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Proceedings ArticleDOI

Vehicle Mass Estimation for Heavy Duty Vehicle

About: The article was published on 2015-09-29. It has received 1 citations till now.
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Patent
Xiaoyu Huang1
20 Jun 2019
TL;DR: In this paper, a method for estimating the mass of a vehicle system includes a number of steps including a first step of providing a vehicle vehicle system having at least a powertrain and a vehicle control module.
Abstract: A method for estimating the mass of a vehicle system includes a number of steps including a first step of providing a vehicle system having at least a powertrain and a vehicle control module. Three different mass estimates are assigned with the last mass estimate being the most accurate. The mass estimates are used in the vehicle control module calculations for vehicle control parameters in the event that the weight of the vehicle changes.
References
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Journal ArticleDOI
TL;DR: Simulation and experimental results show, under a set of qualifying conditions, that both mass and road grade can be estimated with good accuracy.
Abstract: This work proposes a two-stage estimation strategy to determine a heavy-duty vehicle's mass and road grade. The estimation strategy uses standard signals available through the vehicle control area network. The first stage of this approach utilizes an adaptive least-squares estimation strategy to determine the vehicle's mass and an estimate for a constant road grade. Due to the time-varying nature of the road grade, a nonlinear estimator that provides a more-accurate estimate of the road grade is then developed. Simulation and experimental results show, under a set of qualifying conditions, that both mass and road grade can be estimated with good accuracy.

92 citations

Journal ArticleDOI
TL;DR: Experimental results obtained from vehicle road tests show that the proposed estimator is capable of estimating the CG position with acceptable accuracy, and an investigation of the two-layer persistent excitation (PE) condition reveals that, although the CG height estimation largely depends on the excitation level in the maneuver, the CG longitudinal location can be always estimated via the input torque injections.
Abstract: In this paper, a real-time center of gravity (CG) position estimator, which is based on a combined adaptive Kalman filter-extended Kalman filter (AKF-EKF) approach, for lightweight vehicles (LWVs) is proposed. Accurate knowledge of the CG longitudinal location and the CG height in the vehicle frame is helpful to the control of vehicle motions, particularly for LWVs, whose CG positions can be substantially varied by the payloads on board. The proposed estimation method, taking advantage of the separate front/rear torque control capability available in numerous LWV prototypes, only requires that the vehicle be excited longitudinally and/or vertically, thus avoiding potentially dangerous excitation of the vehicle lateral/yaw/roll motions. Moreover, additional parameters, such as vehicle moments of inertia, suspension parameters, and the tire/road friction coefficient (TRFC), are not necessary. A three-degree-of-freedom (3-DOF) vehicle dynamics model, taking the vehicle longitudinal velocity, the front-wheel angular speed, and the rear-wheel angular speed as states, is employed in the filter formulation. The designed estimator consists of two parts: an AKF for filtering noisy states and an EKF for estimating parameters. To minimize the effects of undesirable oscillation and bias in the filtered states, the optimization-based AKF judiciously tunes the suboptimal process noise covariance matrix in real time. Meanwhile, the EKF utilizes the filtered states from the AKF and takes the parameters as random walks. Simulation results exhibit the advantages of the AKF over the standard KF with fixed covariance matrices. Experimental results obtained from vehicle road tests show that the proposed estimator is capable of estimating the CG position with acceptable accuracy. Moreover, an investigation of the two-layer persistent excitation (PE) condition reveals that, although the CG height estimation largely depends on the excitation level in the maneuver, the CG longitudinal location can be always estimated via the input torque injections.

47 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a method for estimating vehicle mass and road grade to compensate for the driving loads of hybrid vehicles and plug-in hybrid electric vehicles (PHEVs).
Abstract: In controlling the longitudinal motion of electrified vehicles such as hybrid vehicles and PHEV (Plug-in Hybrid Electric Vehicles), the variation of the driving resistance loads (or driving loads) such as road grade and actual vehicle mass, is the most influential factor which limits the control performance. Measuring the driving load is not impossible, but it is costly since additional sensors have to be mounted on the vehicle. In this study, methods for estimating vehicle mass and road grade are designed to compensate for the driving loads. The proposed methods are verified using simulation tools and then evaluated experimentally.

27 citations

Proceedings ArticleDOI
21 Nov 2013
TL;DR: The result shows that the real-time multi-data fusion algorithm produces a good estimation of the road grade and vehicle mass with an error of 5%, and the convergence and steady-state error meet the need of real vehicle applications.
Abstract: Vehicle mass and road grade are two key parameters for New Energy Vehicles. It plays an important role in the power distribution of multi-energy power systems and braking energy recovery. Using a 4-wheel drive (4WD) electric mini-car as an experimental platform, a road grade and vehicle mass estimation algorithm based on multi-data fusion technology is studied. Firstly, a Simulink model for GPS (Global Positioning System)/INS (Inertial Navigation System)/wheel-speed data fusion is established, taking advantage of the characteristics of a 4WD electric vehicle. An off-line simulation is conducted with data from a real vehicle test to verify the model. Then the verified algorithm is downloaded and successfully implemented in the Vehicle Control Unit based on MPC561 digital core by Simulink Automatic Code Generation technology. Finally, a hardware-inloop simulation based on CANoe and CANalyzer is conducted for the testing and evaluation of the VCU. The result shows that the real-time multi-data fusion algorithm produces a good estimation of the road grade and vehicle mass with an error of 5%, and the convergence and steady-state error meet the need of real vehicle applications.

23 citations

Proceedings ArticleDOI
03 Jul 2006
TL;DR: In this article, the authors deal with methods to identify the payload parameters of air-sprung vehicles on different levels and examine the described onboard-identification methods for application to truck-semitrailer combinations.
Abstract: Roll over accidents of vehicles belong to the most dangerous vehicle accident scenarios on our roads. In particular, heavy commercial vehicles allow enormous changes of loads and often the drivers are confronted with unknown load conditions, e.g. container freight. This causes a persistent overturning hazard. Due to the varying load situations, the drivers of commercial trucks are therefore requested to permanently adapt their driving style in relation to the actual driving-and roll dynamics of their vehicles, in order to provide sufficient driving safety. This paper therefore deals with methods to identify the payload parameters of air-sprung vehicles on different levels. Special attention is spent to examine the described onboard-identification methods for application to truck-semitrailer combinations. The online-identified relevant payload parameters are the load mass, the position of its centre of gravity and especially the height of its centre of gravity above ground. The aim of this paper is on the one hand to develop an identification approach to be able to provide more driver information about the actual overturning limit of the vehicle and on the other hand to deliver additional information about the current loading conditions of the vehicle as an input to any advanced driver assistance system or to enhanced vehicle-dynamics controllers, which take the payload height into consideration.

15 citations