Hybrid electric vehicle power protection target SOC control method, medium, equipment and program product

Abstract

The invention provides a hybrid electric vehicle power protection target SOC control method, a medium, equipment and a program product, and belongs to the technical field of hybrid electric vehicle control. The method comprises the steps that the current driving scene of a hybrid electric vehicle is recognized based on a data clustering algorithm; an SOC compensation value corresponding to the current driving scene is determined according to the current driving scene of the hybrid electric vehicle, an intelligent power guarantee target SOC value is calculated according to the SOC compensation value corresponding to the current driving scene and the basic target SOC value, and the SOC of the battery is adjusted according to the intelligent power guarantee target SOC value. According to the method, the intelligent power protection function can be realized, and the optimal power protection target SOC is determined according to the current driving working condition, so that the energy consumption is lower, the trouble of manual setting is omitted, and the method is more intelligent and more energy-saving.

Description

Technical Field

[0001] This invention relates to the field of hybrid vehicle control technology, and in particular to a hybrid vehicle's power-saving target SOC control method, medium, equipment, and program product. Background Technology

[0002] The biggest advantage of hybrid electric vehicles (HEVs) over traditional gasoline vehicles is their high fuel economy. To achieve this, vehicle energy control is crucial. Besides switching to series, parallel, or hybrid drive modes at appropriate times, it's also important to consider the battery's lifespan and prevent the State of Charge (SOC) from becoming too low. Therefore, some HEVs allow users to manually set a target SOC for battery maintenance. However, this method cannot determine the optimal SOC based on current driving conditions, resulting in drawbacks such as higher energy consumption, the inconvenience of manual setting, lack of intelligence, and poor energy efficiency. Summary of the Invention

[0003] This invention aims to solve at least one of the technical problems existing in the prior art, and proposes a method, medium, device and program product for controlling the SOC target of hybrid vehicles to maintain power supply.

[0004] In a first aspect, embodiments of the present invention provide a method for controlling the State of Charge (SOC) of a hybrid vehicle to maintain its power supply target, comprising the following steps:

[0005] S100. Identify the current driving scenario of hybrid vehicles based on data clustering algorithms;

[0006] S200: Determine the SOC compensation value corresponding to the current driving scenario based on the current driving scenario of the hybrid vehicle; calculate the intelligent power protection target SOC value based on the SOC compensation value corresponding to the current driving scenario and the basic target SOC value; and adjust the battery SOC based on the intelligent power protection target SOC value.

[0007] Further, step S100 includes the following steps:

[0008] S110. Define the driving scenario and its corresponding cluster center point;

[0009] S120. Real-time calculation of cluster center point parameters within the most recent preset time period T under the current driving scenario;

[0010] S130. Calculate the scenario judgment parameters for each driving scenario based on the cluster center point and cluster center point parameters for each driving scenario;

[0011] S140. Compare the scenario judgment parameters under each driving scenario to identify the current driving scenario of the hybrid vehicle.

[0012] Furthermore, in step S110, four driving scenarios are defined, including: urban congestion, urban smooth traffic, suburban elevated roads, and highways.

[0013] Furthermore, in step S110, the calibrated average vehicle speed and calibrated average driver power demand within the most recent preset time period T are used to define the cluster center point for each driving scenario.

[0014] Furthermore, in step S120, the cluster center point parameters include the average vehicle speed and average power demand within the most recent preset time period T under the current driving scenario.

[0015] Furthermore, the average vehicle speed is obtained by collecting data from the vehicle speed sensor, and the average power demand is obtained by collecting data from the driver's accelerator pedal opening.

[0016] Furthermore, the average vehicle speed is obtained through a vehicle speed sensor, and the average power demand is calculated based on the current vehicle's driving mode, vehicle speed, battery charge, maximum allowable output power of the motor, and maximum allowable output power of the engine.

[0017] Furthermore, in step S130, the calculation formula for the scene judgment parameters under each driving scenario is as follows:

[0018]

[0019] Wherein, S1 is the scenario judgment parameter for urban congestion, S2 is the scenario judgment parameter for urban smooth traffic, S3 is the scenario judgment parameter for suburban elevated roads, S4 is the scenario judgment parameter for highways, Vmean is the average vehicle speed within the most recent preset time period T under the current driving scenario, Pmean is the average demand power within the most recent preset time period T under the current driving scenario, V_CityBlock is the calibrated average vehicle speed for urban congestion, P_CityBlock is the calibrated average driver demand power for urban congestion, V_CityUnBlock is the calibrated average vehicle speed for urban smooth traffic, P_CityUnBlock is the calibrated average driver demand power for urban smooth traffic, V_Suburb is the calibrated average vehicle speed for suburban elevated roads, P_Suburb is the calibrated average driver demand power for suburban elevated roads, V_Highway is the calibrated average vehicle speed for highways, and P_Highway is the calibrated average driver demand power for highways.

[0020] Furthermore, in step S140, the driving scenario corresponding to the smallest scenario judgment parameter among the scenario judgment parameters under each driving scenario is identified as the current driving scenario of the hybrid vehicle.

[0021] Furthermore, during scene switching, the driving scene category can only be updated if the absolute value of the difference between the minimum scene judgment parameter of the most recent preset time period T and the minimum scene judgment parameter of the previous preset time period T is greater than a preset value.

[0022] Furthermore, in step S200, the calculation formula for the intelligent power-saving target SOC value is as follows:

[0023]

[0024] Where SOC_offset is the SOC compensation value and SOC_CompRatio is the correction coefficient.

[0025] Furthermore, SOC_offset is obtained based on the experimental power consumption data of the cluster center points of the vehicle in the current driving scenario.

[0026] In a second aspect, embodiments of the present invention provide an electronic device, comprising:

[0027] One or more processors;

[0028] Memory, used to store one or more programs;

[0029] When the one or more programs are executed by the one or more processors, the one or more processors perform the method as described above.

[0030] Thirdly, embodiments of the present invention provide a computer-readable medium on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps in the method described above.

[0031] Fourthly, embodiments of the present invention provide a computer program product including computer-readable code, characterized in that, when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device executes the method described above.

[0032] The hybrid vehicle battery protection target SOC control method, medium, device, and program products provided by this invention identify the current driving scenario of the hybrid vehicle based on a data clustering algorithm; determine the corresponding SOC compensation value based on the current driving scenario; calculate the intelligent battery protection target SOC value based on the SOC compensation value and the basic target SOC value; and adjust the battery SOC based on the intelligent battery protection target SOC value. This enables intelligent battery protection and determines the optimal battery protection target SOC based on the current driving conditions, resulting in lower energy consumption, eliminating the hassle of manual settings, and making it more intelligent and energy-efficient. Attached Figure Description

[0033] Figure 1 A flowchart illustrating a hybrid vehicle's State of Charge (SOC) control method for maintaining battery power, provided in an embodiment of the present invention;

[0034] Figure 2 This invention provides a definition table of driving scenarios and their corresponding cluster center points in an embodiment of the invention.

[0035] Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0036] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0037] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0038] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0039] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0040] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0041] This invention provides a method for controlling the State of Charge (SOC) of a hybrid vehicle to maintain its power supply target. Figure 1This is a flowchart illustrating a hybrid vehicle's State of Charge (SOC) control method according to an embodiment of the present invention. The method includes the following steps:

[0042] S100: Identify the current driving scenario of hybrid vehicles based on data clustering algorithms.

[0043] In one embodiment, step S100 includes the following steps:

[0044] S110, Define the driving scenario and its corresponding cluster center point.

[0045] For details, please refer to Figure 2 As shown, four driving scenarios are defined: urban congestion, urban smooth traffic, suburban elevated roads, and highways. The cluster center point for each driving scenario is defined using the calibrated average vehicle speed and the calibrated average driver power demand within the most recent preset time period T.

[0046] For example, the cluster center for urban congestion is (CityBlock, calibrated average vehicle speed, CityBlock; CityBlock, calibrated average driver power demand, CityBlock); the cluster center for smooth urban traffic is (CityUnBlock, calibrated average vehicle speed, CityUnBlock; CityUnBlock, calibrated average driver power demand, CityUnBlock); the cluster center for suburban elevated roads is (Suburb, calibrated average vehicle speed, Suburb; CityUnBlock, calibrated average driver power demand, Suburb); and the cluster center for highways is (Highway, calibrated average vehicle speed, Highway; CityUnBlock, calibrated average driver power demand, HighwayUnBlock).

[0047] The calibrated average vehicle speed and calibrated average driver power demand within the most recent preset time period T for each of the above driving scenarios are preset calibration values, and the data are derived from the average value of experimental data under the corresponding driving scenario conditions.

[0048] S120: Calculate the cluster center point parameters within the most recent preset time period T in the current driving scenario in real time.

[0049] Specifically, the cluster center point parameters include the average vehicle speed Vmean and the average demand power Pmean within the most recent preset time period T under the current driving scenario.

[0050] In one embodiment, the average vehicle speed is obtained by a vehicle speed sensor, and the average power demand is obtained by collecting the driver's accelerator pedal opening.

[0051] In one embodiment, the average vehicle speed is acquired by a vehicle speed sensor, and the average power demand is calculated based on the current vehicle's driving mode, vehicle speed, battery charge, the maximum allowable output power of the motor, and the maximum allowable output power of the engine. The method for calculating the average power demand is fairly conventional and is not within the scope of this patent; therefore, it will not be elaborated upon here.

[0052] S130. Calculate the scenario judgment parameters for each driving scenario based on the cluster center point and cluster center point parameters for each driving scenario.

[0053] Specifically, the calculation formulas for the scenario judgment parameters under each driving scenario are as follows:

[0054]

[0055] Wherein, S1 is the scenario judgment parameter for urban congestion, S2 is the scenario judgment parameter for urban smooth traffic, S3 is the scenario judgment parameter for suburban elevated roads, S4 is the scenario judgment parameter for highways, Vmean is the average vehicle speed within the most recent preset time period T under the current driving scenario, Pmean is the average demand power within the most recent preset time period T under the current driving scenario, V_CityBlock is the calibrated average vehicle speed for urban congestion, P_CityBlock is the calibrated average driver demand power for urban congestion, V_CityUnBlock is the calibrated average vehicle speed for urban smooth traffic, P_CityUnBlock is the calibrated average driver demand power for urban smooth traffic, V_Suburb is the calibrated average vehicle speed for suburban elevated roads, P_Suburb is the calibrated average driver demand power for suburban elevated roads, V_Highway is the calibrated average vehicle speed for highways, and P_Highway is the calibrated average driver demand power for highways.

[0056] S140. Compare the scenario judgment parameters under each driving scenario to identify the current driving scenario of the hybrid vehicle.

[0057] Specifically, the driving scenario corresponding to the smallest scenario judgment parameter in each driving scenario is identified as the current driving scenario of the hybrid vehicle.

[0058] For example, comparing S1, S2, S3, and S4, if S4 is the smallest, it is scenario 4 (highway); if S3 is the smallest, it is scenario 3 (suburban elevated road); if S2 is the smallest, it is scenario 2 (smooth traffic in the city); otherwise, it is scenario 1 (congested traffic in the city).

[0059] In one embodiment, during scene switching, the driving scene category can only be updated if the absolute value of the difference between the minimum scene judgment parameter of the most recent preset time period T and the minimum scene judgment parameter of the previous preset time period T is greater than a preset value, so as to avoid frequent changes in driving scenes.

[0060] The preset values ​​are preset calibrable parameters that can be calibrated according to specific experimental conditions.

[0061] S200: Determine the SOC compensation value corresponding to the current driving scenario based on the current driving scenario of the hybrid vehicle; calculate the intelligent power protection target SOC value based on the SOC compensation value corresponding to the current driving scenario and the basic target SOC value; and adjust the battery SOC based on the intelligent power protection target SOC value.

[0062] Specifically, the SOC compensation value SOC_offset is obtained based on the currently identified driving scenario. For example: the SOC_offset value corresponding to scenario 1 is SOC_offset_S1; the SOC_offset value corresponding to scenario 2 is SOC_offset_S2; the SOC_offset value corresponding to scenario 3 is SOC_offset_S3; and the SOC_offset value corresponding to scenario 4 is SOC_offset_S4. The specific SOC_offset_S1-SOC_offset_S4 values ​​are obtained based on experimental power consumption data of the vehicle at the cluster center point in the current driving scenario. For example, at the cluster center point (V_CityBlock, P_CityBlock) in scenario 1 and under normal temperature conditions, the SOC_offset that minimizes the overall vehicle energy consumption is the SOC compensation value corresponding to the current scenario: SOC_offset_S1. In practice, SOC_offset can be obtained by taking values ​​within 10% above and below the basic target SOC with a gradient of 0.5%. The SOC_offset with the lowest overall vehicle power is the SOC_offset_S1 corresponding to this scenario. The method for SOC compensation values ​​for other scenarios follows the same principle.

[0063] In one embodiment, the formula for calculating the target SOC value of intelligent power supply protection is as follows:

[0064]

[0065] Where SOC_offset is the SOC compensation value and SOC_CompRatio is the correction coefficient.

[0066] The base target SOC is a preset base target SOC value for battery protection, which is a constant value, for example, 25%. SOC_CompRatio is a correction coefficient, mainly to account for the impact of temperature on battery performance. It is generally determined by looking up a table based on battery temperature and taking a value close to 1. Since this has a relatively small impact, it can also be directly set to 1. The final intelligent battery protection target SOC result can be calculated based on the currently identified driving scenario. For example, if the current driving scenario is identified as being in urban congestion, the final calculated intelligent battery protection target SOC result = base target SOC + SOC_offset_S1 * SOC_CompRatio.

[0067] This invention identifies the current driving scenario of a hybrid electric vehicle based on a data clustering algorithm; determines the SOC compensation value corresponding to the current driving scenario; calculates the intelligent power-saving target SOC value based on the SOC compensation value and the basic target SOC value; and adjusts the battery SOC based on the intelligent power-saving target SOC value. This enables intelligent power-saving functionality and determines the optimal power-saving target SOC based on the current driving conditions, resulting in lower energy consumption, eliminating the hassle of manual settings, and making it more intelligent and energy-efficient.

[0068] This invention also provides an electronic device. Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the hybrid vehicle power-saving target SOC control methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processors and the memory, configured to enable information interaction between the processors and the memory.

[0069] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0070] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0071] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0072] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps of any of the hybrid vehicle power-saving target SOC control methods described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0073] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described hybrid vehicle power-saving target SOC control method.

[0074] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0075] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0076] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0077] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0078] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0079] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0080] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0081] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0082] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0083] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for controlling the State of Charge (SOC) of a hybrid vehicle to ensure battery power supply, characterized in that, Includes the following steps: S100. Identify the current driving scenario of hybrid vehicles based on data clustering algorithms; S200: Determine the SOC compensation value corresponding to the current driving scenario based on the current driving scenario of the hybrid vehicle; calculate the intelligent power protection target SOC value based on the SOC compensation value corresponding to the current driving scenario and the basic target SOC value; and adjust the battery SOC based on the intelligent power protection target SOC value.

2. The method according to claim 1, characterized in that, Step S100 includes the following steps: S110. Define the driving scenario and its corresponding cluster center point; S120. Real-time calculation of cluster center point parameters within the most recent preset time period T under the current driving scenario; S130. Calculate the scenario judgment parameters for each driving scenario based on the cluster center point and cluster center point parameters for each driving scenario; S140. Compare the scenario judgment parameters under each driving scenario to identify the current driving scenario of the hybrid vehicle.

3. The method according to claim 2, characterized in that, In step S110, four driving scenarios are defined: urban congestion, urban smooth traffic, suburban elevated roads, and highways.

4. The method according to claim 3, characterized in that, In step S110, the calibrated average vehicle speed and calibrated average driver power demand within the most recent preset time period T are used to define the cluster center point for each driving scenario.

5. The method according to claim 4, characterized in that, In step S120, the cluster center point parameters include the average vehicle speed and average power demand within the most recent preset time period T under the current driving scenario.

6. The method according to claim 5, characterized in that, Average vehicle speed is obtained by collecting data from a vehicle speed sensor, and average power demand is obtained by collecting data from the driver's accelerator pedal opening.

7. The method according to claim 5, characterized in that, The average vehicle speed is collected by the vehicle speed sensor, and the average power demand is calculated based on the current vehicle's drive mode, vehicle speed, battery charge, maximum allowable output power of the motor, and maximum allowable output power of the engine.

8. The method according to claim 5, characterized in that, In step S130, the calculation formulas for the scene judgment parameters under each driving scenario are as follows: Wherein, S1 is the scenario judgment parameter for urban congestion, S2 is the scenario judgment parameter for urban smooth traffic, S3 is the scenario judgment parameter for suburban elevated roads, S4 is the scenario judgment parameter for highways, Vmean is the average vehicle speed within the most recent preset time period T under the current driving scenario, Pmean is the average demand power within the most recent preset time period T under the current driving scenario, V_CityBlock is the calibrated average vehicle speed for urban congestion, P_CityBlock is the calibrated average driver demand power for urban congestion, V_CityUnBlock is the calibrated average vehicle speed for urban smooth traffic, P_CityUnBlock is the calibrated average driver demand power for urban smooth traffic, V_Suburb is the calibrated average vehicle speed for suburban elevated roads, P_Suburb is the calibrated average driver demand power for suburban elevated roads, V_Highway is the calibrated average vehicle speed for highways, and P_Highway is the calibrated average driver demand power for highways.

9. The method according to claim 8, characterized in that, In step S140, the driving scenario corresponding to the smallest scenario judgment parameter among the scenario judgment parameters under each driving scenario is identified as the current driving scenario of the hybrid vehicle.

10. The method according to claim 2, characterized in that, When switching scenes, the driving scene category can only be updated if the absolute value of the difference between the minimum scene judgment parameter of the most recent preset time period T and the minimum scene judgment parameter of the previous preset time period T is greater than the preset value.

11. The method according to claim 1, characterized in that, In step S200, the formula for calculating the intelligent power-saving target SOC value is as follows: Where SOC_offset is the SOC compensation value and SOC_CompRatio is the correction coefficient.

12. The method according to claim 11, characterized in that, SOC_offset is obtained based on the experimental power consumption data of the cluster center points of the vehicle in the current driving scenario.

13. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 12.

14. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 12.

15. A computer program product comprising computer-readable code, characterized in that, When the computer-readable code is run in the processor of the electronic device, the processor in the electronic device performs the method as described in any one of claims 1 to 12.

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