A safety monitoring system for electric vehicle charging

Abstract

The present invention discloses a safety monitoring system for electric vehicle charging, relating to the field of electric vehicle safety technology. The system comprises: an environmental monitoring module for monitoring the electric vehicle's charging environment and acquiring corresponding environmental data, including ambient air temperature, wind speed, and light intensity; a temperature monitoring module comprising: a battery monitoring unit for monitoring battery temperature and acquiring battery temperature data; and a charger monitoring unit, wherein the charger monitoring units are provided in plurality and respectively monitor the temperature of the charger input and output terminals, acquiring charger input and output temperature data. By constructing a multi-point temperature time series model and introducing a thermal delay identification and compensation mechanism, the present invention achieves response alignment between the battery and the charger input and output terminals, thereby improving the sensitivity of identifying local thermal imbalances and contact anomalies.

Description

Technical Field

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

[0002] With the widespread adoption of electric vehicles, users are increasingly concerned about charging safety. The charging process involves continuous input of electrical energy and release of heat, requiring the coordinated operation of multiple key components, including the battery, power adapter, and connector. Localized overheating, contact anomalies, or poor heat dissipation during charging can cause electrical failures or even accidents. Therefore, understanding real-time thermal state changes in key components during charging and identifying potential risks is a key research area in electric vehicle safety management.

[0003] Current EV charging safety monitoring solutions typically employ anomaly detection based on temperature thresholds. These methods monitor temperature changes at key locations within the battery or charger, triggering an alarm when the temperature exceeds a fixed, preset value. These methods are simple to implement and have proven valuable in certain charging scenarios, leading to their widespread deployment in both home and public charging facilities.

[0004] After searching, a Chinese patent (publication number: 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 electrically connected to the core processing module, 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. The temperature detection module 1 is used to detect the temperature of the electric vehicle charger and transmit it to the core processing module. 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 and off of the charging circuit. The relay module 2 is used to control the on and off of the electric vehicle power system.

[0005] However, as the operating environment of electric vehicles becomes more complex, such as in high-temperature outdoor environments, confined spaces, or direct sunlight, the surface temperature of the device may fluctuate due to environmental factors rather than actual faults, leading to deviations in early warning judgments. Furthermore, the thermal response behavior of the battery body, connectors, input and output terminals, and other parts is not completely consistent, leaving room for optimization in complex scenarios, especially in improving the ability to identify temperature linkages at multiple locations, the accuracy of anomaly scoring, and adaptability to dynamic environments. 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 technology.

[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;

[0008] The environmental monitoring module is used to monitor the charging environment of the electric vehicle and obtain corresponding environmental data. The environmental data includes:

[0009] Ambient air temperature: directly affects the surface temperature rise of the charger and battery;

[0010] Wind speed: used to quantify the heat dissipation conditions of the charger;

[0011] Light intensity: The shell temperature rises rapidly under direct sunlight;

[0012] The temperature monitoring module includes:

[0013] A battery monitoring unit is used to monitor the battery temperature and obtain battery temperature data;

[0014] A charger monitoring unit, wherein the charger monitoring unit is provided in plurality and monitors the temperature of the charger input end and the temperature of the charger output end respectively, and obtains charger input end temperature data and charger output end temperature data;

[0015] The processing module receives basic charging data and environmental data, and retrieves historical temperature data of the battery, charger input terminal and charger output terminal from a database;

[0016] The processing module receives the battery temperature data, the charger input terminal temperature data and the charger output terminal temperature data, and establishes a time series model for the battery temperature, the charger input terminal temperature and the charger output terminal temperature respectively;

[0017] The processing module calculates the temperature difference and heating rate difference between the three time series models and takes a weighted average of the temperature difference and heating rate difference to obtain a charging stability score, thereby evaluating the consistency of thermal behavior of each part and the stability of the charging process in real time.

[0018] The processing module corrects the electric vehicle battery temperature, charger output terminal temperature, and 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;

[0019] The warning module issues warnings of corresponding levels based on the charging stability score and the difference between the data of each part of the electric vehicle and the corresponding historical data, including:

[0020] If the difference between the corrected battery temperature, the corrected charger output temperature, or the corrected charger input temperature and its corresponding historical data is greater than a preset difference threshold, the early warning module immediately triggers a warning message;

[0021] The warning module sets multiple warning levels based on the charging stability score. When the charging stability score is lower than the corresponding level threshold, a charging abnormality warning of the corresponding warning level is triggered.

[0022] A further technical improvement of the present invention is that the method for obtaining the charging stability score comprises the following steps:

[0023] S1. Set the sampling period, and every other sampling period, the temperature monitoring module collects the battery temperature T B (t), charger input temperature T I (t) and the charger output temperature T O (t), and simultaneously record the timestamp t;

[0024] S2. The processing module establishes data sequences of the battery part, the charger input end part and the charger output end part respectively: {(t k ,T B (t k ))}、{(t k ,T I (t k ))} and {(t k ,T O (t k ))}; Each data sequence is a mapping table of time points and corresponding temperature data; where t k The timestamp of the kth sampling moment;

[0025] S3. For each set of data sequences, calculate in order:

[0026]

[0027]

[0028] Where: v B (t k ) is the heating rate of the battery part, v I (t k ) is the heating rate of the charger input terminal, v O (t k ) is the temperature rise rate at the charger output terminal;

[0029] T i (t k) is the temperature of the part i at the kth moment; i∈{B, I, O}, B is the battery part, I is the charger input part, and O is the charger output part;

[0030] t k+1 , t k are the two sampling moments;

[0031] S4. Set a sliding window and calculate the cross-correlation function between the three sets of data sequences in units of the sliding window;

[0032] The processing module takes the position where the cross-correlation function curve has the largest peak value to obtain the delay Δt ij ;

[0033] S5, set the delay Δt ij Compare with the preset delay threshold, if |Δt ij | is greater than a preset delay threshold, the processing module determines that there is a thermal delay effect between the two points;

[0034] The processing module processes the delayed data sequence by a delay of Δt ij Shift forward to align,

[0035] is the new temperature curve of the corresponding part after thermal delay compensation;

[0036] S6. For each moment t k , calculate the normalized temperature difference between the three sets of data series and the normalized heating rate difference

[0037] S7, the processing module calculates the normalized temperature difference in the most recent sliding window and the normalized heating rate difference Average value and

[0038] S8, the processing module is based on the preset temperature difference weight ω T and the heating rate weight ω S , and by the formula: The charging stability score Q is calculated.

[0039] A further technical improvement of the present invention is that the method for obtaining the battery temperature includes:

[0040] Z1. Collect temperature data at multiple points on the battery body (e.g., center, upper layer, corners), including:

[0041] Maximum temperature of battery body

[0042] Average temperature of battery body

[0043] Temperature difference of the battery body

[0044] At the same time, the temperature T at the battery connection port is collected conn (t);

[0045] Z2. Detect the temperature at the battery connector and the thermal distribution of the battery body to determine whether there is any abnormality at the battery connector or the battery body;

[0046] Z3. If the temperature at the battery connector is abnormal, the battery temperature will be the temperature T at the battery connector. conn (t);

[0047] If the battery body temperature is abnormal, the battery temperature will be the maximum temperature of the battery body.

[0048] If the battery connector and the battery body are normal, the battery temperature will be the average temperature of the battery body.

[0049] A further technical improvement of the present invention is that the abnormality judgment conditions for abnormal temperature at the battery connection port include:

[0050] a1. The temperature of the battery connector is too high, and Where θ1 is the preset temperature difference threshold;

[0051] a2. The temperature at the battery connector rises too quickly, and Where θ2 is the preset heating rate threshold;

[0052] If the temperature at the battery connection port meets any one of conditions a1 or a2, the processing module determines that the temperature at the battery connection port is abnormal.

[0053] A further technical improvement of the present invention is that the judgment condition of abnormal battery body temperature is: ΔT core (t)>θ3; where θ3 is the preset thermal gradient threshold.

[0054] A further technical improvement of the present invention is that: the plurality of charger monitoring units respectively collect the temperature data of the inside and outside of the charger input terminal and the charger output terminal;

[0055] 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;

[0056] The processing module determines whether the internal and external temperature difference of the two parts exceeds a preset temperature difference threshold, and determines whether the internal temperature rise rate of the two parts exceeds a preset rate threshold;

[0057] When the temperature difference between the inside and outside of the charger input exceeds a temperature difference threshold, or the internal temperature rise rate exceeds a rate threshold, the processing module determines that there is potential poor contact or abnormal local heating at the charger input, and immediately triggers a warning message;

[0058] 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 determines that there is a potential abnormality at the charger output terminal and raises the current warning level by n levels, where n is a positive integer not less than 1.

[0059] A further technical improvement of the present invention is that the method for obtaining the temperature difference threshold comprises:

[0060] B1. Real-time collection of environmental data, including ambient temperature T env (t), wind speed V env (t) and light intensity L env (t);

[0061] B2. According to actual thermal management rules, environmental data is divided into different intervals, including: ambient temperature zone, wind speed zone, and light intensity zone;

[0062] B3. Match the current environmental data with the preset decision table and dynamically set the temperature difference threshold;

[0063] The decision table sets corresponding temperature difference thresholds based on different ambient temperature ranges;

[0064] A further technical improvement of the present invention is that the method in which the processing module corrects the electric vehicle battery temperature, the charger output terminal temperature, and the charger input terminal temperature based on environmental data includes:

[0065] H1, the processing module receives the battery temperature T collected by the temperature monitoring module B (t), charger input temperature T I (t) and the charger output temperature T O (t), and when the processing module collects the battery temperature T B (t), charger input temperature T I (t) and the charger output temperature T O (t) Add timestamps to form corresponding time series;

[0066] H2, the processing module synchronously obtains the environmental data uploaded by the environmental monitoring module, including the ambient temperature T env (t), wind speed Venv (t) and light intensity L env (t);

[0067] H3, the processing module builds the environment status code S based on the environment data it receives e (t), used to indicate the current charging environment conditions; and the environmental status code S e (t) include:

[0068] S T =Temperature level;

[0069] S V =wind speed level;

[0070] S L =light level;

[0071] That is S e (t)=(S T ,S V ,S L ), used to select a correction model from a preset heat impact mapping matrix;

[0072] H4, the processing module according to the environmental status code S e (t), 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:

[0073]

[0074] Where, X∈{B, I, O}, represents the battery part, charger output terminal part and charger output terminal part respectively;

[0075] τX represents the thermal inertia parameter of the corresponding part;

[0076] ηX is the response sensitivity of the corresponding part to changes in ambient temperature;

[0077] ∝v and ∝L are the response coefficients of wind speed and light intensity to the temperature at that location;

[0078] 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

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] By constructing a multi-point temperature time series model and introducing a thermal delay identification and compensation mechanism, this method achieves response alignment between the battery and charger input and output terminals, thereby improving the sensitivity of identifying local thermal imbalances and contact anomalies. Furthermore, based on the normalization of temperature differences and heating rate differences, a unified stability scoring standard is established, enhancing the consistency and quantification of anomaly analysis.

[0081] In terms of temperature data judgment, the present invention proposes to jointly consider the temperature of the battery body and the connection port. Through zoned thermal perception and rule judgment, the temperature value representing the current risk is intelligently selected as the "battery temperature". This can effectively identify early hidden dangers such as local overheating of the interface or abnormal internal temperature average, and improve the ability to identify various charging failure modes.

[0082] On the other hand, in terms of environmental adaptation, the present invention constructs a multi-dimensional state matrix based on ambient temperature, wind speed and light intensity, dynamically selects the corresponding temperature correction function, calls the nonlinear compensation function from the thermal response model library, accurately eliminates environmental influences, and achieves temperature restoration and risk judgment that are highly adaptable to complex working conditions, effectively improving the warning accuracy and system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0084] Figure 1 is a system block diagram of the present invention;

[0085] Figure 2 This is the system logic diagram of the present invention. DETAILED DESCRIPTION

[0086] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0087] Example 1

[0088] See also Figure 1-2 As shown, the present invention provides a safety monitoring system for electric vehicle charging, including a temperature monitoring module, an environment monitoring module, a processing module and an early warning module;

[0089] The environmental monitoring module is used to monitor the charging environment of the electric vehicle and obtain corresponding environmental data. The environmental data includes:

[0090] Ambient air temperature: directly affects the surface temperature rise of the charger and battery;

[0091] Wind speed: This quantifies the heat dissipation conditions of the charger. If the charging environment is poorly ventilated (for example, in a confined underground garage), even if the charging power is normal, the heat dissipation will be insufficient, and the charger temperature will rise abnormally.

[0092] Light intensity: Direct sunlight can cause the charger's housing temperature to rise rapidly, especially when charging outdoors. This can cause the charger's internal temperature to rise artificially, affecting the system's temperature rise determination.

[0093] The temperature monitoring module includes:

[0094] A battery monitoring unit is used to monitor the battery temperature and obtain battery temperature data;

[0095] A charger monitoring unit, wherein the charger monitoring unit is provided in plurality and monitors the temperature of the charger input end and the temperature of the charger output end respectively, and obtains charger input end temperature data and charger output end temperature data;

[0096] The processing module receives basic charging data and environmental data, and retrieves historical temperature data of the battery, charger input terminal and charger output terminal from a database;

[0097] The processing module receives the battery temperature data, the charger input terminal temperature data and the charger output terminal temperature data, and establishes a time series model for the battery temperature, the charger input terminal temperature and the charger output terminal temperature respectively;

[0098] In this embodiment, the time series model includes the time-temperature raw data sequence of the corresponding part, the temperature change trend fitting curve, the first-order derivative (heating rate) and second-order derivative (heating acceleration) of the temperature change, local extreme value feature points and statistical characteristic parameters for subsequent charging stability evaluation and abnormality identification, wherein:

[0099] Time-temperature raw data sequence: specifically {(t1,T1),(t2,T2),...,(t n ,T n )};t n is the sampling moment of the corresponding time; T n is the temperature value at the corresponding time point;

[0100] Temperature change trend fitting curve: describes the overall trend of temperature rise and facilitates abnormal comparison, including:

[0101] Segmented fitting: three stages of initial charging, fast charging and steady charging are fitted separately

[0102] Linear fitting: T(t)=a1t+b1; a1 is the slope in the linear fitting model, which represents the linear growth rate of temperature over time; b1 is the intercept in the linear fitting model, which represents the starting value of temperature at time zero (or initial point);

[0103] Exponential fitting: T(t) = a2(1-e -b2t ); a2 is the maximum temperature rise in the exponential fitting model (theoretical final temperature rise value); b2 is the rate parameter in the exponential fitting model, which indicates how quickly the temperature approaches the maximum value (the larger the rate, the faster the rise); T(t) is the temperature value at a certain time t, which is used to describe the relationship between temperature and time;

[0104] Heating rate: The value is approximately Determine the speed of heating and identify slow heating and intense heating problems; i+1 -T i is the temperature difference between two consecutive time points, which is used to numerically approximate the heating rate; t i+1 -t i is the time interval between two consecutive time points;

[0105] Heating acceleration: The second derivative of temperature with respect to time indicates the change in heating rate and determines the heating trend (smooth or drastic);

[0106] Local extreme value feature points: maximum value (local peak value) and minimum value (local valley value), used to capture abnormal spikes, cooling inflection points and other behavioral characteristics;

[0107] Statistical characteristic parameters: mean, variance, maximum, minimum, standard deviation, etc., used to provide a basis for health and stability analysis;

[0108] The processing module calculates the temperature difference and heating rate difference between the three time series models and takes a weighted average of the temperature difference and heating rate difference to obtain a charging stability score, thereby evaluating the consistency of thermal behavior of each part and the stability of the charging process in real time.

[0109] The method for obtaining the charging stability score comprises the following steps:

[0110] S1. Set the sampling period, and every other sampling period, the temperature monitoring module collects the battery temperature T B (t), charger input temperature T I (t) and the charger output temperature T O (t), and simultaneously record the timestamp t;

[0111] The method for obtaining the battery temperature includes:

[0112] Z1. Collect temperature data at multiple points on the battery body (e.g., center, upper layer, corners), including:

[0113] Maximum temperature of battery body

[0114] Average temperature of battery body

[0115] Temperature difference of the battery body

[0116] At the same time, the temperature T at the battery connection port is collected conn (t);

[0117] Z2. Detect the temperature at the battery connector and the thermal distribution of the battery body to determine whether there is any abnormality at the battery connector or the battery body;

[0118] Z3. If the temperature at the battery connector is abnormal, the battery temperature will be the temperature T at the battery connector. conn (t);

[0119] If the battery body temperature is abnormal, the battery temperature will be the maximum temperature of the battery body.

[0120] If the battery connector and the battery body are normal, the battery temperature will be the average temperature of the battery body.

[0121] The abnormal temperature judgment conditions at the battery connector include:

[0122] a1. The temperature of the battery connector is too high, and Where θ1 is the preset temperature difference threshold

[0123] a2. The temperature at the battery connector rises too quickly, and Where θ2 is the preset heating rate threshold;

[0124] If the temperature at the battery connector satisfies any one of conditions a1 or a2, the processing module determines that the temperature at the battery connector is abnormal;

[0125] The judgment condition for abnormal battery body temperature is: ΔT core (t)>θ3; where θ3 is the preset thermal gradient threshold;

[0126] S2. The processing module establishes data sequences of the battery part, the charger input end part and the charger output end part respectively: {(t k ,T B (t k ))}、{(t k ,T I (t k ))} and {(t k ,T O (t k ))}; Each data sequence is a mapping table of time points and corresponding temperature data; where tk The timestamp of the kth sampling moment;

[0127] S3. For each set of data sequences, calculate in order:

[0128]

[0129] Where: v B (t k ) is the heating rate of the battery part, v I (t k ) is the heating rate of the charger input terminal, v O (t k ) is the temperature rise rate at the charger output terminal;

[0130] T i (t k ) is the temperature of the part i at the kth moment; i∈{B, I, O}, B is the battery part, I is the charger input part, and O is the charger output part;

[0131] t k+1 , t k are the two sampling moments;

[0132] S4. Set a sliding window (for example, 300 seconds in this embodiment), and calculate the cross-correlation function between the three sets of data sequences in units of the sliding window. The formula is:

[0133] R ij (τ)=∑ t T i (t)×T j (t+τ); where R ij (τ) specifically includes: R BI (τ) Battery position - charger input position; R BO (τ) Battery position - charger output position; R IO (τ) charger input position - charger output position;

[0134] τ is the assumed time offset (forward or reverse);

[0135] The processing module takes the position where the cross-correlation function curve has the largest peak value to obtain the delay Δt ij , that is, the response time offset between the two data sequences;

[0136] S5, Δt ij Compare with the preset delay threshold, if |Δt ij | is greater than the preset delay threshold, the processing module determines that there is a thermal delay effect between the two points, and the processing module delays the lagging data sequence by the delay amount Δtij Shift alignment forward, e.g.

[0137] It is the new temperature curve of the corresponding part after thermal delay compensation, including:

[0138] Compensated battery temperature;

[0139] Compensated charger input temperature;

[0140] The charger output temperature after compensation;

[0141] S6. For each moment t k , calculate the normalized temperature difference between the three sets of data series and the normalized heating rate difference And the formulas used by the two are:

[0142] T s is the preset temperature difference normalization factor; and include:

[0143]

[0144] S s is the preset heating rate normalization factor; and include:

[0145]

[0146]

[0147] Where, is the temperature rise rate of the corresponding part after thermal delay compensation, and i = (battery part: B; charger input end part: I; charger output end part: O);

[0148] S7, the processing module calculates the normalized temperature difference in the most recent sliding window and the normalized heating rate difference Average value and and:

[0149]

[0150] S8, the processing module is based on the preset temperature difference weight ω T and the heating rate weight ω S , and by the formula: The charging stability score Q is calculated;

[0151] In this embodiment, the warning levels include:

[0152] Q≥0.8, stable (normal charging);

[0153] 0.6≤Q<0.8, mild abnormality (remind the user)

[0154] 0.4≤Q<0.6, moderate abnormality (it is recommended to check the equipment)

[0155] Q<0.4, serious abnormality (automatic power off and alarm);

[0156] The processing module corrects the electric vehicle battery temperature, charger output terminal temperature, and 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;

[0157] The method for the processing module to correct the electric vehicle battery temperature, the charger output terminal temperature, and the charger input terminal temperature based on the environmental data includes:

[0158] H1, the processing module receives the battery temperature T collected by the temperature monitoring module B (t), charger input temperature T I (t) and the charger output temperature T O (t), and when the processing module collects the battery temperature T B (t), charger input temperature T I (t) and the charger output temperature T O (t) Add timestamps to form corresponding time series;

[0159] H2, the processing module synchronously obtains the environmental data uploaded by the environmental monitoring module, including the ambient temperature T env (t), wind speed V env (t) and light intensity L env (t) is used to ensure that the temperature of the corresponding parts of the electric vehicle corresponds to the environmental condition data, providing a basis for subsequent model selection;

[0160] H3, the processing module builds the environment status code S based on the environment data it receives e (t), used to indicate the current charging environment conditions; and the environmental status code S e (t) include:

[0161] S T = Temperature level (low temperature, normal temperature, high temperature);

[0162] SV =wind speed level (still wind, light wind, strong wind);

[0163] S L = Light level (shaded area, moderate sunlight, strong sun);

[0164] That is S e (t)=(S T ,S V ,S L ), used to select a correction model from a preset heat impact mapping matrix;

[0165] The preset thermal impact mapping matrix is ​​a 3*3*3 thermal impact mapping matrix M established through offline training or engineering induction. H , thermal impact mapping matrix M H Each element M H [i][j][k] contains the correction function group under the environmental data: including the function model for each part, empirical response parameters, and nonlinear correction curve;

[0166] H4, the processing module according to the environmental status code S e (t), 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:

[0167]

[0168] Where, X∈{B, I, O}, represents the battery part, charger output terminal part and charger output terminal part respectively;

[0169] τX represents the thermal inertia parameter of the corresponding part;

[0170] ηX is the response sensitivity of the corresponding part to changes in ambient temperature;

[0171] ∝v and ∝L are the response coefficients of wind speed and light intensity to the temperature at that location;

[0172] Moreover, the correction function is fitted by the system based on the measured data under different environmental conditions and has been embedded in the model library;

[0173] In this embodiment, the thermal response model library refers to a set of nonlinear models used to support temperature correction. It is used to describe the thermal response behavior of the device temperature measurement point under different environmental conditions (ambient temperature, wind speed, and light intensity). It can provide corresponding temperature correction functions for different environmental conditions to dynamically compensate for errors caused by the environment on the device temperature data.

[0174] It is constructed through structured experimental sampling and data fitting. Specifically, it includes: collecting temperature rise curve data of key temperature measurement points such as the battery body, charger input and output terminals under multiple representative environmental combinations (such as high temperature + weak wind + strong light, low temperature + strong wind + weak light, etc.), and using nonlinear fitting methods (such as exponential response function or empirical surface function) to extract response model parameters under each environmental state, including thermal inertia coefficient, response sensitivity coefficient, wind speed / light correction factor, 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 table lookup, which serves as a dynamic function of the temperature correction module during system operation;

[0175] 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 include:

[0176] Corrected battery temperature:

[0177] Correction of charger output temperature:

[0178] Correction of charger input temperature:

[0179] The warning module issues warnings of corresponding levels based on the charging stability score and the difference between the data of each part of the electric vehicle and the corresponding historical data, including:

[0180] If the difference between the corrected battery temperature, the corrected charger output temperature, or the corrected charger input temperature and its corresponding historical data is greater than the preset difference threshold, the early warning module immediately triggers a warning message (hard trigger);

[0181] The warning module sets multiple warning levels based on the charging stability score. When the charging stability score is lower than the corresponding level threshold, a charging abnormality warning of the corresponding warning level is triggered.

[0182] Example 2

[0183] 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;

[0184] The environmental monitoring module is used to monitor the charging environment of the electric vehicle and obtain corresponding environmental data. The environmental data includes:

[0185] Ambient air temperature: directly affects the surface temperature rise of the charger and battery;

[0186] Wind speed: This quantifies the heat dissipation conditions of the charger. If the charging environment is poorly ventilated (for example, in a confined underground garage), even if the charging power is normal, the heat dissipation will be insufficient, and the charger temperature will rise abnormally.

[0187] Light intensity: Direct sunlight can cause the charger's housing temperature to rise rapidly, especially when charging outdoors. This can cause the charger's internal temperature to rise artificially, affecting the system's temperature rise determination.

[0188] The temperature monitoring module includes:

[0189] A battery monitoring unit is used to monitor the battery temperature and obtain battery temperature data;

[0190] A charger monitoring unit, wherein the charger monitoring unit is provided in plurality and monitors the temperature of the charger input end and the temperature of the charger output end respectively, and obtains charger input end temperature data and charger output end temperature data;

[0191] Compared with embodiment 1, the plurality of charger monitoring units respectively collect the internal and external temperature data of the charger input terminal and the charger output terminal;

[0192] 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;

[0193] The processing module determines whether the internal and external temperature difference of the two parts exceeds a preset temperature difference threshold, and determines whether the internal temperature rise rate of the two parts exceeds a preset rate threshold;

[0194] When the temperature difference between the inside and outside of the charger input exceeds a temperature difference threshold, or the internal temperature rise rate exceeds a rate threshold, the processing module determines that there is potential poor contact or abnormal local heating at the charger input, and immediately triggers a warning message;

[0195] When the temperature difference between the inside and outside of the charger output exceeds the temperature difference threshold, or the internal temperature rise rate exceeds the rate threshold, the processing module determines that there is a potential abnormality at the charger output and raises the current warning level by n levels, where n is a positive integer not less than 1;

[0196] The method for obtaining the temperature difference threshold includes:

[0197] B1. Real-time collection of environmental data, including ambient temperature T env (t), wind speed V env (t) and light intensity L env (t);

[0198] B2. According to the actual thermal management rules, the environmental data is divided into different intervals, including:

[0199] Ambient temperature zone:

[0200] Low temperature zone (T env <10℃), fast heat dissipation and low shell temperature;

[0201] Normal temperature area: (10℃≤T env ≤30℃), standard conditions;

[0202] High temperature: (T env >30℃), the shell naturally heats up significantly;

[0203] Wind speed zones:

[0204] Still Wind: (V env <0.5m / s), the risk of heat accumulation is high;

[0205] Light wind: (0.5m / s≤V env ≤2.0m / s), natural heat dissipation;

[0206] Strong Wind: (V env ≥2.0m / s), strong heat dissipation;

[0207] Light intensity zones:

[0208] Shaded area: (L env <200W / ㎡), no obvious light heating;

[0209] Moderate sunlight: (200≤L env <600W / ㎡), the shell temperature rises slightly;

[0210] Strong exposure to the sun: (L env ≥600W / ㎡), the shell temperature rises significantly;

[0211] B3. Match the current environmental data with the preset decision table and dynamically set the temperature difference threshold;

[0212] The decision table sets corresponding temperature difference thresholds based on different ambient temperature ranges;

[0213] In this embodiment, the decision table may include:

[0214]

[0215]

[0216] The processing module receives basic charging data and environmental data, and retrieves historical temperature data of the battery, charger input terminal and charger output terminal from a database;

[0217] The processing module receives the battery temperature data, the charger input terminal temperature data and the charger output terminal temperature data, and establishes a time series model for the battery temperature, the charger input terminal temperature and the charger output terminal temperature respectively;

[0218] The processing module calculates the temperature difference and heating rate difference between the three time series models and takes a weighted average of the temperature difference and heating rate difference to obtain a charging stability score, thereby evaluating the consistency of thermal behavior of each part and the stability of the charging process in real time.

[0219] The processing module corrects the electric vehicle battery temperature, charger output terminal temperature, and 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;

[0220] The warning module issues warnings of corresponding levels based on the charging stability score and the difference between the data of each part of the electric vehicle and the corresponding historical data, including:

[0221] If the difference between the corrected battery temperature, the corrected charger output temperature, or the corrected charger input temperature and its corresponding historical data is greater than the preset difference threshold, the early warning module immediately triggers a warning message (hard trigger);

[0222] The warning module sets multiple warning levels based on the charging stability score. When the charging stability score is lower than the corresponding level threshold, a charging abnormality warning of the corresponding warning level is triggered.

[0223] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still 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 environmental 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 includes: Battery monitoring unit, which monitors 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 charger input end temperature data and charger output end temperature data; The processing module receives basic charging data and environmental data, and retrieves historical temperature data of the battery, charger input terminal and charger output terminal from a 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 a time series model respectively; The processing module calculates the temperature difference and heating rate difference between the three time series models and takes a weighted average of them to obtain a charging stability score, including: S1. Set the sampling period, and every other sampling period, the temperature monitoring module collects the battery temperature T B (t), charger input temperature T I (t) and the charger output temperature T O (t), and simultaneously record the timestamp t; S2. The processing module establishes data sequences of the battery part, the charger input end part and the charger output end part respectively: {(t k ,T B (t k ))}、{(t k ,T I (t k ))} and {(t k ,T O (t k ))}; where t k The timestamp of the kth sampling moment; S3. For each set of data sequences, calculate in order: Where: v B (t k ) is the heating rate of the battery part, v I (t k ) is the heating rate of the charger input terminal, v O (t k ) is the temperature rise rate at the charger output terminal; T i (t k ) is the temperature of the part i at the kth moment; i∈{B, I, O}, B is the battery part, I is the charger input part, and O is the charger output part; t k+1 , t k are the two sampling moments; S4. Set a sliding window and calculate the cross-correlation function between the three sets of data sequences in units of the sliding window; The processing module takes the position where the cross-correlation function curve has the largest peak value to obtain the delay Δt ij ; S5, set the delay amount Δt ij Compare with the preset delay threshold, if |Δt ij | 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 processes the delayed data sequence by a delay of Δt ij Shift forward to align, is the new temperature curve of the corresponding part after thermal delay compensation; S6. For each moment t k , 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 Average value and S8, the processing module is based on the preset temperature difference weight ω T and the heating rate weight ω S , and by the formula: The charging stability score Q is calculated; The processing module corrects the electric vehicle battery temperature, charger output terminal temperature, and 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 position, the charger input end position, the charger output end position 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 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 connector and the thermal distribution of the battery body respectively to determine whether there is any abnormality at the battery connector 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 both normal, the battery temperature will be the average temperature of the battery body.

3. The electric vehicle charging safety monitoring system according to claim 2, characterized in that: The abnormal temperature judgment conditions at the battery connector include: a1. The temperature at the battery connector 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 connector is greater than the preset temperature rise rate threshold; If the temperature at the battery connection port meets any one of conditions a1 or a2, the processing module determines that the temperature at the battery connection port is abnormal.

4. The electric vehicle charging safety monitoring system according to claim 3, 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.

5. The electric vehicle charging safety monitoring system according to claim 1, characterized in that: The plurality of charger monitoring units respectively collect temperature data of the inside and outside 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 exceeds the temperature difference threshold, or the internal temperature rise rate exceeds the rate threshold, the processing module will increase the current warning level by n levels; n is a positive integer not less than 1.

6. The electric vehicle charging safety monitoring system according to claim 5, characterized in that: The method for obtaining the temperature difference threshold includes: B1. Real-time collection of environmental data, including ambient temperature, wind speed, and light intensity; B2. According to actual thermal management rules, environmental data is divided into different intervals, including: ambient temperature zone, wind speed zone, and light intensity zone; B3. Match the current environmental data with a preset decision table, which sets corresponding temperature difference thresholds based on different environmental temperature ranges; After the match is successful, the processing module dynamically sets the temperature difference threshold.

7. The electric vehicle charging safety monitoring system according to claim 1, characterized in that: The method for the processing module to correct the electric vehicle battery temperature, the charger output terminal temperature, and the charger input terminal temperature based on the environmental data includes: H1, the processing module receives the battery temperature T collected by the temperature monitoring module B (t), charger input temperature T I (t) and the charger output temperature T O (t), and when the processing module collects data, 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 T env (t), wind speed V env (t) and light intensity L env (t); H3, the processing module builds the environment status code S based on the environment data it receives e (t), S e (t)=(S T ,S V ,S L );S T = Temperature grade, S V = Wind speed level, S L =light level; H4, the processing module according to the environmental status code S e (t), 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: Where, X∈{B, I, O}, represents the battery part, charger output terminal part and charger output terminal part respectively; τX represents the thermal inertia parameter of the corresponding part; ηX is the response sensitivity of the corresponding part to changes in ambient temperature; ∝v and ∝L are the response coefficients of wind speed and light intensity to 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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