Vehicle-in-the-loop (ViL) test benches can accelerate the validation of brake control systems, including Electronic Stability Control (ESC), by enabling repeatable, highly instrumented experiments under controlled boundary conditions. For robust use in
validation, it must be demonstrated which ESC relevant maneuvers and operating conditions can be reproduced on the bench with su¿iciently road-equivalent vehicle and system behavior—and where the ViL environment reaches its limits.
This paper presents a qualification methodology for a bench-based ViL test environment focused on ESC relevant maneuvers. The methodology defines an ESC oriented KPI set for the quantitative comparison of on-road and ViL measurements (including vehicle dynamics response, system and intervention behavior, timing, and repeatability/dispersion) and derives maneuver-specific operating envelopes within which a maneuver is considered road-equivalent. Key drivers of road–rig consistency are addressed through real-time tire-road interaction modeling (e.g., using high-resolution friction and topography inputs), refinement of the relevant vehicle and environment models, and compensation measures for rig-induced artifacts (e.g., latency, inertia e¿ects, and bandwidth limitations). The approach is evaluated using paired measurements from on-road vehicle tests and the ViL bench across representative ESC maneuvers and selected environmental and test conditions (e.g., driver input profiles, road surface characteristics, and temperature). An ablation study quantifies the contribution of major measures, while a structured error budget assigns observed roadrig deviations to dominant contributors.
The results provide maneuver- and condition-dependent qualification statements (“qualified / not qualified”), quantified deviation levels, and explicit operating boundaries for ESC investigations. The proposed methodology establishes a reproducible, measurable basis for ViL applicability and supports downstream virtual validation tasks such as objective function assessment and automated anomaly detection.