动力传动系统
行驶循环
水准点(测量)
对偶(语法数字)
汽车工程
电气化
能源管理
计算机科学
算法
电动汽车
电
工程类
功率(物理)
模拟
能量(信号处理)
数学
扭矩
艺术
统计
物理
文学类
电气工程
热力学
大地测量学
量子力学
地理
作者
Kaixuan Zhang,Jiageng Ruan,Tongyang Li,Hanghang Cui,Changcheng Wu
出处
期刊:Energy
[Elsevier]
日期:2023-04-01
卷期号:269: 126760-126760
被引量:11
标识
DOI:10.1016/j.energy.2023.126760
摘要
Nowadays, the trend of powertrain electrification in the public transportation sector is clear. To meet the dramatic load variation and relatively high handling stability requirements for battery electric buses, the dual-motor four-wheel powertrain architecture attracts great attention in recent years. Although the bus routes are fixed, the driving speed and load vary significantly with time, season, passenger capacity, and traffic conditions, which presents a serious challenge for efficient power coupling in a dual-motor system to reduce energy consumption. This study provides a data-driven fitting cycle for the specific bus route. Then, Deep Deterministic Policy Gradient (DDPG) algorithm is introduced in Energy Management Strategy (EMS) design to improve the vehicle's economic performance with uncertain demand in the unknown cycle. The simulation results show that the proposed DDPG-EMS achieves 93.91%–97.66% of the benchmark Dynamic Programming (DP) – based EMS under various testing cycles. In addition, the comparison of DDPG-EMS agent trained by fitting cycle, standard cycle, and real driving data reached 97.2%–97.66%, 93.91%–97.0%, and 94.41%–96.0% of DP, respectively, which demonstrates the effectiveness of data-driven fitting cycle and reinforcement learning algorithm combination in EMS design for dual-motor electrified bus.
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