Abstract
This paper addresses the problem of predicting the energy consumption for the drivers of Battery electric vehicles (BEVs). Several external factors (e.g., weather) are shown to have huge impacts on the energy consumption of a vehicle besides the vehicle or powertrain dynamics. Thus, it is challenging to take all of those influencing variables into consideration. The proposed approach is based on a hybrid model which improves the prediction accuracy of energy consumption of BEVs. The novelty of this approach is to combine a physics-based simulation model, which captures the basic vehicle and powertrain dynamics, with a data-driven model. The latter accounts for other external influencing factors neglected by the physical simulation model, using machine learning techniques, such as generalized additive mixed models, random forests and boosting. The hybrid modeling method is evaluated with a real data set from TUM and the hybrid models were shown that decrease the average prediction error from 40% of the pure physics model to 10%.
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Acknowledgments
This work was partly supported by the TRANSACT project. TRANSACT (https://transact-ecsel.eu/) has received funding from the Electronic Component Systems for European Leadership Joint Under-taking under grant agreement no. 101007260. This joint undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Belgium, Denmark, Finland, Germany, Poland, Netherlands, Norway, and Spain.
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Huang, YW., Prehofer, C., Lindskog, W., Puts, R., Mosca, P., Kauermann, G. (2023). Predictive Energy Management for Battery Electric Vehicles with Hybrid Models. In: Martins, A.L., Ferreira, J.C., Kocian, A., Tokkozhina, U. (eds) Intelligent Transport Systems. INTSYS 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 486. Springer, Cham. https://doi.org/10.1007/978-3-031-30855-0_13
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