Optimized energy management strategies and sizing of hybrid storage systems for transport applications

Abstract:

Currently with the massive demand of mobility and an increasing concern about the sustainable use of energy resources, more-efficient, best-performed and cost competitive mobility solutions are required. In this topic, the hybrid energy storage systems (HESS) are shown as interesting solutions by combining storage technologies with different power and energy characteristics, where batteries (BTs) and supercapacitors (SCs) stand out as the most important ones.
This PhD thesis deals with the topic of the optimal sizing and operation of HESS (combining BTs and SCs) in order to be integrated in vehicles for public mobility in urban scenarios.

Thus on the one hand, a novel adaptive Energy Management Strategy (EMS) is proposed to deal with the proper split of the power demand among the available energy sources on-board the vehicle. This adaptive EMS is based on fuzzy logic in order to define (in a more intelligent and intuitive way) the desired vehicle operation and to face the management of multiple energy sources and operating conditions in a hybrid electric system. This EMS considers, in addition to the instantaneous power demanded for the vehicle operation and energetic conditions of the HESS, the future energy demand (related to the BTs and SCs) by applying a sliding forward window to estimate their possible energetic behaviors.

On the other hand, a methodology based on genetic algorithms for the co-optimization of the EMS and HESS sizing is proposed. The methodology includes technical, economical and degradation models for the HESS.

The thesis proposal is validated on two relevant case studies in the public transportation sector: Hybrid Electric Tramway and Hybrid Electric Bus.