Multi-factor safety assessment method, system and terminal for energy storage battery

Abstract

The invention provides a multi-factor safety assessment method and system for an energy storage battery and a terminal, and the method comprises the steps: carrying out the multi-factor safety detection of a to-be-detected energy storage battery through simulating different working conditions of the to-be-detected energy storage battery; analyzing the material and the structure of the to-be-detected energy storage battery through a microscope technology, and identifying the structure defect of the to-be-detected energy storage battery; integrating and analyzing the generated data, and identifying the safety risk of the to-be-tested energy storage battery based on a pre-trained battery safety prediction model, so as to generate and output a safety evaluation report of the to-be-tested energy storage battery; therefore, multi-factor safety detection and comprehensive evaluation can be performed on the electrochemical performance, the mechanical performance, the thermal stability and the long-term cycle performance of the energy storage battery to be detected and the material composition and the internal structure of the energy storage battery to be detected, potential safety risks can be found, and an important reference is provided for optimization design and improvement of the energy storage battery.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage batteries, and in particular to a multi-factor safety assessment method, system and terminal for energy storage batteries. Background Art

[0002] Energy storage batteries are batteries that can store electrical energy in the form of chemical energy and convert it back into electrical energy when needed to supply various electrical devices. Energy storage batteries provide balancing and regulation services for the power grid, support the large-scale application of renewable energy, and reduce the peak load and carbon emissions of the power grid. There are many types of energy storage batteries, the most common of which include lithium-ion batteries, lead-acid batteries, sodium-sulfur batteries, etc. Each type of energy storage battery uses different energy storage technologies and has unique characteristics and applicable scenarios. For example, lithium-ion batteries are widely used in the field of energy storage due to their advantages such as high energy density, long cycle life, and low self-discharge rate.

[0003] With the continuous advancement of new energy technologies and the development of the market, energy storage batteries, as key energy storage and balancing tools, will move towards large capacity, large-scale, customization, safety, easy recycling, adaptability, and digitalization. The issue of safety is particularly important. Since energy storage batteries may cause safety risks due to overheating, overcharging, over-discharging or short circuit, it is necessary to conduct a safety assessment of energy storage batteries. It should be noted that there are many factors that affect the safety of energy storage batteries, such as the electrochemical properties, thermal safety properties, mechanical properties, long-term cycle performance, and internal structure of energy storage batteries. However, the safety assessment methods of traditional energy storage batteries often only consider a few factors and cannot fully reflect the true situation of energy storage batteries. More importantly, the potential defects of energy storage batteries cannot be identified in a timely manner, so that energy storage batteries still have safety risks. Summary of the invention

[0004] In view of the shortcomings of the prior art described above, the purpose of the present application is to provide a multi-factor safety assessment method, system and terminal for energy storage batteries, which are used to solve the problem that the safety assessment method of energy storage batteries in the prior art considers very limited factors, resulting in the inability to fully and accurately reflect the actual situation of the energy storage battery and causing the energy storage battery to have safety risks.

[0005] To achieve the above-mentioned purpose and other related purposes, the first aspect of the present application provides a multi-factor safety assessment method for an energy storage battery, the multi-factor safety assessment method for an energy storage battery comprising: simulating different operating conditions of an energy storage battery to be tested, and monitoring the performance parameters of the energy storage battery to be tested under various operating conditions in real time, so as to perform multi-factor safety testing on the energy storage battery to be tested, and generate performance test data of the energy storage battery to be tested; analyzing the material and structure of the energy storage battery to be tested, and identifying the structural defects of the energy storage battery to be tested, and generating the structural test data of the energy storage battery to be tested; integrating and analyzing the generated performance test data and structural test data of the energy storage battery to be tested, generating safety assessment data of the energy storage battery to be tested, and based on a pre-trained battery safety prediction model, identifying the safety risk of the energy storage battery to be tested, and generating safety prediction data of the energy storage battery to be tested; generating and outputting a safety assessment report of the energy storage battery to be tested according to the generated safety assessment data and safety prediction data.

[0006] In some embodiments of the first aspect of the present application, different operating conditions of the energy storage battery to be tested are simulated, and the performance parameters of the energy storage battery to be tested under various operating conditions are monitored in real time to perform multi-factor safety detection on the energy storage battery to be tested. The method of generating performance detection data of the energy storage battery to be tested includes: simulating the vibration environment of the energy storage battery to be tested, causing the energy storage battery to be tested to vibrate according to a preset frequency and acceleration, and monitoring the appearance change, temperature change, voltage change, charging curve, discharge curve and capacity retention rate of the energy storage battery to be tested in real time to obtain vibration test data of the energy storage battery to be tested; simulating the impact environment of the energy storage battery to be tested, causing the energy storage battery to be tested to fall from a predetermined height onto a hard ground, and monitoring the appearance change, temperature change, voltage change, charging curve, discharge curve and capacity retention rate of the energy storage battery to be tested in real time to obtain drop test data of the energy storage battery to be tested; simulating the extrusion environment of the energy storage battery to be tested, Apply a predetermined pressure, and monitor the appearance change, temperature change, voltage change, charging curve, discharging curve and capacity retention rate of the energy storage battery to be tested in real time to obtain the extrusion test data of the energy storage battery to be tested; simulate the thermal stability of the energy storage battery to be tested under overcharge, overdischarge, short circuit, high temperature and low temperature conditions respectively, and monitor the appearance change, temperature change, voltage change, charging curve, discharging curve and capacity retention rate of the energy storage battery to be tested in real time to obtain the thermal stability test data of the energy storage battery to be tested; perform a long-term cycle charge and discharge test on the energy storage battery to be tested, and monitor the appearance change, temperature change, voltage change, charging curve, discharging curve and capacity retention rate of the energy storage battery to be tested in real time to obtain the cycle test data of the energy storage battery to be tested; generate the performance test data of the energy storage battery to be tested according to the obtained vibration test data, drop test data, extrusion test data, thermal stability test data and cycle test data of the energy storage battery to be tested.

[0007] In some embodiments of the first aspect of the present application, the material and structure of the energy storage battery to be tested are analyzed, and the structural defects of the energy storage battery to be tested are identified. The method of generating the structural detection data of the energy storage battery to be tested includes: scanning the surface of the energy storage battery to be tested by a finely focused electron beam through a scanning electron microscope technology to obtain surface feature image data of the energy storage battery to be tested, so as to analyze the surface morphology, roughness and microstructure of the energy storage battery to be tested; using a transmission electron microscope technology, using a high-speed electron beam to penetrate the energy storage battery to be tested, to obtain internal structure image data of the energy storage battery to be tested, so as to analyze the crystal structure, defects and nanoscale internal features of the energy storage battery to be tested; using a non-contact surface scanning technology, using an in-situ non-destructive testing technology, to obtain three-dimensional microscopic morphology data of the surface of the energy storage battery to be tested, so as to analyze the microstructure and morphology of the surface of the energy storage battery material to be tested, and identify damage and crack defects in the microstructure; generating the structural detection data of the energy storage battery to be tested according to the obtained surface feature image data, internal structure image data and three-dimensional microscopic morphology data of the energy storage battery to be tested.

[0008] In some embodiments of the first aspect of the present application, the generated performance test data and structure test data of the energy storage battery to be tested are integrated and analyzed, and the method of generating safety assessment data of the energy storage battery to be tested includes: performing data cleaning on the generated performance test data and structure test data of the energy storage battery to be tested, removing duplicate data and noise data therein, filling missing values ​​therein, and identifying and correcting abnormal values ​​therein; performing data integration on the cleaned performance test data and structure test data of the energy storage battery to be tested to obtain safety test data of the energy storage battery to be tested; analyzing the safety test data of the energy storage battery to be tested, and evaluating the electrochemical performance, thermal stability, mechanical stability and electrical safety performance of the energy storage battery to generate safety assessment data of the energy storage battery to be tested.

[0009] In some embodiments of the first aspect of the present application, the training method of the battery safety prediction model includes: obtaining historical performance test data and historical structure test data of multiple energy storage batteries; performing data cleaning on the historical performance test data and the historical structure test data respectively, and performing data integration on the cleaned data to obtain historical safety test data of each energy storage battery; performing multi-dimensional feature extraction on the obtained historical safety test data of each energy storage battery to generate safety feature data of each energy storage battery to construct a sample training set of each energy storage battery; training a deep learning network model based on the sample training set to construct a battery safety prediction model, learning the safety feature data of each energy storage battery in the sample training set, identifying potential safety risks of each energy storage battery, and generating corresponding safety prediction data.

[0010] In some embodiments of the first aspect of the present application, the method further includes: real-time monitoring of a multi-factor safety detection process of the energy storage battery to be tested, and generating corresponding process monitoring data.

[0011] In some embodiments of the first aspect of the present application, the multi-factor safety detection process of the energy storage battery to be tested is monitored in real time, and the corresponding process monitoring data is generated in a manner including: real-time monitoring of simulated operating condition parameters of the energy storage battery to be tested, and quality control of the multi-factor safety detection process of the energy storage battery to be tested; judging the safety state of the energy storage battery to be tested during the multi-factor safety detection process, and when the energy storage battery to be tested is in an unsafe state, timely interrupting the multi-factor safety detection process and issuing an alarm signal; recording the process monitoring data of the energy storage battery to be tested; wherein the process monitoring data includes but is not limited to: a unique identification code of the energy storage battery to be tested, a start time, an end time, a detection content, an operating condition parameter, the obtained performance detection data, and the safety state of the energy storage battery to be tested.

[0012] In some embodiments of the first aspect of the present application, the safety assessment report includes but is not limited to: the rated power, rated energy, charge and discharge efficiency, energy conversion efficiency, cycle life, calendar life, capacity retention rate, thermal stability, mechanical stability, electrical safety, structural defects and safety risks of the energy storage battery to be tested.

[0013] To achieve the above-mentioned purpose and other related purposes, the second aspect of the present application provides a multi-factor safety assessment system for an energy storage battery, the multi-factor safety assessment system for an energy storage battery comprising: a battery performance detection module, used to simulate different operating conditions of an energy storage battery to be tested, and to monitor the performance parameters of the energy storage battery to be tested under various operating conditions in real time, so as to perform multi-factor safety detection on the energy storage battery to be tested, and to generate performance detection data of the energy storage battery to be tested; a battery structure detection module, used to analyze the material and structure of the energy storage battery to be tested, and to identify the structural defects of the energy storage battery to be tested, and to generate the structural detection data of the energy storage battery to be tested; a battery safety analysis module, connected to the battery performance detection module and the battery structure detection module, used to integrate and analyze the generated performance detection data and structural detection data of the energy storage battery to be tested, generate the safety assessment data of the energy storage battery to be tested, and identify the safety risks of the energy storage battery to be tested based on the pre-trained battery safety prediction model, and generate the safety prediction data of the energy storage battery to be tested; an assessment report output module, connected to the battery safety analysis module, used to generate and output the safety assessment report of the energy storage battery to be tested according to the generated safety assessment data and safety prediction data.

[0014] To achieve the above-mentioned purpose and other related purposes, the third aspect of the present application provides a multi-factor security assessment terminal for an energy storage battery, wherein the multi-factor security assessment terminal for an energy storage battery comprises: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the terminal implements any of the multi-factor security assessment methods for an energy storage battery described above when executing the computer program.

[0015] As described above, the present application provides a multi-factor safety assessment method, system and terminal for energy storage batteries, which performs multi-factor safety testing on the energy storage batteries to be tested by simulating different operating conditions of the energy storage batteries to be tested; analyzes the materials and structures of the energy storage batteries to be tested by microscopy technology to identify the structural defects of the energy storage batteries to be tested; integrates and analyzes the generated data, and identifies the safety risks of the energy storage batteries to be tested based on a pre-trained battery safety prediction model, thereby generating and outputting a safety assessment report for the energy storage batteries to be tested. Therefore, the present application has the following beneficial effects: the present application can perform multi-factor safety testing and comprehensive evaluation on the electrochemical properties, mechanical properties, thermal stability, long-term cycle performance of the energy storage batteries to be tested, as well as the material composition and internal structure of the energy storage batteries to be tested, and help discover potential safety risks, providing an important reference for the optimization design and improvement of energy storage batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Shown is a flow chart of a multi-factor safety assessment method for an energy storage battery in one embodiment of the present application.

[0017] Figure 2 Shown is a flow chart of a multi-factor safety assessment method for energy storage batteries in a specific embodiment of the present application.

[0018] Figure 3 Shown is a structural schematic diagram of a multi-factor safety assessment system for energy storage batteries in one embodiment of the present application.

[0019] Figure 4 Shown is a structural schematic diagram of a multi-factor safety assessment terminal for energy storage batteries in one embodiment of the present application. DETAILED DESCRIPTION

[0020] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0021] In order to solve the problems in the above-mentioned background technology, the present invention provides a multi-factor safety assessment method, system and terminal for energy storage batteries, aiming to solve the problem that the safety assessment method of energy storage batteries in the prior art considers very limited factors, which leads to the inability to fully and accurately reflect the actual situation of the energy storage battery and the safety risks of the energy storage battery. The electrochemical properties, mechanical properties, thermal stability, long-term cycle performance of the energy storage battery, and the material composition and internal structure of the energy storage battery are subjected to multi-factor safety detection and comprehensive evaluation to help discover potential safety risks, thereby providing an important reference for the optimization design and improvement of the energy storage battery. At the same time, in order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention are further described in detail through the following embodiments and in combination with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the invention.

[0022] like Figure 1 FIG. 1 is a flow chart showing a multi-factor safety assessment method for an energy storage battery in an embodiment of the present invention. The multi-factor safety assessment method for an energy storage battery in this embodiment mainly includes the following steps.

[0023] Step S101: Simulate different working conditions of the energy storage battery to be tested, and monitor the performance parameters of the energy storage battery to be tested under various working conditions in real time, so as to perform multi-factor safety detection on the energy storage battery to be tested and generate performance detection data of the energy storage battery to be tested.

[0024] In one embodiment, step S101 includes the following steps.

[0025] ①Simulate the vibration environment of the energy storage battery to be tested, make the energy storage battery to be tested vibrate according to the preset frequency and acceleration, and monitor the appearance change, temperature change, voltage change, charging curve, discharging curve and capacity retention rate of the energy storage battery to be tested in real time to obtain the vibration test data of the energy storage battery to be tested.

[0026] ②Simulate the impact environment of the energy storage battery to be tested, make the energy storage battery to be tested fall from a predetermined height onto a hard ground, and monitor the appearance changes, temperature changes, voltage changes, charging curves, discharging curves and capacity retention rate of the energy storage battery to be tested in real time to obtain the drop test data of the energy storage battery to be tested.

[0027] ③Simulate the extrusion environment of the energy storage battery to be tested, apply a predetermined pressure to the energy storage battery to be tested, and monitor the appearance changes, temperature changes, voltage changes, charging curves, discharging curves and capacity retention rate of the energy storage battery to be tested in real time to obtain the extrusion test data of the energy storage battery to be tested.

[0028] ④ Simulate the thermal stability of the energy storage battery under overcharge, over-discharge, short circuit, high temperature and low temperature conditions respectively, and monitor the appearance change, temperature change, voltage change, charging curve, discharging curve and capacity retention rate of the energy storage battery in real time to obtain the thermal stability test data of the energy storage battery.

[0029] ⑤ Carry out long-term cycle charge and discharge tests on the energy storage battery to be tested, and monitor the appearance changes, temperature changes, voltage changes, charging curves, discharging curves and capacity retention rate of the energy storage battery to be tested in real time to obtain the cycle test data of the energy storage battery to be tested.

[0030] ⑥ Based on the obtained vibration test data, drop test data, extrusion test data, thermal stability test data and cycle test data of the energy storage battery to be tested, generate performance test data of the energy storage battery to be tested.

[0031] By simulating the vibration environment, impact environment and extrusion environment of the energy storage battery to be tested, the present application can detect and evaluate the physical and mechanical properties of the energy storage battery to be tested, such as detecting and evaluating the pressure resistance and impact resistance of the energy storage battery to be tested; by simulating the thermal stability of the energy storage battery to be tested under overcharge, over-discharge, short circuit, high temperature and low temperature conditions, the thermal stability performance of the energy storage battery to be tested under abnormal conditions can be detected and evaluated; by performing long-term cycle charge and discharge tests on the energy storage battery to be tested, the cycle life and stability performance of the energy storage battery to be tested can be evaluated; by monitoring the appearance changes, temperature changes, voltage changes, charging curves, discharge curves and capacity retention rates of the energy storage battery to be tested under various working conditions, the electrochemical properties and long-term storage performance of the energy storage battery to be tested under various conditions can be detected and evaluated; therefore, the present application can provide more accurate and comprehensive energy storage battery quality and performance evaluation results, help users better understand the actual conditions of the energy storage battery, and also provide a comprehensive and reliable data basis for the subsequent identification of potential safety risks of the energy storage battery.

[0032] Step S102: analyzing the material and structure of the energy storage battery to be tested, identifying the structural defects of the energy storage battery to be tested, and generating structural detection data of the energy storage battery to be tested.

[0033] In one embodiment, step S102 includes the following steps.

[0034] ① Through scanning electron microscopy technology, a finely focused electron beam is used to scan the surface of the energy storage battery to obtain surface feature image data of the energy storage battery to be tested, so as to analyze the surface morphology, roughness and microstructure of the energy storage battery to be tested.

[0035] ② Through transmission electron microscopy technology, a high-speed electron beam is used to penetrate the energy storage battery to obtain the internal structure image data of the energy storage battery to be tested, so as to analyze the crystal structure, defects and nanoscale internal characteristics of the energy storage battery to be tested.

[0036] ③ Through non-contact surface scanning technology and in-situ non-destructive testing technology, the three-dimensional microscopic morphology data of the surface of the energy storage battery to be tested is obtained to analyze the microstructure and morphology of the surface of the energy storage battery material to be tested, and to identify damage and crack defects in the microstructure.

[0037] ④ Generate structural detection data of the energy storage battery to be tested based on the obtained surface feature image data, internal structure image data and three-dimensional microscopic morphology data of the energy storage battery to be tested.

[0038] This application uses advanced microscopy technology to analyze the material composition and structural characteristics of energy storage batteries. It can detect defects such as breakage and cracks in the microstructure of energy storage batteries, help discover potential failure points of energy storage batteries and take timely repair measures, and provide important reference for the optimal design and improvement of energy storage batteries, thereby further improving the reliability and safety of energy storage batteries, reducing potential safety risks, and promoting the development and application of energy storage battery technology.

[0039] Step S103: integrating and analyzing the generated performance test data and structure test data of the energy storage battery to be tested, generating safety assessment data of the energy storage battery to be tested, and identifying the safety risk of the energy storage battery to be tested based on the pre-trained battery safety prediction model, and generating safety prediction data of the energy storage battery to be tested.

[0040] In one embodiment, the method of integrating and analyzing the generated performance test data and structure test data of the energy storage battery to be tested to generate the safety assessment data of the energy storage battery to be tested includes the following steps.

[0041] ① Perform data cleaning on the performance test data and structure test data of the energy storage battery to be tested, remove duplicate data and noise data, fill in missing values, and identify and correct outliers.

[0042] ② Integrate the performance test data and structure test data of the energy storage battery to be tested after cleaning to obtain the safety test data of the energy storage battery to be tested.

[0043] ③ Analyze the safety test data of the energy storage battery to be tested, and evaluate the electrochemical performance, thermal stability, mechanical stability and electrical safety performance of the energy storage battery to generate safety assessment data for the energy storage battery to be tested.

[0044] In one embodiment, the training method of the battery safety prediction model includes the following steps.

[0045] ① Obtain historical performance test data and historical structure test data of multiple energy storage batteries.

[0046] ② Clean the historical performance test data and the historical structure test data respectively, and integrate the cleaned data to obtain the historical safety test data of each energy storage battery.

[0047] ③ Perform multi-dimensional feature extraction on the historical safety detection data of each energy storage battery to generate safety feature data of each energy storage battery to construct a sample training set for each energy storage battery.

[0048] ④ Based on the sample training set, a deep learning network model is trained to construct a battery safety prediction model, learn the safety feature data of each energy storage battery in the sample training set, identify the potential safety risks of each energy storage battery, and generate corresponding safety prediction data.

[0049] The battery safety prediction model constructed by training the deep learning network model in this application can effectively learn the correlation between data, accurately predict the performance of energy storage batteries, identify potential safety risks of energy storage batteries and the causes of energy storage battery performance degradation, so as to improve the material composition and structural design of energy storage batteries, thereby improving various performance of energy storage batteries. In addition, the intelligent algorithm used in this application can effectively reduce unnecessary manpower and time costs and improve the efficiency of multi-factor safety assessment.

[0050] It should be noted that this application may use deep learning network models such as multi-layer perceptron, convolutional neural network, recurrent neural network, long short-term memory network, generative adversarial network and autoencoder, or other robot learning algorithms to build a battery safety prediction model. The specific model to be used can be set according to needs and is not limited in this application.

[0051] Step S104: Generate and output a safety assessment report of the energy storage battery to be tested based on the generated safety assessment data and safety prediction data.

[0052] The safety assessment report includes but is not limited to: the rated power, rated energy, charge and discharge efficiency, energy conversion efficiency, cycle life, calendar life, capacity retention rate, thermal stability, mechanical stability, electrical safety, structural defects and safety risks of the energy storage battery to be tested.

[0053] This application can not only provide detailed safety assessment reports to various stakeholders, enabling users to better understand the actual conditions of energy storage batteries, but also provide clear data basis, thereby enhancing the transparency and effectiveness of communication, providing comprehensive testing and evaluation for various performance and internal structures of energy storage batteries, helping to optimize the structural design and material selection of energy storage batteries, and being able to timely discover potential failures and safety risks of energy storage batteries, improve the stability and safety of energy storage batteries, and ultimately promote the development and application of energy storage battery technology.

[0054] In one embodiment, the multi-factor safety assessment method for energy storage batteries further includes: real-time monitoring of the multi-factor safety detection process of the energy storage battery to be tested, and generating corresponding process monitoring data.

[0055] Specifically, the simulated operating condition parameters of the energy storage battery to be tested are monitored in real time, and the quality control of the multi-factor safety detection process of the energy storage battery to be tested is performed; the safety status of the energy storage battery to be tested during the multi-factor safety detection process is judged, and when the energy storage battery to be tested is in an unsafe state, the multi-factor safety detection process is interrupted in time and an alarm signal is issued; the process monitoring data of the energy storage battery to be tested is recorded.

[0056] The process monitoring data includes, but is not limited to: the unique identification code of the energy storage battery to be tested, the start time, end time, test content, operating condition parameters, obtained performance test data and the safety status of the energy storage battery to be tested.

[0057] The purpose of the design of the present application in this embodiment is to timely discover and solve safety problems in the evaluation process by real-time monitoring of the multi-factor safety evaluation process, and to ensure and control the quality of the evaluation process, thereby ensuring the reliability and safety of the energy storage battery evaluation.

[0058] In order to better illustrate and describe the technical solution and technical advantages of the multi-factor safety assessment method of the energy storage battery, the present invention is combined with the following specific embodiments and Figure 2 Further details are given.

[0059] In a specific embodiment, if Figure 2 As shown, a multi-factor safety assessment of the energy storage battery to be tested may include the following steps.

[0060] Step S201: Simulate different operating conditions of the energy storage battery to be tested, and monitor the performance parameters of the energy storage battery to be tested under various operating conditions in real time, so as to perform electrochemical performance, mechanical performance, thermal stability performance, and long-term cycle performance tests on the energy storage battery to be tested, and generate performance test data of the energy storage battery to be tested.

[0061] Step S202: Analyze the internal structure of the energy storage battery to be tested by scanning electron microscopy technology and transmission electron microscopy technology to evaluate its material composition and structural characteristics; and detect the defects of damage and cracks in the microstructure of the energy storage battery by non-contact surface scanning technology; generate structural detection data of the energy storage battery to be tested.

[0062] Step S203: integrating and analyzing the generated performance test data and structure test data of the energy storage battery to be tested, generating safety assessment data of the energy storage battery to be tested, so as to comprehensively analyze the quality and performance of the energy storage battery.

[0063] Step S204: Based on the pre-trained battery safety prediction model, the safety risk of the energy storage battery to be tested is identified, and safety prediction data of the energy storage battery to be tested is generated.

[0064] Step S205: Generate a safety assessment report for the energy storage battery to be tested based on the generated safety assessment data and safety prediction data, and output it to various stakeholders.

[0065] Step S206: real-time monitoring of the multi-factor safety detection process of the energy storage battery to be tested, generating corresponding process monitoring data, so as to perform quality control and safety status assessment on the multi-factor safety detection process of the energy storage battery to be tested, and timely interrupt the multi-factor safety detection process and issue an alarm signal when the energy storage battery to be tested is in an unsafe state.

[0066] In the embodiments of the present application, words such as "first" and "second" are used to distinguish the same or similar items with substantially the same functions and effects. For example, the first XX and the second XX are only used to distinguish different XXs, and do not limit their order. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0067] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" represent examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0068] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers 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, c can be single or multiple.

[0069] like Figure 3 , a schematic diagram of a multi-factor safety assessment system 300 for energy storage batteries in an embodiment of the present invention is shown. The multi-factor safety assessment system 300 for energy storage batteries includes: a battery performance detection module 301, a battery structure detection module 302, a battery safety analysis module 303, and an assessment report output module 304.

[0070] The battery performance detection module 301 is used to simulate different operating conditions of the energy storage battery to be tested, and monitor the performance parameters of the energy storage battery to be tested under various operating conditions in real time, so as to perform multi-factor safety detection on the energy storage battery to be tested and generate performance detection data of the energy storage battery to be tested.

[0071] The battery structure detection module 302 is used to analyze the material and structure of the energy storage battery to be tested, identify the structural defects of the energy storage battery to be tested, and generate structural detection data of the energy storage battery to be tested.

[0072] The battery safety analysis module 303 is connected to the battery performance detection module 301 and the battery structure detection module 302, and is used to integrate and analyze the generated performance detection data and structure detection data of the energy storage battery to be tested, generate safety assessment data of the energy storage battery to be tested, and identify the safety risks of the energy storage battery to be tested based on the pre-trained battery safety prediction model, and generate safety prediction data of the energy storage battery to be tested.

[0073] The assessment report output module 304 is connected to the battery safety analysis module 303 and is used to generate and output a safety assessment report of the energy storage battery to be tested according to the generated safety assessment data and safety prediction data.

[0074] In one embodiment, the multi-factor safety assessment system 300 for energy storage batteries further includes: a process monitoring module 305. The process monitoring module 305 is connected to the battery performance detection module 301 and the battery structure detection module 302, and is used to monitor the multi-factor safety detection process of the energy storage battery to be tested in real time and generate corresponding process monitoring data.

[0075] It should be understood that the specific process of each module executing the above corresponding steps has been described in detail in the above method embodiment, and for the sake of brevity, it will not be repeated here.

[0076] It should also be understood that the division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0077] like Figure 4 As shown, a schematic diagram of the structure of a multi-factor security assessment terminal 400 for an energy storage battery in an embodiment of the present invention is shown. The multi-factor security assessment terminal 400 for the energy storage battery includes: at least one processor 401, a memory 402, at least one network interface 403 and a user interface 405. The various components in the terminal are coupled together through a bus system 404. It can be understood that the bus system 404 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 404 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, various buses are marked as bus systems in Figure X.

[0078] The user interface 405 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0079] It is understood that the memory 402 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable categories of memory.

[0080] The memory 402 in the embodiment of the present invention is used to store various categories of data to support the operation of the multi-factor security assessment terminal 400 of the energy storage battery. Examples of these data include: any executable program used to operate on the multi-factor security assessment terminal 400 of the energy storage battery, such as an operating system 4021 and an application 4022; the operating system 4021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 4022 may include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The multi-factor security assessment method for the energy storage battery provided in any one of the embodiments of the present invention may be included in the application 4022.

[0081] The method disclosed in the above embodiment of the present invention can be applied to the processor 401, or implemented by the processor 401. The processor 401 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 401 or the instruction in the form of software. The above processor 401 may be a general processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 401 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The general processor 401 may be a microprocessor or any conventional processor, etc. In combination with the steps of the accessory optimization method provided in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0082] In an exemplary embodiment, the multi-factor security assessment terminal 400 of the energy storage battery can be used to execute the above method by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD).

[0083] According to the method provided in the embodiments of the present application, the present application also provides a computer program product, which includes: computer program code, when the computer program code is run on a computer, the computer executes the multi-factor safety assessment method for the energy storage battery in any one of the method embodiments shown above.

[0084] According to the method provided in the embodiments of the present application, the present application also provides a computer-readable storage medium, which stores a program code. When the program code is run on a computer, the computer executes the multi-factor safety assessment method for the energy storage battery in any one of the method embodiments shown above.

[0085] The terms "component", "module", "system", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program and / or a computer. By way of illustration, both applications running on a computing device and a computing device can be components. One or more components may reside in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In addition, these components may be executed from various computer-readable media having various data structures stored thereon. Components may, for example, communicate through local and / or remote processes according to signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system and / or a network, such as the Internet interacting with other systems through signals).

[0086] Those of ordinary skill in the art will appreciate that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0087] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0088] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0089] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0091] In the above embodiments, the functions of each functional unit can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions (programs). When loading and executing computer program instructions (programs) on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media integrations. Available media may be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., high-density digital video discs (DVDs), or semiconductor media (e.g., solid state disks (SSDs)), etc.).

[0092] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0093] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0094] In summary, the present application provides a multi-factor safety assessment method, system and terminal for an energy storage battery, which performs multi-factor safety testing on the energy storage battery to be tested by simulating different operating conditions of the energy storage battery to be tested; analyzes the material and structure of the energy storage battery to be tested by microscopy technology to identify the structural defects of the energy storage battery to be tested; integrates and analyzes the generated data, and identifies the safety risks of the energy storage battery to be tested based on a pre-trained battery safety prediction model, thereby generating and outputting a safety assessment report for the energy storage battery to be tested; therefore, the present application can perform multi-factor safety testing and comprehensive evaluation on the electrochemical properties, mechanical properties, thermal stability, long-term cycle performance of the energy storage battery to be tested, as well as the material composition and internal structure of the energy storage battery to be tested, and help discover potential safety risks, providing an important reference for the optimization design and improvement of energy storage batteries.

[0095] Therefore, the present application effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

[0096] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A multi-factor safety assessment method for energy storage batteries, characterized in that: include: Simulate different working conditions of the energy storage battery to be tested, and monitor the performance parameters of the energy storage battery to be tested under various working conditions in real time, so as to perform multi-factor safety testing on the energy storage battery to be tested and generate performance test data of the energy storage battery to be tested; Analyze the material and structure of the energy storage battery to be tested, identify the structural defects of the energy storage battery to be tested, and generate structural detection data of the energy storage battery to be tested; Integrate and analyze the generated performance test data and structure test data of the energy storage battery to be tested, generate safety assessment data of the energy storage battery to be tested, and identify the safety risks of the energy storage battery to be tested based on the pre-trained battery safety prediction model, and generate safety prediction data of the energy storage battery to be tested; Based on the generated safety assessment data and safety prediction data, a safety assessment report for the energy storage battery to be tested is generated and output.

2. The multi-factor safety assessment method for energy storage batteries according to claim 1, characterized in that: Simulate different working conditions of the energy storage battery to be tested, and monitor the performance parameters of the energy storage battery to be tested under various working conditions in real time, so as to perform multi-factor safety testing on the energy storage battery to be tested. The methods for generating performance test data of the energy storage battery to be tested include: Simulate the vibration environment of the energy storage battery to be tested, make the energy storage battery to be tested vibrate according to the preset frequency and acceleration, and monitor the appearance change, temperature change, voltage change, charging curve, discharging curve and capacity retention rate of the energy storage battery to be tested in real time to obtain the vibration test data of the energy storage battery to be tested; Simulate the impact environment of the energy storage battery to be tested, make the energy storage battery to be tested fall from a predetermined height onto a hard ground, and monitor the appearance change, temperature change, voltage change, charging curve, discharging curve and capacity retention rate of the energy storage battery to be tested in real time to obtain the drop test data of the energy storage battery to be tested; Simulate the extrusion environment of the energy storage battery to be tested, apply a predetermined pressure to the energy storage battery to be tested, and monitor the appearance change, temperature change, voltage change, charging curve, discharge curve and capacity retention rate of the energy storage battery to be tested in real time to obtain the extrusion test data of the energy storage battery to be tested; Simulate the thermal stability of the energy storage battery under overcharge, over discharge, short circuit, high temperature and low temperature conditions respectively, and monitor the appearance change, temperature change, voltage change, charging curve, discharging curve and capacity retention rate of the energy storage battery in real time to obtain the thermal stability test data of the energy storage battery; Carry out long-term cycle charge and discharge test on the energy storage battery to be tested, and monitor the appearance change, temperature change, voltage change, charging curve, discharging curve and capacity retention rate of the energy storage battery to be tested in real time to obtain the cycle test data of the energy storage battery to be tested; Performance test data of the energy storage battery to be tested is generated based on the obtained vibration test data, drop test data, extrusion test data, thermal stability test data and cycle test data of the energy storage battery to be tested.

3. The multi-factor safety assessment method for energy storage batteries according to claim 1, characterized in that: Methods for analyzing the material and structure of the energy storage battery to be tested, identifying the structural defects of the energy storage battery to be tested, and generating structural test data of the energy storage battery to be tested include: Through scanning electron microscopy technology, a finely focused electron beam is used to scan the surface of the energy storage battery to be tested, and surface characteristic image data of the energy storage battery to be tested is obtained to analyze the surface morphology, roughness and microstructure of the energy storage battery to be tested; Through transmission electron microscopy technology, a high-speed electron beam is used to penetrate the energy storage battery to obtain the internal structure image data of the energy storage battery to be tested, so as to analyze the crystal structure, defects and nanoscale internal characteristics of the energy storage battery to be tested; Through non-contact surface scanning technology and in-situ non-destructive testing technology, the three-dimensional microscopic morphology data of the surface of the energy storage battery to be tested is obtained to analyze the microstructure and morphology of the surface of the energy storage battery material to be tested, and to identify damage and crack defects in the microstructure; Structural detection data of the energy storage battery to be tested is generated based on the obtained surface feature image data, internal structure image data and three-dimensional microscopic morphology data of the energy storage battery to be tested.

4. The multi-factor safety assessment method for energy storage batteries according to claim 1, characterized in that: The performance test data and structure test data of the energy storage battery to be tested are integrated and analyzed to generate the safety assessment data of the energy storage battery to be tested, including: The generated performance test data and structure test data of the energy storage battery to be tested are cleaned respectively, duplicate data and noise data are removed, missing values ​​are filled, and outliers are identified and corrected; Integrate the performance test data and structure test data of the energy storage battery to be tested after cleaning to obtain safety test data of the energy storage battery to be tested; Analyze the safety test data of the energy storage battery to be tested, and evaluate the electrochemical performance, thermal stability, mechanical stability and electrical safety performance of the energy storage battery to generate safety assessment data for the energy storage battery to be tested.

5. The multi-factor safety assessment method for energy storage batteries according to claim 1, characterized in that: The training method of the battery safety prediction model includes: Obtain historical performance test data and historical structure test data of multiple energy storage batteries; Cleaning the historical performance test data and the historical structure test data respectively, and integrating the cleaned data to obtain historical safety test data of each energy storage battery; Perform multi-dimensional feature extraction on the acquired historical safety detection data of each energy storage battery to generate safety feature data of each energy storage battery so as to construct a sample training set of each energy storage battery; A deep learning network model is trained based on the sample training set to construct a battery safety prediction model, the safety feature data of each energy storage battery in the sample training set is learned, the potential safety risks of each energy storage battery are identified, and corresponding safety prediction data are generated.

6. The multi-factor safety assessment method for energy storage batteries according to claim 1, characterized in that: The method further comprises: Monitor the multi-factor safety detection process of the energy storage battery to be tested in real time and generate corresponding process monitoring data.

7. The multi-factor safety assessment method for energy storage batteries according to claim 6, characterized in that: Real-time monitoring of the multi-factor safety detection process of the energy storage battery to be tested, and the methods of generating corresponding process monitoring data include: Real-time monitoring of the simulated operating condition parameters of the energy storage battery to be tested, and quality control of the multi-factor safety testing process of the energy storage battery to be tested; Determine the safety status of the energy storage battery to be tested during the multi-factor safety detection process. When the energy storage battery to be tested is in an unsafe state, interrupt the multi-factor safety detection process in time and send out an alarm signal; Record the process monitoring data of the energy storage battery to be tested; wherein the process monitoring data includes but is not limited to: the unique identification code of the energy storage battery to be tested, the start time, end time, detection content, operating condition parameters, obtained performance test data and the safety status of the energy storage battery to be tested.

8. The multi-factor safety assessment method for energy storage batteries according to claim 1, characterized in that: The safety assessment report includes but is not limited to: the rated power, rated energy, charge and discharge efficiency, energy conversion efficiency, cycle life, calendar life, capacity retention rate, thermal stability, mechanical stability, electrical safety, structural defects and safety risks of the energy storage battery to be tested.

9. A multi-factor safety assessment system for energy storage batteries, characterized in that: include: The battery performance detection module is used to simulate different working conditions of the energy storage battery to be tested, and monitor the performance parameters of the energy storage battery to be tested under various working conditions in real time, so as to perform multi-factor safety detection on the energy storage battery to be tested and generate performance detection data of the energy storage battery to be tested; The battery structure detection module is used to analyze the material and structure of the energy storage battery to be tested, identify the structural defects of the energy storage battery to be tested, and generate structural detection data of the energy storage battery to be tested; A battery safety analysis module, connected to the battery performance detection module and the battery structure detection module, for integrating and analyzing the generated performance detection data and structure detection data of the energy storage battery to be tested, generating safety assessment data of the energy storage battery to be tested, and identifying the safety risk of the energy storage battery to be tested based on a pre-trained battery safety prediction model, generating safety prediction data of the energy storage battery to be tested; The evaluation report output module is connected to the battery safety analysis module and is used to generate and output a safety evaluation report of the energy storage battery to be tested based on the generated safety evaluation data and safety prediction data.

10. A multi-factor safety assessment terminal for energy storage batteries, characterized in that: include: Processor and memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory so that the terminal implements the multi-factor safety assessment method for the energy storage battery according to any one of claims 1 to 8 when executing.

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