Naina Mohamed, Abdul Razak (2020) Adaptive Cruise Control For Electric Vehicles. Masters thesis, Universiti Teknikal Malaysia Melaka.
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Abstract
Adaptive Cruise Control (ACC) eases driving operations by reducing fatigue and providing comfort when driving on highway roads. ACC system consisting of upper-level and lower-level controllers is developed for an electric vehicle using the Fuzzy Logic Controller (FLC). The ACC is designed in MATLAB Simulink together with longitudinal dynamics model and Burckhardt tyre model. The upper-level controller is a combination of feedforward and fuzzy feedback controllers. The feedforward controller determines the desired acceleration and headway distance. The feedback controller equipped with FLC determines the additional longitudinal force. The lower-level controller distributes the desired tyre longitudinal force and converts the forces into wheel torque commands. The evaluation process is to determine appropriate parameters and fuzzy rules set for the controllers. Feedforward and feedback controllers are evaluated in the selected driving simulation scenario. The feedforward controller had achieved the desired headway distance with reduced relative velocity. The vehicle response has improved more with the combination of feedforward and feedback controllers. The performance is better when the relative velocity is reduced carlier during the same simulation. The simulation results also show the headway distance corresponding to the preceding vehicle speed. The feedforward and feedback controllers’ combination gives safe distance compared to the feedforward controller usage.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Automobiles, Speed, Automatic control, Feedback control systems, Cruise Control, Electric Vehicles |
| Subjects: | T Technology > T Technology (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics |
| Divisions: | Library > Tesis > FKM |
| Depositing User: | F Haslinda Harun |
| Date Deposited: | 06 Jan 2022 12:38 |
| Last Modified: | 06 Jan 2022 12:38 |
| URI: | http://eprints.utem.edu.my/id/eprint/25552 |
| Statistic Details: | View Download Statistic |
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