Electric vehicle charging early warning monitoring system and method

Abstract

The invention relates to the technical field of charging early warning, in particular to an electric vehicle charging early warning monitoring system and method, and the system comprises a power supply module, a voltage monitoring module, a charging environment monitoring module, a data preprocessing module, a cloud data storage module and an early warning module. Key parameters in the charging process of the electric vehicle are comprehensively monitored in real time through a plurality of high-precision monitoring modules, any subtle change in the charging process can be captured in time, the charging state is comprehensively mastered, compared with a traditional system, the monitoring range is wider, data are more accurate, and the early warning effect is better through the data processing and analysis technology in combination with an early warning model. Whether potential safety hazards exist in the charging process or not is rapidly and accurately judged, an early warning signal is sent out in time, field personnel and remote management personnel are reminded to take corresponding measures, occurrence and expansion of accidents are effectively prevented, and the safety of the charging process is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging warning, and particularly to an electric vehicle charging warning monitoring system and method. Background Art

[0002] An electric vehicle charger is a device specifically designed to charge the vehicle battery of an electric vehicle, and is a power conversion device with specific functions used for charging the battery. Electric vehicle chargers can be divided into DC chargers and AC chargers.

[0003] The charging environment is complex and changeable, with many potential safety hazards such as overvoltage, overcurrent, and overheating, which may lead to serious accidents such as battery damage, fire, or even explosion. The functions of traditional charging monitoring systems are relatively limited, making it difficult to comprehensively and real-time monitor various parameters during the charging process, and unable to detect and warn of potential safety risks in a timely and accurate manner. In addition, existing systems do not adequately consider charging environmental factors and are unable to effectively respond to the impact of environmental changes on charging safety. Therefore, the present invention proposes an electric vehicle charging warning monitoring system and method. Summary of the Invention

[0004] Technical problem to be solved: Existing systems do not adequately consider charging environmental factors and are unable to effectively respond to the impact of environmental changes on charging safety.

[0005] In view of the deficiencies of the prior art, the present invention provides an electric vehicle charging warning monitoring system and method, thereby solving the technical problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An electric vehicle charging warning monitoring system and method, including a power supply module, a voltage monitoring module, a charging environment monitoring module, a data preprocessing module, a cloud data storage module, and a warning module;

[0007] Power supply module: The power source of the entire charging warning monitoring system, the key lies in ensuring that the system stably, continuously, and safely obtains power supply, thereby providing reliable power support for each functional module;

[0008] Voltage monitoring module: The voltage monitoring module is responsible for real-time collection and analysis of voltage data during the charging process of the electric vehicle;

[0009] Charging environment monitoring module: Monitors the surrounding environment of the electric vehicle. The charging environment monitoring module is used to real-time monitor the environmental conditions during the charging process of the electric vehicle;

[0010] Data preprocessing module: The collected voltage data is comprehensively stored and processed. The voltage data is processed through a discrete equation, and the temperature, humidity, and combustible gas density are calculated through the mean and variance;

[0011] Cloud data storage module: includes real-time data and associated data;

[0012] Early warning module: If the processed data meets the early warning conditions, when the early warning signal is generated at the charging site, an audible and visual alarm will be immediately triggered.

[0013] In a possible implementation, the real-time data stores the real-time data currently being collected and processed, including the latest sampled values of parameters such as temperature, humidity, combustible gas concentration, smoke concentration, voltage, etc. These data are stored in the form of a time series database (such as InfluxDB) for quick query and analysis of the data change trend in the recent period to meet the high requirements of the system for real-time performance.

[0014] In a possible implementation, by analyzing historical data, the associated data finds that when the temperature exceeds a certain threshold and the combustible gas concentration rises simultaneously, the probability of a fire will increase significantly. Thus, corresponding association rules are established. During the real-time monitoring process, when the data in the preprocessing data module meets the conditions of these association rules, this signal is promptly sent to the early warning module to alert the staff.

[0015] In a possible implementation, the voltage data processing includes a discrete equation formula, and the formula is where R is the resistance, C is the capacitance, and V in (t) is the input voltage. In the discrete time domain, it is approximately represented by a difference equation as where T s is the sampling period, v[k] represents the voltage value at the kth sampling moment, and V in [k] represents the input voltage value at the kth sampling moment. After arrangement, we get:

[0016] In a possible implementation, the method includes the following steps:

[0017] Step 1: The power supply module is the power source of the entire charging early warning monitoring system. The key is to ensure that the system stably, continuously, and safely obtains power supply, and then provides reliable power support for each functional module;

[0018] Step 2: The voltage monitoring module is responsible for real-time collection and analysis of voltage data during the tram charging process. The collected data is transmitted to the data preprocessing module;

[0019] Step 3: Monitor the surrounding environment of the tram. The charging environment monitoring module is used to real-time monitor the environmental conditions during the electric vehicle charging process, including parameters such as temperature, humidity, combustible gas concentration, etc. The collected data is transmitted to the data preprocessing module;

[0020] Step 4: The data preprocessing module processes these data one by one. If the data meets the normal data judgment range, a warning signal will be sent to the warning module. At the same time, the processed data will be uploaded to the cloud data transmission module.

[0021] Step 5: In the cloud data processing module, the data is analyzed to establish corresponding association rules. During the real-time monitoring process, when the data in the preprocessing data module meets the conditions of these association rules, a warning signal is sent in a timely manner to improve the accuracy and timeliness of the warning, which will improve the efficiency of data preprocessing to a certain extent.

[0022] Step 6: If the processed data meets the warning conditions, when a warning signal is generated at the charging site, an audible and visual alarm is immediately triggered to remind the on-site personnel to pay attention. At the same time, in the power supply module, the working status of the power supply, such as normal power supply, backup power supply, overload, fault, etc., is intuitively displayed through LED indicators or displays, which is convenient for maintenance personnel to quickly understand the operation status of the power supply module. Remote alarm: The warning information is sent to relevant personnel such as the vehicle owner and the charging station manager through text messages, mobile application push, emails, etc., to ensure that they can timely understand the warning situation.

[0023] Beneficial effects compared with the prior art:

[0024] 1. In this solution, through multiple high-precision monitoring modules, key parameters (such as voltage, temperature, humidity, combustible gas concentration) during the electric vehicle charging process are comprehensively and real-time monitored to ensure that any subtle changes during the charging process can be captured in a timely manner, and the charging status is comprehensively grasped. Compared with the traditional system, the monitoring range is wider and the data is more accurate. Using data processing and analysis technologies and combining with a warning model, it can quickly and accurately judge whether there are potential safety hazards during the charging process, and send a warning signal in a timely manner to remind on-site personnel and remote management personnel to take corresponding measures, effectively preventing the occurrence and expansion of accidents, and improving the safety of the charging process.

[0025] 2. In this solution, through the cloud data storage module with a hierarchical storage structure, real-time data and historical data are reasonably stored, which is convenient for quick query and analysis. At the same time, through in-depth data mining, such as clustering analysis, association rule mining, etc., potential data rules and abnormal patterns are discovered, providing strong support for warning, improving the intelligent level and warning accuracy of the system. During the real-time monitoring process, when the data in the preprocessing data module meets the conditions of these association rules, a warning signal is sent in a timely manner to improve the accuracy and timeliness of the warning, which will improve the efficiency of data preprocessing to a certain extent. Description of the Drawings

[0026] The above description is only an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and implement it in accordance with the content of the specification, the following will describe the preferred embodiments of the present invention in detail with reference to the accompanying drawings.

[0027] Figure 1 It is a schematic diagram of the overall structure of the present invention. Detailed implementation manners

[0028] The preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention can be implemented in various different forms. Therefore, the present invention is not limited to the embodiments described below.

[0029] The technical solutions in the embodiments of the present application are to solve the problems in the above-mentioned background technology. The general idea is as follows:

[0030] Embodiment 1:

[0031] Please refer to Figure 1 As shown, this embodiment introduces an electric vehicle charging early warning monitoring system, including a power supply module, a voltage monitoring module, a charging environment monitoring module, a data preprocessing module, and an early warning module.

[0032] Power supply module: It is the power source of the entire charging early warning monitoring system. The key lies in ensuring that the system stably, continuously, and safely obtains power supply, and then provides reliable power support for each functional module. The input of the power supply module is usually alternating current (AC) supplied by the mains power grid, and the output provides stable direct current (DC) or alternating current (AC) according to the device requirements. Its power conversion efficiency needs to be high to ensure the efficient use of electric energy, reduce losses. At the same time, in the power supply module, through LED indicators or displays, the working status of the power supply, such as normal power supply, backup power supply, overload, fault, etc., is intuitively displayed, which is convenient for maintenance personnel to quickly understand the operation status of the power supply module. At the same time, it also has the setting of a remote monitoring interface, which can send the status information in the power supply module to the application device in the remote monitoring, thereby improving the management efficiency of the system. To a certain extent, it also has the function of early warning. If the status in the power supply module is unstable, the operator can directly interact through remote monitoring.

[0033] Voltage monitoring module: The voltage monitoring module is responsible for real-time collecting and analyzing the voltage data during the tram charging process, including a voltage sensor and analog-to-digital conversion.

[0034] A high-precision and high-stability voltage sensor is adopted to ensure the accuracy and reliability of the collected voltage data. At the same time, according to the characteristics and requirements of the charging process, a suitable sampling frequency is selected, such as from 10 Hz to 1 kHz, to capture the subtle changes in voltage. A suitable range (0 to 500 V) is selected within the range of measurement to ensure that the voltage change range during charging can be covered. By collecting these data multiple times, the monitored data can be made more delicate, thus ignoring the subtle changes during the charging process to a certain extent. Over time, problems will break out intensively, causing certain hazards. Moreover, during the data collection process, a high-resolution ADC (such as 12-bit or 16-bit) is selected to improve the accuracy of voltage measurement, and the ADC sampling is synchronized with the charging process to avoid data errors caused by asynchronous sampling. After the collection of these data is completed, they will be sent to the data preprocessing module.

[0035] During the monitoring of the power supply module, the charging environment monitoring module monitors the surrounding environment of the electric vehicle, including the temperature around the charging electric vehicle. The charging environment monitoring module is used to monitor the environmental conditions during the charging process of electric vehicles in real time, including parameters such as temperature, humidity, and combustible gas concentration to ensure charging safety. Temperature monitoring: A high-precision temperature sensor is adopted to measure the environmental temperature of the charging area in real time to prevent high temperature from affecting charging safety or low temperature from reducing charging efficiency. Humidity monitoring: A humidity sensor is used to monitor the environmental humidity to avoid the risk of electric leakage caused by the decline of equipment insulation performance due to excessive humidity. Combustible gas concentration monitoring: Combustible gas sensors are installed to detect the concentration of gases such as carbon monoxide and methane to prevent explosion caused by gas leakage encountering electric sparks. Respond quickly to potential fires;

[0036] Sampling frequency setting: According to the change rate of different environmental parameters and the requirements of the charging process, a reasonable sampling frequency is set (such as 1 Hz for temperature, 0.5 Hz for humidity, 2 Hz for combustible gas) to capture parameter changes in time. Signal conditioning and filtering: Filter and amplify / attenuate the collected signals to improve the signal-to-noise ratio and measurement accuracy and ensure data accuracy. Data fusion and analysis: Integrate multi-parameter data and comprehensively analyze the safety of the charging environment. For example, when the temperature is high, the humidity is high, and the smoke concentration rises, judge the fire risk and give an early warning. At this time, the collected temperature data, combustible gas data, and humidity data are transmitted to the data preprocessing module one by one.

[0037] When the above two modules transfer data to the preprocessing module, the preprocessing module will process these data one by one;

[0038] The collected voltage data is processed as a whole. These data are uniformly processed through a discrete equation, and the charging process can be described by a step difference equation or a first-order differential equation, which can be transformed into a first-order difference equation in the discrete time domain. Assuming that in a continuous-time system, the voltage v(t) and current i(t) satisfy the differential equation:

[0039] Among them, R is the resistance, C is the capacitance, and V in (t) is the input voltage. In the discrete-time domain, it is approximately expressed by the difference equation as follows:

[0040] Among them, T s is the sampling period, $v[k]$ represents the voltage value at the k-th sampling moment, and V in [k] represents the input voltage value at the k-th sampling moment. After arrangement, we get:

[0041] Through this difference equation, the voltage value at each sampling moment can be recursively calculated, and then it can be judged whether the conditions of constant voltage and constant current are satisfied.

[0042] Recursive equation

[0043] Recursive equation for the constant-current charging process: During the constant-current charging process, assuming that the voltage V and current I of the battery satisfy Ohm's law, and considering the internal resistance r and electromotive force E of the battery, we have:

[0044] V = E - Ir

[0045] When charging with a constant current I set assuming that the electromotive force E of the battery changes with time, a recursive relationship can be established. Assuming that the change rate of the electromotive force is k, then in the discrete-time domain, the electromotive force can be expressed as:

[0046] E[k + 1] = E[k] + kT s

[0047] The voltage of the battery is then:

[0048] V[k] = E[k] - Ir set r

[0049] By continuously iterating this recursive equation, the change of the voltage can be monitored in real time. If the voltage change meets the expectation and the current remains constant, it indicates that the constant-current state is achieved.

[0050] Next, in order to make the display of these data more intuitive, the voltage values at each sampling moment are calculated by inverse recursion using the above difference equation, and then the curve of the voltage change with time can be plotted, enabling the operator to more intuitively see the change of the voltage. Through the data preprocessing module, it is known that the voltage and current are stable during operation, so this signal will not be sent to the warning module. At the same time, the collected data is uploaded to the cloud storage module for storage and recording.

[0051] Meanwhile, the data preprocessing module will process the temperature, humidity, and combustible gas data, calculate the mean value of the temperature data within a certain time window to reflect the average temperature level during that period. For example, the mean value is calculated every 10 sampling points (assuming a sampling frequency of 1 Hz, i.e., 10 seconds of data) to analyze the overall change trend of the temperature. By calculating the variance of the temperature data, the degree of temperature fluctuation is measured. A larger variance may indicate the instability of the ambient temperature. For example, at an outdoor charging station, the temperature may fluctuate greatly due to solar radiation or heat dissipation from surrounding equipment, which may lead to certain accidents.

[0052] For the humidity data, the mean value is calculated to understand the average humidity level of the environment. For example, when analyzing the humidity change within a day, the mean value is calculated every half hour, and a humidity change curve is plotted. Again, through variance calculation: by calculating the variance of the humidity data, the fluctuation of the humidity is evaluated.

[0053] Combustible gas data: The mean value of the combustible gas concentration data is calculated to judge the approximate concentration level of the combustible gas in the environment. If the mean value gradually increases, it indicates the existence of a gas leakage source.

[0054] After processing these data for evaluation, if the data fluctuation is not significant, no warning will be generated, and at the same time, the data for this period will be uploaded to the cloud storage module.

[0055] Cloud data storage module: Real-time data layer, which stores real-time data that is currently being collected and processed, including the latest sampled values of parameters such as temperature, humidity, combustible gas concentration, voltage, etc. These data are stored in the form of a time series database InfluxDB for quick query and analysis of data change trends in a recent period of time to meet the high requirements of the system for real-time performance. For example, detailed data on the charging environment and charging status within the last 1 hour are recorded. Data with a high sampling frequency (such as multiple data points per second) can be quickly written and read, providing timely data support for real-time monitoring and early warning. By recording these real-time data, similar data records are clustered to discover the internal structure and patterns of the data. Cluster analysis is performed on different historical data, and charging stations are divided into different categories according to factors such as charging load and environmental conditions, providing a basis for formulating personalized management and monitoring strategies for different categories of charging stations. Cluster analysis can also be used for anomaly detection, identifying data points with significant differences from the normal data pattern as abnormal data, further analyzing the reasons and taking corresponding measures. At the same time, association rules between data are mined to discover potential connections between different factors. For example, by analyzing historical data, it is found that when the temperature exceeds a certain threshold and the combustible gas concentration rises simultaneously, the probability of a fire increases significantly, thus establishing corresponding association rules. During real-time monitoring, when the data in the preprocessing data module meets the conditions of these association rules, an early warning signal is sent in a timely manner, improving the accuracy and timeliness of the early warning, and to a certain extent, improving the efficiency of data preprocessing. The charging status of the electric vehicle is detected through the data sources of the voltage monitoring module and the charging environment monitoring module. If one of the data is abnormal, this signal will be transmitted to the next module, thus greatly improving safety.

[0056] Early warning module: If the processed data meets the early warning conditions, when an early warning signal is generated at the charging site, an audible and visual alarm is immediately triggered to alert the on-site personnel. At the same time, in the power supply module, the working status of the power supply, such as normal power supply, backup power supply, overload, fault, etc., is intuitively displayed through LED indicators or a display screen, facilitating maintenance personnel to quickly understand the operating conditions of the power supply module. The early warning information is sent to relevant personnel such as the vehicle owner and charging station management personnel via text messages, mobile applications, emails, etc., to ensure that they can timely understand the early warning situation.

[0057] The specific method of the above online electric vehicle charging early warning and monitoring system includes the following steps:

[0058] Step 1: The power supply module is the power source of the entire charging early warning and monitoring system. The key lies in ensuring that the system stably, continuously, and safely obtains power supply, and then provides reliable power support for each functional module.

[0059] Step 2: The voltage monitoring module is responsible for collecting and analyzing the voltage data during the tram charging process in real time, and the collected data is transmitted to the data preprocessing module.

[0060] Step 3: Monitor the surrounding environment of the tram. The charging environment monitoring module is used to monitor the environmental conditions during the electric vehicle charging process in real time, including parameters such as temperature, humidity, and combustible gas concentration. The collected data is transmitted to the data preprocessing module.

[0061] Step 4: The data preprocessing module processes these data one by one. If it meets the normal data judgment range, it will not send a warning signal to the warning module. At the same time, the processed data will be uploaded to the cloud data transmission module.

[0062] Step 5: In the cloud data processing module, analyze the data and establish corresponding association rules. During the real-time monitoring process, when the data in the preprocessing data module meets the conditions of these association rules, a warning signal is sent in a timely manner to improve the accuracy and timeliness of the warning, which will improve the efficiency of data preprocessing to a certain extent.

[0063] Step 6: If the processed data meets the warning conditions, when a warning signal is generated at the charging site, an audible and visual alarm will be triggered immediately to remind the on-site personnel. At the same time, in the power supply module, through the LED indicator or display screen, the working state of the power supply is visually displayed, such as normal power supply, standby power supply, overload, fault, etc., which is convenient for maintenance personnel to quickly understand the operation status of the power supply module. The warning information is sent to relevant personnel such as the vehicle owner and the charging station management personnel through text messages, mobile application push, emails, etc., to ensure that they can understand the warning situation in a timely manner.

[0064] Finally, it should be noted that: Obviously, the above embodiments are only examples for clearly illustrating the present invention, rather than limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

Claims

1. Electric vehicle charging warning and monitoring system, characterized in that, It includes a power supply module, a voltage monitoring module, a charging environment monitoring module, a data preprocessing module, a cloud data storage module, and an early warning module; Power supply module: The power source of the entire charging early warning monitoring system. The key lies in ensuring that the system stably, continuously, and safely obtains power supply, and then provides reliable power support for each functional module; Voltage monitoring module: The voltage monitoring module is responsible for real-time collection and analysis of voltage data during the tram charging process; Charging environment monitoring module: It monitors the surrounding environment of the tram. The charging environment monitoring module is used to real-time monitor the environmental conditions during the electric vehicle charging process; Data preprocessing module: It overall collects and processes the collected voltage data, processes the voltage data through a discrete equation, and calculates the temperature, humidity, and combustible gas density through the mean and variance; Cloud data storage module: It includes real-time data and associated data; Early warning module: When the processed data meets the early warning conditions, when the early warning signal is generated at the charging site, it immediately triggers an audible and visual alarm.

2. The electric vehicle charging warning and monitoring system according to claim 1, characterized in that, The real-time data stores the real-time data currently being collected and processed, including the latest sampling values of parameters such as temperature, humidity, combustible gas concentration, smoke concentration, voltage, etc. These data are stored in the form of a time series database InfluxDB for quick query and analysis of the data change trend in the recent period to meet the high real-time requirements of the system.

3. The electric vehicle charging warning and monitoring system according to claim 1, wherein The associated data, by analyzing historical data, discovers that when the temperature exceeds a certain threshold and the combustible gas concentration rises simultaneously, the probability of a fire will increase significantly. Thus, corresponding association rules are established. During the real-time monitoring process, when the data in the preprocessing data module meets the conditions of these association rules, this signal is sent to the early warning module in a timely manner to remind the staff.

4. The electric vehicle charging warning and monitoring system according to claim 1, wherein The voltage data processing includes a discrete equation formula, and the formula is where R is the resistance, C is the capacitance, and V in (t) is the input voltage. In the discrete time domain, it is approximately expressed by a difference equation as where T s is the sampling period, v[k] represents the voltage value at the k-th sampling moment, and V in [k] represents the input voltage value at the k-th sampling moment. After arrangement, we get:

5. A method for applying the electric vehicle charging early warning and monitoring system according to claims 1 to 4, characterized in that, The method includes the following steps: Step 1: The power supply module is the power source of the entire charging early warning monitoring system. The key lies in ensuring that the system stably, continuously, and safely obtains power supply, and then provides reliable power support for each functional module; Step 2: The voltage monitoring module is responsible for real-time collection and analysis of voltage data during the tram charging process, and the collected data is transmitted to the data preprocessing module; Step 3: Monitor the surrounding environment of the tram. The charging environment monitoring module is used to real-time monitor the environmental conditions during the electric vehicle charging process, including parameters such as temperature, humidity, combustible gas concentration, etc. The collected data is transmitted to the data preprocessing module; Step 4: The data preprocessing module processes these data one by one. If it meets the normal data judgment range, it will send the early warning signal to the early warning module. At the same time, the processed data will be uploaded to the cloud data transmission module; Step 5: In the cloud data processing module, analyze the data, establish corresponding association rules. During the real-time monitoring process, when the data in the preprocessing data module meets the conditions of these association rules, an early warning signal is sent in a timely manner to improve the accuracy and timeliness of the early warning, which will improve the efficiency of data preprocessing to a certain extent; Step 6: If the processed data meets the warning conditions, when a warning signal is generated at the charging site, an audible and visual alarm will be immediately triggered to alert the on-site personnel. At the same time, the working status of the power supply, such as normal power supply, backup power supply, overload, fault, etc., will be intuitively displayed through LED indicators or a display screen in the power module, facilitating maintenance personnel to quickly understand the operating conditions of the power module. Remote alarm: The warning information will be sent to relevant personnel such as the vehicle owner and charging station management personnel via text messages, mobile application push, emails, etc., to ensure that they can timely understand the warning situation.

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