With the increasing demand for electric vehicles, the requirements of the market are changing ever faster. Therefore, there is a need to improve the electric car’s design time, where simulations could be an appropriate tool for this task. In this paper, the modeling and simulation of an inverter for an electric vehicle are presented. Four different modeling approaches are proposed, depending on the required simulation speed and accuracy in each case. In addition, these models can provide up to 150 different electric modeling and three different thermal modeling variants. Therefore, in total, there were 450 different electrical and thermal variants. These variants are easily selectable and usable and offer different options to calculate the electrical parameters of the inverter. Finally, the speed and accuracy of the different models were compared and the obtained results presented.

This paper proposes an approach for analyzing the benefits that partial-power-processing-based converters can bring to fully electric maritime applications. With the aim of making the system modular and scalable to different powers/energies, series-connected partial power converters are proposed. Serializing these converters entails significant overvoltage issues, and this paper tackles them for one series-connected module failure case. A reliability analysis has been carried out considering that the components of the battery system follow an independent and identical distribution in terms of failure probability. Furthermore, a redundancy factor has been added to allow a certain failure rate in what is known as a fault-tolerant system. Finally, to demonstrate the high efficiency of partial power converters, a 3 kW prototype is tested at different working points that model the charging process of a battery. The experimental results show a peak efficiency of 99.36%.

Power consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-minute interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNN), across different data splits and feature sets. Our analysis reveals that higher data collection frequency generally improves model performance, as indicated by lower RMSE, MAPE, and CV values, alongside higher R² scores. The inclusion of more historical power consumption features has also been found to have a more significant impact on the accuracy of predictions than including climate condition features. Moreover, the SVM-Radial model consistently outperformed others, particularly in capturing complex, non-linear patterns in the data. However, the DNN model, while competent in some metrics, showed elevated MAPE values, suggesting potential overfitting issues. These findings suggest that careful consideration of data frequency, features and model selection is essential for optimizing power prediction, contributing to more efficient power management strategies in building operations.

Power consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-min interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNNs), across different data splits and feature sets, were considered. Our analysis reveals that higher data collection frequency generally improves model performance, as indicated by lower RMSE, MAPE, and CV values, alongside higher R² scores. The inclusion of more historical power consumption features was also found to have a more significant impact on the accuracy of predictions than including climate condition features. Moreover, the SVM-Radial model consistently outperformed others, particularly in capturing complex, non-linear patterns in the data. However, the DNN model, while competent in some metrics, showed elevated MAPE values, suggesting potential overfitting issues. These findings suggest that careful consideration of data frequency, features, and model selection is essential for optimizing power prediction, contributing to more efficient power management strategies in building operations.