No abstract

Regenerative braking is very important for increasing the total range of an electric vehicle. In this study, an embedded controller, including regenerative braking, is designed and implemented for an electric vehicle. Experimental studies are carried out on an electric vehicle driven by two in-wheel electric motors. In-wheel electric motors are preferred in light electric vehicles, since they are both highly efficient and supports regenerative braking. In our embedded controller application, the in-wheel electric motor is operated in both the motor mode and the regenerative braking mode. The in-wheel electric motor control embedded software is developed in the Matlab/Simulink environment. The developed software is embedded in the DSP STM32F407 microcontroller, which has ARM Cortex-M4 core. The in-wheel electric motor is controlled by a fuzzy logic controller in the motor mode, the in-wheel electric motor - in the regenerative braking mode. Different PWM (Pulse Width Modulation) ratios are applied to the wheel electric motor in the regenerative braking mode. The experimental data are recorded in real-time by transferring to a PC on the electric vehicle. The performance of the study is proven with experimental tests.

Thanks to their electrochemical structure, batteries are the elements that can store electrical energy and spend on a load when the electrical energy they store is needed. Today, with the widespread use of electrically powered mobile devices, rechargeable batteries have become widespread and battery technologies have developed. With the idea that the latest technology systems and electric vehicles will become widespread in the future, the studies on batteries are increasing day by day. In this study, charge state estimation of Li-ion battery cell used to provide power in many applications was realized by using adaptive neural fuzzy inference system (ANFIS). A Li-ion battery was discharged using variable electrical loads with a battery discharge circuit modeled on MATLAB Simulink and current, voltage, temperature and current power parameters of the battery were selected as input variables. Battery parameters and charge status data obtained from discharge tests using different electrical loads on MATLAB Simulink were used as training and test parameters of neural network. Using the MATLAB ANFIS toolbox, the system was trained with 80% of the battery parameters obtained in the battery discharge experiments and with 20% as testing data, the success performance was interpreted by applying the adaptive neural fuzzy inference system.

In this paper, a simulation study enhanced to model that the speed control of brushless direct current (BLDC) motors used in electric vehicles with intelligent control methods. The simulation study was prepared in Matlab/Simulink environment. The first control method is Type-1 fuzzy logic control (T1FLC), and the second control method is the Intermittent Type-2 fuzzy logic control (IT2FLC) model. Membership functions for different membership numbers have been created for both types of FLC models. These are 3×3, 5×5, 7×7. Control methods are prepared in Matlab/M-file environment. The model is defined as the input variable of the error, which is the difference between the reference speed and the motor speed, and the output variable of the Pulse Width Modulation (PWM) signal applied to the motor. The simulation study maintains the speed of the BLDC motor up to the reference speed with T1FLC and IT2FLC controllers, depending on the reference speed and applied load values. Depending on the number of different memberships, the effects of controller performances on the control of motor speed have been observed. The graphs and findings of the experiment are shown in the results and discussion section.

Magnetic Levitation System (MLS) has become a current study in the field of engineering due to its advantages such as low energy consumption and minimum friction. MLSs are nonlinear unstable systems. Due to the complexity of the structure and the difficulty of the controls, many advanced control theories can be applied on these systems and the performance of the controllers can be evaluated. In this article, Proportional-Integral-Derivative (PID) and Linear-Quadratic Regulator (LQR) controller methods are applied on MLS mathematically modeled in MATLAB environment. Controller performances were compared in the results found. The results obtained on the applicability of PID and LQR control methods for MLS were evaluated. In addition, the system performance of the controllers was compared with five parameters. These are rise time, settling time, maximum overshoot, overshoot and steady-state error. LQR controller produced great stability and homogeneous response compared to PID controller.