Open Access

Grid-connected multi-string battery energy storage systems (BESS) operating under sustained high-power conditions face heat accumulation, power derating, and cooling-related efficiency losses, leading to trade-offs among thermal management, efficiency, and system availability. To manage these competing objectives, this work proposes a mixed-integer nonlinear programming (MINLP)-based multi-objective optimization (MOO) framework for optimal power allocation across parallel strings under air cooling. The optimization incorporates an equivalent circuit model (ECM) with SOC-, temperature-, and C-rate-dependent internal resistance, temperature-driven derating, and an experimentally derived inverter loss model. High-fidelity control is achieved by co-simulating the optimizer with an electro-thermal BESS simulation that captures spatial thermal dynamics of the battery pack. The simulation provides online state of charge (SOC) and temperature feedback and is validated against industry-measured battery pack temperatures, revealing core temperatures up to 4.5 °C higher than those predicted by conventional 0D average thermal models calibrated to pack surface measurements. Simulation results show that the baseline MINLP controller with evenly weighted objectives achieves approximately 3% higher round-trip efficiency (RTE) and system availability compared to the industrial benchmark droop control while minimizing thermal derating. Linearized optimal control without thermal optimization results in higher maximum string temperatures up to 40 °C and 4% higher derating losses. A Pareto-based objective prioritization improves energy performance by up to 5% while keeping temperatures below the 35 °C derating threshold. System scalability analysis shows that operating an optimal subset of strings outperforms conventional full-string operation, achieving 5.5% higher RTE with comparable availability and thermal performance. The framework is released open-source for adaptation to custom BESS applications.

GaN power transistors require packaging concepts that combine low parasitic inductance with excellent thermal management for demanding converter applications. Therefore in this work, a hybrid half-bridge package embedding two discrete 650 V GaN transistors on AIN DBC is proposed, combining module-level thermal performance with the electrical advantages and flexibility of discrete SMD packages and embedding technologies. Static measurements show no significant differences in transfer characteristics, leakage currents (all IDS, leak <1μA at 600 V), or capacitances compared to reference samples. The package achieves a thermal resistance Rth,j−c below 0.2K/W and a parasitic power loop inductance below 600 pH, with power loop resistance close to twice the nominal RDS, on . This approach enables compact, lowinductive, and thermally efficient half-bridge integration for high-performance power converter applications.

This paper presents a unified framework for the optimal scheduling of battery dispatch and internal power allocation in Battery Energy Storage Systems (BESS). This novel approach integrates both market-based (price-aware) signals and physical system constraints to simultaneously optimize (1) external energy dispatch and (2) internal heterogeneity management of BESS, enhancing its operational economic value and performance. This work compares both model-based Linear Programming (LP) and model-free Reinforcement Learning (RL) approaches for optimization under varying forecast assumptions, using a custom Gym-based simulation environment. The evaluation considers both long-term and short-term performance, focusing on economic savings, State of Charge (SOC) and temperature balancing, and overall system efficiency. In summary, the long-term results show that the RL approach achieved 10% higher system efficiency compared to LP, whereas the latter yielded 33% greater cumulative savings. In terms of internal heterogeneity, the LP approach resulted in lower mean SOC imbalance, while the RL approach achieved better temperature balance between strings. In the short-term evaluation, LP delivers strong optimization under known conditions, whereas RL demonstrates higher adaptability in dynamic environments for real-time BESS control.

This study investigates two models of varying complexity for optimizing intraday arbitrage energy trading of a battery energy storage system using a model predictive control approach. Scenarios reflecting different stages of the system’s life-time are analyzed. The findings demonstrate that the equivalent-circuit-model-based non-linear optimization model outperforms the simpler linear model by delivering more accurate predictions of energy losses and system capabilities. This enhanced accuracy enables improved operational strategies, resulting in increased roundtrip efficiency and revenue, particularly in systems with batteries exhibiting high internal resistance, such as second-life batteries. However, to fully leverage the model’s benefits, it is essential to identify the correct parameters.

This paper presents a novel integration concept for mid-voltage power semiconductor modules. The concept is based on a dielectric liquid that is used for both, cooling and insulation. The semiconductor chips are attached to Cu inlays in a PCB substrate. Dual side cooling is achieved jet by impingement on the bottom side and immersion in the dielectric liquid on the top side. A first laboratory demonstrator was designed and build-up with 2.0 kV rated SiC MOSFETs in a 140×100 mm² module package. Shell immersion cooling fluid S5 LV was applied as coolant. Test results indicate a junction to fluid thermal resistance of 0.54 K/W at a flow rate of 5 l/min and with pressure drop of 12 kPa. The results obtained are on the same level compared with a state- -the-art automotive power of modules with a pin-fin array integrated in the baseplate and water-glycol as coolant.