We offer the following reserach topic:
Batteries, being the most valuable and defining component of electric vehicles (EVs), are of paramount importance to future mobility. The demand for battery testing facilities is soaring, with capacities being reserved years ahead. Here, prototype battery components (cells, modules) are the unit under test (UUT) to potentially thousands of test instruments (e.g., cyclers).
The planning process involves a multitude of tasks, including the organization of workloads, parameters, and test procedures, mapping of UUTs to test equipment, as well as the setup of equipment. The task of reliably planning and managing the usage, control, and inventory of thousands of test channels surpasses human capabilities due to its complexity. Classic optimization methods on the other hand reach their limits due to plant uncertainties (eg., unavailability due unplanned maintenance) and sometimes unpredictable behavior of the unit under test itself. In this work, new ML-based algorithms shall be implemented, analyzed, and compared to existing methods in managing test labs.
The successful completion of the thesis is remunerated with a one-time fee of 3.500,00 EUR before tax.
AVL is one of the world's leading mobility technology companies for development, simulation and testing in the automotive industry, and beyond. The company provides concepts, solutions and methodologies in fields like vehicle development and integration, e-mobility, automated and connected mobility (ADAS/AD), and software for a greener, safer, better world of mobility.
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