New energy automobile charging cable connection early warning device, method and equipment

Abstract

The invention discloses a new energy automobile charging cable connection early warning device, method and equipment, and belongs to the technical field of electric power facilities. The device comprises a real-time charging power detection module used for detecting real-time charging power; the fluctuation identification module is used for identifying whether the fluctuation condition of the real-time charging power exceeds a preset normal range or not; the local temperature acquisition module is used for performing local temperature acquisition through a temperature sensor which is arranged at the connection part of the charging cable in advance; the target position determination module is used for determining a target position according to a local temperature comparison result of each joint; and the oxidation abnormity identification module is used for determining the condition of contact resistance increase caused by local oxidation at the target position and generating early warning information. According to the technical scheme, rapid identification of the local oxidation problem can be realized, the local oxidation problem is processed, the power control precision of the charging cable is improved, and the service life of the charging cable can be prolonged under the condition of timely maintenance.

Description

Technical Field

[0001] The present application belongs to the technical field of electric power facilities, and specifically relates to a new energy vehicle charging cable connection warning device, method and equipment. Background Art

[0002] With the rapid development of science and technology, the number of new energy vehicles has increased dramatically in a few years, and the charging equipment for new energy vehicles has also increased.

[0003] For the charging cables used in the charging process of new energy vehicles, due to the increasing requirements for charging efficiency, the current carried by the charging cables often needs to be precisely controlled. In some cases, such as charging cable failures, such as aging of the insulation layer, poor internal contact, etc., the charging power will be affected to a certain extent, but the charging efficiency cannot meet expectations. Inside the charging cable, the charging cable and the charging pile, as well as the charging cable and the charging gun are often connected to each other through welding or other docking methods. However, as time goes by, it is found that there is still a problem of local oxidation of the wire core at the connection of the charging cable, which has a very obvious effect on the resistivity of the charging cable, and has a greater impact on the charging efficiency of the charging cable. Therefore, how to timely discover and solve the problem of local oxidation is a crucial issue in this field. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a new energy vehicle charging cable connection warning device, method and equipment, the purpose of which is to identify whether there is a local oxidation problem at the connection of the charging cable through real-time power detection and temperature detection, and to achieve rapid identification of local oxidation problems through non-destructive sensing technology, so as to accurately deal with local oxidation problems, improve the power control accuracy of the charging cable, and extend the service life of the charging cable with timely maintenance.

[0005] In a first aspect, an embodiment of the present application provides a new energy vehicle charging cable connection warning device, the device comprising:

[0006] A real-time charging power detection module is used to detect the real-time charging power when it is identified that the charging cable of the new energy vehicle is in working state;

[0007] A fluctuation identification module, used to identify whether the fluctuation of the real-time charging power exceeds a preset normal range;

[0008] A local temperature acquisition module, used for collecting local temperature through a temperature sensor pre-set at the connection of the charging cable when the fluctuation condition exceeds a preset normal range;

[0009] A target position determination module is used to determine the target position according to the local temperature comparison results of each connection;

[0010] The oxidation anomaly recognition module is used to determine that the target position has local oxidation causing increased contact resistance based on the fluctuation of the real-time charging power and the local temperature of the target position, and generate warning information.

[0011] Furthermore, the target location determination module is specifically used to:

[0012] According to the comparison results of the local temperatures of each connection, the connection with the highest local temperature within the preset temperature range is determined as the target position; wherein the preset temperature range is a range obtained by pre-statistical analysis of the local temperature caused by the increase in contact resistance due to local oxidation.

[0013] Furthermore, the device also includes:

[0014] An operating parameter acquisition module, used to acquire electrical parameters of the charging cable during operation, wherein the electrical parameters include electrical parameters in a fast charging mode and electrical parameters in a trickle charging mode;

[0015] The preset temperature range determination module is used to obtain the current operation mode of the charging cable and determine the preset temperature range corresponding to the current operation mode according to the current operation mode.

[0016] Furthermore, the oxidation anomaly identification module is specifically used for:

[0017] Inputting the fluctuation of the real-time charging power and the local temperature of the target position into a pre-built machine learning model, and determining a probability value of the target position being locally oxidized and causing an increase in contact resistance based on an output result of the machine learning model;

[0018] When the probability value is greater than or equal to the first set threshold, a warning message is generated.

[0019] Furthermore, the oxidation anomaly identification module is also specifically used for:

[0020] When the probability value is greater than or equal to the second set threshold and less than the first set threshold, repeated collection information is generated to re-collect the fluctuation of the real-time charging power and the local temperature of each connection.

[0021] Furthermore, the device also includes:

[0022] A disassembly information acquisition module, configured to disassemble the target location and acquire disassembly information after determining that local oxidation exists at the target location causing the contact resistance to increase;

[0023] The feedback module is used to compare the disassembly information with the predicted result of the increase in contact resistance caused by the determined local oxidation to complete information feedback.

[0024] Furthermore, the fluctuation identification module is specifically used for:

[0025] Obtain the physical parameters of the charging cable and the charging parameters of the new energy vehicle;

[0026] Determining a normal range according to the physical parameter and the charging parameter;

[0027] According to the normal range and the preset theoretical charging power, it is determined whether the fluctuation of the real-time charging power exceeds the preset normal range.

[0028] In a second aspect, an embodiment of the present application provides a new energy vehicle charging cable connection warning method, the method comprising:

[0029] When it is identified that the charging cable of the new energy vehicle is in working state, real-time charging power detection is performed;

[0030] Identify whether the fluctuation of the real-time charging power exceeds a preset normal range;

[0031] When the fluctuation exceeds a preset normal range, local temperature collection is performed by a temperature sensor pre-set at the connection of the charging cable;

[0032] Determine the target position based on the local temperature comparison results of each connection;

[0033] According to the fluctuation of the real-time charging power and the local temperature of the target position, it is determined that the target position has local oxidation causing the increase of contact resistance, and an early warning message is generated.

[0034] Further, according to the local temperature comparison results of each connection, the target position is determined, including:

[0035] According to the comparison results of the local temperatures of each connection, the connection with the highest local temperature within the preset temperature range is determined as the target position; wherein the preset temperature range is a range obtained by pre-statistical analysis of the local temperature caused by the increase in contact resistance due to local oxidation.

[0036] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the first aspect.

[0037] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0038] In a fifth aspect, an embodiment of the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.

[0039] In the embodiment of the present application, the real-time charging power detection module is used to detect the real-time charging power when it is identified that the charging cable of the new energy vehicle is in working state; the fluctuation identification module is used to identify whether the fluctuation of the real-time charging power exceeds the preset normal range; the local temperature acquisition module is used to collect local temperature through the temperature sensor pre-set at the connection of the charging cable when the fluctuation exceeds the preset normal range; the target position determination module is used to determine the target position according to the local temperature comparison results of each connection; the oxidation abnormality identification module is used to determine the situation that the contact resistance increases due to local oxidation at the target position according to the fluctuation of the real-time charging power and the local temperature of the target position, and generate early warning information. The above technical scheme can identify whether there is a local oxidation problem at the connection of the charging cable through real-time power detection and temperature detection, and can realize the rapid identification of the local oxidation problem through non-destructive sensing technology, so as to accurately deal with the local oxidation problem, improve the power control accuracy of the charging cable, and extend the service life of the charging cable under timely maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a structural schematic diagram of a new energy vehicle charging cable connection warning device provided in Example 1 of the present application;

[0041] Figure 2 This is a structural schematic diagram of a new energy vehicle charging cable connection warning device provided in Example 2 of the present application;

[0042] Figure 3 It is a flow chart of a new energy vehicle charging cable connection warning method provided in Embodiment 3 of the present application;

[0043] Figure 4 It is a schematic diagram of the structure of the electronic device provided in Example 4 of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical scheme and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for the convenience of description, only the part related to the present application but not all the contents are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow chart describes each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0046] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0047] In conjunction with the accompanying drawings, the new energy vehicle charging cable connection warning device, method and equipment provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0048] Embodiment 1

[0049] Figure 1 Schematic diagram of the structure of the new energy vehicle charging cable connection warning device provided in the first embodiment of the present application. Figure 1 As shown, the device comprises:

[0050] The real-time charging power detection module 110 is used to detect the real-time charging power when it is identified that the charging cable of the new energy vehicle is in a working state;

[0051] The fluctuation identification module 120 is used to identify whether the fluctuation of the real-time charging power exceeds a preset normal range;

[0052] A local temperature acquisition module 130, configured to acquire local temperature through a temperature sensor pre-set at the connection of the charging cable when the fluctuation exceeds a preset normal range;

[0053] A target position determination module 140, for determining a target position according to a local temperature comparison result of each connection;

[0054] The oxidation anomaly identification module 150 is used to determine that there is local oxidation at the target location causing an increase in contact resistance based on the fluctuation of the real-time charging power and the local temperature of the target location, and generate warning information.

[0055] Among them, new energy vehicles can be vehicles that use new energy as a power source. Common ones include pure electric vehicles, hybrid vehicles, etc. Here they are used as charging objects, and the charging cable is connected to them to charge the battery of the new energy vehicle.

[0056] The charging cable is a conductive line that connects the charging pile and the charging interface of the new energy vehicle. It is usually composed of multiple strands of copper wires, wrapped in an insulating layer on the outside, and equipped with corresponding plugs and sockets to adapt to the charging interface standards of different models. It is a key physical connection component for achieving charging.

[0057] The working state refers to the charging cable. The working state can be the situation where charging power is being transmitted. It can be judged by some characteristics, such as a current that meets the charging current range flowing through the charging cable. For example, by setting a high-precision current sensor on the cable line and using current detection technology based on the principle of electromagnetic induction, when the current value is detected to be within a certain charging current range, such as a few amperes to more than ten amperes for slow charging, and tens of amperes or even higher for fast charging, it is determined to be in a working state. Alternatively, the voltage at both ends of the charging cable is also in a normal charging voltage range. For example, different charging modes correspond to different charging voltage ranges, such as the common 220V AC slow charging, 380V AC fast charging, or DC fast charging of several hundred volts, which are determined by a voltage sensor in conjunction with the corresponding voltage judgment logic. This means that power is being transmitted normally to the new energy vehicle, that is, it is in a working state.

[0058] In this solution, intelligent recognition technology can be used. Specifically, it can be achieved by setting up a special state monitoring circuit and algorithm module in the charging system. The monitoring circuit obtains the electrical signals on the charging cable in real time, such as current and voltage signals. It can also be based on a machine learning classification algorithm, such as a support vector machine algorithm, and pre-trained with a large amount of electrical signal data in normal charging and uncharging states. Let the algorithm learn the characteristic patterns of electrical signals in different states, analyze and judge the acquired electrical signals, and when the electrical signal characteristics meet the pre-set working state characteristics of the charging cable, it is determined that the charging cable is in a working state.

[0059] After confirming that it is in working state, the power detection process is started. Specifically, the current and voltage signals on the charging cable can be collected by power sensors, such as a combination of sensors based on the Hall effect and the principle of capacitive voltage division. These sensors are sampled at a set high sampling frequency, such as thousands of times per second, to ensure that real-time signal changes can be captured, and signal data can be continuously obtained. The collected original signal is then passed to the signal conditioning circuit for amplification, filtering and other pre-processing to make it a standard signal suitable for microcontroller processing. After receiving the processed signal, the microcontroller uses the built-in power calculation algorithm to select the corresponding accurate power calculation formula according to different charging circuit types, such as single-phase AC, three-phase AC or DC, to calculate the charging power value in real time, thereby realizing accurate detection of real-time charging power, and transmitting the detected real-time power data to other related modules.

[0060] Among them, the fluctuation of real-time charging power can be the degree of instability of real-time charging power over time, which is specifically reflected in the ups and downs of power values ​​within a certain time range. It can be described by some statistical parameters and time-frequency domain characteristics. For example, in the time domain, by calculating the standard deviation of power data, it can reflect the degree of dispersion of power data relative to the average value. The larger the standard deviation, the more drastic the fluctuation. The sum of the absolute values ​​of the power differences between adjacent sampling points can reflect the frequency and amplitude of power changes. In the frequency domain, signal processing techniques such as Fourier transform and wavelet transform can be used to convert power data to the frequency domain for analysis, observe the main frequency components of power fluctuations and the amplitudes corresponding to each frequency component, such as whether there is an abnormally prominent power fluctuation at a certain frequency, which may imply a certain charging fault or interference factor. These characteristics are combined to comprehensively characterize the fluctuation of real-time charging power.

[0061] The preset normal range can be a standard range that is pre-set in the system to measure whether the real-time charging power fluctuation is normal. The setting process is usually based on a large amount of statistical analysis of normal charging process data, such as collecting real-time charging power data of many different types of new energy vehicles and different charging piles in normal charging scenarios, and using statistical methods, such as calculating statistical quantities such as mean and standard deviation, to determine a reasonable fluctuation range. Generally, the standard deviation range with a certain multiple of the mean as the center is used as the normal range, such as the range of ±2 times the mean standard deviation. At the same time, combined with the design specifications of the charging equipment, such as considering the allowable power fluctuation range of the charging equipment itself, the allowable fluctuation degree of charging piles of different power levels will be different, and the relevant industry standards are comprehensively determined, which is used as a reference for judging whether the power fluctuation is abnormal.

[0062] In this solution, the fluctuation identification module can use the hybrid time-frequency analysis technology to extract the time domain and frequency domain characteristics of the power data, and obtain various statistics and characteristic values ​​that reflect the fluctuation situation as described above. For example, the wavelet transform is combined with the empirical mode decomposition. The wavelet transform can decompose the power data into frequency bands of different scales, which is convenient for analyzing the fluctuation characteristics of different frequency bands. The empirical mode decomposition can adaptively decompose the complex non-stationary power signal into multiple intrinsic mode functions, further refining the characterization of the signal characteristics. Then, the fluctuation characteristic values ​​obtained by these analyses are compared one by one with the pre-set normal range standards. Through logical judgment, such as judging whether the standard deviation of the power data is greater than the upper limit of the standard deviation corresponding to the preset normal range, or whether the amplitude of a key frequency component in the frequency domain exceeds the normal amplitude range, etc., it is accurately identified whether the fluctuation of the real-time charging power exceeds the preset normal range. If it exceeds, the corresponding trigger signal is sent to the relevant modules in the system.

[0063] Among them, the charging cable connection point can be the part where the charging cable is connected to the new energy vehicle charging interface and the charging pile output interface. This part is prone to poor contact and heat due to the plugging and unplugging operations, thermal effects when current passes through, and environmental factors. It is a key part that needs to be paid special attention to during the charging process.

[0064] After receiving the trigger signal from the fluctuation recognition module indicating that the fluctuation situation exceeds the preset normal range, the local temperature acquisition module starts working. First, according to the preset acquisition parameters, such as setting the acquisition frequency to several times per second, it can be reasonably determined according to actual needs and sensor performance, and the acquisition instruction can be sent to the temperature sensor to control the operation of the temperature sensor. For the temperature sensor based on fiber grating technology, the light source inside it emits light of a specific wavelength. After the light passes through the fiber grating, the wavelength of the grating reflected light changes due to the influence of temperature. The reflected light returns to the fiber grating demodulator, which has a high-precision wavelength detection device inside. Based on the principle of optical interference, it can accurately measure the slight change of the wavelength of the reflected light. The demodulator calculates the corresponding temperature value based on the correspondence between the wavelength change and the temperature, and outputs the temperature value to the signal acquisition circuit in the form of a digital signal. After the signal acquisition circuit performs pre-processing such as amplification and filtering on the input signal, it uses a high-precision analog-to-digital converter to convert the analog signal into a digital signal for transmission.

[0065] Among them, the local temperature comparison results of each connection can be the analysis conclusion obtained after comparing the local temperature data collected at each connection of the charging cable, which specifically includes information such as the temperature difference at different connections, temperature distribution rules, and temperature abnormalities. These comparison results are the key basis for subsequently determining the target position.

[0066] The target position may be a specific connection location where potential problems may exist, such as poor contact, local oxidation, etc., which may cause abnormal heating, as determined by comparative analysis of the local temperatures of each connection.

[0067] After the target position determination module receives the local temperature data of each charging cable connection from the local temperature acquisition module, it can perform analysis and processing. For example, firstly, an algorithm based on cluster analysis is used, such as the K-means clustering algorithm, to classify and cluster the temperature data of different connections according to the degree of similarity, and classify the connections with similar temperatures into one category. At the same time, the difference analysis algorithm is combined to calculate the difference between different clusters and the temperature data within the same cluster. By setting a reasonable difference threshold to judge the significance of the temperature difference, the temperature data is comprehensively analyzed and processed. Then, based on the temperature comparison results obtained from these analyses, various possible temperature anomaly modes, such as local high temperature points, temperature gradient anomalies, etc., can be comprehensively considered. Through logical reasoning and empirical rules, the target position where potential problems may exist is finally determined, and the target position information is passed to the oxidation anomaly identification module in a specific data format, such as a structure, JSON format, etc., including key information such as position number and position description.

[0068] The oxidation anomaly identification module can be used to analyze and determine whether there is an oxidation anomaly based on the instability of the real-time charging power over time, through a series of time domain and frequency domain characteristics, and together with the local temperature data of the target location.

[0069] Specifically, oxidation anomaly identification can be achieved through the following code:

[0070] Import necessary libraries

[0071] import numpy as npfrom sklearn.linear_model importLogisticRegression#Here we take the logistic regression model as an example. You can actually change to a more appropriate model according to your needsimport warningswarnings.filterwarnings("ignore")#Ignore some warning messages and adjust them according to the actual situation

[0072] Simulation data generation

[0073] #Assume that there is historical data, each piece of data contains the charging power fluctuation characteristics and local temperature values, as well as the corresponding label whether there is local oxidation that causes increased contact resistance (1 means yes, 0 means no) #Example historical data, the format is [charging power fluctuation characteristics, local temperature value, whether there is an oxidation anomaly label]

[0074] historical_data=[

[0075] [0.1,30,0],

[0076] [0.2,35,0],

[0077] [0.3,40,1],

[0078] [0.4,45,1],

[0079] [0.5,50,1]]

[0080] #Separate features and labels

[0081] X_historical=np.array([data[:2]for data in historical_data])

[0082] y_historical=np.array([data[2]for data in historical_data])

[0083] Model Training

[0084] #Create a logistic regression model instance

[0085] model = LogisticRegression()#training model

[0086] model.fit(X_historical,y_historical)

[0087] Simulate real-time data acquisition

[0088] #Simulate real-time charging power fluctuations (here simply assign an example value)

[0089] real_time_power_fluctuation = 0.45 #Simulate the local temperature of the target location (same simple assignment example value)

[0090] local_temperature=48#Combined into real-time data feature vector, the format should be consistent with the training data feature

[0091] real_time_data=np.array([[real_time_power_fluctuation,local_temperature]])

[0092] Use the model to make predictions and generate warning information based on the results

[0093] #Use the trained model to make predictions and get the probability values ​​(two probability values ​​are returned here, corresponding to the probabilities of category 0 and category 1 respectively. We take category 1, which is the probability of anomaly)

[0094] probability=model.predict_proba(real_time_data)[:,1][0]

[0095] #Assume that the first threshold is set to 0.6. You can adjust this threshold according to the actual situation.

[0096] first_threshold=0.6if probability>=first_threshold:

[0097] #If the probability is greater than or equal to the threshold, generate warning information (here is a simple printing example, the information can actually be sent to the monitoring system, notify the operation and maintenance personnel, etc.)

[0098] warning_message = f"The probability of local oxidation at the target position causing increased contact resistance is {probability:.2f}, which has exceeded the threshold. Please check in time!"

[0099] print(warning_message)else:

[0100] print("The possibility of local oxidation at the current target position causing increased contact resistance is low, and no processing is required for now.")

[0101] The above code first trains a simple logistic regression model based on simulated historical data to learn the relationship between charging power fluctuations and local temperature and whether there is local oxidation causing increased contact resistance. Then it simulates and obtains real-time charging power fluctuations and local temperature data, uses the model to predict the probability of abnormalities, and decides whether to generate warning information based on the set threshold.

[0102] If it involves interacting with hardware devices to obtain real-time data, such as obtaining charging power, temperature and other data from sensors through serial communication, network communication, etc., more complex deep learning models can be used, such as neural network models built based on TensorFlow or PyTorch, and the warning information will be sent to the corresponding monitoring platform or the terminal device of the operation and maintenance personnel through the appropriate communication protocol. The following is a simple illustration of how to send the warning information through the MQTT protocol:

[0103] import paho.mqtt.publish as publish

[0104] Modify the code for generating warning information (change printing to sending via MQTT)

[0105] #Assuming the MQTT server address, port, topic and other information, you need to configure it according to the actual situation

[0106] mqtt_broker="your_mqtt_broker_address"

[0107] mqtt_port=1883

[0108] mqtt_topic="warning_topic"

[0109] if probability>=first_threshold:

[0110] #If the probability is greater than or equal to the threshold, generate an early warning message (this is a simple example, the actual specification can be more detailed)

[0111] warning_message = f"The probability of local oxidation at the target position causing increased contact resistance is {probability:.2f}, which has exceeded the threshold. Please check in time!"

[0112] #Publish warning information to the specified topic through the MQTT protocol so that the monitoring system or operation and maintenance personnel who subscribe to the topic can receive it

[0113] publish.single(mqtt_topic,warning_message,hostname=mqtt_broker,port=mqtt_port)else:

[0114] print("The possibility of local oxidation at the current target position causing increased contact resistance is low, and no processing is required for now.")

[0115] Through the above code, it is possible to implement the whole process of determining the existence of local oxidation at the target location causing increased contact resistance and generating early warning information based on the fluctuation of real-time charging power and the local temperature at the target location.

[0116] The technical solution provided in this embodiment includes a real-time charging power detection module, which is used to detect the real-time charging power when the charging cable of the new energy vehicle is in working state; a fluctuation identification module, which is used to identify whether the fluctuation of the real-time charging power exceeds the preset normal range; a local temperature acquisition module, which is used to collect local temperature through a temperature sensor pre-set at the connection of the charging cable when the fluctuation exceeds the preset normal range; a target position determination module, which is used to determine the target position according to the local temperature comparison results of each connection; an oxidation abnormality identification module, which is used to determine the increase of contact resistance caused by local oxidation at the target position according to the fluctuation of the real-time charging power and the local temperature of the target position, and generate early warning information. The above technical solution can identify whether there is a local oxidation problem at the connection of the charging cable through real-time power detection and temperature detection, and can realize the rapid identification of the local oxidation problem through non-destructive sensing technology, so as to accurately handle the local oxidation problem, improve the power control accuracy of the charging cable, and extend the service life of the charging cable under timely maintenance.

[0117] In the above embodiment, optionally, the target position determination module is specifically used to:

[0118] According to the comparison results of the local temperatures of each connection, the connection with the highest local temperature within the preset temperature range is determined as the target position; wherein the preset temperature range is a range obtained by pre-statistical analysis of the local temperature caused by the increase in contact resistance due to local oxidation.

[0119] Among them, the preset temperature range can be a temperature interval numerical range determined by prior research and statistics. It is a representative temperature range determined by collecting a large amount of local temperature data generated at each connection of the charging cable when the contact resistance increases due to local oxidation, and then using statistical methods for comprehensive analysis. It is equivalent to a judgment benchmark. When comparing the local temperatures of each connection, this range is used to identify which temperature conditions may be associated with increased contact resistance due to local oxidation, providing a key reference standard for accurately finding the target location.

[0120] After receiving the local temperature data of each charging cable connection, this solution compares and analyzes these temperature data according to the difference analysis, cluster analysis and other algorithms mentioned above, and obtains the temperature difference, distribution law and other comparison results of each connection. All the temperature values ​​of the connection can be traversed to screen out those temperature values ​​within the preset temperature range, for example, by comparing and judging one by one with the preset temperature range determined by statistics before. For example, if the temperature value of a certain connection is greater than the lower limit of the preset temperature range and less than the upper limit, it is considered to be within the range, and then among the connections within the preset temperature range, the connection with the highest temperature value is further found. Among them, because the increase in contact resistance caused by local oxidation is often accompanied by an increase in temperature, and the high temperature point in the preset temperature range related to oxidation is likely to be the key part of the oxidation anomaly, so the connection with the highest local temperature and within the preset temperature range is finally determined as the target position.

[0121] The advantage of this arrangement of the present scheme is that by setting a preset temperature range, the target position can be accurately determined based on the local temperature comparison results of each connection, so that targeted focus can be placed on key locations where there may be problems with increased contact resistance due to local oxidation, thereby avoiding indiscriminate troubleshooting of all locations, thereby improving the efficiency and accuracy of problem troubleshooting, and helping to promptly discover potential safety hazards of charging cables, thereby ensuring the safety and stability of the charging process of new energy vehicles, and at the same time reducing unnecessary human and material resources invested in troubleshooting.

[0122] Embodiment 2

[0123] This embodiment is further optimized on the basis of the above embodiment, and the specific optimization is as follows: the device also includes: an operating parameter acquisition module, which is used to obtain electrical parameters of the charging cable during operation, and the electrical parameters include electrical parameters in fast charging mode and electrical parameters in trickle charging mode; a preset temperature range determination module, which is used to obtain the current operating mode of the charging cable and determine the preset temperature range corresponding to the current operating mode according to the current operating mode. Figure 2Schematic diagram of the structure of the new energy vehicle charging cable connection warning device provided in the second embodiment of the present application. Figure 2 As shown, the device comprises:

[0124] The real-time charging power detection module 210 is used to detect the real-time charging power when it is identified that the charging cable of the new energy vehicle is in a working state;

[0125] The fluctuation identification module 220 is used to identify whether the fluctuation of the real-time charging power exceeds a preset normal range;

[0126] A local temperature acquisition module 230, configured to acquire local temperature through a temperature sensor pre-set at the connection point of the charging cable when the fluctuation exceeds a preset normal range;

[0127] A target position determination module 240, for determining a target position according to the local temperature comparison results of each connection;

[0128] The oxidation anomaly identification module 250 is used to determine that the contact resistance increases due to local oxidation at the target location according to the fluctuation of the real-time charging power and the local temperature at the target location, and to generate warning information;

[0129] An operating parameter acquisition module 260, used to acquire electrical parameters of the charging cable during operation, wherein the electrical parameters include electrical parameters in a fast charging mode and electrical parameters in a trickle charging mode;

[0130] The preset temperature range determination module 270 is used to obtain the current operation mode of the charging cable and determine the preset temperature range corresponding to the current operation mode according to the current operation mode.

[0131] Among them, electrical parameters can be various data indicators that reflect the electrical characteristics of the charging cable during operation, including current, such as the amount of charge passing through the conductor cross section per unit time, which directly affects the charging speed and power transmission, voltage, such as different charging modes and different charging stages have different corresponding voltage values, resistance, and power, etc. It should be noted that the electrical parameters in the fast charging mode and the electrical parameters in the trickle charging mode are because these two modes are stages with obvious differences in the charging process of new energy vehicles, and the corresponding electrical parameters will also be different.

[0132] This solution can accurately determine whether the charging cable is currently in fast charging mode or other modes such as trickle charging mode through the operating mode recognition function, and then based on the pre-set correspondence between different operating modes and temperature ranges, for example, in fast charging mode, due to large current and high power, normal heating of the charging cable will make the temperature range relatively high; while in trickle charging mode, due to small current and low power, the normal temperature range is relatively low. According to these different situations, the reasonable temperature range corresponding to each mode is determined respectively to determine the appropriate preset temperature range under the current operating mode, so as to provide accurate and adaptive temperature judgment standards for subsequent modules such as target position determination.

[0133] The current operating mode may be the specific charging working mode that the charging cable is in at the moment. The most common ones are the fast charging mode and trickle charging mode mentioned above. Of course, there may be others such as constant voltage charging mode, constant current charging mode, etc. It reflects the rate and method of power transmission during the charging process and the approximate situation of related electrical parameters. The heating characteristics and electrical performance of the charging cable are different under different operating modes, which has an important impact on whether the temperature is normal.

[0134] In this solution, the operating parameter acquisition module can first deploy corresponding sensors at key positions of the charging cable, such as installing high-precision current sensors on the wires and setting voltage sensors at both ends of the cable. These sensors collect raw signals such as current and voltage in real time, and then pass through the matching signal conditioning circuit, and then use a high-precision analog-to-digital converter to convert the analog signal into a digital signal. Finally, through the built-in data acquisition algorithm, the electrical parameters of the charging cable during operation are continuously acquired at a certain sampling frequency. Whether it is the parameters of high current, high voltage, and high power in the fast charging mode, or the parameters of low current, low voltage, and low power in the trickle charging mode, they can be accurately collected and transmitted to other modules that need this data.

[0135] The current operating mode of the charging cable is obtained, and the preset temperature range corresponding to the current operating mode is determined according to the current operating mode. For example, if the received current value is large, and the voltage value is also in the normal voltage range corresponding to fast charging, and the power value is correspondingly high, the current operating mode is determined to be the fast charging mode through the built-in pattern recognition algorithm; conversely, if the current, voltage, power and other parameters are all in the characteristic range corresponding to the trickle charging, it is determined to be the trickle charging mode. After determining the current operating mode, the preset temperature range corresponding to the current operating mode is searched and determined according to the pre-stored correspondence table of different operating modes and preset temperature ranges.

[0136] The technical solution provided in this embodiment can be more in line with the actual charging situation by accurately collecting the electrical parameters of the charging cable under different operating modes and dynamically determining the corresponding preset temperature range. It can provide accurate temperature judgment basis according to the characteristics of different modes, making the judgment of the target position more scientific and reasonable, and effectively improving the accuracy of identifying abnormalities caused by local oxidation of the charging cable and other problems, further ensuring the safe and stable operation of the new energy vehicle charging system, and also helping to optimize the maintenance strategy of the charging equipment and reduce the risks and losses caused by potential failures.

[0137] Based on the above embodiments, optionally, the oxidation anomaly identification module is specifically used to:

[0138] Inputting the fluctuation of the real-time charging power and the local temperature of the target position into a pre-built machine learning model, and determining a probability value of the target position being locally oxidized and causing an increase in contact resistance based on an output result of the machine learning model;

[0139] When the probability value is greater than or equal to the first set threshold, a warning message is generated.

[0140] Among them, the pre-built machine learning model can be a model trained using a large amount of labeled data before the system is deployed. The labeled data comes from information collected from many actual charging scenarios in the past about charging power fluctuations, local temperatures at the target position of the corresponding charging cable, and whether there are real results of increased contact resistance due to local oxidation. When building, the appropriate model architecture will be selected according to the specific application scenario characteristics, data characteristics, etc. For example, if the data presents a complex nonlinear relationship, a deep neural network is often used. It has multiple hidden layers and can automatically learn the deep feature representation of the data. In addition, in order to take into account both interpretability and better classification performance, decision tree related models can also be selected. The core function of this model is to be able to mine the hidden correlation patterns based on the input real-time charging power fluctuations and local temperature data at the target position, and then output the corresponding probability value, which becomes the key basis for judging abnormal situations.

[0141] In this solution, the acquired real-time charging power fluctuation data and the local temperature data at the target location are preprocessed. For example, normalization may be performed on the power fluctuation data, such as mapping the power value to the range of 0 to 1, so that the model can process data of different magnitudes, and extract some key characteristic indicators, such as the standard deviation of power fluctuations, the maximum fluctuation amplitude, etc., to convert them into a format that meets the model input requirements. For local temperature data, standardization is also performed, such as ensuring that the mean of the temperature data is 0 and the variance is 1, eliminating the dimensional differences under different measurement conditions, and then the two sets of sorted data are arranged in a predetermined order, such as splicing the power fluctuation feature vector in front and the temperature data vector in the back into a comprehensive input vector, and input into the pre-built machine learning model to ensure that the model can accurately receive and process this information.

[0142] When the model receives input data, the internal computing unit will perform a series of operations on the input data according to the corresponding algorithm logic based on the trained parameters, such as the connection weights and biases between the layers of neurons in the neural network. These are determined during the training phase through optimization algorithms, such as the gradient descent algorithm, to minimize the error between the predicted results and the true labels. For example, in a neural network, the data will pass through each hidden layer in turn, and each layer of neurons will perform nonlinear transformations on the input through activation functions (such as ReLU functions, etc.), continuously extracting and integrating features, and finally outputting a probability value in the output layer, indicating the possibility that the contact resistance at the target location is increased due to local oxidation. In this process, the model uses the rules in the large amount of data learned before to make reasonable probability judgments on the current input situation.

[0143] When the probability value is greater than or equal to the first set threshold, a warning message is generated:

[0144] After receiving the probability value output by the model, the oxidation anomaly identification module will compare the probability value with the first preset threshold value pre-set in the system through a simple numerical comparison operation. If the probability value is greater than or equal to this threshold value, assuming that the first preset threshold value is set to 0.6, if the model output probability value is 0.7, the condition is met, which means that the probability of the abnormal situation of local oxidation at the target position causing the increase of contact resistance has reached the level that the system considers to require an alarm.

[0145] This technical solution, through the data analysis and pattern recognition capabilities of the machine learning model, can accurately combine the real-time charging status related data to determine the possibility of local oxidation anomalies at the target location of the charging cable, no longer relying solely on experience judgment, and reducing misjudgments and missed judgments. And based on reasonably set thresholds, early warning information is generated in a timely manner, allowing operation and maintenance personnel to intervene in advance and accurately troubleshoot problems, which helps to ensure the continuous and stable operation of the new energy vehicle charging system, avoid more serious charging failures caused by increased contact resistance due to local oxidation, and improve the safety and reliability of the entire charging facility. At the same time, it can also optimize the allocation of operation and maintenance resources and improve operation and maintenance efficiency.

[0146] On the basis of the above embodiments, optionally, the oxidation anomaly identification module is further specifically used for:

[0147] When the probability value is greater than or equal to the second set threshold and less than the first set threshold, repeated collection information is generated to re-collect the fluctuation of the real-time charging power and the local temperature of each connection.

[0148] The second set threshold value may be a probability critical value pre-set by the system, and its value is smaller than the first set threshold value. For example, if the first set threshold value is set to 0.7, the second set threshold value may be set to 0.4, which is used to further subdivide the probability level of the abnormal situation that the contact resistance increases due to local oxidation at the target position, so as to decide to take different countermeasures. When the probability value is in this interval, the operation of re-collecting relevant data is triggered to assist in more accurately judging whether there is really an abnormal situation.

[0149] When the oxidation anomaly identification module obtains the probability value output by the machine learning model, and judges that the probability value is greater than or equal to the second set threshold and less than the first set threshold, for example, the probability value is 0.5, which is between the two set thresholds. This means that the possibility of local oxidation at the target position causing an increase in contact resistance is at a medium level, and it is not yet certain whether such an abnormal situation exists. Therefore, repeated collection information can be generated. The real-time charging power detection module will use power sensors and other equipment again to obtain new real-time charging power fluctuation data according to the previous detection process; the local temperature acquisition module will control the temperature sensor to measure the temperature of each connection again, so that subsequent analysis and judgment based on the newly collected data can be carried out again to further confirm whether the target position really has an increase in contact resistance due to local oxidation.

[0150] This solution sets a second threshold and generates a mechanism for repeated information collection in the corresponding probability interval, making the judgment of potential local oxidation abnormalities of the charging cable more rigorous and detailed. When there is a certain possibility of abnormality but it is not enough to make a clear judgment, re-collecting key data for re-analysis can avoid misjudgment or missed judgment due to the limitations of single data judgment, further improving the accuracy of abnormal situation identification.

[0151] Based on the above embodiments, optionally, the device further includes:

[0152] A disassembly information acquisition module, configured to disassemble the target location and acquire disassembly information after determining that local oxidation exists at the target location causing the contact resistance to increase;

[0153] The feedback module is used to compare the disassembly information with the predicted result of the increase in contact resistance caused by the determined local oxidation to complete information feedback.

[0154] Disassembly information can be various relevant data and situation descriptions obtained during the specific process of disassembling the target location. It is a comprehensive information collection, including but not limited to the current change and resistance value in the electrical aspect, as well as the visual features in the appearance. This information reflects the actual status of the target location from different angles, providing a detailed basis for the subsequent comparison and analysis with the predicted results.

[0155] When the determination result that the contact resistance increases due to local oxidation at the target position is received, the target position is disassembled. During the disassembly process, disassembly information is collected. For example, resistivity information, appearance status information, etc. The appearance of the connection is analyzed to see if there is oxidation discoloration, corrosion, etc. For the contact resistance value, a professional micro-ohmmeter or other resistance measuring instrument is used to measure the contact resistance before and after disassembly, and the corresponding value is recorded.

[0156] This solution can compare and analyze whether the prediction results are accurate based on the received disassembly information and the prediction results of the increase in contact resistance caused by local oxidation in the system. For example, compare the actual measured contact resistance value with the resistance value range in the prediction result to determine whether it is within a reasonable error range; analyze whether the current change during the actual disassembly is consistent with the predicted current fluctuation pattern; check whether the oxidation characteristics reflected in the actual appearance image are consistent with the predicted appearance, etc. Through these comparative analyses, conclusions such as the difference between the actual situation and the predicted situation and the degree of consistency can be drawn. Finally, the results of these comparative analyses are fed back in accordance with the preset feedback format.

[0157] Through such a setting, after discovering that there is local oxidation at the target location causing the contact resistance to increase, this solution further obtains actual disassembly information and compares it with the predicted results, which helps to deeply understand the difference between the actual abnormal situation and the theoretical expectation, so as to accurately evaluate the accuracy and effectiveness of the current detection and judgment mechanism. This can not only optimize and adjust the existing diagnostic models, judgment thresholds, etc., and improve the accuracy of subsequent judgments on similar problems, but also provide more detailed reference basis for operation and maintenance personnel, so that they can formulate repair and maintenance strategies more reasonably, ensure the more stable and reliable operation of the new energy vehicle charging system, and also facilitate the continuous improvement and improvement of the entire system.

[0158] On the basis of the above embodiments, optionally, the fluctuation identification module is specifically used to:

[0159] Obtain the physical parameters of the charging cable and the charging parameters of the new energy vehicle;

[0160] Determining a normal range according to the physical parameter and the charging parameter;

[0161] According to the normal range and the preset theoretical charging power, it is determined whether the fluctuation of the real-time charging power exceeds the preset normal range.

[0162] Among them, the physical parameters of the charging cable may include data related to various inherent properties of the charging cable itself, such as the material, length, cross-sectional area, material and thickness of the insulation layer of the cable, etc.

[0163] The charging parameters of new energy vehicles may include, for example, various charging-related indicator data involved in charging the new energy vehicle, including the rated capacity of the vehicle battery, the battery charging rate, the current remaining battery power, and the charging modes supported by the vehicle.

[0164] The normal range can be a reasonable range that the real-time charging power should be in under normal charging conditions, determined by comprehensively considering the physical parameters of the charging cable and the charging parameters of the new energy vehicle. It is a key reference standard for subsequent judgment of whether the real-time charging power fluctuation is normal. The determination of this range requires a combination of many factors, and is obtained through certain calculations, analysis, and reference to past experience and industry standards. For example, it may be an upper and lower limit range of a power value. Charging power fluctuations within this range are considered normal, and exceeding this range may indicate an abnormal situation.

[0165] The pre-set theoretical charging power can be a power value that should appear during the ideal charging process, which is pre-calculated or set based on the design specifications of the charging cable, the charging characteristics of the new energy vehicle battery, and related charging standards. It can be used as a benchmark reference to measure the actual real-time charging power fluctuations in combination with the normal range, for example, by comparing the difference between the actual power and the theoretical charging power and observing whether the actual power is within the normal range, to determine whether the charging power has abnormal fluctuations.

[0166] In this solution, for the physical parameters of the charging cable, on the one hand, basic information such as material, length, and cross-sectional area can be directly read from the product specification of the cable. On the other hand, some parameters can be further verified through some testing equipment, such as using a high-precision resistance meter to measure the actual resistance of the cable, and then combining the known length, cross-sectional area and other information to deduce the physical parameters related to its conductive performance, or using professional material analysis instruments to detect the material composition of the insulation layer. For the charging parameters of new energy vehicles, the fluctuation recognition module can communicate with the battery management system of the vehicle to obtain parameter information such as the rated capacity of the battery, the current remaining power, the charging rate, and the charging mode supported by the vehicle from the battery management system.

[0167] After obtaining the physical parameters of the charging cable and the charging parameters of the new energy vehicle, the fluctuation recognition module will use relevant electrical theories and algorithms to determine the normal range. For example, based on the length, cross-sectional area and resistivity of the cable, the resistance value of the cable is calculated by Ohm's law, and then combined with the voltage range corresponding to the charging mode supported by the new energy vehicle, the power calculation formula is used to consider the impact of factors such as the rated capacity, charging rate and remaining power of the battery on the charging power. A comprehensive analysis is performed to calculate the reasonable fluctuation range of power under normal charging conditions, that is, to determine the normal range. This process may also refer to the general standards in the industry and the actual power data statistics under a large number of normal charging scenarios in the past, and make appropriate corrections and improvements to the calculated range to make it more in line with the actual charging situation.

[0168] After determining the normal range and obtaining the preset theoretical charging power, the fluctuation recognition module will receive the real-time charging power data from the real-time charging power detection module in real time. Then, it will determine whether the fluctuation exceeds the preset normal range through comparative analysis.

[0169] This technical solution uses a fluctuation recognition module to comprehensively consider the physical parameters of the charging cable and the charging parameters of the new energy vehicle to determine the normal range of real-time charging power, and combines the pre-set theoretical charging power to judge the actual power fluctuation, so that the judgment of whether the charging power is abnormal is more in line with the actual charging scenario, more scientific and accurate. It no longer relies solely on a fixed, single standard to judge power fluctuations, but fully considers the differences in the characteristics of different cables and vehicles, and can more accurately detect potential charging anomalies, which helps to take corresponding measures in a timely manner to ensure the safety and stability of the new energy vehicle charging process. At the same time, it can also reduce unnecessary detection and maintenance operations caused by misjudgment, and improve the operating efficiency and reliability of the entire charging system.

[0170] Embodiment 3

[0171] Figure 3 1 is a flow chart of a new energy vehicle charging cable connection warning method provided in Example 3 of the present application. Figure 3 As shown, the specific steps include:

[0172] S301, upon recognizing that the charging cable of the new energy vehicle is in a working state, detecting the real-time charging power;

[0173] S302, identifying whether the fluctuation of the real-time charging power exceeds a preset normal range;

[0174] S303: When the fluctuation exceeds a preset normal range, local temperature collection is performed by a temperature sensor pre-set at the connection point of the charging cable;

[0175] S304, determining the target position according to the local temperature comparison results of each connection;

[0176] S305: According to the fluctuation of the real-time charging power and the local temperature of the target position, it is determined that there is a situation where the contact resistance increases due to local oxidation at the target position, and a warning message is generated.

[0177] Further, according to the local temperature comparison results of each connection, the target position is determined, including:

[0178] According to the comparison results of the local temperatures of each connection, the connection with the highest local temperature within the preset temperature range is determined as the target position; wherein the preset temperature range is a range obtained by pre-statistical analysis of the local temperature caused by the increase in contact resistance due to local oxidation.

[0179] In this embodiment, upon identifying that the charging cable of the new energy vehicle is in working condition, real-time charging power detection is performed; whether the fluctuation of the real-time charging power exceeds the preset normal range is identified; when the fluctuation exceeds the preset normal range, local temperature acquisition is performed through a temperature sensor pre-set at the connection of the charging cable; the target position is determined based on the local temperature comparison results of each connection; based on the fluctuation of the real-time charging power and the local temperature of the target position, it is determined that the target position has local oxidation causing the increase of contact resistance, and an early warning message is generated. Through such a setting, this scheme can identify whether there is a local oxidation problem at the connection of the charging cable, and can realize rapid identification of the local oxidation problem through non-destructive sensing technology, so as to accurately handle the local oxidation problem, improve the power control accuracy of the charging cable, and extend the service life of the charging cable under timely maintenance.

[0180] The new energy vehicle charging cable connection warning method provided in the embodiment of the present application corresponds to the new energy vehicle charging cable connection warning device provided in the above embodiment, has the same execution process and beneficial effects, and will not be repeated here to avoid repetition.

[0181] Embodiment 4

[0182] like Figure 4 As shown, an embodiment of the present application also provides an electronic device 400, including a processor 401, a memory 402, and a program or instruction stored in the memory 402 and executable on the processor 401. When the program or instruction is executed by the processor 401, each process of the above-mentioned new energy vehicle charging cable connection warning device embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0183] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0184] Embodiment 5

[0185] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned new energy vehicle charging cable connection warning device embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0186] The processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0187] Embodiment 6

[0188] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, which is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned new energy vehicle charging cable connection warning device embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0189] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0190] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0191] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0192] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0193] The above are only preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions that can be made by those skilled in the art will not deviate from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A new energy vehicle charging cable connection warning device, characterized in that: The device comprises: A real-time charging power detection module is used to detect the real-time charging power when it is identified that the charging cable of the new energy vehicle is in working state; A fluctuation identification module, used to identify whether the fluctuation of the real-time charging power exceeds a preset normal range; A local temperature acquisition module, used for collecting local temperature through a temperature sensor pre-set at the connection of the charging cable when the fluctuation condition exceeds a preset normal range; A target position determination module is used to determine the target position according to the local temperature comparison results of each connection; The oxidation anomaly recognition module is used to determine that the target position has local oxidation causing increased contact resistance based on the fluctuation of the real-time charging power and the local temperature of the target position, and generate warning information.

2. The new energy vehicle charging cable connection warning device according to claim 1 is characterized in that: The target position determination module is specifically used for: According to the comparison results of the local temperatures of each connection, the connection with the highest local temperature within the preset temperature range is determined as the target position; wherein the preset temperature range is a range obtained by pre-statistical analysis of the local temperature caused by the increase in contact resistance due to local oxidation.

3. The new energy vehicle charging cable connection warning device according to claim 2 is characterized in that: The device also includes: An operating parameter acquisition module, used to acquire electrical parameters of the charging cable during operation, wherein the electrical parameters include electrical parameters in a fast charging mode and electrical parameters in a trickle charging mode; The preset temperature range determination module is used to obtain the current operation mode of the charging cable and determine the preset temperature range corresponding to the current operation mode according to the current operation mode.

4. The new energy vehicle charging cable connection warning device according to claim 1, characterized in that: The oxidation anomaly identification module is specifically used for: Inputting the fluctuation of the real-time charging power and the local temperature of the target position into a pre-built machine learning model, and determining a probability value of the target position being locally oxidized and causing an increase in contact resistance based on an output result of the machine learning model; When the probability value is greater than or equal to the first set threshold, a warning message is generated.

5. The new energy vehicle charging cable connection warning device according to claim 4 is characterized in that: The oxidation anomaly identification module is also specifically used for: When the probability value is greater than or equal to the second set threshold and less than the first set threshold, repeated collection information is generated to re-collect the fluctuation of the real-time charging power and the local temperature of each connection.

6. The new energy vehicle charging cable connection warning device according to claim 1, characterized in that: The device also includes: A disassembly information acquisition module, configured to disassemble the target location and acquire disassembly information after determining that local oxidation exists at the target location causing the contact resistance to increase; The feedback module is used to compare the disassembly information with the predicted result of the increase in contact resistance caused by the determined local oxidation to complete information feedback.

7. The new energy vehicle charging cable connection warning device according to claim 1, characterized in that: The fluctuation identification module is specifically used for: Obtain the physical parameters of the charging cable and the charging parameters of the new energy vehicle; Determining a normal range according to the physical parameter and the charging parameter; According to the normal range and the preset theoretical charging power, it is determined whether the fluctuation of the real-time charging power exceeds the preset normal range.

8. A new energy vehicle charging cable connection warning method, characterized in that: The method comprises: When it is identified that the charging cable of the new energy vehicle is in working state, real-time charging power detection is performed; Identify whether the fluctuation of the real-time charging power exceeds a preset normal range; When the fluctuation exceeds a preset normal range, local temperature collection is performed by a temperature sensor pre-set at the connection of the charging cable; Determine the target position based on the local temperature comparison results of each connection; According to the fluctuation of the real-time charging power and the local temperature of the target position, it is determined that the target position has local oxidation causing the increase of contact resistance, and an early warning message is generated.

9. The new energy vehicle charging cable connection warning method according to claim 8, characterized in that: According to the local temperature comparison results of each connection, the target position is determined, including: According to the comparison results of the local temperatures of each connection, the connection with the highest local temperature within the preset temperature range is determined as the target position; wherein the preset temperature range is a range obtained by pre-statistical analysis of the local temperature caused by the increase in contact resistance due to local oxidation.

10. An electronic device, characterized in that: It includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the new energy vehicle charging cable connection warning method as described in any one of claims 8 to 9 are implemented.

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