Plug-in hybrid electric buses total cost of ownership optimization at fleet level based on battery aging. (15th December 2020)

• Record Type: Journal Article
• Authors: López-Ibarra, Jon Ander; Gaztañaga, Haizea; Saez-de-Ibarra, Andoni; Camblong, Haritza
• Is Part Of: Applied energy. Volume 280(2020)
• Publication Date: 2020-12-15
• Subjects: Plug-in hybrid electric buses (PHEBs) -- Total cost of ownership -- Hierarchical energy management strategy -- Battery degradation -- Neuro-fuzzy -- Digitalization; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.115887
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: In this paper a hierarchical energy management strategy design methodology for total cost of ownership management at fleet level is proposed. The decisions are taken from the fleet level point of view, to optimize the whole fleet based on the hierarchical decision maker and management, composed of three levels. The outer part is the offline route-to-bus data exploitation and decision maker, aiming to establish the dynamic programming optimization design. The next level is the offline optimization bus-to-route. Based on the previous decision, the neuro-fuzzy learns from the global optimal solutions. Finally, the trained fuzzy-logic strategy is used to manage the online operation. This fleet is re-organized and the online operation energy management strategy is updated throughout the bus lifetime. These decisions are made based on the evaluated battery lifetime of the fleet, with the aim to meet the planned total cost of ownership requirements. The total cost of ownership for the bus-to-route energy management strategy, has been improved a 7.65 % at fleet level against a charge-sustaining charge-depleting strategy. In the route-to-bus fleet re-organization and update, up to 5.51 % total cost of ownership improvement at fleet level has been obtained against not applying the fleet management. Highlights: A hierarchical three layer fleet management strategy is proposed for the fleet total cost of ownership improvement. The outer level establishes the optimization design and conducts fleet re-organization based on the buses battery state of health. The intermediate level is dedicated to the real-time energy management strategy design based on neuro-fuzzy technique learning from the dynamic programming optimal operation. The inner level online operation is based on a trained fuzzy-logic strategy that is updated throughout the bus lifetime.