Optimization-based online estimation of vehicle mass and road grade: Theoretical analysis and experimental validation

Ritter, Andreas; Widmer, Fabio; Vetterli, Basil; Onder, Christopher H.

doi:10.1016/j.mechatronics.2021.102663

Cited by 14 publications

(2 citation statements)

References 37 publications

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“…The dataset presented in this manuscript has been used in several of our own publications in the context of the research activities mentioned above: (1) The position, odometry, and velocity data served to develop and evaluate a real-time incremental graph construction algorithm 12 . (2) Time-resolved speed, torque, and braking pressure signals were used for the development of the model-based vehicle mass and road grade estimation method 13 . (3) The spatio-temporal nature of the power request signal was used to quantify the relation between grid and battery energy usage on certain road segments, which then served to derive a stochastic model predictive control approach 14 .…”

Section: Background and Summarymentioning

confidence: 99%

ZTBus: A Large Dataset of Time-Resolved City Bus Driving Missions

Widmer,

Ritter,

Onder

2023

Sci Data

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This paper presents the Zurich Transit Bus (ZTBus) dataset, which consists of data recorded during driving missions of electric city buses in Zurich, Switzerland. The data was collected over several years on two trolley buses as part of multiple research projects. It involves more than a thousand missions across all seasons, each mission usually covering a full day of operation. The ZTBus dataset contains detailed information on the vehicle’s power demand, propulsion system, odometry, global position, ambient temperature, door openings, number of passengers, dispatch patterns within the public transportation network, etc. All signals are synchronized in time and include an absolute timestamp in tabular form. The dataset can be used as a foundation for a variety of studies and analyses. For example, the data can serve as a basis for simulations to estimate the performance of different public transit vehicle types, or to evaluate and optimize control strategies of hybrid electric vehicles. Furthermore, numerous influencing factors on vehicle operation, such as traffic, passenger volume, etc., can be analyzed in detail.

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Section: Background and Summarymentioning

confidence: 99%

ZTBus: A Large Dataset of Time-Resolved City Bus Driving Missions

Widmer,

Ritter,

Onder

2023

Sci Data

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“…It often requires expensive optical equipment to directly measure these variables accurately, which is not practical in commercial vehicles [4]. Therefore, the estimation of vehicle states using low-cost sensor measurements and fusion estimation algorithms has become a research topic in the field of vehicle active safety control [5,6].…”

Section: Introductionmentioning

confidence: 99%

A novel maximum correntropy adaptive extended Kalman filter for vehicle state estimation under non-Gaussian noise

Qi¹,

Feng²,

Wan³

et al. 2022

Meas. Sci. Technol.

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For vehicle state estimation, conventional Kalman filters work well under Gaussian assumptions. Still, they are likely to degrade dramatically in the practical non-Gaussian situation (especially the noise is heavy-tailed), showing poor accuracy and robustness. This article presents an estimation technique based on the Maximum Correntropy Criterion (MCC) combined with an adaptive extended Kalman filter (AEKF), and an extended Kalman filter (EKF) based on the MCC has also been studied. A lateral-longitudinal coupled vehicle model is developed, while an observer containing the state vectors such as yaw rate, sideslip angle, vehicle velocity and tire cornering stiffness is designed using easily available in-vehicle sensors and low-cost GPS. After analyzing the algorithmic complexity, the proposed algorithm is validated by Sine Steering Input and Double Lane Change driving scenarios. Finally, it is found that MCC combined with AEKF/EKF (MCAEKF/MCEKF) has stronger robustness and better estimation accuracy than AEKF/EKF in dealing with non-Gaussian noise for vehicle state estimation.

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