of the current charging period and the second charging period in the same charging state interval;
the safety level prediction module is used for predicting the safety level of the battery to be predicted according to the first system range change rate and the second system range change rate of the charging state intervals.
In a third aspect, the present invention provides a computer device comprising: the battery safety level prediction method comprises the steps of storing a battery safety level prediction program, wherein the battery safety level prediction program is used for predicting the battery safety level of the battery, and the battery safety level prediction program is used for predicting the battery safety level of the battery.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the battery safety level prediction method of the first aspect or any one of the embodiments corresponding thereto.
The battery safety level prediction method provided by the invention has the following technical effects:
1. the safety risk level of the battery is estimated through the system extremely poor change rate of different charging state intervals, and the prediction accuracy of the safety risk level of the battery can be effectively improved.
2. By dividing the charging state interval in the charging process, the computing resources and the computing speed can be effectively saved, and the comparison of the system extremely poor change rate of the full charging state interval is not needed; and the charging state interval is individually divided according to the charging habit of the user, so that the method is applicable to the actual working condition of the new energy vehicle and is beneficial to increasing and analyzing the effective data quantity.
3. By setting the current charging period, the charging state interval of the first charging period and the charging state interval of the second charging period, the coverage in the time dimension can be improved, so that misjudgment and missed judgment of the safety risk assessment of the power battery can be reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart illustrating a battery safety level prediction method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another battery safety level prediction method according to an embodiment of the present invention;
FIG. 3 is a flow chart of yet another battery safety level prediction method according to an embodiment of the present invention;
FIG. 4 is a flow chart of one embodiment of a battery safety level prediction method according to the present invention;
FIG. 5 is a flow chart of yet another battery safety level prediction method according to an embodiment of the present invention;
fig. 6 is a block diagram of a battery safety level predicting apparatus according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The lithium ion battery is widely used for an energy system of an electric automobile due to the characteristics of no pollution, high specific energy, long cycle life and the like. The self-discharge of the lithium ion battery system is a scene that the open-circuit voltage continuously drops due to factors such as side reaction of a battery core, micro short circuit, external connection leakage and the like in the use or storage process of the battery.
The existence of the power self-discharging phenomenon not only causes the energy loss of the battery, but also causes the service life reduction of the lithium battery pack due to the self-discharging inconsistency among the battery cores, the discharge capacity reduction is realized, the user experience is influenced, and the use safety of the battery is influenced by the serious failure scene. Wherein, the battery performance is reduced due to the difference of the SOC of the battery after storage in the battery pack caused by the non-uniform self-discharge of the battery. The problem of performance degradation can be frequently found after a period of storage of the battery pack, and when the SOC difference reaches about 20%, the capacity of the battery pack only remains 60% -70%; in terms of safety, lithium precipitation, metal particles and the like are important factors for thermal runaway caused by micro-shorting due to battery design and production problems, so that research and discrimination of self-discharge characteristics through big data application have a crucial influence on the safety of new energy automobiles.
The battery self-discharge is evaluated at present mainly by storing and identifying whether the self-discharge is abnormal or not in a long time under the scenes of normal temperature, high temperature, low temperature and the like of a battery cell and a battery system in a laboratory, and the battery self-discharge is not provided with an evaluation basis after loading.
At present, although the problem of voltage consistency caused by self-discharge or inconsistent aging and the like can be identified based on cloud data uploaded by a vehicle, the self-discharge failure factor and the positioning of the refined failure factor cannot be accurately identified. The self-discharge caused by internal micro-short circuit has certain potential safety hazard under different application scenes and different thermal management conditions of a battery system, and an effective identification method is not available at present.
In accordance with an embodiment of the present invention, there is provided a battery safety level prediction method embodiment, it being noted that the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in an order other than that shown or described herein.
In this embodiment, a battery safety level prediction method is provided, fig. 1 is a flowchart of a battery safety level prediction method according to an embodiment of the present invention, and as shown in fig. 1, the flowchart includes the following steps:
Step S101, determining a plurality of first differential pressure average values of a battery to be predicted in a current charging period; the first differential pressure averages correspond to the charging state intervals one by one.
Specifically, the charging cycle may be understood as the whole charging process of each charging of the battery, and the charging process may include one or more charging state intervals, where each charging state interval corresponds to a differential pressure average value.
The State Of Charge interval (i.e., SOC State Of Charge interval) may be understood as a State Of Charge Of the battery, i.e., a current power ratio Of the battery, which is expressed as a ratio Of a current battery capacity to a total battery capacity, and is commonly expressed as a percentage.
The number of the charging status intervals may be set according to the charging habit of the user, and is not specifically limited herein, for example: the number of the charging state intervals at the end of charging of most vehicles is more than 98% of the charging state intervals, 50% -60% of the charging state intervals or 15% -20% of the charging state intervals, and the number of the charging state intervals can be set to be 3, namely, the charging state intervals are respectively equal to or more than 98%, 60%. Gtoreq.50% and 20%. Gtoreq.15%.
The reason for the three SOC interval divisions described above: (1) In order to save calculation resources and calculation speed, the comparison of the extremely poor change rates of the full SOC interval systems is not performed, and the comparison of the extremely poor change rates of the different SOC interval systems is performed; (2) In order to adapt to different charging habits of users, some vehicles have different SOC states when charging is finished, and the dividing interval is applicable to actual working conditions of new energy vehicles for the algorithm, so that the effective data quantity is increased.
If the vehicle charging is 100% in the current charging period, the current charging period has three charging state intervals of SOC more than or equal to 98%, 60%. Gtoreq.50% and 20%. Gtoreq.15% respectively; if the charging state of the vehicle at the end of charging in the current charging period is more than 60%, the current charging period has two charging state intervals of 60% -or more than or equal to 50% of SOC and 20% -or more than or equal to 15% of SOC; if the charging state at the end of the vehicle charging in the current charging period is more than 20%, the current charging period has a charging state interval of 20%. Gtoreq.SOC.gtoreq.15%.
The invention can sense the self-discharge abnormality of the battery in multiple scenes through the extremely poor change rate of the system, thereby identifying the safety risk of the battery. In order to consider the difference of charging habits and using habits of each electric vehicle and increase the reliability and the application breadth of a calculation result, the invention only takes a battery charging working condition part to extract parameters, and the charging state is divided into three SOC state sections, which are respectively: is more than or equal to 98 percent of SOC interval, 50 percent to 60 percent of SOC interval, and 15 percent to 20 percent of interval.
Step S102, a plurality of second differential pressure average values corresponding to a plurality of charging state intervals of a first charging period one by one are obtained, and a plurality of third differential pressure average values corresponding to a plurality of charging state intervals of a second charging period one by one are obtained; wherein the second charging period is earlier than the first charging period.
Specifically, the first charging period is a charging period of a first preset time set according to physical self-discharge properties of the battery, for example, the first 5 days of the current charging period; the second charging period is a charging period of a second preset time set according to chemical self-discharge properties of the battery, for example, the first 15 days of the current charging period.
Specifically, all the charging state intervals including the current charging period in the first charging period and the second charging period, for example, the current charging period includes 2 charging state intervals, namely, 60%. Gtoreq.50% of SOC and 20%. Gtoreq.15% of SOC; the first charging period and the second charging period may include 2 charging state intervals (i.e., 60%. Gtoreq.50% and 20%. Gtoreq.15% of SOC), and may include 3 charging state intervals (i.e., 98% or higher of SOC, 50% or higher of 60%. Gtoreq.50% and 20%. Gtoreq.15% of SOC).
More specifically, if the current charging cycle includes 2 charging state intervals (60%. Gtoreq.50% and 20%. Gtoreq.15% SOC), and the first charging cycle includes 3 charging state intervals (i.e. SOC.gtoreq.98%, 60%. Gtoreq.50% and 20%. Gtoreq.15% SOC), then only the second differential pressure average of the two charging state intervals of 60%. Gtoreq.50% and 20%. Gtoreq.15% in the first charging cycle needs to be obtained. Similarly, if the second charging period includes 3 charging state intervals (i.e., SOC is greater than or equal to 98%, 60% is greater than or equal to 50% and 20% is greater than or equal to 15%), then only a third differential pressure average value of the two charging state intervals of 60% is greater than or equal to 50% and 20% is greater than or equal to 15% in the second charging period is required.
Step S103, determining a first system range rate of change of the corresponding charging state interval according to the first differential pressure average value and the second differential pressure average value of the current charging period and the first charging period in the same charging state interval.
Specifically, after determining a plurality of first differential pressure averages corresponding to a plurality of charging state intervals of a current period one by one and a plurality of second differential pressure averages corresponding to a plurality of charging state intervals of the first period one by one, the following formula is adopted to determine the first system range change rate of the same charging state interval by the following formula, and the first differential pressure averages and the second differential pressure averages of the same charging state interval:
wherein k is i A first system range rate of change representing an i-th charge state interval, i representing chargeSequence number of electric state interval, dV_avg|SOC2 i A second differential pressure average value, dV_avg|SOC1, representing the first charge period in the ith charge state interval i And (3) representing a first differential pressure average value of the current charging period in the ith charging state interval, wherein m represents a time difference value between the first charging period and the current charging period. The time difference may be days or seconds.
When the time difference may be days, the above formula may be modified as:
The number of days is understood to be the first predetermined time, i.e. 5 days.
When the time difference may be seconds, the above formula may be modified as:
where 86400 represents a time difference of one day (24×60×60=86400 seconds), and t represents a time difference within a certain period of self-discharge, it can be understood that t=5×24×60×60= 432000 seconds.
More specifically, if the current charge cycle includes 2 charge state sections (60%. Gtoreq.50% and 20%. Gtoreq.15% SOC), and the first charge cycle includes 3 charge state sections (i.e., 98% SOC. Gtoreq.98%, 60%. Gtoreq.50% SOC and 20%. Gtoreq.15% SOC), the first system range rate of change of the 60%. Gtoreq.50% SOC charge state sections and the first system range rate of change of the 20%. Gtoreq.15% SOC state sections can be obtained by the above formula.
Step S104, determining the second system range change rate of the corresponding charging state interval according to the first pressure difference average value and the third pressure difference average value of the current charging period and the second charging period in the same charging state interval.
Specifically, after determining a plurality of first differential pressure averages corresponding to a plurality of charging state intervals of the current period one by one and a plurality of third differential pressure averages corresponding to a plurality of charging state intervals of the first period one by one, the following formula is adopted to determine the second system range change rate of the same charging state interval by the following formula, and the first differential pressure averages and the third differential pressure averages of the same charging state interval:
Wherein k is i A second system margin change rate indicating an i-th charge state section, i indicating a sequence number of the charge state section, dV_avg|SOC2 i A third differential pressure average value, dV_avg|SOC1, representing the second charging period in the ith charging state interval i And (3) representing a first differential pressure average value of the current charging period in the ith charging state interval, wherein m represents a time difference value between the second charging period and the current charging period. The time difference may be days or seconds.
When the time difference may be days, the above formula may be modified as:
the number of days is understood to be the first predetermined time, i.e. 15 days.
When the time difference may be seconds, the above formula may be modified as:
where 86400 represents a time difference of one day (24×60=86400 seconds), and t represents a time difference within a certain period of self-discharge, it can be understood that t=15×24×60= 1296000 seconds.
More specifically, if the current charge cycle includes 2 charge state sections (60%. Gtoreq.50% and 20%. Gtoreq.15% SOC), and the second charge cycle includes 3 charge state sections (i.e., 98% SOC. Gtoreq.98%, 60%. Gtoreq.50% and 20%. Gtoreq.15% SOC), the second system range rate of 60%. Gtoreq.50% SOC charge state sections and 20%. Gtoreq.15% SOC state sections can be obtained by the above formula.
In the above steps S103 to S104, the first system range rate of change and the second system range rate of change of each charge state section of the battery during the charging process can be obtained by the above formulas.
Step S105, predicting the safety level of the battery to be predicted according to the first system range change rate and the second system range change rate of the plurality of charging state intervals.
Specifically, the safety level of the battery to be predicted and the self-discharge scene can be judged by comparing the first system range rate of change with the second system range rate of change and the corresponding preset threshold.
In some preferred embodiments, as shown in fig. 2, before step S101, the method further includes steps a 1-a 2:
and a step a1, obtaining the differential pressure value of the battery to be predicted in each time frame in the current charging period.
Specifically, each time frame may be understood as each time or each second, for example, the current charging period is from 8 am to 8 pm today, and then the current charging period includes 12×60×60= 43200 seconds.
As shown in fig. 3, the step a1 further includes steps a11 to a13:
step a11, obtaining a voltage value of each single battery in each time frame in the current charging period.
Specifically, the battery to be predicted includes a plurality of unit batteries; and acquiring full life cycle data of the vehicle through a big data technology, and extracting vehicle charging data from the full life cycle data of the vehicle, namely, the voltage value of each single battery in each time frame in the current charging cycle.
Step a12, determining a voltage maximum value and a voltage minimum value from a plurality of voltage values of each time frame.
Specifically, from each unit cell under each time frameDetermining the maximum voltage V from the battery values max And a voltage minimum value V min 。
And a step a13, determining the pressure difference value of each time frame of the battery to be predicted in the current charging period according to the voltage maximum value and the voltage minimum value.
Specifically, when the maximum voltage V of each time frame of the battery to be predicted is obtained max And a voltage minimum value V min Then, the differential pressure Δv of each time frame of the battery to be predicted in the current charging period can be determined by the following formula:
ΔV＝V max -V min
wherein DeltaV represents the differential pressure value of the battery to be predicted in each time frame in the current charging period, V max Representing the maximum voltage value, V, of the battery to be predicted for each time frame during the current charge cycle min Representing the minimum voltage of the battery to be predicted for each time frame during the current charge cycle.
Step a2, determining a first differential pressure average value of each charging state interval according to differential pressure values of a plurality of time frames in each charging state interval.
Specifically, the first differential pressure mean value of each state of charge interval may be determined by the following formula, and the differential pressure values of a plurality of time frames within each state of charge interval:
wherein dV_avg|SOC i A first differential pressure average value representing an ith charge state interval, i representing a sequence number of the charge state interval,the sum of the differential pressure values of the plurality of time frames in the ith charge state interval is represented, Δv represents the differential pressure value, and n represents the total number of time frames in the ith charge state interval.
As shown in fig. 4, the average value of the differential pressure in the interval greater than or equal to 98% soc in the current charge cycle is calculated by the above formula:
wherein dV_avg|is more than or equal to 98 percent of SOC represents the average value of the pressure difference of a charging state interval with the SOC state being more than or equal to 98 percent; deltaV represents the differential pressure value of each time frame in a charging state interval in which the SOC is greater than or equal to 98%;
as shown in fig. 4, the average value of the differential pressure between 50% and 60% soc interval in the current charge cycle is calculated by the above formula:
wherein dV_avg|50-60%SOC represents the average value of the differential pressure of the SOC in a charging state interval of 50-60%; deltaV represents the differential pressure value of each time frame of the SOC in the 50% -60% charge state interval;
As shown in fig. 4, the average value of the differential pressure between 15% and 20% soc intervals in the current charge cycle is calculated by the above formula:
wherein dV_avg|15-20%SOC represents the average value of the differential pressure of the SOC in a 15-20% charge state interval; deltaV represents the differential pressure value for each time frame of the SOC during the 15% -20% state of charge interval.
In some preferred embodiments, as shown in fig. 5, the step S105 includes the following steps:
step S1051, if there is a first target time frame in the multiple time frames in the current charging period, or the first target system range rate of change is less than a second threshold, determining that the battery to be predicted is normal.
Specifically, the target time frame is a time frame in which the differential pressure value is smaller than a first threshold, that is, Δv < a, where a may be set according to an enterprise trigger range threshold, which is not specifically limited herein; the first target system range rate of change is the maximum of a plurality of first system range rates of change, for example: each charge state interval has a corresponding first system range rate of change, and the largest first system range rate of change is selected as the first target system range rate of change.
Step S1052, if there is a second target time frame in the multiple time frames in the current charging period, the first target system range change rate is greater than or equal to a second threshold, the first target system range change rate is less than a third threshold, and the second target system range change rate is less than a fourth threshold, determining that the battery to be predicted is a first-level security risk level; wherein the second target time frame is a time frame with a differential pressure value greater than or equal to a first threshold value; the second target system range rate of change is the maximum of a plurality of second system range rates of change.
Specifically, the second threshold b and the third threshold c may be set according to the big data driving and the battery self-discharge management and control standard of the battery cell manufacturer, which is not limited herein;
as shown in FIG. 4, if there is a second target time frame, i.e., a time frame in which the differential pressure value ΔV is greater than or equal to a, within the current charging period; the first target system range change rate k1 is greater than or equal to the second threshold b and less than the third threshold c, i.e., b.ltoreq.k1 < c; and the extremely poor change rate k2 of the second target system is smaller than a fourth threshold value c, namely when k2< c, the high probability is characterized as a self-discharge scene caused by abnormal side reaction of the battery body, and the self-discharge scene is a class I security risk level.
Step S1053, if there is a second target time frame in the multiple time frames in the current charging period, the first target system range rate of change is greater than or equal to a second threshold, the first target system range rate of change is less than a third threshold, and the second target system range rate of change is greater than or equal to a fourth threshold, determining that the battery to be predicted is a secondary security risk level.
Specifically, as shown in fig. 4: if a second target time frame exists in the current charging period, namely a time frame with the differential pressure value delta V being more than or equal to a; the first target system range change rate k1 is greater than or equal to the second threshold b and less than the third threshold c, i.e., b.ltoreq.k1 < c; and the extremely poor change rate k2 of the second target system is larger than or equal to a fourth threshold value c, namely when k2 is larger than or equal to c, the probability specificity characterizes that lithium dendrites or other impurities in a certain battery core penetrate through a diaphragm scene, the internal short circuit condition exists, the II-level safety risk level exists, and the safety problem is serious.
The invention focuses on the differential characterization of the self-discharge characteristic and the failure mode of the battery system by refining the pressure difference change stability of a specific SOC interval of the system level, and realizes the battery safety risk characterization.
The embodiment also provides a battery safety level prediction device, which is used for implementing the above embodiment and the preferred implementation, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides a battery safety level prediction apparatus, as shown in fig. 6, including:
the first differential pressure average value determining module is used for determining a plurality of first differential pressure average values of the battery to be predicted in the current charging period; the first differential pressure average values are in one-to-one correspondence with the charging state intervals;
the differential pressure average value acquisition module is used for acquiring a plurality of second differential pressure average values which are in one-to-one correspondence with a plurality of charging state intervals of the first charging period and acquiring a plurality of third differential pressure average values which are in one-to-one correspondence with a plurality of charging state intervals of the second charging period; wherein the second charging period is earlier than the first charging period;
The first system range change rate determining module is used for determining the first system range change rate of the corresponding charging state interval according to a first pressure difference average value and a second pressure difference average value of the current charging period and the first charging period in the same charging state interval;
the second system range change rate determining module is used for determining the second system range change rate of the corresponding charging state interval according to the first pressure difference average value and the third pressure difference average value of the current charging period and the second charging period in the same charging state interval;
the safety level prediction module is used for predicting the safety level of the battery to be predicted according to the first system range change rate and the second system range change rate of the charging state intervals.
In some alternative embodiments, before determining the plurality of first differential pressure averages for the battery to be predicted for the current charge cycle, the apparatus further comprises:
the pressure difference value acquisition module is used for acquiring the pressure difference value of each time frame of the battery to be predicted in the current charging period;
the first differential pressure average value determining module is used for determining the first differential pressure average value of each charging state interval according to the differential pressure values of a plurality of time frames in each charging state interval.
In some alternative embodiments, the battery to be predicted comprises a plurality of single cells; the differential pressure value acquisition module comprises:
the voltage value acquisition unit is used for acquiring the voltage value of each single battery in each time frame in the current charging period;
a voltage maximum and minimum determining unit configured to determine a voltage maximum and a voltage minimum from a plurality of voltage values for each time frame;
and the voltage difference value determining unit is used for determining the voltage difference value of each time frame of the battery to be predicted in the current charging period according to the voltage maximum value and the voltage minimum value.
In some alternative embodiments, the first differential pressure mean determination module determines the first differential pressure mean for each state of charge interval by the following formula, including:
wherein dV_avg|SOC i A first differential pressure average value representing an ith charge state interval, i representing a sequence number of the charge state interval,the sum of the differential pressure values of the plurality of time frames in the ith charge state interval is represented, Δv represents the differential pressure value, and n represents the total number of time frames in the ith charge state interval.
In some alternative embodiments, the first system range rate determination module determines the first system range rate for the corresponding state of charge interval by:
Wherein k is i The system margin change rate indicating the i-th charge state section, i indicating the number of charge state sections, dV_avg|SOC2 i A second differential pressure average value, dV_avg|SOC1, representing the first charge period in the ith charge state interval i And (3) representing a first differential pressure average value of the current charging period in the ith charging state interval, wherein m represents a time difference value between the first charging period and the current charging period.
In some alternative embodiments, the security level prediction module includes:
the first prediction unit is used for determining that the battery to be predicted is normal if a first target time frame exists in a plurality of time frames in the current charging period or the extremely poor change rate of a first target system is smaller than a second threshold value; the target time frame is a time frame in which the differential pressure value is smaller than a first threshold value, and the first target system range change rate is the maximum value of a plurality of first system range change rates.
In some alternative embodiments, the security level prediction module includes:
the second prediction unit is used for determining that the battery to be predicted is of a first-level safety risk level if a second target time frame exists in a plurality of time frames in the current charging period, the first target system range change rate is greater than or equal to a second threshold value, the first target system range change rate is smaller than a third threshold value, and the second target system range change rate is smaller than a fourth threshold value; wherein the second target time frame is a time frame with a differential pressure value greater than or equal to a first threshold value; the second target system range rate of change is the maximum of a plurality of second system range rates of change;
And the third prediction unit is used for determining that the battery to be predicted is a secondary safety risk level if a second target time frame exists in a plurality of time frames in the current charging period, the first target system range change rate is greater than or equal to a second threshold value, the first target system range change rate is less than a third threshold value, and the second target system range change rate is greater than or equal to a fourth threshold value.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The embodiment of the invention also provides computer equipment, which is provided with the battery safety level prediction device shown in the figure 6.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 7, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 7.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods shown in implementing the above embodiments.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.
Claims (10)
1. A battery safety level prediction method, comprising:
determining a plurality of first differential pressure average values of a battery to be predicted in a current charging period; the first differential pressure average values are in one-to-one correspondence with the charging state intervals;
acquiring a plurality of second differential pressure average values corresponding to a plurality of charging state intervals of the first charging period one by one, and acquiring a plurality of third differential pressure average values corresponding to a plurality of charging state intervals of the second charging period one by one; wherein the second charging period is earlier than the first charging period;
determining a first system range change rate of a corresponding charging state interval according to a first differential pressure mean value and a second differential pressure mean value of the current charging period and the first charging period in the same charging state interval;
determining a second system range change rate of the corresponding charging state interval according to a first differential pressure average value and a third differential pressure average value of the current charging period and the second charging period in the same charging state interval;
And predicting the safety level of the battery to be predicted according to the first system range change rate and the second system range change rate of the charging state intervals.
2. The method of claim 1, wherein determining the average value of the plurality of first differential pressures for the battery to be predicted prior to the current charge cycle further comprises:
obtaining a differential pressure value of a battery to be predicted in each time frame in a current charging period;
and determining a first differential pressure average value of each charging state interval according to the differential pressure values of a plurality of time frames in each charging state interval.
3. The method of claim 2, wherein the battery to be predicted comprises a plurality of single cells; the method for obtaining the differential pressure value of the battery to be predicted in each time frame in the current charging period comprises the following steps:
acquiring a voltage value of each single battery in each time frame in a current charging period;
determining a voltage maximum value and a voltage minimum value from a plurality of voltage values of each time frame;
and determining the differential pressure value of the battery to be predicted in each time frame in the current charging period according to the maximum voltage value and the minimum voltage value.
4. A method according to claim 2 or 3, wherein determining a first mean differential pressure for each state of charge interval based on differential pressure values for a plurality of time frames within each state of charge interval comprises:
Wherein dV_avg|SOC i A first differential pressure average value representing an ith charge state interval, i representing a sequence number of the charge state interval,the sum of the differential pressure values of the plurality of time frames in the ith charge state interval is represented, Δv represents the differential pressure value, and n represents the total number of time frames in the ith charge state interval.
5. The method of claim 1 or 2, wherein determining the first system range rate of change for the corresponding state of charge interval based on the first and second differential pressure averages for the current and first charge periods over the same state of charge interval comprises:
wherein k is i The system margin change rate indicating the i-th charge state section, i indicating the number of charge state sections, dV_avg|SOC2 i A second differential pressure average value, dV_avg|SOC1, representing the first charge period in the ith charge state interval i And (3) representing a first differential pressure average value of the current charging period in the ith charging state interval, wherein m represents a time difference value between the first charging period and the current charging period.
6. A method according to claim 2 or 3, wherein predicting the safety level of the battery to be predicted from the first system range rate of change and the second system range rate of change for the plurality of state of charge intervals comprises:
If a first target time frame exists in a plurality of time frames in the current charging period or the extremely poor change rate of the first target system is smaller than a second threshold value, determining that the battery to be predicted is normal;
the target time frame is a time frame in which the differential pressure value is smaller than a first threshold value, and the first target system range change rate is the maximum value of a plurality of first system range change rates.
7. The method of claim 6, wherein predicting the safety level of the battery to be predicted based on the first system range rate of change and the second system range rate of change for the plurality of state of charge intervals comprises:
if a second target time frame exists in the time frames in the current charging period, the first target system range change rate is larger than or equal to a second threshold value, the first target system range change rate is smaller than a third threshold value, and the second target system range change rate is smaller than a fourth threshold value, determining that the battery to be predicted is a first-level security risk level; wherein the second target time frame is a time frame with a differential pressure value greater than or equal to a first threshold value; the second target system range rate of change is the maximum of a plurality of second system range rates of change;
And if a second target time frame exists in the time frames in the current charging period, the first target system range change rate is larger than or equal to a second threshold value, the first target system range change rate is smaller than a third threshold value, and the second target system range change rate is larger than or equal to a fourth threshold value, determining that the battery to be predicted is a secondary safety risk level.
8. A battery safety level prediction apparatus, characterized in that the apparatus comprises:
the first differential pressure average value determining module is used for determining a plurality of first differential pressure average values of the battery to be predicted in the current charging period; the first differential pressure average values are in one-to-one correspondence with the charging state intervals;
the differential pressure average value acquisition module is used for acquiring a plurality of second differential pressure average values which are in one-to-one correspondence with a plurality of charging state intervals of the first charging period and acquiring a plurality of third differential pressure average values which are in one-to-one correspondence with a plurality of charging state intervals of the second charging period; wherein the second charging period is earlier than the first charging period;
the first system range change rate determining module is used for determining the first system range change rate of the corresponding charging state interval according to a first pressure difference average value and a second pressure difference average value of the current charging period and the first charging period in the same charging state interval;
The second system range change rate determining module is used for determining the second system range change rate of the corresponding charging state interval according to the first pressure difference average value and the third pressure difference average value of the current charging period and the second charging period in the same charging state interval;
the safety level prediction module is used for predicting the safety level of the battery to be predicted according to the first system range change rate and the second system range change rate of the charging state intervals.
9. A computer device, comprising:
a memory and a processor communicatively coupled to each other, the memory having stored therein computer instructions that, upon execution, perform the battery safety level prediction method of any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the battery safety level prediction method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311212144.9A CN117148166A (en) | 2023-09-19 | 2023-09-19 | Battery safety level prediction method, device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311212144.9A CN117148166A (en) | 2023-09-19 | 2023-09-19 | Battery safety level prediction method, device, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117148166A true CN117148166A (en) | 2023-12-01 |
Family
ID=88906039
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311212144.9A Pending CN117148166A (en) | 2023-09-19 | 2023-09-19 | Battery safety level prediction method, device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117148166A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115219903A (en) * | 2022-03-11 | 2022-10-21 | 中国第一汽车股份有限公司 | Battery self-discharge rate abnormity judgment method and device based on Internet of vehicles data analysis |
-
2023
- 2023-09-19 CN CN202311212144.9A patent/CN117148166A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115219903A (en) * | 2022-03-11 | 2022-10-21 | 中国第一汽车股份有限公司 | Battery self-discharge rate abnormity judgment method and device based on Internet of vehicles data analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| TWI761083B (en) | Method, system and device for short circuit monitoring of single cell in power battery | |
| US11408942B2 (en) | Method for predicting service life of retired power battery | |
| US10811886B2 (en) | Charge control apparatus capable of high speed cell balancing and energy saving and method thereof | |
| CN113589174B (en) | Method and device for predicting consistency of battery cells of lithium ion power battery system | |
| CN113064089B (en) | Internal resistance detection method, device, medium and system of power battery | |
| CN117148166A (en) | Battery safety level prediction method, device, computer equipment and storage medium | |
| CN111123148A (en) | Method and equipment for judging short circuit in metal secondary battery | |
| CN113093015A (en) | Battery life estimation method and device, battery management system, automobile and medium | |
| CN114252792A (en) | Method and device for detecting internal short circuit of battery pack, electronic equipment and storage medium | |
| KR101416308B1 (en) | Independent cooling method for operating temperature optimizations of medium and large sized batteries electric vehicles | |
| US20240027540A1 (en) | Battery Cell Diagnosing Apparatus and Method | |
| US20230122667A1 (en) | Method and apparatus for monitoring battery backup unit, server, and readable storage medium | |
| CN114264961B (en) | Method and device for detecting short circuit in battery cell and electronic equipment | |
| CN116381544A (en) | Method and device for detecting battery self-discharge abnormality, electronic equipment and storage medium | |
| CN115754740A (en) | Method and device for determining battery cell residual capacity deviation of battery system | |
| CN116008836A (en) | Short circuit detection method and device for battery and storage medium | |
| CN115840151A (en) | Battery capacity consistency analysis method and device and computer equipment | |
| CN115097308A (en) | Method and device for analyzing SOH state of battery cell, electronic equipment and storage medium | |
| CN112731187A (en) | Battery capacity correction method and battery management system | |
| CN115825790B (en) | Early warning method, device and system for battery insulation fault and computer equipment | |
| CN113844332B (en) | Electric excavator, charging method and device for electric excavator and storage medium | |
| CN117885603A (en) | Charging monitoring method, device, storage medium and system | |
| CN117233637A (en) | Lithium battery capacity jump monitoring method and device, computer equipment and storage medium | |
| CN117007981A (en) | Method, device, equipment and medium for determining thermal runaway of lithium ion storage battery | |
| KR20240057271A (en) | Battery management apparatus and operating method of the same |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |