Electric vehicle charging and discharging fault diagnosis method and device, electronic equipment and medium

Abstract

The embodiment of the invention relates to the technical field of vehicle fault diagnosis, and discloses an electric vehicle charging and discharging fault diagnosis method and device, electronic equipment and a medium. According to the method, any subtle change of the vehicle battery in the running state is captured by collecting the battery data of the vehicle battery in real time, the battery data is compared with the preset standard data, whether the current vehicle battery has the early failure or not is accurately judged, and after it is determined that the battery data has the early failure, the vehicle battery can be detected. According to the invention, secondary verification can be carried out on an early fault based on the electrochemical impedance spectrum and the fault detection cloud, the accuracy of fault judgment is further improved, fault misjudgment is avoided, and more reliable battery state information can be provided for a user. The problem that when the new energy vehicle is in the charging and discharging state, the vehicle using safety of a user cannot be guaranteed due to the fact that the user cannot be warned about the fault of the battery is solved, the vehicle using safety of the user is guaranteed, and the vehicle using experience of the user is improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle fault diagnosis technology, and in particular to a method, device, electronic device, and medium for diagnosing charging and discharging faults in electric vehicles. Background Technology

[0002] Currently, with the continuous development of new energy vehicles, users are paying increasing attention to the charging status and fault diagnosis of these vehicles. However, when new energy vehicles are in a charging and discharging state, the battery is prone to failure. If users are not warned of these failures, it will affect their vehicle safety. Summary of the Invention

[0003] The purpose of this invention is to provide a method, device, electronic device and medium for diagnosing charging and discharging faults in electric vehicles, so as to at least avoid the problem that new energy vehicles cannot provide warnings to users of battery failures when they are in the charging and discharging state, thus failing to ensure user safety and improve user experience.

[0004] To address the aforementioned technical problems, in a first aspect, the present invention provides a method for diagnosing charging and discharging faults in electric vehicles, comprising at least:

[0005] The battery management system is used to collect battery data from the vehicle battery in real time.

[0006] If the battery data does not meet the conditions of the preset standard data, then the battery data is determined to be abnormal;

[0007] After determining that the battery data is abnormal, an electrochemical impedance spectroscopy (EIS) analysis is performed on the vehicle battery to obtain the internal resistance spectrum corresponding to the vehicle battery, and the internal resistance spectrum is analyzed using a support vector machine (SVM) to determine whether there is an early fault in the internal resistance spectrum.

[0008] If the internal resistance spectrum shows an early fault, then the fault type of the early fault is marked, and the early fault and its fault type are uploaded to the fault detection cloud.

[0009] At least the fault detection cloud is used to verify the early faults and fault types based on historical data and environmental factors, so as to perform a secondary verification of the early faults;

[0010] If the early fault secondary inspection is present, an early warning report is generated and the battery maintenance process is initiated.

[0011] Optionally, after collecting battery data of the vehicle battery in real time using the battery management system, the method further includes:

[0012] If the battery data meets the conditions of the preset standard data, it is determined that the battery data is not abnormal, and the battery data of the vehicle battery is collected again using the battery management system until the battery data does not meet the conditions of the preset standard data, at which point it is determined that the battery data is abnormal.

[0013] Optionally, after determining that the battery data is abnormal, performing electrochemical impedance spectroscopy (EIS) analysis on the vehicle battery to obtain the corresponding internal resistance spectrum of the vehicle battery, and using support vector machine (SVM) to analyze the internal resistance spectrum to determine whether there is an early fault in the internal resistance spectrum, the method further includes:

[0014] If the internal resistance spectrum does not show the early fault, then the internal resistance spectrum is marked as a temporary fluctuation, and the sampling parameters of the battery management system are adjusted.

[0015] Optionally, after verifying the early faults and fault types based on historical data and environmental factors using the fault detection cloud at least to perform a secondary verification of the early faults, the method further includes:

[0016] If the early fault secondary verification does not exist, the early fault is marked as a false alarm.

[0017] Optionally, after determining that the battery data is abnormal, performing electrochemical impedance spectroscopy (EIS) analysis on the vehicle battery to obtain the corresponding internal resistance spectrum of the vehicle battery, and using support vector machine (SVM) to analyze the internal resistance spectrum to determine whether there is an early fault in the internal resistance spectrum, specifically includes:

[0018] At least one EIS analysis operation is performed on the vehicle battery to obtain the internal resistance spectrum corresponding to the vehicle battery;

[0019] The feature information in the internal resistance spectrum is extracted using SVM, and the feature information is standardized to obtain standard feature information.

[0020] The similarity between the standard feature information and the standard feature state standard in the standard reference library is obtained using SVM.

[0021] The feature state criterion corresponding to the highest similarity is used to determine whether the early fault exists in the internal resistance spectrum; or...

[0022] Optionally, if the early fault secondary inspection exists, an early warning report is generated and the battery maintenance process is initiated, specifically including:

[0023] If the early fault secondary verification exists, then the early warning report is generated;

[0024] The warning report will be sent to the user and the vehicle manufacturer's monitoring center, and the battery maintenance process will be initiated.

[0025] Optionally, before obtaining the similarity between the standard feature information and each feature state standard in the standard reference library using a support vector machine (SVM), the method further includes:

[0026] Collect the internal resistance spectrum of the vehicle battery under at least one operating condition, and perform statistical analysis on the characteristics of the internal resistance spectrum of each battery to at least determine the average value and fluctuation range of the impedance of the vehicle battery data in a specific frequency region, and then establish a standard reference library.

[0027] Secondly, the present invention also provides an electric vehicle charging and discharging fault diagnosis device, comprising at least:

[0028] The data acquisition module is used to collect battery data of the vehicle battery in real time using the battery management system;

[0029] The data comparison module is used to determine that the battery data is abnormal when the battery data does not meet the conditions of the preset standard data.

[0030] The first diagnostic module is used to perform electrochemical impedance spectroscopy (EIS) analysis on the vehicle battery after determining that the battery data is abnormal, to obtain the internal resistance spectrum corresponding to the vehicle battery, and to use support vector machine (SVM) to analyze the internal resistance spectrum to determine whether there is an early fault in the internal resistance spectrum.

[0031] The fault upload module is used to mark the fault type of the early fault when the internal resistance spectrum shows the early fault, and upload the early fault and its fault type to the fault detection cloud.

[0032] The fault verification module is used to verify the early faults and fault types based on historical data and environmental factors using the fault detection cloud at least, so as to perform a secondary verification on the early faults.

[0033] The fault maintenance module is used to generate an early warning report and initiate the battery maintenance process after the early fault secondary inspection is found.

[0034] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that the processor executes the program to implement the steps in the electric vehicle charging and discharging fault diagnosis method according to any one of the first aspects.

[0035] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps in the electric vehicle charging and discharging fault diagnosis method according to any one of the first aspects.

[0036] The technical solution provided in this embodiment firstly collects battery data of the vehicle battery in real time using a battery management system; secondly, if the battery data does not meet the conditions of preset standard data, it is determined that the battery data is abnormal; thirdly, after determining that the battery data is abnormal, an electrochemical impedance spectroscopy (EIS) analysis is performed on the vehicle battery to obtain the corresponding internal resistance spectrum, and a support vector machine (SVM) is used to analyze the internal resistance spectrum to determine whether there is an early fault; furthermore, if an early fault is found in the internal resistance spectrum, the fault type of the early fault is marked, and the early fault and its fault type are uploaded to the fault detection cloud; then, at least the early fault and fault type are verified by the fault detection cloud based on historical data and environmental factors to perform a secondary verification of the early fault; finally, if the secondary verification of the early fault exists, a warning report is generated and the battery maintenance process is initiated.

[0037] Therefore, this invention captures any subtle changes in the vehicle battery's operating state by collecting real-time battery data and comparing it with preset standard data to accurately determine whether the vehicle battery has experienced an early fault. After confirming an early fault, it performs secondary verification based on electrochemical impedance spectroscopy and a fault detection cloud platform, further improving the accuracy of fault diagnosis and preventing misdiagnosis. This provides users with more reliable battery status information. This invention at least avoids the problem of new energy vehicles failing to warn users of battery failures during charging and discharging, thus ensuring user safety and improving the user experience. Attached Figure Description

[0038] Figure 1 This is a flowchart of a method for diagnosing charging and discharging faults in electric vehicles provided by an embodiment of the present invention;

[0039] Figure 2 This is a flowchart of another electric vehicle charging and discharging fault diagnosis method provided in an embodiment of the present invention;

[0040] Figure 3 This is a flowchart of another electric vehicle charging and discharging fault diagnosis method provided in an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of the structure of an electric vehicle charging and discharging fault diagnosis device provided in an embodiment of the present invention;

[0042] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0044] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0045] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0046] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0047] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0048] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0049] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0050] Figure 1 This is a flowchart of a method for diagnosing charging and discharging faults in electric vehicles provided by an embodiment of the present invention. This embodiment is applicable to charging and discharging fault diagnosis scenarios for various types of electric vehicles, including pure electric and plug-in hybrid electric vehicles. This method for diagnosing charging and discharging faults in electric vehicles can be, but is not limited to, executed by the electric vehicle charging and discharging fault diagnosis device described in this embodiment of the present invention. This execution entity can be implemented using software and / or hardware. Figure 1 As shown, the electric vehicle charging and discharging fault diagnosis method includes at least the following steps:

[0051] S1. Utilize the battery management system to collect battery data from the vehicle battery in real time.

[0052] The Battery Management System (BMS) is used for intelligent management and maintenance of each battery cell, monitoring the battery status and constantly monitoring key physical parameters of the battery pack to prevent overcharging and over-discharging. Battery data can be operational status data of the battery or battery pack during charging and discharging. For example, battery data can include voltage, current, and temperature data of individual battery cells and the battery pack. High-precision sensors, such as voltage sensors, current sensors, and temperature sensors, can be used to collect this data. It is understood that voltage sensors can achieve an accuracy of ±0.01V, accurately measuring voltage fluctuations during charging and discharging. This provides an accurate data basis for subsequent early fault analysis and reflects the current charging and discharging state of the battery, providing important information for determining whether the battery is within its normal operating range. It should be noted that real-time acquisition of battery data can capture any subtle changes in the battery's operating status.

[0053] S2. If the battery data does not meet the conditions of the preset standard data, it is determined that the battery data is abnormal.

[0054] The preset standard data can be battery data under normal vehicle battery charging and discharging conditions. This preset standard data can be obtained through pre-experiments and can correspond to a specific value or a range. For common lithium-ion battery cells, the nominal voltage is generally 3.6V-3.7V. During normal use, the charging cut-off voltage is typically 4.1V-4.2V, and the discharging cut-off voltage is typically 2.75V-3.0V. Therefore, the charging voltage value in the preset standard data could be 4.15V, and the discharging voltage value could be 2.85V. In lithium iron phosphate batteries, the nominal voltage of a single cell is approximately 3.2V, the charging cut-off voltage is around 3.65V, and the discharging cut-off voltage is around 2.5V. When the battery cell voltage is higher than the charging cut-off voltage (i.e., if the battery data does not meet the conditions of the preset standard data), overcharging may occur, leading to accelerated battery aging and potentially causing safety issues; if it is lower than the discharging cut-off voltage, it indicates over-discharging, which may cause permanent battery damage. Excessive voltage difference between individual cells, such as exceeding 50-100mV, is also considered abnormal, indicating a problem with battery consistency and potentially affecting the overall performance of the battery pack.

[0055] Accordingly, the normal range of battery pack voltage depends on the number of cells connected in series within the battery pack. For example, a battery pack composed of 96 lithium-ion battery cells with a nominal voltage of 3.7V connected in series has a normal operating voltage range of approximately 288V (96 × 3.0V, near discharge cutoff) to 403.2V (96 × 4.2V, near charge cutoff). Therefore, the operating voltage value of the battery pack in the preset standard data can be [288, 403]. If it exceeds the corresponding range, it indicates that the overall charging or discharging state of the battery pack is abnormal. In addition, if the battery pack voltage fluctuates significantly in a short period of time, while the charging or discharging current does not change significantly, it may also indicate a fault such as poor connection inside the battery pack. The normal operating temperature range of the battery is generally 25℃-40℃. Therefore, the temperature value in the preset standard data can be [25, 40]. Within this temperature range, the performance, lifespan, and safety of the battery can be well guaranteed. When the battery temperature exceeds 50°C, it is considered overheated, which accelerates the internal chemical reaction of the battery, leading to faster battery capacity decay and even potentially causing serious safety accidents such as thermal runaway. If the temperature is below 0°C, especially during continuous discharge in low-temperature environments, the battery's internal resistance increases, charging and discharging efficiency decreases, and usable capacity is reduced. Long-term exposure to low-temperature environments can also affect battery life.

[0056] S3. After determining that the battery data is abnormal, perform electrochemical impedance spectroscopy (EIS) analysis on the vehicle battery to obtain the corresponding internal resistance spectrum of the vehicle battery, and use support vector machine (SVM) to analyze the internal resistance spectrum to determine whether there is an early fault in the internal resistance spectrum.

[0057] Electrochemical impedance spectroscopy (EIS) is used to apply a small-amplitude AC signal (typically in the frequency range of 10 mHz-10 kHz) to the battery and measure its impedance response at different frequencies, thereby generating an internal resistance spectrum reflecting the internal electrochemical processes of the battery. The Support Vector Machine (SVM) algorithm, by finding the optimal hyperplane, can effectively distinguish between normal battery states and early fault states, such as the spectral characteristics of lithium plating and thermal runaway.

[0058] S4. If an early fault is found in the internal resistance spectrum, mark the fault type of the early fault and upload the early fault and its fault type to the fault detection cloud.

[0059] The fault types can include thermal runaway, over-discharge, etc. The data can be uploaded via 5G communication technology. The fault detection cloud platform can be a virtual model built by the automaker based on digital twin technology, highly similar to the physical battery system, capable of simulating and analyzing the entire battery lifecycle.

[0060] S5. At least utilize the fault detection cloud to verify early faults and fault types based on historical data and environmental factors, so as to perform secondary verification on early faults.

[0061] The fault detection cloud platform can be equipped with a battery lifecycle model that simulates battery performance changes under different conditions based on factors such as battery usage history, charge / discharge cycle count, and environmental factors. During specific testing, the system first searches the battery lifecycle model for historical environments that match the current scenario, retrieving relevant historical data such as voltage and temperature. This historical data is then compared with battery data corresponding to earlier faults to determine if they are consistent or similar. If they are, the system further checks if the fault type corresponding to the historical data matches the fault type corresponding to the earlier fault. If both match, a secondary verification of the earlier fault is confirmed, completing the secondary check. This secondary verification improves the accuracy of fault diagnosis, avoids misjudgments, and ensures reliable battery status information for users.

[0062] S6. If the early fault secondary inspection exists, generate an early warning report and start the battery maintenance process.

[0063] The early warning report can be a log file containing early faults and their types. The purpose of the battery maintenance process is to eliminate early faults.

[0064] The technical solution provided in this embodiment firstly collects battery data of the vehicle battery in real time using a battery management system; secondly, if the battery data does not meet the conditions of preset standard data, it is determined that the battery data is abnormal; thirdly, after determining that the battery data is abnormal, an electrochemical impedance spectroscopy (EIS) analysis is performed on the vehicle battery to obtain the corresponding internal resistance spectrum, and a support vector machine (SVM) is used to analyze the internal resistance spectrum to determine whether there is an early fault; furthermore, if an early fault is found in the internal resistance spectrum, the fault type of the early fault is marked, and the early fault and its fault type are uploaded to the fault detection cloud; then, at least the early fault and fault type are verified by the fault detection cloud based on historical data and environmental factors to perform a secondary verification of the early fault; finally, if the secondary verification of the early fault exists, a warning report is generated and the battery maintenance process is initiated.

[0065] Therefore, this embodiment captures any subtle changes in the vehicle battery's operating state by collecting real-time battery data and comparing it with preset standard data to accurately determine whether the vehicle battery has experienced an early fault. After confirming an early fault, it performs secondary verification based on electrochemical impedance spectroscopy and fault detection cloud platforms, further improving the accuracy of fault diagnosis and preventing misdiagnosis. This provides users with more reliable battery status information. This embodiment aims to at least avoid the problem of new energy vehicles failing to warn users of battery faults during charging and discharging, thus ensuring user safety and improving the user experience.

[0066] Based on the above embodiments or implementation methods Figure 2 This is a flowchart of another electric vehicle charging and discharging fault diagnosis method provided in an embodiment of the present invention. Figure 3 This is a flowchart of another electric vehicle charging and discharging fault diagnosis method provided by an embodiment of the present invention. This embodiment of multimodal electric vehicle charging and discharging diagnosis is based on the above embodiment with additions. Figure 2 and Figure 3 As shown, the electric vehicle charging and discharging fault diagnosis method includes at least the following steps:

[0067] S1. Utilize the battery management system to collect battery data from the vehicle battery in real time.

[0068] S6. If the battery data meets the conditions of the preset standard data, it is determined that the battery data is not abnormal, and the battery management system is used to collect the battery data of the vehicle battery again until the battery data does not meet the conditions of the preset standard data, then it is determined that the battery data is abnormal.

[0069] S2. If the battery data does not meet the conditions of the preset standard data, it is determined that the battery data is abnormal.

[0070] S31. Perform EIS analysis on the vehicle battery at least once to obtain the internal resistance spectrum of the vehicle battery.

[0071] S32. Use SVM to extract feature information from the internal resistance spectrum and perform standardization processing on the feature information to obtain standard feature information.

[0072] The characteristic information can be important information from the internal resistance spectrum, such as impedance magnitude, phase angle, resistance, and other related parameters corresponding to specific low-frequency and high-frequency regions on the internal resistance spectrum. Standardization can be a normalization process. It is known that, since the numerical ranges of different characteristic information vary considerably, in order to ensure that each characteristic information plays a balanced role in subsequent analysis, the values ​​of all characteristic information need to be adjusted to the range [0,1]. For example, the characteristic information can be standardized in the following way:

[0073] ;

[0074] In the formula, X represents the standard feature information, X i Representing feature information, X min X represents the minimum value of the feature information. max This represents the maximum value of the feature information.

[0075] S7. Collect the internal resistance spectrum of vehicle batteries under at least one operating condition, and perform statistical analysis on the characteristics of the internal resistance spectrum of each battery to at least determine the average value and fluctuation range of the battery data of the vehicle battery in a specific frequency region, and then establish a standard reference library.

[0076] Statistical analysis can involve calculating the average and fluctuation range of battery impedance data for normal vehicles within a specific frequency range. Correspondingly, early faults such as lithium plating and thermal runaway can also be addressed with corresponding reference standards. The standard reference library should at least contain the average and fluctuation range of impedance data from internal resistance spectra under different conditions.

[0077] S33. Use SVM to obtain the similarity between standard feature information and the standard feature state standard in the standard reference library.

[0078] Before calculating the similarity, the impedance magnitude can be normalized based on its average value and fluctuation range to obtain a standard reference value. For example, the standard reference value could be calculated as: Standard Reference Value = (Average Value - Minimum Fluctuation Range) / (Maximum Fluctuation Range - Minimum Fluctuation Range). Then, the similarity is determined by calculating the difference between the standard reference value and the standard feature information; the smaller the difference, the higher the similarity.

[0079] S34. Take the characteristic state standard corresponding to the highest similarity to determine whether there is an early fault in the internal resistance spectrum.

[0080] The established criteria can be exceeding preset values. For example, a similarity greater than 0.8 and the characteristic state standard corresponding to an abnormal state are considered as early faults. If the similarity exceeds the preset value and corresponds to a normal state, then no early fault exists. In another specific implementation, this embodiment can also output multiple early faults simultaneously. Specifically, a corresponding preset value is set for each early fault, such as 0.6 for thermal runaway and 0.7 for over-discharge. When the similarity to thermal runaway exceeds 0.6 and the similarity to over-discharge exceeds 0.7, it can be considered that the vehicle battery has simultaneously experienced thermal runaway and over-discharge (early faults). If the similarity does not meet the preset value requirements, it indicates that the internal resistance spectrum may represent a new early fault or that there is a problem with the acquisition. The internal resistance spectrum needs to be sent to maintenance personnel for further investigation and maintenance.

[0081] S8. If there is no early fault in the internal resistance spectrum, mark the internal resistance spectrum as a temporary fluctuation and adjust the sampling parameters of the battery management system.

[0082] The sampling parameter can be the sampling frequency, or switching from real-time sampling to fixed-interval sampling.

[0083] S4. If an early fault is found in the internal resistance spectrum, mark the fault type of the early fault and upload the early fault and its fault type to the fault detection cloud.

[0084] S5. At least utilize the fault detection cloud to verify early faults and fault types based on historical data and environmental factors, so as to perform secondary verification on early faults.

[0085] S9. If the early fault does not exist in the secondary case, then mark the early fault as a false alarm.

[0086] S61. If the early fault secondary verification exists, an early warning report will be generated.

[0087] S62. Send the warning report to the user and the vehicle manufacturer's monitoring center, and initiate the battery maintenance process.

[0088] The technical solution provided in this embodiment firstly involves using a battery management system to collect battery data from the vehicle battery in real time. Further, if the battery data meets the conditions of preset standard data, it is determined that the battery data is not abnormal, and the battery management system is used to collect battery data from the vehicle battery again until the battery data no longer meets the conditions of the preset standard data, at which point it is determined that the battery data is abnormal. Further, if the battery data does not meet the conditions of the preset standard data, it is determined that the battery data is abnormal. Further, at least one EIS analysis operation is performed on the vehicle battery to obtain the corresponding internal resistance spectrum. Further, SVM is used to extract feature information from the internal resistance spectrum, and the feature information is standardized to obtain standard feature information. Further, internal resistance spectra of the vehicle battery under at least one operating condition are collected, and the features of each battery's internal resistance spectrum are statistically analyzed to at least determine the average value and fluctuation range of the vehicle battery's impedance in a specific frequency region, thereby establishing a standard reference library. Further, SVM is used to obtain the similarity between the standard feature information and each feature state standard in the standard reference library. Further, the feature state standard corresponding to the highest similarity is selected to determine if an early fault exists in the internal resistance spectrum. Furthermore, if no early fault is found in the internal resistance spectrum, the internal resistance spectrum is marked as a temporary fluctuation, and the sampling parameters of the battery management system are adjusted. Further, if an early fault is found in the internal resistance spectrum, the fault type of the early fault is marked, and the early fault and its type are uploaded to the fault detection cloud. Further, at least the early fault and its type are verified using historical data and environmental factors through the fault detection cloud to perform a secondary verification of the early fault. Further, if the secondary verification of the early fault does not exist, the early fault is marked as a false alarm. If the secondary verification of the early fault exists, a warning report is generated. Finally, the warning report is sent to the user and the vehicle manufacturer's monitoring center, and the battery maintenance process is initiated.

[0089] Therefore, this embodiment captures any subtle changes in the vehicle battery's operating state by collecting real-time battery data and comparing it with preset standard data to accurately determine whether the vehicle battery has experienced an early fault. After confirming an early fault, it performs secondary verification based on electrochemical impedance spectroscopy and fault detection cloud platforms, further improving the accuracy of fault diagnosis and preventing misdiagnosis. This provides users with more reliable battery status information. This embodiment aims to at least avoid the problem of new energy vehicles failing to warn users of battery faults during charging and discharging, thus ensuring user safety and improving the user experience.

[0090] In addition, this embodiment also provides an intelligent electric vehicle charging and discharging status indication system, used to prompt the user about the charging and discharging status of the electric vehicle when implementing electric vehicle charging and discharging fault diagnosis methods. The system includes:

[0091] The terminal display module includes Mini LED arrays deployed on the front and rear bumpers, supporting dynamic rendering and light and shadow control; flexible Micro LED light strips integrated on the sides of the vehicle to achieve dynamic light effect coverage across the entire vehicle body; and a roof-mounted laser projection module with a built-in ambient light sensor, which can generate interactive AR projection interfaces on the ground / wall.

[0092] The interactive extension module includes an embedded vibration module around the charging port, which transmits status information through different vibration modes (such as low-frequency pulses to indicate charging completion and high-frequency vibrations to indicate faults); and a safety-grade fragrance generator that releases a minty warning scent when charging is abnormal and a cedarwood soothing scent when charging is complete, creating a multi-sensory collaborative experience.

[0093] Ambient light sensor module: Utilizing an 8-channel UV-A / B / C band detection module, it supports a 0.1-second response speed and can sense changes in ambient light and darkness in real time.

[0094] Crowd monitoring module: A monitoring network consisting of 12 ultrasonic sensors and 2 fisheye cameras is used to achieve 360° crowd density detection based on the YOLOv8 algorithm and identify the status of people staying within 3 meters.

[0095] Meteorological sensor module: integrates temperature, humidity, air pressure, wind speed and direction detection modules, and supports the collection of extreme weather parameters.

[0096] The dynamic control module specifically includes:

[0097] Lighting adaptive module: used to increase light brightness to 80% when the ambient light is too low, and to activate anti-glare mode (reduce the dynamic frequency of light and shadow) when the light is too high.

[0098] Pedestrian response module: When a person is detected standing within 3 meters, the module automatically switches to silent projection mode (turns off voice broadcast and increases projection brightness) to avoid noise interference in public areas, and resumes normal display mode when no one is present.

[0099] Extreme weather contingency module: Used to reduce the frequency of light and shadow movement in rainy or snowy weather, preheat the projection module in low-temperature environments, and ensure stable operation over a wide temperature range.

[0100] This system also provides a status display matrix to prompt users on the charging and discharging status of intelligent electric vehicles. The status display matrix is ​​shown in Table 1 at least.

[0101] Table 1

[0102]

[0103] In one specific implementation, the intelligent electric vehicle charging and discharging status indication system also includes an APP configuration platform to provide users with customized interaction schemes. The APP configuration platform supports six customizable lighting schemes (including 12 preset dynamic lighting effects), personalized voice recording (supporting mixed broadcasts in five languages ​​including Chinese, English, and Japanese), and AR content editing (customizable logos / advertisements / prompts), achieving a personalized interactive experience for each vehicle. Furthermore, the intelligent electric vehicle charging and discharging status indication system is compatible with Over-the-Air (OTA) technology, enabling dynamic updates of display strategies through the vehicle cloud platform and supporting incremental upgrades of strategy packages to ensure continuous optimization of system functions. When a user is charging their vehicle, the indication system automatically sends status signals (charging / faulty / idle) to charging stations within a 500-meter radius, optimizing the scheduling efficiency of public charging areas and reducing user waiting time. When the user's vehicle has less than 15% remaining battery power, the indication system automatically displays the real-time status (idle / occupied / faulty) of the three nearest charging stations on an AR projection, along with distance and electricity price information, improving the convenience of charging planning.

[0104] Therefore, this indicator system addresses the shortcomings of existing electric vehicle charging status indicators, such as limited information display, reliance on in-vehicle screens or simple lighting leading to misreading in complex scenarios, poor environmental adaptability (lacking lighting and pedestrian awareness, with reduced effectiveness in extreme environments), and fixed interaction methods that fail to support personalized configurations and meet diverse needs. This system utilizes multi-channel information transmission—light, sound, vibration, and odor—to improve information recognition efficiency in complex scenarios, reduce the risk of user misjudgment, enhance indicator effectiveness, ensure reliable information display across different scenarios, and improve the efficiency of public charging networks while meeting diverse user needs.

[0105] Figure 4 This is a schematic diagram of the structure of an electric vehicle charging and discharging fault diagnosis device provided in an embodiment of the present invention. This embodiment is applicable to charging and discharging fault diagnosis scenarios for various types of electric vehicles, including pure electric and plug-in hybrid electric vehicles. This electric vehicle charging and discharging fault diagnosis device can be implemented using software and / or hardware. Figure 4 As shown, the electric vehicle charging and discharging fault diagnosis device 100 includes at least:

[0106] The data acquisition module 110 is used to collect battery data of the vehicle battery in real time using the battery management system.

[0107] The data comparison module 120 is used to determine that the battery data is abnormal when the battery data does not meet the conditions of the preset standard data.

[0108] The first diagnostic module 130 is used to perform electrochemical impedance spectroscopy (EIS) analysis on the vehicle battery after determining that the battery data is abnormal, to obtain the internal resistance spectrum of the vehicle battery, and to use support vector machine (SVM) to analyze the internal resistance spectrum to determine whether there is an early fault in the internal resistance spectrum.

[0109] The fault upload module 140 is used to mark the fault type of the early fault when there is an early fault in the internal resistance spectrum, and upload the early fault and its fault type to the fault detection cloud.

[0110] The fault verification module 150 is used to verify early faults and fault types based on historical data and environmental factors using the fault detection cloud, so as to perform secondary verification on early faults.

[0111] The fault maintenance module 160 is used to generate an early warning report and initiate the battery maintenance process after the early fault secondary inspection is found.

[0112] Optionally, the data comparison module 120 is also used for:

[0113] If the battery data meets the conditions of the preset standard data, it is determined that the battery data is not abnormal, and the battery management system is used to collect the battery data of the vehicle battery again until the battery data does not meet the conditions of the preset standard data, at which point it is determined that the battery data is abnormal.

[0114] Optionally, it also includes:

[0115] The marking adjustment module 170 is used to mark the internal resistance spectrum as a temporary fluctuation and adjust the sampling parameters of the battery management system when there is no early fault in the internal resistance spectrum.

[0116] Optionally, the fault maintenance module 160 is also used for:

[0117] If the secondary verification for an early fault does not exist, the early fault will be marked as a false alarm.

[0118] Optionally, the first diagnostic module 130 is specifically used for:

[0119] At least one EIS analysis operation is performed on the vehicle battery to obtain the corresponding internal resistance spectrum of the vehicle battery; and feature information in the internal resistance spectrum is extracted using SVM, and the feature information is standardized to obtain standard feature information; and the similarity between the standard feature information and each feature state standard in the standard reference library is obtained using SVM; and the feature state standard corresponding to the highest similarity is selected to determine whether there is an early fault in the internal resistance spectrum.

[0120] Optionally, the fault maintenance module 160 is specifically used for:

[0121] When an early fault is detected during secondary inspection, an early warning report is generated; and the early warning report is sent to the user and the vehicle manufacturer's monitoring center, and the battery maintenance process is initiated.

[0122] Optionally, it also includes:

[0123] The reference library construction module 180 is used to collect the internal resistance spectrum of vehicle batteries under at least one operating condition, and to perform statistical analysis on the characteristics of the internal resistance spectrum of each battery in order to at least determine the average value and fluctuation range of the battery data of the vehicle battery in a specific frequency region, and then establish a standard reference library.

[0124] The technical solution provided in this embodiment firstly involves a data acquisition module that uses a battery management system to collect real-time battery data from the vehicle battery. Further, a data comparison module identifies an anomaly in the battery data when it does not meet preset standard data conditions. Further, after identifying an anomaly, a first diagnostic module performs EIS analysis on the vehicle battery to obtain the corresponding internal resistance spectrum, and uses SVM analysis to determine if an early fault exists in the internal resistance spectrum. Further, a fault upload module marks the fault type of the early fault when it is found in the internal resistance spectrum and uploads the early fault and its type to the fault detection cloud. Further, a fault verification module uses the fault detection cloud to verify the early fault and its type based on historical data and environmental factors, performing a secondary verification of the early fault. Finally, a fault maintenance module generates a warning report and initiates the battery maintenance process after the secondary verification of the early fault.

[0125] Therefore, this embodiment captures any subtle changes in the vehicle battery's operating state by collecting real-time battery data and comparing it with preset standard data to accurately determine whether the vehicle battery has experienced an early fault. After confirming an early fault, it performs secondary verification based on electrochemical impedance spectroscopy and fault detection cloud platforms, further improving the accuracy of fault diagnosis and preventing misdiagnosis. This provides users with more reliable battery status information. This embodiment aims to at least avoid the problem of new energy vehicles failing to warn users of battery faults during charging and discharging, thus ensuring user safety and improving the user experience.

[0126] This embodiment provides an electronic device. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. See also: Figure 5The electronic device 1000 includes a processor 1001 and a memory 1002. The memory 1002 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 1001, the steps in any of the electric vehicle charging and discharging fault diagnosis methods described above are performed. Through the above technical solution, the processor 1001 and the memory 1002 are interconnected and communicate with each other through a communication bus and / or other forms of connection mechanism (not shown). The memory 1002 stores a computer program that can be executed by the processor. When the electronic device 1000 is running, the processor 1001 executes the computer program to perform the electric vehicle charging and discharging fault diagnosis method in any optional implementation of the above embodiments, so as to achieve at least the following functions: real-time acquisition of battery data of the vehicle battery using the battery management system; if the battery data does not meet the conditions of preset standard data, it is determined that the battery data is abnormal; after determining that the battery data is abnormal, an electrochemical impedance spectroscopy (EIS) analysis operation is performed on the vehicle battery to obtain the internal resistance spectrum corresponding to the vehicle battery, and the internal resistance spectrum is analyzed using support vector machine (SVM) to determine whether there is an early fault in the internal resistance spectrum; if there is an early fault in the internal resistance spectrum, the fault type of the early fault is marked, and the early fault and its fault type are uploaded to the fault detection cloud; at least the early fault and fault type are verified by the fault detection cloud based on historical data and environmental factors to perform a secondary verification of the early fault; if the secondary verification of the early fault exists, a warning report is generated and the battery maintenance process is initiated.

[0127] This embodiment provides a computer-readable storage medium storing a computer program. When executed by a processor, the program implements the electric vehicle charging and discharging fault diagnosis method provided in all embodiments of this application: real-time acquisition of battery data from the vehicle battery using a battery management system; if the battery data does not meet the conditions of preset standard data, an abnormality in the battery data is determined; after determining that the battery data is abnormal, an electrochemical impedance spectroscopy (EIS) analysis is performed on the vehicle battery to obtain the corresponding internal resistance spectrum, and a support vector machine (SVM) is used to analyze the internal resistance spectrum to determine whether an early fault exists; if an early fault exists in the internal resistance spectrum, the fault type of the early fault is marked, and the early fault and its fault type are uploaded to the fault detection cloud; at least the early fault and fault type are verified using the fault detection cloud based on historical data and environmental factors to perform a secondary verification of the early fault; if the secondary verification of the early fault exists, a warning report is generated, and the battery maintenance process is initiated.

[0128] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0129] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0130] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0131] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for diagnosing charging and discharging faults in electric vehicles, characterized in that, At least including: The battery management system is used to collect battery data from the vehicle battery in real time. If the battery data does not meet the conditions of the preset standard data, then the battery data is determined to be abnormal; After determining that the battery data is abnormal, an electrochemical impedance spectroscopy (EIS) analysis is performed on the vehicle battery to obtain the internal resistance spectrum corresponding to the vehicle battery, and the internal resistance spectrum is analyzed using a support vector machine (SVM) to determine whether there is an early fault in the internal resistance spectrum. If the internal resistance spectrum shows an early fault, then the fault type of the early fault is marked, and the early fault and its fault type are uploaded to the fault detection cloud. At least the fault detection cloud is used to verify the early faults and fault types based on historical data and environmental factors, so as to perform a secondary verification of the early faults; If the early fault secondary inspection is present, an early warning report is generated and the battery maintenance process is initiated.

2. The electric vehicle charging and discharging fault diagnosis method according to claim 1, characterized in that, After the battery management system is used to collect battery data of the vehicle battery in real time, the method further includes: If the battery data meets the conditions of the preset standard data, it is determined that the battery data is not abnormal, and the battery data of the vehicle battery is collected again using the battery management system until the battery data does not meet the conditions of the preset standard data, at which point it is determined that the battery data is abnormal.

3. The electric vehicle charging and discharging fault diagnosis method according to claim 1, characterized in that, After determining that the battery data is abnormal, performing electrochemical impedance spectroscopy (EIS) analysis on the vehicle battery to obtain the corresponding internal resistance spectrum, and using support vector machine (SVM) analysis on the internal resistance spectrum to determine whether there is an early fault in the internal resistance spectrum, the method further includes: If the internal resistance spectrum does not show the early fault, then the internal resistance spectrum is marked as a temporary fluctuation, and the sampling parameters of the battery management system are adjusted.

4. The electric vehicle charging and discharging fault diagnosis method according to claim 1, characterized in that, The method of at least utilizing the fault detection cloud to verify the early faults and fault types based on historical data and environmental factors, in order to perform a secondary verification of the early faults, further includes: If the early fault secondary check is not available, the early fault is marked as a false alarm.

5. The electric vehicle charging and discharging fault diagnosis method according to claim 1, characterized in that, After determining that the battery data is abnormal, an electrochemical impedance spectroscopy (EIS) analysis is performed on the vehicle battery to obtain the corresponding internal resistance spectrum. Then, a support vector machine (SVM) is used to analyze the internal resistance spectrum to determine if there are any early faults. Specifically, this includes: At least one EIS analysis operation is performed on the vehicle battery to obtain the internal resistance spectrum corresponding to the vehicle battery; The feature information in the internal resistance spectrum is extracted using SVM, and the feature information is standardized to obtain standard feature information. The similarity between the standard feature information and the standard feature state standard in the standard reference library is obtained using SVM. The feature state standard corresponding to the highest similarity is taken to determine whether the early fault exists in the internal resistance spectrum.

6. The electric vehicle charging and discharging fault diagnosis method according to claim 1, characterized in that, If the early fault secondary inspection exists, an early warning report is generated and the battery maintenance process is initiated, specifically including: If the early fault secondary verification exists, then the early warning report is generated; The warning report will be sent to the user and the vehicle manufacturer's monitoring center, and the battery maintenance process will be initiated.

7. The electric vehicle charging and discharging fault diagnosis method according to claim 5, characterized in that, Before using a Support Vector Machine (SVM) to obtain the similarity between the standard feature information and the standard feature state standard in the standard reference library, the method further includes: Collect the internal resistance spectrum of the vehicle battery under at least one operating condition, and perform statistical analysis on the characteristics of the internal resistance spectrum of each battery to at least determine the average value and fluctuation range of the impedance of the vehicle battery data in a specific frequency region, and then establish a standard reference library.

8. A device for diagnosing charging and discharging faults in electric vehicles, characterized in that, At least including: The data acquisition module is used to collect battery data of the vehicle battery in real time using the battery management system; The data comparison module is used to determine that the battery data is abnormal when the battery data does not meet the conditions of the preset standard data. The first diagnostic module is used to perform electrochemical impedance spectroscopy (EIS) analysis on the vehicle battery after determining that the battery data is abnormal, to obtain the internal resistance spectrum corresponding to the vehicle battery, and to use support vector machine (SVM) to analyze the internal resistance spectrum to determine whether there is an early fault in the internal resistance spectrum. The fault upload module is used to mark the fault type of the early fault when the internal resistance spectrum shows the early fault, and upload the early fault and its fault type to the fault detection cloud. The fault verification module is used to verify the early faults and fault types based on historical data and environmental factors using the fault detection cloud at least, so as to perform a secondary verification on the early faults. The fault maintenance module is used to generate an early warning report and initiate the battery maintenance process after the early fault secondary inspection is found.

9. An electronic device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the electric vehicle charging and discharging fault diagnosis method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the electric vehicle charging and discharging fault diagnosis method according to any one of claims 1 to 7.

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