About: Vehicle dynamics is a(n) research topic. Over the lifetime, 12909 publication(s) have been published within this topic receiving 204091 citation(s).
Papers published on a yearly basis
31 Oct 2005
TL;DR: In this paper, the authors present a mean value model of SI and Diesel engines, and design and analysis of passive and active automotive suspension components, as well as semi-active and active suspensions.
Abstract: 1. Introduction.- 2.Lateral Vehicle Dynamics.- 3. Steering Control For Automated Lane Keeping.- 4. Longitudinal Vehicle Dynamics.- 5. Introduction to Longitudinal Control.- 6. Adaptive Cruise Control.- 7. Longitudinal Control for Vehicle Platoons.- 8. Electronic Stability Control.- 9. Mean Value Modeling Of SI and Diesel Engines.- 10. Design and Analysis of Passive Automotive Suspensions.- 11. Active Automotive Suspensions.-12. Semi-Active Suspensions.- 13. Lateral and Longitudinal Tires Forces.- 14. Tire-Road Friction Measurement on Highway Vehicles.- 15. Roll Dynamics and Rollover Prevention.- 16. Dynamics and Control of Hybrid Gas Electric Vehicles.
TL;DR: In this paper, two major figures in adaptive control provide a wealth of material for researchers, practitioners, and students to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs.
Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.
01 Dec 2005
TL;DR: Pacejka's model is the subject of continued development, particularly in extending its validity range to modelling higher frequencies and short wavelength obstacles and testing, which are covered in new chapters in the 2nd edition.
Abstract: This book, which is intended for practising and student engineers, provides a complete reference on the mechanical behaviour of pneumatic tyres and their impact on vehicle performance. The comprehensive scope of the book includes developing an understanding of mathematical models of tyre behaviour, the incorporation of these models into vehicle models, and presents an applied understanding of how the tyre influences vehicle handling. The book is supported by practical experimental observations and exercises. Pacejka's tyre model is the subject of continued development, particularly in extending its validity range to modelling higher frequencies and short wavelength obstacles and testing, which are covered in new chapters in the 2nd edition.
••10 Jun 2009
TL;DR: Simulation results over multiple driving cycles indicate better fuel economy over conventional strategies can be achieved and the proposed algorithm is causal and has the potential for real-time implementation.
Abstract: In this paper, a Model Predictive Control (MPC) strategy is developed for the first time to solve the optimal energy management problem of power-split hybrid electric vehicles. A power-split hybrid combines the advantages of series and parallel hybrids by utilizing two electric machines and a combustion engine. Because of its many modes of operation, modeling a power-split configuration is complex and devising a near-optimal power management strategy is quite challenging. To systematically improve the fuel economy of a power-split hybrid, we formulate the power management problem as a nonlinear optimization problem. The nonlinear powertrain model and the constraints are linearized at each sample time and a receding horizon linear MPC strategy is employed to determine the power split ratio based on the updated model. Simulation results over multiple driving cycles indicate better fuel economy over conventional strategies can be achieved. In addition the proposed algorithm is causal and has the potential for real-time implementation.
TL;DR: The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads, and two approaches with different computational complexities are presented.
Abstract: In this paper, a model predictive control (MPC) approach for controlling an active front steering system in an autonomous vehicle is presented. At each time step, a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We present two approaches with different computational complexities. In the first approach, we formulate the MPC problem by using a nonlinear vehicle model. The second approach is based on successive online linearization of the vehicle model. Discussions on computational complexity and performance of the two schemes are presented. The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads
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