A vehicle endurance test data analysis method and device based on matlab

Abstract

The application discloses a kind of matlab-based whole vehicle endurance test data analysis method and equipment, comprising the following steps: test data acquisition, collect test vehicle EVcan and PTcan data during endurance test process;Test data format conversion, the data format of collection is converted into.mat format;Data early processing, the multiple data files collected are uniformly formatted file naming;Test data is solved by MATLAB, and matlab program is characterized according to the total cycle under working condition Recognition, total cycle is disassembled by characteristic point into single cycle working condition, and the variable to be analyzed under each cycle is calculated;Each cycle under each system energy consumption calculation output, and the energy consumption of different cycles is compared and energy consumption is decomposed by the output data.The application can effectively solve the problem of large data volume, low efficiency and low accuracy of manual disassembly.

Description

Technical Field

[0001] This invention belongs to the field of whole vehicle range test data analysis technology, specifically involving a method and computer equipment for whole vehicle range test data analysis of new energy vehicles under CLTC cycle based on MATLAB. Background Technology

[0002] The rapid development of new energy vehicles has made driving range a key focus for the industry and users, becoming a crucial factor influencing the development and performance evaluation of new energy vehicles. This has also made driving range energy consumption decomposition and data analysis an important part of product development. Currently, the driving range standard evaluation for new energy vehicles is based on the CLTC cycle. Taking a vehicle with a 600km driving range as an example, a normal temperature driving range test includes 41.4 CLTC cycles, while a low temperature driving range test includes at least 23.5 cycles. The recovery power, air conditioning consumption, and drive consumption are different under different cycles. Existing methods divide the total energy by the number of cycles, which cannot effectively evaluate the energy consumption of a single cycle system or the energy consumption of the system under different test conditions, and cannot effectively guide development and optimization.

[0003] Therefore, a method is needed to break down and calculate the single-cycle energy consumption of new energy vehicle range tests, effectively evaluate the system energy consumption under different conditions, and thus guide development and optimization. Summary of the Invention

[0004] To address the aforementioned problems in existing technologies, this invention provides a method for analyzing vehicle range test data based on MATLAB. This method includes how to obtain the power consumption of each system in the range test, how to import the data into MATLAB, MATLAB program settings, data calculation, and export. This method can effectively solve the problems of low efficiency and low accuracy in manually splitting large amounts of data.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] As a first aspect of the present invention, a method for analyzing vehicle range test data based on MATLAB is provided, characterized by comprising the following steps:

[0007] Step 1: Data Acquisition. During the range test, data from the EVcan and PTcan of the test vehicle will be collected.

[0008] Step 2: Convert the experimental data format, converting the data collected in Step 1 into .mat format;

[0009] Step 3: Pre-processing of data, naming the collected data files in a unified format;

[0010] Step 4: Solve the experimental data using MATLAB. The MATLAB program identifies the features of the total loop based on the working conditions, decomposes the total loop into single-loop working conditions through feature points, and calculates the variables that need to be analyzed in each loop.

[0011] Step 5: Calculate and output the energy consumption of each system in each cycle, and compare and decompose the energy consumption of different cycles based on the output data.

[0012] Furthermore, in step one, vehicle EVcan and PTcan data are collected during the range test using a canoe, canape, or other data acquisition devices, with the data format being mdf or mf4.

[0013] Furthermore, in step two, the data collected by canoe and canape is converted into .mat format through the data conversion function in the acquisition device, with a data interval of 0.1s.

[0014] Furthermore, in step three, the data file naming format is as follows: each data file uses a uniform filename, and different numbers are added to the end of each filename to distinguish them.

[0015] Furthermore, in step four, the experimental data file processed in step three and the MATLAB file are placed in the same folder, and the motor running program extracts and solves the .mat file using the MATLAB program.

[0016] Furthermore, step four includes:

[0017] S41. Clear all history and errors, and load the .mat file;

[0018] S42. Based on the range analysis requirements, first calculate the energy released by the battery drive and the power recovered during recycling, the DC-DC input and output power, and the motor input and output power;

[0019] S43. After the above calculations, finally output an EXCEL list, showing the calculations for each data analysis group.

[0020] Further, step S42 includes:

[0021] 1) Motor input and output power:

[0022] Battery power P = Battery voltage U * Battery current I;

[0023] When P>0, P=Po is defined, and the battery is in the driving state. When P<0, P=Pi is defined, and the battery is in the recycling state.

[0024] 2) DC-DC input and output power:

[0025] DC-DC input power Pdcdci = DC-DC input current * DC-DC input voltage;

[0026] DC-DC output power Pdcdcio = DC-DC output current * DC-DC output voltage;

[0027] 3) Input and output power of the electric drive:

[0028] 3.1) When the motor torque T>0, the motor is in driving mode:

[0029] Electric drive input power Pdi_1 = Battery output power Po - DC-DC input power Pdcdci;

[0030] Motor output power Pdo_1 = motor speed * motor torque / 9550;

[0031] 3.2) When the motor torque T < 0, the motor is in regeneration state:

[0032] Electric drive input power Pdi_2 = motor speed * motor torque / 9550;

[0033] Motor output power Pdo_2 = Battery input power + DC-DC input power.

[0034] Furthermore, in step five, the Matlab program outputs 12 rows of arrays, which are respectively the cycle operating time, SOC, total energy released by the battery, energy released by the battery drive, energy recovered by the battery, energy released during electric drive, input energy during electric drive, input energy during electric drive recovery, output energy during electric drive recovery, motor drive efficiency, motor recovery efficiency, and DC-DC efficiency.

[0035] As a second aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle range test data analysis method of the present invention.

[0036] The present invention has the following beneficial effects:

[0037] 1. This invention processes data acquired via a bus using a MATLAB program, enabling rapid data extraction, analysis, and application;

[0038] 2. In the MATLAB program, by identifying working condition feature points, tasks that are time-consuming and inaccurate offline can be processed quickly online. The energy consumption under different working condition cycles is effectively broken down, which further guides the development.

[0039] 3. Depending on the analysis requirements, other necessary calculation variables can be added to the MATLAB program. Attached Figure Description

[0040] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0041] Figure 1 This is an overall flowchart of the vehicle range test data analysis method based on MATLAB as described in Embodiment 1 of the present invention;

[0042] Figure 2 This is a schematic diagram of a computer device structure according to Embodiment 2 of the present invention. Detailed Implementation

[0043] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments:

[0044] Example 1

[0045] like Figure 1 As shown in the figure, this embodiment is a method for analyzing vehicle range test data based on MATLAB, including the following steps:

[0046] Step 1: Data Acquisition. During the range test, data from the EVcan and PTcan of the test vehicle are collected.

[0047] In this embodiment, CANoe, CANPEE, or other data acquisition devices are used to collect vehicle EVCAN and PTCAN data during the range test. The recommended data format is MDF or MF4.

[0048] The EVcan and PTcan data of the test vehicle shall include at least the following: battery pack voltage and current, motor speed and torque, and vehicle speed; if it is necessary to analyze the power consumption of thermal systems such as air conditioning, the EVcan and PTcan data of the test vehicle shall also include data such as PTC and compressor.

[0049] Step 2: Convert the experimental data format. Convert the data collected in Step 1 into .mat format:

[0050] In this embodiment, the data collected by canoe and canape is converted into .mat format through the data conversion function in the acquisition device, and the data interval is selected as 0.1s.

[0051] Step 3: Pre-processing the data, naming the collected data files according to a unified format:

[0052] If there are multiple experimental data files collected in step one, after the format conversion in step two, the file naming format needs to be standardized: use a uniform file name and add different numbers to the end of each file name to distinguish the files and facilitate file retrieval in the MATLAB program.

[0053] Step 4: Solve the experimental data using MATLAB. The MATLAB program identifies features of the overall loop based on the operating conditions, decomposes the overall loop into single-loop operating conditions using feature points, and calculates the variables to be analyzed in each loop:

[0054] Place the experimental data file processed in step three and the MATLAB file in the same folder, run the motor program, and use the MATLAB program to extract and solve the .mat file;

[0055] Because a single endurance test is long and generally includes at least 30 cycles, manually breaking down each endurance cycle is labor-intensive and inefficient. The MATLAB program can identify the features of the cycle based on the operating conditions, break down the cycle into single-cycle operating conditions through feature points, and then calculate the variables that need to be analyzed in each cycle.

[0056] S41. First, clear all history and errors, then load the .mat file;

[0057] To facilitate subsequent program solving and easy program review and modification, the analysis variables involved in the .mat file can be uniformly and simplified in naming, as shown in the following example:

[0058] U = Battery pack voltage;

[0059] A = Battery pack current;

[0060] n = motor speed;

[0061] T = Motor torque;

[0062] Vc = Vehicle speed;

[0063] SOC = Battery Pack SOC;

[0064] UDCDC = DC-DC input voltage;

[0065] IDCDC = DCD input current;

[0066] S42. Based on the range analysis requirements, first calculate the energy released by the battery drive and the power recovered during recycling, the DC-DC input and output power, and the motor input and output power:

[0067] 1) Motor input and output power:

[0068] Battery power P = Battery voltage U * Battery current I;

[0069] When P>0, P=Po is defined, and the battery is in the driving state. When P<0, P=Pi is defined, and the battery is in the recycling state.

[0070] 2) DC-DC input and output power:

[0071] DC-DC input power Pdcdci = DC-DC input current * DC-DC input voltage;

[0072] DC-DC output power Pdcdcio = DC-DC output current * DC-DC output voltage;

[0073] 3) Input and output power of the electric drive:

[0074] 3.1) When the motor torque T>0, the motor is in driving mode:

[0075] Electric drive input power Pdi_1 = Battery output power Po - DC-DC input power Pdcdci;

[0076] Motor output power Pdo_1 = motor speed * motor torque / 9550;

[0077] 3.2) When the motor torque T < 0, the motor is in regeneration state:

[0078] Electric drive input power Pdi_2 = motor speed * motor torque / 9550;

[0079] Motor output power Pdo_2 = Battery input power + DC-DC input power;

[0080] 4) If high and low temperature range analysis is involved, the compressor power and the power of the PTC or other air conditioning cooling system can be calculated;

[0081] S43. After the above calculations, finally output an EXCEL list, showing the calculations for each data analysis group.

[0082] In this embodiment, taking the CLTC cycle of electric vehicles as an example, the specific implementation method is as follows:

[0083] The highest speed in the CLTC cycle is 114 km / h, and the second highest speed is 113.7 km / h. Therefore, the cycle can be divided according to vehicle speed.

[0084] When vehicle speed Vc(i) > maximum operating speed - 0.1 && Vc(i+1) < maximum operating speed + 0.1 && i < total cycle time, this feature point is used as the cycle split point. The data before the feature point is one analysis data group, and the data after the feature point is another analysis data group. The power and time are integrated by the integration function trapz to calculate the battery input and output energy, electric drive output and output energy, DC-DC input and output energy, and energy consumed by the compressor, PTC and other systems in each analysis data group. The energy calculated above can be used to further analyze the motor drive recovery efficiency and DC-DC efficiency corresponding to each data analysis group.

[0085] Motor drive efficiency = Energy output by the electric drive during driving / Energy input to the electric drive;

[0086] Motor recovery efficiency = Energy output from electric drive during recovery / Energy input to electric drive;

[0087] DC-DC efficiency = DC-DC output energy / DC-DC input energy.

[0088] Step 5: Calculate and output the energy consumption of each system in each loop:

[0089] The Matlab program will output a 12-line array, representing the cycle time, SOC, total battery output energy, battery drive output energy, battery recovery energy, energy output during electric drive, input energy during electric drive, input energy during electric recovery, output energy during electric recovery, motor drive efficiency, motor recovery efficiency, and DC-DC efficiency. A sample output file is shown in Table 1.

[0090] Table 1. List of output files for energy consumption calculation of each system in the CLTC cycle of electric vehicles (partial data)

[0091] SOC changes in each cycle 3.2 3.1 3 The battery releases electrical energy 1.7 1.67 1.66 Release electrical energy during driving 2.26 2.27 2.26 Recovering electrical energy during recycling -0.47 -0.60 -0.60 Electric motor consumes electrical energy to do work 1.9 2.0 2.0 Electrical energy at the power bus terminal of the motor 2.19 2.21 2.2 Motor recycling and motor work -0.5 -0.6 -0.62 Motor recovers electrical energy from the bus end -0.51 -0.65 -0.65 Motor drive efficiency 88％ 88％ 87.6％ Motor recycling efficiency 89％ 88％ 88％ DCDC efficiency 95％ 94％ 95％

[0092] This data allows for comparison and breakdown of energy consumption across different cycles.

[0093] Depending on the analysis needs, the variables to be analyzed can also be graphically output using the plotting command in the MATLAB program, which facilitates the analysis.

[0094] Example 2

[0095] Figure 2 This is a schematic diagram of the structure of a computer device according to Embodiment 4 of the present invention. Figure 2 A block diagram of an exemplary computer device 12 suitable for implementing embodiments of the present invention is shown. Figure 2 The computer device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0096] like Figure 2 As shown, the computer device 12 is represented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and a bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0097] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (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.

[0098] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0099] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 2 Not shown; usually referred to as a "hard drive"). Although Figure 2 Not 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 (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0100] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0101] The computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with the computer device 12, and / or with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via the input / output (I / O) interface 22. Furthermore, in this embodiment, the display 24 of the computer device 12 is not an independent entity, but is embedded in a mirror, so that when the display surface of the display 24 is not displayed, the display surface of the display 24 and the mirror surface visually blend together. Moreover, the computer device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via the network adapter 20. As shown, the network adapter 20 communicates with other modules of the computer device 12 via the bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0102] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the vehicle range test data analysis method provided in Embodiment 1 of the present invention.

[0103] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0104] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0105] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. A method for analyzing vehicle range test data based on MATLAB, characterized in that, Includes the following steps: Step 1: Data Acquisition. During the range test, data from the EVcan and PTcan of the test vehicle will be collected. Step 2: Convert the experimental data format, converting the data collected in Step 1 into .mat format; Step 3: Pre-processing of data, naming the collected data files in a unified format; Step four: Solve the experimental data using MATLAB. The MATLAB program identifies features of the overall cycle based on the operating conditions, decomposes the overall cycle into single-cycle operating conditions using feature points, and calculates the variables to be analyzed in each cycle; Step four includes: S41. Clear all history and errors, and load the .mat file; S42. Based on the range analysis requirements, first calculate the energy released by the battery drive and the power recovered during recycling, the DC-DC input and output power, and the motor input and output power; step S42 includes: 1) Motor input and output power: Battery power P = Battery voltage U * Battery current I; When P>0, P=Po is defined, and the battery is in the driving state. When P<0, P=Pi is defined, and the battery is in the recycling state. 2) DC-DC input and output power: DC-DC input power Pdcdci = DC-DC input current * DC-DC input voltage; DC-DC output power Pdcdcio = DC-DC output current * DC-DC output voltage; 3) Input and output power of the electric drive: When the motor torque T > 0, the motor is in driving mode: Electric drive input power Pdi_1 = Battery output power Po - DC-DC input power Pdcdci; Motor output power Pdo_1 = motor speed * motor torque / 9550; When the motor torque T < 0, the motor is in regeneration state: Electric drive input power Pdi_2 = motor speed * motor torque / 9550; Motor output power Pdo_2 = Battery input power + DC-DC input power; S43. After the above calculations, finally output an EXCEL list to show the calculations for each data analysis group; Step 5: Calculate and output the energy consumption of each system in each cycle, and compare and decompose the energy consumption of different cycles based on the output data.

2. The method for analyzing vehicle range test data based on MATLAB as described in claim 1, characterized in that, In step one, vehicle EVcan and PTcan data are collected during the range test using canoe, canape or other data acquisition devices, and the data format is mdf or mf4.

3. The method for analyzing vehicle range test data based on MATLAB as described in claim 2, characterized in that, In step two, the data collected by canoe and canape is converted into .mat format through the data conversion function in the acquisition device, with a data interval of 0.1s.

4. The method for analyzing vehicle range test data based on MATLAB as described in claim 1, characterized in that, In step three, the data file naming format is as follows: each data file uses a uniform filename, and different numbers are added to the end of each filename to distinguish them.

5. The method for analyzing vehicle range test data based on MATLAB as described in claim 1, characterized in that, In step four, the experimental data file processed in step three and the MATLAB file are placed in the same folder. The motor running program extracts and solves the .mat file using the MATLAB program.

6. The method for analyzing vehicle range test data based on MATLAB as described in claim 1, characterized in that, In step five, the Matlab program outputs 12 rows of arrays, which are respectively the cycle operating time, SOC, total energy released by the battery, energy released by the battery drive, energy recovered by the battery, energy released during electric drive, input energy during electric drive, input energy during electric drive recovery, output energy during electric drive recovery, motor drive efficiency, motor recovery efficiency, and DC-DC efficiency.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the vehicle range test data analysis method as described in any one of claims 1-6.

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