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<p>This observational study explores the effect of adaptive cruise control (ACC) on energy in electric vehicles (EVs) and contrasts the findings with prior research on internal combustion engine (ICE) vehicles. Using real-world driving data, we show that ACC engagement results in a penalty of +6.62 Wh/km, a 2.5% increase over the fleet-level average of 266 Wh/km. This penalty is smaller than that in ICE vehicles, primarily due to the superior efficiency of EV powertrains and the mitigating role of regenerative braking. On average, human drivers achieve higher regenerative braking efficiency than ACC. However, when braking conditions match, ACC marginally outperforms human drivers across most regions of the speed-deceleration map. This research provides insights into the interplay between energy-efficient technologies and driver-assistance systems, and highlights the need to optimize automation algorithms to leverage the unique characteristics of EV powertrains, maximize energy recovery, and support next-generation energy management solutions in transportation.</p>

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Insights into adaptive cruise control and energy efficiency in electric vehicles

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月1日

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  • Ayman Moawad,
  • Matthew Zebiak,
  • Megan St. Pierre,
  • Dominik Karbowski,
  • Aymeric Rousseau

摘要

This observational study explores the effect of adaptive cruise control (ACC) on energy in electric vehicles (EVs) and contrasts the findings with prior research on internal combustion engine (ICE) vehicles. Using real-world driving data, we show that ACC engagement results in a penalty of +6.62 Wh/km, a 2.5% increase over the fleet-level average of 266 Wh/km. This penalty is smaller than that in ICE vehicles, primarily due to the superior efficiency of EV powertrains and the mitigating role of regenerative braking. On average, human drivers achieve higher regenerative braking efficiency than ACC. However, when braking conditions match, ACC marginally outperforms human drivers across most regions of the speed-deceleration map. This research provides insights into the interplay between energy-efficient technologies and driver-assistance systems, and highlights the need to optimize automation algorithms to leverage the unique characteristics of EV powertrains, maximize energy recovery, and support next-generation energy management solutions in transportation.