Application of Stochastic Programming to Battery Electric Vehicle Charging by Minimization of CVaR

Abstract

We propose a method for making a charging plan robust against stochastic fluctuations based on a stochastic programming framework for in-vehicle battery charging of battery electric vehicles (BEVs). Recharging is required when operating multiple BEVs, but driving energy consumption fluctuate stochastically depending on the daily conditions. When large fluctuations occur, pre-made charging plans will not be executed as planned. In this study, we formulate a charging planning problem that incorporates fluctuations by using CVaR, a risk measure of stochastic fluctuations. Numerical experiments show that the charge plan obtained by the stochastic planning is more robust to fluctuations than the deterministic plan without considering fluctuations.