The use of equivalent circuit models for simulating the operating behaviour of Lithium-Ion batteries is well established in the automotive and the renewable energy sector. However, finding the correct parameter set for these models is still a challenging task. This manuscript proposes a comprehensive methodology for estimating the required, temperature dependent simulation parameters from battery measurements. Based on a specific load current and prior system knowledge, an algorithm first analyses the correlation between current steps and the measured terminal voltage. Secondly, a combination of Particle Swarm Optimisation and Gauss-Newton algorithm fits the initially estimated parameters from the first algorithm to the measurement data. Finally, the dependency of each simulation parameter on both the state of charge and the battery temperature is determined. As this contribution aims at modelling reversible effects of Lithium-Ion batteries, ageing effects are neglected. The validation against measurement data proves that the generated parameter set enables the user to accurately reproduce and investigate the operating behaviour of the chosen battery. Applied to an LFP cell, the comparison between measurements and simulations in standardised real-life automotive driving cycles (Artemis, FTP75 and WLTC) shows a terminal voltage error of less than 1.09% within the typical operational window between SOC0.15 and SOC0.95.

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This paper presents a data-driven surrogate model for predicting cabin airflow and its integration into system-level electric vehicle simulations for energy management analysis. The model employs a graph-based neural network with a mirror-symmetric predictor–corrector architecture and is trained on a dataset generated using computational fluid dynamics (CFD) covering a defined range of inlet velocities and temperatures. The surrogate appropriately reconstructs temperature fields and captures the dominant airflow structures at significantly lower computational cost than CFD. Quantitative evaluation shows high accuracy in passenger-relevant regions, while localized discrepancies remain confined mainly to shear-layer zones. The model enables near-real-time inference and is coupled with a system-level modeling framework for control-oriented simulations that are impractical with CFD. The study is tailored to a specific geometry and operating range, showing that targeted training strategies and physics-based extensions improve robustness, particularly under limited data conditions.

In response to the urgent need for decarbonising the transport sector, this paper analyses the thermal performance of a battery electric minibus under cold ambient conditions. Thermal simulation models of the vehicle cabin and its electric heating circuits for both driver and passenger areas were developed using Modelica and validated with measurement data at −7 °C and 0 °C. The model showed good agreement with the measurements, with cabin temperature deviations within ±1.6 K and heating power deviations below 6%. Results show that the existing electric-only heating system is, in the automatic heating mode selected, insufficient to reach the target cabin temperature of 23 °C, as the optional fuel-powered heater was omitted to ensure fully zero-emission operation. To address this, an extended heating system with an additional heat exchanger was implemented in the simulation, which improved the overall cabin temperature level and also its spatial variation. However, it also increased the heating power demand by 43% at −7 °C (from 4.8 kW to 6.8 kW) and by 17% at 0 °C (from 4.8 kW to 5.6 kW). An additional heat loss analysis revealed that approx. 65–75% of all thermal losses occur through the window areas. Future improvements should therefore focus on optimising the heating strategy and enhancing cabin and heating system insulation to reduce energy demand while maintaining or even improving passenger comfort.

The shift towards sustainable mobility in the passenger- and transport sectors is driving the development of sustainable propulsion technologies. Alongside the advancement of efficient and sustainable powertrains, research increasingly targets the optimization of development processes to address rising system complexity and reduce engineering effort. With the growing adoption of Battery Electric Vehicles (BEVs) and Fuel Cell Electric Vehicles (FCEVs), thermal management is becoming a critical part of the overall vehicle energy management. Considering electric commercial vehicles for public transport, one of the main challenges – beyond the optimal thermal conditioning of powertrain components – is the efficient thermal management of the passengers’ cabin, which significantly impacts overall energy consumption, vehicle range and thermal comfort especially at cold ambient conditions. Particular attention must be paid to the interaction between the Heating, Ventilation and Air Conditioning (HVAC) system and the thermal management system (TMS) of the powertrain components, as can be found in state-of-the-art heat pump systems for electrified vehicles. To enable the early development of operating strategies while accounting for system interactions and determining the optimal system layout, an integrated approach is applied that combines simulation with testing on the Hardware-in-the-Loop (HiL)-ThermoLab testbed. This approach is used in the EU Horizon Europe research project MINDED improving the energy consumption of a battery-electric IVECO eDaily minibus at 0 °C ambient temperature. For the development of optimal HVAC layouts and efficient operating strategies, a multi-physical 1D digital twin model of the passenger cabin, described with the modeling language Modelica and validated with measurement results, is used, while all key refrigerant- and thermal circuit components are physically implemented on the testbed. Hardware components that are not yet available are substituted and necessary environment boundary conditions are emulated on the HiL-ThermoLab testbed using novel dynamic thermo-hydraulic emulators. With the presented methodology and the experimental tests conducted under its application, a simplified heat pump system is developed and investigated for the identification of potentials to reduce energy consumption in the battery-electric IVECO eDaily minibus. The results demonstrate that the HiL-ThermoLab provides an optimal testing environment for identifying efficiency potentials, while the combined use of simulation and hardware testing significantly reduces engineering effort and enables the development of advanced energy management systems at an early development stage.