Safety monitoring system for electric vehicle charging

Abstract

The invention discloses a safety monitoring system for electric vehicle charging, and relates to the technical field of electric vehicle safety, and the system comprises an environment monitoring module which is used for monitoring the charging environment of an electric vehicle and obtaining corresponding environment data, and the environment data comprises the environment air temperature, the wind speed and the illumination intensity; the temperature monitoring module comprises a battery monitoring unit used for monitoring the battery temperature and obtaining battery temperature data; and the charger monitoring units are used for monitoring the temperature of the input end of the charger and the temperature of the output end of the charger respectively and acquiring the temperature data of the input end of the charger and the temperature data of the output end of the charger. According to the method, the multi-point temperature time sequence model is constructed, and a thermal delay identification and compensation mechanism is introduced, so that response alignment between the input ends and the output ends of the battery and the charger is realized, and the identification sensitivity of local thermal unbalance and contact abnormity is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle safety, and particularly to a safety monitoring system for electric vehicle charging. Background Art

[0002] With the large-scale popularization of electric vehicles, users' attention to charging safety has been increasing day by day. During the charging process of an electric vehicle, continuous electrical energy input and heat release occur, involving the coordinated work of multiple key components such as the battery, power adapter, and connector. If local overheating, abnormal contact, or poor heat dissipation occurs during charging, it may lead to electrical failures or even safety accidents. Therefore, how to real-time sense the thermal state changes of key components during charging and timely identify potential risks is an important research direction for current electric vehicle safety management.

[0003] In current electric vehicle charging safety monitoring solutions, an abnormal identification method based on temperature thresholds is usually adopted, that is, by monitoring the temperature change of a key component of the battery or charger and triggering an alarm when it exceeds a fixed preset value. This type of method has a simple structure and is easy to implement, and has certain application value in some charging scenarios, and is widely deployed in household and public charging equipment.

[0004] After retrieval, Chinese Patent (Publication No.: CN106114229B) discloses an electric vehicle safety warning device, which includes a safety monitoring system. The safety monitoring system includes a core processing module and a temperature detection module 1, a temperature detection module 2, a relay module 1, a relay module 2, an alarm circuit module, a display circuit module, and a wireless module electrically connected to the core processing module. The temperature detection module 1 is used to detect the temperature of the electric vehicle charger and transmit it to the core processing module, and the temperature detection module 2 is used to detect the temperature of the electric vehicle battery and transmit it to the core processing module. The relay module 1 is used to control the on / off of the charging circuit, and the relay module 2 is used to control the on / off of the electric vehicle power system.

[0005] However, with the complexity of the electric vehicle usage environment, such as in outdoor high temperature, enclosed space, or direct sunlight conditions, the surface temperature of the device may increase due to environmental factors rather than actual faults, resulting in deviation in early warning judgment. In addition, the thermal response behaviors of the battery body and components such as connectors and input / output terminals are not exactly the same, and there is still room for optimization in complex scenarios, especially in improving the multi-component temperature linkage recognition ability, abnormal scoring accuracy, and dynamic environment adaptability. Therefore, the present invention proposes a safety monitoring system for electric vehicle charging. Summary of the Invention

[0006] The purpose of the present invention is to provide a safety monitoring system for electric vehicle charging to solve the problems mentioned in the above background art.

[0007] The present invention can be implemented through the following technical solutions: A safety monitoring system for electric vehicle charging, comprising: a temperature monitoring module, an environment monitoring module, a processing module, and an early warning module; The environment monitoring module is used to monitor the charging environment of the electric vehicle and obtain corresponding environment data. The environment data includes: Ambient air temperature: which directly affects the surface temperature rise of the charger and the battery; Wind speed: which is used to quantify the heat dissipation conditions of the charger; Light intensity: the temperature of the housing rises rapidly under direct sunlight; The temperature monitoring module includes: A battery monitoring unit, which is used to monitor the battery temperature and obtain battery temperature data; A charger monitoring unit. There are multiple charger monitoring units, which respectively monitor the temperature at the input end and the output end of the charger, and obtain the temperature data at the input end and the output end of the charger; The processing module receives the basic charging data and the environment data, and retrieves the historical temperature data of the battery part, the charger input end part, and the charger output end part from the database; And the processing module receives the battery temperature data, the charger input end temperature data, and the charger output end temperature data, and respectively establishes time series models for the battery temperature, the charger input end temperature, and the charger output end temperature; The processing module calculates the temperature difference and the temperature rise rate difference between the three groups of time series models, and calculates the weighted average of the temperature difference and the temperature rise rate difference to obtain a charging stability score, so as to evaluate the thermal behavior consistency and the charging process stability of each part during the charging process in real time; After the processing module corrects the electric vehicle battery temperature, the charger output end temperature, and the charger input end temperature with the environment data, it obtains the corresponding corrected battery temperature, corrected charger output end temperature, and corrected charger input end temperature, and compares them with the corresponding historical data respectively to calculate the corresponding data difference degree; The early warning module conducts corresponding-level early warnings based on the charging stability score and the data difference degree between each part of the electric vehicle and the corresponding historical data, including: If the data difference degree of any one of the corrected battery temperature, corrected charger output end temperature, or corrected charger input end temperature and its corresponding historical data is greater than the preset difference threshold, the early warning module immediately triggers a warning message; The early warning module sets multiple early warning levels based on the charging stability score. When the charging stability score is lower than the corresponding grading threshold, it triggers a charging anomaly early warning of the corresponding early warning level.

[0008] A further technical improvement of the present invention lies in: the method for obtaining the charging stability score includes the following steps: S1. Set a sampling period, and every other sampling period, the temperature monitoring module collects the battery temperature , the temperature at the input end of the charger and the temperature at the output end of the charger , and record the time stamp t at the same time; S2. The processing module respectively establishes data sequences for the battery part, the input end part of the charger, and the output end part of the charger: , and ; each group of data sequences is a mapping table of time points and corresponding temperature data; where is the time stamp at the kth sampling moment; S3. For each group of data sequences, calculate in order: ; ; ; In the formula: is the heating rate of the battery part, is the heating rate of the input end part of the charger, is the heating rate of the output end part of the charger; is the temperature of part i (battery part: B; input end part of the charger: I; output end part of the charger: O) at the kth moment; , are two sampling moments; S4. Set a sliding window, and calculate the cross-correlation function between every two of the three groups of data sequences in units of the sliding window; And the processing module takes the position with the largest peak value of the cross-correlation function curve to obtain the delay amount ; S5. Compare the delay amount with the preset delay threshold. If is greater than the preset delay threshold, the processing module determines that there is a thermal delay effect between the two points; The processing module shifts the lagging data sequence forward by the delay amount for alignment, ; is the new temperature curve of the corresponding part after thermal delay compensation; S6. For each moment , calculate the normalized temperature difference and the normalized heating rate difference between every two of the three groups of data sequences; S7. The processing module calculates the average value of the normalized temperature difference and the normalized temperature rise rate difference within the most recent sliding window respectively. and the normalized temperature rise rate difference of the average value and ; S8. The processing module is based on the preset temperature difference weight and the temperature rise rate weight , and through the formula: Calculate the charging stability score .

[0009] A further technical improvement of the present invention is that the method for obtaining the battery temperature includes: Z1. Collect multi-point temperature data of the battery body (such as the center, upper layer, corners), including: The maximum temperature of the battery body ; The average temperature of the battery body ; The temperature difference of the battery body ; At the same time, collect the temperature at the battery connection port ; Z2. Detect the temperature at the battery connection port and the thermal distribution of the battery body respectively, and judge whether there is an abnormality at the battery connection port or the battery body; Z3. If the temperature at the battery connection port is abnormal, the battery temperature is the temperature at the battery connection port ; If the temperature of the battery body is abnormal, the battery temperature is the maximum temperature of the battery body ; If both the battery connection port and the battery body are normal, the battery temperature is the average temperature of the battery body .

[0010] A further technical improvement of the present invention is that the abnormal judgment conditions for the temperature at the battery connection port include: a1. The temperature at the battery connection port itself is too high, and ; In the formula, is the preset temperature difference threshold; a2. The temperature rise at the battery connection port is too fast, and ; In the formula, is the preset temperature rise rate threshold; If the temperature at the battery connection port meets any one of the conditions a1 or a2, the processing module judges that the temperature at the battery connection port is abnormal.

[0011] A further technical improvement of the present invention is that the judgment condition for the battery body temperature to be abnormal is: ; wherein, is a preset thermal gradient threshold.

[0012] A further technical improvement of the present invention lies in that: multiple said charger monitoring units respectively collect the internal and external temperature data of the charger input end part and the charger output end part; And the processing module respectively calculates the internal and external temperature differences of the charger input end part and the charger output end part and the internal temperature rising rate of the charger input end part and the charger output end part; And the processing module judges whether the internal and external temperature differences of the two parts exceed the preset temperature difference threshold, and judges whether the internal temperature rising rate of the two parts exceeds the preset rate threshold; When the internal and external temperature difference of the charger input end part exceeds the temperature difference threshold, or the internal temperature rising rate exceeds the rate threshold, the processing module determines that there is potential poor contact or local abnormal heating at the charger input end part, and immediately triggers a warning message; When the internal and external temperature difference of the charger output end part exceeds the temperature difference threshold, or the internal temperature rising rate exceeds the rate threshold, the processing module determines that there is a potential abnormality at the charger output end part, and raises the current warning level by n levels, where n is a positive integer not less than 1.

[0013] A further technical improvement of the present invention lies in that: the method for obtaining the temperature difference threshold includes: B1. Real-time collect environmental data, including environmental temperature , wind speed and light intensity ; B2. Divide the environmental data into different intervals according to the actual thermal management rules, specifically including: environmental temperature partition, wind speed partition and light intensity partition; B3. Match the current environmental data with a preset decision table to dynamically set the temperature difference threshold; In the decision table, corresponding temperature difference thresholds are set based on different environmental temperature intervals; A further technical improvement of the present invention lies in that: the method for the processing module to correct the electric vehicle battery temperature, charger output end temperature, and charger input end temperature based on environmental data includes: H1. The processing module receives the battery temperature , charger input end temperature and charger output end temperature collected by the temperature monitoring module, and when the processing module collects, it adds time stamps to the battery temperature , charger input end temperature and charger output end temperature to form corresponding time series; H2. The processing module synchronously obtains the environmental data uploaded by the environmental monitoring module, including the environmental temperature , wind speed and light intensity ; H3. The processing module constructs an environmental status code based on the environmental data it receives, for representing the current charging environmental conditions; and the environmental status code includes: = temperature level; = wind speed level; = light level; That is , which is used to select a correction model from a preset thermal influence mapping matrix; H4. The processing module selects the corresponding temperature correction model from a predefined thermal response model library according to the environmental status code . Each correction model contains a non-linear correction function for different temperature measurement positions, and the model structure is as follows: ; In the formula, ∈{B, I, O}, representing the battery part, the charger output end part and the charger output end part respectively; represents the thermal inertia parameter of the corresponding part; is the response sensitivity of the corresponding part to the change of environmental temperature; and are the response coefficients of wind speed and light intensity to the temperature influence at this position; H5. The processing module uses the selected correction function to calculate the original temperature data to obtain the corrected temperature data of the corresponding part , and .

[0014] Compared with the prior art, the present invention has the following beneficial effects: By constructing a multi-point temperature time series model and introducing a thermal delay identification and compensation mechanism, the present invention realizes the response alignment between the battery, the charger input end and the output end, thereby improving the recognition sensitivity to local thermal imbalance and contact abnormality. At the same time, based on the normalization processing of temperature difference and heating rate difference, a unified stability scoring standard is established, enhancing the consistency and quantifiability of anomaly analysis.

[0015] In terms of temperature data determination, the present invention proposes to jointly consider the temperature of the battery body and the connection port. Through zonal thermal perception and rule judgment, it intelligently selects the temperature value representing the current risk as the "battery temperature", which can effectively identify early hidden dangers such as local overheating of the interface or abnormal internal temperature uniformity, and improve the recognition ability of various charging failure modes.

[0016] On the other hand, in terms of environmental adaptation, the present invention constructs a multi-dimensional state matrix based on environmental temperature, wind speed, and light intensity, dynamically selects the corresponding temperature correction function, and calls the non-linear compensation function from the thermal response model library to accurately eliminate the environmental impact, realizing temperature restoration and risk judgment highly adaptable to complex working conditions, and effectively improving the warning accuracy and system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 is the system block diagram of the present invention; Figure 2 is the system logic diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0019] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, describe in detail the specific embodiments, structures, features, and effects according to the present invention.

[0020] Embodiment 1: Please refer to Figure 1-2 As shown, the present invention provides a safety monitoring system for electric vehicle charging, including a temperature monitoring module, an environmental monitoring module, a processing module, and a warning module;

[0021] The environmental monitoring module is used to monitor the charging environment of the electric vehicle and obtain the corresponding environmental data. The environmental data includes: Ambient air temperature: directly affects the surface temperature rise of the charger and the battery; Wind speed: used to quantify the heat dissipation conditions of the charger. If the charging environment has poor ventilation (such as a closed space in an underground garage), even if the charging power is normal, the heat dissipation is insufficient, and the temperature at the charger end will rise abnormally; Light intensity: The temperature of the housing rises rapidly under direct sunlight, especially when charging outdoors, resulting in a falsely high internal temperature of the charger and affecting the system temperature rise judgment; The temperature monitoring module includes: A battery monitoring unit, which is used to monitor the battery temperature and obtain battery temperature data; The charger monitoring unit, and a plurality of the charger monitoring units are provided, which respectively monitor the temperature at the input end of the charger and the temperature at the output end of the charger, and obtain the temperature data at the input end of the charger and the temperature data at the output end of the charger; The processing module receives the basic charging data and the environmental data, and retrieves the historical temperature data of the battery part, the input end part of the charger, and the output end part of the charger from the database; And the processing module receives the battery temperature data, the temperature data at the input end of the charger, and the temperature data at the output end of the charger, and respectively establishes time series models for the battery temperature, the temperature at the input end of the charger, and the temperature at the output end of the charger; In this embodiment, the time series model includes the time-temperature original data sequence of the corresponding part, the temperature change trend fitting curve, the first derivative (heating rate) and the second derivative (heating acceleration) of the temperature change, the local extreme feature points, and the statistical characteristic parameters, which are used for subsequent charging stability evaluation and anomaly identification, where: Time-temperature original data sequence: specifically ; is the sampling moment corresponding to the time; is the temperature value at the corresponding time point; Temperature change trend fitting curve: describes the overall trend of temperature rise, which is convenient for anomaly comparison, including: Piecewise fitting: fitting in the three stages of starting charging, fast charging, and stable charging respectively Linear fitting: ; is the slope in the linear fitting model, indicating the linear growth rate of temperature with time; is the intercept in the linear fitting model, indicating the starting value of the temperature at time zero (or at the initial stage); Exponential fitting: ; is the maximum temperature rise amplitude (theoretically the final temperature value) in the exponential fitting model; is the rate parameter in the exponential fitting model, indicating the speed at which the temperature approaches the maximum value (the greater the rate, the faster the rise); is the temperature value at a certain moment t, which is used to describe the change relationship of temperature with time; Heating rate: the numerical value is approximately , judging the heating speed and identifying the problems of slow heating and intense heating; is the difference between the temperatures at two consecutive time points, which is used for numerical approximation to calculate the heating rate; is the time interval between two consecutive time points; Heating acceleration: , the second derivative of temperature with respect to time, indicating the change amount of the heating rate, and judging the heating change trend (smooth or drastic); Local extreme feature points: maximum value (local peak), minimum value (local valley), used to capture behavioral characteristics such as abnormal spikes and cooling inflection points; Statistical characteristic parameters: mean, variance, maximum value, minimum value, standard deviation, etc., used to provide a basis for health and stability analysis; The processing module calculates the temperature difference and the heating rate difference between three groups of time series models, and obtains a weighted average of the temperature difference and the heating rate difference to obtain a charging stability score, so as to evaluate the thermal behavior consistency and charging process stability of each part during the charging process in real time; The method for obtaining the charging stability score includes the following steps: S1. Set a sampling period, and every other sampling period, the temperature monitoring module collects the battery temperature , the temperature at the input end of the charger and the temperature at the output end of the charger , and record the time stamp t at the same time; The method for obtaining the battery temperature includes: Z1. Collect multi-point temperature data of the battery body (such as the center, upper layer, corner), including: The maximum temperature of the battery body ; The average temperature of the battery body ; The temperature difference of the battery body ; Collect the temperature at the battery connection port at the same time ; Z2. Detect the temperature at the battery connection port and the thermal distribution of the battery body respectively to judge whether there is an abnormality at the battery connection port or the battery body; Z3. If the temperature at the battery connection port is abnormal, the battery temperature is the temperature at the battery connection port ; If the temperature of the battery body is abnormal, the battery temperature is the maximum temperature of the battery body ; If both the battery connection port and the battery body are normal, the battery temperature is the average temperature of the battery body ; The abnormal judgment conditions for the temperature at the battery connection port include: a1. The temperature at the battery connection port itself is too high, and ; where is a preset temperature difference threshold a2. The temperature at the battery connection port rises too fast, and ; where is a preset heating rate threshold; If the temperature at the battery connection port meets any one of the conditions a1 or a2, the processing module determines that the temperature at the battery connection port is abnormal; The determination condition for the abnormal temperature of the battery body is: ; where is the preset thermal gradient threshold; S2. The processing module respectively establishes data sequences for the battery part, the charger input end part, and the charger output end part: 、 and ; Each group of data sequences is a mapping table of time points and corresponding temperature data; among them, is the time stamp at the kth sampling moment; S3. For each group of data sequences, calculate in order: ; ; ; Where: is the heating rate of the battery part, is the heating rate of the charger input end part, is the heating rate of the charger output end part; is the temperature of part i (battery part: B; charger input end part: I; charger output end part: O) at the kth moment; 、 are two sampling moments; S4. Set a sliding window (300s in this embodiment for example), and calculate the cross-correlation function between every two of the three groups of data sequences in units of the sliding window. The formula is: ; where Specifically include: Battery part - charger input end part; Battery part - charger output end part; Charger input end part - charger output end part; is the assumed time offset (forward or backward); And the processing module takes the position with the maximum peak value of the cross-correlation function curve to obtain the delay amount , that is, the response time offset between two data sequences; S5. Compare with the preset delay threshold. If is greater than the preset delay threshold, the processing module determines that there is a thermal delay effect between the two points. Then the processing module shifts the lagging data sequence by the delay amount Translate and align forward, for example ; is the new temperature curve of the corresponding part after thermal delay compensation, including: , the battery temperature after compensation; , the temperature at the input end of the charger after compensation; , the temperature at the output end of the charger after compensation; S6. For each moment , calculate the normalized temperature difference and the normalized heating rate difference between every two of the three data sequences, and the formulas for the two are respectively:

[0022] ; is the preset temperature difference normalization factor; and includes: ; ; ; ; is the preset heating rate normalization factor; and includes: ; ; ; In the formula, is the heating rate of the corresponding part after thermal delay compensation, and i = (battery part: B; charger input end part: I; charger output end part: O); S7. The processing module calculates the average values and of the normalized temperature difference and the normalized heating rate difference within the most recent sliding window respectively, and: ; ; S8. The processing module is based on the preset temperature difference weight and the heating rate weight , and calculates the charging stability score through the formula: ; In this embodiment, the warning levels include: ≥0.8, stable (normal charging); 0.6 ≤ <0.8, slightly abnormal (remind the user) 0.4 ≤ <0.6, moderately abnormal (recommend checking the device) <0.4, severely abnormal (automatically cut off power and alarm); After the processing module corrects the temperature of the electric vehicle battery, the temperature at the output end of the charger, and the temperature at the input end of the charger with environmental data, it obtains the corresponding corrected battery temperature, corrected temperature at the output end of the charger, and corrected temperature at the input end of the charger, and compares them with the corresponding historical data respectively to calculate the corresponding data difference degree; The method for the processing module to correct the temperature of the electric vehicle battery, the temperature at the output end of the charger, and the temperature at the input end of the charger based on environmental data includes: H1. The processing module receives the battery temperature collected by the temperature monitoring module , the temperature at the input end of the charger and the temperature at the output end of the charger , and when the processing module collects, for the battery temperature , the temperature at the input end of the charger and the temperature at the output end of the charger add a time stamp to form a corresponding time series; H2. The processing module synchronously obtains the environmental data uploaded by the environmental monitoring module, including the environmental temperature , wind speed and light intensity , which is used to ensure that the temperature of the corresponding part of the electric vehicle corresponds to the environmental condition data one by one and provides a basis for subsequent model selection; H3. The processing module constructs an environmental status code based on the environmental data it receives, which is used to represent the current charging environmental conditions; and the environmental status code includes: = temperature level (low temperature, normal temperature, high temperature); = wind speed level (static wind, light wind, strong wind); = light level (shadow area, medium sunlight, strong sunlight); That is , which is used to select a correction model from a preset thermal influence mapping matrix; The preset thermal influence mapping matrix is a 3*3*3 thermal influence mapping matrix established through offline training or engineering induction , the thermal influence mapping matrix Each element in includes a set of correction functions for this environmental data: including function models for each part, empirical response parameters, and nonlinear correction curves; H4. The processing module, according to the environmental status code , selects the corresponding temperature correction model from the predefined thermal response model library. Each correction model contains nonlinear correction functions for different temperature measurement positions, and the model structure is as follows: ; In the formula, ∈{B, I, O}, representing the battery part, the charger output end part, and the charger input end part respectively; represents the thermal inertia parameter of the corresponding part; is the response sensitivity of the corresponding part to the change in environmental temperature; and are the response coefficients of wind speed and light intensity to the temperature at this position; Moreover, this correction function is fitted by the system according to the measured data under different environmental states and has been embedded in the model library; In this embodiment, the thermal response model library refers to a set of nonlinear models used to support temperature correction, which is used to describe the thermal response behavior of the device temperature measurement points under different environmental conditions (environmental temperature, wind speed, and light intensity), and can provide corresponding temperature correction functions for different environmental states to dynamically compensate for the errors caused by the environment to the device temperature data; It is specifically constructed through structured experimental sampling and data fitting. Specifically, it includes: under multiple representative environmental combinations (such as high temperature + weak wind + strong light, low temperature + strong wind + weak light, etc.), the temperature rise curve data of key temperature measurement points such as the battery body, charger input end, and output end are collected respectively, and nonlinear fitting methods (such as exponential response functions or empirical surface functions) are used to extract the response model parameters under each environmental state, including thermal inertia coefficients, response sensitivity coefficients, wind speed / light correction factors, etc. Finally, the mapping relationship between each state combination and its corresponding correction function is constructed into a model library that can be called by looking up a table and used as a dynamic function of the temperature correction module during the operation of the system; H5. The processing module uses the selected correction function to calculate the original temperature data to obtain the corrected temperature data of the corresponding part , specifically including: Correct the battery temperature: ; Correct the charger output end temperature: ; Correct the charger input end temperature: ; The warning module performs corresponding-level warnings based on the charging stability score and the data difference degree between each part of the electric vehicle and the corresponding historical data, including: If the data difference degree between any one of the corrected battery temperature, the corrected charger output terminal temperature, or the corrected charger input terminal temperature and its corresponding historical data is greater than the preset difference threshold, the warning module immediately triggers a warning message (hard trigger); The warning module sets multiple warning levels based on the charging stability score. When the charging stability score is lower than the corresponding grading threshold, a charging anomaly warning of the corresponding warning level is triggered.

[0023] Embodiment 2: A safety monitoring system for electric vehicle charging, including a temperature monitoring module, an environment monitoring module, a processing module, and a warning module;

[0024] The environment monitoring module is used to monitor the charging environment of the electric vehicle and obtain corresponding environmental data. The environmental data includes: Ambient air temperature: directly affects the surface temperature rise of the charger and the battery; Wind speed: used to quantify the heat dissipation conditions of the charger. If the charging environment has poor ventilation (such as a closed space in an underground garage), even if the charging power is normal, the heat dissipation is insufficient, and the temperature at the charger end will rise abnormally; Light intensity: The temperature of the housing rises rapidly under direct sunlight, especially when charging outdoors, resulting in a falsely high internal temperature of the charger and affecting the system's temperature rise judgment; The temperature monitoring module includes: A battery monitoring unit for monitoring the battery temperature and obtaining battery temperature data; A charger monitoring unit. There are multiple charger monitoring units, which respectively monitor the temperature at the input end of the charger and the temperature at the output end of the charger to obtain the temperature data at the input end of the charger and the temperature data at the output end of the charger; Compared with Embodiment 1, multiple said charger monitoring units respectively collect the internal and external temperature data of the input end part and the output end part of the charger; And the processing module respectively calculates the internal and external temperature differences between the input end part and the output end part of the charger and the internal temperature rise rate of the input end part and the output end part of the charger; And the processing module determines whether the internal and external temperature difference between the two parts exceeds the preset temperature difference threshold, and determines whether the internal temperature rise rate of the two parts exceeds the preset rate threshold; When the internal and external temperature difference of the input end part of the charger exceeds the temperature difference threshold, or the internal temperature rise rate exceeds the rate threshold, the processing module determines that there is potential poor contact or abnormal local heating at the input end part of the charger and immediately triggers a warning message; When the internal and external temperature difference at the charger output end exceeds the temperature difference threshold, or the internal temperature rising rate exceeds the rate threshold, the processing module determines that there is a potential abnormality at the charger output end, and raises the current warning level by n levels, where n is a positive integer not less than 1; The method for obtaining the temperature difference threshold includes: B1. Real-time collect environmental data, including environmental temperature , wind speed and light intensity ; B2. Divide the environmental data into different intervals according to the actual thermal management rules, specifically including: Environmental temperature partition: Low temperature area ( < 10°C), fast heat dissipation, low shell temperature; Normal temperature area: (10°C ≤ 30°C), standard conditions; High temperature: ( > 30°C), significant natural temperature rise of the shell; Wind speed partition: Static wind: ( < 0.5 m / s), high risk of heat accumulation; Light wind: (0.5 m / s ≤ ≤ 2.0 m / s), natural heat dissipation; Strong wind: ( ≥ 2.0 m / s), strong heat dissipation; Light intensity partition: Shadow area: ( < 200 W / ㎡), no obvious light heating; Medium sunlight: (200 ≤ < 600 W / ㎡), slightly rising shell temperature; Strong sun exposure: ( ≥ 600 W / ㎡), obvious temperature rise of the shell; B3. Match the current environmental data with the preset decision table to dynamically set the temperature difference threshold; In the decision table, corresponding temperature difference thresholds are set based on different environmental temperature intervals; In this embodiment, the decision table may include: Ambient temperature range Wind speed range Light intensity range Dynamic internal-external temperature difference threshold Low temperature Strong wind Shadow 6℃ Normal temperature Gentle wind Moderate sunlight 8℃ High temperature Calm wind Intense sunlight 12℃ High temperature Strong wind Shadow 9℃ Normal temperature Calm wind Shadow 7℃ Low temperature Calm wind Shadow 5℃ ... ... ... Further refine according to historical data or experiments The processing module receives the basic charging data and environmental data, and retrieves the historical temperature data of the battery part, charger input end part and charger output end part from the database; And the processing module receives battery temperature data, charger input terminal temperature data, and charger output terminal temperature data, and respectively establishes time series models for the battery temperature, charger input terminal temperature, and charger output terminal temperature; The processing module calculates the temperature difference and the heating rate difference between the three groups of time series models, and obtains a charging stability score by taking the weighted average of the temperature difference and the heating rate difference, so as to evaluate the thermal behavior consistency of each part and the charging process stability during charging in real time; After the processing module corrects the electric vehicle battery temperature, charger output terminal temperature, and charger input terminal temperature with environmental data, it obtains the corresponding corrected battery temperature, corrected charger output terminal temperature, and corrected charger input terminal temperature, and compares them with the corresponding historical data respectively to calculate the corresponding data difference degree; Based on the charging stability score and the data difference degree between each part of the electric vehicle and the corresponding historical data, the warning module issues warnings at corresponding levels, including: If the data difference degree between any one of the corrected battery temperature, corrected charger output terminal temperature, or corrected charger input terminal temperature and its corresponding historical data is greater than the preset difference threshold, the warning module immediately triggers a warning message (hard trigger); The warning module sets multiple warning levels based on the charging stability score. When the charging stability score is lower than the corresponding grading threshold, it triggers a charging anomaly warning at the corresponding warning level.

[0025] The above is only a preferred embodiment of the present invention, and it does not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to it as equivalent embodiments with the equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any simple modification, equivalent change, and modification made to the above embodiment according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A safety monitoring system for electric vehicle charging, comprising a temperature monitoring module, an environment monitoring module, a processing module and an early warning module, characterized in that: The environmental monitoring module is used to monitor the charging environment of the electric vehicle and obtain corresponding environmental data; the environmental data includes ambient air temperature, wind speed and light intensity; The temperature monitoring module comprises: A battery monitoring unit monitors the battery temperature and obtains battery temperature data; A charger monitoring unit, wherein the plurality of charger monitoring units respectively monitor the temperature of the charger input end and the temperature of the charger output end, and obtain the charger input end temperature data and the charger output end temperature data; The processing module receives basic charging data and environmental data, and retrieves historical temperature data of the battery part, the charger input end part and the charger output end part from the database; And the processing module receives the battery temperature data, the charger input terminal temperature data and the charger output terminal temperature data, and establishes time series models respectively; The processing module calculates the temperature difference and heating rate difference between the three groups of time series models, and takes weighted average respectively to obtain the charging stability score; The processing module corrects the electric vehicle battery temperature, the charger output terminal temperature, and the charger input terminal temperature with environmental data, obtains the corresponding corrected battery temperature, corrected charger output terminal temperature, and corrected charger input terminal temperature, and compares them with the corresponding historical data, respectively, and calculates the corresponding data difference; The early warning module issues an early warning of a corresponding level based on the charging stability score and the data difference between the battery location, the charger input terminal location, the charger output terminal location and the corresponding historical data.

2. The electric vehicle charging safety monitoring system according to claim 1, characterized in that: The method for obtaining the charging stability score comprises the following steps: S1. Set the sampling period, and the temperature monitoring module collects the battery temperature once every sampling period. , Charger input temperature and charger output temperature , and record the timestamp t at the same time; S2, the processing module respectively establishes the data sequence of the battery part, the charger input end part and the charger output end part: , and ;in, is the timestamp of the kth sampling moment; S3. For each set of data sequences, calculate in order: ； ； ； Where: is the heating rate of the battery part, is the heating rate of the charger input terminal, is the heating rate at the output end of the charger; is the temperature of the part i (battery part: B; charger input part: I; charger output part: O) at the kth moment; , are the two sampling moments; S4, setting a sliding window, and calculating the cross-correlation function between two of the three sets of data sequences using the sliding window as a unit; And the processing module takes the position where the peak of the cross-correlation function curve is the largest to obtain the delay ; S5, delay amount Compare with the preset delay threshold. If it is greater than a preset delay threshold, the processing module determines that there is a thermal delay effect between the two points; The processing module converts the delayed data sequence into Shift forward to align, ; It is the new temperature curve of the corresponding part after thermal delay compensation; S6. For each moment , calculate the normalized temperature difference between the three sets of data series and the normalized heating rate difference ; S7, the processing module calculates the normalized temperature difference in the most recent sliding window and the normalized heating rate difference The average and ; S8, processing module based on preset temperature difference weight and heating rate weight , and by the formula: Calculate the charging stability score .

3. The electric vehicle charging safety monitoring system according to claim 2, characterized in that: The method for obtaining the battery temperature includes: Z1. The temperature monitoring module collects multi-point temperature data of the battery body, including the maximum temperature of the battery body, the average temperature of the battery body and the temperature difference of the battery body; At the same time, the temperature monitoring module collects the temperature at the battery connection port; Z2, the processing module detects the temperature at the battery connection port and the thermal distribution of the battery body respectively to determine whether there is an abnormality at the battery connection port or the battery body; Z3. If the temperature at the battery connector is abnormal, the battery temperature will be the temperature at the battery connector; If the battery body temperature is abnormal, the battery temperature will use the maximum temperature of the battery body; If the battery connector and the battery body are normal, the battery temperature uses the average temperature of the battery body.

4. The electric vehicle charging safety monitoring system according to claim 3, characterized in that: The abnormal judgment conditions for abnormal temperature at the battery connection port include: a1. The temperature at the battery connection port is greater than the sum of the average temperature of the battery body and the preset temperature difference threshold; a2. The temperature rise rate at the battery connection port is greater than the preset temperature rise rate threshold; If the temperature at the battery connection port satisfies any one of conditions a1 or a2, the processing module determines that the temperature at the battery connection port is abnormal.

5. The electric vehicle charging safety monitoring system according to claim 4, characterized in that: The judgment conditions for abnormal battery body temperature are: The temperature difference of the battery body is greater than the preset thermal gradient threshold.

6. The electric vehicle charging safety monitoring system according to claim 1, characterized in that: The plurality of charger monitoring units respectively collect the internal and external temperature data of the charger input terminal and the charger output terminal; The processing module calculates the temperature difference between the inside and outside of the charger input end and the charger output end and the internal temperature rise rate of the charger input end and the charger output end respectively; The processing module determines whether the internal and external temperature difference between the charger input terminal and the charger output terminal exceeds a preset temperature difference threshold, and determines whether the internal temperature rise rate of the two parts exceeds a preset rate threshold; When the temperature difference between the inside and outside of the charger input exceeds the temperature difference threshold, or the internal temperature rise rate exceeds the rate threshold, the processing module immediately triggers a warning message; When the temperature difference between the inside and outside of the charger output terminal exceeds the temperature difference threshold, or the internal temperature rise rate exceeds the rate threshold, the processing module raises the current warning level by n levels; n is a positive integer not less than 1.

7. The electric vehicle charging safety monitoring system according to claim 6, characterized in that: The method for obtaining the temperature difference threshold comprises: B1. Real-time collection of environmental data, including ambient temperature, wind speed and light intensity; B2. According to the actual thermal management rules, the environmental data is divided into different intervals, including: ambient temperature partition, wind speed partition and light intensity partition; B3. Match the current environmental data with a preset decision table, in which corresponding temperature difference thresholds are set based on different environmental temperature ranges; After the match is successful, the processing module dynamically sets the temperature difference threshold.

8. The electric vehicle charging safety monitoring system according to claim 1, characterized in that: The method for the processing module to correct the battery temperature of the electric vehicle, the output temperature of the charger, and the input temperature of the charger based on the environmental data includes: H1. The processing module receives the battery temperature collected by the temperature monitoring module , Charger input temperature and charger output temperature , and when the processing module collects, timestamps are added to form corresponding time series; H2. The processing module synchronously obtains the environmental data uploaded by the environmental monitoring module, including the ambient temperature , wind speed and light intensity ; H3. The processing module constructs the environment status code based on the environment data it receives. , ; =Temperature level, =Wind speed level, =light level; H4, processing module according to the environmental status code , select the corresponding temperature correction model from the predefined thermal response model library. Each correction model contains a nonlinear correction function for different temperature measurement positions. The model structure is as follows: ； In the formula, ∈{B, I, O}, represent the battery part, charger output terminal part and charger output terminal part respectively; Indicates the thermal inertia parameters of the corresponding parts; is the response sensitivity of the corresponding part to the change of ambient temperature; and is the response coefficient of the influence of wind speed and light intensity on the temperature at that location; H5. The processing module uses the selected nonlinear correction function to calculate the original temperature data and obtain the corrected temperature data of the corresponding part. , .

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