In an industrial joining process, exemplified by deep penetration laser beam welding, ensuring a high quality of welds requires a great effort. The quality cannot be fully established by testing, but can only be produced. The fundamental requirements for a high weld seam quality in laser beam welding are therefore already laid in the process, which makes the use of control systems essential in fully automated production. With the aid of process monitoring systems that can supply data inline to a production process, the foundation is laid for the efficient and cycle-time-neutral control of welding processes. In particular, if novel, direct measurement methods, such as Optical Coherence Tomography, are used for the acquisition of direct geometric quantities, e.g., the weld penetration depth, a significant control potential can be exploited. In this work, an inline weld depth control system based on an OCT keyhole depth measurement is presented. The system is capable of automatically executing an inline control of the deep penetration welding process based only on a specified target weld depth. The performance of the control system was demonstrated on various aluminum alloys and for different penetration depths. In addition, the ability of the control to respond to unforeseen external disturbances was tested. Within the scope of this work, it was thus possible to provide an outlook on future developments in the field of laser welding technology, which could develop in the direction of an intuitive manufacturing process. This objective should be accomplished through the use of intelligent algorithms and innovative measurement technology—following the example of laser beam cutting, where the processing systems themselves have been provided with the ability to select suitable process parameters for several years now.

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 $\mathrm{kW}$ 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 $\mathrm{kW}$ 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.

Agriculture is one of the main sources of greenhouse gas emissions, with agricultural machinery playing an important role through the combustion of fossil fuels. On the upside, many farmers invest in their own renewable energy generation. To leverage the renewable produced energy and cut emissions, a promising solution is the electrification of tractors. With the advent of tractor electrification, the necessity to define the requirements of an electrified tractor emerges. In contrast to automotive and truck engineering, where the usage patterns are narrow, and testing is standardized through load cycles as the WLTP and VECTO LongHaul, the agricultural application of tractors includes a wide variability of operating conditions. Since there are no standardized, publicly available agricultural load cycles, we equipped, during a one-year period, five tractors (77–240 kW) on a farm in the South of Germany with high-resolution GNSS loggers and collected the CAN bus data from the J1939 engine bus and the ISOBUS. The data collected is enriched with information about the tractor models and implements used. Hence, the load and usage information in real-field operation is suitable as input for the development of electrified tractors.

Vehicle-based services such as mobile health clinics can increase spatial accessibility in rural areas. In contrast to stationary infrastructure, vehicle-based services are flexible and can be less capital-intensive to initiate service supply. In particular, rural communities across sub-Saharan Africa experience insufficient access to essential public services necessary for sustainable human development. We consider vehicles as mobile service platforms capable of temporarily transporting service staff, goods, and functions necessary for service delivery spatially closer to rural demand locations. Despite these advantages, public authorities must perform a cost–benefit analysis before allocating resources to a vehicle-based service fleet. This paper analyzes which vehicle-based services beneficially influence the Sustainable Development Goals and quantify their potential for the sub-Saharah African region. Based on a criteria-based selection method, we parse 169 target formulations and extract a set of directly influential Sustainable Development Goals. The remaining goals are the starting point for a literature review to identify existing vehicle-based service concepts addressing the targets. Our evaluation reveals that vehicle-based services can enhance about 128 (76%) of all targets and 16 of the 17 Sustainable Development Goals. Half of these targets require the delivery of consumable goods, whereas 59 (35%) of the Sustainable Development Goal targets relate to the transportation of people, and 24 (14%) require access to a broader spectrum of functionality mounted on top of the vehicle, such as water pumps or refrigerators. In combination with publicly available data, we can identify the SDG for each African country with the greatest potential for a vehicle-based service intervention. Our approach enriches public project appraisals for systematical decision support between stationary and mobile infrastructure.

The electrification of tractors can increase the self-supply of renewable energy produced on the farm and reduce the operating costs of tractors. However, electric tractors face higher upfront costs than their diesel counterparts, as well as limited operating time. A drivetrain that is highly efficient in a wide range of agricultural applications reduces operating costs and enables long operating times. Thus, we propose a method to design electric tractor drivetrain configurations that incorporates longitudinal dynamic simulations to enable the development of such efficient drivetrains. To represent a diverse application profile, we include real-world load cycles recorded from a 104 kW diesel tractor. Our investigation focuses on the axle-individual drivetrain topology (eAxle) and the central motor topology as the configurations that offer the most promising trade-off between efficiency and complexity. The design method includes the top-down design of the topology including its individual components, such as the inverter, motor, and transmission, which are varied based on the load. Our method derives drivetrains with average efficiencies of 83% for an axle-individual topology with two gears. With a 100 kWh battery, such a drivetrain enables operating times of 7.5 h when fertilizing and 2.4 h when seeding.