Planning future charging infrastructure for private EVs: A city-scale assessment of demand and capacity

• Proposed a globally adaptable, city-scale capacity planning model for EV charging infrastructure. • EV electricity consumption in Chongqing reached 57.5 GWh in Dec 2024, resulting in 30 kt CO2 emissions. • Installed 181,622 charging piles during 2022–2024, with spatial distribution aligned to population density. • By 2030, EV electricity demand is projected to reach 1940 GWh, with charging piles exceeding 1.3 million. • Developed a versatile tool for stakeholders to plan sustainable, cost-effective, low-carbon EV infrastructure. Capacity planning for electric vehicle (EV) charging infrastructure has emerged as a critical challenge in developing low-carbon urban energy systems. This study proposes the first demand-driven, multi-objective planning model for optimizing city-scale capacity allocation of EV charging infrastructure. The model employs a bottom-up approach to estimate charging demand differentiated by vehicle type—battery electric vehicles (BEVs), extended-range electric vehicles (EREVs), and plug-in hybrid electric vehicles (PHEVs). Chongqing, a rapidly expanding EV industry cluster in China with a strong industrial base, supportive policies, and diverse urban morphologies, is selected as the case study. The results show that (1) monthly EV electricity consumption in Chongqing rose from 18.9 gigawatt-hours (GWh) in June 2022 to 57.5 GWh in December 2024, with associated carbon emissions increasing from 9.9 kilotons of carbon dioxide (ktCO 2 ) to 30 ktCO 2 ; (2) 181,622 additional charging piles were installed between 2022 and 2024, with the fastest growth observed in Yubei, reflecting a demand-responsive strategy that prioritizes areas with higher population density, higher income levels, and adequate land availability for pile deployment, rather than broad geographic coverage; and (3) between 2025 and 2030, EV electricity demand is projected to reach 1940 GWh, with the number of charging piles exceeding 1.4 million, and charging demand from EREVs and PHEVs expected to overtake BEVs later in the period. While Chongqing serves as the pilot area, the proposed planning platform is adaptable for application in cities worldwide, enabling cross-regional comparisons under diverse socio-economic, geographic, and policy conditions. Overall, this work offers policymakers a versatile tool to support sustainable, cost-effective EV infrastructure deployment aligned with low-carbon electrification targets in the transportation sector. A city-scale capacity planning model for EV charging infrastructure and a case study of Chongqing. The graph presents (a) the operational framework of the planning model; (b1) the distributions of energy intensity and model-specific electricity consumption for BEVs, EREVs, and PHEVs; (b2) monthly variations in operational carbon emissions from top-selling EV models; and (b3) the spatial allocation of charging pile capacity across Chongqing’s districts and counties.