Novel Electric Bus Energy Consumption Model Based on Probabilistic Synthetic Speed Profile Integrated With HVAC

被引:27
|
作者
El-Taweel, Nader A. [1 ]
Zidan, Aboelsood [1 ]
Farag, Hany E. Z. [1 ]
机构
[1] York Univ, Dept Elect Engn & Comp Sci, Toronto, ON M3J 1P3, Canada
关键词
Data models; Energy consumption; Load modeling; Heating systems; Atmospheric modeling; Acceleration; Meteorology; Electric buses; energy consumption; heat ventilation and air conditioning; route topography; speed profile; transportation electrification; weather conditions; BATTERY; HYBRID; WELL;
D O I
10.1109/TITS.2020.2971686
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This paper proposes a novel and generic model to calculate the Electric Bus Energy Consumption (EBEC) without the need for a high-resolution speed profile data. The proposed model generates a set of speed profiles using the basic information of the bus trip: trip time, trip length, and distances between successive bus stops. The generated speed profiles could accurately reflect the various traffic conditions and speed behaviors of real-world situations. Roadway Level of Service (LoS) is incorporated in the proposed model to simulate different traffic conditions. Further, a stochastic model for the bus speed profile is adopted to simulate the probability of the bus to stop at each on-route designated stop. The generated speed profiles are then inputted to an accurate EBEC model that considers the route topography, auxiliary loads (lighting, sound, and radio systems) and the impact of the weather conditions. The operation of the heat, ventilation and air conditioning system (HVAC) is also incorporated in the model using the thermal mass balance principle. Using the proposed model, the characteristics of EBEC on a given route can be evaluated through generating a set of speed profiles for the studied route. The proposed model provides transit network planners with a useful tool to appropriately design electric-based transit networks when there is a lack or unavailability of real-time and high resolution data.
引用
收藏
页码:1517 / 1531
页数:15
相关论文
共 50 条
  • [41] A rule-based model for integrated operation of bus priority signal timings and traveling speed
    Ma, Wanjing
    Liu, Yue
    Han, Baoxin
    JOURNAL OF ADVANCED TRANSPORTATION, 2013, 47 (03) : 369 - 383
  • [42] A Study on the Energy Management Strategy Based on the Accuracy of Speed Profile of Hybrid Electric Vehicle
    Sung, Donghwan
    Lee, Heeyun
    Cha, Suk Won
    2018 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC), 2018,
  • [43] Electric Propulsion Optimization Model Based on Exploitation Profile and Energy Price
    Vucetic, Dubravko
    Tomas, Vinko
    Cuculic, Aleksander
    BRODOGRADNJA, 2011, 62 (02): : 130 - 135
  • [44] Impacts of data preprocessing and selection on energy consumption prediction model of HVAC systems based on deep learning
    Xiao, Ziwei
    Gang, Wenjie
    Yuan, Jiaqi
    Chen, Zhuolun
    Li, Ji
    Wang, Xuan
    Feng, Xiaomei
    ENERGY AND BUILDINGS, 2022, 258
  • [45] Energy consumption dynamic prediction for HVAC systems based on feature clustering deconstruction and model training adaptation
    Liu, Huiheng
    Liu, Yanchen
    Huang, Huakun
    Wu, Huijun
    Huang, Yu
    BUILDING SIMULATION, 2024, 17 (09) : 1439 - 1460
  • [46] An integrated optimization model of metro energy consumption based on regenerative energy and passenger transfer
    He, Deqiang
    Yang, Yanjie
    Chen, Yanjun
    Deng, Jianxin
    Shan, Sheng
    Liu, Jianren
    Li, Xianwang
    APPLIED ENERGY, 2020, 264
  • [47] Power-based electric vehicle energy consumption model: Model development and validation
    Fiori, Chiara
    Ahn, Kyoungho
    Rakha, Hesham A.
    APPLIED ENERGY, 2016, 168 : 257 - 268
  • [48] Energy Consumption Analysis of a Novel Two-speed e-Powertrain System for Electric Vehicle
    Liu, Puhui
    Feng, Shun
    Wei, Wei
    Gu, Yue
    Huang, Xiaowei
    Shen, Jiawei
    2021 IEEE 12TH ENERGY CONVERSION CONGRESS AND EXPOSITION - ASIA (ECCE ASIA), 2021, : 1801 - 1805
  • [49] Simulation of battery energy consumption in an electric car with traction and HVAC model for a given source and destination for reducing the range anxiety of the driver
    Hariharan, C.
    Gunadevan, D.
    Prakash, S. Arun
    Latha, K.
    Raj, V. Antony Aroul
    Velraj, R.
    ENERGY, 2022, 249
  • [50] A novel deep reinforcement learning based methodology for short-term HVAC system energy consumption prediction
    Liu, Tao
    Xu, Chengliang
    Guo, Yabin
    Chen, Huanxin
    INTERNATIONAL JOURNAL OF REFRIGERATION, 2019, 107 : 39 - 51