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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]

To tackle the energy-saving optimization issue of plug-in hybrid electric trucks traversing multiple traffic light intersections continuously, this paper presents a double-layer energy management strategy that utilizes the dynamic programming–twin delayed deep deterministic policy gradient (DP-TD3) algorithm to synergistically optimize the speed planning and energy management of plug-in hybrid electric trucks, thereby enhancing the vehicle's passability through traffic light intersections and fuel economy. In the upper layer, the dynamic programming (DP) algorithm is employed to create a speed-planning model. This model effectively converts the nonlinear constraints related to the position, phase, and timing information of each traffic signal on the road into time-varying constraints, thereby improving computational efficiency. In the lower layer, an energy management model is constructed using the twin delayed deep deterministic policy gradient (TD3) algorithm to achieve optimal allocation of demanded power through the interaction of the TD3 agent with the truck environment. The model's validity is confirmed through testing on a hardware-in-the-loop test machine, followed by simulation experiments. The results demonstrate that the DP-TD3 method proposed in this paper effectively enhances fuel economy, achieving an average fuel saving of 14.61% compared to the dynamic programming–charge depletion/charge sustenance (DP-CD/CS) method. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

Several technological challenges delay the adoption of electrified powertrains in the heavy-duty transport sector. For fuel-cell hybrid electric trucks, key issues include slow cold start, reduced cooling power during high ambient temperatures, and uncertainties regarding durability. In addition, the engineers must handle the complexity of the system. In this article, a Matlab/Simulink library is introduced, which has been developed to aid engineers in the design and optimization of energy management systems and strategies of this complex system that consider mechanical, electrochemical, and thermal energy flows. The library is introduced through five example vehicle models, and through case studies that highlight the various kinds of analysis that can be performed using the provided models. All library code is open source, open for commercial use, and runs in Matlab/Simulink without any need for external libraries. [ABSTRACT FROM AUTHOR]

Mining production, being one of the most energy-intensive industries globally, consumes substantial amounts of fossil fuels and contributes to extensive carbon emissions worldwide. The trend toward electrification and advanced developments in battery technology have shifted attention from diesel power to battery alternatives. These alternatives are appealing, as they contribute to decarbonisation efforts when compared to conventional diesel trucks. This paper presents a comprehensive review of recent technological advancements in powertrains for Mining Haulage Truck (MHT). It also compares these configurations based on mining system-level considerations to assess their future potential. The evaluated configurations include Diesel-Electric Truck (DET), Trolley Assist Truck (TAT), Battery-only Truck (BOT), Battery Trolley with Dynamic charging truck (BT-D), and Battery Trolley with Stationary charging truck (BT-S). According to the analysis, the energy demand for on-board diesel or battery power (excluding trolley power) in these alternative options is as follows: DET—681 kWh, BOT—645 kWh, TAT—511 kWh, BT-S—471 kWh, and BT-D—466 kWh. The paper also illustrates the theory of battery size design based on the current battery technology, battery material selection, battery package design, and battery size selection methods. In the case of tailored battery size selection, BOT, BT-D, and BT-S configurations require LiFePO4 (LFP) battery masses of 25 tonnes, 18 tonnes, and 18 tonnes, respectively. Based on a techno-economic assessment of battery MHT alternatives with a future perspective, it has been determined that BT-D requires the lowest amount of on-board battery energy. Furthermore, over a span of 20 years, BT-S has demonstrated the lowest on-board battery cost. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid electric trucks have become a cutting-edge field in understanding urban traffic emissions due to their enormous potential in low-carbon areas. In order to improve the economy of fuel cell hybrid electric trucks and reduce the decline of fuel cell lifespan, this paper proposes a multi-objective energy management strategy that optimizes weight coefficients. On the basis of establishing a fuel cell battery hybrid system model, three modes of uniform speed, acceleration, and deceleration were identified through clustering analysis of vehicle speed. Reinforcement learning algorithms were used to learn the corresponding weights for different modes, which reduced the decline in fuel cell life while improving the economic efficiency. The simulation results indicate that, under the conditions of no load, half load, and full load, the truck only sacrificed 0.9–5.6%, 1.7–2.6%, and 1.2–1.6% SOC, saving 5.7–6.45%, 5.9–6.67%, and 6.1–6.67% in lifespan loss, and reducing hydrogen consumption by 3.0–7.1%, 2.8–4.4%, and 1.0–3.0%, respectively. [ABSTRACT FROM AUTHOR]

The research provides a comparative theoretical investigation of the operational characteristics of an electric semi-truck and vehicles powered by conventional combustion engines using diesel fuel, hydrotreated vegetable oil (HVO), and methane (including biomethane) in the dual fuel configuration. The Volvo tractor units that are offered for retail in 2024, namely the Volvo FH Electric, Volvo FH500 in dual fuel configuration, and Volvo FH500TC Diesel Euro VI, were chosen for comparison. The considerations encompassed include the road tractor's mass, energy usage, power-to-weight ratio, dynamics, ability to recharge or refuel, payload restrictions, impact on logistics expenses, compliance with regulations on drivers' working hours, and a report on carbon dioxide emissions. The study concludes by discussing and drawing conclusions on the competitiveness of different drive types in truck tractors, specifically in relation to identifying the most suitable areas of application. Synthetic conclusions demonstrate the high effectiveness of the electric drive in urban and suburban conditions. However, vehicles equipped with internal combustion engines using renewable fuels fill the gap in energy-intensive drives in long-distance transport. [ABSTRACT FROM AUTHOR]

Battery electric trucks (BETs) represent a well-suited option for decarbonizing road freight transport to achieve climate targets in the European Union. However, lower ranges than the daily distance of up to 700 km make charging stops mandatory. This paper presents an online algorithm for optimal dynamic charging strategies for long-haul BET based on a dynamic programming approach. In several case studies, we investigate the advantages optimal strategies can bring compared to driver decisions. We further show which charging infrastructure characteristics in terms of charging power, density, and charging station availability should be achieved for BETs in long-haul applications to keep the additional time required for charging stops low. In doing so, we consider the dynamic handling of occupied charging stations for the first time in the context of BET. Our findings show that, compared to driver decisions, optimal charging strategies can reduce the time loss by half compared to diesel trucks. To keep the time loss compared to a diesel truck below 30 min a day, a BET with a 500 kWh battery would need a charging point every 50 km on average, a distributed charging power between 700 and 1500 kW, and an average charger availability above 75%. The presented method and the case studies' results' plausibility are interpreted within a comprehensive sensitivity analysis and subsequently discussed in detail. Finally, we transformed our findings into concrete recommendations for action for the efficient rollout of BETs in long-haul applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With growing demands to save greenhouse gases, the rapid market introduction of battery-electric trucks (BETs) will become increasingly important, with truck manufacturers announcing various models entering the market in the near future. Soon, truck operators will be faced with deciding which battery capacity and cell chemistry to choose in their next purchase. In this study, we evaluate the choice of battery capacity, regarding feasibility and cost-effectiveness, for trucks using NMC and LFP cell chemistry. Our results show that higher energy density allows larger NMC batteries to be installed, resulting in the ability to transport higher payloads at low charging powers. The LFP chemistry has to rely on higher charging powers of up to 700 kW to transport the same payloads. When asked to choose a battery capacity for the individual use case, the smallest battery size should always be selected when only charging powers up to 300 kW are available. However, the reduction in publicly charged energy can lead to cost advantages of larger battery capacities at higher charging powers. When deciding between the two cell chemistries, the LFP chemistry shows advantages in most cases. Only at high payloads and low charging powers the NMC chemistry shows cost advantages. [ABSTRACT FROM AUTHOR]

Polymer electrolyte membrane fuel cells (PEMFCs) are employed in trucks and large commercial vehicles utilizing hydrogen as fuel due to their rapid start-up characteristics and responsiveness. However, addressing the requirement for high power output in the low-current section presents a challenge. To solve this issue, a multi-stack can be applied using two stacks. Furthermore, thermal management, which significantly affects the performance of the stacks, is essential. Therefore, in this study, a hydrogen electric truck system model was developed based on a Hyundai Xcient hydrogen electric truck model using MATLAB/SimscapeTM 2022b. In addition, the system's performance and thermal characteristics were evaluated and analyzed under different road environments and wind conditions while driving in Korea. [ABSTRACT FROM AUTHOR]

Mitigating CO2 emissions from long-haul commercial trucking is a major challenge that must be addressed to achieve substantial reductions in greenhouse gas (GHG) emissions from the transportation sector. Extensive recent research and development programs have shown how significant near-term reductions in GHGs from commercial vehicles can be achieved by combining technological advances. This paper reviews progress in technology for engine efficiency improvements, vehicle resistance and drag reductions, and the introduction of hybrid electric powertrains in long-haul trucks. The results of vehicle demonstration projects by major vehicle manufacturers have shown peak brake thermal efficiency of 55% in heavy-duty diesel engines and have demonstrated freight efficiency improvements of 150% relative to a 2009 baseline in North America. These improvements have been achieved by combining multiple incremental improvements in both engine and vehicle technologies. Powertrain electrification through hybridization has been shown to offer some potential reductions in fuel consumption. These potential benefits depend on the vehicle use, the details of the powertrain design, and the duty cycle. To date, most papers have focused on standard drive cycles, leaving a research gap in how hybrid electric powertrains would be designed to minimize fuel consumption over real-world drive cycles, which are essential for a reliable powertrain design. The results of this paper suggest that there is no "one-size-fits-all" solution to reduce the GHGs in long-haul trucking, and a combination of technologies is required to provide an optimum solution for each application. [ABSTRACT FROM AUTHOR]

To fulfil the CO2 emission reduction targets of the European Union (EU), heavy-duty (HD) trucks need to operate 15% more efficiently by 2025 and 30% by 2030. Their electrification is necessary as conventional HD trucks are already optimized for the long-haul application. The resulting hybrid electric vehicle (HEV) truck gains most of the fuel saving potential by the recuperation of potential energy and its consecutive utilization. The key to utilizing the full potential of HEV-HD trucks is to maximize the amount of recuperated energy and ensure its intelligent usage while keeping the operating point of the internal combustion engine as efficient as possible. To achieve this goal, an intelligent energy management strategy (EMS) based on ECMS is developed for a parallel HEV-HD truck which uses predictive discharge of the battery and adaptive operating strategy regarding the height profile and the vehicle mass. The presented EMS can reproduce the global optimal operating strategy over long phases and lead to a fuel saving potential of up to 2% compared with a heuristic strategy. Furthermore, the fuel saving potential is correlated with the investigated boundary conditions to deepen the understanding of the impact of intelligent EMS for HEV-HD trucks. [ABSTRACT FROM AUTHOR]

This paper investigates the charging needs and charger utilisation of a system of battery electric trucks and their public fast chargers, along the highway between the Swedish cities of Helsingborg and Stockholm, in a possible future scenario in which all trucks are battery electric. The system is investigated via an agent-based model which simulates a typical day with current levels of truck traffic on the road. The traffic flow is based on hourly truck flow data during a day and the annual flow of trucks. The findings indicate major potential for a well-functioning system of public chargers for trucks, with high utilisation, few queuing problems at charging stations, cheap public fast charging and a robust response to queues caused by peaks or increases in the traffic flow. The number of 900 kW chargers along the road needed to achieve this has been estimated at 140, and the utilisation factor for the chargers is predicted to be 30%. [ABSTRACT FROM AUTHOR]

This study presents an analysis of the charging infrastructure requirements for autonomous electric trucks (AETs) in a specified geographical region, focusing on the state of Texas as a case study. A discrete-time, agent-based model is used to simulate the AET fleet and consider various model parameters such as trip distance/duration, the number of trips, and charging speeds. The framework incorporates unique properties of the Texas road network to assess the sensitivity of charging infrastructure needs. By synergizing electrification and automation, AETs offer benefits such as reduced carbon emissions, enhanced transportation safety, decreased congestion, and improved operational costs for fleets. By simulating daily trips and energy consumption patterns, an analysis of the charging infrastructure needs for cities along the Texas highway triangle formed by I-35, I-45 and I-10 revealed that the total charging energy and average charging power for these major cities ranges between 443~533 MWh/day and 18.5~22 MW, with costs in the range of USD $7.74~$15.93 million for each city, depending on charging infrastructure design and exclusive of any enhancements to the distribution grid infrastructure needed to support the charging infrastructure. This data-driven approach may be replicated for other regions by adapting the simulation parameters to allow policymakers and stakeholders to assess the charging infrastructure requirements and related investments needed to support the transition to electric and autonomous heavy-duty trucking. [ABSTRACT FROM AUTHOR]

This paper presents an equivalent consumption minimization strategy (ECMS) based on model predictive control for series hybrid electric mine trucks (SHE-MTs), the objective of which is to minimize fuel consumption. Two critical works are presented to achieve the goal. Firstly, to gain the real-time speed trajectory on-line, a speed prediction model is established by utilizing a recurrent neural network (RNN). Specifically, a hybrid optimization algorithm based on the genetic algorithm (GA) and the particle swarm optimization algorithm (PSOA) is used to enhance the prediction precision of the speed prediction model. Then, on this basis, an ECMS based on MPC (ECMS-MPC) is proposed. In this process, to improve the real-time and working condition adaptability of the ECMS-MPC, the power-optimal fuel consumption mapping model of the range extender is established, and the equivalent factor (EF) is real-time adjusted by means of the PSOA. Finally, taking a cement mining road as the research object, the proposed strategy is simulated with the collected actual vehicle data. The experimental results indicate that the prediction precision of the proposed speed prediction model is over 98%, realizing on-line speed prediction effectively. Furthermore, compared to the existing real-time EMSs, its fuel-saving rate had an increase of more than 13%. This indicates that the designed ECMS-MPC is able to offer a novel and effective method for the on-line energy management of the SHE-MTs. [ABSTRACT FROM AUTHOR]

Because of the rising demand for CO2 emission limits and the high cost of fuel, the electrification of heavy-duty vehicles has become a hot topic. Manufacturers have tried a variety of designs to entice customers, but the outcomes vary depending on the application and availability of recharging. Without affecting vehicle range, plug-in hybrids provide a potential for the automobile industry to reach its CO2 reduction objectives. However, the actual CO2 emission reductions will largely rely on the energy source, user behavior, and vehicle design. This research compares a series plug-in hybrid medium-duty truck against two baselines: nonhybrid and pure electric commercial trucks. As well as evaluating and contrasting the different tools to quantify CO2 emissions, this manuscript offers fresh information on how to simulate various powertrain components used in electrified vehicles. According to the findings, plug-in hybrids with batteries larger than 50 kWh can reduce emissions by 30%, while still meeting the 2030 well-to-wheel CO2 regulations. The recommended battery size for plug-in hybrid is 100 kWh, and for electric vehicles it is 320 kWh. The range of a plug-in hybrid is 18% longer than that of nonhybrid, 6% longer than that of a full hybrid, and 76% longer than that of a pure electric powertrain with a fully charged battery. [ABSTRACT FROM AUTHOR]

This paper investigates the economic consequences of a haulage company replacing its line-haul diesel trucks with battery-electric ones. It also examines how large truck batteries should be, whether the haulage companies should use public fast chargers to complement their own, and whether public fast chargers have the potential to be profitable. The potential extra cost of losing payload capacity is estimated and there is an investigation of whether a charge-point operator should meet the peak demand for charging. The case under analysis is designed to represent a typical line-haul service between terminals in a major logistics system, with the finding that, in this case, a transition to battery-electric trucks seems cost effective for the company. Moreover, it is advisable for the company to use public fast chargers and these will likely become profitable given that the utilisation factor of the investigated public fast chargers may realistically exceed 20%. [ABSTRACT FROM AUTHOR]

One possible step for reducing humans' use of fossil fuel due to transport tasks is to replace diesel trucks with battery electric ones. This paper introduces the energy distribution diagram, which makes it easy to visualise the trucks' daily energy consumption over their full service life. The energy distribution is used to investigate which driving patterns are suitable for cost-effective battery electric trucks when compared to commercial diesel trucks. It is shown that the battery capacity that results in the lowest cost per kilowatt-hour propulsion energy depends on the driving pattern, and an algorithm for selecting the most cost-effective capacity is presented. In many instances, it was found that battery electric trucks competed favourably with diesel trucks, especially when the trucks had low variations in daily energy consumption. It is beneficial to determine the circumstances under which they may be cheaper, as this will facilitate the transition to battery electric trucks in segments with a reduced overall cost of ownership. [ABSTRACT FROM AUTHOR]

The decarbonization of the transport sector, and thus of road-based transport logistics, through electrification, is essential to achieve European climate targets. Battery electric trucks offer the greatest well-to-wheel potential for CO2 saving. At the same time, however, they are subject to restrictions due to charging events because of their limited range compared to conventional trucks. These restrictions can be kept to a minimum through optimal charging stop strategies. In this paper, we quantify these restrictions and show the potential of optimal strategies. The modeling of an optimal charging stop strategy is described mathematically as an optimization problem and solved by a genetic algorithm. The results show that in the case of long-distance transport using trucks with battery capacities lower than 750 kWh, a time loss is to be expected. However, this can be kept below 20 min for most battery capacities by optimal charging stops and sufficient charging infrastructure. [ABSTRACT FROM AUTHOR]

The energy storage system of electric-drive heavy mining trucks takes on a critical significance in the characteristics including excellent load capacity, economy, and high efficiency. However, the existing battery-based system does not apply to harsh cold environments, which is the common working condition for the above trucks. A type of cycle hydro-pneumatic energy storage system for the trucks was proposed in this study. The dynamic model of the system, including the dynamic and thermodynamic models of hydraulic and pneumatic parts, was built to analyze the performance of the system. Subsequently, the thermodynamic characteristics were clarified during the energy storage and released through the real test condition-based simulation. The power and energy performances of the system were studied in practice based on the above characteristics. The analysis of the results showed that the system reduced 22.03% driving power at the optimal braking energy recovery rate, the energy density was nearly 12.6 MJ/m3, the maximum input power was higher than 230 kW, and the cycle efficiency was about 40.6%. The results of this study will be conducive to the application of the hydro-pneumatic energy storage system for the electric-drive mining trucks and reducing the resulting carbon emission. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have become increasingly popular because they are highly efficient and sustainable. However, EVs have intensive electric loads. Their penetrations into the power system pose significant challenges to the operation and control of the power distribution system, such as a voltage drop or transformer overloading. Therefore, grid operators need to prepare for high-level EV penetration into the power system. This study proposes data-driven, parameterized, individual, and aggregated EV charging models to predict EV charging loads in the urban residential sector. Actual EV charging profiles in Saskatchewan, Canada, were analyzed to understand the characteristics of EV charging. A location-based algorithm was developed to identify residential EV charging from raw data. The residential EV charging data were then used to tune the EV charging model parameters, including battery capacity, charging power level, start charging time, daily EV charging energy, and the initial state of charge (SOC). These parameters were modeled by random variables using statistic methods, such as the Burr distribution, the uniform distribution, and the inverse transformation methods. The Monte Carlo method was used for EV charging aggregation. The simulation results show that the proposed models are valid, accurate, and robust. The EV charging models can predict the EV charging loads in various future scenarios, such as different EV numbers, initial SOC, charging levels, and EV types (e.g., electric trucks). The EV charging models can be embedded into load flow studies to evaluate the impact of EV penetration on the power distribution systems, e.g., sustained under voltage, line loss, and transformer overloading. Although the proposed EV charging models are based on Saskatchewan's situation, the model parameters can be tuned using other actual data so that the proposed model can be widely applied in different cities or countries. [ABSTRACT FROM AUTHOR]

Transport of ore and overburden in open-pit mines is often performed with large haul trucks. Battery-electric operation of haul trucks can significantly reduce the CO 2 emissions and the fossil fuel dependence of open-pit mining. This simulation study investigates the feasibility and economy of operating large haul trucks battery-electrically, charging from an electric road system in the form of overhead trolley lines. The used simulation model is verified against measurements on diesel-electric haul trucks in normal operation. The simulation model is then tested with five drive cycles representing typical operations in the Aitik copper mine in northern Sweden. This simulation study shows that battery-electric operation with an electric road is feasible. It is shown that battery-electric operation is much cheaper than diesel-electric operation under reasonable assumptions. It is also much cheaper than diesel-electric operation assisted by an electric road. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Loaders are widely used in the construction of earthworks for construction projects. Due to the large volume and mass of these machines, they have shortcomings such as low driving efficiency and high energy consumption. To address these shortcomings, this work applied eclectic drive technology to a loader's traction system. A wheel electric drive system with a switched reluctance driver (SRD) was developed. The operating principle and basic structure of the SRD system were analyzed. A new voltage PWM-controlled strategy with dynamic adjustable turn-on and turn-off angles and a single conducting phase in a fixed period was developed. Then, an SRD simulation model was established in MATLAB/Simulink. A simulation of the working condition for the loader was performed. A test bench for the SRD was built and accordingly the transportation and operation conditions were tested. The corresponding speed–time curve of transportation and operation was obtained. Simulation and experiment results showed that the electric drive system with SRD had excellent responses to changes in torque and speed and adapted well to the various working conditions of the loader. The system could effectively traction the loader and verified the feasibility and applicability of the wheel electric drive system. [ABSTRACT FROM AUTHOR]

Electric retrofitting (e-retrofitting) is a viable option for accelerating the renewal of heavy-duty vehicle fleets to reduce the related emissions. We introduce a simulation-based assessment of e-retrofitting strategies for heavy-duty vehicles. Our simulation tool, an electric vehicle fleet simulation toolbox, comprises three modules, namely driving cycles, vehicle dynamics, and vehicle profiles. The first allows for the creation of realistic driving cycles based on GPS data from real routes. The vehicle dynamics and vehicle profiles incorporate, e.g., the modelling of the powertrain and driving conditions. Ten realistic driving cycles were created and used for investigating and comparing three different powertrain alternatives, including the original diesel powertrain, electric with a single-speed transmission and electric with a multi-speed transmission. The vehicles were simulated in two different heavy-load scenarios. First, driving with a cargo load represented by the maximum vehicle weight and second, driving with snow ploughing. We found that the multi-speed transmission in an electric heavy-duty truck significantly improved its traction performance and gradeability. On the other hand, the effect on the electric powertrain efficiency, and thereby on the energy consumption, remained rather minor. Considering the given workload scenarios, our results advocate employing rather than omitting the gearbox in the e-retrofit truck process. [ABSTRACT FROM AUTHOR]

Energy management strategies have a significant impact on the hydrogen economy of fuel cell trucks and the lifetime of battery and fuel cell systems. This contribution presents the design and optimal calibration of an energy management strategy that is adaptive to the battery and ambient temperatures. Indeed, fuel cell trucks face critical operating conditions due to high ambient temperatures or high loads on long uphill roads. However, the presented adaptive energy management strategy shifts the electric loads to the fuel cell system to limit the battery usage, avoiding accelerated degradation due to battery temperature peaks without hindering the hydrogen economy. The strategy design and calibration involves a multi-objective optimization of performance indicators related to hydrogen consumption, fuel cell degradation, battery thermal state, equivalent charge/discharge cycles, and charge control. This work uses AVL CAMEO to systematically vary the adaptive curve parameters to explore the trade-off between the key performance indicators. The calibration considers real-world driving cycles of road freight vehicles, including measured speed, road elevation, and variable vehicle mass. Moreover, the energy management design is robust because the performance indicators are evaluated over 8935 km, covering an extensive range of real-world driving scenarios. Eventually, the adaptive and predictive energy management strategy proposed in this work can meet all the performance targets thanks to the optimal calibration, and it is particularly effective in avoiding battery temperature peaks. [ABSTRACT FROM AUTHOR]

This paper proposes a 300 kW 24-slot/10-pole 6-phase stator-shifted fractional-slot concentrated winding spoke-type ferrite permanent magnet machine for electric truck applications. The proposed motor consists of a stator with dual three-phase windings positioned 75 degrees apart to reduce higher-order MMF harmonic order, and a rotor with an inexpensive and high-resistance ferrite permanent magnet in the spoke configuration. The simulated result of the stator-shifted machine is compared with a fabricated stator-shifted machine, and the results show good agreement with each other. To further reduce the torque ripple from 2.5 to 0.9% while maintaining a high maximum torque of 2980 Nm, circular voids with a diameter of 11 mm are embedded in the rotor. The proposed motor is evaluated for irreversible demagnetization, mechanical and thermal stability, and fault tolerant ability. To assess the proposed motor performance, the electric truck simulation model is constructed using MATLAB/Simulink and used to compare with the reported 12-slot/10-pole rare-earth permanent magnet-based machine. Compared to a previously reported six-phase rare-earth permanent magnet based flat-type machine, the proposed motor can save 4.3 kWh of energy with a USD 2512 lower cost while retaining a similar motor performance. [ABSTRACT FROM AUTHOR]

This paper presents an initial investment cost analysis of public transportation systems operating with wireless charging electric vehicles (EVs). There are three different types of wireless charging systems, namely, stationary wireless charging (SWC), in which charging happens only when the vehicle is parked or idle, quasi-dynamic wireless charging (QWC), in which power is transferred when a vehicle is moving slowly or in stop-and-go mode, and dynamic wireless charging (DWC), in which power can be supplied even when the vehicle is in motion. This analysis compares the initial investment costs for these three types of charging systems for a wireless charging-based public transportation system. In particular, this analysis is focused on the energy logistics cost in transportation, which is defined as the cost of transferring and storing the energy needed to operate the transportation system. Performing this initial investment analysis is complicated, because it involves considerable tradeoffs between the costs of batteries in the EV fleet and different kinds of battery-charging infrastructure. Mathematical optimization models for each type of EV and infrastructure system are used to analyze the initial costs. The optimization methods evaluate the minimum initial investment needed to deploy the public transportation system for each type of EV charging solution. To deal with the variable cost estimates for batteries and infrastructure equipment in the current market, a cost-sensitivity analysis is performed. The goal of this analysis is to identify the market cost conditions that are most favorable for each type of wireless charging solution. Furthermore, the cost analysis quantitatively verifies the qualitative comparison of the three different wireless charging types conducted in the previous research. [ABSTRACT FROM AUTHOR]

This article is designed to demonstrate that electric roads are an affordable way to electrify all forms of road transport—not only cars, but also buses and trucks. Electric roads represent a way to power electric vehicles without relying solely on batteries. The idea is that when an electric vehicle reaches an electric road, it stops using power from the battery and instead uses power directly from the road itself. The primary challenge for electric vehicles is still the perception of a compromised quality of life in owning an electric vehicle due to a limited range compared with petrol and diesel cars, today. This paper introduces a new technology, currently experiencing rapid development, that can not only overcome range anxiety but make electric vehicles better, in terms of range, than petrol and diesel cars today. Furthermore, not only can this research help to arrange this, but it can also help, for the first time, to cost-effectively electrify heavy-duty transport, such as trucks and buses, which would be a huge breakthrough in terms of sustainability, as it is very important to start supplying electricity to heavy-duty vehicles. The case study provides a very hypothetical example of a trip with and without an electric road, covering a total of 26,011 km of highways and main roads. The results indicate that building electric roads is cheaper than many other alternatives. If a large battery is replaced with a smaller battery for each new vehicle sold, after 3 years, enough savings will be made to electrify all highways and main roads in Turkey. This paper can help transport operators and policymakers develop strategies to accelerate the adoption of electric vehicles by appropriately implementing electric road infrastructure. [ABSTRACT FROM AUTHOR]

Real-time simulations refer to the simulations of a physical system where model equations for one time-step are solved within the same time period as in reality. An FPGA/CPU-based real-time simulation platform is presented in this paper, with a full-electric vehicle model implemented in a central processing unit (CPU) board and an electric drive model implemented in a field programmable gate arrays (FPGA) board. It has been a challenge to interface two models solved with two different processors. In this paper, one open-loop and three closed-loop interfaces are proposed. Real-time simulation results show that the best method is to transmit electric machine speed from the vehicle model to the electric derive model, with feedback electric machine torque calculated in FPGA. In addition, a virtual vehicle testing tool (CarMaker) is used when building the vehicle model, achieving more accurate modeling of vehicle subsystems. The presented platform can be used to verify advanced vehicle control functions during hardware-in-the-loop (HIL) testing. Vehicle anti-slip control is used as an example here. Finally, experiments were performed by connecting the real-time platform with a back-to-back electric machine test bench. Results of torque, rotor speed, and d&q axis currents are all in good agreement between simulations and experiments. [ABSTRACT FROM AUTHOR]

Nowadays, robust and efficient solid waste collection is crucial to motivate citizens to participate in the circular economy by sorting recyclable solid waste. Vocational vehicles, including garbage trucks, contribute significantly to CO 2 emissions; therefore, it is strongly recommended, and in the European Union it is mandatory, to replace conventional-fuel-based garbage trucks with electric ones. For providing sustainable and energy-efficient solid waste collection with a heterogeneous fleet, in-depth mathematical computations are needed to support solving complex decision-making problems, including crew rostering and vehicle routing, because the distance and capacity of electric garbage trucks differ from conventional-fuel-based ones. However, the literature on solid waste collection using electric garbage trucks is still relatively scarce. The main contribution of this paper is developing an optimization problem for balancing travel distance assigned to each garbage truck of a heterogeneous fleet. The problem is based on specific requirements of the Municipal Solid Waste Management in Cracow, Poland, where the working time of routes is balanced and the total time of collection service can be minimized. For the problem, an MIP program was developed to generate optimal crew schedules, so that the hitherto network of segregated solid waste pickup nodes can be served using a heterogeneous fleet in which the share of electric garbage trucks is up to 30%. We study the impact of the changed composition of the fleet on modifications in crew rostering due to the shorter range of an electric vehicle compared to a conventional-fuel-based one. [ABSTRACT FROM AUTHOR]

This work investigates the fuel energy and emission reductions possible with the hybridization of a Class 8 tractor-trailer. The truck tractor has two drive axles: one powered by an internal-combustion-engine-based powertrain (CP) and the other powered by an electric powertrain (EP) consisting of an electric drive system supplied by a battery pack, resulting in a through-the-road hybrid. The EP has two modes of operation depending on the direction of power flow: motoring/battery discharging and generating/battery recharging. Switched optimal control is used to select between the two modes of EP operation, and a recently developed distributed switched optimal control is applied. The control is distributed between the CP, the EP, and the vehicle motion operation components. Control-oriented, component-specific power flow models are set forth to describe the dynamics and algebraic relationships. Four different tractor-trailers are simulated: the original CP and three hybrids with engine sizes of 15 L, 11 L, and 7 L. Simulations are performed over a short test cycle and two regulatory driving cycles to compare the fuel use, total energy, and emissions. Results show that the hybrids have reduced fuel use, total energy, and emissions compared to the original CP; the reductions and reference velocity tracking error increases as the engine size is decreased. Particularly, fuel use is reduced by at least 4.1 % under a charge sustaining operation and by 9.8 % when the battery energy can be restored with an off-board charger at the end of the cycle. [ABSTRACT FROM AUTHOR]

The further development of electric mobility requires major scientific efforts to obtain reliable data for vehicle and drive development. Practical experience has repeatedly shown that vehicle data sheets do not contain realistic consumption and range figures. Since the fear of low range is a significant obstacle to the acceptance of electric mobility, a reliable database can provide developers with additional insights and create confidence among vehicle users. This study presents a detailed, yet easy-to-implement and modular physical model for both passenger and commercial battery electric vehicles. The model takes consumption-relevant parameters, such as seasonal influences, terrain character, and driving behavior, into account. Without any a posteriori parameter adjustments, an excellent agreement with known field data and other experimental observations is achieved. This validation conveys much credibility to model predictions regarding the real-world impact on energy consumption and cruising range in standardized driving cycles. Some of the conclusions, almost impossible to obtain experimentally, are that winter conditions and a hilly terrain each reduce the range by 7–9%, and aggressive driving reduces the range by up to 20%. The quantitative results also reveal the important contributions of recuperation and rolling resistance towards the overall energy budget. [ABSTRACT FROM AUTHOR]

Electromobility has gained significance over recent years and the requirements on the performance and efficiency of electric vehicles are growing. Lithium-ion batteries are the primary source of energy in electric vehicles and their performance is highly dependent on the operating temperature. There is a compelling need to create a robust modeling framework to drive the design of vehicle batteries in the ever-competitive market. This paper presents a system-level modeling methodology for thermal simulations of large battery packs for electric trucks under real-world operating conditions. The battery pack was developed in GT-SUITE, where module-to-module discretization was performed to study the thermal behavior and temperature distribution within the pack. The heat generated from each module was estimated using Bernardi's expression and the pack model was calibrated for thermal interface material properties under a heat-up test. The model evaluation was performed for four charging/discharging and cooling scenarios typical for truck operations. The results show that the model accurately predicts the average pack temperature, the outlet coolant temperature and the state of charge of the battery pack. The methodology developed can be integrated with the powertrain and passenger cabin cooling systems to study complete vehicle thermal management and/or analyze different battery design choices. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The need for deploying fast-charging stations for electric vehicles (EVs) is becoming essential in recent years. This need is justified by the increasing charging demand and supported by new charging technologies making EV chargers more efficient. In this paper, we provide a survey on EV fast-charging models and introduce a data-driven approach with an optimization model for deploying EV fast-chargers for both electric vehicles and heavy trucks traveling through a network of suburban highways. This deployment aims at satisfying EV charging demands while respecting the limits imposed by the electric grid. We also consider the availability of local photovoltaic (PV) farm and integrate its produced power to the proposed charging network. Finally, through a case study on Paris-Saclay area, we provide locations for EV charging stations and analyze the benefits of integrating PV power at different prices, production costs and charging capacities. The obtained results also suggest potential enhancements to the charging network in order to accommodate the increasing charging demand for EVs in the future. [ABSTRACT FROM AUTHOR]

This article proposes a novel framework to develop computationally efficient energy consumption models of electric and internal combustion engine vehicles. The number of calculations in a conventional energy consumption model prevents the model's usage in applications where time is limited. As many fleet operators around the world are in the process of transitioning towards electric vehicles, a computationally efficient energy consumption model will be valuable to analyse the vehicles they trial. A vehicle's energy consumption depends on the vehicle characteristics, drive cycles and vehicle mass. The proposed modelling framework considers these aspects, is computationally efficient, and can be run using open source software packages. The framework is validated through two use cases: an electric bus and a diesel truck. The model error's standard deviation is less 5% and its mean is less than 2%. The proposed model's mean computation time is less than 20 ms, which is two orders of magnitude lower than that of the baseline model. Finally, a case study was performed to illustrate the usefulness of the modelling framework for a fleet operator. [ABSTRACT FROM AUTHOR]

Reducing greenhouse gas emissions in the transport sector is known to be an important contribution to climate change mitigation. Some parts of the transport sector are particularly difficult to decarbonize; this includes the heavy-duty vehicle sector, which is considered one of the "hard-to-abate" sectors of the economy. Transitioning from diesel trucks to hydrogen fuel cell trucks has been identified as a potential way to decarbonize the sector. However, the current and future costs and efficiencies of the enabling technologies remain unclear. In light of these uncertainties, this paper investigates the investments required to decarbonize New Zealand's heavy-duty vehicle sector with green hydrogen. By combining system dynamics modelling literature and hydrogen transition modelling literature a customized methodology is developed for modelling hydrogen transitions with system dynamics modelling. Results are presented in terms of the investments required to purchase the hydrogen production capacity and the investments required to supply electricity to the hydrogen production systems. Production capacity investments are found to range between 1.59 and 2.58 billion New Zealand Dollars, and marginal electricity investments are found to range between 4.14 and 7.65 billion New Zealand Dollars. These investments represent scenarios in which 71% to 90% of the heavy-duty vehicle fleet are replaced with fuel cell trucks by 2050. The wide range of these findings reflects the large uncertainties in estimates of how hydrogen technologies will develop over the course of the next thirty years. Policy recommendations are drawn from these results, and a clear opportunity for future work is outlined. Most notably, the results from this study should be compared with research investigating the investments required to decarbonize the heavy-duty vehicle sectors with alternative technologies such as battery-electric trucks, biodiesel, and catenary systems. Such a comparison would ensure that the most cost effective decarbonization strategy is employed. [ABSTRACT FROM AUTHOR]

A combustion engine-driven vehicle can be made more fuel efficient over some drive cycles by, for example, introducing electric machines and solutions for electrical energy storage within the vehicle's driveline architecture. The possible benefits of different hybridization concepts depend on the architecture, i.e., the type of energy storage, and the placement and sizing of the different driveline components. This paper examines a diesel electric plug-in hybrid truck, where the powertrain includes a diesel engine supported with two electric motors, one supporting the crank shaft and one the turbocharger. Numerical optimal control was used to find energy-optimal control strategies during two different accelerations; the trade-off between using electrical energy and diesel fuel was evaluated using a simulation platform. Fixed-gear acceleration was performed to evaluate the contribution from the two electric motors in co-operation, and individual operation. A second acceleration test case from 8 to 80 km/h was performed to evaluate the resulting optimal control behavior when taking gear changes into account. A cost factor was used to relate the cost of diesel fuel to electrical energy. The selection of the cost factor relates to the allowed usage of electrical energy: a high cost factor results in a high amplification from electrical energy input to total system energy savings, whereas a low cost factor results in an increased usage of electrical energy for propulsion. The difference between fixed-gear and full acceleration is mainly the utilization of the electric crank shaft motor. For the mid-range of the cost factors examined, the crank shaft electric motor is used at the end of the fixed-gear acceleration, but the control sequence is not repeated for each gear during the full acceleration. The electric motor supporting the turbocharger is used for higher cost factors than the crank shaft motor, and the amplification from electrical energy input to total energy savings is also the highest. [ABSTRACT FROM AUTHOR]

Medium and heavy-duty battery electric trucks (BETs) may play a key role in mitigating greenhouse gas (GHG) emissions from road freight transport. However, technological challenges such as limited range and cargo carrying capacity as well as the required charging time need to be efficiently addressed before the large-scale adoption of BETs. In this study, we apply a geospatial data analysis approach by using a battery electric vehicle potential (BEVPO) model with the datasets of road freight transport surveys for analyzing the potential of large-scale BET adoption in Finland and Switzerland for trucks with gross vehicle weight (GVW) of over 3.5 t. Our results show that trucks with payload capacities up to 30 t have the most potential for electrification by relying on the currently available battery and plug-in charging technology, with 93% (55% tkm) and 89% (84% tkm) trip coverage in Finland and Switzerland, respectively. Electric road systems (ERSs) would be essential for covering 51% trips (41% tkm) of heavy-duty trucks heavier than 30 t in Finland. Furthermore, range-extender technology could improve the trip electrification potential by 3–10 percentage points (4–12 percentage points of tkm). [ABSTRACT FROM AUTHOR]

Decarbonising heavy-duty trucks is challenging due to high journey power and energy requirements. With a growing fleet of commercial vehicles in the UK, biomethane can provide significant reductions in greenhouse gas (GHG) emissions compared to fossil diesel. Methane is a potent GHG with a global warming potential (GWP) of 23–36, therefore reducing levels in the atmosphere can have a significant impact on climate change. There are a range of anthropogenic sources of methane that could be collected and processed to provide sustainable energy (upcycled), e.g., agricultural waste and the waste water system. This paper explores the impact of using upcycled methane in transport in South East England, evaluating local sources of anthropogenic methane and the environmental and economic impact of its use for a heavy-duty truck compared to fossil and battery electric alternatives. Analysis concludes that the use of upcycled methane in transport can provide significant reductions in lifecycle GHG emissions compared to diesel, fossil natural gas or battery electric trucks, and give net negative GHG emissions where avoided environmental methane emissions are considered. Furthermore, upcycling solutions can offer a lower cost route to GHG reduction compared to electrification. [ABSTRACT FROM AUTHOR]

The advancement of electric mobility as a measure to comply with international climate targets and sustain renewable resources in the future has led to an electrification of the mobility sector in recent years. This trend has not been spared in the logistics and commercial vehicle sector. Emerging electric powertrain concepts for long-haul vehicles have since been developed and adapted to different use cases and axle concepts. In this paper, the authors show the influence of the powertrain topology and the associated design of the electric machine on the efficiency and energy consumption of commercial vehicles. For this, existing series or prototype long-haul axle topologies are analyzed regarding their efficiency and operating points within four driving cycles. Additionally, a sensitivity analysis on the influence of the total gearbox ratio tests the assumed designs. We find that single-machine topologies offer efficiency advantages over multiple-machine topologies. However, this study highlights a joint consideration of application-specific machine design and topology to realize the full technological potential. [ABSTRACT FROM AUTHOR]