Torque ripple reduction of Switched Reluctance Motor using
DITC and adaptive turn-on technique for electric vehicle
applications
Abstract. Direct Instantaneous Torque Control (DITC) with an adaptive turn-on angle technique is
presented in this paper to improve the torque ripple of the Switched Reluctance Motor (SRM) for Electric
Vehicle (EV) applications. Torque ripple suppression is achieved by employing two operating modes
during the commutation interval. Firstly, both the outgoing and incoming phase states are modified to track
the required torque during the incoming phase's minimum inductance area. As soon as the incoming phase
leaves its minimum inductance zone, the outgoing phase is demagnetized, and only the incoming phase
state is modified for torque tracking. In addition, a closed-loop regulator is used to dynamically control the
turn-on angle that drives the incoming current to reach its first peak at the instant of switching between the
two operation modes when the rotor and stator poles initiate overlap, thus increasing the motor's efficiency.
Simulation results showed that the proposed control method has superior advantages over the traditional
DITC and Average Torque Controller (ATC). Furthermore, the simulation results were further verified
experimentally using a four-phase 4kW, 8/6 SRM prototype.
Keywords: Direct Instantaneous Torque Control, Switched Reluctance Motors, Switching Angles, Finite
Element Analysis.
1. Introduction
Due to its advantages, switched reluctance motor (SRM) has increasingly been considered for traction
systems in electric vehicle (EV) applications. Unlike conventional motors, the rotor is devoid of coils and
magnets, simplifying the motor's structure, reducing production costs, and enabling high-speed operation in
harsh conditions [1-4]. In addition, its high fault tolerance ability is also a significant advantage in
industrial applications because each phase operates independently [5, 6]. However, significant torque
ripples, which yields high vibrations and acoustic noise, is the major drawback of SRM [7, 8]. Additionally,
the discrete mechanism for torque production and the nonlinear magnetic characteristics present a
significant challenge to accurate modeling and control of the SRM [9].
Two strategies have been presented in the literature to overcome these limitations from the perspective
of modifying the machine's design or implementing advanced control techniques [10-13]. Different
structural modifications have been proposed as machine design solutions, such as changing the
arrangement, shape, and number of motor poles. Nevertheless, while this method can improve torque
ripple, it usually minimizes the maximum torque production and has a limited operating range [13].
Consequently, more attention has been paid to control techniques. A variety of control parameters,
including switching angles, reference currents, and supply voltages, are optimized by these techniques to
improve the SRMs' performance. In [14-17], various offline optimization approaches were implemented for
optimizing switching angles, aiming to enhance efficiency, minimize copper losses, and mitigate torque
ripple of SRM drives. However, the offline optimization-based methods require accurate motor modeling
and much effort to determine the switching angles for each operational point in the speed-torque plane.[18]
and [19] propose analytical methods for adjusting the switching angles that maximize motor efficiency.
However, the analytical approach is inappropriate for electric vehicle applications because it ignores
significant torque ripples. Based on the hybrid flux-current control, a novel ATC is proposed in [20] that
enhances the SRM's energy conversion ratio and realizes more precise torque tracking, complicating the
control system. In [21, 22], the ATC is combined with intermittent control. Although the motor efficiency
is enhanced, the torque ripple increases significantly due to intermittent phases within a stroke. Using
lookup tables with precalculated optimum control parameters for SRM, [23-25] aims to diminish torque
ripple and enhance motor efficiency based on ATC. However, this strategy is time-consuming because it
necessitates obtaining the optimal parameters for each operation point over the speed-torque curve. [26]
presents an improved direct Instantaneous Torque Control (ITC) to reduce torque ripple and optimize
torque per ampere and motor efficiency in EV applications. This technique is affected by the accuracy of
the torque-to-current conversion technique. Using PWM modulation, [12] introduces a direct ITC method
that employs additional sectors in the commutation region to suppress torque ripples. A combination of
direct instantaneous force control and instantaneous torque control is used in [27] to diminish acoustic
noise and torque ripple. In [28], the optimum switching angles for the DITC strategy are obtained by
employing a multistage ant colony algorithm to optimize the system efficiency and torque ripple of the
SRM. In [8, 29, 30], indirect ITC based on an improved torque sharing function (TSF) is used to reduce the
SRM's torque ripple. Furthermore, secondary objectives are also considered, like minimizing copper loss
and maximizing the torque/ampere.
Aiming to improve SRM's performance, a modified DITC with an adaptive turn-on angle controller is
proposed in this research. The main paper's contributions can be listed as:
• Improving DITC switching scheme by adopting two switching modes in the commutation region
to reduce the torque ripples. In the first mode, both sequential phases track the commanded torque
within the low inductance area of the incoming phase. In the second mode, at the position of
beginning overlapping the stator and rotor poles, the outgoing phase is demagnetized, and only the
incoming phase state is adjusted to track the commanded torque.
• Designing a closed-loop turn-on controller to improve system efficiency by driving the incoming
current to its first peak at the instant the transition between the switching modes takes place.
• Evaluating the proposed controller's effectiveness based on simulation and experiment results is
compared to traditional DITC and ATC.
This paper continues with the following sections: Section II demonstrates the mathematical modeling of
the SRM. Section III briefly describes the torque control methods used in SRM drives. The proposed
control scheme and proposed turn-on angle controller are introduced in Section IV. The simulation and
experiment results are exhibited in Section V. Finally, this research's conclusion is given in Section VI.
2. Motor modeling
SRM is a double salient electrical machine that generates electromagnetic torque due to the rotor's
propensity to move into the maximum inductance position. Therefore, the magnetic characteristics of the
motor vary nonlinearly with both phase current (𝑖) and rotor positions (𝜃). The electrical modeling of SRM
is governed by phase equations 1-3, which represent the voltage (𝑉𝑘 ), flux linkage (𝜆𝑘 ), and
electromagnetic torque (𝑇𝑒 ) [14].
𝑑𝜆 (𝑖 ,𝜃)
𝑉𝑘 = 𝑅𝑖𝑘 + 𝑘 𝑘
𝑑𝑡
𝜆𝑘 (𝑖𝑘 , 𝜃) = 𝐿𝑘 (𝑖𝑘 , 𝜃) 𝑖𝑘
1 𝑑𝐿𝑘 2
𝑇𝑘 =
𝑖 𝑘
2 𝑑𝜃
(1)
(2)
(3)
where, R, and 𝐿𝑘 are the stator resistance and phase inductance, respectively.
The total torque (𝑇𝑒 ) for 𝑚 phase’s SRM is given by:
𝑇𝑒 = ∑𝑚
𝑘=1 𝑇𝑘
(4)
The average value of total torque (𝑇𝑎𝑣𝑔 ) over one electric cycle is determined as:
1
𝜏
𝑇𝑎𝑣𝑔 = ∫0 𝑇𝑒 (𝑡). 𝑑𝑡
𝜏
(5)
Torque ripple is the difference between maximum and minimum torque expressed as a percent of
average torque:
𝑇𝑟𝑝 =
𝑇𝑒 (max)−𝑇𝑒 (min)
𝑇𝑎𝑣𝑔
× 100%
(6)
The system efficiency ( 𝜂) is defined as a ratio of output mechanical power (𝑃𝑜𝑢𝑡 ) to the input electrical
power (𝑃𝑖𝑛 ):
𝜂=
𝑃𝑜𝑢𝑡
𝑃𝑖𝑛
× 100% , with 𝑃𝑜𝑢𝑡 = 𝜔 𝑇𝑎𝑣𝑔 and 𝑃𝑖𝑛 = 𝑃𝑜𝑢𝑡 + 𝑃𝑙𝑜𝑠𝑠
(7)
Where 𝜔 is the angular velocity of the rotor and 𝑃𝑙𝑜𝑠𝑠 is the power losses which mainly consist of copper
(𝑃𝑐𝑢 ) and iron (𝑃𝐹𝑒 ) losses in SRM drive [32]. Depending on the rms stator phase current (𝐼), 𝑃𝑐𝑢 can be
determined as:
𝑃𝑐𝑢 = 𝑚𝐼 2 𝑅
(8)
The iron losses can be estimated by improved Steinmetz equation [33, 34]:
𝑎+𝑏𝐵𝑚𝑎𝑥
𝑃𝐹𝑒 = 𝐶ℎ 𝑓𝐵𝑚𝑎𝑥
+
1
2𝜋2
𝑑𝐵 2
𝐶𝑒 ( )
(9)
𝑑𝑡 𝑎𝑣𝑔
where 𝑓 and 𝐵 are frequency and flux density, respectively. The coefficients 𝐶ℎ , 𝐶𝑒 , 𝑎, and 𝑏 are the
Steinmetz parameters that can be obtained by fitting the sheet loss data.
Parameter
Base speed(rpm)
Output power (kW)
Rated voltage (v)
Stator resistance (Ω)
𝜃𝑢 (⁰)
𝜃𝑚 (⁰)
Bore diameter (mm)
Shaft diameter (mm)
(a)
Table 1 8/6 SRM data
Value
Parameter
1500
Stack length (mm)
4
Air gap length (mm)
600
Rotor pole Height (mm)
0.642
Stator pole Height (mm)
Rotor pole arc (⁰)
30
Stator pole arc (⁰)
8
96.7
Stator outside diameter (mm)
36
Turns per pole
Value
151
0.4
18.1
29.3
21.5
20.45
179.5
88
(b)
(c)
(d)
Fig.1 Obtained FEA data; a) Electromagnetic torque; b) Inductance;
c) Flux linkage; d) Single phase simulation of SRM
Modeling and iron loss estimation of the SRM require obtaining the magnetic characteristics. In this
research, the 2D finite element analysis (FEA) is used to estimate the static magnetization curves
{(𝜆𝑘 (𝑖𝑘 , 𝜃), 𝐿𝑘 (𝑖𝑘 , 𝜃), 𝑇𝑘 (𝑖𝑘 , 𝜃)} and the flux density in various motor parts (yoke and poles for both the
stator and rotor) by the FEMM software. More detail about the estimation of the magnetization curves and
the flux density is presented in [33]. Fig.1 shows the single-phase simulation model and static
magnetization characteristics samples for 8/6 SRM obtained by FEA. Rotor positions 0° and 30°
(mechanical degree) correspond to unaligned and aligned positions, respectively. The inspected motor data
used in FEA are given in table 1.
As observed from the obtained SRM's torque and inductance characteristics, the braking torque
(negative) is generated during falling inductance regions. In contrast, the motoring torque (positive) is
produced during rising inductance regions. Furthermore, the torque production for a given current also
increases significantly at 𝜃𝑚 , where the rotor leaves the zone of minimum inductance when the rotor and
stator poles initiate alignment. Therefore, the control parameters (𝑖 and switching angles) play a crucial role
in determining the performance of the SRM.
However, choosing the optimal switching angles (𝜃𝑜𝑛 𝑎𝑛𝑑 𝜃𝑜𝑓𝑓 ) is usually determined by resolving two
or more conflicting objectives, as every goal needs various switching angles. Therefore, this paper intends
to realize the lowest torque ripple and improve the efficiency of the SRM drive through the optimal
selection of the switching angles utilized on the proposed controller.
3. Torque control strategies
EVs require rapid torque response to handle the road conditions such as climbing, braking, and
accelerating. Therefore, the torque controller is essential for EVs. In SRM, two torque control strategies are
presented in the literature: ATC and ITC [20, 31]. In ATC, the reference current is maintained at a constant
value throughout the conduction zone. Accordingly, the controller adjusts the switching angles and
reference current for every operating point in the speed-torque curve. Fig. 2 illustrates the block diagram of
ATC. First, the out-loop speed controller produces torque reference. Then, the torque regulator processes
the torque error to determine the required reference current. Finally, the AHB converter is driven by the
hysteresis current controller based on current error within the optimized switching angles. Despite its
simple structure, ATC exhibits significant torque ripples. In contrast, the electric current is continuously
regulated based on torque requirements and rotor position within stroke duration in ITC. Generally, this
strategy involves either indirect ITC (IITC), or direct ITC (DITC) [26]. The reference torque in IITC is
decomposed between the machine phases based on the torque sharing function (TSF) to generate the
reference current. The block diagram of IITC is given in Fig.3. As can be seen, ATC and IITC have almost
the same structure with differences in torque-to-current transformation methods. For instance, ATC utilizes
a predetermined lookup table to convert the estimated average torque over the conduction interval to
reference current. The ITC, on the other hand, can significantly reduce torque ripples of SRM because it
estimates the reference current instantaneously at every sample point. However, IITC has a complicated
control algorithm requiring TSF and a torque-current inverse model.
On the other hand, the DITC controls the torque directly by configuring the states of the AHB in the
commutation interval and single-phase conduction according to the hysteresis scheme, as shown in Fig.4.
Generally, asymmetrical half-bridge (AHB) converters can operate in three switching states: excitation (S =
1), freewheeling (S = 0), and demagnetization (S = -1). In SRM drive, torque production involves a
significant ripple during commutation regions due to the discrete conduction mechanism. The DITC control
technique determines the phase switching states based on rotor position and torque errors to mitigate torque
ripple, as demonstrated in Fig. 5, Where ∆𝑇𝑚𝑎𝑥 and ∆𝑇𝑚𝑖𝑛 are the maximum and minimum torque error
thresholds, respectively. For smooth torque production in single-phase conduction, the freewheeling and
excitation states are used. In contrast, the demagnetization state is avoided to reduce torque ripples as much
as possible, as shown in Fig.5(a). While in the commutation region, the overlapping phases' states should
not be modified at the same time to avoid increasing switching losses and torque ripple. Moreover, state
changing priority of sequential phases is set according to the torque error to ensure smooth transitions
between phases. When the torque error signal is negative, the outgoing phase's switching state is first
adjusted to decrease torque production; When the torque error signal is positive, the incoming phase's
switching state is first changed to increase torque production, as illustrated in Fig.5(b).
Fig.2 Block diagram of ATC
Fig.3 Block diagram of IITC
Fig.4 Block diagram of DITC
Fig.5 Switching rules in a DITC; a) Single-phase conduction; b) Commutation interval
While the conventional DITC strategy can effectively improve torque ripples, the switching angles are
typically fixed. This increases the power losses, which in turn reduces the efficiency and overall system
performance. Consequently, this paper proposes optimized switching rules for the DITC strategy with an
adaptive turn-on angle controller to enhance the performance of the SRM drive.
4
4.1
The proposed control strategy
The proposed DITC scheme
A modified switching strategy in DITC is proposed to achieve torque ripples suppression and efficiency
improvement of SRM by considering the excitation current and rate of change of motor inductance with
rotor position. For simplicity, only two phases' inductance and current against their rotor position are
utilized for an illustration, as shown in Fig 6, where 𝜃𝑜𝑛 (𝑜) and 𝜃𝑜𝑛 (𝑖) are the turn-on angles of outgoing
phase and incoming phase, 𝜃𝑚 (𝑜) and 𝜃𝑚 (𝑖) are the begin overlapping angles between stator and
rotor poles for outgoing phase and incoming phase, 𝑖𝑜 and 𝑖𝑖 are the outcoming and incoming phase
current, 𝐿𝑜 and 𝐿𝑖 are the outcoming and incoming inductance, respectively. Additionally, two small
regions are defined in the commutation interval, each with its operation mode.
In Region 1, the incoming phase generates a relatively small torque as the inductance is almost flat
(𝑑𝐿⁄𝑑𝜃 ≅ 0) according to Eq. (3). Therefore, based on the maximum error torque threshold (∆𝑇𝑚𝑎𝑥 ) only
the excitation and freewheeling state are used for incoming current to simplify the control approach and
utilize the minimum inductance region to rise the incoming current quickly and reach its peak at 𝜃𝑚 (𝑖) . In
contrast, the inductance slope for the outgoing phase is high, leading to higher torque production.
Therefore, the state of the outgoing phase is modified to pursue the commanded torque and torque ripple
reduction by using all three converter states, where the excitation and freewheeling are activated according
to minimum torque error thresholds, the demagnetization state is avoided whenever possible by activating it
only if the torque reaches −∆𝑇𝑚𝑎𝑥 . The switching state in region 1 is demonstrated in Fig.7 (a).
In Region 2, the inductance of the incoming phase leaves the minimum inductance zone, resulting in
significantly increased incoming phase torque production, while the inductance rate change of the outgoing
phase decreases, causing reduced outgoing phase torque production. Therefore, only the incoming phase is
considered for torque tracking and ripple reduction. At the same time, the demagnetization state is
maintained for the outgoing phase regardless of the torque error, as shown in Fig.7 (b). However, in the
single-phase conduction period, the same switching scheme in traditional DITC is used, as shown in Fig.
5(a).
In summary, the operating modes of sequential currents should be changed at 𝜃𝑚 (𝑖) , where the
demagnetization of the outgoing phase current should be used, while the minimum inductance region is
utilized to build the incoming phase current until 𝜃𝑚 (𝑖) .
4.2 The turn-on angle
The proposed controller aims to reduce torque ripple and improve the efficiency of SRM by considering
the most effective interval of the inductance profile. Therefore, the 𝜃𝑜𝑛 (𝑖) angle should be selected to force
the first incoming-phase current peak to occur at 𝜃𝑚 (𝑖) , where the incoming phase leaves the minimum
inductance region resulting in better torque tracking capability. For instance, torque production of the
motor will not be fully exploited if the phase current reaches its peak after 𝜃𝑚 . Conversely, If the phase
current peak occurs before 𝜃𝑚 , torque generation will be minimal due to the low inductance slope, resulting
in higher copper losses.
However, the required rising time of the incoming current to reach its first peak varies with motor speed
and commanded torque, where the current rising time increases if either motor speed or load torque
increases. Several analytical approaches have been proposed in the literature to determine the turn-on angle
that fulfills the first peak current at 𝜃𝑚 (𝑖) [18, 19]. However, all these methods use the reference current as
a variable to determine the turn-on angle, which is not available in DITC, where the only control variable is
torque. Therefore, a new automatic turn-on angle controller is developed according to the phase current, as
given in Fig 8. The developed controller consists of two sub-algorithms. The first one monitors the first
peak of current and detects its position (𝜃𝑝 ). Then a comparison between 𝜃𝑝 and 𝜃𝑚 is made to adjust 𝜃𝑜𝑛
as follow:
1. If 𝜃𝑝 < 𝜃𝑚 , torque production will be minimal in low inductance region, resulting in higher
copper losses, and reducing motor efficiency. Therefore, the turn-on angle should be advanced
by a suitable increment ∆𝜃𝑜𝑛 .
2. If 𝜃𝑝 > 𝜃𝑚 , torque production will not be fully utilized, generating insufficient torque and
increase torque ripple. Therefore, the turn-on angle should be retreated by a suitable decrement
−∆𝜃𝑜𝑛 .
This way, the 𝜃𝑜𝑛 is modified every electric cycle until the error between the two angles (𝜃𝑚 (𝑖) , 𝜃𝑝 ) is
restricted to a small pre-specified tolerance.
Fig.6 Two phases inductance and current waveforms against rotor position
Fig.7 Switching rules of the proposed DITC; a) Region 1; b) Region 2
Fig.8 Turn-on angle controller flowchart
5. Results and discussion
5.1 Simulation results
The proposed control scheme is tested in the MATLAB/Simulink environment. Lookup tables that
stored obtained FEA magnetic characteristics are used in the simulation model to estimate the
instantaneous electromagnetic torque and phase current. A comparison is carried out with the traditional
DITC and optimized ATC to show the effectiveness of the proposed controller, as follows.
Fig.9 shows the steady-state simulation results of ATC at base speed (1500 rpm) under 20 Nm load
torque, where the hysteresis band of phase current is set to 0.2 A. In addition, the switching angles are
optimized offline to realize the optimum balance between maximum efficiency and minimum torque ripple
according to a multi-objective function that uses weight factors. The weight factors used to obtain the
optimum switching angles are set to 0.5 for both the torque ripple and efficiency. The switching angles
optimization algorithm is discussed in detail in previous authors’ work [14].
Figure 10 and Figure 11 show the steady state simulation results of traditional DITC and the proposed
DITC at base speed under 20 Nm load torque, respectively. The switching angles are kept constant in
traditional DITC, where the turn-on angle is set to 1⁰, and the turn-off angle is set to 23⁰. in contrast, the
proposed closed loop 𝜃𝑜𝑛 controller is used to regulate the turn-on angle, which improves the system's-
(a)
(b)
Fig. 9. Steady-state simulation results of the optimized ATC at 1500 rpm
and 20 Nm load torque;(a) Torque, (b) Current
(a)
(b)
Fig. 10. Steady-state simulation results of the traditional DITC at 1500 rpm
and 20 Nm load torque;(a) Torque, (b) Current
(a)
(b)
Fig. 11. Steady-state simulation results of the proposed controller at 1500 rpm
and 20 Nm load torque;(a) Torque, (b) Current
efficiency by driving the incoming current to reach its first peak 𝜃𝑚 (𝑖) . The maximum and minimum torque
error thresholds for both traditional DITC and the proposed DITC are defined as 0.4 and 0.2, respectively.
Fig. 12 (a) illustrates the variation of copper and iron losses for the proposed controller under different
load torque at 1000 rpm speed, while Fig. 12 (b) displays the losses variation for different speeds at
constant torque of 10 Nm. As it is apparent from Fig. 12, both losses are increased with a load torque
increase at a constant speed, while at constant torque, the iron losses increase significantly, and the copper
loss is very slightly raised with speed increase.
The steady state efficiency and torque ripple under 10 Nm loading torque over the entire speed are given
in Fig.13. The proposed strategy marginally improves the efficiency compared with the ATC as both
methods are optimized to increase efficiency, and both approaches have higher efficiency than the
traditional DITC. Furthermore, the proposed controller significantly reduces the torque ripple, which has
the lowest torque ripple until 2500 rpm, while the ATC provides the lowest torque ripple after that speed.
The dynamic response of the three controllers subjected to sudden changes in load torque and reference
speed, where the reference speed changed from 1000 rpm to 2200 rpm at 0.6 sec and the load torque
changed from 17 Nm to 10 Nm at 0.4 sec, is presented in Figs. 14-16. The figures show that the traditional
DITC and the proposed controller have a fast dynamic response at low-speed operations. However, the
ATC has a somewhat faster response at high-speed operations. In terms of torque production, the proposed
controller provides the smoothest torque resulting in the minimum torque ripple, pursued by traditional
DITC and then ATC. The dynamic torque ripple and efficiency for the three controllers are given in
Figs.17-19.
Although the proposed controller has little impact on efficiency improvements compared with the
optimized ATC, the proposed method remarkably minimizes the torque ripple. However, higher efficiency
improvements may be achieved by optimizing the turn-off angle, but torque ripple will be affected
negatively.
The phase current, first peak current, first peak position, and the turn-on angle of the closed-loop turn-on
angle controller are shown in Fig. 20. As can be noted from Fig. 20 (b), the position of the peak current is
driven to 𝜃𝑚 . However, the proposed controller requires a settling time to adjust the turn-on angle, leading
to a bit higher torque ripple in the transient response operation.
(a)
(b)
Fig. 12. The variation of copper and iron losses;(a) At constant speed of 1000 rpm,
(b)with constant torque of 10 Nm
(a)
(b)
Fig. 13. Simulation results under 10 Nm load torque;(a) Efficiency, (b) Torque ripple
(a)
(b)
Fig. 14. The dynamic response of the optimized ATC;(a) Speed, (b) Torque
(a)
(b)
Fig. 15. The dynamic response of the traditional DITC;(a) Speed, (b) Torque
(a)
(b)
Fig. 16. The dynamic response of the proposed DITC;(a) Speed, (b) Torque
(a)
(b)
Fig. 17. Dynamic Simulation results of ATC;(a) Torque ripple, (b) efficiency
(a)
(b)
Fig. 18. Dynamic Simulation results of traditional DITC;(a) Torque ripple, (b) efficiency
(a)
(b)
Fig. 19. Dynamic Simulation results of proposed DITC;(a) Torque ripple, (b) efficiency
(a)
(b)
(c)
(d)
Fig. 20. Closed-loop turn-on angle controller;(a) Phase current, (b) Position of the first peak, (c) First peak
current magnitude, (d) Turn-on angle
5.2 Experimental verification
Fig. 21 illustrates the experimental platform constructed to verify the proposed controller's efficacy. The
(LAH 50-P) current sensor and an incremental encode were used to measure the phase current and rotor
position, respectively. The TMS320F28335 DSP board is utilized to execute the control algorithm. The
(DAQ NI USB-6009) data acquisition board and LabView software were used to acquire and record the
data. An electromagnetic brake (MAGTORL model 4605c), with 6Nm maximum loading torque, is
connected to the SRM via a torque transducer. Hence, the motor is tested in low power operation of 100 V
with maximum 6 Nm torque.
Figs. 22-23 show the experimental torque and current waveform results of the optimized ATC and
Traditional DITC at operational speeds of 300 rpm and 600 rpm. The hysteresis band of phase current for
ATC is still set to 0.2 A, and the switching angles of traditional DTIC are also still fixed at 1⁰ and 23⁰.
Fig. 24 demonstrates the experimental results of torque, current, and current against rotor position
waveform of the proposed controller at the same operational speeds and loading torque. As shown in the
figures, the proposed controller provides the smoothest torque waveform. The torque ripple at high-speed
operation (600 rpm) increases significantly in both traditional DITC and the proposed controller, but the
proposed controller still provides the lowest torque ripple. Furthermore, the proposed turn-on controller
modifies the 𝜃𝑜𝑛 angle, forcing the current's first peak at 𝜃𝑚 = 8°, which in turn improves the system
efficiency.
Although the improvement of the proposed controller's torque ripple is more evident in the simulation
system than in the experiment results, both experimental and simulation results show that the
proposed controller effectively improves motor performance. The main reasons for these dissimilarities are
motor modeling errors and experimental noise.
Fig. 21 The experimental platform.
(a)
(b)
Fig. 22 Experimental results of ATC and traditional DITC at 300 rpm
and 5.7Nm loading torque; (a)ATC, (b) traditional DITC
(a)
(b)
Fig. 23 Experimental results of ATC and traditional DITC at 600 rpm and
3Nm loading torque; (a) ATC, (b) traditional DITC
(a)
(b)
Fig. 24 Experimental results of the proposed controller at 300 rpm and 600 rpm; (a) 300 rpm, (b) 600 rpm
7. Conclusion
A Modified DITC algorithm with an adaptive closed-loop turn-on angle controller is proposed to
achieve the lowest torque ripple of SRM for EV applications. Based on the inductance profile of SRM, two
operating modes are employed in the commutation intervals to reduce the torque ripple. Both incoming and
outgoing phase states are adjusted to track the torque in the incoming phase's minimum inductance zone.
After that, only the incoming phase is responsible for pursuing the commanded torque. Moreover, the turnon angle is modified to force the first current peak when the stator and rotor poles begin overlapping,
leading to utilizing the most efficient inductance profile and improving system efficiency. Simulations and
experiment results validated torque ripple improvement compared to traditional DITC and optimized ATC.
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[2]
[3]
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