Chance Constrained Optimization for Energy Management in Electric Vehicles
Abstract
E-powertrain of future electric vehicles could consist of energy generation units (e.g., fuel cells and photovoltaic modules), energy storage systems (e.g., batteries and supercapacitors), energy conversion units (e.g., bidirectional DC/DC converters and DC/AC inverters) and an electric machine, which can work in both generating and motoring modes [1- 6]. An energy management system is responsible to operate the above-mentioned components in a way that the technical constraints are satisfied. This task should be accomplished by solving an optimization problem, which could aim at minimizing the total operation costs [5]. The optimization problem has been widely addressed by deterministic approaches [7], which take into account the forecasted values of active-reactive load profile. However, as shown in Figure 1 (a), it is impossible to accurately forecast the values, meaning that the solutions coming from deterministic approaches could lead to infeasible operations (i.e., constraint violations). Therefore, stochastic optimization approaches [8] should be utilized to fi nd optimal solution strategies while considering uncertain parameters.
Cite
@article{arxiv.2012.03935,
title = {Chance Constrained Optimization for Energy Management in Electric Vehicles},
author = {Erfan Mohagheghi and Joan Gubianes Gasso and Abebe Geletu and Pu Li},
journal= {arXiv preprint arXiv:2012.03935},
year = {2020}
}