Gear limit torque based online fuel saving method

Abstract

The application provides an online fuel-saving method based on gear limit torque, and relates to the technical field of engine control, comprising the following steps: S1. inputting vehicle number; S2. calculating cumulative mileage; S3. removing data with poor quality; S4. generating working condition distribution diagram; S5. fitting optimal specific fuel consumption curve; S6. fitting moving optimal specific fuel consumption curve; S7. generating data; S8. OTA remote upgrading of user vehicle. Through the big data intelligent analysis platform algorithm, the working condition distribution of engine operation data is automatically analyzed online, and the optimal personalized calibration parameter is set for the current driver in combination with the platform same type driver working condition analysis situation, so that the optimal limit torque suitable for the current driver is output in each gear and speed interval, the effect of one vehicle one calibration is achieved, and the driver driving behavior is standardized.

Description

Technical Field

[0001] This application relates to the field of engine control technology, and in particular to an online fuel-saving method based on gear-position torque limiting. Background Technology

[0002] Engine matching engineers need to conduct matching tests with vehicle manufacturers to calibrate the corresponding limiting torque curves for different engine models, gears, and speed ranges to appropriately limit the current engine's external characteristic torque. This calibration is only performed for specific engine models and does not extend to each individual user.

[0003] The existing technical solutions have the following drawbacks:

[0004] (1). Uniqueness: The parameters are calibrated in batches for a certain engine model based only on the experience of the supporting engineers. However, for the same model of vehicle, the usage scenarios and driving habits of the drivers may be different, so the parameters are not universally applicable.

[0005] (2). Complexity: Manual calibration requires engineers to conduct repeated experiments in various scenarios to determine appropriate parameters. Calibration is labor-intensive and time-consuming, and the determined curve parameters may not be optimal. Summary of the Invention

[0006] To overcome the shortcomings of related technologies that only calibrate for engine models and do not reach every user, this application provides an online fuel-saving method based on gear torque limiting. This solution uses big data intelligent analysis platform technology to automatically analyze the engine operating data (engine speed, engine torque, throttle, vehicle speed, mileage, etc.) of each user's vehicle online and automatically generate the optimal limiting torque curve corresponding to each gear and speed range.

[0007] The technical solution adopted by the embodiments of this application to solve its technical problem is:

[0008] An online fuel-saving method based on gear-position torque limiting includes the following steps:

[0009] S1. Enter the vehicle number;

[0010] S2. Calculate cumulative mileage;

[0011] S3. Remove data with poor quality;

[0012] S4. Generate a working condition distribution diagram;

[0013] S5. Fit the optimal specific fuel consumption curve;

[0014] S6. Fit the moving optimal specific fuel consumption curve;

[0015] S7. Generate data;

[0016] S8.OTA remote upgrade for user vehicles.

[0017] Furthermore, in step S2, the cumulative mileage is calculated by counting back 30 days from the day the function is triggered, whereby...

[0018] If the 30-day mileage is greater than 6000km, then use this start and end point; if the 30-day mileage is less than 6000km, then push the start point forward to when the mileage is greater than 6000km.

[0019] Furthermore, in step S3, a day with poor data quality is defined as a problem day. Data from problem days is not included in the calculation of the operating condition distribution map in step S4. The following three situations determine that the data quality of that day is poor:

[0020] A. No increase in mileage: Check the data for each day within the date range. Compare the mileage at the moment the region changes with the moment of the last change. If there is no change, it is considered a problem day and should be removed.

[0021] B. No increase in fuel consumption: Check the data for each day of the date range. Compare the fuel consumption at the moment the region changes with the moment of the last change. If there is no change, it is considered a problem day and should be removed.

[0022] C. Set a fuel consumption reference value for each model. If there is an obvious error, it is judged as data for a problematic day and removed.

[0023] Furthermore, in step S4, the time from step S2 is used to draw a thermal distribution map of the operating conditions, and the speed and torque distribution is rasterized to finally obtain the speed and torque distribution. The drawing steps are as follows:

[0024] (1). Range division: The speed is divided into 100rpm intervals, and the torque interval division standard is: the reference torque corresponding to the current model is rounded up to the nearest 100 / 10;

[0025] (2). Classification: Classify each speed and torque separately, calculate the number of items in each category, and calculate the ratio of each category to the total number of categories.

[0026] Furthermore, in step S5, based on the universal characteristic curve of the basic engine, the optimal fuel consumption curves of each engine model are extended and fitted, that is, the optimal specific fuel consumption point at each speed.

[0027] Furthermore, in step S6, fitting the optimal moving ratio fuel consumption curve consists of the following three steps:

[0028] A. The index for shifting the optimal fuel consumption curve;

[0029] B. Rules for shifting the optimal fuel consumption curve;

[0030] C. Set a torque limiter based on the gear position.

[0031] Furthermore, in step S6, sub-step A is further subdivided into the following steps: ① Calculate the total on the line; the distribution of the speed and torque ranges of all vehicles on a certain model's big data platform, and the total proportion A of the optimal control line; ② Calculate the median. Calculate the total proportion B of vehicles whose distribution proportion is below A for this model, and take the median.

[0032] Furthermore, in sub-step B of step S6, the online proportion C of the target vehicle is calculated. ① When C > A, no movement is made; ② When B ≤ C ≤ A, the torque value of the optimal fuel consumption curve is lowered - the reference torque of the corresponding model is rounded up * 5%; ③ When the sum < B, the torque value of the optimal fuel consumption curve is lowered - the reference torque of the corresponding model is rounded up * 10%.

[0033] Furthermore, in sub-step C of step S6, the gear ranges are divided into low, medium, and high gear ranges; the low gear range is set according to the torque corresponding to each speed based on the external characteristic value of the engine model; the medium gear range is filled with the value after shifting the optimal specific fuel consumption curve below 1600 rpm, and the speed limit is set above 1600 rpm; the high gear range is filled with the value after shifting the optimal specific fuel consumption curve.

[0034] Furthermore, in step S7, the table set in step S6 is filled into the engine data to generate the data. The data generated in step S7 is then used to remotely upgrade the electronic control data of the engine controller via OTA.

[0035] The advantages of the embodiments of this application are:

[0036] Through big data intelligent analysis platform algorithms, the engine operating data is automatically analyzed online to determine the distribution of operating conditions. Combined with the operating condition analysis of drivers of the same model on the platform, the optimal personalized calibration parameters are set for the current driver. This ensures that the most suitable limit torque is output for the current driver in each gear and speed range, achieving the effect of one calibration per vehicle and standardizing driver behavior. Attached Figure Description

[0037] Figure 1 A flowchart illustrating the online fuel-saving method based on gear torque limiting provided for embodiments of this application. Detailed Implementation

[0038] The technical solution in this application aims to address the shortcomings of related technologies that only calibrate and match engine models without reaching every user. The overall approach is as follows:

[0039] Example 1:

[0040] Please see Figure 1An online fuel-saving method based on gear-position torque limiting includes the following steps:

[0041] S1. Enter the vehicle number;

[0042] S2. Calculate cumulative mileage;

[0043] In step S2, the cumulative mileage is calculated by counting back 30 days from the day the function is triggered, whereby...

[0044] If the 30-day mileage is greater than 6000km (configurable), then use this start and end point; if the 30-day mileage is less than 6000km (configurable), then push the start point forward to when the mileage is greater than 6000km (configurable).

[0045] S3. Remove data with poor quality;

[0046] In step S3, a day with poor data quality is defined as a problem day. Data from problem days is not included in the calculation of the operating condition distribution map in step S4. The following three situations determine that the data quality of a day is poor:

[0047] A. No increase in mileage: Check the data for each day within the date range. Compare the mileage at the moment the region changes with the moment of the last change. If there is no change, it is considered a problem day and should be removed.

[0048] B. No increase in fuel consumption: Check the data for each day of the date range. Compare the fuel consumption at the moment the region changes with the moment of the last change. If there is no change, it is considered a problem day and should be removed.

[0049] C. Set a fuel consumption reference value for each model. If there is an obvious error, it is judged as data for a problematic day and removed.

[0050] This application ensures the accuracy of subsequent operating condition data generation by removing data with poor quality, i.e., problematic and invalid data.

[0051] S4. Generate a working condition distribution diagram;

[0052] In step S4, the thermal distribution diagram of the operating condition is plotted using the time data from step S2. The speed and torque distribution is then rasterized to obtain the final speed and torque distribution. The plotting steps are as follows:

[0053] (1). Range division: The speed is divided into ranges of 100 rpm (600, 700, 800...2200), and the torque range division standard is: the reference torque corresponding to the current model is rounded up to the nearest hundred / 10;

[0054] (2). Classification: Classify each speed and torque separately, calculate the number of items in each category, and calculate the ratio of each category to the total number of categories.

[0055] S5. Fit the optimal specific fuel consumption curve;

[0056] In step S5, based on the universal characteristic curve of the basic engine, the optimal fuel consumption curves of each engine model are extended and fitted, that is, the optimal specific fuel consumption point at each speed.

[0057] S6. Fit the moving optimal fuel consumption curve;

[0058] In step S6, fitting the optimal moving ratio fuel consumption curve consists of the following three steps:

[0059] A. Fitting the optimal fuel consumption curve shift index; Step A in step S6 is further subdivided into the following steps: ① Calculate the total on the line; the distribution of speed and torque ranges of all vehicles on a certain model's big data platform, and the total proportion A on the optimal control line; ② Calculate the median. Calculate the total proportion B of vehicles whose distribution proportion is below A for this model, and take the median.

[0060] B. Fitting the optimal fuel consumption curve shift rules; In sub-step B of step S6, the online proportion C of the target vehicle is counted. ① When C > A, no shift is made; ② When B ≤ C ≤ A, the torque value of the optimal fuel consumption curve is shifted down by -(the reference torque of the corresponding model is rounded up * 5%) Nm; ③ When the sum < B, the torque value of the optimal fuel consumption curve is shifted down by -(the reference torque of the corresponding model is rounded up * 10%) Nm.

[0061] C. Set up a torque limit table based on gear position. In sub-step C of step S6, the gear positions are divided into low, medium, and high gear ranges; the low gear range is set according to the torque corresponding to each speed based on the external characteristic value of the engine model; for the medium gear range, the value after shifting the optimal specific fuel consumption curve is filled in below 1600 rpm, and the speed limit is set above 1600 rpm; for the high gear range, the value after shifting the optimal specific fuel consumption curve is filled in.

[0062] S7. Generate data;

[0063] S8.OTA remote upgrade for user vehicles.

[0064] In step S7, the table set in step S6 is filled into the engine data to generate the data. The data generated in step S7 is then used to remotely upgrade the electronic control data of the engine controller via OTA.

[0065] This application utilizes big data intelligent analysis platform technology and applies algorithms to automatically analyze engine operating data online, simultaneously generating optimal limiting torque curves for each gear and speed range to appropriately limit engine output torque. This method can customize personalized optimal calibration parameters for different customers, thereby standardizing driver behavior and achieving fuel savings.

[0066] Finally, it should be noted that the above embodiments are merely examples for clearly illustrating this application and are not intended to limit the implementation. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this application.

Claims

1. An online fuel-saving method based on gear-position torque limiting, characterized in that, Includes the following steps: S1. Enter the vehicle number; S2. Calculate cumulative mileage; S3. Remove data with poor quality; S4. Generate a working condition distribution diagram; S5. Fit the optimal specific fuel consumption curve; S6. Fit the moving optimal fuel consumption curve; S7. Generate data; S8 OTA remote upgrade for user vehicles; In step S4, the time from step S2 is used to draw a thermal distribution map of the operating conditions, and the speed and torque distribution is rasterized to finally obtain the speed and torque distribution. The drawing steps are as follows: (1). Dividing the interval: The speed interval is divided into 100 rpm intervals, and the torque interval is divided according to the following standard: the reference torque corresponding to the current model is rounded up to the nearest hundred / 10; (2). Classification: Classify each speed and torque separately, calculate the number of items in each category, and calculate the ratio of each category to the total number of categories; In step S6, fitting the optimal moving ratio fuel consumption curve consists of the following three steps: A. The index for shifting the optimal fuel consumption curve; B. Rules for shifting the optimal fuel consumption curve; C. Set up a torque limiter based on gear position; In step S6, sub-step A is further subdivided into the following steps: ① Calculate the total on the line; the distribution of the speed and torque range of all vehicles on the big data platform of a certain model, and the total proportion A of the optimal control line; ② Calculate the median; the total proportion B of vehicles whose distribution proportion is below A of this model, and take the median. In step B of step S6, the online proportion C of the target vehicle is calculated. ① When C > A, no movement is made; ② When B ≤ C ≤ A, the torque value of the optimal fuel consumption curve is shifted downwards - the reference torque of the corresponding model is rounded up * 5%; ③ When the sum < B, the torque value of the optimal fuel consumption curve is shifted downwards - the reference torque of the corresponding model is rounded up * 10%. In step C of step S6, the gear ranges are divided into low, medium, and high gear ranges; the low gear range is set according to the torque corresponding to each speed according to the external characteristic value of the engine model; the medium gear range is filled with the value after shifting the optimal specific fuel consumption curve below 1600 rpm, and the speed limit is set above 1600 rpm; the high gear range is filled with the value after shifting the optimal specific fuel consumption curve.

2. The online fuel-saving method based on gear torque limiting as described in claim 1, characterized in that, In step S2, the cumulative mileage is calculated by counting back 30 days from the day the function is triggered, whereby... If the 30-day mileage is greater than 6000km, then use this start and end point; if the 30-day mileage is less than 6000km, then push the start point forward to when the mileage is greater than 6000km.

3. The online fuel-saving method based on gear torque limiting as described in claim 1, characterized in that, In step S3, a day with poor data quality is defined as a problem day. Data from problem days is not included in the calculation of the operating condition distribution map in step S4. The following three situations determine that the data quality of a day is poor: A. No increase in mileage: Check the data for each day within the date range. Compare the mileage at the moment the region changes with the moment of the last change. If there is no change, it is considered a problem day and should be removed. B. No increase in fuel consumption: Check the data for each day of the date range. Compare the fuel consumption at the moment the region changes with the moment of the last change. If there is no change, it is considered a problem day and should be removed. C. Set a fuel consumption reference value for each model. If there is an obvious error, it is judged as data for a problematic day and removed.

4. The online fuel-saving method based on gear torque limiting as described in claim 1, characterized in that, In step S5, based on the universal characteristic curve of the basic engine, the optimal fuel consumption curves of each engine model are extended and fitted, that is, the optimal specific fuel consumption point at each speed.

5. The online fuel-saving method based on gear torque limiting as described in claim 1, characterized in that, In step S7, the table set in step S6 is filled into the engine data to generate the data. The data generated in step S7 is then used to remotely upgrade the electronic control data of the engine controller via OTA.

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