The efficiency of PSO-based MPPT technique of an electric vehicle within the city

Kariem, Habib; Touti, Ezzedine; Fetouh, Tamer

doi:10.1177/0020294019882973

Cited by 14 publications

(5 citation statements)

References 37 publications

(50 reference statements)

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“…f( ) and g( ) are the activation functions of the hidden layer and the output layer. The function f( ) can also be called ''Transfer function,'' The commonly used is the sigmoid function given as equation (1). The functions g( ) use is a linear function.…”

Section: Bp Neural Networkmentioning

confidence: 99%

“…Moreover, optical fiber has strong plasticity, which is suitable for various shapes of composite components, and is widely used in the field of aerospace. 1,2 There are many kinds of fiber optic sensors 3 used in composite structure health monitoring, including Bragg grating fiber optic sensors, Fabry Perot-Paro (F-P) optical fiber sensor, distributed optical fiber sensors, micro bend fiber optic sensor, etc. At present, various optical fibers have been widely used in the health monitoring of composite material structures, and there are many research results.…”

Section: Introductionmentioning

confidence: 99%

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A static load position identification method for optical fiber-composite structures based on particle swarm optimization- back Propagation neural network algorithm

Shen

Tian

2022

Measurement and Control

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Aiming at researching on health monitoring of composite materials, a static load position identification method for optical-fiber composite structures based on the Particle Swarm Optimization-Back Propagation (PSO-BP) neural network algorithm is proposed. Based on the 2 × 2 optical fibers-composite structures, the PSO-BP algorithm is used to establish a nonlinear mapping between the fiber output intensity and the position. At first, a three-layer BP neural network is established. The number of the hidden layer is 30. And then the PSO algorithm is used to optimize the initial weights and thresholds of the BP neural network. Finally, a BP neural network is built using optimized initial weights and thresholds. A total of 515 sets of data samples are collected by the experimental system, of which 500 sets are used for training and 15 sets are used for the final model prediction. Simulation results show that the Mean Square Error (MSE) of the static load position prediction based on the PSO-BP algorithm is 0.0485. Compared with the position prediction model established by the BP neural network, Radial Basis Function (RBF) neural network and Support Vector Regression Machine (SVRM), the PSO-BP neural network model has a higher accuracy. The proposed method has an important application value for the research of health self-diagnosis of composite structures.

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Section: Bp Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A static load position identification method for optical fiber-composite structures based on particle swarm optimization- back Propagation neural network algorithm

Shen

Tian

2022

Measurement and Control

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“…In addition, each particle has a memory allowing it to remember its best performance and the best performance achieved by "neighboring" particles (informants). In addition, each particle has a group of informants, historically called its neighborhood [41,42]. A swarm of particles, which are potential solutions to the optimization problem, will look for the global optimum with their movements.…”

Section: A the Pso Algorithmmentioning

confidence: 99%

A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System

et al. 2021

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This research focuses on a photovoltaic system that powers an Electric Vehicle when moving in realistic scenarios with partial shading conditions. The main goal is to find an efficient control scheme to allow the solar generator producing the maximum amount of power achievable. The first contribution of this paper is the mathematical modelling of the photovoltaic system, its function and its features, considering the synthesis of the step-up converter and the maximum power point tracking analysis. This research looks at two intelligent control strategies to get the most power out, even with shading areas. Specifically, we show how to apply two evolutionary algorithms for this control. They are the “particle swarm optimization method” and the “grey wolf optimization method”. These algorithms were tested and evaluated when a battery storage system in an Electric Vehicle is fed through a photovoltaic system. The Simulink/Matlab tool is used to execute the simulation phases and to quantify the performances of each of these control systems. Based on our simulation tests, the best method is identified.

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“…27 The overall system is monitored by a control unit, which applies to the hybrid model and the complete electrical model. 28,29 Several advantages and issues characterize each version. The fully electric vehicle is more environment friendly.…”

Section: Electric and Hybrid Vehicle Architecturesmentioning

confidence: 99%

Energy optimization of an electric car using losses minimization and intelligent predictive torque control

Kraiem

Shaaban

2020

Journal of Algorithms & Computational Technology

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Autonomy is considered an important criterion that characterizes the performance of electric vehicles. It is represented by the distance that could be traveled by a fully electric vehicle which mainly depends on several parameters such as the vehicle model, type of battery, type of motor, etc. In this context, to improve the autonomy of electric vehicles, this paper represents an optimization study for the electric motor based on two contributions. The first devise an energy optimization algorithm to reduce the motor losses by calculation of the stator flux reference according to the electromagnetic torque and the rotation speed. The second is concerned with controller parameters adjustment using the Particle Swarm Optimization (PSO) technique to improve the efficacy and robustness of the drive. The performance of this strategy is evaluated in terms of torque, flux ripples, and transient response to step variations of the torque control. A comparative study of the designed PI controllers based on PSO with four other control algorithms and tuning methods is established in order to prove the efficiency of PI_PSO. The analysis, modeling, and simulation results are presented to verify the validity of the proposed overall optimization study.

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The efficiency of PSO-based MPPT technique of an electric vehicle within the city

Cited by 14 publications

References 37 publications

A static load position identification method for optical fiber-composite structures based on particle swarm optimization- back Propagation neural network algorithm

A static load position identification method for optical fiber-composite structures based on particle swarm optimization- back Propagation neural network algorithm

A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System

Energy optimization of an electric car using losses minimization and intelligent predictive torque control

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