Construction of Life Prediction Process for Engine Parts by Using Real-World Driving Data and Simulation Models

2024-01-2244

04/09/2024

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WCX SAE World Congress Experience
Authors Abstract
Content
To help ensure that engine components are as reliable as customers need them to be, we have thus far evaluated them by establishing development target values based on market requirements, having engineers design parts to meet these requirements, then performing durability tests. These durability requirements are calculated to provide a margin of safety for use in the marketplace. However, depending on the part, these evaluation criteria can be overly aggressive against how it is used in the market, having led to a decrease in development efficiency as engine systems become more advanced. Therefore, in this study, we focused on the subject of high-cycle fatigue, which affects numerous components and is highly scalable, and built up a process for estimating the life span of components that would enable us to conduct appropriate evaluations that reflect how parts are truly used in the market. Recently, more and more vehicles are equipped with Telematics Control Units, (TCUs) which are being used by an expanding array of services, and have earned such vehicles the label of connected cars. We utilized the large-scale, real-time data (driving data) that these units provide. By combining such data with simulations that calculate engine transient behavior, we went on to construct a process capable of estimating the life span of engine components in each vehicle based on how it is used (stress) and its failure mechanisms. This process allows the stresses that components will face in the market to be recognized during the development stage, and makes possible efficient development that satisfies the requirements of the market while reducing the pursuit of excessive quality.
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DOI
https://doi.org/10.4271/2024-01-2244
Pages
7
Citation
Tanaka, K., Yoshii, K., and Takahashi, K., "Construction of Life Prediction Process for Engine Parts by Using Real-World Driving Data and Simulation Models," SAE Technical Paper 2024-01-2244, 2024, https://doi.org/10.4271/2024-01-2244.
Additional Details
Publisher
Published
Apr 09
Product Code
2024-01-2244
Content Type
Technical Paper
Language
English