CN114735015B - Load assessment method for commercial diesel vehicle - Google Patents
Load assessment method for commercial diesel vehicle Download PDFInfo
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- CN114735015B CN114735015B CN202210391249.4A CN202210391249A CN114735015B CN 114735015 B CN114735015 B CN 114735015B CN 202210391249 A CN202210391249 A CN 202210391249A CN 114735015 B CN114735015 B CN 114735015B
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- load
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- engine
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- 238000000034 method Methods 0.000 title claims abstract description 7
- 238000001228 spectrum Methods 0.000 claims abstract description 39
- 239000000446 fuel Substances 0.000 claims abstract description 7
- 230000003203 everyday effect Effects 0.000 claims abstract description 6
- 238000012545 processing Methods 0.000 claims abstract description 5
- 230000006855 networking Effects 0.000 claims abstract description 4
- 238000001914 filtration Methods 0.000 claims 1
- 239000012634 fragment Substances 0.000 claims 1
- 238000011156 evaluation Methods 0.000 abstract description 4
- 238000012544 monitoring process Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000008676 import Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
- B60W40/13—Load or weight
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2510/00—Input parameters relating to a particular sub-units
- B60W2510/06—Combustion engines, Gas turbines
- B60W2510/0638—Engine speed
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Mathematical Physics (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Combined Controls Of Internal Combustion Engines (AREA)
Abstract
The invention discloses a load evaluation method of a diesel commercial vehicle, which relates to the technical field of vehicle load monitoring and solves the technical problem of inaccurate load evaluation, and the method comprises the following steps: the vehicle networking platform receives and stores engine data which is acquired and uploaded by the vehicle-mounted terminal in real time; marking time break points, stopping and idling of each engine data according to the speed, the rotating speed and the acquisition time stamp, and generating a road spectrum segment slice; generating road spectrum slice data for each engine every day by using a big data offline processing engine according to the road spectrum slice of each engine; checking the oil consumption value of each segment in the road spectrum slice data of a certain engine to obtain a stable driving cycle segment of the vehicle; selecting hundred kilometer fuel consumption value division from all road spectrum slice data of the vehicle history: no load, medium load, heavy load and full load 4 load regions; and judging which section of the load sections the comprehensive oil consumption value of the stable driving cycle section belongs to, and completing load assessment of the vehicle.
Description
Technical Field
The invention relates to the technical field of vehicle load monitoring, in particular to a load evaluation method for a diesel commercial vehicle.
Background
At present, no technical scheme for automatically monitoring the load of a vehicle exists in the market, namely, external equipment is required to be purchased to acquire load data, for example, an external sensor is added on the vehicle, or weighing equipment such as a wagon balance is used, but both schemes involve the increase of hardware, installation and maintenance cost.
In the prior art, it is common to use manual downloading of single engine data to import Excel to calculate road spectrum slice data, and then use a certain amount of road spectrum slice data to divide loading intervals: no load, medium load, heavy load, full load. Because Excel is used for manually calculating road spectrum slice data, the number of engine samples used for characteristic cannot be selected too much, and finally load interval division is deviated and load estimation is inaccurate.
Disclosure of Invention
The invention aims to solve the technical problem of the prior art and aims to provide a load assessment method for a diesel commercial vehicle, which can improve load estimation accuracy.
The technical scheme of the invention is as follows: a method for load assessment of a diesel commercial vehicle, comprising:
the vehicle networking platform receives and stores engine data which is acquired and uploaded by the vehicle-mounted terminal in real time;
marking time break points, stopping and idling of each engine data according to the speed, the rotating speed and the acquisition time stamp, and generating a road spectrum segment slice;
generating road spectrum slice data for each engine every day by using a big data offline processing engine according to the road spectrum slice of each engine;
checking the oil consumption value of each segment in the road spectrum slice data of a certain engine to obtain a stable driving cycle segment of the vehicle;
selecting hundred kilometer fuel consumption value division from all road spectrum slice data of the vehicle history: no load, medium load, heavy load and full load 4 load regions;
and judging which section of the load sections the integrated fuel consumption value of the stable driving cycle section belongs to, and completing load assessment of the vehicle.
As a further improvement, the road spectrum segment slices are generated by deleting the low-speed small segments of the creep and splicing the driving segments and the idle segments.
Further, before generating the road spectrum slice data, calculating the maximum vehicle speed, the average vehicle speed, the accumulated mileage and the accumulated oil quantity of each road spectrum slice.
Further, segments of the road spectrum slice data that are too small in mileage or idle are filtered.
Further, the comprehensive oil consumption value of the stable driving cycle segment is obtained through a weighting algorithm.
Advantageous effects
Compared with the prior art, the invention has the advantages that:
according to the invention, by using a big data offline computing engine, a road spectrum slicing algorithm and a load estimation algorithm, a great amount of road spectrum slicing data of history is automatically computed and stored every day, load intervals are automatically divided, a characteristic sample of a stable driving cycle of a vehicle is found based on the road spectrum slicing data, the load of the vehicle is finally estimated, and the load estimation precision is high.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a road spectrum slice data diagram;
FIG. 3 is a steady driving cycle segment data graph;
fig. 4 is a schematic diagram of vehicle load estimation.
Detailed Description
The invention will be further described with reference to specific embodiments in the drawings.
Referring to fig. 1-4, a method for evaluating the load of a commercial diesel vehicle is characterized by comprising the following steps:
the vehicle networking platform receives and stores engine data which is acquired and uploaded by the vehicle-mounted terminal in real time, and stores massive data;
marking time break points, stopping and idling of each engine data according to the speed, the rotating speed and the acquisition time stamp, and generating a road spectrum segment slice;
generating road spectrum slice data for each engine every day by using a big data offline processing engine according to the road spectrum slice of each engine;
checking the oil consumption value of each segment in the road spectrum slice data of a certain engine to obtain a stable driving cycle segment of the vehicle;
selecting hundred kilometer fuel consumption value division from all road spectrum slice data of the vehicle history: no load, medium load, heavy load and full load 4 load regions;
and judging which section of the load sections the comprehensive oil consumption value of the stable driving cycle section belongs to, and completing load assessment of the vehicle.
In the embodiment, after marking the engine data, by deleting the small creep speed segment and splicing the driving segment and the idle speed segment to generate the road spectrum segment slice, the influence of the creep speed oil consumption on load assessment can be reduced, so that the assessment accuracy is improved.
Before the big data offline processing engine is used for generating road spectrum slice data for each engine every day, the maximum speed, the average speed, the accumulated mileage and the accumulated oil quantity of each road spectrum slice are calculated, and the comprehensive oil consumption value of the stable driving cycle segment can be conveniently calculated later.
When the load of the vehicle is estimated, segments with too small mileage or idling in road spectrum slice data are filtered, the error of oil consumption is large, the idle oil consumption can not reflect the load of the vehicle, and segments with too small mileage or idling are filtered to improve the estimation accuracy.
The comprehensive fuel consumption value of the stable driving cycle segment is obtained through a weighting algorithm, and a weighting coefficient can be set according to the operating condition of the vehicle so as to improve the evaluation accuracy.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these do not affect the effect of the implementation of the present invention and the utility of the patent.
Claims (1)
1. A method for load assessment of a diesel commercial vehicle, comprising:
the vehicle networking platform receives and stores engine data which is acquired and uploaded by the vehicle-mounted terminal in real time;
marking time break points, stopping and idling of each engine data according to the speed, the rotating speed and the acquisition time stamp, and generating a road spectrum segment slice;
generating road spectrum slice data for each engine every day by using a big data offline processing engine according to the road spectrum slice of each engine;
checking the oil consumption value of each segment in the road spectrum slice data of a certain engine to obtain a stable driving cycle segment of the vehicle;
selecting hundred kilometer fuel consumption value division from all road spectrum slice data of the vehicle history: no load, medium load, heavy load and full load 4 load regions;
judging which section of the load sections the integrated fuel consumption value of the stable driving cycle section belongs to, and finishing load assessment of the vehicle;
the small creep low-speed segments are deleted, and the driving segments and the idle segments are spliced to generate road spectrum segment slices;
before generating road spectrum slice data, calculating the maximum speed, average speed, accumulated mileage and accumulated oil quantity of each road spectrum slice;
filtering fragments with too small mileage or idling in the road spectrum slice data;
and obtaining the comprehensive oil consumption value of the stable driving cycle segment through a weighting algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210391249.4A CN114735015B (en) | 2022-04-14 | 2022-04-14 | Load assessment method for commercial diesel vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210391249.4A CN114735015B (en) | 2022-04-14 | 2022-04-14 | Load assessment method for commercial diesel vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114735015A CN114735015A (en) | 2022-07-12 |
| CN114735015B true CN114735015B (en) | 2024-03-29 |
Family
ID=82281043
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210391249.4A Active CN114735015B (en) | 2022-04-14 | 2022-04-14 | Load assessment method for commercial diesel vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114735015B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012238257A (en) * | 2011-05-13 | 2012-12-06 | Yazaki Corp | Driving evaluation apparatus and driving evaluation system |
| CN109767023A (en) * | 2019-01-16 | 2019-05-17 | 北京经纬恒润科技有限公司 | A kind of predictor method and system of vehicle load state |
| CN112819031A (en) * | 2021-01-04 | 2021-05-18 | 中国汽车技术研究中心有限公司 | Vehicle-mounted weight prediction method and system, electronic device and medium |
-
2022
- 2022-04-14 CN CN202210391249.4A patent/CN114735015B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012238257A (en) * | 2011-05-13 | 2012-12-06 | Yazaki Corp | Driving evaluation apparatus and driving evaluation system |
| CN109767023A (en) * | 2019-01-16 | 2019-05-17 | 北京经纬恒润科技有限公司 | A kind of predictor method and system of vehicle load state |
| CN112819031A (en) * | 2021-01-04 | 2021-05-18 | 中国汽车技术研究中心有限公司 | Vehicle-mounted weight prediction method and system, electronic device and medium |
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| CN114735015A (en) | 2022-07-12 |
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