Optimized torque control of switched reluctance motor at all operational regimes using neural network

Rahman, K.M.; Gopalakrishnan, S.; Fahimi, Babak; Rajarathnam, A.V.; Ehsani, M.

doi:10.1109/28.924774

Cited by 68 publications

(17 citation statements)

References 15 publications

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“…Several intelligent algorithm methods have also been reported to obtain the position in previous research. Two networks are trained separately [15], one for a low-speed condition and the other is for a high-speed condition. In this method, the control parameters are recreated.…”

Section: Of 21mentioning

confidence: 99%

Position Sensorless Control of Switched Reluctance Motor with Consideration of Magnetic Saturation Based on Phase-Inductance Intersection Points Information

Cai

Wang

et al. 2018

Energies

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The precise estimation of position is an essential concern for the control of a switched reluctance motor (SRM). Given the prominent role of position, the promising sensorless control approach for an SRM drive should be capable of providing accurate position. An inductance-based approach has been widely applied in the position estimation. However, the estimated accuracy suffers from the magnetic saturation effect, resulting in the poor performance of sensorless operation. This paper presents a reversible synchronization of commutation with the corresponding rotor position for SRM. With the consideration of magnetic saturation, the proposed approach is taken as a suitable candidate and plays an essential role in accommodating the requirement of optimal control. The relationship between a typical inductance position and magnetic circuit saturation is investigated. The instant of the intersections is sensed by comparing the instantaneous inductance of adjacent phases. Thus, the predicted position is obtained with the information of the special point and the calculated average speed. Compared with other existing methods, the proposed approach has the certain advantages, such as the ability to update the estimated speed and position six times per electrical period, which guarantees the estimated accuracy. The proposed approach is also valid even when the motor is operated at an acceleration state and heavy load operation. In addition, the requirement of the educated inductance structure is not unnecessary, less memory space is needed in the chip, and the accumulated error is eliminated. The simulation and experimental findings demonstrate the feasibility and practicality of the proposed position estimation approach with carrying out the inertial operation, load mutation, and high-speed test.

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Section: Of 21mentioning

confidence: 99%

Position Sensorless Control of Switched Reluctance Motor with Consideration of Magnetic Saturation Based on Phase-Inductance Intersection Points Information

Cai

Wang

et al. 2018

Energies

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“…Hence, the angular velocity at t moment can be derived as

\begin{matrix} right leftthickmathspace.5em \\ Y (t) & = \frac{2 J - B T_{normals}}{2 J + B T_{normals}} \times Y ()(, t - T_{s}) + \frac{T_{s}}{2 J + B T_{s}} \times X (t) \\ + \frac{T_{normals}}{2 J + B T_{normals}} \times X ()(, t - T_{s}) . \end{matrix}

For the 8/6 SRM prototype, J = 0.00011 kg m 2 and B = 4 μN s/rad. The rotor position at the time of t can be obtained as

θ false(t false) = θ false(t - T_{s} false) + ω ()(, t) \times \frac{T_{s}}{2} + ω (t - T_{normals}) \times \frac{T_{normals}}{2} .

For the sake of simplicity, the motor is operated at speed open‐loop and only the current chopped control strategy is adopted herein to avoid phase current beyond the rated current at low speed [24]. Hence, the control signals of the MOSFETs are only determined by the rotor position and phase current.…”

Section: Hardware Simulation Of the Whole Srm Systemmentioning

confidence: 99%

New circuits for emulating switched reluctance motor windings

Liang

Chen

Shi

et al. 2016

IET Electric Power Applications

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This study sets forth two new universal circuits capable of emulating the behaviour of non-linear inductance, which are composed of only off-the-shelf circuit devices. One of the proposed inductor emulators is dependent on a controllable resistor whose resistance value is programmable, while the other is carried out by using a multiplier to produce the emulator current. The values of the controllable resistance and the input voltage of the multiplier can be easily computed according to the requirement through digital signal processing techniques by means of digital signal processor chip. The phase inductance of switched reluctance motor (SRM) winding is of high non-linearity due to doubly salient structure and magnetic saturation. Based on the analytical expression of phase inductance, the two proposed universal inductor emulators are applied to mimic the characteristics of SRM windings. The experimental and simulated results of the winding emulators provide the conclusive evidences for validating their practicability. Moreover, the emulators for the whole SRM drive system are designed and the corresponding experimental results further verify the correctness of the proposed SRM winding emulators.

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“…The high-speed control parameters are the phase turn-on and turn-off angles. A detailed description of this optimal control scheme of the SRM is presented in [15] and [16].…”

Section: Control Considerationsmentioning

confidence: 99%

Evaluation of soft switching for EV and HEV motor drives

Ehsani

Rahman

Bellar

et al. 2001

IEEE Trans. Ind. Electron.

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Soft switching has the potential of reducing switch stresses and of lowering the switching losses as compared to hard switching. For this reason, several soft-switching topologies have been presented in the literature. Each topology has some advantages. Their operation, however, requires additional active and/or passive elements. This introduces additional cost and complexity. To understand the effectiveness of the soft-switching technique, when applied to electric vehicle (EV) and hybrid electric vehicle (HEV) systems, it may be necessary to first evaluate their system requirements and performance. This evaluation process would require knowledge of the vehicle dynamics. The vehicle load requires a special torque-speed profile from the drivetrain for minimum power ratings to meet the vehicle's operational constraints, such as initial acceleration and gradability. The selection of motor and its control for EV and HEV applications are dictated mainly by this special torque-speed requirement. As a consequence, this requirement will have a strong influence on the converter operation. This paper makes an attempt to evaluate EV and HEV running in both standard Federal Test Procedure 1975 city driving and highway driving cycles. A simplified analysis will be carried out for several of the most commonly used electric motors operating on the optimal torque-speed profile. Special attention is given to the converter conduction and switching losses. By analyzing the switching losses, and by assuming that an ideal soft-switching scheme will have zero switching losses, one can evaluate the improvement in the system efficiency if a soft-switching control is used. The relative significance of soft switching for EV and HEV systems will then be established.Index Terms-Electric vehicle/hybrid electric vehicle soft-switching evaluation, soft-switched electric vehicle/hybrid electric vehicle motor drives, soft switching.

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Optimized torque control of switched reluctance motor at all operational regimes using neural network

Cited by 68 publications

References 15 publications

Position Sensorless Control of Switched Reluctance Motor with Consideration of Magnetic Saturation Based on Phase-Inductance Intersection Points Information

Position Sensorless Control of Switched Reluctance Motor with Consideration of Magnetic Saturation Based on Phase-Inductance Intersection Points Information

New circuits for emulating switched reluctance motor windings

Evaluation of soft switching for EV and HEV motor drives

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