CN111931810A - Energy-saving driving behavior analysis system based on multiple vehicles - Google Patents
Energy-saving driving behavior analysis system based on multiple vehicles Download PDFInfo
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- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
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Abstract
The invention discloses an energy-saving driving behavior analysis system based on multiple vehicles, which comprises a vehicle data acquisition module, a data processing module, an oil consumption comparison module, a sample library establishing module, a driving behavior characteristic identification module, an energy-saving driving behavior principal component analysis module, a multiple vehicle clustering analysis module and an optimal energy-saving driving suggestion module. The invention can provide economic and reliable driving advice for a driver in the driving process and theoretical support for the design and improvement of vehicles by vehicle enterprises, thereby achieving the effects of energy conservation and emission reduction.
Description
Technical Field
The invention belongs to the technical field of intelligent transportation and automobile energy conservation, and particularly relates to an energy-saving driving behavior analysis system based on multiple automobiles.
Background
The research of automobile fuel saving technology is always the direction of effort of each automobile host factory, factors influencing automobile fuel consumption are many, including automobile self characteristics, road traffic and natural environment conditions, driving behaviors also have certain influence on fuel consumption, and the situation that the fuel consumption difference is large can also occur due to the difference of the driving behaviors of the same automobile type, so that the establishment of an energy-saving driving behavior model, the analysis of the relation between the driving behaviors and the fuel consumption, and the training of drivers to optimize the driving behaviors is one of the most direct and effective fuel saving means at present. Meanwhile, the oil consumption conditions of multiple vehicles in the same vehicle type are analyzed and compared with the theoretical oil consumption, so that the vehicle enterprises can know whether the actual oil consumption greatly exceeds the theoretical oil consumption after the vehicle type is put on the market, and theoretical and data support is provided for the vehicle enterprises on the subsequent theoretical design of the vehicle oil consumption.
The Chinese patent 'an energy-saving driving behavior analysis method based on big data', an authorization notice number CN105160883B, an authorization notice date 2017.08.25, wherein the patent collects vehicle-related information, environmental information and traffic information through a vehicle-mounted terminal and a road side system, and sends the collected information to an internet of vehicles information service background through the vehicle-mounted terminal; the information service background judges and records the times of rapid acceleration, rapid deceleration and rapid turning of a driver by establishing a single-vehicle data analysis model, carries out classification and quantification according to the times of driving behaviors to obtain weight scores of different levels of each driving behavior, accumulates the weight scores to obtain an energy-saving behavior score corresponding to the driver in a certain effective driving time, and finally feeds back an energy-saving driving suggestion to a user in real time or in a mode of voice broadcasting and screen display of an on-vehicle terminal when a section of travel is finished.
However, in the above scheme, the operation of the driving behaviors is not comprehensive enough, only three driving behaviors of rapid acceleration, rapid deceleration and rapid turning are considered, and other driving behaviors are related to the fuel consumption. In addition, the scheme only provides a method for analyzing the energy-saving behavior of a single vehicle, and does not consider an analysis method of the energy-saving behavior of multiple vehicles.
Disclosure of Invention
In view of the problems in the background art, the invention aims to provide an energy-saving driving behavior analysis system based on multiple vehicles, which provides economic and reliable driving advice for a driver during driving and theoretical support for vehicle design and improvement of vehicle enterprises.
In order to achieve the above object, the present invention provides a multi-vehicle-based energy-saving driving behavior analysis system, comprising:
the vehicle data acquisition module: collecting data in the driving process of the automobile in real time and uploading the data;
a data processing module: carrying out data cleaning and statistics on the uploaded data;
the oil consumption comparison module: receiving data of the data processing module, comparing the data with the theoretical oil consumption of the vehicle type, and screening out sample data lower than the theoretical oil consumption;
a sample library establishing module: screening out vehicles with fuel consumption lower than the theoretical fuel consumption, and establishing a sample library for subsequent analysis by collecting data in the driving process of the vehicles and the driving fuel consumption of the vehicles in real time;
the driving behavior feature recognition module: identifying the driving behaviors through the data of the sample library, and classifying the driving behaviors according to the vehicle speed, the rotating speed and the accelerator;
energy-conserving driving action principal components analysis module: analyzing energy-saving driving behaviors related to oil consumption from the driving behaviors in the driving behavior feature recognition module;
the multi-vehicle cluster analysis module: clustering the energy-saving driving behavior data set after the energy-saving driving behavior principal component analysis module characteristic extraction, solving the Euclidean distance between the driving behavior sample data of any two drivers, and clustering M drivers;
the best energy-saving driving suggestion module: and giving corresponding services according to the driving habits of the drivers in the same cluster.
Preferably, the vehicle data acquisition module acquires the vehicle speed, the engine rotating speed, the actual torque of the engine, the gear, the accelerator opening and the fuel consumption rate of the engine in the driving process of the automobile on line in real time and uploads the real-time information to the information service background of the Internet of vehicles.
Preferably, the data processing module carries out data preprocessing on the vehicle speed, the engine rotating speed, the actual torque of the engine, the gear, the accelerator opening and the fuel consumption rate of the engine, wherein the data preprocessing comprises deleting abnormal values and filling missing values; the statistical work of the uploaded data is vehicle running oil consumption, and an oil consumption value is obtained by accumulating the fuel consumption rate of the engine.
Further preferably, the filling method of the specific deficiency value is to take an average value of the collected data of the same signal parameter for filling.
Preferably, the driving behavior is recognized by the expression of the vehicle state: the vehicle speed, the rotating speed and the accelerator are divided into 6 driving behaviors: the method comprises the following steps of sharp acceleration times, sharp deceleration times, high-speed running time ratio, high-rotating-speed running time ratio, large-throttle running time ratio and full-throttle running time ratio.
The invention has the beneficial effects that: in the original driving behaviors of rapid acceleration, rapid deceleration and rapid turning, 14 items of high-speed running time ratio, large-throttle running mileage ratio and the like are added, and the reason that the multi-index analysis influences the oil consumption is realized. The method can analyze the energy-saving driving behaviors of multiple vehicles, find the actual oil consumption difference of the multiple vehicles in the same vehicle type, and find the driving behavior characteristic expression causing the difference. The method is characterized in that the driving behaviors of multiple vehicles are clustered by applying a machine learning algorithm comprising a principal component analysis algorithm and a clustering algorithm, and corresponding services such as optimal energy-saving driving behavior suggestions can be given to drivers in the same cluster due to the similarity of the driving habits.
The invention can provide economic and reliable driving advice for a driver in the driving process and theoretical support for the design and improvement of vehicles by vehicle enterprises, thereby achieving the effects of energy conservation and emission reduction.
Drawings
FIG. 1 is a block diagram of the architecture of the present invention
FIG. 2 is a rapid acceleration model of the present invention
FIG. 3 is a model of the present invention for rapid deceleration
FIG. 4 is a high vehicle speed travel time fraction model of the present invention
FIG. 5 is a high speed travel time fraction model of the present invention
FIG. 6 is a model of the ratio of the driving time of the throttle valve
FIG. 7 is a full throttle travel time fraction model of the present invention
FIG. 8 is a flowchart of an energy-saving driving behavior analysis of the present invention
Detailed Description
The technical solutions of the present invention (including the preferred ones) are described in further detail below by way of fig. 1 to 8 and enumerating some alternative embodiments of the present invention. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without any inventive step, are within the scope of the present invention.
As shown in fig. 1, the multi-vehicle-based energy-saving driving behavior analysis system designed by the present invention includes:
the vehicle data acquisition module: collecting data in the driving process of the automobile in real time and uploading the data; the vehicle data acquisition module acquires the speed, the engine speed, the actual torque of the engine, the gear, the accelerator opening and the fuel consumption rate of the engine in the running process of the automobile on line in real time and uploads the real-time information to the internet of vehicles information service background.
A data processing module: carrying out data cleaning and statistics on the uploaded data; the data processing module carries out data preprocessing on the vehicle speed, the engine rotating speed, the actual torque of the engine, the gear, the accelerator opening and the fuel consumption rate of the engine, and the data preprocessing comprises deleting abnormal values and filling missing values; the statistical work of the uploaded data is vehicle running oil consumption, and an oil consumption value is obtained by accumulating the fuel consumption rate of the engine.
Preferably, the filling method of the specific deficiency value is to perform filling by averaging the acquired data of the same signal parameter.
The oil consumption comparison module: and receiving data of the data processing module, comparing the data with the theoretical oil consumption of the vehicle type, and screening out sample data lower than the theoretical oil consumption.
A sample library establishing module: and screening the vehicles with the oil consumption lower than the theoretical oil consumption, and establishing a sample library for subsequent analysis by acquiring data in the driving process of the vehicles and the driving oil consumption of the vehicles in real time.
The above features can be summarized as follows: one) vehicle data collection and preprocessing:
the vehicle-mounted terminal is used for collecting vehicles and sending collected information to the Internet of vehicles information service background through the vehicle-mounted terminal; and preprocessing vehicle data such as vehicle speed, engine rotating speed, actual torque of the engine, gear, accelerator opening, fuel consumption rate of the engine and the like, calculating the hundred kilometer fuel consumption of the effective driving mileage of the vehicle, comparing the hundred kilometer fuel consumption with a theoretical fuel consumption value of the vehicle type, and screening sample data lower than the theoretical fuel consumption.
After one), two) driving behavior characteristic identification: and identifying the driving behavior characteristics by using a driving behavior characteristic identification module.
The driving behavior feature recognition module: the driving behaviors are identified through data of a sample library, and the identification method is that 6 driving behaviors are identified through vehicle state expressions such as vehicle speed, rotating speed and accelerator shown in a table 1.
TABLE 1 Driving behavior characteristics
As shown in fig. 2, the present invention provides a rapid acceleration model: the vehicle data acquisition module is used for acquiring a vehicle speed signal and an accelerator opening degree signal in the driving process of the automobile on line in real time. The driving behavior characteristic identification module analyzes the vehicle speed signal and the accelerator opening signal into values, and calculates the acceleration value a of the vehicle in one second before and after the vehicle (V)1-V0) T, calculating instantaneous acceleration value and rapid acceleration threshold value (a)Fast accelerationUnit m/s2) By comparison, if aInstant heating>＝aFast accelerationAnd judging whether the accelerator opening at the moment is larger than 0, recording as 1-time rapid acceleration behavior if the accelerator opening is larger than 0, and quitting the identification of the rapid acceleration behavior if the condition is not met. And finally, the driving behavior feature recognition module counts the accumulated rapid acceleration behavior times of one trip.
As shown in fig. 3, the model of rapid deceleration according to the present invention: the vehicle data acquisition module is used for acquiring a vehicle speed signal and a brake pedal signal in the driving process of the automobile on line in real time. The driving behavior characteristic identification module analyzes the vehicle speed signal and the accelerator opening signal into values and calculates a deceleration value a of the vehicle in one second before and afterInstantaneous decrease＝|V1-V0I/t, calculating instantaneous deceleration value and emergencyDeceleration threshold (a)Fast decelerationUnit m/s2) By comparison, if aInstantaneous decrease>＝aFast decelerationThen, it is determined whether the braking state at this time is equal to "1" (braking state), and if the braking state is equal to "1", it is recorded as 1-time rapid deceleration behavior. And if the condition is not met, quitting the identification of the rapid deceleration behavior. And finally, the driving behavior feature recognition module counts the accumulated times of the rapid deceleration behavior of one trip.
As shown in fig. 4, a high vehicle speed travel time ratio model of the present invention: and an ON gear signal and a vehicle speed signal in the driving process of the automobile are acquired ON line in real time through a vehicle data acquisition module. The driving behavior characteristic identification module analyzes the vehicle speed signal and the ON gear signal into values, and respectively calculates and accumulates the vehicle speed when the ON gear state is '1' (ON)>0km/h, vehicle speed>Travel time T (units: mins), T of 90km/hHigh vehicle speed(unit: mins). Finally, the driving behavior feature recognition module calculates the high vehicle speed time ratio etaHigh vehicle speed＝tHigh vehicle speed/T。
As shown in fig. 5, the high-speed travel time ratio model of the present invention: the method comprises the steps of collecting an ON gear signal, a vehicle speed signal and a rotating speed signal in the driving process of the automobile ON line in real time through a vehicle data collecting module. The driving behavior characteristic identification module analyzes the vehicle speed signal, the rotating speed signal and the ON gear signal into values, and respectively calculates and accumulates the vehicle speed when the ON gear state is '1' (ON)>0km/h, rotational speed>Running time T (units: mins), T) of 1400rpmHigh rotational speed(unit: mins). Finally, the driving behavior feature recognition module calculates the high vehicle speed time ratio etaHigh rotational speed＝tHigh rotational speed/T。
As shown in fig. 6, the model of the large throttle travel time ratio of the present invention: an ON gear signal, a vehicle speed signal and an accelerator opening degree signal in the driving process of the automobile are acquired ON line in real time through a vehicle data acquisition module. The driving behavior characteristic identification module analyzes the vehicle speed signal, the accelerator opening signal and the ON gear signal into values, and respectively calculates and accumulates the vehicle speed when the ON gear state is '1' (open)>0km/h，50％<Throttle opening degree<80% of the travel time T (units: mins), TBig throttle(unit: mins). Finally, theDriving behavior feature recognition module calculates high vehicle speed time ratio etaBig throttle＝tBig throttle/T。
As shown in fig. 7, the model of the full-throttle travel time ratio of the present invention is: an ON gear signal, a vehicle speed signal and an accelerator opening degree signal in the driving process of the automobile are acquired ON line in real time through a vehicle data acquisition module. The driving behavior characteristic identification module analyzes the vehicle speed signal, the accelerator opening signal and the ON gear signal into values, and respectively calculates and accumulates the vehicle speed when the ON gear state is '1' (open)>0km/h，80％<Running time T (unit: mins), T of accelerator opening degreeFull throttle(unit: mins). Finally, the driving behavior feature recognition module calculates the high vehicle speed time ratio etaFull throttle＝tFull throttle/T。
Thirdly), analyzing the multi-vehicle energy-saving driving behavior: by applying the energy-saving driving behavior principal component analysis module, the multi-vehicle cluster analysis module and the optimal energy-saving driving suggestion module, the energy-saving driving behavior expression that the oil consumption of a plurality of vehicles in the same vehicle type is lower than the theoretical oil consumption in hundreds of kilometers can be analyzed, the reason of low oil consumption in driving can be obtained, and the optimal energy-saving driving suggestion can be provided.
Energy-conserving driving action principal components analysis module: analyzing energy-saving driving behaviors related to oil consumption from the driving behaviors in the driving behavior feature recognition module;
the multi-vehicle cluster analysis module: clustering the energy-saving driving behavior data set after the energy-saving driving behavior principal component analysis module characteristic extraction, solving the Euclidean distance between the driving behavior sample data of any two drivers, and clustering M drivers;
the best energy-saving driving suggestion module: and giving corresponding services according to the driving habits of the drivers in the same cluster.
FIG. 8 shows an analysis flow chart of the present invention: the method comprises the steps of firstly, collecting and uploading data in the driving process of multiple vehicles in the same vehicle type in real time, carrying out data cleaning on the data uploaded to a platform, counting the hundred kilometer oil consumption values of the multiple vehicles, comparing the data with the design theoretical oil consumption of the vehicle type, and screening sample data lower than the theoretical oil consumption. And identifying the driving behavior by using the speed, the rotating speed and the accelerator information of the sample data, and classifying different driving behavior characteristics. And extracting the energy-saving driving behavior characteristics related to the oil consumption by a correlation analysis and principal component analysis method, and finally performing cluster analysis on the extracted characteristic data set to obtain the optimal energy-saving driving suggestion.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and any modification, combination, replacement, or improvement made within the spirit and principle of the present invention is included in the scope of the present invention.
Claims (5)
1. An energy-saving driving behavior analysis system based on multiple vehicles is characterized by comprising:
the vehicle data acquisition module: collecting data in the driving process of the automobile in real time and uploading the data;
a data processing module: carrying out data cleaning and statistics on the uploaded data;
the oil consumption comparison module: receiving data of the data processing module, comparing the data with the theoretical oil consumption of the vehicle type, and screening out sample data lower than the theoretical oil consumption;
a sample library establishing module: screening out vehicles with fuel consumption lower than the theoretical fuel consumption, and establishing a sample library for subsequent analysis by collecting data in the driving process of the vehicles and the driving fuel consumption of the vehicles in real time;
the driving behavior feature recognition module: identifying the driving behaviors through the data of the sample library, and classifying the driving behaviors according to the vehicle speed, the rotating speed and the accelerator;
energy-conserving driving action principal components analysis module: analyzing energy-saving driving behaviors related to oil consumption from the driving behaviors in the driving behavior feature recognition module;
the multi-vehicle cluster analysis module: clustering the energy-saving driving behavior data set after the energy-saving driving behavior principal component analysis module characteristic extraction, solving the Euclidean distance between the driving behavior sample data of any two drivers, and clustering M drivers;
the best energy-saving driving suggestion module: and giving corresponding services according to the driving habits of the drivers in the same cluster.
2. The multi-vehicle based energy saving driving behavior analysis system according to claim 1, characterized in that: the vehicle data acquisition module acquires the speed, the engine speed, the actual torque of the engine, the gear, the accelerator opening and the fuel consumption rate of the engine in the running process of the automobile on line in real time and uploads the real-time information to the internet of vehicles information service background.
3. The multi-vehicle based energy saving driving behavior analysis system according to claim 1, characterized in that: the data processing module carries out data preprocessing on the vehicle speed, the engine rotating speed, the actual torque of the engine, the gear, the accelerator opening and the fuel consumption rate of the engine, and the data preprocessing comprises deleting abnormal values and filling missing values; the statistical work of the uploaded data is vehicle running oil consumption, and an oil consumption value is obtained by accumulating the fuel consumption rate of the engine.
4. The multi-vehicle based energy saving driving behavior analysis system according to claim 3, characterized in that: the specific missing value filling method is to take the average value of the collected data of the same signal parameter for filling.
5. The multi-vehicle based energy saving driving behavior analysis system according to claim 1, characterized in that: the driving behavior recognition method comprises the following steps of through the representation of the vehicle state: the vehicle speed, the rotating speed and the accelerator are divided into 6 driving behaviors: the method comprises the following steps of sharp acceleration times, sharp deceleration times, high-speed running time ratio, high-rotating-speed running time ratio, large-throttle running time ratio and full-throttle running time ratio.
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| CN113250836A (en) * | 2021-06-07 | 2021-08-13 | 一汽解放汽车有限公司 | Engine control method, engine control device, computer equipment and storage medium |
| CN113581188A (en) * | 2021-06-30 | 2021-11-02 | 桂林电子科技大学 | Commercial vehicle driver driving style identification method based on Internet of vehicles data |
| CN114049699A (en) * | 2021-11-15 | 2022-02-15 | 浙江飞碟汽车制造有限公司 | Vehicle fuel economy evaluation method based on data analysis |
| CN117382641A (en) * | 2023-10-19 | 2024-01-12 | 徐州徐工汽车制造有限公司 | Method, device and medium for optimizing energy consumption performance of vehicle |
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| CN113250836A (en) * | 2021-06-07 | 2021-08-13 | 一汽解放汽车有限公司 | Engine control method, engine control device, computer equipment and storage medium |
| CN113581188A (en) * | 2021-06-30 | 2021-11-02 | 桂林电子科技大学 | Commercial vehicle driver driving style identification method based on Internet of vehicles data |
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