Energy storage device with conductive polymer sponge and intelligent monitoring system thereof

Abstract

The embodiment of the invention provides an energy storage device with conductive polymer sponge and an intelligent monitoring system of the energy storage device, and belongs to the field of electrochemistry, the energy storage device comprises a shell, and an electrode assembly, electrolyte and a current collector which are arranged in the shell; the electrode assembly comprises a positive electrode, a negative electrode and a diaphragm arranged between the positive electrode and the negative electrode, and the positive electrode and the negative electrode are composite electrodes of conductive polymer sponge; the conductive polymer sponge is a polymer sponge which is prepared through an in-situ chemical oxidation polymerization technology and has a porous network, and the energy storage device further comprises a control system used for controlling the operation state of the energy storage device. According to the embodiment of the invention, the polymer sponge with a highly interconnected porous network is prepared through an in-situ chemical oxidation polymerization technology on the basis of a unique microstructure of the conductive polymer sponge, and the three-dimensional structure of the polymer sponge increases the effective surface area of an electrode and improves electrochemical reaction active sites, so that more efficient charge transmission and storage are realized.

Description

Technical Field

[0001] The invention belongs to the technical field of electrochemical energy storage, and in particular relates to an energy storage device with a conductive polymer sponge and an intelligent monitoring system thereof. Background Art

[0002] With the continuous development of science and technology, people have an increasing demand for energy storage devices. Energy storage devices can store electrical energy for use when needed. At present, the energy storage devices on the market mainly include batteries, supercapacitors and electrochemical capacitors. However, these energy storage devices have some problems, such as low energy density, short cycle life, poor safety, etc.

[0003] In order to solve these problems, researchers have been looking for new energy storage materials and technologies. Conductive polymer sponge is a new type of energy storage material, which has the advantages of high specific surface area, high conductivity and good chemical stability. Applying conductive polymer sponge to energy storage devices can improve the energy density and cycle life of energy storage devices, while also improving the safety of energy storage devices.

[0004] However, the application of conductive polymer sponges in energy storage devices still faces some challenges. For example, the preparation process of conductive polymer sponges is relatively complicated and costly; the performance of conductive polymer sponges is easily affected by environmental factors such as temperature and humidity; conductive polymer sponges are prone to expansion and contraction during charging and discharging, resulting in damage to the electrode structure, etc.

[0005] Therefore, it is necessary to develop an energy storage device with a conductive polymer sponge and an intelligent monitoring system thereof to solve the above problems. Summary of the invention

[0006] In view of this, an embodiment of the present invention provides an energy storage device having a conductive polymer sponge and an intelligent monitoring system thereof to solve the above problems.

[0007] To achieve the above object, the present invention provides an energy storage device having a conductive polymer sponge, the energy storage device comprising: a housing, and an electrode assembly, an electrolyte and a current collector arranged inside the housing; The electrode assembly comprises a positive electrode and a negative electrode, and a separator disposed between the positive electrode and the negative electrode, wherein the positive electrode and the negative electrode are conductive polymer sponge composite electrodes; the conductive polymer sponge is a polymer sponge having a porous network; the electrolyte is filled between the positive electrode and the negative electrode; the current collector is connected to the positive electrode and the negative electrode respectively; The energy storage device also includes a control system, which includes: A material performance monitoring module, used for real-time monitoring of the performance parameters of the conductive polymer sponge composite electrode; An electrical parameter measurement module, used to measure the electrical parameters of the energy storage device and generate an electrical parameter report of the energy storage device after processing the electrical parameters; An automatic control module is used to adjust the operating state of the energy storage device according to the performance parameters of the conductive polymer sponge composite electrode and / or the electrical parameter report of the energy storage device.

[0008] In some possible implementations, the material property monitoring module includes a first embedded microsensor array, and the first embedded microsensor array includes: An electrochemical impedance spectroscopy sensor for real-time monitoring of the interfacial electron transport properties of the conductive polymer sponge composite electrode; A strain sensor for real-time monitoring of the microstructural deformation of the conductive polymer sponge composite electrode; A temperature sensor, used to monitor the temperature change of the conductive polymer sponge composite electrode in real time; The electrochemical active site imaging sensor is used to monitor the surface morphology changes of the conductive polymer sponge composite electrode in real time.

[0009] In some possible implementations, the electrical parameter measurement module includes: A second sensor array is used to collect analog signals of the terminal voltage of the energy storage device, the charge and discharge current of the energy storage device, the resistance of the conductive polymer sponge composite electrode, the internal temperature change of the energy storage device, and the energy conversion efficiency of the energy storage device; A data processing unit, used to perform signal amplification, bandpass filtering, signal normalization and anti-interference processing on the analog signal to obtain a standardized analog data signal; An analog-to-digital converter, used for converting the standardized analog data signal into a standard digital signal; a microcontroller for filtering, statistically analyzing and monitoring abnormalities of digital signals of the standard, performing real-time trend calculations, and generating electrical parameter reports; The communication interface unit is used to transmit the electrical parameter report to a host computer monitoring system, a cloud storage platform or an edge computing node.

[0010] In some possible implementations, the automatic control module includes: A load monitoring unit, used to collect load characteristic data of the energy storage device, wherein the load characteristic data includes load type, power demand, working mode, current characteristics, voltage characteristics, temperature characteristics and time characteristics; A charge and discharge strategy control unit, configured to construct a dynamic charge and discharge decision model according to the electrical parameter report of the energy storage device and in combination with the load characteristic data, and dynamically adjust the charge and discharge strategy of the energy storage device through the dynamic charge and discharge decision model; An electrode material health status assessment unit, used to construct an electrode material performance health assessment model based on the performance parameters of the conductive polymer sponge composite electrode, and determine the performance parameters of the conductive polymer sponge composite electrode through the electrode material performance health assessment model; A dynamic performance optimization unit is used to construct a dynamic performance compensation model according to the charging and discharging strategy of the energy storage device and the performance parameters of the conductive polymer sponge composite electrode, and adjust the working parameters of the energy storage device through the dynamic performance compensation model.

[0011] In some possible implementations, the charge and discharge strategy control unit specifically includes: A real-time parameter matching subunit, for evaluating the current load demand according to the electrical parameters, and predicting the change trend of the load based on the historical data of the load characteristic data in combination with a machine learning algorithm; A charge and discharge optimization subunit, for dynamically generating an optimal charge and discharge curve based on physical modeling or optimization algorithm according to the change trend of the load to build a dynamic charge and discharge decision model, and optimizing the charge and discharge time and intensity through the dynamic charge and discharge decision model; The energy balance control subunit is used to obtain the cycle life of the energy storage device according to the monitored charge and discharge cycle and health status of the energy storage device, and to control the charge and discharge rate and charge and discharge depth of the energy storage device according to the cycle life and the energy conversion efficiency; wherein the charge and discharge depth refers to the degree of discharge of the energy storage device, which is expressed by the ratio of the remaining power of the energy storage device to the total power.

[0012] In some possible implementations, the electrode material health status assessment unit specifically includes: A material performance index library determination subunit is used to obtain performance parameters of the conductive polymer sponge composite electrode, and to construct a material performance index library according to the performance parameters, and to store performance parameters related to the conductive polymer sponge composite electrode through the material performance index library, wherein the performance parameters include interface electron transport characteristics, microstructure deformation, temperature change and surface morphology change; A model building subunit, used to train the performance parameters in the material performance index library using machine learning and statistical analysis algorithms to build the electrode material performance health assessment model; The health assessment subunit is used to determine the health status of the conductive polymer sponge composite electrode through the electrode material performance health assessment model.

[0013] In some possible implementations, the degree of degradation of the conductive polymer sponge composite electrode material is measured by the following indicators: The electron transfer capability refers to evaluating the conductivity and charge transfer impedance of the conductive polymer sponge composite electrode material through the interfacial electron transfer characteristics of the conductive polymer sponge composite electrode; the micro stress refers to analyzing the stress changes generated by the conductive polymer sponge composite electrode material during the charge and discharge process through the microstructural deformation of the conductive polymer sponge composite electrode, and determining the mechanical properties of the conductive polymer sponge composite electrode material according to the stress changes; The change of surface active sites refers to the loss and change of active sites of the conductive polymer sponge composite electrode determined by the change of the surface morphology of the conductive polymer sponge composite electrode material.

[0014] In some possible implementations, the porosity of the conductive polymer sponge composite electrode ranges from 60% to 85%, and the pore size ranges from 10 nm to 500 nm.

[0015] In some possible implementations, the thickness of the positive electrode and the negative electrode is 0.1 mm to 2 mm, and the distance between the positive electrode and the negative electrode is 0.05 mm to 0.5 mm.

[0016] In some possible implementations, the electrolyte includes: a liquid electrolyte and a solid electrolyte, the liquid electrolyte is an organic electrolyte, and the solid electrolyte is a polymer gel electrolyte; the ion conductivity of the electrolyte is greater than 1 mS / cm; The current collector is made of aluminum foil or copper foil with a thickness of 10 μm to 50 μm.

[0017] In a second aspect, an embodiment of the present invention further provides an intelligent monitoring system for the energy storage device of the first aspect, the intelligent monitoring system comprising a monitoring unit and a communication interface unit; The communication interface unit is connected to the energy storage device and is used to transmit the electrical parameter report of the energy storage device to the monitoring unit, and the monitoring unit includes a host computer monitoring system, a cloud storage platform or an edge computing node; The monitoring unit is used to monitor the operating status of the energy storage device according to the electrical parameter report.

[0018] The above technical solution has the following beneficial technical effects: Based on the unique microstructure of the conductive polymer sponge, the embodiment of the present invention prepares a polymer sponge with a highly interconnected porous network through in-situ chemical oxidation polymerization technology. The three-dimensional structure of the polymer sponge can increase the effective surface area of ​​the electrode and improve the active sites of the electrochemical reaction, thereby achieving more efficient charge transfer and storage.

[0019] The embodiment of the present invention realizes intelligent management of the conductive polymer sponge energy storage device through the control system, thereby improving the performance, reliability and service life of the system. The system supports communication interfaces such as CAN, USB and Bluetooth, can realize edge computing and real-time data processing, and provides users with a more intelligent and efficient energy storage solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention. Figure 1 This is a functional block diagram of an energy storage device with a conductive polymer sponge according to an embodiment of the present invention.

[0021] Figure 2 This is a scanning electron microscope (SEM) image of a polymer sponge with a porous network prepared by in-situ chemical oxidative polymerization technology according to an embodiment of the present invention; Figure 3 is a functional block diagram of a material property detection module according to an embodiment of the present invention; Figure 4 is a functional block diagram of an electrical parameter measurement module according to an embodiment of the present invention; Figure 5 is a functional block diagram of an automatic control module according to an embodiment of the present invention; Figure 6 is a functional block diagram of a charge and discharge strategy control unit in an embodiment of the present invention; Figure 7 is a functional block diagram of an electrode material health status assessment unit according to an embodiment of the present invention; Figure 8 It is a functional block diagram of an intelligent control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the embodiments of the present invention. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0023] Embodiment 1 The present embodiment provides an energy storage device with a conductive polymer sponge, the energy storage device comprising a shell, and an electrode assembly, an electrolyte and a current collector arranged inside the shell; the electrode assembly comprises a positive electrode and a negative electrode, and a separator arranged between the positive electrode and the negative electrode, the positive electrode and the negative electrode are composite electrodes of a conductive polymer sponge; the conductive polymer sponge is a polymer sponge with a porous network prepared by in-situ chemical oxidation polymerization technology; the electrolyte is filled between the positive electrode and the negative electrode; the current collector comprises a positive current collector and a negative current collector, which are connected to the positive electrode and the negative electrode, respectively.

[0024] like Figure 1 As shown, the energy storage device also includes a control system, and the control system includes: A material performance monitoring module, used for real-time monitoring of the performance parameters of the conductive polymer sponge composite electrode; In this embodiment, the material performance detection module uses a first embedded microsensor array, which is interconnected through an integrated circuit board and prepared using a microelectronic process, and can monitor the microstructural changes of the conductive polymer sponge composite electrode material in real time. The main parameters monitored include the electrode surface morphology, changes in electrochemical active sites, and the microstress of the conductive polymer sponge composite electrode material.

[0025] An electrical parameter measurement module, used to measure the electrical parameters of the energy storage device and generate an electrical parameter report of the energy storage device after processing the electrical parameters; In this embodiment, the electrical parameter measurement module also uses a sensor array, including a voltage sensor, a current sensor, an internal resistance sensor, a temperature sensor and a charge and discharge efficiency sensor; the voltage sensor is used to convert the high voltage of the energy storage device into a measurable small voltage signal, and measure the terminal voltage of the energy storage device to obtain the charging state and remaining power of the energy storage device. The current sensor is used to measure the charging and discharging current of the energy storage device to control the charging and discharging process and protect the energy storage device from overcurrent damage. The internal resistance sensor is used to measure the change in the internal resistance of the conductive polymer sponge composite electrode of the energy storage device to reflect the health status and performance degradation of the energy storage device. The temperature sensor is used to measure the temperature of the energy storage device and its surrounding environment to ensure that the energy storage device operates within an appropriate temperature range to avoid overheating or overcooling. The charge and discharge efficiency sensor is used to calculate the charge and discharge efficiency of the energy storage device by measuring the difference between the input and output energy, evaluate the energy conversion efficiency of the energy storage system, and optimize the system performance.

[0026] Specifically, the embedded microsensor array used in the material performance detection module focuses on the monitoring of the microscopic performance of materials, including electrochemical impedance spectroscopy sensors, strain sensors, temperature sensors and electrochemical active site imaging sensors. These sensors are specially designed to capture the microstructural changes of conductive polymer sponge composite electrode materials, and analyze the intrinsic performance characteristics of electrode materials by monitoring the electronic transmission characteristics, microscopic deformation, temperature changes and surface morphology. The electrical parameter measurement module uses a sensor array for the overall electrical performance monitoring of the energy storage device, which consists of a voltage sensor, a current sensor, an internal resistance sensor, a temperature sensor and a charge and discharge efficiency sensor. The main goal of this sensor array is to comprehensively obtain the electrical parameters of the energy storage device, including terminal voltage, charge and discharge current, resistance of the conductive polymer sponge composite electrode, temperature changes inside the energy storage device and energy conversion efficiency, to provide macroscopic and systematic data support for the performance evaluation of the energy storage device. The two sensor arrays can be monitored complementary to each other. Although there is overlap in parts such as temperature sensors, the monitoring purposes and scales are different. The embedded microsensor array used in the material performance detection module focuses on the temperature changes at the microscopic scale of the material, while the electrical parameter measurement module uses a dedicated sensor array to focus on the status of the entire energy storage device. This design enables the monitoring component to comprehensively obtain the performance characteristics of the composite electrode material and energy storage device from both microscopic and macroscopic dimensions.

[0027] The embodiment of the present invention can build a more intelligent and comprehensive monitoring network by establishing a correlation and fusion mechanism for sensor data. Material microscopic performance sensors provide internal state analysis of materials, and electrical parameter measurement sensors evaluate the overall performance of the system, ultimately forming a multi-level, high-precision comprehensive intelligent monitoring and early warning system.

[0028] An automatic control module is used to adjust the operating state of the energy storage device according to the performance parameters of the conductive polymer sponge composite electrode and / or the electrical parameter report of the energy storage device.

[0029] Specifically, when it is detected that the charging state of the conductive polymer sponge composite electrode is not ideal, the automatic control module can adjust the charging current, voltage or charging time to optimize the charging process and improve the charging efficiency and electrode life. The automatic control module can also control the discharge current, voltage or discharge time to ensure that the energy storage device discharges within a safe range and maximizes the use of its stored energy. When it is detected that the temperature of the electrode or energy storage device is too high or too low, the automatic control module can start the cooling or heating system to maintain a suitable operating temperature range and improve performance and safety. By monitoring the performance and electrical parameters of the conductive polymer sponge composite electrode material, the automatic control module can promptly detect potential faults or abnormal conditions and take corresponding protective measures, such as shutdown, alarm or switching to a backup energy storage device. The automatic control module can also adaptively adjust the operating state of the energy storage device based on the real-time detection results to adapt to different working conditions and requirements.

[0030] The embodiment of the present invention monitors and analyzes the performance parameters and electrical parameters of the conductive polymer sponge composite electrode material in real time through the automatic control module, and can accurately adjust the operating state of the energy storage device to improve its performance, reliability and safety. This can ensure that the energy storage device operates in the best state and provides stable and reliable energy support for various applications. The specific adjustment strategy and method will be designed and optimized according to the specific energy storage system and application requirements. The embodiment of the present invention is based on the unique microstructure of the conductive polymer sponge (such as Figure 2 As shown in the figure, a polymer sponge with a highly interconnected porous network is prepared by in-situ chemical oxidation polymerization technology. Its three-dimensional structure increases the effective surface area of ​​the electrode and improves the active sites of the electrochemical reaction, thereby achieving more efficient charge transfer and storage. Among them, the in-situ chemical oxidation technology uses a strong oxidant to be injected into the groundwater in situ to achieve the degradation of organic pollutants and form environmentally harmless compounds. In-situ chemical oxidation technology can be applied to the in-situ remediation of contaminated soil, aquifers, and groundwater. The pollutants that can be effectively treated mainly include: volatile organic compounds, such as chlorinated solvents and benzene series; semi-volatile organic compounds, such as pesticides, polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs). Commonly used typical oxidants include hydrogen peroxide, Fenton's reagent, ozone and potassium permanganate.

[0031] The preparation process of the conductive polymer sponge according to the embodiment of the present invention comprises the following steps: S1, selecting appropriate polymerization monomers and oxidants, for example, the polymerization monomers are polyaniline (PANI), polypyrrole (PPy) or their complexes; the oxidants are ammonium persulfate, ferric chloride, etc.

[0032] S2, controlling the reaction temperature between 0-50°C to ensure the smooth progress of the polymerization reaction.

[0033] S3, in situ chemical oxidation polymerization, pours the reaction solution into a mold or reaction container. Within a certain period of time, the polymerization reaction will occur in the solution to form a polymer sponge with a three-dimensional porous network.

[0034] Among them, in the in-situ chemical oxidation polymerization technology, the role of the oxidant is to initiate the polymerization reaction and promote the polymerization monomers to form polymers. Specifically, the oxidant will react chemically with the polymerization monomers, oxidize them and form free radicals or ions, and these active species will further initiate the polymerization reaction to form polymer chains. In this process, the oxidant does not directly destroy the microstructure of the organic matter in the polymerization monomers. On the contrary, the polymerization reaction is achieved by the formation of chemical bonds between the polymerization monomers. Specifically, unsaturated structures such as double bonds or triple bonds in the polymerization monomers will undergo addition reactions under the action of the oxidant to form new chemical bonds, thereby connecting the polymerization monomers into polymer chains. Therefore, the in-situ chemical oxidation polymerization technology forms a three-dimensional porous network through polymerization reactions, rather than by destroying the microstructure of organic matter in the polymerization monomers.

[0035] S4, subsequent heat treatment and surface functionalization, the prepared polymer sponge is subjected to subsequent heat treatment to improve its conductivity and stability. The sponge structure can be further optimized by surface functionalization treatment, such as doping, grafting, etc.; S5, adjusting the polymerization conditions. By controlling the polymerization conditions, such as reaction time, concentration, temperature, etc., the porosity, conductivity and mechanical strength of the polymer sponge can be adjusted.

[0036] For example, adjusting the reaction time, increasing the monomer concentration, the amount of oxidant, temperature, solvent selection, additives, stirring speed, etc. Prolonging the reaction time can increase the molecular weight and cross-linking degree of the polymer, thereby improving the mechanical strength, but will lead to a decrease in porosity. Increasing the monomer concentration will lead to an increase in the molecular weight of the polymer, thereby improving the mechanical strength, but will also reduce the porosity. The amount of oxidant will affect the rate and degree of polymerization. Increasing the amount of oxidant will increase the conductivity, but will affect the porosity and mechanical strength. The reaction temperature has an effect on the rate of polymerization and the structure of the product. Higher temperatures will accelerate the reaction, but will also lead to polymer degradation or structural changes. Different solvents will affect the rate of polymerization and the properties of the product. Choosing a suitable solvent can adjust the porosity and conductivity. Adding some additives or additives, such as surfactants, cross-linking agents, etc., can change the structure and properties of the polymer, thereby affecting the porosity, conductivity and mechanical strength. Proper stirring can promote the uniform reaction, affect the distribution and structure of the polymer, and then affect the porosity and mechanical strength. It should be noted that these adjustment methods are interrelated and have different effects on different polymer systems. Therefore, when making adjustments, systematic experimental studies are needed to determine the optimal polymerization conditions. In addition, other methods, such as template method, freeze drying, etc., can be combined to further control the porosity and structure of polymer sponges.

[0037] The working principle of the energy storage device of the embodiment of the present invention: During the charging process, rapid charge transfer is achieved through the three-dimensional network structure of the conductive polymer sponge composite electrode, redox reactions occur between the positive and negative electrodes, and the ions in the electrolyte quickly migrate in the sponge pores and store electrical energy. During discharge, the charges are efficiently transmitted along the interconnected conductive network to release the stored electrical energy. The embodiment of the present invention uses in-situ chemical oxidation polymerization technology to prepare a polymer sponge with a highly interconnected porous network. Its three-dimensional structure increases the effective surface area of ​​the electrode and increases the active sites of the electrochemical reaction, thereby achieving more efficient charge transfer and storage. In addition, the embodiment of the present invention uses a control system to intelligently manage the energy storage device of the conductive polymer sponge, thereby improving the reliability and service life of the energy storage device.

[0038] like Figure 3 As shown, the material property detection module includes a first embedded micro sensor array, and the first embedded micro sensor array includes the following sensors: The electrochemical impedance spectroscopy sensor is used to detect the electrochemical properties of the conductive polymer sponge composite electrode.

[0039] Electrochemical impedance spectroscopy (EIS) is used to study the electrochemical process at the interface between electrodes and electrolytes. The method for measuring the electrochemical properties of conductive polymer sponge composite electrodes using an electrochemical impedance spectroscopy sensor is as follows: Install the conductive polymer sponge composite electrode in the electrochemical test cell and ensure good contact between the electrode and the test cell. Prepare appropriate electrolyte solutions compatible with the electrode materials and ensure that the various components of the electrochemical test system (such as working electrode, reference electrode, counter electrode, etc.) are properly connected. Set the parameters of the electrochemical impedance spectroscopy test, such as frequency range, amplitude, etc. Select a suitable measurement mode, such as constant potential or constant current mode. Start the electrochemical impedance spectroscopy measurement and let the system stabilize for a period of time to ensure the accuracy of the measurement results. Record the impedance response of the electrode within the set frequency range. After obtaining the electrochemical impedance spectroscopy data, data analysis methods can be used for processing and analysis. Interpret the impedance spectroscopy data by fitting an equivalent circuit model and extract information about the electrode interface characteristics, such as charge transfer resistance, double layer capacitance, etc. Based on the results of the data analysis, interpret the electrochemical properties of the conductive polymer sponge composite electrode, such as conductivity, electrochemical activity, interface stability, etc.; compare with the standard sample to evaluate the improvement or change in electrode performance.

[0040] The strain sensor is used to monitor the microstructural deformation of the conductive polymer sponge composite electrode in real time.

[0041] The temperature sensor is used to detect the temperature change of the conductive polymer sponge composite electrode in real time.

[0042] Specifically, the temperature can be measured by directly contacting the temperature sensor with the surface of the conductive polymer sponge composite electrode through heat conduction between the temperature sensor and the conductive polymer sponge composite electrode. This method can provide accurate temperature readings, but it is necessary to ensure good contact between the temperature sensor and the conductive polymer sponge composite electrode. The temperature sensor can also be integrated inside the conductive polymer sponge composite electrode, for example, by embedding a temperature sensitive element in the electrode material. In this way, the temperature changes inside the conductive polymer sponge composite electrode can be monitored in real time, providing more accurate information. Alternatively, multiple temperature sensors can be distributed at different locations of the conductive polymer sponge composite electrode to obtain more comprehensive temperature distribution information. This method can detect hot spots or temperature non-uniformity on the conductive polymer sponge composite electrode.

[0043] In order to achieve real-time detection, the temperature sensor can be connected to a data acquisition system or monitoring equipment to transmit and record the temperature data in real time. These data can be used to monitor the temperature change trend of the electrode and take corresponding control measures to ensure that the electrode works within a safe temperature range.

[0044] The electrochemical active site imaging sensor is used to observe the surface morphology changes of the conductive polymer sponge composite electrode in real time.

[0045] Specifically, it can be measured using a scanning electrochemical microscope (SECM), which can image the surface morphology with high resolution in an electrochemical environment. By placing a very small electrode (called a probe) close to the surface of a conductive polymer sponge composite electrode and measuring the change in current or potential, information about the surface morphology can be obtained. It can also be measured using an atomic force microscope (AFM), which uses a tiny probe to scan the surface of a conductive polymer sponge composite electrode and images the surface morphology by detecting the interaction force between the probe and the surface. AFM can provide atomic-level resolution and observe surface morphological changes in real time. It can also be measured using an electrochemical quartz crystal microbalance (EQCM), which monitors changes in the mass of the electrode surface by measuring changes in the resonant frequency of a quartz crystal. When the surface morphology of a conductive polymer sponge composite electrode changes, such as adsorption, deposition, or dissolution of a material, it will cause a change in mass, so that changes in the surface morphology can be observed in real time.

[0046] The embodiments of the present invention can observe the surface morphology changes of the conductive polymer sponge composite electrode in real time and non-invasively, and provide important information about the electrode structure, active site distribution and reaction process.

[0047] In this embodiment, the electrochemical impedance spectroscopy sensor is used to monitor the interfacial electron transport characteristics of the conductive polymer sponge composite electrode material, measure the impedance change during the transmission of electrons at the material interface, and reflect the electron transport capacity and interface quality of the electrode material; wherein the electron transport characteristics refer to the transmission capacity and transmission speed of electrons inside the material under the action of the electric field. In the electrochemical impedance spectroscopy sensor, the electron transport characteristics are used to describe the charge transfer process between the electrode surface and the electrolyte. For example, when detecting the interfacial electron transport characteristics of the conductive polymer sponge composite electrode, the electron transport characteristics can refer to the electron transport speed and efficiency on the electrode surface, as well as the ion transport speed and efficiency in the electrolyte. These characteristics can affect the performance indicators of the sensor such as sensitivity, response time and selectivity.

[0048] The strain sensor is used to detect the microstructural deformation of the conductive polymer sponge composite electrode material and obtain the microscopic deformation data of the conductive polymer sponge composite electrode material under the action of external stress; the temperature sensor is used to monitor the temperature change of the conductive polymer sponge composite electrode material in real time and obtain the real-time change curve of the temperature of the conductive polymer sponge composite electrode material; the electrochemical active site imaging sensor is used to observe the surface morphology changes of the conductive polymer sponge composite electrode and obtain the morphological evolution image of the active sites on the electrode surface.

[0049] like Figure 4 As shown, the electrical parameter measurement module includes: The second sensor array is used to collect analog signals of the terminal voltage of the energy storage device, the charge and discharge current of the energy storage device, the resistance of the conductive polymer sponge composite electrode, the internal temperature change of the energy storage device and the energy conversion efficiency of the energy storage device.

[0050] A data processing unit, used to perform signal amplification, bandpass filtering, signal normalization and anti-interference processing on the analog signal to obtain a standardized analog data signal; The second sensor array can use a voltage sensor to connect to the positive and negative poles of the energy storage device to collect the terminal voltage, and then convert the terminal voltage into an analog signal. The voltage sensor can be a sensor based on resistance voltage division, Hall effect or other principles. The charge and discharge current can be collected by connecting the current sensor in series in the charge and discharge circuit of the energy storage device, and the charge and discharge current can be converted into an analog signal. The current sensor can be a sensor based on the Hall effect, fluxgate or other principles. A resistance measurement circuit can be used to connect to the conductive polymer sponge composite electrode to collect the resistance of the conductive polymer sponge composite electrode, and the resistance value can be calculated by applying a certain current or voltage and measuring the voltage or current at both ends of the electrode. The temperature change can be collected by using a temperature sensor (such as a thermocouple, thermistor or infrared sensor) to contact or approach the energy storage device or the conductive polymer sponge composite electrode to convert the temperature change into an analog signal. The energy conversion efficiency can be obtained by measuring the input and output energy of the energy storage device and calculating the ratio thereof. A power sensor can be used to measure the input and output power.

[0051] These analog signals can be converted into digital signals through an analog to digital converter (ADC) for subsequent processing and analysis. During the acquisition process, attention should be paid to signal amplification, filtering and calibration to ensure that the acquired signals are accurate and reliable.

[0052] An analog-to-digital converter, used for converting the standardized analog data signal into a standard digital signal; a microcontroller for filtering, statistically analyzing and detecting anomalies of the digital signals of the standard, performing real-time trend calculations, and generating electrical parameter reports; Specifically, the microcontroller stores the collected digital signal in a certain data buffer, defines a time window, slides at a fixed time interval, and calculates the average value of the digital signal in each time window. The average values ​​are connected to form a real-time trend curve. By observing the changes in the trend curve, the real-time trend of the electrical parameters can be analyzed, such as whether it is stable, rising, falling or fluctuating.

[0053] Among them, the method of anomaly detection is as follows: First, the microcontroller sets appropriate thresholds based on the characteristics of the digital signal. These thresholds can be fixed values ​​or dynamic thresholds derived from historical data or statistical analysis. The collected digital signal is then compared with the set thresholds. For example, if the signal value exceeds or falls below the threshold, it is considered abnormal. When anomalies are detected, the microcontroller can mark or record these abnormal conditions for subsequent analysis and processing.

[0054] The items of abnormal detection may include but are not limited to overvoltage / undervoltage, detecting whether the voltage signal exceeds the normal range; overcurrent / undercurrent, detecting whether the current signal exceeds or is lower than the set threshold; temperature abnormality, monitoring whether the temperature signal rises or falls abnormally; pressure abnormality, detecting whether the pressure signal exceeds the expected range; liquid level abnormality, monitoring whether the liquid level signal changes abnormally; vibration abnormality, detecting whether the vibration signal exceeds the normal level; other parameter abnormalities, according to specific application requirements, abnormal conditions of other electrical parameters can also be detected.

[0055] In this embodiment, by performing anomaly detection and real-time trend calculation, the microcontroller can promptly detect abnormal conditions of electrical parameters and provide real-time trend analysis to ensure the normal operation and safety of the system. The specific implementation method varies depending on the model of the microcontroller and the application scenario, and needs to be adjusted and optimized according to the specific situation.

[0056] In this embodiment, the data processing unit can process the analog signal collected by the second sensor array, and the analog signal types include voltage, current, resistance and temperature analog signals, and the signal range is -5V to +5V, and outputs the pre-processed standardized digital signal through signal amplification, bandpass filtering, signal normalization and anti-interference processing. The analog-to-digital converter has a 16-bit conversion accuracy, a sampling rate of 100Hz, and a conversion error of ±0.1%. It is directly connected to the output end of the data acquisition and processing unit to realize the accurate conversion of analog signals to digital signals.

[0057] Specifically, the microcontroller is used to perform complex processing of digital signals, including filtering, statistical analysis, anomaly detection, real-time trend calculation, and generate detailed electrical parameter reports to provide intelligent analysis support for system monitoring.

[0058] The installation positions of the multi-channel electrical parameter acquisition system in this embodiment are divided into two types: internal and external. The internal installation points include the electrode material contact points, the current acquisition terminal and the signal conversion module; the external installation positions include the key parts of the energy storage device housing, the heat dissipation interface and the communication interface to ensure the optimal layout of the sensor and data acquisition system. In this embodiment, the measurement accuracy of the measurement system can reach ±0.1%, the data sampling frequency is 100Hz, and it can monitor key parameters such as voltage, current, internal resistance, charge and discharge efficiency and temperature in real time, providing comprehensive and accurate data support for the performance evaluation and health status monitoring of the energy storage device.

[0059] like Figure 5 As shown, in some embodiments, the automatic control module includes: The load monitoring unit is used to collect load characteristic data of the energy storage device, wherein the load characteristic data includes load type, power demand, working mode, current characteristics, voltage characteristics, temperature characteristics and time characteristics.

[0060] A charge and discharge strategy control unit is used to construct a dynamic charge and discharge decision model according to the electrical parameter report of the energy storage device and in combination with the load characteristic data, and dynamically adjust the charge and discharge strategy of the energy storage device through the dynamic charge and discharge decision model.

[0061] In this embodiment, constructing a dynamic charge and discharge decision model includes the following steps: The electrical parameters of the energy storage device and the load characteristic data are cleaned, filtered, calibrated and other pre-processing operations or standard data are performed to ensure the accuracy and reliability of the data.

[0062] Features related to charging and discharging decisions are extracted from the preprocessed standards, such as voltage change rate, current fluctuation, temperature trend, etc.

[0063] According to the extracted features related to charging and discharging decisions, select appropriate machine learning or deep learning models, such as neural networks, decision trees, support vector machines, etc. Use historical charging and discharging data to train the selected machine learning or deep learning model to allow the model to learn the charging and discharging laws and characteristics of the energy storage device.

[0064] Use test data to evaluate the trained model and check the accuracy and generalization ability of the model. Based on the evaluation results, optimize and adjust the model, such as adjusting model parameters and increasing the amount of training data.

[0065] In actual operation, the charge and discharge strategy control unit inputs the real-time acquired energy storage device data into the trained dynamic charge and discharge decision model, and the dynamic charge and discharge decision model outputs the optimal charge and discharge decision based on the input data.

[0066] For example, for a lithium-ion energy storage device, the charge and discharge strategy control unit can obtain data such as the voltage, current, temperature, and load power of the energy storage device in real time. Then, through feature engineering, the health status, remaining capacity, charge and discharge efficiency and other features of the energy storage device are extracted. Next, a suitable model, such as a neural network or decision tree, is selected to train the historical data. The trained model can predict the remaining available time, maximum charge and discharge power and other information of the energy storage device based on real-time data, and determine the optimal charge and discharge strategy based on this information.

[0067] In this embodiment, sensors can be used to directly measure the current, voltage, power and other parameters of the load to obtain load characteristic data. The load characteristic data is collected by a load monitoring system, such as an electric meter or a sensor, to monitor the power demand, volatility, etc. of the load, including load type (such as resistive load, inductive load, capacitive load, etc.), power demand (i.e., the power size of the load, in watts), working mode (i.e., the working mode of the load, such as continuous work, intermittent work, periodic work, etc.), current characteristics (i.e., the current fluctuation of the load, including peak current, average current, etc.), voltage characteristics (i.e., the voltage requirements of the load, such as rated voltage, working voltage range, etc.), temperature characteristics (temperature changes of the load, which affect the performance of the energy storage device), and time characteristics (the working time and rest time of the load, as well as the start and stop time of the load).

[0068] In this implementation, by acquiring load characteristic data, the charge and discharge strategy control unit can dynamically adjust the charge and discharge strategy of the energy storage device according to the real-time load demand to achieve optimal energy management and efficiency improvement. For example, when the load demand is large, the discharge power of the energy storage device is increased; when the load demand is small, the discharge power of the energy storage device is reduced or charged to avoid damage to the energy storage device caused by excessive discharge or charging.

[0069] The dynamic charge and discharge decision model can control the energy storage system to dynamically adjust the charge and discharge strategy according to the current load demand in the following ways. For example, the dynamic charge and discharge decision model obtains the characteristic data of the current load in real time, such as power demand, change trend, etc. The dynamic charge and discharge decision model is used to predict the future changes of the load to obtain the load change trend so as to make a charge and discharge plan in advance. According to the load demand and load change trend, the dynamic charge and discharge decision model calculates the optimal charge and discharge power and time to achieve efficient energy management.

[0070] In addition, the dynamic charge and discharge decision model dynamically adjusts the charge and discharge strategy according to the load changes monitored in real time to meet the changes in load demand. When determining the charge and discharge strategy, the dynamic charge and discharge decision model will consider the safety and life of the energy storage system to avoid damage to the energy storage device caused by excessive or frequent charge and discharge. By optimizing the charge and discharge strategy, the dynamic charge and discharge decision model can maximize the energy utilization efficiency of the energy storage system and reduce energy waste. The dynamic adjustment capability of the dynamic charge and discharge decision model can help the energy storage system better cope with load fluctuations, achieve supply and demand balance, and improve power supply stability.

[0071] For example, when the load demand increases, the dynamic charge and discharge decision model will automatically increase the charging power or reduce the discharging power to meet the load demand; when the load demand decreases, the dynamic charge and discharge decision model will adjust the charge and discharge strategy accordingly to avoid overcharging or unnecessary discharge. In addition, the dynamic charge and discharge decision model can also adjust the charge and discharge strategy in advance according to the load change trend to better adapt to the load change.

[0072] This embodiment can dynamically adjust the charging and discharging strategy according to the current load demand through the dynamic charging and discharging decision model, thereby achieving efficient, stable and safe operation of the energy storage system.

[0073] The electrode material health status assessment unit is used to construct an electrode material performance health assessment model based on the performance parameters of the conductive polymer sponge composite electrode, and determine the performance parameters of the conductive polymer sponge composite electrode through the electrode material performance health assessment model.

[0074] In this embodiment, the electrode material performance health assessment model can be constructed based on the following steps: Based on the first embedded microsensor array, real-time data of electrode materials under different conditions (such as charge and discharge cycles, temperature changes, etc.) are collected. These data may include parameters such as voltage, current, temperature, and resistance.

[0075] The collected data is preprocessed, including data cleaning, noise reduction, normalization and other operations to obtain standard data to ensure the quality and accuracy of the data.

[0076] Extract meaningful features from the preprocessed standard data, which can reflect the performance and health status of the electrode material. Meaningful features include peak current, average resistance, capacity decay, etc.

[0077] Select a suitable machine learning algorithm or deep learning model, use the extracted meaningful features as input, and the health status of the electrode material as output to train the model. Models include neural networks, support vector machines, random forests, etc.

[0078] Use the test set data to evaluate the trained electrode material performance health assessment model. The evaluation indicators may include accuracy, recall rate, F1 value, etc. Optimize and adjust the model based on the evaluation results.

[0079] The trained electrode material performance health assessment model is applied to the actual electrode material health status assessment. By real-time monitoring of the performance data of the electrode material, the electrode material performance health assessment model is used to predict and analyze the performance parameters of the conductive polymer composite electrode, and potential problems and failures are discovered in a timely manner.

[0080] In the process of building the electrode material performance health assessment model, it is also necessary to ensure the accuracy and stability of the embedded microsensor array, and improve the performance of the sensor through calibration and regular maintenance. For the training data, accurate annotation is required to indicate the health status of the electrode material. As time goes by and the electrode material is used, the model needs to be updated and retrained to adapt to new working conditions and performance changes. Consider fusing the data of multiple sensors to obtain a more comprehensive and accurate assessment of the health of the electrode material.

[0081] Through the above steps, the constructed electrode material performance health assessment model can be used to evaluate the health status of the conductive polymer sponge composite electrode using the trained electrode material performance health assessment model. Ensure that when using the model for evaluation, the input data has similar characteristics and ranges to the data used in model training in order to obtain accurate evaluation results. In addition, this embodiment can also regularly update and improve the model to adapt to changes in electrode material performance and new application requirements, realize real-time monitoring and evaluation of the health status of electrode materials, and provide important decision-making basis for energy storage device management systems.

[0082] A dynamic performance optimization unit is used to construct a dynamic performance compensation model according to the charging and discharging strategy of the energy storage device and the performance parameters of the conductive polymer sponge composite electrode, and adjust the working parameters of the energy storage device through the dynamic performance compensation model.

[0083] In this embodiment, environmental parameters refer to external environmental conditions that affect the performance of the energy storage device, including temperature, humidity, altitude, vibration and shock, electromagnetic interference, etc. For example, the ambient temperature will affect the performance and life of the energy storage device, the high humidity environment will cause corrosion and reduced insulation performance of the energy storage device, and changes in altitude will affect the air density and pressure, thereby affecting the heat dissipation and insulation performance of the energy storage device. The energy storage device will be subject to vibration and shock during transportation, installation or use, which will affect the structure and performance of the energy storage device, and strong electromagnetic interference will affect the electronic control system of the energy storage device. Environmental sensors can be used to measure parameters such as temperature, humidity, and altitude.

[0084] For example, for an energy storage device for an electric vehicle, the acquisition of environmental parameters includes the following steps: Install environmental sensors such as temperature sensors and humidity sensors on electric vehicles to monitor ambient temperature and humidity in real time.

[0085] Through data analysis algorithms, historical ambient temperature and humidity data are analyzed to establish a relationship model between environmental parameters and the performance of energy storage devices.

[0086] During vehicle driving, ambient temperature and humidity data are collected in real time, and the impact of environmental parameters on the performance of the energy storage device is predicted based on the relationship model.

[0087] Based on the prediction results, the operating parameters of the energy storage device, such as charging current, discharge power, etc., are automatically adjusted to optimize its performance and life.

[0088] By continuously monitoring and adjusting the operating parameters of the energy storage device, its performance and reliability under different environmental conditions can be improved and its service life can be extended.

[0089] In this embodiment, the dynamic performance optimization unit is a real-time, adaptive intelligent control system that combines performance prediction and real-time adjustment functions. First, the dynamic performance optimization unit builds a dynamic performance compensation model by continuously monitoring the electrical parameters of the energy storage device (such as voltage, current, internal resistance, etc.), material microstructure changes (such as deformation, changes in surface morphology), and environmental data (such as temperature, humidity, etc.).

[0090] Specifically, the construction of the dynamic performance compensation model can be based on the following steps: Use sensors or monitoring equipment to continuously collect electrical parameters of the energy storage device, such as voltage, current, power, etc.; at the same time, collect data on changes in the microstructure of the material, such as crystal structure, molecular arrangement, etc.; as well as environmental data, such as temperature, humidity, pressure, etc.

[0091] The collected data is preprocessed, including data cleaning, noise reduction, normalization and other operations to obtain standard data to ensure the quality and accuracy of the data.

[0092] Extract meaningful features from the preprocessed standard data, which can reflect the performance and health status of the energy storage device. Features can include the changing trend of electrical parameters, characteristic parameters of material microstructure, statistical characteristics of environmental data, etc.

[0093] Select appropriate machine learning or deep learning algorithms, such as neural networks, support vector machines, random forests, etc., as the basis for building a dynamic performance compensation model.

[0094] The extracted features are used as input and the performance indicators of the energy storage device are used as output to train the model. During the training process, techniques such as cross-validation can be used to optimize the performance and generalization ability of the model.

[0095] Use the test set data to evaluate the trained model. Evaluation indicators may include mean square error, mean absolute error, coefficient of determination, etc. Optimize and adjust the model based on the evaluation results.

[0096] The trained model is applied to the actual energy storage device. By real-time monitoring of the electrical parameters, material microstructure changes, and environmental data of the energy storage device, the model is used for prediction and compensation to improve the performance and stability of the energy storage device.

[0097] In addition, in the process of building a dynamic performance compensation model, the following points need to be combined: Ensure that the collection and processing of electrical parameters, material microstructure changes, and environmental data are carried out synchronously to ensure the accuracy and reliability of the model.

[0098] As time goes by and the energy storage device is used, the model needs to be updated and retrained to adapt to new operating conditions and performance changes.

[0099] Multiple variables (such as electrical parameters, material microstructure changes, environmental data, etc.) are integrated to obtain a more comprehensive and accurate performance compensation model.

[0100] According to the needs of actual applications, ensure that the model has sufficient real-time performance and can complete prediction and compensation operations in a short time.

[0101] Through the above steps, a dynamic performance compensation model can be constructed by continuously monitoring the electrical parameters of the energy storage device, changes in the material microstructure, and environmental data, so as to achieve real-time monitoring and compensation of the performance of the energy storage device and improve the efficiency and reliability of the energy storage device.

[0102] Specifically, the dynamic performance compensation model analyzes historical data and real-time data to identify key factors that affect the performance of the energy storage device, such as the number of cycles, charge and discharge depth, and temperature fluctuations. When signs of performance degradation of the energy storage device are detected, the dynamic performance optimization unit automatically adjusts the operating parameters, such as charge and discharge current, pulse mode, and frequency, according to the predicted degradation trend to compensate for the performance degradation of the energy storage device and optimize the working state of the energy storage device. In this way, the dynamic performance optimization unit can not only predict future performance changes of the energy storage device, but also perform adaptive regulation in a timely manner when performance degradation occurs, ensuring that the energy storage device maintains optimal performance throughout its life cycle, and providing users with accurate recommendations on the maintenance and replacement of energy storage devices.

[0103] like Figure 6 As shown, in some embodiments, the charge and discharge strategy control unit includes: A real-time parameter matching subunit, for evaluating the current load demand according to the electrical parameters, and predicting the change trend of the load based on the historical data of the load characteristic data in combination with a machine learning algorithm; In this embodiment, the real-time parameter matching subunit evaluates the current load demand, which refers to the power demand of the system in a specific period of time, involving power size, duration and fluctuation characteristics. The evaluation is based on historical load data, combined with machine learning or prediction algorithms (such as regression analysis, time series analysis, etc.), predicts the change trend of the load, and adjusts the charging and discharging strategy of the energy storage device in real time to ensure the best match between the load demand and the state of the energy storage device (such as voltage, temperature).

[0104] Specifically, in order to evaluate the current load demand and predict its changing trend, the real-time parameter matching subunit can take the following steps: Collect historical load data, including load values, timestamps, and other information over a period of time.

[0105] The collected data is preprocessed, including data cleaning, missing value processing, outlier detection and other operations to obtain standard data to ensure the quality and accuracy of the data.

[0106] Extract meaningful features from the preprocessed standard data, which can reflect the change pattern and trend of the load. Features can include statistical features such as the average value, variance, peak value, valley value, etc. of the load, as well as temporal features such as seasonality and periodicity of the load.

[0107] Choose an appropriate machine learning or forecasting algorithm, such as linear regression, time series analysis, neural network, etc., as the basis for the forecasting model.

[0108] The prediction model is trained using the extracted features and historical load data. During the training process, techniques such as cross-validation can be used to optimize the performance and generalization ability of the model.

[0109] Use the test set data to evaluate the trained model. Evaluation indicators may include mean square error, mean absolute error, coefficient of determination, etc. Optimize and adjust the model based on the evaluation results.

[0110] Use the trained model to evaluate the current load demand and predict the load change trend. The prediction results can include information such as load values ​​and change trends in the future.

[0111] According to the predicted load change trend, the parameters of the energy storage system are adjusted in real time to achieve the best energy storage and release effect. For example, the charging power of the energy storage system can be increased in advance according to the load increase trend to meet future load requirements; or the discharge power of the energy storage system can be reduced in advance according to the load reduction trend to avoid over-discharge.

[0112] Through the above steps, the real-time parameter matching subunit can evaluate the current load demand and predict the load change trend based on the historical load data and machine learning or prediction algorithms, thereby realizing real-time parameter matching and optimization control.

[0113] The charge and discharge optimization subunit is used to dynamically generate an optimal charge and discharge curve based on physical modeling or optimization algorithm according to the change trend of the load to build a dynamic charge and discharge decision model, and optimize the charge and discharge time and intensity through the dynamic charge and discharge decision model.

[0114] Specifically, the charge and discharge optimization subunit dynamically generates the optimal charge and discharge curve (i.e., a dynamic charge and discharge decision model) based on physical modeling or optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) according to the electrochemical properties of the conductive polymer sponge composite electrode material. The electrochemical properties can be obtained through sensors in the battery management system (BMS), including parameters such as the internal resistance and discharge efficiency of the energy storage device, and temperature conditions. The temperature conditions include the temperature of the energy storage device itself or the working environment, and optimizes the charge and discharge time and intensity to maximize energy efficiency and extend the life of the energy storage device.

[0115] The energy balance control subunit is used to obtain the cycle life of the energy storage device according to the monitored charge and discharge cycle and health status of the energy storage device, and to control the charge and discharge rate and charge and discharge depth of the energy storage device according to the cycle life and the energy conversion efficiency; wherein the charge and discharge depth refers to the degree of discharge of the energy storage device, which is expressed by the ratio of the remaining power of the energy storage device to the total power.

[0116] During the regulation process, the energy balance regulation subunit will take into account the cycle life and energy conversion efficiency of the energy storage device. For example, it will evaluate the cycle life by monitoring the charge and discharge cycles and health status of the energy storage device, calculate the energy loss by the input and output energy difference, and balance between charging and discharging to ensure stable power supply of the energy storage system while reducing energy loss. In this way, the charge and discharge rate and depth can be finely regulated while ensuring the performance of the energy storage device. The charge and discharge depth refers to the degree of discharge of the energy storage device, which is expressed by the ratio of the remaining power of the energy storage device to the total power, so as to maximize the service life of the energy storage device.

[0117] like Figure 7As shown, in some embodiments, the electrode material health status assessment unit includes: The material performance index library determines a subunit, which is used to obtain the performance parameters of the conductive polymer sponge composite electrode and construct a material performance index library based on the performance parameters. The material performance index library stores performance parameters related to the conductive polymer sponge composite electrode, and the performance parameters include interface electron transport characteristics, microstructure deformation, temperature change and surface morphology change.

[0118] Specifically, the performance parameter collection subunit collects microstructure data such as electrochemical impedance spectroscopy, deformation data, temperature and surface morphology, and constructs a material performance index library, which is used to store performance parameters related to polymer sponge composite electrode materials, including electrochemical properties, mechanical properties and surface structure data, which will be used as input features in the evaluation model. Among them, electrochemical properties include conductivity, electrochemical stability, capacitive performance and current density; conductivity refers to the conductivity of the material, which refers to its ability to conduct electricity. For conductive polymer sponge composite electrodes, conductivity is an important property because it directly affects the charge transfer efficiency of the electrode. Electrochemical stability refers to the stability of conductive polymer sponge composite electrode materials in electrochemical environments. It includes the stability of conductive polymer sponge composite electrode materials during charging and discharging, as well as tolerance to electrolytes. Capacitive performance refers to the ability of materials to store charge. In conductive polymer sponge composite electrodes, capacitive performance is very important for energy storage applications. Current density refers to the current passing through a unit area. It is related to the conductivity and active surface area of ​​the electrode, and is crucial for applications such as energy storage devices and supercapacitors.

[0119] Mechanical properties include flexibility, strength, elastic modulus and fatigue life. Flexibility refers to the ability of the storage and polymer sponge composite electrode material to withstand deformation such as bending, stretching or twisting. For some applications, such as flexible electronic devices, flexibility is a key property. Strength refers to the ability of the storage and polymer sponge composite electrode material to resist external force damage. It is very important to ensure the integrity and stability of the electrode during use. The elastic modulus is the stiffness of the storage and polymer sponge composite electrode material when subjected to force. It is of great significance for designing and optimizing the structure and performance of the electrode. Fatigue life refers to the durability of the storage and polymer sponge composite electrode material under repeated loading and unloading. The surface structure includes porosity, pore size distribution and chemical composition. Porosity refers to the ratio of the volume of pores in the polymer sponge composite electrode material to the total volume. For conductive polymer sponge composite electrodes, high porosity can provide a larger active surface area, which is conducive to charge transfer and material diffusion. Pore size distribution refers to the size distribution of pores in the polymer sponge composite electrode material. Different pore size distributions can affect the permeability, adsorption performance and reaction kinetics of the electrode. Surface roughness refers to the degree of irregularity on the surface of the polymer sponge composite electrode material. It can affect the contact area and interfacial properties between the electrode and the electrolyte. The chemical composition of the surface has an important influence on the electrochemical properties and surface reactions of the polymer sponge composite electrode materials. For example, the functional groups on the surface can participate in electrochemical reactions or interact with the electrolyte. These performance parameters are interrelated and jointly determine the comprehensive performance and application range of the conductive polymer sponge composite electrode.

[0120] The model building subunit is used to train the performance parameters in the material performance index library using machine learning and statistical analysis algorithms to build the electrode material performance health assessment model.

[0121] The health assessment subunit is used to determine the health status of the conductive polymer sponge composite electrode through the electrode material performance health assessment model.

[0122] Specifically, machine learning techniques include support vector machines (SVM), random forests (RF), etc., and statistical analysis techniques include regression analysis, principal component analysis (PCA), etc. The construction and evaluation subunits can identify subtle changes in the microstructure of electrode materials from multi-dimensional sensor data and quantify the degree of material degradation. These technologies help the electrode material performance health assessment model to automatically extract rules from data for prediction and classification.

[0123] Wherein, the degradation degree of the conductive polymer sponge composite electrode material is measured by the following indicators: The electron transport capability refers to evaluating the conductivity and charge transfer impedance of the conductive polymer sponge composite electrode material through the interfacial electron transport characteristics of the conductive polymer sponge composite electrode; Micro stress refers to analyzing the stress changes generated by the conductive polymer sponge composite electrode material during the charge and discharge process through the microstructural deformation of the conductive polymer sponge composite electrode, and determining the mechanical properties of the conductive polymer sponge composite electrode material according to the stress changes; The surface active site change refers to the loss and change of the active sites of the conductive polymer sponge composite electrode determined by the surface morphology change of the conductive polymer sponge composite electrode material.

[0124] For example, a scanning electron microscope (SEM) is used to monitor the changes in the surface morphology of the conductive polymer sponge composite electrode material, and the loss and change of active sites of the conductive polymer sponge composite electrode material are evaluated based on the changes in the surface morphology, which is crucial to the efficiency of the electrode reaction.

[0125] In some embodiments, the porosity of the conductive polymer sponge composite electrode ranges from 60% to 85%, and the pore size ranges from 10nm to 500nm. In this embodiment, the porosity of the electrode ranges from 60% to 85%, and the pore size distribution ranges from 10 to 500 nanometers, with a high specific surface area of ​​200 to 500 square meters per gram. This structure can improve the electrochemical performance of the electrode and enhance energy storage efficiency.

[0126] In some embodiments, the thickness of the positive electrode and the negative electrode is 0.1 mm to 2 mm, and the distance between the positive electrode and the negative electrode is 0.05 mm to 0.5 mm. The electrode assembly in this embodiment is composed of an upper positive electrode and a lower negative electrode conductive polymer sponge, and the electrodes are separated by a microporous ceramic or polymer diaphragm to avoid short circuit between the positive and negative electrodes. The thickness of the electrode ranges from 0.1 to 2 mm, and the distance between the electrodes is controlled at 0.05 to 0.5 mm, which can ensure the efficiency of charge transfer and the stability of energy storage.

[0127] In some embodiments, the electrolyte includes: a liquid electrolyte and a solid electrolyte, the liquid electrolyte is an organic electrolyte, and the solid electrolyte is a polymer gel electrolyte; the ionic conductivity of the electrolyte is greater than 1mS / cm; the material of the current collector is aluminum foil or copper foil, with a thickness of 10μm to 50μm. The electrolyte system in this embodiment includes two types of liquid electrolytes and solid electrolytes. The liquid electrolyte is an organic electrolyte, and the solid electrolyte is a polymer gel electrolyte. The ionic conductivity of the electrolyte is required to be greater than 1mS / cm, and it has good stability and a wide temperature range, from -20°C to 60°C.

[0128] In addition, the current collector is made of aluminum foil or copper foil with a thickness of 10-50 microns, and the surface adhesion is enhanced by nanostructure treatment. The electrical connection part adopts welding or crimping technology to ensure efficient current transmission and connection stability.

[0129] The embodiment of the present invention realizes intelligent management of the conductive polymer sponge energy storage device through the control system, thereby improving the performance, reliability and service life of the system. In addition, the control system supports communication interfaces such as CAN, USB and Bluetooth, and can realize edge computing and real-time data processing, providing users with a more intelligent and efficient energy storage solution.

[0130] A large number of tests have proved that the energy storage device of the embodiment of the present invention has the following performance advantages: specific capacity greater than 200 mAh / g; cycle stability>5000 times, capacity retention rate>90%; excellent rate performance, 1C-20C charging and discharging without obvious attenuation; wide temperature range adaptability, -20°C to 60°C operating temperature; energy density greater than 200 Wh / kg.

[0131] Embodiment 2 like Figure 8 As shown, an embodiment of the present invention further provides an intelligent monitoring system for an energy storage device, the intelligent monitoring system comprising a monitoring unit and a communication interface unit; the communication interface unit is connected to the energy storage device, and is used to transmit an electrical parameter report of the energy storage device to the monitoring unit, the monitoring unit comprising a host computer monitoring system, a cloud storage platform or an edge computing node; the monitoring unit is used to monitor the operating status of the energy storage device according to the electrical parameter report.

[0132] Specifically, the electrical parameter report is transmitted to a monitoring unit such as a host computer monitoring system, a cloud storage platform or an edge computing node through a communication interface unit to realize real-time data sharing and remote monitoring. In this embodiment, the communication interface unit can transmit the electrical parameter report to the host computer monitoring system, the cloud storage platform or the edge computing node through several methods such as wired connection, wireless connection, cloud service integration and edge computing node transmission to realize real-time data sharing and remote monitoring; wherein, the wired connection can use a serial communication interface (such as RS232, RS485, etc.) or an Ethernet interface to directly connect the communication interface unit to the host computer monitoring system, the cloud storage platform or the edge computing node through a physical cable. After the connection is established, the communication interface unit can send the electrical parameter report to the target device according to a specific communication protocol (such as Modbus, TCP / IP, etc.). Wireless connection refers to the use of wireless communication technology (such as Wi-Fi, Bluetooth, ZigBee, etc.) to wirelessly connect the communication interface unit to the host computer monitoring system, the cloud storage platform or the edge computing node. It is necessary to ensure that both the communication interface unit and the target device support the corresponding wireless communication standards and are correctly configured and paired when connected. Once connected, the communication interface unit can wirelessly transmit electrical parameter reports to the target device.

[0133] Cloud service integration, if the goal is to upload data to a cloud storage platform, the communication interface unit can be integrated with the cloud service provider's API. By calling the corresponding API function, the electrical parameter report is sent to the cloud storage platform to achieve remote storage and sharing of data. In some embodiments, the uploaded data can be processed and displayed in real time on the cloud through the real-time data monitoring and analysis functions provided by the cloud service.

[0134] Edge computing node transmission, for edge computing nodes, the communication interface unit can send electrical parameter reports to edge computing devices through the network. Edge computing nodes can process, analyze and make decisions on data in real time, providing faster and localized responses. This method is suitable for scenarios where real-time data processing and control are required close to the data source.

[0135] In practical applications, the specific transmission method depends on the system requirements, the support capabilities of the communication interface unit, and the characteristics of the target device. In addition, the security, stability, and real-time requirements of data transmission need to be considered to ensure that data can be accurately and timely transmitted to the destination to achieve effective real-time sharing and remote monitoring.

[0136] In addition, in the design of the communication interface unit, specific protocols, compression technologies, encryption mechanisms, and optimization solutions for edge computing and cloud collaboration can also be used to meet different needs and application scenarios. For example, choosing a suitable communication protocol can ensure reliable transmission and correct parsing of data. The protocol includes TCP / IP (Transmission Control Protocol / Internet Protocol), UDP (User Datagram Protocol), HTTP (HyperText Transfer Protocol), etc. According to specific application requirements, you can choose to customize or use existing standard protocols. The use of compression technology can reduce the amount of data transmission and improve transmission efficiency. Compression technology can be used to compress electrical parameter reports. Compression algorithms include LZ77, LZ78, Huffman coding, etc. Compression technology can reduce data storage space and transmission time without losing data accuracy. In order to protect the security and privacy of data, an encryption mechanism can be designed in the communication interface unit. Encryption can be done by encrypting the data so that it exists in ciphertext during transmission, and only the authorized recipient can decrypt and read the data. Encryption algorithms include AES, RSA, etc. In the scenario of edge computing and cloud collaboration, specific optimization solutions can be implemented. For example, a part of the data processing and analysis tasks can be assigned to the edge computing nodes to reduce the data transmission volume and delay; the big data analysis and machine learning algorithms in the cloud can be used to conduct in-depth analysis and mining of the data uploaded by the edge computing nodes; and the data transmission efficiency between the edge computing nodes and the cloud can be improved by optimizing the communication protocol and data format.

[0137] In the description of the embodiments of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the devices or elements referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the embodiments of the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0138] In the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0139] Although the embodiments of the present invention have been described with reference to preferred embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the embodiments of the present invention. In particular, the various technical features mentioned in the various embodiments may be combined in any manner as long as there is no structural conflict. The embodiments of the present invention are not limited to the specific embodiments disclosed herein, but include all technical solutions falling within the scope of the claims.

[0140] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

Claims

1. An energy storage device having a conductive polymer sponge, characterized in that: The energy storage device comprises: a shell, and an electrode assembly, an electrolyte and a current collector arranged inside the shell; The electrode assembly comprises a positive electrode and a negative electrode, and a separator disposed between the positive electrode and the negative electrode, wherein the positive electrode and the negative electrode are conductive polymer sponge composite electrodes; the conductive polymer sponge is a polymer sponge having a porous network; the electrolyte is filled between the positive electrode and the negative electrode; the current collector is connected to the positive electrode and the negative electrode respectively; The energy storage device also includes a control system, which includes: A material performance monitoring module, used for real-time monitoring of the performance parameters of the conductive polymer sponge composite electrode; An electrical parameter measurement module, used to measure the electrical parameters of the energy storage device and generate an electrical parameter report of the energy storage device after processing the electrical parameters; An automatic control module is used to adjust the operating state of the energy storage device according to the performance parameters of the conductive polymer sponge composite electrode and / or the electrical parameter report of the energy storage device.

2. The energy storage device according to claim 1, characterized in that: The material performance monitoring module includes a first embedded microsensor array, and the first embedded microsensor array includes: An electrochemical impedance spectroscopy sensor for real-time monitoring of the interfacial electron transport properties of the conductive polymer sponge composite electrode; A strain sensor for real-time monitoring of the microstructural deformation of the conductive polymer sponge composite electrode; A temperature sensor, used to monitor the temperature change of the conductive polymer sponge composite electrode in real time; The electrochemical active site imaging sensor is used to monitor the surface morphology changes of the conductive polymer sponge composite electrode in real time.

3. The energy storage device according to claim 2, characterized in that: The electrical parameter measurement module comprises: A second sensor array is used to collect analog signals of the terminal voltage of the energy storage device, the charge and discharge current of the energy storage device, the resistance of the conductive polymer sponge composite electrode, the internal temperature change of the energy storage device, and the energy conversion efficiency of the energy storage device; A data processing unit, used to perform signal amplification, bandpass filtering, signal normalization and anti-interference processing on the analog signal to obtain a standardized analog data signal; An analog-to-digital converter, used for converting the standardized analog data signal into a standard digital signal; A microcontroller is used to filter, perform statistical analysis and abnormality monitoring on the digital signal of the standard, perform real-time trend calculations, and generate electrical parameter reports.

4. The energy storage device according to claim 3, characterized in that: The automatic control module comprises: A load monitoring unit, used to collect load characteristic data of the energy storage device, wherein the load characteristic data includes load type, power demand, working mode, current characteristics, voltage characteristics, temperature characteristics and time characteristics; A charge and discharge strategy control unit, configured to construct a dynamic charge and discharge decision model according to the electrical parameter report of the energy storage device and in combination with the load characteristic data, and dynamically adjust the charge and discharge strategy of the energy storage device through the dynamic charge and discharge decision model; An electrode material health status assessment unit, used to construct an electrode material performance health assessment model based on the performance parameters of the conductive polymer sponge composite electrode, and determine the performance parameters of the conductive polymer sponge composite electrode through the electrode material performance health assessment model; A dynamic performance optimization unit is used to construct a dynamic performance compensation model according to the charging and discharging strategy of the energy storage device and the performance parameters of the conductive polymer sponge composite electrode, and adjust the working parameters of the energy storage device through the dynamic performance compensation model.

5. The energy storage device according to claim 4, characterized in that: The charge and discharge strategy control unit specifically includes: A real-time parameter matching subunit, for evaluating the current load demand according to the electrical parameters, and predicting the change trend of the load based on the historical data of the load characteristic data in combination with a machine learning algorithm; A charge and discharge optimization subunit, for dynamically generating an optimal charge and discharge curve based on physical modeling or optimization algorithm according to the change trend of the load to build a dynamic charge and discharge decision model, and optimizing the charge and discharge time and intensity through the dynamic charge and discharge decision model; The energy balance control subunit is used to obtain the cycle life of the energy storage device according to the monitored charge and discharge cycle and health status of the energy storage device, and to control the charge and discharge rate and charge and discharge depth of the energy storage device according to the cycle life and the energy conversion efficiency; wherein the charge and discharge depth refers to the degree of discharge of the energy storage device, which is expressed by the ratio of the remaining power of the energy storage device to the total power.

6. The energy storage device according to claim 5, characterized in that: The electrode material health status assessment unit specifically includes: A material performance index library determination subunit is used to obtain performance parameters of the conductive polymer sponge composite electrode, and to construct a material performance index library according to the performance parameters, and to store performance parameters related to the conductive polymer sponge composite electrode through the material performance index library, wherein the performance parameters include interface electron transport characteristics, microstructure deformation, temperature change and surface morphology change; A model building subunit, used to train the performance parameters in the material performance index library using machine learning and statistical analysis algorithms to build the electrode material performance health assessment model; The health assessment subunit is used to determine the health status of the conductive polymer sponge composite electrode through the electrode material performance health assessment model.

7. The energy storage device according to claim 6, characterized in that: The degradation degree of the conductive polymer sponge composite electrode material is measured by the following indicators: The electron transport capability refers to evaluating the conductivity and charge transfer impedance of the conductive polymer sponge composite electrode material through the interfacial electron transport characteristics of the conductive polymer sponge composite electrode; Micro stress refers to analyzing the stress changes generated by the conductive polymer sponge composite electrode material during the charge and discharge process through the microstructural deformation of the conductive polymer sponge composite electrode, and determining the mechanical properties of the conductive polymer sponge composite electrode material according to the stress changes; The change of surface active sites refers to the loss and change of active sites of the conductive polymer sponge composite electrode determined by the change of the surface morphology of the conductive polymer sponge composite electrode material.

8. The energy storage device according to claim 1, characterized in that: The porosity of the conductive polymer sponge composite electrode ranges from 60% to 85%, and the pore size ranges from 10nm to 500nm.

9. The energy storage device according to claim 1, characterized in that: The thickness of the positive electrode and the negative electrode is 0.1 mm to 2 mm, and the distance between the positive electrode and the negative electrode is 0.05 mm to 0.5 mm; The electrolyte comprises: a liquid electrolyte and a solid electrolyte, wherein the liquid electrolyte is an organic electrolyte and the solid electrolyte is a polymer gel electrolyte; the ion conductivity of the electrolyte is greater than 1 mS / cm; The current collector is made of aluminum foil or copper foil with a thickness of 10 μm to 50 μm.

10. An intelligent monitoring system for the energy storage device according to any one of claims 1 to 9, characterized in that: The intelligent monitoring system includes a monitoring unit and a communication interface unit; The communication interface unit is connected to the energy storage device and is used to transmit the electrical parameter report of the energy storage device to the monitoring unit, and the monitoring unit includes a host computer monitoring system, a cloud storage platform or an edge computing node; The monitoring unit is used to monitor the operating status of the energy storage device according to the electrical parameter report.

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