Technology Choice for Road Freight Electrification

Abstract

Heavy goods vehicles (HGVs) are high carbon emitters despite their relatively low numbers. For example, in the UK, they contribute 21% of transport emissions. Road freight decarbonisation is a global problem, and thus, solutions need to be compatible internationally and meet the requirements of diverse logistics and geographies. They should also attempt to replicate the benefits of scale that diesel platforms provide, and ideally allow the government to replace diesel excise duty. There exist multiple pathways to decarbonising road freight. Of the long-term solutions available, electrification is seen as the most promising due to its high efficiency and highest potential to reduce emissions. However, barriers such as payload capacity loss and charging downtime prevent it from being implementable at a cost comparable to diesel HGVs. Other barriers, such as expensive grid connections for charging infrastructure, make the business case unclear. This thesis develops simple techno-economic models that quantify these barriers and integrate vehicle and infrastructure models to compare technology pathways. The basic methodology is divided into a journey cost model and an infrastructure cost breakeven model. The journey cost model calculates the cost implications of the time and payload penalties. This is done by arranging charging stops based on battery size, modelling the journey time increase, and estimating the payload loss due to the selected battery size. The corresponding penalties result in a requirement for additional vehicles, and thus, increased logistics costs. The cost breakeven model formulates infrastructure income and expenditure by sizing the infrastructure appropriately, considering usage requirements. This is done on the basis of a finance case, which defines the payback period and other economic parameters. This helps obtain the usage requirement for an infrastructure unit to achieve a specified payback period. The model is applied to static charging, battery swapping, dynamic charging using electric road systems (ERS), and a new logistics method called 'pony express' (tractor swapping). The primary outcome of the thesis is a quantitative comparison of solutions in terms of vehicle configurations and infrastructure under different conditions. The solutions are compared for their cost and feasibility using a combination of the journey cost model and the infrastructure cost breakeven model. The lowest cost configuration of this platform was shown to be dynamic charging, based on its feasibility for enough coverage along a journey. In less ideal conditions and where chargers are feasible, battery electric vehicles with pony express appear to provide the next best solution. Battery swapping is an uncertain solution with numerous technical challenges that need to be studied in greater detail. For less dense regions without any infrastructure feasibility, range-extended electric vehicles are a possible and flexible choice. The proposed solutions can benefit from economies of scale by implementing a modular vehicle platform with an electric drivetrain, with different on-board energy options depending on infrastructure availability. This common modular platform could be the key to being a common global solution. Additional recommendations from the work include consideration of battery health as a result of charging strategies, as identified from in-service data. The potential to recoup lost diesel fuel duty is also studied and is found to be only possible with dynamic charging. Optimum battery sizes are estimated, both for large journey datasets using the simple model, and for specific case studies using detailed simulations and different charging strategies. Finally, issues such as the decision of switching to dynamic charging based on coverage, and predicting the change in technology choice through the future with technology improvement are studied. It is seen that even with large improvements in battery technology and reduced costs, dynamic charging still stands out with logistics cost benefits.