基于深度学习的编码解码器模型用于自动驾驶电动卡车车队的速度规划
S Karthik1, G Rohith2, K B Devika3
1Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, India.
Heliyon
|July 1, 2024
概括
本研究引入了一种深度学习模型,用于优化电动卡车车队的速度,提高长途旅行的能源效率和续航里程. 这种新的方法确保了考虑到电池充电和道路状况的稳定排队.
科学领域:
- * * 运输工程 运输工程
- * 人工智能 * 人工智能
- * * 可持续能源 可持续能源
背景情况:
- *电动卡车排队对于在长途运输中扩大电动汽车的续航里程至关重要.
- *优化排队速度以提高能源效率是一个重大挑战.
- * 数据驱动的卡车排队解决方案是有限的,第一原则的方法很难.
研究的目的:
- * 开发一种新的深度学习方法,以优化自动驾驶电动卡车车队的速度配置.
- * 在考虑电池充电状态 (SOC),交通和道路条件的同时,确保排队串稳定性.
- *为高效的长途电动卡车路线规划和运营决策提供框架.
主要方法:
- * 使用了一种序列对序列编码器-解码器深度学习模型.
- * 在各种高速公路驱动周期上训练模型,以便长途适用.
- * 执行了超参数调整,以选择最合适的模型架构.
- *通过预测各种SOC配置文件的驱动周期来测试框架.
主要成果:
- * 证明了该模型能够预测电动卡车车队的驱动周期和速度配置文件.
- * 展示了框架在考虑多个操作约束方面的有效性.
- * 介绍了关于路线预测及其对政策决策的影响的案例研究.
结论:
- * 拟议的深度学习框架可以有效地规划电动卡车可行的路线.
- * 它有效地管理了包括电池容量,放电率和充电基础设施在内的限制.
- *这种方法确保了稳定的车队运行,并优化了长途电动卡车运输的能源利用.
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