Repeatability of Tests for Validation of Iron Loss Models in Electrical Machines

Prediction of iron loss in electrical machines (e-machines) is known to be a challenging task. The concept of a building factor (BF) to account for the discrepancy between predicted and measured loss is widely used. From the literature, these BFs may need to be as high as 1.5–2, i.e., a discrepancy of up to 100%, leading to a different impact on total losses for every e-machine. To calibrate the BF, modeling, and testing are required. This article describes an extensive test campaign on permanent magnet synchronous machines (PMSMs) at no-load with magnetized and dummy rotors. Several challenges to accurately quantify no-load iron loss by dynamic testing for model calibration are reported, highlighting the benefits of rotor temperature monitoring, and performing repeatability checks. For accurate prediction of iron loss impacted by mechanical stress (due to cut edge damage or shrink-fit for example), advanced modeling approaches require electrical steel properties (BH and loss density) as a function of tensile and compressive stress. Such measurements with a dedicated single-sheet tester (SST) have not yet been standardized. Repeatability issues and the spread of measured data are presented and their influence is mitigated by a precycling procedure. A novel method to compare the impact of stress on several electrical steel grades including thicknesses from 0.1 to 0.35 mm indicates different grades follow similar trends. However, the changes are grade-specific, requiring characterization of each material grade at multiple stress levels, frequency, and flux density for advanced modeling of e-machine performance.


I. INTRODUCTION
T HE accurate prediction of iron losses in rotor and stator cores of electrical machines (e-machines) is required to meet efficiency requirements, as they are one of the main loss components.However, this is a complicated task due to several factors.Geometries of the magnetic circuits in the stator and rotor are complex and e-machine specific.The magnetic flux pattern in the stator rotates with areas where the direction of the flux density changes with time.Whenever the rotor is moving, mechanical losses are present, and the movement raises the difficulty to measure rotor temperature.Most importantly, manufacturing introduces tolerances on part dimensions and material properties that may be different than those provided in the database.Many publications have reported that processes such as cutting, welding, or shrink fitting of lamination stacks introduce local damage to the rotor and stator cores, which results in an increase in iron loss [1].
Several building factors (BFs) are introduced to account for known discrepancies between prediction and measurement [2].These factors are specific to each e-machine and its manufacturing processes.Therefore, several calibration tasks, at different development stages of a product may be necessary (see Fig. 1).
Estimations of the iron loss BF can follow several methods, depending on the e-machine type and manufacturing-incurred change being investigated.Tables I and II gather values of iron loss BFs from the literature, dedicated to cut edge damage from punching and stator shrink-fit, respectively.The range of Manuscript received 28 March 2023; accepted 3 May 2023.Date of publication 19 May 2023; date of current version 24 October 2023.Corresponding author: J. Soulard (e-mail: J.Soulard@warwick.ac.uk).
Color versions of one or more figures in this article are available at https://doi.org/10.1109/TMAG.2023.3276192.
Digital Object Identifier 10.1109/TMAG.2023.3276192values is wide, from 5% to 75% iron loss increase.The tables include three influencing factors only: motor type [permanent magnet synchronous machines (PMSMs) or induction machine (IM)], electric steel grade, or test conditions.Some studies combine modeling and experiments, while some are derived from advanced modeling methods, using different test procedures and material data.Moses et al. [1], Bourchas et al. [3], and Krings et al. [4] confirm BF values are process dependent with BF for laser cutting expected to be different than for punching (see Table I).The combined effect of manufacturing and assembly-induced stresses requires complex investigations to separate contributions from each cause [6].
Based on testing capability, project scopes, and availability of test objects, the calibration procedure of permanent magnet (PM) machine iron loss BF using no-load tests with a   magnetized and non-magnetized (dummy) rotor at different speeds was followed.This method, described in [2], is not specific to any manufacturing damage and the e-machine is tested under relevant conditions, i.e., the local flux densities in the rotor and stator are the same as when the e-machine is in its application.
Iron loss prediction with finite element analysis (FEA) simulations requires electrical steel magnetic properties as input data [4].Typical BH curves at 50 Hz and iron loss densities for standardized ranges of sinusoidal flux density variations are readily available from suppliers.A dataset including the influence of residual stress is now required by more advanced models to account for manufacturing impacts, such as shrink-fit or cut-edge damage.Although some data and advanced research equipment may be described in [7], no standard exists, and many grades have not been qualified yet, especially among thinner gauges for automotive and highspeed applications.
Both procedures involve intensive experimental investigations.As described in [21], any measurement value has an uncertainty related to two aspects: the accuracy of the chosen measurement apparatus (including sensor precision) is the core subject of the reference, while the variations introduced by the test object's relative positioning in the experimental setup and operator's influence are only described briefly.Both types of uncertainty contribute to the repeatability and reproducibility of the measurements, which is rarely discussed thoroughly in references dealing with iron losses in electric machines.
The importance of considering the impact of manufacturing on the stator and rotor core during the design stage is highlighted in Section II, using an interior PM synchronous motor for automotive applications.Section III describes the identified challenges while running an extensive experimental campaign with several PM synchronous motors, tested at no-load with an end-of-line commercial rig and a dynamometer.The collection of electrical steel data including the impact of tensile and compressive stress aligned with magnetization via a bespoke commercial single-sheet tester (SST) is discussed in Section IV, together with analysis of obtained datasets for different grades.Conclusions on how to improve the repeatability of iron loss build factor calibration are drawn in Section V.
The details of the tested motors and the electrical steel grade are not relevant for the purposes of this article.Therefore, they are not provided.The usage of normalized values emphasizes how impactful manufacturing or experimental issues may have on certain material or e-machine characteristics.

II. IMPACT OF BFS ON MOTOR PERFORMANCE
A three-phase, 48-slot/eight-pole interior PM (IPM) motor for electric vehicle traction application from Motor-CAD template has been selected for the investigation of iron loss BF [22].The model of template motor e8 is inspired by the IPM machine in the Nissan Leaf.It is an on-board drive system designed to meet the requirements of the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) drive cycle.The motor model was simulated with BFs of 1 and 2, i.e., assuming the manufacturing impact may double the iron loss contribution at all working conditions, which is a realistic worst case scenario according to the literature and straightforward to simulate and analyze.
The ratio of total losses with BF = 2 to the total loss for BF = 1 is shown as color map as a function of the torque and speed in Fig. 2. The reader may be more familiar with the efficiency map under maximal torque per copper loss control.Instead of the efficiency, the increase of total loss is presented to visualize where the constant iron loss BF influences the performances the most.
The color scale on the right of Fig. 2 indicates the total losses may be up to 1.8 times higher when an iron loss BF of 2 is considered in the IPM.Most importantly, the figure indicates that the low torque region (≤50 N • m) has been most affected, while it is not as significant in the high torque region.By way of example, at peak torque 276 N • m and base speed 4000 r/min, the increase in a total loss is only 10% since copper losses are the main loss contributor at high torque (high current).The WLTP working points were added in Fig. 2   points from 4000 to 6500 r/min are lower than 23 N • m, a region where the total loss increase is 70% or higher.Therefore, a significant difference in motor and drive cycle performance predictions can be expected due to manufacturing influence on iron loss.
Table III shows the drive cycle efficiencies and energy loss split with different iron loss BFs.Simulations were run with the electrical steel grade M270-35A (0.35 mm lamination thickness) and with a 0.2 mm grade, using material properties in the database of the design tool.
The first observation is that iron loss is the highest contributor to total loss in all cases, even more, if the impact of manufacturing is included.Assuming both grades are impacted in such a way that BF = 2, the total loss during the WLTP drive cycle has increased by 53% with 0.35 mm laminations, and 45% with 0.2 mm, leading to a reduction of efficiency on the drive cycle by 2.4% and 1.7%, respectively.
All these values are case study specific as they depend on the application, motor type, and design choices (a compromise between copper and iron losses in the stator, choice of e-steel grade, number of poles and gear ratio, etc.).However, this simple investigation quantifies how serious the impact of manufacturing may be on total losses or other performance.The designer may then decide whether more advanced investigations are necessary.The method is applicable to all types of e-machines.

III. REPEATABILITY OF NO-LOAD TESTS WITH PM MOTORS
This section is dedicated to iron loss experimental identification in PMSMs, where it is not possible to control the rotor excitation easily.The procedure includes two dynamic tests at no-load varying the set point for the speed, using a separation method as the iron loss cannot be measured directly.
The measurements with a magnetized rotor identify the total no-load losses, which include iron loss and mechanical loss.The test with the dummy rotor quantifies the mechanical losses only.Iron loss values are obtained by subtracting the loss for the non-magnetized rotor from the measured loss for the magnetized rotor.
Since the torque was measured, the power loss is obtained by multiplying by the speed at which the test is run.This is an indirect identification of the no-load iron losses that create uncertainty issues as discussed in the introduction [21].Table IV indicates the uncertainty associated with the measurement equipment, i.e., the characteristics of sensors used for torque and temperature measurement in the end-of-line test rig, as well as for the dynamometer rig equipped with a stateof-art control system newly commissioned on campus.The uncertainties introduced by repeatability issues and how much they influence the mean values of the measured quantities and deviations are described in Sections III-A-III-D.
Several motors were constructed using a mix of high-volume manufacturing and manual processes, mostly on campus.The water-cooling system embedded in the motor housing was activated during testing.For the elevated temperature of the coolant (65 • C), heat soaking was used before starting the repeats of drive cycles.

A. Proposed Zigzag Test Cycle for No-Load Loss Measurements
Gauge repeatability and reproducibility methodology was used for both no-load tests with several motors including units equipped with magnetized and dummy rotors.A measurement campaign on a single motor took up to five days with an 8-h testing shift for the obtention of 30 valid test cycles.
The V-test cycle was employed first with the intention to minimize the time between two measurement points, allowing for many set points within the speed sweep every 100 r/min starting at −6500 r/min and up to +6500 r/min.
Results for one of the motors with a magnetized rotor with the coolant temperature set at 20 • C are presented in Fig. 3 As expected, the no-load losses increase with the speed, heating up the motor as the repeats take place before stabilizing after 2 V-tests.When further analyzing the measured values, a significant difference was obtained between no-load losses at positive and negative speed, which was assumed to be due to different rotor temperatures (and, therefore, flux density levels).
To confirm the assumption, the zigzag test cycle was introduced, switching between positive and negative speeds successively to obtain as close as possible temperatures for negative and positive speeds.The speed sweep increment was increased to 1000 r/min to keep the drive cycle within reasonable duration, due to increased transients between measurement points.Results of the zigzag tests confirmed the influence of temperature on torque values is significant as reduced rotor temperature visibly increased the no-load torque due to a lower reduction of flux density from temperature.

Reduced values of standard deviations (σ ) in Table V
indicate that improved repeatability of no-load torque measurement can be achieved by the proposed zigzag test cycle, with reduction of the torque variation between positive and negative speeds thanks to keeping the rotor temperature within 5 • C across all speeds.The residual difference (less than 1%) may be linked to torque sensor behavior combined with a variation of axial force due to rotor skewing depending on the direction of rotation [23].

B. Drift of No-Load Torque During Repeated Test Cycles
In this test, the no-load torque was measured at both positive and negative 6000 r/min with a dummy rotor.The zigzag test cycles were repeated 43 times.The no-load torque equals the mechanical loss because of the dummy rotor.The measured torque decreased as the repeats took place until test 20, as shown in Fig. 4. At this stage, the end-of-line rig had to be modified so the motor under test was dismounted and remounted, introducing a step change for repeat 21 before the decrease continued until it finally stabilized from repeat 30.
Besides the critical influence of rotor temperature, the no-load dummy rotor measurement campaigns highlighted the need to let the bearings settle both while rotating the tested unit first after manufacturing completion and whenever the unit is mounted on the test rig.The same behavior was encountered on the dynamometer test rig.Using a mean value from the first ten tests would lead to no-load iron loss equal to 0.65 p.u., while using average values from the last ten repeats gives 0.77 p.u.
Visible differences between positive and negative speed torque results (around 0.04 p.u.) can be observed after 30 repeats.In this case, this difference cannot be explained by the skewing.The recommended approach is to combine both speed directions in an average value of the loss, but it increases the confidence interval significantly.

C. Measurement Deviations Between Test Rigs
The same motor with a magnetized rotor was tested on the end-of-line test rig and on the dynamometer with a newly commissioned control system.The latter uses a Yokogawa WT5000 Precision Power Analyzer and speed is obtained from the encoder pulse timing according to the actual speed, while other sensors and measurement apparatus are the same.
The deviations between the dynamometer and end-of-line test rig during the zigzag test cycle can be observed in Fig. 5(a), using the dynamometer's no-load torque value at 6000 r/min as a reference.The increase in no-load torque values measured by a dynamometer varies from 1% (at −1000 r/min) to 9% (at ±6000 r/min).Nevertheless, after normalizing the measurement results by dividing the reference value at −6000 r/min, the zigzag test route overlapped well between the two test rigs, as shown in Fig. 5(b).This indicates good repeatability for no-load tests at different speeds by different test rigs, although the absolute values of no-load torques differ in Fig. 5(a), while the rotor temperatures were within a couple of degrees.Part of the differences are due to mechanical loss variations.

D. Influence of Rotor Temperature on No-Load Test
Previous investigations clearly indicated the influence of temperature on the measured no-load losses.Fig. 6 shows the measured torque values as a function of the output of the rotor IR sensor, gathering several sets of data and combining positive and negative 6000 r/min.
For rotor temperature below 40 • C, the average total no-load torque is 1 p.u. with reduced impact of the temperature across the range of 25 • C-40 • C, but with 4% variations across repeats.The torque values for rotor temperature above 60 • C are much lower (−0.15p.u.) with a larger impact of the temperature for this range.The measurements with the dummy rotors on the same figure indicate a more linear dependency of the mechanical loss with the temperature, reducing the loss as the temperature increases by 0.05 p.u. between 25 • C and 60 • C. If the rotor temperature value is not accessible, the identified iron loss may be calculated from any of the measured values in Fig. 6.The iron loss can then vary from 0.74 p.u. (hot magnetized rotor and cold dummy rotor) to 0.91 p.u. (cold magnetized rotor and hot dummy rotor.The highest no-load iron loss can be 23% higher than the lowest value.Therefore, it is important to control the temperature variations in the rotor and bearings to a minimum for both no-load tests. One possibility, if the temperature cannot be controlled would be to postprocess the measured data to account for temperature influence.In general, the temperature coefficient of PM properties is given as a single value, assuming a constant rate of change over the tolerated temperature range.For example, neodymium iron boron magnet (NdFeB) grade N38UH loses approximately 0.12% in remanent flux density for every degree Celsius above 20 • C.However, discrepancies have been found in [24] between the first-order fitting temperature coefficient and the measured properties of a NdFeB.Instead, a second-order temperature influence model was introduced to ensure a better match with the measured data.Such data is not readily available.It should also be noted that the IR sensor used in this study indicates an average temperature of the magnet and the electrical steel in the rotor.
The presented measurement campaign highlights some of the challenges met while quantifying iron loss in an IPM motor using dynamic no-load tests with magnetized and dummy rotors.Rotor temperature, mounting, and test rig itself may contribute to high uncertainties in the iron loss values for calibration purposes.Even though iron loss prediction models keep improving, high values of BF may still be required in the future, due to measurement challenges.

IV. MEASUREMENTS OF ELECTRICAL STEEL PROPERTIES UNDER STRESS FOR ADVANCED IRON LOSS MODELING
To investigate the impact of shrink fitting and cut edge damage on e-machine performance, the electrical steel magnetic properties under stress conditions are required.Collection of this data using a bespoke commercial equipment from Brockhaus to apply stress on a single sheet in the direction of magnetization is described here, first describing repeatability challenges, and then focusing on quantifying different grades' sensitivity to stress.

A. Improved Magnetic Measurement Repeatability for a SST With Applied Mechanical Stress
The process involving applying stress in combination with a single-sheet test is currently not covered by an IEC standard.Issues with repeatability were identified when measuring properties of e-steel strips of dimensions 300 × 30 mm under stress across three users, especially when testing thinner grades.
To apply uniaxial stress to a strip, it must be held at both ends with clamping blocks.The clamping introduces unwanted stress into the strip, the effect of which is exaggerated by operator differences.For a given magnetic polarization, the permeability and the iron losses of the material are measured for the soft magnetic material.The permeability is found to be proportionally more affected by the user variance than the loss measurements at low frequencies (50 Hz).Fig. 7 provides a baseline variation between users before clamping, using the same sample to avoid sample-to-sample variation.A small variation between users is observed, which could be attributed to precise sample positioning within the SST.Variations of the same magnitude were obtained while testing different strips of the same material grade by the same operator.
Once the same sample is clamped, significant user-to-user variation arises, as shown in Fig. 8.All users experience reduced repeatability of permeability results.The clamping effects dramatically the identified relative permeability, therefore, the BH curve.
A mitigation regime of pre-cycling is proposed against the effect of clamping.The stress cycling involves clamping the sample, then incrementally cycling the strip under tensile stress up to 50% of its tensile strength: 0% → 15% → 0% → 30% → 0% → 50% → 0%.The stress cycling settles the sample within the clamping blocks and shows a promising ability to minimize the measurement spread and the difference between  operators.Additionally, to minimize other inconsistencies, all measurements in Figs.7-13 were conducted with the strip aligned to 1 mm of the center of the excitation and measurement coils of the SST.A consistent bolt-tightening scheme (sequence and applied force) was employed where applicable.
Fig. 9 gives the improved repeatability of the permeability of a strip that has been clamped and gone through post-stress cycling.The corresponding maximum-to-minimum range is improved from 50.1% to 19.2% for 0.8 T, considering three repeats with three operators given in Table VI.Most importantly the mean permeability post-cycling is now only 1.3% different from the unclamped value.Without clamping, the difference was 9.6%.
Similarly, both the mean and the variability of the iron loss measurements are affected by the strip clamping as presented in Table VII.The relative standard deviation of the loss density (standard deviation divided by the mean) is reduced with the introduction of stress cycling and the procedure reduces the inter-user variation of the mean.

B. BH Curves and Permeability of Electrical Steel Under Applied Mechanical Stress-Benchmarking
It is well reported that the magnetic properties of electrical steel are deteriorated when subjected to mechanical stress.However, only restricted data sets are available in the literature.
A comprehensive set of measurements of the magnetic properties under stress in tension and compression was made, with 0.1 (two different suppliers), 0.2, and 0.35 mm samples.This dataset complements the measurements by Moses et al. [1], which are compared in Fig. 10 for 0.2 mm grades.
The loss density without stress is significantly different so the materials are likely to come from different suppliers.The presented data are normalized with the loss without stress for the respective material so that the sensitivity of the loss to the stress is compared, instead of focusing on which material has the lower or higher losses.
For the same nominal thickness, the influence of tensile stress is different between Moses and the material tested here.The figure also reveals the differing influences of compressive  stress (negative applied field axis) between transverse and rolling direction strips, but minor differences in tension.The reported variations highlight the importance to multiply data sets for different grades and suppliers since chemical composition may vary and influence the stress response.Benchmarking test procedures for the application of stress are still required within the research community.

C. Impact of Applied Mechanical Stress and Frequency on Loss Density for 0.2 mm
The purpose of this section is to introduce key data plots, useful to compare the influences of stress on the losses and BH curves of electrical steels at the same time.As these plots are quite complex, the loss increases for the 0.2 mm grade are presented twice, first in Figs.11 and 12, then in Fig. 13(c) and (d) in Section IV-D.
The impact of tensile stress on the iron losses of a 0.2 mm grade as a function of flux density for different frequencies is shown in Fig. 11.The maximum impact of tensile stress on losses has a defined peak that occurs at 0.5 T for 50 Hz and 0.7 T for 1000 Hz, reaching 33% and 20%, respectively.It is also noted that for low frequency (<200 Hz) and above 1.6 T, the iron losses are slightly reduced under 100 MPa tensile stress.
The impact of the stress on loss has a different pattern for compressive stress (see Fig. 12).The maximum impact on losses are observed at minimum flux density with an increase as high as +350%.This means e-machines working under reduced load conditions could be significantly impacted by shrink-fit for example.
For all frequencies measured, the impact of stress when the electrical steel is saturated is reduced.However, for compressive stress, the values are much higher (>25% at all frequencies).For both tensile and compressive impact on the losses inflect at the same value of flux density (1.3 T), above which higher frequencies properties are more impacted by stress than lower frequencies for this material.This occurs at the knee point of the BH curve.

D. Comparison of Impact of Applied Mechanical Stress on BH and Loss Density for Three Grades
To go deeper into the analysis of stress impact, the percentage changes in losses under tensile and compressive stress are overlayed alongside the B-H curves in Fig. 13 for three grades [thickest (a) and (b) to thinnest (e) and (f)], both at 50 Hz and for 1000 Hz (left and right), respectively.
The purpose of the investigations is not to compare absolute properties but, to highlight trends and quantify stress impacts for comparison.All data are normalized using each material's own absolute values of the flux density at 10 4 A/m and the loss density without stress at each flux density level and frequency.The loss density increase is shown both as the diameter of the circle and by its color shade.
For the three tested grades, compressive stress is confirmed to be much more impacting than tensile stress both in terms of the BH curve (reduced permeability) and loss increase.The three grades show the same lack of impact on BH at higher flux density as in Section IV-C.
Comparing loss increase at 50 Hz maximal impact, the thicker the grade, the higher the loss increase.However, the reduced loss increases at 1 kHz for 0.35 and 0.2 mm are not as pronounced for 0.1 mm as the eddy current loss contribution is still relatively low for the thinnest gauge.
These trends were extracted from only three grades with tests using the same test rig and prestress procedure.Though aligned with reported behavior in [1] and other references, continued efforts are required to confirm that identified trends are valid across a larger variety of electrical steel products, as two grades of 0.1 mm from different suppliers led to different loss increase amplitudes.
Constant BF is not likely to be an accurate way to model the impact of loss on e-machine performance.Trends identified at the material level may not apply once inserted in devices.As permeability is influenced, the flux density patterns within the stator and rotor cores will be different, impacting the eddy currents loss in a complex shape magnetic circuit.The usage of flux-weakening to reach a higher speed is also going to lead to complex loss behavior.E-machines with the saturated magnetic circuit at the high load will be less impacted than the ones designed with reduced flux density levels below the base speed.It can also be predicted that SMPM with surface-mounted magnets will be less impacted in terms of torque than IPM since the relative permeability of PM material is close to 1, reducing the influence of the electrical steel permeability.Induction motors' magnetizing current will increase while reluctance torques in IPM, synchronous reluctance, and switched reluctance machines will suffer from the reduction in permeability as well as the loss increase of the electrical steel.

V. CONCLUSION
The importance of considering a BF during the design stage was highlighted in a case study, providing a simple simulation method to identify at early design stages whether the impact of manufacturing on iron loss is likely to create serious performance issues.The constant BF (BF = 2) method is applicable to any e-machine type and application.
Calibrating models to accurately predict iron loss and e-machine performance at variable speed and torque is still a challenging task.
From running an extensive campaign of dynamic testing of PM synchronous machines for iron loss BF calibration, it was concluded that monitoring rotor temperatures (magnets and bearings) and adapting the drive cycle for repeatability checks are key considerations for accurate identification of iron losses at no-load.Postprocessing of measured data or adaptation of modeling conditions are both challenging since they would require access to the nonlinear behavior of the magnet remanent flux density with temperature, not readily available from datasheets.
Advanced modeling methods to account for cut edge damage or impact of stator or rotor shrink-fit are available in the literature.However, they require a dedicated collection of electrical steel data including the impact of tensile and compressive mechanical stress.Such measurements are not covered by standards yet.A precycling procedure was proposed and implemented with satisfactory results to increase the repeatability of the measured values, especially for thinner grades.
Data collected on different grades of electrical steel were postprocessed presenting original figures to allow for comparison of the BH curves and loss increases with stress and highlight trends with respect to flux density, frequency, stress, material grades, and suppliers.Due to grain size, chemical composition, and thickness variations, it was confirmed that measurements must be conducted for different material grades and suppliers to provide accurate input data for advanced iron loss prediction in e-machines.The stress cycling procedure was proposed by their Warwick Manufacturing Group (WMG) colleague Dr. Frank Zhou.
The data that support the findings of this study are subject to third-party restrictions.Reasonable request will be addressed by J. Soulard.

Fig. 1 .
Fig. 1.Development of e-machine products using iterations and repeated BF calibrations.
(a) and (b).The first figure shows the normalized no-load torque (using values at 6000 r/min as reference) as a function of the speed, while the second figure presents the values of the rotor temperature measured by open-loop infrared (IR) sensor.

Fig. 3 .
Fig. 3. Comparison of V-test cycle and Zigzag test cycle.(a) No-load torque versus speed including test cycles (zigzag has been taken as the reference for torque per-unit values).(b) Corresponding stabilized rotor temperatures while repeating the two test cycles.

Fig. 4 .
Fig. 4. Drift of measured no-load torque at 6000 r/min with a dummy rotor during repeated zigzag test cycles over four days.

Fig. 5 .
Fig. 5. Measurement deviations between test rigs during the Zigzag test cycle.(a) No-load torque (dynamometer at ±6000 r/min has been taken as the reference for per-unit values).(b) Normalized no-load torque (torque at ±6000 r/min has been taken as the reference for each test rig).

Fig. 6 .
Fig.6.Influence of rotor temperature on no-load torque with both dummy rotor and magnetized rotor at ±6000 r/min.(Torque of magnetized rotor with coolant at 20 • C has been taken as the reference for per-unit value.)

Fig. 7 .
Fig. 7. Relative permeability of e-steel strip measured by three users before clamping in the SST at 50 Hz.The maximum-to-minimum range is given by the "error" bars.

Fig. 8 .
Fig. 8. Permeability of e-steel strip measured by three users after clamping in the SST.The maximum-to-minimum range is given by the "error" bars.

Fig. 9 .
Fig. 9. Permeability of e-steel strip measured by three users after clamping and stress-cycling in the SST.The maximum-to-minimum range is given by the "error" bars.

Fig. 10 .
Fig. 10.Percentage change in losses with respect to an unstressed strip at 50 Hz and 1.0 T for 0.2 mm thick material.Data published by Moses et al. [1] and material the same thickness measured after stress-cycling.

Fig. 11 .
Fig. 11.Loss increases for a 0.2 mm thick strip under 100 MPa tensile stress compared to no stress loss.Created as an average of three rolling and three transverse direction strips.

Fig. 12 .
Fig. 12. Percentage change in losses for a 0.2 mm thick strip under 60 MPa compressive stress with respect to unstressed losses.Created as an average of three rolling and three transverse direction strips.

ACKNOWLEDGMENT
This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) Studentship through Motor Design Ltd., under Grant 1942187; in part by Innovate U.K. Grants for projects Virbius under Grant 113168, CompETe under Grant 113254, and HVStack under Grant 43016; and in part by Prosperity Partnerships Enabling Low Carbon Technologies (ELCATS) under Grant EP/R004927/1.

TABLE I REPORTED
IMPACT OF CUT-EDGE DAMAGE FROM PUNCHING ON IRON LOSS

TABLE II REPORTED
IMPACT OF SHRINK-FIT ON IRON LOSS as white dots.Most WLTP drive cycle operating Authorized licensed use limited to the terms of the applicable license agreement with IEEE.Restrictions apply.

TABLE III INFLUENCE
OF BF ON MOTOR DRIVE CYCLE PERFORMANCE

TABLE IV RANGE
AND PRECISION OF SENSORS OF AN END-OF-LINE TEST RIG

TABLE V COMPARISON
OF TORQUE VARIATION (±2σ /MEAN) BETWEEN TEST CYCLES

TABLE VI REPEATABILITY
OF SST PERMEABILITY MEASUREMENTS AT 0.8 T, 50 Hz

TABLE VII REPEATABILITY
OF SST LOSS MEASUREMENTS AT 0.8 T, 50 Hz