Speed Control Of Electric Vehicle Research Articles

Enhanced PD Controller for Speed Control of Electric Vehicle Based on Gorilla Troops Algorithm

Enhanced PD Controller for Speed Control of Electric Vehicle Based on Gorilla Troops Algorithm

Switched capacitor voltage boost converter for BLDC motor speed control of electric vehicles

BLDC motors are extensively used in various industries, including CNC machine tools, industrial robots, and electric vehicles. Because of their compact size, high efficiency, high torque-to-power ratios, and low maintenance requirements due to their brushless operation, BLDC motors are the backbone of many industrial automation systems. However, they pose significant challenges when it comes to speed control. In this study, the speed of BLDC motors is controlled by PI-based speed controllers. In the described method, the BLDC motor is commutated using Hall Effect sensors. A proposed approach uses PI to regulate the speed of BLDC motors in an open-loop PWM method. The speed-controlled BLDC motor is analysed using MATLAB/simulink. The hardware of the proposed system is also implemented.

Design an Optimal Fractional Order PID Controller for Speed Control of Electric Vehicle

Design an Optimal Fractional Order PID Controller for Speed Control of Electric Vehicle

Adaptive Speed Control of Electric Vehicles Based on Multi-Agent Fuzzy Q-Learning

Electric vehicles (EVs) are expected to account for a significant percentage of the vehicle market in the coming years. The speed regulator is the inseparable part of EVs to solve challenges such as managing battery energy or adapting to road conditions. Since the operation of the EV system is time-variant due to parametric variations and road situations, the regulation of such systems is a complex task that cannot be effectively accomplished by the conventional deterministic control methodologies. To address the control challenges corresponding to the EV, the ultra-local model (ULM) with extended state observer (ESO) is developed for the speed tracking problem of the system. The Fuzzy Q-learning multi-age system (MAS) is also adopted to adaptively regulate the gains of the ULM controller in an online manner. The robustness of the established controller has been assessed by experimental data based on the new European driving cycle (NEDC) and altering some critical parameters of the EV test system. Moreover, the real-time verifications for the tracking problem of NEDC are also conducted by hardware-In-the Loop (HiL) simulation in the EV system.

Heuristic design and modelling of modified interleaved boost converter for E-mobility control

PurposeThis study aims to present a modified interleaved boost converter (MIBC) topology for improving the reliability and efficiency of power electronic systems.Design/methodology/approachThe MIBC topology was implemented with two parallel converters, operated with a −180 degree phase shift. Using this methodology, ripples are reduced. The state-space model was analysed with a two-switch MIBC for different modes of operation. The simulation was carried out and validated using a hardware prototype.FindingsThe performance of the proposed MIBC shows better output voltage, current and power than the interleaved boost converter (IBC) for the solar PV array. The output power of the proposed converter is 1.353 times higher than that of existing converters, such as boost converter (BC) and IBC. The output power of the four-phase IBC is 30 kW, whereas that of the proposed two-phase MIBC is 40.59 kW. The efficiency of MIBC was better than that of IBC (87.01%). By incorporating interleaved techniques, the total inductor current is reduced by 29.60% compared with the existing converter.Practical implicationsThe proposed MIBC can be used in a grid-connected system with an inverter circuit for DC-to-AC conversion, electric vehicle speed control, power factor correction circuit, high-efficiency converters and battery chargers.Originality/valueThe work presented in this paper is a modified version of IBC. This modified MIBC was modelled using the state-space approach. Furthermore, the state-space model of a two-phase MIBC was implemented using a Simulink model, and the same was validated using a hardware setup.

Speed control of electric vehicle by using type-2 fuzzy neural network

Speed control of electric vehicle by using type-2 fuzzy neural network

Electrical vehicle speed control by AI technique

This research paper in regards to the programmed vehicle driving framework without driver. This part presents a speed control of electric vehicle (EV) to improve the comportment and solidness under different street essential state . The control circuit utilizing shrewd fuzzy PI regulator is proposed. Boundaries which manage the working of PI regulator are progressively changed with the help of fuzzy canny control. This work manages the issue of way following for an electrical vehicle which has four electromechanical wheels framework. A regulator dependent on fuzzy rationale send the speed and the guiding guidelines to the robot to guarantee that the robot follows the direction , during the plan of this regulator the exertion was focused on the effortlessness and proficiency . The handling and reaction time are fundamental components in the process control, which is the thing that roused the decision of the construction and the boundaries of the various pieces of the fuzzy regulator.

Electronically Tunable ACO Based Fuzzy FOPID Controller for Effective Speed Control of Electric Vehicle

The phenomenal growth of the Electric Vehicle (EV) technology demands efficient and intelligent control strategies for the propulsion system. In this work, a novel fuzzy fractional order PID (FOPID) controller using Ant Colony Optimization (ACO) algorithm has been proposed to control EV speed effectively. The controller parameters and the fuzzy logic controller's membership functions are tuned and updated in real-time using the multi-objective ACO technique. The proposed controller's speed tracking performance is verified using the new European driving cycle (NEDC) test in the MATLAB-Simulink platform. The proposed controller outperforms the ACO-based fuzzy integer-order PID (IOPID), FOPID, and traditional IOPID controllers. The sensitivity analysis confirms the robustness of the proposed controller for varying parameters of the EV model. The stabilization of EV speed in the presence of external disturbance is also confirmed. In the proposed work, an attempt is made to analyze the system's stability using Matignon's theorem, considering the linearized EV model. The proposed controller gives optimum speed tracking performance compared to the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) based fuzzy FOPID controllers. Additionally, the optimized fuzzy FOPID controller is realized using a second-generation current conveyor with extra inputs (EX-CCII) and fractional-order capacitors with electronic tunability. The controller circuit's performance evaluation is carried out in the Cadence Analog Design Environment using GPDK 180 nm CMOS process.

Nonlinear Model Predictive Speed Control of Electric Vehicles Represented by Linear Parameter Varying Models With Bias Terms

This paper investigates a novel approach to design a nonlinear optimal model predictive controller for the speed control of constrained nonlinear electric vehicles (EVs). The proposed approach employs a linear parameter varying model including bias terms and a model predictive scheme. The controller design conditions are derived in terms of linear matrix inequalities (LMIs), which can be solved through convex optimization techniques. Due to considering bias terms in the system dynamic, the proposed approach can be regarded as the general case of the existing results. Furthermore, practical limitations on the amplitude of the input signal are considered and formulated in terms of LMIs. An EV dynamic with bias term is presented and hardware-in-the-loop real time and experiments are carried out to illustrate the effectiveness and merits of the proposed approach over the existing results.

Analysis, control and design of speed control of electric vehicles delayed model: multi‐objective fuzzy fractional‐order controller

The purpose of this study is to suggest an optimal multi-objective fuzzy fractional-order controller (MOFFOPID) for the speed control of EV systems with time-delay. It is presumed that while the EV is in movement, the armature winding resistance of the direct current (DC) motor varies with time due to temperature changes and this makes it impossible to develop an accurate dynamic model for an EV system. Consequently, in order to have a fast and accurate tracking of the set-point, besides a smooth control signal, a new multi-objective stochastic optimisation is used for the online adjustment of the parameters of MOFFOPID controller. Moreover, a comparison is made between the results of the current study and those of some of the most recent studies on the same topic, which have used online multi-objective PI and online multi-objective fuzzy PI, to assess the efficiency of the suggested controller. Finally, the experimental results based on a TMS320F28335 DSP are implemented on a DC motor to verify the effectiveness of the proposed MOFFOPID controller in controlling the speed of the DC motor which has non-linear features. The results of the simulation confirm the desirable performance of suggested controller.

Robust MRAS speed control of hybrid electric vehicle using sliding-mode and fuzzy-logic adaptation mechanism

In this paper model reference adaptive system (MRAS) with two adaptation techniques are proposed for speed control of electric vehicle (EV), which is achieved by controlling the throttle position. The first proposed adaptation scheme is sliding mode (SM), which is based on Lyapunov theory of stability and the second adaptation mechanism is based on fuzzy-logic (FL) technique. The dominant pole-based reference models (RMs) are designed that give the desired performance of the vehicle. These two adaptation techniques are developed to achieve robust performance of the nonlinear vehicle with the target to improve fuel economy, reduced pollution, improved driving safety and reduced manufacturing cost. A detailed comparison between the proposed and conventional schemes is also carried out. In this paper a robust proportional integral (PI) controller is designed using Kharitonov theorem and also determines the stability margin of linearised system.

Design of control system instrumentation using reliability criteria

This article focuses on the design of reliable control system instrumentation. It describes a design methodology using structural modeling. This method provides instrumentation structural links from graphical tree representation, and this graphical tool allows obtaining different paths for accessing the physical quantities concerning the fixed mission by the designer for the control system. The main of our contribution is to assess the reliability thanks to structural links of control system during design phase. The mission of the speed control of electric vehicle is used as an illustrative example.

The Fuzzy Decoupling Control of the Electric Vehicle Steering and Speed Systems

Based on the electric vehicle steering and speed models, this paper discussed the control problems of the electric vehicle steering and speed systems. Firstly, the electric vehicle speed control system is designed using the fuzzy PID controller. Then the electric vehicle steering controller is a fuzzy controller. Due to the coupled effect of speed in the steering control system, designing a fuzzy decoupling controller, reducing the coupled effect of speed, in order to control the system of steering model and speed model independently, and to achieve the purpose of decoupling. Finally, the result of simulation proves that no matter how speed changes, steering control system still has good tracking performance with decoupling controller. The systemic robustness is much stronger and it also enhances the utility of the control system.