Energy-efficient Hybrid Model Predictive Trajectory Planning for Autonomous Electric Vehicles

2024/11/09 by Fan Ding, Xuewen Luo, Ding, Fan +15
Engineering · #Artificial Intelligence (cs.AI) #Electric Vehicles and Infrastructure #Electric and Hybrid Vehicle Technologies #FOS: Computer and information sciences #Robotics (cs.RO) #Vehicle Dynamics and Control Systems

paper · pdf · doi:10.48550/arxiv.2411.06111

openalex publication_date 2024/11/09 · openalex created_date 2024/11/15 · openalex updated_date 2026/07/28

Abstract

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.

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