Similar articles

Abstract: The concept of hybrid ships has gained significant attention in recent years, as they offer an effective means of enhancing energy utilization and reducing environmental pollution. However, the navigational environments of ships are often subject to changes, which in turn affect their energy efficiency in a complex manner. It is therefore evident that enhancing the energy efficiency of hybrid ships is a worthwhile goal. In this study, we take a diesel-electric hybrid ship navigating in inland waterways as the research object, and propose a hierarchical optimization method for ship energy efficiency. The upper-layer control establishes a predictive model for propulsion motor speed and fuel consumption through multivariate time series predictions, and employs the model predictive control (MPC) method to optimize the propulsion motor speed. The lower-layer control utilizes an equivalent fuel consumption minimization method, which is based on improving the equivalence factor. This involves combining the variation of the supercapacitor’s state of charge (SOC) with the propulsion motor speed obtained from the MPC optimization in the upper-layer control. Furthermore, a proportional integral (PI) controller is used to adjust the equivalence factor, in order to adapt the equivalent fuel consumption minimization method to the working conditions. Our results demonstrate that the proposed hierarchical optimization method can reduce the energy efficiency operating indicator (EEOI) by approximately 11.54% and the fuel consumption by approximately 9.47% in comparison to the pre-optimization scenario.

考虑通航环境的混合动力船舶航速与能量管理的分层优化控制

[1]BalsamoF, CapassoC, MiccioneG, et al., 2017. Hybrid storage system control strategy for all-electric powered ships. Energy Procedia, 126:1083-1090.

[2]BassamAM, PhillipsAB, TurnockSR, et al., 2017. Development of a multi-scheme energy management strategy for a hybrid fuel cell driven passenger ship. International Journal of Hydrogen Energy, 42(1):623-635.

[3]CarltonJS, 2018. Marine Propellers and Propulsion. 4th Edition. Elsevier, Amsterdam, the Netherlands.

[4]ChenZH, LiuYG, ZhangYJ, et al., 2022. A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles. Energy, 243:122727.

[5]GaoSC, ZongYH, JuF, et al., 2024. Scenario-oriented adaptive ECMS using speed prediction for fuel cell vehicles in real-world driving. Energy, 304:132028.

[6]GeertsmaRD, NegenbornRR, VisserK, et al., 2017. Pitch control for ships with diesel mechanical and hybrid propulsion: modelling, validation and performance quantification. Applied Energy, 206:1609-1631.

[7]GuoLL, GaoBZ, GaoY, et al., 2017. Optimal energy management for HEVs in eco-driving applications using bi-level MPC. IEEE Transactions on Intelligent Transportation Systems, 18(8):2153-2162.

[8]HanL, JiaoXH, ZhangZ, 2020. Recurrent neural network-based adaptive energy management control strategy of plug-in hybrid electric vehicles considering battery aging. Energies, 13(1):202.

[9]HasanvandS, RafieiM, GheisarnejadM, et al., 2020. Reliable power scheduling of an emission-free ship: multiobjective deep reinforcement learning. IEEE Transactions on Transportation Electrification, 6(2):832-843.

[10]HeinK, XuY, WilsonG, et al., 2021. Coordinated optimal voyage planning and energy management of all-electric ship with hybrid energy storage system. IEEE Transactions on Power Systems, 36(3):2355-2365.

[11]HoltropJ, 1984. A statistical re-analysis of resistance and propulsion data. International Shipbuilding Progress, 31(363):272-276.

[12]HoltropJ, MennenGGJ, 1982. An approximate power prediction method. International Shipbuilding Progress, 29(335):166-170.

[13]HouJ, SunJ, HofmannHF, 2018. Mitigating power fluctuations in electric ship propulsion with hybrid energy storage system: design and analysis. IEEE Journal of Oceanic Engineering, 43(1):93-107.

[14]HouJ, YaoDW, WuF, et al., 2021. Online vehicle velocity prediction using an adaptive radial basis function neural network. IEEE Transactions on Vehicular Technology, 70(4):3113-3122.

[15]IMO (International Maritime Organization), 2023. 2023 IMO Strategy on Reduction of GHG Emissions from Ships. IMO.

[16]KalikatzarakisM, GeertsmaRD, BoonenEJ, et al., 2018. Ship energy management for hybrid propulsion and power supply with shore charging. Control Engineering Practice, 76:133-154.

[17]KhanMMSK, FaruqueMO, NewazA, 2017. Fuzzy logic based energy storage management system for MVDC power system of all electric ship. IEEE Transactions on Energy Conversion, 32(2):798-809.

[18]LiYP, WangF, TangXL, et al., 2022. Real-time multiobjective energy management for electrified powertrains: a convex optimization-driven predictive approach. IEEE Transactions on Transportation Electrification, 8(3):3139-3150.

[19]LiuCX, LiXY, ChenY, et al., 2023. Real-time energy management strategy for fuel cell/battery vehicle based on speed prediction DP solver model predictive control. Journal of Energy Storage, 73:109288.

[20]LiuH, WangYC, GaoDJ, 2021. Research on control strategy of hybrid electric ship based on minimum equivalent fuel consumption. IOP Conference Series: Earth and Environmental Science, 632:032029.

[21]LiuYS, GeYS, TanJW, et al., 2018. Emission characteristics of offshore fishing ships in the Yellow Bo Sea, China. Journal of Environmental Sciences, 65:83-91.

[22]SulligoiG, VicenzuttiA, MenisR, 2016. All-electric ship design: from electrical propulsion to integrated electrical and electronic power systems. IEEE Transactions on Transportation Electrification, 2(4):507-521.

[23]TianX, HeR, SunXD, et al., 2020. An ANFIS-based ECMS for energy optimization of parallel hybrid electric bus. IEEE Transactions on Vehicular Technology, 69(2):1473-1483.

[24]UNCTAD (United Nations Conference on Trade and Development), 2022. Review of Maritime Transport 2022: Navigating Stormy Waters. United Nations Publications.

[25]WuP, PartridgeJ, BucknallR, 2020. Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships. Applied Energy, 275:115258.

[26]XiePL, TanS, GuerreroJM, et al., 2021. MPC-informed ECMS based real-time power management strategy for hybrid electric ship. Energy Reports, 7(S1):126-133.

[27]ZahediB, NorumLE, LudvigsenKB, 2014. Optimized efficiency of all-electric ships by dc hybrid power systems. Journal of Power Sources, 255:341-354.

[28]ZhangN, MaXH, JinLM, 2017. Energy management for parallel HEV based on PMP algorithm. The 2nd International Conference on Robotics and Automation Engineering (ICRAE), p.177-182.

[29]ZhuJY, ChenL, WangXF, et al., 2020. Bi-level optimal sizing and energy management of hybrid electric propulsion systems. Applied Energy, 260:114134.

[30]ZhuLS, HanJG, PengDK, et al., 2014. Fuzzy logic based energy management strategy for a fuel cell/battery/ultra-capacitor hybrid ship. The 1st International Conference on Green Energy, p.107-112.