@article {pub6240,
	title = {Design of Fair and Interpretable Electric Vehicle Charging Policies through Genetic Programming},
	author = {Steffen Limmer AND Angus Kenny AND Tapabrata Ray AND Felix Lanfermann AND Hemant Kumar Singh AND Andrea Castellani},
	year = {2025},
	month = {December},
	abstract = {Controlled charging of a group of electric vehicles (EVs) subject to power limits, such as those imposed by transformer capacity constraints, may result in inefficient and unfair energy distribution among EVs. The present work aims to learn fair and efficient charging policies on historical data using multi-objective genetic programming. Two variants of this approach are proposed and evaluated in simulation experiments. Compared to several baseline methods, including different manually designed charging policies, the proposed approach increases efficiency by 40 \% or more and fairness by 60 \% or more. An analysis of the automatically designed policies in terms of interpretability comes to the conclusion that the best performing policies are of high complexity, containing on average more than 13 variables and more than 16 operators. However, it is shown that it is possible to significantly reduce the complexity without losing much in the quality of the charging control.},
	publisher = {Elsevier},
	journal = {Applied Energy},
	volume = {404}
}