Battery electric trucks place high demands on battery systems in terms of energy efficiency, available driving range, reliability, and power capability. Reconfigurable battery systems (RBSs) offer a flexible alternative to conventional fixed battery-pack architectures by allowing the electrical configuration of the battery system to be changed during operation. Depending on the architecture, reconfiguration can be performed at cell, module, or pack level. This thesis focuses on module-level prioritization, where groups of cells are treated as selectable modules instead of controlling each cell individually. This flexibility creates a control problem, where the system must decide which modules should be used at each time step. This thesis investigates module prioritization strategies for a reconfigurable battery system intended for battery electric truck applications.

The evaluated strategies prioritize modules based on fixed ordering, module voltage, state of charge, and resistance-related analytic criteria. The strategies are implemented in a simulation model that describes the electrical behaviour of cells, modules, and battery packs, including voltage, resistance, current distribution, state of charge, and power losses. The simulation study evaluates the strategies under variations in initial conditions, system size, power profile, and hysteresis setting. In addition, the strategies are implemented on a physical prototype to investigate practical feasibility and compare experimental behaviour with simulation trends.

The results show that resistance-aware prioritization generally gives the lowest energy losses in larger battery-electric-truck-oriented configurations, where ohmic losses dominate. In contrast, state-of-charge-based prioritization gives the longest operating duration by distributing module usage more evenly. Voltage-based prioritization provides a practical compromise, since it performs close to the best strategies in several cases while relying on directly measurable module voltage. The results also show clear trade-offs between energy loss, usable energy, switching behaviour, and computational effort. Prototype experiments confirm that the strategies can be implemented on physical hardware, although the small prototype scale makes the quantitative ranking more sensitive to converter losses and practical implementation factors. Overall, the thesis shows that module prioritization can influence RBS performance and provides useful guidance for future strategy design.