A thermal runaway early warning method for sodium-ion hybrid energy storage system based on thermal simulation model

Abstract

The application provides a sodium-ion hybrid energy storage system thermal runaway early warning method based on a thermal simulation model, belongs to the technical field of computer data processing, and aims to improve the accuracy of precursor identification of thermal runaway of a multi-source heterogeneous energy storage node. By constructing a heterogeneous thermal simulation architecture based on a multi-modal state space, a three-dimensional dynamic mapping mechanism of a spatial physical coordinate, a target battery material, and a thermal physical property parameter matrix is established, the material of the corresponding battery can be automatically identified according to the spatial node of the hardware distributed optical fiber positioning, and the exclusive specific heat capacity, thermal conductivity coefficient and other basic physical parameters and early warning thresholds are called, so that the calculation deviation of a single model in processing mixed materials is eliminated, and the accuracy of precursor identification of thermal runaway of a multi-source heterogeneous energy storage node is improved.

Description

Technical Field

[0001] This invention relates to the field of computer data processing technology, and in particular to a method for early warning of thermal runaway in a sodium-ion hybrid energy storage system based on a thermal simulation model. Background Technology

[0002] In recent years, sodium-ion batteries have seen a gradual increase in their application in large-scale energy storage systems, such as grid-side and user-side energy storage power stations, due to the abundance of sodium in the Earth's crust, low acquisition costs, and good low-temperature discharge performance. With the advancement of industrialization, sodium-ion energy storage systems are beginning to coexist with traditional lithium-ion battery systems in the same energy storage power stations, forming a hybrid energy storage architecture. However, sodium-ion batteries exhibit significant objective physical differences from traditional lithium-ion batteries in thermodynamic characteristics such as self-heating initiation temperature, thermal runaway trigger critical point, and temperature rise rate.

[0003] For hybrid energy storage systems, sodium-ion batteries differ from batteries made of other materials in terms of temperature curves and heat generation rates. Using a single thermal simulation model cannot accurately fit the heat generation patterns of heterogeneous materials, which can easily lead to residuals in thermophysical calculations, resulting in false or missed alarms for early signs of thermal runaway. The massive amounts of data collected by hardware sensing networks contain complex background noise interference, and the noise characteristics of different device levels and subsystems are different. The lack of targeted differentiated noise compensation mechanisms can easily lead to weak early fault signals being submerged or over-filtered and erased, indicating room for improvement. Summary of the Invention

[0004] This invention provides a method for early warning of thermal runaway in sodium-ion hybrid energy storage systems based on thermal simulation models, in order to improve the accuracy of identifying precursors to thermal runaway in multi-source heterogeneous energy storage nodes.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In the first aspect, a method for early warning of thermal runaway in a sodium-ion hybrid energy storage system based on a thermal simulation model is provided and applied to a monitoring terminal. The method includes: acquiring temperature data along the line of a distributed optical fiber arranged in the hybrid energy storage system and the spatial node coordinates corresponding to the temperature data.

[0007] Based on a pre-constructed topology mapping relationship, the material type of the target battery corresponding to the spatial node coordinates is determined; wherein, the topology mapping relationship includes the correspondence between the physical length coordinates of the optical fiber and different battery material types;

[0008] Based on the material type, retrieve the thermal property parameters and preset warning thresholds that match the target battery from the multimodal physical parameter library;

[0009] The temperature data along the line and the thermal property parameters are input into the thermal simulation model for calculation to obtain the real-time thermal state characteristics of the target battery.

[0010] Determine whether the real-time thermal state characteristics exceed the preset warning threshold. If they do, trigger a thermal runaway warning signal.

[0011] Optionally, the step of determining the material type of the target battery corresponding to the spatial node coordinates based on a pre-constructed topological mapping relationship includes:

[0012] Extract the fiber optic cabling trajectory data and the spatial distribution data of each target battery from the hybrid energy storage system;

[0013] The spatial node coordinates are compared with the fiber optic cabling trajectory data to locate the physical spatial location of the spatial node coordinates.

[0014] Based on the physical spatial location and the spatial distribution data of each target battery, identify the target battery corresponding to the current spatial node coordinates and extract the material type corresponding to the target battery.

[0015] Optionally, the multimodal physical parameter library includes a first subset of material parameters and a second subset of material parameters;

[0016] The first subset of material parameters corresponds to the first type of battery material and includes the first specific heat capacity data, the first thermal conductivity, and the first thermal runaway critical temperature, which serves as the preset warning threshold.

[0017] The second subset of material parameters corresponds to the second type of battery material and includes the second specific heat capacity data, the second thermal conductivity, and the second thermal runaway critical temperature, which serves as the preset warning threshold; the first type of battery material is different from the second type of battery material.

[0018] Optionally, the step of inputting the temperature data along the line and the thermal property parameters into a thermal simulation model for calculation to obtain the real-time thermal state characteristics of the target battery includes:

[0019] Based on the material type, configure the corresponding thermodynamic mapping relationship and thermodynamic characteristic parameter set for the thermal simulation model;

[0020] By combining the temperature data along the line, the ambient temperature data, and the thermal property parameters, the current internal heat accumulation value and temperature rise rate of the target battery are calculated.

[0021] The accumulated internal heat value and the rate of temperature change are output as the real-time thermal state characteristics.

[0022] Optionally, the dynamic updating of the topology mapping relationship through the node self-discovery mechanism also includes the following steps:

[0023] When the hybrid energy storage system connects to a new target battery or replaces an existing target battery, it monitors the node change status on the system communication bus and sends a configuration query command to the new node when it captures the online broadcast signal of the new node.

[0024] The system receives configuration response data returned by the new node and parses out the hardware unique identifier and the feature field representing the battery material type from the configuration response data, which are used as the device identifier and material type of the target battery to be accessed, respectively.

[0025] Read the physical topology connection sequence of the system communication bus to obtain the communication port number or rack slot number corresponding to the target battery that is connected;

[0026] Query the preset physical structure mapping table of the computer room, convert the communication port number or rack slot number into three-dimensional spatial absolute coordinates as the physical installation position, and determine the physical length coordinates of the optical fiber corresponding to the physical installation position;

[0027] The physical length coordinates of the optical fiber are associated and bound with the material type, and the mapping records in the topology mapping relationship are updated online.

[0028] Optionally, it also includes a dynamic expansion step for the multimodal physical parameter library:

[0029] Determine whether the obtained material type belongs to a known material type that already exists in the multimodal physical parameter library;

[0030] If the material type is unknown, a parameter synchronization request is triggered to obtain the thermal property parameters corresponding to the unknown material type and the preset warning threshold.

[0031] The obtained thermal property parameters of the unknown material type and the preset warning threshold are used as a new subset of material parameters and dynamically written into the multimodal physical parameter library.

[0032] Optionally, it also includes a step of performing joint state verification by combining impedance characteristics:

[0033] Obtain the active diagnostic trigger command for the target battery;

[0034] According to the material type of the target battery, a preset frequency band mapping table is queried to identify the frequency boundaries of the first type of characteristic impedance and the second type of characteristic impedance in the AC response spectrum for different material types.

[0035] Using the frequency boundary between the first type of characteristic impedance and the second type of characteristic impedance as the cutoff condition, the preset low-frequency diffusion band is eliminated, and the remaining frequency range is extracted as the target characteristic band corresponding to the material type.

[0036] The control system's excitation source injects AC excitation signals into the target battery only within the target's characteristic frequency band.

[0037] The electrical response signal of the target battery to the AC excitation signal is collected, and the corresponding target frequency band impedance spectrum data is extracted.

[0038] The target frequency band impedance spectrum data is fused with the real-time thermal state characteristics to verify the warning status of the target battery.

[0039] Optionally, it also includes an online model parameter correction step:

[0040] During system operation, real-time terminal voltage data and real-time operating current data of the target battery are collected synchronously.

[0041] The temperature data along the line, the real-time terminal voltage data, and the real-time operating current data are input into the online status observer;

[0042] The online condition observer is used to calculate the current aging condition assessment value and polarization deviation value of the target battery;

[0043] Based on the aging state assessment value and polarization deviation value, the impedance network parameters pre-configured inside the thermal simulation model are adaptively overwritten and updated.

[0044] The thermal simulation model outputs the real-time thermal state characteristics using the updated impedance network parameters.

[0045] Optionally, the step of fusing the target frequency band impedance spectrum data with the real-time thermal state characteristics to verify the warning state of the target battery includes:

[0046] The internal resistance mutation rate is calculated based on the impedance spectrum data of the target frequency band, and it is determined whether the internal resistance mutation rate reaches the first safety threshold.

[0047] Determine whether the real-time thermal state characteristics calculated using the updated impedance network parameters exceed the preset warning threshold.

[0048] When the internal resistance mutation rate reaches the first safety threshold and the real-time thermal state characteristics exceed the preset warning threshold, the thermal runaway warning signal is triggered.

[0049] Secondly, a thermal runaway early warning system based on a thermal simulation model is provided, including:

[0050] The data acquisition module is used to acquire the temperature data along the distributed optical fiber arranged in the hybrid energy storage system and the spatial node coordinates corresponding to the temperature data.

[0051] The material determination module is used to determine the material type of the target battery corresponding to the spatial node coordinates based on a pre-built topology mapping relationship; wherein, the topology mapping relationship includes the correspondence between the physical length coordinates of the optical fiber and different battery material types;

[0052] The parameter calling module is used to call the thermal property parameters matching the target battery and the preset warning threshold from the multimodal physical parameter library according to the material type.

[0053] The model solving module is used to input the temperature data along the line and the thermal property parameters into the thermal simulation model for solving, so as to obtain the real-time thermal state characteristics of the target battery.

[0054] The early warning triggering module is used to determine whether the real-time thermal state characteristics exceed the preset early warning threshold. If they do, a thermal runaway early warning signal is triggered.

[0055] Thirdly, an electronic device is provided, comprising: a processor and a memory; the memory is used to store a computer program, which, when executed by the processor, causes the electronic device to perform the thermal runaway early warning method for a sodium-ion hybrid energy storage system based on a thermal simulation model as described in the first aspect.

[0056] In one possible design, the electronic device described in the third aspect may further include a transceiver. This transceiver may be a transceiver circuit or an interface circuit. The transceiver can be used for communication between the electronic device described in the third aspect and other electronic devices.

[0057] In the embodiments of the present invention, the electronic device described in the third aspect may be a terminal, or a chip (system) or other component or assembly disposed in the terminal, or a system containing the terminal.

[0058] Fourthly, a computer-readable storage medium is provided, comprising: a computer program or instructions; when the computer program or instructions are executed on a computer, the computer causes the computer to perform the thermal runaway early warning method for a sodium-ion hybrid energy storage system based on a thermal simulation model as described in the first aspect.

[0059] In summary, the above methods and systems have the following technical effects:

[0060] 1. In this invention, by constructing a heterogeneous thermal simulation architecture based on multimodal state space, and by establishing a three-dimensional dynamic mapping mechanism of spatial physical coordinates, target battery material, and thermal property parameter matrix, the material of the corresponding battery can be automatically identified based on the spatial node located by hardware distributed optical fiber, and dedicated basic physical parameters such as specific heat capacity and thermal conductivity and warning thresholds can be called. This eliminates the calculation deviation when a single model processes mixed materials and improves the accuracy of identifying the precursors of thermal runaway in multi-source heterogeneous energy storage nodes.

[0061] 2. In this invention, by introducing a bus-level node self-discovery and parameter library dynamic expansion mechanism, when a new module is connected, the system can automatically obtain its hardware identifier and material type, and bind and update them online with the physical length coordinates of the optical fiber. For unknown new materials, the system can automatically trigger parameter synchronization and establish a new material subset, eliminating the tedious process of manually recalibrating the system structure and updating the underlying code, and improving the system's large-scale deployment capability and maintainability.

[0062] 3. In this invention, the most sensitive mid-to-high frequency bands in the early stage of thermal runaway are locally excited based on the battery material, reducing the scanning time and hardware data throughput of invalid frequency bands, thus reducing the overall energy consumption of the system. Furthermore, by using an online state observer combined with feedback from real-time terminal voltage and operating current, the impedance network polarization and aging parameters inside the thermal simulation model are adaptively updated, overcoming the parameter distortion phenomenon under battery dynamic conditions and ensuring the timeliness of early warning and judgment in large-scale scenarios. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of the architecture of a thermal runaway early warning system based on a thermal simulation model provided in an embodiment of the present invention.

[0064] Figure 2 A flowchart illustrating the thermal runaway early warning method based on a thermal simulation model provided in an embodiment of the present invention;

[0065] Figure 3 The logical block diagram of a thermal runaway early warning system based on a thermal simulation model provided in an embodiment of the present invention. Detailed Implementation

[0066] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0067] In this embodiment of the invention, "predefined" or "preconfigured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This embodiment of the invention does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or electronic device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or electronic device. The type of memory can be any form of storage medium, and this embodiment of the invention does not limit this.

[0068] In this embodiment of the invention, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0069] In the description of the embodiments of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this invention, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0070] The network architecture and business scenarios described in the embodiments of this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0071] A schematic diagram of the architecture of a thermal runaway early warning system. Figure 1 As shown, the early warning system includes: a monitoring terminal, distributed optical fibers, and M target batteries, where M is an integer greater than 2.

[0072] The monitoring terminal can be a terminal device with transmitting, receiving, and processing functions, or a chip or chip system that can be installed on the terminal device. This terminal device can also be referred to as user equipment (UE), access terminal equipment, subscriber unit, user station, mobile station (MS), mobile station, remote station, remote terminal equipment, mobile device, user terminal equipment, terminal equipment, wireless communication equipment, user agent, or user device. The terminal devices in the embodiments of this application may be servers, industrial control computers, mobile phones, cellular phones, smartphones, tablets, wireless data cards, personal digital assistant computers (PDAs), wireless modems, handsets, laptop computers, machine-type communication (MTC) terminal devices, computers with wireless transceiver capabilities, virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, wireless terminal devices in industrial control, wireless terminal devices in self-driving, wireless terminal devices in remote medical care, wireless terminal devices in smart grids, wireless terminal devices in transportation safety, wireless terminal devices in smart cities, wireless terminal devices in smart homes, vehicle-mounted terminal devices, roadside units (RSUs) with terminal device functions, etc. The terminal equipment in this application may also be a control module, control unit, control component, control chip, or control unit built into the energy storage container as one or more components or units. Alternatively, the terminal equipment may also be customer-premises equipment (CPE).

[0073] The target batteries are distributed within the hybrid energy storage system. Distributed optical fibers are arranged within the hybrid energy storage system and communicate with a monitoring terminal. The distributed optical fibers can collect temperature data along the target batteries and feed back the temperature data and corresponding spatial node coordinates to the monitoring terminal. Optionally, the system may further include a system communication bus and a system excitation source communicated with the monitoring terminal. The system communication bus is used to monitor the node change status of the target batteries, and the system excitation source is used to inject AC excitation signals into the target batteries to collect impedance spectrum data.

[0074] Figure 2 This is a flowchart illustrating the method provided in an embodiment of the present invention. This thermal runaway early warning method for a sodium-ion hybrid energy storage system based on a thermal simulation model can be applied to the aforementioned monitoring terminal. The specific process is as follows:

[0075] S201, The monitoring terminal acquires the temperature data along the distributed optical fiber deployed in the hybrid energy storage system and the spatial node coordinates corresponding to the temperature data.

[0076] The monitoring terminal can obtain the temperature data along the line and the spatial node coordinates corresponding to the temperature data from the distributed optical fiber that is connected to the monitoring terminal.

[0077] For example, distributed optical fibers are deployed in the hybrid energy storage system. These distributed optical fibers can collect temperature data along the target battery and feed the temperature data and the corresponding spatial node coordinates back to the monitoring terminal.

[0078] S202, the monitoring terminal determines the material type of the target battery corresponding to the spatial node coordinates according to the pre-constructed topology mapping relationship; wherein, the topology mapping relationship includes the correspondence between the physical length coordinates of the optical fiber and different battery material types;

[0079] Scenario 1:

[0080] The topology mapping relationship includes the correspondence between the physical length coordinates of optical fibers and different battery material types. In other words, the topology mapping relationship includes optical fiber cabling trajectory data in the hybrid energy storage system and spatial distribution data of each target battery. The monitoring terminal extracts the optical fiber cabling trajectory data and spatial distribution data of each target battery from the hybrid energy storage system, and this data reflects the actual spatial location distribution of the target batteries.

[0081] Based on this, the monitoring terminal can identify the target battery corresponding to the current spatial node coordinates from the target batteries according to the actual spatial location distribution of each target battery.

[0082] For example, the monitoring terminal can compare the coordinates of the spatial nodes with the fiber optic cabling trajectory data to locate the physical spatial position of the spatial node coordinates; based on the physical spatial position and the spatial distribution data of each target battery, it can identify the target battery corresponding to the current spatial node coordinates and extract the material type of the target battery. In one example, the physical length Lx is calculated by comparing the flight time of the reflected fiber optic signal. If Lx meets the corresponding coordinate interval limit, the corresponding three-dimensional absolute spatial coordinates are output through reverse addressing to complete the extraction of the material type.

[0083] Scenario 2:

[0084] The monitoring terminal dynamically updates the topology mapping relationship through a node self-discovery mechanism. Based on this, the monitoring terminal can update the mapping records in the topology mapping relationship online according to the spatial nodes located by the distributed optical fiber.

[0085] For example, when the hybrid energy storage system connects to a new target battery or replaces an existing target battery, it monitors the node change status on the system communication bus and sends a configuration query command to the new node when it captures the online broadcast signal of the new node.

[0086] The system receives configuration response data returned by the new node and parses out the hardware unique identifier and the feature field representing the battery material type from the configuration response data, which are used as the device identifier and material type of the target battery to be accessed, respectively; it reads the physical topology connection order of the system communication bus and obtains the communication port number or rack slot number corresponding to the target battery to be accessed.

[0087] Query the preset physical structure mapping table of the computer room, convert the communication port number or rack slot number into three-dimensional spatial absolute coordinates as the physical installation position, and determine the physical length coordinates of the optical fiber corresponding to the physical installation position;

[0088] The physical length coordinates of the optical fiber are associated and bound with the material type, and the mapping records in the topology mapping relationship are updated online. The update of the topology mapping relationship is then completed.

[0089] In one example, the association between the three-dimensional spatial absolute coordinates and the physical length coordinates of the optical fiber is achieved by constructing a multidimensional hash table. The key of the hash table is the three-dimensional spatial absolute coordinate (X, Y, Z), and the value is a coordinate interval structure containing the start and end lengths of the optical fiber. If the calculated physical length belongs to this interval, the corresponding three-dimensional spatial absolute coordinates are output.

[0090] Scenario 3:

[0091] The monitoring terminal can also further update and improve the topology mapping relationship and parameter calling through the dynamic expansion step of the multimodal physical parameter library.

[0092] For example, the monitoring terminal determines whether the obtained material type belongs to a known material type that already exists in the multimodal physical parameter library; if it belongs to an unknown material type, a parameter synchronization request is triggered to obtain the thermal property parameters and preset warning thresholds corresponding to the unknown material type; the obtained thermal property parameters and preset warning thresholds of the unknown material type are dynamically written into the multimodal physical parameter library as a new subset of material parameters.

[0093] In one example, after triggering the parameter synchronization request, a synchronization waiting timer is set. If the timer expires and the thermal property parameters of the unknown material type are not obtained, the unknown material type is temporarily mapped to a conservative subset of material parameters with the lowest safety threshold and the smallest specific heat capacity in the multimodal physical parameter library. This ensures the continuity of the early warning system and simultaneously sends warning logs to the cloud monitoring platform.

[0094] S203, the monitoring terminal retrieves the thermal property parameters matching the target battery and the preset early warning threshold from the multimodal physical parameter library according to the material type;

[0095] The multimodal physical parameter library contains a first subset of material parameters and a second subset of material parameters. The first subset of material parameters corresponds to a first type of battery material and includes first specific heat capacity data, first thermal conductivity, and a first thermal runaway critical temperature as the preset warning threshold. The second subset of material parameters corresponds to a second type of battery material and includes second specific heat capacity data, second thermal conductivity, and a second thermal runaway critical temperature as the preset warning threshold. The first type of battery material and the second type of battery material are different.

[0096] For example, the first type of battery material is different from the second type of battery material, and can be a ternary lithium-ion battery and a lithium iron phosphate battery, respectively. The monitoring terminal can call up specific basic physical parameters such as specific heat capacity and thermal conductivity, as well as warning thresholds.

[0097] S204, The monitoring terminal inputs the temperature data along the line and the thermal property parameters into the thermal simulation model for calculation to obtain the real-time thermal state characteristics of the target battery;

[0098] The monitoring terminal uses the collected data and accessed specific thermophysical parameters for thermal simulation model calculations. For example, based on the material type, it configures a corresponding thermodynamic mapping relationship and a set of thermodynamic characteristic parameters for the thermal simulation model.

[0099] Since distributed optical fibers can only measure the surface temperature of the target battery or the external ambient temperature, the monitoring terminal uses a lumped parameter thermal model for inverse calculation when calculating the current internal heat accumulation value and temperature rise rate of the target battery.

[0100] Specifically, the monitoring terminal, based on the lumped parameter thermal model and Newton's law of cooling, substitutes the surface temperature measured by fiber optic positioning, the ambient temperature, and the aforementioned specially configured battery equivalent mass, specific heat capacity, and heat transfer coefficient into the energy conservation logic to deduce the core temperature inside the battery. Then, it calculates the surface temperature rise rate by comparing the surface temperature at the previous moment with the currently measured surface temperature, and integrates the temperature difference between the core temperature and the surface temperature over time to output the current internal heat accumulation value of the target battery. The calculated internal core temperature and the internal heat accumulation value are combined to form the real-time thermal state characteristics for output.

[0101] In one specific embodiment of the present invention, the core of configuring the corresponding thermodynamic mapping relationship and thermodynamic characteristic parameter set for the thermal simulation model lies in calling differentiated thermodynamic equations and characteristic parameter sets for batteries with different chemical systems. For example, the thermal simulation model uses the Bernardi heat generation model to solve the conventional electrochemical heating branch and uses the Arrhenius equation to solve the side reaction heating branch. The key variables required in the solution process, such as activation energy, enthalpy of reaction of the corresponding chemical system, and specific heat capacity data, are dynamically configured and distributed by the multimodal physical parameter library based on the aforementioned identified material type.

[0102] In one example, during system operation, real-time terminal voltage data and real-time operating current data of the target battery are collected simultaneously.

[0103] The temperature data along the line, the real-time terminal voltage data, and the real-time operating current data are input into the online status observer;

[0104] For example, the system uses the extended Kalman filter (EKF) algorithm to construct an online state observer. This observer uses the synchronously acquired real-time terminal voltage and operating current as input observation features to perform iterative convergence and directly outputs the current first type characteristic impedance estimate of the target battery. Subsequently, by linearly comparing the estimate with the factory nominal initial internal resistance, the battery's state of aging assessment value (SOH) is obtained.

[0105] The online condition observer is used to calculate the current aging condition assessment value and polarization deviation value of the target battery;

[0106] Based on the aging state assessment value and polarization deviation value, the impedance network parameters pre-configured within the thermal simulation model are adaptively updated to ensure the accuracy of the thermal simulation calculation.

[0107] S205, the monitoring terminal determines whether the real-time thermal state characteristics exceed the preset warning threshold. If they do, a thermal runaway warning signal is triggered.

[0108] The preset warning thresholds correspond to the thermal runaway critical temperatures of different battery materials. To ensure system safety, the monitoring terminal can also perform joint status verification by combining impedance characteristics.

[0109] For example, the monitoring terminal acquires the active diagnostic trigger command of the target battery; it queries a preset frequency band mapping table according to the material type of the target battery to identify the frequency boundaries of the first and second type characteristic impedances in the AC response spectrum for different material types; it uses the frequency boundaries of the first and second type characteristic impedances as truncation conditions to eliminate preset low-frequency diffusion bands and extract the remaining frequency range as the target characteristic frequency band corresponding to the material type; it controls the system excitation source to inject AC excitation signals into the target battery only within the target characteristic frequency band; it collects the electrical response signal of the target battery to the AC excitation signal and extracts the corresponding target frequency band impedance spectrum data; it fuses the target frequency band impedance spectrum data with the real-time thermal state characteristics to verify the warning state of the target battery. When the internal resistance mutation rate calculated based on the target frequency band impedance spectrum data reaches a first safety threshold, and the real-time thermal state characteristics exceed the preset warning threshold, the thermal runaway warning signal is triggered.

[0110] Specifically, the triggering condition for the active diagnosis trigger command is that when the real-time thermal state characteristics output by the thermal simulation model reach 80% of the preset warning threshold (i.e., the warning level), the extraction command is triggered to avoid increasing system power consumption by injecting global real-time AC excitation.

[0111] After acquiring the electrical response signal, the signal-to-noise ratio (SNR) of the signal is calculated. If the SNR is lower than the preset threshold (e.g., 15dB), the joint verification is abandoned and the independent early warning result of the thermal simulation model is directly adopted.

[0112] The online state observer employs the Extended Kalman Filter (EKF) algorithm, using the battery's first-type characteristic impedance. With polarization internal resistance Establish state-space equations for the state variables, based on the real-time terminal voltage. With real-time operating current The state update equation for the observer at time k is:

[0113]

[0114] in, For Kalman gain, The model predicts the terminal voltage.

[0115] Extract the current first-type characteristic impedance estimate from the state variables after the observer converges. ;

[0116] The current period Compared with the factory-nominal initial internal resistance Compare and calculate the aging condition assessment value SOH:

[0117]

[0118] Simultaneously, the first type of characteristic impedance is extracted by comparing the impedance spectrum data of the target frequency band at time k. and The first type of characteristic impedance at time t Calculate the internal resistance mutation rate :

[0119]

[0120] The calculated If the mutation rate increases sharply when compared with the first safety threshold, it indicates that there may be a micro-short circuit in the separator inside the battery, which is a precursor to thermal runaway. At this time, the thermal state characteristics are fused together by "AND" logic to output a high-confidence thermal runaway warning signal.

[0121] In summary, the monitoring terminal can determine the material type of the target battery corresponding to the spatial node coordinates based on the pre-built topological mapping relationship, and call the exclusive basic physical parameters and early warning thresholds, eliminating the calculation deviation when a single model processes mixed materials, and improving the accuracy of identifying the precursors of thermal runaway in multi-source heterogeneous energy storage nodes. This can balance performance and cost.

[0122] The above combination Figure 2 The method provided by the embodiments of the present invention is described in detail below. A thermal runaway early warning device for performing the method provided by the embodiments of the present invention is described in detail below. The device includes a monitoring terminal and is configured to:

[0123] Acquire the temperature data along the route of the distributed optical fiber deployed in the hybrid energy storage system and the spatial node coordinates corresponding to the temperature data;

[0124] Based on a pre-constructed topology mapping relationship, the material type of the target battery corresponding to the spatial node coordinates is determined; wherein, the topology mapping relationship includes the correspondence between the physical length coordinates of the optical fiber and different battery material types;

[0125] Based on the material type, retrieve the thermal property parameters and preset warning thresholds that match the target battery from the multimodal physical parameter library;

[0126] The temperature data along the line and the thermal property parameters are input into the thermal simulation model for calculation to obtain the real-time thermal state characteristics of the target battery.

[0127] Determine whether the real-time thermal state characteristics exceed the preset warning threshold. If they do, trigger a thermal runaway warning signal.

[0128] The electronic device provided in this embodiment of the invention, exemplarily, can be a network device, or a chip (system) or other component or assembly that can be disposed in a network device. The electronic device may include a processor. Optionally, the electronic device may also include a memory and / or a transceiver. The processor is coupled to the memory and transceiver, for example, by means of a communication bus connection.

[0129] In the several embodiments provided in this invention, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0130] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0131] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, an electronic device, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0132] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for early warning of thermal runaway in a sodium-ion hybrid energy storage system based on a thermal simulation model, applied to a monitoring terminal, characterized in that, Includes the following steps: Acquire the temperature data along the route of the distributed optical fiber deployed in the hybrid energy storage system and the spatial node coordinates corresponding to the temperature data; Based on a pre-constructed topology mapping relationship, the material type of the target battery corresponding to the spatial node coordinates is determined; wherein, the topology mapping relationship includes the correspondence between the physical length coordinates of the optical fiber and different battery material types; Based on the material type, retrieve the thermal property parameters and preset warning thresholds that match the target battery from the multimodal physical parameter library; The temperature data along the line and the thermal property parameters are input into the thermal simulation model for calculation to obtain the real-time thermal state characteristics of the target battery. Determine whether the real-time thermal state characteristics exceed the preset warning threshold. If they do, trigger a thermal runaway warning signal.

2. The method for early warning of thermal runaway in a sodium-ion hybrid energy storage system based on a thermal simulation model according to claim 1, characterized in that, The step of determining the material type of the target battery corresponding to the spatial node coordinates based on the pre-constructed topological mapping relationship includes: Extract the fiber optic cabling trajectory data and the spatial distribution data of each target battery from the hybrid energy storage system; The spatial node coordinates are compared with the fiber optic cabling trajectory data to locate the physical spatial location of the spatial node coordinates. Based on the physical spatial location and the spatial distribution data of each target battery, identify the target battery corresponding to the current spatial node coordinates and extract the material type corresponding to the target battery.

3. The method for early warning of thermal runaway in a sodium-ion hybrid energy storage system based on a thermal simulation model according to claim 1, characterized in that, The multimodal physical parameter library contains a first subset of material parameters and a second subset of material parameters; The first subset of material parameters corresponds to the first type of battery material and includes the first specific heat capacity data, the first thermal conductivity, and the first thermal runaway critical temperature, which serves as the preset warning threshold. The second subset of material parameters corresponds to the second type of battery material and includes the second specific heat capacity data, the second thermal conductivity, and the second thermal runaway critical temperature, which serves as the preset warning threshold; the first type of battery material is different from the second type of battery material.

4. The method for early warning of thermal runaway in a sodium-ion hybrid energy storage system based on a thermal simulation model according to claim 3, characterized in that, The step of inputting the temperature data along the line and the thermal property parameters into a thermal simulation model for calculation to obtain the real-time thermal state characteristics of the target battery includes: Based on the material type, configure the corresponding thermodynamic mapping relationship and thermodynamic characteristic parameter set for the thermal simulation model; By combining the temperature data along the line, the ambient temperature data, and the thermal property parameters, the current internal heat accumulation value and temperature rise rate of the target battery are calculated. The accumulated internal heat value and the rate of temperature change are output as the real-time thermal state characteristics.

5. The method for early warning of thermal runaway in a sodium-ion hybrid energy storage system based on a thermal simulation model according to claim 1, characterized in that, The dynamic updating of topology mapping relationships through node self-discovery mechanism also includes the following steps: When the hybrid energy storage system connects to a new target battery or replaces an existing target battery, it monitors the node change status on the system communication bus and sends a configuration query command to the new node when it captures the online broadcast signal of the new node. The system receives configuration response data returned by the new node and parses out the hardware unique identifier and the feature field representing the battery material type from the configuration response data, which are used as the device identifier and material type of the target battery to be accessed, respectively. Read the physical topology connection sequence of the system communication bus to obtain the communication port number or rack slot number corresponding to the target battery that is connected; Query the preset physical structure mapping table of the computer room, convert the communication port number or rack slot number into three-dimensional spatial absolute coordinates as the physical installation position, and determine the physical length coordinates of the optical fiber corresponding to the physical installation position; The physical length coordinates of the optical fiber are associated and bound with the material type, and the mapping records in the topology mapping relationship are updated online.

6. The method for early warning of thermal runaway in a sodium-ion hybrid energy storage system based on a thermal simulation model according to claim 5, characterized in that, It also includes a dynamic expansion step for the multimodal physics parameter library: Determine whether the obtained material type belongs to a known material type that already exists in the multimodal physical parameter library; If the material type is unknown, a parameter synchronization request is triggered to obtain the thermal property parameters corresponding to the unknown material type and the preset warning threshold. The obtained thermal property parameters of the unknown material type and the preset warning threshold are used as a new subset of material parameters and dynamically written into the multimodal physical parameter library.

7. The method for early warning of thermal runaway in a sodium-ion hybrid energy storage system based on a thermal simulation model according to claim 1, characterized in that, It also includes the step of combining impedance characteristics for joint state verification: Obtain the active diagnostic trigger command for the target battery; According to the material type of the target battery, a preset frequency band mapping table is queried to identify the frequency boundaries of the first type of characteristic impedance and the second type of characteristic impedance in the AC response spectrum for different material types. Using the frequency boundary between the first type of characteristic impedance and the second type of characteristic impedance as the cutoff condition, the preset low-frequency diffusion band is eliminated, and the remaining frequency range is extracted as the target characteristic band corresponding to the material type. The control system's excitation source injects AC excitation signals into the target battery only within the target's characteristic frequency band. The electrical response signal of the target battery to the AC excitation signal is collected, and the corresponding target frequency band impedance spectrum data is extracted. The target frequency band impedance spectrum data is fused with the real-time thermal state characteristics to verify the warning status of the target battery.

8. The method for early warning of thermal runaway in a sodium-ion hybrid energy storage system based on a thermal simulation model according to claim 7, characterized in that, It also includes an online model parameter correction step: During system operation, real-time terminal voltage data and real-time operating current data of the target battery are collected synchronously. The temperature data along the line, the real-time terminal voltage data, and the real-time operating current data are input into the online status observer; The online condition observer is used to calculate the current aging condition assessment value and polarization deviation value of the target battery; Based on the aging state assessment value and polarization deviation value, the impedance network parameters pre-configured inside the thermal simulation model are adaptively overwritten and updated. The thermal simulation model outputs the real-time thermal state characteristics using the updated impedance network parameters.

9. The method for early warning of thermal runaway in a sodium-ion hybrid energy storage system based on a thermal simulation model according to claim 8, characterized in that, The step of fusing the target frequency band impedance spectrum data with the real-time thermal state characteristics to verify the warning status of the target battery includes: The internal resistance mutation rate is calculated based on the impedance spectrum data of the target frequency band, and it is determined whether the internal resistance mutation rate reaches the first safety threshold. Determine whether the real-time thermal state characteristics calculated using the updated impedance network parameters exceed the preset warning threshold. When the internal resistance mutation rate reaches the first safety threshold and the real-time thermal state characteristics exceed the preset warning threshold, the thermal runaway warning signal is triggered.

10. A thermal runaway early warning system based on a thermal simulation model, applied to the thermal runaway early warning method for a sodium-ion hybrid energy storage system based on a thermal simulation model as described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to acquire the temperature data along the distributed optical fiber arranged in the hybrid energy storage system and the spatial node coordinates corresponding to the temperature data. The material determination module is used to determine the material type of the target battery corresponding to the spatial node coordinates based on a pre-built topology mapping relationship; wherein, the topology mapping relationship includes the correspondence between the physical length coordinates of the optical fiber and different battery material types; The parameter calling module is used to call the thermal property parameters matching the target battery and the preset warning threshold from the multimodal physical parameter library according to the material type. The model solving module is used to input the temperature data along the line and the thermal property parameters into the thermal simulation model for solving, so as to obtain the real-time thermal state characteristics of the target battery. The early warning triggering module is used to determine whether the real-time thermal state characteristics exceed the preset early warning threshold. If they do, a thermal runaway early warning signal is triggered.

Images