Particular aspects concerning the design of a brushless DC electric motor driving a mini-scooter

Marian-Stefan, Nicolae; Presura, Raluca-Cristina; Vlad, Ion; Nicolae, Petre-Marian; Smarandescu, Ionut-Daniel

doi:10.1109/icate.2016.7754712

2016 International Conference on Applied and Theoretical Electricity (ICATE) 2016

DOI: 10.1109/icate.2016.7754712

|View full text |Cite

Particular aspects concerning the design of a brushless DC electric motor driving a mini-scooter

Nicolae Marian-Stefan

Raluca-Cristina Presura

Ion Vlad

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Torque estimation of electric vehicle motor using adaptive-network based fuzzy inference systems

Kerem

2021

International Journal of Automotive Engineering and Technologies

View full text Add to dashboard Cite

This paper presents to estimating studies of the torque data of the Electric Vehicle (EV) motor using Adaptive-Network Based Fuzzy Inference Systems (ANFIS). The real-time data set of the Outer-Rotor Permanent Magnet Brushless DC (ORPMBLDC) motor which was designed and manufactured for using in ultralight EV, was used in these estimation process. The current, the power and the motor speed parameters are defined as input variables, and the torque parameter defined as output variable. Five distinct ANFIS models were designed for torque estimation process and the performances of each model were compared. The most effective model for testing data set among the ANFIS models was anfis: 2 with 98 nodes and 36 fuzzy rules, and the worst model was anfis: 5 with 286 nodes and 125 fuzzy rules. Performance results of all designed models were presented in tables and graphs.

show abstract

Torque estimation of electric vehicle motor using adaptive-network based fuzzy inference systems

Kerem

2021

International Journal of Automotive Engineering and Technologies

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Particular aspects concerning the design of a brushless DC electric motor driving a mini-scooter

Cited by 1 publication

References 9 publications

Torque estimation of electric vehicle motor using adaptive-network based fuzzy inference systems

Torque estimation of electric vehicle motor using adaptive-network based fuzzy inference systems

Contact Info

Product

Resources

About