Recursive least square vehicle mass estimation based on acceleration partition

Yang, Feng; Liu, Xiong; Yu, Zhuoping; Qu, Tong

doi:10.3901/cjme.2014.03.448

Cited by 13 publications

(5 citation statements)

References 8 publications

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“…Figure 8, we can see that the estimates capture the given actual mass for three different cases. Hence, compared to estimation mechanisms in other existing approaches [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], it is of tremendous benefit to predict the unknown mass, without perceiving the information of a road grade. Furthermore, Figure 9 shows the estimation results of unloaded truck based on two different actual field test datasets.…”

Section: Vehicle Mass Estimation Using the Characteristics Of Engine mentioning

confidence: 99%

“…Here, we address that using multiple forgetting factors improves the transient and steady-state perspectives of estimation performance. Commonly, the existing mass estimation techniques rely on longitudinal dynamics, along with state estimation algorithms, such as the recursive least squares algorithm (RLS) [3] and Kalman filter (KF) [4]. Based on perturbation theory, Fathy [5] simplifies the mass estimation model with the differential equation of longitudinal dynamics.…”

Section: Introductionmentioning

confidence: 99%

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

Jung

Choi

2020

Energies

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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.

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Section: Vehicle Mass Estimation Using the Characteristics Of Engine mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jung

Choi

2020

Energies

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“…And most importantly, all these longitudinal dynamics based estimation techniques are unable to work if the vehicle is parking on a slope, which means these estimators cannot apply to the HSA control. One may argue that the latest mass value estimated before parking can be employed to the HSA controller; 35 however, the mass of the commercial vehicle may have already changed before hill start due to the occupants getting on and off, or goods loading and unloading. Besides, the convergence speed and complexity of the above methods also limit its application in HSA.…”

Section: Introductionmentioning

confidence: 99%

Two-layer mass-adaptive hill start assist control method for commercial vehicles

Peng

Wang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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This study aims to develop a two-layer mass-adaptive control framework to improve the hill start assist performance of commercial vehicles equipped with an electronic parking brake system. In the first layer, the desired pressure of the hill start assist control is proposed, then a logic threshold control method is employed to track the time-varying desired pressure. Due to the frequently changing load of the commercial vehicles, the second layer is utilized to estimate the vehicle mass online. The proposed mass-adaptive control method is evaluated via a co-simulation platform involving MATLAB/Simulink, TruckSim and AMESim, and is compared with a one-layer scheme without mass estimation. Finally, we further demonstrate the feasibility and robustness of the two-layer mass-adaptive hill start assist framework in vehicle experiments.

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“…Kim et al 12 designed an estimator that combined both longitudinal and lateral dynamics on the basis of RLS. Feng et al 13 regarded vehicle mass along with resistance as unknown variables in his estimator. By using two different RLS algorithms based on acceleration partitions, vehicle mass and resistance could be estimated independently.…”

Section: Introductionmentioning

confidence: 99%

Two-layer structure algorithm for estimation of commercial vehicle mass

Cai

Wang

Jia

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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Aiming at the problem of mass estimation for commercial vehicle, a two-layer structure mass estimation algorithm was proposed. The first layer was the grade estimation algorithm based on recursive least squares method and the second layer was a mass estimation algorithm using the extended Kalman filter. The estimated grade was introduced as the observation quantity of the second layer. The influence of the suspension deformation on grade estimation was considered in the first layer algorithm, which was corrected in real time according to the mass and road grade estimated by the second layer algorithm. The proposed estimation algorithm was validated via a co-simulation platform involving TruckSim and MATLAB/Simulink. Finally, a road test was carried out, and the evaluation method using the root mean square error was proposed. According to the test, the average value of the root mean square error reduces from 871.65 to 772.52, grade estimation is more accurate, and the convergence speed of mass estimation is faster, compared with estimation results of the extended Kalman filter method.

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Recursive least square vehicle mass estimation based on acceleration partition

Cited by 13 publications

References 8 publications

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

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

Two-layer mass-adaptive hill start assist control method for commercial vehicles

Two-layer structure algorithm for estimation of commercial vehicle mass

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