High-voltage lithium battery module equipment

Abstract

The invention discloses high-voltage lithium battery module equipment, and relates to the field of lithium batteries, the high-voltage lithium battery module equipment comprises an energy supply module, the top end of the energy supply module is provided with a planning management unit, the right side of the energy supply module is provided with an alarm display module, the bottom end of the front surface of the energy supply module is provided with a heat dissipation module, the data acquisition module is used for identifying the charging and discharging state of the energy supply module and the working state of the heat dissipation module based on the current charging and discharging stage of the energy supply module, planning a data acquisition object and path, extracting data based on a planning strategy, carrying out risk identification by constructing a band analysis model, obtaining fluctuation diffusion data and outputting the fluctuation diffusion data; performing risk identification on the fluctuation diffusion data through the three-dimensional battery module model, and updating an energy supply strategy; according to the method, targeted acquisition is carried out for a key fluctuation time period, redundant data volume is reduced, a fluctuation diffusion state is identified through a wave band analysis model, risk evolution is simulated in combination with a three-dimensional battery module model, and the accuracy and timeliness of risk judgment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium batteries, and in particular to a high-voltage lithium battery module device. Background Art

[0002] As the world pays more attention to the use of renewable energy, the power generation of renewable energy such as solar and wind power continues to increase, which has promoted the demand for efficient energy storage systems. Lithium batteries, as an efficient energy storage solution, have become a key technology connecting renewable energy and power grids. The popularity of electric vehicles (EVs) has increased the demand for high-voltage lithium batteries. High-performance, high-energy-density lithium battery modules have become an important part of the electric vehicle power system, promoting the research and development and advancement of related technologies.

[0003] In scenarios such as new energy electric vehicles and grid-level energy storage stations, high-voltage lithium battery packs face the thermal accumulation effects of the charging and discharging process and the discreteness of single-cell battery parameters. The existing technology has insufficient prediction accuracy for the diffusion of abnormal fluctuations in battery packs and lacks a multi-dimensional risk linkage response mechanism. As the scale of battery modules expands, traditional monitoring systems are inefficient in extracting the voltage or temperature fluctuation characteristics of massive single cells and are unable to predict potential faults through fluctuation trends. It is urgent to establish a data collection and modeling analysis system based on time series characteristics. There is a technical gap in the industry's three-dimensional presentation of abnormal conditions within battery modules. Summary of the invention

[0004] (I) Technical problems to be solved: In view of the above-mentioned shortcomings of the prior art, the present invention provides a high-voltage lithium battery module device that can effectively solve the problems of the prior art.

[0005] (II) Technical solution: To achieve the above objectives, the present invention is implemented through the following technical solutions: The present invention discloses a high-voltage lithium battery module device, comprising an energy supply module, a planning management unit is installed on the top of the energy supply module, an alarm display module is installed on the right side of the energy supply module, and a heat dissipation module is installed at the bottom of the front of the energy supply module, wherein: Energy supply module, used to deploy several lithium battery cells to charge or discharge electric energy to designated transportation equipment; The planning management unit is used to identify the charging and discharging status of the energy supply module and the working status of the heat dissipation module based on the current charging and discharging stage of the energy supply module, plan the data collection object and path, extract data based on the planning strategy, identify risks by building a band analysis model, obtain fluctuation diffusion data, identify risks of the fluctuation diffusion data through a three-dimensional battery module model, and update the energy supply strategy; The alarm display module is used to provide corresponding scale alarm information feedback based on the risk identification results of the planning management unit; The heat dissipation module is used to provide a heat dissipation mechanism for each working component and functional module, and to feed back heat dissipation response data to the planning management unit.

[0006] Furthermore, the planning management unit is deployed with submodules at the lower level, and the submodules include: a state identification module, a band planning module, a band acquisition module, a construction module and a risk identification module, wherein: The state recognition module is used to retrieve the original working information of each lithium battery cell of the energy supply module and the operating data based on the working target of the current cycle, perform normalization processing, and output a single cell data set based on the target completion degree; The band planning module is used to analyze the charging and discharging fluctuation trends of several lithium battery cells in the current cycle before the target is completed based on the monomer data set of target completion, extract key fluctuation features, mark the time periods involving key fluctuation features, and plan the implementation path and timing of several lithium battery cell collection plans based on the marked data; The band collection module is used to collect and implement corresponding paths and timings for time periods involving key fluctuation characteristics according to several lithium battery monomer collection schemes provided by the band planning module to obtain several fluctuation data; A construction module is used to construct a band analysis model, which receives the fluctuation data provided by the band acquisition module and outputs the fluctuation diffusion state of the data-related object in a preset period; A risk identification module is used to construct a three-dimensional battery module model, perform simulation demonstration in the three-dimensional battery module model based on the fluctuation state provided by the construction module, and make risk judgments; The energy supply control module is used to receive the adjustment strategy of the risk identification module based on the risk judgment result.

[0007] Furthermore, the energy supply control module triggers the module jump based on the risk judgment result of the risk identification module. When the risk identification module determines that there is an abnormality in the fluctuation diffusion state data submitted by the current construction module, it adjusts to the energy supply control module. The energy supply control module triggers the alarm display module based on the risk content, provides alarm feedback, and selects to trigger the adjustment of the working content of the energy supply module according to the preset alarm feedback result, wherein the high, medium and low risk contents of the alarm feedback result correspond to the adjustment intensity of the charging and discharging state of the energy supply module.

[0008] Furthermore, the state identification module is interactively connected to the band planning module through a wireless network, the band planning module is interactively connected to the band acquisition module through a wireless network, the band acquisition module is interactively connected to the construction module through a wireless network, the construction module is interactively connected to the risk identification module through a wireless network, and the risk identification module is interactively connected to the energy supply control module through a wireless network.

[0009] Furthermore, the output process of the state recognition module based on the single data set of target completion is: Obtaining the original working parameter set and operating performance data of the lithium battery cell within a preset period, wherein the original working parameter set includes basic parameters of voltage, current and temperature, and the operating performance data includes a real-time power output value and a cumulative energy throughput value based on the current charge and discharge target; Performing multi-dimensional normalization processing on the original working parameter set to generate a standardized parameter vector that matches the physical characteristics of the lithium battery cell, wherein the normalization processing includes a range method conversion based on the rated parameters of the cell and data alignment in the time dimension; Input the standardized parameter vector and the operating performance data into a preset target completion evaluation matrix, and calculate the target deviation coefficient of each lithium battery cell in the current charge and discharge stage, wherein the target deviation coefficient is determined by a dynamic ratio of actual output energy to target energy; The lithium battery monomers are clustered and analyzed according to the target deviation coefficient to generate classification results including high matching group, medium matching group and low matching group, and the standardized parameter vectors of the monomers in each group are associated with the target deviation coefficient and stored to form a monomer data set indexed by the target completion degree.

[0010] Furthermore, the band planning module calculates the monomer fluctuation contribution coefficient according to the amplitude change rate and duration of each lithium battery monomer in the key fluctuation period, generates a collection priority sequence in descending order of the coefficient, and constructs an adjacency matrix according to the physical deployment position of the monomer based on the priority sequence to generate the shortest collection path covering the high-priority monomer, and sets the collection trigger timing of each monomer according to the start time and cycle length of the key fluctuation period, including: For continuous fluctuation periods, configure periodic polling collection timing; For periods of sudden fluctuations, configure event-driven trigger acquisition timing.

[0011] Furthermore, the band analysis model of the planning management unit receives fluctuation data within a key fluctuation feature time period, performs a joint analysis of the data in the time domain and the frequency domain, extracts the fluctuation amplitude, frequency duration and change gradient parameters, constructs a multidimensional fluctuation feature matrix based on the parameters, calculates the transmission path of the fluctuation energy in the battery module through preset diffusion coefficient weights, and combines the spatial topological connection relationship and electrothermal coupling parameters of the lithium battery cells to predict the diffusion direction of the fluctuation within a preset period and the set of associated cells, and outputs fluctuation diffusion state data including the diffusion path, influence range and attenuation rate.

[0012] Furthermore, the working logic of the three-dimensional battery module model is: Based on the spatial topological connection relationship and electrothermal coupling parameters of the lithium battery cells in the module, the fluctuation diffusion state data submitted by the construction module is received and injected; By calculating the conduction path and superposition effect of the fluctuation energy in the three-dimensional battery module model in real time, calling the preset multi-dimensional risk assessment matrix, and synchronously comparing the fluctuation parameters to be measured with the threshold conditions in the historical fault feature library, the risk level is determined; When the risk level corresponding to the fluctuation amplitude exceeds the first threshold, an energy supply module single-unit isolation instruction is generated; when it exceeds the second threshold, an energy supply module single-unit power reduction instruction is generated, and the associated alarm level mapping relationship is output to the energy supply control module.

[0013] Furthermore, the fluctuation diffusion data acquired by the planning management unit includes: voltage, temperature fluctuation characteristic values, fluctuation propagation rate and spatial distribution characteristics.

[0014] Furthermore, the heat dissipation module encapsulates the operating parameters, temperature change gradient and mode switching records of the heat dissipation execution into a heat dissipation response data packet, and periodically transmits it back to the risk identification module. The heat dissipation response data packet includes: timestamp, temperature control efficiency coefficient and heat dissipation energy consumption index, and receives the adjustment strategy control of the energy supply control module.

[0015] (III) Beneficial effects: Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects: 1. By setting up a planning management unit, the operating data of lithium battery cells is normalized based on the target completion degree, the key fluctuation characteristics in the charging and discharging process are extracted, and the dynamic collection path and timing are planned. Targeted collection is only carried out for key fluctuation periods, reducing the amount of redundant data and optimizing resource usage. The fluctuation diffusion state is identified through the band analysis model, and the risk evolution is simulated in combination with the three-dimensional battery module model, so as to improve the accuracy and timeliness of risk judgment and provide early risk warning.

[0016] 2. By constructing a three-dimensional battery module model, the fluctuation diffusion state output by the band analysis model is visualized and simulated, which can intuitively locate the abnormal fluctuation area, assist in quick decision-making, combine the fluctuation time series and three-dimensional diffusion to verify the risk, reduce the misjudgment rate, and fill the gap in the spatial risk analysis of the battery module.

[0017] 3. By triggering energy supply strategy adjustments of different intensities according to the risk level and linking the alarm display module (3) for feedback, the risk-graded response can avoid the problem of excessive intervention or insufficient response, balance safety and battery performance, and form a closed-loop control with alarm feedback and energy supply strategy adjustment to improve the system's fault tolerance. Through risk grading and dynamic strategy adaptation, more refined battery management is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 It is a schematic diagram of the overall structure of the present invention; Figure 2 It is a schematic diagram of the framework of the present invention.

[0020] The numbers in the figure represent, respectively, 1. Energy supply module; 2. Planning management unit; 21. State identification module; 22. Band planning module; 23. Band acquisition module; 24. Construction module; 25. Risk identification module; 26. Energy supply control module; 3. Alarm display module; 4. Heat dissipation module. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] The present invention will be further described below in conjunction with the embodiments.

[0023] ① Embodiment 1: A high voltage lithium battery module device of this embodiment, such as Figure 1-Figure 2 As shown, it includes an energy supply module 1, a planning management unit 2 is installed on the top of the energy supply module 1, an alarm display module 3 is installed on the right side of the energy supply module 1, and a heat dissipation module 4 is installed at the bottom of the front of the energy supply module 1, wherein: Energy supply module 1, used to deploy a number of lithium battery cells to charge or discharge electrical energy to designated transport equipment; The planning management unit 2 is used to identify the charging and discharging state of the energy supply module 1 and the working state of the heat dissipation module 4 based on the current charging and discharging stage of the energy supply module 1, plan the data collection object and path, extract data based on the planning strategy, perform risk identification by building a band analysis model, obtain fluctuation diffusion data, perform risk identification on the fluctuation diffusion data through a three-dimensional battery module model, and update the energy supply strategy; The planning management unit 2 has submodules deployed at the lower level, including: a state identification module 21, a band planning module 22, a band acquisition module 23, a construction module 24 and a risk identification module 25, wherein: The state recognition module 21 is used to retrieve the original working information of each lithium battery cell of the energy supply module 1 and the operation data based on the working target of the current cycle, perform normalization processing, and output a cell data set based on the target completion degree; The output process of the state recognition module 21 based on the single data set of target completion is: Obtaining the original working parameter set and operating performance data of the lithium battery cell within a preset period, the original working parameter set includes basic parameters of voltage, current and temperature, and the operating performance data includes real-time power output value and cumulative energy throughput value based on the current charging and discharging target; Perform multi-dimensional normalization on the original working parameter set to generate a standardized parameter vector that matches the physical characteristics of the lithium battery cell. The normalization process includes the range method conversion based on the rated parameters of the cell and the data alignment in the time dimension. Input the standardized parameter vector and the operating performance data into the preset target completion evaluation matrix, and calculate the target deviation coefficient of each lithium battery cell in the current charging and discharging stage. The target deviation coefficient is determined by the dynamic ratio of the actual output energy to the target energy. Perform cluster analysis on lithium battery cells according to the target deviation coefficient to generate classification results including high matching group, medium matching group and low matching group, and store the standardized parameter vector of cells in each group in association with the target deviation coefficient to form a cell data set indexed by target completion degree; The band planning module 22 is used to analyze the charging and discharging fluctuation trends of several lithium battery cells in the current cycle before the target is completed based on the monomer data set of the target completion degree, extract key fluctuation features, mark the time periods involving key fluctuation features, and plan the implementation path and timing of several lithium battery cell acquisition plans based on the marked data; the band planning module 22 calculates the monomer fluctuation contribution coefficient according to the amplitude change rate and duration of each lithium battery cell in the key fluctuation period, generates an acquisition priority sequence in descending order of the coefficients, constructs an adjacency matrix for the monomers according to the physical deployment position based on the priority sequence, generates the shortest acquisition path covering high-priority monomers, and sets the acquisition trigger timing of each monomer according to the start time and cycle length of the key fluctuation period, including: For continuous fluctuation periods, configure periodic polling collection timing; For sudden fluctuation periods, configure event-driven trigger acquisition timing; The band collection module 23 is used to collect and implement corresponding paths and timings for the time periods involving key fluctuation characteristics according to the several lithium battery monomer collection schemes provided by the band planning module 22, and obtain several fluctuation data; A construction module 24 is used to construct a band analysis model, which receives the fluctuation data provided by the band acquisition module 23 and outputs the fluctuation diffusion state of the data-related object in a preset period; The risk identification module 25 is used to construct a three-dimensional battery module model, perform simulation demonstration in the three-dimensional battery module model based on the fluctuation state provided by the construction module 24, and perform risk judgment; The working logic of the 3D battery module model is: Based on the spatial topological connection relationship and electrothermal coupling parameters of the lithium battery cells in the module, the fluctuation diffusion state data submitted by the construction module 24 is received and the fluctuation diffusion state data is injected; By calculating the conduction path and superposition effect of the fluctuation energy in the three-dimensional battery module model in real time, calling the preset multi-dimensional risk assessment matrix, and synchronously comparing the fluctuation parameters to be measured with the threshold conditions in the historical fault feature library, the risk level is determined; When the risk level corresponding to the fluctuation amplitude exceeds the first threshold, an isolation instruction for the energy supply module 1 is generated; when it exceeds the second threshold, a power reduction instruction for the energy supply module 1 is generated, and the associated alarm level mapping relationship is output to the energy supply control module 26; The energy supply control module 26 is used to receive the adjustment strategy of the risk identification module 25 based on the risk judgment result; The alarm display module 3 is used to provide corresponding scale alarm information feedback based on the risk identification result of the planning management unit 2; The heat dissipation module 4 is used to provide a heat dissipation mechanism for each working component and functional module, and to feed back heat dissipation response data to the planning management unit 2; as a heat dissipation mechanism providing unit, the heat dissipation module 4 can feed back heat dissipation response data in real time, and is linked with the operating status of the system, effectively reducing the risk of failure caused by overheating and improving the operating life and efficiency of the battery; The heat dissipation module 4 encapsulates the operation parameters, temperature change gradient and mode switching records of the heat dissipation execution into a heat dissipation response data packet, and periodically transmits it back to the risk identification module 25. The heat dissipation response data packet includes: timestamp, temperature control efficiency coefficient and heat dissipation energy consumption index, and receives the adjustment strategy control of the energy supply control module 26; The state identification module 21 is interactively connected to the band planning module 22 through a wireless network, the band planning module 22 is interactively connected to the band acquisition module 23 through a wireless network, the band acquisition module 23 is interactively connected to the construction module 24 through a wireless network, the construction module 24 is interactively connected to the risk identification module 25 through a wireless network, and the risk identification module 25 is interactively connected to the energy supply control module 26 through a wireless network.

[0024] Compared with the prior art, the charging and discharging status of lithium battery cells is monitored in real time through the planning management unit 2, and the constructed band analysis model is used to identify potential risks. The real-time risk identification capability can detect abnormalities in time and reduce the risk of battery failure. Through the collaboration of the band planning module 22 and the band acquisition module 23, the device can automatically plan and implement data acquisition plans according to the fluctuation trend of the charging and discharging status of lithium battery cells. Through intelligent data acquisition and analysis methods, the accuracy and timeliness of data are effectively improved. For different types of fluctuations, such as continuous fluctuations and sudden fluctuations, the device flexibly responds by optimizing the acquisition timing to ensure that fluctuations in critical periods are efficiently captured, thereby improving the accuracy of data analysis. The three-dimensional battery module model constructed by the risk identification module 25 can simulate and visualize the fluctuation diffusion state in real time, and make corresponding isolation instructions or power reduction instructions according to the risk level, thereby improving the safety and reliability of the battery module; Through the organic combination of various modules, an integrated intelligent management platform is formed, which can realize multi-dimensional data processing, risk assessment, heat dissipation management and other functions, and optimize the overall operating efficiency of lithium batteries.

[0025] ②Example 2: At other levels, this embodiment also provides another optimization mechanism based on Embodiment 1, specifically a band analysis model, which receives fluctuation data within a key fluctuation feature period, performs a joint analysis of the data in the time domain and the frequency domain, extracts the fluctuation amplitude, the frequency duration and the change gradient parameters, constructs a multi-dimensional fluctuation feature matrix based on the parameters, calculates the transmission path of the fluctuation energy in the battery module through the preset diffusion coefficient weight, combines the spatial topological connection relationship and the electrothermal coupling parameters of the lithium battery cells, predicts the diffusion direction of the fluctuation within a preset period and the associated monomer set, and outputs the fluctuation diffusion state data including the diffusion path, the influence range and the attenuation rate; Fluctuation diffusion data include: voltage, temperature fluctuation characteristic values, fluctuation propagation rate and spatial distribution characteristics.

[0026] Compared with the existing technology, by jointly analyzing the fluctuation data in the time domain and frequency domain, the fluctuation amplitude, frequency duration and change gradient parameters can be extracted more comprehensively and deeply, and the fluctuation characteristics can be captured more accurately, thereby enhancing the ability to identify abnormal behaviors. Compared with the traditional model, the new analysis model can extract multiple fluctuation characteristic parameters and construct a multi-dimensional fluctuation characteristic matrix. Through a rich parameter set, it provides more information basis for risk assessment and fault prediction, making the model's prediction ability more accurate. The transmission path of the fluctuation energy in the battery module is calculated by the preset diffusion coefficient weight. This function helps to analyze how the fluctuation propagates in the system and promptly discover potential risk areas. It combines the spatial topological connection relationship of the lithium battery cells with the electrothermal coupling parameters, and considers the influence of multiple factors in the fluctuation analysis, making the obtained fluctuation diffusion state data more realistic and effective. The fluctuation diffusion state data output by the model includes the diffusion path, impact range and attenuation rate, which enables users to clearly understand the degree and scope of the impact of the fluctuation on the system.

[0027] ③Example 3: In this embodiment, the energy supply control module 26 triggers the module jump based on the risk judgment result of the risk identification module 25. When the risk identification module 25 determines that there is an abnormality in the fluctuation diffusion state data submitted by the current construction module 24, it adjusts to the energy supply control module 26. The energy supply control module 26 triggers the alarm display module 3 based on the risk content, provides alarm feedback, and selects to trigger the adjustment of the working content of the energy supply module 1 according to the preset alarm feedback result, wherein the high, medium and low risk contents of the alarm feedback result correspond to the adjustment intensity of the charging and discharging state of the energy supply module 1.

[0028] Compared with the prior art, through real-time risk judgment of the risk identification module 25, the system can quickly evaluate the fluctuation diffusion state data submitted by the construction module 24 to ensure immediate response in abnormal situations. The energy supply control module 26 can trigger different alarm feedbacks according to the risk content. This hierarchical alarm mechanism can flexibly respond to problems of different severity. Compared with the single alarm mode generally used in the prior art, the hierarchical feedback can better match the actual risk level, so as to take more appropriate measures. According to the preset alarm feedback results, the system can accurately adjust the charging and discharging status of the energy supply module 1 to ensure that the adjustment intensity of the work content can be reasonably planned according to the risk level. By combining the risk judgment results with the adjustment strategy of the work content, more intelligent decision-making support is provided. From risk judgment to alarm feedback, and then to the adjustment of the actual work content, the entire process achieves a high degree of automated linkage.

[0029] In summary, the present invention normalizes the operating data of lithium battery cells based on their target completion, extracts key fluctuation characteristics during the charging and discharging process, and plans dynamic acquisition paths and timings, and only performs targeted acquisitions during key fluctuation periods, thereby reducing the amount of redundant data and optimizing resource usage. The fluctuation diffusion state is identified through a band analysis model, and risk evolution is simulated in combination with a three-dimensional battery module model, thereby improving the accuracy and timeliness of risk judgment and providing early risk warnings. By constructing a three-dimensional battery module model, the fluctuation diffusion state output by the band analysis model is visualized and simulated, so that abnormal fluctuation areas can be intuitively located to assist in rapid decision-making. The fluctuation timing and three-dimensional diffusion are combined to verify risks, thereby reducing the misjudgment rate and filling the gap in the spatial risk analysis of battery modules. By triggering energy supply strategy adjustments of different intensities according to the risk level and linking the alarm display module 3 for feedback, the risk-graded response can avoid the problem of excessive intervention or insufficient response, balance safety and battery performance, and form a closed-loop control with alarm feedback and energy supply strategy adjustment to improve the system's fault tolerance. Through risk grading and dynamic strategy adaptation, more refined battery management is achieved.

[0030] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A high voltage lithium battery module device, characterized in that: The energy supply module (1) comprises a planning management unit (2) installed at the top of the energy supply module (1), an alarm display module (3) installed on the right side of the energy supply module (1), and a heat dissipation module (4) installed at the bottom of the front of the energy supply module (1), wherein: An energy supply module (1) is used to deploy a plurality of lithium battery cells to charge or discharge electric energy to a designated transport device; The planning management unit (2) is used to identify the charging and discharging state of the energy supply module (1) and the working state of the heat dissipation module (4) based on the current charging and discharging stage of the energy supply module (1), plan data collection objects and paths, extract data based on the planning strategy, perform risk identification by building a band analysis model, obtain fluctuation diffusion data, perform risk identification on the fluctuation diffusion data by using a three-dimensional battery module model, and update the energy supply strategy; An alarm display module (3) is used to provide corresponding scale alarm information feedback based on the risk identification result of the planning management unit (2); The heat dissipation module (4) is used to provide a heat dissipation mechanism for each working component and functional module, and to feed back heat dissipation response data to the planning management unit (2).

2. A high voltage lithium battery module device according to claim 1, characterized in that: The planning management unit (2) has submodules deployed at a lower level, and the submodules include: a state identification module (21), a band planning module (22), a band acquisition module (23), a construction module (24) and a risk identification module (25), wherein: A state recognition module (21) is used to retrieve original working information of each lithium battery cell of the energy supply module (1) and operating data based on the working target of the current cycle, perform normalization processing, and output a cell data set based on the target completion degree; The band planning module (22) is used to analyze the charging and discharging fluctuation trends of several lithium battery cells in the current cycle before the target is completed based on the single cell data set of the target completion degree, extract key fluctuation features, mark the time period involving the key fluctuation features, and plan the implementation path and timing of several lithium battery cell collection plans based on the marked data; The band collection module (23) is used to perform corresponding path and time sequence collection for a period involving key fluctuation characteristics according to a number of lithium battery monomer collection schemes provided by the band planning module (22), thereby acquiring a number of fluctuation data; A construction module (24) is used to construct a band analysis model, the band analysis model receives the fluctuation data provided by the band acquisition module (23), and outputs the fluctuation diffusion state of the data-related object in a preset period; A risk identification module (25) is used to construct a three-dimensional battery module model, perform a simulation demonstration in the three-dimensional battery module model based on the fluctuation state provided by the construction module (24), and perform risk judgment; The energy supply control module (26) is used to receive the adjustment strategy based on the risk judgment result from the risk identification module (25).

3. A high voltage lithium battery module device according to claim 2, characterized in that: The energy supply control module (26) triggers module jump based on the risk judgment result of the risk identification module (25). When the risk identification module (25) determines that the fluctuation diffusion state data submitted by the current construction module (24) is abnormal, it is adjusted to the energy supply control module (26). The energy supply control module (26) triggers the alarm display module (3) based on the risk content, provides alarm feedback, and selects to trigger the adjustment of the working content of the energy supply module (1) according to the preset alarm feedback result, wherein the high, medium and low risk content of the alarm feedback result corresponds to the adjustment intensity of the charging and discharging state of the energy supply module (1).

4. A high voltage lithium battery module device according to claim 2, characterized in that: The state identification module (21) is interactively connected to the band planning module (22) via a wireless network, the band planning module (22) is interactively connected to the band acquisition module (23) via a wireless network, the band acquisition module (23) is interactively connected to the construction module (24) via a wireless network, the construction module (24) is interactively connected to the risk identification module (25) via a wireless network, and the risk identification module (25) is interactively connected to the energy supply control module (26) via a wireless network.

5. A high voltage lithium battery module device according to claim 2, characterized in that: The output process of the state recognition module (21) based on the single data set of target completion is as follows: Obtaining the original working parameter set and operating performance data of the lithium battery cell within a preset period, wherein the original working parameter set includes basic parameters of voltage, current and temperature, and the operating performance data includes a real-time power output value and a cumulative energy throughput value based on the current charge and discharge target; Performing multi-dimensional normalization processing on the original working parameter set to generate a standardized parameter vector that matches the physical characteristics of the lithium battery cell, wherein the normalization processing includes a range method conversion based on the rated parameters of the cell and data alignment in the time dimension; Input the standardized parameter vector and the operating performance data into a preset target completion evaluation matrix, and calculate the target deviation coefficient of each lithium battery cell in the current charge and discharge stage, wherein the target deviation coefficient is determined by a dynamic ratio of actual output energy to target energy; The lithium battery monomers are clustered and analyzed according to the target deviation coefficient to generate classification results including high matching group, medium matching group and low matching group, and the standardized parameter vectors of the monomers in each group are associated with the target deviation coefficient and stored to form a monomer data set indexed by the target completion degree.

6. A high voltage lithium battery module device according to claim 2, characterized in that: The band planning module (22) calculates the monomer fluctuation contribution coefficient according to the amplitude change rate and duration of each lithium battery monomer in the key fluctuation period, generates a collection priority sequence in descending order of the coefficients, constructs an adjacency matrix according to the physical deployment position of the monomers based on the priority sequence, generates the shortest collection path covering the high-priority monomers, and sets the collection trigger timing of each monomer according to the start time and cycle length of the key fluctuation period, including: For continuous fluctuation periods, configure periodic polling collection timing; For periods of sudden fluctuations, configure event-driven trigger acquisition timing.

7. A high voltage lithium battery module device according to claim 1, characterized in that: The band analysis model of the planning management unit (2) receives the fluctuation data within the key fluctuation feature period, performs a joint analysis of the data in the time domain and the frequency domain, extracts the fluctuation amplitude, the frequency duration and the change gradient parameters, constructs a multi-dimensional fluctuation feature matrix based on the parameters, calculates the transmission path of the fluctuation energy in the battery module through a preset diffusion coefficient weight, combines the spatial topological connection and the electrothermal coupling parameters of the lithium battery cells, predicts the diffusion direction of the fluctuation within a preset period and the associated monomer set, and outputs the fluctuation diffusion state data including the diffusion path, the influence range and the attenuation rate.

8. A high voltage lithium battery module device according to claim 2, characterized in that: The working logic of the three-dimensional battery module model is: Based on the spatial topological connection relationship and electrothermal coupling parameters of the lithium battery cells in the module, receiving the fluctuation diffusion state data submitted by the construction module (24), and injecting the fluctuation diffusion state data; By calculating the conduction path and superposition effect of the fluctuation energy in the three-dimensional battery module model in real time, calling the preset multi-dimensional risk assessment matrix, and synchronously comparing the fluctuation parameters to be measured with the threshold conditions in the historical fault feature library, the risk level is determined; When the risk level corresponding to the fluctuation amplitude exceeds a first threshold, a power supply module (1) single-unit isolation instruction is generated; when it exceeds a second threshold, a power supply module (1) single-unit module power reduction instruction is generated, and an associated alarm level mapping relationship is output to the power supply control module (26).

9. A high voltage lithium battery module device according to claim 1, characterized in that: The fluctuation diffusion data acquired by the planning management unit (2) includes: voltage and temperature fluctuation characteristic values, fluctuation propagation rates and spatial distribution characteristics.

10. A high voltage lithium battery module device according to claim 1, characterized in that: The heat dissipation module (4) encapsulates the operating parameters, temperature change gradient and mode switching records of the heat dissipation execution into a heat dissipation response data packet, and periodically transmits it back to the risk identification module (25). The heat dissipation response data packet includes: a timestamp, a temperature control efficiency coefficient and a heat dissipation energy consumption index, and receives the adjustment strategy control of the energy supply control module (26).

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