自律走行電気自動車のための省エネ型ハイブリッドモデル予測軌道計画
Energy-efficient Hybrid Model Predictive Trajectory Planning for Autonomous Electric Vehicles
既存の自動運転アルゴリズムに追加ハードウェアなしで統合できる省エネ軌道計画器EHMPPを提案し、シミュレーションで回生エネルギーの11.74%増加と最適出力でのモータ追従を実証した。
著者: Fan Ding, Xuewen Luo, Gaoxuan Li, Hwa Hui Tew, Junn Yong Loo, Chor Wai Tong, A. S. M Bakibillah, Ziyuan Zhao, Zhiyu Tao
分類: cs.RO, cs.AI
原文アブストラクト
To tackle the twin challenges of limited battery life and lengthy charging durations in electric vehicles (EVs), this paper introduces an Energy-efficient Hybrid Model Predictive Planner (EHMPP), which employs an energy-saving optimization strategy. EHMPP focuses on refining the design of the motion planner to be seamlessly integrated with the existing automatic driving algorithms, without additional hardware. It has been validated through simulation experiments on the Prescan, CarSim, and Matlab platforms, demonstrating that it can increase passive recovery energy by 11.74\% and effectively track motor speed and acceleration at optimal power. To sum up, EHMPP not only aids in trajectory planning but also significantly boosts energy efficiency in autonomous EVs.
