Trip energy consumption estimation for electric buses

被引:99
作者
Ji, Jinhua [1 ]
Bie, Yiming [1 ]
Zeng, Ziling [2 ]
Wang, Linhong [1 ]
机构
[1] Jilin Univ, Sch Transportat, Changchun 130022, Peoples R China
[2] Chalmers Univ Technol, Dept Architecture & Civil Engn, S-41296 Gothenburg, Sweden
来源
COMMUNICATIONS IN TRANSPORTATION RESEARCH | 2022年 / 2卷
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Electric bus; Trip energy consumption; Regression model; Operational data; Cold region; CHARGING INFRASTRUCTURE; BATTERY CAPACITY; MODEL; OPTIMIZATION; COST;
D O I
10.1016/j.commtr.2022.100069
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
This study aims to develop a trip energy consumption (TEC) estimation model for the electric bus (EB) fleet planning, operation, and life-cycle assessment. Leveraging the vast variations of temperature in Jilin Province, China, real-world data of 31 EBs operating in 14 months were collected with temperatures fluctuating from -27.0 to 35.0 & DEG;C. TEC of an EB was divided into two parts, which are the energy required by the traction and battery thermal management system, and the energy required by the air conditioner (AC) system operation, respectively. The former was regressed by a logarithmic linear model with ambient temperature, curb weight, travel distance, and trip travel time as contributing factors. The optimum working temperature and regression parameters were obtained by combining Fibonacci and Weighted Least Square. The latter was estimated by the operation time of the AC system in cooling mode or heating mode. Model evaluation and sensitivity analysis were conducted. The results show that: (i) the mean absolute percentage error (MAPE) of the proposed model is 12.108%; (ii) the estimation accuracy of the model has a probability of 99.7814% meeting the requirements of EB fleet scheduling; (iii) the MAPE has a 1.746% reduction if considering passengers' boarding and alighting.
引用
收藏
页数:13
相关论文
共 43 条
[21]   Studies of Energy Consumption by a City Bus Powered by a Hybrid Energy Storage System in Variable Road Conditions [J].
Lebkowski, Andrzej .
ENERGIES, 2019, 12 (05)
[22]   Optimal Charging Schedule Planning and Economic Analysis for Electric Bus Charging Stations [J].
Leou, Rong-Ceng ;
Hung, Jeng-Jiun .
ENERGIES, 2017, 10 (04)
[23]   Mixed bus fleet scheduling under range and refueling constraints [J].
Li, Lu ;
Lo, Hong K. ;
Xiao, Feng .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 104 :443-462
[24]   The effects of dynamic traffic conditions, route characteristics and environmental conditions on trip-based electricity consumption prediction of electric bus [J].
Li, Pengshun ;
Zhang, Yi ;
Zhang, Yi ;
Zhang, Kai ;
Jiang, Mengyan .
ENERGY, 2021, 218
[25]   Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption [J].
Liu, Kai ;
Wang, Jiangbo ;
Yamamoto, Toshiyuki ;
Morikawa, Takayuki .
APPLIED ENERGY, 2018, 227 :324-331
[26]   Impact of road gradient on energy consumption of electric vehicles [J].
Liu, Kai ;
Yamamoto, Toshiyuki ;
Morikawa, Takayuki .
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2017, 54 :74-81
[27]   Optimal charging plan for electric bus considering time-of-day electricity tariff [J].
Liu, Yuhan ;
Wang, Linhong ;
Zeng, Ziling ;
Bie, Yiming .
JOURNAL OF INTELLIGENT AND CONNECTED VEHICLES, 2022, 5 (02) :123-137
[28]   Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning [J].
Pamula, Teresa ;
Pamula, Wieslaw .
ENERGIES, 2020, 13 (09)
[29]   Solution approaches for integrated vehicle and crew scheduling with electric buses [J].
Perumal, Shyam S. G. ;
Dollevoet, Twan ;
Huisman, Dennis ;
Lusby, Richard M. ;
Larsen, Jesper ;
Riis, Morten .
COMPUTERS & OPERATIONS RESEARCH, 2021, 132
[30]   Data-driven decomposition analysis and estimation of link-level electric vehicle energy consumption under real-world traffic conditions [J].
Qi, Xuewei ;
Wu, Guoyuan ;
Boriboonsomsin, Kanok ;
Barth, Matthew J. .
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2018, 64 :36-52