Assessing Finite Control Set Model Predictive Speed Controlled PMSM Performance for Deployment in Electric Vehicles

Murali, Abhishek; Sultana, Razia; Gade, Chandrasekhar Reddy; Annamalai, Chitra; Subramaniam, Umashankar

doi:10.3390/wevj12010041

Cited by 14 publications

(3 citation statements)

References 29 publications

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“…In the literature, many control methods for the PMSM have been developed, including scalar control [9][10][11], field-oriented control (FOC) [12][13][14], direct torque control (DTC) [15][16][17][18][19], PI control [20][21][22], and model predictive control (MPC) [23][24][25]. However, all these results are developed for either continuous-time or discrete-time cases but are not adapted for sampled-data implementation.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Dynamical Modeling and Control of Permanent Magnet Synchronous Motors: Hardware-in-the-Loop Verification

2023

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The stabilization of a permanent magnet synchronous motor using digital controllers requires the design of both the feedback law and an appropriate sampling frequency. Moreover, the design approach must be robust against existing uncertainties, such as disturbances and parameter variations. In this paper, we develop a stabilizing state feedback nonlinear control scheme for the permanent magnet synchronous motor. Moreover, we consider the case where the feedback signal is transmitted over a digital platform, and we derive the stabilizing sampling frequency, such that the stability of the closed-loop system is maintained. We design the controller by emulation, where the closed-loop stability is first established in continuous time; we then take into account the effect of sampling. The feedback law consists of two parts: feedback linearization and robust linear quadratic regulator for the linearized mode. The robustness is achieved by augmenting the state space model, with additional states representing the tracking errors of the motor speed and the motor current. Then, to cope with sampling, we estimate the maximally allowable sampling interval to reduce the sampling frequency while preserving the closed-loop stability. The overall system is modeled as a hybrid dynamical system, which allows handling both the continuous-time and discrete-time dynamics. The effectiveness of the proposed technique is illustrated by simulation and verified experimentally using a hardware-in-the-loop setup. Upon implementing the proposed approach, the obtained sampling interval was around 91 ms, making it suitable for digital implementation setups.

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Section: Introductionmentioning

confidence: 99%

Hybrid Dynamical Modeling and Control of Permanent Magnet Synchronous Motors: Hardware-in-the-Loop Verification

2023

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“…This controller examines only a finite set of possible switching states of power converters, solves the cost function for each of them, and chooses a switching state that minimizes the cost function. Thus, it possesses a simple nature, nonlinearities in the control design, an easy implementation, and a fast dynamic response in tracking the reference values of the controlled variables [26][27][28][29]. As a result, this controller is used in this study for improving the dynamic performance of the proposed HESS Bq-ZSI.…”

Section: Introductionmentioning

confidence: 99%

Dual-Source Bidirectional Quasi-Z-Source Inverter Development for Off-Road Electric Vehicles

Mande

Trovão

et al. 2022

WEVJ

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In this paper, a battery pack and a supercapacitor bank hybrid energy storage system (HESS) with a new control configuration is proposed for electric vehicles (EVs). A bidirectional quasi-Z-source inverter (Bq-ZSI) and a bidirectional DC-DC converter are used in the powertrain of the EV. The scheme of the control for the proposed HESS Bq-ZSI using finite control set model predictive control (FCS-MPC) is first deduced to enhance the dynamic performance. With the idea of managing battery degradation mitigation, the fractional-order PI (FOPI) controller is then applied and associated with a filtering technique. The Opal-RT-based real-time simulation is next executed to verify the performance and effectiveness of the proposed HESS control strategy. As a result, the proposed HESS Bq-ZSI with this control scheme provides a quick response to the mechanical load and stable DC link voltage under the studied driving cycle. Moreover, the comparative results also show that the proposed HESS Bq-ZSI equipped with the new control configuration enables the reduction of the root-mean-square value, the mean value, and the standard deviation by 57%, 59%, and 27%, respectively, of the battery current compared to the battery-based inverter. Thus, the proposed HESS Bq-ZSI using these types of controllers can help to improve the EV system performance.

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“…The correctness of the model parameters determines the machine behaviors anticipated by the model at the next extreme. Soft computational approaches for intelligent control, including neural networks, neuro-fuzzy and fuzzy logic are well-known and have been used by many researchers in the driving field [12]. This paper [13] establishes a hybrid intelligent controller, which reduces the torque, ripples and improves performance.…”

Section: Introductionmentioning

confidence: 99%

ANFIS based Direct Torque Control of PMSM Motor for Speed and Torque Regulation

Basappa

Viswanathan

2022

IJEEI

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Nowadays, the Permanent Magnet Synchronous Motors (PMSM) are gaining popularity among electric motors due to their high efficiency, high-speed operation, ruggedness, and small size. PMSM motors comprise a trapezoidal electromotive force which is also called synchronous motors. Direct Torque Control (DTC) has been extensively applied in speed regulation systems due to its better dynamic behavior. The controller manages the amplitude of torque and stator flux directly using the direct axis current. To manage the motor speed, the torque error, flux error, and projected location of flux linkage are employed to adjust the inverter switching sequence via Space Vector Pulse Width Modulation (SVPWM). One of the most common problems encountered in a PMSM motor is Torque ripple, which is recreated by power electronic commutation and a better controller reduces the ripples to increase the drive's performance. Conventional controllers such as PI, PID and SVPWM-DTC were compared with the proposed Adaptive Neuro-Fuzzy Inference System (ANFIS) in terms of performance measures such as speed and torque ripple. In this work, the Two-Gaussian membership function of the ANFIS controller is used in conjunction with a PMSM motor to reduce torque ripple up to 0.53 Nm and maintain the speed with a distortion error of 2.33 %.

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Assessing Finite Control Set Model Predictive Speed Controlled PMSM Performance for Deployment in Electric Vehicles

Cited by 14 publications

References 29 publications

Hybrid Dynamical Modeling and Control of Permanent Magnet Synchronous Motors: Hardware-in-the-Loop Verification

Hybrid Dynamical Modeling and Control of Permanent Magnet Synchronous Motors: Hardware-in-the-Loop Verification

Dual-Source Bidirectional Quasi-Z-Source Inverter Development for Off-Road Electric Vehicles

ANFIS based Direct Torque Control of PMSM Motor for Speed and Torque Regulation

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