提升自动驾驶电动车能效，通过优化轨迹规划实现11.74%能量回收
Energy-efficient Hybrid Model Predictive Trajectory Planning for Autonomous Electric Vehicles
- 融合模型预测控制与节能策略，优化轨迹规划
- 仿真中能量回收率提升11.74%，精准控制电机转速与加速度
- 无需额外硬件，可集成现有自动驾驶系统，适合电动车能效优化
为应对电动汽车续航有限和充电时间长的双重挑战，本文提出一种节能型混合模型预测轨迹规划器（EHMPP），采用节能优化策略。EHMPP聚焦于改进运动规划器设计，可无缝集成至现有自动驾驶算法中，无需额外硬件。在Prescan、CarSim和Matlab平台的仿真验证表明，该方法可使被动能量回收率提高11.74%，并有效实现电机转速与加速度在最优功率下的精确跟踪。综上，EHMPP不仅支持轨迹规划，更显著提升自动驾驶电动车的能源效率。
原文摘要 · Abstract (English)
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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