An electric vehicle endurance post-sale diagnosis method and device and vehicle

Abstract

The application discloses a method and device for diagnosing the endurance of an electric vehicle after sale and a vehicle, and the method comprises the following steps: determining endurance influence factors according to a preset endurance problem classification rule; obtaining vehicle operation data and part state data corresponding to a target vehicle according to the endurance influence factors; comparing the vehicle operation data and the part state data with statistical information of the same vehicle model to determine abnormal endurance influence factors; and generating an endurance after-sale diagnosis result of the target vehicle according to the abnormal endurance influence factors. The application realizes accurate diagnosis of endurance problems through big data comparison, improves the efficiency and standardization of the endurance after-sale processing of the electric vehicle, reduces the endurance complaint rate of the user, and guarantees the vehicle experience.

Description

Technical Field

[0001] This invention belongs to the field of new energy vehicle technology, specifically relating to a method, device, and vehicle for after-sales diagnostics of electric vehicle range. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the number of electric vehicles on the road continues to rise, and users are paying increasing attention to vehicle range performance. Range-related complaints have become one of the core issues in after-sales service for new energy vehicles. Currently, the handling of range complaints for electric vehicles is mostly reactive, meaning that after users submit complaints, after-sales personnel address the issue through persuasive language, on-site testing, or retrieving individual vehicle data.

[0003] However, these approaches have significant drawbacks: reassuring statements lack data support, are highly generic, and fail to accurately address users' actual problems, potentially even exacerbating negative emotions; on-site testing is time-consuming and labor-intensive, influenced by factors such as environment and driving habits, making it difficult to reproduce real-world usage scenarios, thus limiting the reliability of test results; single-vehicle data retrieval and analysis are inefficient, relying on staff's personal experience, and cannot identify common problems through batch data mining, enabling only post-event remediation rather than proactive prediction and intervention.

[0004] Furthermore, existing technologies have failed to establish standardized rules for classifying battery life issues and a data comparison system, resulting in vague identification of factors affecting battery life, inaccurate location of abnormal causes, and a lack of uniformity and authority in after-sales diagnostic results. Therefore, there is an urgent need for an after-sales solution that can accurately classify, proactively diagnose, and perform batch analysis of battery life issues to improve the efficiency of handling battery life complaints, reduce user anxiety, and optimize brand reputation.

[0005] Therefore, it is necessary to provide improved technical solutions to overcome the above-mentioned technical problems existing in the prior art. Summary of the Invention

[0006] The purpose of this application is to provide a method, device, and vehicle for after-sales diagnosis of electric vehicle range. By determining range-influencing factors through preset classification rules, and by comparing and analyzing big data statistical information of the same vehicle model, abnormal influencing factors can be accurately located and diagnostic results can be generated, thereby achieving proactive, efficient, and standardized handling of range-related after-sales issues.

[0007] To achieve the above objectives: In a first aspect, embodiments of this application provide a method for after-sales diagnostics of electric vehicle range, including: The range-affecting factors are determined based on the preset range problem classification rules; Based on the range impact factor, obtain the vehicle operation data and component status data corresponding to the target vehicle; The vehicle operation data and component status data are compared with big data statistics of the same model to determine the factors affecting abnormal range. Based on the abnormal range impact factors, the after-sales diagnostic results for the target vehicle's range are generated.

[0008] In one embodiment, the preset range problem classification rules include vehicle energy consumption classification rules and battery state classification rules; determining the range influencing factors according to the preset range problem classification rules includes: Based on the vehicle energy consumption classification rules and battery status classification rules, range impact factors are determined.

[0009] In one embodiment, the range-affecting factors include energy consumption factors and battery factors; the energy consumption factors include driving energy consumption, air conditioning energy consumption, low-voltage energy consumption, parking energy consumption, and abnormal sleep energy consumption; the battery factors include battery state of charge, battery cell state, and battery temperature. The step of obtaining vehicle operation data and component status data corresponding to the target vehicle based on the range impact factor includes: Based on the energy consumption factors and battery factors, the target vehicle's speed, ambient temperature, relative acceleration, energy recovery mode, tire pressure, wiper operating time, proportion of uphill sections, air conditioning temperature difference, window opening ratio, vehicle internal and external circulation ratio, low-voltage accessory power, parking accessory energy consumption, external discharge energy consumption, sentry mode energy consumption, battery health status, battery capacity, and battery state of charge are obtained.

[0010] In one embodiment, comparing the vehicle operation data and component status data with big data statistics of the same vehicle model to determine the abnormal range influencing factors includes: Based on big data statistics of the same vehicle model, establish normal threshold ranges corresponding to the operating data and component status data of each vehicle. If the vehicle operation data or component status data of the target vehicle exceeds the corresponding normal threshold range, the range impact factor corresponding to the data is determined to be an abnormal range impact factor.

[0011] In one embodiment, the method further includes: If all vehicle operation data and component status data of the target vehicle are within the corresponding normal threshold range, then the abnormal range impact factor is determined to be user cognitive bias.

[0012] In one embodiment, after generating the after-sales diagnostic results for the target vehicle's range, the method further includes: Based on the battery life after-sales diagnostic results, push corresponding explanations of the causes, popular science content and optimization suggestions to users; The forms of the push include at least one of in-vehicle interface push, terminal application push, and SMS push.

[0013] In one embodiment, based on the after-sales diagnosis result of the battery life, pushing corresponding cause explanations, popular science content, and optimization suggestions to the user further includes: If the target vehicle is currently in a charging state and the battery charge state usage range before charging is greater than a preset short-term charging threshold, based on the after-sales diagnosis result of the battery life, push corresponding cause explanations, popular science content, and optimization suggestions to the user.

[0014] In a second aspect, an embodiment of the present application provides an after-sales diagnosis device for the battery life of an electric vehicle, including: An impact factor determination module for determining battery life impact factors according to preset battery life problem classification rules; An information acquisition module for acquiring vehicle operation data and component status data corresponding to the target vehicle according to the battery life impact factors; An abnormal factor determination module for comparing the vehicle operation data and component status data with the big data statistical information of the same vehicle model to determine abnormal battery life impact factors; A diagnosis module for generating an after-sales diagnosis result of the battery life of the target vehicle according to the abnormal battery life impact factors.

[0015] In one embodiment, the device further includes a push module for pushing corresponding cause explanations, popular science content, and optimization suggestions to the user based on the after-sales diagnosis result of the battery life.

[0016] In a third aspect, an embodiment of the present application provides a vehicle, including the after-sales diagnosis device for the battery life of an electric vehicle as described in the second aspect.

[0017] The after-sales diagnosis method, device, and vehicle for the battery life of an electric vehicle provided by the embodiments of the present application, by presetting two types of classification rules for vehicle energy consumption and battery status, clarify the specific types of battery life impact factors, and achieve the standardized classification of battery life problems; comprehensively collect vehicle operation and component status data based on battery life impact factors, establish a normal threshold range in combination with the big data statistical information of the same vehicle model, and accurately locate abnormal impact factors through data comparison, avoiding diagnostic deviations caused by relying on manual experience; generate customized diagnosis results for different abnormal impact factors, and actively push cause explanations, popular science content, and optimization suggestions to the user when the push conditions are met, realizing the active intervention and accurate appeasement of after-sales battery life problems; at the same time, common battery life problems can be mined through batch data comparison, providing data support for vehicle product optimization, reducing the occurrence of battery life complaints from the source, and significantly improving the after-sales processing efficiency of electric vehicles and the user's vehicle use experience. Description of the Drawings

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the electric vehicle range after-sales diagnostic method provided in an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the electric vehicle range after-sales diagnostic device provided in an embodiment of the present invention. Detailed Implementation

[0021] 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.

[0022] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0023] It should be understood that although the terms first, second, third, etc., may be used herein to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this document, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if," as used herein, can be interpreted as "when," "when," or "in response to determination." Furthermore, as used herein, the singular forms "a," "an," and "the" are intended to also include the plural forms unless the context indicates otherwise. It should be further understood that the terms "comprising," "including," indicate the presence of the stated feature, step, operation, element, component, item, kind, and / or group, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms "or" and "and / or" as used herein are to be interpreted as inclusive, or mean any one or any combination thereof. Therefore, "A, B, or C" or "A, B, and / or C" means "any one of the following: A; B; C; A and B; A and C; B and C; A, B, and C". Exceptions to this definition will only occur if the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0024] It should be understood that although the steps in the flowcharts of this application's embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0025] It should be noted that step designations such as S110 and S120 are used in this document for the purpose of more clearly and concisely describing the corresponding content, and do not constitute a substantial limitation on the order. In specific implementation, those skilled in the art may execute S120 first and then S110, etc., but these should all be within the protection scope of this application.

[0026] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0027] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0028] First Embodiment Referring to Figure 1, an electric vehicle range after-sales diagnostic method is provided in an embodiment of this application. This method can be executed by the electric vehicle range after-sales diagnostic device provided in this embodiment. The device can be implemented in software and / or hardware. In this embodiment, the electric vehicle range after-sales diagnostic device is taken as the executing entity of the method. The electric vehicle range after-sales diagnostic method provided in this embodiment includes the following steps: Step S110: Determine the battery life influencing factors according to the preset battery life problem classification rules.

[0029] It is understandable that the preset range problem classification rules are based on the core influencing dimensions of electric vehicle range degradation, specifically including vehicle energy consumption classification rules and battery status classification rules. These two types of rules correspond to the two core scenarios of range degradation, ensuring the comprehensiveness and relevance of range influencing factors.

[0030] In one embodiment, the preset range problem classification rules include vehicle energy consumption classification rules and battery state classification rules; determining range influencing factors according to the preset range problem classification rules includes: Based on vehicle energy consumption classification rules and battery status classification rules, range impact factors are determined.

[0031] It is understandable that by using vehicle energy consumption classification rules and battery status classification rules, the complex range problem can be broken down into clear influencing factors, providing clear guidance for subsequent data collection and anomaly detection.

[0032] Step S120: Based on the range impact factor, obtain the vehicle operation data and component status data corresponding to the target vehicle.

[0033] It is understandable that there is a one-to-one correspondence between range-affecting factors and vehicle operation data and component status data. Based on the divided energy consumption factors and battery factors, it is necessary to comprehensively collect relevant data of the target vehicle to ensure that the data covers all key scenarios that affect range.

[0034] In one embodiment, the range-affecting factors include energy consumption factors and battery factors; energy consumption factors include driving energy consumption, air conditioning energy consumption, low-voltage energy consumption, parking energy consumption, and abnormal sleep energy consumption; battery factors include battery state of charge, battery cell state, and battery temperature; based on the range-affecting factors, vehicle operation data and component status data corresponding to the target vehicle are obtained, including: Based on energy consumption factors and battery factors, obtain the target vehicle's speed, ambient temperature, relative acceleration, energy recovery mode, tire pressure, wiper operating time, proportion of uphill sections, air conditioning temperature difference, window opening ratio, vehicle internal and external circulation ratio, low-voltage accessory power, parking accessory energy consumption, external discharge energy consumption, sentry mode energy consumption, battery health status, battery capacity, and battery state of charge.

[0035] Understandably, the specific data collected includes: vehicle speed, relative acceleration, and the proportion of uphill sections related to driving energy consumption; air conditioning temperature difference, window opening ratio, and vehicle internal / external air circulation ratio related to air conditioning energy consumption; low-voltage accessory power related to low-voltage energy consumption; parking accessory energy consumption, external discharge energy consumption, and sentry mode energy consumption related to parking energy consumption; vehicle dormancy state data related to abnormal dormancy energy consumption; battery health status, battery capacity, battery state of charge, and battery temperature related to battery status; and auxiliary data such as ambient temperature, tire pressure, wiper operating time, and energy recovery mode. This data is collected in real time through onboard sensors, battery management system, and vehicle infotainment system, and uploaded to a big data platform for storage and processing.

[0036] Step S130: Compare the vehicle operation data and component status data with big data statistics of the same model to determine the factors affecting abnormal range.

[0037] It is understandable that big data statistics on the same vehicle model are derived from statistical analysis of a large amount of operational and component status data from vehicles of the same model, thus possessing broad representativeness and authority. Based on this statistical information, a normal threshold range is established for each collected data item. This threshold range can reflect the normal data fluctuation range of vehicles of the same model under normal driving scenarios.

[0038] In one embodiment, vehicle operating data and component status data are compared with big data statistics of the same vehicle model to determine abnormal range influencing factors, including: Based on big data statistics of the same vehicle model, normal threshold ranges are established for the vehicle operation data and component status data of each vehicle. If the vehicle operation data or component status data of the target vehicle exceeds the corresponding normal threshold range, the range impact factor corresponding to the data is determined to be an abnormal range impact factor.

[0039] This can be understood as comparing various data points of the target vehicle with their corresponding normal threshold ranges. If any data point exceeds this range, the range impact factor corresponding to that data point is determined to be an abnormal range impact factor. For example, if the tire pressure of the target vehicle is lower than the normal threshold range for the same model, the driving energy consumption factor corresponding to "tire pressure" is determined to be abnormal; if the battery health status is lower than the normal threshold range for the same model, the battery-related factor corresponding to "battery health status" is determined to be abnormal.

[0040] In one embodiment, it further includes: If all vehicle operation data and component status data of the target vehicle are within the corresponding normal threshold range, then the abnormal range impact factor is determined to be user cognitive bias.

[0041] Furthermore, if all vehicle operation data and component status data of the target vehicle are within the corresponding normal threshold range, it indicates that there is no physical cause for abnormal range in the vehicle itself. In this case, the abnormal range influencing factor is determined to be user cognitive bias, that is, there is a difference between the user's expectation of the electric vehicle's range and the actual range performance.

[0042] Step S140: Generate the after-sales diagnostic results for the target vehicle's range based on the abnormal range impact factors.

[0043] It is understandable that customized after-sales diagnostic results for different abnormal battery life factors need to be generated. The diagnostic results should clearly identify the core cause of the abnormal battery life, providing a basis for subsequent user push notifications and after-sales processing.

[0044] For example, if the abnormal influencing factor is "low temperature environment leads to decreased battery activity", the diagnostic results should clearly explain the mechanism by which ambient temperature affects battery range; if the abnormal influencing factor is "aggressive driving leads to increased energy consumption", the diagnostic results should point out the relationship between driving habits and range performance; if the abnormal influencing factor is user cognitive bias, the diagnostic results should clearly state that the vehicle range is not abnormal, and focus on explaining the reasons for the difference between the actual range and the advertised range.

[0045] In one embodiment, after generating the after-sales diagnostic results for the target vehicle's range, the method further includes: Based on the after-sales diagnostic results of the battery range test, corresponding explanations of the causes, popular science content and optimization suggestions will be pushed to users; the push can be in the form of at least one of the following: push through the vehicle interface, push through the terminal application, and push through SMS.

[0046] It is understandable that, in order to proactively intervene in battery range issues and reassure users, relevant content needs to be pushed to users after generating diagnostic results. The push can take the form of at least one of the following: push notifications via the vehicle's infotainment system, push notifications via the terminal application, or push notifications via SMS. Among these, SMS notifications are mainly used to inform users of abnormal battery range alerts, and detailed information is provided to guide users to view through the vehicle's infotainment system or application to avoid information overload.

[0047] Furthermore, the content pushed out needs to be customized based on the diagnostic results: for energy consumption-related abnormal factors, corresponding energy-saving suggestions will be pushed, such as controlling vehicle speed when driving at high speeds, setting the air conditioning temperature appropriately, and adjusting the energy recovery mode; for battery-related abnormal factors, battery maintenance tips will be pushed, such as fully charging and discharging to correct the battery's state of charge and maintaining the temperature of the charging gun in low-temperature environments; for user cognitive biases, popular science content on the range of new energy vehicles will be pushed, such as an introduction to factors affecting range and methods for estimating range in actual driving scenarios.

[0048] In one embodiment, based on the battery life after-sales diagnostic results, the system pushes corresponding explanations of the causes, popular science content, and optimization suggestions to the user, and further includes: If the target vehicle is currently charging, and the battery state of charge usage range before charging is greater than the preset short-term charging threshold, then based on the range after-sales diagnostic results, the user will be pushed the corresponding explanation of the cause, popular science content and optimization suggestions.

[0049] Understandably, to avoid false push notifications caused by short-term charging or short-distance driving, a push notification trigger condition is set: the push operation is only executed when the target vehicle is currently charging and the battery's state of charge usage range before charging is greater than a preset short-term charging threshold (e.g., 60%). This setting ensures the targeting and effectiveness of push notifications, improving the user experience.

[0050] In summary, the implementation method of this application, through standardized classification, multi-dimensional data collection, big data comparison and analysis, and customized diagnosis and push, achieves proactive, accurate and efficient handling of electric vehicle range after-sales issues, solves the defects of existing technologies such as passive response, ambiguous diagnosis and low efficiency, and improves the standardization of after-sales handling and user satisfaction.

[0051] Second Embodiment Based on the first embodiment of this application, an electric vehicle range after-sales diagnostic device is provided in this embodiment, see reference. Figure 2 The device includes: The impact factor determination module 21 is used to determine the range impact factor according to the preset range problem classification rules.

[0052] The information acquisition module 22 is used to acquire vehicle operation data and component status data corresponding to the target vehicle based on the range impact factor.

[0053] The abnormal factor determination module 23 is used to compare vehicle operation data and component status data with big data statistical information of the same model to determine the abnormal range impact factors.

[0054] The diagnostic module 24 is used to generate after-sales diagnostic results for the target vehicle's range based on abnormal range impact factors.

[0055] In one embodiment, the influence factor determination module 21 is further configured to: Based on the vehicle energy consumption classification rules and battery status classification rules, range impact factors are determined.

[0056] In one embodiment, the information acquisition module 22 is further configured to: Based on energy consumption factors and battery factors, obtain the target vehicle's speed, ambient temperature, relative acceleration, energy recovery mode, tire pressure, wiper operating time, proportion of uphill sections, air conditioning temperature difference, window opening ratio, vehicle internal and external circulation ratio, low-voltage accessory power, parking accessory energy consumption, external discharge energy consumption, sentry mode energy consumption, battery health status, battery capacity, and battery state of charge.

[0057] In one embodiment, the anomaly factor determination module 23 is further configured to: Based on big data statistics of the same vehicle model, normal threshold ranges are established for the vehicle operation data and component status data of each vehicle. If the vehicle operation data or component status data of the target vehicle exceeds the corresponding normal threshold range, the range impact factor corresponding to the data is determined to be an abnormal range impact factor.

[0058] In one embodiment, the anomaly factor determination module 23 is further configured to: If all vehicle operation data and component status data of the target vehicle are within the corresponding normal threshold range, then the abnormal range impact factor is determined to be user cognitive bias.

[0059] In one embodiment, the device further includes a push module for pushing corresponding explanations of causes, popular science content, and optimization suggestions to the user based on the after-sales diagnostic results of battery life.

[0060] It should be noted that the description of the device for distributed IoT data storage above is similar to the description of the method for distributed IoT data storage above, and the beneficial effects of the same method will not be repeated. For technical details not disclosed in the device embodiments of the distributed IoT data storage of this invention, please refer to the description of the method embodiments of the distributed IoT data storage of this invention.

[0061] Third Embodiment This application provides a vehicle that includes the electric vehicle range after-sales diagnostic device as described in the second embodiment. By equipping the vehicle with this device, it can proactively diagnose range problems, locate anomalies, and accurately push notifications, significantly improving the efficiency of range-related after-sales processing, reducing the incidence of user range complaints, and optimizing the user's driving experience.

[0062] In this application, the same or similar terms, concepts, technical solutions and / or application scenario descriptions are generally described in detail only when they appear for the first time. When they appear again, they are generally not repeated for the sake of brevity. When understanding the technical solutions and other contents of this application, the same or similar terms, concepts, technical solutions and / or application scenario descriptions that are not described in detail later can be referred to their previous relevant detailed descriptions.

[0063] In this application, the descriptions of the various embodiments have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0064] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0065] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. For those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for after-sales diagnostics of electric vehicle range, characterized in that, The method includes: The range-affecting factors are determined based on the preset range problem classification rules; Based on the range impact factor, obtain the vehicle operation data and component status data corresponding to the target vehicle; The vehicle operation data and component status data are compared with big data statistics of the same model to determine the factors affecting abnormal range. Based on the abnormal range impact factors, the after-sales diagnostic results for the target vehicle's range are generated.

2. The method according to claim 1, characterized in that, The preset range problem classification rules include vehicle energy consumption classification rules and battery status classification rules; the determination of range influencing factors based on the preset range problem classification rules includes: Based on the vehicle energy consumption classification rules and battery status classification rules, range impact factors are determined.

3. The method according to claim 2, characterized in that, The factors affecting driving range include energy consumption factors and battery factors; the energy consumption factors include driving energy consumption, air conditioning energy consumption, low voltage energy consumption, parking energy consumption, and abnormal sleep energy consumption; the battery factors include battery state of charge, battery cell state, and battery temperature. The step of obtaining the vehicle operation data and component status data corresponding to the target vehicle based on the range impact factor includes: Based on the energy consumption factors and battery factors, the target vehicle's speed, ambient temperature, relative acceleration, energy recovery mode, tire pressure, wiper operating time, proportion of uphill sections, air conditioning temperature difference, window opening ratio, vehicle internal and external circulation ratio, low-voltage accessory power, parking accessory energy consumption, external discharge energy consumption, sentry mode energy consumption, battery health status, battery capacity, and battery state of charge are obtained.

4. The method according to claim 3, characterized in that, The step of comparing the vehicle operation data and component status data with big data statistics of the same vehicle model to determine the abnormal range impact factors includes: Based on big data statistics of the same vehicle model, establish normal threshold ranges corresponding to the operating data and component status data of each vehicle. If the vehicle body operation data or component status data of the target vehicle exceeds the corresponding normal threshold range, the range impact factor corresponding to the data is determined to be an abnormal range impact factor.

5. The method according to claim 4, characterized in that, The method further includes: If all vehicle operation data and component status data of the target vehicle are within the corresponding normal threshold range, then the abnormal range impact factor is determined to be user cognitive bias.

6. The method according to claim 1, characterized in that, After obtaining the after-sales diagnostic results for the target vehicle's range based on the abnormal range impact factor, the method further includes: Based on the battery life after-sales diagnostic results, push corresponding explanations of the causes, popular science content and optimization suggestions to users; The push notifications can take at least one of the following forms: vehicle infotainment interface push notifications, terminal application push notifications, and SMS push notifications.

7. The method according to claim 6, characterized in that, The method of pushing corresponding explanations, popular science content, and optimization suggestions to users based on the battery life after-sales diagnostic results also includes: If the target vehicle is currently charging, and the battery state of charge usage range before charging is greater than the preset short-term charging threshold, then based on the range after-sales diagnostic results, the user will be pushed with corresponding explanations of the reasons, popular science content, and optimization suggestions.

8. An electric vehicle range after-sales diagnostic device, characterized in that, The device includes: The impact factor determination module is used to determine the range impact factor according to the preset range problem classification rules; The information acquisition module is used to acquire the vehicle body operation data and component status data corresponding to the target vehicle based on the range influencing factor. The abnormal factor determination module is used to compare the vehicle operation data and component status data with big data statistics of the same vehicle model to determine the abnormal range impact factors. The diagnostic module is used to generate after-sales diagnostic results for the target vehicle's range based on the abnormal range impact factors.

9. The apparatus according to claim 8, characterized in that, The device also includes a push module, which is used to push corresponding explanations of the causes, popular science content and optimization suggestions to the user based on the battery life after-sales diagnostic results.

10. A vehicle, characterized in that, Includes the electric vehicle range after-sales diagnostic device as described in any one of claims 8-9.

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