Search Results (41)

Road freight contributes over half of China’s transport carbon emissions, making its decarbonization critical for carbon neutrality. This study combines total cost of ownership (TCO) and life cycle assessment (LCA) to analyze the economic efficiency and carbon emission effects of diesel, electric, and hydrogen fuel cell trucks. Combined with the LSTM neural network and vehicle ownership model, this study predicts the fleet emission reduction potential from 2020 to 2050. The results show that all new energy trucks can achieve TCO parity with diesel trucks before 2050, and electrification shows better economic competitiveness than hydrogen fuel cell technology across all vehicle types in the Chinese context. Fuel cell trucks powered via solar-powered water electrolysis exhibit the lowest carbon intensity, and grid decarbonization can significantly improve the emission reduction effects of electric and fuel cell trucks. Freight fleet carbon emissions are expected to peak around 2030. In an ideal scenario, emission reductions of 19.5%, 41.9%, and 82.9% can be achieved by 2030, 2040, and 2050, respectively. Heavy-duty trucks are the main emission contributors (47–58%) and the main target of emission reduction strategies. Short-term reduction depends on fuel economy, while long-term reduction prioritizes new energy substitution. Policy recommendations include promoting alternative fuel trucks, upgrading emission standards, and adopting differential taxation. Full article

Heavy-duty trucks, especially those used in urban areas, are responsible for a disproportionally large share of the external costs of the transportation sector. Policy instruments that target these trucks could thus be efficient measures to reduce negative impact from the traffic sector. This paper presents how heavy-duty trucks operated in Sweden’s two largest cities, Gothenburg and Stockholm, in the year 2022 and how zero-emission zones or environmental zones with an entrance fee targeting heavy-duty trucks could affect not only urban traffic but all trucks on Swedish roads. The analysis is based on GPS data from 69,000 trucks in operation in Sweden in the year 2022. Of these trucks, 4% visited the two cities for more than 100 days (frequent visitors) and 40% visited at least once during the year. Although zero-emission zones would have the strongest impact, environmental zones with an entrance fee could be a more flexible way to create a strong enough incentive for frequent visitors to electrify. An entrance fee of 100 Euro per day in combination with current investment subsidies would make electric trucks competitive for frequent visitors and still allow for others to continue using conventional trucks during a transition period. Full article

This study presents an integrated simulation framework that combines agent-based transport modeling with probabilistic load-flow analysis to quantify power system loading of long-haul heavy-duty electrification. The approach is applied to a case study considering fully electrified road freight in the Skåne region in Sweden, using high-resolution transport demand data and the actual power grid model used by the grid owner in the study area. The synthetic freight population covers the full long-haul truck segment intersecting Skåne. Both public en-route fast charging and end-of-trip depot charging are considered. The analysis reveals two fundamentally different charging demand profiles: a heavily fluctuating profile for public en-route charging, accounting on average for 82% of the total daily charging energy, and a stable profile for end-of-trip depot charging, covering on average the remaining 18%. The latter is achieved through a Linear Programming (LP) optimization model that flattens the load by scheduling charging across depot stay windows. These profiles serve as inputs to a probabilistic load-flow simulation that computes loading distributions for substation transformers. The simulation results show that in 4 of the 43 primary substations studied, the maximum transformer loading exceeds 100% following the introduction of truck charging, with peak loading at the most affected substation rising from 99% to 159%. This stress is primarily caused by the public charging demand, which peaks from late morning to noon, aligning with the early stages of logistics operations. However, there is no clear correlation between the magnitude of the truck charging load and the impact on transformer loading, since this is also highly dependent on local grid conditions. These findings highlight the value of integrated transport-energy simulations for planning resilient infrastructure and guiding targeted grid reinforcements. Full article

As the electrification of heavy-duty trucks accelerates, conventional charging methods face challenges, including long charging durations and reduced transportation efficiency. This paper compares and evaluates various charging methods for electric heavy-duty trucks (EHDTs), including slow charging, fast charging, battery swapping, and electric roads, from both technological and economic perspectives. A case study in a harbor setting further examines the cost and efficiency implications of a 22 kW slow charger, a 150 kW fast charger, and battery swapping (the swappable battery is charged with 150 kW). The analysis provides insights into selecting the most suitable charging solution by assessing annual charging costs, truck and infrastructure cost amortization, and downtime across different scenarios. The findings of this paper indicate that slow charging is cost-effective in low-demand operations but becomes impractical as operational demand increases, leading to excessive downtime exceeding 37,000 h annually in high-demand scenarios. Fast charging significantly reduces downtime but requires higher infrastructure investment and charging costs. Battery swapping minimizes downtime to less than 300 h annually in high-demand scenarios, and, despite a higher initial infrastructure cost, it emerges as the most cost-effective option over five years for medium- and high-utilization fleets, with a total cost of approximately €1.67 million in the studied harbor case. Thus, selecting a suitable charging solution depends on operational priorities, such as minimizing cost or maximizing fleet availability within a specific use-case context. Full article

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. Full article

Global greenhouse gas emissions must be reduced to meet the targets of the Paris Climate Accords. This study quantifies the potential energy cost savings of a holistic disposition approach for mixed-electrified heavy-duty truck fleets. Electrifying heavy-duty trucks reduces energy costs compared to traditional diesel-powered baselines. On-site energy generation further decreases electrification expenses. Bidirectional vehicle-to-grid participation also contributes to lowering energy costs. A mixed-integer linear programming optimization algorithm has been developed to incorporate these three approaches into the fleet’s disposition decisions. Real-world data have been utilized, including commercial order datasets, diesel prices, on-site-generated electrical energy prices, and vehicle-to-grid prices. Cost savings start at an average of 17% for small fleets with limited electrification and unfavorable price scenarios. However, they can reach net revenue generation for large fleets with high electrification and favorable price scenarios. A daily surplus of fleet energy costs can be achieved, with vehicle-to-grid revenues surpassing the costs of energy consumed. Ensuring battery electric heavy-duty trucks are available during high-revenue periods and operating during low-revenue times can lower overall fleet energy costs for commercial operators and improve power grid stability. By turning energy costs into net surpluses, this approach provides a financial incentive that could accelerate the transition to greenhouse-gas-neutral transport. Full article

The electrification of long-haul freight transport introduces significant challenges in fleet planning, charging decisions, and reliability management under uncertainty. This study proposed a Stochastic Electric Freight Operations Planning Problem on Predefined Routes with Partial Recharging and Heterogeneous Fleets (SEFOP-PR-HF), to support corridor-based electric truck operations under uncertain demand. The model represents real-world interregional logistics, where vehicles operate on fixed long-haul routes and may perform partial recharging at fast-charging stations. Freight demand is modeled as a normally distributed random variable, and Chance-Constrained Programming (CCP) is employed to ensure probabilistic feasibility of vehicle capacity and battery constraints. The objective is to minimize total long-term system cost, including fleet acquisition and charging expenditures, while maintaining operational reliability. A Mixed-Integer Linear Programming (MILP) formulation is applied for multiple corridor instances using real heavy-duty electric truck data. Computational results show that incorporating demand uncertainty improves robustness but raises total cost by 6–33% compared to deterministic solutions. Sensitivity analyses further reveal how reliability levels and demand variability influence fleet allocation and charging strategies. Full article

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO₂-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NO_x, VOCs, SO_x, PM_2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. Full article

As of mid-2025, California maintains a target (and legal agreement with truck OEMs) to achieve 100% zero-emission medium- and heavy-duty (M/HD) truck sales by 2036. While the US federal government has relaxed its targets, fuel economy standards continue to incentivize electrification. To meet these ambitions, the adequate rollout of charging infrastructure at scale is needed. This paper reviews existing studies on M/HD charging and investment needs in California and the U.S. This paper introduces a novel matrix that delineates charging needs by charging power, truck type (Class 2b-8), charger-to-vehicle ratios, and charger investment costs. Results indicate that California may require 151,000 to 156,000 depot and public chargers on the road by 2030, growing to 434,000 to 460,000 chargers on the road by 2035. Corresponding investment—including new installation and replacement—could reach USD 7.1 to USD 7.4 billion by 2030 and USD 16.4 to USD 17.8 billion by 2035. Meeting this scale of infrastructure deployment represents not only a technical challenge but also a sustainability imperative, demanding unprecedented coordination among policymakers, utilities, and fleet operators to overcome barriers like financing and permitting and to ensure infrastructure growth aligns with climate commitments and equitable access. Full article

The global urgency to mitigate climate change by reducing transport-related emissions drives the accelerated electrification of road freight transport. This paper presents a comprehensive meta-study forecasting the development and corresponding power demand of electric trucks and tractor units in Hamburg up to 2050, emphasizing the shift from conventional to electric vehicles. Utilizing historical registration data and existing commercial and institutional reports from 2007 to 2024, the analysis estimates future distributions of electric heavy-duty vehicles across Hamburg’s 103 city quarters. Distinct approaches are evaluated to explore potential heavy-duty vehicle distribution in the city, employing Mixed-Integer Linear Programming to quantify and minimize distribution uncertainties. Power demand forecasts at this detailed geographical level enable effective infrastructure planning and strategy development. The findings serve as a foundation for Hamburg’s transition to electric heavy-duty vehicles, ensuring a sustainable, efficient, and reliable energy supply aligned with the city’s growing electrification requirements. Full article

Recently, the electrification and automation of heavy-duty trucks has gained significant attention from both industry and academia, driven by new legislation introduced by the European Union. During a typical drive cycle, the mass of an urban service truck can vary substantially as waste is collected, yet most existing studies rely on a single controller with fixed gains. This limits the ability to adapt to mass changes and results in suboptimal energy usage. Within the framework of the EU-funded OBELICS and ESCALATE projects, this study proposes a novel control strategy for a semi-autonomous refuse truck. The approach combines a particle swarm optimization algorithm to determine optimal controller gains and a multiple model controller to adapt these gains dynamically based on real-time vehicle mass. The main objectives of the proposed method are to (i) optimize controller parameters, (ii) reduce overall energy consumption, and (iii) minimize speed tracking error. A cost function addressing these objectives is formulated for both autonomous and manual driving modes. The strategy is evaluated using a real-world drive cycle from Eskişehir City, Turkiye. Simulation results show that the proposed MMC-based method improves vehicle performance by $5.19\%$ in autonomous mode and $0.534\%$ in manual mode compared to traditional fixed-gain approaches. Full article

HTs account for less than 7% of the automotive market in China, yet they contribute to more than 40% of the total carbon emissions from vehicles, with nitrogen oxide and particulate matter emissions exceeding 50% of the total vehicular emissions. BS for HTs has emerged as a crucial approach to reducing carbon emissions.As the number of BSHTs increases, the construction and operation of BSSs have become a pressing issue. This study focuses on the optimal charging strategy for BSSs by considering factors such as charging modes, charging durations, and real-time electricity prices. An optimal charging model, BS-CDE, is developed to formulate the operational cost problem of BSSs as a MOOP. By enhancing the traditional NSGA-II algorithm in aspects such as operators and parameter adjustments, the model is solved to obtain the optimal charging strategy, thereby reducing the operational costs of BSSs. Simulation results demonstrate that the proposed model effectively simulates the actual charging and battery-swapping processes for HTs. The results provide valuable guidance for the initial battery configuration and charging strategies of BSSs. Compared with traditional methods, the proposed model incorporates the actual operational scenarios of BSHTs while addressing multiple objectives during the charging process. Experimental results demonstrate that the proposed algorithm outperforms traditional methods, improving the HV and Sp metrics by 6.2% and 13.9%, respectively. Full article

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. Full article

Electrification of the transport sector introduces operational issues in the electricity distribution network, such as excessive voltage deviation, substation overloading, and adverse power quality impacts on other network loads. These concerns are expected to grow as electrification expands to incorporate heavy vehicles such as trucks and buses due to their greater energy requirements and higher charging loads. Two strategies are proposed to support medium- and heavy-duty chargers which address their high power demand and mitigate power quality disturbances and the overloading of substations. The first is a dedicated feeder connected at the secondary of the substation directly to the charging station which aims to reduce the impact of high load on other customers. The second is the addition of a dedicated substation that solely provides power for charging stations in major corridors, alleviating stress on existing zone substations. Hosting capacity is measured using a voltage deviation index, describing the deviation in line voltage, which should experience a sag of no more than 6% of the nominal voltage, and a substation charging capacity index, describing the available capacity of each zone substation as a ratio of its total power capacity. Verification of the proposed strategies was performed on an MV-LV distribution network representative of an industrial Australian town with heavy-vehicle charging. Results showed that the network could handle ten 250 kW chargers, which was tripled to 35 with a dedicated feeder. The dedicated feeder alternatively allowed up to 10 megawatt-scale chargers, which was again tripled when a dedicated substation was introduced. Full article

The reliance of the commercial transportation industry on fossil fuels has long contributed to pollutant and greenhouse gas emissions. Since full electrification of medium- and heavy-duty vehicles faces limitations due to the large battery capacity required for extended driving ranges, this study explores a Range-Extended Electric Vehicle (REEV) for medium-duty Class 6 pick-up and delivery trucks. This hybrid architecture combines an electric powertrain with an internal combustion engine range-extender. Maximizing the efficiency of REEVs requires an Energy Management Strategy (EMS) to optimally split the power between the two power sources. In this work, a hierarchical EMS is developed through model-based design and validated via Hardware-In-The-Loop (HIL) simulations. The proposed EMS demonstrated a 7% reduction in fuel consumption compared to a baseline control strategy, while maintaining emissions and engine start frequency comparable to a benchmark globally optimal EMS obtained with dynamic programming. Furthermore, HIL results confirmed the strategy’s real-time implementation feasibility, highlighting the practical viability of the controller. This research underscores the potential of REEVs in significantly reducing emissions and fuel consumption, as well as providing a sustainable alternative for medium-duty truck applications. Full article