What is required for a motor, which is the key component in vehicle electrification, is not only high performance as a component but also high consistency with the system.
Model-based development using simulation is also becoming indispensable for motor development.
JMAG, which has a high track record in motor design, proposes a new workflow for model-based development.
How the development of motors for EVs will proceed in system design, component design, prototype / performance evaluation, and system verification is presented.
All the examples shown are from simulations using JMAG.
1. System Design
In this phase, with system design, from the performance requirements for the final product to component requirements, that is, motor requirement specifications, are broken down.
The specifications of the motor are determined while confirming that the system requirements are met with heavy use of simulations, where previously motor specifications were determined using experience and rule-based processes. By simulation it is possible to investigate a very large design space at high speed, so it is possible to narrow down the design space more systematically without missing any cases while leaving freedom for a later process.
In system design, from EV requirements, drive motor and drive requirements are deduced. First, after identifying the type of motor and the output based on system requirements, the power flow of candidate motors is examined; and after basic motor requirements are determined, motor type selection is carried out from evaluations of required maximum torque and maximum power. An I-shaped IPM motor is selected for reasons of high efficiency and low magnet quantity (low price).
By examining specific motor geometries and configurations at system design, it is possible to develop efficiently with less rework.
※You can see the details by clicking on the image or title below.
Based on the requirements for the motor chosen in the system design in section 1, the specific motor geometry and composition are determined. In this section, the way in which simulation is being used is evolving.
Traditionally, simulation has been used to confirm the performance of motors designed using experience and rules, but with the new workflow, from parametric analysis that makes use of the characteristics of simulation a large design space is searched and the optimum motor meeting the requirements is found.
In this case study rectangular conductors and distributed windings which allow a good fill factor were selected in order to increase the power density. This was done based on the results of parametric analysis for wire type, slot shape, and winding method. Since the rotor has many design variables, from numerous parametric analyses the magnet positioning and geometry was optimized. Taking step skew into account, slot opening width is determined such that cogging torque is minimized. Finally, it is confirmed that the demagnetization resistance is sufficient to withstand even harsh environments.
To adapt control parameters concerning driving a designed motor, detailed motor characteristics are handed over to the control design process.
※You can see the details by clicking on the image or title below.
What is confirmed during this phase includes the design’s intended performance and whether or not this performance can be demonstrated in actual machines rather than simulations alone, as well as the likelihood of issues occurring.
Today’s latest simulation techniques are run thoroughly through computers using the likes of HPC (High Performance Computing).
Using virtual prototypes results in not only a significant reduction of trial costs, but functions can also be confirmed over a considerably wide range of operation conditions. Elements not usually able to be confirmed in real machines are also observed, such as motor internal magnetic fields, loss, temperature, and force distribution. This offers both a smarter way of confirming functions as well as further ideas for improvement.
The efficiency map is confirmed here first. Machines can encounter issues in temperature management which can result in substantially time consuming and labor intensive work, but simulations make it possible to generate these issues without difficulty. Magnetic hotspots that cause demagnetization and electromagnetic forces that cause vibrations are analyzed in detail to check that the designed motor satisfies all the required functions.
※You can see the details by clicking on the image or title below.
It is confirmed that, when using the motor for production, the final product demonstrates the desired performance.
The control and multiphysics phenomena such as heat and vibration, etc., are confirmed as part of system validation.
MIL/HIL techniques are used for control. Control actions can be validated extensively and in detail using a highly accurate motor model that also captures detailed information such as spatial harmonics, eccentricity, etc., as well as motor magnetic saturation. The integration of physical phenomenon is performed with linked simulations through cooling and sound vibrations. Virtual prototypes and evaluations obtain detailed heat distribution and electromagnetic force distribution which can be transferred to other external 3rd party tools for highly accurate validations.
The corresponding shows the confirmation of electric current and torque responsiveness during times of inverter failure as one example of control validation. This simulation is conducted with JMAG-RT using MATLAB/Simulink. Motor vibration when connected to gears and inverters is also confirmed.
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#01 Verification of cruising distance
System design begins with vehicle system requirements. Given vehicle system requirements the requirements of the motor drive, battery, control, cooling, and drive system subsystem are determined. In this case study the requirements are: weight – 1,300 kg, size – 4,300 x 1,800 x 1,600, maximum speed – 160 km/h, acceleration performance – 0-100 km/h in 10 seconds, acceleration – 10 km/h/s, noise no greater than 55 dB at a cruising speed of 100 km/h. When vehicle weight and maximum speed are chosen, the maximum speed, maximum output, and maximum torque of the motor can be estimated. However, at this point it is not known whether the motor drive specifications meet the vehicle system requirements. Because of this, basic motor design is carried out, and whether the cruising distance, one of the requirements, is met or not is verified. For this, basic design information is required for batteries and drive systems as well.
Fig. 1-1 shows the maximum cruising distance for the electrical power consumption calculated from the variation in efficiency obtained from JC08 mode running simulation. Using the basic designed motor, whether the required cruising distance (400 km) with the assumed battery capacity (40 kWh) can be obtained is confirmed.
In the running simulation, the JC08 vehicle speed history in Fig. 1-2 is input and the variation in efficiency in Fig. 1-3 is obtained. This verification is carried out in the course of breaking down the vehicle system requirement specifications to the requirement specifications of subsystems such as the motor drive system. At this stage, the motor information to be input are requirement specifications (maximum output, maximum torque, and efficiency).
Fig. 1-1 Cruising distance and battery power consumption history
Fig.1-2 JC08 mode vehicle speed history
Fig.1-3 Motor efficiency during JC08 running mode
If the cruising distance requirement is not met at this stage, then the subsystem requirement specifications are insufficient. Therefore in this case, the subsystem requirements are be redetermined.
At this point, an efficiency map is generated from the requirement specifications of the motor drive system using JMAG-Express and JMAG-RT Viewer and verified by running simulation.
#02 Vibration reduction measures
At the system design stage, vibration reduction measures are considered. The vehicle system requirement of cruising at 100 km/h with in-vehicle noise not greater than 55 dB is input. Resonances from electromagnetic excitation forces from a motor in the frequency range of 5-10 kHz, a vibration range that people are sensitive to, should be avoided as much as possible. Therefore at this stage the combinations of the number of slots and poles are narrowed down.
The electromagnetic excitation force focused on is a component called zero-th order annular mode (Fig. 2-1).
Fig. 2-1 Zero-th order annular mode
Fig. 2-2 Number of pole slots and ring zero-order electromagnetic force frequency
The stator oscillates in the radial direction. Breathing mode shape 0 is likely to generate noise, therefore it is hoped that this noise does not lie in the frequency band where measures are required. The breathing mode shape 0 frequency is determined by the number of pole slots and the rotation speed. In Fig. 2-2, the excitation force frequency vs. rotation speed for each combination of number of poles and slots is shown. The combinations between excitation force frequencies in the 5-10 kHz range at a rotation speed of 6,000 rpm are excluded.
#03 JC08 mode running simulation
Using the basic designed motor efficiency map (#04), Simulink is used to obtain the variation in efficiency when running in JC08 mode.
Fig. 3-1 shows the simulation model. The required power for air resistance and acceleration based on vehicle speed is obtained, and the required power is converted to a load on the motor drive system. Vehicle speed is converted to motor speed. Once the motor speed and load are known, the efficiency at an operating point from the efficiency map can be obtained.
The requirement specifications (maximum output, maximum torque, and efficiency) of the motor drive system are required for this simulation. In addition, machine design requirement specifications needed to obtain vehicle weight, which is required to calculate rolling resistance. Requirement specifications for control, drive systems, electricity, cooling, and batteries are derived from vehicle system requirement specifications, and verification is made as to whether the requirements are met using running simulation.
The efficiency map used is an efficiency map created after the basic design of the motor was completed. It is possible to generate a simple efficiency map by generating a JMAG-RT model from JMAG-Express and using JMAG-RT Viewer.
Fig. 3-1 Running simulation model
#04 Design selection based on maximum output, maximum torque, and maximum efficiency
Inputs are motor drive system requirement specifications. Consider a motor with a maximum rotation speed of 10,000 rpm or more, a maximum torque of 260 Nm or more, a maximum power of 75 kW or more, a system efficiency averaging 90% or more when in JC08 mode, and meeting vibration reduction requirements in the 5-10 kHz range. A basic design of a motor that meets these requirements is searched for, and the motor type, number of poles, number of slots, number of turns, and rotor diameter is determined. After determining the basic design, a running simulation is performed and the validity of the requirement specifications is confirmed.
For an induction motor and a PM motor, two types of motors – IPM and SPM – were investigated. For an IPM motor two types were considered, I-shaped and V-shaped, so that a total of 4 types were compared.
Parametric analysis is performed changing motor types, number of poles, number of slots, number of turns, and rotor diameter. This is performed using JMAG-Express and JMAG-RT Viewer. A model is generated with JMAG-Express and a JMAG-RT model is output. An efficiency map is created using JMAG-RT Viewer, and maximum output, maximum torque, and maximum efficiency are evaluated. When creating an efficiency map with the JMAG-RT Viewer, DC voltage and maximum current, which are in inverter requirement specifications, are input.
For selecting a design, first, whether the maximum output and maximum torque requirements are met are confirmed. The red lines in Fig. 4-1 are the requirement lines. Designs using V-shaped magnets were eliminated. Next, the remaining design efficiency maps are compared. Although the SPM motor was able to obtain a high maximum torque, its rotation speed did not attain the maximum rotation speed and was excluded (Fig. 4-2). The induction motor was able to obtain high efficiency and output at high rotation speeds (Fig. 4-3). However, focus was put on performance in the JC08 running mode region. By doing so the PM motor exhibited better efficiency results. Finally, I-shaped and V-shaped magnets were compared. Both have similar performance characteristics, but a design with I-shaped magnets, whose high-efficiency range is broad in the medium speed – low load region was adopted (Fig. 4-4, Fig. 4-5). At this stage,from varying the number of poles, number of slots, number of turns, and rotor diameter as parameters, an IPM motor with I-shaped magnets, 8 poles, 48 slots, and a rotor diameter of 130 mm was chosen. From the space allowed inside the vehicle, a stator outer diameter of 212 mm and motor height (including coil ends) of 250 mm was chosen.
Fig. 4-1 Designs investigated, maximum output and maximum torque The red lines are the target lines
Fig. 4-2 SPM motor efficiency map The filled-in SPM motor circle in Fig. 4-1
Fig. 4-3 Induction machine efficiency map The operating range in JC08 running mode
Fig. 4-4 IPM motor (I-shaped) efficiency map
Fig. 4-5 IPM motor (V-shaped) efficiency map
#05 Resistance
In this subsection the winding method is decided. Parametric analysis is performed while changing the winding method in JMAG-Express. The windings investigated are a distributed winding and a concentrated winding. Fig. 5-1 shows the relationship between phase resistance and torque. Distributed winding results and concentrated winding results are shown. It can be seen that for the concentrated winding the resistance value can be lower than for the distributed winding (6-slot pitch, full-pitch winding). However, when looking at the torque, it can be seen that the distributed winding is advantageous. From these results, a distributed winding was adopted to obtain the necessary torque.
Fig. 5-1 Tradeoffs between winding methods, torque and resistance
#06 Iron loss
Next, the shape of the slot is determined. Slot shapes were compared and investigated while changing the back yoke width and tooth width (Fig. 6-1). Fig. 6-2 shows the relationship between phase resistance and iron loss. In this subsection parametric analysis is performed with JMAG-Express. A tradeoff can be seen between the two. When the back yoke becomes narrow the coil space increases so the resistance can be lowered, however the area of the core decreases causing the magnetic flux density to increase thus causing the iron loss to increase. Parametric analysis was performed, from which dimensions were chosen to minimize iron loss and resistance. Related documents:
◆[L-OP-78] User-Defined Geometry Model Library
Fig. 6-1 Slot geometry dimensions varied
Fig. 6-2 Iron loss / resistance tradeoff
#07 Torque
Finally, the wire shape is chosen. At this stage, the winding method and slot area are fixed. What kind of winding to wind in the slot area is investigated. Round wire and rectangular wire are investigated. Since the number of turns has been chosen the size of the rectangular wire was first chosen. Next, the round wire strand diameter and number of strands were adjusted. The fill factor can be increased by decreasing the strand diameter and increasing the number of strands. However resistance increases. Fig. 7-1 shows the relationship. The red dashed line shows the resistance value of the rectangular wire as a comparision. In order to obtain the same resistance as the rectangular wire, it became necessary to have two 2-mm diameter strands. Producing 2-mm diameter windings is costly. Whereas, rectangular wire is made with a shape that can be inserted into the slot of a manufactured the coil shape. A rectangular wire was adapted considering that resistance can be reduced and considering manufacuring costs. The Fig. 7-2 wire shape diagram was produced using the JMAG-Designer wire template. Whether the resulting wire obtained by JMAG-Express could actually be wound was checked using JMAG-Designer.
Fig. 7-1 Round wire strand diameter and resistance In order to increase the fill factor the diameter and number of strands for the round wire are adjusted. The rectangular wire is set at 4 cm x 3.5 cm.
Fig. 7-2 Winding diagram
#08 Torque / loss correlation diagram
Up to this point the basic design of the motor and the geometry of the stator have been determined. Next, the detailed dimensions of the rotor geometry are determined. In this subsection multi-objective optimization is performed using JMAG-Designer, and multi-case parametric analysis is also performed. In this subsection the point is that for the drive motor the driving conditions are wide, so it is necessary to evaluate characteristics at multiple operating points instead of a single operating point. In the case of the IPM motor, it is also necessary to prevent the rotor from being destroyed by centrifugal force. For this reason, magnet detailed dimensions and positions are chosen so that the torque is maximized and the iron loss is minimized within the assumed operating region. Furthermore, constraint conditions are set for the maximum stress inside the rotor and the phase voltage.
Fig. 8-1 shows the torque and iron loss for all the candidate geometries. The green geometries were rejected due to voltage limits being exceeded although their torque was high. The red points do not satisfy the stress constraint at high speed rotation. The blue points are the remaining candidate designs, among which the design with the maximum torque is chosen. Multi-objective optimization solves the problem of torque maximization and iron loss minimization. Torque and iron loss are evaluated at two speeds of 1,200 rpm and 9,000 rpm. In the objective function, the values of two operating points are normalized and summed. Stress and voltage limitation were set as constraint condition. The torque and iron loss in the figure are displayed in a form resulting from the operating point values being normalized and averaged.
Fig. 8-2 shows the modified rotor dimensions. Analysis was performed for a low speed of 1,200 rpm and a high speed of 9,000 rpm, and current phase was defined as design parameter.
Fig. 8-1 Torque and iron loss One point represents one geometry.
The basic characteristics of the motor have been determined from determining the stator geometry and rotor geometry. Next, rotor step skew is investigated in order to suppress cogging torque, torque ripple, and current ripple. Without losing basic characteristics, only harmonics are minimized. In order to optimize skew angle, cogging torque and induced voltage are computed with JMAG-Designer at multiple skew angles. Similarly, using parametric analysis the slot opening is investigated. .
Fig. 9-1 shows the cogging torque waveform when changing the step skew from 0 to 3.75 degrees with a slot opening of 5 mm. The number of steps is 2. Analysis was carried out in a 2D model, and the influence of skew was evaluated by varying the phase of the torque waveform (the skew graph function in JMAG-Designer). From the results it was found that a 1/2 slot pitch (3.75 degrees) exhibits the peak minimum value. Fig. 9-2 shows a no-load induced voltage waveform. From applying skew, it can be seen that slot harmonic components are eliminated.
Next, the cogging torque (Fig. 9-3) and the induced voltage waveform (Fig. 9-4) are shown when changing the slot opening from 0.5 mm to 5 mm. From Fig. 9-3 and Fig. 9-4, it could be seen that by reducing the slot opening, it is possible to further reduce the cogging torque and the induced voltage harmonics components.
Details of the tooth shape were determined by this investigation.
Fig. 9-1 Skew angle and cogging torque
Fig. 9-2 Skew angle and induced voltage waveform
Fig. 9-3 Slot opening width and cogging torque
Fig. 9-4 (a) Whole waveform
Fig. 9-4 (b) Enlarged
Fig. 9-4 Slot opening width and back EMF waveform
#10 Magnet demagnetization and coercive force distribution
The detailed geometry is determined, then the resistance to demagnetization of the magnet is evaluated. If there is a problem improvements are made. As a countermeasure to increase the coercive force, Dy is concentrated in regions internal to the magnet where demagnetization is likely to occur. Magnet temperature is raised and lowered to confirm whether thermal irreversible demagnetization does not occur. Parametric analysis is performed while changing temperature using JMAG-Designer.
As a result, it is confirmed that when the magnet temperature is set at 150 deg C, demagnetization did not occur even at the maximum load current of 250 A (Fig. 10-1). Demagnetization was confirmed when the temperature was raised to 180 deg C (Fig. 10-2).
Although this can be avoided by increasing the coercivity in the vicinity of the magnet surface (Fig. 10-3), in this case study this strategy is not taken due to costs.
Since JMAG-Designer can define the distribution of coercive force, it is possible to investigate Dy diffusion magnets.
Fig. 10-1 Demagnetization distribution (magnet temperature: 150 deg C)
Fig. 10-2 Demagnetization distribution (magnet temperature: 180 deg C)
Fig. 10-3 Coercive force distribution
#11 Motor characteristics for control design
At this stage the motor specifications are determined and prototyping is possible. However, with this workflow rather than prototyping the development will proceed on a model basis. On the other hand, in control the control parameters are matched as in subsection #12.
In order to perform detailed control design, highly accurate motor characteristics are required. JMAG-RT sends inductance, flux linkage, torque, etc. resulting from taking magnetic saturation and spatial harmonics into account, as motor characteristics to control design. (Fig. 11).
Fig. 11 Motor characteristics sent to the control simulator
On the control side, parameter conformance is performed using detailed motor characteristics sent from the motor design side (Fig. 12-1). While changing the control gain, the responsiveness to the speed command and torque command is measured, and the control gain (speed Wm, current Wc) for the shortest response time is searched (Fig. 12-2).
Figure 12-3 shows simulation results from changing the combination of speed control gain and current control gain for a current/speed control model. From the combination of parameters control may or may not be possible, and even if possible the responsiveness may be low. Targets set are 200 Nm and 1,200 rpm. The results show that the combination of a Wc of 1,000 and a Wm of 100 is good.
Parameter conformance is a very time-consuming task, as the number and combination of suitable parameters and the number of operating points to be conformed to are large. Conformance is carried out using the motor characteristics generated by JMAG-RT, without waiting for an actual prototype.
Fig. 12-1 Simulation model
Fig. 12-2 Gain combinations used for conformance
Fig. 12-3 Conformance results
#13 Efficiency map
Prototyping act as the input for efficiency maps, but the workflow itself uses virtual prototypes and evaluates motor characteristics. This is similar to evaluating a real prototype, but it is still required to check whether a real prototype can actually obtain the characteristics as planned in the basic design stage. Modeling begins by basing the form of the virtual prototype as close as possible to an actual machine prototype, and then evaluating its performance. It is mainly important that the more detailed parts that are modelled, and that actual drive conditions including detailed geometries and control are reproduced.
Fig. 13-1 shows an efficiency map made by obtaining loss from repeated calculations of FEA that use the current waveform as input. The current waveform is from the actual run time that accounts for control and the circuit at each operating point. Iron loss is calculated as post processing. Eddy current of magnet and coil are calculated. Each time the efficiency map is evaluated during basic design stages, sinusoidal current is assumed and the effect of the eddy current is not accounted for. Fig. 13-1 additionally displays a classification of loss at 4 representative operating points. Copper loss is the highest at point A because due to high load at a large flowing electrical current. Point B maintains a large proportion of iron loss because the PWM carrier frequency component is largely included at a low speed and a low load. It is understood that at Point C and Point D, rotation speed increases and AC loss grows larger. Fig. 13-2(a)-(f) displays the loss density distribution of each part. Stray load loss that is generated in detailed parts such as the press ring is also evaluated here.
Eddy currents are primarily due to magnetic flux leaking from the core surface of the press ring, rotor, stator surface, and coil end parts. Loss is generated whenever magnetic flux has an axial component in the air gap. Fig. 13-2(f) displays the relationship between flux the frequency component and loss density. The carrier frequency component flux line and loss density distribution are shown in the stator core and the magnet part. Virtual prototypes make it possible to both visualize and analyze the main causes of loss generations.
Fig. 13-1 Efficiency map during prototype and verification stages
Fig. 13-2 (a) Press ring
Fig. 13-2 (b) Magnet
Fig. 13-2 (c) Rotor core
Fig. 13-2 (d) Stator core
Fig. 13-2 (e) Coil end
Fig. 13-2 (f) Magnetic flux frequency component and loss density distribution
The magnet’s resistance to demagnetization is evaluated, and the temperature of the magnet is measured for this purpose. This involves estimating the demagnetizing temperature of the design stage. In order to avoid that temperature in actual drive conditions, all necessary cooling functions are estimated also.
This is where worst-case scenarios are considered and the operating condition where the eddy current generates in the magnet is maximized (Fig. 14-1). It is necessary that the maximum temperature is suppressed to less than 150 deg C, which is the temperature where demagnetization resistance deteriorates. For this temperature management, it was assumed that 120 W of heat was removed from the rotor surface, and it is necessary that a cooling system satisfies this.
Fig. 14-2 displays magnet loss density. Loss is concentrated on terminal parts of the magnet due to the skin effect. On the other hand, magnet internal temperature rises inside parts (Fig. 14-3) because the magnet is buried in the rotor core, making it difficult to cool directly. For this evaluation, magnetic field, loss, and temperature are analyzed in linked analyses using JMAG-Designer.
The effectiveness of oil that flows throughout the inside of the motor case as a coolant is additionally confirmed. Fig. 14-4 displays the analysis results of oil flowing throughout the case interior. The outflow inlet as well as areas where the oil is able to come into contact with the rotating shaft with ease can be understood. Fig. 14-5 displays the results of calculating the temperature with JMAG by mapping the heat transfer coefficient obtained from this result. Although differences in temperature can be seen in areas immersed in oil and those less immersed in oil, it is understood that even coils that generate large amounts of heat are suppressed to 60 deg C. Because the temperature of the magnet at this time is at approximately 30 deg C at even the largest parts, this confirms the effectiveness of using oil for cooling. Particleworks by Prometech Software was used in addition to JMAG for this evaluation to calculate the heat transfer coefficient caused by oil flow as well as for calculating the actual oil flow itself.
Fig. 14-2 Loss density distribution at maximum magnet loss
Fig. 14-3 Magnet internal part heat distribution This displays the magnet surface and internal cross section temperature distribution (longitudinally and latitudinally).
Fig 14-4 Oil flow in motor case
Fig. 14-5 Temperature distribution
Example provided in cooperation with: Prometech Software, Inc.
#15 Torque ripple
Confirming cogging torque is shown in Fig. 15-1, and was able to fall below the level as intended during the design period. This design was constructed in 2D, where magnetic flux in the axial direction is considered to have not been taken into account. Torque ripple during operation (6,000rpm, 80N･m; operating conditions) is also confirmed. The magnetic saturation here additionally includes the effects of inverter switching.
Fig. 15-1 shows a comparison of cogging torque investigated during the design stage (2D model) and the cogging torque during the prototype stage (3D model). Cogging torque is predicted to be higher in the design stage. Fig. 15-3 shows gap magnetic flux density distribution with no step skew during 2D analysis compared with 3D analysis, where the difference in the size of magnetic flux density between slots can be understood. Magnetic flux is only in the magnetic circuit plane in 2D, but crosses the gap between teeth in 3D, and is thought to generate differences similar to those in Fig. 15-2.
Fig. 15-3 shows torque ripple when a 3D model with a step skew is driven by the inverter with a current that includes the harmonic component. Step skew results from the design stage can be confirmed in a 3D analysis.
This is where a medium-sized analysis model is calculated in many steps. Parallel calculation such as MPP and SMP, etc., is used for this.
Fig. 15-1 A comparison of cogging torque between the design stage and the verification stage
Fig. 15-2 Gap magnetic flux density distribution
Fig. 15-3 Torque ripple during running time with an inverter
#16,17 Electromagnetic force distribution and eigenmode
To evaluate only the vibration of the motor alone, an analysis of electromagnetic force is carried out first (Fig. 16). A harmonic analysis of the electromagnetic forces on the teeth is run in JMAG Designer, and the electromagnetic force component can be visualized for various frequencies.
In order to next confirm resonance, eigenmodes are calculated (Fig. 17), and radiated sound is evaluated last. Vibration and sound pressure is calculated here by mapping the electromagnetic force distribution obtained through magnetic field analysis to a structural analysis in JMAG-Designer.
Fig. 17 Eigenmode of the stator as the source of sound radiation
#18 ECU control test
ECU control tests are carried out to validate system quality. Unexpected conditions are also implemented for assumed test cases to confirm that there have been no omissions or insufficiencies. Motor responsiveness is confirmed when IGBT is both opened and closed, and during both low temperatures and high temperatures, whereas HILs is used for safe tests and automation. These are conducted using highly accurate plant models generated from JMAG-RT.
Fig. 18 display typical test items and test results where JMAG-RT and Simulink are used, confirming that there is no current overflow due to simulated IGBT failure when either permanently opened or closed. Testing during real machine failure is both dangerous and comes at an additional cost, but Model-Based Development make safe and effective tests entirely possible. Motor behaviour each time a high torque of 170Nm is applied during run time is the next item to be confirmed. The inverter maximum current (250A) does not cause conflict even with a load of 170Nm for a single second, and it was confirmed that speed quickly returned to the command value. The command value could be followed without delays even when changing the temperature. This confirmed that no problems had occurred.
A plant model generated from JMAG-RT is used here for simulations. For JMAG-RT models to reproduce magnetic saturation and spatial harmonics in detail, motor behaviour is recreated equal to that of a real machine. This includes recreating unstable drive conditions. JMAG-RT models can additionally use various HILs as-is.
Test items
Results
1. Confirming safety during IGBT failure (open)
2. Confirming safety during IGBT failure (closed)
3. Behavior during high load torque
4. Confirming controllability during -100 deg C motor temperature
5. Confirming controllability during 100 deg C motor temperature
Fig. 18 Typical test items and test results for ECU control.
#19 Verification of cruising distance
This confirms whether the required drive autonomy can be run again using the final motor. Motor efficiency is lower than at the time of the basic design, so the influence that this has during drive autonomy is checked.
In Fig. 19-1, the run simulation in #01 was performed again, changing the efficiency map, and the efficiency map uses phenomenon that occurred at #13. Drive autonomy is shortened due to a decline in motor efficiency, and yet running for over 400km is still confirmed to be possible. Fig. 19-2 displays these differences in efficiency when driven.
Fig. 19-1 Run distance and battery power history
Fig. 19-2 Motor efficiency during JC08 mode running time
#20 Vibration of Gearbox
Vibrations that did not cause issues with the motor alone will also change the eigenvalue and the behavior of the vibration as the system itself is built. Here is where motor electromagnetic forces cause vibrations, so analysis is carried out by mapping the stator electromagnetic force distribution in an NVH simulation model.
Not just motors, but geometry that includes gearboxes and inverters are used to evaluate vibrations. The source of vibrations in the gearbox are gear transmission errors and the motor electromagnetic force. Fig. 21‐2 shows vibrations with speed on the hozirontal axis and acceleration on the vertical axis. The NVH simulation model for each operating point is given the motor teeth radial electromagnetic force and torque ripple. It was confirmed that the 1st stage transmission error and 48th order motor excitation modes from Fig. 21-2 were at 5,712rpm and 3,024rpm respectively. Fig. 21-3 and Fig. 21-4 show the mode of vibration. The vibration analysis with gearboxes is done using JMAG and Romax-Designer.
Fig. 20-4 Mode of vibration 48th order motor excitation, 3,024 (rpm)
Example provided in cooperation with: Romax Technology
#21 Evaluation of Vibrations when Running Vehicle
A simulation is conducted that includes the entire vehicle body as part of evaluating the vehicle when it is being run.
This evaluation of vehicle vibrations includes not just the motor, but also components such as the inverter and the battery (Fig. 21-1). The source of these vibrations is the motor torque ripple. Because actual vibrations transfer through the shaft and the driver’s seat, etc., and to the driver of the vehicle themselves, vibrations in the acceleration of the driver’s seat are evaluated on a vertical axis with time displayed on a horizontal axis. Here, the motor is made to reach a target velocity with ease via rotation speed of 0rpm to 2,000rpm within a period of 3 seconds (approximately 28km/h). As shown in Fig. 21-2 and Fig. 21-3, it is understood that a significant amount of vibration is generated in the driver’s seat for a period of 1.5 seconds when the vehicle is stopped. Simulating vibrations from when the vehicle is being run is realized through the use of JMAG, Adams, Simulink.
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