行驶循环
内燃机
电气化
汽车工程
燃料效率
聚类分析
能源消耗
燃烧
电动汽车
计算机科学
工程类
电
人工智能
电气工程
功率(物理)
化学
物理
有机化学
量子力学
作者
Yiming Ye,Xuan Zhao,Jiangfeng Zhang
标识
DOI:10.1016/j.trd.2023.103900
摘要
Due to the fundamental differences in motors and internal combustion engines, the real-time energy consumption profiles of internal combustion engine vehicles (ICEVs) and electric vehicles (EVs) are different, which motivates the need to identify the driving cycle for different types of vehicles. Existing EV driving cycle studies did not provide comparisons with ICEV driving cycles using the same geographical locations. This paper proposes a systematic method to develop the driving cycle cycles for EVs and ICEVs and combine the self-organizing map and support vector machine to eliminate the inconsistent clustering results in the driving segment classification, improving the clustering performance by 6.06–16.05%. Then, the driving cycles for the EV and ICEV are established under peak and off-peak traffic periods, and compared in the same traffic environment to illustrate the influence of driving cycle electrification. The emissions and energy consumption are also analyzed and compared for both types of vehicles.
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