Method for conveniently checking information through NFC (Near Field Communication) output applied to lithium battery

Abstract

The invention relates to the technical field of battery management, and discloses an NFC output convenient information viewing method applied to a lithium battery, and the method comprises the steps: receiving a situation vector which is transmitted by a terminal and is used for describing an expected task through near field communication (NFC); and an edge AI coprocessor in the intelligent protection board combines real-time state data in the battery and the situation vector, generates enhanced battery information including predictive endurance, dynamic safety boundary and other information in real time through a local AI prediction model, and transmits the enhanced battery information back to the terminal through NFC. And after the task is finished, the intelligent protection board records actual performance data of the battery, and the AI prediction model is subjected to self-adaptive adjustment and optimization locally by using the data. According to the invention, convenient off-line information interaction, accurate context awareness prediction and personalized self-learning optimization are realized, and the intelligent level, safety and user experience of lithium battery use are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, specifically to a method for conveniently viewing information via NFC output on lithium batteries. Background Technology

[0002] Lithium-ion batteries, due to their high energy density, long cycle life, and low self-discharge rate, have been widely used in various electronic devices, especially in fields with high requirements for endurance and safety, such as drones, power tools, portable energy storage, and intelligent robots. To ensure the safe and reliable operation of lithium-ion battery packs, a Battery Management System (BMS), also commonly referred to as a protection board, is typically installed. Its core function is to monitor the battery pack's voltage, current, temperature, and other states in real time, and to provide protection against abnormal conditions such as overcharging, over-discharging, overcurrent, and extreme temperatures, while also estimating the battery's remaining capacity (State of Charge, SOC).

[0003] However, as lithium batteries are increasingly used in fields requiring high performance and high reliability, existing technologies are gradually revealing their inherent limitations. On one hand, obtaining battery internal status information remains cumbersome, typically relying on Bluetooth communication requiring power-on and stable pairing, or serial communication interfaces requiring physical connections. This approach is inefficient in scenarios such as rapid safety checks before task execution, especially when the battery is not installed in the device or is powered off, making it nearly impossible to quickly ascertain its health status and critical information.

[0004] On the other hand, traditional protection boards provide relatively limited information. They can tell users the current remaining battery percentage, which is static, historical data. However, they cannot answer users' more pressing questions about future dynamic performance, such as, "With the current battery status, can it support the upcoming high-power flight mission to achieve its intended goals?" This disconnect between battery information and the user's actual application scenario makes it difficult for users to make accurate operational decisions based on existing data, often leading to range anxiety or safety risks.

[0005] Furthermore, even when some high-end systems attempt to introduce cloud-based intelligent analytics to provide predictive maintenance recommendations, this approach suffers from significant drawbacks in real-time performance and accuracy due to its strong dependence on network connectivity, inherent data transmission latency, and the decoupling of general cloud models from the actual aging state of individual batteries. Simultaneously, this centralized architecture prevents the model from undergoing personalized local adaptive optimization based on the unique usage habits and degradation paths of each battery. Therefore, current technologies generally lack a comprehensive solution that integrates convenient offline interaction, accurate context awareness, efficient edge intelligent prediction, and continuous local self-learning capabilities. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method for conveniently viewing information via NFC output on lithium batteries, resolving the technical deficiencies of current lithium battery information viewing methods. For example, using a physical display screen requires openings in the battery casing, which compromises the integrity of the casing, reduces its waterproof and dustproof performance, and increases manufacturing costs. Other wireless viewing methods typically only provide information about the battery's current physical state, offering limited information and lacking the ability to predict battery performance in specific future operating scenarios.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of this invention provides a method for conveniently viewing information via NFC output on a lithium battery, comprising the following steps: Step (a) The protection board of the lithium battery collects the internal status data of the battery pack in real time through its internal IC chip; Step (b) The terminal receiving device sends a context vector containing external working information to the NFC module connected to the protection board via near field communication (NFC); The protection board in step (c) generates enhanced battery information based on the received context vector and the collected internal state data. This enhanced battery information includes predictions of the future performance of the battery. In step (d), the protection board sends the enhanced battery information to the terminal receiving device for display via the NFC module.

[0008] In one possible implementation, the specific process of generating enhanced battery information in step (c) is as follows: the edge AI coprocessor inside the protection board runs an AI prediction model, which receives the context vector and the internal state data as input to the model and outputs the enhanced battery information containing the prediction results. This process can be expressed by the following formula: ; in: This indicates enhanced battery information that includes the predicted results; This represents the function described in the AI ​​prediction model; The vector representing the internal state data may include, for example, parameters such as the voltage of each individual cell in the battery pack, the total current, the temperature at each measuring point, and the internal resistance of the battery. Represents the context vector; This represents a set of weight parameters for the AI ​​prediction model.

[0009] In one possible implementation, the context vector includes at least one piece of information selected from task distance, ambient temperature, and preset working mode. The context vector can be expressed by the following formula: ; in, This represents the vector concatenation operation. Represents the task feature vector. Represents the environmental feature vector. This represents a user preference vector.

[0010] In one possible implementation, the prediction results included in the enhanced battery information are predictions of battery life under the scenario defined by the context vector.

[0011] In one possible implementation, the method further includes a step of optimizing subsequent viewing results using the enhanced battery information, including: Step (e) After the task is executed, the protection board records the actual performance data of the battery pack; In step (f), during the next interaction via near field communication (NFC), the protection board adjusts the method for generating the enhanced battery information based on the actual performance data and the previously generated prediction results.

[0012] This adjustment process allows subsequent predictions to be revised based on historical data.

[0013] In one possible implementation, the specific process of adjusting the method for generating the enhanced battery information in step (f) is as follows: the protection board calculates the difference between the predicted result and the actual performance data, and updates the parameters of the AI ​​prediction model used to generate the predicted result based on the difference. This update process can be expressed by the following formula: ; ; in: The difference between the predicted result and the actual performance data is represented by the loss function. Calculated; This refers to the performance prediction portion of the prediction results; A vector representing the actual performance data; and These represent the weight parameters of the AI ​​prediction model before and after the update, respectively. This represents a learning rate parameter used to control the update step size; Represents the loss function Regarding the old parameters The gradient.

[0014] In one possible implementation, the enhanced battery information also includes a dynamic safety operating boundary. This dynamic safety operating boundary is a set of safe operating parameters that the protection board calculates based on the scenario vector and the internal state data, and which is recommended to the user when viewing the information. For example, it may include recommended maximum continuous discharge current, peak discharge current, power limits, etc.

[0015] In one possible implementation, prior to step (b), a preparatory step is included: the lithium battery broadcasts a status beacon indicating its macroscopic health status via Bluetooth Low Energy (BLE). The terminal receiving device receives and parses the status beacon to prompt the user to initiate information viewing via Near Field Communication (NFC) if necessary.

[0016] In one possible implementation, the protection board and the NFC module transmit data via the 485 communication protocol.

[0017] A second aspect of the present invention provides a system for convenient viewing of information via NFC output for lithium batteries, comprising: The protection board is used to collect internal state data of the lithium battery and is configured to receive context vectors. The NFC module is connected to the protection board; The protection board is also configured to perform any of the methods described in the first aspect of the invention to generate and output enhanced battery information.

[0018] This invention provides a method for conveniently viewing information via NFC output on lithium batteries. It offers the following advantages: 1. This invention utilizes Near Field Communication (NFC) as the primary means of information exchange, eliminating the need for holes in the battery casing to mount a physical display or data interface. This design directly solves the problem described in the original technical solution where openings compromised the structural integrity of the battery casing. By maintaining the casing's airtightness, the product's waterproof and dustproof ratings are significantly improved, and manufacturing costs are effectively reduced by saving materials such as the display screen and related assembly processes.

[0019] 2. This invention elevates the information viewed by users beyond simply the current physical state to intelligent predictions of future performance. By combining a "context vector" containing external operational information obtained from the terminal device with the battery's own "internal state data," an AI prediction model is used to generate predictions of battery performance under specific future scenarios. This addresses the pain point of the original technical solution, which lacked a single information dimension and could not meet users' needs for predicting key information such as future battery life, making the information obtained by users more forward-looking and valuable for decision-making.

[0020] 3. This invention introduces a self-optimization mechanism based on actual usage data. After task execution, the protection board records "actual performance data" and compares it with the previous "prediction result," thereby adjusting its internal AI prediction model. This mechanism enables the accuracy of information prediction to adaptively correct itself based on the aging characteristics of individual battery cells and the user's specific usage habits, making the information viewed by the user increasingly closer to the actual situation, providing a level of personalization and high precision that traditional static information viewing methods cannot match. Attached Figure Description

[0021] Figure 1 This is a system structure block diagram of the present invention; Figure 2 This is a flowchart of the method of the present invention; Figure 3 This is a schematic diagram illustrating an application scenario of the present invention; Figure 4 This is a schematic diagram of the internal structure and data flow of the intelligent protection board of the present invention; Figure 5 This is a data transmission flowchart of the present invention.

[0022] The components include: 1. Lithium battery pack; 2. Smart protection board; 21. Data acquisition IC chip; 22. Microcontroller; 23. Edge AI coprocessor; 24. Memory; 25. Board-level communication interface; 3. Dual-mode communication module; and 4. Terminal receiving device. Detailed Implementation

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] See attached document Figure 1 , Figure 1 This is a system structure block diagram according to an embodiment of the present invention. This embodiment provides a system for convenient viewing of information via NFC output from a lithium battery, comprising: a lithium battery pack 1, a smart protection board 2, a dual-mode communication module 3, and a terminal receiving device 4.

[0025] The lithium battery pack 1 serves as the energy source for the system and consists of one or more battery cells connected in series or parallel. The intelligent protection board 2 is located inside the lithium battery pack 1 and is electrically connected to the electrodes and temperature acquisition points of the lithium battery pack 1, used to monitor and manage the operating status of the lithium battery pack 1.

[0026] The intelligent protection board 2 integrates a data acquisition IC chip 21, a microcontroller (MCU) 22, an edge AI coprocessor 23, and a memory 24. The data acquisition IC chip 21 is used to collect various internal status data of the lithium battery pack 1, such as the voltage of each individual cell, the total current of the battery pack, and the temperature at multiple preset locations.

[0027] A microcontroller (MCU) 22, connected to the data acquisition IC chip 21, receives and processes internal status data and executes basic protection logic, such as overcharge, over-discharge, overcurrent, and over-temperature protection. An edge AI coprocessor 23, connected to the MCU 22, has a hardware architecture suitable for performing neural network operations to execute AI prediction models. A memory 24, connected to both the edge AI coprocessor 23 and the MCU 22, stores preset AI prediction models, running code, and temporary data generated during operation.

[0028] The dual-mode communication module 3 is connected to the microcontroller (MCU) 22 on the smart protection board 2 via a board-level communication interface 25, for example, a 485 communication protocol interface. The dual-mode communication module 3 integrates near-field communication (NFC) circuitry and Bluetooth Low Energy (BLE) circuitry, giving it two independent wireless communication capabilities.

[0029] Terminal receiving device 4 is an external device with NFC and Bluetooth communication capabilities, such as a smartphone. There is no physical connection between terminal receiving device 4 and dual-mode communication module 3; they exchange data via NFC or BLE wireless communication protocols. Terminal receiving device 4 runs a dedicated application that is used to construct and send context vectors containing external operational information, and to receive and display enhanced battery information generated by smart protection board 2.

[0030] In this embodiment, the data interaction process between the various components of the system is as follows: The smart protection board 2 acquires the internal state data of the lithium battery pack 1 through the data acquisition IC chip 21. Simultaneously, the terminal receiving device 4 sends a context vector to the dual-mode communication module 3 via NFC, which then transmits the context vector to the smart protection board 2 via the board-level communication interface 25. The edge AI coprocessor 23 of the smart protection board 2 performs calculations based on the internal state data and the context vector to generate enhanced battery information. Finally, this enhanced battery information is wirelessly transmitted to the terminal receiving device 4 via the NFC circuit of the smart protection board 2, the board-level communication interface 25, and the dual-mode communication module 3.

[0031] See attached document Figure 2 , Figure 2 This is a flowchart of a method according to an embodiment of the present invention. This embodiment provides a method for conveniently viewing information via NFC output on a lithium battery, and this method can be applied to the system of the aforementioned embodiment. The method may include the following steps: S201, the intelligent protection board 2 periodically broadcasts status beacons via dual-mode communication module 3 in Bluetooth Low Energy (BLE) mode.

[0032] S202, the terminal receiving device 4 sends the context vector to the smart protection board 2 via near field communication (NFC).

[0033] S203, Smart Protection Board 2 generates enhanced battery information that includes prediction results.

[0034] S204, the smart protection board 2 transmits enhanced battery information back to the terminal receiving device 4 via NFC.

[0035] S2o5, after the task is completed, the intelligent protection board 2 adjusts the method for generating enhanced battery information based on actual performance data.

[0036] In a specific execution flow, step S201 is executed first, which is an optional preparatory step. The microcontroller (MCU) 22 of the smart protection board 2 periodically instructs the Bluetooth Low Energy (BLE) circuit in the dual-mode communication module 3 to broadcast. The broadcast data packet is a status beacon, whose data structure contains the unique identification code of the lithium battery pack 1 (…). ) and a macroscopic health status level initially assessed by the intelligent protection board 2 based on internal status data ( ) A dedicated application on terminal receiving device 4 scans and receives the status beacon in the background, and when the parsed... When preset prompt conditions are met, the application sends a notification to the user, prompting the user to perform subsequent NFC information viewing operations if necessary.

[0037] Subsequently, step S202 is executed. The user brings the terminal receiving device 4 close to the NFC antenna area of ​​the dual-mode communication module 3 to establish an NFC communication link. The dedicated application on the terminal receiving device 4 constructs a standardized context vector based on the user's input or data collected by the phone's sensors. This context vector It includes external work information, such as a task feature vector composed of task distance and estimated load type. ), an environmental feature vector composed of ambient temperature and altitude ( ), and a user preference vector composed of the performance modes selected by the user ( The context vector ( The NFC write command is sent to the dual-mode communication module 3 and transmitted to the smart protection board 2 via the board-level communication interface 25.

[0038] Subsequently, step S203 is executed. The microcontroller (MCU) 22 of the intelligent protection board 2 receives the situation vector ( After that, the internal state data of the lithium battery pack 1 is first acquired in real time through its data acquisition IC chip 21, and then constructed into an internal state data vector. The vector ( This includes parameters such as the voltage of each individual cell, total current, temperature at each measuring point, and internal anode material of the battery.

[0039] Next, the microcontroller (MCU) 22 will send the context vector ( ) and internal state data vector ( The data is transmitted together to the edge AI coprocessor 23 inside the device. The edge AI coprocessor 23 then calls an AI prediction model stored in memory 24. The received two vectors are then used as input to the model for an inference operation. This operation can be expressed by the following formula: ; in, This indicates enhanced battery information in the computational output; This represents the function that the AI ​​prediction model represents; This represents a set of weight parameters for the AI ​​prediction model. The output... It contains the prediction results for this specific scenario. For example, predicting battery life. In another embodiment, It can also include a dynamic safety working boundary ( This boundary is based on and The calculated parameters are a set of safe operating parameters recommended to the user, such as maximum continuous discharge current and peak power limit.

[0040] Subsequently, step S204 is executed. The intelligent protection board 2 will generate a result containing the prediction ( Enhanced battery information () The information is transmitted back to the terminal receiving device 4 via an NFC communication link. A dedicated application on the terminal receiving device 4 receives the information and displays it visually on the display interface.

[0041] Subsequently, step S204 is executed. The intelligent protection board 2 will generate a result containing the prediction ( Enhanced battery information () The information is transmitted back to the terminal receiving device 4 via an NFC communication link. A dedicated application on the terminal receiving device 4 receives the information and displays it visually on the display interface.

[0042] After the above process is completed, once the lithium battery pack 1 has completed one task, step S205 can be executed. During the task execution, the intelligent protection board 2 will continuously record various performance parameters of the lithium battery pack 1 and organize them into an actual performance data vector after the task is completed. The next time the user interacts via NFC, the edge AI coprocessor 23 of the smart protection board 2 will perform an update of the model parameters.

[0043] The update process is as follows: First, using a preset loss function ( ) Calculate the prediction result generated last time Compared with the actual performance data vector recorded this time The difference between ( Subsequently, based on this difference value ( The gradient descent algorithm is used to calculate a model for correcting AI predictions. Weight parameters gradient of ) This process involves updating the weight parameters, and ultimately completing the update. This process can be expressed by the following formula: ; ; in, and These represent the weight parameters of the AI ​​prediction model before and after the update, respectively. It is a preset.

[0044] The learning rate parameter is used to control the update step size. This step allows the AI ​​prediction model to be adjusted based on actual usage data, thereby outputting results in subsequent predictions that better reflect the actual condition of the lithium battery pack.

[0045] See attached document Figure 3 , Figure 3 This is a schematic diagram illustrating an application scenario according to an embodiment of the present invention. To further illustrate the specific process of the aforementioned system and method in practical applications, the present invention provides the following embodiment. This embodiment describes a scenario where the technical solution of the present invention is applied to a lithium battery pack 1 of a drone 5.

[0046] In this application scenario, a user plans the flight mission of the drone 5 through a dedicated application on their terminal receiving device 4 (e.g., a smartphone). During the mission planning phase, the dedicated application acquires or receives external operational information related to the flight mission. For example, the user plans an aerial photography route on a map, and the application calculates the mission distance accordingly; the application obtains local weather data through a network interface to get the ambient temperature; the user selects a preset "high-speed movement" operating mode on the program interface. This information is combined to construct a context vector. .

[0047] Before drone 5 takes off, the user brings the terminal receiving device 4 close to the NFC sensing area on the drone 5. The terminal receiving device 4 then uses NFC communication to transmit a context vector containing the aforementioned mission distance, ambient temperature, and preset operating mode. The information is sent to the lithium battery pack 1 inside the drone 5. The smart protection board 2 inside the lithium battery pack 1 receives the situation vector. [00XX] The intelligent protection board 2 receives the context vector ( At the same time, through its internal data acquisition IC chip 21, it collects the internal state data of the lithium battery pack 1 in real time, such as the voltage of each cell, the current temperature of the battery, the internal resistance of the battery, etc., and constructs them into an internal state data vector. Subsequently, the edge AI coprocessor 23 inside the smart protection board 2 executes a pre-built AI prediction model ( ), and will and As input to the model.

[0048] After model computation, enhanced battery information containing prediction results is generated. ).Should This includes a battery life prediction for this "high-speed motion" mode flight mission; for example, the predicted available flight time is 18 minutes. Meanwhile, this... It also includes a dynamic safety working boundary ( For example, it is recommended in this boundary that the maximum peak discharge current of this flight should be controlled below a certain value to cope with the high load brought about by the "high speed motion" mode.

[0049] Smart Protection Board 2 will provide this enhanced battery information ( The data is transmitted back to the terminal receiving device 4 via NFC. After receiving the data, the dedicated application displays "Predicted flight time: 18 minutes" on the user interface and prompts "High-speed motion mode is enabled, it is recommended to pay attention to instantaneous power".

[0050] Users execute flight missions based on this information. Throughout the flight of the UAV 5, the intelligent protection board 2 continuously records the actual performance parameters of the battery at a preset frequency, such as real-time voltage drop curves, current output curves, and temperature change curves. After the flight mission ends, these recorded data are integrated into an actual performance data vector for this mission. ).

[0051] After the flight mission concludes and the drone 5 lands, when the user interacts with the lithium battery pack 1 again via NFC using the terminal receiving device 4 (e.g., during a pre-flight check), the edge AI coprocessor 23 inside the smart protection board 2 is triggered to update the model parameters. It updates the stored prediction results from the previous mission (flight duration 18 minutes) with the actual performance data vector recorded for this mission. Compare the two, calculate the difference between them, and use this to evaluate the AI ​​prediction model ( ) internal weight parameters ( A fine-tuning is then performed. In this way, if the actual flight time is only 17.5 minutes due to battery aging or specific environmental factors, the updated model will predict a more accurate 17.5 minutes when encountering similar scenario vectors in the future, thus achieving adaptive optimization of predictive capabilities.

[0052] See attached document Figures 4-5 , Figure 4 This is a schematic diagram of the internal structure and data flow of a smart protection board according to an embodiment of the present invention. The diagram further illustrates the specific connection relationships and data interaction processes between the various components within the smart protection board 2.

[0053] In one specific implementation, the operation of the intelligent protection board 2 is as follows: The data acquisition IC chip 21 continuously samples parameters such as voltage, current, and temperature of the lithium battery pack 1, and sends this raw data to the microcontroller (MCU) 22 via the internal data bus. The microcontroller (MCU) 22 processes and formats this raw data to form a structured internal state data vector. .

[0054] When an information viewing request is triggered, the microcontroller (MCU) 22 receives a context vector sent by the external terminal receiving device 4 from the dual-mode communication module 3 via the board-level communication interface 25. ). Upon receiving the complete Then, the microcontroller (MCU) 22 performs a data integration operation, that is, it integrates the latest internal state data vector. With the received context vector Pack.

[0055] Subsequently, the microcontroller (MCU) 22 will package the... and The two data vectors are sent together to the edge AI coprocessor 23, along with an instruction to perform inference operations. Upon receiving the instruction and data, the edge AI coprocessor 23 loads a pre-set AI prediction model from memory 24. The weight parameters of ) ), and perform a forward propagation operation. The output of this operation is the result containing the prediction ( Enhanced battery information () .

[0056] The edge AI coprocessor 23 will generate the computation The data is sent back to the microcontroller (MCU) 22. The microcontroller (MCU) 22 receives... Then, it is sent to the dual-mode communication module 3 through the board-level communication interface 25, and finally sent to the terminal receiving device 4 by the NFC circuit of the module.

[0057] In the adaptive update process of model parameters, the microcontroller (MCU) 22 is responsible for buffering and processing a series of key performance data collected by the data acquisition IC chip 21 within a complete task cycle, and integrating them into a structured actual performance data vector after the task is completed. Then it is stored in a designated area of ​​memory 24. Meanwhile, the prediction result generated in the previous interaction ( ) are also stored together.

[0058] During the next NFC interaction that triggers the update process, the microcontroller (MCU) 22 reads from the memory 24. and The two data vectors are then transmitted to the edge AI coprocessor 23, along with an instruction to perform a model update operation. The edge AI coprocessor 23 calculates the model weight parameters based on its built-in loss function and optimization algorithm (such as gradient descent). Update volume () .

[0059] Finally, the edge AI coprocessor 23 uses the calculated update amount to directly modify and overwrite the AI ​​prediction model stored in memory 24. The weight parameters of ) This completes a localized and adaptive update of the model. The entire update process is completed in a closed loop within the intelligent protection board 2, without the need for external device intervention or cloud server computing power support.

[0060] 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 method for conveniently viewing information via NFC output on a lithium battery, characterized in that, Includes the following steps: Step (a) The protection board of the lithium battery collects the internal status data of the battery pack in real time through its internal IC chip; Step (b) The terminal receiving device sends a context vector containing external working information to the NFC module connected to the protection board via near-field communication; The protection board in step (c) generates enhanced battery information based on the received context vector and the collected internal state data. This enhanced battery information includes predictions of the future performance of the battery. In step (d), the protection board sends the enhanced battery information to the terminal receiving device for display via the NFC module.

2. The method for conveniently viewing information via NFC output applied to lithium batteries according to claim 1, characterized in that, The enhanced battery information generated in step (c) specifically involves: The edge AI coprocessor inside the protection board runs an AI prediction model, taking the context vector and the internal state data as input, and outputs the enhanced battery information containing the prediction results.

3. The method for conveniently viewing information via NFC output applied to lithium batteries according to claim 1, characterized in that, The context vector includes at least one piece of information selected from task distance, ambient temperature, and preset working mode.

4. The method for conveniently viewing information via NFC output applied to lithium batteries according to claim 1, characterized in that, The prediction results included in the enhanced battery information are predictions of battery life under the scenario defined by the context vector.

5. The method for conveniently viewing information via NFC output applied to lithium batteries according to claim 1, characterized in that, The method also includes steps to optimize subsequent viewing results using the enhanced battery information: Step (e) After the task is executed, the protection board records the actual performance data of the battery pack; In step (f), during the next interaction via near field communication (NFC), the protection board adjusts the method for generating the enhanced battery information based on the actual performance data and the previously generated prediction results.

6. A method for conveniently viewing information via NFC output in lithium batteries according to claim 5, characterized in that, The adjustment to the method for generating the enhanced battery information in step (f) is specifically as follows: The protection board calculates the difference between the predicted result and the actual performance data, and updates the parameters of the AI ​​prediction model used to generate the predicted result based on the difference.

7. The method for conveniently viewing information via NFC output applied to lithium batteries according to claim 1, characterized in that, The enhanced battery information also includes a dynamic safety operating boundary, which is a safety operating parameter recommended to the user when viewing the information, calculated based on the context vector and the internal state data.

8. The method for conveniently viewing information via NFC output applied to lithium batteries according to claim 1, characterized in that, Prior to step (b), it also includes the following step: The lithium battery broadcasts a status beacon indicating its macroscopic health status via Bluetooth Low Energy, prompting the user to initiate information viewing via near-field communication when necessary.

9. A method for conveniently viewing information via NFC output applied to lithium batteries according to claim 1, characterized in that, The protection board and the NFC module transmit data via the 485 communication protocol.

10. A system for convenient information viewing via NFC output in lithium batteries, characterized in that, include: The protection board is used to collect internal state data of the lithium battery and is configured to receive context vectors. The NFC module is connected to the protection board; The protection board is further configured to perform the method as described in any one of claims 1-9 to generate and output enhanced battery information.

Images