Electric Vehicles Plug-In Duration Forecasting Using Machine Learning for Battery Optimization

Yukai Chen, Khaled Sidahmed Sidahmed Alamin, Daniele Jahier Pagliari, Sara Vinco, Enrico Macii and Massimo Poncino
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Yukai Chen: Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, 10129 Turin, Italy
Khaled Sidahmed Sidahmed Alamin: Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, 10129 Turin, Italy
Daniele Jahier Pagliari: Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, 10129 Turin, Italy
Sara Vinco: Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, 10129 Turin, Italy
Enrico Macii: Interuniversity Department of Regional and Urban Studies and Planning (DIST), Politecnico di Torino, 10129 Turin, Italy
Massimo Poncino: Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, 10129 Turin, Italy

Energies, 2020, vol. 13, issue 16, 1-19

Abstract: The aging of rechargeable batteries, with its associated replacement costs, is one of the main issues limiting the diffusion of electric vehicles (EVs) as the future transportation infrastructure. An effective way to mitigate battery aging is to act on its charge cycles, more controllable than discharge ones, implementing so-called battery-aware charging protocols. Since one of the main factors affecting battery aging is its average state of charge (SOC), these protocols try to minimize the standby time, i.e., the time interval between the end of the actual charge and the moment when the EV is unplugged from the charging station. Doing so while still ensuring that the EV is fully charged when needed (in order to achieve a satisfying user experience) requires a “just-in-time” charging protocol, which completes exactly at the plug-out time. This type of protocol can only be achieved if an estimate of the expected plug-in duration is available. While many previous works have stressed the importance of having this estimate, they have either used straightforward forecasting methods, or assumed that the plug-in duration was directly indicated by the user, which could lead to sub-optimal results. In this paper, we evaluate the effectiveness of a more advanced forecasting based on machine learning (ML). With experiments on a public dataset containing data from domestic EV charge points, we show that a simple tree-based ML model, trained on each charge station based on its users’ behaviour, can reduce the forecasting error by up to 4× compared to the simple predictors used in previous works. This, in turn, leads to an improvement of up to 50% in a combined aging-quality of service metric.

Keywords: electric vehicles; light gradient boosting; battery charging; intelligent charging; optimal charging behavior; battery aging (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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