An eco-driving strategy for electric buses at signalized intersection with a bus stop based on energy consumption prediction

被引:0
|
作者
Pan, Yingjiu [1 ]
Xi, Yi [1 ]
Fang, Wenpeng [1 ]
Liu, Yansen [1 ]
Zhang, Yali [1 ]
Zhang, Wenshan [1 ]
机构
[1] Changan Univ, Sch Automobile, Xian 710064, Peoples R China
基金
中国国家自然科学基金;
关键词
Connected electric buses; Energy consumption prediction; Eco-driving; Deep reinforcement learning; Signalized intersection with a bus stop; AUTOMATED VEHICLES; MODEL;
D O I
10.1016/j.energy.2025.134672
中图分类号
O414.1 [热力学];
学科分类号
摘要
The interaction between signalized intersections and downstream bus stops often leads to increased energy consumption in electric city buses. To address this issue, this study proposes a specialized eco-driving strategy tailored for electric buses operating at signalized intersections and bus stops. First, this study investigates how driving behavior affects energy consumption and develops a predictive model for energy consumption utilizing real-world driving data. Second, an optimized reward function is formulated based on the constructed energy consumption prediction model, incorporating considerations of safety, efficiency, and comfort. Subsequently, an acceleration and deceleration strategy are established using the Soft Actor-Critic framework to generate an ecovelocity curve. The effectiveness of this strategy is evaluated against real-world driving data. When compared to the driving behaviors observed in three distinct real-world scenarios, the proposed strategy demonstrates energy savings of 31.19 %, 20.84 %, and 30.29 % for electric buses navigating signalized intersections and bus stops continuously.
引用
收藏
页数:16
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