Heavy goods transportation is responsible for around 27% of CO₂ emissions from road transport in the EU and for 5% of total CO₂ emissions in the EU. The decarbonization of long-distance transport in particular remains a major challenge. The combination of battery electric trucks (BETs) with on-route high-power charging (HPC) offers a promising solution. Planning and setting up the required infrastructure is a critical success factor here. We propose a methodology to evaluate the charging infrastructure needed to support the large-scale introduction of heavy-duty BETs in Germany, considering different levels of electrification, taking the European driving and rest time regulations into account. Our analysis employs MATSim, an activity-based multi-agent transport simulation, to assess potential bottlenecks in the charging infrastructure and to simulate the demand-based distribution of charging stations. The MATSim simulation is combined with an extensive pre-processing of transport-related data and a suitable post-processing. This approach allows for a detailed examination of the required charging infrastructure, considering the impacts of depot charging solutions and the dynamic nature of truck movements and charging needs. The results indicate a significant need to augment HPC with substantial low power overnight charging facilities and highlight the importance of strategic infrastructure development to accommodate the growing demand for chargers for BETs. By simulating various scenarios of electrification, we demonstrate the critical role of demand-oriented infrastructure planning in reducing emissions from the road freight sector until 2030. This study contributes to the ongoing discourse on sustainable transportation, offering insights into the infrastructure requirements and planning challenges associated with the transition to battery electric heavy-duty vehicles.

The electrification of long-haul freight transport requires a comprehensive public charging infrastructure along motorways. This study presents a framework combining multi-agent transport simulation (MATSim) with evolutionary bi-objective optimization (NSGA-II) to determine the number and spatial distribution of high-power charging (HPC) points for battery-electric trucks (BETs) on the German motorway network. Beyond infrastructure sizing, the approach also quantifies the impact of BET charging on the duration and distance of long-haul truck trips. The optimization simultaneously addresses the perspectives of two key stakeholders: charge point operators (CPOs), who seek to maximize charger utilization, and logistics operators, who aim to minimize waiting times. The results yield a range of Pareto-optimal configurations balancing the two objectives. A multi-iteration replanning step further lets trucks adapt their routes to experienced waiting times for a more realistic performance assessment, reducing mean waiting times by up to 92%. We evaluate five electrification levels from 1% to 20% across two charging network scenarios with 347 and 779 potential locations, respectively. For the balanced solutions—the knee-point configurations that best reconcile both objectives—at a 10% electrification level, the optimized network reaches a temporal charger utilization of 23% to 32% at mean waiting times of about 1.4 to 1.9 min per charging process. Compared with an internal combustion engine truck (ICET) reference, BET trip durations increase by only 0.9% to 1.3% due to charging detours. Overall, the fast-charging network planned by the German federal government appears sufficient for the HPC demand at electrification levels up to 10% to 15%, whereas additional low-power charging (LPC) infrastructure beyond the planned locations will be needed to cover overnight charging requirements.