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

TL;DR

This paper presents EHMPP, an energy-efficient hybrid model predictive planner for autonomous electric vehicles that enhances energy recovery and tracking without extra hardware, validated through multiple simulation platforms.

Contribution

Introduction of EHMPP, a novel energy-saving trajectory planning method integrated with existing autonomous driving systems for electric vehicles.

Findings

Increases passive recovery energy by 11.74%

Effectively tracks motor speed and acceleration at optimal power

Validated across Prescan, CarSim, and Matlab platforms

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.