 Real-time optimal control of an LNG-fueled hybrid electric ship considering battery degradationsBo Pang, Siyang Liu, Haijia Zhu, Yanbiao Feng and Zuomin Dong Energy, 2024, vol. 296, issue C Abstract: Liquified natural gas (LNG) is a low-cost, cleaner fuel that reduces air pollutants and carbon dioxide (CO2) emissions. However, LNG-fueled engines have a methane slip issue, leading to increased carbon dioxide equivalent (CO2e or CDE) emissions. This research introduced a new approach to combine the LNG-fueled engine with hybrid electric propulsion and real-time optimal control to maximize fuel efficiency and minimize CO2e emissions. In addition to low-cost LNG fuel, the approach controls and reduces the battery degradation costs to provide a clean and economical marine propulsion solution. An integrated globally optimal propulsion system design and real-time optimal control strategy for the LNG-fueled hybrid electric ships effectively addressed the methane slip-triggered emissions issue of natural gas (NG) engines, reducing CO2e emissions by 12.28% and the high-cost and relatively short life of the hybrid propulsion system's battery energy storage system (BESS), decreasing battery capacity loss by 12%. The optimal sizing of the NG-diesel dual-fuel compression ignition (CI) engine and BESS and the optimal baseline power control and energy management strategy (EMS) are jointly created using dynamic programming (DP) for the vessel's statistical data-based, normal operation profile. As marine vessels have more variations of propulsion power demand than road vehicles due to varying ocean operation conditions, two new methods are introduced to accurately predict the varying propulsion power demands and dynamically update the BESS's state of health (SOH) in real-time. The former applies an extended Kalman filter (EKF) on both real-time vessel operation data and the statistical vessel operation cycle to more accurately predict vessel power demand, and the latter compares the instantly measured battery output voltage data with the battery performance and degradation models built using extensive battery test data to capture the state of the BESS more precisely. These two new update schemes lead to an extended model predictive control (MPC) approach for the real-time optimal power control and energy management of the hybrid electric ship operation, considering the NG fuel consumption, CO2e emissions and BESS degradation associated costs. The newly introduced NG-engine hybrid electric propulsion system component size and EKF-MPC real-time control optimization techniques have been tested using the technical specification and operation data of a real medium-sized vehicle and passenger ferry in operation, with reduced CO2e emissions and battery capacity loss and lowered ferry operating costs by $305,286 over 10 years of operations. Keywords: Real-time optimal control; Hybrid electric propulsion system; Battery degradation modelling; Model predictive control; NG/LNG fuel and engine (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:296:y:2024:i:c:s0360544224009435 DOI: 10.1016/j.energy.2024.131170 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.