LAPSE:2023.22422
Published Article
LAPSE:2023.22422
A New Adaptive Mass Estimation Approach of Heavy Truck Based on Engine Torque Local Convex Minimum Characteristic at Low Speeds
DaeYi Jung, Gyoojae Choi
March 24, 2023
This paper proposes a new mass estimation for a vehicle system, utilizing the characteristics of engine torque local convex minimum, where the mass can be estimated based on the driving forces and the longitudinal accelerations only. Fundamentally, this approach generally requires no other information about an aerodynamic effect, a road grade, or a rolling friction, which is usually demanded by the existing well-known longitudinal dynamics and adaptive filter-based estimation methods. The effectiveness of the proposed approach was evaluated and validated by both TruckSim/Simulink co-simulation and actual field test data. It is found that the proposed estimation technique is more favorable for a situation where the vehicle is exposed to low-speed regions. In addition to this new mass estimation strategy, other new and current existing methods were explored and are reviewed here. Moreover, this study suggested a guideline for a hybrid-type mass estimation strategy to predict a mass by combining a new method with an existing one for every speed.
Keywords
(Extended) Kalman filter, detection algorithm, recursive least square, vehicle longitudinal dynamics, vehicle mass estimation
Suggested Citation
Jung D, Choi G. A New Adaptive Mass Estimation Approach of Heavy Truck Based on Engine Torque Local Convex Minimum Characteristic at Low Speeds. (2023). LAPSE:2023.22422
Author Affiliations
Jung D: School of Mechanical and Automotive Engineering, Kunsan National University, Gunsan 54150, Korea
Choi G: School of Mechanical and Automotive Engineering, Kunsan National University, Gunsan 54150, Korea
Journal Name
Energies
Volume
13
Issue
7
Article Number
E1649
Year
2020
Publication Date
2020-04-02
Published Version
ISSN
1996-1073
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Original Submission
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PII: en13071649, Publication Type: Journal Article
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LAPSE:2023.22422
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doi:10.3390/en13071649
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Mar 24, 2023
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