CN115585926A - Power battery expansion force monitoring system and method - Google Patents
Power battery expansion force monitoring system and method Download PDFInfo
- Publication number
- CN115585926A CN115585926A CN202211201924.9A CN202211201924A CN115585926A CN 115585926 A CN115585926 A CN 115585926A CN 202211201924 A CN202211201924 A CN 202211201924A CN 115585926 A CN115585926 A CN 115585926A
- Authority
- CN
- China
- Prior art keywords
- data
- expansion force
- battery
- monitoring
- power battery
- 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
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L5/00—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
Abstract
The invention provides a system and a method for monitoring expansion force of a power battery, which are used for acquiring monitoring data of the power battery, wherein the monitoring data comprises an expansion force value in a battery core, temperature data and electric quantity data of the power battery; based on the monitoring data, predicting by using a pre-established expansion force prediction model to obtain an expansion force prediction value of the battery cell; judging whether the predicted value of the expansion force exceeds a preset value or not, and outputting a judgment result; and when the judgment result is that the predicted value of the expansion force exceeds a preset value, generating early warning information. Through the inside expansion force value of monitoring electric core, combine data such as temperature, the residual capacity of battery, utilize the machine training model to predict the expansion force value that battery electric core will come the moment, predict the future expansion force variation trend of battery, prejudge the safe state of battery, carry out dangerous early warning, very big improvement power battery system's security.
Description
Technical Field
The invention relates to the technical field of battery monitoring, in particular to a power battery expansion force monitoring system and method.
Background
In recent 20 years, corresponding green clean energy strategies have been launched in various countries around the world in order to meet the challenges of climate environmental problems such as global warming. The power battery is used as a power source of green intelligent vehicles and is an important component in a new energy system. However, in recent years, safety accidents in the field of power battery application are in an increasing trend, accidents caused by various reasons are frequent, and the importance of the safety problem of the battery in the technical field of the power battery is gradually reduced.
According to the relevant statistical research data, the main causes of the current power battery accidents include: (1) changing the stress condition of the power battery: battery extrusion, breakage, etc. due to external mechanical reasons such as traffic accidents; (2) power battery voltage mismanagement: the abnormal battery voltage caused by the faults or quality problems of electronic components due to improper charging and discharging processes; (3) insufficient thermal condition management of the power battery: the temperature condition of the battery is not perfect and sensitive in detection, early warning and disposal, so that the temperature of the battery is out of control. Analysis shows that the safety causes of the three power batteries directly or indirectly cause abnormal change of the expansion force in the batteries before accidents occur. Therefore, finding an effective method and system for monitoring the cell expansion force of the power battery is an important way for improving the safety of the power battery. The conventional method and system for monitoring the expansion force of the power battery monitor the external expansion force between power battery modules or groups, have large volume and complex structure, can be developed only in a laboratory environment in a battery research and development stage, and cannot be applied to a working condition environment.
Disclosure of Invention
Based on the existing problems, the invention provides a system and a method for monitoring the expansion force of a power battery, and aims to overcome the defects that the expansion force in the battery cannot be monitored in real time under a working condition environment in the prior art.
The utility model provides a power battery bulging force monitoring system, power battery comprises a plurality of electric cores, includes:
the data acquisition module is used for acquiring monitoring data of the power battery, wherein the monitoring data comprises an expansion force value in the battery cell, temperature data and electric quantity data of the power battery;
the prediction module is connected with the data acquisition module and used for predicting to obtain a predicted expansion force value of the battery cell by utilizing a pre-established expansion force prediction model based on the monitoring data;
the judging module is connected with the predicting module and used for judging whether the predicted value of the expansion force exceeds a preset value or not and outputting a judging result;
and the early warning module is connected with the judging module and used for generating early warning information when the judging result is that the expansion force predicted value exceeds a preset numerical value.
Further, the data acquisition module comprises:
the pressure sensor is embedded in the battery cell and used for measuring pressure data in the battery cell;
and the expansion force calculation unit is connected with the pressure sensor and used for calculating the expansion force value inside the battery cell according to the pressure data inside the battery cell.
Further, the data acquisition module comprises:
the pressure sensor is arranged outside the battery cell and used for measuring pressure data outside the battery cell;
and the expansion force calculation unit is connected with the pressure sensor and used for calculating the expansion force value of the interior of the battery cell based on a pre-established expansion force mapping model between the interior and the exterior of the battery cell according to the pressure data of the exterior of the battery cell.
Further, the monitoring data further comprises at least one of the following data:
barometric pressure data measured by a barometric pressure sensor;
gas data measured by a gas sensor;
acoustic data measured by the acoustic sensor.
Further, the method also comprises a model establishing module used for establishing an expansion force prediction model, wherein the model establishing module comprises:
the data collection unit is used for collecting test data of the battery under the simulation scene;
the data processing unit is connected with the data collecting unit and is used for processing the test data to form a plurality of time series sample sets;
the data dividing unit is connected with the data processing unit and divides a plurality of time sequence sample sets into a training set, a testing set and a verification set;
the model training unit is connected with the data dividing unit and used for training, testing and verifying the constructed initial model by using the training set, the testing set and the verifying set to obtain an expansion force prediction model;
the testing data comprises an expansion force value inside a battery core of the testing battery, and temperature data and electric quantity data of the testing battery;
the simulation scene comprises normal discharge and abnormal discharge under extreme conditions.
Further, the data acquisition module comprises:
the data acquisition unit is used for continuously collecting monitoring data of the power battery;
the preprocessing unit is connected with the data acquisition unit and used for merging the monitoring data according to time to form time sequence data;
and the prediction module is used for acquiring the predicted value of the expansion force of the battery cell by utilizing a pre-established expansion force prediction model based on the time sequence data of the preset time period.
Further, the data acquisition module further comprises:
the persistence configuration unit is used for configuring a plurality of storage rules of the monitoring data;
the selection unit is connected with the persistence configuration unit and used for selecting the storage rule;
the execution unit is respectively connected with the preprocessing unit, the selection unit and the persistence configuration unit and is used for:
deleting time series data which are not required to be used for prediction after the prediction module executes prediction when the selected storage rule is the first persistence;
when the selected storage rule is the second persistence, storing the time sequence data into a circular buffer, and covering the earliest time sequence data in the circular buffer by the newly generated time sequence data;
and when the selected saving rule is the third persistence, periodically storing the time-series data, and periodically uploading the stored monitoring data to a remote database and then deleting the monitoring data.
Further, the early warning module sends the early warning information to a battery management system;
and the battery management system shields the corresponding battery cell according to the early warning information and/or reports the power battery failure to the main control system.
A power battery expansion force monitoring method uses the power battery expansion force monitoring system, and comprises the following steps:
step A1, acquiring monitoring data of a power battery, wherein the monitoring data comprises an expansion force value in a battery cell, temperature data and electric quantity data of the power battery;
step A2, based on the monitoring data, obtaining a predicted value of the expansion force of the battery cell by utilizing a pre-established expansion force prediction model;
step A3, judging whether the expansion force predicted value exceeds a preset number:
if yes, executing the step A4;
if not, returning to the step A1;
and A4, generating early warning information when the judgment result is that the expansion force predicted value exceeds a preset value.
Further, in step A2, the process of establishing the expansion force prediction model includes the following steps:
step A21, collecting test data of a battery under a simulation scene;
step A22, processing the test data to form a time series sample set;
step A23, dividing a time sequence sample set into a training set, a test set and a verification set;
step A24, training, testing and verifying the constructed initial model by using a training set, a testing set and a verifying set to obtain an expansive force prediction model;
the testing data comprises an expansion force value inside a battery core of the testing battery, and temperature data and electric quantity data of the testing battery;
the simulation scene comprises normal discharge and abnormal discharge under extreme conditions.
The method has the advantages that by monitoring the expansion force value in the battery cell and combining the data of the temperature, the residual electric quantity and the like of the battery, the expansion force value of the battery cell at the future time is predicted by utilizing the machine training model, the change trend of the future expansion force of the battery is predicted, the safety state of the battery is predicted, the danger early warning is carried out, and the safety of the power battery system is greatly improved.
Drawings
FIG. 1 is a block diagram of a power cell expansion force monitoring system according to the present invention;
FIG. 2 is a block diagram of a data acquisition module of an expansion force monitoring system for a power battery according to the present invention;
FIG. 3 is a block diagram of a model building module of a power cell expansive force monitoring system of the present invention;
FIG. 4 is a flow chart illustrating steps of a method for monitoring expansion force of a power battery according to the present invention;
FIG. 5 is a flow chart of the steps of a model building process of a power battery swelling force monitoring method of the present invention;
fig. 6 (a) is a schematic diagram of a structure in which a pressure sensor is embedded in a square cell according to an embodiment of the present invention;
fig. 6 (b) is a schematic diagram of a structure in which a pressure sensor is embedded in a circular cell according to another embodiment of the present invention;
fig. 7 (a) is a schematic structural view illustrating the disposition of a battery module according to another embodiment of the present invention;
fig. 7 (b) is a schematic structural view of the disposition of a battery module according to another embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
Referring to fig. 1, the invention provides a system for monitoring swelling force of a power battery, wherein the power battery comprises a plurality of battery cells, and the system comprises:
the data acquisition module (1) is used for acquiring monitoring data of the power battery, wherein the monitoring data comprises an expansion force value in the battery cell, temperature data and electric quantity data of the power battery;
the prediction module (2) is connected with the data acquisition module (1) and is used for predicting to obtain a predicted value of the expansion force of the battery cell by utilizing a pre-established expansion force prediction model based on the monitoring data;
the judging module (3) is connected with the predicting module (2) and is used for judging whether the expansion force predicted value exceeds a preset value or not and outputting a judging result;
and the early warning module (4) is connected with the judging module (3) and used for generating early warning information when the judgment result is that the expansion force predicted value exceeds a preset value.
Referring to fig. 2, further, the data acquisition module (1) includes:
the pressure sensor (101) is embedded in the battery cell and used for measuring pressure data in the battery cell;
and the expansion force calculation unit (102) is connected with the pressure sensor (101) and is used for calculating the expansion force value inside the battery cell according to the pressure data inside the battery cell.
Further, the data acquisition module (1) comprises:
the pressure sensor (101) is arranged outside the battery cell and used for measuring pressure data outside the battery cell;
and the expansion force calculation unit (102) is connected with the pressure sensor (101) and is used for calculating the expansion force value of the interior of the battery cell based on a pre-established expansion force mapping model between the interior and the exterior of the battery cell according to the pressure data of the exterior of the battery cell.
As a preferred embodiment of the present invention, the battery cell is a square battery cell, as shown in fig. 6 (a), the inside of the square battery cell includes an anode sheet (1011), a cathode sheet (1012) and a separator (1013), the pressure sensor (101) can be embedded in the metal casing (1014) of the battery cell and fixed at the outermost of the anode sheet, the cathode sheet and the separator layer, preferably, the pressure sensor is embedded in the inside of the battery cell, for example, embedded at a certain position of the anode sheet, the cathode sheet and the separator folded layer of the square battery cell without affecting the normal operation of the battery cell. The signal acquisition interface of the pressure sensor is added to the outer end of the cell except the anode column (1017), the cathode column (1016) and the pressure release valve (1015) so as to transmit the detection value of the pressure sensor to the external data acquisition module (1).
As another preferred embodiment of the present invention, the battery cell is a cylindrical battery cell, as shown in fig. 6 (b), the circular battery cell includes a positive electrode sheet (1011), a negative electrode sheet (1012) and a separator (1013), and the upper end includes a positive electrode cap (1018), a gasket (not shown), an exhaust valve (1015) and a current blocking device (not shown), wherein the positive electrode sheet (1011), the negative electrode sheet (1012) and the separator (1013) form a regular hexagonal prism around the center, and the surface of the battery cell is covered by a stainless steel sheet. Preferably, the pressure sensor (101) is embedded outside the electrode plate and inside the stainless steel sheath. Preferably, the pressure sensor is embedded in the anode sheet, the cathode sheet or the diaphragm of the cell under the condition of not influencing the normal operation of the cell.
For the selection of the pressure sensor, the pressure sensor with small volume, good flexibility of materials and high sensitivity is selected. The pressure sensor with the volume, the measuring range, the sensitivity and the working environment parameters meeting the application scene can be selected. Preferably, the touch sensor is selected as the pressure sensor, is small in size and high in sensitivity, and is suitable for being installed inside the battery core.
As another preferred embodiment of the present invention, for example, the pressure sensor is not easily embedded into the battery cell, the pressure sensor may be made of a novel electronic skin by a 3D printing or printing process, may be in a special shape such as a reciprocating fold line or a zigzag line, and is disposed on the stainless steel battery cell casing, and the magnitude of the expansion force inside the battery cell is estimated by sensing the stress generated by the expansion force conducted by the battery cell casing.
As another preferred embodiment of the present invention, the pressure sensor is disposed outside the battery cell, or inside the battery module, on the outer wall of the battery module, inside the battery pack. Through calibration in a laboratory environment, a mapping relation between a measured value of the pressure sensor outside the battery cell and a measured value inside the battery cell is pre-established, so that the expansion force value inside the battery cell can be calculated according to the measured value of the external pressure sensor, and the expansion force inside the battery cell can be indirectly obtained.
The signal acquisition interface of the pressure sensor at the upper end of the battery core adopts a contact type contact, and the battery module is easily assembled by an automatic production line mode.
As some preferred embodiments, as shown in fig. 7 (a), a contact data acquisition interface is reserved at the upper end of a square battery cell embedded with a pressure sensor, and a plurality of battery cells are assembled into a battery module by means of structural components such as a module base (1020), a module top cover and the like, wherein a battery cell expansion force sampling harness and a conductive connecting plate (1019) are covered under the module top cover, and the expansion force sampling harness is communicated with the pressure sensor embedded in the battery cell through a contact at the top end of the battery cell and can read a pressure value of the pressure sensor.
In some embodiments, as shown in fig. 7 (b), a plurality of regular hexagonal prism-shaped battery cells embedded in the pressure sensors combine the regular hexagonal prism-shaped battery cells together through a battery cell support to form a battery module, the upper part of the battery module draws current through a bus bar (1022), and a sampling PCBA (1023) at the bottom of the battery module is responsible for collecting pressure values of the pressure sensors embedded in the battery cells. In particular, in some embodiments, a cylindrical battery cell embedded in a pressure sensor or a battery cell with other shapes may be adopted, and both the cylindrical battery cell and the battery cell may be combined into a battery module by the method shown in fig. 2, and the signal acquisition circuit monitors the internal pressure value of the battery cell in real time and transmits the value to the data acquisition module (1); all there are insulation board (1021) about the battery module, and sampling PCBA (1023) sets up on insulation board (1021) of below.
As a preferred real-time mode of the invention, the power battery management system BMS is connected to an electrical subsystem and a thermal management subsystem, the electrical subsystem is configured to acquire voltage data, current data, and electric quantity data of the battery cell and/or the battery module, and is responsible for monitoring and managing the voltage, current, electric quantity, and the like of the battery cell and/or the battery module, and is also responsible for executing commands of the BMS, such as cell balancing operation, cell shielding operation, and the like; therefore, the data acquisition module (1) acquires the electric quantity data from the electric subsystem as the electric quantity data of the power battery. The thermal management subsystem is used for monitoring the temperature, air pressure, smoke and other data of the single battery cell and/or the battery module and sending the data to the BMS; therefore, the data acquisition module (1) acquires the temperature data, the air pressure data and the smoke data of the single battery cell or the battery module or the battery pack from the thermal management subsystem as the temperature data, the air pressure data and the smoke data of the power battery.
The power battery swelling force monitoring system acquires monitoring data and sends a swelling force value inside the battery cell to the BMS according to the monitoring period of the BMS, and meanwhile, the power battery swelling force monitoring system predicts and acquires a swelling force predicted value inside the battery cell and judges whether the battery cell is in a safe state or not, and when the battery cell is in an unsafe state, early warning information is sent to the BMS.
Further, the monitoring data further comprises at least one of the following data:
air pressure data measured by an air pressure sensor;
gas data measured by a gas sensor;
acoustic data measured by the acoustic sensor.
As a preferred embodiment of the present invention, a temperature sensor and a fuel gauge are further mounted on the battery cell, the temperature sensor measures a temperature value of the battery cell, and the temperature data of the battery pack includes a temperature value of each battery cell. The electricity meter measures the residual electricity of the battery cells, and the residual electricity of the battery pack comprises the residual electricity of each battery cell. Preferably, the temperature sensor and the electricity meter which are realized by the flexible printed circuit board are selected to be arranged on the battery core, so that the space occupied by the sensor is reduced. Preferably, the temperature sensor is arranged on each battery module, the fuel gauge is arranged on each battery module, the temperature value measured by the temperature sensor is used as the temperature data of the battery pack, and the residual electric quantity measured by the fuel gauge is used as the electric quantity data of the battery pack.
As a preferred embodiment of the present invention, the battery cell may be a regular hexagonal prism battery cell, or may be a soft package battery cell, and may be battery cells with different shapes.
In any type of battery core, the monitoring data of the battery core can be acquired only by embedding the corresponding sensor in the manufacturing process.
Referring to fig. 3, further, a model building module (5) is further included for building an expansion force prediction model, and the model building module (5) includes:
the data collection unit (501) is used for collecting test data of the battery under the simulation scene;
the data processing unit (502) is connected with the data collecting unit (501) and is used for processing the test data to form a time series sample set;
the data dividing unit (503) is connected with the data processing unit (502) and divides the time series sample set into a training set, a testing set and a verification set;
the model training unit (504) is connected with the data dividing unit (503) and is used for training, testing and verifying the constructed initial model by using the training set, the testing set and the verifying set to obtain an expansive force prediction model;
the testing data comprises an expansion force value inside a battery core of the testing battery, temperature data of the testing battery and residual capacity;
the simulation scene comprises normal discharge and abnormal discharge under extreme conditions.
The expansion force prediction model comprises CNN, RNN, LSTM, GRU and the like, and the network topology can comprise an input layer, a full connection layer, a convolution layer, an output layer and the like.
Preferably, the LSTM or GRU model can be used as a specific regression prediction model because the LSTM or GRU model has better learning ability on historical information.
Firstly, selecting a test battery, and deploying the test battery and a corresponding data acquisition system in an actual working condition environment or a laboratory environment, wherein the acquired physical quantity at least corresponds to the physical quantity which can be detected in a production environment; the simulation scene can cover the scenes which can occur in the production environment as much as possible besides two scenes of normal discharge and abnormal discharge under extreme conditions. Continuously monitoring a plurality of physical quantity change processes of the battery cell and the battery pack in a simulation process, namely acquiring test data, stamping a timestamp on the test data, and sequencing the test data according to a time sequence to form a time sequence sample set; carrying out dislocation transformation and data set conversion operation on the time series data set to obtain a transformed time series sample set; the time series sample set is as follows 6: 3:1 into a training set, a test set and a verification set which are used as models to be trained; and training, verifying and testing the model until the obtained training model meets the required testing precision, and obtaining the expansion force prediction model. Converting a data set refers to changing the dimension values of the original feature vectors.
Further, the data acquisition module (1) comprises:
the data acquisition unit (103) is used for continuously collecting monitoring data of the power battery;
the preprocessing unit (104) is connected with the data acquisition unit (103) and is used for merging the monitoring data according to time to form time sequence data;
and the prediction module (2) is used for acquiring a predicted value of the expansion force of the battery cell by utilizing a pre-established expansion force prediction model based on the time sequence data of the preset time period.
The invention can accurately monitor the internal expansion force of the single battery cell of the power battery, can also estimate the internal expansion force by monitoring the external expansion force of the single battery cell, integrates the historical data information learning capacity of a machine learning algorithm model, can utilize the correlation of multiple physical characteristics of the current battery, and can fully utilize the correlation among historical data, thereby realizing that the safety state of the battery is judged by not solely utilizing the instantaneous value of one physical quantity of the expansion force of the cell, but the expansion force of the battery is predicted by the variation trend of multiple physical characteristics of the battery, and the safety state of the cell of the power battery is judged, so that the prediction of the expansion force is more stable, reliable and credible.
Further, the data acquisition module (1) further comprises:
a persistence configuration unit (105) for configuring a plurality of retention rules of the monitoring data;
a selection unit (106) connected with the persistence configuration unit (105) and used for selecting the storage rule;
an execution unit (107) respectively connected to the preprocessing unit (104) and the persistence configuration unit (105) and configured to:
deleting time-series data that are not to be reused for the prediction after the prediction is performed by the prediction module (2) when the selected retention rule is of the first persistence;
when the selected saving rule is the second persistence, saving the time sequence data to the ring buffer, and covering the earliest time sequence data in the ring buffer by the newly generated time sequence data;
and when the selected saving rule is a third persistence, periodically storing the time series data, and periodically uploading the stored monitoring data to a remote database and then deleting the monitoring data.
Further, the early warning module (4) sends the early warning information to a battery management system;
and the battery management system shields the corresponding battery cell according to the early warning information and/or reports the power battery failure to the main control system.
Referring to fig. 4, the present invention further provides a method for monitoring expansion force of a power battery, where the system for monitoring expansion force of a power battery is used, and includes:
step A1, acquiring monitoring data of a power battery, wherein the monitoring data comprises an expansion force value in a battery cell, temperature data of the power battery and residual electric quantity;
step A2, based on the current monitoring data, obtaining a predicted value of the expansion force of the battery cell by utilizing a pre-established expansion force prediction model;
step A3, judging whether the expansion force predicted value exceeds a preset number:
if yes, executing the step A4;
if not, returning to the step A1;
and A4, generating early warning information when the judgment result is that the expansion force predicted value exceeds a preset value.
Referring to fig. 5, further, in step A2, the process of establishing the expansion force prediction model includes the following steps:
step A21, collecting test data of a battery under a simulation scene;
step A22, processing the test data to form a time series sample set;
step A23, dividing a time sequence sample set into a training set, a testing set and a verification set;
step A24, training, testing and verifying the constructed initial model by using a training set, a testing set and a verifying set to obtain an expansive force prediction model;
the testing data comprises an expansion force value inside a battery core of the testing battery, temperature data of the testing battery and residual capacity;
the simulation scene comprises normal discharge and abnormal discharge under extreme conditions.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.
Claims (10)
1. The utility model provides a power battery expansibility monitoring system, power battery comprises a plurality of electric cores, its characterized in that includes:
the data acquisition module is used for acquiring monitoring data of the power battery, wherein the monitoring data comprises an expansion force value in the battery cell, temperature data and electric quantity data of the power battery;
the prediction module is connected with the data acquisition module and used for predicting to obtain a predicted expansion force value of the battery cell by utilizing a pre-established expansion force prediction model based on the monitoring data;
the judging module is connected with the predicting module and used for judging whether the predicted expansion force value exceeds a preset value or not and outputting a judging result;
and the early warning module is connected with the judging module and used for generating early warning information when the judging result is that the expansion force predicted value exceeds a preset value.
2. The system for monitoring expansion force of power battery as claimed in claim 1, wherein said data acquisition module comprises:
the pressure sensor is embedded in the battery cell and used for measuring pressure data in the battery cell;
and the expansion force calculation unit is connected with the pressure sensor and used for calculating the expansion force value in the battery cell according to the pressure data in the battery cell.
3. The system for monitoring expansion force of power battery as claimed in claim 1, wherein said data acquisition module comprises:
the pressure sensor is arranged outside the battery cell and used for measuring pressure data outside the battery cell;
and the expansion force calculation unit is connected with the pressure sensor and used for calculating the expansion force value of the interior of the battery cell based on a pre-established expansion force mapping model between the interior and the exterior of the battery cell according to the pressure data of the exterior of the battery cell.
4. The system for monitoring expansion force of power battery as claimed in claim 1, wherein said monitored data further comprises at least one of the following data:
barometric pressure data measured by a barometric pressure sensor;
gas data measured by a gas sensor;
acoustic data measured by the acoustic sensor.
5. The system for monitoring expansion force of power battery as claimed in claim 1, further comprising a model building module for building said expansion force prediction model, said model building module comprising:
the data collection unit is used for collecting test data of the battery under the simulation scene;
the data processing unit is connected with the data collecting unit and is used for processing the test data to form a plurality of time series sample sets;
the data dividing unit is connected with the data processing unit and divides a plurality of time sequence sample sets into a training set, a testing set and a verification set;
the model training unit is connected with the data dividing unit and used for training, testing and verifying the constructed initial model by using the training set, the testing set and the verifying set to obtain the expansion force prediction model;
wherein the test data comprises an expansion force value of the interior of the cell of the test battery, temperature data and electric quantity data of the test battery;
wherein the simulation scene comprises normal discharge and abnormal discharge under extreme conditions.
6. The system for monitoring expansion force of power battery as claimed in claim 1, wherein said data acquisition module comprises:
the data acquisition unit is used for continuously collecting the monitoring data of the power battery;
the preprocessing unit is connected with the data acquisition unit and is used for merging the monitoring data according to time to form time sequence data;
the prediction module is used for acquiring the expansion force prediction value of the battery cell by utilizing a pre-established expansion force prediction model based on the time sequence data of a preset time period.
7. The system for monitoring expansion force of power battery as claimed in claim 6, wherein said data acquisition module further comprises:
the persistence configuration unit is used for configuring a plurality of storage rules of the monitoring data;
the selection unit is connected with the persistence configuration unit and used for selecting the storage rule;
the execution unit is respectively connected with the preprocessing unit and the persistence configuration unit and is used for:
deleting the time series data that are not to be reused for prediction after the prediction module performs prediction when the selected retention rule is a first persistence;
when the selected saving rule is a second persistence, saving time series data to a circular buffer, and overwriting the earliest time series data in the circular buffer by the newly generated time series data;
when the selected saving rule is a third persistence, the time-series data is stored periodically, and the stored monitoring data is deleted after being uploaded to a remote database periodically.
8. The system for monitoring the expansion force of the power battery as claimed in claim 1, wherein the early warning module sends the early warning information to a battery management system;
and the battery management system shields the corresponding battery cell according to the early warning information and/or reports the power battery failure to a master control system.
9. A power battery expansion force monitoring method, wherein a power battery expansion force monitoring system according to any one of claims 1-8 is used, and the method comprises the following steps:
step A1, acquiring monitoring data of the power battery, wherein the monitoring data comprises an expansion force value in the battery core, temperature data and electric quantity data of the power battery;
step A2, based on the monitoring data, obtaining a predicted value of the expansion force of the battery cell by utilizing a pre-established expansion force prediction model;
step A3, judging whether the expansion force predicted value exceeds a preset number:
if yes, executing the step A4;
if not, returning to the step A1;
and A4, generating early warning information when the judgment result is that the expansion force predicted value exceeds a preset value.
10. The expansion force monitoring method for the power battery according to claim 9, wherein in the step A2, the expansion force prediction model is established by the following steps:
step A21, collecting test data of a battery under a simulation scene;
step A22, processing the test data to form a time series sample set;
step A23, dividing the time sequence sample set into a training set, a testing set and a verification set;
step A24, training, testing and verifying the constructed initial model by using the training set, the testing set and the verifying set to obtain the expansion force prediction model;
wherein the test data comprises an internal expansion force value of the cell of the test battery, temperature data and electric quantity data of the test battery;
wherein the simulation scene comprises normal discharge and abnormal discharge under extreme conditions.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211201924.9A CN115585926A (en) | 2022-09-29 | 2022-09-29 | Power battery expansion force monitoring system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211201924.9A CN115585926A (en) | 2022-09-29 | 2022-09-29 | Power battery expansion force monitoring system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115585926A true CN115585926A (en) | 2023-01-10 |
Family
ID=84778317
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211201924.9A Pending CN115585926A (en) | 2022-09-29 | 2022-09-29 | Power battery expansion force monitoring system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115585926A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115879378A (en) * | 2023-01-04 | 2023-03-31 | 清陶(昆山)能源发展股份有限公司 | Training method and device for expansion force prediction model of battery cell |
| CN116577679A (en) * | 2023-07-12 | 2023-08-11 | 苏州精控能源科技有限公司 | Thermal runaway early warning method and system for large energy storage battery, electronic equipment and medium |
-
2022
- 2022-09-29 CN CN202211201924.9A patent/CN115585926A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115879378A (en) * | 2023-01-04 | 2023-03-31 | 清陶(昆山)能源发展股份有限公司 | Training method and device for expansion force prediction model of battery cell |
| CN115879378B (en) * | 2023-01-04 | 2023-10-20 | 清陶(昆山)能源发展股份有限公司 | Training method and device for expansion force prediction model of battery cell |
| CN116577679A (en) * | 2023-07-12 | 2023-08-11 | 苏州精控能源科技有限公司 | Thermal runaway early warning method and system for large energy storage battery, electronic equipment and medium |
| CN116577679B (en) * | 2023-07-12 | 2023-09-12 | 苏州精控能源科技有限公司 | Thermal runaway early warning method and system for large energy storage battery, electronic equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115585926A (en) | Power battery expansion force monitoring system and method | |
| CN110828919B (en) | Battery thermal runaway early warning system and method | |
| CN116455085B (en) | Intelligent monitoring system of battery energy storage power station | |
| CN110187225A (en) | A kind of lithium battery internal short-circuit voltage and current method for detecting abnormality and system | |
| CN111261957B (en) | Lithium ion energy storage battery thermal runaway protection system and method based on distribution control | |
| WO2023036037A1 (en) | Intelligent battery monitoring and early warning method and system based on multiple types and quantities of sensors | |
| KR20230057894A (en) | Apparatus of Detecting Abnormal Portent Cell in Batter Pack and Method thereof | |
| CN115494404A (en) | Storage battery pack online monitoring method | |
| CN218916625U (en) | Expansion force detection device of battery cell | |
| CN114734874A (en) | Method for acquiring key detection area of battery core, intelligent safety detection method, device and system of battery and vehicle | |
| CN109326839A (en) | A kind of flexible lithium ion battery method for managing security and system | |
| CN217157440U (en) | Intelligent battery monitoring and early warning system based on multiple types and multiple quantity sensors | |
| CN109904533A (en) | The battery life analysis system and method for battery for electric automobile packet | |
| CN117872159A (en) | Lithium battery cell charge and discharge abnormality detection method and system based on data analysis | |
| CN111596157B (en) | Power system fault condition prediction method, device and system and storage medium | |
| CN117673406A (en) | Intelligent monitoring system of all-vanadium redox flow battery | |
| CN109878748A (en) | A kind of power supply system and method for multivoltage unmanned aerial vehicle platform | |
| CN112630665B (en) | Lithium battery life prediction system based on intelligent network connection | |
| CN110646740A (en) | Monitoring method and monitoring device for battery pack | |
| CN117954717A (en) | Battery pack, power consumption device, battery pack detection method, and computer storage medium | |
| CN115172911A (en) | Analysis and prediction system and warning method for automobile lithium battery | |
| CN110429326B (en) | Self-protection lithium battery and protection method thereof | |
| CN114006064A (en) | Dynamic temperature monitoring system based on electric bicycle lithium battery temperature | |
| CN113156543A (en) | Remote-measuring automatic weather station system and weather forecasting method thereof | |
| CN117007999B (en) | Battery pack fault diagnosis method, device and system |
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 |