Master Thesis: Development of a Vehicle Speed Estimation

The accurate estimation of a vehicle's longitudinal speed is essential for a wide range of applications, including vehicle dynamics control, advanced driver assistance systems, and autonomous driving. In this proposal, we outline a research project aimed at developing a robust function for estimating a vehicle's longitudinal speed. The proposed method will utilize data from rotational speed sensors for each axle and the measurements of acceleration from an inertial measurement unit (IMU).

Suitable background
Mechanical/Design Engineering with interest in the field of system identification and adaptive control.

Description of thesis work

Research Objective
The primary objective of this research is to design and implement a function that estimates a vehicle's longitudinal speed using data from rotational speed sensors and an IMU. This function will provide a real-time and accurate estimation of the vehicle's speed, allowing for improved vehicle control and safety.

The project will utilize rotational speed sensors installed on each axle of the vehicle. An IMU will be used to measure the vehicle’s acceleration.

This project relies on the effective fusion of data from both the rotational speed sensors and the IMU. Proper data fusion techniques will be developed to ensure that the resulting longitudinal speed estimate is robust and accurate.

Methodology

Literature Review: To provide an in-depth review of the existing literature on vehicle speed estimation, focusing on different methods for data/signal estimator and their limitations.
Data Collection: Gather data from the rotational speed sensors and the IMU during controlled experiments and real-world driving scenarios.
Data Processing: Process the collected data to remove noise, calibrate sensors, and ensure data quality.
Algorithm Development: Develop a speed estimation algorithm that integrates the data from the rotational speed sensors and the IMU. This algorithm will consider factors such as wheel slip, tire deformation, and sensor noise to provide accurate speed estimates.
Validation: Validate the developed algorithm through extensive testing in various driving conditions and scenarios, including low-speed maneuvers and high-speed driving.
Real-Time Implementation: Implement the speed estimation function on a real-time platform suitable for integration with vehicle control systems.

Timeline

This project is expected to be completed within 6 months.
We are looking for a team of two students with knowledge of electromobility.
You are curious, open-minded and are interested in collaborating with people with another background than yourself. During the master thesis you are mainly located at our plant in Eskilstuna. The recruitment process is ongoing, the positions can be filled before the application period has expired.

Thesis Level: Master

Language: English

Starting date: January 24

Number of students: 2

Tutor
David Berggren, Design Engineer, +46 737 656409