Charging management method, system and device of new energy automobile and electronic equipment

Abstract

The invention discloses a charging management method, system and device for a new energy automobile and electronic equipment, and the method comprises the steps: obtaining the basic data of the automobile, and enabling the basic data of the automobile to comprise the vehicle identification data, the vehicle travel data and the vehicle battery data; based on the basic data of the vehicle, charging strategy planning is carried out according to an intelligent charging SOC planning value algorithm, and vehicle personalized charging configuration parameters corresponding to the vehicle using mode are obtained; and based on the vehicle personalized charging configuration parameters, the vehicle is controlled to be intelligently charged in the corresponding vehicle using mode. According to the method, intelligent planning can be performed on the battery charging strategies of different vehicle using modes according to the basic data of the vehicle, so that autonomous vehicle using planning setting of a vehicle owner is combined with driving behavior analysis, the accuracy, reliability and individuation of a battery management system are improved, and the service life of the battery is prolonged.

Description

Technical Field

[0001] This invention relates to the field of intelligent connected vehicles, and more particularly to a charging management method, system, device, and electronic device for new energy vehicles. Background Technology

[0002] The current state of battery health management strategies for new energy vehicles mainly focuses on the application of battery management systems (BMS). However, due to the complexity and uncertainty of battery performance, the system may make misjudgments or omissions. Furthermore, new energy vehicle batteries are affected by various environmental factors during use, such as temperature, humidity, and vibration. These factors may adversely affect battery performance and lifespan, and existing battery health management strategies often fail to fully consider the impact of these factors. Summary of the Invention

[0003] In view of the above problems, this application provides a charging management method for new energy vehicles, which can intelligently plan battery charging strategies for different usage modes based on the vehicle's basic data, thereby combining the owner's autonomous vehicle usage planning settings with driving behavior analysis, improving the accuracy, reliability and personalization of the battery management system, and extending battery life.

[0004] Firstly, this application provides a charging management method for new energy vehicles. The method includes: acquiring basic vehicle data, including vehicle identification data, vehicle trip data, and vehicle battery data; based on the basic vehicle data, performing charging strategy planning according to an intelligent charging SOC planning value algorithm to obtain personalized charging configuration parameters for the vehicle corresponding to the usage mode; and controlling the vehicle to perform intelligent charging in the corresponding usage mode based on the personalized charging configuration parameters.

[0005] In the technical solution of this application embodiment, the basic data of the vehicle is first obtained. Then, based on the basic data of the vehicle, a charging strategy is planned according to the intelligent charging SOC planning value algorithm to obtain the personalized charging configuration parameters of the vehicle corresponding to the usage mode. Based on the personalized charging configuration parameters of the vehicle, the vehicle is controlled to perform intelligent charging in the corresponding usage mode. It can intelligently plan the battery charging strategy for different usage modes according to the basic data of the vehicle, thereby combining the owner's autonomous vehicle usage planning settings with driving behavior analysis, improving the accuracy, reliability and personalization of the battery management system, and extending battery life.

[0006] In some embodiments, the vehicle personalized charging configuration parameters include at least a current coefficient, a charging cutoff SOC coefficient, and a temperature protection threshold. Based on the vehicle personalized charging configuration parameters, the vehicle is controlled to perform intelligent charging in the corresponding usage mode, including at least one of the following: based on the charging cutoff SOC coefficient, the vehicle is controlled to charge in the corresponding usage mode to stop charging when the vehicle's battery charge reaches the target SOC planning value; based on the current coefficient, the vehicle is controlled to charge at the XCU standard charging power or the optimal charging power in the corresponding usage mode; based on the temperature protection threshold, the vehicle is controlled to perform constant temperature protection during charging in the corresponding usage mode, and a warning process is initiated when the battery temperature meets the temperature protection threshold.

[0007] In some embodiments, the vehicle usage mode includes at least one of a custom mode, a daily commuting mode, a long-distance travel mode, and a long-term storage mode; the target SOC planning value includes at least one of a first preset threshold, a second preset threshold, a third preset threshold, and a fourth preset threshold; based on the cutoff SOC planning value, controlling the vehicle to charge in the corresponding usage mode includes at least one of the following: when the vehicle is charging in the custom mode, controlling the vehicle to stop charging when the battery level reaches the first preset threshold; when the vehicle is charging in the daily commuting mode, controlling the vehicle to stop charging when the battery level reaches the second preset threshold; when the vehicle is charging in the long-distance travel mode, controlling the vehicle to stop charging when the battery level reaches the third preset threshold; when the vehicle is charging in the long-term storage mode, controlling the vehicle to stop charging when the battery level reaches the fourth preset threshold.

[0008] In some embodiments, the intelligent charging SOC planning value algorithm is obtained by training the battery life extension model based on the corresponding enterprise standard data of the vehicle; based on the basic data of the vehicle, the charging strategy is planned according to the intelligent charging SOC planning value algorithm to obtain the vehicle personalized charging configuration parameters corresponding to the vehicle usage mode, including: inputting the basic data of the vehicle into the battery life extension model for calculation, and outputting the corresponding vehicle personalized charging configuration parameters.

[0009] In some embodiments, after inputting the vehicle's basic data into the battery life extension model for calculation and outputting the corresponding personalized charging configuration parameters for the vehicle, the method further includes: correcting the actual charging cutoff SOC under the corresponding vehicle use mode according to the charging cutoff SOC coefficient to obtain the target SOC planning value; and controlling the vehicle to stop charging when it reaches the target SOC planning value under the corresponding vehicle use mode.

[0010] In some embodiments, the method further includes: storing the vehicle's personalized charging configuration parameters in packets and transmitting them in the order of the data packets after the vehicle is powered on.

[0011] On the other hand, this application provides a charging management system for new energy vehicles. The system includes: a cloud platform configured to acquire basic vehicle data and, based on the basic vehicle data, perform charging strategy planning according to an intelligent charging SOC planning value algorithm to obtain personalized charging configuration parameters for the vehicle corresponding to the usage mode. The basic vehicle data includes vehicle identification data, vehicle trip data, and vehicle battery data. A vehicle-side platform is connected to the cloud platform and configured to send the basic vehicle data to the cloud platform and, based on the personalized charging configuration parameters, control the vehicle to perform intelligent charging in the corresponding usage mode.

[0012] In some embodiments, the cloud platform includes: a big data platform configured to deploy an intelligent charging SOC planning value algorithm and, based on vehicle basic data, obtain personalized charging configuration parameters for the vehicle according to the intelligent charging SOC planning value algorithm; a remote monitoring platform configured to periodically acquire personalized charging configuration parameters for the vehicle from the big data platform and send them to the corresponding vehicle according to vehicle identification data; and a vehicle-side platform including: an on-board TBOX configured to receive personalized charging configuration parameters for the vehicle sent by the remote monitoring platform and send them to the vehicle's XCU controller via a CAN bus; and a battery management system configured to receive personalized charging configuration parameters for the vehicle sent by the XCU controller and verify, store, and execute the personalized charging configuration parameters.

[0013] On the other hand, this application provides a charging management device for new energy vehicles. The device includes: an acquisition module for acquiring basic vehicle data, including vehicle identification data, vehicle trip data, and vehicle battery data; a charging planning module for planning a charging strategy based on the vehicle's basic data and an intelligent charging SOC planning value algorithm to obtain personalized charging configuration parameters corresponding to the vehicle's usage mode; and a control module for controlling the vehicle to perform intelligent charging in the corresponding usage mode based on the personalized charging configuration parameters.

[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:

[0017] Figure 1 A flowchart of a charging management method for new energy vehicles according to an embodiment of this application is shown;

[0018] Figure 2 This application illustrates an architecture diagram of a charging management system for new energy vehicles according to an embodiment of the present application.

[0019] Figure 3 This illustration shows a schematic diagram of the personalized configuration parameter format according to an embodiment of this application;

[0020] Figure 4 This paper illustrates a functional diagram of the intelligent charging SOC planning value algorithm according to an embodiment of this application.

[0021] Figure 5 A schematic diagram of a charging management system for a new energy vehicle according to an embodiment of this application is shown;

[0022] Figure 6 A block diagram of a charging management device for a new energy vehicle according to an embodiment of this application is shown;

[0023] Figure 7 A schematic diagram of an electronic device according to an embodiment of this application is shown. Detailed Implementation

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] The current state of battery health management strategies for new energy vehicles mainly focuses on the application of battery management systems (BMS). This system can monitor various battery parameters in real time, such as voltage, current, and temperature, to ensure that the battery operates in a safe and efficient state. However, due to the complexity and uncertainty of battery performance, the system may make misjudgments or omissions. Furthermore, new energy vehicle batteries are affected by various environmental factors during use, such as temperature, humidity, and vibration. These factors may adversely affect the battery's performance and lifespan, and existing battery health management strategies often fail to fully consider the impact of these factors.

[0033] In view of this, this application proposes a charging management method for new energy vehicles, which can intelligently plan battery charging strategies for different usage modes based on the vehicle's basic data, thereby combining the owner's autonomous vehicle usage planning settings with driving behavior analysis, improving the accuracy, reliability, and personalization of the battery management system, and extending battery life.

[0034] In the technical solution of this application embodiment, the basic data of the vehicle is first obtained. Then, based on the basic data of the vehicle, a charging strategy is planned according to the intelligent charging SOC (remaining percentage of battery capacity) planning value algorithm to obtain the personalized charging configuration parameters of the vehicle corresponding to the usage mode. Based on the personalized charging configuration parameters of the vehicle, the vehicle is controlled to perform intelligent charging in the corresponding usage mode. It can intelligently plan the battery charging strategy for different usage modes according to the basic data of the vehicle, thereby combining the owner's autonomous vehicle usage planning settings with driving behavior analysis, improving the accuracy, reliability and personalization of the battery management system, and extending battery life.

[0035] Figure 1 A flowchart of a charging management method for new energy vehicles according to an embodiment of this application is shown.

[0036] like Figure 1 As shown, the charging management method 100 for new energy vehicles provided in this application includes steps S110 to S130.

[0037] Step S110: Obtain basic vehicle data, including vehicle identification data, vehicle trip data, and vehicle battery data.

[0038] For example, basic data for each vehicle can be obtained, such as vehicle identification data, vehicle trip data, and vehicle battery data. The vehicle identification data represents the basic information of each vehicle, such as the vehicle model, terminal number, and VIN code (Vehicle Identification Number). The vehicle trip data represents the driver's trip and behavior information while driving the vehicle, such as trip start time, trip start location, trip end time, trip end location, distance traveled, cumulative distance traveled, trip energy consumption, cumulative power consumption, average speed, maximum speed, emergency braking and deceleration, and component energy consumption. The vehicle battery data represents the internal information of the vehicle's battery, such as the battery cell voltage, current, and battery status.

[0039] Step S120: Based on the vehicle's basic data, a charging strategy is planned according to the intelligent charging SOC planning value algorithm to obtain the vehicle's personalized charging configuration parameters corresponding to the vehicle usage mode.

[0040] For example, the intelligent charging SOC planning algorithm is an algorithm that analyzes and calculates based on the vehicle's basic data to obtain the recommended charging parameters for different vehicle usage modes. For instance, based on the acquired vehicle basic data, the intelligent charging SOC planning algorithm can be used to plan personalized charging strategies and obtain personalized charging configuration parameters for multiple usage modes. Usage modes may include, for example, custom mode, daily commuting mode, long-distance travel mode, and long-term storage mode. Personalized charging configuration parameters may include parameters related to the upper limit of charging SOC value, charging power, and battery constant temperature protection. The intelligent charging SOC planning algorithm can be trained on a network model based on the input vehicle dataset.

[0041] Step S130: Based on the vehicle's personalized charging configuration parameters, control the vehicle to perform intelligent charging in the corresponding usage mode.

[0042] For example, based on the vehicle's personalized charging configuration parameters, when the owner sets the corresponding driving mode through a mobile APP or HMI, the charging upper limit SOC value, charging power, and whether to enable battery constant temperature protection for that mode can be planned and set so that the vehicle can intelligently charge in the corresponding driving mode.

[0043] In the technical solution of this application embodiment, the basic data of the vehicle is first obtained. Then, based on the basic data of the vehicle, a charging strategy is planned according to the intelligent charging SOC planning value algorithm to obtain the personalized charging configuration parameters of the vehicle corresponding to the usage mode. Based on the personalized charging configuration parameters of the vehicle, the vehicle is controlled to perform intelligent charging in the corresponding usage mode. It can intelligently plan the battery charging strategy for different usage modes according to the basic data of the vehicle, thereby combining the owner's autonomous vehicle usage planning settings with driving behavior analysis, improving the accuracy, reliability and personalization of the battery management system, and extending battery life.

[0044] In one example, the charging management method for new energy vehicles in this application is based on a charging management system for new energy vehicles. This system can obtain basic vehicle data from the vehicle and upload the data to a cloud platform for analysis and calculation to obtain personalized charging configuration parameters for the vehicle. Users can set usage scenarios through an App or HMI according to their usage needs. Under different usage modes, the cloud platform intelligently plans charging strategies based on the personalized charging configuration parameters of the vehicle according to the owner's trip planning and driving habits.

[0045] Figure 2 This paper illustrates the architecture diagram of a charging management system for a new energy vehicle according to an embodiment of this application.

[0046] like Figure 2 As shown, the charging management system for new energy vehicles can sequentially upload basic vehicle data and charging-related data of charging piles to the cloud via the charging pile cloud, charging piles, vehicle-side BMS, and TBOX (vehicle-to-everything network terminal). The cloud-based intelligent charging SOC planning algorithm receives, parses, and analyzes the data. Based on the analyzed personalized charging configuration parameters, it issues relevant instructions for intelligent charging planning and transmits these parameters. When the owner sets the corresponding driving mode according to their needs via the APP or vehicle HMI, the cloud plans the upper limit SOC value and charging power of intelligent charging based on the personalized charging configuration parameters of the corresponding mode. It also activates the constant temperature protection function according to the corresponding mode, thereby extending battery life and optimizing performance.

[0047] For example, the intelligent charging SOC planning value algorithm is obtained by training the battery life extension model based on the corresponding enterprise standard data of the vehicle; based on the basic data of the vehicle, the charging strategy is planned according to the intelligent charging SOC planning value algorithm to obtain the personalized charging configuration parameters of the vehicle corresponding to the usage mode. For example, the basic data of the vehicle is input into the battery life extension model for calculation, and the corresponding personalized charging configuration parameters of the vehicle are output.

[0048] Specifically, enterprise standard data of vehicles can be acquired periodically. The relevant production standard data of vehicles (enterprise standard data) can be used as input for training and updating of the battery life extension model to obtain an accurate intelligent charging SOC planning value algorithm. Based on the basic data input of the vehicle, the intelligent charging SOC planning value algorithm can output personalized configuration parameters (vehicle personalized charging configuration parameters) for each vehicle. Among them, vehicle personalized charging configuration parameters can include parameters related to charging planning strategy, such as current coefficient, charging cutoff SOC coefficient, and temperature protection threshold. At the same time, the battery life extension effect and update status of the vehicle can be tracked based on the enterprise standard data to continuously optimize the intelligent charging strategy.

[0049] In the technical solution of this application embodiment, the basic data of the vehicle is input into the battery life extension model for calculation, and the personalized charging configuration parameters of the vehicle for the application vehicle mode are output. Then, the intelligent charging SOC planning value algorithm is used for analysis and calculation, and the battery is intelligently planned and managed according to the scenario, reducing battery loss, ensuring battery health, improving the accuracy and reliability of the battery management system, and making it intelligent and personalized.

[0050] For example, the generated personalized charging configuration parameters for vehicles can be packaged, stored in a designated location in the cloud, and then transmitted to the corresponding vehicles. For instance, the personalized charging configuration parameters for vehicles can be packaged, stored, and transmitted in the order of the data packets after the vehicle is powered on.

[0051] Specifically, at a fixed time each day, such as 0:00, the generated personalized charging configuration parameters for vehicles can be packaged and stored in the personalized configuration parameter table of the Hive database (data warehouse tool) on the big data platform. For example, the current coefficient, charging cutoff SOC coefficient, and temperature protection threshold related parameters corresponding to each VIN code can be packaged into 1200-byte packets, and the total number of personalized configuration parameter packets, total byte length, single packet byte length, and data packet order can be specified to generate personalized parameter data format. Then, after the vehicle corresponding to the VIN code in the personalized configuration parameter table is powered on, the personalized configuration parameters corresponding to that VIN are sent to the corresponding vehicle in the order of data packets. Each vehicle only sends personalized configuration parameters once a day.

[0052] Figure 3 A schematic diagram of the personalized configuration parameter format of an embodiment of this application is shown.

[0053] like Figure 3 As shown, HEX data strings can be generated for personalized configuration parameters according to the personalized parameter data format. Each pair of numbers is one byte, and each data packet can include a data packet number, a data identifier, and a data body, thereby transmitting the personalized configuration parameters to the corresponding vehicles in the order of the data packets.

[0054] In the technical solution of this application embodiment, the personalized charging configuration parameters of the vehicle are packaged and stored, and after the vehicle is powered on, they are transmitted to the corresponding vehicle in the order of data packets, so as to control the vehicle to perform intelligent charging based on the personalized charging configuration parameters, thereby improving the data transmission and the reliability of the vehicle battery management system.

[0055] Next, we will explain in detail how to perform intelligent battery charging planning based on the personalized charging configuration parameters transmitted to the vehicle.

[0056] For example, the vehicle's personalized charging configuration parameters include at least a current coefficient, a charging cutoff SOC coefficient, and a temperature protection threshold. Based on these parameters, the vehicle is controlled to perform intelligent charging in the corresponding usage mode, including at least one of the following: based on the charging cutoff SOC coefficient, the vehicle is controlled to charge in the corresponding usage mode to stop charging when the battery charge reaches the target SOC planning value; or based on the current coefficient, the vehicle is controlled to charge at the XCU standard charging power or the optimal charging power in the corresponding usage mode; or based on the temperature protection threshold, the vehicle is controlled to perform constant temperature protection during charging in the corresponding usage mode, and a warning process is initiated when the battery temperature meets the temperature protection threshold.

[0057] For example, after transmitting the vehicle's personalized charging configuration parameters to the vehicle, the vehicle can be controlled to charge in the corresponding usage mode based on the charging cutoff SOC coefficient. For instance, firstly, the actual charging cutoff SOC (i.e., 100% SOC) in the corresponding usage mode is corrected according to the charging cutoff SOC coefficient to calculate the charging cutoff SOC planning value, i.e., the target SOC planning value. The corrected target SOC planning value is less than or equal to 100% SOC according to the corresponding usage mode. Then, the vehicle is controlled to stop charging when it reaches the target SOC planning value in the corresponding usage mode, thereby avoiding battery life degradation caused by excessive fast charging, high temperature, frequent deep discharge, etc., as well as the problem of battery life being affected by overcharging and over-discharging.

[0058] Specifically, the vehicle usage mode includes at least one of the following: custom mode, daily commuting mode, long-distance travel mode, and long-term storage mode; the target SOC planning value includes at least one of the following: a first preset threshold, a second preset threshold, a third preset threshold, and a fourth preset threshold; based on the cutoff SOC planning value, the vehicle is controlled to charge in the corresponding usage mode, including at least one of the following: when the vehicle is charging in custom mode, charging is stopped when the vehicle's battery level reaches the first preset threshold; or when the vehicle is charging in daily commuting mode, charging is stopped when the vehicle's battery level reaches the second preset threshold; or when the vehicle is charging in long-distance travel mode, charging is stopped when the vehicle's battery level reaches the third preset threshold; or when the vehicle is charging in long-term storage mode, charging is stopped when the vehicle's battery level reaches the fourth preset threshold.

[0059] Specifically, the charging strategy for each vehicle usage mode is planned as follows:

[0060] 1. Custom Mode (Default)

[0061] Users can customize settings according to their own usage needs:

[0062] Charging limit: The charging limit SOC setting range is 60% to 100% (first preset threshold);

[0063] Charging power: Uses the default XCU standard charging power (without extending charging time);

[0064] Temperature protection: The battery temperature protection function needs to be enabled or disabled by the user.

[0065] 2. Daily Commuting Mode (Smart Mode)

[0066] Charging limit: The charging limit SOC setting range is 80% to 100% (adjustable according to travel distance habits, i.e., the second preset threshold);

[0067] Charging power: Uses the default XCU standard charging power (without extending charging time);

[0068] Temperature protection: The battery temperature protection function needs to be enabled or disabled by the user.

[0069] For everyday driving, it is recommended to enable the daily commuting mode. The system will plan the charging limit (charge limit SOC) based on your travel distance habits and battery characteristics, ensuring travel distance and charging speed while extending battery life.

[0070] 3. Long-distance travel mode (Battery SOC limits and preheating performance are planned in advance according to the customer's itinerary to improve the customer's travel efficiency.)

[0071] Charging limit: The charging limit SOC setting range is 100% (third preset threshold);

[0072] Charging power: Uses the default XCU standard charging power (without extending charging time);

[0073] Constant temperature protection: The constant temperature protection function is automatically activated. After activation, the maximum SOC for charging is 100%. When the battery enters a low temperature state (meeting the temperature protection threshold), it will perform heat preservation after charging (the heat preservation state lasts for a maximum of 6 hours).

[0074] 4. Long-term (healthy) storage mode (guides the user to insert the charging gun for storage, and automatically starts charging).

[0075] Charging limit: The charging limit SOC setting range is ≤80% (fourth preset threshold);

[0076] Charging power: Cloud-based charging planning (optimal charging power);

[0077] Temperature protection: The temperature protection function is automatically turned off, and the battery is intelligently managed within a suitable temperature range to avoid high-temperature storage, thereby improving lifespan and safety.

[0078] The charging modes can include fast charging and slow charging (or home fast charging). When the system charges according to the optimal charging power planned in the cloud, it adopts fast charging mode, which appropriately extends the charging time, thereby extending battery life and improving safety. When using the XCU standard charging power for slow charging (or home fast charging), the system will pause charging when it is close to the SOC deadline based on the vehicle usage time or learn the user's charging habits, and resume charging in advance based on the vehicle usage time habits, so as to ensure battery health, maintain a longer battery life and stable power output, and avoid safety problems such as leakage and short circuit.

[0079] In the technical solution of this application embodiment, based on the charging cutoff SOC coefficient in the vehicle's personalized charging configuration parameters, the vehicle is controlled to charge in the corresponding usage mode so that charging stops when the vehicle's battery charge reaches the target SOC planning value. Based on the current coefficient, the vehicle is controlled to charge at the XCU standard charging power or the optimal charging power in the corresponding usage mode. Based on the temperature protection threshold, the vehicle is controlled to perform constant temperature protection during charging in the corresponding usage mode, and a warning process is initiated when the battery temperature meets the temperature protection threshold. This introduces advanced algorithms to optimize the battery management strategy, improve the accuracy and reliability of the battery management system, and achieve battery life extension and performance optimization.

[0080] In the technical solution of this application embodiment, the actual charging cutoff SOC under the corresponding vehicle use mode is corrected according to the charging cutoff SOC coefficient to obtain the target SOC planning value. When the vehicle is charging in custom mode, charging is stopped when the vehicle's battery level reaches a first preset threshold; when the vehicle is charging in daily commuting mode, charging is stopped when the vehicle's battery level reaches a second preset threshold; when the vehicle is charging in long-distance travel mode, charging is stopped when the vehicle's battery level reaches a third preset threshold; and when the vehicle is charging in long-term storage mode, charging is stopped when the vehicle's battery level reaches a fourth preset threshold. This allows for intelligent planning and management of the battery based on specific scenarios, thereby extending battery life and optimizing performance.

[0081] Figure 4 A schematic diagram of the intelligent charging SOC planning value algorithm according to an embodiment of this application is shown.

[0082] like Figure 4 As shown, for different types of batteries, the intelligent charging SOC planning algorithm (also known as the XCU algorithm) can implement corresponding battery charging and discharging strategies (intelligent charging strategy planning) under different vehicle usage modes. For ternary lithium battery packs, taking intelligent mode as an example, when intelligent mode is on and long-term healthy storage mode is off, the charging cutoff SOC planning value range (target SOC planning value) is: 100% ≥ recommended value ≥ 85%. For lithium iron phosphate battery packs, taking intelligent mode as an example, when intelligent mode is on and long-term healthy storage mode is off, the charging cutoff SOC planning value is: 100%; when long-distance mode is on and long-term healthy storage mode is off, the charging cutoff SOC planning value is: 100%; when long-term healthy storage mode is on, the charging cutoff SOC planning value is: 80%.

[0083] Figure 5 A schematic diagram of a charging management system for a new energy vehicle according to an embodiment of this application is shown.

[0084] like Figure 5 As shown in the embodiment of this application, the charging management system 500 for new energy vehicles includes: a cloud platform 510 and a vehicle-side platform 520.

[0085] The cloud platform 510 is configured to acquire basic vehicle data and, based on this data, perform charging strategy planning according to the intelligent charging SOC planning value algorithm to obtain personalized charging configuration parameters for the vehicle corresponding to the usage mode. The basic vehicle data includes vehicle identification data, vehicle trip data, and vehicle battery data. The vehicle-side platform 520 communicates with the cloud platform 510 and is configured to send the basic vehicle data to the cloud platform 510, and, based on the personalized charging configuration parameters, control the vehicle to perform intelligent charging in the corresponding usage mode.

[0086] For example, the cloud platform 510 includes: a big data platform 511 and a remote monitoring platform 512. The big data platform 511 is configured to deploy an intelligent charging SOC planning value algorithm and, based on the vehicle's basic data, obtain personalized charging configuration parameters for the vehicle according to the intelligent charging SOC planning value algorithm; the remote monitoring platform 512 communicates with the big data platform 511 and is configured to periodically obtain the personalized charging configuration parameters for the vehicle from the big data platform 511 and send them to the corresponding vehicle according to the vehicle identification data.

[0087] Specifically, an intelligent charging SOC planning algorithm can be deployed on the intelligent connected vehicle big data platform 511 (cloud BMS). Based on the basic information (vehicle basic data) uploaded from the vehicle to the big data platform 511 via the remote monitoring platform 512, calculations are performed to generate personalized configuration parameters for each vehicle, which are then stored in a designated location, such as the personalized configuration parameter table in the big data platform 511 hive database. The remote monitoring platform 512 (remote control cloud) periodically (e.g., daily) retrieves the parameters to be distributed (vehicle personalized charging configuration parameters) from the personalized configuration parameter table of the big data platform 511 and transmits them to the designated vehicle based on the VIN code. The specific functions of the big data platform 511 based on the intelligent charging SOC planning algorithm are described in the table below:

[0088]

[0089] The specific functions of the remote monitoring platform 512 in acquiring personalized configuration parameters and transmitting them to the corresponding vehicles are described in the table below:

[0090]

[0091]

[0092] For example, the vehicle-side platform 520 includes: an onboard TBOX 521 and a battery management system (BMS). The onboard TBOX 521 is communicatively connected to the remote monitoring platform 512 and configured to receive personalized vehicle charging configuration parameters sent by the remote monitoring platform 512 and send them to the vehicle's XCU controller via the CAN bus; the battery management system 522 is communicatively connected to the onboard TBOX 521 and configured to receive personalized vehicle charging configuration parameters sent by the XCU controller, and to verify, store, and execute the personalized vehicle charging configuration parameters.

[0093] Specifically, the vehicle-mounted TBOX521 receives personalized configuration parameters (data packets) from the cloud platform 510, parses them, and transmits them to the XCU controller via CAN communication. The battery management system 522 (BMS) receives the personalized configuration parameters transmitted by the XCU controller, stores them locally, and performs intelligent charging based on the received personalized configuration parameters after verification. The specific functions of the vehicle-side platform 520 in receiving and executing personalized configuration parameters are described in the table below:

[0094]

[0095]

[0096] In the technical solution of this application embodiment, basic vehicle data is obtained through the cloud platform of the charging management system of new energy vehicles. Based on the basic vehicle data, charging strategy planning is performed according to the intelligent charging SOC planning value algorithm to obtain personalized charging configuration parameters for the vehicle corresponding to the usage mode. The basic vehicle data is sent to the cloud platform through the vehicle-side platform 520. Based on the personalized charging configuration parameters, the vehicle is controlled to perform intelligent charging in the corresponding usage mode. By improving the battery health status management strategy and algorithm of new energy vehicles through the system, significant impacts are made on improving battery safety, extending battery life, improving charging and discharging efficiency, intelligent management and environmental benefits. This helps to promote the sustainable development of the new energy vehicle industry and brings users a safer, more efficient and convenient user experience.

[0097] Figure 6 A block diagram of a charging management device for a new energy vehicle according to an embodiment of this application is shown.

[0098] like Figure 6 As shown, the charging management device 600 for new energy vehicles provided in this application includes:

[0099] The acquisition module 610 is used to acquire basic vehicle data, including vehicle identification data, vehicle trip data, and vehicle battery data.

[0100] The charging planning module 620 is used to plan charging strategies based on the vehicle's basic data and the intelligent charging SOC planning value algorithm to obtain personalized charging configuration parameters for the vehicle corresponding to the usage mode.

[0101] The control module 630 is used to control the vehicle to perform intelligent charging in the corresponding vehicle use mode based on the vehicle's personalized charging configuration parameters.

[0102] For example, the vehicle's personalized charging configuration parameters include at least a current coefficient, a charging cutoff SOC coefficient, and a temperature protection threshold. Based on these parameters, the vehicle is controlled to perform intelligent charging in the corresponding usage mode, including at least one of the following: based on the charging cutoff SOC coefficient, the vehicle is controlled to charge in the corresponding usage mode to stop charging when the battery charge reaches the target SOC planning value; based on the current coefficient, the vehicle is controlled to charge at the XCU standard charging power or the optimal charging power in the corresponding usage mode; based on the temperature protection threshold, the vehicle is controlled to perform constant temperature protection during charging in the corresponding usage mode, and a warning process is initiated when the battery temperature meets the temperature protection threshold.

[0103] For example, the vehicle usage mode includes at least one of a custom mode, a daily commuting mode, a long-distance travel mode, and a long-term storage mode; the target SOC planning value includes at least one of a first preset threshold, a second preset threshold, a third preset threshold, and a fourth preset threshold; based on the cutoff SOC planning value, controlling the vehicle to charge in the corresponding usage mode includes at least one of the following: when the vehicle is charging in the custom mode, controlling the vehicle to stop charging when the battery level reaches the first preset threshold; when the vehicle is charging in the daily commuting mode, controlling the vehicle to stop charging when the battery level reaches the second preset threshold; when the vehicle is charging in the long-distance travel mode, controlling the vehicle to stop charging when the battery level reaches the third preset threshold; when the vehicle is charging in the long-term storage mode, controlling the vehicle to stop charging when the battery level reaches the fourth preset threshold.

[0104] For example, the intelligent charging SOC planning value algorithm is obtained by training the battery life extension model based on the corresponding enterprise standard data of the vehicle; based on the basic data of the vehicle, the charging strategy is planned according to the intelligent charging SOC planning value algorithm to obtain the personalized charging configuration parameters of the vehicle corresponding to the usage mode, including: inputting the basic data of the vehicle into the battery life extension model for calculation, and outputting the corresponding personalized charging configuration parameters of the vehicle.

[0105] For example, after inputting the vehicle's basic data into the battery life extension model for calculation and outputting the corresponding personalized charging configuration parameters for the vehicle, the method further includes: correcting the actual charging cutoff SOC under the corresponding vehicle use mode according to the charging cutoff SOC coefficient to obtain the target SOC planning value; and controlling the vehicle to stop charging when it reaches the target SOC planning value under the corresponding vehicle use mode.

[0106] For example, the device 600 also includes a storage module for: storing vehicle personalized charging configuration parameters in packets, and transmitting them in the order of the data packets after the vehicle is powered on.

[0107] Figure 7 A schematic diagram of an electronic device according to an embodiment of this application is shown.

[0108] 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.

[0109] like Figure 7 As shown, for ease of understanding, an embodiment of this application illustrates a specific electronic device 700.

[0110] Electronic device 700 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 700 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.

[0111] like Figure 7 As shown, the electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. The RAM 703 may also store various programs and data required for the operation of the electronic device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0112] Multiple components in electronic device 700 are connected to I / O interface 705. These components include: input unit 706, such as a keyboard or mouse; output unit 707, such as various types of displays or speakers; storage unit 708, such as a disk or optical disk; and communication unit 709, such as a network interface card (NIC), modem, or wireless transceiver. Communication unit 709 allows electronic device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0113] The computing unit 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 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 701 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 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of any one or more of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 701 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).

[0114] 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 sequential list of executable instructions for implementing logical functions, and can be embodied 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.

[0115] 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.

[0116] 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. A charging management method for new energy vehicles, characterized in that, The method includes: Acquire basic vehicle data, including vehicle identification data, vehicle trip data, and vehicle battery data; Based on the vehicle's basic data, a charging strategy is planned according to the intelligent charging SOC planning value algorithm to obtain the vehicle's personalized charging configuration parameters corresponding to the usage mode. Based on the vehicle's personalized charging configuration parameters, the vehicle is controlled to perform intelligent charging in the corresponding usage mode.

2. The charging management method for new energy vehicles according to claim 1, characterized in that, The personalized charging configuration parameters for the vehicle include at least a current coefficient, a charging cutoff SOC coefficient, and a temperature protection threshold; the step of controlling the vehicle to perform intelligent charging in the corresponding usage mode based on the personalized charging configuration parameters includes at least one of the following: Based on the charging cutoff SOC coefficient, the vehicle is controlled to charge in the corresponding vehicle use mode, so as to stop charging when the vehicle's battery charge reaches the target SOC planning value. Based on the current coefficient, the vehicle is controlled to charge at the XCU standard charging power or the optimal charging power in the corresponding vehicle use mode. Based on the temperature protection threshold, the vehicle is controlled to perform constant temperature protection during charging in the corresponding vehicle use mode, and an early warning process is initiated when the battery temperature meets the temperature protection threshold.

3. The charging management method for new energy vehicles according to claim 2, characterized in that, The vehicle usage mode includes at least one of a custom mode, a daily commuting mode, a long-distance travel mode, and a long-term storage mode; the target SOC planning value includes at least one of a first preset threshold, a second preset threshold, a third preset threshold, and a fourth preset threshold; controlling the vehicle to charge in the corresponding vehicle usage mode based on the cutoff SOC planning value includes at least one of the following: When the vehicle is charging in the custom mode, the charging is stopped when the vehicle's battery level reaches the first preset threshold. When the vehicle is charging in the daily commuting mode, the charging is stopped when the vehicle's battery level reaches the second preset threshold. When the vehicle is charging in the long-distance travel mode, the charging is stopped when the vehicle's battery level reaches the third preset threshold. When the vehicle is being charged in the long-term storage mode, the charging is stopped when the vehicle's battery level reaches the fourth preset threshold.

4. The charging management method for new energy vehicles according to claim 1, characterized in that, The intelligent charging SOC planning value algorithm is obtained by training the battery life extension model based on the corresponding enterprise standard data of the vehicle. Based on the vehicle's basic data, a charging strategy is planned according to the intelligent charging SOC planning algorithm to obtain personalized charging configuration parameters for the vehicle corresponding to the usage mode, including: The basic data of the vehicle is input into the battery life extension model for calculation, and the corresponding personalized charging configuration parameters of the vehicle are output.

5. The charging management method for new energy vehicles according to claim 4, characterized in that, After inputting the vehicle's basic data into the battery life extension model for calculation and outputting the corresponding personalized charging configuration parameters for the vehicle, the method further includes: Based on the charging cutoff SOC coefficient, the actual charging cutoff SOC under the corresponding vehicle usage mode is corrected to obtain the target SOC planning value. When the vehicle is charged to the target SOC planning value in the corresponding vehicle use mode, charging is stopped.

6. The charging management method for new energy vehicles according to claim 5, characterized in that, The method further includes: The personalized charging configuration parameters for the vehicle are stored in packets and transmitted in the order of the data packets after the vehicle is powered on.

7. A charging management system for new energy vehicles, characterized in that, The system includes: The cloud platform is configured to acquire basic vehicle data and, based on the basic vehicle data, perform charging strategy planning according to the intelligent charging SOC planning value algorithm to obtain personalized charging configuration parameters for the vehicle corresponding to the usage mode. The basic vehicle data includes vehicle identification data, vehicle trip data, and vehicle battery data. The vehicle-side platform communicates with the cloud platform and is configured to send basic vehicle data to the cloud platform, as well as control the vehicle to perform intelligent charging in the corresponding usage mode based on the vehicle's personalized charging configuration parameters.

8. The charging management system for new energy vehicles according to claim 7, characterized in that, The cloud platform includes: The big data platform is configured to deploy an intelligent charging SOC planning value algorithm, and based on the vehicle's basic data, obtain the vehicle's personalized charging configuration parameters according to the intelligent charging SOC planning value algorithm; The remote monitoring platform communicates with the big data platform and is configured to periodically obtain the personalized charging configuration parameters of the vehicle from the big data platform and send them to the corresponding vehicle according to the vehicle identification data. The vehicle-side platform includes: The vehicle-mounted TBOX is connected to the remote monitoring platform and configured to receive the vehicle's personalized charging configuration parameters sent by the remote monitoring platform and send them to the vehicle's XCU controller via the CAN bus. The battery management system communicates with the vehicle-mounted TBOX and is configured to receive the vehicle personalized charging configuration parameters sent by the XCU controller, and to verify, store, and execute the vehicle personalized charging configuration parameters.

9. A charging management device for new energy vehicles, characterized in that, The device includes: The acquisition module is used to acquire basic vehicle data, including vehicle identification data, vehicle trip data, and vehicle battery data. The charging planning module is used to plan the charging strategy based on the vehicle's basic data and according to the intelligent charging SOC planning value algorithm to obtain the vehicle's personalized charging configuration parameters corresponding to the vehicle usage mode. The control module is used to control the vehicle to perform intelligent charging in the corresponding vehicle usage mode based on the vehicle's personalized charging configuration parameters.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-6.

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