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A Road Slope Estimation Method Based on Adaptive Extended Kalman Filtering

A technology for extending Kalman and road slope, which is applied in the direction of measuring inclination, instrumentation, surveying and navigation, etc., can solve the problems of reducing the estimation accuracy of road slope, and achieve the goal of overcoming the problem of estimation divergence, improving accuracy and scope of application, and driving comfortably Effect

Active Publication Date: 2021-05-25
重庆大学溧阳智慧城市研究院
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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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

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  • A Road Slope Estimation Method Based on Adaptive Extended Kalman Filtering
  • A Road Slope Estimation Method Based on Adaptive Extended Kalman Filtering
  • A Road Slope Estimation Method Based on Adaptive Extended Kalman Filtering

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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...

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Abstract

The invention discloses a road slope estimation method based on adaptive extended Kalman filter, which obtains vehicle state data through a data acquisition device, and calculates model parameters in combination with vehicle state data and vehicle intrinsic parameters: then based on the relationship model between slope and vehicle state data , to build an extended Kalman filter estimation model; finally, the Kalman filter estimation model is improved to an adaptive extended Kalman filter algorithm model. The method provided by the present invention utilizes the self-adaptive extended Kalman filter algorithm capable of rapid convergence and real-time estimation, and dynamically and real-time estimates the road slope through the vehicle driving state data, thereby providing real-time road slope information for the driver and assisting decision-making for automatic driving , green driving, and automatic transmission shift control provide an important basis to achieve safe, economical, and comfortable driving; improve the accuracy and scope of slope estimation under the influence of uncertain dynamic noise.

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

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Application Information

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Patent Type & Authority Patents(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
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