Intelligent battery detection system based on digital twinning

Abstract

The invention relates to the technical field of digital twinning, and particularly discloses an intelligent battery detection system based on digital twinning, which comprises battery detection equipment, an edge computing gateway, a cloud computing platform and a digital twinning visual interaction platform, the battery detection equipment is used for detecting charging and discharging data of a to-be-detected battery and uploading operation data of the battery detection equipment and the charging and discharging data of the to-be-detected battery to the edge computing gateway; the edge computing gateway is used for preprocessing the received data and uploading the preprocessed data to the cloud computing platform; and the cloud computing platform is used for analyzing and processing the preprocessed charging and discharging data of the to-be-detected battery to obtain a future state index of the to-be-detected battery, and then uploading the preprocessed operation data of the battery detection equipment and the future state index of the to-be-detected battery to the digital twin visual interaction platform for display. According to the invention, the real-time performance and accuracy of battery state prediction can be greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to an intelligent battery detection system based on digital twin. Background Art

[0002] With the development of intelligent technology, batteries are increasingly used, particularly in new energy vehicles, energy storage systems, and portable devices. Battery performance directly impacts the user experience and lifespan of these devices. Therefore, efficiently and accurately detecting battery status has become a pressing challenge in battery management. Traditional battery testing techniques rely primarily on manual and single-device testing. However, these technologies have limitations and are unable to meet modern demands for efficient, intelligent, and real-time monitoring.

[0003] For example, patent CN118566734A, "Digital Twin Battery Monitoring and Prediction System, Method, Device, and Medium," discloses a digital twin battery monitoring and prediction system consisting of a monitoring module, a twin module, and a maintenance module. The monitoring module monitors battery pack operating data in real time and transmits it to the twin module. The twin module receives this data, assesses and predicts the battery pack's health status, and triggers the maintenance module's automated maintenance process based on the prediction results.

[0004] For example, patent CN111581850A "A full-cycle power battery management system using digital twin technology" discloses a full-cycle power battery management system using digital twin technology. It adopts cloud-edge collaborative technology, and the edge and cloud sides use algorithms with different long and short time scales respectively. The battery aging status judgment results based on the cloud twin model are used to assist in the classification and reorganization of batteries in different aging states.

[0005] Existing detection systems, including those described in the aforementioned patents, lack sufficient interactive visualization capabilities to meet user needs for real-time battery status monitoring and operational management. While modern battery detection systems can collect large amounts of data through sensors and upload them to cloud platforms for storage and analysis, traditional battery management systems often lack effective visualization capabilities. Users can only view battery status data via text or tables, lacking an intuitive, interactive graphical interface. This presentation method not only makes it difficult for users to quickly identify potential battery failures, but also easily causes data overload, making it difficult for users to extract useful information from the large amount of data.

[0006] Furthermore, existing battery management systems lack the ability to coordinate edge computing with cloud computing. While edge computing technology can perform data preprocessing and preliminary analysis on the device side, the results are often crude and inaccurate due to the limitations of edge computing capabilities. Consequently, battery management systems often need to transmit large amounts of raw data to the cloud for processing, which then conducts in-depth analysis and status prediction. Summary of the Invention

[0007] In response to the defects and shortcomings in the existing technology, the present invention provides an intelligent battery detection system based on digital twins, which can greatly improve the real-time and accuracy of battery status prediction.

[0008] As a first aspect of the present invention, a digital twin-based intelligent battery detection system is provided, wherein the digital twin-based intelligent battery detection system includes a battery detection device, an edge computing gateway, a cloud computing platform, and a digital twin visualization interaction platform; wherein,

[0009] The battery testing device is configured to receive the parsed charge and discharge control instructions issued by the edge computing gateway to control the charge and discharge process of the battery to be tested, detect the charge and discharge data of the battery to be tested during the charge and discharge process, and upload the operating data of the battery testing device itself and the charge and discharge data of the battery to be tested to the edge computing gateway;

[0010] The edge computing gateway is used to preprocess the operating data of the battery testing device itself and the charge and discharge data of the battery to be tested, and upload the preprocessed battery testing device operating data and the preprocessed charge and discharge data of the battery to be tested to the cloud computing platform; wherein the edge computing gateway also parses the charge and discharge control instructions issued by the digital twin visualization interactive platform, and sends the parsed charge and discharge control instructions to the battery testing device;

[0011] The cloud computing platform is used to store the preprocessed battery testing equipment operating data and the preprocessed charge and discharge data of the battery to be tested, and to analyze and process the preprocessed charge and discharge data of the battery to be tested to obtain real-time status indicators and future status indicators of the battery to be tested, and then upload the preprocessed battery testing equipment operating data and the real-time status indicators and future status indicators of the battery to be tested to the digital twin visualization interaction platform;

[0012] The digital twin visualization interaction platform is used to display the pre-processed battery detection equipment operating data and the real-time status indicators and future status indicators of the battery to be detected to the user; wherein, the digital twin visualization interaction platform is also used to send the charge and discharge control instructions issued by the user in the human-computer interaction interface to the edge computing gateway.

[0013] Furthermore, the battery testing device will be equipped with corresponding host computer control software, through which the user can read the operating data of the battery testing device itself and the charge and discharge data of the battery to be tested, set the charge and discharge parameters, and issue charge and discharge control instructions to the battery testing device; or, the user can directly establish a connection with the battery testing device through the communication protocol supported by the battery testing device, and then write the protocol parsing and data parsing programs by himself according to the communication protocol document provided by the manufacturer to realize data reading and charge and discharge control instruction issuance for the battery testing device.

[0014] Furthermore, the edge computing gateway includes a protocol conversion module, a data acquisition and cleaning module, a control instruction parsing module and a first data interface module, wherein:

[0015] The protocol conversion module is configured to establish communication with the battery testing device according to a communication protocol supported by the battery testing device, convert the operating data of the battery testing device itself and the charge and discharge data of the battery to be tested obtained from the battery testing device into data in a unified communication protocol and data format, and send the converted operating data of the battery testing device itself and the converted charge and discharge data of the battery to be tested to the data acquisition and cleaning module;

[0016] The data acquisition and cleaning module is used to clean the converted battery testing device operating data and the converted charge and discharge data of the battery to be tested, and then send the cleaned battery testing device operating data and the cleaned charge and discharge data of the battery to be tested to the first data interface module;

[0017] The first data interface module is used to send the operating data of the battery detection device after cleaning and the charge and discharge data of the battery to be tested after cleaning to the cloud computing platform; wherein, the first data interface module is also used to receive the charge and discharge control instructions issued by the digital twin visualization interaction platform, and send the charge and discharge control instructions to the control instruction parsing module;

[0018] The control instruction parsing module is used to parse the charge and discharge control instructions and send the parsed charge and discharge control instructions to the protocol conversion module; wherein, the protocol conversion module converts the parsed charge and discharge control instructions into an instruction format supported by the battery detection device, and sends the converted charge and discharge control instructions to the battery detection device.

[0019] Furthermore, the cloud computing platform includes a second data interface module, a data cache module, a persistent storage module and a battery twin module; wherein,

[0020] The second data interface module is used to receive the pre-processed battery detection equipment operation data and the pre-processed charge and discharge data of the battery to be detected uploaded by the edge computing gateway;

[0021] The data cache module is used to store the currently pre-processed battery detection equipment operation data and the currently pre-processed charge and discharge data of the battery to be detected;

[0022] The persistent storage module is used to store the historical pre-processed battery detection equipment operation data and the historical pre-processed battery charge and discharge data to be detected;

[0023] The battery twin module is used to analyze and process the preprocessed charge and discharge data of the battery to be detected to obtain the real-time status indicator and future status indicator of the battery to be detected, and then send the real-time status indicator and future status indicator of the battery to be detected to the data cache module and the persistent storage module respectively; wherein, the preprocessed battery detection equipment operation data and the real-time status indicator and future status indicator of the battery to be detected are uploaded to the digital twin visualization interaction platform through the second data interface module through the persistent storage module.

[0024] Furthermore, the battery twin module includes multiple battery twin models, wherein each battery twin model is responsible for predicting a certain status indicator of the battery to be tested;

[0025] Among them, the battery twin model is constructed based on the historical data in the persistent storage module and can be updated based on the current data in the data cache module. The battery twin module can not only predict the real-time status indicators of the battery to be tested, but also predict the future status indicators of the battery to be tested.

[0026] Furthermore, the digital twin visualization interactive platform includes a third data interface module, a three-dimensional model display module, a status data display module, an alarm notification module and a human-computer interaction module; wherein,

[0027] The third data interface module is configured to receive the pre-processed battery testing equipment operation data and the real-time status indicator and future status indicator of the battery to be tested uploaded by the cloud computing platform;

[0028] The three-dimensional model display module is used to provide the user with a three-dimensional view of the battery testing device, the battery to be tested, and the entire testing environment, and allow the user to view the battery testing device, the battery to be tested, and the entire testing environment by rotating, zooming in, and zooming out;

[0029] The status data display module is used to display a three-dimensional view of the battery to be tested, the pre-processed battery testing equipment operation data, and the real-time status indicators and future status indicators of the battery to be tested to the user;

[0030] The alarm notification module is configured to issue an alarm to a user through the human-computer interaction module when an abnormality occurs in the real-time status indicator or future status indicator of the battery to be tested;

[0031] The human-computer interaction module is used to send the charge and discharge control instructions issued by the user in the human-computer interaction interface to the edge computing gateway through the third data interface module.

[0032] Furthermore, the charge and discharge control instructions include equipment stop / reset, data acquisition frequency, charge and discharge step parameters and equipment protection parameters.

[0033] Furthermore, the battery testing equipment is a battery capacity tester, a cycle life tester, a chemical composition capacity cabinet or a handheld internal resistance tester.

[0034] Furthermore, the real-time status indicators of the battery to be detected include voltage, current and internal resistance, and the future status indicators of the battery to be detected include internal resistance, remaining charge and discharge time, SOH, potential faults and remaining life.

[0035] As a second aspect of the present invention, a smart battery detection method based on digital twins is provided, and the smart battery detection method based on digital twins comprises the following steps:

[0036] Step 1: Data collection and transmission between battery testing equipment and edge computing gateway

[0037] First, the battery testing device establishes a communication connection with the protocol conversion module in the edge computing gateway; the battery testing device continuously and uninterruptedly detects the charge and discharge data of the battery to be tested during the charging and discharging process through the built-in sensor, and the protocol conversion module in the edge computing gateway converts the battery testing device's own operating data and the charge and discharge data of the battery to be tested obtained from the battery testing device into data with a unified communication protocol and data format, and sends the converted battery testing device's own operating data and the converted charge and discharge data of the battery to be tested to the data acquisition and cleaning module in the edge computing gateway; the data acquisition and cleaning module cleans the converted battery testing device's own operating data and the converted charge and discharge data of the battery to be tested;

[0038] Step 2: Upload data to the cloud computing platform

[0039] After the data acquisition and cleaning module completes data cleaning, it sends the cleaned battery detection device's own operating data and the cleaned battery charge and discharge data to the first data interface module in the edge computing gateway. The first data interface module of the edge computing gateway, acting as a client, actively sends a request to the second data interface module in the cloud computing platform to upload the cleaned battery detection device's own operating data and the cleaned battery charge and discharge data to the cloud computing platform.

[0040] Step 3: Data Storage and Analysis

[0041] After receiving the real-time data uploaded by the edge computing gateway, the second data interface module in the cloud computing platform writes the data into the data cache module and the persistent storage module in the cloud computing platform in sequence for subsequent calls;

[0042] Step 4: Battery status prediction

[0043] The battery twin module in the cloud computing platform reads the currently preprocessed charge and discharge data of the battery to be tested from the data cache module, inputs it into the battery twin model for model update, and then writes the battery status indicator to be tested predicted after the model update back to the data cache module and the persistent storage module;

[0044] Step 5: 3D visualization

[0045] The digital twin visualization interactive platform regularly obtains the latest predicted battery status indicator to be tested through the second data interface module in the cloud computing platform, and synchronizes it to the three-dimensional model display module in the digital twin visualization interactive platform to display it to the user;

[0046] Step 6: User interacts with the digital twin visualization interactive platform

[0047] The status data display module in the digital twin visualization interactive platform displays various status indicators of the battery to be tested, and charging and discharging control instructions are issued by clicking virtual buttons on the human-computer interaction interface;

[0048] Step 7: The edge computing gateway receives the charge and discharge control instructions and sends them to the battery detection device

[0049] The charge and discharge control instructions issued by the user are passed to the edge computing gateway through the digital twin visualization interactive platform. The edge computing gateway parses the received charge and discharge control instructions, and converts the parsed charge and discharge control instructions into the instruction format supported by the battery detection device according to the communication protocol of the battery detection device, and sends them to the battery detection device.

[0050] The digital twin-based intelligent battery detection system provided by the present invention has the following advantages:

[0051] (1) Significant improvement in real-time performance and accuracy: The present invention realizes real-time monitoring and dynamic prediction of battery status by introducing digital twin technology. Traditional battery detection methods usually rely on offline data analysis and static models, resulting in delayed prediction results and low accuracy. This system can significantly improve the real-time performance and accuracy of battery status prediction by building a battery twin model in a cloud computing platform and continuously updating the model based on real-time data;

[0052] (2) Significant optimization of user interaction experience: This system provides users with an intuitive and immersive battery status monitoring experience through a digital twin visualization interactive platform. The platform integrates functions such as three-dimensional model display, status data visualization, alarm notification, and human-computer interaction, allowing users to view the three-dimensional model of the battery and its detection equipment by rotating, zooming in, and zooming out, and to grasp the key indicators of the battery in real time. At the same time, the alarm notification function can promptly alert the user when the battery status is abnormal, ensuring that the user can quickly take countermeasures. This interactive design not only improves the user's operational convenience, but also enhances the system's practicality and user experience;

[0053] (3) System flexibility and scalability: This system is designed with a universal protocol conversion module that is compatible with the communication protocols and data formats of various battery testing equipment. This design enables the system to flexibly access different types of testing equipment and adapt to diverse testing needs. In addition, the modular design of the cloud computing platform (including modules such as data caching, persistent storage, and battery twins) facilitates the functional expansion of the system. Users can add or optimize battery twin models according to actual needs to further enhance the system's predictive capabilities and application scope. This flexibility and scalability design enables the present invention to adapt to the rapid development of battery testing technology and meet the needs of different application scenarios;

[0054] (4) Efficient utilization of resources: Traditional battery detection systems usually transmit all data to the cloud for processing, which not only increases the burden of network transmission, but may also cause data processing delays. The present invention can effectively reduce the amount of data uploaded to the cloud by implementing data collection, cleaning and preprocessing functions in the edge computing gateway, thereby reducing the load on the cloud computing platform and network transmission pressure. At the same time, the edge computing gateway can also locally parse and issue user instructions, further improving the system's response speed and operating efficiency. This hierarchical computing design concept makes the present invention more advantageous when processing large-scale battery detection data. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present invention, but do not constitute a limitation of the present invention.

[0056] Figure 1 This is an architectural diagram of the digital twin-based intelligent battery detection system provided by the present invention.

[0057] Figure 2 This is a workflow diagram of the digital twin-based intelligent battery detection system provided by the present invention. DETAILED DESCRIPTION

[0058] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0059] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions 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 embodiments described are only 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 efforts should fall within the scope of protection of the present invention.

[0060] It should be noted that the terms "first," "second," and the like in the specification and claims of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate for the embodiments of the present invention described herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatuses.

[0061] In this embodiment, a smart battery detection system based on digital twin is provided. Figure 1-2 As shown, the digital twin-based intelligent battery detection system includes battery detection equipment, edge computing gateway, cloud computing platform and digital twin visualization interaction platform; wherein,

[0062] The battery testing device is configured to receive the parsed charge and discharge control instructions issued by the edge computing gateway to control the charge and discharge process of the battery to be tested, detect the charge and discharge data of the battery to be tested during the charge and discharge process, and upload the operating data of the battery testing device itself and the charge and discharge data of the battery to be tested to the edge computing gateway;

[0063] It should be noted that the charge and discharge control instructions include equipment stop / reset, data acquisition frequency, charge and discharge step parameters, equipment protection parameters, etc.

[0064] It should be noted that the battery testing equipment is the starting point of the intelligent battery testing system. According to the type of battery to be tested, the testing function, etc., the battery testing equipment can be divided into battery capacity testers, cycle life testing equipment, chemical composition cabinets, handheld internal resistance testers, etc., but no matter which type of equipment is directly connected to the battery to be tested, receiving charge and discharge control instructions to perform different operations such as constant current charge and discharge, constant voltage charge and discharge, and shelving on the battery to be tested. At the same time, the charge and discharge data of the battery to be tested during the charging and discharging process are collected through sensors, among which the charge and discharge data include voltage, current, temperature and other data.

[0065] The edge computing gateway is used to preprocess the operating data of the battery testing device itself and the charge and discharge data of the battery to be tested, and upload the preprocessed battery testing device operating data and the preprocessed charge and discharge data of the battery to be tested to the cloud computing platform; wherein the edge computing gateway also parses the charge and discharge control instructions issued by the digital twin visualization interactive platform, and sends the parsed charge and discharge control instructions to the battery testing device;

[0066] The cloud computing platform is used to store the preprocessed battery testing equipment operating data and the preprocessed charge and discharge data of the battery to be tested, and to analyze and process the preprocessed charge and discharge data of the battery to be tested to obtain real-time status indicators and future status indicators of the battery to be tested, and then upload the preprocessed battery testing equipment operating data and the real-time status indicators and future status indicators of the battery to be tested to the digital twin visualization interaction platform;

[0067] It should be noted that the real-time status indicators of the battery to be tested include voltage, current and internal resistance, and the future status indicators of the battery to be tested include internal resistance, remaining charge and discharge time, SOH, potential faults and remaining life.

[0068] The digital twin visualization interaction platform is used to display the pre-processed battery detection equipment operating data and the real-time status indicators and future status indicators of the battery to be detected to the user; wherein, the digital twin visualization interaction platform is also used to send the charge and discharge control instructions issued by the user in the human-computer interaction interface to the edge computing gateway.

[0069] Preferably, the battery testing device will be equipped with corresponding host computer control software, through which the user can read the operating data of the battery testing device itself (connection status, power, energy consumption, etc.) and the charge and discharge data of the battery to be tested (voltage, current, temperature, etc.), set the charge and discharge parameters, and issue charge and discharge control instructions to the battery testing device; alternatively, the user can directly establish a connection with the battery testing device through the communication protocol supported by the battery testing device (CAN, Modbus-TCP, Modbus-RTU, TCP / IP, etc.), and then write the protocol parsing and data parsing programs according to the communication protocol documentation provided by the manufacturer to realize data reading and charge and discharge control instruction issuance for the battery testing device.

[0070] In an embodiment of the present invention, the battery detection device communicates with the edge computing gateway to provide detection device and battery data to the entire detection system, and at the same time controls the battery charging and discharging process according to instructions issued by the upstream system or user.

[0071] Preferably, the edge computing gateway is the data transfer station of the entire detection system, which undertakes the data transmission task of the battery detection equipment and performs certain data preprocessing and control instruction parsing work on the edge computing gateway. The edge computing gateway includes a protocol conversion module, a data acquisition and cleaning module, a control instruction parsing module and a first data interface module, wherein,

[0072] The protocol conversion module is configured to establish communication with the battery testing device according to a communication protocol supported by the battery testing device, convert the operating data of the battery testing device itself and the charge and discharge data of the battery to be tested obtained from the battery testing device into data in a unified communication protocol and data format, and send the converted operating data of the battery testing device itself and the converted charge and discharge data of the battery to be tested to the data acquisition and cleaning module;

[0073] It should be noted that, as mentioned above, the battery detection equipment supports a variety of communication protocols and data formats. The detection system of the present invention also has a set of pre-defined communication protocols and data formats. In order to make the detection system of the present invention applicable to various types of detection equipment at the same time, the edge computing gateway needs to use the protocol conversion module to convert the internal data and instructions of the detection system of the present invention with the internal data and instructions of the battery detection equipment so that the two can communicate normally.

[0074] The data acquisition and cleaning module is used to clean the converted battery testing device operating data and the converted charge and discharge data of the battery to be tested, and then send the cleaned battery testing device operating data and the cleaned charge and discharge data of the battery to be tested to the first data interface module;

[0075] It should be noted that the data acquisition and cleaning module reads the data in the battery testing equipment at a predetermined frequency. Since the raw data collected from the battery testing equipment may contain noise or abnormal data, the data needs to be verified, cleaned, and pre-processed before the next step of processing and transmission to ensure the quality of the uploaded data and reduce the computing pressure of the cloud computing platform.

[0076] The first data interface module is used to send the operating data of the battery detection device after cleaning and the charge and discharge data of the battery to be tested after cleaning to the cloud computing platform; wherein, the first data interface module is also used to receive the charge and discharge control instructions issued by the digital twin visualization interaction platform, and send the charge and discharge control instructions to the control instruction parsing module;

[0077] It should be noted that the edge computing gateway communicates with other modules of the detection system of the present invention through the first data interface module. The first data interface module simultaneously assumes the role of server and client, that is, the edge computing gateway can actively initiate data to other modules as a client, such as reporting real-time data to the cloud computing platform; it can also act as a server to receive requests from other modules, such as receiving operation instructions from the digital twin visualization interaction platform.

[0078] The control instruction parsing module is used to parse the charge and discharge control instructions and send the parsed charge and discharge control instructions to the protocol conversion module; wherein, the protocol conversion module converts the parsed charge and discharge control instructions into an instruction format supported by the battery detection device, and sends the converted charge and discharge control instructions to the battery detection device.

[0079] Specifically, the core module of the edge computing gateway is the protocol conversion module, and its core function is to establish communication with the battery detection equipment according to the communication protocol, and to encapsulate the data acquisition and control instructions of the detection equipment into a data interface supported by the internal communication protocol defined in advance by the detection system of the present invention. In this way, other modules or sub-modules within the detection system of the present invention no longer need to pay attention to the model of the battery detection equipment and the communication protocol it supports.

[0080] The following is an example of an edge computing gateway in the detection system of the present invention. Using the building block edge gateway M300 of the human Internet of Things, it is connected to the battery module detection equipment (only one detection channel) that supports the Modbus-RTU communication protocol and the three-phase smart meter in the battery module detection equipment through the RS485 interface. It can read the real-time power and total energy consumption data in the three-phase smart meter, the real-time voltage and current of the battery module detection equipment detection channel in real time, and can also send constant current charging / discharging, constant voltage charging / discharging, and stop detection instructions to the battery module detection equipment. These data acquisition interfaces and instruction sending interfaces are based on the protocol conversion function of the M300 gateway and are encapsulated as data nodes and method nodes in the pre-designed OPC UA communication protocol server. The cloud computing platform and the digital twin visualization interaction platform can obtain real-time data or send control instructions by calling the corresponding nodes in the OPC UA server.

[0081] Preferably, the cloud computing platform is the core computing module of the detection system, responsible for storing, analyzing and processing the data uploaded by the edge computing gateway. The cloud computing platform includes a second data interface module, a data cache module, a persistent storage module and a battery twin module; wherein,

[0082] The second data interface module is used to receive the pre-processed battery detection equipment operation data and the pre-processed charge and discharge data of the battery to be detected uploaded by the edge computing gateway;

[0083] It should be noted that the cloud computing platform communicates with other modules of the detection system of the present invention through the second data interface module, such as receiving real-time data uploaded by the edge computing gateway, and sending the real-time status indicators of the battery predicted by the battery twin module to the digital twin visualization interaction platform.

[0084] The data cache module is used to store the currently pre-processed battery detection equipment operation data and the currently pre-processed charge and discharge data of the battery to be detected;

[0085] It should be noted that the data cache module is used to store real-time data within the recent period to ensure data timeliness and read and write efficiency.

[0086] The persistent storage module is used to store the historical pre-processed battery detection equipment operation data and the historical pre-processed battery charge and discharge data to be detected;

[0087] It should be noted that the data persistence storage module is responsible for preserving historical data for a long time to facilitate subsequent data analysis, backtracking, and troubleshooting.

[0088] The battery twin module is used to analyze and process the preprocessed charge and discharge data of the battery to be detected to obtain the real-time status indicator and future status indicator of the battery to be detected, and then send the real-time status indicator and future status indicator of the battery to be detected to the data cache module and the persistent storage module respectively; wherein, the preprocessed battery detection equipment operation data and the real-time status indicator and future status indicator of the battery to be detected are uploaded to the digital twin visualization interaction platform through the second data interface module through the persistent storage module.

[0089] Specifically, the battery twin module includes multiple battery twin models, wherein each battery twin model is responsible for predicting a certain status indicator of the battery to be tested;

[0090] Among them, the battery twin module is responsible for digital twin modeling and state prediction of the battery. The battery twin model is constructed based on the historical data in the persistent storage module and can be updated based on the current data in the data cache module. With the help of the real-time update mechanism of the battery twin model, the battery twin module can not only predict the real-time status indicators (voltage, current, internal resistance, etc.) of the battery to be tested, but also predict the future status indicators of the battery to be tested (battery internal resistance, SOH, remaining charge and discharge time, potential faults, remaining life, etc.).

[0091] A specific embodiment of the battery twin model in the battery twin module is described below. The remaining state of health (SOH) of a battery is an important indicator during battery use. A SOH prediction model can be constructed based on the long short term memory (LSTM) neural network in deep learning. The input of this SOH prediction model is the partial voltage and current curves during the battery's charge and discharge process, as well as the battery's SOH at the previous moment. The output is the remaining battery capacity at the end of the partial voltage and current curves, as well as the voltage and current curves over a subsequent period of time.

[0092] Before starting the prediction, the SOH prediction model needs to be trained based on existing historical data so that it can learn the general mapping rules between voltage, current curves and SOH from a large amount of data. In the early stages of the battery testing process, due to the short testing time, sufficient voltage and current curve data has not yet been accumulated. At this time, the SOH prediction model can only rely on the general rules learned from historical data to predict the SOH of the current battery to a large extent. As the battery testing time increases, the personalized characteristics of the specific battery contained in the voltage / current curve data will gradually increase. Therefore, if the real-time voltage / current curve data at a short interval is input into the SOH prediction model at fixed intervals, the SOH prediction model will gradually learn the mapping rules of the specific battery, and the predicted SOH and voltage / current curve will become more and more consistent with the actual situation of the current tested battery.

[0093] Preferably, the digital twin visualization interactive platform is developed based on 3D engine tools such as Unity 3D. By integrating 3D models and interactive visualization functions, a virtual scene with a one-to-one correspondence with real detection equipment and batteries is constructed. In the virtual scene, the real-time status indicators of the battery and the operating status of the battery detection equipment are displayed to the user, allowing the user to intuitively monitor the battery detection process and perform operation management. The digital twin visualization interactive platform includes a third data interface module, a 3D model display module, a status data display module, an alarm notification module, and a human-computer interaction module; wherein,

[0094] The third data interface module is configured to receive the pre-processed battery testing equipment operation data and the real-time status indicator and future status indicator of the battery to be tested uploaded by the cloud computing platform;

[0095] It should be noted that the digital twin visualization interactive platform communicates with other modules of the detection system of the present invention through the third data interface module, such as receiving the real-time battery status indicators sent by the cloud computing platform for display, and sending the operation instructions issued by the user in the interactive interface to the edge computing gateway.

[0096] The three-dimensional model display module is used to provide the user with a three-dimensional view of the battery testing device, the battery to be tested, and the entire testing environment, and allows the user to view the battery testing device, the battery to be tested, and the entire testing environment by rotating, zooming in, and out, thereby improving the user's monitoring experience;

[0097] The status data display module is used to combine with the three-dimensional model display module to display to the user a three-dimensional view of the battery to be tested, the pre-processed battery testing equipment operating data, and the real-time status indicators and future status indicators of the battery to be tested. This allows the user to not only see the battery's appearance and structure, but also display key status indicators such as the battery's voltage, current, temperature, and SOH in real time, helping the user to more clearly understand the battery's status change trend during the testing process and facilitate maintenance decision-making.

[0098] The alarm notification module is configured to issue an alarm to a user through the human-computer interaction module when an abnormality occurs in the real-time status indicator or future status indicator of the battery to be tested;

[0099] It should be noted that the alarm notification module monitors the battery and device status in real time, alerting users when an anomaly occurs. When the system detects an abnormal battery status (such as excessive voltage, excessive temperature, or abnormal internal resistance), the alarm notification module promptly alerts the user through the human-computer interface, allowing the user to take necessary countermeasures.

[0100] The human-computer interaction module is used to send the charge and discharge control instructions issued by the user in the human-computer interaction interface to the edge computing gateway through the third data interface module.

[0101] It's important to note that users interact with the platform through this module. They can click buttons on the human-computer interface to view real-time data for specific objects or send commands, such as stopping testing or setting charge and discharge parameters. All commands are forwarded to the edge computing gateway, which then sends them to the battery testing device.

[0102] A specific embodiment of the digital twin visualization interaction platform is described below. The digital twin visualization interaction platform is a battery capacity rapid detection visualization platform developed based on Unity 3D. The platform places scenes and three-dimensional models of battery capacity cabinets that are consistent with the real-world appearance and location. On this basis, various charts reflecting the status information of the current battery detection process (such as the detection process used and the number of batteries detected) are integrated. There is also a battery model on the three-dimensional model of the capacity cabinet. Through the color, light and shadow effects of the battery model in the interactive platform, users can more intuitively identify the current charge and discharge status of the battery.

[0103] As mentioned above, the interactive platform can also obtain real-time data of current physical objects such as sub-capacity cabinets and batteries. When the battery on the sub-capacity cabinet is manually removed, the battery model at the corresponding position of the sub-capacity cabinet model on the interactive platform will also disappear, maintaining one-to-one synchronization.

[0104] By collecting real-time data, combining it with historical data and the twin model, the interactive platform can also calculate the current capacity of the battery in real time and display it on the interactive platform. Through the collaboration of various modules such as battery testing equipment, edge computing gateway, cloud computing platform, battery twin module, and three-dimensional visualization interactive platform, the digital twin intelligent battery testing system proposed in this invention can achieve real-time monitoring of battery testing equipment and dynamic prediction of battery status indicators to be tested. It can also conduct human-computer interaction through three-dimensional visualization, improving the real-time performance, accuracy, and user experience of the battery testing process.

[0105] In another embodiment of the present invention, a smart battery detection method based on digital twins is provided. The smart battery detection method based on digital twins includes the following steps:

[0106] Before the detection system of the present invention works, it is necessary to build battery twin models based on existing historical data and deploy these models in advance to the cloud computing platform.

[0107] Step 1: Data collection and transmission between battery testing equipment and edge computing gateway

[0108] First, the battery testing device establishes a communication connection with the protocol conversion module in the edge computing gateway; the battery testing device continuously and uninterruptedly detects the charge and discharge data (voltage, current, temperature, etc.) of the battery to be tested during the charge and discharge process through the built-in sensor. The protocol conversion module in the edge computing gateway converts the battery testing device's own operating data and the charge and discharge data of the battery to be tested obtained from the battery testing device into data with a unified communication protocol and data format, and sends the converted battery testing device's own operating data and the converted charge and discharge data of the battery to be tested to the data acquisition and cleaning module in the edge computing gateway; the data acquisition and cleaning module cleans the converted battery testing device's own operating data and the converted charge and discharge data of the battery to be tested;

[0109] Step 2: Upload data to the cloud computing platform

[0110] After the data acquisition and cleaning module completes data cleaning, it sends the cleaned battery detection device's own operating data and the cleaned battery charge and discharge data to the first data interface module in the edge computing gateway. The first data interface module of the edge computing gateway, acting as a client, actively sends a request to the second data interface module in the cloud computing platform to upload the cleaned battery detection device's own operating data and the cleaned battery charge and discharge data to the cloud computing platform.

[0111] Step 3: Data Storage and Analysis

[0112] After receiving the real-time data uploaded by the edge computing gateway, the second data interface module in the cloud computing platform writes the data into the data cache module and the persistent storage module in the cloud computing platform in sequence for subsequent calls;

[0113] Step 4: Battery status prediction

[0114] The battery twin module in the cloud computing platform reads the currently preprocessed charge and discharge data of the battery to be tested from the data cache module, inputs it into the battery twin model for model update, and then writes the battery status indicator to be tested predicted after the model update back to the data cache module and the persistent storage module;

[0115] Step 5: 3D visualization

[0116] The digital twin visualization interactive platform regularly obtains the latest predicted battery status indicator to be tested through the second data interface module in the cloud computing platform, and synchronizes it to the three-dimensional model display module in the digital twin visualization interactive platform to display it to the user;

[0117] Step 6: User interacts with the digital twin visualization interactive platform

[0118] On-site users can interact with the digital twin visualization interactive platform in real time. The status data display module in the digital twin visualization interactive platform displays various status indicators of the battery to be tested, and charge and discharge control instructions can be issued by clicking virtual buttons on the human-computer interaction interface.

[0119] Step 7: The edge computing gateway receives the charge and discharge control instructions and sends them to the battery detection device

[0120] When the user issues an operation instruction through the digital twin visualization interactive platform, the charge and discharge control instruction issued by the user is passed to the edge computing gateway through the digital twin visualization interactive platform. The edge computing gateway parses the received charge and discharge control instruction and converts the parsed charge and discharge control instruction into the instruction format supported by the battery detection device according to the communication protocol of the battery detection device, and sends it to the battery detection device.

[0121] By looping through steps 1 to 7 above, a closed-loop feedback mechanism can be implemented in which the user can grasp the status of the detection system in real time and the detection system can respond to user operations in a timely manner, ensuring the flexibility and controllability of the battery detection process and effectively improving the accuracy and efficiency of battery detection.

[0122] In this embodiment of the present invention, the edge computing gateway features a multifunctional design: it not only handles data collection and transmission but also performs protocol conversion, data cleaning, and command parsing. The protocol conversion module enables the system to be compatible with the communication protocols of various battery testing devices; the data collection and cleaning module denoises and preprocesses raw data to ensure data quality; and the control command parsing module enables the system to efficiently parse and issue user commands. The design and implementation of these functions are crucial components of the present invention.

[0123] In an embodiment of the present invention, the modular architecture of the cloud computing platform: The modular design of the cloud computing platform is another innovation of the present invention. The platform includes core modules such as data cache, persistent storage and battery twins, which can realize efficient storage, analysis and modeling of data. The data cache module ensures the timeliness and read and write efficiency of the data; the persistent storage module provides a guarantee for the long-term preservation of historical data; and the battery twin module realizes real-time monitoring and dynamic prediction of the battery status. The collaborative work of these modules enables the cloud computing platform to perform well in processing large-scale battery detection data.

[0124] In an embodiment of the present invention, a digital twin visualization and interactive platform is designed and applied: The digital twin visualization and interactive platform of the present invention provides users with an intuitive battery status monitoring experience by integrating three-dimensional model display, status data visualization, and human-computer interaction functions. The platform can display the appearance and structure of the battery and its detection equipment in a three-dimensional view and display the key status indicators of the battery in real time. The alarm notification function can promptly alert the user when the battery status is abnormal, ensuring that the user can take swift countermeasures. The human-computer interaction function allows users to operate the management system through the interface, issue control instructions, or view specific data.

[0125] In an embodiment of the present invention, a closed-loop feedback mechanism of multi-module collaboration: The present invention realizes a closed-loop feedback mechanism through the collaborative work of battery detection equipment, edge computing gateway, cloud computing platform and digital twin visualization interaction platform. The operation process of the system includes multiple links such as data acquisition, data transmission, data analysis, state prediction and visualization display. Each link is closely connected to ensure the efficient operation of the system. Users can monitor the battery status in real time through the visual interaction platform and issue instructions through interface operations. These instructions will be quickly parsed and issued to the battery detection equipment. This closed-loop feedback mechanism makes the present invention excellent in the real-time, accuracy and user experience of battery detection.

[0126] An embodiment of the present invention provides an intelligent battery detection system based on digital twins. By combining battery detection equipment, edge computing gateways, cloud computing platforms and digital twin visualization interactive platforms, it monitors the status of battery detection equipment in real time, and dynamically predicts and displays various battery indicators and status to be tested (such as battery internal resistance, SOH, remaining charge and discharge time, internal anomalies, remaining life, etc.), thereby improving the real-time performance, accuracy and user experience of the battery detection process.

[0127] It will be understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present invention, and the present invention is not limited thereto. Those skilled in the art will appreciate that various modifications and improvements can be made without departing from the spirit and substance of the present invention, and such modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. An intelligent battery detection system based on digital twins, characterized in that: The digital twin-based intelligent battery detection system includes battery detection equipment, edge computing gateway, cloud computing platform and digital twin visualization interaction platform; wherein, The battery testing device is configured to receive the parsed charge and discharge control instructions issued by the edge computing gateway to control the charge and discharge process of the battery to be tested, detect the charge and discharge data of the battery to be tested during the charge and discharge process, and upload the operating data of the battery testing device itself and the charge and discharge data of the battery to be tested to the edge computing gateway; The edge computing gateway is used to preprocess the operating data of the battery testing device itself and the charge and discharge data of the battery to be tested, and upload the preprocessed battery testing device operating data and the preprocessed charge and discharge data of the battery to be tested to the cloud computing platform; wherein the edge computing gateway also parses the charge and discharge control instructions issued by the digital twin visualization interactive platform, and sends the parsed charge and discharge control instructions to the battery testing device; The cloud computing platform is used to store the preprocessed battery testing equipment operating data and the preprocessed charge and discharge data of the battery to be tested, and to analyze and process the preprocessed charge and discharge data of the battery to be tested to obtain real-time status indicators and future status indicators of the battery to be tested, and then upload the preprocessed battery testing equipment operating data and the real-time status indicators and future status indicators of the battery to be tested to the digital twin visualization interaction platform; The digital twin visualization interaction platform is used to display the pre-processed battery detection equipment operating data and the real-time status indicators and future status indicators of the battery to be detected to the user; wherein, the digital twin visualization interaction platform is also used to send the charge and discharge control instructions issued by the user in the human-computer interaction interface to the edge computing gateway.

2. The digital twin-based intelligent battery detection system according to claim 1, characterized in that: The battery testing device will be equipped with corresponding host computer control software. Through the host computer control software, the user can read the operating data of the battery testing device itself and the charge and discharge data of the battery to be tested, set the charge and discharge parameters, and issue charge and discharge control instructions to the battery testing device; alternatively, the user can directly establish a connection with the battery testing device through the communication protocol supported by the battery testing device, and then write the protocol parsing and data parsing program according to the communication protocol documentation provided by the manufacturer to realize the data reading and charge and discharge control instruction issuance of the battery testing device.

3. The digital twin-based intelligent battery detection system according to claim 1, characterized in that: The edge computing gateway includes a protocol conversion module, a data acquisition and cleaning module, a control instruction parsing module and a first data interface module, wherein: The protocol conversion module is configured to establish communication with the battery testing device according to a communication protocol supported by the battery testing device, convert the operating data of the battery testing device itself and the charge and discharge data of the battery to be tested obtained from the battery testing device into data in a unified communication protocol and data format, and send the converted operating data of the battery testing device itself and the converted charge and discharge data of the battery to be tested to the data acquisition and cleaning module; The data acquisition and cleaning module is used to clean the converted battery testing device operating data and the converted charge and discharge data of the battery to be tested, and then send the cleaned battery testing device operating data and the cleaned charge and discharge data of the battery to be tested to the first data interface module; The first data interface module is used to send the operating data of the battery detection device after cleaning and the charge and discharge data of the battery to be tested after cleaning to the cloud computing platform; wherein, the first data interface module is also used to receive the charge and discharge control instructions issued by the digital twin visualization interaction platform, and send the charge and discharge control instructions to the control instruction parsing module; The control instruction parsing module is used to parse the charge and discharge control instructions and send the parsed charge and discharge control instructions to the protocol conversion module; wherein, the protocol conversion module converts the parsed charge and discharge control instructions into an instruction format supported by the battery detection device, and sends the converted charge and discharge control instructions to the battery detection device.

4. The digital twin-based intelligent battery detection system according to claim 1, characterized in that: The cloud computing platform includes a second data interface module, a data cache module, a persistent storage module and a battery twin module; wherein, The second data interface module is used to receive the pre-processed battery detection equipment operation data and the pre-processed charge and discharge data of the battery to be detected uploaded by the edge computing gateway; The data cache module is used to store the currently pre-processed battery detection equipment operation data and the currently pre-processed charge and discharge data of the battery to be detected; The persistent storage module is used to store the historical pre-processed battery detection equipment operation data and the historical pre-processed battery charge and discharge data to be detected; The battery twin module is used to analyze and process the preprocessed charge and discharge data of the battery to be detected to obtain the real-time status indicator and future status indicator of the battery to be detected, and then send the real-time status indicator and future status indicator of the battery to be detected to the data cache module and the persistent storage module respectively; wherein, the preprocessed battery detection equipment operation data and the real-time status indicator and future status indicator of the battery to be detected are uploaded to the digital twin visualization interaction platform through the second data interface module through the persistent storage module.

5. The digital twin-based intelligent battery detection system according to claim 4 is characterized in that: The battery twin module includes multiple battery twin models, wherein each battery twin model is responsible for predicting a certain status indicator of the battery to be tested; Among them, the battery twin model is constructed based on the historical data in the persistent storage module and can be updated based on the current data in the data cache module. The battery twin module can not only predict the real-time status indicators of the battery to be tested, but also predict the future status indicators of the battery to be tested.

6. The digital twin-based intelligent battery detection system according to claim 1, characterized in that: The digital twin visualization interaction platform includes a third data interface module, a three-dimensional model display module, a status data display module, an alarm notification module and a human-computer interaction module; wherein, The third data interface module is configured to receive the pre-processed battery testing equipment operation data and the real-time status indicator and future status indicator of the battery to be tested uploaded by the cloud computing platform; The three-dimensional model display module is used to provide the user with a three-dimensional view of the battery testing device, the battery to be tested, and the entire testing environment, and allow the user to view the battery testing device, the battery to be tested, and the entire testing environment by rotating, zooming in, and zooming out; The status data display module is used to display a three-dimensional view of the battery to be tested, the pre-processed battery testing equipment operation data, and the real-time status indicators and future status indicators of the battery to be tested to the user; The alarm notification module is configured to issue an alarm to a user through the human-computer interaction module when an abnormality occurs in the real-time status indicator or future status indicator of the battery to be tested; The human-computer interaction module is used to send the charge and discharge control instructions issued by the user in the human-computer interaction interface to the edge computing gateway through the third data interface module.

7. The digital twin-based intelligent battery detection system according to claim 1, characterized in that: The charge and discharge control instructions include equipment stop / reset, data acquisition frequency, charge and discharge step parameters and equipment protection parameters.

8. The digital twin-based intelligent battery detection system according to claim 1, characterized in that: The battery testing equipment is a battery capacity tester, a cycle life tester, a chemical composition cabinet or a handheld internal resistance tester.

9. The digital twin-based intelligent battery detection system according to claim 1, characterized in that: The real-time status indicators of the battery to be tested include voltage, current and internal resistance, and the future status indicators of the battery to be tested include internal resistance, remaining charge and discharge time, SOH, potential faults and remaining life.

10. A digital twin-based smart battery detection method, applied to the digital twin-based smart battery detection system according to any one of claims 1 to 9, characterized in that: The digital twin-based intelligent battery detection method includes the following steps: Step 1: Data collection and transmission between battery testing equipment and edge computing gateway First, the battery testing device establishes a communication connection with the protocol conversion module in the edge computing gateway; the battery testing device continuously and uninterruptedly detects the charge and discharge data of the battery to be tested during the charging and discharging process through the built-in sensor, and the protocol conversion module in the edge computing gateway converts the battery testing device's own operating data and the charge and discharge data of the battery to be tested obtained from the battery testing device into data with a unified communication protocol and data format, and sends the converted battery testing device's own operating data and the converted charge and discharge data of the battery to be tested to the data acquisition and cleaning module in the edge computing gateway; the data acquisition and cleaning module cleans the converted battery testing device's own operating data and the converted charge and discharge data of the battery to be tested; Step 2: Upload data to the cloud computing platform After the data acquisition and cleaning module completes data cleaning, it sends the cleaned battery detection device's own operating data and the cleaned charge and discharge data of the battery to be detected to the first data interface module in the edge computing gateway; The first data interface module of the edge computing gateway acts as a client and actively sends a request to the second data interface module in the cloud computing platform to upload the operating data of the battery detection device after cleaning and the charge and discharge data of the battery to be detected after cleaning to the cloud computing platform; Step 3: Data Storage and Analysis After receiving the real-time data uploaded by the edge computing gateway, the second data interface module in the cloud computing platform writes the data into the data cache module and the persistent storage module in the cloud computing platform in sequence for subsequent calls; Step 4: Battery status prediction The battery twin module in the cloud computing platform reads the currently preprocessed charge and discharge data of the battery to be tested from the data cache module, inputs it into the battery twin model for model update, and then writes the battery status indicator to be tested predicted after the model update back to the data cache module and the persistent storage module; Step 5: 3D visualization The digital twin visualization interactive platform regularly obtains the latest predicted battery status indicator to be tested through the second data interface module in the cloud computing platform, and synchronizes it to the three-dimensional model display module in the digital twin visualization interactive platform to display it to the user; Step 6: User interacts with the digital twin visualization interactive platform The status data display module in the digital twin visualization interactive platform displays various status indicators of the battery to be tested, and charging and discharging control instructions are issued by clicking virtual buttons on the human-computer interaction interface; Step 7: The edge computing gateway receives the charge and discharge control instructions and sends them to the battery detection device The charge and discharge control instructions issued by the user are passed to the edge computing gateway through the digital twin visualization interactive platform. The edge computing gateway parses the received charge and discharge control instructions, and converts the parsed charge and discharge control instructions into the instruction format supported by the battery detection device according to the communication protocol of the battery detection device, and sends them to the battery detection device.

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