In battery electric buses (e-buses), the substantial energy consumption of the heating, ventilation, and air conditioning (HVAC) system can cause significant reductions of the available travel range. Additionally, HVAC systems are often operated at higher levels than what required for the thermal comfort of the passengers. Therefore, this paper proposes a method to experimentally investigate the influence of the HVAC system on the energy consumption and thermal comfort in a 12$\mathrm{m}$ e-bus. An appropriate thermal comfort model is identified and the required climatic input parameters are selected and measured with self-developed sensor stations. The energy consumption of the e-bus, the state of charge (SoC) of the battery and the available travel range are measured by an embedded data logger. Climatic measurements are then performed with heating on and off on a Berlin bus line in winter conditions. The results show that the energy consumption of the e-bus is increased by a factor of $1.9$ with heating on, while both the SoC and travel range are reduced accordingly. Comparing the thermal comfort with heating on and off, a decrease from “comfortable” to “slightly uncomfortable but acceptable” is observed.

The feasibility of electric vertical take-off and landing aircraft (eVTOL) relies on high-performance batteries with elevated energy and power densities for long-distance flight. However, systemic evaluation of battery chemistries for eVTOLs remains limited. This paper fills this research gap through a comprehensive investigation of current and emerging battery technologies. First, the properties of current battery chemistries are benchmarked against eVTOL requirements, identifying nickel-rich lithium-ion batteries (LIB), such as NMC and NCA, as the best suited for this application. Through comparison of 300 commercial battery cells, the Molicel INR21700-P45B cell is identified as the best candidate. Among next-generation batteries, SiSu solid-state batteries (SSBs) emerge as the most promising alternative. The performance of these cells is evaluated using a custom eVTOL battery simulation model for two eVTOL aircraft: the Volocopter VoloCity and the Archer Midnight. Results indicate that the Molicel INR21700-P45B underperforms in high-load scenarios, with a state of charge (SoC) at the end of the flight below the 30% safety margin. Simulated SoC values for the SiSu cell remain above this threshold, reaching 64.9% for the VoloCity and 64.8% for the Midnight. These results highlight next-generation battery technologies for eVTOLs and demonstrate the potential of SSBs to enhance flight performance.

In electric buses, heating, ventilation and air conditioning are responsible for up to 50% of the energy consumption. It is therefore necessary to identify improved thermal settings to minimize the energy consumption, while guaranteeing good thermal comfort. Hence, an accurate prediction of the passengers’ thermal sensation (TS) is needed. One of the most widely used models for TS prediction is the PMV-PPD model, which has been shown to provide reliable results in uniform, steady-state climatic conditions. Since these are not present in an urban bus, the accuracy of the PMV-PPD model diminishes. Additionally, some of the parameters needed are difficult to obtain (i.e., clothing insulation). This paper presents seven different machine learning models (ML) for the prediction of TS using three different sets of parameters. The first set comprises five parameters similar to the PMV-PPD model, the second uses only two, and the third uses all parameters available. To obtain the necessary data, climatic measurements in an electric bus in Berlin, Germany, were made. These measurements were performed in summer for ambient temperatures between 14.7 °C and 32.0 °C. Person-related information as well as the thermal comfort assessment were obtained via surveys. Despite the relatively small data set, four of our seven ML models performed well with a median accuracy between 70.3% and 69.4%. This could also be observed when using only two parameters. Hence, the efforts to gain experimental data can be reduced significantly. For the PMV-PPD model, a median shift of +1 was observed for mild and warm TS. The median accuracy rises from 48.8% without shift to 68.8% with shift.