Battery electric trucks (BETs) are a promising option to reduce emissions from heavy-duty vehicles. However, the transition to BETs will cause an additional demand for electricity. Future charging strategies will influence the future peak load as well as the operational and technical feasibility of BETs. We simulated 2410 representative single-day German truck driving profiles with three different charging strategies: (1) as slow as possible, (2) as fast as possible, and (3) slowly at depots and as fast as possible at public locations. Assuming a 33% electrification rate by 2030 and near-complete fleet conversion by 2045, we scaled our results to the German truck fleet. We found that charging as fast as possible leads to additional peak loads up to 6 GW in 2030 and 18 GW in 2045, while the other charging strategies reduce additional peak loads to 3 GW in 2030 and 8 GW in 2045. Therefore, implementing wise charging strategies will reduce future peak load.

The decarbonization of heavy-duty vehicles requires transitioning from fossil fuel to zero-emission trucks, such as battery electric trucks and hydrogen fuel cell electric trucks. However, the absence of a pervasive and dependable network of high-capacity charging and hydrogen refuelling stations risks of compromising, rather than fostering, the greening of this hard-to-abate sector. The present study aims to contribute to the extant literature by presenting an innovative analysis of the EU network-level dispensing infrastructure cost of both compressed and subcooled liquid hydrogen refuelling stations, in addition to megawatt charging stations. It is calculated that, assuming the entire trucks fleet conversion into 35 MPa and 70 MPa-compressed hydrogen fuel cell electric trucks, annual investments of 3.8 and 7.2 billion euros will be necessary by 2050 for the establishment of a hydrogen refuelling network infrastructure in Europe. Conversely, the financial outlay required for the electrification of 2 million battery electric trucks in Europe by 2050 is estimated to be between 8.1 and 12.9 billion euros per year. Similarly, the cost of network-level subcooled liquid hydrogen refuelling infrastructure is calculated to be 1.0 billion euros. While the 70 MPa-hydrogen refuelling and the ultra-fast electric charging levelized dispensing costs are comparable, the levelized costs associated solely with the refuelling of 35 MPa-compressed hydrogen are estimated to be 50–65% lower than those of electric charging. Subcooled liquid hydrogen exhibits the lowest dispensing infrastructure costs; however, upstream liquefaction emerges as a dominant cost driver, substantially increasing total supply chain costs. These results highlight the critical role of stations-infrastructure economics in shaping technology pathways for heavy-duty transport decarbonisation. By providing transparent and comparable cost benchmarks, the study supports evidence-based policy design, coordinated stations planning, and strategic prioritisation of hydrogen and electric charging networks in Europe.

Abstract Road freight transport is essential to modern economies, yet its decarbonization remains challenging. While previous studies often focused on average-duty or long-haul applications, the logistics sector is highly heterogeneous, spanning a wide range of truck usage patterns. This study assesses the economic viability of battery electric trucks (BETs) and fuel cell electric trucks (FCETs) using microdata from four million trucks across Europe. Under baseline assumptions for cost and technical maturity, BETs outperform diesel trucks in total cost of ownership for 70-90% of heavy-duty road freight activity by 2030. When accounting for limited charging infrastructure until 2030, 25% of kilometres, corresponding to 19% of vehicles, remain economically and technically feasible-substantially higher than the 5-9% share of the total truck fleet expected under the 2030 EU CO 2 standards. By 2035, infrastructure roll-out and improved costs increase the share of kilometres to 77%. In contrast, the window for FCET cost-competitiveness is narrow.

Greenhouse gas emissions from heavy-duty vehicles (HDVs) must be drastically reduced. Battery electric trucks (BETs) are the main option for low-carbon road freight transport, but they require recharging infrastructure. However, a thorough cost analysis of public charging is lacking, especially for the Megawatt Charging System (MCS). This study estimates the infrastructure-related levelised cost of megawatt charging for battery electric trucks in Europe based on simulated truck operations and techno-economic modelling. The analysis combines empirical driving data with cost assumptions for MCS infrastructure. The reported values are infrastructure-only costs and include annualised capital expenditure, installation costs, grid connection costs and operating expenditure. They exclude electricity prices, taxes, levies, land costs and operator margins. Low- and high-cost scenarios differ in assumed charger hardware and installation costs, while grid connection costs and utilisation assumptions are held constant across scenarios. The results show that utilisation is the key driver of cost reductions over time. The infrastructure-related levelised cost of MCS declines to 0.03–0.07 EUR/kWh by 2050 under the analysed cost assumptions. The total annual infrastructure costs for Europe are estimated at 6.6–10.8 billion EUR, or 2.9–4.7 EUR cents/km. The results support policy decisions on infrastructure deployment and highlight the importance of coordinated rollout and demand growth.

Battery electric vehicles (BEV) offer a promising pathway to decarbonizing transportation. However, existing studies have primarily focused on BEV passenger cars while overlooking motorcycles and the interplay between these modes, an omission that is critical in emerging markets. This research addresses this gap by examining the BEV market diffusion of both vehicle types in an integrated framework, utilizing an agent-based model that involves monetary factors, infrastructure availability, consumer preferences, and local socioeconomic conditions. Applying the model to the Greater Jakarta Area, a metropolitan city in an emerging market, the results indicate that robust policy measures could boost the diffusion of both BEV passenger cars and motorcycles to approximately 82 % of the total vehicle stock by 2050. BEV passenger cars may achieve price parity as early as 2024, whereas BEV motorcycles are expected to follow in 2026. Consumer preferences and infrastructure availability are critical to BEV diffusion, especially for electric motorcycles. In addition, total motorcycle stocks are projected to decline after 2036 due to the potential mode shift to passenger cars as socioeconomic conditions improve. Overall, strong supports on both BEV types' uptake could lower transport-sector greenhouse-gas emissions by up to 65 % by 2050.

The increasing diffusion of electric vehicles contributes to a growing electricity demand in the coming years. At the same time, this integrates millions of mobile storage units into the electricity system, which has a rising need for flexibility to balance the intermittent generation from photovoltaic systems and wind turbines. To capture the potential of electric cars as a flexibility resource, we simulate 7,000 vehicle driving profiles in an agent-based model, generating load profiles as well as charging power and state-of-charge boundaries for the German car fleet, which serve as restrictions in energy system optimization. In a scenario-based study for Germany in 2030 and 2045, we compare the installed electric capacities in the optimized system, depending on whether electric vehicle charging is uncontrolled, controlled, or bidirectional. Here we show that a bidirectionally charged car fleet has the potential to replace 32 GW (84 %) of stationary battery storage and 31 GW (64 %) of hydrogen-fired peaking power plants, while enabling an additional solar power expansion of 7 GW (2 %) until 2045. Notably, implementing vehicle-to-grid can limit hydrogen-fired electricity generation to winter months and enable a shift toward combined heat and power plants. On the demand side, it can reduce the expansion of electrolyzers by 19 GW (28 %) and power-to-heat capacities by 25 GW (60 %). Overall, the integrated energy system can substantially benefit from the implementation of smart and especially bidirectional charging as it lowers the need for future capacity expansion in the electricity system but also in coupled hydrogen and heat systems.

Battery electric trucks (BET) reduce greenhouse gas emissions in the transport sector but require public charging infrastructure. Truck fast charging networks have been planned in various studies and countries. However, existing charging infrastructure optimization studies ignore relevant actual constraints, such as the size of parking areas or available grid power, leading to unrealistic results. Here, we derive a minimal public fast charging network for BET in Germany with actual real-world capacity limitations. We add capacity constraints to a flow refueling location model (FRLM) which makes the optimization more challenging as it is no longer sufficient to ensure that every path can be travelled but it must be determined which vehicle uses which charging location. The constraint is implemented as hourly maximum number of vehicles that can be served at each location and obtained via queuing theory from local traffic flows. We apply the model to 236,000 origin-destination traffic flows. For 300 km BET range, we identify 124 optimal charging locations. For 15 % BET in stock, e.g. by 2030, this would require 2 to 30 charging points per location with an average of 16 charging points using 17 % of the available truck parking lots per location. Our findings provide input for governments and public charging infrastructure planners. These results indicate that well positioned large initial charging locations can already cover significant shares of BET traffic.

This study highlights the crucial role of Battery Electric Vehicles (BEVs) in decarbonizing transportation systems in emerging markets, focusing on the challenge posed by limited charging infrastructure, which hampers BEV adoption. It proposes a flow-based optimization model that incorporates multiple driving ranges, multi-period assessment, and capacitated charging stations that based on a queuing model to determine the optimal fast-charging infrastructure for a densely populated emerging-market city, specifically the Greater Jakarta Area. Given a widespread access to home charging, this study focuses on meeting the en-route charging demand of long-distance drivers. The proposed model shows that endogenously integrating variations in BEV ranges provides more detailed distributions of station locations, all while maintaining reasonable solution times. Findings indicate an optimal ratio of 218 BEVs per fast charger by 2050, consisting of 372 sites equipped with approximately 62,800 chargers. Over half of these sites require more than 100 chargers to be installed and are mainly positioned along highway corridors. Given the high population density and limited land availability in the area, this study also explores profitability and land utilization aspects, offering strategic guidance to plan the rollout of charging infrastructure in regions with similar characteristics.

Zero-emission trucks will benefit from rapidly falling costs of batteries and fuel cells, which will enable their fast market diffusion. Industry and policy must prepare for battery-electric trucks with respect to their manufacturing and supply, adequate charging infrastructure and electricity grid expansions, as well as regulation.

Heavy road freight transport is responsible for about 7% of energy-related greenhouse gas emissions in Germany and Europe. The electrification of trucks via batteries, overhead lines or fuel cells are promising options to meet the European climate targets for heavy road freight. The development of the nec essary infrastructures and the development of the market ramp-up of the alternative truck technologies will be presented.

The energy transition fosters a dynamic landscape marked by renewable energy, electrification, and complex interactions among actors and technologies. Employing model experiments and comparisons shows promise for exploring these connections and enhancing model clarity and precision. This study adopts a multi-model approach, integrating a model comparison to probe how the electrification of demand-side sectors and strategic load shifts of battery electric vehicles and heat pumps might impact Germany's generation adequacy by 2030. Specific demand models from the transport and heating sectors and a future load structure projection model are interlinked with three electricity system models. The comparative analysis of the three electricity system models unveils discrepancies in dispatch decisions for power plants, flexibility options' load shifts, and their effects on generation adequacy, directly tied to model attributes.The comparison underscores methodological variations (linear optimization versus agent-based simulation, myopic foresight versus perfect foresight) as pivotal, emphasizing the significance of considering load change and start-up costs for power plants. The results show that with optimized load shifting by electric vehicles and heat pumps, the adequacy of power generation is less strained despite increased electricity demand. Moreover, load shifts mitigate curtailment of renewables and consumers, reducing carbon emissions by lowering conventional power generation.

Battery electric trucks (BETs) can dramatically reduce tail-pipe emissions from heavy-duty vehicles. However, their limited range necessitates frequent charging. Megawatt charging has been discussed as requirement and potential barrier for fast BET market diffusion. Here, we simulate all daily trips of 2,410 trucks in Germany as BETs with lowest charging power required. Assuming 450 - 700 km (2030 - 2050) range, the share of vehicles replaceable by BETs is higher than 90 %. Most charging events (>95 % of night charging, >75 % of day charging) need maximally 44 kW. Higher power, mostly 45 - 350 kW, is mainly needed during the day by up to 25 % of charging vehicles. Megawatt charging (MCS) higher than 350 kW is important for long-haul operation and delivers about 20 % of all BETs' electricity demand by 2035. In conclusion, low power depot charging is the main charging location for BETs, MCS is needed for long-haul operation.

Battery electric trucks (BETs) are the most promising option for fast and large-scale CO _2 emission reduction in road freight transport. Yet, the limited range and longer charging times compared to diesel trucks make long-haul BET applications challenging, so a comprehensive fast charging network for BETs is required. However, little is known about optimal truck charging locations for long-haul trucking in Europe. Here we derive optimized truck charging networks consisting of publicly accessible locations across the continent. Based on European truck traffic flow estimates for 2030 and actual truck stop locations we construct a long-term charging network that minimizes the total number of required locations. Our approach introduces an origin-destination (OD) pair sampling method and includes local capacity constraints to compute an optimized stepwise network expansion along the highest demand routes in Europe. For an electrification target of 15% BET share in long-haul and without depot charging, our results suggest that about 91% of electric long-haul truck traffic across Europe can be enabled already with a network of 1,000 locations, while 500 locations would suffice for about 50%. We furthermore show how the coverage of OD flows scales with the number of locations and the size of the stations. Ideal locations to cover many truck trips are at highway intersections and along major European road freight corridors (TEN-T core network).

Low-carbon road freight transport is pivotal in mitigating global warming. Nonetheless, electrifying heavy-duty vehicles poses a tremendous challenge due to high technical requirements and cost competitiveness. Data on future truck costs are scarce and uncertain, complicating assessments of the future role of zero-emission truck (ZET) technologies. Here we derive most likely cost developments for price setting ZET components by meta forecasting from more than 200 original sources. We find that costs are primed to decline much faster than expected, with significant differences between scientific and near-market estimates. Specifically, battery system costs could drop by 64% to 75% and fall below 150 kWh-1 by no later than 2035, whereas fuel cell system costs may exhibit even higher cost reductions but are unlikely to reach 100 kWh-1 before the early 2040s. This fast cost decline supports an optimistic view on the ZET market diffusion and has substantial implications for future energy and transport systems. The costs of battery and fuel cell systems for zero-emission trucks are primed to decline much faster than expected, boosting prospects for their fast global diffusion and electrification of freight transport, with battery-electric trucks probably leading.

In order to reduce national and global greenhouse gas (GHG) emissions, many countries worldwide have committed themselves to a more sustainable development of their transport sector. Promoting the use of electrical vehicles (EVs) rather than combustion engine cars is one political strategy to achieve a reduction in GHG emissions. To implement targeted and effective promotion measures governments can refer to market diffusion models for EVs. However, in our study we identify that in existing models the consideration of environmental measures is underrepresented. Hence, this paper addresses this gap in current market diffusion models for EVs by particular focusing on environmental effects as additional influencing factors of the market diffusion. Results are drawn for the German car market with a market diffusion simulation until 2050 applying the market diffusion model ALADIN considering the introduction of distinct CO2 tax trajectories. The results are analyzed based on scenarios, where (i) no CO2 tax, (ii) the current governmental plan for a CO2 tax, and (iii) a considerable high CO2 tax is applied. Additional insights when incrementally increasing the CO2 tax are provided. The scenario analysis shows that the market diffusion is highly dependent on the evolution of external factors. A CO2 tax considerably higher than the current governmental plan by 2030 (such as 150€/t, based on its monetary value by 2020) is required to have a meaningful impact on the market diffusion of EVs. Moreover, applying a considerable high CO2 tax leads to a slower growth of BEV and PHEV from 2040 onwards that is compensated by a growth in FCEV vehicles.

Electric battery trucks (BETs) have the potential to significantly reduce emissions from heavyduty vehicles. However, adopting BETs for long-haul operations depends on the availability of sufficient charging infrastructure. In this study, we use a trip chain model to assess the charging requirements for BETs in long-haul operations in Europe in 2030. Our model accounts for truck driving regulations and different stop types. We find that the number of overnight chargers (50-100 kW) required is 4-5 times higher than the number of megawatt chargers (0.7-1.2 MW) needed to support a BET share of 15% in long-haul operations. We estimate that approximately 40,000 overnight and 9,000 megawatt chargers are required, with an average of eight overnight and two megawatt chargers per charging area serving an average of two and 11 BETs daily, respectively. These findings provide insights for planning charging infrastructure for BETs in long-haul operations in Europe.

ABSTRACT Following the Paris Agreement, virtually all countries worldwide have committed themselves to undertaking efforts to limit global warming to 1.5 °C. Within the European Union (EU), the recent ‘Fit for 55’ policy package proposes ambitious greenhouse gas (GHG) mitigation policies for all sectors as part of the EU's contribution to limiting global warming. Yet, it is unclear whether the proposed policies are sufficient for the EU to limit global warming to 1.5 °C and it remains an open policy problem how to translate global temperature targets into sector-specific emission budgets and further into sector-specific policies. Here, we derive GHG budgets for transport in EU27 and obtain GHG mitigation pathways for Europe consistent with 1.5 °C global warming. We do not provide a comprehensive assessment of the ‘Fit for 55’ transport package but we discuss the main policies for road transport in light of the GHG emission budgets, their level of ambition, and suggest amendments to these policies as well as improvements to the ‘Fit for 55’ package. Our results suggest that parts of the ‘Fit for 55’ for transport are still not ambitious enough to align with a 1.5 °C scenario. Key policy insights A Paris-compatible residual carbon budget for EU transport is 10–12 Gt CO2. The budget implies net zero emissions for EU transport by 2044–2048 latest. We find the current ‘Fit for 55’ proposal for transport is not ambitious enough. A faster phase-out of cars and trucks with combustion engines is required and there is a need for ambitious standards for fast charging e-vehicles. CO2 pricing of transport is not a substitute but a complement to fleet targets.

Road transport accounted for 20% of global total greenhouse gas emissions in 2020, of which 30% come from road freight transport (RFT). Modeling the modern challenges in RFT requires the integration of different freight modeling improvements in, e.g., traffic, demand, and energy modeling. Recent developments in 'Big Data' (i.e., vast quantities of structured and unstructured data) can provide useful information such as individual behaviors and activities in addition to aggregated patterns using conventional datasets. This paper summarizes the state of the art in analyzing Big Data sources concerning RFT by identifying key challenges and the current knowledge gaps. Various challenges, including organizational, privacy, technical expertise, and legal challenges, hinder the access and utilization of Big Data for RFT applications. We note that the environment for sharing data is still in its infancy. Improving access and use of Big Data will require political support to ensure all involved parties that their data will be safe and contribute positively toward a common goal, such as a more sustainable economy. We identify promising areas for future opportunities and research, including data collection and preparation, data analytics and utilization, and applications to support decision-making.