CN-122009178-A - Energy-saving driving method, device, equipment and storage medium based on real-time load identification

CN122009178ACN 122009178 ACN122009178 ACN 122009178ACN-122009178-A

Abstract

The application discloses an energy-saving driving method, device, equipment and storage medium based on real-time load identification, which relate to the technical field of vehicle control and comprise the following steps of acquiring running data of a vehicle in real time; the method comprises the steps of inputting running data into a preset load identification model to obtain load information of a vehicle, training the preset load identification model through historical running data and historical load information of the vehicle, generating an energy-saving driving strategy containing control parameters of a power system according to the load information, and controlling the vehicle to drive in an energy-saving mode according to the energy-saving driving strategy. The application can reduce the power energy consumption of vehicle driving.

Inventors

XU BINGHUA
Xiong Yanyun
HU ZHICONG
WEI YANG
LUO WEIJIA
LI YIFAN
ZHANG RUNHUAI
LIU YIKAI
LI JINGUI
CHEN ZELIN
HUANG JINLONG
CHEN REN
WU BOHAN

Assignees

东风柳州汽车有限公司

Dates

Publication Date: 20260512
Application Date: 20260303

Claims (10)

1. An energy-saving driving method based on real-time load identification is characterized by comprising the following steps: acquiring running data of a vehicle in real time; Inputting the operation data into a preset load identification model to obtain load information of the vehicle, wherein the preset load identification model is obtained through training historical operation data and historical load information of the vehicle; and generating an energy-saving driving strategy containing control parameters of the power system according to the load information, and controlling the vehicle to perform energy-saving driving according to the energy-saving driving strategy.
2. The method of claim 1, wherein prior to the step of acquiring the vehicle's operational data in real time, further comprising: Acquiring an original operation data sequence acquired by a vehicle in a historical operation process, and acquiring historical load information corresponding to the original operation data sequence in time; Generating a characteristic parameter set according to the original operation data sequence; training the initial model based on the characteristic parameter set and the historical load information to obtain a preset load identification model.
3. The method of claim 2, wherein the step of generating a set of characteristic parameters from the original sequence of operational data comprises: Performing time synchronization and resampling processing on the original operation data sequence to generate equidistant time sequence data; constructing a quality sensitive candidate feature set based on the equidistant time series data; Carrying out statistical feature expansion processing on the quality sensitive candidate feature set to generate a high-dimensional candidate feature set; and carrying out feature screening on the high-dimensional candidate feature set, and determining a feature parameter set associated with the load change.
4. The method of claim 3, wherein the step of constructing a quality-sensitive candidate feature set based on the equally spaced time-series data comprises: Constructing a unit torque acceleration response feature based on the equally spaced time series data; extracting dynamic response characteristics of the vehicle in the process of increasing the speed from zero to a preset threshold value from the equidistant time sequence data, wherein the dynamic response data at least comprises one of acceleration rising time, peak acceleration and acceleration change rate; extracting torque demand characteristics of the vehicle in a constant-speed driving stage from the equidistant time sequence data; and determining a quality sensitive candidate feature set according to the unit torque acceleration response feature, the dynamic response feature and the torque demand feature.
5. The method of claim 3, wherein the step of feature screening the set of high-dimensional candidate features to determine a set of feature parameters associated with a load change comprises: performing correlation analysis on the candidate features in the high-dimensional candidate feature set and the historical load information to obtain a correlation analysis result; and determining a characteristic parameter set associated with the load change from the high-dimensional candidate characteristic set according to the correlation analysis result.
6. The method of claim 1, wherein the step of generating an energy efficient driving strategy including powertrain control parameters based on the load information comprises: Acquiring a preset load partition gear shifting characteristic map; Determining a target gear shifting strategy matched with the current load from the preset load partition gear shifting characteristic map according to the load information; and generating an energy-saving driving strategy based on the target gear shifting strategy.
7. The method of claim 1, wherein the step of generating an energy efficient driving strategy including powertrain control parameters based on the load information comprises: acquiring a predicted value of future power demand; correcting the future power demand predicted value according to the load information to obtain a corrected final power demand predicted value; and generating an energy-saving driving strategy based on the final power demand predicted value.
8. An energy efficient driving apparatus based on real-time load identification, the apparatus comprising: The acquisition module is used for acquiring the running data of the vehicle in real time; The identification module is used for inputting the operation data into a preset load identification model to obtain load information of the vehicle, and the preset load identification model is obtained through training historical operation data and historical load information of the vehicle; And the generation module is used for generating an energy-saving driving strategy containing control parameters of the power system according to the load information and controlling the vehicle to carry out energy-saving driving according to the energy-saving driving strategy.
9. An energy efficient driving apparatus based on real time load identification, characterized in that the apparatus comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the energy efficient driving method based on real time load identification as claimed in any one of claims 1 to 7.
10. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the energy efficient driving method based on real-time load recognition according to any one of claims 1 to 7.

Description

Energy-saving driving method, device, equipment and storage medium based on real-time load identification Technical Field The application relates to the technical field of vehicle control, in particular to an energy-saving driving method, device and equipment based on real-time load identification and a storage medium. Background Under the scenes of logistics transportation and the like, the load state of the vehicle frequently changes, and the existing energy-saving control technology generally predicts the power demand based on fixed vehicle calibration parameters and a dynamic model, so that the influence caused by the dynamic change of the load is difficult to adapt, the control strategy is not matched with the actual working condition, and the energy consumption is high. Therefore, how to reduce the power consumption of the vehicle driving is still a problem to be solved. The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art. Disclosure of Invention The application mainly aims to provide an energy-saving driving method, device and equipment based on real-time load identification and a storage medium, and aims to solve the technical problem of how to reduce the power energy consumption of vehicle driving. In order to achieve the above purpose, the present application provides an energy-saving driving method based on real-time load recognition, the method comprising: acquiring running data of a vehicle in real time; Inputting the operation data into a preset load identification model to obtain load information of the vehicle, wherein the preset load identification model is obtained through training historical operation data and historical load information of the vehicle; and generating an energy-saving driving strategy containing control parameters of the power system according to the load information, and controlling the vehicle to perform energy-saving driving according to the energy-saving driving strategy. In an embodiment, before the step of acquiring the operation data of the vehicle in real time, the method further includes: Acquiring an original operation data sequence acquired by a vehicle in a historical operation process, and acquiring historical load information corresponding to the original operation data sequence in time; Generating a characteristic parameter set according to the original operation data sequence; training the initial model based on the characteristic parameter set and the historical load information to obtain a preset load identification model. In an embodiment, the step of generating a set of characteristic parameters from the original sequence of operational data comprises: Performing time synchronization and resampling processing on the original operation data sequence to generate equidistant time sequence data; constructing a quality sensitive candidate feature set based on the equidistant time series data; Carrying out statistical feature expansion processing on the quality sensitive candidate feature set to generate a high-dimensional candidate feature set; and carrying out feature screening on the high-dimensional candidate feature set, and determining a feature parameter set associated with the load change. In an embodiment, the step of constructing a quality-sensitive candidate feature set based on the equally spaced time series data comprises: Constructing a unit torque acceleration response feature based on the equally spaced time series data; extracting dynamic response characteristics of the vehicle in the process of increasing the speed from zero to a preset threshold value from the equidistant time sequence data, wherein the dynamic response data at least comprises one of acceleration rising time, peak acceleration and acceleration change rate; extracting torque demand characteristics of the vehicle in a constant-speed driving stage from the equidistant time sequence data; and determining a quality sensitive candidate feature set according to the unit torque acceleration response feature, the dynamic response feature and the torque demand feature. In an embodiment, the step of performing feature screening on the high-dimensional candidate feature set to determine a feature parameter set associated with a load change includes: performing correlation analysis on the candidate features in the high-dimensional candidate feature set and the historical load information to obtain a correlation analysis result; and determining a characteristic parameter set associated with the load change from the high-dimensional candidate characteristic set according to the correlation analysis result. In an embodiment, the step of generating an energy-saving driving strategy including a control parameter of the power system according to the load information, and controlling the vehicle to perform energy-saving driving accordin