Quantitative analysis method for fuel consumption based on user working condition test data

Abstract

The application discloses a kind of oil consumption quantitative analysis method based on user working condition test data, combine user working condition test data with standard test data, through the time proportion deviation of subdivision speed section, lock the abnormal speed section leading to oil consumption difference, and based on abnormal speed section, the driving condition characteristics shown when driving in this abnormal speed interval in test phase are observed, then the time length, speed, instantaneous fuel consumption, form distance and other data corresponding to this driving condition are statistically analyzed, the oil consumption quantitative value of this driving condition in user working condition test is obtained, which indicates the direction for subsequent actual road fuel consumption optimization.The application increases targeted quantitative index analysis on the basis of traditional qualitative analysis based on user working condition, not only greatly shortens test data analysis time and improves work efficiency, but also can more objectively and accurately analyze fuel consumption difference, thereby providing reliable basis for identifying actual road working condition fuel consumption analysis of the vehicle under research and subsequent vehicle.

Description

Technical Field

[0001] This invention relates to the field of automotive fuel consumption testing technology, and in particular to a method for quantitative analysis of fuel consumption based on user operating condition test data. Background Technology

[0002] In the early stages of automobile development, fuel economy testing typically involves using a rotating drum to simulate standard operating conditions and test fuel consumption. Later in the development process, user-specific fuel consumption testing is conducted. User-specific fuel consumption differs significantly from standard operating conditions. User-specific fuel consumption testing is a road-based testing method, usually using the refueling method (based on the fuel pump stopping, the amount of fuel added is divided by the mileage to calculate the fuel consumption per 100 kilometers) to analyze fuel consumption results. This method can only qualitatively describe the fuel consumption of the user group, but cannot quantify specific indicators, making it difficult to propose a clear fuel consumption optimization plan for the vehicle.

[0003] Specifically, user-condition fuel consumption testing typically involves selecting a test route and planning a specific strategy. The day before the test, all test vehicles must be fully fueled (all vehicles are tested until the fuel pump clicks off), and the odometer readings are reset to zero. All vehicles are then parked in designated locations. On the day of the test, vehicles are retrieved and connected to a computer and 581 device. INCA software is used to collect ECU data. After the test, the data collected by INCA throughout the entire process is analyzed to qualitatively determine the fuel consumption differences. The analysis method based on this test includes the following steps: 1) Using MDA software, the data collected by INCA is opened, and signals such as VKPH (vehicle speed), EPEDPON (accelerator pedal opening), PE enrichment switch, and EXHMPACT (exhaust temperature enrichment) are retrieved to confirm whether there are any abnormalities in the process data; 2) The user-condition VKPH (vehicle speed), EPEDPON (accelerator pedal opening), PE enrichment switch, and EXHMPACT (exhaust temperature enrichment) data are qualitatively compared with the corresponding data from the standard WLTC (Wheel Trim Test) cycle to provide a fuel consumption analysis conclusion.

[0004] In actual testing and analysis, to ensure that the power enters the PE enrichment process and improves power, it is necessary to analyze the operating conditions under a specific throttle opening. However, the throttle was kept at 50% throughout the test, which could not trigger PE enrichment. Therefore, the frequency of triggering high throttle was relatively high during the test drive. In order to ensure that the power enters the PE enrichment process, the throttle opening can be higher than 80%, which may lead to an increase in actual fuel consumption. In addition, due to the large amount of vehicle driving data, qualitative analysis is not only slow to process, but also cannot objectively and accurately reflect the real fuel consumption situation. Therefore, it is difficult to provide reliable quantitative support for fuel consumption improvement. Summary of the Invention

[0005] In view of the above, the present invention aims to provide a method for quantitative analysis of fuel consumption based on user operating condition test data, so as to solve the aforementioned technical problems.

[0006] The technical solution adopted in this invention is as follows:

[0007] This invention provides a method for quantitative analysis of fuel consumption based on user operating condition test data, including:

[0008] The process data collected in the user's fuel consumption test is correlated with the vehicle speed range in the standard condition to identify the corresponding vehicle speed range between the standard condition and the user's condition.

[0009] The vehicle speed data in the process data is further segmented according to multiple preset vehicle speed thresholds, and corresponding segmentation is performed within the corresponding vehicle speed segment in the standard working condition.

[0010] By comparing the time percentage deviation between the user's operating conditions and the standard operating conditions for the corresponding detailed segments, abnormal speed segments can be identified.

[0011] Determine the vehicle's driving status type within the abnormal speed range;

[0012] By combining the instantaneous fuel consumption data, duration data, and driving distance data corresponding to the driving state type in the process data, the fuel consumption value under the driving state type is calculated and used as a quantitative indicator to guide fuel consumption optimization.

[0013] In at least one possible implementation, the association of process data collected during user fuel consumption testing with vehicle speed ranges in standard operating conditions includes:

[0014] Obtain the first maximum vehicle speed and the first average vehicle speed from the process data;

[0015] Obtain the second maximum speed and the second average speed within the preset speed range under standard operating conditions;

[0016] If the difference between the first maximum speed and the second maximum speed is less than or equal to the first lower limit, and the difference between the first average speed and the second average speed is less than or equal to the second lower limit, then the corresponding relationship between the speed ranges of the standard operating condition and the user's operating condition is determined.

[0017] In at least one possible implementation, obtaining the first maximum vehicle speed and the first average vehicle speed from the process data includes: pre-segmenting the process data into initial vehicle speed segments, and calculating the first maximum vehicle speed and the first average vehicle speed within the initial vehicle speed segments.

[0018] In at least one possible implementation, determining the abnormal speed segment includes:

[0019] Obtain the percentage of driving time in each detailed segment of both user working conditions and standard working conditions;

[0020] If the absolute value of the difference between the time percentage of the refined segment corresponding to the user's operating condition and the standard operating condition is greater than or equal to the preset time percentage deviation threshold, then the refined segment is determined to be the abnormal speed segment.

[0021] In at least one possible implementation, determining the vehicle's driving state type within the abnormal speed range includes:

[0022] Using the process data, the driving characteristics of the test vehicle in the abnormal speed range were obtained; Vio

[0023] Based on the driving characteristics, the driving state type is determined.

[0024] In at least one possible implementation, obtaining the fuel consumption value under the driving state type includes calculating the fuel consumption value under the driving state type according to the following formula: Q = L * T / 36S, where Q is the fuel consumption of the driving state type, L is the instantaneous fuel consumption of the driving state type, T is the duration of the driving state type, and S is the driving distance of the driving state type.

[0025] In at least one of the possible implementations, the driving state type includes: idling, acceleration, constant speed, and deceleration.

[0026] The main design concept of this invention lies in combining user-defined driving condition test data with standard test data. By subdividing the time percentage deviation of different speed segments, it identifies abnormal speed segments that cause fuel consumption differences. Based on these abnormal speed segments, it analyzes the driving characteristics exhibited during the test phase when driving within these abnormal speed ranges. Then, it statistically analyzes the corresponding data such as duration, speed, instantaneous fuel consumption, and travel distance to derive a quantitative value for fuel consumption under these user-defined driving condition tests. This provides direction for subsequent optimization of fuel consumption in real-world driving conditions. This invention adds targeted quantitative indicator analysis to the traditional qualitative analysis based on user-defined driving conditions. This not only significantly shortens test data analysis time and improves work efficiency, but also enables a more objective and accurate analysis of fuel consumption differences, thus providing a reliable basis for analyzing fuel consumption under real-world driving conditions for vehicles under development and future models. Attached Figure Description

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below with reference to the accompanying drawings, wherein:

[0028] Figure 1 A flowchart illustrating the fuel consumption quantification analysis method based on user operating condition test data provided in this embodiment of the invention. Detailed Implementation

[0029] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0030] This invention proposes an embodiment of a fuel consumption quantification analysis method based on user operating condition test data. Specifically, as follows: Figure 1 As shown, it includes:

[0031] Step S1: Associate the process data collected in the user working condition fuel consumption test with the vehicle speed range in the standard working condition, and identify the corresponding vehicle speed range between the standard working condition and the user working condition.

[0032] Step S2: The vehicle speed data in the process data is further segmented according to multiple preset vehicle speed thresholds, and the corresponding vehicle speed segments in the standard working condition are further segmented accordingly.

[0033] Step S3: Compare the time percentage deviation between the user's operating conditions and the standard operating conditions to determine the abnormal speed segments;

[0034] Step S4: Determine the vehicle's driving status type within the abnormal speed range;

[0035] Step S5: Combine the instantaneous fuel consumption data, duration data, and driving distance data corresponding to the driving state type in the process data to calculate the fuel consumption value under the driving state type, and use it as a quantitative indicator to guide fuel consumption optimization.

[0036] Furthermore, the step of associating the process data collected during the user's fuel consumption test with the vehicle speed range in the standard operating conditions includes:

[0037] Obtain the first maximum vehicle speed and the first average vehicle speed from the process data;

[0038] Obtain the second maximum speed and the second average speed within the preset speed range under standard operating conditions;

[0039] If the difference between the first maximum speed and the second maximum speed is less than or equal to the first lower limit, and the difference between the first average speed and the second average speed is less than or equal to the second lower limit, then the corresponding relationship between the speed ranges of the standard operating condition and the user's operating condition is determined.

[0040] Based on this concept, obtaining the first maximum vehicle speed and the first average vehicle speed in the process data includes: pre-segmenting the process data into initial vehicle speed segments, and calculating the first maximum vehicle speed and the first average vehicle speed in the initial vehicle speed segments.

[0041] Furthermore, the determination of abnormal speed segments includes:

[0042] Obtain the percentage of driving time in each detailed segment of both user working conditions and standard working conditions;

[0043] If the absolute value of the difference between the time percentage of the refined segment corresponding to the user's operating condition and the standard operating condition is greater than or equal to the preset time percentage deviation threshold, then the refined segment is determined to be the abnormal speed segment.

[0044] Furthermore, the determination of the vehicle's driving state type within the abnormal speed range includes:

[0045] Using the process data, the driving characteristics of the test vehicle in the abnormal speed range are obtained;

[0046] Based on the driving characteristics, the driving state type is determined.

[0047] Based on this, the process of obtaining the fuel consumption value under the driving state type includes calculating the fuel consumption value under the driving state type according to the following formula: Q = L * T / 36S, where Q is the fuel consumption of the driving state type, L is the instantaneous fuel consumption of the driving state type, T is the duration of the driving state type, and S is the driving distance of the driving state type.

[0048] Finally, it should be noted that the aforementioned driving state types include: idling, acceleration, constant speed, and deceleration.

[0049] Regarding step S1, specifically, the process data collected from the user's fuel consumption test is used to identify the corresponding segment to the standard driving condition using two variables: maximum vehicle speed Vmax and average vehicle speed Vavg. Taking a certain vehicle model and a certain road condition as an example, the maximum vehicle speed is 79 km / h and the average vehicle speed is 32 km / h. The standard driving condition (WLTC) has preset low-speed segments (e.g., 0-60 km / h) and medium-speed segments (e.g., 60-90 km / h). The preset second maximum vehicle speed for the low-speed and medium-speed segments in the standard driving condition is 76.6 km / h and the preset second average vehicle speed is 27.6 km / h. Comparing the process data in the aforementioned example with the data in the standard driving condition, it can be seen that the two are basically equivalent (i.e., the maximum vehicle speed and the average vehicle speed are both less than or equal to their respective preset lower limits). Therefore, a correlation can be established between the two, and the corresponding segment can be identified as 0-90 km / h (the preset low-speed segment and medium-speed segment).

[0050] Regarding step S2, specifically, the process data is refined into several segments according to multiple preset speed thresholds to obtain several first refined speed segments (seven first refined speed segments as shown in Table 1 below), and the first time proportion of each first refined speed segment is calculated; similarly, the corresponding segments in the standard operating condition are divided according to each first refined speed segment to obtain several second refined speed segments of the standard operating condition, and the second time proportion of each second refined speed segment is obtained.

[0051] Regarding step S3, specifically, the first time percentage and the second time percentage for the corresponding speed segment are compared. Speed ​​segments with an absolute difference greater than or equal to a preset time percentage deviation threshold (e.g., 10%) are identified as having significant differences and requiring further refinement of the speed segment. As shown in the refined speed segment time percentage table (Table 1), compared to the standard operating conditions, the time percentage difference for user-controlled speeds below 20 km / h is significant. This indicates that the refined speed segment results in higher fuel consumption than the standard. Therefore, further processing can be performed on speed segments below 20 km / h to analyze the specific difference in fuel consumption (explained later).

[0052] Table 1. Detailed breakdown of vehicle speed time segments

[0053] Refine vehicle speed segments User working time percentage Standard operating condition time percentage 0≤v＜10 36.0％ 24.1％ 10≤v＜20 5.4％ 16.2％ 20≤v＜30 7.5％ 17.0％ 30≤v＜40 11.7％ 10.9％ 40≤v＜50 16.1％ 14.3％ 50≤v＜60 14.1％ 9.9％ 60≤v 9.2％ 8％

[0054] To illustrate the above solution, we will use real user fuel consumption data as an example:

[0055] The test was conducted on March 5, 2022, at an ambient temperature of 15℃-20℃. Test vehicle 1 achieved a fuel consumption of 8.6L / 100km under user-defined conditions and 7.98L / 100km under standard operating conditions. Test vehicle 2, the same model, achieved 7.4L / 100km under user-defined conditions and 7.24L / 100km under standard operating conditions. The difference in fuel consumption under user-defined conditions was 1.2L / 100km, while the difference under standard operating conditions was 0.74L / 100km. This indicates significant room for improvement in actual road fuel consumption, requiring further fuel consumption analysis to provide quantitative optimization guidance.

[0056] Accordingly, following the aforementioned embodiment, the process data of test vehicle 1 and test vehicle 2 were analyzed. Test vehicle 1 had a maximum speed of 56 km / h and an average speed of 15 km / h; test vehicle 2 had a maximum speed of 54 km / h and an average speed of approximately 15 km / h. The throttle opening of both vehicles was generally below 25% (similar driving styles). Based on their maximum and average speeds, a corresponding segment in the standard operating condition (such as a preset low-speed segment) could be identified and associated. Next, the process data of test vehicle 1 and test vehicle 2 were further segmented, and the corresponding low-speed segment identified in the WLTC operating condition was further segmented. The time percentage data for the corresponding segmented speeds was statistically analyzed, as shown in Table 2, which displays the measured time percentages for both vehicles.

[0057] Table 2. Percentage of Actual Measurement Time for Both Vehicles

[0058] Refine vehicle speed segments Standard operating condition time percentage Test vehicle 1 time percentage Test vehicle 2 time percentage 0≤v＜10 32.1％ 49％ 48.1％ 10≤v＜20 21.9％ 12.6％ 12.0％ 20≤v＜30 22.4％ 15.4％ 14.6％ 30≤v＜40 11.9％ 14.5％ 15.2％ 40≤v＜50 8.8％ 8.0％ 8.9％ 50≤v＜60 2.9％ 0.6％ 1.3％ 60≤v 0.0％ 0.0％ 0.0％

[0059] As can be seen from Table 2 comparing the time percentages of each refined speed segment in the low-speed range of the WLTC operating condition, the time percentages of both vehicles in the user operating condition at the speed segment 0≤v<10 deviate significantly from the time percentages of the standard operating condition. Therefore, it can be determined that the refined speed segment 0≤v<10 is the main reason for the difference in fuel consumption between the two vehicles compared to the standard operating condition.

[0060] Continuing from the previous text, regarding steps S4 and S5, specifically, for the identified abnormal speed range, the vehicle's driving state type within that abnormal speed range is determined (e.g., by using preset driving characteristics from the user's operating condition test process data, it can be determined whether the vehicle is idling, accelerating, maintaining a constant speed, or decelerating within that speed range; furthermore, it can be understood that the vehicle may not be traveling in a single driving state within a certain speed range, thus allowing for the statistical analysis of more types of driving state characteristics, which can be used as a basis for subsequent steps). Then, by combining the instantaneous fuel consumption, duration, and driving distance corresponding to that driving state type from the process data, the fuel consumption per 100 kilometers under that driving state is calculated. Based on this, a reliable guiding direction can be provided for optimizing fuel consumption on actual roads. For example, a quantitative difference value can be further calculated from the fuel consumption data of a certain driving state.

[0061] Based on the previous example, in actual operation, using the abnormal speed range of 0≤v<10, the driving characteristics of the vehicle in this speed range during the user's working condition test were analyzed, which mainly reflected the idling state type. Therefore, the instantaneous fuel consumption, idling duration, and idling distance of test vehicle 1 and test vehicle 2 were read from the test process data respectively (of course, the instantaneous fuel consumption, duration, and distance of the vehicle when driving at a speed of 0≤v<10 can also be obtained as a convenient implementation method). The idling fuel consumption and the difference between the two vehicles were calculated according to the following formula: Q=L*T / 36S, where Q is the fuel consumption per 100km in the idling state, in L / 100km; L is the instantaneous fuel consumption in the idling state, in L / h; T is the idling duration, in s; and S is the distance traveled in the idling state, in km. The above calculations show that the idle fuel consumption of test vehicle 1 is about 0.8L / 100km higher than that of test vehicle 2. This provides a reliable data analysis basis for subsequent targeted optimization of fuel consumption on actual roads.

[0062] In summary, the main design concept of this invention lies in combining user-defined driving condition test data with standard test data. By subdividing the time percentage deviation of different speed segments, it identifies abnormal speed segments that cause fuel consumption differences. Based on these abnormal speed segments, it examines the driving characteristics exhibited during the test phase when driving within these abnormal speed ranges. Then, it performs statistical analysis on the corresponding data such as duration, speed, instantaneous fuel consumption, and travel distance to derive a quantitative value for fuel consumption under these user-defined driving condition tests. This provides direction for subsequent optimization of fuel consumption in real-world driving conditions. This invention adds targeted quantitative indicator analysis to the traditional qualitative analysis based on user-defined driving conditions. This not only significantly shortens test data analysis time and improves work efficiency but also enables a more objective and accurate analysis of fuel consumption differences, thus providing a reliable basis for analyzing fuel consumption under real-world driving conditions for vehicles under development and future models.

[0063] In this embodiment of the invention, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects have an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0064] The above description of the structure, features, and effects of the present invention is based on the embodiments shown in the figures. However, the above are only preferred embodiments of the present invention. It should be noted that the technical features involved in the above embodiments and their preferred methods can be reasonably combined and matched by those skilled in the art to form a variety of equivalent solutions without departing from or changing the design concept and technical effects of the present invention. Therefore, the present invention is not limited to the scope of implementation shown in the figures. Any changes made in accordance with the concept of the present invention, or modifications to equivalent embodiments, that do not exceed the spirit covered by the specification and figures, should be within the protection scope of the present invention.

Claims

1. A method for quantitative analysis of fuel consumption based on user operating condition test data, characterized in that, include: The process data collected in the user's fuel consumption test is correlated with the vehicle speed range in the standard condition to identify the corresponding vehicle speed range between the standard condition and the user's condition. The vehicle speed data in the process data is further segmented into segments according to multiple preset vehicle speed thresholds, and corresponding vehicle speed segments are further segmented within the corresponding vehicle speed segments in the standard operating conditions. Obtain the percentage of driving time in each detailed segment of both user working conditions and standard working conditions; If the absolute value of the difference between the time percentage of the refined segment corresponding to the user's working condition and the standard working condition is greater than or equal to the preset time percentage deviation threshold, then the refined segment is determined to be an abnormal speed segment. Using the process data, determine the vehicle driving state type in the abnormal speed range; By combining the instantaneous fuel consumption data of the vehicle driving state type, the duration data of the vehicle driving state type, and the driving distance data of the vehicle driving state type in the process data, the fuel consumption value under the vehicle driving state type is calculated and used as a quantitative indicator to guide fuel consumption optimization.

2. The fuel consumption quantitative analysis method based on user operating condition test data according to claim 1, characterized in that, The process data collected during the user's fuel consumption test is correlated with the vehicle speed range in the standard operating conditions, including: Obtain the first maximum vehicle speed and the first average vehicle speed from the process data; Obtain the second maximum speed and the second average speed within the preset speed range under standard operating conditions; If the difference between the first maximum speed and the second maximum speed is less than or equal to the first lower limit, and the difference between the first average speed and the second average speed is less than or equal to the second lower limit, then the corresponding relationship between the speed ranges of the standard operating condition and the user's operating condition is determined.

3. The fuel consumption quantitative analysis method based on user operating condition test data according to claim 2, characterized in that, The step of obtaining the first maximum vehicle speed and the first average vehicle speed in the process data includes: pre-segmenting the process data into initial vehicle speed segments, and calculating the first maximum vehicle speed and the first average vehicle speed in the initial vehicle speed segments.

4. The fuel consumption quantitative analysis method based on user operating condition test data according to claim 1, characterized in that, The determination of the vehicle driving status type in the abnormal speed range includes: Using the process data, the driving characteristics of the test vehicle in the abnormal speed range are obtained; Based on the driving characteristics, the vehicle driving state type is determined.

5. The fuel consumption quantitative analysis method based on user operating condition test data according to claim 1, characterized in that, The process of obtaining the fuel consumption value under the vehicle driving state type includes calculating the fuel consumption value under the vehicle driving state type according to the following formula: Q=L*T / 36S, where Q is the fuel consumption of the vehicle driving state type, L is the instantaneous fuel consumption of the vehicle driving state type, T is the duration of the vehicle driving state type, and S is the driving distance of the vehicle driving state type.

6. The fuel consumption quantitative analysis method based on user operating condition test data according to any one of claims 1 to 5, characterized in that, The vehicle driving status types include: idling, acceleration, constant speed, and deceleration.

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