Load Spectrum Compilation Method of Hybrid Electric Vehicle Reducers Based on Multi-Criteria Decision Making

• Published in: Energies (Basel) 2022-05, Vol.15 (9), p.3293
• Main Authors: Li, Jie, Han, Chongyang, Wu, Weibin, Tang, Ting, Ran, Xiao, Zheng, Zefeng, Sun, Shunli
• Format: Article
• Language: English
• Subjects: Accuracy; Data collection; Decision making; Electric vehicles; Fatigue life; fatigue life prediction; hybrid electric vehicle; Life prediction; load spectrum; Materials fatigue; multi-criteria decision making; Multiple criteria decision making; Multiple criterion; Parameter estimation; Power sources; rainflow counting; reducer; Reliability analysis; Roads & highways; Simulation; Spectra; Working conditions
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15093293

This article proposes a method for compiling the load spectra of reducers for hybrid electric vehicles. Selecting typical working conditions for real vehicle data collection, the load data under each typical working condition were divided into five categories according to the state of the power source and the data were preprocessed. The optimal sample loads for compiling load spectra were obtained based on a multi-criteria decision-making method, rainflow counting for optimal sample loads was performed according to different power source output patterns, non-parametric extrapolation was performed to obtain the full-life two-dimensional load spectrum after dimensionality reduction, and a full-life eight-level programmed load spectrum that could be used for bench tests was obtained. Using the programmed load spectrum and the extracted sample load as the load input, a fatigue life prediction simulation of the reducer gear of a hybrid electric vehicle was carried out. The reducer gear fatigue life from the programmed load spectrum was compared to the gear fatigue life under actual load. The fatigue life of the reducer gear when the programmed load spectrum was used as the input was 1.412 × 103. When the actual load was used as the input load, the fatigue life of the reducer gear was 1.933 × 103. The relative error between the two is only 26%, which is in the normal range. The results show that the programmed load spectrum is effective and reliable and that the load spectrum compilation method provides a basis for accurately evaluating the reliability of the hybrid electric vehicle reducer.