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With the increasing demand for sustainable transportation in the face of challenges such as climate change and urbanization, optimizing the energy efficiency of Electric City Buses (ECBs) is essential. This study employs explainable artificial intelligence techniques, specifically SHapley additive expansion (SHAP), to assess the influence of factors such as vehicle speed, acceleration, and braking on the energy consumption of the drivetrain. The data is segmented into distinct scenarios, including acceleration, starting, curves, uphill, and downhill driving. In driving conditions like curves or uphill and downhill routes, the brake pedal position, alongside the accelerator position and vehicle speed, emerged as key factors impacting drivetrain consumption. Secondly, the study delves into analyzing driving behavior during bus stop entries and leaving instances, employing methods like Deep Autoencoder-based Clustering (DAC) and Self-Organizing Map (SOM). This analysis identified groups with energy-efficient and energy-inefficient driving behaviors, with certain clusters showing high acceleration and low braking use, particularly during nighttime or low-traffic conditions. These insights highlight the potential for energy savings by promoting smoother, more consistent driving styles, especially as electric buses approach or depart from bus stops.

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Clustering Analysis to Determine the Optimizing Potentials in Drivetrain Consumption with SHAP Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Sunilkumar Raghuraman,
  • Daniel Baumann,
  • Marc Schindewolf,
  • Eric Sax

摘要

With the increasing demand for sustainable transportation in the face of challenges such as climate change and urbanization, optimizing the energy efficiency of Electric City Buses (ECBs) is essential. This study employs explainable artificial intelligence techniques, specifically SHapley additive expansion (SHAP), to assess the influence of factors such as vehicle speed, acceleration, and braking on the energy consumption of the drivetrain. The data is segmented into distinct scenarios, including acceleration, starting, curves, uphill, and downhill driving. In driving conditions like curves or uphill and downhill routes, the brake pedal position, alongside the accelerator position and vehicle speed, emerged as key factors impacting drivetrain consumption. Secondly, the study delves into analyzing driving behavior during bus stop entries and leaving instances, employing methods like Deep Autoencoder-based Clustering (DAC) and Self-Organizing Map (SOM). This analysis identified groups with energy-efficient and energy-inefficient driving behaviors, with certain clusters showing high acceleration and low braking use, particularly during nighttime or low-traffic conditions. These insights highlight the potential for energy savings by promoting smoother, more consistent driving styles, especially as electric buses approach or depart from bus stops.