Abstract
In this paper, we want to present an ICT architecture with a range prediction component, which sets up on machine learning algorithms based on consumption data. By this, the range component and therefore ICT system adapts to new vehicles and environmental conditions on runtime and distinguishes itself by low customization and maintenance costs.
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Kretzschmar, J., Gebhardt, K., Theiß, C., Schau, V. (2016). Range Prediction Models for E-Vehicles in Urban Freight Logistics Based on Machine Learning. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_17
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DOI: https://doi.org/10.1007/978-3-319-40973-3_17
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