CN116125325B - Method and device for detecting consistency of battery cells, vehicle and storage medium - Google Patents
Method and device for detecting consistency of battery cells, vehicle and storage medium Download PDFInfo
- Publication number
- CN116125325B CN116125325B CN202211556315.5A CN202211556315A CN116125325B CN 116125325 B CN116125325 B CN 116125325B CN 202211556315 A CN202211556315 A CN 202211556315A CN 116125325 B CN116125325 B CN 116125325B
- Authority
- CN
- China
- Prior art keywords
- value
- voltage
- characteristic
- determining
- boundary
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000009499 grossing Methods 0.000 claims abstract description 41
- 230000002159 abnormal effect Effects 0.000 claims abstract description 22
- 238000004364 calculation method Methods 0.000 claims abstract description 18
- 238000012545 processing Methods 0.000 claims description 14
- 238000001514 detection method Methods 0.000 claims description 12
- 238000004140 cleaning Methods 0.000 claims description 8
- 238000012423 maintenance Methods 0.000 abstract description 9
- 230000005856 abnormality Effects 0.000 description 23
- 239000011159 matrix material Substances 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/18—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries of two or more battery modules
-
- 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]
- G01R31/3644—Constructional arrangements
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Secondary Cells (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention provides a method and a device for detecting the consistency of battery cells of a battery, a vehicle and a storage medium, wherein the method comprises the following steps: determining the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data of the multiple battery cells at the same moment; determining a smoothing characteristic value according to the maximum voltage value, the minimum voltage value and the voltage parameter value; and when the number of the smooth characteristic values exceeding the boundary range of the target characteristic values is larger than a preset number, determining that the voltage of the battery cell is abnormal in consistency. The invention determines the smooth characteristic value by determining the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data, determines the boundary range of the target characteristic value, and determines that the voltage of the battery cell is abnormal in consistency when the number of the smooth characteristic value exceeding the boundary range of the target characteristic value is greater than the preset number, thereby carrying out risk identification on the abnormal voltage of the battery cell in advance so as to facilitate timely maintenance, avoid fault alarm, have low calculation force requirement and be suitable for detecting most abnormal voltage of the battery cell.
Description
Technical Field
The invention relates to the technical field of new energy, in particular to a method and a device for detecting consistency of battery cells of a battery, a vehicle and a storage medium.
Background
With the development of new energy technology, the requirements of people on a battery system are also higher and higher, and the consistency of battery cells in the battery system can have great influence on the battery system.
At present, when consistency detection is carried out on the battery cells, noise reduction treatment can be carried out on the single voltages of the encoder and the decoder under different working conditions, obvious difference analysis is carried out on the noise reduced voltages, whether obvious differences exist between the single voltages or not is judged, consistency of the battery cell voltages is detected, in the detection process, the detection is realized by combining with a deep learning method such as a neural network, however, when the method is adopted for carrying out the consistency detection on the battery cells, the required calculation force requirement is higher, and the method is not suitable for offline analysis, so that the applicability of the battery cell detection is poor.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art.
Therefore, a first object of the present invention is to provide a method for detecting the consistency of cells of a battery, which determines a smooth characteristic value by determining a maximum voltage value, a minimum voltage value and a voltage parameter value in voltage data of a plurality of cells at the same time, determines a boundary range of a target characteristic value, and determines that the voltage of the cells is abnormal in consistency when the number of the smooth characteristic value exceeding the boundary range of the target characteristic value is greater than a preset number, so as to perform risk identification on the abnormal voltage of the cells in advance, to facilitate timely maintenance, avoid occurrence of fault alarm, and have low calculation force requirement, thus being suitable for detecting the abnormal voltage of most of the cells.
Therefore, a second object of the present invention is to provide a device for detecting the uniformity of cells of a battery.
To this end, a third object of the invention is to propose a vehicle.
To this end, a fourth object of the present invention is to propose a computer readable storage medium.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a method for detecting cell uniformity of a battery, the method comprising: acquiring voltage data of a plurality of battery cells at different moments; determining the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data of the multiple battery cells at the same moment; determining a smoothing feature value from the maximum voltage value, the minimum voltage value, and the voltage parameter value; and when the number of the smooth characteristic values exceeding the boundary range of the target characteristic values is larger than the preset number, determining that the voltage of the battery cell is abnormal in consistency.
According to the method for detecting the consistency of the battery cells of the battery, the smooth characteristic value is determined by determining the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data of the plurality of battery cells at the same moment, the boundary range of the target characteristic value is determined, and when the number of the smooth characteristic value exceeding the boundary range of the target characteristic value is greater than the preset number, the consistency abnormality of the voltage of the battery cells is determined, so that the risk identification of the voltage abnormality of the battery cells is performed in advance, the timely maintenance is facilitated, the fault alarm is avoided, the calculation force requirement is low, and the method is suitable for detecting the voltage abnormality of most battery cells.
In some embodiments, determining a smoothing feature value from the maximum voltage value, the minimum voltage value, and the voltage parameter value includes: calculating the difference value between the maximum voltage value and the voltage parameter value and the difference value between the minimum voltage value and the voltage parameter value, and taking the difference value as a characteristic value; and carrying out data smoothing processing on the characteristic value at intervals of a first preset time step according to a first preset window length to obtain the smoothed characteristic value.
In some embodiments, prior to determining the maximum voltage value, the minimum voltage value, and the voltage parameter value in the voltage data of the plurality of cells at the same time, determining the boundary range of the target feature value comprises: determining a history feature value in a history discrete database; performing sliding calculation on the historical characteristic value at intervals of a first preset mileage step length according to a second preset window length, and determining an upper limit boundary value of the characteristic value and a lower limit boundary value of the characteristic value corresponding to the second preset window length; and determining the boundary range of the target characteristic value according to the upper limit boundary value of the characteristic value and the lower limit boundary value of the characteristic value.
In some embodiments, determining the upper boundary value of the feature value and the lower boundary value of the feature value corresponding to the second preset window length includes: the historical characteristic values are arranged according to a preset sequence; determining a history feature value of three-quarters and a history feature value of one-quarter of the history feature value corresponding to the maximum voltage value every second preset window length, and determining a history feature value of three-quarters and a history feature value of one-quarter of the history feature value corresponding to the minimum voltage value every second preset window length; and determining an upper limit boundary value of the characteristic value according to the historical characteristic value under the three-quarter bit corresponding to the maximum voltage value, the historical characteristic value under the one-quarter bit and a preset maximum characteristic boundary upper limit value, and determining a lower limit boundary value of the characteristic value according to the historical characteristic value under the three-quarter bit corresponding to the minimum voltage value, the historical characteristic value under the one-quarter bit and a preset minimum characteristic boundary lower limit value.
In some embodiments, determining the boundary range of the target feature value from the upper boundary value of the feature value and the lower boundary value of the feature value comprises: and smoothing the upper limit boundary value of the characteristic value at intervals of a second preset mileage step length according to a third preset window length to obtain an upper limit boundary of the target characteristic value, and smoothing the lower limit boundary value of the characteristic value to obtain a lower limit boundary of the target characteristic value.
In some embodiments, acquiring voltage data for a plurality of cells at different times includes: acquiring charging initial voltage data in the process of charging the battery cell, and cleaning data of time, temperature, current and SOC value corresponding to the charging initial voltage data to obtain cleaned charging voltage data; and taking the charging voltage data meeting a preset SOC value range in the cleaned charging voltage data as the voltage data.
In some embodiments, the voltage parameter values include: a median voltage value or an average voltage value.
In order to achieve the above object, an embodiment of a second aspect of the present invention provides a device for detecting cell uniformity of a battery, the device comprising: the acquisition module is used for acquiring voltage data of the multiple battery cores at different moments; the first determining module is used for determining the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data of the plurality of battery cells at the same moment; the second determining module is used for determining a smoothing characteristic value according to the maximum voltage value, the minimum voltage value and the voltage parameter value; and the third determining module is used for determining that the voltage of the battery cell is abnormal in consistency when the number of the smooth characteristic values exceeding the boundary range of the target characteristic values is larger than a preset number.
According to the detection device for the consistency of the battery cells of the battery, the smooth characteristic value is determined by determining the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data, the boundary range of the target characteristic value is determined, and when the number of the smooth characteristic value exceeding the boundary range of the target characteristic value is larger than the preset number, the consistency abnormality of the battery cells is determined, so that the risk identification of the voltage abnormality of the battery cells is performed in advance, the maintenance is convenient in time, the fault alarm is avoided, the calculation force requirement is low, and the detection device is suitable for detecting most of the voltage abnormality of the battery cells.
In order to achieve the above object, an embodiment of a third aspect of the present invention proposes a vehicle including: the device for detecting the consistency of the battery cells in the embodiment.
According to the vehicle provided by the embodiment of the invention, the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data are determined to determine the smooth characteristic value, the boundary range of the target characteristic value is determined, and when the number of the smooth characteristic value exceeding the boundary range of the target characteristic value is greater than the preset number, the consistency abnormality of the voltage of the battery cell is determined, so that the risk identification of the voltage abnormality of the battery cell is performed in advance, the maintenance is convenient in time, the fault alarm is avoided, the calculation force requirement is low, and the vehicle is suitable for detecting the voltage abnormality of most battery cells.
In order to achieve the above object, an embodiment of a fourth aspect of the present invention proposes a computer-readable storage medium having stored thereon a cell uniformity detection program of a battery, which when executed by a processor, implements the cell uniformity detection method of the battery as described in the above embodiment.
Additional aspects and advantages of the invention 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 invention.
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of a method of detecting cell uniformity of a battery according to one embodiment of the invention;
FIG. 2 is a graphical illustration of an upper boundary of a target feature value and a lower boundary of the target feature value according to one embodiment of the invention;
FIG. 3 is a flow chart of a method of detecting cell uniformity of a battery in accordance with an embodiment of the present invention;
FIG. 4 is a block diagram of a device for detecting cell uniformity of a battery according to an embodiment of the present invention;
Fig. 5 is a block diagram of a vehicle according to one embodiment of the invention.
Detailed Description
Embodiments of the present invention will be described in detail below, by way of example with reference to the accompanying drawings.
The consistency of the battery cells in the battery system can be gradually deteriorated along with the increase of the service time, and after the consistency of the battery cells is problematic, the battery system can possibly generate phenomena of overvoltage, undervoltage, low endurance mileage and the like, thereby influencing the normal operation of the vehicle.
Therefore, by adopting the method for detecting the consistency of the battery cells of the embodiment of the invention, the voltage characteristic value of the charging data is extracted by extracting the charging data in each charging process, the boundary range of the target characteristic value is formulated, and whether the battery cells are abnormal or not is determined by judging the relation between the voltage characteristic value and the boundary range of the target characteristic value.
The following describes a method for detecting cell uniformity according to an embodiment of the present invention with reference to fig. 1, and as shown in fig. 1, the method for detecting cell uniformity of a battery according to an embodiment of the present invention at least includes steps S1 to S4.
Step S1, voltage data of a plurality of battery cells at different moments are obtained.
The power battery consists of a plurality of electric cores, voltage data of the electric cores at different moments are a voltage data matrix of m rows and n columns, and if the number of the electric cores is n, the voltage value of each electric core is respectively recorded as V11, V12 … V1n and the like at the same moment; at different times, the voltage values of the same cell are respectively recorded as V11 and V21 … Vm1.
And S2, determining the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data of the plurality of battery cells at the same time.
In an embodiment, after voltage data of a plurality of battery cells at different moments are obtained, a maximum voltage value, a minimum voltage value and a voltage parameter value in the voltage data of the plurality of battery cells are determined at the same moment, wherein the voltage parameter value comprises a median voltage value or an average voltage value, and when the voltage parameter value is determined, the voltage data of each battery cell at the same moment is taken as an example, and the voltage parameter value in the voltage data can be determined by solving the median voltage value or the average voltage value of the voltage data. By determining the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data, data support can be provided for consistency detection of the battery cells.
And S3, determining a smoothing characteristic value according to the maximum voltage value, the minimum voltage value and the voltage parameter value.
The method comprises the steps of determining a characteristic value, determining a maximum voltage value, a minimum voltage value and a voltage parameter value, determining a value according to the maximum voltage value, the minimum voltage value and the voltage parameter value after determining the maximum voltage value, the minimum voltage value and the voltage parameter value, and performing data smoothing on the characteristic value to obtain a smoothed characteristic value.
In an embodiment, after determining the maximum voltage value, the minimum voltage value, and the voltage parameter value in the voltage data, the feature value may be obtained by calculating the maximum voltage value, the minimum voltage value, and the voltage parameter value, and performing data smoothing processing on the feature value to obtain a smoothed feature value.
And S4, when the number of the smooth characteristic values exceeding the boundary range of the target characteristic values is larger than a preset number, determining that the voltage of the battery cell is abnormal in consistency.
The boundary range of the target characteristic value consists of an upper limit boundary of the target characteristic value and a lower limit boundary of the target characteristic value, and the voltage consistency of the battery cell is detected by determining the boundary range of the target characteristic value according to the relation between the boundary range of the target characteristic value and the smooth characteristic value.
In an embodiment, after determining the smoothing eigenvalues, if the smoothing eigenvalues exceeding the preset number exceed the boundary range of the target eigenvalues, for example, when five continuous smoothing eigenvalues exceed the boundary range of the target eigenvalues, it is considered that the voltage consistency of the battery cell is abnormal, so as to realize risk identification of the abnormal battery cell.
According to the method for detecting the consistency of the battery cells of the battery, the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data are determined to determine the smooth characteristic value, the boundary range of the target characteristic value is determined, and when the number of the smooth characteristic value exceeding the boundary range of the target characteristic value is larger than the preset number, the consistency abnormality of the battery cells is determined, so that the risk identification of the voltage abnormality of the battery cells is performed in advance, the maintenance is convenient in time, the fault alarm is avoided, the calculation force requirement is low, and the method is suitable for detecting the voltage abnormality of most battery cells.
In some embodiments, determining the smoothing feature value from the maximum voltage value, the minimum voltage value, and the voltage parameter value includes: calculating the difference value between the maximum voltage value and the voltage parameter value and the difference value between the minimum voltage value and the voltage parameter value in the voltage data of the multiple battery cells at the same moment, and taking the difference value as a characteristic value; and carrying out data smoothing processing on the characteristic value at intervals of a first preset time step according to the first preset window length to obtain a smoothed characteristic value.
In an embodiment, after determining the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data, calculating the characteristic value, for example, calculating the difference between the maximum voltage value and the voltage parameter value of the plurality of battery cells at the same time, calculating the difference between the minimum voltage value and the voltage parameter value, and taking the calculated difference as the characteristic value.
After the feature value is obtained, performing data smoothing processing on the feature value, for example, performing data smoothing processing on the feature value by adopting a filtering mode according to a first preset window length, for example, 300, and performing data smoothing processing on the feature value at intervals of a first preset time step, for example, a step length from t1 to t2, so as to obtain a smoothed feature value.
For example, taking the calculation of the characteristic value of each cell at the same time as an example, the maximum voltage value is denoted as Vmax, the minimum voltage value is denoted as Vmin, and when vmax=v1n, vmin=v11, the characteristic values are respectively The matrix formed by the characteristic values is called a characteristic matrix, for example, the characteristic matrix is marked as V', the characteristic matrix comprises the difference value between the maximum voltage value and the voltage parameter value and the difference value between the minimum voltage value and the voltage parameter value, and when the characteristic values are subjected to data smoothing, the characteristic values are processed at intervals of a first preset time step according to a first step, for exampleAndThe data smoothing of the characteristic values is realized in such a way, so that the smooth characteristic values are obtained, and the consistency anomaly detection is carried out on the battery cells according to the smooth characteristic values.
In some embodiments, prior to determining the maximum voltage value, the minimum voltage value, and the voltage parameter value in the voltage data of the plurality of cells at the same time, determining the boundary range of the target feature value includes: determining a history feature value in a history discrete database; sliding calculation is carried out on the historical characteristic values at intervals of a first preset mileage step length according to a second preset window length, and an upper limit boundary value of the characteristic values and a lower limit boundary value of the characteristic values corresponding to the second preset window length are determined; and determining an upper limit boundary of the target characteristic value and a lower limit boundary of the target characteristic value, which correspond to the boundary range of the target characteristic value, according to the upper limit boundary value of the characteristic value and the lower limit boundary value of the characteristic value.
In the embodiment, when determining the boundary range of the target characteristic value, firstly extracting characteristic values and carrying out data smoothing processing on voltage data in the same type of battery cells and the same type of vehicle history database to obtain history characteristic values in a history discrete database, wherein the history characteristic values in the history discrete database are history characteristic values subjected to primary smoothing processing;
After the historical characteristic values in the historical discrete database are determined, sliding calculation is carried out on the historical characteristic values at intervals of a first preset mileage step length according to a second preset window length, for example, at intervals of 100km according to a window length of 5000km, an upper limit boundary value of the characteristic values and a lower limit boundary value of the characteristic values are determined in each window length, and an upper limit boundary value and a lower limit boundary value of the characteristic values corresponding to each window length are determined according to the upper limit boundary value of the characteristic values and the lower limit boundary value of the characteristic values so as to provide a boundary for detecting the consistency abnormality of the battery cells.
In some embodiments, determining the upper boundary value of the feature value and the lower boundary value of the feature value corresponding to the second preset window length includes: the historical characteristic values are arranged according to a preset sequence; determining a history feature value under three-quarters of a history feature value corresponding to the maximum voltage value and a history feature value under one-quarter of the history feature value every second preset window length, and determining a history feature value under three-quarters of a history feature value corresponding to the minimum voltage value and a history feature value under one-quarter of the history feature value every second preset window length; and determining an upper limit boundary value of the characteristic value according to the historical characteristic value under the three-quarter bit corresponding to the maximum voltage value, the historical characteristic value under the one-quarter bit and a preset maximum characteristic boundary upper limit value, and determining a lower limit boundary value of the characteristic value according to the historical characteristic value under the three-quarter bit corresponding to the minimum voltage value, the historical characteristic value under the one-quarter bit and a preset minimum characteristic boundary lower limit value.
In an embodiment, after the historical feature values are determined, the historical feature values are arranged according to a preset sequence, for example, the historical feature values are arranged in an ascending order, three-quarter bits corresponding to each window length, namely, 75 bits, for example, denoted as V max,thre75,Vmin,thre75 and one-quarter bits corresponding to each window length, for example, 25 bits, for example, denoted as V min,thre25,Vmax,thre25, are calculated, and after the historical feature values corresponding to three-quarters of each window length, the historical feature values corresponding to one-quarter bits and the preset maximum feature boundary upper limit value are determined, the upper limit boundary values of the feature values, for example, the upper limit boundary values three_up=min [ V max,thre75+C*(Vmax,thre75-Vmax,thre25 ], three_up_max ] are determined according to the historical feature values corresponding to three-quarters of the historical feature values corresponding to one-quarter bits, the historical feature values corresponding to one-quarter bits and the preset maximum feature boundary upper limit value; determining a lower limit boundary value of the feature value, for example, a lower limit boundary value thre_down=max [ V min,thre75+C*(Vmin,thre75-Vmin,thre25 ], thre_up_min ] of the feature value according to the historical feature value under the three-quarter bit, the historical feature value under the one-quarter bit and a preset minimum feature boundary lower limit value; wherein, C is a super parameter, for example, the value of C can be any one of values 1-3; thre_up_max is a preset maximum feature boundary upper limit value, and thre_up_min is a preset minimum feature boundary lower limit value. And determining the boundary range of the target characteristic value by determining the upper limit boundary value of the characteristic value and the lower limit boundary value of the characteristic value.
In some embodiments, determining an upper boundary of a target feature value and a lower boundary of the target feature value corresponding to a target feature value boundary range from an upper boundary of a feature value and a lower boundary of a feature value comprises: and smoothing the upper limit boundary value of the characteristic value at intervals of a second preset mileage step length according to a third preset window length to obtain an upper limit boundary of the target characteristic value, and smoothing the lower limit boundary value of the characteristic value to obtain a lower limit boundary of the target characteristic value.
In an embodiment, when determining the upper limit boundary of the target feature value and the lower limit boundary of the target feature value, smoothing the upper limit boundary of the feature value every second preset mileage step length according to a third preset window length to obtain the upper limit boundary of the target feature value, and smoothing the lower limit boundary of the feature value to obtain the lower limit boundary of the target feature value, for example, smoothing the upper limit boundary of the feature value every 1Km according to a preset window length of 1000Km, and smoothing the lower limit boundary of the feature value every 1Km according to a preset window length of 1000Km to screen out a larger deviation value of the upper limit boundary of the feature value and the lower limit boundary of the feature value, thereby avoiding the influence of the accidentally occurring upper limit boundary of the feature value and the lower limit boundary of the feature value on the range of the target feature value, and further improving the accuracy of determining the upper limit boundary of the target feature value and the lower limit boundary of the target feature value.
For example, as shown in fig. 2, an upper boundary of the target feature value and a lower boundary of the target feature value are shown in an embodiment of the present invention. As can be seen from fig. 2, the curves of the upper limit boundary of the determined target characteristic value and the lower limit boundary of the target characteristic value are relatively stable, and when the continuous plurality of smooth characteristic values do not satisfy the boundary range of the target characteristic value, the voltage consistency of the battery cell is considered to be abnormal, thereby realizing the risk identification of the voltage of the battery cell.
In some embodiments, acquiring voltage data for a plurality of cells at different times includes: acquiring charging initial voltage data in the process of charging the battery cell, and cleaning data of time, temperature, current and SOC value corresponding to the charging initial voltage data to obtain cleaned charging voltage data; and taking the charging voltage data meeting the preset SOC value range in the cleaned charging voltage data as voltage data.
In an embodiment, when voltage data of each battery cell is obtained at different moments, data cleaning is performed on initial voltage data of charging in a battery cell charging process, for example, data cleaning is performed on time, temperature, current and an SOC value corresponding to the initial voltage data to obtain cleaned charging voltage data, after the cleaned charging voltage data is determined, charging voltage data in a charging state is extracted, charging voltage data meeting a preset SOC value range is selected, for example, charging voltage data between 10 and 80 of the SOC value is selected, and the problem of high voltage outlier caused by an extreme high SOC value and a low SOC value is avoided, so that misjudgment of consistency of the battery cells is reduced, and accuracy is improved. It can be understood that if data abnormality occurs in the charge initial voltage data, for example, the charge initial voltage data exceeds a preset voltage threshold, the voltage data is considered to be abnormal, at this time, the temperature, the current and the SOC corresponding to the abnormal charge initial voltage data are determined, and the abnormal charge initial voltage data and the time, the temperature, the current and the SOC corresponding to the abnormal charge initial voltage data are deleted, so as to clean the charge initial voltage data.
The method for detecting the cell uniformity of the battery according to the embodiment of the present invention is illustrated and described with reference to fig. 3, and is a flowchart of the method for detecting the cell uniformity of the battery according to an embodiment of the present invention as shown in fig. 3.
Step S11, start.
Step S12, cleaning the charging initial voltage data, and cleaning data of time, temperature, current and SOC value corresponding to the charging initial voltage data.
And S13, taking the charging voltage data meeting the preset SOC value range in the cleaned charging voltage data as voltage data.
Step S14, calculating the difference value between the maximum voltage value and the voltage parameter value and the difference value between the minimum voltage value and the voltage parameter value in the voltage data of the plurality of battery cells at the same moment, and taking the difference value as a characteristic value.
And S15, carrying out data smoothing processing on the characteristic value at intervals of a first preset time step according to a first preset window length to obtain a smoothed characteristic value.
Step S16, determining the historical characteristic values in the historical discrete database.
And S17, performing sliding calculation on the historical characteristic values every other first preset mileage step length according to the second preset window length to determine an upper limit boundary value of the characteristic values and a lower limit boundary value of the characteristic values corresponding to the second preset window length.
And S18, determining an upper limit boundary of the target characteristic value and a lower limit boundary of the target characteristic value corresponding to the boundary range of the target characteristic value according to the upper limit boundary value of the characteristic value and the lower limit boundary value of the characteristic value.
And S19, when the number of the smooth characteristic values exceeding the boundary range of the target characteristic values is larger than a preset number, determining that the voltage of the battery cell is abnormal in consistency.
Step S20, end.
According to the method for detecting the consistency of the battery cells of the battery, the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data are determined to determine the smooth characteristic value, the boundary range of the target characteristic value is determined, and when the number of the smooth characteristic value exceeding the boundary range of the target characteristic value is larger than the preset number, the consistency abnormality of the battery cells is determined, so that the risk identification of the voltage abnormality of the battery cells is performed in advance, the maintenance is convenient in time, the fault alarm is avoided, the calculation force requirement is low, and the method is suitable for detecting the voltage abnormality of most battery cells.
The following describes a device for detecting cell consistency of a battery according to an embodiment of the present invention.
As shown in fig. 4, the device 2 for detecting the cell consistency of the battery according to the embodiment of the present invention includes an acquisition module 20, a first determination module 21, a second determination module 22, and a third determination module 23, where the acquisition module 20 is configured to acquire voltage data of a plurality of cells at different moments; the first determining module 21 determines the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data of the plurality of battery cells at the same time; the second determining module 22 is configured to determine a smoothing characteristic value according to the maximum voltage value, the minimum voltage value and the voltage parameter value; the third determining module 23 is configured to determine that the voltage of the battery cell is abnormal in consistency when the number of the smoothed feature values exceeding the boundary range of the target feature value is greater than a predetermined number.
According to the detecting device 2 for the consistency of the battery cells, disclosed by the embodiment of the invention, the smooth characteristic value is determined by determining the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data, the boundary range of the target characteristic value is determined, and when the number of the smooth characteristic value exceeding the boundary range of the target characteristic value is greater than the preset number, the consistency abnormality of the battery cells is determined, so that the risk identification of the abnormality of the battery cells is realized, the maintenance is performed in advance, the fault alarm is avoided, the calculation force requirement is low, and the detecting device is suitable for detecting the abnormality of most battery cells.
In some embodiments, the first determining module 21 is specifically configured to: calculating the difference value between the maximum voltage value and the voltage parameter value and the difference value between the minimum voltage value and the voltage parameter value, and taking the difference value as a characteristic value; and carrying out data smoothing processing on the characteristic value at intervals of a first preset time step according to the first preset window length to obtain a smoothed characteristic value.
In some embodiments, the second determining module 22 is specifically configured to determine a historical feature value in a historical discrete database; sliding calculation is carried out on the historical characteristic values at intervals of a first preset mileage step length according to a second preset window length, and an upper limit boundary value of the characteristic values and a lower limit boundary value of the characteristic values corresponding to the second preset window length are determined; and determining an upper limit boundary of the target characteristic value and a lower limit boundary of the target characteristic value corresponding to the target characteristic value boundary range according to the upper limit boundary value of the characteristic value and the lower limit boundary value of the characteristic value.
In some embodiments, the second determining module 22 is specifically configured to arrange the historical feature values according to a preset order; determining a historical characteristic value under three-quarters of the historical characteristic value and a historical characteristic value under one-quarter of the historical characteristic value corresponding to the maximum voltage value every second preset window length, and determining a historical characteristic value under three-quarters of the historical characteristic value and a historical characteristic value under one-quarter of the historical characteristic value corresponding to the minimum voltage value every second preset window length; and determining an upper limit boundary value of the characteristic value according to the historical characteristic value under the three-quarter bit corresponding to the maximum voltage value, the historical characteristic value under the one-quarter bit and a preset maximum characteristic boundary upper limit value, and determining a lower limit boundary value of the characteristic value according to the historical characteristic value under the three-quarter bit corresponding to the minimum voltage value, the historical characteristic value under the one-quarter bit and a preset minimum characteristic boundary lower limit value.
In some embodiments, the second determining module 22 is specifically configured to perform smoothing processing on the upper limit boundary value of the feature value at intervals of a second preset mileage step length according to a third preset window length to obtain an upper limit boundary of the target feature value, and perform smoothing processing on the lower limit boundary value of the feature value to obtain a lower limit boundary of the target feature value.
In some embodiments, the obtaining module 20 is specifically configured to obtain charging initial voltage data in a charging process of the battery cell, and perform data cleaning on time, temperature, current and SOC value corresponding to the charging initial voltage data to obtain cleaned charging voltage data; and taking the charging voltage data meeting a preset SOC value range in the cleaned charging voltage data as the voltage data.
In some embodiments, the voltage parameter values include: a median voltage value or an average voltage value.
A vehicle of an embodiment of the invention is described below.
As shown in fig. 5, a vehicle 3 according to an embodiment of the present invention includes the detection device 2 of the cell uniformity of the battery of the above-described embodiment.
According to the vehicle 3 provided by the embodiment of the invention, the smooth characteristic value is determined by acquiring the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data, the boundary range of the target characteristic value is determined, and when the number of the smooth characteristic value exceeding the boundary range of the target characteristic value is greater than the preset number, the consistency abnormality of the voltage of the battery cell is determined, so that the risk identification of the voltage abnormality of the battery cell is performed in advance, the maintenance is convenient in time, the fault alarm is avoided, the calculation force requirement is low, and the vehicle 3 is suitable for detecting the voltage abnormality of most battery cells.
The computer readable storage medium according to the fourth aspect of the present invention stores a program for detecting the cell uniformity of the battery, and when the program for detecting the cell uniformity of the battery is executed by the processor, the method for detecting the cell uniformity of the battery according to the above embodiment is implemented.
In the description of the present specification, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means 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 invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (6)
1. The method for detecting the consistency of the battery cells of the battery is characterized by comprising the following steps of:
acquiring voltage data of a plurality of battery cells at different moments;
determining the maximum voltage value, the minimum voltage value and the voltage parameter value in the voltage data of a plurality of battery cells at the same moment, wherein the voltage parameter value comprises a median voltage value or an average voltage value;
Determining a smoothing feature value according to the maximum voltage value, the minimum voltage value, and the voltage parameter value, wherein determining the smoothing feature value according to the maximum voltage value, the minimum voltage value, and the voltage parameter value includes: calculating the difference value between the maximum voltage value and the voltage parameter value and the difference value between the minimum voltage value and the voltage parameter value, and taking the difference value as a characteristic value; performing data smoothing processing on the characteristic value at intervals of a first preset time step according to a first preset window length to obtain a smoothed characteristic value;
when the number of the smooth characteristic values exceeding the boundary range of the target characteristic values is larger than the preset number, determining that the voltage of the battery cell is abnormal in consistency, wherein,
Determining maximum voltage values, minimum voltage values and voltage parameter values in voltage data of a plurality of battery cells at the same moment, wherein before the voltage parameter values comprise median voltage values or average voltage values, determining a boundary range of the target characteristic values comprises determining historical characteristic values in a historical discrete database; performing sliding calculation on the historical characteristic value at intervals of a first preset mileage step length according to a second preset window length, and determining an upper limit boundary value of the characteristic value and a lower limit boundary value of the characteristic value corresponding to the second preset window length; determining the boundary range of the target characteristic value according to the upper limit boundary value of the characteristic value and the lower limit boundary value of the characteristic value, wherein,
Determining an upper limit boundary value of the characteristic value and a lower limit boundary value of the characteristic value corresponding to the second preset window length comprises: the historical characteristic values are arranged according to a preset sequence; determining a history feature value of three-quarters and a history feature value of one-quarter of the history feature value corresponding to the maximum voltage value every second preset window length, and determining a history feature value of three-quarters and a history feature value of one-quarter of the history feature value corresponding to the minimum voltage value every second preset window length; and determining an upper limit boundary value of the characteristic value according to the historical characteristic value under the three-quarter bit corresponding to the maximum voltage value, the historical characteristic value under the one-quarter bit and a preset maximum characteristic boundary upper limit value, and determining a lower limit boundary value of the characteristic value according to the historical characteristic value under the three-quarter bit corresponding to the minimum voltage value, the historical characteristic value under the one-quarter bit and a preset minimum characteristic boundary lower limit value.
2. The method for detecting cell uniformity of a battery according to claim 1, wherein determining a boundary range of said target feature value according to an upper limit boundary value of said feature value and a lower limit boundary value of said feature value comprises:
and smoothing the upper limit boundary value of the characteristic value at intervals of a second preset mileage step length according to a third preset window length to obtain an upper limit boundary of the target characteristic value, and smoothing the lower limit boundary value of the characteristic value to obtain a lower limit boundary of the target characteristic value.
3. The method for detecting cell uniformity of a battery according to claim 1, wherein acquiring voltage data of a plurality of cells at different times comprises:
Acquiring charging initial voltage data in the process of charging the battery cell, and cleaning data of time, temperature, current and SOC value corresponding to the charging initial voltage data to obtain cleaned charging voltage data;
And taking the charging voltage data meeting a preset SOC value range in the cleaned charging voltage data as the voltage data.
4. A device for detecting cell uniformity of a battery, comprising:
the acquisition module is used for acquiring voltage data of the multiple battery cores at different moments;
the first determining module is used for determining maximum voltage values, minimum voltage values and voltage parameter values in voltage data of the multiple battery cells at the same moment, wherein the voltage parameter values comprise median voltage values or average voltage values;
The second determining module is configured to determine a smoothing feature value according to the maximum voltage value, the minimum voltage value, and the voltage parameter value, where determining the smoothing feature value according to the maximum voltage value, the minimum voltage value, and the voltage parameter value includes: calculating the difference value between the maximum voltage value and the voltage parameter value and the difference value between the minimum voltage value and the voltage parameter value, and taking the difference value as a characteristic value; performing data smoothing processing on the characteristic value at intervals of a first preset time step according to a first preset window length to obtain a smoothed characteristic value;
the third determining module is configured to determine that the voltage of the battery cell is abnormal in consistency when the number of the smooth feature values exceeding the boundary range of the target feature values is greater than a preset number, where the first determining module is configured to:
determining maximum voltage values, minimum voltage values and voltage parameter values in voltage data of a plurality of battery cells at the same moment, wherein before the voltage parameter values comprise median voltage values or average voltage values, determining a boundary range of the target characteristic values comprises determining historical characteristic values in a historical discrete database; performing sliding calculation on the historical characteristic value at intervals of a first preset mileage step length according to a second preset window length, and determining an upper limit boundary value of the characteristic value and a lower limit boundary value of the characteristic value corresponding to the second preset window length; determining the boundary range of the target characteristic value according to the upper limit boundary value of the characteristic value and the lower limit boundary value of the characteristic value, wherein,
Determining an upper limit boundary value of the characteristic value and a lower limit boundary value of the characteristic value corresponding to the second preset window length comprises: the historical characteristic values are arranged according to a preset sequence; determining a history feature value of three-quarters and a history feature value of one-quarter of the history feature value corresponding to the maximum voltage value every second preset window length, and determining a history feature value of three-quarters and a history feature value of one-quarter of the history feature value corresponding to the minimum voltage value every second preset window length; and determining an upper limit boundary value of the characteristic value according to the historical characteristic value under the three-quarter bit corresponding to the maximum voltage value, the historical characteristic value under the one-quarter bit and a preset maximum characteristic boundary upper limit value, and determining a lower limit boundary value of the characteristic value according to the historical characteristic value under the three-quarter bit corresponding to the minimum voltage value, the historical characteristic value under the one-quarter bit and a preset minimum characteristic boundary lower limit value.
5. A vehicle, characterized by comprising: the battery cell uniformity detection apparatus according to claim 4.
6. A computer-readable storage medium, wherein a program for detecting cell uniformity of a battery is stored on the computer-readable storage medium, and when the program for detecting cell uniformity of a battery is executed by a processor, the method for detecting cell uniformity of a battery according to any one of claims 1 to 3 is implemented.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211556315.5A CN116125325B (en) | 2022-12-06 | 2022-12-06 | Method and device for detecting consistency of battery cells, vehicle and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211556315.5A CN116125325B (en) | 2022-12-06 | 2022-12-06 | Method and device for detecting consistency of battery cells, vehicle and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116125325A CN116125325A (en) | 2023-05-16 |
| CN116125325B true CN116125325B (en) | 2024-10-01 |
Family
ID=86308994
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211556315.5A Active CN116125325B (en) | 2022-12-06 | 2022-12-06 | Method and device for detecting consistency of battery cells, vehicle and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116125325B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104656023A (en) * | 2013-11-22 | 2015-05-27 | 广州汽车集团股份有限公司 | Method and system for evaluating battery monomer consistency |
| CN111257755A (en) * | 2020-03-31 | 2020-06-09 | 上海玫克生储能科技有限公司 | Method for preventive detection and diagnosis of battery pack |
| CN116754984A (en) * | 2023-07-06 | 2023-09-15 | 阳光储能技术有限公司 | Battery consistency detection method and related device |
Family Cites Families (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103730936B (en) * | 2014-01-03 | 2015-09-30 | 桂林电子科技大学 | The balanced management method of electric automobile power battery balanced management system |
| CN105449734A (en) * | 2015-06-02 | 2016-03-30 | 武汉众宇动力系统科技有限公司 | Battery balancing device and charge and discharge balancing method thereof |
| CN105116342B (en) * | 2015-08-20 | 2018-06-15 | 中国矿业大学 | A kind of detection method based on battery consistency detection sorter |
| CN106828142B (en) * | 2016-12-23 | 2019-08-20 | 惠州市蓝微新源技术有限公司 | A kind of power battery active equalization method |
| CN110018418A (en) * | 2018-01-05 | 2019-07-16 | 神讯电脑(昆山)有限公司 | Battery check device and its detection method |
| CN108287318A (en) * | 2018-01-30 | 2018-07-17 | 上海华普汽车有限公司 | A kind of detection method and detecting system based on power cell of vehicle packet |
| CN109709488A (en) * | 2018-12-29 | 2019-05-03 | 蜂巢能源科技有限公司 | Battery core mould group is electrically connected detection method and device |
| CN110032670B (en) * | 2019-04-17 | 2022-11-29 | 腾讯科技（深圳）有限公司 | Method, device and equipment for detecting abnormity of time sequence data and storage medium |
| CN110045298B (en) * | 2019-05-06 | 2021-05-11 | 重庆大学 | Method for diagnosing parameter inconsistency of power battery pack |
| CN110706039A (en) * | 2019-10-16 | 2020-01-17 | 爱驰汽车有限公司 | Electric vehicle residual value rate evaluation system, method, equipment and medium |
| CN110907833B (en) * | 2019-10-16 | 2021-01-29 | 北京理工大学 | Power battery fault diagnosis method based on sliding window |
| CN110703107B (en) * | 2019-11-05 | 2022-03-08 | 中国第一汽车股份有限公司 | Consistency judgment method, device and equipment for power battery and storage medium |
| CN112051508B (en) * | 2020-07-28 | 2023-03-28 | 国网江西省电力有限公司电力科学研究院 | Method for evaluating performance consistency of secondary utilization lead-acid storage battery |
| CN115236536A (en) * | 2021-04-23 | 2022-10-25 | 北汽福田汽车股份有限公司 | Method for evaluating voltage consistency after lithium iron phosphate battery is installed on vehicle and vehicle |
| CN113933735B (en) * | 2021-09-30 | 2023-07-14 | 蜂巢能源科技有限公司 | Battery fault determining method, device, storage medium and processor |
| CN113928122B (en) * | 2021-09-30 | 2023-05-23 | 蜂巢能源科技有限公司 | Method and device for determining abnormal voltage of battery cell |
| CN114415037A (en) * | 2022-01-20 | 2022-04-29 | 重庆唐古拉科技有限公司 | Battery pack abnormal cell positioning and identifying method, system, equipment and medium |
| CN114430080B (en) * | 2022-01-27 | 2022-07-29 | 中国汽车工程研究院股份有限公司 | Power battery cell abnormal self-discharge identification method based on operation data |
| CN115356633A (en) * | 2022-07-31 | 2022-11-18 | 常伟 | Power battery short plate battery core detection algorithm based on two-wheel vehicle |
| CN115372828A (en) * | 2022-08-22 | 2022-11-22 | 上海褒科智能科技有限公司 | Battery cell consistency evaluation method based on charging segment data and unsupervised algorithm |
-
2022
- 2022-12-06 CN CN202211556315.5A patent/CN116125325B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104656023A (en) * | 2013-11-22 | 2015-05-27 | 广州汽车集团股份有限公司 | Method and system for evaluating battery monomer consistency |
| CN111257755A (en) * | 2020-03-31 | 2020-06-09 | 上海玫克生储能科技有限公司 | Method for preventive detection and diagnosis of battery pack |
| CN116754984A (en) * | 2023-07-06 | 2023-09-15 | 阳光储能技术有限公司 | Battery consistency detection method and related device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116125325A (en) | 2023-05-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| DE102019108607B3 (en) | System and method for determining charging profiles | |
| CN106600463B (en) | Local shape similarity ultra-short term load prediction method and device | |
| CN114430080A (en) | Power battery cell abnormal self-discharge identification method based on operation data | |
| DE112016006166T5 (en) | State estimator and state estimator | |
| WO2015197483A1 (en) | Device and method for controlling a state of charge of an electric energy accumulator | |
| WO2011015307A1 (en) | Method for controlling a battery and device for implementing the method | |
| EP4031886A1 (en) | Condition value for rechargeable batteries | |
| DE102015016987A1 (en) | Method for detecting a degradation of a rechargeable battery cell and apparatus for carrying out the method | |
| DE102020215201A1 (en) | Method and device for predicting an aging state of a device battery in a battery-operated device | |
| CN116308300B (en) | Power equipment state monitoring evaluation and command method and system | |
| CN116643178B (en) | SOC estimation method and related device of battery management system | |
| CN114266430A (en) | Overhead transmission line risk assessment method and device based on variation and assimilation | |
| CN117517966A (en) | Fault diagnosis method for battery pack | |
| CN116736166A (en) | Battery cell abnormality detection method and device of battery pack and battery pack | |
| CN116739829A (en) | Big data-based power data analysis method, system and medium | |
| CN116125325B (en) | Method and device for detecting consistency of battery cells, vehicle and storage medium | |
| CN117879087B (en) | Dynamic control method of lithium battery energy storage system | |
| CN118082592B (en) | Intelligent charging data management system and method based on big data analysis | |
| DE102018108184A1 (en) | Method and device for determining the state of a rechargeable battery and computer program | |
| CN117388564A (en) | Power battery consistency abnormality detection method based on real vehicle operation data | |
| CN116488578A (en) | Photovoltaic equipment analysis method based on AI visual assistance | |
| CN107831388B (en) | Method and system for identifying overload of cluster 10kV cable line | |
| EP4046228B1 (en) | Method of adapting an anode overvoltage of a lithium-ion battery and method of improving a state of health (soh) of the lithium-ion battery | |
| CN112083340B (en) | Storage battery performance detection method and device, storage medium and terminal | |
| DE102017103348A1 (en) | Operating strategy management system and method for operating an electrical energy storage |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |