Reliability Modeling and Assessment of Electric Vehicle Motor Using Fault Tree and Fuzzy Petri Nets

Wang, Bing; Liang, Yanping; Chen, Yang; Sang, Zhibo

doi:10.14257/ijgdc.2016.9.8.12

Cited by 8 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To show the applicability and flexibility of the proposed RPNs for knowledge representation and acquisition, a case study of assessing the reliability of an electric motor system (Wang et al, 2016) is presented in this section.…”

Section: Illustrative Examplementioning

confidence: 99%

“…To demonstrate the rationality and advantages of the proposed RPN model, it is compared with the extended FPNs (EFPNs) (Wang et al, 2016), the cloud reasoning Petri nets (CRPNs; Liu et al, 2018), the FPR method (Yeung & Ysang, 1998), and the dynamic uncertain causality graph (UCG; Li et al, 2020). The reasoning results of the terminating place obtained by these methods are listed in Table 7.…”

Section: Illustrative Examplementioning

confidence: 99%

See 1 more Smart Citation

Knowledge representation and acquisition using R‐numbers Petri nets considering conflict opinions

et al. 2021

View full text Add to dashboard Cite

As a vital modelling technique, fuzzy Petri nets (FPNs) have been widely used in various areas for knowledge representation and reasoning. However, the conventional FPNs have many deficiencies in representing inaccurate knowledge, acquiring knowledge parameters and conducting approximate reasoning when used in the real world. In this article, a new version of FPNs, called R‐numbers Petri nets (RPNs), is proposed to overcome the shortcomings and enhance the effectiveness of FPNs. Based on R‐numbers, expert knowledge is depicted in the form of weighted R‐numbers production rules. The interrelationships among input places (or transitions) are modelled by the R‐numbers Maclaurin symmetric mean operator in the knowledge reasoning process. In addition, the conflict opinions of experts are handled with the proposed RPN model in order to obtain more precise knowledge parameters. Finally, the effectiveness and practicality of the proposed RPNs are illustrated by a realistic example concerning reliability analysis of an electric vehicle motor.

show abstract

Section: Illustrative Examplementioning

confidence: 99%

Section: Illustrative Examplementioning

confidence: 99%

Knowledge representation and acquisition using R‐numbers Petri nets considering conflict opinions

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Based on the data listed in Table 11, the failure rate of motor controller parts is calculated by substituting these parameters into (8)(9)(10)(11)(12). The calculation results are listed in Table 12.…”

Section: B Evaluation Of Failure Rates Of Motor Controller Partsmentioning

confidence: 99%

“…The reliability of drive motor and electronic converter for power supply was also studied by scholars. For example, the reliability of the drive motor of EVs was studied in [9], [10] by using a combined fault tree and Petri net approach, and the fault logical causes by the winding insulation and bearing with higher fault rate were investigated in [11] by the approach of fault tree analysis. The reliability of bidirectional DC/DC converters of EVs was studied in [12]; the age and life of power converter components were estimated in [13] based on a survey, and so on.…”

Section: Introductionmentioning

confidence: 99%

A Detailed Reliability Study of the Motor System in Pure Electric Vans by the Approach of Fault Tree Analysis

et al. 2020

View full text Add to dashboard Cite

Attributing to the unique feature of zero carbon emission, electric vehicles (EVs) are attracting increasing interest in recent years, but their reliability, particularly the reliability of their critical components, is still a matter of concern today. In order to address this issue, much effort has been made before to assess the reliability of drive motor in the EVs. However, drive motor and motor controller are logically integrated and requested to work as one system in the EVs. In contrast to the individual reliability analysis of them, the combined assessment of the two parts can provide a more reliable prediction to the reliability of the entire motor system. Moreover, both drive motor and motor controller are composed of multiple components. The structure, type, and characteristics of these components may affect the reliability of the motor system as well. But these issues have not been considered in the previous research. In order to fill this gap of knowledge, the reliability of the entire motor system of pure electric vans that includes both drive motor and motor controller is investigated in this paper. In the research, the theoretical failure rates of subassemblies and components in drive motor and motor controller are predicted first. Then based on the failure rate prediction results, the reliability of the entire motor system (comprising both drive motor and motor controller) is assessed. Based on the assessment results, some interesting conclusions with respect to the most vulnerable subassemblies and components in the entire motor system and the potential disadvantage of existing reliability research are finally obtained. It is deemed that these new findings will be of great significance to the future reliability design and maintenance of pure electric vans. INDEX TERMS Reliability, electric vehicles, motor system, motor controller, fault tree analysis.

show abstract

“…In order to accurately assess the reliability of lithium-ion batteries, a reliability model considering the dependency among cells for the overall degradation of lithium-ion battery packs was built in [12]. Apart from these, the reliability of the stator and rotor components in permanent magnet synchronous motors for BEVs has been studied using a combined fault tree and Petri net approach [13,14]. The fault logic of failures caused by key components of the drive motor (i.e., stator and rotor windings and bearings) has also been investigated [15][16][17] using the approach of fault tree analysis (FTA); the results showed that different components have different effects on the reliability of the entire drive motor and also suggested that reliability issues in the drive motor and motor controller should be investigated together when assessing the reliability of the motor system; otherwise, an unreliable reliability prediction may be obtained.…”

Section: Introductionmentioning

confidence: 99%

Reliability Study of BEV Powertrain System and Its Components—A Case Study

et al. 2021

View full text Add to dashboard Cite

The powertrain system is critical to the reliability of a battery electric vehicle (BEV). However, the BEV powertrain is a complex system; it includes the motor, motor controller, power distribution unit, battery system, etc. The failure of any of these components may result in the failure of the entire powertrain system and eventually cause serious traffic accidents on the road. However, how much does each component affect the reliability of the entire system, and which components are the most vulnerable in the entire system? These questions are still unanswered today. To develop a reliability design for a BEV powertrain system, it is essential to conduct detailed research by investigating the most vulnerable component parts of the entire powertrain. In the present study, a fault-tree model of the entire powertrain and its subsystems was developed. Based on this model, the failure rates of all components were calculated first. Then, trends in the reliability indices for the entire powertrain and its components were estimated against BEV service life. From the estimation results, we learned that with increased service time, the reliability of the entire powertrain system is indeed much lower than that of its individual subsystems. Moreover, through comparative research, we found that the battery module is the most unreliable component not only of the battery system, but the entire powertrain system. Additionally, it was interesting to find that the reliability of the motor components was higher than that of other subsystem components, but that the reliability indices for the entire motor were not the highest among all the powertrain subsystems studied in this paper. We believe the findings of the present study will be of great significance to an improved understanding of the reliability design and maintenance of BEVs.

show abstract

Reliability Modeling and Assessment of Electric Vehicle Motor Using Fault Tree and Fuzzy Petri Nets

Abstract: Performing reliability analysis of electric vehicle motor has an important impact on its safety. To do so, this paper proposes its reliability modeling and evaluation issues of electric vehicle motor by using fault tree (FT)

Cited by 8 publications

References 16 publications

Knowledge representation and acquisition using R‐numbers Petri nets considering conflict opinions

Knowledge representation and acquisition using R‐numbers Petri nets considering conflict opinions

A Detailed Reliability Study of the Motor System in Pure Electric Vans by the Approach of Fault Tree Analysis

Reliability Study of BEV Powertrain System and Its Components—A Case Study

Contact Info

Product

Resources

About