State-space construction of Hybrid Petri nets with multiple stochastic firings

TL;DR

This paper introduces an algorithm for constructing state spaces of hybrid Petri nets with multiple stochastic transitions and computes their transient probabilities using various integration methods, demonstrated on a battery system case study.

Contribution

It extends existing models to handle multiple stochastic firings and details algorithms for state space construction and probability computation.

Findings

Algorithm successfully constructs state space with multiple stochastic firings.

Different integration methods are compared for efficiency and accuracy.

Case study demonstrates practical feasibility and performance considerations.

Abstract

Hybrid Petri nets have been extended to include general transitions that fire after a randomly distributed amount of time. With a single general one-shot transition the state space and evolution over time can be represented either as a Parametric Location Tree or as a Stochastic Time Diagram. Recent work has shown that both representations can be combined and then allow multiple stochastic firings. This work presents an algorithm for building the Parametric Location Tree with multiple general transition firings and shows how its transient probability distribution can be computed using multi-dimensional integration. We discuss the (dis-)advantages of an interval arithmetic and a geometric approach to compute the areas of integration. Furthermore, we provide details on how to perform a Monte Carlo integration either directly on these intervals or convex polytopes, or after transformation to standard simplices. A case study on a battery-backup system shows the feasibility of the approach and discusses the performance of the different integration approaches.