An Embedded Controller Application with Regenerative Braking for the Electric Vehicle

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.

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“…For the designed system, the linearized state equations in equation 13 can be expressed as in equation 20.…”

Section: Linear Modelmentioning

confidence: 99%

Performance Comparison of PID and LQR Controllers for Control of Non-Linear Magnetic Levitation System

Karabacak

Yaşar

Sarıtaş

2023

DEUFMD

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“…Therefore, the control strategies for in-wheel motor-driven electric vehicles (IWMDEV) have been widely studied. Karabacak et al designed a fuzzy logic controller in the motor mode and a pulse width modulation controller in the RB mode for two IWMDEV (2IWMDEV), and the algorithm is embedded in a DSP microcontroller [15] . Gang and Zhi proposed an RB control method for four IWMDEV (4IWMDEV) under urban scenes using motor efficiency maps, and the simulation results validated the effectiveness of the proposed method under urban driving cycles [16] .…”

Section: Introductionmentioning

confidence: 99%

Integrated control of anti-lock and regenerative braking for in-wheel-motor-driven electric vehicles

Yong,

Dong,

Zhang

et al. 2024

Complex Engineering Systems

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The in-wheel motor is increasingly used in electric vehicles due to the significantly improved controllability, response capability, and energy recovery efficiency based on this technology. However, the independent control of in-wheel motors will lead to braking torque distribution problems, especially in a situation where anti-lock braking systems (ABS) are triggered, which may cause the braking energy to be unrecoverable without the coordinated control between anti-lock and RB for two in-wheel motor-driven electric vehicles based on the RB efficiency map. Control-oriented wheel dynamics and slip ratio models of the system are generated. A sliding mode intervention of regenerative braking (RB) control. This paper presents an integrated algorithm to realize the control-based ABS controller is designed to prevent the wheels from locking and to maintain the slip ratio within a desired level, and the stability and robustness of the controller to uncertainties and disturbances are discussed. Moreover, the braking strength of the driver is calculated and divided into different modes to derive a dynamic braking torque distribution to improve the energy recovery efficiency. The hardware-in-the-loop simulation results show that the recovered energy of the proposed strategy under ABS-triggered maneuver is increased by 52.9% than that of the Proportional, Integral, and Derivative controller and can effectively improve the braking performance and stability.

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“…In different methanol mixtures for an internal combustion engine, the effect on engine emissions and performance is estimated using an ANFIS model [13]. Li-ion batteries are used in electric vehicles [14][15]. Efficient use of storable energy resources, which are becoming more and more needed today, is of great importance.…”

Section: Introductionmentioning

confidence: 99%

Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS)

Karabacak

Özkan

Sarıtaş

2020

International Journal of Energy Applications and Technologies

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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.

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An Embedded Controller Application with Regenerative Braking for the Electric Vehicle

Cited by 8 publications

References 15 publications

Performance Comparison of PID and LQR Controllers for Control of Non-Linear Magnetic Levitation System

Performance Comparison of PID and LQR Controllers for Control of Non-Linear Magnetic Levitation System

Integrated control of anti-lock and regenerative braking for in-wheel-motor-driven electric vehicles

Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS)

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