There are several global and local position estimation and refinement techniques based on the GNSS (Global Navigation Satellite System) and environmental monitoring (e.g., LIDAR, Light Detection and Ranging). These are usually based on a combination of multiple sensors using some form of sensor fusion, together with a filtering or observation technique. The behavior of these algorithms may vary depending on the applied sensor signals and on their accuracy under different environmental conditions and for different vehicle types. In the case of systems that also use GNSS signals, different procedures must also be prepared for signal dropouts and, in the worst case, drastic fluctuations in accuracy. The aim of this research is to present and compare the performance of different estimation procedures for different vehicles and environmental conditions.

In applied battery research, use-case-driven prediction is becoming increasingly important, particularly for predicting real-life load profiles. This study proposes techniques to forecast lifetime load profiles for traction batteries, comparing urban- and highway-dominated vehicular use cases. Both charging and discharging scenarios are analyzed. We examine the uncertainty in these profiles and conduct a sensitivity analysis to understand the relationship between load profiles and user behavior. In this study, we introduce a novel methodology that maps behavioral and environmental parameters to battery load clusters, enabling us to identify high-risk aging scenarios. Based on parameter studies, we perform load profile clustering to identify critical use case groups and observe key parameter interactions. We present a case study of an idealized driver under Hungarian environmental conditions to predict outlier battery usage in fleets. This novel approach enables more robust predictions of aging and performance degradation for automotive traction batteries across different user clusters.