Battery health state monitoring method based on Bluetooth Beacon long data packet broadcast
The battery health status monitoring method using Bluetooth Beacon long data packet broadcast solves the compatibility, real-time performance, security, and maintenance cost issues of electric bicycle battery monitoring systems. It achieves seamless access to full-domain battery data and real-time risk identification, thereby improving the safety and maintenance efficiency of electric bicycle batteries.
Patent Information
- Application Number
- CN202511499730.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-20
- Publication Date
- 2026-02-13
AI Technical Summary
Existing electric bicycle battery monitoring systems suffer from problems such as incompatibility between regulatory platforms and multiple brands of equipment, data silos, poor real-time performance making it difficult to capture sudden anomalies, weak security making them vulnerable to attacks, coarse-grained risk assessment, and high operation and maintenance costs.
A battery health status monitoring method based on Bluetooth Beacon long data packet broadcasting is adopted. Through a unified data reporting standard protocol, Bluetooth gateway, mobile app and cloud server, combined with a machine learning-driven anomaly detection and battery health assessment framework, real-time data collection, analysis and early warning are realized.
It enables seamless access and centralized management of battery data across the entire domain, real-time identification of potential signs of thermal runaway, improved safety and operational efficiency, and reduced the risk of fire accidents.
Smart Images
Figure CN121531328A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of electric bicycle battery fire safety monitoring, in particular to a battery health state monitoring method based on Bluetooth Beacon long data packet broadcast. BACKGROUND
[0002] In recent years, electric bicycle fire accidents have occurred frequently, causing casualties and property losses, and there is an urgent need for a solution that does not increase the production cost of electric bicycle batteries, does not require a large amount of government funds and manpower, and only solves various management problems of electric bicycle batteries from the technical and standard levels.
[0003] Traditional electric bicycle battery monitoring relies on brand private protocols, which makes it impossible for the supervision platform to be compatible with multiple brands of equipment, forming a data island; existing technologies mainly rely on regular manual inspection or data analysis after charging, making it difficult to capture sudden abnormalities (such as premonitory signs of thermal runaway during charging); traditional wireless transmission is vulnerable to man-in-the-middle attacks or data tampering, and local devices lack encryption capabilities; existing solutions rely mainly on threshold alarms, which have a high false alarm rate and are difficult to predict implicit degradation; and manual inspection has low coverage and relies on experience. Therefore, we propose a battery health state monitoring method based on Bluetooth Beacon long data packet broadcast. SUMMARY
[0004] The purpose of the present application is to solve the problems mentioned in the background art, and the present application provides a battery health state monitoring method based on Bluetooth Beacon long data packet broadcast.
[0005] In order to achieve the above-mentioned purpose, the present application specifically adopts the following technical solutions:
[0006] A battery health state monitoring method based on Bluetooth Beacon long data packet broadcast, comprising: an electric bicycle battery health state data reporting standard protocol, a power module, a Bluetooth gateway, a mobile phone App and a cloud server; wherein the electric bicycle battery health state data reporting standard protocol realizes a comprehensive supervision standard for all electric bicycle batteries, all electric bicycle brand batteries report data according to a unified standard protocol, ensuring that the Bluetooth gateway and the mobile phone App obtain the health state data of all brand electric bicycle batteries; the power module is used to measure the voltage, current, temperature, internal resistance and other information of the battery, and broadcast the measurement values to the outside through the Bluetooth Beacon long data packet protocol; the Bluetooth gateway and the mobile phone App are used to receive Bluetooth Beacon long data packets and transmit the received data to the cloud server; the cloud server is used to decrypt, analyze and store the data in the Bluetooth Beacon long data packet sent by the Bluetooth gateway, discover abnormal conditions, notify the electric bicycle owner, property management, fire-related units in the first time, and realize the monitoring and early warning of the health state of the electric bicycle battery.
[0007] Further, the power module is built-in with a BMS and a Bluetooth unit, wherein the BMS is used to measure the voltage, current, temperature, and internal resistance information of the battery, and broadcast the measurement values to the Bluetooth gateway or the mobile phone App through the Bluetooth Beacon long data packet protocol; the Bluetooth unit supports the Bluetooth function of the Beacon long data packet.
[0008] Further, the Bluetooth gateway receives the Bluetooth beacon long data packet of multiple battery BMSs, obtains the RSSI value of the battery BMS Bluetooth beacon device connected to the Bluetooth gateway, and transmits the broadcast data to the cloud server through the network.
[0009] Further, the Bluetooth gateway is installed at the elevator or the doorway, when the Bluetooth gateway receives the battery BMS Bluetooth beacon data, the Bluetooth gateway automatically voice alarms (batteries and electric bicycles are prohibited from entering the doorway), and at the same time sends a signal to the elevator light curtain interface, and the elevator does not close the door.
[0010] Further, the Bluetooth gateway is installed in the charging shed, when the Bluetooth gateway receives the battery BMS Bluetooth beacon data, the cloud server judges that the battery of the electric bicycle is about to have thermal runaway, the cloud server sends an instruction to the charging device interface in the first time to cut off the battery charging of the dangerous electric bicycle, and at the same time notifies the owner, property, and fire department, and eliminates the fire hazard in the first time.
[0011] Further, the mobile phone App is used to realize the battery inspection of the electric bicycle, and the property, fire department, and grid staff can use the mobile phone App to regularly inspect all electric bicycle batteries in the jurisdiction, and timely find potential safety hazards, and the mobile phone App receives the Bluetooth beacon long data packet of multiple battery BMSs, and transmits the broadcast data to the cloud server through the network.
[0012] Further, the decryption of the cloud server is based on the Advanced Encryption Standard (AES) algorithm and a dynamic key management mechanism, which ensures the confidentiality and integrity of the Bluetooth beacon data transmitted from the mobile phone App or the Bluetooth gateway when processed in the cloud, prevents unauthorized access or data tampering, and thus supports real-time risk assessment and instruction issuance.
[0013] Further, the analysis of the cloud server is based on the received Bluetooth beacon data and the built-in intelligent algorithm model, which real-time analyzes the key parameters such as battery temperature, voltage, and current to accurately identify potential thermal runaway signs, and generates a risk assessment report, thereby supporting rapid decision-making and proactive intervention.
[0014] Further, the intelligent algorithm model comprises a machine learning driven anomaly detection mechanism and a battery health assessment framework; wherein the anomaly detection mechanism monitors the historical trend and real-time fluctuation of battery parameters in real time through a machine learning model, identifies deviation patterns such as sudden temperature rise, abnormal voltage fluctuation or unstable current, the battery health assessment framework learns the normal degradation trajectory of the battery based on historical operation data statistical analysis and multi-dimensional feature extraction, evaluates the health status and predicts the remaining service life, generates a health score in combination with real-time parameters and environmental factors, and works together with the anomaly detection mechanism to optimize the accuracy of the risk assessment report.
[0015] Further, the method comprises the following steps:
[0016] Step S1, the power module measures the voltage, current, temperature and internal resistance parameters of the electric bicycle battery in real time through the built-in BMS, and encapsulates these measurement values into a Bluetooth Beacon long data packet according to the electric bicycle battery health status data reporting standard protocol;
[0017] Step S2, the Bluetooth unit of the power module supports the broadcast function of the Beacon long data packet, and broadcasts the encapsulated data packet at a preset frequency (such as once per second) to ensure that the Bluetooth gateway or mobile phone App can receive it within the effective range;
[0018] Step S3, after the Bluetooth Beacon long data packet is received by the Bluetooth gateway or mobile phone App, the source identification (such as the unique MAC address of the battery BMS) of the data packet is automatically parsed, and the relevant RSSI value is obtained to estimate the distance; at the same time, the Bluetooth gateway is installed at key positions such as elevator entrance, corridor or charging shed, and the mobile phone App is used by the property, fire or grid staff;
[0019] Step S4, the Bluetooth gateway or mobile phone App transmits the received broadcast data to the cloud server in real time through wireless network (such as Wi-Fi or cellular network), ensuring the integrity and real-time of the data during transmission;
[0020] Step S5, the cloud server decrypts the received data based on the advanced encryption standard (AES) algorithm and dynamic key management mechanism, verifies the confidentiality and integrity of the data, and prevents unauthorized access or tampering;
[0021] Step S6: The cloud server uses its built-in intelligent algorithm model (including a machine learning-driven anomaly detection mechanism and a battery health assessment framework) to analyze the decrypted data in real time, and analyze the historical trends and real-time fluctuations of key parameters such as battery temperature, voltage, and current. The anomaly detection mechanism identifies deviation patterns such as sudden temperature rise, abnormal voltage, or unstable current, while the battery health assessment framework combines historical operating data and environmental factors to generate a health score and risk assessment report, and predict the remaining service life.
[0022] Step S7: At the Bluetooth gateway location (e.g., elevator entrance), if the Bluetooth gateway collects a long beacon data packet, it indicates that a battery or electric bicycle has been detected. The Bluetooth gateway will issue a voice alarm saying "Batteries and electric bicycles are prohibited from entering the corridor," and at the same time send a relay signal to the elevator light curtain so that the elevator door does not close. At the Bluetooth gateway location (charging shed), if the cloud server analyzes and finds potential signs of thermal runaway or abnormal risks, it will immediately trigger an early warning mechanism: the cloud server will send a command to the charging equipment interface to immediately cut off the charging power of the dangerous battery.
[0023] Step S8: The cloud server synchronously sends the early warning information (including battery location, risk level, and detailed report) to the electric bicycle owner, property management personnel, and fire department via push notification. It supports real-time viewing and inspection functions on the mobile app. At the same time, the mobile app allows users to manually upload inspection data or receive cloud commands to achieve closed-loop monitoring and proactive intervention to eliminate fire hazards.
[0024] Step S9: The entire monitoring process is continuously and cyclically executed to ensure real-time and comprehensive monitoring of the health status of electric bicycle batteries; the cloud server stores all historical data and early warning records for subsequent algorithm optimization and regulatory auditing.
[0025] The beneficial effects of this invention are as follows:
[0026] 1. This invention forces all batteries to broadcast data in the same Bluetooth Beacon long data packet format through a unified reporting standard protocol, completely breaking down brand barriers and achieving seamless access and centralized management of battery data across the entire domain.
[0027] 2. This invention utilizes dual-channel real-time data acquisition via a Bluetooth gateway and a mobile app, combined with a cloud-based millisecond-level risk analysis model, to complete the entire process from data reception, decryption, analysis to command issuance within 3 seconds. Especially when deployed in critical areas such as elevator entrances and charging sheds, the interconnected gateway can proactively intervene through physical interception (e.g., disabling the elevator) or remote power outages, transforming reactive measures into proactive prevention.
[0028] 3. This invention employs AES-256 end-to-end encryption with dynamic key management to ensure the confidentiality of data throughout the entire data link from BMS to the cloud. The key is rotated every 30 minutes and bound to a unique device identifier, so even if a single communication is intercepted, historical data cannot be decrypted.
[0029] 4. The anomaly detection mechanism of this invention monitors the historical trends and real-time fluctuations of battery parameters in real time through a machine learning model, and identifies deviation patterns such as sudden temperature rise, abnormal voltage fluctuations, or unstable current. The battery health assessment framework is based on statistical analysis of historical operating data and multi-dimensional feature extraction. It uses a deep neural network model to learn the normal degradation trajectory of the battery, assesses its health status and predicts its remaining service life. It combines real-time parameters and environmental factors to generate a health score and works in conjunction with the anomaly detection mechanism to jointly optimize the accuracy of the risk assessment report.
[0030] 5. The mobile inspection function of the mobile app in this invention supports scanning no less than 100 battery devices within a 30-meter radius in a single scan, automatically generating a heat map report containing location tags, which improves the efficiency of a single person's inspection by more than 10 times. At the same time, historical data is automatically archived in the cloud, providing a basis for decision-making to optimize the maintenance cycle.
[0031] 6. This invention enables the monitoring and early warning of battery health status through a standard protocol for reporting electric bicycle battery health status data, a battery BMS module with Bluetooth functionality, a Bluetooth gateway, data communication between a mobile app and a cloud server, thereby preventing fire accidents caused by electric bicycle batteries from the source. Attached Figure Description
[0032] Figure 1 This is a module connection diagram of the present invention;
[0033] Figure 2 This is a flowchart of the process of the present invention. Detailed Implementation
[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.
[0035] Please see Figure 1 - Figure 2This invention provides a battery health status monitoring method based on Bluetooth Beacon long data packet broadcasting, comprising: a standard protocol for reporting electric bicycle battery health status data, a power module, a Bluetooth gateway, a mobile app, and a cloud server; wherein, the standard protocol for reporting electric bicycle battery health status data realizes a comprehensive regulatory standard for all electric bicycle batteries, and all electric bicycle brand batteries report data according to a unified standard protocol, ensuring that the Bluetooth gateway and mobile app can obtain health status data of electric bicycle batteries of all brands; the power module is used to measure information such as battery voltage, current, temperature, and internal resistance, and broadcasts the measured values externally through the Bluetooth Beacon long data packet protocol; the Bluetooth gateway and mobile app are used to receive Bluetooth Beacon long data packets and transmit the received data to the cloud server; the cloud server is used to decrypt, analyze, and store the data in the Bluetooth Beacon long data packets sent by the Bluetooth gateway, and upon detecting abnormalities, immediately notify the electric bicycle owner, property management, and relevant fire departments, realizing the monitoring and early warning of the electric bicycle battery health status.
[0036] In this embodiment, preferably, the power module integrates a Battery Management System (BMS) and a Bluetooth unit. The BMS measures the battery's voltage, current, temperature, and internal resistance, and broadcasts the measured values to a Bluetooth gateway or mobile app via the Bluetooth Beacon long data packet protocol. The Bluetooth unit supports Bluetooth Beacon long data packet functionality. The coordinated operation of the BMS and Bluetooth unit enables efficient and stable real-time broadcasting of battery health data, ensuring accurate transmission of measured values to the Bluetooth gateway or mobile app. Through the precise data acquisition of the BMS and the protocol support of the Bluetooth unit, the two work together to optimize the data transmission process, reduce power consumption, and extend the overall lifespan of the power module. Simultaneously, it supports maintaining a stable signal connection in complex environments, avoiding data loss or delay, providing a solid data foundation for subsequent decryption, analysis, and anomaly warning on the cloud server, ultimately achieving comprehensive and reliable monitoring of the electric bicycle battery's health status.
[0037] In this embodiment, preferably, the Bluetooth gateway receives long data packets from multiple battery BMS Bluetooth beacon devices, obtains the RSSI value of the battery BMS Bluetooth beacon devices connected to the Bluetooth gateway, and transmits the broadcast data to the cloud server via the network. This allows for real-time reception and processing of monitoring data from multiple batteries, combining RSSI values to analyze signal strength and connection stability. This ensures the cloud server efficiently performs data decryption, status analysis, and anomaly warnings, improving the comprehensiveness and reliability of battery health status monitoring.
[0038] In this embodiment, preferably, the Bluetooth gateway is installed at the elevator or stairwell entrance. When the Bluetooth gateway receives Bluetooth beacon data from the battery BMS, it automatically issues a voice alarm (batteries and electric bicycles are prohibited from entering the stairwell) and simultaneously sends a signal to the elevator light curtain interface, preventing the elevator doors from closing. This setup can detect and prevent batteries carrying potential safety risks from entering the stairwell or elevator in real time, effectively preventing fires caused by battery overheating, short circuits, or other abnormal conditions. Combined with battery health status data, when the beacon information broadcast by the BMS shows that the voltage, temperature, or capacity exceeds the safety threshold, the Bluetooth gateway immediately triggers a linkage mechanism. It not only alerts the user through a voice alarm but also coordinates with the elevator system to forcibly suspend operation, ensuring that high-risk batteries are isolated outside public areas. This greatly improves the safety of the electric bicycle usage environment and seamlessly integrates with cloud data analysis, providing immediate physical response support for abnormal warnings, ultimately achieving a closed-loop management system from data monitoring to proactive protection.
[0039] In this embodiment, preferably, the Bluetooth gateway is installed in the charging shed. When the Bluetooth gateway receives Bluetooth beacon data from the battery BMS, the cloud server determines that the electric bicycle battery is about to experience thermal runaway. The cloud server immediately sends a command to the charging equipment interface to cut off the charging of the dangerous electric bicycle's battery, and simultaneously notifies the owner, property management, and fire department to eliminate fire hazards immediately. This setup enables real-time monitoring and intervention of potentially dangerous batteries that are charging. Once the cloud server identifies abnormal increases in parameters such as voltage and temperature based on the beacon information broadcast by the BMS and reaches the thermal runaway warning threshold, it immediately triggers a charging interruption command, effectively cutting off the energy supply source. At the same time, it simultaneously pushes warning information to the owner's mobile APP, property management system, and fire emergency platform, building a multi-party collaborative rapid response mechanism. This not only significantly reduces the probability of fires in charging scenarios but also transforms post-event handling into pre-event prevention. Through deep integration with cloud data analysis models, it achieves fully automated management from risk identification to proactive intervention, significantly improving the safety protection level of centralized electric bicycle charging areas in the community.
[0040] In this embodiment, preferably, a mobile app is used to inspect electric bicycle batteries. Property management, fire department personnel, and grid workers can use the app to conduct routine inspections of all electric bicycle batteries in their jurisdiction, promptly identifying potential safety hazards. The app also receives long Bluetooth beacon data packets from multiple battery management systems (BMS) and transmits the broadcast data to a cloud server via the network. This setup enables real-time battery health status inspections on mobile devices, allowing property management, fire department personnel, and grid workers to efficiently collect Bluetooth beacon data from multiple electric bicycles during daily inspections, including key parameters such as voltage and temperature. This data is then synchronized in real-time to the cloud server for in-depth analysis. This proactively identifies potential hazards such as battery aging and short circuits even in non-charging scenarios, sending early warning information to relevant responsible parties. This forms a closed-loop risk prevention and control system covering the entire battery lifecycle, effectively preventing thermal runaway events and assisting decision-makers in optimizing inspection routes and frequencies, further improving the efficiency of community battery safety management.
[0041] In this embodiment, preferably, the cloud server's decryption is based on the Advanced Encryption Standard (AES) algorithm and a dynamic key management mechanism. This ensures that Bluetooth beacon data transmitted from the mobile app or Bluetooth gateway remains confidential and intact during cloud processing, preventing unauthorized access or data tampering, thereby supporting real-time risk assessment and command issuance. This setup effectively ensures end-to-end security during data transmission, especially when handling sensitive battery parameters such as voltage and temperature. The powerful encryption capabilities of the AES algorithm and the periodic rotation mechanism of the dynamic key prevent man-in-the-middle attacks or data theft. Simultaneously, it allows the cloud server to quickly parse the data stream after decryption, run risk assessment models in real time (e.g., anomaly detection algorithms based on machine learning), identify early signals such as overvoltage, overtemperature, or internal short circuits, and automatically issue precise commands, such as remotely pausing charging, triggering local alarms, or notifying maintenance personnel. Furthermore, dynamic key management can be integrated with a user access control system to ensure that only authorized roles (such as property managers or fire departments) can access critical data. This improves response efficiency while strengthening the compliance and auditability of the entire system, ultimately achieving a more proactive and intelligent closed-loop battery health management system.
[0042] In this embodiment, preferably, the cloud server's analysis is based on received Bluetooth beacon data and a built-in intelligent algorithm model. It analyzes key parameters such as battery temperature, voltage, and current in real time to accurately identify potential signs of thermal runaway and generate a risk assessment report, thereby supporting rapid decision-making and proactive intervention. This setup effectively ensures end-to-end security during data transmission, especially when handling sensitive battery parameters such as voltage and temperature. The strong encryption capabilities of the AES algorithm and the periodic rotation mechanism of dynamic keys prevent man-in-the-middle attacks or data theft. Simultaneously, the cloud server can efficiently parse the data stream after decryption, run a machine learning-based risk assessment model in real time, accurately identify early abnormal signals such as overvoltage, overtemperature, or internal short circuits, and automatically trigger remote intervention measures such as pausing charging, local alarms, or maintenance notifications. Furthermore, dynamic key management can seamlessly integrate with the user access control system, ensuring that only authorized roles (such as property managers or fire departments) access critical data, thereby improving response efficiency while strengthening system compliance, auditability, and the overall intelligent management loop.
[0043] In this embodiment, preferably, the intelligent algorithm model includes a machine learning-driven anomaly detection mechanism and a battery health assessment framework. The anomaly detection mechanism uses a machine learning model to monitor historical trends and real-time fluctuations of battery parameters, identifying deviation patterns such as sudden temperature increases, abnormal voltage fluctuations, or unstable current. The battery health assessment framework, based on statistical analysis of historical operating data and multi-dimensional feature extraction, uses a deep neural network model to learn the normal degradation trajectory of the battery, assessing its health status and predicting its remaining lifespan. It combines real-time parameters with environmental factors to generate a health score, working in conjunction with the anomaly detection mechanism to jointly optimize the accuracy of the risk assessment report. This setup enables real-time integration of multi-source sensor data, combined with external variables such as ambient temperature and humidity, and charging / discharging frequency, dynamically adjusting model thresholds to effectively reduce false alarm rates and improve early warning sensitivity. Simultaneously, it supports the automatic generation of visual reports, including heatmaps, trend curves, and changes in health scores, helping users intuitively understand the evolution of battery status. Based on the prediction results, it triggers tiered response strategies, such as prioritizing high-risk battery cells or optimizing maintenance plans, thereby ensuring system stability while achieving efficient resource allocation and a closed-loop full lifecycle management system.
[0044] The synergistic combination of the above components can effectively solve the following core problems in the prior art:
[0045] Fragmented regulation and insufficient compatibility: Traditional electric bicycle battery monitoring relies on brand-specific protocols, resulting in regulatory platforms being incompatible with multiple brands of devices and creating data silos. This invention, through a unified reporting standard protocol, forces all batteries to broadcast data using the same Bluetooth Beacon long data packet format, completely breaking down brand barriers and achieving seamless access and centralized management of battery data across the entire domain.
[0046] Poor real-time performance and lack of proactive protection: Existing technologies mainly rely on periodic manual inspections or post-charging data analysis, making it difficult to promptly detect sudden anomalies (such as precursors to thermal runaway during charging). This invention utilizes dual-channel real-time data acquisition via a Bluetooth gateway and a mobile app, combined with a cloud-based millisecond-level risk analysis model, to complete the entire process from data reception, decryption, analysis to command issuance within 3 seconds. Especially when deployed in key areas such as elevator entrances and charging sheds, the interconnected gateway can proactively intervene through physical interception (such as disabling the elevator) or remote power cutoff, transforming reactive measures into proactive prevention.
[0047] Weak security: Traditional wireless transmission is vulnerable to man-in-the-middle attacks or data tampering, and local devices lack encryption capabilities. This invention employs AES-256 end-to-end encryption with dynamic key management to ensure the confidentiality of data throughout the entire link from BMS to the cloud. The key is rotated every 30 minutes and bound to a unique device identifier, so even if a single communication is intercepted, historical data cannot be decrypted.
[0048] Coarse-grained risk assessment: Existing solutions mostly rely on threshold alarms, resulting in high false alarm rates and difficulty in predicting hidden degradation. The intelligent algorithm model of this invention integrates a dual mechanism. The anomaly detection mechanism uses a machine learning model to monitor historical trends and real-time fluctuations of battery parameters, identifying deviation patterns such as sudden temperature increases, abnormal voltage fluctuations, or unstable current. The battery health assessment framework, based on statistical analysis of historical operating data and multi-dimensional feature extraction, uses a deep neural network model to learn the normal degradation trajectory of the battery, assessing its health status and predicting its remaining lifespan. Combining real-time parameters and environmental factors, a health score is generated and works in conjunction with the anomaly detection mechanism to jointly optimize the accuracy of the risk assessment report.
[0049] High maintenance costs: manual inspections are infrequent and rely on experience. The mobile inspection function of the mobile app in this invention supports scanning no fewer than 100 battery devices within a 30-meter radius in a single operation, automatically generating a heat map report with location tags, which improves the efficiency of a single inspection by more than 10 times. At the same time, historical data is automatically archived in the cloud, providing a basis for decision-making to optimize maintenance cycles.
[0050] In summary, this invention enables the monitoring and early warning of battery health status through a standard protocol for reporting electric bicycle battery health status data, a battery BMS module with Bluetooth functionality, a Bluetooth gateway, data communication between a mobile app and a cloud server, thereby preventing fires caused by electric bicycle batteries from the source.
[0051] The working principle and usage process of this invention include the following steps:
[0052] Step S1: The power module measures the voltage, current, temperature and internal resistance parameters of the electric bicycle battery in real time through the built-in BMS, and encapsulates these measured values into Bluetooth Beacon long data packets according to the standard protocol for reporting the health status data of electric bicycle batteries.
[0053] Step S2: The Bluetooth unit of the power module supports the Beacon long data packet broadcasting function, broadcasting the encapsulated data packet to the outside at a preset frequency (such as once per second) to ensure that the Bluetooth gateway or mobile app can receive it within the effective range.
[0054] Step S3: After receiving the long Bluetooth Beacon data packet, the Bluetooth gateway or mobile app automatically parses the source identifier of the data packet (such as the unique MAC address of the battery BMS) and obtains the relevant RSSI value to estimate the distance. Meanwhile, the Bluetooth gateway is installed in key locations such as elevator entrances, corridors, or charging sheds, while the mobile app is used by property management, fire department, or grid workers.
[0055] Step S4: The Bluetooth gateway or mobile app transmits the received broadcast data to the cloud server in real time via a wireless network (such as Wi-Fi or cellular network) to ensure that the data remains intact and real-time during transmission.
[0056] Step S5: The cloud server decrypts the received data based on the Advanced Encryption Standard (AES) algorithm and dynamic key management mechanism to verify the confidentiality and integrity of the data and prevent unauthorized access or tampering.
[0057] Step S6: The cloud server uses its built-in intelligent algorithm model (including a machine learning-driven anomaly detection mechanism and a battery health assessment framework) to analyze the decrypted data in real time, and analyze the historical trends and real-time fluctuations of key parameters such as battery temperature, voltage, and current. Among them, the anomaly detection mechanism identifies deviation patterns such as sudden temperature rise, abnormal voltage, or unstable current, while the battery health assessment framework combines historical operating data and environmental factors to generate a health score and risk assessment report, and predict the remaining service life.
[0058] Step S7: At the Bluetooth gateway location (e.g., elevator entrance), if the Bluetooth gateway collects a long beacon data packet, it indicates that a battery or electric bicycle has been detected. The Bluetooth gateway will issue a voice alarm saying "Batteries and electric bicycles are prohibited from entering the corridor," and at the same time send a relay signal to the elevator light curtain so that the elevator door does not close. At the Bluetooth gateway location (charging shed), if the cloud server analyzes and finds potential signs of thermal runaway or abnormal risks, it will immediately trigger an early warning mechanism: the cloud server will send a command to the charging equipment interface to immediately cut off the charging power of the dangerous battery.
[0059] Step S8: The cloud server synchronously sends the early warning information (including battery location, risk level, and detailed report) to electric bicycle owners, property management personnel, and fire departments via push notification. It supports real-time viewing and inspection functions on the mobile app. At the same time, the mobile app allows users to manually upload inspection data or receive cloud commands to achieve closed-loop monitoring and proactive intervention to eliminate fire hazards.
[0060] Step S9: The entire monitoring process is continuously and cyclically executed to ensure real-time and comprehensive monitoring of the health status of electric bicycle batteries; the cloud server stores all historical data and early warning records for subsequent algorithm optimization and regulatory auditing.
[0061] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for monitoring battery health status based on Bluetooth Beacon long data packet broadcasting, characterized in that, include: The system comprises an electric bicycle battery health status data reporting standard protocol, a power module, a Bluetooth gateway, a mobile app, and a cloud server. The standard protocol enables comprehensive monitoring of all electric bicycle batteries, ensuring all brands report data according to this unified protocol. This guarantees that the Bluetooth gateway and mobile app can access the health status data of all brands of electric bicycle batteries. The power module measures battery voltage, current, temperature, internal resistance, and other information, broadcasting these values via Bluetooth Beacon long data packet protocol. The Bluetooth gateway and mobile app receive Bluetooth Beacon long data packets and transmit the received data to the cloud server. The cloud server decrypts, analyzes, and stores the data within the Bluetooth Beacon long data packets received from the Bluetooth gateway. Upon detecting anomalies, it immediately notifies the electric bicycle owner, property management, and relevant fire departments, enabling monitoring and early warning of the electric bicycle battery's health status.
2. The battery health status monitoring method based on Bluetooth Beacon long data packet broadcasting according to claim 1, characterized in that, The power module has a built-in BMS and Bluetooth unit. The BMS is used to measure the battery's voltage, current, temperature, and internal resistance information, and broadcasts the measured values to the Bluetooth gateway or the mobile app via the Bluetooth Beacon long data packet protocol. The Bluetooth unit supports Bluetooth Beacon long data packet functionality.
3. The battery health status monitoring method based on Bluetooth Beacon long data packet broadcasting according to claim 1, characterized in that, The Bluetooth gateway receives long data packets from multiple battery BMS Bluetooth beacon devices, obtains the RSSI value of the battery BMS Bluetooth beacon devices connected to the Bluetooth gateway, and transmits the broadcast data to the cloud server via the network.
4. The battery health status monitoring method based on Bluetooth Beacon long data packet broadcast according to claim 1, characterized in that, The Bluetooth gateway is installed in the elevator or at the entrance of the building. When the Bluetooth gateway receives Bluetooth beacon data from the battery BMS, it automatically issues a voice alarm (batteries and electric bicycles are prohibited from entering the building) and sends a signal to the elevator light curtain interface, preventing the elevator doors from closing.
5. The battery health status monitoring method based on Bluetooth Beacon long data packet broadcasting according to claim 1, characterized in that, The Bluetooth gateway is installed in the charging shed. When the Bluetooth gateway receives Bluetooth beacon data from the battery BMS, the cloud server determines that the electric bicycle battery is about to experience thermal runaway. The cloud server immediately sends a command to the charging equipment interface to cut off the charging of the dangerous electric bicycle battery, and at the same time notifies the owner, property management and fire department to eliminate the fire hazard as soon as possible.
6. The battery health status monitoring method based on Bluetooth Beacon long data packet broadcast according to claim 1, characterized in that, The mobile app is used to inspect electric bicycle batteries. Property management, fire department, and grid workers can use the mobile app to conduct routine inspections of all electric bicycle batteries in their jurisdiction, promptly identify potential safety hazards, and the mobile app receives long Bluetooth beacon data packets from multiple battery BMSs and transmits the broadcast data to the cloud server via the network.
7. The battery health status monitoring method based on Bluetooth Beacon long data packet broadcast according to claim 1, characterized in that, The cloud server's decryption is based on the Advanced Encryption Standard (AES) algorithm and a dynamic key management mechanism, ensuring that Bluetooth beacon data transmitted from the mobile app or Bluetooth gateway remains confidential and intact when processed in the cloud, preventing unauthorized access or data tampering, thereby supporting real-time risk assessment and command issuance.
8. The battery health status monitoring method based on Bluetooth Beacon long data packet broadcast according to claim 1, characterized in that, The cloud server's analysis is based on received Bluetooth beacon data and a built-in intelligent algorithm model. It analyzes key parameters such as battery temperature, voltage, and current in real time to accurately identify potential signs of thermal runaway and generate risk assessment reports, thereby supporting rapid decision-making and proactive intervention.
9. A battery health status monitoring method based on Bluetooth Beacon long data packet broadcasting according to claim 8, characterized in that, The intelligent algorithm model includes a machine learning-driven anomaly detection mechanism and a battery health assessment framework. The anomaly detection mechanism uses a machine learning model to monitor the historical trends and real-time fluctuations of battery parameters, identifying deviation patterns such as sudden temperature increases, abnormal voltage fluctuations, or unstable current. The battery health assessment framework, based on statistical analysis of historical operating data and multi-dimensional feature extraction, uses a deep neural network model to learn the normal degradation trajectory of the battery, assesses its health status, and predicts its remaining lifespan. It combines real-time parameters with environmental factors to generate a health score and works in conjunction with the anomaly detection mechanism to jointly optimize the accuracy of the risk assessment report.
10. A battery health status monitoring method based on Bluetooth Beacon long data packet broadcasting according to claim 1, characterized in that, Includes the following steps: Step S1: The power module measures the voltage, current, temperature and internal resistance parameters of the electric bicycle battery in real time through the built-in BMS, and encapsulates these measured values into Bluetooth Beacon long data packets according to the standard protocol for reporting the health status data of the electric bicycle battery. Step S2: The Bluetooth unit of the power module supports the broadcast function of Beacon long data packets, and broadcasts the encapsulated data packets to the outside at a preset frequency (such as once per second) to ensure that the Bluetooth gateway or mobile app can receive them within the effective range. Step S3: After receiving the long Bluetooth Beacon data packet, the Bluetooth gateway or mobile app automatically parses the source identifier of the data packet (such as the unique MAC address of the battery BMS) and obtains the relevant RSSI value to estimate the distance. Meanwhile, the Bluetooth gateway is installed in key locations such as elevator entrances, corridors, or charging sheds, while the mobile app is used by property management, fire department, or grid workers. Step S4: The Bluetooth gateway or mobile app transmits the received broadcast data to the cloud server in real time via a wireless network (such as Wi-Fi or cellular network) to ensure that the data remains intact and real-time during transmission. Step S5: The cloud server decrypts the received data based on the Advanced Encryption Standard (AES) algorithm and dynamic key management mechanism to verify the confidentiality and integrity of the data and prevent unauthorized access or tampering. Step S6: The cloud server uses its built-in intelligent algorithm model (including a machine learning-driven anomaly detection mechanism and a battery health assessment framework) to analyze the decrypted data in real time, and analyze the historical trends and real-time fluctuations of key parameters such as battery temperature, voltage, and current. The anomaly detection mechanism identifies deviation patterns such as sudden temperature rise, abnormal voltage, or unstable current, while the battery health assessment framework combines historical operating data and environmental factors to generate a health score and risk assessment report, and predict the remaining service life. Step S7: At the Bluetooth gateway location (e.g., elevator entrance), if the Bluetooth gateway collects a long beacon data packet, it indicates that a battery or electric bicycle has been detected. The Bluetooth gateway will issue a voice alarm saying "Batteries and electric bicycles are prohibited from entering the corridor," and at the same time send a relay signal to the elevator light curtain so that the elevator door does not close. At the Bluetooth gateway location (charging shed), if the cloud server analyzes and finds potential signs of thermal runaway or abnormal risks, it will immediately trigger an early warning mechanism: the cloud server will send a command to the charging equipment interface to immediately cut off the charging power of the dangerous battery. Step S8: The cloud server synchronously sends the early warning information (including battery location, risk level, and detailed report) to the electric bicycle owner, property management personnel, and fire department via push notification. It supports real-time viewing and inspection functions on the mobile app. At the same time, the mobile app allows users to manually upload inspection data or receive cloud commands to achieve closed-loop monitoring and proactive intervention to eliminate fire hazards. Step S9: The entire monitoring process is continuously and cyclically executed to ensure real-time and comprehensive monitoring of the health status of electric bicycle batteries; the cloud server stores all historical data and early warning records for subsequent algorithm optimization and regulatory auditing.