Analysis and Optimisation of a New Differential Steering Concept

The emergence of electric drives opens up new opportunities in vehicle design. For example, powerful in-wheel motors pro -vide unprecedented flexibility in chassis design and are suitable for distributed drive solutions, although implying non-trivial vehicle dynamics control problems. This work aims at a new differential steering concept relying only on passive steering linkages where the necessary steering moment about the kingpins is generated by traction force differences produced by in-wheel motors. For the analysis of the proposed steering concept, a tailored multi-body system model is introduced along with the associated steering control system. In addition, this work explores the general applicability of such a new steering concept by using multi-objective optimisation. For this purpose, various design objectives and constraints are defined with respect to the dynamic, steady-state and low-speed steering performance of the vehicle. The resulting behaviour of the proposed steering concept is investigated by various simulation experiments demonstrating a comparable steering performance to that of conventional passenger cars

Dynamic Performance of Estimator-based Speed Sensorless Control of Induction Machines Using Extended and Unscented Kalman Filters

This paper presents an estimator-based speed sensorless field-oriented control (FOC) method for induction machines, where the state estimator is based on a self-contained, non-linear model. This model characterises both the electrical and the mechanical behaviours of the machine and describes them with seven state variables. The state variables are estimated from the measured stator currents and from the known stator voltages by using an estimator algorithm. An important aspect is that one of the state variables is the load torque and, hence, it is also estimated by the estimator. Using this feature, the applied estimator-based speed sensorless control algorithm may be operated adequately besides varying load torque. In this work, two different variants of the control algorithm are developed based on the extended and the unscented Kalman filters (EKF, UKF) as state estimators. The dynamic performance of these variants is tested and compared using experiments and simulations. Results show that the variants have comparable performance in general, but the UKF-based control provides better performance if a stochastically varying load disturbance is present