Search Results (365)

Hybrid electric mining trucks operating in open-pit environments encounter highly variable gradients and payload conditions that standard energy management strategies fail to address adequately. Existing approaches are predominantly calibrated for full-load scenarios and neglect the accelerated battery degradation resulting from sustained high-power cycling, undermining long-term operational viability. This study presents a multi-objective energy management framework that couples real-time driving condition recognition with dynamic programming (DP) optimization for a 130-tonne hybrid mining truck. Field data collected from an open-pit mine in Heilongjiang Province were used to construct six physically representative driving conditions via principal component analysis and K-means clustering. A Bidirectional Gated Recurrent Unit (Bi-GRU) network (2 layers, 128 hidden units per direction) was trained on a route-based temporal split, attaining 95.8% classification accuracy across all six conditions. Condition-specific powertrain modes were subsequently defined, and a DP formulation with a weighted-sum cost function was solved to jointly minimize diesel consumption and battery capacity fade—quantified through a semi-empirical effective electric quantity metric. A marginal rate of substitution (MRS) analysis was conducted to identify the optimal trade-off between fuel economy and battery life preservation. In the DP cost function, the weight coefficient μ (ranging from 0 to 1) governs the relative emphasis placed on battery degradation minimization versus fuel consumption minimization: μ = 0 corresponds to pure fuel minimization, whereas μ = 1 corresponds to pure battery degradation minimization. The MRS analysis identified μ = 0.1 as the knee point of the Pareto trade-off: relative to pure fuel minimization (μ = 0), this setting reduces effective electric quantity by 6.1% while increasing fuel consumption by only 1.4% (MRS = 4.36). Against a rule-based baseline, the proposed strategy improves fuel economy by 12.3% and extends battery service life by 15.7%. Co-simulation results were validated against onboard fuel-flow measurements; absolute simulated and measured fuel consumption values are reported route-by-route, with deviations within 4.5%. A three-layer BP neural network (3 inputs, two hidden layers of 20 and 10 neurons, 1 output) trained on the DP solution reproduces near-optimal performance—with fuel consumption and effective electric quantity increases below 1.0% and 1.1%, respectively—while reducing computation time by over 96% (from approximately 52,860 s to 1836 s for the 1800 s driving cycle), demonstrating practical feasibility for real-time deployment. Full article

Fire trucks equipped with truck-mounted electric water cannons are key mobile firefighting assets for urban and industrial fire response. However, due to the inherent mechanical inertia of the cannon body, its low-frequency motion response cannot match high-frequency control commands, making the system prone to oscillations and control instability. To address this command–execution frequency mismatch, this paper proposes a decoupled dual closed-loop control architecture for truck-mounted electric water cannons on mobile fire trucks: the fast loop is used for fire-source tracking and rapid localization, while the slow loop is used for water-jet aiming alignment. In the fast loop, a 2-D quadrant positioning rule drives the pan–tilt unit to achieve rapid fire tracking and accurate centering. In the slow loop, Kalman-filter-based state estimation and delay-aligned prediction generate feedforward aiming commands; these commands are fused with error feedback and further processed through command limiting and trajectory optimization, ultimately producing smooth and executable angle references. The visual perception module ran at 58 FPS, satisfying the real-time requirement of the proposed system. In five repeated extinguishment tests under controlled open-site conditions, the proposed method successfully completed all trials and reduced the mean extinguishment time to 13.55 s, compared with 15.83 s for the incremental-PID baseline and 23.76 s for the coupled proportional baseline, while also showing smoother correction and less redundant oscillation. Full article

Transitioning to electrified freight transport is a challenging task, and this article explores how charging strategies can be evaluated based on operational factors. The purpose is to develop a method that synthesises the core insights of the current literature and is easy to use and understand for practitioners. Therefore, it is an aim to make it as simple as possible, covering the core factors needed to find the right charging strategy, while deliberately excluding factors which only have a minor effect. The method is intended to be used to start developing an electrification strategy and can serve as a tool to decide which solutions to investigate further with more detailed methods. A cost–benefit analysis, which includes both monetary and subjective measures, is used to analyse various types of chargers and charging strategies. The novelty of the article lies in applying a systems perspective that enables a more comprehensive evaluation of charging strategies than studies that focus on specific operational factors. The results highlight five operational factors for evaluation: charging cost, productivity, flexibility, robustness, and business risk. The findings suggest that many hauliers can manage most, if not all, of their charging needs independently. Consequently, it is likely that many can begin electrifying soon, as public charging is often not critical for the electrification of their trucks. Furthermore, the article presents a decision tree to create an overview of how different driving characteristics match different charging strategies. Full article

Task scheduling for vehicle–crane coordination is crucial for the operational efficiency of electrified automated container terminals (ACTs). However, under fully shared dispatching, existing studies rarely capture how charging-induced capacity fluctuations disrupt bidirectional service–arrival matching and propagate service-window shifts. To address this gap, this study proposes a comprehensive spatio-temporal optimization approach. Firstly, a bi-objective model is established to minimize service–arrival mismatch and vehicle energy consumption under state-of-charge (SOC) and charger-capacity constraints, explicitly quantifying vehicle–crane alignment at both handling interfaces. Secondly, an enhanced multi-objective algorithm (ST-NSGA-II) is developed, integrating a feasibility-preserving recursive decoding mechanism and a spatio-temporal variable neighborhood search (VNS) procedure. Finally, numerical experiments demonstrate that ST-NSGA-II significantly reduces mismatch and energy consumption compared to standard NSGA-II in large-scale scenarios. It also outperforms MOEA/D in Pareto-set quality, yielding a higher hypervolume (1.301 vs. 0.960) and a lower Spacing value (0.102 vs. 0.185). The results demonstrate that the proposed spatio-temporal optimization approach can effectively reduce handover mismatch compared to conventional scheduling modes, thereby achieving seamless task scheduling for vehicle–crane coordination. 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

The transportation sector’s decarbonization represents one of the most critical challenges in achieving global climate targets. This study presents a comprehensive analysis of an integrated renewable energy system that produces green hydrogen through a hybrid solar photovoltaic (PV) and wind power configuration. The proposed system combines a 1.2 MWp solar array with 800 kW wind turbines, feeding a 1 MW proton exchange membrane (PEM) electrolyzer for hydrogen production. The hydrogen is subsequently compressed, stored at 350 (for trucks and buses) and 700 bar (for cars), and then utilized either directly for fuel cell electric vehicles (FCEVs) or reconverted to electricity via a 250 kW stationary PEM fuel cell to support electric vehicle (EV) charging infrastructure. Through detailed techno-economic simulation using HOMER Pro and MATLAB/Simulink 2022a, we demonstrate that the hybrid configuration achieves a 71% electrolyzer capacity factor, producing 55.8 tonnes of hydrogen annually with a levelized cost of 5.82 €/kg. The system ensures over 60 h of grid-independent operation while reducing CO₂ emissions by 1656 tones annually compared to conventional grid-powered alternatives. Results indicate that hybrid renewable hydrogen systems can provide economically viable solutions for sustainable mobility infrastructure, with projected cost reductions making them competitive with fossil fuel alternatives by 2030. Full article

The integration of truck platooning and electrification presents a promising avenue for improving operational efficiency and environmental sustainability in freight transportation. Realizing the energy and cost saving as well as emission reduction benefits requires a holistic design of truck routing, scheduling, and platooning strategies that account for practical operational constraints. This study investigates the integrated planning problem of routing, scheduling, and platooning for a mixed fleet of conventional trucks (CTs) and electric trucks (ETs), referred to as mixed fleet truck platooning (MFTP) problem. The MFTP incorporates charging scheduling and key operational factors, such as platooning leader–follower positioning under the battery constraints of ETs, charging station availability and capacity, and the positional configuration of trucks within a platoon. The objective is to minimize the total operation cost of the MFTP system, including charging cost, fuel cost, travel labor cost, charging labor cost, and platoon formation labor cost, while ensuring timely arrivals across multiple origin–destination (OD) pairs. The proposed MFTP is formulated as a novel mixed-integer linear program (MILP). Extensive numerical experiments on the simplified Illinois interstate highway network are conducted to examine the effectiveness and efficiency of the proposed model. Numerical results show that incorporating platooning reduces the total operational cost by 7.6% relative to the non-platooning scenario. The findings also shed some light on planning mixed fleets of CTs and ETs with platooning, offering valuable managerial insights for decision-makers. Full article

The decarbonization of the transportation sector necessitates the adoption of practical measures that can be implemented within existing fleets. One such measure is the installation of solar panels on trucks, which has shown potential to reduce fuel consumption in heavy-duty vehicles (HDVs). This study presents lessons learned from a monitoring project involving 200 commercial trucks retrofitted with 300–500 W solar panels, aimed at supplementing battery charging and minimizing alternator operation. The system incorporated commercially available flexible photovoltaic (PV) modules, adhesive mounting techniques, a charge controller, and a data logger housed within a control box. Documentation covered installation procedures, wiring practices, and safety considerations across various truck models, with additional insights from electrical contractors regarding labor time and costs. Results indicate that adhesive-based mounting can be carried out safely and reliably without structural modifications, although wiring and control box placement constitute the most significant portions of the installation process. The project further identified variability in installation duration and economic viability, depending on vehicle configuration and technician expertise. Overall, the findings affirm that vehicle-integrated photovoltaic (VIPV) retrofits are both technically feasible and operationally robust. They also underscore the practical requirements, constraints, and workforce considerations essential for scaling deployment within commercial fleets. Full article

Vehicle recharge time is a key barrier to widespread adoption of battery electric trucks, where megawatt class charging could be used to achieve refueling times comparable to internal combustion vehicles. This work presents the design and validation of a megawatt-capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current sensor mismatch and to verify protection logic for multiple bus voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs-class charging -capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent-circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current–sensor mismatch and to verify protection logic for multiple bus-voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs. Full article

Autonomous trucks can be used in different loading combinations, including different axle configurations, tire types, and lateral wander mode scenarios. In this research, four different truck types have been selected with varying gross weights and axle configurations. The four different truck types include a 5-axle long-haul semi-truck, a 6-axle electric autonomous truck, a 6-axle autonomous truck platoon leader, and a 5-axle autonomous truck platoon follower. Furthermore, three different tire footprint scenarios, consisting of a conventional dual wheel assembly, a wide base tire, and a new generation wide base tire, have been used. In order to utilize the possibility of lateral wander programmed into the autonomous trucks, three different lateral wander models, including zero lateral wander, a human-driven probabilistic lateral wander, and an optimum uniform wander mode, have been used. Finite element analysis has been employed to incorporate the effects of various scenarios on a conventional pavement section. Results showed improved pavement life with the use of uniform wander mode, where trucks T1 and T2 improved the pavement life by 47% and 56%, respectively, when compared to truck T3. Furthermore, the use of uniform wander mode decreases rutting and fatigue damage by 36% and 28%, respectively, on average for all scenarios. The use of new generation wide-base tires is recommended, since it reduces damaging strains by 38% when compared to the dual tire configuration. 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

Decarbonization has emerged as a crucial objective in the optimization of port collection and distribution networks. To investigate the synergistic effects of carbon trading mechanisms and the implementation of electric autonomous container trucks (EACTs), this study develops a multi-objective bi-level programming model that simultaneously minimizes transportation cost, carbon trading cost, and transportation time. The model is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), generating a Pareto-optimal solution set, from which the optimal solution is selected using a normalized ideal point method. Simulation-based case studies validate the feasibility and practical applicability of the proposed model. The results show that the optimized network significantly outperforms the traditional road-dominant mode. Under the baseline carbon price of 70 CNY/ton, the optimal deployment rate of EACTs reaches 25.03% and 33.87%. Sensitivity analysis reveals a distinct non-linear threshold effect: increasing the carbon price to 90 CNY/ton drives the EACT adoption rate to 32.76% and 45.38%, resulting in a 6.98% reduction in carbon emissions and a 12.75% decrease in total operational costs compared to the baseline scenario. Additionally, strict carbon quotas (e.g., 3000 tons) are found to further compel a modal shift, peaking EACT usage at 35.08% and 46.71%. These quantitative findings offer actionable insights for optimizing multimodal transport structures and refining carbon trading policies. 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