Abstract
The presence of motion control or active safety systems in vehicles have become increasingly important for improving vehicle performance and handling and negotiating dangerous driving situations. The performance of such systems would be improved if combined with knowledge of vehicle dynamic parameters. Since some of these parameters are difficult to measure, due to technical or economic reasons, estimation of those parameters might be the only practical alternative. In this paper, an estimation strategy of important vehicle dynamic parameters, pertaining to motion control applications, is presented. The estimation strategy is of a modular structure such that each module is concerned with estimating a single vehicle parameter. Parameters estimated include: longitudinal, lateral, and vertical tire forces – longitudinal velocity – vehicle mass. The advantage of this strategy is its independence of tire parameters or wear, road surface condition, and vehicle mass variation. Also, because of its modular structure, each module could be later updated or exchanged for a more effective one. Results from simulations on a 14-DOF vehicle model are provided here to validate the strategy and show its robustness and accuracy.
Highlights
IntroductionThe presence of active safety systems, such as Antilock Braking, Traction Control and Stability Control systems, have become increasingly important
The presence of active safety systems, such as Antilock Braking, Traction Control and Stability Control systems, have become increasingly important. Such systems or motion control systems help maintain driver’s control over the vehicle in dangerous driving situations, thereby reducing accident rates. Such systems are capable of enhancing vehicle performance and handling
[5] Antonov et al proposed an advanced method for calculating vertical tire forces based on modelled suspension system and using estimated forces acting on vehicle centre of gravity
Summary
The presence of active safety systems, such as Antilock Braking, Traction Control and Stability Control systems, have become increasingly important. Longitudinal or lateral tire forces can be estimated based on analytical tire models, such as linear model [6], Dugoff model [7], or Magic Formula model [8] Such models rely on parameters that are specific to each tire and vary with tire aging or road surface condition. In [16], Bae et al proposed an averaging recursive least squares estimator that uses longitudinal force, acceleration, and GPS-based road grade measurements to determine vehicle mass and aerodynamic drag. Other parameters estimated in that strategy were vehicle mass and longitudinal velocity – body roll and pitch angles. The rest of this paper is structured as follows: Section-2 presents the outline of the modular estimation strategy with details on developed modules.
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