CN-121973658-A - Electric automobile charging equipment fault diagnosis method, system, equipment and storage medium

CN121973658ACN 121973658 ACN121973658 ACN 121973658ACN-121973658-A

Abstract

The invention discloses a fault diagnosis method, a fault diagnosis system, fault diagnosis equipment and fault diagnosis storage media for electric automobile charging equipment. The method comprises the steps of monitoring and collecting electrical parameters, temperature parameters, environment parameters and interaction control signals of the charging equipment in real time, recording communication messages, detecting signal integrity of the interaction control signals, generating a full-flow interaction log of the charging equipment and a vehicle, preprocessing detection results of the electrical parameters, the temperature parameters, the environment parameters and the signal integrity, extracting multi-dimensional characteristic parameters representing the running state of the equipment, identifying whether the multi-dimensional characteristic parameters are abnormal or not based on a pre-built normal working condition characteristic library, and carrying out root cause analysis by combining the multi-dimensional characteristic parameters and the full-flow interaction log when the abnormal characteristic parameters exist in the multi-dimensional characteristic parameters. The invention can find early signal degradation symptoms and realize accurate tracing and positioning of faults.

Inventors

LENG PENGCHENG
JIANG XIAODAN
LENG PENGFEI
LI JINPENG
ZHOU YIFAN
Fei Kangda
MA ZHENYU

Assignees

江苏安科瑞电能服务股份有限公司

Dates

Publication Date: 20260505
Application Date: 20260327

Claims (10)

1. A method for diagnosing faults of charging equipment of an electric automobile, comprising the steps of: Monitoring and collecting electrical parameters, temperature parameters and environmental parameters of the charging equipment in the process of charging the vehicle in real time and interactive control signals reflecting the communication states of the charging equipment and the vehicle, and recording communication messages of the charging equipment and the vehicle in the whole process from gun insertion starting to charging ending; Performing signal integrity detection on the interaction control signal to obtain a signal integrity detection result; generating a full-flow interaction log of the charging equipment and the vehicle according to the time sequence of the communication message; Preprocessing the electrical parameter, the temperature parameter, the environmental parameter and the signal integrity detection result, and extracting a multi-dimensional characteristic parameter representing the running state of equipment, wherein the multi-dimensional characteristic parameter is fused with an electrical characteristic extracted from the electrical parameter, a temperature characteristic extracted from the temperature parameter, an environmental characteristic extracted from the environmental parameter and a signal integrity characteristic extracted from the interactive control signal; identifying whether the multidimensional feature parameters are abnormal or not based on a pre-constructed normal working condition feature library, wherein the normal working condition feature library is a feature reference set which is constructed based on historical operation data of the charging equipment and reflects the normal operation state of the charging equipment; When the abnormal characteristic parameters exist in the multi-dimensional characteristic parameters, root cause analysis is conducted by combining the multi-dimensional characteristic parameters and the full-flow interaction log, and the interaction stage and the specific position of the fault corresponding to the abnormal characteristic parameters are located.
2. The method for diagnosing a fault in a charging device of an electric vehicle according to claim 1, wherein recording a communication message of the charging device and the vehicle from a start of a gun to an end of charging, comprises: Recording negotiation request and response messages of the charging equipment and the vehicle in a charging parameter negotiation stage, a real-time state reporting message in the charging stage and a fault code interaction message; And marking communication overtime, checking error and protocol violation as abnormal communication information in the recording process, and taking the abnormal communication information as a component part of the full-flow interaction log.
3. The electric vehicle charging device fault diagnosis method according to claim 1, wherein the interactive control signal is a CC/CP control signal of the charging device and the vehicle; performing signal integrity detection on the interactive control signal, including: detecting PWM duty cycle, rising edge time, falling edge time and waveform integrity of the CC/CP control signal; and if the PWM duty ratio exceeds a standard interval of 20% -80%, the rising edge time exceeds a first preset threshold, the falling edge time exceeds a second preset threshold, or the waveform integrity is abnormal, determining that the CC/CP control signal is not compliant.
4. The electric vehicle charging equipment fault diagnosis method according to claim 1, wherein the normal operating condition feature library is pre-constructed by: Acquiring historical operation data of the charging equipment, wherein the historical operation data comprises electrical parameters, temperature parameters, environmental parameters and interaction control signals of the charging equipment in a normal operation state; Preprocessing the historical operation data, extracting electrical characteristics, temperature characteristics, environmental characteristics and signal integrity characteristics in the historical operation data, and splicing the electrical characteristics, the temperature characteristics, the environmental characteristics and the signal integrity characteristics in the historical operation data to form a multidimensional characteristic vector; the multidimensional feature vectors are used as training data and input into a machine learning model for training, so that the machine learning model learns the distribution rule of the feature vectors under the normal working condition of the charging equipment, and the normal working condition feature library is obtained; the machine learning model includes at least one of a random forest, a convolutional neural network, or a long-term short-term memory network.
5. The method for diagnosing a fault of an electric vehicle charging device according to claim 1, wherein the root cause analysis is performed by combining the multidimensional feature parameter and the full-flow interaction log, and locating the interaction stage and the specific position of the fault comprises: Inputting the abnormal characteristic parameters into a fault diagnosis model to obtain target fault types corresponding to the abnormal characteristic parameters, wherein the fault diagnosis model is obtained by classifying and training according to historical fault data corresponding to each fault type and is used for matching the input abnormal characteristic parameters with the abnormal characteristics extracted from the historical fault data to identify the corresponding fault types; extracting a communication message of an interaction stage corresponding to the target fault type from the full-flow interaction log according to the target fault type corresponding to the abnormal characteristic parameter; Performing root cause analysis based on the time stamp and the content of the communication message and the target fault type, and determining the specific position of the fault corresponding to the abnormal characteristic parameter; generating a fault report of the fault corresponding to the abnormal characteristic parameter, wherein the fault report comprises at least one of fault details, risk level assessment, root cause analysis conclusion, emergency treatment step, maintenance guide and spare part recommendation list.
6. The electric vehicle charging equipment failure diagnosis method according to claim 1, characterized in that the method further comprises: in response to receiving a remote spot check instruction, controlling a simulation test unit accessed to an internal circuit of the charging equipment to simulate a preset fault state; Recording response data of the charging equipment to the simulated fault state in real time, and comparing the response data with a threshold value in a built-in standard database to generate a compliance test report; Uploading the compliance test report to a supervision platform through an encryption channel so as to support remote forced detection and energy efficiency evaluation of the charging equipment.
7. The method for diagnosing a fault in an electric vehicle charging device according to claim 6, wherein the simulation test unit comprises a controllable resistor network connected to an internal circuit of the charging device, and simulating a preset fault state comprises at least one of: simulating temperature abnormality by switching a precise resistor connected in series in a temperature sensor circuit; simulating abnormal interactive control signals by switching precise resistors connected in series or in parallel in a CP circuit or a CC circuit; The built-in standard database comprises a mandatory standard database comprising at least one standard threshold of GB 44263, GB 39752, GB 46519.
8. An electric vehicle charging equipment fault diagnosis system, characterized by comprising: the data acquisition module is used for monitoring and acquiring electrical parameters, temperature parameters and environmental parameters of the charging equipment in the process of charging the vehicle in real time and interactive control signals reflecting the communication states of the charging equipment and the vehicle, and recording communication messages of the charging equipment and the vehicle in the whole process from the starting of the gun insertion to the ending of charging; The signal detection module is used for carrying out signal integrity detection on the interaction control signal to obtain a signal integrity detection result; The log generation module is used for generating a full-flow interaction log of the charging equipment and the vehicle according to the time sequence of the communication message; The feature extraction module is used for preprocessing the electrical parameter, the temperature parameter, the environment parameter and the signal integrity detection result, extracting a multi-dimensional feature parameter representing the running state of equipment, wherein the multi-dimensional feature parameter is fused with an electrical feature extracted from the electrical parameter, a temperature feature extracted from the temperature parameter, an environment feature extracted from the environment parameter and a signal integrity feature extracted from the interactive control signal; the abnormal recognition module is used for recognizing whether the multidimensional characteristic parameter is abnormal or not based on a pre-constructed normal working condition characteristic library, wherein the normal working condition characteristic library is a characteristic reference set which is constructed based on historical operation data of the charging equipment and reflects the normal operation state of the charging equipment; And the diagnosis analysis module is used for carrying out root cause analysis by combining the multi-dimensional characteristic parameters and the full-flow interaction log when the abnormal characteristic parameters exist in the multi-dimensional characteristic parameters, and positioning the interaction stage and the specific position of the fault corresponding to the abnormal characteristic parameters.
9. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program which, when executed by the processor, implements the method of any of claims 1-7.
10. A computer readable storage medium, having stored thereon program instructions, which when executed, implement the method of any of claims 1-7.

Description

Electric automobile charging equipment fault diagnosis method, system, equipment and storage medium Technical Field The invention relates to the technical field of automobile computer control and intelligent power grids, in particular to a fault diagnosis method, system and equipment for charging equipment of an electric automobile and a storage medium. Background With the high-speed development of the new energy automobile industry, the running stability and the fault response efficiency of the electric automobile charging equipment serving as a core supporting infrastructure have become key factors for restricting the high-quality development of the industry. The current fault monitoring and diagnosing scheme of charging equipment in the market still takes the traditional passive alarm as a core mode, and each equipment manufacturer and each operation platform build a monitoring system based on a self technical system, so that a unified industry standard and data interaction specification are not formed. From the equipment manufacturer side, the mainstream enterprises rely on a built-in fault alarm module of the charging equipment to realize fault feedback, only basic operation parameters of equipment core hardware can be acquired, and when obvious faults occur to the equipment or the parameters exceed a threshold value, the equipment alarms, and the early warning and fault pre-judging capability is avoided. From the operation platform end, each large charging operator adopts a customized communication protocol, the platform can only receive single-point fault data uploaded by equipment, the whole charging process cannot be monitored, and the problems of communication faults, abnormal interaction and the like generated in the three-party interaction process of the power grid, the charging pile and the vehicle are difficult to identify and trace. The defects of the prior art are that quality monitoring of an electric car-charging pile interaction signal is lacking, the charging pile fault cannot be analyzed in combination with early symptoms of fault occurrence such as signal quality degradation, single-point fault data can be recorded, the retrospective analysis of the whole charging process cannot be realized, daily monitoring data cannot be combined with newly released national mandatory standards (such as GB 44263-2024, GB 39152-2024 and GB 46519-2025), and data support cannot be provided for remote forced detection and energy efficiency evaluation of charging equipment. Disclosure of Invention The invention aims to overcome the defects in the prior art and provides a fault diagnosis method, a fault diagnosis system, a fault diagnosis device and a fault diagnosis storage medium for electric automobile charging equipment. To achieve the above object, a first aspect of the present invention provides a fault diagnosis method for a charging device of an electric vehicle, including: Monitoring and collecting electrical parameters, temperature parameters and environmental parameters of the charging equipment in the process of charging the vehicle in real time and interactive control signals reflecting the communication states of the charging equipment and the vehicle, and recording communication messages of the charging equipment and the vehicle in the whole process from gun insertion starting to charging ending; Performing signal integrity detection on the interaction control signal to obtain a signal integrity detection result; generating a full-flow interaction log of the charging equipment and the vehicle according to the time sequence of the communication message; Preprocessing the electrical parameter, the temperature parameter, the environmental parameter and the signal integrity detection result, and extracting a multi-dimensional characteristic parameter representing the running state of equipment, wherein the multi-dimensional characteristic parameter is fused with an electrical characteristic extracted from the electrical parameter, a temperature characteristic extracted from the temperature parameter, an environmental characteristic extracted from the environmental parameter and a signal integrity characteristic extracted from the interactive control signal; identifying whether the multidimensional feature parameters are abnormal or not based on a pre-constructed normal working condition feature library, wherein the normal working condition feature library is a feature reference set which is constructed based on historical operation data of the charging equipment and reflects the normal operation state of the charging equipment; When the abnormal characteristic parameters exist in the multi-dimensional characteristic parameters, root cause analysis is conducted by combining the multi-dimensional characteristic parameters and the full-flow interaction log, and the interaction stage and the specific position of the fault corresponding to the abnormal characteristic parameters are located. Further,