Details

Autor(en) / Beteiligte

• Li, Yangyang
• Wang, Shuqian
• Duan, Xiongbo
• Liu, Shujing
• Liu, Jingping
• Hu, Song

Titel

Multi-objective energy management for Atkinson cycle engine and series hybrid electric vehicle based on evolutionary NSGA-II algorithm using digital twins

Ist Teil von

• Energy conversion and management, 2021-02, Vol.230, p.113788, Article 113788

Ort / Verlag

Oxford: Elsevier Ltd

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •High-accuracy combined simulation–optimization platform for the vehicle is developed.•NSGA-II is put forward, clarified and applied for optimization of full engine MAPs.•Reduction rates of fuel consumption and NO are decreased by up to 12.48% and 92.64%.•Cumulative fuel consumption and NO of optimized vehicle reduced by 4.58% and 46.1%. In order to develop higher performance Atkinson cycle gasoline engine and explore its fuel-saving potential on series hybrid electric vehicles, this study is pioneered in digital twins by GT-Power software, MATLAB/Simulink software and multi objective evolutionary optimization using evolutionary non-dominated sorting genetic algorithm. In the first stage, an experimental investigation is carried out and a corresponding 1-D GT-Power simulation model is developed and validated by the experimental data for an Otto cycle engine and then modified into the Atkinson cycle engine. In the second stage, the digital twins engine model takes the spark timing, exhaust gas recirculation rate, intake variable valve timing, exhaust variable valve timing as well as lambda as the inputs of the simulation optimization platform for the Atkinson cycle engine. The optimum values of aforementioned input parameters are identified by the evolutionary non-dominated sorting genetic algorithm to minimize the brake specific fuel consumption and nitric oxide under different speeds and loads, the reduction rates of fuel consumption and nitric oxide are decreased by up to 12.48% and 92.64%, respectively. In the third stage, the optimized performance MAPs are implemented in the series hybrid electric vehicle with the Atkinson cycle engine, the results show that the cumulative fuel consumption and nitric oxide volume fraction of the optimized vehicle under new European driving cycle reduced by 4.58% and 46.1%, respectively. It is concluded that the proposed evolutionary non-dominated sorting genetic algorithm method can identify the optimum conditions of vehicle well and improve its fuel economy as well as emission. Furthermore, the combined simulation platform for both engine and vehicle can be applied to evaluate and optimize the energy distribution and performance of vehicles with different technologies or strategies in the future. Besides, all these will provide theoretical basis and digital model support for the development of efficient and energy-saving new energy vehicles.