Thermal degradation of traction
machines
Project – DYMEDEC
PhD fellow: Zhe Huang
Project manager: Azra Selimovic
Supervisors: Mats Alaküla, Avo Reinap
Project info.
 Dynamic modeling of cooling for electrical drives (DYMEDEC)
 Industrial PhD project
 2013-09-01 to 2016-05-31
 3 088 500 SEK
 Partners: Volvo Group and Lund University
 Network: BEVI, SP, Chalmers, Dahrentråd...
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Project goal and approach –
Dynamic thermal and lifetime model
T*,*
Vehicle
and drive
cycle
Powertrain
model
Electrical
machine
losses
Electrical
machine
cooling
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Qheat
Thermal
model
Qcool

Life model
Degradation model and test verification
Off line learning phase
Identify critical
failure modes
Implement
Impelement
degradation
measurement
Choose
degradation
parameters
Possible test object
(full machine/
motorrete/
bearing...)
Build degradation model
Data collection
and
postprocessing
Implement stress
Impelement
stress
(lab environment:
current induced
thermal cycle)
EM Degradation
model
Usage of degradation model
Load cycles
On line data
monitoring
Design EM
with the
required life
span
RUL
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Off line condition
monitoring in
workshop
Detailed diagnostics
+ schedule spare
part
Results
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Conclusions
 Thermal and degradation model
-
Inputs: driving cycle, vhecile specifications, defined powertain
Outputs: temperature distribution while driving, expected lifetime
Design traction machine with required lifetime
 Accelerated lifetime test
-
Improved test method
Stator segment VS single material
Electrically heated winding and liquid cooling VS oven heating
 Thermal-mechanical model
-
Impregnation used to enhance the heat transfer induces mechanical stress to winding
coating (primary insulation) under thermal stress
 improve the degradation model (thermal-mechanical stress VS insulation lifetime)
 Condition monitoring model
-
Global physical properties changes due to local insulation material aging
 improve the degradation model (usage time VS insulation quality)
 Prognostic measurement – State of Health (SOH)
-
Povide quick health check of the traction motor before vehicle start daily
Simple hardware and software requirement (high frequency logging device needed)
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Publications
 Zhe Huang, Francisco J. Marquez-Fernandez, Yury Loayza, Avo Reinap, and Mats Alaküla.
"Dynamic thermal modeling and application of electrical machine in hybrid drives."
International Conference on Electrical Machines (ICEM), pp. 2158-2164. IEEE, 2014.
 Zhe Huang, Avo Reinap, and M. Alaküla. "Predictive monitoring of turn-to-turn insulation in
single tooth coils." 10th International Symposium on Diagnostics for Electrical Machines,
Power Electronics and Drives (SDEMPED), IEEE, 2015.
 Zhe Huang, Avo Reinap, and M. Alaküla. “Degradation and Fatigue of Epoxy Impregnated
Traction Motors Due to Thermal and Thermal Induced Mechanical Stress – Part I: Thermal
Mechanical Simulation of Single Wire due to Evenly Distributed Temperature”, 8th
International Conference on Power Electronics, Machines and Drives (PEMD), IET, 2016.
 Zhe Huang, Avo Reinap, and M. Alaküla. “Degradation and Fatigue of Epoxy Impregnated
Traction Motors Due to Thermal and Thermal Induced Mechanical Stress – Part II: Thermal
Mechanical Simulation of Multiple Wires due to Evenly and Unevenly Distributed
Temperature”, 8th International Conference on Power Electronics, Machines and Drives
(PEMD), IET, 2016.
 Zhe Huang, Avo Reinap, and M. Alaküla. “Dielectric Properties Modeling and Measurement
of Single Tooth Coil Insulation System under Accelerated Degradation Test”, International
Conference on Electrical Machines (ICEM), IEEE, 2016, full paper submitted.
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Thanks for your attention!
Thermal model
Detailed Loss Model
Mechanical Dynamics Model
Detailed Thermal Model (1)
Detailed Thermal Model (2)
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Lifetime model
10
5
• Degradation caused by thermal stress
4
Life Time [hours]
Life time versus constant thermal stress
- 𝐿 = 𝐴𝑒 𝐵/𝑇
- Hot spot temperature in winding is the critical
parameter for lifetime
10
10
3
2
10
150
• Life time definition
- Standard defines L50
-
From L50 to Ln...
Cumulative distribution function shape factor 𝛽
𝐹 𝑡 =1
𝑡 𝛽
− 𝜂
−𝑒
• Temperature cycle counting algorithms
- Mean edge (MIL Handbook)
- RainFlow
(Fatigue estimation; consider the deepest cycle)
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Polyurethane TI155
Polyester TI180
Polyesterimide TI200
Polyester&Polyamide-imide TI220
200
250
o
Temp [ C]
300
Lifetime model – results
Diving Cycle
US06
Thermal Index
[°C]
155
180
200
220
Life time by Mean edge
[hours]
4 773
24 389
124 741
935 644
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Life time by RainFlow
[hours]
2 495
20 550
105 104
1 005 422
Test set up
Off line learning phase
160
140
Identify critical
failure modes
120
100
0.025
Choose
degradation
parameters
Implement
Impelement
degradation
measurement
0.02
0.015
0.01
0.005
0.08
0
0.04
0.1
0.12
0.06
0.14
Possible test object
(full machine/
motorette/
bearing...)
 High voltage DC –
leakage current
(transient: time
constant; steady state:
insulation R)
 Low voltage AC –
impedance (insulation
C)
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Implement stress
Impelement
stress
(lab environment:
current induced
thermal cycle)
Data collection
and
postprocessing
Improve model step 1 – Thermalmechanical stress VS aging
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Improve model step 2 – Predictable
properties change VS aging
Single tooth winding under test
Differnt winding configurations and fill factors
Results – Insulation resistance
developement during degradation:
analytical modeling VS measurement
Analytical modeling
for
condition monitoring
Results – Insulation resistance at different
aging time of EM and coating thickness
Capacitance (phase to ground) change
during degradation: measurement at
different temperature cycle stresses
FEA modeling for
condition monitoring
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Degradation model application – Traction
machine prognostics
 Theory
- Traction machine on-line condition monitoring (State of Health) by parasitic capacitance
changes with machine aging
 Meaurement set-up
- On a PMSM (hybrid car traction)
- Fault case: phase and ground failure

-
Results
Method validation by lab equipment
Small influence by rotor position – lock rotor
Up to 10 Mhz logging and current sensing requirement -> higher than control need but can be
shared with battery diagnostic
Current response with and without phase to ground fault,
in time domain (left) and in frequency domain (right)
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PMSM under measurement