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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.

Electric truck platooning offers a promising solution to extend the range of electric vehicles during long-haul operations. However, optimizing the platoon speed to ensure efficient energy utilization remains a critical challenge. The existing research on implementing data-driven solutions for truck platooning remains limited and implementing first principles solution is still a challenge. However, recognizing the resemblance of truck platoon data to a time series serves as a compelling motivation to explore suitable analytical techniques to address the problem. This paper presents a novel deep learning approach using a sequence-to-sequence encoder-decoder model to obtain the speed profile to be followed by an autonomous electric truck platoon considering various constraints such as the available state of charge (SOC) in the batteries along with other vehicles and road conditions while ensuring that the platoon is string stable. To ensure that the framework is suitable for long-haul highway operation, the model has been trained using various known highway drive cycles. Encoder-decoder models were trained and hyperparameter tuning was performed for the same. Finally, the most suitable model has been chosen for the application. For testing the entire framework, drive cycle/speed prediction corresponding to different desired SOC profiles has been presented. A case study showing the relevance of the proposed framework in predicting the drive cycle on various routes and its impact on taking critical policy decisions during the planning of electric truck platoons has also been presented. This study would help to efficiently plan the feasible routes for electric trucks considering multiple constraints such as battery capacity, expected discharge rate, charging infrastructure availability, route length/travel time, and other on-road operating conditions while also maintaining stability.

Summary: Various options are discussed to de-fossilize heavy-duty vehicles, including battery-electric vehicles (BEVs), electric road systems (ERS), and indirect electrification via hydrogen fuel cells or e-fuels. We investigate their power sector implications in future scenarios of Germany, with high renewable energy shares, using an open-source capacity expansion model and route-based truck traffic data. Power sector costs are the lowest for flexibly charged BEVs that also carry out vehicle-to-grid operations and the highest for e-fuels. If BEVs and ERS-BEVs are not optimally charged, power sector costs increase but are still substantially lower than in scenarios with hydrogen or e-fuels. This is because indirect electrification is less energy efficient, which outweighs potential flexibility benefits. BEVs and ERS-BEVs favor solar photovoltaic energy, while hydrogen and e-fuels favor wind power and increase fossil electricity generation. Results remain qualitatively robust in sensitivity analyses. Science for society: Battery-electric vehicles (BEVs) have emerged in the passenger car sector as the most promising option to de-fossilize transportation. But for heavy-duty vehicles, the technology space is open with alternative options. Aside from BEVs, electric road systems for dynamic power transfer and indirect electrification via hydrogen fuel cells or e-fuels are all alternatives. Although these options differ with respect to technology readiness, market development, and infrastructure requirements, we are interested in their interaction with future power sectors with large shares of variable renewable energy sources. Using an open-source model applied to Germany, we find that direct electrification with smart charging leads to the lowest power sector costs. The difference between pure BEVs and electric road systems is small. The use of hydrogen and e-fuels, by contrast, incurs much higher power sector costs because these options are less energy efficient, which outweighs their potential flexibility benefits.

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.

Many shipping companies tackle the challenge of potentially replacing conventional trucks with electric ones in economically developed countries. The aim of this work is to describe the principle of scaling models for analysing the operation of a fleet of vehicles, when the decision to use a particular type of model is made considering the accuracy and completeness of the initial data, as well as the goals of modelling. At the end of the report, information is provided on the TraPodSim simulation system developed by the authors, which is based on a multi-agent simulation model created using AnyLogic software.

With the continual expansion of urban road networks and global commitments to net zero, electric vehicles (EVs) have been considered to be the most viable solution to decarbonize the transportation sector. In recent years, the electric road system (ERS) has been introduced and piloted in a few countries and regions to decarbonize heavy-duty vehicles. However, little research has been carried out on its reliability. This paper fills the gap and investigates the reliability of electric truck power supply systems for electric road (ETPSS–ER), which considers both the power system and truck traffic networks. First, a brief introduction of electric roads illustrates the working principle of EV charging on roads. Then, an optimized electric truck (ET) travel pattern model is built, based on which the corresponding ET charging load demand, including both static charging and dynamic charging, is conducted. Then, based on the new ET travel pattern model, a daily travel-pattern-driven Monte Carlo simulation-based reliability assessment method for ETPSS–ER system is presented. Case studies based on the IEEE RBTS system shows that ETs driving on ERS systems can meet the daily travel demands. The case studies also examine the impacts of increasing number of ETs, extra wind power, and battery energy storage systems (BESS) on the reliability of ERS power systems.

In addition to passenger vehicles, battery-electric trucks and buses could offer substantial flexibility to the energy system. Using a Bass diffusion model, we extrapolated the unidirectional charging needs and availability of trucks in five of eleven typical applications, as well as city buses, for Germany until 2040. Combined, these heavy-duty vehicles could provide up to 23 GW of down-regulating flexibility potential (i.e., in case of excess power supply) in 2040. The resulting revenues could contribute to reducing electricity costs for depot operators. These results illustrate the need to provide easy and automated market access to heavy-duty vehicle fleets.

Electrification of road transport is crucial to limit global warming. Battery electric vehicles with stationary charging infrastructure have received considerable attention in the scientific literature for both cars and trucks, while dynamic charging via electric road systems (ERS) has received much less attention and their future role in low-carbon road transport is uncertain. Here, we envision three potential scenarios for the future of ERS in European low-carbon transport. We sketch a potential European ERS network and discuss the political, technological, and market steps needed to realize these. We argue that existing field trials, tests, and research projects have collected sufficient evidence to make the next step: Decide and act. Decision-makers will never have perfect information about all aspects of ERS or competing technologies, but the urgency of the climate crisis requires a commitment one way or the other. A clear decision with respect to ERS would send a clear directive and would help focus time, effort, and money on the necessary infrastructure and policies to implement ambitious GHG abatement targets in road transport.

With the advancement of vehicle electrification and intelligence, distributed drive electric trucks have emerged as the preferred choice for heavy-duty electric trucks. However, the control of yaw stability remains a significant issue. To tackle this concern, this study introduces a layered control strategy for yaw moment. Specifically, the upper layer utilizes a yaw moment controller based on linear quadratic regulator (LQR) to compute the additional yaw moment required. Additionally, in order to enhance the performance of the yaw moment controller, the weight matrix in LQR is optimized using a hybrid Genetic Algorithm and Particle Swarm Optimization algorithm (GA-PSO). The lower layer consists of a torque distribution layer, which establishes an objective function for minimizing tire utilization rate. Quadratic Programming algorithm is then employed to compute the optimal torque distribution value, thereby improving the vehicle’s stability. Subsequently, the stability control effects of the vehicle are simulated and compared on the Matlab/Simulink Trucksim joint simulation platform using four control strategies: the proposed control strategy, SMC, LQR, and without yaw moment control. These simulations are conducted under two working conditions: serpentine and double lane change. The results demonstrate that the proposed approach reduces the average yaw rate by 14.4%, 19.6%, and 42.15% while optimizing the average sideslip angle by 25.9%, 24.8%, and 52.3% in comparison to the other three control strategies. Consequently, the proposed control strategy significantly enhances the driving stability of the vehicle. Furthermore, the optimized allocation method reduces the average tire utilization rate by 42.6% in contrast to the average allocation method, thereby improving the stability control margin of the vehicle. These findings successfully validate the efficiency of the yaw stability control strategy presented in this article.

Nowadays, the Heavy-Duty Truck industry is facing one of the most critical questions in the universe. How would it be possible to decrease the CO2 emissions? Performing all possible technological steps in conventional engines is only a “part” of the solution. Electrification is a must for both keeping the environment clean and fulfilling the stringent emission regulations successfully. In this paper, the electrification part of the solution is examined. The main goal is to develop a transmission speed and ratio selection methodology that can be used at the early stages of battery electric truck development, especially for conceptual decisions or hardware selection. The authors are focused on the effect of electric axle (e-axle) transmission speed and ratio selection in 27 Tons Battery Electric (BE) Road Truck application. First of all, a one-dimensional vehicle model is created in GT-Suite software. Then, e-axle, electric motor (e-motor) & battery, regenerative braking model block, electric driveline transmission & differential submodels, and the integrated control algorithm are also implemented. In order to complete a thorough analysis, five different transmission designs (2-speed single, 2 + 1 speed split, 2 + 2 speed split, 3-speed single, 4-speed single) with thousands of gear ratio sets are studied. Satisfying the performance requirements with minimum energy consumption in defined Vecto routes is the vital target of these studies. Dual e-motor studies show that the most effective way of decreasing energy consumption is using e-motors at the same speed (up to 5% benefit) and turning off one (up to 0.6% benefit) below specific torque values if the same e-motors are used. Instead of using complex split transmissions, increasing the number of gears in the single transmission is a prominent configuration because it operates the e-motors at a higher efficient point. In the light of these studies, 3-speed single transmission is capable of providing gradeability requirements with the lowest energy consumption and less system complexity.

The journey towards transportation electrification started with small electric vehicles (i.e., electric cars), which have enjoyed an increasing level of global interest in recent years. Electrification of commercial vehicles (e.g., trucks) seems to be a natural progression of this journey, and many commercial vehicle manufacturers have shifted their focus on medium- and heavy-duty vehicle electrification over the last few years. In this paper, we present a comprehensive review and analysis of the existing works presented in the literature on commercial vehicle charging. The paper starts with a brief discussion on the significance of commercial vehicle electrification, especially heavy- and medium-duty vehicles. The paper then reviews two major charging strategies for commercial vehicles, namely the return-to-base model and the on route charging model. Research challenges related to the return-to-base model are then analysed in detail. Next, different methods to charge commercial vehicles on route during their driving cycles are summarized. The paper then analyzes the challenging issues related to charging commercial vehicles at public charging stations. Future works relevant to these challenges are highlighted. Finally, the possibility of accommodating vehicle to grid technology for commercial vehicles is discussed.

Drone logistics is considered as a disruptive business model reshaping logistics in the next decades. Most prominent potential advantages of drone delivery are cost savings, high speed, and high flexibility. Additionally, drones are also considered as a means of green transportation as they are electric vehicles which are potentially emission-free. To which extent these claimed potentials exist depends on the application scenario as well as the environmental and technological conditions. In this study a stationary drone delivery system is considered where parcels are delivered from a central depot to customers either by drone or electric truck. The minimal total energy consumption for serving all customers is determined when using only an electric truck or a mixed fleet of electric trucks and drones. In a simulation study the effects of structural characteristics (like numbers of customers and customer density) and environmental conditions (like wind speed and traffic conditions) on potential energy savings using drones are estimated. The results indicate that structural characteristics and environmental conditions heavily affect the energy saving potential of drones. In urban settings with high customer density, the energy saving potential is limited to at most 1% while in rural settings drones can help to save 5% of total energy. Under drone-favoring conditions like calm winds and heavy traffic, the energy saving potential can double.

New York City’s food distribution system is among the largest in the United States. Food is transported by trucks from twelve major distribution centers to the city’s point-of-sale locations. Trucks consume large amounts of energy and contribute to large amounts of greenhouse gas emissions. Therefore, there is interest to increase the efficiency of New York City’s food distribution system. The Gowanus district in New York City is undergoing rezoning from an industrial zone to a mix residential and industrial zone. It serves as a living lab to test new initiatives, policies, and new infrastructure for electric vehicles. We analyze the impact of electrification of food-distribution trucks on greenhouse gas emissions and electricity demand in this paper. However, such analysis faces the challenges of accessing available and granular data, modeling of demands and deliveries that incorporate logistics and inventory management of different types of food retail stores, delivery route selection, and delivery schedule to optimize food distribution. We propose a framework to estimate truck routes for food delivery at a district level. We model the schedule of food delivery from a distribution center to retail stores as a vehicle routing problem using an optimization solver. Our case study shows that diesel trucks consume 300% more energy than electric trucks and generate 40% more greenhouse gases than diesel trucks for food distribution in the Gowanus district.

Recently, due to the industrial and e-commerce revolution, the freight transport sector has grown rapidly and has become one of the key factors for economic development. Coupled with the growth of this sector, significant energy and environmental problems have arisen. Therefore, a huge effort has been made around the world in order to develop some solutions that could mitigate these problems. One of these promising solutions is electrifying the urban freight transport sector including the trucks and freight commercial vehicles fleets. In Palestine, as in other developing countries, the implications of this solution are still unquantified and the efficiency of the application is unpredicted. Therefore, this has necessitated a comprehensive study that considers all the relevant factors, such as the source of the electricity (renewable energy, oils, natural gas, etc.), fuel rates, and electricity rates. In this study, a prediction model has been developed using the historical data for the number of trucks and freight commercial vehicles over the period 2006–2020. Next, the total travelled kilometers, the total fuel consumption, and the total electricity consumption by the urban freight transport sector have been determined based on two scenarios (5% and 20% penetration of electric freight transport modes) during the next 10 years. Finally, the amounts of reduction in GHG emissions and the energy cost have been determined. The results have shown a significant reduction in the amount of GHG emission during the next 10 years by electrifying 20% of the freight transport sector in Westbank, Palestine. Moreover, an annual reduction of 66 million U.S $ could be achieved in total energy costs in this region.

Son yıllarda lojistik sektörü kaynaklı çevresel problemlerin önüne geçilmesi ve enerji tüketim miktarının azaltılmasına yönelik çalışmalar ağırlık kazanmıştır. Elektrikli araç kullanımı, lojistik sektöründe bu amaçla uygulanabilecek yenilikçi yaklaşımlardan bir tanesidir ve yaygın olarak çalışılan konulardan biridir. Bu çalışma kapsamında lojistik sektöründe elektrikli kamyonların kullanılması durumunda rota optimizasyonu yapan bir matematiksel model geliştirilmesi amaçlanmıştır. Elektrikli araç rotalama problemi, açık uçlu araç rotalama konsepti ile birlikte dikkate alınmıştır. Problemin çözümü için karışık tamsayılı programlama modeli önerilmiştir ve probleme, toplam mesafe minimizasyonu, kullanılan toplam araç sayısı minimizasyonu ve harcanan toplam enerji miktarı minimizasyonu olmak üzere üç farklı amaç fonksiyonu tanımlanmıştır. Önerilen model, örnek problemler üzerinde test edilerek doğrulanmış ve farklı amaç fonksiyonları için karşılaştırmalı sonuçlar elde edilmiştir. Ulaşılan sonuçlar, önerilen model ile küçük boyutlu gerçek hayat uygulamalarında kısa işlem süreleri ile optimum sonuçların elde edilebileceğini göstermiştir. Elektrikli araçların rota optimizasyonu yapılarak lojistik kaynaklı çevre kirlenmesinin azaltılacağı ve enerji verimliliği sağlanacağı öngörülmektedir.

The world industry is on a threshold of a new development cycle which is characterized by emergence of breakthrough technologies: distributed ledger technology, drone-type devices, 3D printing, virtual reality, Internet of things, augmented reality etc. Due to these technologies fundamental changes will take place in the majority of economic branches. The automotive industry is not an exception. This industry plays one of the key roles in economy in a great number of countries and changes occurring in it lead not only to creation of new products, technical processes but these changes also entail improvement of the industrial base which has been already created. The Republic of Belarus considers an automotive industry as one of key industries for its economy and its efficiency has a high dependency factor pertaining to speed of innovation emergence and implementation that is strategically important aspect of increase in competitiveness both on current sales markets and on potential ones. The paper pays a special attention to two main trends arising in the course of forming a new industrialization: increase in environmentally-friendly fuel systems and digital transformation. Problems of world ecology have exerted a considerable impact on developments in the field of alternative energy sources. Speed of development and promotion of electric vehicles considerably exceed “breakthrough” innovation developments of the past. However a great number of factors can have a significant effect on the future of electric vehicles and two of them are thoroughly considered in the paper: cost of energy (batteries) and price of oil (at the price of about 20 USD for barrel the development rate will considerably decrease). Besides the matter of environmental friendliness the paper pays attention to services of digital transformation. The world has already presented examples pertaining to usage of remote control for dump trucks, telematics etc. Such an approach makes it possible for organizations to optimize substantially an expenditure budget and doing so we increase resource use efficiency. The paper also contains a review of innovations in the automotive industry of the Republic of Belarus: BelGee electric cars, Belkommunmash electrobuses, developments of large industrial equipment.

Increase of non-renewable energy consumption and CO2 emissions has brought heavy burdens to our planet. Heavy-duty vehicles as a large energy consumer benefit a lot from platooning due to reduced air drag. Comparing to ICE trucks, electric trucks can gain more energy savings from platooning while also reducing CO2 emissions. This paper explores the energy consumption of electric HDV platooning under highway situations and proposes an adaptive downhill coasting speed method with regenerative braking. Simulations show that electric HDV platooning can reach at most 33.4% energy savings with our proposed adaptive coasting speed profile. With a sacrifice of 22.2% travel time, the energy savings can be further increased to 49.3%.

Full electrification of the transport sector is a necessity to combat climate change and a pressing societal issue: climate agreements require a fuel shift of all the modes of transport, but while uptake of passenger electric vehicles is increasing, long haul trucks rely almost completely on fossil fuels. Providing highways with proper charging infrastructure for future electric mobility demand is a problem that is not fully investigated in literature: in fact, previous work has not addressed grid planning and infrastructure design for both passenger vehicles and trucks on highways. In this work, the authors develop a methodology to design the electrical infrastructure that supplies static and dynamic charging for both modes of transport. An algorithm is developed that selects substations for the partial electrification of a highway and, finally, the design of the electrical infrastructure to be implemented is produced and described, assessing conductors and substations sizing, in order to respect voltage regulations. The system topology of a real highway (E18 in Norway) and its traffic demand is analyzed, together with medium-voltage substations present in the area.

The imperative to decarbonize long-haul, heavy-duty trucking for mitigating both global climate change as well as air pollution is clear. Given recent developments in battery and ultra-fast charging technology, some of the prominent barriers to electrification of trucking are dissolving rapidly. Here we shed light on a significant yet less-understood barrier, which is the general approach to retail electricity pricing. We show that this is a near term pathway to $0.06/kWh charging costs that will make electric trucking substantially cheaper than diesel. This pathway includes (i) reforming demand charges to reflect true, time-varying system costs; (ii) avoiding charging during a few specific periods (