Charging pile charging fault diagnosis method, device and equipment and storage medium

Abstract

The embodiment of the invention provides a charging pile charging fault diagnosis method and device, equipment and a storage medium, and the method comprises the steps: determining whether a charging fault exists or not through a preset safety threshold value dynamic prediction model based on the temperature rise rate collected in advance in real time and the output current of a target charging pile in the charging process of the target charging pile, and determining whether the charging fault exists or not; and if the charging fault indeed exists, determining a fault end based on the historical charging data of the target charging pile, and generating early warning information based on the fault end. According to the method, through real-time detection, historical data verification and dynamic model optimization, accurate positioning and quick response of a charging fault are realized, and the charging safety and the operation and maintenance efficiency are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of charging pile technology, and in particular to a charging pile charging fault diagnosis method, device, equipment and storage medium. Background Technology

[0002] Against the backdrop of the rapid development of electric vehicles, DC charging, as an efficient and convenient charging method, is widely used in the new energy vehicle field. However, the quality of DC charging stations on the market varies greatly, and users often find it difficult to accurately detect abnormalities in the charging stations. Prolonged charging at abnormal charging stations can potentially cause irreversible and serious damage to critical components such as the vehicle's DC charging socket, battery, or relays.

[0003] In existing technologies, fixed thresholds for physical quantities such as temperature and current are preset inside the charging pile. When the monitored data exceeds the threshold, an alarm or shutdown is triggered.

[0004] However, the above methods lack adaptability and have a single detection dimension, resulting in poor stability and safety of the charging system. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for diagnosing charging pile charging faults, in order to improve the detection rate of charging pile charging faults, enhance the stability and safety of the charging system, and ensure charging efficiency.

[0006] In a first aspect, embodiments of this application provide a method for diagnosing charging faults in a charging pile, applied to a cloud device, the method comprising:

[0007] During the charging process of the target charging pile, based on the pre-collected real-time temperature rise rate and the output current of the target charging pile, a pre-set safety threshold dynamic prediction model is used to determine whether there is a charging fault. The safety threshold dynamic prediction model includes a temperature rise prediction model and a current prediction model. The temperature rise prediction model is used to predict the temperature rise rate safety threshold, and the current prediction model is used to predict the current safety threshold.

[0008] If a charging fault does exist, the faulty end is determined based on the historical charging data of the target charging pile. The faulty end may be the charging pile end or the vehicle end.

[0009] Early warning information is generated based on the faulty end.

[0010] In one possible implementation, the method further includes:

[0011] Collect temperature rise data of multiple vehicles under different ambient temperatures and charging currents;

[0012] Machine learning algorithms are used to analyze the mapping relationship between ambient temperature, charging current and temperature rise data, and a temperature rise prediction model is established.

[0013] In one possible implementation, the method further includes:

[0014] Collect historical data on battery health status, battery temperature, and charging at multiple charging stations for multiple vehicles;

[0015] The current prediction model is established based on the battery health status, battery temperature, historical data, and a pre-set initial threshold.

[0016] In one possible implementation, during the charging process at the target charging pile, based on the pre-collected real-time temperature rise rate and the output current of the target charging pile, a pre-set safety threshold dynamic prediction model is used to determine whether a charging fault exists, including:

[0017] Based on the temperature rise prediction model and the current prediction model, the temperature rise safety threshold and the output current safety threshold corresponding to the target charging pile are determined.

[0018] If the number of times the temperature rise rate exceeds the temperature rise safety threshold is greater than a preset threshold, then a temperature rise fault is determined to exist.

[0019] If the output current exceeds the output current safety threshold, then a current fault does exist.

[0020] In one possible implementation, the method further includes:

[0021] Collect the termination current of the target charging pile after it finishes charging;

[0022] If the termination current does not drop below the preset termination current safety threshold within a preset time, then an interruption fault does exist.

[0023] In one possible implementation, determining the faulty terminal based on the historical charging data of the target charging pile, if a charging fault does exist, includes:

[0024] If it is determined that there is one or more of the following faults: temperature rise fault, current fault, and termination fault, then analyze the frequency of other vehicles having the same fault when charging at the target charging station in the historical charging data.

[0025] If the frequency is greater than a preset frequency threshold, the charging pile terminal is identified as the faulty terminal.

[0026] In one possible implementation, the method further includes:

[0027] If the frequency is less than a preset frequency threshold, the vehicle end is identified as the faulty end.

[0028] Secondly, embodiments of this application provide a charging pile charging fault diagnosis device, comprising:

[0029] The first determining module is used to determine whether a charging fault exists during the charging process of the target charging pile, based on the pre-collected real-time temperature rise rate and the output current of the target charging pile, through a pre-set safety threshold dynamic prediction model. The safety threshold dynamic prediction model includes a temperature rise prediction model and a current prediction model. The temperature rise prediction model is used to predict the temperature rise rate safety threshold, and the current prediction model is used to predict the current safety threshold.

[0030] The second determining module is used to determine the faulty end based on the historical charging data of the target charging pile if a charging fault does exist. The faulty end includes the charging pile end or the vehicle end.

[0031] The generation module is used to generate early warning information based on the faulty end.

[0032] In one possible implementation, the device further includes:

[0033] The first acquisition module is used to collect temperature rise data of multiple vehicles under different ambient temperatures and charging currents.

[0034] The first module is used to analyze the mapping relationship between ambient temperature, charging current and temperature rise data using machine learning algorithms, and to establish the temperature rise prediction model.

[0035] In one possible implementation, the device further includes:

[0036] The second data acquisition module is used to collect battery health status, battery temperature, and historical charging data from multiple charging stations for multiple vehicles.

[0037] The second module is used to establish the current prediction model based on the battery health status, the battery temperature, the historical data, and a pre-set initial threshold.

[0038] In one possible implementation, the first determining module is specifically used for:

[0039] Based on the temperature rise prediction model and the current prediction model, the temperature rise safety threshold and the output current safety threshold corresponding to the target charging pile are determined.

[0040] If the number of times the temperature rise rate exceeds the temperature rise safety threshold is greater than a preset threshold, then a temperature rise fault is determined to exist.

[0041] If the output current exceeds the output current safety threshold, then a current fault does indeed exist. In one possible implementation, the first determining module is further specifically used for:

[0042] Collect the termination current of the target charging pile after it finishes charging;

[0043] If the termination current does not drop below the preset termination current safety threshold within a preset time, then an interruption fault does exist.

[0044] In one possible implementation, the second determining module is specifically used for:

[0045] If it is determined that there is one or more of the following faults: temperature rise fault, current fault, and termination fault, then analyze the frequency of other vehicles having the same fault when charging at the target charging station in the historical charging data.

[0046] If the frequency is greater than a preset frequency threshold, the charging pile terminal is identified as the faulty terminal.

[0047] In one possible implementation, the second determining module is further configured to:

[0048] If the frequency is less than a preset frequency threshold, the vehicle end is identified as the faulty end.

[0049] Thirdly, embodiments of this application provide a cloud device, including: a memory and a processor;

[0050] The memory stores computer-executed instructions;

[0051] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0052] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0053] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0054] The charging pile fault diagnosis method, device, equipment, and storage medium provided in this application, during the charging process of the target charging pile, based on the pre-collected real-time temperature rise rate and the output current of the target charging pile, uses a pre-set safety threshold dynamic prediction model to determine whether a charging fault exists. If a charging fault does exist, the faulty end is identified based on the historical charging data of the target charging pile, and an early warning information is generated based on the faulty end. The above method, through real-time detection, historical data verification, and dynamic model optimization, achieves accurate location and rapid response to charging faults, significantly improving charging safety and operation and maintenance efficiency. Attached Figure Description

[0055] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0056] Figure 1 A flowchart illustrating the charging pile charging fault diagnosis method provided in this application embodiment. Figure 1 ;

[0057] Figure 2 A flowchart illustrating the charging pile charging fault diagnosis method provided in this application embodiment. Figure 2 ;

[0058] Figure 3 A flowchart illustrating the charging pile charging fault diagnosis method provided in this application embodiment. Figure 3 ;

[0059] Figure 4 A flowchart illustrating the charging pile charging fault diagnosis method provided in this application embodiment. Figure 4 ;

[0060] Figure 5 A flowchart illustrating the charging pile charging fault diagnosis method provided in this application embodiment. Figure 5 ;

[0061] Figure 6 A flowchart illustrating the charging pile charging fault diagnosis method provided in this application embodiment. Figure 6 ;

[0062] Figure 7 This is a schematic diagram of the charging pile charging fault diagnosis device provided in the embodiments of this application;

[0063] Figure 8 A schematic diagram of the structure of the cloud device provided in the embodiments of this application.

[0064] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0065] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0066] Against the backdrop of the rapid development of electric vehicles, DC charging, as an efficient and convenient charging method, is widely used in the new energy vehicle field. However, the quality of DC charging stations on the market varies greatly, and users often find it difficult to accurately detect abnormalities in charging stations. Prolonged charging at abnormal charging stations can potentially cause irreversible and serious damage to critical components such as the vehicle's DC charging socket, battery, or relays. Therefore, it is necessary to implement comprehensive monitoring and early warning systems for the following scenarios:

[0067] 1. Due to wear, aging, and loose insertion during long-term use, DC charging guns often experience poor contact between the charging terminal and the DC charging socket. This can cause abnormal heating of the DC charging socket during high-current charging, severely affecting charging power, and may even lead to unexpected interruptions in DC charging. For example, under high-power charging conditions, poor contact may cause localized overheating, thereby affecting the stability and safety of the charging system, reducing charging efficiency, and prolonging charging time.

[0068] 2. Abnormal DC charging current response is also a problem that requires close attention. Abnormally high current can cause significant damage to the battery. For example, abnormally high current may damage the internal structure of the battery, shorten its lifespan, or even lead to safety accidents. As the core component of an electric vehicle, the safety and stability of the battery are crucial; therefore, accurate monitoring and early warning of the charging current are key to ensuring the safe operation of the vehicle.

[0069] 3. If DC charging current still exists after charging is complete, forcibly disconnecting the fast charging positive and negative relays at this time will cause serious damage to the relays. Relays play a crucial control role in the charging system; improper operation may cause them to malfunction, thus affecting the normal operation of the entire charging system. For example, if residual current is not detected and handled appropriately after charging is complete, and the relay is forcibly disconnected instead, the relay contacts may be damaged, reducing its reliability and lifespan.

[0070] In existing technologies, fixed thresholds for physical quantities such as temperature and current are preset inside the charging pile. When the monitored data exceeds the threshold, an alarm or shutdown is triggered. However, the above method lacks adaptability and has a single detection dimension, resulting in poor stability and safety of the charging system.

[0071] Based on the aforementioned technical problems, the inventor's technical concept is as follows: Existing technologies primarily diagnose charging failures at the charging pile end, neglecting vehicle-side scenarios. Existing pile-side technologies focus more on scenarios where charging stops abnormally, without considering abnormal charging current response or vehicle-side charging socket temperature. This results in poor stability and safety of the charging system. Therefore, by establishing a DC charging temperature and current fault monitoring model, abnormal temperature rise data and current anomalies in the charging socket during DC charging can be accurately identified, ensuring early detection of problems and preventing potential safety hazards and equipment damage. An efficient early warning mechanism can be established to promptly push abnormal information to users, after-sales service, charging pile manufacturers, or maintenance personnel, prompting all parties to take appropriate measures. This improves the safety and reliability of DC charging, ensuring the normal use of electric vehicles and protecting users' rights.

[0072] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0073] Figure 1 A flowchart illustrating the charging pile charging fault diagnosis method provided in this application embodiment. Figure 1 ,like Figure 1 As shown, the method includes:

[0074] Step S101: During the charging process of the target charging pile, based on the pre-collected real-time temperature rise rate and the output current of the target charging pile, a pre-set safety threshold dynamic prediction model is used to determine whether there is a charging fault.

[0075] In this step, in order to accurately determine the specific fault and cause of the charging pile during the charging process, the cloud device monitors the temperature rise rate and the output current of the target charging pile in real time after the vehicle starts charging at the target charging pile, thereby determining whether there is a charging fault. The temperature rise rate refers to the rate of temperature change of the charging socket.

[0076] Specifically, based on the temperature rise prediction model and the current prediction model, the temperature rise safety threshold and the output current safety threshold corresponding to the target charging pile are determined. If the number of times the temperature rise rate exceeds the temperature rise safety threshold is greater than the preset number threshold, then a temperature rise fault is determined to exist. If the output current exceeds the output current safety threshold, then a current fault does exist. The end current after the target charging pile finishes charging is collected. If the end current does not drop below the preset end current safety threshold within a preset time, then an interruption fault does exist.

[0077] Optionally, it is also necessary to collect parameters such as the current ambient temperature, the target charging station model, and the vehicle battery status during the charging process. The model dynamically adjusts the temperature rise rate and the safe threshold range of the current based on these parameters. For example, a slightly higher temperature rise rate is allowed in high-temperature environments, and the current deviation threshold of older charging stations may be appropriately relaxed.

[0078] It should be noted that during real-time monitoring, the data obtained from real-time monitoring is used to determine faults through a dynamic prediction model for safety thresholds. The dynamic prediction model for safety thresholds includes a temperature rise prediction model and a current prediction model. The temperature rise prediction model is used to predict the safety threshold for the temperature rise rate, and the current prediction model is used to predict the safety threshold for the current.

[0079] Step S102: If a charging fault does exist, the faulty end is determined based on the historical charging data of the target charging pile.

[0080] In this step, existing technologies all diagnose charging failures at the charging pile end, without considering the vehicle-side scenario. This leads to inaccurate charging fault identification, affecting the stability and safety of the charging system, while also reducing charging efficiency and prolonging charging time. Therefore, after identifying the charging fault through the above steps, it is also necessary to determine the faulty end based on the historical charging data of the target charging pile.

[0081] Specifically, if it is determined that there is one or more of the following faults: temperature rise, current, and termination, then the frequency of other vehicles having the same fault when charging at the target charging station is analyzed in the historical charging data. If the frequency is greater than a preset frequency threshold, the charging station end is identified as the faulty end; if the frequency is less than the preset frequency threshold, the vehicle end is identified as the faulty end.

[0082] Step S103: Generate early warning information based on the faulty end.

[0083] In this step, a charging fault is identified through the aforementioned steps, and the faulty end is accurately located. Then, an early warning message is generated based on the faulty end and sent to the relevant user or maintenance personnel, thereby achieving efficient early warning and improving charging efficiency.

[0084] Specifically, during the fault diagnosis process described above, the specific cause of the fault can be determined. Corresponding warning messages are then generated for different faulty charging points and pushed to users, charging station manufacturers, and maintenance personnel through various channels. For users, warning messages can be sent via mobile app, SMS, etc., reminding them of the charging station malfunction and preventing repeated charging at faulty stations, which could further damage the vehicle. Simultaneously, the warning messages provide users with the location information of nearby working charging stations, facilitating timely changes in charging locations. For charging station manufacturers and maintenance personnel, the warning messages can include detailed information such as the specific location of the charging station, the type of malfunction, and its severity. This helps them quickly locate the problematic charging station and take appropriate repair measures. Maintenance personnel can also use the warning messages to carry necessary tools and parts, improving repair efficiency.

[0085] For example, a charging pile terminal failure:

[0086] On the user side: The app pushes a notification that "charging station is abnormal, it is recommended to replace it" and recommends nearby available charging stations.

[0087] On the maintenance side: Automatically generate maintenance work orders, including abnormal data waveforms, fault location conclusions (such as "the nozzle is worn and needs to be replaced"), and mark them as high priority.

[0088] Vehicle-side fault:

[0089] User terminal: The message reads "Vehicle charging system malfunction. Please contact the service center to check the socket or BMS."

[0090] Operations and maintenance: Push vehicle maintenance suggestions (such as "Check the oxidation of the charging socket").

[0091] The charging pile fault diagnosis method provided in this application, during the charging process of the target charging pile, uses a pre-set safety threshold dynamic prediction model based on the pre-collected real-time temperature rise rate and the output current of the target charging pile to determine whether a charging fault exists. If a charging fault does exist, the faulty end is identified based on the historical charging data of the target charging pile, and an early warning message is generated based on the faulty end. This method, through real-time detection, historical data verification, and dynamic model optimization, achieves accurate fault location and rapid response, significantly improving charging safety and operation and maintenance efficiency.

[0092] Based on the above embodiments, Figure 2A flowchart illustrating the charging pile charging fault diagnosis method provided in this application embodiment. Figure 2 ,like Figure 2 As shown, the method may further include:

[0093] Step S201: Collect temperature rise data of multiple vehicles under different ambient temperatures and charging currents.

[0094] Step S202: Use machine learning algorithms to analyze the mapping relationship between ambient temperature, charging current and temperature rise rate, and establish a temperature rise prediction model.

[0095] To fully account for charging faults during the charging process, after-sales vehicle charging data is collected via cloud devices, gathering temperature rise data for normal charging piles under different charging currents and various external ambient temperatures. Through in-depth analysis of this data, the normal temperature rise rate T℃ / min under different charging currents is accurately identified, forming the normal temperature rise rate distribution of DC charging under different ambient temperatures and charging currents, thus obtaining a temperature rise prediction model.

[0096] The charging pile fault diagnosis method provided in this application collects temperature rise data of multiple vehicles under different ambient temperatures and charging currents. It then uses machine learning algorithms to analyze the mapping relationship between ambient temperature, charging current, and temperature rise rate, establishing a temperature rise prediction model. This method, by collecting multi-dimensional data in the cloud and constructing a temperature rise distribution model, achieves accurate quantification of the charging safety baseline, providing a reliable basis for real-time anomaly detection. This significantly improves the timeliness and accuracy of fault warnings, providing data support for charging pile health management and battery life optimization.

[0097] Based on the above embodiments, Figure 3 A flowchart illustrating the charging pile charging fault diagnosis method provided in this application embodiment. Figure 3 ,like Figure 3 As shown, the method may include:

[0098] Step S301: Collect historical data on battery health status, battery temperature, and charging at multiple charging stations for multiple vehicles.

[0099] Step S302: Based on battery health status, battery temperature, historical data, and a pre-set initial threshold, establish a current prediction model.

[0100] Data from multiple vehicles can be obtained through the vehicle's battery management system (BMS), for example:

[0101] Voltage and Current: The BMS monitors the voltage and charge / discharge current of each battery cell in real time through sensors.

[0102] Temperature: Temperature sensors (such as NTC thermistors) distributed throughout the battery module collect the temperature of each area.

[0103] Cycle count: Records the number of complete charge-discharge cycles of the battery.

[0104] Internal resistance change: The battery internal resistance is measured by AC injection or pulse testing.

[0105] The battery health status is calculated using the parameters mentioned above.

[0106] Battery temperature can be obtained through the BMS's built-in temperature sensor network, for example:

[0107] Sensor deployment:

[0108] Each battery module is equipped with 1-3 temperature sensors to monitor hot spots (such as cell connections).

[0109] Example: A battery contains 10 modules and a total of 25 temperature probes are deployed.

[0110] Sampling frequency:

[0111] During charging: sampling once per second (1Hz), increasing to 5Hz at high temperatures.

[0112] For daily use: Sample once per minute to save energy.

[0113] Data processing:

[0114] Outlier filtering: Remove sudden values ​​caused by sensor malfunctions (such as >100℃ or <-20℃).

[0115] Temperature weighting: Calculate the average temperature of the battery cell, or select the highest temperature as the key indicator.

[0116] Transmission path:

[0117] The data is transmitted to the vehicle gateway via the CAN bus and then uploaded to the cloud via the 4G / 5G network.

[0118] Historical data from multiple charging stations can be obtained through charging station controllers, cloud databases, and third-party platforms, for example:

[0119] Local logs of charging stations:

[0120] Record content:

[0121] Charging parameters: output current, voltage, power, and charging time.

[0122] Error codes: such as overcurrent (Error 0x12) and communication timeout (Error 0x55).

[0123] Environmental data: pile tip temperature and humidity (collected by built-in sensors).

[0124] The allowable charging current of the charging vehicle is determined as the initial threshold, and dynamic correction rules are established based on battery health status, battery temperature, and historical data to obtain the current prediction model.

[0125] The charging pile fault diagnosis method provided in this application collects battery health status and battery temperature data from multiple vehicles and historical charging data from multiple charging piles. Based on battery health status, battery temperature, historical data, and a pre-set initial threshold, a current prediction model is established. By fusing battery status, environmental parameters, and historical data, the current prediction model achieves adaptive safety control of the charging process. This solution significantly improves battery safety and equipment compatibility while ensuring charging efficiency, providing reliable technical support for the large-scale operation of electric vehicles.

[0126] Based on the above embodiments, Figure 4 A flowchart illustrating the charging pile charging fault diagnosis method provided in this application embodiment. Figure 4 ,like Figure 4 As shown, step S101 specifically includes:

[0127] Step S401: Based on the temperature rise prediction model and the current prediction model, determine the temperature rise safety threshold and the output current safety threshold corresponding to the target charging pile.

[0128] Step S402: If the number of times the temperature rise rate exceeds the temperature rise safety threshold is greater than the preset number threshold, then a temperature rise fault is determined to exist.

[0129] Step S403: If the output current exceeds the safe threshold for output current, then there is indeed a current fault.

[0130] The current environment, charging current, and target charging pile information are input into the temperature rise prediction model. Based on the historical data distribution, the temperature rise prediction model generates a dynamic threshold under the current operating conditions, i.e., the temperature rise safety threshold.

[0131] The safe threshold for output current is obtained by comparing the current battery health status, battery temperature, and historical overcurrent rate of the charging pile with the allowable charging current of the vehicle being charged.

[0132] The system can periodically calculate the temperature rise rate of the target charging station and compare it with a temperature rise safety threshold. If the number of times the temperature rise rate exceeds the threshold reaches a preset threshold, a temperature rise fault is identified. For example, if the temperature rise is calculated every 60 seconds, and the number of times the detected temperature rise rate exceeds the normal temperature rise rate T+Δ℃ / min during a DC charging process is N1, exceeding a preset number N2, the system will immediately trigger a DC charging anomaly warning, confirming a temperature rise fault.

[0133] The output current at the charging pile is monitored in real time. If it exceeds the safe threshold, it indicates a current fault. Specific current faults are divided into continuous overcurrent and transient overcurrent. Therefore, for DC charging current overcurrent, it is necessary to distinguish between small continuous overcurrent and large instantaneous overcurrent. For example, if the current exceeds the threshold and the duration is >3 seconds, it is a continuous overcurrent. If the number of times the current instantaneously exceeds the threshold reaches a preset value, it is determined to be a transient overcurrent.

[0134] The charging pile charging fault diagnosis method provided in this application, based on a temperature rise prediction model and a current prediction model, determines the temperature rise safety threshold and output current safety threshold corresponding to the target charging pile. If the number of times the temperature rise rate exceeds the temperature rise safety threshold is greater than a preset number threshold, a temperature rise fault is determined to exist. If the output current exceeds the output current safety threshold, a current fault is indeed found. This method, through a dynamic safety threshold and number threshold strategy, achieves high-precision detection and classification of charging anomalies, significantly improving safety while optimizing the allocation of operation and maintenance resources, providing a reliable guarantee for the charging safety of electric vehicles.

[0135] Based on the above embodiments, Figure 5 A flowchart illustrating the charging pile charging fault diagnosis method provided in this application embodiment. Figure 5 ,like Figure 5 As shown, step S101 further includes:

[0136] Step S501: Collect the end current of the target charging pile after it finishes charging.

[0137] Step S502: If the termination current does not drop below the preset termination current safety threshold within a preset time, then an interruption fault does exist.

[0138] After DC charging is completed, the vehicle's BMS (Battery Management System) sends a stop charging signal to the charging station, requesting to stop charging. After that, the current at the charging station remains greater than the threshold N for T seconds.

[0139] Specifically, the charging pile will immediately start monitoring after receiving the stop charging command sent by the vehicle-side BMS (such as CAN message 0x1806F456).

[0140] Continuous data acquisition time window: typically 5-10 seconds after the command is issued (preset according to charging pile specifications). The current between the positive and negative terminals of the charging gun is measured in real time using a Hall sensor or shunt resistor, and the current drop curve is recorded. A pre-set end current safety threshold and a preset time threshold are set according to the target charging pile type. Timing begins when the BMS sends the stop command; if the current does not drop below the safety threshold within the preset time, it is considered a stop fault.

[0141] Optionally, if the current remains consistently high (e.g., 100A), it is determined that the relay is stuck or the communication has failed.

[0142] If the current does not return to zero within a preset time (e.g., it is still 10A after 5 seconds), it is determined to be a power module response delay.

[0143] Optionally, throughout the charging process, cloud-based devices continuously utilize big data analytics and machine learning algorithms to constantly optimize the criteria for judging abnormal temperature rise and charging current. As data accumulates, the diagnostic model can more accurately adapt to different charging scenarios, charging pile types, and changes in the external environment, thereby improving the accuracy and reliability of detecting overheating of DC charging sockets and abnormal charging current.

[0144] The charging pile charging fault diagnosis method provided in this application collects the termination current of the target charging pile after charging ends. If the termination current does not drop below a preset termination current safety threshold within a preset time, a termination fault is indeed found. By collecting the termination current in real time and strictly determining the termination fault, hardware damage and safety accidents caused by residual current are effectively avoided. Combining dynamic thresholds and preset time, this method significantly optimizes operation and maintenance response efficiency while improving charging safety, providing a key guarantee for the reliability of electric vehicle charging infrastructure.

[0145] Based on the above embodiments, Figure 6 A flowchart illustrating the charging pile charging fault diagnosis method provided in this application embodiment. Figure 6 ,like Figure 6 As shown, step S102 specifically includes:

[0146] Step S601: If it is determined that there is one or more of the following: temperature rise fault, current fault, and termination fault, then analyze the frequency of other vehicles having the same fault when charging at the target charging station in the historical charging data.

[0147] Step S602: If the frequency is greater than the preset frequency threshold, the charging pile end is identified as the faulty end.

[0148] Step S603: If the frequency is less than the preset frequency threshold, the vehicle end is identified as the faulty end.

[0149] When a vehicle at a target charging station triggers one or more of the following faults: temperature rise, current overcurrent, or shutdown, the system automatically retrieves charging records for all vehicles at that station over the past 30 days. It then filters out abnormal events that perfectly match the current fault type (e.g., abnormal temperature rise, overcurrent, residual shutdown). The system calculates the fault frequency, using a pre-set frequency threshold. If the calculated fault frequency is greater than the threshold, the charging station is considered faulty; if the frequency is less than the threshold, the vehicle is considered faulty.

[0150] For example, regarding fault information judgment:

[0151] If there are temperature rise faults, current faults, or shutdown faults, it may be due to power module malfunction and communication failure.

[0152] If only the temperature rise fault exists, it may be due to poor contact in the vehicle-end socket.

[0153] If there is a current fault or an interruption fault, the pile end output module may be abnormal, causing the current control to fail.

[0154] If only an interruption fault exists, the relay at the vehicle end may be stuck.

[0155] Optional, possible causes of abnormal temperature rise:

[0156] Charging station issues: Wear and tear on the charging gun head, increased contact resistance → increased heat generation when current flows. Vehicle-side issues: Oxidation or looseness of the vehicle's charging socket → poor contact leading to localized overheating.

[0157] Possible causes of abnormal current:

[0158] Problem at the pile end: Power module malfunction, communication protocol error → uncontrolled output current

[0159] Vehicle-side issues: BMS current request error, internal battery short circuit → misleading charging station output

[0160] The charging pile fault diagnosis method provided in this application determines that one or more of the following faults exist: temperature rise, current, and termination. It then analyzes the frequency of other vehicles experiencing the same fault at the target charging pile in historical charging data. If the frequency is greater than a preset frequency threshold, the charging pile is identified as the faulty end; if the frequency is less than the preset threshold, the vehicle is identified as the faulty end. By comparing the multi-vehicle fault frequency analysis with the preset threshold, precise isolation between vehicle-side and charging pile-side faults is achieved. This significantly improves charging safety and operational efficiency, providing reliable technical support for large-scale electric vehicle charging networks.

[0161] Based on the above method embodiments, Figure 7 This is a schematic diagram of the charging pile charging fault diagnosis device provided in the embodiments of this application, as shown below. Figure 7 As shown, the charging pile charging fault diagnosis device 700 includes:

[0162] The first determining module 701 is used to determine whether a charging fault exists during the charging process of the target charging pile, based on the pre-collected real-time temperature rise rate and the output current of the target charging pile, through a pre-set safety threshold dynamic prediction model. The safety threshold dynamic prediction model includes a temperature rise prediction model and a current prediction model. The temperature rise prediction model is used to predict the temperature rise rate safety threshold, and the current prediction model is used to predict the current safety threshold.

[0163] The second determining module 702 is used to determine the faulty end based on the historical charging data of the target charging pile if a charging fault does exist. The faulty end includes the charging pile end or the vehicle end.

[0164] The generation module 703 is used to generate early warning information based on the faulty end.

[0165] In one possible implementation, the charging pile charging fault diagnosis device 700 further includes:

[0166] The first acquisition module 704 is used to collect temperature rise data of multiple vehicles under different ambient temperatures and charging currents.

[0167] The first module 705 is used to analyze the mapping relationship between ambient temperature, charging current and temperature rise data using machine learning algorithms, and to establish a temperature rise prediction model.

[0168] In one possible implementation, the charging pile charging fault diagnosis device 700 further includes:

[0169] The second data acquisition module 707 is used to collect battery health status, battery temperature, and historical charging data from multiple charging stations for multiple vehicles.

[0170] The second module 707 is used to establish a current prediction model based on battery health status, battery temperature, historical data, and a pre-set initial threshold.

[0171] In one possible implementation, the first determining module 701 is specifically used for:

[0172] Based on the temperature rise prediction model and the current prediction model, the temperature rise safety threshold and the output current safety threshold corresponding to the target charging pile are determined.

[0173] If the number of times the temperature rise rate exceeds the temperature rise safety threshold is greater than the preset threshold, then a temperature rise fault is determined to exist.

[0174] If the output current exceeds the safe threshold, then there is indeed a current fault.

[0175] In one possible implementation, the first determining module 701 is further specifically used for:

[0176] Collect the end current of the target charging pile after it finishes charging;

[0177] If the termination current does not drop below the preset termination current safety threshold within the preset time, then an interruption fault does exist.

[0178] In one possible implementation, the second determining module 702 is specifically used for:

[0179] If it is determined that there is one or more of the following faults: temperature rise fault, current fault, and termination fault, then analyze the frequency of other vehicles having the same fault when charging at the target charging station in the historical charging data.

[0180] If the frequency is greater than the preset frequency threshold, the charging station will be identified as a faulty terminal.

[0181] In one possible implementation, the second determining module 702 is further configured to:

[0182] If the frequency is less than the preset frequency threshold, the vehicle end is identified as the faulty end.

[0183] The charging pile charging fault diagnosis device provided in this embodiment can execute the charging pile charging fault diagnosis method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0184] Figure 8 This is a schematic diagram of the structure of a cloud device provided in an embodiment of this application. Figure 8 As shown, the vehicle provided in this embodiment includes:

[0185] At least one processor 801 and memory 802.

[0186] Optionally, the vehicle also includes a communication component 803. The processor 801, memory 802, and communication component 803 are connected via a bus 84.

[0187] In a specific implementation, at least one processor 801 executes computer execution instructions stored in memory 802, causing at least one processor 801 to perform the above-described method.

[0188] The specific implementation process of processor 801 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0189] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0190] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0191] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0192] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0193] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0194] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0195] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0196] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0197] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0198] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0199] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0200] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0201] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for diagnosing charging faults in charging piles, characterized in that, Applied to cloud devices, the method includes: During the charging process of the target charging pile, based on the pre-collected real-time temperature rise rate and the output current of the target charging pile, a pre-set safety threshold dynamic prediction model is used to determine whether there is a charging fault. The safety threshold dynamic prediction model includes a temperature rise prediction model and a current prediction model. The temperature rise prediction model is used to predict the temperature rise rate safety threshold, and the current prediction model is used to predict the current safety threshold. If a charging fault does exist, the faulty end is determined based on the historical charging data of the target charging pile. The faulty end may be the charging pile end or the vehicle end. Early warning information is generated based on the faulty end.

2. The method according to claim 1, characterized in that, The method further includes: Collect temperature rise data of multiple vehicles under different ambient temperatures and charging currents; Machine learning algorithms are used to analyze the mapping relationship between ambient temperature, charging current and the temperature rise data, and a temperature rise prediction model is established.

3. The method according to claim 1, characterized in that, The method further includes: Collect historical data on battery health status, battery temperature, and charging at multiple charging stations for multiple vehicles; The current prediction model is established based on the battery health status, battery temperature, historical data, and a pre-set initial threshold.

4. The method according to claim 1, characterized in that, During the charging process at the target charging pile, based on the pre-collected real-time temperature rise rate and the output current of the target charging pile, a pre-set safety threshold dynamic prediction model is used to determine whether a charging fault exists, including: Based on the temperature rise prediction model and the current prediction model, the temperature rise safety threshold and the output current safety threshold corresponding to the target charging pile are determined. If the number of times the temperature rise rate exceeds the temperature rise safety threshold is greater than a preset threshold, then a temperature rise fault is determined to exist. If the output current exceeds the output current safety threshold, then a current fault does exist.

5. The method according to claim 4, characterized in that, The method further includes: Collect the termination current of the target charging pile after it finishes charging; If the termination current does not drop below the preset termination current safety threshold within a preset time, then an interruption fault does exist.

6. The method according to claim 1, characterized in that, If a charging fault does exist, the faulty terminal is determined based on the historical charging data of the target charging pile, including: If it is determined that there is one or more of the following faults: temperature rise fault, current fault, and termination fault, then analyze the frequency of other vehicles having the same fault when charging at the target charging station in the historical charging data. If the frequency is greater than a preset frequency threshold, the charging pile terminal is identified as the faulty terminal.

7. The method according to claim 6, characterized in that, The method further includes: If the frequency is less than a preset frequency threshold, the vehicle end is identified as the faulty end.

8. A charging pile charging fault diagnosis device, characterized in that, include: The first determining module is used to determine whether a charging fault exists during the charging process of the target charging pile, based on the pre-collected real-time temperature rise rate and the output current of the target charging pile, through a pre-set safety threshold dynamic prediction model. The safety threshold dynamic prediction model includes a temperature rise prediction model and a current prediction model. The temperature rise prediction model is used to predict the temperature rise rate safety threshold, and the current prediction model is used to predict the current safety threshold. The second determining module is used to determine the faulty end based on the historical charging data of the target charging pile if a charging fault does exist. The faulty end includes the charging pile end or the vehicle end. The generation module is used to generate early warning information based on the faulty end.

9. A cloud device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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