A robust energy management strategy for fuel cell and ultracapacitor hybrid electric vehicles under uncertainty via a jellyfish-search-based approach

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This paper presents the development of an energy management system (EMS) for a fuel cell hybrid electric vehicle comprising a fuel cell (FC) and an ultracapacitor (UC). In previous studies, hydrogen consumption was the key priority, overlooking other considerations such as lifetime and characteristics of both the FC and the UC. Due to this and the restricted number of iterations, optimization strategies reported in the literature may suffer from suboptimal solutions. In addition, ignoring practical operating scenarios, such as uncertainties due to road conditions, operating status, temperature, and aging, results in poor performance. This work proposes EMS, which considers: (1) fuel usage, (2) lifespan, and the slow dynamic response of the FC, and (3) the lifetime of the UC. Because of its fast and less convergence time characteristics, the Jellyfish Search (JS) optimizer is used. To achieve resilient performance under uncertainties, robust optimization-based EMS using min-max optimization and JS is adopted. The developed EMS was tested under two different driving cycles. Based on the simulation results, the proposed EMS shows good performance. JS has a short computational time of about 0.15 s for each decision while satisfying all the system constraints, such as keeping the SoC within a suitable level (25% to 95%), and reducing the occurrences of severe changes in the power demand of fuel cells, thus increasing the life span of system components. Moreover, by adding robust optimization (RO), the system was able to meet the system requirement with DS 100% even under uncertainties.

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