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To minimize the negligence when charging electric vehicles, it is deemed important to have an internet of things (IoT) based monitoring system using a notification feature. The monitoring system of electric vehicle battery charging used a voltage divider and temperature sensor (DS18B20) installed on the Arduino Mega 2560 microcontroller with the addition of an ESP8266 Wi-Fi module for sending microcontroller data into the Blynk platform. A notification feature was added as the reminder that the battery has been overcharging or overheating. This study applied the Mamdani fuzzy logic method to determine the conditions when notifications must appear. The results of the application of the Mamdani fuzzy logic method were able to determine the conditions for push notifications to appear using the parameters as desired; by so doing, it is possible to create a battery monitoring system with accurate push notification feature to prevent the battery from being overcharged and overheated. [ABSTRACT FROM AUTHOR]

This paper reviews the existing studies employing total cost of ownership (TCO) analysis to evaluate the comparative economic viability of battery electric trucks (BETs) and diesel trucks (DTs). A key finding is that until recent years, BETs have not been cost-competitive with DTs. Light-duty trucks and medium-duty trucks started to become competitive in 2021 (1) according to some estimates, whereas heavy-duty trucks might remain to be not competitive even in future decades. However, (2) TCO estimates differ across continents. (3) The combing effect of fuel prices and taxes is most likely responsible for the fact that BETs enjoy a stronger competitive position relative to DTs in Europe, Asia, and Oceania, whereas, in North America, most estimates assign them poor competitiveness, both presently and in the coming years. (4) Most studies underline that significant cost disproportions persist in the heavy-duty truck segment due to its demanding operational requirements and a lack of robust high-powered charging infrastructure. Consequently, substantial financial incentives and subsidies will be required for heavy-duty trucks to enhance their economic viability, potentially accelerating cost parity from post-2035 to the near future. This paper identifies several constraints in its TCO analysis, including limited data on residual values, variability in discount rates, depreciation costs, and a lack of longitudinal and market data for BETs. [ABSTRACT FROM AUTHOR]

With the widespread adoption of renewable energy sources like wind power and photovoltaic (PV) power, uncertainties in the renewable energy output and the battery-swapping demand for electric heavy-duty trucks make it challenging for battery-swapping stations to optimize battery-charging management centrally. Uncoordinated large-scale charging behavior can increase operation costs for battery-swapping stations and even affect the stability of the power grid. To mitigate this, this paper proposes a multi-timescale battery-charging optimization for electric heavy-duty truck battery-swapping stations, taking into account the source–load–storage uncertainty. First, a model incorporating uncertainties in renewable energy output, time-of-use pricing, and grid load fluctuations is developed for the battery-swapping station. Second, based on day-ahead and intra-day timescales, the optimization problem for battery-charging strategies at battery-swapping stations is decomposed into day-ahead and intra-day optimization problems. We propose a day-ahead charging strategy optimization algorithm based on intra-day optimization feedback information-gap decision theory (IGDT) and an improved grasshopper algorithm for intra-day charging strategy optimization. The key contributions include the following: (1) the development of a battery-charging model for electric heavy-duty truck battery-swapping stations that accounts for the uncertainty in the power output of energy sources, loads, and storage; (2) the proposal of a day-ahead battery-charging optimization algorithm based on intra-day-optimization feedback information-gap decision theory (IGDT), which allows for dynamic adjustment of risk preferences; (3) the proposal of an intra-day battery-charging optimization algorithm based on an improved grasshopper optimization algorithm, which enhances algorithm convergence speed and stability, avoiding local optima. Finally, simulation comparisons confirm the success of the proposed approach. The simulation results demonstrate that the proposed method reduces charging costs by 4.26% and 6.03% compared with the two baseline algorithms, respectively, and improves grid stability, highlighting its effectiveness for managing battery-swapping stations under uncertainty. [ABSTRACT FROM AUTHOR]

This study analyzes decarbonization costs for the Swedish heavy-duty road fleet under five scenarios, one taken from the EUCalc model as a reference scenario and four others driven by interviews: a high-speed transition with 100% battery electric vehicles (BEVs) across all distances; a high-speed transition with BEVs taking 100% of the market in local and regional distances and 40% in long distances, with the remaining 60% being fuel cell vehicles (FCVs) by 2050; a low-speed transition with BEVs market share increasing by 15% every five years, starting at 10% from 2025 for local and 2030 for regional and long distances; and a low-speed transition similar to the previous scenario, but with 60% of the electrified long-distance fleet to be FCVs. The system's expenses are then calculated through numerical modeling. The study links research on the costs of sustainability transition to a circular economy by analyzing the effect of charging range and temperature on battery degradation for BEVs and their impact on the batteries' valorization. In full electrification scenarios, despite lower operating expenses, the system incurs a higher total cost because of higher investment expenses. Charging–discharging pattern and temperature impact the remaining capacity, and therefore salvage value, of end-of-life batteries. • Develops a model evaluating the decarbonization costs of Swedish heavy-duty trucks. • Integrates cost savings from batteries' remaining capacity into the cost analysis. • Incorporates loading capacity and charging time loss into the cost analysis. • BEVs have a lower TCO in all scenarios and distances compared to diesel trucks. • A 70% EoL battery buyback rate leads to higher salvage value in BEVs. [ABSTRACT FROM AUTHOR]

Purpose: The study explores the critical domain of safe suspension design for electric military trucks operating in cross-country terrains, extending beyond the prevalent focus on electric cars with in-wheel drive. The research objectives include non-linear suspension design, considerations for extreme cross-country terrains, and the study of varying velocities on ride dynamics. Methods: A novel non-linear suspension model for a multi-axle electric military truck with an in-wheel motor is proposed. The model captures the complexities of extreme military operations and introduces the time delay factor between three axles to analyze suspension performance parameters at different velocities using Simulink. Three types of road profiles are considered: sinusoidal bump, ISO 8608 class C, and class F. With 14 degrees of freedom and non-linear behavior, the proposed model offers valuable insights into the road-holding capacity and ride comfort of electric military trucks in challenging cross-country environments. Results: The findings indicate that the suspension performance of the electric military truck is higher compared to an electric car. Further, the findings predict the safe velocity range for enhancing the comfort and stability of electric military trucks, thus offering a unique perspective for designing a safer suspension system. Conclusion: A novel non-linear suspension system for a multi-axle, in-wheel driven electric military truck was modeled by considering various road profiles, such as a sinusoidal bump and cross-country terrains. Based on this, the effect of different velocities on the suspension performance parameters was studied, and a safer range of velocities for a secure suspension design was established as per ISO 2631. [ABSTRACT FROM AUTHOR]

The transportation sector is responsible for significant greenhouse gas (GHG) emissions, with medium- and heavy-duty trucks (MHDTs) being major contributors. In response, medium- and heavy-duty electric trucks (MHDETs) are being explored as zero-emission alternatives. However, the weight of high-capacity batteries needed for long-haul trips and heavy loads could increase the axle loads as well as the gross vehicle weight (GVW) of heavy-duty electric trucks (HDETs), leading to extra damage on flexible highway pavements. This study simulated the GVW increment of HDETs and assessed its effects on pavement damage, environmental impacts, and life-cycle costs of four typical highway pavement structures in Illinois (known as thick–weak, thick–strong, thin–weak, and thin–strong). The utilisation of electric trucks resulted in significant reductions in diesel combustion related global warming potential (GWP) emissions and costs (in $2022) by 73% and 11.5%, respectively. However, the results demonstrated that with a 100% penetration of HDETs carrying an additional weight of 8 kips per truck, the pavement deterioration accelerated compared to traffic with conventional trucks. As a consequence, GWP and costs were reduced by 69% and 10.6%, respectively. [ABSTRACT FROM AUTHOR]

In order to improve the vehicle driving range of a pure electric light commercial vehicle, this paper proposes to reduce the electric power consumption per 100 kilometers by reducing power consumption and increasing coasting energy recovery power. Firstly, for ECO energy-saving mode, the paper proposes the energy-saving control technology based on different vehicle usage scenarios and working conditions, which significantly reduces the energy consumption of the vehicle in the whole scenario. Secondly, in the view of the coasting energy recovery deceleration of commercial vehicles with empty and full load varies greatly, the paper proposes the energy recovery control based on the vehicle mass to achieve the same coasting deceleration under different loads to recover more energy. Then, for the problem that coasting energy recovery is usually designed with low adhesion coefficient road surface, the paper proposes adaptive adjustment of coasting energy recovery based on road adhesion coefficient. Finally, the pure electric light truck is tested on real road. The results show that the ECO mode reduces the electricity consumption per 100 kilometers by 2.9%, and by 2.1% for scenarios of turning on the air conditioning and opening the car door. The energy recovery strategy based on the vehicle' s mass shows a 2.7% reduction in electricity consumption per 100 kilometers. [ABSTRACT FROM AUTHOR]

Background: As road transport companies increasingly integrate electric trucks (eTrucks) into urban fleets, evaluating their performance in real-world conditions is essential for effective fleet management and infrastructure planning. Methods: This study introduces TraPodSim, a simulation system designed to assess the key performance indicators (KPIs) of eTrucks and other vehicle types. Using real geographic data, transportation routes, and technical vehicle specifications, the system simulates daily operations under user-defined conditions. Results: TraPodSim produces 20 physical indicators, providing detailed insights into the daily performance of each vehicle in the fleet. These indicators help evaluate fleet efficiency, energy consumption, and overall operational effectiveness. Conclusions: TraPodSim offers transport companies a valuable tool for optimizing fleet configurations and analyzing the use of private or public battery-charging stations, enabling the efficient integration of eTrucks into existing transportation networks. [ABSTRACT FROM AUTHOR]

Taking into account the requirements to reduce the release of harmful emissions into the environment, the EU's environmental standards when transitioning to the Euro 7 standard in 2025 will actually lead vehicles having to operate without producing emissions in all driving situations. Carmakers believe that the new, much stricter regulations will mark the end of the internal combustion engine era. For example, in 2030, the manufacturer SEAT will cease its activities, leaving behind the Cupra brand, which will be exclusively electric in the future. This trend will apply not only to private vehicles (passenger cars), but also to utility vehicles, which is the subject of our research, namely the spatial tubular frame in the Bogdan ERCV27 garbage truck, presented in the form of a solid model. The peculiarity of the studied model is the installation of a battery block behind the driver's cabin, causing an additional load to be placed on the spatial frame of the garbage truck, which in terms of its architecture is more like the body of a bus. During the conditions involving various modes of operation of a full-scale Bogdan ERCV27 garbage truck sample, questions about the strength and uniformity of its load-bearing spatial frame inevitably arise, which are decisive, even at the stage of designing and preparing the technical documentation. The main static load mode, which, despite its name, also covers dynamic conditions, was modeled using the appropriate coefficient kd = 2.0. The maximum stresses on the model during the "bending" mode were 381.13 MPa before structure optimization and 270.5 MPa as a result of the improvement measures. The spatial frame mass was reduced by 4.13%. During the "torsion" mode, the maximum deformation values were 12.1–14.5 mm, which guarantees the normal operation of the aggregates and units of the truck. [ABSTRACT FROM AUTHOR]

Abstract The e-commerce sector’s rapid expansion has led to an increase in delivery activities both within and across cities, fuelling the growth of the courier, express, and parcel (CEP) services. CEP service providers are crucial for the distribution of goods across all types of cities, especially for last-mile delivery. However, CEP service providers need innovative approaches for their last-mile distribution in small- and medium-sized cities to reduce transport costs and negative environmental impacts. For this reason, this paper analyses the quantitative impacts of logistics measures of CEP service providers for last-mile delivery in small- and medium-sized cities, especially the resulting transport costs and environmental impacts, in the framework of a case study for the investigation area of the Aachen city region. A simulation-based analysis was conducted using the agent-based transport simulation MATSim and the linked route optimisation Jsprit. The results revealed that electric trucks are not cost-effective as a stand-alone logistics measure for last-mile delivery in small- and medium-sized cities. However, combining electric trucks with other sustainable logistics measures, such as parcel shops and parcel lockers, results in a viable logistics measure for last-mile delivery. It is possible to reduce total transport costs by at least 5.4% and CO2 emissions by at least 61.1%. Hence, CEP service providers should replace diesel trucks with a mix of sustainable logistics measures for last-mile delivery in small- and medium-sized cities to achieve better operational efficiency and lesser environmental impacts.

In the United States, the transportation sector  is the largest single source of greenhouse gas (GHG) and nitrogen oxide (NOx) emissions, and heavy-duty trucks contribute a disproportionately large share. Therefore, the trucking industry has been seeking ways to minimize emissions, such as adopting zero-emission vehicles and improving truck operating strategies to reduce truck miles. Battery-powered vehicles have different limitations than those with internal combustion engines. In this study, researchers from the University of Southern California investigated the adoption of battery electric heavy-duty trucks (BEHDTs) in the short-haul freight movement sector and the drayage industry. Drayage is a short-haul pickup and delivery service for transporting freight among ports, warehouses, and other facilities. With drayage routing, vehicles have limited weight and volume capacities and often make many stops. Routing involves optimizing for multiple factors, like fuel, distance traveled, and timeliness. This brief summarizes the findings from that research and provides implications for the field.View the NCST Project Webpage

The adoption of battery electric trucks (BETs) as a replacement for diesel trucks has potential to significantly reduce greenhouse gas (GHG) emissions from the freight transportation sector. However, BETs have shorter driving range and lower payload capacity, which need to be taken into account when dispatching them. This paper addresses the energy-efficient dispatching of BET fleets, considering backhauls and time windows. To optimize vehicle utilization, customers are categorized into two groups: linehaul customers requiring deliveries and backhaul customers requiring pickups, where the deliveries need to be made following the last-in-first-out principle. The objective is to determine a set of energy-efficient routes that integrate both linehaul and backhaul customers, while considering factors such as limited driving range, payload capacity of BETs and the possibility of en route recharging. The problem is formulated as a mixed-integer linear programming (MILP) model and propose an adaptive large neighborhood search (ALNS) metaheuristic algorithm to solve it. The effectiveness of the proposed strategy is demonstrated through extensive experiments using a real-world case study from a logistics company in Southern California. The results indicate that the proposed strategy leads to a significant reduction in total energy consumption compared to the baseline strategy, ranging from 7% to 40%, while maintaining reasonable computational time. This research contributes to the development of sustainable transportation solutions in the freight sector by providing a practical and more efficient approach for dispatching BET fleets. The findings emphasize the potential of BETs in achieving energy savings and advancing the goal of green logistics.  View the NCST Project Webpage

The future charging requirements of electric trucks will lead to new demands on the power grid. In order not to slow the expansion of the charging infrastructure for electric trucks, the power grid must be strengthened for this purpose. However, due to the limited penetration of electric trucks in fleets to date, grid planners lack information on their time- and location-dependent charging demand. The question arises as to how the charging demand of electric trucks can be realistically taken into account in power grid simulations. This paper therefore presents a methodology that makes it possible to quantify the charging demand of electric trucks at typical charging locations and derives initial parameters for power system planning with electric trucks. For location-based charging demand modeling, the arrival and departure behavior of trucks at representative logistics centers is combined with mobility data and vehicle parameters. This allows the determination of time series-based charging demand. A charging demand analysis at five different logistics center types shows that that energy demand, peak load, and temporal behavior vary greatly depending on the center type. It is therefore advisable to take these different charging location types into account when designing the electricity grids. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have emerged as a promising solution to address environmental concerns, especially benefiting urban delivery and last-mile fleets due to their unique operational characteristics. Despite the potential advantages, the adoption of electric trucks (eTrucks) into delivery fleets has been slow, mainly due to the challenge posed by eTrucks' limited driving range. Consequently, a reliable method for predicting the eTrucks' energy consumption in fleet route planning is essential, and the accuracy of the velocity trajectory forecast forming the fundamental basis. This paper introduces a data-driven approach to predict the velocity and energy consumption of medium-duty (MD) eTrucks, considering various road features, payload, and traffic conditions. A gated recurrent unit (GRU) is trained using traffic-labeled characteristic features specific to each road segment within a delivery route. For every predefined route, the GRU generates the velocity profile by analyzing a sequence of traffic states predicted from the maximum entropy Markov model (MEMM). Corresponding eTruck energy consumption is estimated using an autonomie truck model. Real-world EV data are used to evaluate the proposed method, and the results demonstrate that the model effectively utilizes the information, achieving high accuracy in predicting both eTruck velocity and energy consumption. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the forthcoming years, it is expected that there will be a notable increase in the market penetration of electrically powered trucks with the objective of reducing greenhouse gas emissions in the transport sector. It is therefore essential to implement a comprehensive public charging infrastructure along highways in the medium term, enabling vehicles to be charged overnight or during driving breaks, particularly in the context of long-distance transportation. This paper presents a simulation model that supports the planning and technical design of truck charging parks at German highway rest areas. It also presents a transferable mobility model for the volume of trucks and the parking times of long-distance trucks at rest areas. Subsequently, a simulation is offered for the purpose of designing the charging infrastructure and analysing peak loads in the local energy system. The potential of the models is demonstrated using various charging infrastructure scenarios for an exemplary reference site. Subsequently, the extent to which the charging infrastructure requirements and the service quality at the location depend on external conditions is explained. In addition, the influence of the range of offers and the business models on the efficiency of infrastructure use is established. Based on the findings, general recommendations for the design of truck charging parks at rest areas are then given and discussed. [ABSTRACT FROM AUTHOR]

In the United States, new laws and regulations in several states require increasing percentages of vehicles produced in the future to be zero emission to address climate-related issues. Manufacturers are producing more Electric Vehicles (EVs), but potential customers are hesitant to adopt EVs due to range anxiety. Log truck owners are particularly hesitant to invest in electric log trucks (ELTs) due to forest harvest operations remote nature. However, regenerative braking utilizing gravitational potential energy provides the opportunity to increase the range of ELTs. This manuscript presents the workflow for calculating and mapping energy transportation cost on an ELT using a GIS-enabled tool and demonstrates the tool's applicability through a case study. The study area, the McDonald Dunn Research Forest, included 5,607 ha of forest stands partitioned into 357 harvest polygons averaging 15.7 ha, 326 harvest landings and 295 km of road. Using remotely sensed data from the study site, the tool digitalizes the wood supply chain and optimizes routing based on minimum energy expenditure, providing results such as trip energy cost, distance, and time. The results show the tools forest operations planning potential, applicability as a GIS navigation system, and provides EV buyers with a tool to make informed decisions when investing in technology that could combat climate-related issues. EV adoption will reduce reliance on fossil fuels and potentially ameliorate other environmental problems. [ABSTRACT FROM AUTHOR]

The present work intends to make a scientific contribution to future drive technology in medium-duty road freight transportation that is as objective and fact-based as possible. In cooperation with a medium-sized forwarding company, 1-day transports, previously driven with diesel trucks, were examined. Using a physically based model, which was first validated by comparing simulated CNG drive data with real-world diesel data, the findings were transferred to battery electric trucks (BETs) and fuel cell trucks (FCETs) and extrapolated to 2050 based on expected technological developments. The model makes statements based on the results of the investigated application regarding specific consumption, greenhouse gas (GHG) emissions, consumption shares and recuperation. The CNG combustion technology (ICET-CNG) serves as a reference. BETs in this application have the lowest emission and consumption values: BET2050 will consume a third of the energy and emit a fifth of the GHGs of ICET-CNG2024. The weight of the battery leads to higher consumption values. FCETs have higher fuel consumption due to their longer drive trains. This is partially compensated by their lower weight: FCET2050 will consume 40% of the energy and emit a third of the GHGs of ICET2024. In long-distance traffic, aerodynamic drag is the dominant consumption factor, accounting for 40%, which should be addressed in further truck development. Recuperation extends the range by 3–7%. [ABSTRACT FROM AUTHOR]

The load spectrum is the foundation for the fatigue life simulation of the vehicle frame. Load spectrum extraction based on virtual iteration (VI) has the disadvantage of relatively low efficiency. With an electric truck as the research object, the wheel force, the axle acceleration, the leaf spring deformation, and bracket strain were measured on a proving ground. A full vehicle multibody dynamics (MBD) model was built. Spatial attention 1D convolutional neural network (SA-1DCNN) deep learning and VI method were used for load spectrum extraction simulation. Simulation and measured responses were compared in the time domain, root mean square (RMS) value, and pseudo damage to validate the simulation. The frame fatigue life simulation result was validated with a road test. The results indicate that both methods can achieve reasonable results, and the efficiency of the SA-1DCNN method is 30 % higher than that of the VI method. The proposed method can improve load spectrum extraction efficiency. [ABSTRACT FROM AUTHOR]

Fighting climate change has become a major task worldwide. One of the key energy sectors to emit greenhouse gases is transportation. Therefore, long term strategies all over the world have been set up to reduce on-road combustion emissions. In this context, the road freight sector faces significant challenges in decarbonization, driven by its limited availability of low-emission fuels and commercialized zero-emission vehicles compared with its high energy demand. In this work, we develop the Mobility and Energy Transportation Analysis (META) Model, a python-based optimization model to quantify the impact of transportation projected policies on freight transport by projecting conventional and alternative fuel technologies market acceptance as well as greenhouse gas (GHG) emissions. Along with introducing e-fuels as an alternative refueling option for conventional vehicles, META investigates the market opportunities of Mobile Carbon Capture (MCC) until 2050. To accurately assess this technology, a techno-economic analysis is essential to compare MCC abatement cost to alternative decarbonization technologies such as electric trucks. The novelty of this work comes from the detailed cost categories taken into consideration in the analysis, including intangible costs associated with heavy-duty technologies, such as recharging/refueling time, cargo capacity limitations, and consumer acceptance towards emerging technologies across different regions. Based on the study results, the competitive total cost of ownership (TCO) and marginal abatement cost (MAC) values of MCC make it an economically promising alternative option to decarbonize the freight transport sector. Both in the KSA and EU, MCC options could reach greater than 50% market shares of all ICE vehicle sales, equivalent to a combined 35% of all new sales shares by 2035. [ABSTRACT FROM AUTHOR]

The concepts of cubic spherical neutrosophic sets (CSNSs), introduced and investigated by Gomathi et al. [5], offer a geometric representation of collection of neutrosophic sets (NSs), enhancing their ability to capture uncertainty. The formulation characterizes information using points on a sphere with a defined center and radius, providing a more precise depiction of fuzziness inherent in uncertain data. The cubic spherical neutrosophic Archimedean triangular norms(ATN) and conorms (ATCN), expanding the model's capabilities to handle uncertainty. These algebraic operators enable the aggregation and combination of uncertain information, offering a more comprehensive approach to decision-making. The research further presents a method for solving multiple-criteria decision-making problems within the cubic spherical neutrosophic context, leveraging the newly integrated norms and conorms. The algorithm utilizes the cosine similarity measure of cubic spherical neutrosophic sets, exemplified through an application involving the selection of the most effective electric truck. This extended framework provides decision-makers with enhanced tools to navigate complex decision landscapes amidst uncertainty, facilitating more informed and robust choices across diverse domains. [ABSTRACT FROM AUTHOR]

Making road transport more sustainable is a vital part in reducing emissions, where the transition to electric freight vehicles (EFVs) are important. In this transition, there is a need for available electric vehicle charging infrastructure (EVCI). Many logistic companies are installing their own EVCI, which is primarily used during the night. This study explores how providers could share and users book charging on digital platforms for EFVs by conducting a qualitative study of a pilot project for sharing. The study shows that providers need financial incentive, available EVCI while not having charging as a primary business model. Users tend to have uncertainties about charging, avoid public charging, and would benefit from driver support. A digital platform enables a wide range of actors to participate, while providing payment solutions and vehicle identification. The study presents several sustainability drivers and barriers. Economic drivers include increased revenues, low-cost charging, and a possibility to invest in cheaper vehicles. Environmental drivers include improved usage, potentially fewer batteries, and supporting the sustainability transition. Social drivers include a community of sharing, easy comparison, and improving predictability. Economic barriers include limited demand, limited usage, and a lack of interest from the provider. Environmental barriers include the risk of under-utilization, overcapacity where many providers install EVCI, and the risk of stressing the grid when utilizing fast charging for large trucks. Social barriers include uncertainty about reliability, grid limitations, and a lack of trust of actors in the system. HIGHLIGHTS: Companies sharing their fast chargers complement public charging Limited demand for fast charging due to few large electric trucks Actors providing charging need accessible physical space and bookable chargers Platforms for charging vehicles provide environmental, economic and social benefits [ABSTRACT FROM AUTHOR]

Battery swapping is an emerging energy supply method for Electric Heavy Trucks (EHTs), and Rack and Pinion Drives (RPDs) in widely used in Battery Swapping System (BSS). Fault diagnosis for RPDs improves the safety and efficiency of battery swapping, however, unique operation conditions of driving gear in RPDs, including low rotational speed, reciprocating motion, and speed fluctuation pose steep challenges for the fault diagnosis. To address these issues, in this study, a novel signal representation method, Planar Dot Pattern (PDP) is proposed: first, spatial relative features of adjacent teeth is employed in PDP, which is suitable for representation of weak fault feature caused by low rotational speed; then, tooth-by-tooth representation method is proposed in PDP to maximizing utilization of the short samples caused by reciprocating motion; finally, an adaptive delay factor is defined in PDP to reduce the impact of speed fluctuation. Additionally, exclusive feature extraction method for PDP based on dot distribution analysis is proposed, which enhances the accuracy and efficiency of fault diagnosis. The recognition of fault patterns is achieved based on sparse representation, with the proposed dot distribution features. The fault diagnosis accuracy based on the vibration data of BSS experimental platform was 98.21%, which is more accurate than conventional fault diagnosis methods. [ABSTRACT FROM AUTHOR]

Container ports are increasingly deploying electric trucks (ETs) for inter-terminal transportation (ITT). However, en-route recharging activities, which reduce ITT efficiency, are necessary for ETs because of limited battery capacity. This study proposes a real-time strategy to schedule the en-route recharging activities for ETs. The strategy dynamically monitors the ITT system, and simultaneously designates charging piles for ETs and determines their positions in the service sequences. An ET may be designated to a remote but shorter-queued pile. The total queue time of the ETs served by a designated pile can be reduced by adjusting the service sequence. The effectiveness is demonstrated in comparison with an alternative strategy in a real-world case. The results indicate that the proposed strategy effectively reduces container throughput time by 21.8% and ET energy consumption by 12.3%. Sensitivity analyses suggest that the strategy is applicable for ports with multiple charging stations and moderate ET fleet size. [ABSTRACT FROM AUTHOR]

As the country that emits the most carbon in the world, China needs significant and urgent changes in carbon emission control in the transportation sector in order to achieve the goals of reaching peak carbon emissions before 2030 and achieving carbon neutrality by 2060. Therefore, the promotion of new energy vehicles has become the key factor to achieve these two objectives. For the reason that the comprehensive transportation cost directly affects the end customer's choice of heavy truck models, this work compares the advantages, disadvantages, and economic feasibility of diesel, liquefied natural gas (LNG), electric, hydrogen, and methanol heavy trucks from a total life cycle cost and end-user perspective under various scenarios. The study results show that when the prices of diesel, LNG, electricity, and methanol fuels are at their highest, and the price of hydrogen is 35 CNY/kg, the total life cycle cost of the five types of heavy trucks from highest to lowest are hydrogen heavy trucks (HHT), methanol heavy trucks (MHT), diesel heavy trucks (DHT), electric heavy trucks (EHT), and LNG heavy trucks (LNGHT), ignoring the adverse effects of cold environments on car batteries. When the prices of diesel, LNG, electricity, and methanol fuels are at average or lowest levels, and the price of hydrogen is 30 CNY/kg or 25 CNY/kg, the life cycle cost of the five heavy trucks from highest to lowest are HHT, DHT, MHT, EHT, and LNGHT. When considering the impact of cold environments, even with lower electricity prices, EHT struggle to be economical when LNG prices are low. If the electricity price is above 1 CNY/kWh, regardless of the impact of cold environments, the economic viability of EHT is lower than that of HHT with a purchase cost of 500,000 CNY and a hydrogen price of 25 CNY/kg. Simultaneously, an exhaustive competitiveness analysis of heavy trucks powered by diverse energy sources highlights the specific categories of heavy trucks that ought to be prioritized for development during various periods and the challenges they confront. Finally, based on the analysis results and future development trends, the corresponding policy recommendations are proposed to facilitate high decarbonization in the transportation sector. [ABSTRACT FROM AUTHOR]

The transportation sector is undergoing a transformative shift, driven by advancements like autonomous and electric vehicle technologies. In this research, we investigate employment, carbon emissions, and total cost of ownership of autonomy and electrification in the US trucking industry. We utilize life cycle assessment and multi‐regional input‐output modeling to develop a comprehensive life cycle sustainability assessment approach. According to the results, while enhanced fuel economy due to autonomous systems can lead up to a 18% and 41% reduction in emissions and costs, electrification of diesel trucks shows remarkable potential, achieving up to a 40% decline in emissions and a 12% saving in life cycle costs. Autonomy and electrification combined could lead to a 50% decrease in emissions and 46% savings in life cycle costs. On the other hand, autonomy, while enhancing fuel efficiency and reducing costs, causes job losses due to improved efficiency and the elimination of driver positions. Introducing autonomy to diesel trucks results in a 27% decrease in jobs within the US trucking sector, attributed to improved fuel efficiency and subsequent job losses. Transition to autonomy and electrification requires a deliberate balance between environmental, social, and economic aspects. Managerial strategies should consider the use of the proposed composite indicators when setting emission reduction, cost cutting, and managing employment implications. Flexible re‐skilling and training programs should be developed to adapt to the changing skill requirements due to electrification and automation. [ABSTRACT FROM AUTHOR]

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Charge management, which lowers charging costs for fleets and prevents straining the electrical grid, is critical to the successful deployment of medium- and heavy-duty battery-electric trucks (MHD BETs). This study introduces an energy demand and cost management framework that optimizes depot charging for MHD BETs by combining an energy consumption machine learning model and a linear program optimization model. The framework considers key factors impacting real-world MHD BET operations, including vehicle and charger configurations, duty cycles, use cases, geographic and climate conditions, operation schedules, and utilities' time-of-use (TOU) rates and demand charges. The framework was applied to a hypothetical fleet of 100 MHD BETs in California under three different utilities for 365 days, with results compared to unmanaged charging. The optimized charging solution avoided more than 90% of on-peak charging, reduced fleet charging peak load by 64–75%, and lowered fleet energy variable costs by 54–64%. This study concluded that the proposed charge management framework significantly reduces energy costs and peak loads for MHD BET fleets while making recommendations for fleet electrification infrastructure planning and the design of utility TOU rates and demand charges. [ABSTRACT FROM AUTHOR]

The hydrogen pressure typically increases to approximately 700-bar for storage in high-pressure tanks. Some industry applications use such a high-pressure hydrogen but some others need lower-pressure hydrogen where the pressure is reduced through pressure reduction valves. Some energy is lost during pressure reduction which would be significant in large-scale systems. A multi-level hydrogen storage can address this issue, where the hydrogen is stored at various pressures. This paper addresses an electricity-hydrogen nexus system integrated with multi-level hydrogen storage and solar PV sites. The designed system supplies electric and fuelcell vehicles at 3 different charging points including an electric vehicle charging station, fuelcell car refueling station, and fuelcell truck refueling station. The hydrogen is stored at 30, 350, and 700-bar pressure levels and each level feeds various users. The water electrolysis is employed to generate hydrogen at 30-bar pressure and 2 compressors increase the pressure to 350 and 700-bar. The cost function, defined as the daily energy cost, is optimized at $1045.65 per day. The water electrolyzer produces the most hydrogen during nighttime when electricity is inexpensive. Under normal operating conditions, the fuel cell is not utilized due to its low efficiency of about 60%. However, it serves as a backup resource in the event of a system outage, such as when the external grid fails, in hours 22 to 24. During this time, the fuel cell is activated and produces 154 kWh. • Multi-Level Hydrogen Storage. • Electricity-Hydrogen Nexus System Integration. • Optimized Hydrogen Production and Pressure Management. • Cost Optimization and Efficiency. • Backup Resource Utilization. [ABSTRACT FROM AUTHOR]

Climate change risks have triggered the international community to find efficient solutions to reduce greenhouse gas (GHG) emissions mainly produced by the energy, industrial, and transportation sectors. The problem can be significantly tackled by promoting electric vehicles (EVs) to be the dominant technology in the transportation sector. Accordingly, there is a pressing need to increase the scale of EV penetration, which requires simplifying the manufacturing process, increasing the training level of maintenance personnel, securing the necessary supply chains, and, importantly, developing the charging infrastructure. A new modular trend in EV manufacturing is being explored and tested by several large automotive companies, mainly in the USA, the European Union, and China. This modular manufacturing platform paves the way for standardised manufacturing and assembly of EVs when standard scalable units are used to build EVs at different power scales, ranging from small light-duty vehicles to large electric buses and trucks. In this context, modularising EV electric systems needs to be considered to prepare for the next EV generation. This paper reviews the main modular topologies presented in the literature in the context of EV systems. This paper summarises the most promising topologies in terms of modularised battery connections, propulsion systems focusing on inverters and rectifiers, modular cascaded EV machines, and modular charging systems. [ABSTRACT FROM AUTHOR]

The recent development of battery electric trucks (BETs) suggests that they could play a vital role in transitioning to zero-emission road freight. To facilitate this transition, it is important to understand under which conditions BETs can be a viable alternative to internal combustion engine trucks (ICETs). Concurrently, the advancement of autonomous driving technology adds uncertainty and complexity to analyzing how the cost competitiveness of future zero-emissions trucks, such as autonomous electric trucks (AETs) may develop. This study examines the cost performance of BETs and AETs compared to ICETs, and how it varies over different market and technology conditions, charging strategies, and transport applications. Focus is on heavy-duty tractor-trailer trucks operating full truckload shuttle-flows in Sweden. Due to the inherent uncertainty and interactions among the analyzed factors, the analysis is performed as computational experiments using a simulation model of BET, AET, and ICET shuttle flow operations and associated costs. In total, 19,200 experiments are performed by sampling the model across 1200 scenarios representing various transport applications and technical and economic conditions for sixteen charging strategies with different combinations of depot, destination, and en route charging. The results indicate that both BETs and AETs are cost competitive compared to ICETs in a large share of scenarios. High asset utilization is important for offsetting additional investment costs in vehicles and chargers, highlighting the importance of deploying these vehicles in applications that enable high productivity. The cost performance for BETs is primarily influenced by energy related costs, charging strategy, and charging infrastructure utilization. The AET cost performance is in addition heavily affected by remote operations cost, and costs for the automated driving system. When feasible, relying only on depot charging is in many scenarios the most cost-effective charging strategy, with the primary exceptions being highly energy-demanding scenarios with long distances and heavy goods in which the required battery is too heavy to operate the truck within vehicle weight regulations if not complemented by destination, or en route charging. However, many experiments do not lead to a reduced payload capacity for BETs and AETs compared to ICETs, and a large majority of the considered scenarios are feasible to operate with a BET or AET within current gross vehicle weight regulations. [ABSTRACT FROM AUTHOR]

As a core component of the powertrain system of Electric Logistics Vehicles (ELVs), the gearbox is crucial for ensuring the reliability and stability of ELV operations. Traditional fault diagnosis methods for gearboxes primarily rely on the analysis of vibration signals during operation. This paper presents research on diagnosing gear tooth wear faults in ELV powertrains using motor current signals. Firstly, an experimental test platform was constructed based on the structural principle of the powertrain of ELV models. Subsequently, a pure electric light truck powertrain gearbox with tooth wear was tested. Time–frequency domain analysis, amplitude analysis, ANOVA analysis, kurtosis analysis, and zero−crossing points analysis were used to analyze the U−phase current of the motor connected to the gearbox to study the characteristics of the phase current of the drive motor after tooth wear. The results indicate that while the time–frequency domain characteristics of the U−phase currents are not significantly altered by tooth wear faults, the amplitude, variance, and kurtosis of the current increase with the severity of the wear. Conversely, the number of zero−crossing points decreases. These findings provide valuable insights into new methodologies for diagnosing faults in ELV powertrain systems, potentially enhancing the efficiency and effectiveness of troubleshooting processes. [ABSTRACT FROM AUTHOR]

Purpose: Electrification is a promising solution for decarbonising the road freight transport system, but it is challenging to understand its impact on the system. The purpose of this research is to provide a system-level understanding of how electrification impacts the road freight transport system. The goal is to develop a model that illustrates the system and its dynamics, emphasising the importance of understanding these dynamics in order to comprehend the effects of electrification. Design/methodology/approach: The main methodological contribution of the study is the combination of the multi-layer model with system dynamics methodology. A mixed methods approach is used, including group model building, impact analysis, and literature analysis. Findings: The study presents a conceptual multi-layer dynamic model, illustrating the complex causal relationships between variables in the different layers and how electrification impacts the system. It distinguishes between direct and induced impacts, along with potential policy interventions. Moreover, two causal loop diagrams (CLDs) provide practical insights: one explores factors influencing electric truck attractiveness, and the other illustrates the trade-off between battery size and fast charging infrastructure for electric trucks. Originality/value: The study provides stakeholders, particularly policymakers, with a system-level understanding of the different impacts of electrification and their ripple effects. This understanding is crucial for making strategic decisions and steering the transition towards a sustainable road freight transport system. [ABSTRACT FROM AUTHOR]

This paper investigates the competition between two charge point operators at the same site for future battery electric long-haul trucks. The charge point operators are located along one of the busiest highways in Sweden. The investigation is carried out using an agent-based model where trucks select charge point operators based on charging prices and the length of any queues, while charge point operators adjust their prices and number of chargers to improve their profitability. The study aims to predict conditions for trucks and charge point operators in a future public fast-charging market. Our findings indicate the potential for a well-functioning future public fast-charging market with small queuing problems, high utilisation, and reasonable prices for public fast charging. Assuming a price for electricity of EUR 0.08/kWh and a minimum profit margin of EUR 0.001/kWh for charge point operators, the findings indicate that the price level outside rush hours will be low, approximately EUR 0.1/kWh. The prices during rush hours will likely be much higher, but it is harder to predict the value due to uncertainties of how charge point operators will act in the future market. Still, from the model result, the price during rush hours is suggested to be just above EUR 0.5/kWh, with an average charging price of around EUR 0.15/kWh. It also seems likely that it is profitable for charge point operators to build enough chargers so that charging queues are short. [ABSTRACT FROM AUTHOR]

In response to the EU's stringent zero-carbon emission standards for 2035 and global initiatives to phase out fossil-fuel-powered vehicles, there is an urgent need for innovative solutions in vehicle propulsion systems. While much of the current research focuses on electric passenger cars, commercial vehicles remain relatively underexplored despite their significant potential impact on carbon neutrality goals. This study presents an open-source Simulink model specifically tailored for the analysis of electric commercial trucks, concentrating on the 6.5-ton category. Developed to assess the influence of various power components and control strategies on driving range, the model incorporates three validated powertrain configurations and features such as regenerative braking and one-pedal drive. Simulations are conducted under two real-world driving scenarios in the city of Taipei in Taiwan to evaluate different configurations' effects on energy consumption and efficiency. Results indicate that optimizing the vehicle configuration can reduce power consumption by 26.3% and extend driving range by an additional 25.1 km on a single battery charge. By making the model and its source code publicly available, this research not only fills a critical gap in specialized evaluation tools for electric commercial vehicles but also serves as a valuable resource for both industrial assessments and educational purposes in the field of vehicle electrification. [ABSTRACT FROM AUTHOR]

This article presents an original research methodology that combines insights from patents and academic research, offering a unique perspective on energy recovery technologies for trucks equipped with refrigeration units. The purpose of the study is to perform a functional analysis of existing solutions and to suggest a mechanism for exposing unexplored areas and opportunities for innovation. To achieve this goal, a systematic opportunity scan is presented, investigating patents and conducting a state-of-the-art search of existing technologies. This scan classifies a diverse range of solutions, elucidating their interconnections and providing an overview of the existing technological area, covering system components and technical trends. Thus, the main functions and components are listed, as well as the system requirements. Once the functions have been surveyed, a morphological matrix is proposed, and five main functions are analyzed. This methodology makes it possible to list the majority of the possible solutions for the functions analyzed, taking into account the components observed in the literature review and patents, including new components raised by the research group. Finally, with the morphological matrix structure, it was possible to combine unexplored elements, achieving innovative solutions. [ABSTRACT FROM AUTHOR]