Range extender working condition generation method and device and vehicle

Abstract

The embodiment of the invention discloses a range extender working condition generation method and device and a vehicle. The range extender working condition generation method comprises the steps that whole vehicle working condition big data information is acquired, wherein the whole vehicle big data information comprises multiple pieces of range extender power data; according to the whole vehicle working condition big data information, performing interval division on the range extender power data, and determining statistical data of power distribution and power change rate of each interval; and generating the characteristic working condition of the range extender based on the statistical data of the power distribution and the power change rate. According to the range extender working condition generation method and device and the vehicle provided by the embodiment of the invention, required working conditions can be provided for testing and verifying various performances of the range extender.

Description

Technical Field

[0001] Embodiments of the present invention relate to range extender technology, and more particularly to a method, device, and vehicle for generating operating conditions for a range extender. Background Art

[0002] During the development phase of range-extender vehicles, various operating condition tests are conducted to verify the range extender's performance. Currently, these operating condition tests are mostly durability tests, such as rated power and thermal shock tests, but these tests lack verification of actual user operating conditions. Summary of the Invention

[0003] Embodiments of the present invention provide a method, device, and vehicle for generating operating conditions for a range extender, so as to provide the required operating conditions for testing and verifying various performances of the range extender.

[0004] In a first aspect, an embodiment of the present invention provides a method for generating a range extender operating condition, comprising:

[0005] Obtaining vehicle operating condition big data information; the vehicle operating condition big data information includes multiple range extender power data;

[0006] Dividing the range extender power data into intervals based on the vehicle operating condition big data information, and determining statistical data on power distribution and power change rate in each interval;

[0007] Based on the statistical data of the power distribution and the power change rate, a characteristic operating condition of the range extender is generated.

[0008] Optionally, dividing the range extender power data into intervals according to the vehicle operating condition big data information includes:

[0009] According to the vehicle operating condition big data information, the multiple range extender power data are divided into multiple intervals according to power size.

[0010] Optionally, determining the statistical data of the power distribution and the power change rate in each interval includes:

[0011] Determine the usage proportion of different power according to the vehicle operating condition big data information, wherein the different power includes the power at different times;

[0012] The difference between the power at time k and the power at time k-1 is taken as the power change rate.

[0013] Optionally, generating a characteristic operating condition of the range extender based on the statistical data of the power distribution and the power change rate includes:

[0014] Based on the statistical data of the power distribution and the power change rate, randomly extracting a preset number of powers from the vehicle operating condition big data information;

[0015] The power extracted after the preset number of times is distributed over time according to the extraction time sequence;

[0016] The power distribution over time is used as the characteristic operating condition of the range extender.

[0017] Optionally, the randomly extracting a preset number of powers from the vehicle operating condition big data information based on the statistical data of the power distribution and the power change rate includes:

[0018] Based on the statistical data of the power distribution and the power change rate, randomly extracting a power as the initial power from the vehicle operating condition big data information;

[0019] According to the initial power, the interval in which the initial power is located is determined, and according to the mean and standard deviation of the power change rate, a preset number of powers are randomly extracted from the vehicle operating condition big data information.

[0020] Optionally, randomly extracting a preset number of powers from the vehicle operating condition big data information according to the mean and standard deviation of the power change rate includes:

[0021] If the power randomly extracted from the vehicle operating condition big data information for the mth time satisfies a preset condition, determining the interval in which the power randomly extracted from the vehicle operating condition big data information for the mth time falls; m is an integer greater than 1, and the preset condition includes that the difference between the mean of ΔPm and the mean of ΔP0 and the difference between the standard deviation of ΔPm and the standard deviation of ΔP0 are within a preset range, ΔP0 is the power change rate of the multiple range extender power data, and ΔPm is the change rate of the power randomly extracted from the vehicle operating condition big data information for the mth time and the power extracted m-1 times before;

[0022] If the power randomly extracted from the vehicle operating condition big data information for the mth time does not meet the preset conditions, continue to randomly extract power from the vehicle operating condition big data information until the preset number of extractions is completed.

[0023] Optionally, the m-th time and the previous m-1 times are the m-th time and the previous m-1 times corresponding to the power extracted in the same interval.

[0024] Optionally, the interval sizes of the intervals are the same.

[0025] In a second aspect, an embodiment of the present invention provides a range extender operating condition generating device, comprising:

[0026] A data acquisition module is used to acquire big data information on vehicle operating conditions; the big data information on vehicle operating conditions includes power data of multiple range extenders;

[0027] a data determination module, configured to divide the range extender power data into intervals based on the vehicle operating condition big data information, and determine statistical data on power distribution and power change rate in each interval;

[0028] An operating condition generating module is used to generate a characteristic operating condition of the range extender based on the statistical data of the power distribution and the power change rate.

[0029] In a third aspect, an embodiment of the present invention provides a vehicle including a range extender, wherein the range extender operating condition generation method described in the first aspect is applied to the range extender.

[0030] The range extender operating condition generation method, device, and vehicle provided in an embodiment of the present invention include: obtaining whole vehicle operating condition big data information; the whole vehicle operating condition big data information includes multiple range extender power data; based on the whole vehicle operating condition big data information, the range extender power data is divided into intervals, and statistical data of power distribution and power change rate in each interval is determined; based on the statistical data of power distribution and power change rate, characteristic operating conditions of the range extender are generated. The range extender operating condition generation method, device, and vehicle provided in an embodiment of the present invention generate characteristic operating conditions of the range extender based on the power data of the range extender actually used by the user included in the whole vehicle operating condition big data information, the statistical data of power distribution and power change rate determined, thereby providing the required operating conditions for testing and verifying various performance characteristics of the range extender. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of a method for generating a range extender operating condition provided in the first embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of power distribution in vehicle operating condition big data information provided by the first embodiment of the present invention;

[0033] Figure 3 This is a flow chart of a method for generating a range extender operating condition provided by a second embodiment of the present invention;

[0034] Figure 4 is a schematic diagram of a power distribution obtained by extraction provided by the second embodiment of the present invention;

[0035] Figure 5 is a schematic diagram of a power data sequence provided by the second embodiment of the present invention;

[0036] Figure 6 This is a structural block diagram of a range extender operating condition generating device provided in a third embodiment of the present invention;

[0037] Figure 7 This is a structural diagram of an electronic device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0038] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0039] Example 1

[0040] Figure 1 This is a flow chart of a method for generating a range extender operating condition provided in a first embodiment of the present invention. This embodiment is applicable to generating a range extender operating condition, etc. The range extender is a vehicle range extender. The method can be executed by a range extender operating condition generating device, which can be integrated into an electronic device such as a computer. The device can be implemented in the form of software and / or hardware. The method specifically includes the following steps:

[0041] Step 110: Obtain vehicle operating condition big data information; the vehicle operating condition big data information includes multiple range extender power data.

[0042] Among them, the whole vehicle operating condition big data information includes the operating condition data information of multiple users actually using the vehicle (the vehicle type is the type of vehicle to which the range extender is applied), and the operating condition data information of each user actually using the vehicle includes the range extender power data of the user at different times in multiple time periods of using the vehicle, such as the actual operating power changes of the vehicle's range extender in a time period of the user using the vehicle and the corresponding time.

[0043] Step 120: Divide the power distribution into intervals based on the vehicle operating condition big data information, and determine the statistical data of the power distribution and power change rate in each interval.

[0044] Specifically, based on the vehicle operating condition big data information, the power data of multiple range extenders are divided into multiple intervals according to the power size. For example, Figure 2 This is a schematic diagram of power distribution in vehicle operating condition big data information provided by the first embodiment of the present invention, with reference to Figure 2 The power range is 0-70kW, and 0-70kW is divided into 7 intervals, namely [0-10Kw), [10-20kW), [20-30kW), [30-40kW), [40-50kW), [50-60kW), and [60-70Kw]. Figure 2The vertical axis represents the count value, which can be understood as the number of power occurrences in each interval. The power change rate is the difference in power between the last and last moment of a user's vehicle use, such as the difference between the last second and the previous second. Power change rate statistics include the mean and standard deviation. Based on the power distribution and interval divisions, the power distribution and power change rate statistics for each interval can be determined.

[0045] Step 130: Generate characteristic operating conditions of the range extender based on statistical data of power distribution and power change rate.

[0046] Specifically, a power level is randomly extracted from the vehicle's operating condition big data as the initial power level. Subsequent randomly extracted powers must meet a preset condition, which is related to the statistical data of the power change rate, until a preset number of extractions are completed. The power levels extracted after the preset number of extractions are then sorted by the time of extraction to form a power distribution over time. This power distribution over time serves as the characteristic operating condition of the range extender.

[0047] The range extender operating condition generation method provided in this embodiment includes: obtaining vehicle operating condition big data information; the vehicle operating condition big data information includes multiple range extender power data; dividing the range extender power data into intervals based on the vehicle operating condition big data information, and determining statistical data on the power distribution and power change rate for each interval; and generating characteristic operating conditions for the range extender based on the statistical data on the power distribution and power change rate. The range extender operating condition generation method provided in this embodiment generates characteristic operating conditions for the range extender based on the power data of the range extender actually used by the user, as included in the vehicle operating condition big data information, and the determined statistical data on the power distribution and power change rate, thereby providing the required operating conditions for testing and verifying various performance characteristics of the range extender.

[0048] Example 2

[0049] Figure 3 This is a flow chart of a method for generating a range extender operating condition provided in a second embodiment of the present invention. This embodiment is applicable to generating a range extender operating condition, etc. The range extender is a vehicle range extender. The method can be executed by a range extender operating condition generating device, which can be integrated into an electronic device such as a computer. The device can be implemented in the form of software and / or hardware. The method specifically includes the following steps:

[0050] Step 210: Obtain vehicle operating condition big data information; the vehicle operating condition big data information includes multiple range extender power data.

[0051] Among them, the whole vehicle operating condition big data information includes the operating condition data information of multiple users actually using the vehicle (the vehicle type is the type of vehicle to which the range extender is applied), and the operating condition data information of each user actually using the vehicle includes the range extender power data of the user at different times in multiple time periods of using the vehicle, such as the actual operating power changes of the vehicle's range extender in a time period of the user using the vehicle and the corresponding time.

[0052] Step 220: Divide the power data of the multiple range extenders into multiple intervals according to the power size based on the big data information of the vehicle operating condition.

[0053] Among them, the interval size of each interval is the same. For example, refer to Figure 2 The power range is 0-70kW, and 0-70kW is divided into 7 intervals, namely [0-10kW), [10-20kW), [20-30kW), [30-40kW), [40-50kW), [50-60kW), and [60-70kW].

[0054] Step 230: Determine the usage ratio of different power according to the big data information of the vehicle operating condition.

[0055] Specifically, the power usage ratio of each range extender in the vehicle operating condition big data information is determined according to the vehicle operating condition big data information. The power usage ratio of each range extender can be the power usage time ratio of each range extender. For example, the vehicle operating condition big data information includes 25kW power and 35kW power, and the total usage time of 25kW power is 10h, and the total usage time of 35kW power is 35h, then the usage ratio of 25kW power is 25%, and the usage ratio of 35kW power is 75%.

[0056] It should be noted that the vehicle operating condition big data information includes multiple range extender power data. The above power, power usage time and usage ratio are only for illustrative purposes and are not limited here.

[0057] Step 240: The difference between the power at time k and the power at time k-1 is taken as the power change rate.

[0058] The power change rate is the power difference between the last moment and the previous moment when the user uses the vehicle, such as the power difference between the kth second and the k-1th second, where k is greater than 1.

[0059] Step 250: Based on the statistical data of power distribution and power change rate, a power is randomly selected from the vehicle operating condition big data information as the initial power.

[0060] Among them, the initial power is randomly extracted from the power data of each range extender in the big data information of the vehicle operating condition. The initial power is random and has randomness.

[0061] Step 260: If the power randomly extracted from the vehicle operating condition big data information for the mth time meets the preset conditions, determine the interval in which the power randomly extracted from the vehicle operating condition big data information for the mth time is located.

[0062] Where m is an integer greater than 1. The preset conditions include that the difference between the mean of ΔPm and the mean of ΔP0 and the difference between the standard deviation of ΔPm and the standard deviation of ΔP0 are within the preset range. ΔP0 is the power change rate of each range extender power data. ΔPm is the change rate of the power randomly extracted from the vehicle operating condition big data information for the mth time and the power extracted for the previous m-1 times. The mth time and the previous m-1 times are the mth time and the previous m-1 times corresponding to the power extracted in the same interval. Reference Figure 2 Taking the interval [10-20kW) as an example, if the currently extracted power is 11kW, and the power in the interval [10-20kW) has been extracted 5 times before, then this is the 6th time the power in this interval has been extracted. If the power extracted this time meets the preset conditions, it is determined that the interval in which the power extracted this time is located is the interval [10-20kW).

[0063] Step 270: If the power randomly extracted from the vehicle operating condition big data information for the mth time does not meet the preset conditions, continue to randomly extract power from the vehicle operating condition big data information until the preset number of extractions is completed.

[0064] For example, the preset number of times is 600, and the data is sampled once per second, for a total of 600 seconds, or 10 minutes. In addition, the number of power data in each interval is greater than the preset threshold. Since the power is randomly sampled, the number of power data in each interval corresponding to the randomly sampled power usually meets actual needs.

[0065] Step 280: Arrange the power extracted after the preset number of times according to the extraction time to form a power distribution over time.

[0066] For example, Figure 4 is a schematic diagram of a power data sequence provided by the second embodiment of the present invention. Figure 5 This is a schematic diagram of a power distribution corresponding to a power data sequence provided by the second embodiment of the present invention. Figure 4 and Figure 5 , the power data sequence is obtained by random sampling for 600s. The power data sequence is the distribution of power over time. Figure 4 As shown, Figure 4 The vertical axis is the count value, which can be understood as the number of times the power of each interval extracted during the random sampling process appears. Figure 5 The power distribution shown is similar to Figure 2It can be seen from the power distribution shown that the range extender operating condition generation method described in this embodiment can generate typical operating condition power sequence data that conforms to the big data rules based on big data.

[0067] Step 290: Use the power distribution over time as the characteristic operating condition of the range extender.

[0068] It should be noted that the specific values ​​of the parameters in this embodiment can be determined according to actual working conditions and are not limited here.

[0069] The range extender operating condition generation method provided in this embodiment is based on the power data of the range extender actually used by users, which is included in the vehicle operating condition big data information, and the statistical data of power distribution and power change rate are determined to generate characteristic operating conditions that conform to the big data distribution characteristics. This is used for testing and verifying various performance of the range extender during the vehicle development stage.

[0070] Example 3

[0071] Figure 6 This is a structural block diagram of a range extender operating condition generation device provided by the third embodiment of the present invention. Figure 6 The range extender operating condition generation device includes: a data acquisition module 310, a data determination module 320, and an operating condition generation module 330. The data acquisition module 310 is used to acquire vehicle operating condition big data information; the vehicle operating condition big data information includes multiple range extender power data; the data determination module 320 is used to divide the range extender power data into intervals based on the vehicle operating condition big data information, and determine the power distribution and power change rate statistics for each interval; and the operating condition generation module 330 is used to generate the characteristic operating condition of the range extender based on the power distribution and power change rate statistics.

[0072] Based on the above implementation, the data determination module 320 includes:

[0073] The proportion determination unit is used to determine the proportion of different power usage based on the big data information of the vehicle operating conditions. The different power includes the power at different times;

[0074] The change rate determining unit is used to take the difference between the power at time k and the power at time k-1 as the power change rate.

[0075] In one embodiment, the data determination module 320 includes:

[0076] The interval division unit is used to divide the power distribution into multiple intervals according to the power size based on the big data information of the vehicle operating conditions.

[0077] Optionally, the operating condition generation module 330 includes:

[0078] The power extraction submodule is used to randomly extract a preset number of powers from the vehicle operating condition big data based on the statistical data of power distribution and power change rate;

[0079] The power distribution submodule is used to form a power distribution over time according to the order of extraction time of the power extracted after completing the preset number of times;

[0080] The operating condition determination submodule is used to use the power distribution over time as the characteristic operating condition of the range extender.

[0081] Optionally, the power extraction submodule includes:

[0082] A power determination unit is used to randomly select a power as the initial power from the vehicle operating condition big data information based on the statistical data of power distribution and power change rate;

[0083] The power extraction unit is used to determine the interval of the initial power based on the initial power, and randomly extract a preset number of powers from the vehicle operating condition big data information based on the mean and standard deviation of the power change rate.

[0084] Optionally, the power extraction unit includes:

[0085] an interval determination subunit, configured to determine an interval in which the power randomly extracted from the vehicle operating condition big data information for the mth time falls if the power randomly extracted from the vehicle operating condition big data information for the mth time satisfies a preset condition; m is an integer greater than 1, the preset condition includes that the difference between the mean of ΔPm and the mean of ΔP0 and the difference between the standard deviation of ΔPm and the standard deviation of ΔP0 are within a preset range, ΔP0 is a power change rate of the plurality of range extender power data, and ΔPm is a change rate between the power randomly extracted from the vehicle operating condition big data information for the mth time and the power extracted m-1 times previously;

[0086] The power extraction subunit is used to continue randomly extracting power from the vehicle operating condition big data information if the power randomly extracted from the vehicle operating condition big data information for the mth time does not meet the preset conditions until the preset number of extractions is completed.

[0087] This embodiment further provides a vehicle, including: a range extender, wherein the range extender operating condition generation method as described in any embodiment of the present invention is applied to the range extender.

[0088] The range extender operating condition generation device and vehicle provided in this embodiment and the range extender operating condition generation method provided in any embodiment of the present invention belong to the same inventive concept and have corresponding beneficial effects. For technical details not detailed in this embodiment, please refer to the range extender operating condition generation method provided in any embodiment of the present invention.

[0089] Example 4

[0090] Figure 7 This is a structural diagram of an electronic device provided in Example 4 of the present invention. Figure 7 A block diagram of an exemplary electronic device 412 suitable for implementing embodiments of the present invention is shown. Figure 7 The electronic device 412 shown is only an example and should not limit the functionality and scope of use of the embodiments of the present invention.

[0091] like Figure 7 As shown, electronic device 412 is implemented as a general purpose device. Components of electronic device 412 may include, but are not limited to, one or more processors 416, a storage device 428, and a bus 418 connecting various system components (including storage device 428 and processor 416).

[0092] Bus 418 represents one or more of several types of bus structures, including a storage device bus or storage device controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, the Industry Subversive Alliance (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0093] The electronic device 412 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 412, including volatile and non-volatile media, removable and non-removable media.

[0094] The storage device 428 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 430 and / or cache memory 432. The electronic device 412 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 434 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 Not shown, often called a "hard drive"). Although Figure 7Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk, such as a Compact Disc Read-Only Memory (CD-ROM), a Digital Video Disc-Read Only Memory (DVD-ROM), or other optical media, may be provided. In these cases, each drive may be connected to bus 418 via one or more data medium interfaces. Storage device 428 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0095] A program / utility 440 having a set (at least one) of program modules 442 may be stored, for example, in storage device 428. Such program modules 442 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 442 generally implement the functions and / or methodologies of the embodiments described herein.

[0096] The electronic device 412 may also communicate with one or more external devices 414 (e.g., a keyboard, a pointing terminal, a display 424, etc.), and may also communicate with one or more terminals that enable a user to interact with the electronic device 412, and / or any terminal that enables the electronic device 412 to communicate with one or more other computing terminals (e.g., a network card, a modem, etc.). Such communication may be performed through an input / output (I / O) interface 422. Furthermore, the electronic device 412 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 420. Figure 7 As shown, the network adapter 420 communicates with other modules of the electronic device 412 via the bus 418. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 412, including but not limited to: microcode, terminal drivers, redundant processors, external disk drive arrays, Redundant Arrays of Independent Disks (RAID) systems, tape drives, and data backup storage systems.

[0097] The processor 416 executes various functional applications and data processing by running the programs stored in the storage device 428, such as implementing the range extender operating condition generation method provided in an embodiment of the present invention, which includes:

[0098] Obtaining big data information on vehicle operating conditions; the big data information on vehicle operating conditions includes power data of multiple range extenders;

[0099] Based on the big data of vehicle operating conditions, the range extender power data is divided into intervals, and the power distribution and power change rate statistics of each interval are determined;

[0100] Based on the statistical data of power distribution and power change rate, the characteristic operating conditions of the range extender are generated.

[0101] Example 5

[0102] The fifth embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for generating a range extender operating condition as provided in the embodiment of the present invention is implemented. The method includes:

[0103] Obtaining big data information on vehicle operating conditions; the big data information on vehicle operating conditions includes power data of multiple range extenders;

[0104] Based on the big data of vehicle operating conditions, the range extender power data is divided into intervals, and the power distribution and power change rate statistics of each interval are determined;

[0105] Based on the statistical data of power distribution and power change rate, the characteristic operating conditions of the range extender are generated.

[0106] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0107] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0108] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0109] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or terminal. In cases involving a remote computer, the remote computer may be connected to the user's computer through 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., through the Internet using an Internet service provider).

[0110] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, re-modulations, combinations, and substitutions are possible for those skilled in the art without departing from the scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for generating a range extender operating condition, characterized in that: include: Obtaining vehicle operating condition big data information, wherein the vehicle operating condition big data information includes multiple range extender power data; Dividing the range extender power data into intervals based on the vehicle operating condition big data information, and determining statistical data on power distribution and power change rate in each interval; Based on the statistical data of the power distribution and the power change rate, a characteristic operating condition of the range extender is generated.

2. The range extender operating condition generation method according to claim 1, characterized in that: The step of dividing the range extender power data into intervals based on the vehicle operating condition big data information includes: According to the vehicle operating condition big data information, the multiple range extender power data are divided into multiple intervals according to power size.

3. The range extender operating condition generation method according to claim 1, characterized in that: The statistical data of determining the power distribution and power change rate of each interval includes: Determine the usage proportion of different power according to the vehicle operating condition big data information, wherein the different power includes the power at different times; The difference between the power at time k and the power at time k-1 is taken as the power change rate.

4. The range extender operating condition generation method according to any one of claims 1 to 3, characterized in that: The generating of the characteristic operating condition of the range extender based on the statistical data of the power distribution and the power change rate includes: Based on the statistical data of the power distribution and the power change rate, randomly extracting a preset number of powers from the vehicle operating condition big data information; The power extracted after the preset number of times is distributed over time according to the extraction time sequence; The power distribution over time is used as the characteristic operating condition of the range extender.

5. The range extender operating condition generation method according to claim 4, characterized in that: The statistical data based on the power distribution and the power change rate, randomly extracting a preset number of powers from the vehicle operating condition big data information, includes: Based on the statistical data of the power distribution and the power change rate, randomly extracting a power as the initial power from the vehicle operating condition big data information; According to the initial power, the interval in which the initial power is located is determined, and according to the mean and standard deviation of the power change rate, a preset number of powers are randomly extracted from the vehicle operating condition big data information.

6. The range extender operating condition generation method according to claim 5, characterized in that: The randomly extracting a preset number of powers from the vehicle operating condition big data information according to the mean and standard deviation of the power change rate includes: If the power randomly extracted from the vehicle operating condition big data information for the mth time satisfies a preset condition, determining the interval in which the power randomly extracted from the vehicle operating condition big data information for the mth time falls; m is an integer greater than 1, and the preset condition includes that the difference between the mean of ΔPm and the mean of ΔP0 and the difference between the standard deviation of ΔPm and the standard deviation of ΔP0 are within a preset range, ΔP0 is the power change rate of the multiple range extender power data, and ΔPm is the change rate of the power randomly extracted from the vehicle operating condition big data information for the mth time and the power extracted m-1 times before; If the power randomly extracted from the vehicle operating condition big data information for the mth time does not meet the preset conditions, continue to randomly extract power from the vehicle operating condition big data information until the preset number of extractions is completed.

7. The range extender operating condition generation method according to claim 6, characterized in that: The m-th time and the m-1 times before are the m-th time and the m-1 times before corresponding to the power extracted in the same interval.

8. The range extender operating condition generation method according to any one of claims 1 to 3, characterized in that: The interval sizes of all intervals are the same.

9. A range extender operating condition generating device, characterized in that: include: A data acquisition module is used to acquire big data information of vehicle operating conditions, wherein the big data information of vehicle operating conditions includes power data of multiple range extenders; a data determination module, configured to divide the range extender power data into intervals based on the vehicle operating condition big data information, and determine statistical data on the power distribution and power change rate of each interval; An operating condition generating module is used to generate a characteristic operating condition of the range extender based on the statistical data of the power distribution and the power change rate.

10. A vehicle, characterized in that: include: A range extender, wherein the range extender operating condition generation method as described in any one of claims 1 to 8 is applied to the range extender.

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