Observer design based on nonlinear suspension model with unscented Kalman filter

This paper presents a new approach to estimating suspension state information and parameter in real-time. An observer with unscented Kalman filter is designed based on a nonlinear quarter car model. The proposed observer could estimate the sprung mass, vertical velocity of sprung and unsprung mass for the nonlinear suspension systems with vehicle load variation. The designed observer has low sensitivity and robust to unknown road surfaces. The efficiency of the estimator is validated through the simulations with two different types of road excitation and payload variations. The simulation results clearly indicate that compared with the extended Kalman filter estimator, the unscented Kalman filter is more accurate and robust. The estimated state information and parameters could be used in the design of suspension control systems

Towards self-powered sensing using nanogenerators for automotive systems

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.nanoen.2018.09.032 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Harvesting energy from the working environment of vehicles is important for wirelessly monitoring their operation conditions and safety. This review aims at reporting different sensory and energy harvesting technologies developed for automotive and active safety systems. A few dominant sensing and power harvesting mechanisms in automotive systems are illustrated, then, triboelectric, piezoelectric and pyroelectric nanogenerators, and their potential for utilization in automotive systems are discussed considering their high power density, flexibility, different operating modes, and cost in comparison with other mechanisms. Various ground vehicles’ sensing mechanisms including position, thermal, pressure, chemical and gas composition, and pressure sensors are presented. A few novel types self-powered sensing mechanisms are presented for each of the abovementioned sensor categories using nanogenerators. The last section includes the automotive systems and subsystems, which have the potential to be used for energy harvesting, such as suspension and tires. The potential of nanogenerators for developing new self-powered sensors for automotive applications, which in the near future, will be an indispensable part of the active safety systems in production cars, is also discussed in this review article

Robust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation

This work provides novel robust and regularized algorithms for parameter estimation with applications in vehicle tractive force prediction and mass estimation. Given a large record of real world data from test runs on public roads, recursive algorithms adjusted the unknown vehicle parameters under a broad variation of statistical assumptions for two linear gray-box models

Robust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation

This dissertation provides novel robust and regularized algorithms from linear system identification for parameter estimation with applications in vehicle tractive force prediction and mass estimation

Optimale Regelung eines prädiktiven Energiemanagements von Hybridfahrzeugen

Diese Arbeit verbindet die aktuellen Trends der Automobilindustrie (E-Mobilität, autonomes Fahren und das vernetzte Fahrzeug) in einem Entwurf für ein Fahrerassistenzsystem für Hybridfahrzeuge, das die Geschwindigkeitsregelung als auch die Regelung des Energiemanagements optimal übernimmt