Optimal Design And Control Of Li-Battery/Ultracapacitor Hybrid Energy Storage System

The underground load-haul-dump(LHD)vehicles and harbor tugboats normally operate under dynamic on-off power loads,and hybrid electric as well as pure electric powertrain technologies have the ability to improve energy efficiency and reduce emissions.Nonetheless,the dramatic and frequent power variations impose a heavy burden on the conventional battery energy storage system(ESS),considerably shortening its operation life.Hybrid ESS(HESS)that combines high energy density Li-batteries with frequent charge/discharge tolerant,high power,and long cycle life ultracapacitors(UCs)serves as a promising alternative.However,the optimal design and control of the HESS with efficient search algorithms,which considers the battery capacity loss and life-cycle cost(LCC),is a complex and challenging task.Taking the HESS-based LHD and tugboat as research object,this dissertation focuses on the modelling of HESS and LCC,the multi-objective optimization and nested optimization of HESS,and the validation of the nested optimization on hybrid electric tugboats.Firstly,a HESS model that can reflect the battery capacity loss has been presented based on a semi-active HESS configuration.A multi-objective optimization is formulated regarding the energy consumption and HESS' LCC based on dynamic programming(DP)-based optimal energy management strategy(EMS)and Pareto optimal solutions.A backward simulation model of LHDs and a HESS' LCC model are established.The optimal HESS and battery ESS options are compared.Secondly,a novel nested,dual-level optimization taking the HESS component size as well as battery depth of discharge(DOD)as optimization variables and the LHD's working hours as a constraint has been introduced and suitable global optimization algorithms,including a genetic algorithm(GA)and an advanced surrogate-based Multi-Start Space Reduction(MS SR)search method,specialized developed for computation-intensive,black-box global optimization problems,have been proposed.At the bottom level,control optimization focuses on the minimization of energy consumption using the DP-based optimal EMS.At the top level,HESS optimization is carried out to achieve the minimum LCC of the HESS.Then,the comparison between the optimal solution selected from the Pareto front and the one achieved from MSSR is conducted.Finally,the dynamic power profile of tugboats is built and the proposed nested optimization method has been applied to reduce the number of battery replacement as well as the LCC of the hybrid electric harbor tugboats and to verify the effectiveness of the nested optimization and search method with the minimum battery capacity loss in the inner loop and the least LCC in the outer loop.This research forms the foundation for the optimal design and control of the HESS in pure electric and hybrid electric vehicles and vessels,particularly for those with dynamically changing power loads.It also serves as a useful platform for testing advanced global optimization methods.