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Motor and transmission multi-objective optimum design for tracked hybrid electric vehicles considering equivalent inertia of track system

Authors
Kwon, K.Lee, J.Min, S.
Issue Date
Dec-2020
Publisher
IEEE
Keywords
Equivalent inertia model; Hybrid electric vehicles; Mechanical power transmission; Motor and transmission; Multi-objective optimization; Optimization; Series hybrid electric vehicles; Surrogate model; Torque; Track system; Tracking; Traction motors; Transportation
Citation
IEEE Transactions on Transportation Electrification
Indexed
SCIE
SCOPUS
Journal Title
IEEE Transactions on Transportation Electrification
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/1455
DOI
10.1109/TTE.2021.3081115
ISSN
2372-2088
2332-7782
Abstract
In the development of hybrid electric vehicles (HEVs), a series hybrid powertrain is mainly utilized in tracked vehicles to reduce energy consumption. In order to achieve high energy efficiency while maintaining the required driving performance, key design parameters of traction systems, such as transmission ratio and motor torque and power, need to be optimized. With the aim of effectively analyzing a complex track system, this paper proposes an equivalent inertia model, which collectively represents the motion of each component of the tracked vehicle. The equivalent inertia model showed that the inertial effect was 34.8% higher than when the total mass of the vehicle was considered exclusively. Based on this inertia model, design objectives, such as energy efficiency and driving performance, were defined as quantified functions. Because of the balanced relationships between the objective functions, this study formulated a multi-objective optimization problem that includes motor stack length and transmission gear ratio as design variables. Based on the multi-objective optimization results, a Pareto front was obtained, which illustrates the balanced relationships between the objective functions. Comparing the initial HEV design, the optimum designs can improve energy efficiency and driving performance as a maximum of 13.0% and 2.9%, respectively. IEEE
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