[1]

Deng Y, Chen Z, Yan P, Zhong R. 2023. Battery swapping and management system design for electric trucks considering battery degradation. Transportation Research Part D: Transport and Environment 122:103860

doi: 10.1016/j.trd.2023.103860
[2]

Pearre NS, Kempton W, Guensler RL, Elango VV. 2011. Electric vehicles: how much range is required for a day's driving? Transportation Research Part C: Emerging Technologies 19(6):1171−84

doi: 10.1016/j.trc.2010.12.010
[3]

Zhang R, Yao E. 2015. Electric vehicles' energy consumption estimation with real driving condition data. Transportation Research Part D: Transport and Environment 41:177−87

doi: 10.1016/j.trd.2015.10.010
[4]

Liu G, Ouyang M, Lu L, Li J, Hua J. 2015. A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications. Applied energy 149:297−314

doi: 10.1016/j.apenergy.2015.03.110
[5]

Fiori C, Ahn K, Rakha HA. 2016. Power-based electric vehicle energy consumption model: Model development and validation. Applied Energy 168:257−68

doi: 10.1016/j.apenergy.2016.01.097
[6]

Miri I, Fotouhi A, Ewin N. 2021. Electric vehicle energy consumption modelling and estimation—a case study. International Journal of Energy Research 45(1):501−20

doi: 10.1002/er.5700
[7]

Xie Y, Li Y, Zhao Z, Dong H, Wang S, Liu J, et al. 2020. Microsimulation of electric vehicle energy consumption and driving range. Applied energy 267:115081

doi: 10.1016/j.apenergy.2020.115081
[8]

Doluweera G, Hahn F, Bergerson J, Pruckner M. 2020. A scenario-based study on the impacts of electric vehicles on energy consumption and sustainability in Alberta. Applied Energy 268:114961

doi: 10.1016/j.apenergy.2020.114961
[9]

Du LL, Li B, Zhang HD. 2013. Estimation on state of charge of power battery based on the grey neural network model. Applied Mechanics and Materials 427:1158−62

doi: 10.4028/www.scientific.net/amm.427-429.1158
[10]

Li Y, Chattopadhyay P, Xiong S, Ray A, Rahn CD. 2016. Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-charge. Applied Energy 184:266−75

doi: 10.1016/j.apenergy.2016.10.025
[11]

Sheng H, Xiao J. 2015. Electric vehicle state of charge estimation: Nonlinear correlation and fuzzy support vector machine. Journal of Power sources 281:131−37

doi: 10.1016/j.jpowsour.2015.01.145
[12]

Wang Y, Lu C, Bi J, Sai Q, Zhang Y. 2021. Ensemble machine learning based driving range estimation for real-world electric city buses by considering battery degradation levels. IET Intelligent Transport Systems 15(6):824−36

doi: 10.1049/itr2.12064
[13]

Zhang J, Wang Z, Liu P, Zhang Z. 2020. Energy consumption analysis and prediction of electric vehicles based on real-world driving data. Applied Energy 275:115408

doi: 10.1016/j.apenergy.2020.115408
[14]

How DNT, Hannan MA, Hossain Lipu MS, Sahari KSM, Ker PJ, et al. 2020. State-of-charge estimation of li-ion battery in electric vehicles: a deep neural network approach. IEEE Transactions on Industry Applications 56(5):5565−74

doi: 10.1109/TIA.2020.3004294
[15]

Tian J, Xiong R, Shen W, Lu J. 2021. State-of-charge estimation of LiFePO4 batteries in electric vehicles: a deep-learning enabled approach. Applied Energy 291:116812

doi: 10.1016/j.apenergy.2021.116812
[16]

Chandran V, Patil CK, Karthick A, Ganeshaperumal D, Rahim R, et al. 2021. State of charge estimation of lithium-ion battery for electric vehicles using machine learning algorithms. World Electric Vehicle Journal 12(1):38

doi: 10.3390/wevj12010038
[17]

Manoharan A, Begam KM, Aparow VR, Sooriamoorthy D. 2022. Artificial neural networks, gradient boosting and support vector machines for electric vehicle battery state estimation: a review. Journal of Energy Storage 55:105384

doi: 10.1016/j.est.2022.105384
[18]

Basso R, Kulcsár B, Sanchez-Diaz I. 2021. Electric vehicle routing problem with machine learning for energy prediction. Transportation Research Part B: Methodological 145:24−55

doi: 10.1016/j.trb.2020.12.007
[19]

Ji J, Bie Y, Zeng Z, Wang L. 2022. Trip energy consumption estimation for electric buses. Communications in Transportation Research 2:100069

doi: 10.1016/j.commtr.2022.100069
[20]

Bi J, Wang Y, Zhang J. 2018. A data-based model for driving distance estimation of battery electric logistics vehicles. EURASIP Journal on Wireless Communications and Networking 2018:251

doi: 10.1186/s13638-018-1270-7
[21]

Bi J, Wang Y, Sai Q, Ding C. 2019. Estimating remaining driving range of battery electric vehicles based on real-world data: a case study of Beijing, China. Energy 169:833−43

doi: 10.1016/j.energy.2018.12.061
[22]

Wang Y, Lu C, Bi J, Sai Q, Qu X. 2023. Lifecycle cost optimization for electric bus systems with different charging methods: collaborative optimization of infrastructure procurement and fleet scheduling. IEEE Transactions on Intelligent Transportation Systems 24(3):2842−61

doi: 10.1109/TITS.2022.3223028
[23]

Dong C, Chang N. 2023. Overview of the identification of traffic accident-prone locations driven by big data. Digital Transportation and Safety 2(1):67−76

doi: 10.48130/DTS-2023-0006
[24]

Lyu N, Wang Y, Wu C, Peng L, Thomas AF. 2022. Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions. Journal of Intelligent and Connected Vehicles 5(1):17−35

doi: 10.1108/JICV-07-2021-0008