Bi-objective optimal design of plug-in hybrid electric propulsion system for ships. (15th June 2019)

• Record Type: Journal Article
• Authors: Jianyun, Zhu; Li, Chen; Lijuan, Xia; Bin, Wang
• Is Part Of: Energy. Volume 177(2019)
• Page Start: 247
• Page End: 261
• Publication Date: 2019-06-15
• Subjects: Hybrid electric propulsion system -- Bi-objective optimization -- Fuel consumption -- GHG emissions -- NSGA-II -- Sensitivity analysis; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.04.079
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Growing concerns about reducing fuel consumption and global greenhouse gas (GHG) emissions have forced the shipping industry to accelerate the development of plug-in hybrid electric propulsion systems (HEPSs). However, the design optimization of plug-in HEPSs with the single objective of saving fuel may result in increased GHG emissions. This study proposes a bi-objective optimization by considering not only fuel consumption but also GHG emissions. The NSGA-II method is developed to explore the Pareto optimal solution set. A real-time hardware-in-the-loop experimental platform is built to validate the effectiveness of the optimization. The experimental results show that the optimal design selected from the Pareto solution set of the bi-objective optimization is closer to the ideal point than the optimal designs via the single-objective optimization pursuing either minimum fuel consumption or minimum GHG emissions. Further, sensitivity analysis is conducted. It is found that three variables (motor rotor diameter, motor rotor length, and gear ratio) are of local optimum at the Pareto front; and two (number of battery modules and lower bound of the battery state of charge) are of strong sensitivity regarding the contradiction between fuel consumption and GHG emissions. Highlights: Bi-objective optimization is proposed by considering fuel consumption and GHG emissions. NSGA-II is developed for the optimal sizing of plug-in hybrid electric systems. The optimal solution is sensitive to battery capacity and SOC lower bound.