System and method for monitoring and analyzing state of energy storage battery based on flexible strain sensor

Abstract

The invention discloses an energy storage battery state monitoring analysis system and method based on flexible strain sensors, and relates to the technical field of energy storage equipment monitoring, the energy storage battery state monitoring analysis system based on the flexible strain sensors comprises a plurality of flexible strain sensors, a measurement module and an analysis module; the plurality of flexible strain sensors are correspondingly embedded among the plurality of battery cells; the flexible strain sensor is used for generating impedance change by sensing the pressure change of the side surface of the battery cell; the plurality of flexible strain sensors are connected with the measurement module; the measurement module is used for collecting impedance data of the plurality of flexible strain sensors; the analysis module is connected with the measurement module; and the analysis module is used for acquiring impedance data based on the plurality of flexible strain sensors for analysis and calculation to obtain an energy storage battery state monitoring analysis result. The hysteresis of energy storage battery state monitoring analysis is reduced, the accuracy of the monitoring result is improved, and the application range of energy storage battery state monitoring analysis is expanded.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage device monitoring, and particularly to a state monitoring and analysis system and method for energy storage batteries based on flexible strain sensors. Background Art

[0002] Lithium battery energy storage systems have become an important means to solve the mismatch between energy supply and demand, improve the flexibility and stability of the power grid due to their high energy density, fast response ability, and environmental friendliness. With the accumulation of charge and discharge cycles, fluctuations in ambient temperature, and the progress of internal complex chemical reactions, energy storage lithium batteries (hereinafter referred to as "energy storage batteries" or "batteries") will gradually exhibit performance degradation problems such as reduced capacity, increased internal resistance, and decreased thermal stability, which not only affect the overall efficiency and economic benefits of the energy storage system, but may also lead to safety accidents, posing potential threats to personnel safety, equipment safety, and even environmental safety. Therefore, the online monitoring technology of energy storage battery status has become a powerful support for ensuring the safe and stable operation, optimizing the operation, and extending the life of the energy storage system. The related technologies of online monitoring of energy storage battery status aim to achieve real-time monitoring and analysis of key parameters of the battery such as voltage, current, internal resistance, temperature, SOC, and SOH through advanced material technologies and preparation processes, sensing theories and measurement technologies, data processing technologies, and intelligent analysis algorithms, and then analyze and predict the health status, potential faults, remaining life, etc. of the battery.

[0003] Energy storage batteries are prone to thermal runaway problems when mechanical, electrical, thermal and other environmental conditions are abnormal, which may lead to serious accidents such as fires and even explosions. However, current online monitoring of energy storage batteries still lacks effective monitoring and warning means for thermal runaway risks. As the internal electrochemical reaction of the battery progresses, during the entire process of the development of battery thermal runaway, it usually exhibits characteristics such as reduced battery capacity, increased cell temperature, changes in the composition and concentration of released gases, and battery swelling. The existing energy storage battery monitoring process has hysteresis and inaccurate detection results. Summary of the Invention

[0004] The purpose of the present application is to provide a state monitoring and analysis system and method for energy storage batteries based on flexible strain sensors, which can reduce the hysteresis of energy storage battery state monitoring and analysis, improve the accuracy of monitoring results, and expand the applicable range of energy storage battery state monitoring and analysis.

[0005] To achieve the above object, the present application provides the following solutions:

[0006] In a first aspect, the present application provides a state monitoring and analysis system for an energy storage battery based on a flexible strain sensor. The energy storage battery includes a plurality of battery cells. The state monitoring and analysis system for the energy storage battery based on the flexible strain sensor includes: a plurality of flexible strain sensors, a measurement module, and an analysis module; the plurality of flexible strain sensors are correspondingly embedded between the plurality of battery cells; the flexible strain sensors are configured to generate impedance changes by sensing pressure changes on the sides of the battery cells; the plurality of flexible strain sensors are all connected to the measurement module; the measurement module is configured to collect impedance data of the plurality of flexible strain sensors; the analysis module is connected to the measurement module; the analysis module is configured to obtain an analysis and calculation based on the impedance data of the plurality of flexible strain sensors to obtain a state monitoring and analysis result of the energy storage battery.

[0007] In a second aspect, the present application further provides a method for monitoring and analyzing the state of an energy storage battery based on a flexible strain sensor. The method for monitoring and analyzing the state of an energy storage battery based on the flexible strain sensor is applied to the above-mentioned analysis module. The method for monitoring and analyzing the state of an energy storage battery based on the flexible strain sensor includes: obtaining impedance data of the plurality of flexible strain sensors; substituting the impedance data of the plurality of flexible strain sensors into a flexible strain sensor pressure analysis model to calculate pressure data of the plurality of flexible strain sensors; the flexible strain sensor pressure analysis model is constructed based on a neural network model; calculating a state monitoring and analysis result of the energy storage battery by using the DS evidence theory according to the pressure data of the plurality of flexible strain sensors.

[0008] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application:

[0009] In the present application, the plurality of flexible strain sensors are correspondingly embedded between the plurality of battery cells; the flexible strain sensors are configured to generate impedance changes by sensing pressure changes on the sides of the battery cells; the plurality of flexible strain sensors are all connected to the measurement module; the measurement module is configured to collect impedance data of the plurality of flexible strain sensors; the analysis module is connected to the measurement module; the analysis module is configured to obtain an analysis and calculation based on the impedance data of the plurality of flexible strain sensors to obtain a state monitoring and analysis result of the energy storage battery. By correspondingly embedding the plurality of flexible strain sensors between the plurality of battery cells, the present application can achieve in-situ monitoring for eliminating conduction delay, reduce the hysteresis of the state monitoring and analysis of the energy storage battery, and improve the accuracy of the monitoring result. At the same time, since the flexible strain sensor does not need to be integrated with the battery cell, accurate state monitoring and analysis of the energy storage battery can be achieved without higher battery cell manufacturing capabilities, expanding the applicable range of the state monitoring and analysis of the energy storage battery. Description of the Drawings

[0010] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic structural diagram of an energy storage battery state monitoring and analysis system based on a flexible strain sensor provided by an embodiment of the present application.

[0012] Figure 2 It is a schematic flowchart of a method for monitoring and analyzing the state of an energy storage battery based on a flexible strain sensor provided by an embodiment of the present application.

[0013] Symbol description:

[0014] Flexible strain sensor - 1, measurement module - 2, and analysis module - 3. Specific embodiments

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0016] Currently, the most commonly used early warning characteristic information for battery thermal runaway is temperature and gas concentration. However, there are only a few seconds or dozens of seconds from the start of temperature rise to rapid heating, ignition, and combustion of the battery. At the same time, the complex structure inside the battery cluster leads to uneven gas concentration distribution, and these factors will affect the reliability of timely detection and early warning of thermal runaway risks. Currently, relevant research work has found that monitoring characteristic information such as the internal pressure of the battery cluster can detect the thermal runaway risk of the battery earlier than temperature and gas, etc., thus greatly enhancing the safety of the energy storage system. Currently, the sensing devices that can realize in-situ monitoring of the degree of expansion and deformation (i.e., reflecting the pressure magnitude) inside the battery cluster include fiber optic sensors and flexible sensors. The former can monitor characteristic quantities such as internal pressure, strain, and temperature of the battery. However, due to the relatively easy damage of the optical fiber, the long-term operation reliability of the fiber optic sensor is difficult to guarantee. The flexible sensor is not easily damaged, can be made with a thickness of millimeters, and can be conformally embedded inside the battery cluster with the battery core. At the same time, it can cooperate with intelligent algorithms to realize strain (pressure) monitoring at multiple points.

[0017] Embodiment 1, as Figure 1As shown in the figure, this embodiment provides a state monitoring and analysis system for energy storage batteries based on flexible strain sensors. The energy storage battery includes multiple battery cells. The state monitoring and analysis system for energy storage batteries based on flexible strain sensors includes: multiple flexible strain sensors 1, a measurement module 2, and an analysis module 3;

[0018] Multiple flexible strain sensors 1 are correspondingly embedded between multiple battery cells; the flexible strain sensors 1 are used to generate impedance changes by sensing the pressure changes on the sides of the battery cells.

[0019] Multiple flexible strain sensors 1 are all connected to the measurement module 2; the measurement module 2 is used to collect the impedance data of multiple flexible strain sensors 1.

[0020] The analysis module 3 is connected to the measurement module 2; the analysis module 3 is used to obtain the state monitoring and analysis results of the energy storage battery through analysis and calculation based on the impedance data of multiple flexible strain sensors 1.

[0021] In the actual application process, the impedance characteristics of the flexible strain sensors 1 change significantly with slight changes in pressure strain; the measurement module measures the impedance network composed of the liquid metal contacts of each flexible sensor; the analysis module 3 uses intelligent algorithms to quantitatively analyze the degree of expansion and deformation on the sides of each battery cell inside the battery cluster.

[0022] Furthermore, the flexible strain sensor 1 includes: multiple liquid metal contacts and a flexible substrate; multiple liquid metal contacts are evenly arranged on the impedance network structure of the flexible substrate; the output end of the flexible substrate is connected to the measurement module 2.

[0023] Optionally, the flexible sensors are distributed between adjacent battery cells inside the battery cluster. After being conformally fabricated with the gaps between the battery cells, they are embedded between adjacent battery cells, and the flexible sensors are in close contact with the adjacent battery cells without gaps.

[0024] In the actual application process, the preparation method of the flexible sensor is as follows: the flexible substrate of the flexible sensor adopts a multi-sensing layer integration process; the patterned liquid metal contacts are a centimeter-scale liquid metal connection layout process, and are processed by screen printing and mask spraying; the local liquid metal contacts are processed at a 20-micron process level, and soft lithography and reversible bonding processes are adopted, and the liquid metal flow channels for blind-end perfusion are processed by texture bonding processes to reserve exhaust holes below 20 microns; the liquid metal used for the wire connection structure is processed by laser-induced graphene on a PI film and integrated with the liquid metal sensing layer.

[0025] Furthermore, the arrangement method of multiple liquid metal contacts is an array arrangement in a patterned design.

[0026] In the actual application process, a liquid metal material (liquid metal contact) is poured into the hollow flow channels processed on the flexible substrate according to the designed pattern. The patterned liquid metal contact has impedance-pressure strain characteristics, that is, the impedance value changes significantly and regularly with the slight change of the pressure strain.

[0027] Furthermore, the energy storage battery state monitoring and analysis system based on the flexible strain sensor further includes: an external environment monitoring module.

[0028] The external environment monitoring module is connected to the analysis module 3; the external environment monitoring module is used to detect the external environment data of multiple battery cells; the analysis module 3 is also used to perform analysis and calculation based on the impedance data of multiple flexible strain sensors 1 and the external environment data of multiple battery cells to obtain the energy storage battery state fusion monitoring and analysis result. The external environment data includes at least one or more of: temperature, humidity, gas concentration, and smoke.

[0029] Optionally, the external environment monitoring module includes at least one or more of: a temperature sensor, a humidity sensor, a gas concentration sensor, and a smoke sensor.

[0030] Embodiment 2, as Figure 2 shown, this embodiment also provides a method for monitoring and analyzing the state of an energy storage battery based on a flexible strain sensor. The method for monitoring and analyzing the state of an energy storage battery based on a flexible strain sensor is applied to the above analysis module 3. The method for monitoring and analyzing the state of an energy storage battery based on a flexible strain sensor includes:

[0031] S1. Obtain the impedance data of multiple flexible strain sensors.

[0032] S2. Substitute the impedance data of multiple flexible strain sensors into the flexible strain sensor pressure analysis model to calculate the pressure data of multiple flexible strain sensors; the flexible strain sensor pressure analysis model is constructed based on a neural network model.

[0033] S3. Use the DS evidence theory to calculate the energy storage battery state monitoring and analysis result according to the pressure data of multiple flexible strain sensors.

[0034] Furthermore, before calculating the energy storage battery state monitoring and analysis result based on the pressure data of multiple flexible strain sensors using the DS evidence theory, it further includes:

[0035] Obtain the external environment data of multiple battery cells; the external environment data includes at least one or more of: temperature, humidity, gas concentration, and smoke.

[0036] Furthermore, use the multi-source information fusion algorithm based on the DS evidence theory according to the pressure data of multiple flexible strain sensors to calculate the energy storage battery state fusion monitoring and analysis result.

[0037] Further, the training process of the flexible strain sensor pressure analysis model is as follows:

[0038] Obtain the impedance data for training; the impedance data for training is the impedance data of the flexible strain sensor in multiple experimental processes; divide the impedance data for training into a training set and a validation set; use the pressure value as a label, and input the training set into the neural network model for training; use the validation set to select the optimal parameters of the neural network model, and use the model corresponding to the minimum value of the mean square error loss function as the trained neural network model, and use the trained neural network model as the flexible strain sensor pressure analysis model.

[0039] Further, the iterative calculation method of the neural network model is the error backpropagation analysis method.

[0040] Optionally, the activation function of the neural network model is the Sigmoid function.

[0041] Optionally, if the training result of the neural network model does not converge, the step size in the gradient descent method can be adjusted, the hidden layer can be increased, or the activation function can be adjusted for measurement.

[0042] In the actual application process, the input of the neural network is the impedance data of multiple flexible strain sensors, and the output is the pressure data of multiple flexible strain sensors. The learning data of the neural network is obtained through self-experiments. By repeatedly setting known strain values and simultaneously measuring the impedance at the ports of the liquid metal contact impedance network, the corresponding input and output are obtained. The cost function (loss function) takes the square error between the output parameter and the corresponding true value, the activation function takes the Sigmoid function, and the error backpropagation method of the neural network is used to iteratively calculate the values of each measurement coefficient. After iterative convergence, the measurement calculation basis for the expansion deformation is obtained. If non-convergence occurs, the step size in the gradient descent method can be adjusted, the hidden layer can be increased, or the activation function can be adjusted for measurement.

[0043] The technical effects of this application are as follows:

[0044] In this application, multiple flexible strain sensors are correspondingly embedded between multiple battery cells, which can achieve in-situ monitoring to eliminate conduction time delay, reduce the hysteresis of the energy storage battery state monitoring and analysis, and improve the accuracy of the monitoring results. At the same time, since the flexible strain sensor does not need to be integrated with the battery cell, accurate energy storage battery state monitoring and analysis can be achieved without higher battery cell manufacturing capabilities, expanding the scope of application of the energy storage battery state monitoring and analysis.

[0045] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0046] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A state monitoring and analysis system for an energy storage battery based on a flexible strain sensor, wherein the energy storage battery comprises a plurality of battery cells, characterized in that: The energy storage battery state monitoring and analysis system based on flexible strain sensors comprises: a plurality of flexible strain sensors, a measurement module and an analysis module; The plurality of flexible strain sensors are correspondingly embedded between the plurality of battery cells; the flexible strain sensors are used to generate impedance changes by sensing pressure changes on the sides of the battery cells; A plurality of flexible strain sensors are connected to the measurement module; the measurement module is used to collect impedance data of the plurality of flexible strain sensors; The analysis module is connected to the measurement module; the analysis module is used to obtain impedance data based on multiple flexible strain sensors for analysis and calculation to obtain energy storage battery status monitoring analysis results.

2. The energy storage battery state monitoring and analysis system based on flexible strain sensor according to claim 1 is characterized in that: The flexible strain sensor comprises: a plurality of liquid metal contacts and a flexible substrate; The plurality of liquid metal contacts are arranged on the impedance network structure of the flexible substrate; The output end of the flexible substrate is connected to the measurement module.

3. The energy storage battery state monitoring and analysis system based on flexible strain sensor according to claim 2 is characterized in that: The liquid metal contacts are arranged in an array in a patterned design.

4. The energy storage battery state monitoring and analysis system based on flexible strain sensor according to claim 1 is characterized in that: The energy storage battery state monitoring and analysis system based on the flexible strain sensor also includes: an external environment monitoring module; The external environment monitoring module is connected to the analysis module; the external environment monitoring module is used to detect the external environment data of the multiple battery cells; the analysis module is also used to perform analysis and calculation based on the impedance data of the multiple flexible strain sensors and the external environment data of the multiple battery cells to obtain the energy storage battery status fusion monitoring and analysis results.

5. The energy storage battery state monitoring and analysis system based on flexible strain sensor according to claim 4 is characterized in that: The external environment data includes at least one or more of temperature, humidity, gas concentration and smoke.

6. A method for monitoring and analyzing the state of an energy storage battery based on a flexible strain sensor, wherein the method for monitoring and analyzing the state of an energy storage battery based on a flexible strain sensor is applied to the analysis module according to any one of claims 1 to 5, characterized in that: The energy storage battery state monitoring and analysis method based on the flexible strain sensor includes: Acquiring impedance data of the plurality of flexible strain sensors; Substituting impedance data of the plurality of flexible strain sensors into a flexible strain sensor pressure analysis model to calculate and obtain pressure data of the plurality of flexible strain sensors; the flexible strain sensor pressure analysis model is constructed based on a neural network model; The DS evidence theory is used to calculate the energy storage battery status monitoring analysis result based on the pressure data of the plurality of flexible strain sensors.

7. The method for monitoring and analyzing the state of an energy storage battery based on a flexible strain sensor according to claim 6 is characterized in that: The training process of the flexible strain sensor pressure analysis model is as follows: Acquiring impedance data for training; the impedance data for training is impedance data of the flexible strain sensor during multiple experiments; The impedance data used for training is divided into a training set, a validation set and a test set; Using the pressure value as the label, the training set is input into the constructed neural network model for training; Use the validation set to select the optimal parameters of the neural network model, and take the model corresponding to the minimum value of the mean square error loss function as the trained neural network model; Use the test set to evaluate the performance of the trained neural network model; According to the results of model performance evaluation, the trained neural network model is used as the pressure analysis model of the flexible strain sensor.

8. The method for monitoring and analyzing the state of an energy storage battery based on a flexible strain sensor according to claim 7 is characterized in that: The iterative calculation method of the neural network model is an error inverse analysis method.

9. The method for monitoring and analyzing the state of an energy storage battery based on a flexible strain sensor according to claim 6 is characterized in that: Before obtaining the energy storage battery state monitoring analysis result based on the pressure data of the plurality of flexible strain sensors and the DS evidence theory, the method further includes: Acquire external environmental data of the plurality of battery cells; the external environmental data at least includes: one or more of temperature, humidity, gas concentration and smoke.

10. The method for monitoring and analyzing the state of an energy storage battery based on a flexible strain sensor according to claim 9 is characterized in that: Based on the pressure data of the plurality of flexible strain sensors and the DS evidence theory and the multi-source information fusion algorithm, the energy storage battery state fusion monitoring and analysis result is calculated.

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