Authors:
Sunilkumar Raghuraman
1
;
2
;
Daniel Baumann
2
;
Marc Schindewolf
2
and
Eric Sax
2
Affiliations:
1
Daimler Busses, Mannheim, Germany
;
2
Karlsruhe Institute of Technology, Karlsruhe, Germany
Keyword(s):
Electric City Bus, Drivetrain Energy Consumption, Influencing Factors, Drive Style Analysis, Clustering.
Abstract:
In response to the growing need for sustainable mobility amidst global challenges like climate change and urbanization, ensuring energy-efficient operation of Electric City Buses (ECBs) is crucial. This study initially utilizes techniques associated with explainable artificial intelligence, such as SHapley Additive ExPlanations (SHAP), to determine the impact of various factors such as vehicle speed, acceleration, braking on drivetrain consumption. The data is categorized into distinct scenarios such as acceleration, starting, curve, uphill and downhill for this analysis. In driving scenarios such as curves, uphill, or downhill, the position of the brake pedal, along with the accelerator pedal and vehicle speed, were identified as significant factors affecting drivetrain consumption. Secondly, the study delves into analyzing driving behavior during bus stop entries, employing methods like Deep Autoencoder-based Clustering (DAC) and Self-Organizing Map (SOM). In the results of the DAC
and SOM analysis, it was found that Cluster 2, identified through the DAC model, exhibited substantial energy consumption, characterized by higher acceleration and lesser brake pedal usage. Conversely, the SOM analysis showed that the orange and blue clusters have greater energy efficiency, with a higher distance covered and lower energy consumption, contrasting with other clusters that consumed more energy for reaching the busstop.
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