Abstract
Plug-in hybrid (PH) buses offer range and operating flexibility of buses with conventional internal combustion engines with environmental. However, when they are frequently charged, they also enable societal benefits (emissions- and noise-related) associated with electric buses. Thanks to geofencing, pure electric drive of PH buses can be assigned to specific locations via a back-office system. As a result, PH buses not only can fulfil zero-emission (ZE) zones set by city authorities, but they can also minimize total energy use thanks to selection of locations favouring (from energy perspective) electric drive. Such a location-controlled behaviour allows executing targeted air quality improvement and noise reduction strategies as well reducing energy consumption. However, current ZE zone assignment strategies used by PH buses are static—they are based on the first-come-first serve rule and do not consider traffic conditions. In this article, we propose a novel recommendation system, based on artificial intelligence, that allows PH buses operating efficiently in a dynamic environment, making the best use of the available resources so that emission- and noise-pollution levels are minimized.
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Acknowledgements
The authors would like to acknowledge the Spanish Ministerio de Economía, Industria y Competitividad and ERDF for the support provided under contracts RTI2018-100754-B-I00 (iSUN project) and FPU17/00563. This work was partially funded by the University of Cadiz (contracts PR2018-056 & PR2018-062).
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Ruiz, P., Arias, A., Massobrio, R., de la Torre, J.C., Seredynski, M., Dorronsoro, B. (2020). Intelligent Electric Drive Management for Plug-in Hybrid Buses. In: Dorronsoro, B., Ruiz, P., de la Torre, J., Urda, D., Talbi, EG. (eds) Optimization and Learning. OLA 2020. Communications in Computer and Information Science, vol 1173. Springer, Cham. https://doi.org/10.1007/978-3-030-41913-4_8
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