Abstract
In this paper, identification of an appropriate hybrid energy storage system (HESS) architecture, introduction of a comprehensive and accurate HESS model, as well as HESS design optimization using a nested, dual-level optimization formulation and suitable optimization algorithms for both levels of searches have been presented. At the bottom level, design optimization focuses on the minimization of power loss in batteries, converter, and ultracapacitors (UCs), as well as the impact of battery depth of discharge (DOD) to its operation life, using a dynamic programming (DP)-based optimal energy management strategy (EMS). At the top level, HESS optimization of component size and battery DOD is carried out to achieve the minimum life-cycle cost (LCC) of the HESS for given power profiles and performance requirements as an outer loop. The complex and challenging optimization problem is solved using an advanced Multi-Start Space Reduction (MSSR) search method developed for computation-intensive, black-box global optimization problems. An example of load-haul-dump (LHD) vehicles is employed to verify the proposed HESS design optimization method and MSSR leads to superior optimization results and dramatically reduces computation time. This research forms the foundation for the design optimization of HESS, hybridization of vehicles with dynamic on-off power loads, and applications of the advanced global optimization method.
Highlights
With their highly efficient electric drives and electric energy storage systems (ESSs), electrified vehicles (EVs) provide promising transportation and construction solutions with high energy efficiency, extra-low emissions, reduced maintenance cost, as well as the possibility to use renewable energy to replace fossil fuels [1,2,3]
To apply genetic algorithm (GA) to solve this problem, the fitness function shown in Equation (32) is used to evaluate the status of each solution and the ranges of variables are listed in Equation (34)
Due to the fact that the first four variables are discrete and the other one is continuous, which belongs to a mixed integer problem, the first four variables need to be converted to integers before the dynamic programming (DP) algorithm
Summary
With their highly efficient electric drives and electric energy storage systems (ESSs), electrified vehicles (EVs) provide promising transportation and construction solutions with high energy efficiency, extra-low emissions, reduced maintenance cost, as well as the possibility to use renewable energy to replace fossil fuels [1,2,3]. Though, have studied the impact of depth of discharge (DOD) to the battery operation life and capacity loss Another key functional component of the HESS is its DC/DC converter. The needs to optimize the EMS that controls the operation of the HESS and to optimize the sizes of HESS components, batteries, UCs and DC/DC converter, for a given HESS architecture, add additional complexity to the design optimization problem. To solve the HESS component size and EMS optimization problems, a nested HESS design optimization formulation based on the HESS performance/power-loss model and its optimal operation, and a dedicated global optimization search method that combines Dynamic Programming (DP) and MSSR SBGO has been introduced.
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