Simultaneous mass and time-varying grade estimation for heavy-duty vehicles

Vahidi, Ardalan; Druzhinina, Maria; Stefanopoulou, Anna G.; Peng, Huei

doi:10.1109/acc.2003.1242508

Cited by 53 publications

(43 citation statements)

References 12 publications

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“…Method 1 and method 2 above are used to solve the single objective function in Equation (18). In order to solve the original multiple objectives optimization problem described in the problem formulation at the previous section, multiple objectives GA (MOGA) is also implemented.…”

Section: Optimization Methods Using Pso and Gamentioning

confidence: 99%

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Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study

Rozaqi

Rijanto

Kanarachos

2017

J. Mechatron. Electr. Power Veh. Technol.

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This paper proposes a new method of concurrent SOC and SOH estimation using a combination of recursive least square (RLS) algorithm and particle swarm optimization (PSO). The RLS algorithm is equipped with multiple fixed forgetting factors (MFFF) which are optimized by PSO. The performance of the hybrid RLS-PSO is compared with the similar RLS which is optimized by single objective genetic algorithms (SOGA) as well as multi-objectives genetic algorithm (MOGA). Open circuit voltage (OCV) is treated as a parameter to be estimated at the same time with internal resistance. Urban Dynamometer Driving Schedule (UDDS) is used as the input data. Simulation results show that the hybrid RLS-PSO algorithm provides little better performance than the hybrid RLS-SOGA algorithm in terms of mean square error (MSE) and a number of iteration. On the other hand, MOGA provides Pareto front containing optimum solutions where a specific solution can be selected to have OCV MSE performance as good as PSO.

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Section: Optimization Methods Using Pso and Gamentioning

confidence: 99%

“…The parameter estimates are calculated using RLS with multiple fixed forgetting factors (MFFF-RLS) as follows [18,19].…”

Section: ̂=̂( ) =̂mentioning

confidence: 99%

Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study

Rozaqi

Rijanto

Kanarachos

2017

J. Mechatron. Electr. Power Veh. Technol.

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“…Unfortunately, the load torque depends on vehicle mass and road grade which are unknown parameters. See reference [11] for challenges and methods to simultaneously estimate the vehicle mass and the road grade. The load torque may be estimated using the automatic transmission torque converter turbine speed measurement, if the torque converter is in the unlocked state, or using a wheel speed measurement.…”

Section: Application Of the Recursive Trigonometric Interpolation mentioning

confidence: 99%

Computationally efficient filtering algorithms for engine torque estimation

Stotsky

2005

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…Recursive Least Squares methods are common for estimation of multiple parameters, for instance, Vahidi [14] estimates road gradient and mass simultaneously. Many approaches use a combination of RLS and Kalman Filtering methods to simultaneously estimate road gradient and vehicle mass, including Raffone [8] and Vahidi [14]. Nonlinear observer structures are also used, by the current authors [9] [10] [15] [7] and by McIntyre [16] and Rajamani [17].…”

Section: Introductionmentioning

confidence: 99%

Modelling and Simulation of Rapidly Changing Road Gradients

Wragge-Morley¹,

Herrmann²,

Burgess³

et al. 2016

SAE Int. J. Passeng. Cars - Mech. Syst.

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In vehicle dynamics modelling, the road profile is generally treated in one of two ways; either the gradient is a property that changes over a length scale far greater than that of the vehicle's wheelbase, or as a very detailed road surface model for determining the behaviour of vehicle suspensions. Occasionally, for modelling the behaviour of off-road vehicles, step-climbing manoeuvres are modelled. We propose an extension of these step-climbing models to a general, continuously varying road gradient model for cases where the distance over which the large gradient change occurs are of similar length-scale as the vehicle wheelbase. The motivation behind this work comes from a road gradient and vehicle mass estimation problem where it was noticed that very sudden gradient changes have a significant impact on the powertrain, but in a way that is not proportional to the attitude change of the vehicle. We present an analysis applicable to these cases where the road gradient change is substantially different from the vehicle attitude change. The analysis is built up from a very basic step climbing exercise to a general case solution for two dimensional longitudinal vehicle dynamics. Methods of handling road gradient and gradient change for modelling purposes are discussed. A method is proposed that defines the gradient change as an input to a backwards dynamic model that allows us to compute torque required to achieve a pre-defined datum speed during a gradient transition. The results of simulations for simple gradient change cases are shown. We discuss the implication of these road gradient transients for vehicle parameter estimation based on powertrain behaviour.

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Simultaneous mass and time-varying grade estimation for heavy-duty vehicles

Cited by 53 publications

References 12 publications

Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study

Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study

Computationally efficient filtering algorithms for engine torque estimation

Modelling and Simulation of Rapidly Changing Road Gradients

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