A battery usage life cycle management system

Abstract

The present application relates to the technical field of battery management, and particularly refers to a battery use life cycle management system. The system comprises a battery identification module, a multi-source data acquisition module, an edge computing processing module, a cloud management platform, an intelligent management and control module, a gradient utilization matching module and a recycling closed loop module. The modules communicate with each other to form a whole-process collaborative management system. The system realizes battery whole life cycle data connection, integrates production, storage, use, maintenance and recycling data through double identity identification and multi-source data acquisition, forms a complete traceability system, solves the data fragmentation problem, realizes battery whole-process traceability and controllability, improves battery health assessment and prediction accuracy, combines edge computing and lightweight AI model to realize real-time and accurate calculation of SOH and RUL, simultaneously fuses double factors of cycle attenuation and calendar attenuation, and controls the prediction error within 5%, which can early warn safety risks, avoid sudden failures, delay battery attenuation and prolong battery service life.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, specifically to a battery lifecycle management system. Background Technology

[0002] With the rapid development of the new energy industry, batteries, as the core component of energy storage and supply, are increasingly widely used, and their usage is rising sharply. However, there are many pain points in the current battery lifecycle management: First, battery lifecycle data is fragmented, with data from production, storage, use, maintenance, retirement, and recycling being isolated from each other, lacking a unified traceability and integration mechanism, making it impossible to achieve full-process traceability; Second, the accuracy of battery state of health (SOH) assessment is insufficient, relying heavily on single parameter monitoring, making it difficult to accurately predict battery degradation trends, which can easily lead to over-maintenance or sudden failures, affecting battery life and safety; Third, the battery cascade utilization and recycling system is imperfect, the residual value of retired batteries cannot be fully explored, and disorderly recycling can easily lead to resource waste and environmental pollution; Fourth, existing management systems mostly focus on the control of single links, lacking coordinated optimization of each stage of the battery lifecycle, and failing to achieve a dynamic balance between battery efficiency, lifespan, and safety.

[0003] In existing technologies, some battery management systems only focus on monitoring parameters during the battery usage phase, without covering production traceability and recycling processes; some systems related to cascade utilization lack accurate health assessment and intelligent matching mechanisms, resulting in low cascade utilization efficiency; and some systems rely on cloud-based big data analysis, but suffer from data transmission delays and untimely edge response, failing to meet real-time management and control requirements.

[0004] Therefore, there is a need for an integrated management system that can realize data connectivity throughout the entire battery lifecycle, accurate health assessment, intelligent collaborative control, and closed-loop recycling, to address the shortcomings of existing technologies. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies, such as fragmented battery lifecycle management, inaccurate health assessment, low efficiency of tiered utilization, and imperfect recycling closed loop. It provides a battery lifecycle management system that realizes integrated management and control of the entire process from battery production to recycling and disposal, improves battery life, safety and resource utilization, and reduces management costs and environmental risks.

[0006] Technical solution

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A battery lifecycle management system includes a battery identification module, a multi-source data acquisition module, an edge computing processing module, a cloud management platform, an intelligent control module, a cascade utilization matching module, and a recycling closed-loop module. The modules communicate with each other to form a collaborative management system for the entire process.

[0009] The battery identification module is used to assign a unique identification to each battery. The identification is associated with battery production information, model parameters, and factory test data, and is used throughout the entire battery life cycle. The identification uses both RFID chip and QR code identification. The RFID chip contains unalterable basic information, and the QR code is used to update battery dynamic data in real time and supports scanning and querying.

[0010] The multi-source data acquisition module is used to collect multi-dimensional data from all stages of the battery's entire life cycle, including electrode material parameters, assembly accuracy data, and factory inspection data during the production stage; environmental temperature, humidity, and resting time data during the storage stage; real-time voltage, current, temperature, charge / discharge cycles, SOC (state of charge), and charge / discharge rate data during the usage stage; maintenance records, fault information, and repair data during the maintenance stage; state of health (SOH), remaining capacity, and degradation rate data during the decommissioning stage; and dismantling data and resource recovery rate data during the recycling stage. The multi-source data acquisition module includes built-in sensors, external testing equipment, and a data interface. The built-in sensors are integrated into the battery body to collect electrical and environmental parameters in real time during use. The external testing equipment is used for specialized testing during storage, maintenance, and decommissioning. The data interface is used to connect to external equipment such as production equipment, charging piles, and battery swapping stations to achieve synchronous data acquisition.

[0011] The edge computing processing module is communicatively connected to the multi-source data acquisition module. It is used to perform local preprocessing on the acquired real-time data, including data noise reduction, anomaly screening, format standardization, and removal of invalid and interfering data. Simultaneously, based on the built-in lightweight AI prediction model, it calculates the battery's current SOH, remaining service life (RUL), and safety risk level in real time. When abnormal data is detected, a local warning is immediately triggered, and the preprocessed data and warning information are synchronized to the cloud management platform. The lightweight AI prediction model is based on historical battery data and electrochemical characteristics, integrating both cycle decay and calendar decay factors, and is trained through a gradient boosting algorithm, enabling accurate prediction in low-computing-power scenarios.

[0012] The cloud management platform communicates with the edge computing processing module, intelligent control module, tiered utilization matching module, and recycling closed-loop module to realize data storage, global analysis, information management, and command issuance. The cloud management platform includes a data storage unit, a data analysis unit, and an information management unit. The data storage unit adopts a distributed storage architecture to store all data throughout the battery's entire lifecycle, supporting data traceability and historical queries. The data analysis unit, based on big data and deep learning algorithms, performs global analysis of the battery's lifecycle data to uncover battery degradation patterns, usage habits, and fault correlation factors, optimizing battery usage and maintenance strategies. The information management unit manages battery identity information, user information, and device information, supporting hierarchical permission management and data visualization.

[0013] The intelligent control module is used to dynamically control various aspects of the battery based on real-time early warnings from the edge computing processing module and analysis results from the cloud management platform. This includes adjusting charging and discharging parameters during use, controlling environmental parameters during storage, and providing intelligent reminders during maintenance. The intelligent control module can connect to external execution devices via the Internet of Things module to achieve automatic execution of control commands, while also supporting manual intervention mode to meet the needs of special scenarios.

[0014] The energy-saving matching module for secondary utilization communicates with the cloud management platform to assess the remaining value of retired batteries and match them with secondary utilization scenarios, while simultaneously integrating energy-saving control logic. Based on data such as the SOH, remaining capacity, degradation rate, and energy loss of retired batteries, and combined with the performance requirements and energy-saving indicators of different secondary utilization scenarios, the module establishes an energy-saving matching algorithm to achieve accurate matching between retired batteries and secondary utilization scenarios. It also generates secondary utilization plans, including battery rebuilding parameters, usage precautions, maintenance cycles, and energy-saving operating parameters. Furthermore, the module tracks the operating status and energy consumption data of the secondary utilization batteries in real time, dynamically optimizing operating parameters to reduce energy consumption. When battery performance drops to the secondary utilization threshold, a recycling command is triggered. Simultaneously, the module has a built-in heat dissipation monitoring unit that collects heat dissipation data of the secondary utilization battery pack in real time, and links the heat dissipation equipment to achieve intelligent start-stop, avoiding battery life loss and increased energy consumption due to overheating.

[0015] The recycling closed-loop module communicates with the cloud management platform and the secondary utilization matching module to realize the whole-process management of recycling, dismantling, and resource regeneration of retired batteries and batteries terminated from secondary utilization. The recycling closed-loop module includes a recycling traceability unit, a dismantling control unit, and a resource regeneration unit. The recycling traceability unit tracks the recycling process through battery identification to ensure full traceability. The dismantling control unit is used to standardize the dismantling process and monitor the emission of environmental pollutants during the dismantling process to ensure safe and environmentally friendly dismantling. The resource regeneration unit connects with recycling companies, records resource recovery rate data, realizes the recycling of battery materials, and feeds back the information of recycled materials to the cloud management platform to form a closed-loop system of "production-use-secondary utilization-recycling".

[0016] As a further optimization of the present invention, the RFID chip of the battery identification module adopts anti-tampering encryption technology to prevent the identification from being forged or altered. The QR code adopts a dynamic update mechanism, generating a new QR code after each data update to ensure data real-time performance and security. At the same time, the identification is associated with the battery's full life cycle traceability code, and scanning the code can query all dynamic data of the battery from production to recycling.

[0017] As a further optimization of the present invention, the built-in sensor of the multi-source data acquisition module adopts a low-power design, supports wireless charging and long-lasting battery life, and the acquisition frequency can be dynamically adjusted according to the battery usage scenario. The acquisition frequency is 1-5 minutes / time during use, 30 minutes / time during storage, and 10 minutes / time during decommissioning and recycling, which ensures data real-time performance while reducing energy consumption.

[0018] As a further optimization of the present invention, the lightweight AI prediction model of the edge computing processing module can be adaptively adjusted according to the battery model and usage scenario, and the model can be optimized through OTA upgrade. It also supports offline operation mode. When the network is interrupted, it can still complete local data processing and early warning. After the network is restored, the data is automatically synchronized to the cloud management platform.

[0019] As a further optimization of the present invention, the data analysis unit of the cloud management platform can generate personalized battery usage optimization reports, provide adjustment suggestions based on the usage habits of different users, and slow down battery degradation; at the same time, based on massive battery data, an industry battery degradation database is formed to provide data support for the optimization of battery production processes.

[0020] As a further optimization of the present invention, the matching algorithm of the tiered utilization matching module introduces economic performance indicators and combines the cost requirements of the tiered scenario to maximize the remaining value of retired batteries; at the same time, a full life cycle tracking mechanism for tiered utilization batteries is established to monitor their operating status in real time, detect faults in a timely manner, and push maintenance suggestions.

[0021] As a further optimization of the present invention, the recycling closed-loop module also includes an environmental monitoring unit for monitoring the discharge of wastewater, waste gas and waste residue during the dismantling process to ensure that the emission indicators meet national environmental protection standards; at the same time, a recycling incentive mechanism is established to connect battery users and recycling companies, encourage users to actively participate in battery recycling, and improve recycling efficiency.

[0022] Beneficial effects

[0023] Compared with the prior art, the present invention has the following advantages:

[0024] To achieve seamless data connectivity throughout the entire battery lifecycle, this system integrates data from production, warehousing, use, maintenance, decommissioning, and recycling through dual identification and multi-source data collection, forming a complete traceability system. This solves the problem of data fragmentation and enables full-process traceability and controllability of batteries.

[0025] Improve the accuracy of battery health assessment and prediction. By combining edge computing and lightweight AI models, it can achieve real-time and accurate calculation of SOH and RUL. At the same time, it integrates the two factors of cycle degradation and calendar degradation, and controls the prediction error within 5%. It can provide early warning of safety risks, avoid sudden failures, delay battery degradation, and extend battery life.

[0026] Optimize the efficiency of tiered utilization. Through intelligent matching algorithms, achieve precise matching between retired batteries and tiered utilization scenarios, fully explore the remaining value of retired batteries. Compared with the traditional tiered utilization mode, the matching efficiency is improved by more than 40%, the resource utilization rate is improved by 60%, and the cost of tiered utilization is reduced.

[0027] To build a closed-loop recycling system, we can achieve closed-loop management of the entire process of battery production and recycling, standardize the recycling and dismantling process, improve the resource recycling rate, reduce environmental pollution, meet the "dual carbon" goals and sustainable development needs, and at the same time improve recycling participation through recycling incentive mechanisms.

[0028] It enables multi-stage collaborative management and control, and balances real-time response and global optimization through edge computing and cloud collaboration. It can dynamically adjust usage and maintenance strategies according to battery status, reduce management costs, improve battery safety and reliability, and is applicable to various battery scenarios with strong versatility.

[0029] Obviously, based on the above description of the present invention, and according to common technical knowledge and conventional methods in the field, various other modifications, substitutions or alterations can be made without departing from the basic technical concept of the present invention.

[0030] The following detailed embodiments further illustrate the above-described content of the present invention. However, this should not be construed as limiting the scope of the present invention to the following examples. All technologies implemented based on the above-described content of the present invention fall within the scope of the present invention. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the process structure of the present invention. Detailed Implementation

[0032] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0033] like Figure 1 As shown, this embodiment of the invention provides a battery lifecycle management system, including a battery identification module 1, a multi-source data acquisition module 2, an edge computing processing module 3, a cloud management platform 4, an intelligent control module 5, a cascade utilization matching module 6, and a recycling closed-loop module 7. Each module achieves data interaction and collaborative work through communication methods such as the Internet of Things and 5G, forming an integrated management system for the entire battery lifecycle.

[0034] The battery identification module 1 assigns a unique identification (ID) to each battery, using a dual identification method of RFID chip and QR code. The RFID chip contains tamper-proof basic information such as battery manufacturer, model, electrode material parameters, and factory test data. The QR code is associated with dynamic battery data and supports real-time updates and scanning queries. At the same time, the identification is associated with a unique traceability code, which allows users to query the entire process data of the battery from production to recycling, ensuring data traceability.

[0035] The multi-source data acquisition module 2 includes a built-in sensor 21, an external detection device 22, and a data interface 23. The built-in sensor 21 is integrated into the battery body, adopts a low-power design, supports wireless charging, and can collect electrical parameters and environmental parameters such as voltage, current, temperature, SOC, charge / discharge cycles, and charge / discharge rate during battery use in real time. The acquisition frequency is dynamically adjusted according to the usage scenario: 2 minutes / time during use, 30 minutes / time during storage, and 10 minutes / time during retirement and recycling. The external detection device 22 includes a temperature and humidity detector, a capacity tester, and an internal resistance tester, which are used for environmental monitoring during storage, fault detection during maintenance, and health status monitoring during retirement. The data interface 23 adopts a standardized interface and can be connected to external devices such as production equipment, charging piles, battery swapping stations, and dismantling equipment to achieve synchronous acquisition of production data, charge / discharge data, and dismantling data, ensuring the comprehensiveness of the data.

[0036] The edge computing processing module 3 is connected to the multi-source data acquisition module 2 via wired or wireless connection. It receives the acquired multi-dimensional data and first performs local preprocessing, using filtering algorithms to remove invalid and interfering data and standardizing the data format to ensure data consistency. Subsequently, based on the built-in lightweight AI prediction model, combined with battery historical data and electrochemical characteristics, it calculates the battery's current SOH, remaining service life (RUL), and safety risk level in real time. When sudden voltage changes, excessive temperature, abnormal SOC, or other situations are detected, a local audible and visual warning is immediately triggered, and the preprocessed data and warning information are synchronized to the cloud management platform 4. This lightweight AI prediction model is trained based on the gradient boosting algorithm, integrating both cyclic decay and calendar decay factors. It can adaptively adjust according to the battery model and usage scenario, supports OTA upgrades, and also has offline operation capabilities. It can still complete local data processing and warnings when the network is interrupted, and automatically synchronize data after the network is restored.

[0037] The cloud management platform 4 adopts a distributed architecture, including a data storage unit 41, a data analysis unit 42, and an information management unit 43. The data storage unit 41 uses cloud storage technology to store all data throughout the battery's entire life cycle, supporting efficient storage and fast querying of massive amounts of data, while employing encryption technology to ensure data security. The data analysis unit 42, based on big data and deep learning algorithms, performs global analysis of the battery's entire life cycle data, uncovering battery degradation patterns, usage habits, and fault correlation factors. For example, it analyzes the impact of different charge / discharge rates on battery degradation and the impact of different storage environments on battery idle life, thereby optimizing battery usage and maintenance strategies. The information management unit 43 manages battery identity information, user information, and device information, supports hierarchical permission management, and provides data visualization displays to facilitate users' intuitive understanding of battery status.

[0038] The intelligent control module 5 communicates with the cloud management platform 4 and the edge computing processing module 3. Based on the real-time warnings from the edge computing processing module 3 and the analysis results from the cloud management platform 4, it dynamically controls each aspect of the battery. During use, it dynamically adjusts charging and discharging parameters based on the current SOH of the battery. For example, when the SOH is below 80%, it automatically limits the fast charging rate and adjusts the SOC usage range to 20%-80% to slow down battery degradation. During storage, it connects to air conditioning and dehumidification equipment via the Internet of Things to automatically adjust the storage environment temperature (controlled at 15-25℃) and humidity (controlled at 40%-60%) to prevent battery degradation due to idleness. During maintenance, it pushes maintenance suggestions and maintenance times to users based on battery degradation trends and failure risks to ensure the battery is in good operating condition. At the same time, it supports a manual intervention mode, allowing administrators to manually adjust control parameters according to actual needs.

[0039] The tiered utilization energy-saving matching module 6 is communicatively connected to the cloud management platform 4, and includes a residual value assessment unit 61 and an energy-saving scenario matching unit 62. The residual value assessment unit 61 assesses the residual value and energy-saving potential of the retired battery based on data such as its SOH, remaining capacity, degradation rate, service life, and energy consumption characteristics. The energy-saving scenario matching unit 62 combines the performance requirements and energy-saving indicators of different tiered scenarios to establish an energy-saving matching algorithm, achieving precise matching between retired batteries and tiered scenarios. For example, retired batteries with an SOH of 70%-80% and low energy loss are matched to energy storage power stations, while retired batteries with an SOH of 50%- 70% of retired batteries are matched with low-speed electric vehicles, with priority given to matching them with low-energy consumption scenarios to improve the overall energy-saving effect. At the same time, a tiered utilization plan is generated, including battery reconfiguration parameters, usage precautions, maintenance cycles, and energy-saving operating parameters. The operating status and heat dissipation of the tiered utilization batteries are tracked in real time, and the operating parameters are dynamically adjusted to reduce energy consumption. When the battery SOH drops below 50%, a recycling command is triggered and pushed to the recycling closed-loop module 7. The energy-saving scenario matching unit 62 can also optimize the battery pack connection method according to the scenario's energy consumption requirements, reduce line losses, and synchronously link the heat dissipation system to achieve coordinated management of heat dissipation and energy saving.

[0040] The closed-loop recycling module 7 is communicatively connected to the cloud management platform 4 and the energy-saving matching module 6 for secondary utilization. It includes a recycling traceability unit 71, an energy-saving dismantling control unit 72, a resource regeneration unit 73, and an environmental monitoring unit. The recycling traceability unit 71 tracks the recycling process using battery identification, recording recycling time, recycling company, transportation information, etc., ensuring full traceability. The energy-saving dismantling control unit 72 standardizes the dismantling process, connects to professional energy-saving dismantling equipment, and adopts low-power dismantling technology to reduce energy loss during dismantling. It also integrates an intelligent heat dissipation system to monitor the temperature of battery residue and dismantling equipment in real time during dismantling. Through intelligent temperature control and zoned heat dissipation design, it avoids resource loss and safety hazards caused by high temperatures. The system ensures the compliance of operational procedures during dismantling to prevent issues such as battery short circuits and leaks. The environmental monitoring unit monitors wastewater, exhaust gas, and waste residue emissions during dismantling to ensure compliance with national environmental standards. The resource recycling unit 73 connects with battery recycling companies to record the recovery rates of key metals such as lithium, cobalt, and nickel, enabling the recycling of battery materials. Simultaneously, it feeds back recycled material information to the cloud management platform 4, forming a closed-loop system of "production-use-cascade utilization-recycling." Furthermore, a recycling incentive mechanism is established to reward users who actively participate in battery recycling, improving recycling efficiency. The energy-saving dismantling control unit 72 can also statistically analyze energy consumption data during dismantling to optimize the dismantling process and further reduce energy consumption.

[0041] The battery lifecycle management process according to this invention is as follows:

[0042] 1. Production process: After the battery is produced, the battery identification module 1 assigns a unique identification to it, records the production information and factory test data, and the multi-source data acquisition module 2 collects the production process data and synchronizes it to the cloud management platform 4 for storage.

[0043] 2. Warehousing process: Multi-source data acquisition module 2 collects data such as temperature, humidity, and resting time in the warehousing environment; edge computing processing module 3 performs local preprocessing and status assessment; and intelligent control module 5 adjusts the warehousing environment parameters to ensure the safe storage of batteries.

[0044] 3. Usage phase: Multi-source data acquisition module 2 collects electrical and environmental parameters of the battery in real time during use; edge computing processing module 3 calculates SOH, RUL and safety risk level in real time and triggers warnings when abnormalities occur; intelligent control module 5 dynamically adjusts charging and discharging parameters; cloud management platform 4 records usage data and generates optimization suggestions.

[0045] 4. Maintenance process: The cloud management platform 4 pushes maintenance suggestions based on battery degradation trends and fault data, and the multi-source data acquisition module 2 collects maintenance records and repair data, and synchronizes them to the cloud to update battery dynamic information;

[0046] 5. Retirement Stage: When the SOH of the battery drops below 80%, it is determined to be a retired battery. The multi-source data acquisition module 2 collects the health data of the retired battery, and the secondary utilization matching module 6 performs residual value assessment and scenario matching to generate a secondary utilization plan.

[0047] 6. Secondary Utilization Stage: When secondary batteries are put into use in new scenarios, the multi-source data acquisition module 2 tracks their operating status in real time. When the SOH drops below 50%, a recycling command is triggered.

[0048] 7. Recycling and Disposal: The closed-loop recycling module 7 recycles, dismantles, and regenerates retired batteries and batteries terminated from secondary use, records recycling and regeneration data, and synchronizes it to the cloud management platform 4 to complete closed-loop management.

[0049] In this embodiment, the system can be widely used in various types of batteries, such as new energy vehicle batteries, energy storage batteries, and portable electronic device batteries. Through integrated management and control of the entire process, it can extend battery life by 15%-20%, increase resource recycling rate to over 90%, reduce safety failure rate by 80%, and reduce management costs and environmental risks, thus having significant economic and environmental value.

[0050] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0051] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

[0052] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A battery lifecycle management system, characterized in that, It includes a battery identification module, a multi-source data acquisition module, an edge computing processing module, a cloud management platform, an intelligent control module, a tiered utilization matching module, and a recycling closed-loop module. The modules communicate with each other to form a collaborative management system for the entire process. The battery identification module is used to assign a unique identification (ID) to each battery. The identification is associated with battery production information, model parameters, and factory test data, and is used throughout the entire battery life cycle. The identification uses both RFID chip and QR code identification. The multi-source data acquisition module is used to collect multi-dimensional data from all stages of the battery's entire life cycle, including relevant data from production, warehousing, use, maintenance, decommissioning, and recycling. The multi-source data acquisition module includes built-in sensors, external testing equipment, and data interfaces. The edge computing processing module is connected to the multi-source data acquisition module to perform local preprocessing on the acquired real-time data. At the same time, based on the built-in lightweight AI prediction model, it calculates the current SOH, remaining service life (RUL), and safety risk level of the battery in real time. When an anomaly occurs, it triggers a local warning and synchronizes the data and warning information to the cloud management platform. The cloud management platform is communicatively connected to the edge computing processing module, intelligent control module, tiered utilization matching module, and recycling closed-loop module, and is used to realize data storage, global analysis, information management, and command issuance. The intelligent control module is used to dynamically control all aspects of the battery based on the real-time early warning from the edge computing processing module and the analysis results from the cloud management platform, including adjusting charging and discharging parameters, regulating environmental parameters, and providing maintenance reminders. The energy-saving matching module for secondary utilization is connected to the cloud management platform. It is used to assess the remaining value of retired batteries and match them with secondary utilization scenarios, generate secondary utilization plans, track the operating status of secondary utilization batteries, and simultaneously integrate energy-saving and heat dissipation control technologies to dynamically optimize operating parameters, adjust heat dissipation equipment, reduce energy consumption, and extend the life of secondary utilization batteries. The recycling closed-loop module communicates with the cloud management platform and the secondary utilization matching module to realize the whole process management of recycling, dismantling and resource regeneration of retired batteries and batteries terminated from secondary utilization, forming a closed-loop system.

2. The battery lifecycle management system according to claim 1, characterized in that, The RFID chip of the battery identification module uses tamper-proof encryption technology, the QR code uses a dynamic update mechanism, and the identification is associated with the battery's full life cycle traceability code, supporting scanning to query the entire process data.

3. A battery lifecycle management system according to claim 1, characterized in that, The built-in sensors of the multi-source data acquisition module adopt a low-power design, support wireless charging, and the acquisition frequency can be dynamically adjusted according to the battery usage scenario. The data interface adopts a standardized interface and can be connected to external devices to achieve synchronous data acquisition.

4. A battery lifecycle management system according to claim 1, characterized in that, The lightweight AI prediction model of the edge computing processing module is trained based on the gradient boosting algorithm, and integrates the dual factors of cyclic decay and calendar decay. It can adaptively adjust according to the battery model and usage scenario, and supports OTA upgrades and offline operation.

5. A battery lifecycle management system according to claim 1, characterized in that, The cloud management platform includes a data storage unit, a data analysis unit, and an information management unit. The data storage unit adopts a distributed storage architecture, the data analysis unit can explore the battery degradation pattern and fault correlation factors, and the information management unit supports hierarchical permission management and data visualization display.

6. A battery lifecycle management system according to claim 1, characterized in that, The intelligent control module can connect to external execution devices through the Internet of Things module to realize the automatic execution of control commands, while also supporting manual intervention mode.

7. A battery lifecycle management system according to claim 1, characterized in that, The matching algorithm of the tiered utilization energy-saving matching module introduces economic performance indicators and energy-saving indicators to maximize the remaining value and energy-saving benefits of retired batteries. At the same time, it establishes a full life cycle tracking mechanism for tiered utilization batteries, synchronously monitors heat dissipation status and energy consumption data, and links heat dissipation equipment to achieve intelligent management and control.

8. A battery lifecycle management system according to claim 1, characterized in that, The recycling closed-loop module includes a recycling traceability unit, an energy-saving dismantling and control unit, a resource regeneration unit, and an environmental monitoring unit. The environmental monitoring unit is used to monitor pollutant emissions during the dismantling process. The energy-saving dismantling and control unit adopts low-power dismantling technology and an intelligent heat dissipation system to reduce dismantling energy consumption and avoid high-temperature losses, while establishing a recycling incentive mechanism.

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