A self-adaptive joint optimization framework for marine hybrid energy storage system design considering load fluctuation characteristics

Jiangfan Huang, Qing An, Mingyu Zhou, Ruoli Tang, Zhengcheng Dong, Jingang Lai, Xin Li and Xiangguo Yang

Applied Energy, 2024, vol. 361, issue C, No S0306261924003568

Abstract: Recently, with the development of new energy technologies, all-electric ships (AESs) with hybrid energy storage system (HESS) are becoming a promising solution to reduce fuel consumption and emissions. However, the high maneuverability of ships during the actual navigation places higher performance requirements on the HESS, which presents a nonlinear and multi-objective challenge for the HESS design. Therefore, it is necessary to consider the coupling between HESS sizing and energy management strategy (EMS). In this paper, a self-adaptive joint optimization framework (SJOF) for marine HESS design considering load fluctuation characteristics is proposed, which can find the optimal decision solution with excellent system economic and battery life performance for AES HESS design. Based on the rain flow counting (RFC) method, a multi-objective joint optimization method considering life cycle cost (LCC) and battery degradation index (BDI) is introduced into SJOF. Besides, a novel EMS including the self-adaptive segmentation mechanism (SSM) and power allocation is proposed, which can achieve the most efficient energy scheduling.

Keywords: All-electric ship (AES); Hybrid energy storage system (HESS); Energy management strategy (EMS); Joint optimization framework; Rain flow counting (RFC) (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2024.122973

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