Title :
Optimal power management for electric tugboats with unknown load demand
Author :
Thanh Long Vu ; Dhupia, Jaspreet S. ; Ayu, Aaron Alexander ; Kennedy, Louis ; Adnanes, Alf Kare
Author_Institution :
Sch. of Mech. & Aerosp. Eng., Nanyang Technol. Univ., Singapore, Singapore
Abstract :
This paper, inspired by the research on energy management for land-based hybrid electric vehicles (HEVs), presents an optimal power management scheme for electric tugboats that optimally splits the power supply from engines and batteries in response to the load demand, while minimizing the engine fuel consumption and maintaining the battery life. For this purpose, an optimization problem is formulated, in which the cost function consists of power load demand tracking, engine fuel consumption and change in battery state of charge. Utilizing the mixed-integer programming, the optimal power planning for the engines and batteries is determined. The proposed optimal algorithm can control the operation, i.e. starting time and stopping time, for several engines, which is a key difference from several other optimal algorithms developed for land-based HEVs. Since the load demand is unknown, a novel prediction scheme is introduced to anticipate the load demand, allowing the implementation of optimization scheme. Numerical illustration is presented on an industry-consulted harbor tugboat model to show the effectiveness of the proposed schemes.
Keywords :
boats; energy management systems; hybrid electric vehicles; integer programming; HEV; battery life; battery state of charge; cost function; electric tugboats; energy management; engine fuel consumption; industry-consulted harbor tugboat model; land-based hybrid electric vehicles; mixed-integer programming; optimal power management scheme; optimal power planning; optimization scheme; power load demand tracking; power supply; prediction scheme; starting time; stopping time; Batteries; Cost function; Engines; Fuels; Power demand; Switches; Power management; hybrid electric vehicles; marine powertrain systems; optimization;
Conference_Titel :
American Control Conference (ACC), 2014
Conference_Location :
Portland, OR
Print_ISBN :
978-1-4799-3272-6
DOI :
10.1109/ACC.2014.6859004