Search Results (339)

The deployment of electric trucks is recognized as a crucial tool for reducing dependence on traditional fossil fuels and mitigating pollution from transportation systems. However, insufficient and unbalanced distribution of charging stations may hinder the use of electric trucks. This study develops an explicit mixed-integer linear program to optimize the siting and sizing of charging stations for electric trucks in general transport networks. The model incorporates the queuing dynamics of electric trucks at charging stations through a formulated set of first-come-first-served constraints, enabling the direct computation of the charging waiting time for each truck. The objective function minimizes the total system cost, comprising the charging station construction cost, the electric truck procurement cost, the electricity consumption cost, and the operational cost, consisting of travel times, queuing times, and the delay penalties of the trucks. To address the computational challenges in solving large-scale network problems, we propose a hybrid solution strategy combining a rolling horizon framework with a genetic algorithm, which enhances computational efficiency through problem decomposition and iterative optimization. Finally, numerical experiments are conducted on three road networks, including the Sioux Falls network and the Chicago network, to validate the effectiveness of the proposed model and algorithm. Full article

The paper primarily focuses on the control strategy of an electric dump truck equipped with an M100 methanol range extender. In response to the significant adverse impact of the constant power control strategy on the lifespan of power batteries and the large rotational speed fluctuations of range extenders under the power-following control strategy, a constant-speed and variable-torque range extender control strategy based on the rule-based control strategy is proposed. This strategy enables power following within the range of 70 kW to 130 kW and fixed-point operation at 50 kW and 150 kW. Through co-simulation using AVL Cruise and MATLAB R2022b/Simulink, the results indicate that under the China Heavy-duty Commercial Vehicle Test Cycle-Dynamic (CHTC-D), with an average vehicle speed of 23.19 km/h, the constant-speed and variable-torque range extender control strategy achieves a higher methanol saving rate compared to both the constant power control strategy and the power-following control strategy, thereby demonstrating better fuel economy. The methanol consumption per 100 km for the dump truck using the constant power control strategy, the power-following control strategy, and the constant-speed and variable-torque control strategy are 62.89 L, 64.49 L, and 62.53 L, respectively. Compared with the same type of diesel range-extended electric dump truck, its fuel usage cost has a significant advantage. Full article

With the development of vehicle-to-grid (V2G) technology, electric vehicles (EVs) are increasingly participating in grid interactions. However, V2G-induced energy consumption and battery aging intensify range anxiety among users, reduce participation willingness, and decrease discharge capacity and revenue due to capacity loss. In this study, aging models for power batteries in electric passenger vehicles and electric trucks are established. A time-of-use electricity price model and an economic model considering battery aging costs are constructed. Two scenarios were established for daily use and V2G operation. The impacts of different scenarios and charging/discharging patterns on battery life and user profit are analyzed. The results indicate that the additional V2G discharging process increases the cyclic aging rate of EV batteries. Within the studied parameter ranges, the cyclic aging rate increased by 5.89% for electric passenger vehicles and 3.72% for electric trucks, respectively. Additionally, the initial V2G revenue may struggle to cover early-stage battery aging costs, but the subsequent slowdown in degradation may eventually offset these costs. With appropriate charging and discharging strategies, the maximum revenue per year reaches 18,200 CNY for electric trucks and 5600 CNY for electric passenger vehicles. This study may provide theoretical support for optimizing EV charging/discharging strategies and formulating policies in V2G scenarios. Full article

Traditional approaches to overcoming energy loss from resistances of vehicular transit velocities have focused primarily on reducing aerodynamic drag through streamlining air flow. These approaches have overlooked significant reductions in resistance on highways by reducing rolling losses and the aerodynamic drag associated with boundary layer separation and leading-edge stagnation regions. Ground effect vehicles are able to make significant strides towards reducing these two resistances. These vehicles can approach an 80% reduction in resistance compared to conventional frame streamlining alone. These substantial reductions to resistance enable a more effective and broader range of electric vehicles, including electric trucks and railcars. Lower resistance enables higher speeds at the same power consumption. Examples of digital prototype performances include up to 50% and 30% reductions in resistance through mitigating rolling/drivetrain and boundary layer separation losses, respectively. Digital prototypes are able to reach a lift-to-drag efficiency of 25 while maintaining a 0.2 aspect ratio. A cascade of additional advantages arises from aerodynamic lift-enabling rubber tires on steel rails for multimodal and widespread service. This paper details the mechanisms of how to achieve substantial reductions in energy consumption and enable transit transformations. The technology enables open-ended evolution with far greater possibilities than current transit options. The technological evolution includes electric automobiles, delivery trucks, semi-trucks, and railcars using batteries and solar sheets with significant competitive advantages over fossil fuels. Full article

To reduce emissions in the existing transportation system and lower carbon dioxide (CO₂) output, battery electric vehicles (BEVs) offer a promising approach due to their higher energy efficiency. However, their driving range still falls short compared to conventional vehicles. Optimizing the heating, ventilation, and air conditioning (HVAC) system can help save energy and improve passenger comfort. This study investigates an advanced thermal management system for an electric truck cabin with heating panels and added insulation. A one-dimensional (1D) cabin thermal model was also developed and validated with experimental data. The model integrates insulation, heating panels, and a 1D comfort simulation. It is functional mock-up unit (FMU) compatible and connects to larger system simulations and real-time applications. The results show that energy consumption can be reduced by up to 50% with these thermal measures. In the future, further research and new approaches will be necessary to identify even more efficient subsystems and cost-effective solutions. Full article

The transition to sustainable transport in the logistics sector requires innovative strategies, yet companies still face uncertainty regarding the operational, economic, and environmental feasibility of replacing diesel trucks with electric ones. Electric trucks represent a sustainable alternative, contributing to the reduction in pollutant gas emissions, noise reduction in traffic, and lower operational costs, in addition to building sustainable logistics through recharges from renewable energy sources. Although electric trucks offer sustainability benefits, existing research often lacks analyses based on real-world delivery conditions. In this context, the objective of this paper is to analyze the logistical impact of introducing electric trucks for beverage transportation. This study includes assessments of planned route profiles, economic evaluation during operation, emission mitigation costs, and charging analyses under different pricing models in consumer units. These elements were selected to reflect the actual challenges companies face. The results demonstrate that electric trucks can reduce fuel costs by 83.90% and significantly lower carbon emissions, confirming their viability for last-mile freight transport operations. Therefore, the results demonstrate that the process of replacing diesel trucks with electric ones is a viable alternative for companies due to the savings generated during operation and the reduction in pollutant emissions. Full article

Vehicle electrification has been proposed as a strategy for decarbonizing the transport sector. However, companies operating fleets of light-duty internal combustion engine vehicles (ICEVs) for personnel and freight transportation still lack the data and decision-making tools necessary to evaluate the transition to electric vehicles (EVs). This study proposes a novel methodology that combines the use of web applications with longitudinal vehicle dynamics to determine energy consumption and regenerative braking potential. In addition, it incorporates energy consumption data, taxes, subsidies and vehicle discounts to conduct a comparative analysis of the total cost of ownership of EVs versus IECVs. The proposed methodology was applied to evaluate the feasibility of an energy transition in a fleet of vans and pickup trucks used for transporting personnel and materials. The results show that the model can estimate energy consumption with an average error of 7.6% compared to monitored data. Replacing 10 ICEVs with 5 electric vans and 5 electric pickup trucks could reduce energy consumption by up to 62%. The operating cost of the electric van is 8.5% lower than its ICEV counterpart, while the electric pickup achieves a 13.8% reduction in operating costs compared to the combustion model. The technical findings and the methodology of this study are expected to provide a solid basis for companies to evaluate the energy and economic feasibility of electrifying their fleets. Full article

The reclamation of exhausted open-pit coal mines is an energy-intensive and costly process. Traditional methods offer no economic return. This study explores the feasibility of using autonomous electric dump trucks (EDTs) to fill the pit, leveraging regenerative braking during descent to generate energy and reduce operational costs. A comprehensive energy balance model was developed based on the operational cycle of the Komatsu HD605-7 (E-Dumper) in the unique downhill-loaded logistics of the Pery quarry. The model incorporates vehicle dynamics equations, including rolling resistance, gradient, and aerodynamic forces, to calculate net energy consumption per cycle. Three energy storage system (ESS) configurations were compared: NMC/NCA batteries, LiFePO₄ (LFP) batteries, and a hybrid LFP + supercapacitor (SC) system. Simulation results demonstrate that the net energy per cycle decreases with increasing payload capacity, even becoming negative (net energy generation) for loads above 110 tons due to powerful regenerative braking on the 13% descent grade. The hybrid LFP + SC system proved most efficient, achieving the lowest specific energy consumption (kWh/ton) by effectively capturing high-power regenerative currents. While LFP batteries have a lower energy density, their superior cycle life, thermal stability, and safety make them the optimal choice for the harsh mining environment. The proposed operation strategy, utilizing EDTs in a downhill-loaded cycle, transforms mine reclamation from a cost center into a potentially energy-neutral or even energy-positive process. A hybrid ESS with LFP batteries and supercapacitors is recommended as the most reliable and efficient solution for this specific application. Full article

To resolve the inherent coupling conflict between trajectory tracking and lateral stability in distributed electric drive buses, this paper proposes a hierarchical cooperative control framework. A simplified two-degree-of-freedom (2-DOF) vehicle model is first established, and kinematically derived reference states for stable motion are computed. At the upper level, a model predictive controller (MPC) generates real-time steering commands while explicitly minimizing lateral tracking error. At the lower level, a proportional integral derivative (PID)-based roll moment controller and a linear quadratic regulator (LQR)-based direct yaw moment controller are designed, with four-wheel torque distribution achieved via quadratic programming subject to friction circle and vertical load constraints. Co-simulation results using TruckSim and MATLAB/Simulink demonstrate that, during high-speed single-lane-change maneuvers, peak lateral error is reduced by 11.59–18.09%, and root-mean-square (RMS) error by 8.67–14.77%. Under medium-speed double-lane-change conditions, corresponding reductions of 3.85–12.16% and 4.48–11.33% are achieved, respectively. These results fully validate the effectiveness of the proposed strategy. Compared with the existing MPC–direct yaw moment control (DYC) decoupled control framework, the coordinated control strategy proposed in this paper achieves the optimal trade-off between trajectory tracking and lateral stability while maintaining the quadratic programming solution delay below 0.5 milliseconds. Full article

Accurate carbon emission estimation across all operational stages of container terminals is essential for advancing low-carbon development in the transportation sector and designing effective emission reduction pathways. This study develops a two-layer carbon accounting framework that integrates vessel berthing–waiting and terminal operations, tailored to the operational characteristics of Shanghai Port container terminals. The Ship Traffic Emission Assessment Model (STEAM) is applied to estimate emissions during berthing, while a bottom-up method is employed for mobile-mode container handling operations. Targeted mitigation strategies—such as shore power adoption, operational optimization, and “oil-to-electricity” or “oil-to-gas” transitions—are evaluated through comparative analysis. Results show that vessels generate substantial emissions during erthing, which can be significantly reduced (by over 60%) through shore power usage. In terminal operations, internal transport trucks have the highest emissions, followed by straddle carriers, container tractors, and forklifts; in stacking, tire cranes dominate emissions. Comprehensive comparisons indicate that “oil-to-electricity” can reduce total emissions by approximately 39%, while “oil-to-gas” can achieve reductions of about 73%. These findings provide technical and policy insights for supporting the green transformation of container terminals under the national dual-carbon strategy. Full article

Fuel cell trucks are a promising solution to reduce the disproportionately high greenhouse gas emissions of heavy-duty long-haul transportation. However, unlike conventional diesel vehicles, they lack combustion engine waste heat for cabin heating. As a result, electric heaters are often employed, which increase auxiliary energy consumption and reduce driving range. To address this challenge, advanced control strategies are needed to improve heating efficiency while maintaining passenger comfort. This study proposes and validates a methodology for implementing Model Predictive Control (MPC) in the cabin heating system of a fuel cell truck. Vehicle experiments were conducted to characterize dynamic heating behavior, passenger comfort indices, and to provide validation data for the mathematical models. Based on these models, an MPC strategy was developed in a Model-in-the-Loop simulation environment. The proposed approach achieves energy savings of up to 8.1% compared with conventional control using purely electric heating, and up to 21.7% when cabin heating is coupled with the medium-temperature cooling circuit. At the same time, passenger comfort is maintained within the desired range (PMV within ±0.5 under typical winter conditions). The results demonstrate the potential of MPC to enhance the energy efficiency of fuel cell trucks. The methodology presented provides a validated foundation for the further development of predictive thermal management strategies in heavy-duty zero-emission vehicles. Full article

At the international level, new measures, policies, and technologies are being developed to reduce greenhouse gas emissions and, more broadly, air pollutants. Road transportation is one of the main contributors to such emissions, as vehicles are extensively used in logistics operations, and many fleet owners of fossil-fueled trucks are adopting new technologies such as electric, hybrid, and hydrogen-based vehicles. This paper addresses the Hybrid Fleet Capacitated Vehicle Routing Problem with Time Windows (HF-CVRPTW), with the objectives of minimizing costs and mitigating environmental impacts. A mixed-integer linear programming model is developed, incorporating split deliveries, scheduled arrival times at stores, and a carbon cap-and-trade mechanism. The model is tested on a real case study provided by Decathlon, evaluating the performance of internal combustion engine (ICE), electric (EV), and hydrogen fuel cell (HV) vehicles. Results show that when considering economic and emission trading costs, the optimal fleet deployment priority is to use ICE vehicles first, followed by EVs and then HVs, but considering only total emissions, the result is the reverse. Further analysis explores the conditions under which alternative fuel, electricity, or hydrogen prices can achieve competitiveness, and a further analysis investigates the impact of different electricity generation and hydrogen production pathways on overall indirect emissions. 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

To address the issue of poor yaw stability in distributed-drive electric pickup trucks at medium-to-high speeds, particularly under the influence of continuously varying tire forces and road adhesion coefficients, a novel Kalman filter-based method for estimating the road adhesion coefficient, combined with a Tube-based Model Predictive Control (Tube-MPC) algorithm, is proposed. This integrated approach enables real-time estimation of the dynamically changing road adhesion coefficient while simultaneously ensuring vehicle yaw stability is maintained under rapid response requirements. The developed hierarchical yaw stability control architecture for distributed-drive electric pickup trucks employs a square root cubature Kalman filter (SRCKF) in its upper layer for accurate road adhesion coefficient estimation; this estimated coefficient is subsequently fed into the intermediate layer’s corrective yaw moment solver where Tube-based Model Predictive Control (Tube-MPC) tracks desired sideslip angle and yaw rate trajectories to derive the stability-critical corrective yaw moment, while the lower layer utilizes a quadratic programming (QP) algorithm for precise four-wheel torque distribution. The proposed control strategy was verified through co-simulation using Simulink and Carsim, with results demonstrating that, compared to conventional MPC and PID algorithms, it significantly improves both the driving stability and control responsiveness of distributed-drive electric pickup trucks under medium- to high-speed conditions. Full article

Mining is a branch of industry that continuously implements new technologies to optimise processes, in terms of increasing performance and decreasing the negative impact on the environment. To enhance the efficiency of the rock material fragmentation process, it is proposed to replace the existing hydraulic hammer with a fully electric alternative. This study used simple data-acquisition methods, including acoustic signals, vibrations, electric currents, and video recordings, to compare the efficiency of both hammers. Measurements were taken for hydraulic and electric hammers under comparable conditions, using the same boom, operator, and rock material. Two methodologies are proposed: the first one is based on acoustic data measurement and parametrisation, and the second one, for validation purposes, is based on visual (video) data analysis. The acoustic approach analyses the stability parameter ($\alpha$) of the $\alpha$-stable distribution, the shape parameter ($\nu$) of the t Location–Scale distribution that can assess signal impulsiveness, and three additional basic parameters (number of peaks, sum of amplitudes of impulses, and duration of the cycle for each truck/loader). The visual approach, used for validation, detects individual rock sizes and roughly evaluates the number of oversized pieces. However, it is a manual approach, due to challenging conditions. The results of both methods demonstrate that the electric hammer is more than three times more efficient. This increased efficiency is attributable to adaptive impact frequency control, a feature that is unavailable in hydraulic hammers. Full article