  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.
