Effective charging judgment method, system and device for new energy automobile and electronic equipment

Abstract

The invention discloses an effective charging judgment method, system and device for a new energy automobile and electronic equipment, and the method comprises the steps: initializing and loading an effective charging judgment rule and a threshold parameter, and obtaining the multi-dimensional charging data of the automobile in real time, the multi-dimensional charging data at least comprises charging duration, charging electric quantity, power change, battery temperature, environment temperature and charging type; carrying out filtering processing on the multi-dimensional charging data based on a judgment rule to remove the short-time charging data and obtain filtered multi-dimensional charging data; and for the corresponding charging types, multi-parameter association analysis is performed on the filtered multi-dimensional charging data based on a decision rule and a threshold parameter, so that effective charging data in the filtered multi-dimensional charging data can be determined, and the charging types comprise a fast charging type and a slow charging type. According to the method, the charging effectiveness of the new energy vehicle can be judged based on the multi-dimensional data, and the accuracy of effective charging identification is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent charging management for new energy vehicles, and in particular to a method, system, device, and electronic device for determining effective charging of new energy vehicles. Background Technology

[0002] Currently, most methods for statistically analyzing the charging of new energy vehicles rely on a single parameter (such as state of charging) to determine charging effectiveness. This method suffers from inaccurate judgments in practical applications, leading not only to insufficient data validity but also hindering the implementation of battery health management strategies and preventing comprehensive and precise monitoring of battery performance under different charging environments. Summary of the Invention

[0003] In view of the above problems, this application provides a method for determining the effective charging of new energy vehicles, which can determine the effectiveness of charging of new energy vehicles based on multi-dimensional data, improve the accuracy of effective charging identification, and reduce the false and false judgment rates.

[0004] Firstly, this application provides a method for determining effective charging of new energy vehicles. The method includes: initializing and loading effective charging determination rules and threshold parameters, and acquiring multi-dimensional charging data of the vehicle in real time. The multi-dimensional charging data includes at least charging duration, charging capacity, power change, battery temperature, ambient temperature, and charging type. Based on the determination rules, the multi-dimensional charging data is filtered to remove short-term charging data, resulting in filtered multi-dimensional charging data. For the corresponding charging type, multi-parameter correlation analysis is performed on the filtered multi-dimensional charging data based on the determination rules and threshold parameters to determine the effective charging data in the filtered multi-dimensional charging data. The charging type includes fast charging and slow charging.

[0005] In the technical solution of this application embodiment, the determination rules and threshold parameters for effective charging are first initialized and loaded, and multi-dimensional charging data of the vehicle are acquired in real time. Then, the multi-dimensional charging data is filtered based on the determination rules to remove short-term charging data, resulting in filtered multi-dimensional charging data. Then, for the corresponding charging type, multi-parameter correlation analysis is performed on the filtered multi-dimensional charging data based on the determination rules and threshold parameters to determine the effective charging data in the filtered multi-dimensional charging data. This enables the determination of the effectiveness of new energy vehicle charging based on multi-dimensional data, improves the accuracy of effective charging identification, and reduces the false positive and false negative rates.

[0006] In some embodiments, the threshold parameters include a first charging duration threshold and / or a first charging capacity threshold corresponding to the fast charging mode. The determination rule indicates the rules for determining valid charging data based on the first charging duration threshold and / or the first charging capacity threshold. For the corresponding charging type, multi-parameter correlation analysis is performed on the filtered multi-dimensional charging data based on the determination rule and the threshold parameters to determine the valid charging data in the filtered multi-dimensional charging data, including: for the fast charging type, if the charging duration is greater than or equal to the first charging duration threshold, the filtered multi-dimensional charging data is determined to be valid charging data; and / or for the fast charging type, if the charging capacity is greater than or equal to the first charging capacity threshold, the filtered multi-dimensional charging data is determined to be valid charging data.

[0007] In some embodiments, the threshold parameters include a second charging duration threshold and / or a second charging capacity threshold corresponding to the slow charging mode. The determination rule indicates the rules for determining valid charging data based on the second charging duration threshold and / or the second charging capacity threshold. For the corresponding charging type, multi-parameter correlation analysis is performed on the filtered multi-dimensional charging data based on the determination rule and the threshold parameters to determine the valid charging data in the filtered multi-dimensional charging data, including: for the slow charging type, if the charging duration is greater than or equal to the second charging capacity threshold, the filtered multi-dimensional charging data is determined to be valid charging data; and / or for the slow charging type, if the charging capacity is greater than or equal to the second charging capacity threshold, the filtered multi-dimensional charging data is determined to be valid charging data.

[0008] In some embodiments, filtering of multidimensional charging data based on judgment rules to remove short-term charging data includes: filtering short-term charging data in multidimensional charging data based on judgment rules when power change, battery temperature, and ambient temperature meet short-term charging thresholds; wherein, short-term charging data characterizes abnormal charging behavior.

[0009] In some embodiments, the method further includes: storing valid charging data and short-term charging data respectively, and marking the short-term charging data to statistically analyze the short-term charging frequency; and obtaining charging statistics data and charging analysis results based on the stored valid charging data and short-term charging data.

[0010] In some embodiments, the method further includes: providing users with charging statistics and charging analysis results in real time based on a mobile app or in-vehicle HMI.

[0011] In some embodiments, the method further includes: dynamically adjusting a threshold parameter based on multidimensional charging data collected over a preset time; wherein the multidimensional charging data collected over a preset time characterizes changes in the user's charging behavior.

[0012] On the other hand, this application provides an effective charging determination system for new energy vehicles, which is used to perform the steps of the method in any of the above embodiments.

[0013] On the other hand, this application provides an effective charging determination device for new energy vehicles. The device includes: a data acquisition module, used to initialize and load the determination rules and threshold parameters for effective charging, and to acquire multi-dimensional charging data of the vehicle in real time, wherein the multi-dimensional charging data includes at least charging duration, charging capacity, power change, battery temperature, ambient temperature, and charging type; a data filtering module, used to filter the multi-dimensional charging data based on the determination rules to remove short-term charging data and obtain filtered multi-dimensional charging data; and a data analysis module, used to perform multi-parameter correlation analysis on the filtered multi-dimensional charging data based on the determination rules and threshold parameters for the corresponding charging type to determine the effective charging data in the filtered multi-dimensional charging data, wherein the charging type includes fast charging type and slow charging type.

[0014] On the other hand, this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method of any of the above embodiments.

[0015] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart of the effective charging determination method for new energy vehicles according to an embodiment of this application is shown; Figure 2A and Figure 2B This invention illustrates a functional diagram of an effective charging determination system for new energy vehicles according to an embodiment of this application. Figure 3 A block diagram of an effective charging determination device for new energy vehicles according to an embodiment of this application is shown; Figure 4 A schematic diagram of an electronic device according to an embodiment of this application is shown. Detailed Implementation

[0017] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0019] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] In the description of the embodiments in this application, the term "and / or" 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 document generally indicates that the preceding and following related objects have an "or" relationship.

[0022] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0023] In the description of the embodiments of this application, the technical terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0024] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0025] Currently, most methods for statistically analyzing the charging of new energy vehicles rely on a single parameter (such as charging status) to determine charging effectiveness. This method suffers from inaccuracies in practical applications, failing to consider crucial factors such as charging duration and charge amount, and cannot identify invalid charging behavior. This not only leads to insufficient data validity but also hinders the implementation of battery health management strategies, making it impossible to comprehensively and precisely monitor battery performance under different charging modes and environments.

[0026] Existing technical solutions primarily use changes in charging signals to determine the charging duration and number of charging cycles for electric vehicles. Specifically, this technical approach includes the following aspects: ① Charging signal monitoring: The charging management system determines the start and end of charging by monitoring changes in the charging signal in real time. It records these real-time changes to facilitate analysis of the charging process.

[0027] ② Calculation of charging time: The system calculates the charging time based on the duration of changes in the charging signal.

[0028] ③Statistics on the number of charging cycles: The system counts the number of charging cycles by monitoring changes in the charging signal. When the system detects that the charging signal has changed from a non-charging state to a charging state, the charging cycle is incremented by one; conversely, when the signal changes back to a non-charging state, the charging process ends, and the charging cycle is recorded.

[0029] In view of this, this application proposes a method for determining the effective charging of new energy vehicles, which can determine the effectiveness of charging of new energy vehicles based on multi-dimensional data, improve the accuracy of effective charging identification, and reduce the false and false judgment rates.

[0030] In the technical solution of this application embodiment, the determination rules and threshold parameters for effective charging are first initialized and loaded, and multi-dimensional charging data of the vehicle are acquired in real time. Then, the multi-dimensional charging data is filtered based on the determination rules to remove short-term charging data, resulting in filtered multi-dimensional charging data. Then, for the corresponding charging type, multi-parameter correlation analysis is performed on the filtered multi-dimensional charging data based on the determination rules and threshold parameters to determine the effective charging data in the filtered multi-dimensional charging data. This enables the determination of the effectiveness of new energy vehicle charging based on multi-dimensional data, improves the accuracy of effective charging identification, and reduces the false positive and false negative rates.

[0031] Figure 1 A flowchart illustrating the effective charging determination method for new energy vehicles according to an embodiment of this application is shown.

[0032] like Figure 1 As shown, the effective charging determination method 100 for new energy vehicles provided in this application includes steps S110 to S130.

[0033] Step S110: Initialize and load the determination rules and threshold parameters for valid charging, and acquire multi-dimensional charging data of the vehicle in real time. The multi-dimensional charging data includes at least charging time, charging amount, power change, battery temperature, ambient temperature and charging type.

[0034] For example, the threshold parameter may include charging-related parameters set based on the data type of the collected data, such as charging duration and charging capacity. The determination rule is a relevant rule for valid charging indicated by the set threshold parameter. Before performing charging data analysis, the determination rule and threshold parameter for valid charging can be initialized and set first, and then multi-dimensional charging data of the vehicle can be acquired in real time. For example, raw data related to the vehicle charging period can be collected, which may include charging duration, charging capacity, power change, battery temperature, ambient temperature, charging type, vehicle status and other data.

[0035] Step S120: Filter the multidimensional charging data based on the judgment rules to remove short-term charging data and obtain filtered multidimensional charging data.

[0036] For example, short-term charging data may include abnormal charging data that indicates invalid charging, such as short charging time or abnormal power changes during charging. Based on the judgment rules set by relevant parameters, the multidimensional charging data is filtered to remove short-term charging data, thereby cleaning and preprocessing the multidimensional charging data, and then the processed data is further analyzed.

[0037] Step S130: For the corresponding charging type, perform multi-parameter correlation analysis on the filtered multi-dimensional charging data based on the judgment rules and threshold parameters to determine the valid charging data in the filtered multi-dimensional charging data, wherein the charging type includes fast charging type and slow charging type.

[0038] For example, different judgment rules and threshold parameters for effective charging are set for different charging characteristics of fast charging and slow charging types. Based on this, multi-parameter correlation analysis is performed on the filtered multi-dimensional charging data to ensure that the judgment method is adapted to different charging modes. For example, multi-dimensional analysis can be performed based on charging time, charging power, and charging amount to identify and determine the effective charging data and improve the accuracy of charging effectiveness analysis.

[0039] In the technical solution of this application embodiment, the determination rules and threshold parameters for effective charging are first initialized and loaded, and multi-dimensional charging data of the vehicle are acquired in real time. Then, the multi-dimensional charging data is filtered based on the determination rules to remove short-term charging data, resulting in filtered multi-dimensional charging data. Then, for the corresponding charging type, multi-parameter correlation analysis is performed on the filtered multi-dimensional charging data based on the determination rules and threshold parameters to determine the effective charging data in the filtered multi-dimensional charging data. This enables the determination of the effectiveness of new energy vehicle charging based on multi-dimensional data, improves the accuracy of effective charging identification, and reduces the false positive and false negative rates.

[0040] In one example, the effective charging determination method for new energy vehicles of this application is implemented based on an effective charging determination system for new energy vehicles. This system can filter short-term charging data from real-time collected multi-dimensional charging-related data, identify effective charging data through a combination of multiple determination rules for statistical analysis, and communicate with external systems to provide real-time and historical charging statistics query functions, thereby improving the accuracy of charging effectiveness determination and realizing multiple business functions, which are described in detail below.

[0041] Figure 2A and Figure 2B A functional schematic diagram of the effective charging determination system for new energy vehicles according to an embodiment of this application is shown.

[0042] like Figure 2AAs shown, the effective charging determination system for new energy vehicles collects real-time raw data (multi-dimensional charging data) related to the charging of new energy vehicles at charging piles by receiving sensor data. Multi-dimensional charging data may include charging amount, charging time, power, ambient temperature, vehicle status, charging interface type (fast charging or slow charging), battery temperature, etc. Then, the collected data is sequentially received, processed (e.g., cleaning and preprocessing the raw data, and determining its validity), and stored (e.g., saving the results of the determination of the validity of the charging data). Data communication is then carried out with external systems through data interfaces, such as by connecting to a mobile APP or vehicle HMI to provide charging management information to the owner (user), or by providing data analysis and charging statistics results to battery R&D engineers through a cloud platform.

[0043] like Figure 2B As shown, the effective charging determination system for new energy vehicles can determine effective charging through several functional modules. For example, it primarily receives sensor data (multi-dimensional charging data, i.e., charging information) in real time through the BMS (Battery Management System) and transmits the collected data to the TBOX (On-Board Optimizer) via a gateway through the CAN bus. The TBOX then uploads the charging information to the data platform via a private network. The data platform (which may include a data receiving module, a data processing module, a data storage module, and an interface module) receives the raw data, cleans and preprocesses it, determines the validity of the filtered data, classifies charging behavior according to the determination rules, and saves the final determination results. The saved data (charging statistics) can be provided to users through the interface module by connecting to an external system. For example, the charging statistics results can be provided to users via a mobile app or vehicle system over a public network to enable user data access. The following details the function of each module in the effective charging determination system for new energy vehicles.

[0044] Battery Data Acquisition Module (BMS) Function: Real-time collection of raw data related to vehicle charging (multi-dimensional charging data), including charging capacity, charging time, power, ambient temperature, vehicle status, charging interface type (fast charging or slow charging), battery temperature, etc.

[0045] Data flow: After being cleaned and preprocessed, the collected raw data is transmitted to the data processing module (in the data platform).

[0046] Data upload module (TBOX) Function: Receives multi-dimensional charging data in real time from the battery data acquisition module during each charging period of the vehicle, including charging time, charging capacity, power change, battery temperature, ambient temperature, and charging status.

[0047] Data flow: Data is encrypted and uploaded to the cloud via the Internet of Vehicles (IoV) system to ensure privacy and security.

[0048] Data receiving module Function: Receives uploaded encrypted data and performs basic verification and decoding to ensure that the data is not lost or damaged.

[0049] Data flow: Successfully decoded data is passed to the data processing module.

[0050] Data processing module Function: Performs multi-parameter correlation analysis on the raw data transmitted by the data acquisition module (battery data acquisition module), and determines the validity of charging events by integrating various judgment rules, such as charging time and cumulative charging amount. For fast charging and slow charging modes, different judgment criteria are set according to power and power parameters to ensure the adaptability and accuracy of the algorithm.

[0051] Data flow: The judgment result is transmitted to the data storage module (in the data platform) for subsequent management.

[0052] Data storage module Function: Stores valid charging data for historical retrieval, charging statistics, and performance analysis. This module also supports integration with a Battery Management System (BMS), providing fundamental data support for battery health management.

[0053] Data flow: Data can be output to the car owner's terminal or the backend cloud via the interface module.

[0054] Interface module Function: Responsible for data communication with external systems (such as APP, cloud platform), providing real-time and historical charging statistics query functions. The module supports access to in-vehicle HMI and user APP, making it convenient for users to view and manage charging status.

[0055] Data flow: Presenting charging statistics and analysis results to R&D engineers and users.

[0056] Next, we will describe in detail how to achieve effective charging determination based on the various functional modules of the effective charging determination system for new energy vehicles.

[0057] For example, the loading judgment rules and threshold parameters can be initialized first, and relevant multi-dimensional charging data can be captured periodically by the data acquisition module and transmitted to the data processing module. The multi-dimensional charging data is filtered based on the judgment rules to remove short-term charging data. For example, based on the judgment rules, short-term charging data in the multi-dimensional charging data is filtered when the power change, battery temperature, and ambient temperature meet the short-term charging threshold. Here, short-term charging data represents abnormal charging behavior.

[0058] Specifically, short-term charging data refers to invalid charging data. The short-term charging threshold can be a critical value set based on multi-dimensional data such as power changes, battery temperature, and ambient temperature during charging to meet abnormal charging behaviors such as short-term plugging and unplugging, unexpected interruption, and abnormal power fluctuation. The data processing module can perform preliminary filtering on the collected raw charging data (i.e., multi-dimensional charging data) according to the judgment rules (filtering rules) set by the short-term charging threshold to identify and filter short-term charging events, so as to avoid invalid data from interfering with the effective judgment and statistics of charging, thereby providing more realistic and reliable data support.

[0059] In the technical solution of this application embodiment, based on the judgment rules, when the power change, battery temperature, and ambient temperature meet the short-time charging threshold, the short-time charging data in the multi-dimensional charging data is filtered, thereby identifying and filtering abnormal events such as short-time charging. By monitoring and analyzing charging events in real time, abnormal charging behavior is incorporated into the judgment system to avoid interference from invalid data on valid charging judgment and subsequent statistics, providing a more accurate data foundation for battery management and subsequent performance evaluation.

[0060] For example, the threshold parameters include a first charging time threshold or a first charging capacity threshold corresponding to the fast charging mode, and the judgment rule indicates the rule for determining valid charging data based on the first charging time threshold or the first charging capacity threshold. For the corresponding charging type, multi-parameter correlation analysis is performed on the filtered multi-dimensional charging data based on the judgment rule and the threshold parameters to determine the valid charging data in the filtered multi-dimensional charging data. For example, for the fast charging type, if the charging time is greater than or equal to the first charging time threshold, the filtered multi-dimensional charging data is determined to be valid charging data; or for the fast charging type, if the charging capacity is greater than or equal to the first charging capacity threshold, the filtered multi-dimensional charging data is determined to be valid charging data.

[0061] Specifically, for the cleaned and filtered multi-dimensional charging data, the data processing module can determine the charging type based on the collected charging type data. For different charging types, multi-dimensional analysis is performed by combining charging capacity, charging power, and charging duration to identify valid charging behaviors. For example, for fast charging, the corresponding parameter calculation path can be entered based on the initialized judgment rules and threshold parameters to perform valid charging analysis based on charging duration and charging capacity. For example, the charging duration can be calculated based on the time difference between the start and end signals of charging; the charging capacity can be calculated by accumulating changes in current and voltage values ​​using the calculus integration method, which can ensure the accuracy of the charging data. Based on the calculation results, the judgment module can determine the valid charging judgment conditions according to different charging modes (fast charging or slow charging). In fast charging mode, if the charging duration is less than 1 minute (first charging duration threshold) or the charging capacity is less than 1 kWh (first charging capacity threshold), the charging behavior is judged as invalid; otherwise, it is considered valid charging.

[0062] For example, the threshold parameters include a second charging duration threshold or a second charging capacity threshold corresponding to the slow charging mode. The judgment rule indicates the rules for determining valid charging data based on the second charging duration threshold or the second charging capacity threshold. For the corresponding charging type, multi-parameter correlation analysis is performed on the filtered multi-dimensional charging data based on the judgment rule and the threshold parameters to determine the valid charging data in the filtered multi-dimensional charging data. For example, for the slow charging type, if the charging duration is greater than or equal to the second charging capacity threshold, the filtered multi-dimensional charging data is determined to be valid charging data; or for the slow charging type, if the charging capacity is greater than or equal to the second charging capacity threshold, the filtered multi-dimensional charging data is determined to be valid charging data.

[0063] Specifically, the charging type is first determined to be slow charging based on the collected charging type data. Based on the initial loading judgment rules and threshold parameters, for slow charging, if the charging time is less than 3 minutes (second charging time threshold) or the charging amount is less than 0.5 kWh (second charging amount threshold), the charging behavior is determined to be invalid; otherwise, it is valid charging.

[0064] In the technical solution of this application embodiment, for fast charging, when the charging time is greater than or equal to a first charging time threshold, or when the charging amount is greater than or equal to a first charging amount threshold, the filtered multi-dimensional charging data is determined to be valid charging data. For slow charging, when the charging time is greater than or equal to a second charging amount threshold, or when the charging amount is greater than or equal to a second charging amount threshold, the filtered multi-dimensional charging data is determined to be valid charging data. This can further filter invalid charging data, thereby refining the charging judgment and statistical parameters to different charging scenarios. By combining the characteristics of fast charging and slow charging to set corresponding judgment criteria, the method is ensured to adapt to different charging modes, improving the accuracy of charging effectiveness analysis. At the same time, by using multiple parameters for comprehensive analysis, a more comprehensive charging effectiveness assessment is provided, which can more accurately reflect the actual charging situation of electric vehicles and form a comprehensive charging behavior feature library.

[0065] For example, based on the judgment result, valid charging data and invalid charging data can be recorded and statistically analyzed separately. For example, firstly, valid charging data and short-term charging data are stored separately, and short-term charging data is marked to statistically analyze the short-term charging frequency; then, based on the stored valid charging data and short-term charging data, charging statistics and charging analysis results are obtained.

[0066] Specifically, effective charging behavior (corresponding data) can be recorded. The recorded content can include charging mode (fast charging or slow charging), battery type, charging duration, charging capacity, and SOC (suspension charge rate) variation range (initial SOC value and final SOC value). This data is used for statistical analysis of charging behavior to obtain statistical data and effectiveness judgment results for effective charging. For example, effective charging data can be stored in a data storage module for historical query, charging statistics, and performance analysis. For ineffective charging, ineffective charging data (short-term charging data) is marked and recorded. The frequency and characteristics of ineffective charging are statistically analyzed to obtain corresponding statistical data and analysis results of ineffective charging characteristics, so as to improve charging behavior in the future.

[0067] In the technical solution of this application embodiment, effective charging data and short-term charging data are stored separately, the short-term charging data is marked, and the short-term charging frequency and corresponding characteristics are statistically analyzed to obtain charging statistics data and charging analysis results, so as to facilitate historical query, charging statistics and performance analysis, and provide basic data support for battery health management.

[0068] For example, the stored data can also be provided to users through an interface module, such as a mobile app or in-vehicle HMI, to provide users with charging statistics and charging analysis results in real time.

[0069] Specifically, the interface module of the effective charging determination system for new energy vehicles can be connected to the in-vehicle HMI, user APP, or other cloud platforms to provide users with real-time and historical charging statistics query functions. This allows charging statistics and charging analysis results to be presented to users or R&D engineers, enabling real-time communication with users and providing transparency, effectiveness judgment, and adjustment suggestions regarding charging status. The real-time user interaction and feedback mechanism allows users to have a deeper understanding of the charging process, improves user participation, and enhances the practicality of new energy vehicle charging management.

[0070] For example, the threshold parameters can be dynamically adjusted based on subsequent charging behavior to improve the accuracy of the judgment. For instance, the threshold parameters can be dynamically adjusted based on multi-dimensional charging data collected over a preset time period. The multi-dimensional charging data collected over a preset time period characterizes changes in the user's charging behavior. That is, after determining the charging validity based on the initially loaded threshold parameters, the user's charging habits can be understood by collecting multi-dimensional charging data over a period of time, and the threshold parameters can be continuously adjusted based on this to improve the accuracy of the validity judgment.

[0071] In the technical solution of this application embodiment, the threshold parameter is dynamically adjusted based on the multi-dimensional charging data collected over a preset time, thereby improving the accuracy of the charging effectiveness judgment based on changes in user charging habits.

[0072] In one example, the process of real-time multi-dimensional charging data acquisition and charging effectiveness determination based on the effective charging determination system for new energy vehicles is as follows: (1) BMS signal transmission and TBOX upload The vehicle's battery management system (BMS) is responsible for collecting charging data in real time, including charging mode (fast charging or slow charging), SOC (state of charge), current, voltage, and battery type.

[0073] (2) The BMS transmits the data to the TBOX, which then uploads it to the cloud big data platform (data platform) for subsequent charging analysis.

[0074] (3) Data platform reception and preliminary analysis After receiving the uploaded data, the data platform first determines the charging mode (fast charging or slow charging) and then enters the corresponding calculation path to analyze the charging time and charging capacity respectively.

[0075] (4) Calculation of charging time and charging capacity Charging duration calculation: The charging duration is calculated based on the time difference between the start and end signals of charging.

[0076] Power calculation: Based on the calculus integration method, the charging power is calculated by accumulating changes in current and voltage values ​​to ensure the accuracy of charging data.

[0077] (5) Invalid charging judgment At the end of charging, the data platform determines the effectiveness of the charging based on a fixed threshold standard (threshold parameter): Slow charging: If the charging time is less than 3 minutes or the charging amount is less than 0.5 kWh, the charging behavior is considered invalid.

[0078] Fast charging: If the charging time is less than 1 minute or the charging amount is less than 1 kilowatt-hour, the charging behavior is considered invalid.

[0079] (6) Records and statistics of effective and ineffective charging Effective charging: Record effective charging behavior, including charging mode (fast charging or slow charging), battery type, charging time, charging capacity, and SOC change range (starting and ending SOC values), for subsequent statistical analysis.

[0080] Invalid charging: Mark and record invalid charging, and statistically analyze the frequency and characteristics of invalid charging so that it can be used to improve charging behavior in future analysis.

[0081] In another example, a dynamic calculation algorithm can be used to set an initial threshold parameter, then determine the validity of the charging data and record it. The relevant threshold parameter can be dynamically adjusted based on subsequent charging behavior. The following are pseudocode examples and statistical result examples: Pseudocode example: 1. # Define threshold 2. slow_charge_time_threshold=3 # Slow charging time threshold (minutes) 3. slow_charge_energy_threshold=0.5 # Slow charging energy threshold (kWh) 4. fast_charge_time_threshold=1 # Fast charging time threshold (minutes) 5. fast_charge_energy_threshold=1 # Fast charging energy threshold (kWh) 6.def process_charge_event(charge_mode,charge_time,charge_energy,battery_type,soc_start,soc_end): 7. # Determine the effectiveness of charging 8. if charge_mode=="slow": 9.is_valid=charge_time>=slow_charge_time_threshold and charge_energy>= slow_charge_energy_threshold 10.else: 11.is_valid=charge_time>=fast_charge_time_threshold and charge_energy>= fast_charge_energy_threshold 12. Record valid and invalid charging. 13. if is_valid: 14.log_valid_charge(charge_mode, battery_type, charge_time, charge_energy, soc_start, soc_end) 15.else: 16.log_invalid_charge(charge_mode,battery_type,charge_time,charge_energy) 17. #Example Recording Function 18.def log_valid_charge(charge_mode,battery_type,charge_time,charge_energy,soc_start,soc_end): 19. #Record effective charging behavior 20.print("Valid charging record:",charge_mode,battery_type,charge_time,charge_energy,soc_start,soc_end) 21.def log_invalid_charge(charge_mode,battery_type,charge_time,charge_energy): 22. #Record invalid charging behavior 23.print("Invalid charging record:",charge_mode,battery_type,charge_time,charge_energy) Statistical example: Recording time: 2024-10-29 15:35:00 Charging mode: Fast charging Battery type: Ternary lithium battery Charging time: 25 minutes Charging capacity: 10.2 kWh Initial SOC: 20% SOC completion: 80% SOC variation range: 20%-80% Starting charging voltage: 300V Charging end voltage: 400V Charging current: 60A (average) Charging effectiveness: Effective Note: If the fast charging threshold is met, it will be recorded as a valid charge. This application provides an effective charging determination system for new energy vehicles, which is used to execute the steps of the method in any of the above embodiments.

[0082] Figure 3 A block diagram of an effective charging determination device for a new energy vehicle according to an embodiment of this application is shown.

[0083] like Figure 3 As shown, this application provides an effective charging determination device 300 for new energy vehicles. The device 300 includes: The data acquisition module 310 is used to initialize and load the determination rules and threshold parameters for effective charging, and to acquire multi-dimensional charging data of the vehicle in real time. The multi-dimensional charging data includes at least charging time, charging amount, power change, battery temperature, ambient temperature and charging type.

[0084] The data filtering module 320 is used to filter multidimensional charging data based on judgment rules to remove short-term charging data and obtain filtered multidimensional charging data.

[0085] The data analysis module 330 is used to perform multi-parameter correlation analysis on the filtered multi-dimensional charging data based on the judgment rules and threshold parameters for the corresponding charging type, so as to determine the effective charging data in the filtered multi-dimensional charging data. The charging type includes fast charging type and slow charging type.

[0086] For example, the threshold parameters include a first charging time threshold corresponding to the fast charging mode and / or a first charging capacity threshold corresponding to the fast charging mode, and the determination rule indicates the rule for determining valid charging data based on the first charging time threshold and / or the first charging capacity threshold; the data analysis module 330 is further configured to: for the fast charging type, determine the filtered multidimensional charging data as valid charging data when the charging time is greater than or equal to the first charging time threshold; and / or for the fast charging type, determine the filtered multidimensional charging data as valid charging data when the charging capacity is greater than or equal to the first charging capacity threshold.

[0087] For example, the threshold parameters include a second charging duration threshold and / or a second charging capacity threshold corresponding to the slow charging mode, and the determination rule indicates the rule for determining valid charging data based on the second charging duration threshold and / or the second charging capacity threshold; the data analysis module 330 is further configured to: for the slow charging type, determine the filtered multidimensional charging data as valid charging data when the charging duration is greater than or equal to the second charging capacity threshold; and / or for the slow charging type, determine the filtered multidimensional charging data as valid charging data when the charging capacity is greater than or equal to the second charging capacity threshold.

[0088] For example, the data filtering module 320 is further configured to: filter short-time charging data in multi-dimensional charging data based on judgment rules, provided that the power change, battery temperature, and ambient temperature meet the short-time charging threshold; wherein, the short-time charging data characterizes abnormal charging behavior.

[0089] For example, the effective charging determination device 300 for new energy vehicles further includes a storage module, used for: storing effective charging data and short-term charging data respectively, and marking the short-term charging data to count the short-term charging frequency; and obtaining charging statistics data and charging analysis results based on the stored effective charging data and short-term charging data.

[0090] For example, the effective charging determination device 300 for new energy vehicles also includes a data communication module for providing users with charging statistics and charging analysis results in real time based on a mobile APP or vehicle HMI.

[0091] For example, the effective charging determination device 300 for new energy vehicles also includes a threshold parameter adjustment module, which is used to: dynamically adjust the threshold parameter based on multi-dimensional charging data collected over a preset time; wherein the multi-dimensional charging data collected over a preset time represents changes in the user's charging behavior.

[0092] Figure 4 A schematic diagram of an electronic device according to an embodiment of this application is shown.

[0093] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method in any of the above embodiments.

[0094] like Figure 4 As shown, for ease of understanding, embodiments of this application illustrate a specific electronic device 400.

[0095] Electronic device 400 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 400 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0096] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. The RAM 403 may also store various programs and data required for the operation of the electronic device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0097] Multiple components in electronic device 400 are connected to I / O interface 405. These components include: input unit 406, such as a keyboard or mouse; output unit 407, such as various types of displays or speakers; storage unit 408, such as a disk or optical disk; and communication unit 409, such as a network interface card (NIC), modem, or wireless transceiver. Communication unit 409 allows electronic device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0098] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods described above. For example, in some embodiments, any one or more of the methods described above can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of any one or more of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 401 can be configured to perform any one or more of the methods described above by any other suitable means (e.g., by means of firmware).

[0099] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this application, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0100] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application 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 therein. 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 this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. An effective charging determination method for a new energy vehicle, characterized by comprising: The method comprises: initializing and loading the determination rule and threshold parameter of valid charging, and acquiring multi-dimensional charging data of the vehicle in real time, wherein the multi-dimensional charging data at least includes charging duration, charging power, power change, battery temperature, ambient temperature and charging type; filtering the multi-dimensional charging data based on the determination rule to remove short-time charging data, to obtain filtered multi-dimensional charging data; for the corresponding charging type, performing multi-parameter correlation analysis on the filtered multi-dimensional charging data based on the determination rule and the threshold parameter, to determine the valid charging data in the filtered multi-dimensional charging data, wherein the charging type includes fast charging type and slow charging type.

2. The method according to claim 1, wherein The threshold parameter includes a first charging duration threshold corresponding to the fast charging mode and / or a first charging power threshold corresponding to the fast charging mode, and the determination rule indicates the rule for determining the valid charging data based on the first charging duration threshold and / or the first charging power threshold; for the corresponding charging type, performing multi-parameter correlation analysis on the filtered multi-dimensional charging data based on the determination rule and the threshold parameter, to determine the valid charging data in the filtered multi-dimensional charging data, comprising: for the fast charging type, in the case that the charging duration is greater than or equal to the first charging duration threshold, determining that the filtered multi-dimensional charging data is the valid charging data; and / or for the fast charging type, in the case that the charging power is greater than or equal to the first charging power threshold, determining that the filtered multi-dimensional charging data is the valid charging data.

3. The method according to claim 1, wherein The threshold parameter includes a second charging duration threshold corresponding to the slow charging mode and / or a second charging power threshold corresponding to the slow charging mode, and the determination rule indicates the rule for determining the valid charging data based on the second charging duration threshold and / or the second charging power threshold; for the corresponding charging type, performing multi-parameter correlation analysis on the filtered multi-dimensional charging data based on the determination rule and the threshold parameter, to determine the valid charging data in the filtered multi-dimensional charging data, comprising: for the slow charging type, in the case that the charging duration is greater than or equal to the second charging power threshold, determining that the filtered multi-dimensional charging data is the valid charging data; and / or for the slow charging type, in the case that the charging power is greater than or equal to the second charging power threshold, determining that the filtered multi-dimensional charging data is the valid charging data.

4. The method according to claim 1, wherein The filtering of the multi-dimensional charging data based on the determination rule to remove short-time charging data comprises: filtering the short-time charging data in the multi-dimensional charging data based on the determination rule, in the case that the power change, the battery temperature and the ambient temperature meet the short-time charging threshold; wherein the short-time charging data represents abnormal charging behavior.

5. The method according to any one of claims 1 to 4, wherein The method further comprises: storing the valid charging data and the short-time charging data respectively, and marking the short-time charging data to count the frequency of short-time charging; Based on the stored effective charging data and the short-time charging data, charging statistical data and charging analysis results are obtained.

6. The method according to claim 5, wherein The method further comprises: Based on a mobile phone APP or a vehicle-mounted HMI, the charging statistical data and the charging analysis results are provided to a user in real time.

7. The method according to claim 6, wherein The method further comprises: Based on the collected multi-dimensional charging data of a preset time, the threshold parameter is dynamically adjusted. The collected multi-dimensional charging data of a preset time represents a change in the charging behavior of the user.

8. An effective charging determination system for a new energy vehicle, characterized by comprising: The system is used to perform the steps of the method of any one of claims 1-7.

9. A device for determining the effective charging of a new energy vehicle, characterized in that, The device comprises: A data acquisition module is configured to initialize and load a determination rule and a threshold parameter of effective charging, and to acquire multi-dimensional charging data of a vehicle in real time, wherein the multi-dimensional charging data at least includes charging duration, charging power, power change, battery temperature, ambient temperature, and charging type; A data filtering module is configured to filter the multi-dimensional charging data based on the determination rule to remove short-time charging data, and to obtain filtered multi-dimensional charging data; A data analysis module is configured to perform multi-parameter correlation analysis on the filtered multi-dimensional charging data based on the determination rule and the threshold parameter for a corresponding charging type, to determine effective charging data in the filtered multi-dimensional charging data, wherein the charging type includes fast charging type and slow charging type.

10. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method of any one of claims 1-7.

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