The passenger car market has experienced a radical shift: the rise of SUV, crossover vehicles, but also Battery Electric Vehicle (BEV) and Plug-In Hybrid Vehicle (PHEV), has blurred the borders between traditional vehicle segments as well as body types, resulting in reduced applicability of conventional taxonomies of vehicle types. This study aims to provide an overview of the vehicle market by proposing a new, machine-learning-based segmentation of the entire German vehicle fleet covering the past years. We merge over 40 million registered vehicles with a technical specifications database and apply data-mining techniques to derive an improved market segmentation. We demonstrate that unsupervised learning techniques, specifically Ward and k-means clustering, yield clusters with enhanced separation, clarity, and practical usability. Clustering was applied to both raw technical features and engineered features designed to capture aspects of economy, ecology, usability, and performance. The silhouette scores can reach 0.19, a significant increase over the +0.05/−0.05 scores of the existing vehicle segments or chassis types.

This study explores the importance of considering regional aspects and different calculation approaches when assessing the environmental impact of passenger cars in Germany. The transportation sector, in general, needs to improve its transition to comply with national and international goals, and more efficient measures are necessary. To achieve this, the spatial heterogeneity of underlying data, such as vehicle stocks, cubic capacity classes as a proxy for consumption values, and annual mileage, is investigated with respect to regional differences. Using data samples for the year 2017, the average emission values per car and year are calculated as well as Germany’s total emission values from the utilization of passenger cars. Conducting a spatially informed allocation algorithm, battery electric vehicles (BEVs) are added to certain regional fleets, replacing cars with internal combustion engines (ICEs). The results show significant regional differences in the underlying data, with a divergence between rural and urban areas as well as northern and southern regions, while the spread in mileage values is higher than that in consumption values. Comparing the tank-to-wheel (TtW) and well-to-wheel (WtW) approaches reveals different values with an increased spread as more BEVs are introduced to the fleet. Using the presented concept to allocate BEVs, emissions can be reduced by 1.66% to 1.35%, depending on the calculation perspective, compared to the extrapolation of historical values. Furthermore, rural areas benefit more from optimized allocation compared to urban ones. The findings suggest that regional distribution strategies could lead to more efficient reductions in GHG emissions within the transportation sector while incorporating both TtW and WtW approaches, leading to more comparable and precise analyses.

Transitioning to battery electric vehicles (BEVs) is crucial for decarbonizing transportation. However, the market penetration of BEVs depends significantly on cost structures and innovative technologies. This review comprehensively analyzes the key cost and performance parameters along the vertical integration axis of BEV electrification, from the cell to energy system integration. At the cell level, the analysis shows the emergence of sodium-ion technology as a lowcost alternative. In contrast, at the pack level, advanced integration concepts increase the competitiveness of cell chemistries with lower energy density. At the vehicle level, future cost reductions increasingly depend on battery innovations. Finally, synergies with renewable energy systems through bidirectional charging are essential to unlock BEVs' full economic and environmental potential. The future success of electric mobility thus depends on the proper selection of cell technology and holistic system integration.