  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.
