A Modified Crow Search Algorithm is used to Minimize Torque Ripple for Switched Reluctance Motor Drives in Electric Vehicle Application

Abstract

The popularly used Switched Reluctance Motor (SRM) is an electric motor that employs reluctance torque and includes salient poles on the stator and rotor. A Modified Crow Search Algorithm (MCSA) based on an asymmetric converter with a PI-PWM controller is used in this paper to control the speed of a Switched Reluctance Motor (SRM) while also minimizing torque ripple. To control the settling time, the controller takes the optimum value of the torque ripple & the square root of the speed error. The motor works simultaneously with two phases during normal operation, but it is designed to satisfy the load specifications of the faulty single-phase & two-phases. A Modified Crow Search Algorithm has been used successfully in the improvement of parameters that controls the speed of SRM. Settling time, Rise time, Steady-state error, Peak Overshoot & Torque ripple parameters can be reduced in this presented method. Optimization technique is implemented to adjust the PI controller gain parameters in order of improving torque and speed control. The efficiency of the proposed optimization algorithm based on speed, current, torque, and flux is shown through simulation results. The motors that can be utilised in EVs include switching reluctance motors, permanent magnet synchronous motors, induction motors, brushless DC motors, and brushed DC motors. The SRM has established as one of the best solutions for electric vehicle propulsion because to its clear benefits over other types of electric drive systems. This paper will model the SRM drive for EV applications using MATLAB/Simulink.

Keywords

switched reluctance motor (SRM)proportional integral (PI)pulse width modulation (PWM)modified crow search algorithm (MCSA)electric vehicle (EV)

1. Introduction

The SRM operates on a principle of variable reluctance. The rotating magnetic field is generated by a force electronic switching circuit. The basic idea is that the magnetic circuit depends on an air gap. The motor reluctance can be changed by altering the air gap between the stator and rotor. A fap actuators in mid-sized aircraft require a five-phase switching reluctance motor. Electrical systems in complex aircraft are modeled to provide the required capability when it is needed (Martin & Kumar, 2021). However, just a little amount of power is required by the landing gear and secondary battle control. As needed, the system is turned on and off to save energy.

This method is used to adjust the coefficients of a traditional proportion-integration speed controller for SRM drives by taking torque ripple reduction into evaluation (Daryabeigi & Dehkordi, 2014). A function that factors in both speed errors is solved using the SBFA method. To achieve speed control of a switching reluctance motor with the shortest possible settling time and no overrun. The algorithm was created using a combination of differential evolution and particle swarm optimization, and it was used to manage the speed of a switching reluctance motor when the speed changed suddenly (Mahendiran et al., 2012). Other artificial intelligence technologies, such as fuzzy logic controllers, fuzzy PI controllers and particle swarm optimization based tuning of fuzzy PI controllers were also used to control the speed of switching reluctance motors (Templos-Santos et al., 2019).

A speed management and torque ripple reduction control method for switching reluctance motors using Pareto-based NSGA-II optimization. It is chosen because it has a more even distribution of individuals on the Pareto front and a lower processing complexity (Kalaivani et al., 2013). The turn-on angle and overlap angle, have an impact on speed range, top speed, copper loss and effectiveness (Xue et al., 2009). A genetic algorithm is used to optimise the turn-on angle and overlap angle at varied expected torque needs while operating under torque ripple minimization control in order to maximise the speed range and minimise copper loss. The torque output oscillations in the SRM speed control loop were corrected for using the sliding mode control method. To maintain a consistent motor speed, the sliding mode controller in use modifies the value of the reference current (Inanc & Ozbulur, 2003).

By using hybrid Optimizing Liaison Gravitational Search Algorithm for torque ripple is minimized and its speed is controlled in SRM. When compared to the Gravitational Search Algorithm (GSA) algorithm, the torque ripple coefficient, ISE of speed, and current are all lowered by 12.81 percent, 38.60 percent, and 16.74 percent, respectively. An adaptive intelligent control built on the Lyapunov stability theory was created for precise and effective speed control of SR motors (Hajatipour & Farrokhi, 2008) a powerful starting torque, low acoustic noise multi-layer switching reluctance motor (MSRM). The motor is very nonlinear, hence finite element analysis (FEA) is used in the numerical investigation (Daldaban & Ustkoyuncu, 2008).

A four-phase 8/6 and 1.0-kW SRM was developed. System-level simulations, both static and dynamic, were utilised to confirm the overall effectiveness of the intended SRM. Using finite-element analysis, a static characteristic curves of the flux linkage and torque were also confirmed (Anwar et al., 2001). As a result, DE, BBBC and PSO will be employed to optimise the SRM's parameters and manage its speed (Wang, 2018). For a 10 kW WT application, an optimised design technique for the outer rotor doubly salient permanent magnet generator ORDSPMG is described. With the help of the symbiotic organism search (SOS) algorithm and parametric two-dimensional finite element analysis (2D-FEA), the machine parameters are optimised quickly and effectively (Guerroudj et al., 2021).

One of the best techniques for controlling the switching reluctance motor (SRM) with better dynamic performance and fewer torque ripple is direct torque control (DTC). However, because the phase current is extended into the negative torque zone in this technique, it consumes more source current and reduces the net torque to ampere ratio (Reddy et al., 2020). The main issue is torque ripple, which may be reduced by changing the motor's geometrical design or implementing alternative control schemes. The machine's complexity rises as the geometrical design of the motor changes. However, the contemporary power electronic components has aided in the improvement of the drive's complexity (Keerthana and Sundaram, 2020). The inaccuracy is decreased, demonstrating that voltage vector selection may cover a wide range of speeds. The torque ripples for variations in load torque and drive speed range from 3.25 percent to 1.7 percent, according to modelling results (Pushparajesh et al., 2021). A nonlinear modulating factor dependent on the rotor position and magnitude of the phase currents. This factor manipulates the currents in two adjacent phases during commutation and reduces the torque ripple effectively. Unlike the conventionally available torque-sharing functions, this method instantaneously modulates every phase current obtained mathematically based on the other phase current in order to maintain the net torque constant. This method requires minimal offline analysis and offers maximum possible torque with a minimal ripple (Rana & Teja, 2021). For the SRM drive, a Vienna-type rectifier and an alternative Asymmetric H-Bridge converter are used. It boosts the DC-link voltage while also improving the quality of the input current. As a result, the SRM drive's power factor (PF) improves. This converter configuration has a lower Total Harmonic Distortion (THD). As+ a result, it will focus on improving the SRM drive's power quality. It's even more beneficial for reducing torque ripples (Fantin Irudaya Raj & Appadurai, 2021).

A speed control of switched reluctance motor (SRM) with torque ripple reduction using direct Instantaneous torque control (DITC). The reference values of the instantaneous torque is generated from speed control by PI controller, from the comparison between the reference torques and the estimated torque using hysteresis controller in addition to selects different turn-off angles so that improve the performance of torque ripple, the latter achieves the switching signals needed for the converter to get the desired results (Labiod et al., 2015). The optimization function in the implementation includes torque, loss, and torque ripple. The SLSTM is optimised in stages until a specified convergence condition is met. It is effective in finding the most stable and best solution for the SRM drive system. Furthermore, when compared to the initial design and CLSTM (Diao et al., 2021), it can achieve greater output performance, such as higher average torque and reduced torque ripple, as well as a higher level of resilience. The nonlinear properties of the switching reluctance motor are obtained, which seems to be the best current waveform in the inductor's effective working range, and then a control strategy of the turn-on angle is created to actualize the ideal winding current waveform. Using the coincidence of the freewheeling zero point and the position angle at the end of the maximum inductance period as the control objective, a control strategy for the turn-off angle that makes full use of the effective inductance working interval is presented, which enhances system efficiency. The goal of this research is a modified Crow Search method is used to adjust the parameters of the PI controllers in order to see which one performs better when used to regulate the speed of an SRM.

The major contributions of the proposed research work are listed below,

The research that has been presented uses an asymmetrical Converter with a PI-PWM controller and a modified Crow Search Algorithm to implement torque ripple reduction and SRM speed control.

Metaheuristics has been shown to be effective in solving complex, difficult optimization problems in order to improve global and local searching skills and the each crow fitness function is evaluated.

Optimization algorithm is used for fine-tuning the gain parameters of PI controller in order to achieve better speed control and torque reduction.

The simulation results demonstrate the suggested PI controller with MCSA optimization's effective performance.

In section 1, a brief introduction to the SR motor will be given. Section 2 discusses about the overview of the Speed, torque ripple reduction and mathematical modeling for SRM. Section 3 given the proposed PI Controller and optimization technique employed for speed control and torque ripple reduction of SRM drives. The analysis and simulation results for minimising torque ripples are reported in Section 4. The paper is concluded in Section 5.

2. Switched Reluctance Motor

SRMs have main poles on the stator and rotor. The main pole is called salient poles. These machines have no field torque/permanent magnet in the rotor. The main poles in the stator carries the concentrated coils excited in series by the DC voltage pulse. Without a permanent magnet, the rotor is constructed from passive and laminated magnetic materials. The SR machine's stator and rotor are both constructed from layered steel laminates (Anwar et al., 2001). The stator's electrical windings are arranged in a concentric pattern. To create the stator phase, each stator pole winding is coupled to a pole that is diametrically opposed. The stator and rotor bearings seek the position with the least amount of resistance; this is the fundamental operating concept shared by all machines. The 8/6 pole switching reluctance motor is depicted in Figure 1. The stator's excited current is at its highest level. The following rotor poles line up with the produced magnetic field's direction when the SR motor phase is excited. This situation is called aligned position. To achieve continuous rotation, the stages of the “On phase” and “Off phase” buttons are switched serially in each phase.

Figure 1.

8/6 Pole SRM.

The SRM is a doubly salient, singly excited variable speed machine with Ns stator poles and Nr rotor poles, normally denoted as Ns / Nr SRM. Around each stator pole, a coil is wound with the number of turns based on the applied voltage and desired speed. To form one phase, totally opposed stator coils are connected in series or parallel. Ps = 2 mq stator poles are found in a 4-phase eight-six switched reluctance machine. A single phase, two phases, three phases, four phases, or more than four phases can be used. However, 8/6 four-phase 6/4 engines are the most typical variants. A three-phase machine has a topology of 12/8 or 18/12 when q = 2 or 3. The selection of an arc angle of the stator pole is βs and rotor pole is βr which is an undesirable major part of the engine design (Labiod et al., 2015). In typical configurations, the stator poles’ arc angle is often a little less than the rotor poles’ arc angle. The interaction current of the phase winding is almost nil and the SRM magnetic circuit is symmetrical and saturated. The torque is produced entirely by the phase winding's inductance. Table 1 show the parameter and specification values of SRM. The Switched reluctance machine has a simple structure and is durable.

Table 1.

Parameter and Specification Values of SRM.

Motor Parameters	Specification Values
Rated power	10 KW
Number of phases	4
Number of stator poles	8
Stator inductance	3.2 × 10⁻³H
Magnet flux linkage	0.085Vs/rad
Number of rotor poles	6
Maximum phase inductance	60 Mh
Minimum phase inductances	10 Mh

2.1 Mathematical Modeling of SRM

The stator and rotor pole points are almost identical if anything is done to keep a strategic distance from zero torque areas. The asymmetrical converter of the attractive circuit in the Switched Reluctance Motor (SRM) stages achieves just about zero standard flux linkage. SRM will operate with phases because in the short-circuited phase no instigated voltage or current will appear. As a result, the Switched Reluctance Motor is more forgiving than any AC engine where stage co-operation is the primary service standard.

The resistance voltage drop is multiplied by the flux linkage rate change to determine the applied voltage in the Switched Reluctance Motor.

\begin{aligned} V_{D r o p} & = R_{s t a} I + \frac{d_{φ} (θ, I)}{d t} \end{aligned}

(1)

\begin{aligned} φ & = L (θ, I) I \end{aligned}

(2)

where,

\begin{matrix} R_{s t a} \to S t a t o r r e s i s t a n c e \end{matrix}

\begin{matrix} φ \to F l u x l i n k a g e \end{matrix}

\begin{matrix} L \to I n d u c t a n c e d e p e n d s u p o n r o t o r p o s i t i o n \end{matrix}

\begin{matrix} V_{D r o p} = R_{s t a} I + L (θ, I) \frac{d I}{d t} + I \frac{d θ}{d t} \frac{d_{L} (θ, I)}{d θ} \end{matrix}

(3)

Phase voltage equation becomes,

\begin{aligned} V_{D r o p} & = R_{s t a} I + L (θ, I) \frac{d I}{d t} + ω_{m} I \frac{d_{L} (θ, I)}{d θ} \end{aligned}

(4)

\begin{aligned} θ & = ω_{m} I \frac{d_{L} (θ, I)}{d θ} \end{aligned}

(5)

\begin{aligned} K_{b} & = \frac{d_{L} (θ, I)}{d θ} \end{aligned}

(6)

where,

\begin{matrix} K_{b} \to R e s i s t i v e v o l t a g e d r o p . \end{matrix}

\begin{matrix} ω_{m} \to A i n d u c t i v e v o l t a g e d r o p . \end{matrix}

\begin{matrix} I \to I n d u c e d E M F . \end{matrix}

The Instantaneous input power is determined by adding the winding resistance loss and the air gap power. The following formula determines the instantaneous input power

\begin{aligned} P & = V I \end{aligned}

(7)

\begin{aligned} P_{i n p u t} & = R_{s t a} I^{2} + I^{2} \frac{d_{L} (θ, I)}{d θ} + L (θ, I) \frac{d I}{d t} \end{aligned}

(8)

Time is given by,

\begin{aligned} t = \frac{θ}{ω_{m}} \end{aligned}

(9)

Air gap power equations,

\begin{aligned} P_{a} & = \frac{1}{2} I^{2} \frac{d_{L} (θ, I)}{d θ} \end{aligned}

(10)

\begin{aligned} P_{a} & = \frac{1}{2} I^{2} \frac{d_{L} (θ, I)}{d θ} ω_{m} \end{aligned}

(11)

\begin{aligned} P_{a} & = ω_{m} T_{e} \end{aligned}

(12)

Evaluating torque,

\begin{aligned} T_{e} = I^{2} \frac{d_{L} (θ, I)}{d θ} \end{aligned}

(13)

As a consequence, in SRM, the torque and Phase Current are used to calculate rotor position.

2.2 Torque Ripple Minimization of SRM Drive

By modifying the current profile and choosing the proper turn on and turn off angles, the torque ripple is decreased. A proposal includes speed control with torque ripple minimization. The method is used to identify the optimal configurations of 6 operating parameters for enhancing the performance of the SRM drive, along with a set of simulation results. The performance of the SRM drive can be increased and torque ripple minimized greatly by using the operating settings.

Speed error and current error are measured using the integral squared errors of both speed and current, as shown in below.

\begin{aligned} IS E_{speed} & = \int (ω_{ref} - ω_{m})^{2 \; \;} dt \end{aligned}

(14)

\begin{aligned} IS E_{current} & = \int (I_{ref} - I_{phase})^{2 \; \;} dt \end{aligned}

(15)

Measurement of torque ripple, T_ripple,

\begin{aligned} T_{r i p p l e} = \frac{T_{m a x} - T_{m i n}}{T_{m e a n}} \end{aligned}

(16)

3. Proposed Methodology

A switching reluctance motor's torque, ripple, and speed control are reduced in the proposed work. Using an asymmetrical converter based on the Modified Crow Search algorithm and a PI-PWM controller (SRM). The best integral square speed error and flux torque ripple value are used to determine the controller's settling times.

The SRM speed control and the torque ripple reduction scheme are proposed as given in Figure 2. The block diagram consists of PWM converter, Encoder, Controllers and commutator. Reference speed is set as input to the error detector. The feedback signal reaches the error detector from SRM through an encoder. The Pulse Width Modulation converter with a 4-phase SRM is connected to the current controller with the output of the speed controller.

Figure 2.

Proposed Speed control and torque ripple reduction of SRM.

Battery provides the required power supply. The PWM converter supplies 96 V as a DC voltage to each phase. To maximize the inductance of exciting coils, a torque is produced which causes the rotor poles to move in the same direction as energized stator poles. In the SRM, the rotor location sensor is fixed to a shaft that sends the signal to the Proportional-Integral (PI) controller together with the rotor's position in relation to the reference axis. The main dimension of the SRM is shown in Table 2. The tuning of PI controller can be done with a single or multiple objectives like reduction of steady state error, reduction of overshoot, settling time or any of the performance parameters like the Integral Square Error, the Integral Absolute Error or any other such objectives may be considered as the prime importance of tuning the PI controller.

Table 2.

Main Dimension of SRM.

Parameter	Value
Speed	6000 rpm
Stator resistance	1.3 ohm
Voltage	380 V
Peak Current	3 A
Rated torque	0.4 Nm
Inertia	0.0013 kg mm

The Proportional Integral (PI) controller controls the motor that reduces ripples. The gains of the Proportional Integral (PI) controller are $K_{p}$ = 0.1 and $K_{i}$ = 0.8.

The Proportional-Integral (PI) controller design's pulse-transfer feature is given by,

\begin{aligned} H_{\frac{u}{e}} (S) = \frac{k}{μ} [1 + \frac{λ T_{s}}{S - 1}] \end{aligned}

(17)

where,

T_{s} \to A s a m p l i n g p e r i o d .

When a proportional-integral (PI) controller is required, $T_{d e r}$ the derivative time, is set to zero; assumes the form,

\begin{aligned} K (s) = K_{p} (1 + \frac{1}{T_{d e r} s}) \end{aligned}

(18)

Equivalently,

\begin{aligned} K (s) = K_{p} \frac{s + 1 / T_{i}}{s} = K_{p} \frac{s + z}{s} \end{aligned}

(19)

where,

S = 1 / T_{i}

The following is a statement of the problem of setting the Proportional-Integral (PI) controller parameters of a critically damped second-order system: Find a gain $K_{p}$ and a zero $1 / T_{i}$ that allows the feedback system to meet the transient output requirements.

3.1 Modeling of PI controller

In order to achieve the best possible control for a specific process, the most important step is to tune the proportional gain $K_{p}$ and integral gain $K_{i}$ gain parameters. The equation of PI controller is,

\begin{aligned} u (t) = K_{p} e (t) + K_{i} \int_{0}^{t} e (t) \end{aligned}

(20)

where,

\begin{matrix} K_{p} \; represent\; the\; Proportional\; gain \end{matrix}

\begin{matrix} K_{i} denotes\; the\; Integral\; gain \end{matrix}

3.2 Modified Crow Search Algorithm (MCSA)

A Modified Crow Search Algorithm (MCSA) that develops a theoretical framework for examining the stability and robustness properties of quadratic optimization schemes, a modern metaheuristic based on the behavior of crows intelligent group action. Metaheuristics have been shown to be effective in solving complex, difficult optimization problems. MCSA is a new entrant in this category, as a result of these advances. CSA is focused on crow's strategic actions, when looking for their food. Local minima Converter genie and unbalanced exploration and extraction phases are common problems with this algorithm. A cosine function that accelerates discovery while slows exploitation in the iterative process is initially suggested as a solution to this issue. Second, the concept of opposition-based learning is used to enhance CSA's capacity for exploration. Hence, optimized results can be obtained by optimizing the PI controller gains by MCSO algorithm. A number of non-inferior points exist as the optimal solution in a multi-objective optimization problem. The min-max PI approach is used in this research to extract the most satisfying solution from the non-inferior solution set. The multi-objective problem is converted into its equivalent single-objective optimization problem using this method. A PI controller is designated to each objective as follows

\begin{aligned} μ_{f i} (X) = {\begin{array}{ll} 1 & f o r f_{i} (X) \leq f_{i}^{m i n} \\ 0 & f o r f_{i} (X) \geq f_{i}^{m a x} \\ \frac{f_{i}^{m a x} - f_{i} (X)}{f_{i}^{m a x} - f_{i}^{m i n}} & f_{i}^{m i n} \leq f_{i} (X) \leq f_{i}^{m a x} \end{array} \end{aligned}

(21)

Where, f is the objective function value. The lower and higher bounds in the above equation are found by single objective optimization for the associated objective function. The decision maker is required to identify the achievement degree for each objective function (μ_ref,i) in the range of [0,1] such that 0 represents the least relevance and 1 shows the highest significance for the relevant objective function. Finally, the multi-objective problem is converted into a single-objective one as follows:

\begin{aligned} G (X) = m i n_{x \in Ω} {m a x | μ_{r e f, i} - μ_{f, i} (X) |} \end{aligned}

(22)

Where $μ_{r e f, i}$ is the controller reference function; $μ_{f}$ is the fuzzy membership function. It is worth noting that evaluating the cost and emission function necessities the stochastic power flow run.

The pseudo-code for MCSA is given;

Begin

Max_G : number of max generations;

SP : size of the crow flock;

FL : Flight light of crows;

Set G = 0;

Initialize SP crows

Calculate fitness of each crow

While G < max_G

For I = 1 : SP

Calculate the,

D A P_{J}^{G}

, K_p and K_i values

If rand > =

D A P_{J}^{G}

For J = 1: dimension

X_{I, J}^{G + 1} = X_{I, J}^{G} + F L^{G} * (M_{D (J), J}^{G} - X_{I, J}^{G}) J = 1 \dots M

END

Else

If rand < 0.5

X_{I}^{G + 1} = B e s t + C 1 * C 2

Else

X_{I}^{G + 1} = B e s t - C 1 * C 2

End

3.2.1 Dynamic Awareness Probability (DAP)

Since the start of the optimization process, the Awareness Probability (AP) value in a traditional Crow Search Algorithm (CSA) has been constant and set to 0.1 for all crows. In this research, to increase the traditional Crow Search Algorithm's (CSAs) exploration or exploitation strategy selection, Each crow's AP value is altered for each iteration based on how well it performed using AP (DAP) that was inspired by the research. Each crow in this method has its fitness function assessed. Following this, Equation (23) is used to assign a rank to each crow.

\begin{aligned} R a n k_{I} = i, i = 1, 2, \dots S P \end{aligned}

(23)

Each swarm member's Awareness Probability (AP) is the only factor that determines an individual's is their position in the individual rank.

\begin{aligned} D A P_{i}^{G} = A P_{m i n} + (A P_{m a x} - A P_{m i n i}) \frac{R a n k_{I}}{S P} \end{aligned}

(24)

where,

SP is represent the size of population

AP_mini is the minimum values of awareness probabilitly

AP_max is the maximum values of awareness probabilitly

From equation (24), it can be inferred that the Awareness Probability (AP) value rises as the crow's rank rises. The Awareness Probability (AP) of the best crow is set to the minimum value while that of the worst crow is set to the maximum value when both are placed first and last, respectively. The best crow will look locally (exploit), whereas the poorest crow will look globally. The flowchart of the proposed model is depicted in Figure 3. In the proposed stochastic model, appropriate distribution functions are used to model the electric vehicle uncertainties associated with different arrival times, departure times, and different numbers of EVs in each fleet (representing different battery sizes).

Figure 3.

Flowchart of the Proposed Model.

(Figure 3)

4. Simulation Result and Discussion

To validate thè proposed approach, PI gains of controllers are optimized by Modified Crow Search Algorithm (MCSA) optimization technique. Overshoot and settling time is maintained within the specified limits and the steady-state error is made as small as possible with the proposed techniques. A torque ripple has been simulated using the optimization technique, and the results are compared with those of a conventional PI controller.

A proportional controller is used to minimize the output signal's rise time although the integral controller reduces the steady state error. To eliminate the steady state error caused by the torque parameter, the real-time $T_{m a x}, T_{m i n}$ values are calculated using motor simulation. The motor that reduces ripples is controlled by the PI controller and the gains are $K_{p}$ = 0.1 and $K_{i}$ = 0.8.

Figure 4 shows the flux response with the aid of PI controller. It is inferred that, it reaches the steady-state value at 2.8sec. By changing the process inputs, the PI controller tries to eradicate the error, which is the difference between the calculated variable and the desired value. To increase the speed of response and remove the steady state error, a combination of proportional and integral terms is used. The proportional term's output response is equal to the current error value. By multiplying the error value by a proportional gain, denoted by $K_{p}$ , the proportional factor is adjusted.

Figure 4.

Flux response obtained using PI controller.

The stator current response obtained using Proportional-Integral (PI) controller is shown in Figure 5. The motor draws less current initially and the machine begins to reach steady state quickly at 2.2 s For Proportional-Integral (PI) controller, the proportional gain is set as $K_{p}$ = 10 and the integral gain as $K_{i}$ = 100. In the simulation results, the time is 1 s and the sampling time is 0.01 s They are sufficient to ensure that the system stabilizes during intermittent processes and that the information of complex processes can be recorded.

Figure 5.

Current Response Using PI Controller.

As demonstrated in Figure 6, the torque peaks at a start-up torque and then settles at a steady state value at 2.8 sec. At low speeds, the unavoidable inaccuracies in voltage measurement. The variable frequency drive's output frequency is 0, the motor cannot be regulated. A minimum frequency in the range of 0.5 Hz to 1 Hz may be reached to a stationary position.

Figure 6.

Torque response of PI controller.

The speed response produced using a PI controller is shown in Figure 7. The motor is initially operating at a speed of 500 rpm at t = 1.4 Sec. and the changed to the speed of 3000 rpm at 2.7 Sec. The graph on the scope depicts speed versus time. Increasing the reference speed, it is observed from the plot that the controller is able to track the set point changes.

Figure 7.

Speed Response Using PI Controller.

The motor torque is equal to the drive torque. Figure 8 depicts the accelerator signal and drive torque. When there is a single phase open circuit failure, the open circuit lasts from 0.02 to 0.08 s. Thus, phase B alone cannot receive the current. At a 10 Nm load, the measured values of Tmax, Tmin, and average torque are 56.68 Nm. Figure 9 displays the single phase open circuit fault as a plot of the MCSA results for flux (V*S), current (A), and torque ripple (Nm). Determine the Proportional-Integral gain values of 0.1 and 0.8 and obtained the torque ripple is 1.232%.

Figure 8.

Accelerator signal and drive torque.

Figure 9.

Flux, Current and Torque Responses Obtained During Single Phase Open Circuit Fault Using ECSA.

The open circuit period in a single phase short circuit faults ranges from 0.02 to 0.08 s. T_max and T_min values have been observed, and the average torque at 10 Nm of load is 44.23 Nm. Figure 10 displays the results of the MCSA algorithm for flux (V*S), current (A) and torque ripple (Nm) in the presence of a single phase short circuit fault. Torque ripple is estimated from the data and is found to be 1.579%.

Figure 10.

Flux, Current and Torque responses obtained during Single phase short circuit fault in MCSA.

In the event of double phase open circuit faults, the time instant of the fault is established to be 0.02 to 0.08 s for phase A and 0.16 to 0.25 s for phase B. Therefore, it is impossible to supply current to both phases A and B. Tmax and Tmin values have been observed, and the average torque at 10 Nm of load is 55.05 Nm. Figure 11 shows the flux (V*S), current (A) during double phase open circuit and the torque ripple (Nm) and the torque ripple of 1.512% is obtained.

Figure 11.

Flux, Current and Torque Responses Obtained During Double Phase Open Circuit Fault in MCSA.

In Double phase short circuit faults, the time interval value is set between 0.02 s to 0.08 s The values of T_max, T_min is observed and at a 10 Nm load, the average torque is 41.95 Nm. The proportional-integral gain values of 0.1 and 0.8 provide a torque ripple value of 4.530%. Figure 12 shows the flux (V*S), current (A) and the torque (Nm) obtained during double phase short circuit, in case of MCSA optimization.

Figure 12.

Flux, Current and Torque responses obtained during Double phase short circuit fault in MCSA.

Table 3 shows Switched Reluctance Motors (SRM's) performance after MCSA optimization procedures. The suggested MCSA based on PI min-max performs well in optimising all targets, particularly torque ripple. Regarding the optimization findings, it should be highlighted that it is the random and heuristic nature of algorithm (MCSA) which helps to solve the proposed nonlinear optimization problem by minimizing the ISE of speed and torque ripple and to come to an optimal and practical solution. The Proportional-Integral (PI) gain of MCSA optimization methods are shown in Table 4. During single and double phase, the speed responses of traditional Proportional-Integral (PI) controllers and MCSA optimization techniques are clarified.

Table 3.

Performance of SRM with MCSA Optimization.

PARAMETER/ OPTIMIZATION	PI	MCSA
Rise time, t_r (sec)	0.5	0.33
Settling time t_s (sec)	1.4	1
Peak overshoot (%)	1.40	0.028
Torque ripple (%)	2.5	2
Steady state error (%)	0.05	0.03
ISE	0.80	0.44
IAE	0.47	0.32
ITAE	0.57	0.16

Table 4.

PI Gains of MCSA Optimization Algorithm.

PARAMETER	K_i speed	K_p speed	K_i current	K_p current	$θ_{o n}$	$θ_{o f f}$
ISE Speed	0.9284	9.7639	0.6365	22.9729	50.5478	79.8997
ISE Current	0.6256	5.9212	0.2393	14.8354	48.8382	80.5002
Ripple	0.6116	7.6493	0.4537	23.2817	50.2686	80.0337

Figure 13 shows the results of the proposed MCSA method on the convergence rate of the speed error with five different iteration indexes (i.e., j = 1 (blue), 3 (orange), 5 (yellow), 7 (purple). First, a slow convergence rate of the x₁ (i.e., maximum speed error of 70/48 r/min and recovery time of 30/21 ms) is observed during j = 1 for the conventional method and the proposed method because there is no stored data (John & Eastham 1995) during this iteration to improve the control inputs. Next, it can be revealed that as the iteration index j increases, the convergence rate of the proposed method decays to zero much faster (i.e., maximum speed error of 35/25/18/15 r/min and recovery time of 18/17/15/14 ms at (j = 3, 5, and 7 respectively) than the maximum speed error of 50/33/24/20 r/min and recovery time of 28/26/24/21 ms at j = 3, 5, and 7 respectively). This is because the improved control input contains the speed and load torque dynamics that helps in capturing the additional learning information to effectively reject the repetitive disturbances. In the paper, the iteration index j = 3 is chosen because it offers an acceptable speed tracking performance with less training time. Note that, to speed up the convergence rate of the x, the iterative learning gain K is chosen according to the convergence law, considering the trade-off between the satisfactory control performance (i.e., fast tracking performance and reduced torque ripples) and the training time.

Figure 13.

Convergence Rate of the Speed Error.

Table 5 compares the performance of various optimizations. It is clear that the MCSA-based PI controller outperforms the other improvements. The speed of the SRM is controlled by various types of controllers and overshoot is absent in all of them, according to the tabulation. Using a PI controller based on the Modified Crow Search Algorithm improves the settling time of the motor's speed response. The ripple occurs at the intervel of 0.2 to 0.4 s The operating reference speed of motor is 1500 rpm. At t = 0.25 s, MCSA reaches the maximum speed at 1500 rpm, as compared to various optimization techniques.

Table 5.

Comparision of Various Optimizations.

		ISE speed
Author and Year	Optimization	K_i speed	K_p speed	Ripple coefficient of torque (T_i)	Computational time	Convergence rate	Accuracy (%)	Torque Ripple Efficiency (%)
Manjula., (Manjula et al., 2021) (2021)	MPSO	0.9	9.7	0.4867	1.9	1.3	8	81.77
Nutan Saha et.al., (Saha and Panda, 2017) (2016)	MOLGSA	0.2	9.7	0.648	1.7	1.1	8	85.03
Proposed method	MCSA	0.92	0.75	0.415	1.0	0.53	9	91.2

5. Experimental Results

If the motor is constructed properly and operated effectively, a very long constant-power range is feasible, as demonstrated by the SRM designs discussed in Section V. We will discuss the experimental findings in this section to show that the SRM is capable of producing a constant-power range that is enlarged. However, the experimental motor is a modest, commercially accessible motor. The dynamic model is used to calculate the ideal control parameters. Due to its reliability, robustness and ease of construction, the Switched Reluctance Motor is commonly used in high-speed electric traction requirements. Figure 14 shows how the hardware is implemented.

Figure 14.

Hardware Implementation.

The proposed PI + DITC was implemented in a real time testing platform a DSPACE ACE kit 1006 CLP that included a processor board with an AMD OpteronTM running at 2.6 GHz. The DSPACE ACE kit 1006 CLP is a rapid-prototyping tool that allows controllers to be designed in Simulink, a well-known simulation and prototyping environment for modeling dynamic systems. Once these controllers have been designed, the code is simply generated and loaded into a DSP for real-time control, thus allowing the control algorithm to be tested rapidly. The proposed PI + DITC was tested in a 8/6 SRM prototype, using a set up including the DSPACE ACE kit 1106 and a DC motor acting as a load. The experimental values of torque were obtained from the product of the torque constant by the instantaneous armature current of the DC motor, introducing the corrections to take into account the iron and mechanical losses of the DC motor at different speeds.

A closed loop control is accomplished by using an optical encoder to obtain input from the motor's output. The feedback signal is fed into the controller, which generates the required control signal. In order to verify the feasibility of the proposed SRM speed control system based on the switching angle range, the power test is carried out for the 8/6 pole SRM with rated power of 10 KW, rated voltage of 380 V and rated speed of 6000 r/min. The experimental prototype is a field image of the motor and control system's power test.

In Proportional-Integral (PI) controller, the faults are created in one of the phases, where four phase converters are used. Two sensor signals and three phases are used to supply the same pulse to both circuits. Figure 15 shows the signals and the torque as the problem develops.

Figure 15.

Flux, Current and Torque Vs Time Response Using PI Controller.

The speed Vs time response obtained using PI controller signal is plotted in Figure 16. The reference speed value is 1500 rpm and the maximum voltage value is 1.33 V.

Figure 16.

Speed – time response of PI Controller.

The key benefit of SRM is that it can work, even if one of the four phases is switched off. The faults are caused by an open circuit in 4 phase Converters with one phase off and the other three phases provide the same pulse to both circuits, as uses two sensor signals. If a malfunction happens, the torque changes, and the signal is plotted in Figure 17.

Figure 17.

Flux, Current and Torque Vs Time Response Using MCS Algorithm.

Figure 18 shows the speed Vs time response obtained using Modified Crow Search (MCS) algorithm. The reference speed value is set as 1500 rpm. As would be expected theoretically, an improvement in the power factor is indicated by the measured rms phase current decreasing while keeping constant power.

Figure 18.

Speed Vs time response of MCS algorithm.

The load torque is first set to 4 Nm, and then 12 Nm is suddenly added. At about 4 s, a load torque of 12 Nm is suddenly applied, and the current rapidly increases. At about 2 s, a load torque of 12 Nm is suddenly applied, and the current increases nearly 500 ms later. This demonstrates that the PI speed control presented in this study, based on the MCSA, can swiftly suppress changes in load torque and has advantages under load with sudden significant pulsation. PWM speed regulation control based on the optimal range of switching angle.

According to Figure 19 (a), the real speed calculated using MCS-PI converges to the step + 100 rpm at t = 0.05 s and subsequently to the step + 200 rpm at t = 0.61 s without experiencing any significant overshoot and the steady state error is equal to zero. Although the rotor speed tracks the reference speed when MCS-PI is used, there is a significant oscillation. Additionally, Figure 19(b) displays the electromagnetic torque that was measured using MCS-PI. Within the rated limit, torque ripple is permissible. Figure 19(c) illustrates the real phase current and verifies that MCS-PI reduces the current ripple.

Figure 19.

Simulation Results Using MCS-PI (a) Rotor Speed (b) Electromagnetic Torque. (c) Stator Currents for Each Inductor.

Figure 20 displays the experimental findings under operating conditions that are identical to the simulation. The simulation results, which are depicted in Figure 19, and these experimental results are quite comparable. Experimental testing is done on the controller's performance when the reference speed is changed from 200 rpm to 100 rpm at t = 0.6 s.

Figure 20.

Experimental results using MCS algorithm-PI (a) Rotor speed using MCS -PI (b) Rotor speed using MCS –PI.

During double phase faults, the speed responses of Proportional-Integral (PI) controllers with optimization technique is clarified. The motor is turning at 1500 rpm as a guide. The external load torque is initially zero& the motor produces frictional and torques. Computer simulation in MATLAB software is used to demonstrate the efficiency of the presented control in terms of speed and reduction of 4 phase 8/6 torque ripples. The position of rotor, stator flux coupling, excitation current torque, and flux coupling are all tightly coupled, resulting in high and non-linear torque ripple as the rotor position and phase current shift. It lowers torque ripple by controlling the current profile and selecting the on and off angles.

2 V and 100 A are the DC supply voltage and reference current, respectively. RPS is used to mount the Switched Reluctance Motor (SRM) shaft. At 30 degrees and 45 degrees, the converters turn-off and turn-on angles remain unchanged. The established torque waveform is regulated by this switching angle. The torque ripple is measured, and the effect is 0.415%.

6. Conclusion

This paper presents the Modified Crow Search Algorithm-based speed management of a switching reluctance motor with torque ripple reduction by minimizing the ISE of speed and torque ripple. An optimal proportional and integral gains, as well as the turn on and turn off angles, are calculated for both the speed and current controllers. The performance of the controller includes computing the integral square errors of speed, torque coefficient, and the integral square errors of speed and torque ripple. The potential of the SRM for automotive application is clearly proved, and a design methodology is presented. The SRM can definitely outperform BLDCs and IMs in terms of performance. Despite running over the voltage and current ratings of the motors, about 40% more power than the design rated power is achieved at high speeds. These designs operate at high speed with excellent efficiency. Optimization methods are utilised to change the gain settings of the PI controller in order to enhance torque reduction and speed control. According to the results, torque ripple and settling times are decreased by MCSA-based speed controllers, which increase SRM drive performance.

Footnotes

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

References

Anwar

M. N.

Husain

Radun

A. V.

(2001). A comprehensive design methodology for switched reluctance machines. IEEE Transactions on Industry Applications, 37(6), 1684–1692. https://doi.org/10.1109/28.968179

Daldaban

Ustkoyuncu

(2008). Multi-layer switched reluctance motor to reduce torque ripple. Energy Conversion and Management, 49(5), 974–979. https://doi.org/10.1016/j.enconman.2007.10.011

Daryabeigi

Dehkordi

B. M.

(2014). Smart bacterial foraging algorithm based controller for speed control of switched reluctance motor drives. International Journal of Electrical Power & Energy Systems, 62(1), 364–373. https://doi.org/10.1016/j.ijepes.2014.04.055 .

Diao

Sun

Lei

Bramerdorfer

Guo

Zhu

(2021). Robust design optimization of switched reluctance motor drive systems based on system-level sequential Taguchi method. IEEE Transactions on Energy Conversion, 36(4), 3199–3207. https://doi.org/10.1109/TEC.2021.3085668

Fantin Irudaya Raj

Appadurai

(2021). Minimization of torque ripple and incremental of power factor in switched reluctance motor drive. In Recent trends in communication and intelligent systems (pp. 125–133). Springer.

Guerroudj

Karnavas

Y. L.

Charpentier

J.-F.

Chasiotis

I. D.

Bekhouche

Saou

El-Hadi Zaïm

(2021). Design optimization of outer rotor toothed doubly salient permanent magnet generator using symbiotic organisms search algorithm. Energies, 14(8), 2055. https://doi.org/10.3390/en14082055

Hajatipour

Farrokhi

(2008). Adaptive intelligent speed control of switched reluctance motors with torque ripple reduction. Energy Conversion and Management, 49(5), 1028–1038. https://doi.org/10.1016/j.enconman.2007.09.019

Inanc

Ozbulur

(2003). Torque ripple minimization of a switched reluctance motor by using continuous sliding mode control technique. Electric Power Systems Research, 66(3), 241–251. https://doi.org/10.1016/S0378-7796(03)00093-2

John

Eastham

A. R.

(1995). Speed control of switched reluctance motor using sliding mode control strategy. In Proc. IEEE-IAS Annu. Meeting (pp. 263–270.

10.

Kalaivani

Subburaj

Willjuice Iruthayarajan

(2013). Speed control of switched reluctance motor with torque ripple reduction using non-dominated sorting genetic algorithm (NSGA-II). International Journal of Electrical Power & Energy Systems, 53, 69–77. https://doi.org/10.1016/j.ijepes.2013.04.005

11.

Keerthana

Sundaram

(2020). State of art of control techniques adopted for torque ripple minimization in switched reluctance motor drives. In 2020 4th international conference on trends in electronics and informatics (ICOEI)(48184) (pp. 105–110). IEEE.

12.

Labiod

Srairi

Mahdad

Benchouia

M. T.

Benbouzid

M. E. H.

(2015). Speed control of 8/6 switched reluctance motor with torque ripple reduction taking into account magnetic saturation effects. Energy Procedia, 74, 112–121. https://doi.org/10.1016/j.egypro.2015.07.530 .

13.

Mahendiran

T. V.

Thanushkodi

Thangam

(2012). “Speed control of switched reluctance motor using new hybrid particle swarm optimization”.

14.

Manjula

Kalaivani

Gengaraj

(2021). PSO Based torque ripple minimization of switched reluctance motor using FPGA controller. Intelligent Automation and Soft Computing, 29(2), 451–465. https://doi.org/10.32604/iasc.2021.016088

15.

Martin

G. W.

Kumar

(2021). Novel bio-inspired algorithm for speed control and torque ripple reduction of switched reluctance motor in aerospace application. Journal of Electrical Engineering & Technology, 16(3), 1359–1374. https://doi.org/10.1007/s42835-021-00690-z

16.

Pushparajesh

Nandish

B. M.

Marulasiddappa

H. B.

(2021). Hybrid intelligent controller based torque ripple minimization in switched reluctance motor drive. Bulletin of Electrical Engineering and Informatics, 10(3), 1193–1203. https://doi.org/10.11591/eei.v10i3.3039

17.

Rana

A. K.

Teja

A. R.

(2021). A mathematical torque ripple minimization technique based on a nonlinear modulating factor for switched reluctance motor drives. IEEE Transactions on Industrial Electronics, 69(2), 1356–1366. https://doi.org/10.1109/TIE.2021.3063871

18.

Reddy

P. K.

Ronanki

Perumal

(2020). Efficiency improvement and torque ripple minimisation of four-phase switched reluctance motor drive using new direct torque control strategy. IET Electric Power Applications, 14(1), 52–61. https://doi.org/10.1049/iet-epa.2019.0432

19.

Saha

Panda

(2017). Speed control with torque ripple reduction of switched reluctance motor by hybrid many optimizing liaison gravitational search technique. Engineering Science and Technology, an International Journal, 20(3), 909–921. https://doi.org/10.1016/j.jestch.2016.11.018

20.

Templos-Santos

J. L.

Aguilar-Mejia

Peralta-Sanchez

Sosa-Cortez

(2019). Parameter tuning of PI control for speed regulation of a PMSM using bio-inspired algorithms. Algorithms, 12(3), 54. https://doi.org/10.3390/a12030054

21.

Wang

J.-J.

(2018). Parameter optimization and speed control of switched reluctance motor based on evolutionary computation methods. Swarm and Evolutionary Computation, 39, 86–98. https://doi.org/10.1016/j.swevo.2017.09.004

22.

Xue

X. D.

Eric Cheng

K. W.

S. L.

(2009). Optimization and evaluation of torque-sharing functions for torque ripple minimization in switched reluctance motor drives. IEEE transactions on Power Electronics, 24(9), 2076–2090. https://doi.org/10.1109/TPEL.2009.2019581