Computation of the Speed of Four In-Wheel Motors of an Electric Vehicle Using a Radial Basis Neural Network
| Autor: | Hasan Kürüm, Merve Yildirim, Arif Gülten, Mehmet Cem Catalbas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
electronic differential system
Engineering business.product_category 020209 energy 02 engineering and technology Electronic differential speed estimation law.invention Robustness (computer science) Control theory CODESYS law Electric vehicle lcsh:Technology (General) 0202 electrical engineering electronic engineering information engineering MATLAB computer.programming_language Artificial neural network lcsh:T58.5-58.64 business.industry lcsh:Information technology 020208 electrical & electronic engineering electric vehicle in-wheel motor Tachometer radial basis neural network lcsh:TA1-2040 lcsh:T1-995 business lcsh:Engineering (General). Civil engineering (General) computer Induction motor |
| Zdroj: | Engineering, Technology & Applied Science Research, Vol 6, Iss 6, Pp 1288-1293 (2016) Engineering, Technology & Applied Science Research, Vol 6, Iss 6 (2016) |
| ISSN: | 1792-8036 2241-4487 |
| Popis: | This paper presents design and speed estimation for an Electric Vehicle (EV) with four in-wheel motors using Radial Basis Neural Network (RBNN). According to the steering angle and the speed of EV, the speeds of all wheels are calculated by equations derived from the Ackermann-Jeantand model using CoDeSys Software Package. The Electronic Differential System (EDS) is also simulated by Matlab/Simulink using the mathematical equations. RBNN is used for the estimation of the wheel speeds based on the steering angle and EV speed. Further, different levels of noise are added to the steering angle and the EV speed. The speeds of front wheels calculated by CoDeSys are sent to two Induction Motor (IM) drives via a Controller Area Network-Bus (CAN-Bus). These speed values are measured experimentally by a tachometer changing the steering angle and EV speed. RBNN results are verified by CoDeSys, Simulink, and experimental results. As a result, it is observed that RBNN is a good estimator for EDS of an EV with in-wheel motor due to its robustness to different levels of sensor noise. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|