Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Adaptive extended kalman filtering-based road slope estimation method

A technology of extending Kalman and road slope, applied in the direction of measuring inclination, instrument, mapping and navigation, etc., can solve the problem of reducing the accuracy of road slope estimation, to overcome the problem of estimation divergence, improve the accuracy and scope of application, and achieve comfortable driving. Effect

Active Publication Date: 2017-06-13
重庆大学溧阳智慧城市研究院
View PDF2 Cites 22 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing research mainly focuses on the real-time performance of slope estimation, ignoring various uncertain disturbances and model errors in the driving process, which will greatly reduce the accuracy of road slope estimation in practical applications.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Adaptive extended kalman filtering-based road slope estimation method
  • Adaptive extended kalman filtering-based road slope estimation method
  • Adaptive extended kalman filtering-based road slope estimation method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0061] As shown in the figure, the adaptive extended Kalman filter-based dynamic road gradient estimation method provided in this embodiment includes the following four steps:

[0062] Step 1: Obtain the vehicle state data through the data acquisition device, combine the vehicle state data and the inherent parameters of the vehicle, and calculate other relevant parameters of the model:

[0063] 1) Obtain real-time vehicle status data through the OBD-II interface, including engine torque T, vehicle speed v, engine speed n, accelerator opening Th, brake signal Br, gear position information Ge, and store them in the mobile device terminal through the Bluetooth device.

[0064] 2) Based on the corresponding inherent parameters of the vehicle (tire rolling radius r, final drive ratio i 0 , road rolling resistance coefficient f, drive train mechanical efficiency η, vehicle air resistance coefficient C d , vehicle frontal area A), and other parameter information in the calculation m...

Embodiment 2

[0079] Further detailed description of the present embodiment; the state of the vehicle is collected by OpenXC, and the adaptive extended Kalman filter algorithm is used to realize the dynamic estimation of the road slope; the following four steps are described in detail:

[0080] Step 1: Obtain the vehicle state data through the OpenXC data acquisition device, and calculate other relevant parameters of the model based on the combination of the vehicle state data and the inherent parameters of the vehicle:

[0081] 1) attached figure 1 It is the process of transmitting and acquiring vehicle status data acquired by in-vehicle sensors. In order to realize the intelligent control of automobiles, all major automobiles currently use the CAN bus to connect the controllers, actuators and sensors inside the car, and transmit the data to the OBD-II interface in a unified manner. In the embodiment, the OpenXC provided by Ford Motor Company of the United States is adopted to be inserted...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an adaptive extended kalman filtering-based road slope estimation method which comprises the following steps: acquiring vehicle state data through a data acquisition device, and calculating model parameters according to the vehicle state data and fixed parameters of a vehicle; then building an extended kalman filtering estimation model based on a relation model between a slope and the vehicle state data; finally, improving the kalman filtering estimation model into an adaptive extended kalman filtering algorithm model. According to the method provided by the invention, by the use of an adaptive extended kalman filtering algorithm capable of realizing rapid convergence and real-time estimation, the road slope can be dynamically estimated in real time according to the vehicle driving state data, so that real-time road slope information can be supplied to a driver, and important basis can be supplied for automatic driving assistant decision making, green driving and automatic transmission gear shifting control, to realize safe, economical and comfortable driving; the slope estimation precision under uncertain dynamic noise influence is improved, and the application range is extended.

Description

technical field [0001] The invention relates to the technical field of intelligent traffic systems, in particular to a dynamic road gradient estimation method based on adaptive extended Kalman filter. Background technique [0002] With the development of vehicle intelligence, people have higher and higher demands on the safety, comfort and economy of travel and driving. In addition to the dynamic performance of the vehicle itself and the human driving operation, the road slope is also an important factor affecting the driving of the vehicle. Unreasonable driving operations such as unreasonable acceleration, deceleration, and frequent gear shifting are prone to safety hazards when the vehicle passes the ramp, increasing fuel consumption and exhaust emissions of the vehicle, and affecting driving comfort. Therefore, obtaining accurate road slope information in real time is of great significance for improving vehicle safety and fuel economy. [0003] At present, the algorithm...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G01C9/00
CPCG01C9/00
Inventor 孙棣华赵敏廖孝勇黄秋光
Owner 重庆大学溧阳智慧城市研究院
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com