This paper analyzes the effect of GPS sampling interval on the performance of the longitudinal dynamic model for estimating electric bus trip energy consumption based on real-world operational data. The performance of the estimation model under different sampling intervals is primarily evaluated by the MAE, RMSE, MAPE, and probability distribution functions. It is observed that the relationships between sampling intervals and the MAE, RMSE, and MAPE are all roughly S-shaped. The estimation accuracy is similar when the sampling interval is less than or equal to 13 s. The probability distribution functions of residuals are no longer stably consistent with that of the observed trip energy consumption when the sampling interval is larger than 16 s. In addition, the adaptability of the estimation model under different sampling intervals is analyzed from the perspective of bus operation management. Results indicate that the threshold value of the sampling interval is 16 s at the current battery rated capacity of 162.3 kWh. The threshold value of the sampling interval will become smaller with the decrease in battery rated capacity and increase in daily operation mileage.
ShenJDusmezSKhalighA. Optimization of sizing and battery cycle life in battery/ultracapacitor hybrid energy storage systems for electric vehicle applications. IEEE Trans Ind Inform2014; 10: 2112–2121.
6.
LeeJShonHPapakonstantinouI, et al. Optimal fleet, battery, and charging infrastructure planning for reliable electric bus operations. Transp Res D Transp Environ2021; 100: 103066.
7.
LiuXQuXMaX. Optimizing electric bus charging infrastructure considering power matching and seasonality. Transp Res D Transp Environ2021; 100: 103057.
8.
LiuXLiuXZhangX, et al. Optimal location planning of electric bus charging stations with integrated photovoltaic and energy storage system. Comput Aided Civ Infrastruct Eng2023; 38: 1424–1446.
9.
MaXZhangXLiX, et al. Impacts of free-floating bikesharing system on public transit ridership. Transp Res D Transp Environ2019; 76: 100–110.
10.
BieYJiJWangX, et al. Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption. Comput Aided Civ Infrastruct Eng2021; 36: 1530–1548.
11.
HuangDWangS. A two-stage stochastic programming model of coordinated electric bus charging scheduling for a hybrid charging scheme. Multimodal Transp2022; 1(1): 100006.
12.
BieYWangDQiH. Prediction model of bus arrival time at signalized intersection using GPS data. J Transp Eng2012; 138: 12–20.
13.
FigliozziMAStollNB. A study of bus high-resolution GPS speed data accuracy. Transp Res Rec2018; 2672: 187–198.
14.
BieYGongXLiuZ. Time of day intervals partition for bus schedule using GPS data. Transp Res Part C Emerg Technol2015; 60: 443–456.
15.
DaiZMaXChenX. Bus travel time modelling using GPS probe and smart card data: a probabilistic approach considering link travel time and station dwell time. J Intell Transp Syst2019; 23: 175–190.
16.
SongXTianJTaoP, et al. Traffic state estimation of bus line with sparse sampled data. IEEE Access2020; 8: 216127–216140.
17.
WangYLiKZengX, et al. Energy consumption characteristics based driving conditions construction and prediction for hybrid electric buses energy management. Energy2022; 245: 123189.
18.
WangGFangZXieX, et al. Pricing-aware real-time charging scheduling and charging station expansion for large-scale electric buses. ACM Trans Intell Syst Technol2021; 12: 1–26.
19.
Al-OgailiASAl-ShetwiAQAl-MasriHMK, et al. Review of the estimation methods of energy consumption for battery electric buses. Energies2021; 14: 7578.
20.
RhodeSVan VaerenberghSPfriemM. Power prediction for electric vehicles using online machine learning. Eng Appl Artif Intell2020; 87: 103278.
21.
AbediaslHAnsariAHosseiniV, et al. Real-time vehicular fuel consumption estimation using machine learning and on-board diagnostics data. Proc IMechE, Part D: J Automobile Engineering2023; 15. DOI: 10.1177/09544070231185609
22.
LiuBShiQHeZ, et al. A driving-cycle predictive control approach for energy consumption optimization of hybrid electric vehicle. Proc IMechE, Part D: J Automobile Engineering2022; 236: 2507–2518.
23.
GongCHuMLiS, et al. Equivalent consumption minimization strategy of hybrid electric vehicle considering the impact of driving style. Proc IMechE, Part D: J Automobile Engineering2019; 233: 2610–2623.
24.
PamułaTPamułaD. Prediction of electric buses energy consumption from trip parameters using deep learning. Energies2022; 15: 1747.
25.
LiuYLiangH. A data-driven approach for electric bus energy consumption estimation. IEEE Trans Intell Transp Syst2022; 23: 17027–17038.
26.
GalletMMassierTHamacherT. Estimation of the energy demand of electric buses based on real-world data for large-scale public transport networks. Appl Energy2018; 230: 344–356.
27.
GuoZLiuCLuoJ. Quantitative research on vehicle energy consumptions based on the longitudinal-vertical dynamics of the tracked vehicle. Proc IMechE, Part D: J Automobile Engineering2023; 12. DOI: 10.1177/09544070231174274
28.
QuXYuYZhouM, et al. Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: a reinforcement learning based approach. Appl Energy2020; 257: 114030.
29.
WangJBesselinkINijmeijerH. Battery electric vehicle energy consumption prediction for a trip based on route information. Proc IMechE, Part D: J Automobile Engineering2018; 232: 1528–1542.
LiuYLyuCZhangY, et al. DeepTSP: deep traffic state prediction model based on large-scale empirical data. Commun Transp Res2021; 1: 100012.
32.
AbdelatyHAl-ObaidiAMohamedM, et al. Machine learning prediction models for battery-electric bus energy consumption in transit. Transp Res D Transp Environ2021; 96: 102868.
33.
El-TaweelNAZidanAFaragHEZ. Novel electric bus energy consumption model based on probabilistic synthetic speed profile integrated with HVAC. IEEE Trans Intell Transp Syst2021; 22: 1517–1531.
34.
JiJBieYZengZ, et al. Trip energy consumption estimation for electric buses. Commun Transp Res2022; 2: 100069.
35.
De CauwerCVerbekeWCoosemansT, et al. A data-driven method for energy consumption prediction and energy-efficient routing of electric vehicles in real-world conditions. Energies2017; 10: 608.
36.
CuiQDuSLiuC, et al. A stochastic optimal energy management strategy considering battery health for hybrid electric bus. Proc IMechE, Part D: J Automobile Engineering2020; 234: 3112–3127.
37.
VepsäläinenJRitariALajunenA, et al. Energy uncertainty analysis of electric buses. Energies2018; 11: 3267.
38.
Saadon Al-OgailiARamasamyAJuhana Tengku HashimT, et al. Estimation of the energy consumption of battery driven electric buses by integrating digital elevation and longitudinal dynamic models: Malaysia as a case study. Appl Energy2020; 280: 115873.
39.
MaXMiaoRWuX, et al. Examining influential factors on the energy consumption of electric and diesel buses: a data-driven analysis of large-scale public transit network in Beijing. Energy2021; 216: 119196.
40.
HjelkremOALervågKYBabriS, et al. A battery electric bus energy consumption model for strategic purposes: validation of a proposed model structure with data from bus fleets in China and Norway. Transp Res D Transp Environ2021; 94: 102804.
41.
LiXWangTLiJ, et al. Energy consumption estimation for electric buses based on a physical and data-driven fusion model. Energies2022; 15: 4160.
42.
JiangJYuYMinH, et al. Trip-level energy consumption prediction model for electric bus combining Markov-based speed profile generation and Gaussian processing regression. Energy2023; 263: 125866.
43.
XuHTuRLiT, et al. Interpretable bus energy consumption model with minimal input variables considering powertrain types. Transp Res D Transp Environ2023; 119: 103742.
44.
BassoFFeijooFPezoaR, et al. The impact of electromobility in public transport: an estimation of energy consumption using disaggregated data in Santiago, Chile. Energy2024; 286: 129550.
45.
SunSBatistaSFAMenéndezM, et al. Powering up urban mobility: a comparative study of energy efficiency in electric and diesel buses across various lane configurations. Sustain Cities Soc2024; 101: 105086.
46.
XuYGbologahFELeeDY, et al. Assessment of alternative fuel and powertrain transit bus options using real-world operations data: life-cycle fuel and emissions modeling. Appl Energy2015; 154: 143–159.
47.
DehdariPWlcekHFurmansK. An updated literature review of CO2e calculation in road freight transportation. Multimodal Transp2023; 2(2): 100068.
48.
LajunenA. Energy consumption and cost-benefit analysis of hybrid and electric city buses. Transp Res Part C Emerg Technol2014; 38: 1–15.
49.
GaoZLinZLaClairTJ, et al. Battery capacity and recharging needs for electric buses in city transit service. Energy2017; 122: 588–600.
50.
BieYLiuYLiS, et al. HVAC operation planning for electric bus trips based on chance-constrained programming. Energy2022; 258: 124807.
