Michel Mensler

The CONTSID Toolbox: A Software Support for Data-based Continuous-time Modelling

This chapter describes the continuous-time system identification (CONTSID) toolbox for MATLAB®, which supports continuous-time (CT) transfer function and state-space model identification directly from regularly or irregularly time-domain sampled data, without requiring the determination of a discrete-time (DT) model. The motivation for developing the CONTSID toolbox was first to fill in a gap, since no software support was available to serve the cause of direct time-domain identification of continuous-time linear models but also to provide the potential user with a platform for testing and evaluating these data-based modelling techniques. The CONTSID toolbox was first released in 1999 [15]. It has gone through several updates, some of which have been reported at recent symposia [11, 12, 16]. The key features of the CONTSID toolbox can be summarised as follows: it supports most of the time-domain methods developed over the last thirty years [17] for identifying linear dynamic continuous-time parametric models from measured input/output sampled data; it provides transfer function and state-space model identification methods for single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems, including both traditional and more recent approaches; it can handle irregularly sampled data in a straightforward way; it may be seen as an add-on to the system identification (SID) toolbox for MATLAB® [26]. To facilitate its use, it has been given a similar setup to the SID toolbox; it provides a flexible graphical user interface (GUI) that lets the user analyse the experimental data, identify and evaluate models in an easy way.

Energy Management of a High Efficiency Hybrid Electric Automatic Transmission

The energy management of a hybrid vehicle defines the vehicle power flow that minimizes fuel consumption and exhaust emissions. In a combined hybrid the complex architecture requires a multi-input control from the energy management. A classic optimal control obtained with dynamic programming shows that thanks to the high efficiency hybrid electric variable transmission, energy losses come mainly from the internal combustion engine. This paper therefore proposes a sub-optimal control based on the maximization of the engine efficiency that avoids multi-input control. This strategy achieves two aims: enhanced performances in terms of fuel economy and a reduction of computational time.