Applying fuzzy logic control to analyze real-time control for charging and discharging power of electric vehicles

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189978 ◽

2021 ◽

pp. 1-7

Author(s):

Feng Liu

Keyword(s):

Fuzzy Logic ◽

Real Time ◽

Electric Vehicle ◽

Electric Vehicles ◽

Fuzzy Logic Control ◽

Power Grid ◽

Regional Distribution ◽

Real Time Control ◽

Time Control ◽

Logic Control

The disorderly charging of large-scale electric vehicles will aggravate the peak-valley difference of the power grid, and affect the power quality and life of the transformer. The fuzzy logic control strategy for charging and discharging optimization of charging vehicles under the framework of fuzzy logic control from the perspective of the group is considered in this article. A real-time control method based on the clustering characteristics of the charging end time is proposed according to the different charging requirements of the connected electric vehicles and fuzzy logic control is adopted to solve the problem of optimal charging and discharging power of the entire cluster and a single electric vehicle. A fuzzy logic control model considering the charging and discharging of electric vehicles is established orienting at minimize daily load fluctuations and control penalties in the upper layer. The charging and discharging cost of electric vehicle owners is considered to solve the optimal control problem of the charging and discharging power of a single electric vehicle. Taking the data of the typical regional distribution network load as an example, it is verified that the real-time charging optimization strategy under fuzzy logic control through simulation can ensure the reliable operation of the power grid while considering the interests of all parties.

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Optimal Slip Ratio Based Fuzzy Control of Acceleration Slip Regulation for Four-Wheel Independent Driving Electric Vehicles

Mathematical Problems in Engineering ◽

10.1155/2013/410864 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 19

Author(s):

Guodong Yin ◽

Shanbao Wang ◽

Xianjian Jin

Keyword(s):

Fuzzy Logic ◽

Electric Vehicle ◽

Electric Vehicles ◽

Fuzzy Logic Control ◽

Fuzzy Controller ◽

Angular Acceleration ◽

Slip Rate ◽

Wheel Slip ◽

Slip Ratio ◽

Logic Control

To improve the driving performance and the stability of the electric vehicle, a novel acceleration slip regulation (ASR) algorithm based on fuzzy logic control strategy is proposed for four-wheel independent driving (4WID) electric vehicles. In the algorithm, angular acceleration and slip rate based fuzzy controller of acceleration slip regulation are designed to maintain the wheel slip within the optimal range by adjusting the motor torque dynamically. In order to evaluate the performance of the algorithm, the models of the main components related to the ASR of the four-wheel independent driving electric vehicle are built in MATLAB/SIMULINK. The simulations show that the driving stability and the safety of the electric vehicle are improved for fuzzy logic control compared with the conventional PID control.

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Experimental Investigation on Swash Plate Axial Piston Pumps With Conical Cylinder Blocks Using Fuzzy Logic Control

Fluid Power Systems and Technology ◽

10.1115/imece2002-39325 ◽

2002 ◽

Cited By ~ 1

Author(s):

M. K. Bahr Khalil ◽

J. Svoboda ◽

R. B. Bhat

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Control ◽

Fuzzy Logic Controller ◽

Dynamic Performance ◽

Real Time Control ◽

Axial Piston Pumps ◽

Time Control ◽

Logic Control ◽

Swash Plate ◽

Axial Piston

Analytical studies on the dynamic performance of variable displacement swash plate axial piston pumps show that the pump performance can significantly improved by replacing the conventional PD process controller with fuzzy logic controller. Electrically controlled, constant power regulated, swash plate pumps with conical cylinder blocks have been recently extensively studied. Comparative study has been carried out to compare the pump dynamic performance when the conventional PD process controller is replaced by a proposed fuzzy logic one. The study reveals some superior performance characteristics when fuzzy logic controller is used. In the present study an experimental setup is built to measure the dynamic performance of a typical 9-piston pump that has conical shaped cylinder block. The pump is of 40 cc/rev geometrical size, type A4VSO, that is manufactured by Rexroth. The experimental setup consists of a hydraulic test bed interfaced with real time control and data acquisition system. The setup is used firstly for testing the pump static characteristics. Subsequently, the setup is used to measure the time response of the pump, which is equipped with the conventional PD controller, to the stepwise changes in the load pressure. Pump model verification is then discussed based on the comparison between the theoretical and experimental results. The pump is afterwards interfaced with real time control software for prototyping the proposed fuzzy logic controller that replaces the currently used PD one. With the fuzzy logic control, measuring the pump time response under the same loading conditions is repeated. Experimental results are presented, compared with the analytical findings and discussed.

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Real-Time Simulation Study for a Series Hybrid Electric Vehicle

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.128-129.965 ◽

2011 ◽

Vol 128-129 ◽

pp. 965-969

Author(s):

Xu Dong Liu ◽

Qing Wu Fan ◽

Bang Gui Zheng ◽

Jian Min Duan

Keyword(s):

Fuzzy Logic ◽

Real Time ◽

Electric Vehicle ◽

Control Strategy ◽

Fuzzy Logic Control ◽

Hybrid Electric Vehicle ◽

Real Time Simulation ◽

Logic Control ◽

Time Simulation ◽

Series Hybrid Electric Vehicle

To shorten design period and reduce development costs, computer modeling & simulation is important for HEV design and development. In this paper, real-time simulation for a Series Hybrid Electric Vehicle (SHEV) is made to test its fuzzy logic control strategy based on dSPACE-DS1103 development kits. The whole real-time simulation schematic is designed and the vehicle forward-facing simulation model is set up. Driver behavior is simulated by two potentiometers and introduced into the system to realize close-loop control. A real-time monitoring interface is also developed to observe the experiment results. Experiment results show that the real-time simulation platform works well and the SHEV fuzzy logic control strategy is effective.

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Fuzzy logic control for a hydraulic hybrid excavator based on torque prediction and genetic algorithm optimization

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407017724590 ◽

2017 ◽

Vol 232 (8) ◽

pp. 983-994 ◽

Cited By ~ 2

Author(s):

Hua Zhou ◽

Peng-Yu Zhao ◽

Ying-Long Chen

Keyword(s):

Genetic Algorithm ◽

Fuzzy Logic ◽

Control Strategy ◽

Fuzzy Logic Control ◽

Membership Functions ◽

Real Time Control ◽

Hydraulic Hybrid ◽

Time Control ◽

Logic Control ◽

Hybrid Excavator

Optimization of the control strategy, whose primary mission is to solve the problem associated with energy management, is an effective way to minimize the fuel consumption of the hydraulic hybrid excavator. As a widely used control strategy, fuzzy logic control can be adopted to realize suboptimal power split with robustness and adaptation, which is one of the most logical approaches for multidomain, nonlinear and time-varying plant. However, the membership functions are difficult to determine according to manual experiences; meanwhile, the optimization-based membership functions are difficult to utilize in real time control. This paper aims to improve the fuel consumption of a hydraulic hybrid excavator by proposing a fuzzy control strategy whose membership functions are optimized by the genetic algorithm, which considers predicted torque of the internal combustion engine (ICE) as a known quantity to realize real time control. The needed torque of the ICE is predicted by superposition of the previous torque. A fuzzy logic control strategy is then designed with membership functions optimized by the genetic algorithm according to the predicted needed torque to achieve better performance. Finally, a numerical experiment is carried out to verify the proposed control strategy.

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