Chance Constrained Optimization for Energy Management in Electric Vehicles

TL;DR

This paper explores the use of chance constrained optimization to improve energy management in electric vehicles by effectively handling uncertainties in load forecasts, ensuring feasible operation within technical constraints.

Contribution

It introduces a chance constrained optimization framework specifically designed for electric vehicle energy management, addressing uncertainties in load predictions.

Findings

Enhanced feasibility of energy management solutions under uncertainty

Reduced constraint violations compared to deterministic methods

Improved robustness of energy operation strategies

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.