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arXiv · 2012.03935

Chance Constrained Optimization for Energy Management in Electric Vehicles

Erfan Mohagheghi·Joan Gubianes Gasso·Abebe Geletu·Pu Li
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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.

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Erfan Mohagheghi, Joan Gubianes Gasso, Abebe Geletu, Pu Li. 2020-12-05. Chance Constrained Optimization for Energy Management in Electric Vehicles. https://doi.org/10.17352/tcsit.000019

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