The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, as the battery is a key element in the energy performance of an EV powertrain system. The equivalent circuit model (ECM) technique at the cell level is commonly employed for this purpose, offering a balance of accuracy and efficiency in representing battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell has been developed to ensure high accuracy and performance. Modelica, an acausal and object-oriented equation-based modeling language, has been used for its advantageous features, including the development of extendable, modifiable, modular, and reusable models. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from lithium-ion (Li-ion) battery cells with four different commonly used cathode materials, have been utilized. This approach allows for the representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed, and the simulation results of the US Environmental Protection Agency Federal Test Procedure (FTP-75) driving cycle have been compared with an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both models across different system points. Specifically, the maximum vehicle velocity deviation during the cycle reaches 1.22 km/h, 8.2% lower than the corresponding value of the reference application. The maximum deviation of SoC is limited to 0.06%, and the maximum value of relative voltage deviation is 1.49%. The verified model enables the exploration of multiple potential architecture configurations for EV powertrains using Modelica.

Development and application of the open-source GPU-based fluid-thermal simulation code, NekRS, are described. Time advancement is based on an efficient kth-order accurate timesplit formulation coupled with scalable iterative solvers. Spatial discretization is based on the high-order spectral element method (SEM), which affords the use of fast, low-memory, matrix-free operator evaluation. Recent developments include support for nonconforming meshes using overset grids and for GPU-based Lagrangian particle tracking. Results of large-eddy simulations of atmospheric boundary layers for wind-energy applications as well as extensive nuclear energy applications are presented.

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, since these serve as a key element in the energy performance of an EV powertrain system. The Equivalent Circuit Model (ECM) technique at the cell level is commonly employed for this purpose, combining accuracy and efficiency in the representation of battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell is being developed using Modelica, an equation-based modeling language. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from Li-ion battery cells with 4 different commonly used cathode materials, have been utilized. This approach allows for a representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed and simulation results of the FTP75 driving cycle have been compared against an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both software across different system points. Specifically, the maximum deviation of SoC is limited to 0.08% and the maximum value of relative voltage deviation is 1.4%. The verified model enables the exploration of multiple possible architecture configurations of EV powertrains with Modelica.

Electric vehicles (EVs) and photovoltaics (PVs) are expected to be broadly adopted in future power systems. However, the temporal variability of EV load and PV production presents challenges for integrating them into the power grid. This study evaluates and assesses the synergies between EVs and PV systems to maximize solar energy utilization for EV load coverage. The configurations studied include EV charging via the national grid as a reference case (Case 1) and two solar energy harvesting options: EVs powered directly by vehicle-mounted PVs (Case 2) and EV chargers connected to residential PV installations (Case 3). These cases are evaluated across different urban environments with large EV fleets and dissimilar weather conditions: Berlin and Los Angeles. A customized operation profile based on the worldwide harmonized light-duty test cycle (WLTC) and a charge-right-away (CRA) strategy is used. Energy performance analysis is conducted through dynamic simulations using the Modelica language, with environmental and economic indices derived. Key findings highlight the superior performance of residential PV systems in both cities compared to current solar EV technologies, with both solutions offering significant benefits over the reference case. Cases 2 and 3 result in a 44% and 59% reduction in annual energy consumption, greenhouse gas emissions, and charging costs in Berlin, while in Los Angeles, the reductions are 67% and 98%. The average daily solar driving range reaches 20.3% in Berlin and 30.4% in Los Angeles.

Research activity in the field of combined cooling heating power (or trigeneration) systems with high renewable energy source (RES) contributions has increased rapidly over the last few years, in line with the European Union legislation about energy communities. However, technical challenges arise regarding the synergetic, sustainable and optimal integration of RES in local energy systems. In the present study, the operation of a trigeneration system located in the student residences of Democritus University of Thrace in Greece is examined. The system involves a combination of highly promising renewable and storage technologies, including solar thermal energy and biomass for heat generation, hot water tanks for thermal energy storage, absorption refrigeration for cooling, along with Organic Rankine Cycle and photovoltaic systems for electricity generation. System modeling and simulation have been implemented in Dymola environment with the use of Modelica equation-based modeling language. The accuracy of the model response has been validated against available measurements. Specific design and control measures have been proposed, simulated in a transient fashion and evaluated in terms of (i) RES generation, (ii) solar fraction and (iii) temporal flexibility. The measures examined, including the placement of a Li-ion battery, resulted in an increase of 24.6% in the heating demand solar contribution and of 7.9% in the renewable energy generated for the electricity demand, over the examined periods.

This paper presents a modeling framework for large-capacity lithium-ion battery energy storage systems (BESSs), developed within the Modelica LIBSystems library and focused on system-level integration. The framework builds on a combined analysis of the electrical, thermal and degradation behavior at the cell level to model the BESS interconnection to the electrical grid. A semi-empirical aging model was incorporated following its validation at the cell level against capacity loss experimental measurements. Two case studies were conducted for a 10.5 MW/15 MWh BESS installed in the isolated power system of Terceira Island. The first analyzed the short-term response to a 5% load step decrease under 60% and 80% renewable penetration scenarios, yielding a frequency nadir improvement of 3 mHz and 21 mHz, respectively. The second projected long-term degradation under two dispatch strategies: one derived from historical time series, and another synthetically constructed to induce more frequent and deeper cycling. After 1000 days of operation, the state of health declined to 95.2% in the historical-based case and to 93.5% under the aggressive profile. The proposed framework establishes a unified, cross-domain modeling workbench for Li-ion BESS applications, enabling evaluation of the system design, control strategies, operation conditions, and system-level performance across both dynamic and long-term horizons.