The launch of both battery electric vehicles (BEVs) and autonomous vehicles (AVs) on the global market has triggered ongoing radical changes in the automotive sector. On the one hand, the new characteristics of the BEV powertrain compared to the combustion type have resulted in new central parameters, such as vehicle range, which then become an important selling point. On the other hand, electric components are as yet not optimized and the sensors needed for autonomous driving are still expensive, which introduces changes to the vehicle cost structure. This transformation is not limited to the vehicle itself but also extends to its mobility and the necessary infrastructure. The former is shaped by new user behaviors and scenarios. The latter is impacted by the BEV powertrain, which requires a charging and energy supply infrastructure. To enable manufacturers and researchers to develop and optimize BEVs and AVs, it is necessary to first identify the relevant parameters and costs. To this end, we have conducted an extensive literature review. The result is a complete overview of the relevant parameters and costs, divided into the categories of vehicle, infrastructure, mobility, and energy.

The transportation sector needs to significantly lower greenhouse gas emissions. European manufacturers in particular must develop new vehicles and powertrains to comply with recent regulations and avoid fines for exceeding $C{O}_2$ emissions. To answer the question regarding which powertrain concept provides the best option to lower the environmental impacts, it is necessary to evaluate all vehicle life-cycle phases. Different system boundaries and scopes of the current state of science complicate a holistic impact assessment. This paper presents a scaleable life-cycle inventory (LCI) for heavy-duty trucks and powertrains components. We combine primary and secondary data to compile a component-based inventory and apply it to internal combustion engine (ICE), hybrid and battery electric vehicles (BEV). The vehicles are configured with regard to their powertrain topology and the components are scaled according to weight models. The resulting material compositions are modeled with LCA software to obtain global warming potential and primary energy demand. Especially for BEV, decisions in product development strongly influence the vehicle’s environmental impact. Our results show that the lithium-ion battery must be considered the most critical component for electrified powertrain concepts. Furthermore, the results highlight the importance of considering the vehicle production phase.

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.

Automobile manufacturers are in the midst of the greatest phase of transformation in their history. The paradigms they used over recent decades are gradually dissolving. Two new megatrends are drastically changing the market. On the one hand, there is sustainability, which is causing the ever-increasing replacement of the combustion engine by the electric motor and is pushing the sector towards "greener" mobility. On the other hand, there is automation, which is the source of a shift towards autonomous driving. How do these megatrends affect the vehicle development process? Which potentials and changes do they cause? The answer is the key to enable vehicle manufacturers to face these changes. To answer these questions, we analyze the current development process, taking into account the individual development steps, and discuss the modifications promoted by sustainability and automation.

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.