The infrastructure cost for depot charging of battery electric trucks

The feasibility of heavy battery electric trucks

Road freight transport contributes to a large portion of greenhouse gas (GHG) emissions. Transitioning diesel to battery electric (BE) trucks is an attractive sustainability solution. To evaluate the BE transition in New Zealand (NZ), this study analysed the life-cycle GHG emissions and total cost of ownership (TCO) of diesel and BE trucks based on real industry data. The freight pickup and delivery (PUD) operations were simulated by a discrete-event simulation (DES) model. Spreadsheet models were constructed for life-cycle assessment (LCA) and TCO for a truck operational lifetime of 10 years (first owner), this being the typical usage of a tier-one freight company in New Zealand (NZ). The whole-of-life emissions from the diesel and BE trucks are 717,641 kg and 62,466 kg CO2e, respectively. For the use phase (first owner), the emissions are 686,754 kg and 8714 kg CO2e, respectively; i.e., the BE is 1.27% of the diesel truck. The TCO results are 528,124 NZ dollars (NZD) and 529,573 NZD (as of 2022), respectively. The battery price and road user charge are the most sensitive variables for the BE truck. BE truck transitions are explored for freight companies, customers, and the government. For the purchase of BE trucks, the break-even point is about 9.5 years, and straight-line depreciation increases freight costs by 8.3%. Government subsidy options are evaluated. The cost of emission credits on the emissions trading scheme (ETS) is not expected to drive the transition. An integrated model is created for DES freight logistics, LCA emissions, and TCO costs supported by real industry data. This allows a close examination of the transition economics.

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

This study investigates the effects of key technological and operating factors on daily routing of battery electric truck (BET) in urban goods delivery. The key factors are service area, number of customers, battery capacity, number of charging stations, vehicle capacity, and charging rate. A primary cost metric used is daily vehicle routing cost (DVRC), which is defined as the sum of driver salary associated with driving time and idling time for en-route battery recharging activities, and electricity (energy) cost. It is estimated with a proposed green EVRP (G-EVRP) model. Overall, BET works with the economies of scale. However, BET may be better suited for a market place with high number of customers (customer density) at a city or a county scale but often technologically or financially constrained at a regional (intercity) scale. For a given network size and number of customers, battery capacity must be carefully chosen to minimize the BET DVRC. Vehicle capacity affects the number of vehicles needed to dispatch and the optimal fleet size is a compromise between the level of consolidation and that of en-route recharging activity. Little effect is found in number of charging stations on BET daily routing in the study setting as optimal routing strategies typically result in either zero or very low en-route recharging activity level.

To achieve net zero emissions, the global transportation sector needs to reduce emissions by 90% from 2020 to 2050, and road freight has a significant potential to reduce emissions. In this context, emission reduction paths should be explored for road freight over the fuel life cycle. Based on panel data from 2015 to 2020 in China, China's version of the GREET model was established to evaluate the impact of crude oil mix, electricity mix, and vehicle technology on China's reduction in road freight emissions. The results show that the import share of China's crude oil has increased from 2015 to 2020, resulting in an increase in the greenhouse gas (GHG) emission intensity of ICETs in the well-to-tank (WTT) stage by 7.3% in 2020 compared with 2015. Second, the share of China's coal-fired electricity in the electricity mix decreased from 2015 to 2020, reducing the GHG emission intensity of battery electric trucks (BETs), by approximately 6.5% in 2020 compared to 2015. Third, different vehicle classes and types of BETs and fuel cell electric trucks (FCETs) have different emission reduction effects, and their potentials for energy-saving and emission reduction at various stages of the fuel life cycle are different. In addition, in a comparative study of vehicle technology, the results show that (1) for medium-duty trucks (MDTs) and heavy-duty trucks (HDTs), FCETs have lower GHG emission intensity than BETs, and replacing diesel-ICETs can significantly reduce GHG emissions from road freight; (2) for light-duty trucks (LDTs), BETs and FCETs have the highest GHG emission reduction potential; thus, improving technologies such as electricity generation, hydrogen fuel production, hydrogen fuel storage, and transportation will help to improve the emission reduction capabilities of BETs and FCETs. Therefore, policymakers should develop emission standards for road freight based on vehicle class, type, and technology.

Development of battery technology is making battery electric heavy duty trucks technically and commercially viable and several manufacturers have introduced battery electric trucks recently. However, the national and sectoral differences in freight transport operations affect the viability of electric trucks. The aim of this paper is to develop a methodology for estimating the potential of electric trucks and demonstrate the results in Switzerland and Finland. Commodity-level analysis of the continuous road freight survey data were carried out in both countries. As much as 71% of Swiss road freight transport tonne-kilometers may be electrified using battery electric trucks but Finland has very limited potential of 35%, due to the use of long and heavy truck-trailer combinations. Within both countries the electrification potential varies considerably between commodities, although in Finland more so than in Switzerland. Commodities which are constrained by payload volume rather than weight and are to large extent carried using medium duty or <26t rigid trucks trucks seem to provide high potential for electrification even with the current technology. Electric trucks increase the annual electricity consumption by only 1–3%, but truck charging is likely to have a large impact on local grids near logistics centres and rest stations along major roads. A spatial analysis by routing the trips reported in the datasets used in this study should be carried out. Future research should also include comparison between the alternate ways of electrifying road freight transport, i.e. batteries with charging, batteries with battery swapping and electrified road systems.

Comparative life cycle assessment of heavy-duty drivetrains: A Norwegian study case

This paper presents an integrated approach to optimizing microgrid management and electric truck logistics for transportation research. The experiment involves a 100 kW solar photovoltaic system, a 500 kWh battery energy storage system, the electric demand of a commercial building, and a heavy-duty vehicle charging system. The study aims to demonstrate how synchronized optimization of a microgrid control algorithm and a truck route algorithm can reduce overall system costs. The microgrid management system is designed to meet the constraints and requirements of a commercial electric truck charging scheduler. This integrated approach is an improvement over previous systems as it uses the scheduler’s outputs—such as time frames and energy requirements—as constraints for microgrid management. The truck scheduling algorithm iteratively learned to optimize charging times, ensuring that charging occurs during low-cost periods or when renewable energy is available. The electric vehicle scheduler adjusted truck arrival times based on the availability of clean energy sources, creating a feedback loop that continuously improves cost efficiency. Results indicated significant cost savings, with electric utility costs for electric vehicle (EV) charging being only 0% to 20% of the original value while the transportation system is only 23% to 64%, compared with the baseline scenario without the co-optimization framework. These findings suggest that the proposed integrated approach can effectively reduce costs and improve the efficiency of microgrid and electric truck operations. Uncoordinated charging schedules leads to a higher power demand than a well-organized battery electric truck (BET) dispatching strategy. Optimizing truck charging times and energy needs based on microgrid conditions can significantly reduce electricity and transportation costs.

The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.

The successful introduction of battery electric trucks heavily depends on public charging infrastructure. But even as the first trucks capable of long-haul transportation are being built, no coherent fast-charging networks are yet available. This paper presents a methodology for assessing fast charging networks for electric trucks in Germany from the literature. It aims to establish a quantitative understanding of the networks’ performance and robustness to deviations from idealized system parameters and identify crucial charging sites from a transportation planning perspective. Additionally, the study explores the quantification of adaptation effects displayed by agents in response to charging site outages. To achieve these objectives, a comprehensive methodology incorporating infrastructure, vehicle and operational strategy modeling, simulation, and subsequent evaluation is presented. Factors such as charging station locations, C-rates, mandatory rest periods, and vehicle parameters are taken into account, along with the distribution of traffic according to publicly available data. The study aims to offer a comprehensive understanding of charging networks’ performance and resilience. This will be applied in a case study on two proposed networks and newly created derivatives. The proposed network offers over 99% coverage for long-haul transport but leads to a time loss of approximately 7% under reference conditions. This study advances the understanding of the performance and resilience of proposed charging networks, providing a solid foundation for the design and implementation of robust and efficient charging infrastructure for electric trucks.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Electric Trucks offer one of the most promising alternatives to vehicles in the field of transport of goods. In battery electric trucks, heat is generated by components present in the electric truck such as battery of the electric vehicle, electric drive system, Endurance Brake System etc. which require cooling and Thermal management system to control and monitor the cooling system.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;The thermal management system considered here includes two coolant tanks. The first coolant tank performs thermal management for the battery and Electric-Drive(e-Drive) components which can heat up to 60&lt;sup&gt;0&lt;/sup&gt;C and the second coolant tank performs thermal management for HPR circuit, and it is used to break the charging circuit to protect the battery getting charged beyond 100% using regenerative braking concept. HPR (High performance resistor) is the component which can heat up to ~95&lt;sup&gt;0&lt;/sup&gt;C and make sure the battery is not getting charged beyond the safe limits. Since HPR is a critical component and operates at high temperatures, it has a separate coolant tank.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;However, in the conventional thermal management systems, the second coolant tank is used less than 5% in entire lifetime of the vehicle. It is underutilized because of less frequent operation of the EB circuit and the first coolant circuit experiences more frequent operation due to more frequent operation of the battery. This disparity in utilization results in an inefficient use of the coolant in the thermal management system. The inefficient use of the coolant provides inefficient cooling and reduce electric vehicle performance. The proposed solution is to predict Cooling and heating requests for system level components so that coolant from Battery Circuit/e-Drive Circuit and HPR Circuit can be dynamically transferred between the circuits depending on the enable requests and climatic conditions. This will improve the overall cooling efficiency and reduce the time taken for components to cool. In the MATLAB model, Percentage decrease in time taken to cool is measured around 30%&lt;/div&gt;&lt;/div&gt;

Integrated planning of BEV public fast-charging stations

Vehicle to Grid regulation services of electric delivery trucks: Economic and environmental benefit analysis

The electrification of heavy-duty trucks stands as a critical and challenging cornerstone in the low-carbon transition of the transportation sector. This paper employs the total cost of ownership (TCO) as the economic evaluation metric, framed within the context of China’s ambitious goals for heavy truck electrification by 2035. A detailed TCO model is developed, encompassing not only the vehicles but also their related energy replenishing infrastructures. This comprehensive approach enables a sophisticated examination of the economic feasibility for different deployment contexts of both fuel cell and battery electric heavy-duty trucks, emphasizing renewable energy utilization. This study demonstrates that in the context where both fuel cell components and hydrogen energy are costly, fuel cell trucks (FCTs) exhibit a significantly higher TCO compared to battery electric trucks (BETs). Specifically, for a 16 ton truck with a 500 km range, the TCO for the FCT is 0.034 USD/tkm, representing a 122% increase over its BET counterpart. In the case of a 49 ton truck designed for a 1000 km range, the TCO for the FCT is 0.024 USD/tkm, marking a 36% premium compared to the BET model. The technological roadmap suggests a narrowing cost disparity between FCTs and BETs by 2035. For the aforementioned 16 ton truck model, the projected TCO for the FCT is expected to be 0.016 USD/tkm, which is 58% above the BET, and for the 49 ton variant, it is anticipated at 0.012 USD per ton-kilometer, narrowing the difference to just 4.5% relative to BET. Further analysis within this study on the influences of renewable energy pricing and operational range on FCT and BET costs highlights a pivotal finding: for the 49 ton truck, achieving TCO parity between FCTs and BETs is feasible when renewable energy electricity prices fall to 0.022 USD/kWh or when the operational range extends to 1890 km. This underscores the critical role of energy costs and efficiency in bridging the cost gap between FCTs and BETs.

This paper introduces a vehicle routing problem with soft time windows and charging behavior (VPRTW&CB). A multi-objective algorithm approach is proposed to solve the problem, by which optimal delivery routes for electric vehicles are reached. In order to find the best delivery scheme, we analyze the impact of vehicle types and charging time on total costs, then obtain the optimal vehicle type and charging time. Next, the evaluation index is set up to compare the comprehensive competitiveness of diesel trucks and electric trucks with charging mode as well as battery swapping mode. The results show that in a distribution area with a radius of 60 km, electric trucks with a load of 1.8 tons and a maximum driving range of about 180 kilometers are the most economical. Electric trucks using charging mode are the most competitive because of their lower infrastructure costs and better emission reduction effects. The competitiveness of swapping-type electric trucks is second to that of charging-type electric trucks, which is mainly due to their higher infrastructure costs. In addition, the competitiveness of the diesel trucks is the lowest because of their poor performance in driving costs and emission reduction effects. Finally, we receive the optimized low carbon city distribution scheme.