A high-resolution dataset on the electric passenger vehicle characteristics in China and the European Union

This dataset provides highly resolved market characteristics of electric passenger vehicles in China (CN_characteristics_2023.xlsx) and the EU (EU_characteristics_2023) for 2023, calculated using a bottom-up multi-dataset fusion approach. The market characteristics include electric vehicle (EV) market penetration, segment mix, sales-weighted average battery capacity, and battery chemistry mix, and are highly resolved across spatial, segmental, and powertrain levels. The dataset covers 31 provinces and municipalities in China, as well as the 27 EU member states plus Iceland and Norway. Vehicles are classified into five segments based on wheelbase. Two EV types—Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV)—are distinguished, along with six battery chemistries: LFP, LMO, NCM-L, NCM-M, NCM-H, and NCA.Additionally, the dataset includes bottom-up characteristic calculation code for the EU (EU_Bottom_up_Multi_dataset_fusion.m) as an example, together with an EU EV specification and registration sample (EU_Variant_2023.xlsx), which covers EVs accounting for nearly 70% of total EU sales in 2023. This file contains both the input specifications for the fusion process (e.g., vehicle segment, electric range, battery capacity) and the corresponding registration data, representing the direct outputs of the fusion process. A counterpart file is provided for China (CN_VAC_2023.xlsx), which similarly combines input specifications and registrations derived through the fusion process, conducted at the prefecture-level city resolution. To prevent this table from becoming overly lengthy and complex, we present only the vehicle registrations of each provincial capital as a representative subset.The dataset further includes the gravimetric energy density clustering code (BEV_Energy_density_clustering.m) used in obtaining sub-NCM chemistries for Chinese BEVs, its input file (BEV_energy_density_CN.xlsx), and its outputs (BEV_NCM_clustered.xlsx).