A New Adaptive Mass Estimation Approach of Heavy Truck Based on Engine Torque Local Convex Minimum Characteristic at Low Speeds

DaeYi Jung and Gyoojae Choi
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DaeYi Jung: School of Mechanical and Automotive Engineering, Kunsan National University, Gunsan 54150, Korea
Gyoojae Choi: School of Mechanical and Automotive Engineering, Kunsan National University, Gunsan 54150, Korea

Energies, 2020, vol. 13, issue 7, 1-19

Abstract: 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: vehicle mass estimation; (Extended) Kalman filter; recursive least square; detection algorithm; vehicle longitudinal dynamics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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