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Data-driven smart charging for heterogeneous electric vehicle fleets

能源与人工智能(英文) 2020年第1期1卷 74-86页

作者： Oliver Frendo jerome graf Nadine Gaertner Heiner Stuckenschmidt SAP SE Dietmar-Hopp-Allee 16Walldorf 69190Germany University of Mannheim Schloss68131 Mannheim

The ongoing electrification of mobility comes with the challenge of charging electric vehicles(EVs)sufficiently while charging infrastructure capacities are *** charging algorithms produce charge plans for indi-vidual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a *** practice,EV charging processes follow nonlinear charge profiles such as constant-current,constant-voltage(CCCV).Smart charging must consider charge profiles in order to avoid gaps between charge plans and actual EV power *** valid models of charge profiles and their parameters for a diverse set of EVs are not publicly *** this work we propose a data-driven approach for integrating a machine learning model to pre-dict arbitrary charge profiles into a smart charging *** train machine learning models with a dataset consisting of charging processes from the workplace gathered in 2016–2018 from a heterogeneous EV fleet of 1001 EVs with 18 unique *** charging process includes the time series of charging *** pre-processing,the dataset contains 10.595 charging processes leading to 1.2 million data points in *** then compare different machine learning models for charge profile predictions finding that XGBoost yields the most accurate predictions with a mean absolute error(MAE)of 126W and a relative MAE of *** show that smart charging with the integrated XGBoost model leads to a more effective infrastructure usage with up to 21%more energy charged compared to smart charging without considering charge ***,an ablation study on regression model features shows the EV’s model is not a necessary attribute for accurate charge profile ***,charging features are required including the number of phases used for charging.

关键词： Smart charging Electric vehicles Data-driven approach Machine learning

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