Enhancing the Reliability Assessment System for Crucial Powertrain Components through Design, Simulation, and Experiment

Abstract

Article Enhancing the Reliability Assessment System for Crucial Powertrain Components through Design, Simulation, and Experiment Xiaochun Zeng 1,2,3, Gongcheng Wang 1,* , Ying Liu 2,3, Jing Liu 1,2,3, Yinglian Feng 2,3,4, Xiuyong Shi 5,6, Diming Lou 5,6, Lei Zhang 4, Tao Wei 1, and Zhong Li 1 1 Jiangling Motors Co. Ltd., Nanchang 330001, China 2 Jiangxi Province Women Science and Technology Workers Association, Nanchang 330036, China 3 Jiangxi Mechanical Engineering Society, Nanchang 330002, China 4 Magna PT Powertrain (Jiangxi), Nanchang 330200, China 5 Nanchang Automotive Institute of Intelligence & New Energy, Nanchang 330052, China 6 Tongji University, Shanghai 200092, China * Correspondence: gwang7@jmc.com.cn Received: 5 September 2023 Accepted: 19 September 2023 Published: 20 September 2023 Abstract: This article conducted innovative research on heat balance, cooling and structural strength by using Computer Aided Engineering (CAE), design, and experiment. These multifaceted approaches rely on the meticulous application of dynamic analysis, combustion analysis, fluid dynamics, and finite element analysis. This research has devised a fatigue-oxidation-creep multi-fatigue life prediction technique for thermal engines, coupled with an innovative water jacket optimization method. By addressing the design challenges arising from the demanding requirements of high explosion pressure, formidable power, and significant torque in power systems, this study has made significant strides in advancing the field. And this method was used to establish the development capability of powertrain electric drive components. To enhance reliability and prediction accuracy, a simulation and test calibration method was devised for Crucial Powertrain Components. This article establishes a robust foundation by providing comprehensive data support for reliability target decomposition, facilitating the efficient derivation of component reliability metrics. Additionally, it explores potential customer scenario failures and investigates the underlying mechanisms of powertrain malfunctions. By establishing a correlation matrix between failure modes, mechanisms, and reliability experiments, the coverage of powertrain failure mode verification experiments was significantly increased by over 95%. Ultimately, this article contributes to the formation of a comprehensive technical system for evaluating the reliability of crucial powertrain components, integrating design, simulation, and experiment.