4.7 Article

Multi-Objective Design Optimization of a Novel Dual-Mode Power-Split Hybrid Powertrain

Journal

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 71, Issue 1, Pages 282-296

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3130580

Keywords

Mechanical power transmission; Optimization; Engines; Gears; Vehicle dynamics; Dynamic programming; Shafts; Dual-mode powertrain; dynamic programming; direct transmit points; multi-objective optimization

Funding

  1. National Natural Science Foundation of China [52072051]
  2. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body [31915003]
  3. Sichuan Key Laboratory of Vehicle Measurement, Control and Safety [QCCK20220-006]

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This paper explores the performance potential of a novel dual-mode power-split hybrid powertrain and analyzes the steady-state power split characteristics of the system. A mode switching strategy is developed and verified, and a novel multi-objective optimization algorithm with high computational efficiency is proposed.
This paper aims to explore the performance potential of a novel dual-mode power-split hybrid powertrain. First, the steady-state power split characteristics for the proposed hybrid powertrain in different modes are analyzed. The mode switching strategy is developed to maximize powertrain efficiency, which is verified by using dynamic programming based on direct transmit points (DTPs). Moreover, a novel multi-objective evolutionary algorithm based on the decomposition (MOEA/D) method used in a nested way with dynamic programming is proposed to solve the multi-objective optimization of the PS-HEVs for the first time, and the computational efficiency and superiority of the proposed algorithm is compared with the commonly used NSGA-II algorithm. The results show that the MOEA/D based multi-objective optimization framework has similar performance in the search for the Pareto frontier but significantly higher computational efficiency than that of the NSGA-II algorithm. The obtained Pareto frontier provides optimal design candidates for hybrid powertrain systems.

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