Electric bicycle and charging station linkage monitoring system and monitoring method thereof

Abstract

The invention relates to the technical field of electric vehicle charging linkage monitoring, in particular to an electric bicycle and charging station linkage monitoring system and a monitoring method thereof.The electric bicycle and charging station linkage monitoring system comprises a charging station monitoring system, a charging station and an electric bicycle. Abnormal conditions are found in time, potential safety hazards such as overcharge and overheating of the battery are prevented, and the occurrence probability of accidents such as fire disasters is reduced. And by accurately controlling the charging process, the over-charging and over-discharging of the battery are avoided, and the loss of the battery is reduced, so that the service life of the battery is prolonged. And the charging efficiency is optimized: the charging efficiency can be improved, the charging time can be reduced and the user experience can be improved by analyzing, intelligently scheduling and dynamically adjusting the charging power through the collected data. And policy supervision is supported, real-time data is provided for the government and a supervision mechanism, supervision of charging safety of the electric bicycle is supported, and healthy development of the industry is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging linkage monitoring, in particular to an electric bicycle and charging station linkage monitoring system and a monitoring method thereof. Background Art

[0002] At present, the technological development of electric bicycle charging stations is moving towards intelligence, networking, green and low-carbon.

[0003] However, the monitoring of electric bicycle charging stations in the prior art has the following technical defects:

[0004] 1. Reduced charging safety: There is a lack of effective functional modules for real-time monitoring of the battery status and charging process of electric bicycles, which makes it impossible to detect abnormal situations in time and prevent safety hazards such as battery overcharging and overheating. The probability of accidents such as fire is high.

[0005] 2. Short battery life: The charging process cannot be accurately controlled, so overcharging and over-discharging of the battery cannot be effectively avoided, which increases battery loss and shortens the battery life.

[0006] 3. Low charging efficiency: It is impossible to analyze the effectively collected data, intelligently schedule and dynamically adjust the charging power, and it is impossible to further improve the charging efficiency and reduce the charging time, resulting in a poor user experience.

[0007] 4. The lack of real-time data is not conducive to policy supervision, cannot meet the supervision of electric bicycle charging safety, and is not conducive to the healthy development of the industry. Summary of the invention

[0008] In order to overcome the technical defects in the prior art and effectively solve the technical problems in the background technology, the present invention provides the following technical solutions:

[0009] A monitoring system for linkage between an electric bicycle and a charging station, the monitoring system comprising the following modules: a charging station monitoring system, a charging station and an electric bicycle:

[0010] The charging station monitoring system, as the core of the entire linkage monitoring method, is responsible for collecting and processing data from charging stations and electric bicycles to monitor and manage the charging process;

[0011] The charging station includes the following two main components: a unified gateway and a charging pile: the unified gateway serves as a communication bridge between the components inside the charging station and the external monitoring system. The unified gateway is responsible for data transmission and conversion to ensure accurate information transmission; the charging pile is directly connected to the electric bicycle, responsible for providing power for charging, and communicates with the monitoring system through the unified gateway to report charging status and receive instructions;

[0012] The electric bicycle includes the following two main components: T-BOX and BMS-battery: the T-BOX serves as the on-board communication unit of the electric bicycle and is responsible for communicating with the unified gateway of the charging station, transmitting battery status information, and receiving instructions from the monitoring system; the BMS-battery is used to monitor the battery status and ensure that the battery operates within a safe range; the BMS works in conjunction with the T-BOX to achieve real-time monitoring of the battery status.

[0013] As a preferred technical solution of the present invention, the charging station monitoring system communicates with the charging station through a unified gateway to monitor the working status of the charging pile.

[0014] As a preferred technical solution of the present invention, the charging station monitoring system communicates with the T-BOX of the electric bicycle to obtain the status information of the BMS-battery, thereby realizing remote monitoring of the battery of the electric bicycle.

[0015] As a preferred technical solution of the present invention, the charging pile is directly connected to the T-BOX of the electric bicycle to perform power transmission and status information exchange.

[0016] A monitoring method for an electric bicycle and a charging station linkage monitoring system, the monitoring method comprising the following process steps:

[0017] S1: Start: The user starts the electric bicycle and prepares for charging;

[0018] S2: plugging in the electric bicycle power supply: the user plugs the charging port of the electric bicycle into the charging pile of the charging station;

[0019] S3: Select charging pile: The charging station monitoring system guides the user to select an idle charging pile through the display screen or mobile application;

[0020] S4: Connect charging port: The system confirms that the charging port is correctly connected;

[0021] S5: Socket to determine power access: The system checks whether the charging port is correctly connected and has power supply;

[0022] S6: Feedback disconnection: If the connection is incorrect or there is a power problem, the system will feedback to the user and disconnect;

[0023] S7: Synchronization confirmation information: Once the connection is confirmed, the system will synchronize the charging information;

[0024] S8: BMS battery data collection: The BMS of the electric bicycle starts to collect battery status data;

[0025] S9: T-BOX data acquisition and upload: T-BOX acquires BMS data and uploads it to the charging station monitoring system;

[0026] S10: Obtain vehicle data: The system receives and records battery data from the T-BOX;

[0027] S11: Pile information monitoring: The monitoring system continuously monitors the status of the charging pile;

[0028] S12: Synchronous charging information upload: Upload key information of the charging process to the cloud or database for further analysis and monitoring;

[0029] S13: Obtain charging pile data: The system obtains data during the charging process from the charging pile;

[0030] S14: Data linkage monitoring and analysis: Linking charging pile data with BMS data for analysis and evaluation;

[0031] S15: Charging data display: display the analysis results to the user;

[0032] S16: End: After charging is completed, the system ends the current charging session.

[0033] As a preferred technical solution of the present invention, the data linkage monitoring and analysis strategy in S14 is to achieve real-time monitoring and optimization of the charging process and battery status by integrating advanced algorithms and methods, including:

[0034] (1) Real-time data acquisition and preprocessing: Use high-precision sensors and analog-to-digital converters to collect key parameters of electric bicycles in real time, and use filtering algorithms to remove noise to ensure data accuracy;

[0035] (2) Battery state estimation: Use Kalman filtering or extended Kalman filtering algorithm to estimate the health status of the battery in real time, and predict the remaining life and performance changes of the battery based on the real-time data of the battery;

[0036] (3) Charging strategy optimization: Based on multi-objective optimization theory, develop particle swarm optimization or genetic algorithm to optimize the charging strategy to minimize charging time and cost while meeting charging efficiency and battery safety.

[0037] (4) Abnormal detection and fault diagnosis: Using machine learning algorithms, abnormal behavior during the charging process is detected. When an abnormality is detected, the system will automatically trigger the fault diagnosis process to quickly locate the problem and take corresponding measures;

[0038] (5) Data linkage analysis: Through data linkage analysis technology, the charging pile data and BMS data are integrated, and data mining and pattern recognition algorithms are used to analyze the potential rules and trends in the charging process to provide decision support for the operation of charging stations;

[0039] (6) Safety early warning control: Develop fuzzy logic control or adaptive control algorithms to warn of possible safety risks during the charging process, dynamically adjust charging parameters according to changes in battery status and charging environment, and ensure the safety of the charging process;

[0040] (7) Establish a feedback mechanism: Design a user-friendly interface to provide users with real-time charging status and battery health information through mobile applications or charging station displays. At the same time, collect user feedback and use the feedback information to further optimize charging strategies and improve user experience;

[0041] (8) Remote monitoring and management: Build a remote monitoring platform, use cloud computing and big data technology to achieve remote monitoring of charging stations and electric bicycles, and through remote access and control, understand the operating status of charging stations in real time and conduct remote management and maintenance;

[0042] (9) Energy consumption analysis and optimization: Use energy consumption analysis algorithms to evaluate the energy consumption of charging stations. Through optimization algorithms, the rational allocation and use of energy can be achieved, energy consumption can be reduced, and energy utilization efficiency can be improved.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. Improve charging safety: By real-time monitoring of the battery status and charging process of electric bicycles, abnormal conditions can be discovered in time to prevent safety hazards such as battery overcharging and overheating, and reduce the probability of accidents such as fire.

[0045] 2. Extend battery life: By precisely controlling the charging process, avoid overcharging and over-discharging of the battery, reduce battery loss, and thus extend the battery life.

[0046] 3. Optimize charging efficiency: By analyzing the collected data, intelligently scheduling and dynamically adjusting the charging power, the charging efficiency can be improved, the charging time can be reduced, and the user experience can be enhanced.

[0047] 4. Support policy supervision: Provide real-time data to governments and regulators, support the supervision of electric bicycle charging safety, and promote the healthy development of the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 FIG. 4 is a block diagram of the monitoring system of the present invention.

[0049] Figure 2 It is a flow chart of the monitoring method of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] See also Figure 1-2 The present invention provides a technical solution: a monitoring system for linkage between an electric bicycle and a charging station, the monitoring system comprising the following modules: a charging station monitoring system, a charging station and an electric bicycle:

[0052] As the core of the entire linkage monitoring method, the charging station monitoring system is responsible for collecting and processing data from charging stations and electric bicycles to monitor and manage the charging process;

[0053] The charging station consists of the following two main components: a unified gateway and a charging pile: the unified gateway serves as a communication bridge between the components inside the charging station and the external monitoring system. The unified gateway is responsible for data transmission and conversion to ensure accurate information delivery; the charging pile is directly connected to the electric bicycle, responsible for providing power for charging, and communicates with the monitoring system through the unified gateway to report charging status and receive instructions;

[0054] The electric bicycle contains the following two main components: T-BOX and BMS-battery: T-BOX, as the on-board communication unit of the electric bicycle, is responsible for communicating with the unified gateway of the charging station, transmitting battery status information such as power, temperature, charging requirements, etc., and receiving instructions from the monitoring system; BMS-battery is used to monitor the battery status, such as voltage, current, temperature, etc., to ensure that the battery works within a safe range; BMS (battery management system) works together with T-BOX (on-board communication unit) to achieve real-time monitoring of the battery status.

[0055] Furthermore, the charging station monitoring system communicates with the charging station through a unified gateway to monitor the working status of the charging pile.

[0056] Furthermore, the charging station monitoring system communicates with the T-BOX of the electric bicycle to obtain the status information of the BMS-battery and realize remote monitoring of the electric bicycle battery.

[0057] Furthermore, the charging pile is directly connected to the T-BOX of the electric bicycle to transmit power and exchange status information.

[0058] Through this modular design and communication mechanism, intelligent linkage monitoring between electric bicycles and charging stations can be achieved, improving charging safety and efficiency, while providing real-time monitoring and management functions for users and operators.

[0059] A monitoring method for an electric bicycle and a charging station linkage monitoring system, the monitoring method comprising the following process steps:

[0060] S1: Start: The user starts the electric bicycle and prepares for charging;

[0061] S2: plugging in the electric bicycle power supply: the user plugs the charging port of the electric bicycle into the charging pile of the charging station;

[0062] S3: Select charging pile: The charging station monitoring system guides the user to select an idle charging pile through the display screen or mobile application;

[0063] S4: Connect charging port: The system confirms that the charging port is correctly connected;

[0064] S5: Socket to determine power access: The system checks whether the charging port is correctly connected and has power supply;

[0065] S6: Feedback disconnection: If the connection is incorrect or there is a power problem, the system will feedback to the user and disconnect;

[0066] S7: Synchronize confirmation information: Once the connection is confirmed, the system will synchronize charging information, including charging pile status, available power, etc.

[0067] S8: BMS battery data collection: The BMS (battery management system) of the electric bicycle starts to collect battery status data, such as voltage, current, temperature, etc.

[0068] S9: T-BOX data acquisition and upload: T-BOX (vehicle communication unit) acquires BMS data and uploads it to the charging station monitoring system;

[0069] S10: Obtain vehicle data: The system receives and records battery data from the T-BOX;

[0070] S11: Pile information monitoring: The monitoring system continuously monitors the status of the charging pile, including current, voltage, charging time, etc.

[0071] S12: Synchronous charging information upload: Upload key information of the charging process to the cloud or database for further analysis and monitoring;

[0072] S13: Obtain charging pile data: The system obtains data during the charging process from the charging pile;

[0073] S14: Data linkage monitoring and analysis: Link the charging pile data with the BMS data to evaluate the charging efficiency, battery health status, etc.

[0074] S15: Charging data display: Display the analysis results to the user, including charging progress, estimated completion time, battery status, etc.;

[0075] S16: End: After charging is completed, the system ends the current charging session.

[0076] Furthermore, the data linkage monitoring and analysis strategy in S14 is to achieve real-time monitoring and optimization of the charging process and battery status by integrating advanced algorithms and methods, including:

[0077] (1) Real-time data acquisition and preprocessing: Use high-precision sensors and analog-to-digital converters to collect key parameters of electric bicycles in real time, and use filtering algorithms to remove noise to ensure data accuracy;

[0078] (2) Battery State Estimation (SOH) Algorithm: Use Kalman filter or extended Kalman filter algorithm to estimate the health status of the battery in real time, and predict the remaining life and performance changes of the battery based on the real-time data of the battery;

[0079] (3) Charging strategy optimization algorithm: Based on multi-objective optimization theory, develop particle swarm optimization (PSO) or genetic algorithm to optimize the charging strategy and minimize charging time and cost while meeting charging efficiency and battery safety.

[0080] (4) Anomaly detection and fault diagnosis: Using machine learning algorithms, such as support vector machines (SVM) or neural networks, abnormal behaviors during the charging process are detected. When an abnormality is detected, the system will automatically trigger the fault diagnosis process to quickly locate the problem and take corresponding measures;

[0081] (5) Data linkage analysis: Through data linkage analysis technology, the charging pile data and BMS data are integrated, and data mining and pattern recognition algorithms are used to analyze the potential rules and trends in the charging process to provide decision support for the operation of charging stations;

[0082] (6) Safety early warning control: Develop fuzzy logic control or adaptive control algorithms to warn of possible safety risks during the charging process, dynamically adjust charging parameters according to changes in battery status and charging environment, and ensure the safety of the charging process;

[0083] (7) Establish a feedback mechanism: Design a user-friendly interface to provide users with real-time charging status and battery health information through mobile applications or charging station displays. At the same time, collect user feedback and use the feedback information to further optimize charging strategies and improve user experience;

[0084] (8) Remote monitoring and management: Build a remote monitoring platform and use cloud computing and big data technologies to achieve remote monitoring of charging stations and electric bicycles. Through remote access and control, charging station operators can understand the operating status of charging stations in real time and conduct remote management and maintenance.

[0085] (9) Energy consumption analysis and optimization: Use energy consumption analysis algorithms to evaluate the energy consumption of charging stations. Through optimization algorithms, the rational allocation and use of energy can be achieved, energy consumption can be reduced, and energy utilization efficiency can be improved.

[0086] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An electric bicycle and charging station linkage monitoring system, characterized in that: The monitoring system includes the following modules: charging station monitoring system, charging station and electric bicycle: The charging station monitoring system, as the core of the entire linkage monitoring method, is responsible for collecting and processing data from charging stations and electric bicycles to monitor and manage the charging process; The charging station includes the following two main components: a unified gateway and a charging pile: the unified gateway serves as a communication bridge between the components inside the charging station and the external monitoring system. The unified gateway is responsible for data transmission and conversion to ensure accurate information transmission; the charging pile is directly connected to the electric bicycle, responsible for providing power for charging, and communicates with the monitoring system through the unified gateway to report charging status and receive instructions; The electric bicycle includes the following two main components: T-BOX and BMS-battery: the T-BOX serves as the on-board communication unit of the electric bicycle and is responsible for communicating with the unified gateway of the charging station, transmitting battery status information, and receiving instructions from the monitoring system; the BMS-battery is used to monitor the battery status and ensure that the battery operates within a safe range; the BMS works in conjunction with the T-BOX to achieve real-time monitoring of the battery status.

2. The electric bicycle and charging station linkage monitoring system according to claim 1, characterized in that: The charging station monitoring system communicates with the charging station through a unified gateway to monitor the working status of the charging pile.

3. The electric bicycle and charging station linkage monitoring system according to claim 1, characterized in that: The charging station monitoring system communicates with the T-BOX of the electric bicycle to obtain the status information of the BMS-battery, thereby realizing remote monitoring of the battery of the electric bicycle.

4. The electric bicycle and charging station linkage monitoring system according to claim 1, characterized in that: The charging pile is directly connected to the T-BOX of the electric bicycle to perform power transmission and status information exchange.

5. The monitoring method of the electric bicycle and charging station linkage monitoring system according to any one of claims 1 to 4, characterized in that: The monitoring method comprises the following process steps: S1: Start: The user starts the electric bicycle and prepares for charging; S2: plugging in the electric bicycle power supply: the user plugs the charging port of the electric bicycle into the charging pile of the charging station; S3: Select charging pile: The charging station monitoring system guides the user to select an idle charging pile through the display screen or mobile application; S4: Connect charging port: The system confirms that the charging port is correctly connected; S5: Socket to determine power access: The system checks whether the charging port is correctly connected and has power supply; S6: Feedback disconnection: If the connection is incorrect or there is a power problem, the system will feedback to the user and disconnect; S7: Synchronization confirmation information: Once the connection is confirmed, the system will synchronize the charging information; S8: BMS battery data collection: The BMS of the electric bicycle starts to collect battery status data; S9: T-BOX data acquisition and upload: T-BOX acquires BMS data and uploads it to the charging station monitoring system; S10: Obtain vehicle data: The system receives and records battery data from the T-BOX; S11: Pile information monitoring: The monitoring system continuously monitors the status of the charging pile; S12: Synchronous charging information upload: Upload key information of the charging process to the cloud or database for further analysis and monitoring; S13: Obtain charging pile data: The system obtains data during the charging process from the charging pile; S14: Data linkage monitoring and analysis: Linking charging pile data with BMS data for analysis and evaluation; S15: Charging data display: display the analysis results to the user; S16: End: After charging is completed, the system ends the current charging session.

6. The monitoring method of the electric bicycle and charging station linkage monitoring system according to claim 5, characterized in that: The data linkage monitoring and analysis strategy in S14 is to achieve real-time monitoring and optimization of the charging process and battery status by integrating advanced algorithms and methods, including: (1) Real-time data acquisition and preprocessing: High-precision sensors and analog-to-digital converters are used to collect key parameters of electric bicycles in real time, and filtering algorithms are used to remove noise to ensure data accuracy; (2) Battery state estimation: Use Kalman filtering or extended Kalman filtering algorithm to estimate the health status of the battery in real time, and predict the remaining life and performance changes of the battery based on the real-time data of the battery; (3) Charging strategy optimization: Based on multi-objective optimization theory, develop particle swarm optimization or genetic algorithm to optimize the charging strategy to minimize charging time and cost while meeting charging efficiency and battery safety. (4) Abnormal detection and fault diagnosis: Using machine learning algorithms, abnormal behavior during the charging process is detected. When an abnormality is detected, the system will automatically trigger the fault diagnosis process to quickly locate the problem and take corresponding measures; (5) Data linkage analysis: Through data linkage analysis technology, the charging pile data and BMS data are integrated, and data mining and pattern recognition algorithms are used to analyze the potential rules and trends in the charging process to provide decision support for the operation of charging stations; (6) Safety early warning control: Develop fuzzy logic control or adaptive control algorithms to warn of possible safety risks during the charging process, dynamically adjust charging parameters according to changes in battery status and charging environment, and ensure the safety of the charging process; (7) Establish a feedback mechanism: Design a user-friendly interface to provide users with real-time charging status and battery health information through mobile applications or charging station displays. At the same time, collect user feedback and use the feedback information to further optimize charging strategies and improve user experience; (8) Remote monitoring and management: Build a remote monitoring platform, use cloud computing and big data technology to achieve remote monitoring of charging stations and electric bicycles, and through remote access and control, understand the operating status of charging stations in real time and conduct remote management and maintenance; (9) Energy consumption analysis and optimization: Use energy consumption analysis algorithms to evaluate the energy consumption of charging stations. Through optimization algorithms, the rational allocation and use of energy can be achieved, energy consumption can be reduced, and energy utilization efficiency can be improved.

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