Data Driven Energy Economy Prediction of Electric Buses Using Machine Learning
DOI:
https://doi.org/10.48001/978-81-966500-7-0-7Keywords:
BEB, GHG, Machine Learning, Support Vector Machines, Random Forest, Artificial Neural NetworksAbstract
Electrification of transportation systems is increasing, in particular city buses raise enormous potential. Deep understanding of real-world driving data is essential for vehicle design and fleet operation. Various technological aspects must be considered to run alternative powertrains efficiently. Uncertainty about energy demand results in conservative design which implies inefficiency and high costs. Both, industry, and academia miss analytical solutions to solve this problem due to complexity and interrelation of parameters. Precise energy demand prediction enables significant cost reduction by optimized operations. This paper aims at increased transparency of battery electric buses’ (BEB) energy economy.We introduce novel sets of explanatory variables to characterize speed profiles, which we utilize in powerful machine learning methods. We develop and comprehensively assess 5 different algorithms regarding prediction accuracy, robustness, and overall applicability. Achieving a prediction accuracy of more than 94%, our models performed excellent in combination with the sophisticated selection of features. The presented methodology bears enormous potential for manufacturers, fleet operators and communities to transform mobility and thus pave the way for sustainable, public transportation
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Aljohani, T. M., Ebrahim, A., & Mohammed, O. (2021). Real-Time metadata-driven routing optimization for electric vehicle energy consumption minimization using deep reinforcement learning and Markov chain model. Electric Power Systems Research, 192. https://doi.org/10.1016/j.epsr.2020.106962
Asamer, J., Graser, A., Heilmann, B., & Ruthmair, M. (2016). Sensitivity analysis for energy demand estimation of electric vehicles. Transportation Research Part D: Transport and Environment, 46, 182–199. https://doi.org/10.1016/j.trd.2016.03.017
Chen, Y., Wu, G., Sun, R., Dubey, A., Laszka, A., & Pugliese, P. (2021). A Review and Outlook on Energy Consumption Estimation Models for Electric Vehicles. SAE International Journal of Sustainable Transportation, Energy, Environment, Policy, 2(1). https://doi.org/10.4271/13-02-01-0005
De Cauwer, C., Van Mierlo, J., & Coosemans, T. (2015). Energy consumption prediction for electric vehicles based on real-world data. Energies, 8(8), 8573–8593. https://doi.org/10.3390/en8088573
Ericsson, E. (2001). Independent driving pattern factors and their influence on fuel-use and exhaust emission factors. Transportation Research Part D: Transport and Environment, 6(5), 325–345. https://doi.org/10.1016/S1361-9209(01)00003-7
European Commission Directorate-General for Mobility and Transport. (2018). EU transport in figures. https://doi.org/10.2832/729667
Gallo, M., & Marinelli, M. (2020). Sustainable mobility: A review of possible actions and policies. Sustainability (Switzerland), 12(18). https ://doi.org /10.3390 /su12187499
Garg, T. K., & Mittal, P. (2021). Logistics networks: a sparse matrix application for solving the transshipment problem. Journal of Mathematical and Computational Science. https://doi.org/10.28919/jmcs/6654
Göhlich, D., Kunith, A., & Ly, T. (2014). Technology assessment of an electric urban bus system for berlin. WIT Transactions on the Built Environment, 138, 137–149. https://doi.org/10.2495/UT140121
Kontou, A., & Miles, J. (2015). Electric Buses: Lessons to be Learnt from the Milton Keynes Demonstration Project. Procedia Engineering, 118, 1137–1144. https://doi.org/10.1016/j.proeng.2015.08.455
Lajunen, A., & Lipman, T. (2016). Lifecycle cost assessment and carbon dioxide emissions of diesel, natural gas, hybrid electric, fuel cell hybrid and electric transit buses. Proceedings of the ICE -Energy, 106, 329–342. https://doi.org/10.1016/j.energy.2016.03.075
Li, P., Zhang, Y., Zhang, Y., & Zhang, K. (2021). Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data. Applied Energy, 298. https://doi.org/10.1016/j.apenergy.2021.117204
Marc, G., Tobias, M., & Thomas, H. (2018). Estimation of the energy demand of electric buses based on real-world data for large-scale public transport networks. Applied Energy, 230, 344–356.
Mittal, P. (2020). Impact of Digital Capabilities and Technology Skills on Effectiveness of Government in Public Services. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020. https://doi.org/10.1109/ICDABI51230.2020.9325647
Mittal, P., & Gautam, S. (2023). Logistic Regression and Predictive Analysis in Public Services of AI Strategies. TEM Journal, 12(2), 751–756. https://doi.org/10.18421/TEM122-19
Nijmeijer, H., Wang, J., & Besselink, I. (2017). Battery electric vehicle energy consumption modelling for range estimation. International Journal of Electric and Hybrid Vehicles, 9(2), 79. https://doi.org/10.1504/ijehv.2017.10006163
Pamuła, T., & Pamuła, D. (2022). Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning. Energies, 15(5). https://doi.org/10.3390/en15051747
Perugu, H., Collier, S., Tan, Y., Yoon, S., & Herner, J. (2023). Characterization of battery electric transit bus energy consumption by temporal and speed variation. Energy, 263. https://doi.org/10.1016/j.energy.2022.125914
Recalde, A., Cajo, R., Velasquez, W., & Alvarez-Alvarado, M. S. (2024). Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review. Energies, 17(13). https://doi.org/http://dx.doi.org/10.3390/en17133059
Sennefelder, R. M., Martín-Clemente, R., & González-Carvajal, R. (2023). Energy Consumption Prediction of Electric City Buses Using Multiple Linear Regression. Energies, 16(11). https://doi.org/10.3390/en16114365
Simonis, C., & Sennefelder, R. (2019). Route specific driver characterization for data-based range prediction of battery electric vehicles. 2019 14th International Conference on Ecological Vehicles and Renewable Energies, EVER 2019. https://doi.org/10.1109/EVER.2019.8813579
SRIDHAR, S., & TARUN, G. (2024). Machine Learning-Based Data-Driven Energy Economy Forecasting for Electric City Buses. International Journal For Advanced Research in Science and Technology, 14(5).
Ullah, I., Liu, K., Yamamoto, T., Al Mamlook, R. E., & Jamal, A. (2022). A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability. Energy and Environment, 33(8), 1583–1612. https://doi.org/10.1177/0958305X211044998
Ushakov, D., Dudukalov, E., Mironenko, E., & Shatila, K. (2022). Big data analytics in smart cities’ transportation infrastructure modernization. Transportation Research Procedia, 63, 2385–2391. https://doi.org/10.1016/j.trpro.2022.06.274
Xiong, R., Cao, J., & Yu, Q. (2018). Reinforcement learning-based real-time power management for hybrid energy storage system in the plug in hybrid electric vehicle. Applied Energy, 211, 538–548. https://doi.org/10.1016/j.apenergy.2017.11.072
Ziliaskopoulos, A. K., & Waller, S. T. (2000). An Internet-based geographic information system that integrates data, models and users for transportation applications.Transportation Research Part C: Emerging Technologies, 8(1-6), 427–444. https://doi.org/10.1016/S0968-090X(00)00027-9
Zogaan, W. A. (2022). Power Consumption prediction using Random Forest model. International Journal of Mechanical Engineering, 7(5), 974–5823.
