Interior Heating and Its Influence on Electric Bus Consumption

Čulík, Kristián; Štefancová, Vladimíra; Hrudkay, Karol; Morgos, Jan

doi:10.3390/en14248346

Cited by 17 publications

(13 citation statements)

References 51 publications

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“…Eufrásio et al [26] conducted a study in São Paulo and reported an average consumption of 1.19-1.27 kWh/km, of which they found higher energy consumption on hotter days. Different studies provide statistical analyses on energy consumption in relation to temperature [27,28]. Culik et al [27] performed a statistical analysis on 14,888 trips and found that lower temperatures increased energy consumption.…”

Section: Data Analysis In Beb Operationsmentioning

confidence: 99%

Towards Efficient Battery Electric Bus Operations: A Novel Energy Forecasting Framework

Würtz,

Bogenberger,

Göhner

et al. 2024

WEVJ

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As the adoption of battery electric buses (BEBs) in public transportation systems grows, the need for precise energy consumption forecasting becomes increasingly important. Accurate predictions are essential for optimizing routes, charging schedules, and ensuring adequate operational range. This paper introduces an innovative forecasting methodology that combines a propulsion and auxiliary energy model with a novel concept, the environment generator. This approach addresses the primary challenge in electric bus energy forecasting: estimating future environmental conditions, such as weather, passenger load, and traffic patterns, which significantly impact energy demand. The environment generator plays a crucial role by providing the energy models with realistic input data. This study validates various models with different levels of model complexity against real-world operational data from a case study of over one year with 16 electric buses in Göttingen, Germany. Our analysis thoroughly examines influencing factors on energy consumption, like altitude, temperature, passenger load, and driving patterns. In order to comprehensively understand energy demands under varying operational conditions, the methodology integrates data-driven models and physical simulations into a modular and highly accurate energy predictor. The results demonstrate the effectiveness of our approach in providing more accurate energy consumption forecasts, which is essential for efficient electric bus fleet management. This research contributes to the growing body of knowledge in electric vehicle energy prediction and offers practical insights for transit authorities and operators in optimizing electric bus operations.

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Section: Data Analysis In Beb Operationsmentioning

confidence: 99%

Towards Efficient Battery Electric Bus Operations: A Novel Energy Forecasting Framework

Würtz,

Bogenberger,

Göhner

et al. 2024

WEVJ

View full text Add to dashboard Cite

show abstract

“…This means that these items may directly or indirectly collect, process and/or exchange data via a computer network and the Internet. Concurrently, other growing trends on a global scale are the advances in electromobility [2][3][4][5][6][7][8][9], obtaining energy from renewable sources and alternative fuels [10][11][12][13][14][15][16] and reducing exhaust emissions from means of transport [17][18][19][20][21][22][23][24]. The analysis of the literature and the state of knowledge confirmed that there are many technologies on the market for obtaining data from photovoltaic systems, electric car chargers and electricity meters taken from the power grid [2,25,26].…”

Section: Introductionmentioning

confidence: 99%

A Method of Assessing the Selection of Carport Power for an Electric Vehicle Using the Metalog Probability Distribution Family

et al. 2023

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This article presents a method for assessing the selection of carport power for an electric vehicle using the Metalog probability distribution family. Carports are used to generate electricity and provide shade for vehicles parked underneath them. On the roof of the carport, there is a photovoltaic system consisting of photovoltaic panels and an inverter. An inverter with Internet of Things functions generates data packets which describe the operation of the entire system at certain intervals and sends them via wireless transmission to a cloud server. The transmitted data can be processed offline and used to determine the charging capacity of individual electric vehicles. This article presents the use of the Metalog family of distributions to predict the production of electricity by a photovoltaic carport with the accuracy of the probability distribution. Based on the calculations, an electric vehicle was selected that can be charged from the carport.

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“…Large numbers of electric vehicles with traction batteries with high energy capacity may be helpful for this purpose. Electric buses with batteries with a capacity of over 200 kWh and passenger vehicles with batteries with a capacity of up to 100 kWh can be charged with DC chargers with adjustable output power [47]. This solution is also helpful for owners of PV systems who have built a system with too much power and are unable to use the energy produced.…”

Section: Introductionmentioning

confidence: 99%

Strategic Model for Charging a Fleet of Electric Vehicles with Energy from Renewable Energy Sources

Caban,

Małek,

Šarkan

2024

Energies

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The ever-growing number of electric vehicles requires increasing amounts of energy to charge their traction batteries. Electric vehicles are the most ecological when the energy for charging them comes from renewable energy sources. Obtaining electricity from renewable sources such as photovoltaic systems is also a way to reduce the operating costs of an electric vehicle. However, to produce cheap electricity from renewable energy sources, you first need to invest in the construction of a photovoltaic system. The article presents a strategic model for charging a fleet of electric vehicles with energy from photovoltaic systems. The model is useful for sizing a planned photovoltaic system to the energy needs of a vehicle fleet. It uses the Metalog family of probability distributions to determine the probability of producing a given amount of energy needed to power electric vehicle chargers. Using the model, it is possible to determine the percentage of energy from photovoltaic systems in the total energy needed to charge a vehicle fleet. The research was carried out on real data from an operating photovoltaic system with a peak power of 50 kWp. The approach presented in the strategic model takes into account the geographical and climatic context related to the location of the photovoltaic system. The model can be used for various renewable energy sources and different sizes of vehicle fleets with different electricity demands to charge their batteries. The presented model can be used to manage the energy produced both at the design stage of the photovoltaic system and during its operation.

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Interior Heating and Its Influence on Electric Bus Consumption

Cited by 17 publications

References 51 publications

Towards Efficient Battery Electric Bus Operations: A Novel Energy Forecasting Framework

Towards Efficient Battery Electric Bus Operations: A Novel Energy Forecasting Framework

A Method of Assessing the Selection of Carport Power for an Electric Vehicle Using the Metalog Probability Distribution Family

Strategic Model for Charging a Fleet of Electric Vehicles with Energy from Renewable Energy Sources

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