A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV

Ozkop, E.; Altaş, İsmail H.; Okumuş, Halil İbrahim; Sharaf, A.Μ.

doi:10.1080/00207217.2015.1010183

Cited by 19 publications

(9 citation statements)

References 18 publications

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“…Fuzzy sets: Each input parameter is represented by a fuzzy set Ak with a membership function µ, see Equations (14) and (15). The most commonly used triangular membership function was used in this study [35].…”

Section: Fuzzy Control Schemementioning

confidence: 99%

A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System

Zhao

2020

Energies

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The energy consumption of air-conditioning systems is a major part of energy consumption in buildings. Optimal control strategies have been increasingly developed in building heating, ventilation, and air-conditioning (HVAC) systems. In this paper, a load forecast fuzzy (LFF) control strategy was proposed. The predictive load based on the SVM method was used as the input parameter of the fuzzy controller to perform feedforward fuzzy control on the HVAC system. This control method was considered as an effective way to reduce energy consumption while ensuring indoor comfort, which can solve the problem of hysteresis and inaccuracy in building HVAC systems by controlling the HVAC system in advance. The case study was conducted on a ground source heat pump system in Tianjin University to validate the proposed control strategy. In addition, the advantages of the LFF control strategy were verified by comparing with two feedback control strategies, which are the supply water temperature (SWT) control strategy and the room temperature fuzzy (RTF) control strategy. Results show that the proposed LFF control strategy is capable not only to ensure the minimum indoor temperature fluctuations but also decrease the total energy consumption.

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Section: Fuzzy Control Schemementioning

confidence: 99%

A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System

Zhao

2020

Energies

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“…便和驾驶路感要求 [9] ，有利于提高电动汽车的操纵稳定性和轻便性。目前，对于电子差速技术的研究，多数学者基于自适应控制 [6] 、滑模变结构控制 [7] 、鲁棒控制 [8] 等方法优化分配各轮毂电机的转矩或者转速，使汽车的横摆角速度能够跟踪其理想值，从而提高轮毂电机驱动汽车的转向稳定性 [6][7][8] 。同时，有学者提出了基于理想助力特性的差动助力转向模型 [9][10] [12] ( ) ( )…”

Section: 轮毂电机驱动汽车电子差速与差动助力转向的协调控制unclassified

Coordinated Control of Electronic Differential and Differential Assist Steering for Electric Vehicle Driven by In-wheel Motors

Lu¹

2017

JME

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Abstract：Coordinated control of electronic differential and differential assist steering is proposed to improve vehicle handing stability and steering portability when electric vehicle driven by in-wheel motors steering. Coordinated optimal control of the vehicle stability and assist steering of steering wheel is realized by re-distributing torques of left and right in-wheel motors. Two kinds of control methods are analyzed to conclude consistency of control trend and state respond, which can validate the feasibility of coordinated control. Meanwhile，the coordinated control strategy is designed by setting a weight function. The effect of speed and steering wheel angle on vehicle yaw rate and steering wheel torque is analyzed, and the weight function according to the vehicle speed would be designed. Matlab co-simulation with CarSim and road testing have carried out and demonstrated that the coordinated control strategy can give consideration to the vehicle stability of differential steering and assist steering for drivers. Reducing steering torque based on differential steering control in low speed and improving the stability on electronic differential in high speed is realized comprehensively.

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“…Several approaches have been proposed to eliminate the chattering phenomenon. However, these approaches, which are based on the use of a boundary layer in the sliding mode, are known to degrade the robustness [16]. To conquer the vulnerability of single SMC method and guarantee the system stability, the combination of SMC and FLC could be useful [17].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Sliding Mode Control for the Vehicle Height and Leveling Adjustment System of an Electronic Air Suspension

Sun

Cai

Yuan

et al. 2018

Chin. J. Mech. Eng.

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The accurate control for the vehicle height and leveling adjustment system of an electronic air suspension (EAS) still is a challenging problem that has not been effectively solved in prior researches. This paper proposes a new adaptive controller to control the vehicle height and to adjust the roll and pitch angles of the vehicle body (leveling control) during the vehicle height adjustment procedures by an EAS system. A nonlinear mechanism model of the full-car vehicle height adjustment system is established to reflect the system dynamic behaviors and to derive the system optimal control law. To deal with the nonlinear characters in the vehicle height and leveling adjustment processes, the nonlinear system model is globally linearized through the state feedback method. On this basis, a fuzzy sliding mode controller (FSMC) is designed to improve the control accuracy of the vehicle height adjustment and to reduce the peak values of the roll and pitch angles of the vehicle body. To verify the effectiveness of the proposed control method more accurately, the full-car EAS system model programmed using AMESim is also given. Then, the co-simulation study of the FSMC performance can be conducted. Finally, actual vehicle tests are performed with a city bus, and the test results illustrate that the vehicle height adjustment performance is effectively guaranteed by the FSMC, and the peak values of the roll and pitch angles of the vehicle body during the vehicle height adjustment procedures are also reduced significantly. This research proposes an effective control methodology for the vehicle height and leveling adjustment system of an EAS, which provides a favorable control performance for the system.

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A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV

Cited by 19 publications

References 18 publications

A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System

A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System

Coordinated Control of Electronic Differential and Differential Assist Steering for Electric Vehicle Driven by In-wheel Motors

Fuzzy Sliding Mode Control for the Vehicle Height and Leveling Adjustment System of an Electronic Air Suspension

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