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<p>With the growing prevalence of battery electric vehicles, enhancing their energy efficiency through control optimization has become a subject of interest. In this work, we use dynamic programming (DP) as an offline, globally optimal benchmark to compute acceleration-based trajectories under simultaneous boundary conditions on travel time, speed, and distance. We formulate an acceleration-centric DP that minimizes battery-energy consumption; specifically, the objective is to minimize the integral of battery power over the acceleration maneuver subject to the boundary conditions and powertrain constraints. A physics-based longitudinal vehicle model, along with an empirically derived battery and motor model, is used to analyze the energy dynamics of the system. The resulting optimal acceleration trajectories are shown to reduce energy consumption by 3.4% relative to a constant acceleration-based approach. This study contributes an improved perspective on energy-optimal control of electric vehicles by introducing an acceleration-centric control algorithm that enables global optimization under boundary conditions in accelerated driving situations. This positions dynamic programming (DP) as an offline benchmark and yields a driving mode-related (ECO/Normal/Sport) torque-reference library for controller design and evaluation. The reference library can support practical selection of driving modes according to driver preferences and provide implementable, energy-aware acceleration trajectories for specific driving environments.</p>

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Control Optimization of Acceleration Profile for a Battery Electric Vehicle

  • 期刊论文

  • 发表日期：

    2025年12月10日

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  • Jehwi Yeon,
  • Sungtak Hong,
  • Jaekwang Jung,
  • Yunho Lee,
  • Jungwon Han,
  • Namwook Kim

摘要

With the growing prevalence of battery electric vehicles, enhancing their energy efficiency through control optimization has become a subject of interest. In this work, we use dynamic programming (DP) as an offline, globally optimal benchmark to compute acceleration-based trajectories under simultaneous boundary conditions on travel time, speed, and distance. We formulate an acceleration-centric DP that minimizes battery-energy consumption; specifically, the objective is to minimize the integral of battery power over the acceleration maneuver subject to the boundary conditions and powertrain constraints. A physics-based longitudinal vehicle model, along with an empirically derived battery and motor model, is used to analyze the energy dynamics of the system. The resulting optimal acceleration trajectories are shown to reduce energy consumption by 3.4% relative to a constant acceleration-based approach. This study contributes an improved perspective on energy-optimal control of electric vehicles by introducing an acceleration-centric control algorithm that enables global optimization under boundary conditions in accelerated driving situations. This positions dynamic programming (DP) as an offline benchmark and yields a driving mode-related (ECO/Normal/Sport) torque-reference library for controller design and evaluation. The reference library can support practical selection of driving modes according to driver preferences and provide implementable, energy-aware acceleration trajectories for specific driving environments.