Efficient simulation of hybrid systems: A hybrid bond graph approach

Accurate and efficient simulations facilitate cost-effective design and analysis of large, complex, embedded systems, whose behaviors are typically hybrid, i.e. continuous behaviors interspersed with discrete mode changes. In this paper we present an approach for deriving component-based computational models of hybrid systems using hybrid bond graphs (HBGs), a multi-domain, energy-based modeling language that provides a compact framework for modeling hybrid physical systems. Our approach exploits the causality information inherent in HBGs to derive component-based computational models of hybrid systems as reconfigurable block diagrams. Typically, only small parts of the computational structure of a hybrid system change when mode changes occur. Our key idea is to identify the bonds and elements of HBGs whose causal assignments are invariant across system modes, and use this information to derive space-efficient reconfigurable block diagram models that may be reconfigured efficiently when mode changes occur. This reconfiguration is based on the incremental reassignment of causality implemented as the Hybrid Sequential Causal Assignment Procedure, which reassigns causality for the new mode based on the causal assignment of the previous mode. The reconfigurable block diagrams are general, and they can be transformed into simulation models for generating system behavior. Our modeling and simulation methodology, implemented as the Modeling and Transformation of HBGs for Simulation (MoTHS) tool suite, includes a component-based HBG modeling paradigm and a set of model translators for translating the HBG models into executable models. In this work, we use MoTHS to build a high-fidelity MATLAB Simulink model of an electrical power distribution system.