Evidence map›Paper›PMID 39205052›Full record

ArticleSensors (Basel, Switzerland)2024

Accelerated Fatigue Test for Electric Vehicle Reducer Based on the SVR-FDS Method.

Yudong Wu, Zhanhao Cui, Wang Yan, Haibo Huang, Weiping Ding

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yudong WuSchool of Intelligent Manufacturing, Chengdu Technological University, Chengdu 611730, China.ORCID 0000-0003-4446-5611
Zhanhao CuiSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China.
Wang YanSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China.
Haibo HuangSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China.
Weiping DingSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China.

Funding

the Natural Science Foundation of Sichuan Province 2022NSFSC1892the Talent Program (Ph.D. Fund) of Chengdu Technological University 2024RC025
6 · The paper itself

Abstract

The reducer serves as a pivotal component within the power transmission system of electric vehicles. On one hand, it bears the torque load within the power transmission system. On the other hand, it also endures the vibration load transmitted from other vehicle components. Over extended periods, these dynamic loads can cause fatigue damage to the reducer. Therefore, the reliability and durability of the reducer during use are very important for electric vehicles. In order to save time and economic costs, the durability of the reducer is often evaluated through accelerated fatigue testing. However, traditional approaches to accelerated fatigue tests typically only consider the time-domain characteristics of the load, which limits precision and reliability. In this study, an accelerated fatigue test method for electric vehicle reducers based on the SVR-FDS method is proposed to enhance the testing process and ensure the reliability of the results. By utilizing the support vector regression (SVR) model in conjunction with the fatigue damage spectrum (FDS) approach, this method offers a more accurate and efficient way to evaluate the durability of reducers. It has been proved that this method significantly reduces the testing period while maintaining the necessary level of test reliability. The accelerated fatigue test based on the SVR-FDS method represents a valuable approach for assessing the durability of electric vehicle reducers and offering insights into their long-term performance.

Indexed as

accelerated fatigue testelectric vehicle reducerSVR–FDSvibration

Identifiers

PMID39205052
PMCPMC11360812

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.