Determination of improved climatic conditions for thermal comfort and energy efficiency in electric buses

Cigarini, Francesco; Schminkel, Peer; Sonnekalb, Michael; Best, Pascal; Göhlich, Dietmar

doi:10.1016/j.apergo.2022.103856

Cited by 5 publications

(9 citation statements)

References 14 publications

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“…Additionally, this high energy consumption does not necessarily result in an improved passengers' satisfaction, as the HVAC system is often operated at higher levels than what is necessary for the thermal comfort of the passengers. This is shown by recent on-road studies in e-buses, which were performed in Amsterdam [4] and Berlin [5] for mean external temperatures of 13.4 °C and 5.3-7.8 °C, respectively. In both cases, the results of the performed climatic measurements and of the passengers' surveys indicate that the thermal settings of the HVAC system can be decreased, respectively, up to 1.4 K [4] and 2.5 K [5] with respect to the standard set temperature without significantly affecting the thermal wellbeing.…”

Section: Introductionmentioning

confidence: 74%

“…This is shown by recent on-road studies in e-buses, which were performed in Amsterdam [4] and Berlin [5] for mean external temperatures of 13.4 °C and 5.3-7.8 °C, respectively. In both cases, the results of the performed climatic measurements and of the passengers' surveys indicate that the thermal settings of the HVAC system can be decreased, respectively, up to 1.4 K [4] and 2.5 K [5] with respect to the standard set temperature without significantly affecting the thermal wellbeing. These findings show the need for a revision of these thermal settings especially in e-buses with the goal of improving the overall energy efficiency, while guaranteeing a good thermal comfort to the passengers.…”

Section: Introductionmentioning

confidence: 74%

“…In this case, the climatic conditions can vary greatly depending on, e.g., the proximity to entrance doors or the height above the ground. Additional variations can ensue with time due to changes in outer temperature, solar radiation through the windows and external air flow through the doors [3,5,13]. To better capture these phenomena, dynamic thermophysiological models, such as the twonodes model [3,8,9,14] or local models [15,16], are sometimes used.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome the limitations of the aforementioned studies, ML is used in this paper to assess passengers' thermal comfort in an e-bus. For this purpose, a data set obtained from climatic measurements in an e-bus in Berlin, Germany [5] containing both climatic parameters and personal information of 278 passengers is used for modeling. Because of the limited number of input data and the discrete nature of the declared TS, passengers were arranged in two groups representing mild and warm thermal sensation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assessment of Thermal Comfort in an Electric Bus Based on Machine Learning Classification

Santoyo Alum,

Fay,

Cigarini

et al. 2023

Applied Sciences

Self Cite

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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.

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Section: Introductionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessment of Thermal Comfort in an Electric Bus Based on Machine Learning Classification

Santoyo Alum,

Fay,

Cigarini

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…Heating, air conditioning, cooling, and ventilation in particular play a role in auxiliary consumers [43,44]. In Germany, it is known that at low temperatures, the heating requirement for electric buses can account for about 50% of the energy consumption [45]. For smaller vehicle capacities, fewer empirical data are available regarding energy consumption.…”

Section: Bus Typesmentioning

confidence: 99%

Optimizing Fleet Structure for Autonomous Electric Buses: A Route-Based Analysis in Aachen, Germany

Sistig,

Sinhuber,

Rogge

et al. 2024

Sustainability

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Intelligent transportation systems enhance the potential for sustainable, user-friendly, and efficient transport. By eliminating driver costs, autonomous buses facilitate the redesign of networks, timetables, and fleet structure in a cost-effective manner. The electrification of bus fleets offers the opportunity to further improve the environmental sustainability of transportation networks, but requires adjustments to vehicle schedules due to the limited range and charging requirements. This paper examines the intricate relationship between electrification and autonomous buses. To this end, timetables for autonomous electric buses of different sizes were developed for a real bus route in Aachen, Germany. The resulting electric vehicle scheduling problem was then solved using an adaptive large neighborhood search to determine the number of vehicles needed and the total cost of ownership. By eliminating driver costs, vehicles with lower passenger capacity become much more attractive, albeit at a slightly higher cost. In comparison, the incremental costs of electrification are low if the right approach is taken. Fluctuations in typical passenger numbers can be used to modify timetables and vehicle schedules to accommodate the charging needs of autonomous electric buses. In particular, electric bus concepts with fewer charging stations and lower charging power benefit from adapting the timetable to passenger numbers. The results demonstrate that the specific requirements of electric buses should be considered when adapting networks and timetables in order to design a sustainable transport network.

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A Literature Review on Bus Comfort On-Board

Maltinti,

Coni,

Rombi

et al. 2024

Computational Science and Its Applications – ICCSA 2024 Workshops

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Determination of improved climatic conditions for thermal comfort and energy efficiency in electric buses

Cited by 5 publications

References 14 publications

Assessment of Thermal Comfort in an Electric Bus Based on Machine Learning Classification

Assessment of Thermal Comfort in an Electric Bus Based on Machine Learning Classification

Optimizing Fleet Structure for Autonomous Electric Buses: A Route-Based Analysis in Aachen, Germany

A Literature Review on Bus Comfort On-Board

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