Optimization of Hybrid Energy Storage Systems for Vehicles with Dynamic On-Off Power Loads Using a Nested Formulation

Jiajun Liu, Huachao Dong, Tianxu Jin, Li Liu, Babak Manouchehrinia and Zuomin Dong
Additional contact information
Jiajun Liu: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Huachao Dong: Department of Mechanical Engineering, University of Victoria, Victoria, BC V8W2Y2, Canada
Tianxu Jin: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Li Liu: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Babak Manouchehrinia: Department of Mechanical Engineering, University of Victoria, Victoria, BC V8W2Y2, Canada
Zuomin Dong: Department of Mechanical Engineering, University of Victoria, Victoria, BC V8W2Y2, Canada

Energies, 2018, vol. 11, issue 10, 1-25

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.

Keywords: nested optimization; hybrid energy storage system; surrogate-based optimization method; electrified vehicles (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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