Battery aging and behavioral pattern identification: A fleet analytics framework for regulatory compliance testing

Battery Electric Vehicle s (BEVs) face stringent regulatory requirements for battery durability, with Euro 7 and Advanced Clean Cars II (ACCII) regulations mandating State of Health (SoH) thresholds of 70 % after 8 years or 160,000 km and 10 years or 240,000 km, respectively. Current testing protocols rely on homogeneous profiles that inadequately represent real-world usage variability, creating recall and over-engineering risks. This paper addresses this gap by proposing a data-driven framework linking usage behavior patterns with battery aging dynamics to identify representative test profiles. The framework consists of three integrated steps: (1) predicting battery age and usage at regulatory boundaries through mathematical formulation, (2) estimating SoH using machine learning models including ensemble methods and statistical approaches, and (3) clustering vehicles into groups with homogeneous aging behavior. Unlike existing approaches, it accounts for real-world heterogeneity across diverse usage patterns without assuming predefined load profiles. Validation used a large-scale Volkswagen AG fleet of roughly 850,000 vehicles across five countries (China, Germany, Italy, Norway, United States). Analysis across regions demonstrates how usage behavior diversity impacts battery aging, validating the need for behavioral pattern-aware methodologies. The framework was complemented by Center for Advanced Life Cycle Engineering (CALCE) battery cycling validation (192 cells, 24 test groups), achieving Homogeneity 0.9157 and Completeness 0.9509. The modular design enables future enhancements including multi-view clustering and advanced SoH prediction models. This framework provides manufacturers a systematic approach to regulatory compliance while reducing unnecessary testing and mitigating over-engineering risk. • Regional usage differences significantly impact battery state of health loss. • Predict vehicle battery age and mileage at regulatory compliance boundaries. • Integrated data-driven framework links battery aging to real-world usage patterns. • Clustering identifies vehicle groups with distinct battery degradation behaviors.