Testing a Battery Management System via Criticality-based Rare Event Simulation

TL;DR

This paper presents a novel criticality-based simulation method to efficiently identify rare critical events in safety-critical systems, demonstrated through a battery management system case study in automotive engineering.

Contribution

It introduces an optimization-driven rare event simulation approach tailored for safety-critical systems, improving detection efficiency over traditional methods.

Findings

Effective identification of critical parameter configurations

Demonstrated approach's applicability to automotive battery management

Lessons learned for industrial safety validation

Abstract

For the validation of safety-critical systems regarding safety and comfort, e.g., in the context of automated driving, engineers often have to cope with large (parametric) test spaces for which it is infeasible to test through all possible parameter configurations. At the same time, critical behavior of a well-engineered system with respect to prescribed safety and comfort requirements tends to be extremely rare, speaking of probabilities of order $10^{-6}$ or less, but clearly has to be examined carefully for valid argumentation. Hence, common approaches such as boundary value analysis are insufficient while methods based on random sampling from the parameter space (simple Monte Carlo) lack the ability to detect these rare critical events efficiently, i.e., with appropriate simulation budget. For this reason, a more sophisticated simulation-based approach is proposed which employs optimistic optimization on an objective function called "criticality" in order to identify effectively the set of critical parameter configurations. Within the scope of the ITEA 3 TESTOMAT project (http://www.testomatproject.eu/) the collaboration partners OFFIS e.V. and AKKA Germany GmbH conducted a case study on applying criticality-based rare event simulation to the charging process of an automotive battery management system given as a model. The present technical report documents the industrial use case, the approach, application and experimental results, as well as lessons learned from the case study.