The paper presents the energy loss minimization of a hybrid energy storage system used in an electric vehicle, composed by a battery and a supercapacitor. The optimization is carried out by searching the optimal power sharing between the energy storage devices. The power sharing factor is defined as a discrete time variable, with constant values during each subdivision of the driving cycle. The elements of the optimal solution vector are the power sharing factors and the time instants that define the subdivisions. The particle swarm optimization algorithms have been validated using the Rastrigin test function, and three versions of the boundary behaviour have been compared in case of the constrained optimization. The algorithms have been tested for the energy loss minimization in case of a simple driving cycle, and their performance has been assessed by statistical analysis for different swarm sizes.

In electric vehicles battery life can be prolonged by using hybrid energy storage systems (HESS ), which combine high energy density batteries with supercapacitors, characterized by high power density. This paper deals with the control of electronic power converters from an active parallel HESS. The load of the HESS is the electrical motor drive of an electric vehicle. The interfaces between the DC-link and the power sources are four-phase bidirectional DC-DC converters driven in current control mode, based on the current references supplied by an active parallel HESS power distribution algorithm. We present a rule-based fuzzy energy management algorithm for a HESS powered electric vehicle and its simulation in MATLAB/Simulink® environment using the Quasi-Static Simulation (QSS ) and Fuzzy Logic toolboxes. Also, simulation results in driving and regenerative braking operation modes of the electric vehicle are presented.

The paper presents a strategy of energy loss minimization within a hybrid energy storage system of an electrical vehicle, composed by a battery and a supercapacitor. The optimization of the power sharing between these energy storage devices is performed for the New European Driving Cycle, using the Particle Swarm Optimization algorithm. The minimum energy storage required to pass through the driving cycle is taken into account as a time-variable constraint during the optimization. The dimension of the search space increases with the dimension of the optimization vector, which has to be kept low in order to keep the complexity of the problem manageable. It is shown, that the subdivision, and piecewise optimization of the driving cycle improves the result by means of relaxation of the constraint represented by minimum level of the required energy storage.

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