Intelligent fuzzy controller using particle swarm optimization for control of permanent magnet synchronous motor for electric vehicle

In order to improve the electrical conversion efficiency of an electric tractor motor, a load torque based control strategy (LTCS) is designed in this paper by using a particle swarm optimization algorithm (PSO). By mathematically modeling electric-mechanical performance and theoretical energy waste of the electric motor, as well as the transmission characteristics of the drivetrain, the objective function, control relationship, and analytical platform are established. Torque and rotation speed of the motor’s output shaft are defined as manipulated variables. LTCS searches the working points corresponding to the best energy conversion efficiency via PSO to control the running status of the electric motor and uses logic and fuzzy rules to fit the search initialization for load torque fluctuation. After using different plowing forces to imitate all the common tillage forces, the simulation of traction experiment is conducted, which proves that LTCS can make the tractor use electrical power efficiently and maintain agricultural applicability on farmland conditions. It provides a novel method of fabricating a more efficient electric motor used in the traction of an off-road vehicle.

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“…(2) The mechanical dynamic characteristics of the motor output shaft should be controlled by the PSO method presented by Elwer et al [10].…”

Section: Analysis Of the Simulation Resultmentioning

confidence: 99%

“…However, the optimization of electric motor control strategy was also ignored [9]. Elwer et al optimized the scaling factors of a fuzzy controller used for enhancing the rapidity, accuracy, and robustness of the electric vehicle motor [10]. However, the controller is not helpful in the improvement of motor energy conversion efficiency.…”

Section: Introductionmentioning

confidence: 99%

Design of a Load Torque Based Control Strategy for Improving Electric Tractor Motor Energy Conversion Efficiency

Liu

Zhou

2016

Mathematical Problems in Engineering

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“…Several authors have dealt with this problem for controllers: Control of permanent magnet synchronous motor for electric vehicle [211], load frequency control (LFC) [212], optimal control of a grid independent photovoltaic system [213], [214], and control of flexible AC transmission systems (FACTS) devices, particularly, a thyristor controlled series capacitor (TCSC) [215]. For this type of problem, the suggested definition of the particle is as follows [216]: (46) where is the shape function defined by integer value, e.g., for Gaussian functions;…”

Section: G Power System Identification and Controlmentioning

confidence: 99%

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Valle

Venayagamoorthy

Mohagheghi

et al. 2008

IEEE Trans. Evol. Computat.

1,972

851

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Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.Index Terms-Classical optimization, particle swarm optimization (PSO), power systems applications, swarm intelligence.

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“…However, the actual process of power generation system with nonlinear, time-varying, non-stable, and the con-trolled object load varied, complex confounding factors. To obtain satisfactory control results, we need to constantly tune the PID parameters online, so this paper proposes a Fuzzy-PID control algorithm to achieve maximum power tracking control [12,13]. Fuzzy-PID control combines fuzzy control and traditional PID control.…”

Section: Fuzzy-pid Controllermentioning

confidence: 99%

Maximum Power Tracking Control for Current Power System Based on Fuzzy-PID Controller

Mao

Huang

Yang

et al. 2015

Intelligent Robotics and Applications

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Abstract. In order to solve the problem of maximum energy capture in the dynamic changing current, the maximum power point tracking (MPPT) control strategy of an extensible vertical-axis blade current power generation system was developed. Consider the current speed with the characteristics of randomness and uncertainties. Affixed set of PID parameters can hardly achieve desired generator speed. Therefore, this paper presents a Fuzzy-PID control, which combines the characteristics of traditional PID and fuzzy control. The simulation results show that the Fuzzy-PID control is able to guarantee desired dynamic characteristics, such as fast response, robustness and stability, and is able to effectively track the maximum power point. IntroductionWith the high speed development of economy, global problems gradually change seriously, such as air pollution, resource shortage and ecological deterioration. In order to solve the energy crisis and air pollution effectively, development of new energy and the use the renewable energy to replace the non-renewable energy have become the trend [1]. The global ocean area is vast, which potential energy is enormous. The ocean current energy mainly includes the kinetic energy of water flow, which has many advantages in comparison to other renewable energy. The ocean current energy has strong regularity and stability, which are predictable. The ocean current generation device lies below sea level, which does not occupy the land area, would not affect the surrounding landscape and would not pollute the environment. Therefore, the development and utilization of the ocean current energy is connected by more and more people [2].

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Intelligent fuzzy controller using particle swarm optimization for control of permanent magnet synchronous motor for electric vehicle

Cited by 9 publications

References 8 publications

Design of a Load Torque Based Control Strategy for Improving Electric Tractor Motor Energy Conversion Efficiency

Design of a Load Torque Based Control Strategy for Improving Electric Tractor Motor Energy Conversion Efficiency

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Maximum Power Tracking Control for Current Power System Based on Fuzzy-PID Controller

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