Novel speed estimation technique for vector‐controlled switched reluctance motor drive

Khan, Yawer Abbas; Verma, Vimlesh

doi:10.1049/iet-epa.2018.5572

Cited by 20 publications

(21 citation statements)

References 31 publications

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“…Therefore, the vehicle speed information needs to be obtained accurately and in real time. At present, there are two main methods for obtaining the longitudinal speed of the distributed driving vehicle: one is to use an optical sensor or a high-precision GPS speedometer; the other is an estimation method based on common on-board sensors [24]. The former method is not suitable for large-scale production and general research because the cost is too high, so the second method has become the mainstream of current research.…”

Section: B Design Of Hill Start Controller 1) Vehicle Dynamics Analysismentioning

confidence: 99%

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Hill-Start of Distributed Drive Electric Vehicle Based on Pneumatic Electronic Parking Brake System

Wang

et al. 2020

IEEE Access

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The hill-start assist system (HAS) ensures that the vehicle starting on the ramp will not roll backwards, which improves the convenience of the driving in the case of starting on the ramp and parking. However, the traditional hill-start method is not suitable for distributed driving electric vehicles under some circumstances. Therefore, based on the pneumatic electronic parking brake system (EPB), a hill-start control method suitable for distributed driving electric vehicles is proposed in this paper, so as to realize the vehicle's fast and stable starting on the ramp. Firstly, by analyzing the change of the force of the vehicle during the starting of the ramp, a dynamic allocation strategy based on the front and rear axle loads is adopted in terms of demand torque. Secondly, according to the force analysis of the vehicle on the ramp, the control target of hill-start is determined, and the logic threshold control method is used to make the actual pressure change with the ideal pressure in real time. Then, a driving torque correction controller based on the sliding mode algorithm is designed to prevent driving wheels from slipping on bad roads, resulting in a reduction in the total driving force and a rollback of the vehicle. Finally, to verify its feasibility, Matlab, Trucksim and AMEsim were used to establish the controller model, the vehicle model and the pneumatic EPB system model for co-simulation verification. The simulation results show that the proposed control strategy can prevent the vehicle from slipping under different road conditions with less brake wear.

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Section: B Design Of Hill Start Controller 1) Vehicle Dynamics Analysismentioning

confidence: 99%

“…The former formula of the formula (24) represents the friction work of the parking brake system, and the latter formula is a condition to avoid the parking brake force becoming the resistance of the hill-start.…”

Section: Brake Wearmentioning

confidence: 99%

Hill-Start of Distributed Drive Electric Vehicle Based on Pneumatic Electronic Parking Brake System

Wang

et al. 2020

IEEE Access

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“…Also, ‘ i d ’, ‘ i q ’ and ‘ i 0 ’ are the d ‐axis, q ‐axis and zero‐phase currents, respectively. The voltage equations in dq 0‐frame can be expressed as [8]

\begin{matrix} right leftthickmathspace.5em \\ [\begin{matrix} 1em4pt \\ V_{d} \\ V_{q} \\ V_{0} \end{matrix}] = & R [\begin{matrix} 1em4pt \\ i_{d} \\ i_{q} \\ i_{0} \end{matrix}] + [\begin{matrix} 1em4pt \\ L_{normalav} & 0 & \frac{Δ L}{\sqrt{2}} \\ 0 & L_{normalav} & 0 \\ \frac{Δ L}{\sqrt{2}} & 0 & L_{normalav} \end{matrix}] [\begin{matrix} 1em4pt \\ \dot{i_{d}} \\ \dot{i_{q}} \\ \dot{i_{0}} \end{matrix}] \\ + 2 ω [\begin{matrix} 1em4pt \\ 0 & - L_{normalav} & 0 \\ L_{normalav} & 0 & \frac{Δ L}{\sqrt{2}} \\ 0 & 0 & 0 \end{matrix}] [\begin{matrix} 1em4pt \\ i_{d} \\ i_{q} \\ i_{0} \end{matrix}] \end{matrix}

where ‘ V d ’, ‘ V q ’ and ‘ V 0 ’ are the d ‐axis, q ‐axis and zero‐phase voltages, respectively, and ‘ ω ’ is the rotor speed in electrical radians/s. The mean torque can be given by [8]

T_{e} = \sqrt{2} P normalΔ L i_{0} i_{q}

The reference zero‐phase current is given by [30]

i_{0}^{*} = \frac{\sqrt{2}}{normalΔ L} Ф_{normalr}^{*}

where ‘

Ф_{r}

…”

Section: Vector‐control Strategy With Unipolar Excitationmentioning

confidence: 99%

“…Finally, we are left with intelligent estimation techniques. These estimators are independent of the motor model [8]. The estimators available in the literature which come under this classification are artificial neural network (ANN) [28] and fuzzy [29] based estimators.…”

Section: Introductionmentioning

confidence: 99%

Improved MRAS based speed estimation for a vector controlled switched reluctance motor drive

Khan

Verma

2020

IET Electric Power Applications

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This study deals with the unipolar excitation‐based speed sensorless vector controlled switched reluctance motor drive. For speed sensorless operation, Z‐MRAS (Z‐model reference adaptive system) based speed estimator is proposed where ‘Z’ is a fictitious quantity. This novel structure is completely independent of the stator resistance and DC component of self‐inductance. Also, in the formulation, integrator and differentiator terms are absent. The proposed formulation is perceived as stable in the entire four‐quadrant zone of operation. Detailed stability and sensitivity analysis has been carried out for the proposed estimation scheme. The practicality of the proposed formulation is confirmed through simulation in MATLAB/SIMULINK platform and by carrying out the experimentation on a dSPACE‐1104 based prototype.

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“…[12] proposes a new current vector control method based on the dq0 coordinate system and emphasizes the effect of zero-phase current on the torque control. Another vector control about speed-sensorless SRM drive is also proposed based on the dq0 coordinate system [13].…”

Section: Introductionmentioning

confidence: 99%

Minimization torque ripple for SRM based on flux linkage partition in DB-DTFC

Shang

et al. 2020

MATEC Web Conf.

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This paper proposes a novel deadbeat torque and flux control (DB-DTFC) to reduce torque ripple for switched reluctance motor (SRM). DB-DTFC combines the advantages of direct torque control (DTC) and space-vector modulation (SVM). DB-DTFC leads current vector control into DTC in order to find the equation between torque and current through deadbeat prediction theory i.e. a beat reaches a given point. In addition, the deadbeat calculation module here is similar to that of permanent magnet synchronous motor. Based on dq0 reference frame of SRM, the most suitable dq0 axis current of next moment corresponding to different torque errors is calculated and predicted. According to the calculated dq0 axis current, the optimal space voltage vectors can be selected to reduce torque ripple. In order to verify the effectiveness and correctness of the proposed scheme, DB-DTFC is verified and compared with the DTC-SVM by simulation.

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Novel speed estimation technique for vector‐controlled switched reluctance motor drive

Cited by 20 publications

References 31 publications

Hill-Start of Distributed Drive Electric Vehicle Based on Pneumatic Electronic Parking Brake System

Hill-Start of Distributed Drive Electric Vehicle Based on Pneumatic Electronic Parking Brake System

Improved MRAS based speed estimation for a vector controlled switched reluctance motor drive

Minimization torque ripple for SRM based on flux linkage partition in DB-DTFC

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