Real-Time Monitoring of Thermal Response and Life-Time ...
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Real-Time Monitoring of Thermal Response andLife-Time Varying Parameters in Power Modules
Christoph H. van der Broeck∗,∗∗, Timothy A. Polom∗∗, Robert D. Lorenz∗∗ and Rik W. De Doncker∗∗Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University
Jaegerstrasse 17-19, 52066 Aachen, Germany, Email: [email protected]∗∗ Wisconsin Electric Machines and Power Electronics Consortium (WEMPEC), University of Wisconsin-Madison
1415 Engineering Drive, 53706 Madison, WI, Email: [email protected]
Abstract—This paper introduces new technologies for mon-itoring thermal response and life-time varying parameters ofpower electronic modules in real-time without interruptingnormal converter operation. The technologies are embeddedin an adaptive thermal observer that integrates temperaturemeasurements and electrothermal models to estimates devicelosses and temperatures at reliability-critical locations of thepower module. The observer structure includes a model ref-erence adaptive system (MRAS) for adaptively calibrating de-vice switching-loss models. This improves thermal monitoringaccuracy, avoids time-consuming off-line calibrations and createsthe opportunity for efficiency estimations during power moduleoperation. The adaptive observer together with small-signalloss-excitation and system-identification technologies extract thethermal impedance of the power module in magnitude andphase over multiple frequency decades. This in-situ thermalimpedance spectroscopy (ITIS) has the unique feature of con-tinuously providing information on the thermal interface of thepower devices without interrupting normal inverter operation.The extracted information, i.e. life-time varying electrical andthermal parameters, enable diagnosing power module state-of-health with improved accuracy compared to prior technologies. Iteven allows separating different degradation effects, such as bondwire lift-off, device and base-plate solder delamination as well asdeterioration of the convection process. With these new features,next generation monitoring systems can extract thermal responseand degradation and thus protect power modules from severethermal stress and overload. Furthermore, the obtained state-of-health information can be selectively used to reduce thermalstress and to schedule predictive maintenance leading to a saferand more reliable operation of future converter systems.
I. INTRODUCTION
The power density of power electronic systems has in-creased significantly over the last decades due to numerousimprovements in the multi-physics integration of power de-vices [1], [2]. Continuing this trend is of great importance forthe development of next generation electric vehicles [3], [4].However, an even stronger miniaturization of power electronicmodules without jeopardizing reliability and safety is onlypossible with a simultaneous development of electrothermaland degradation monitoring systems [5]–[12]. They guaranteereliable and safe converter operation by real-time monitoringand control of thermal stress [13]–[21], early diagnosis ofcritical degradation [9], [11], [22], [23] and timely initiationof predictive maintenance [24]–[27].
For the real-time electrothermal monitoring of power elec-tronic modules, observers are effective tools that can esti-mate temperatures and device losses [15], [18], [28]–[30].Observers, which have been used in power converters and
drives for decades [31]–[33], were first proposed for elec-trothermal monitoring of power electronic devices in [8]. Inthis application, they combine junction temperature informa-tion with electrothermal real-time models to estimate losses,junction temperature, and 3-D distributed temperatures, e.g.at critical module interconnects, with minimal sensitivity tomodel inaccuracies [18]. The junction temperature informa-tion is either obtained on the basis of temperature-sensitiveelectrical or optical parameters [34], [35], or it is measuredwith on-chip temperature sensors [36]. Depending on theapplication, a wide range of thermal real-time models canbe applied in observers. For estimating junction temperaturesand device losses only, a 1-D Cauer model [17], [37] mayprovide sufficient information and accuracy. However, if 3-D distributed temperatures shall be estimated in real time,the combination of finite difference modeling [38] and modeltruncation techniques [39]–[41] yield compact and precisereal-time models. The required model derivation process isdiscussed in [30], [42], [43].
This work presents an adaptive thermal observer derivedfrom [18] that monitors instantaneous, i.e. switching periodaveraged, and excitation period averaged device losses andtemperatures with nearly zero lag and minimal sensitivityto model inaccuracies . The instantaneous device losses andtemperature estimation is essential for a high-bandwidth elec-trothermal monitoring system that is required for precisedata acquisition and fast thermal overload protection [8],[15], [28], [44]. The excitation period averaged device lossesand temperature are key control variables for active thermalcontrol of power electronic inverters and must be free of lag[14], [21], [45], [46].
Inaccuracies of device loss models are a strong limitation ofall loss and temperature estimation methods. Unlike thermalmodels of fluid cooled power modules, which can be derivedwith a comparatively high accuracy of approximately 5 %[47], [48], the extraction of accurate switching loss modelsis difficult: The precise measurement of switching transientsand consequent extraction of switching loss energies overthe entire operation range of a power module requires ahigh expertise on modeling measurement equipment [49].However, even cautious state-of-the-art modeling results ina best case accuracy of 20 % [50]–[52]. Furthermore, thedevice characteristics and parasitics of different converters thatare realized with the same type of power module vary [53].This means that a generic loss model must be calibrated for
every power converter individually. The resulting process istime consuming and hence not feasible for a fast majority ofapplications. Thermal observers can intrinsically compensateinaccuracies of device loss models within their feedback band-width. However, above the observer feedback-bandwidth, anydevice-loss inaccuracies results in deviations of the tempera-tures estimates [18]. To overcome this limitation, a adaptivedevice loss model is introduced in this work that utilizes amodel reference adaptive system for self-calibration.
Another important scope of this work lies in monitoringthe thermal interface characteristics of the power moduleand consequently diagnosing degradation mechanisms. State-of-the-art approaches have identified the static junction-to-ambient thermal resistance as an indicator for delaminationprocesses [5] that can be extracted online according to [54],[55]. However, their accuracy is bounded by the previouslyaddressed poor precision of most device loss models. Also,these techniques are unable to distinguish different degrada-tion mechanisms. The transient thermal impedance Zth pro-vides more in-depth information about localized delaminations[56]–[59]. It is estimated either by external excitation with anadditional circuitry [41] or by generating a load current stepvia the converter control algorithm according to [58], whenthe normal converter operation interrupted. However, none ofthese approaches can extract the transient thermal impedancewithout interrupting the normal converter operation.
To address these limitation, this work proposes a newmethod for extracting the thermal impedance of the powermodule in the frequency domain referred to as in-situ thermalimpedance spectroscopy. In-situ thermal impedance spec-troscopy (ITIS) identifies the magnitude and phase of thepower module thermal impedances over multiple frequencydecades without interrupting normal converter operation. Itapplies periodic small-signal device-losses and subsequentlyextracts the excitation-response via temperature sensing andfiltering to reconstruct the thermal impedance of the powermodule. As the thermal impedance can be extracted withnegligible error, it allows separate identification of differentdegradation mechanisms [60], e.g. die-attach or DCB-attachsolder layer due to fatigue [61] as well as a deteriorationof the convection process [62]. The extracted state-of-healthinformation allows operating the converter at reduced stresslevels if a critical degradation is reached , e.g. by reducing therated power or by active thermal control [7], [8], [13], [14],[63]. Furthermore, the degradation information can be used toschedule a predictive-maintenance of power converters [24]–[26] to ensure in-time replacement of critical components.
This paper, which is based on the work in [64] and [65],is organized as follows: Section II reviews the electrothermalmodels and applicable junction temperature measurement con-cepts that are the basis for the thermal and state-of-healthmonitoring structure. In Section III, the thermal observerstructure is presented that estimates losses and 3-D distributedtemperatures. Section IV shows how the thermal monitoringsystem can be enhanced with an adaptive switching losscalibration yielding more precise thermal monitoring. SectionV introduces in-situ thermal impedance spectroscopy (ITIS)
Fig. 1. Hybridpack2 (HP2) power module (left) and meshing of its finitedifference model (right)
and illustrates how it can be used to track life-time varyingthermal parameters and diagnose degradation in power mod-ules. Finally, in Section VI and Section VII the performanceof the monitoring system and the self-sensing algorithms areevaluated in simulations and experiments for the Hybridpack2(HP2) power module (FS800R07A2E3B13) depicted in Fig.1.
II. ELECTROTHERMAL MODELING AND JUNCTIONTEMPERATURE MEASUREMENT
This section introduces the electrothermal model and thejunction temperature measurement that are used for this work.
A. Thermal Model
A key component of the thermal monitoring system is thethermal real-time model of the power module. This workuses 3-d finite difference modeling (FDM) to subdivide apower module into small cubes, as illustrated in Fig. 1. Thethermal characteristic of each cube is represented by a thermalcapacitance and thermal resistances to its neighbors, resultingin a large thermal equivalent circuit represented by Fig. 2. Theentire thermal equivalent circuit of the power module can besummarized in a state space model (1).
dθ
dt= A · θ +B · P loss T out = C · θ (1)
The model (1) requires the device losses P loss, the transitionmatrix A, the input matrix B and the output matrix C tocalculate the temperature vector T out. The output temperaturevector T out includes temperatures at all four devices, tem-perature nodes below the devices at the device solder layer,at the DCB-to-baseplate solder layer, at the NTC and thespatially-averaged base plate temperature. These local nodesallow monitoring temperatures of critical material interfacesand are visualized in Fig. 3.
With model reduction techniques, in this case the balancedtruncation method, the state space model is significantlyreduced in size such that is applicable as a real-time model.The final model is characterized by the matrices A, B andC. For the HP2 power module in Fig. 1, the final model has14 states and can estimate the thermal behavior accuratelywithin a bandwidth of 100 Hz. The model is discretized withthe zero-order hold method [66], [67] with a sampling timeof Ts = 1 ms to make it applicable on a digital signalprocessor. The sampling time of Ts = 1 ms achieves anaccurate discretization if the losses do not vary significantlyover one sampling time, as discussed in [42]. A more detailed
p
Dz
bRth,d
bRth,l
qloss
aRth,r
aRth,baRth,u
aRth,d
aRth,f
aRth,l
Cth
bRth,r
bRth,u
m,n,pT
x
y
z
m-1,n,pT m+1,n,pT
m,n+1,pTm-1,n+1,pTm+1,n+1,pT
m,n-1,pTm-1,n-1,pT m+1,n-1,pT
mDx m+1Dxm-1Dx
n-1
Dy
nDy
n+1
Dy
Fig. 2. Excerpt thermal network resulting from application of FDM
Device Area IGBTA DiodeA DiodeB IGBTB
Device
Upper Sd Layer
Lower Sd Layer
NTC Base Plate
Si
Sd
Cu
Al O2 3
Cu
Sd
Cu
H O2
AC
DC+
DC-
Fig. 3. Monitoring nodes of the extracted thermal real-time model depictedfor one phase of the power module
derivation of the electrothermal model can be found in [29],[41], [42] and [18].
B. Loss Model
Next, a model for the prediction of instantaneous andaveraged losses of the IGBT and the diode is presented basedon [42].
1) Instantaneous loss model: First, the conduction lossesof the IGBT and the diode are addressed. They depend onforward voltages and currents as shown in (2).
P IGBTcond = vIGBT
ce (ice,Tj) · ice and PDiodecond = vDiode
T (id,Tj) · id(2)
The forward voltages vIGBTce and vDiode
T are modeled based onextracted data sheet parameters from [68], summarized in Tab.I. They are approximated as a function of the device junctiontemperature Tj and the device currents ice and id via (3).
vIGBTce (ice,Tj) = Vce(Tj) +Rce(Tj)ice + Sce(Tj)
√ice
vDiodeT (id,Tj) = VT(Tj) +RT(Tj)id + ST(Tj)
√id (3)
TABLE ICONDUCTION LOSS MODEL PARAMETERS OF THE HP2
Tj Vce Rce Sce VT RT ST
25 °C 0.54 V 0 Ω 0.03 V/A0.5 334 mV 0 Ω 0.06 V/A0.5
125 °C 0.31 V 0.2 mΩ 0.04 V/A0.5 222 mV 0 Ω 0.06 V/A0.5
TABLE IISWITCHING LOSS MODEL PARAMETERS OF THE HP2
E0 K0 α β KT Err0 Krec
0 KrecT
1.2 mJ 0.1 mJ/A 1.75 0.82 1 µJ K−10.5 mJ 4.4 µJ A−1 0.02 K−1
∗for V refdc = 400 V , Rref
g = 2.2 Ω and T ref = 20 °C
The switching losses of the IGBT and the diode are predictedwith parametric loss models given by (4) and (5) as a functionof the device currents ice and id, the dc-link voltage Vdc, thegate resistance Rg and the device temperatures differences∆Tj with respect to the reference temperature T ref
j . Theparameters of the model are summarized in Tab. II and werederived in [42].
P IGBTsw =
(E0 +K0ice
(Vdc
V refdc
)α(Rg
Rrefg
)β+ ∆TjKT
)· fsw
with ∆Tj = Tj − T refj (4)
PDsw =Erec
0 +Krec0 ice
(Vdc
V refdc
)α(Rg
Rrefg
)−β
(1 + ∆Tj ·KrecT ) · fsw
with Erec0 = Err
0 ·Vdc
V refdc
(5)
2) Averaged loss model: Based on the instantaneous lossmodel, a model for the averaged device losses is derived byintegration of (2), (4) and (5) over one excitation period T0of the current resulting in (6) - (9).
P IGBTcond = VceI
(1
2π+M cos(ϕ0)
8
)+RceI
2
(1
8+M cos(ϕ0)
3π
)+ Sce · I
32 · (0.139 + 0.1144 ·M cos(ϕ0)) (6)
PDcond = VTI ·
(1
2π− M cos(ϕ0)
8
)+RTI ·
(1
8− M cos(ϕ0)
3π
)+ ST · I · (0.139− 0.1144 ·M · cos(ϕ0)) (7)
P IGBTsw =
(E0
2+K0
πI
(Vdc
V refdc
)α(Rg
Rg
)β+
∆TjKT
2
)· fpwm (8)
PDsw =
Erec0
2+Krec
0
πI
(Vdc
V refdc
)α(Rg
Rrefg
)−β
· (1 + ∆TjKrecT ) · fpwm
(9)
The averaged losses can be computed based on the modulationindex M = V /(Vdc/2), the current amplitude I , the loadangle ϕ0, the gate resistance Rg, the device temperatures Tjand the parameters in Tab. I and Tab. II.
C. Junction Temperature Measurement
The monitoring technologies that are developed in this workrequire to measure junction temperature. Various junction tem-perature extraction methods have been proposed in literature:Temperature sensitive electrical parameters (TSEPs) can bemeasured, e.g. with smart gate drivers, to extract junctiontemperature information [34], [69]. Examples of TSEPs aredevice forward voltage [70]–[73], gate current [74], [75],turn-on delay [76], [77], gate resistance [78] or gate voltage
Power Device
Power Module
Measurement Delay
Measurement Noise
Thermal Interface
Loss Estimation
Adaptive Loss Model Identification
Thermal Observer
Section IV
Section V
Section III.
Converter
Operation
Ki
K (I)i
Tspatial
T (k)spatial
T (k)spatial
T (k)spatial
T
T
T
Tj
T (k)j
T (k)j
T (k)j
T (k)j
T (k-n)j
T (k-n)j
T (k-n)j
DTj
DTj
Ploss
DPloss
P (X ,DR )loss conv gate
P (X )loss conv
P (X )loss conv
Ploss
Ploss
Ploss
Ploss
Ploss,S
Ploss,S
X conv
avg
avg
avg
avg
avg
avgavgavg avg
avg avg
t
t
t
t t
t
t
avg
ttt
tt
avgavg
avg
DP (k)loss
DP (k)loss
DPloss
DPloss
DPloss
-1T zs
-1T zs
Vdc
fsw
Dfsw
Dfsw
Rgate
Tj
abcd
DK0
dDK0
dt
DK0
abci
0
Kp
dT
-nz
-nz
-11 - z
-11 - z
k
= AT + B P loss
T (k)= C q (k)
T (k)= C q (k) + Tf
q (k+1)= A q (k)+B P (k) loss
q (k+1)= A q (k)+B P (k) loss
dt
out
out
out
out
d
d
d
d
d
d
Low-Frequency Temperature Estimation
High-Frequency Temperature Estimation
DRgate
DRgate
A
In-situ Thermal Impedance Spectroscopy (ITIS)
Orthogonal Correlation
Tspatial
TjZ (jw )th 0
T
DTj
ex
ex exexex ex
exex
T (k)= C q (k)
q (k+1)= A q (k)+B P (k) loss
out
outd d
d
Excitation-Frequency Temperature Estimation
Kr
-1T (1- cos(w T )z )s 0 s
-1 -11-2cos(w T )z + z0 s
P0P0
Ploss
Ploss
DPloss
DRgate
w0
sin(w t)0
DR (X )g conv
ex
ex
ex
B
B
Fig. 4. Thermal and state-of-health monitoring system for power electronic modules comprising a thermal observer structure (Section III), an adaptive lossmodel calibration unit (Section IV) and a thermal impedance spectroscope (Section V)
[79]. Recent research also investigated the electrolumines-cence of the body diode in SiC devices as a temperaturesensitive optical parameter [80]–[83] Alternatively, on-chipjunction-temperature sensors [36], [84], [85] can be applied.This work utilized a decapsulated power module and high-bandwidth infrared (IR) sensors, which were placed abovethe two diodes and IGBTs of a half bridge, for experimen-tal evaluation. Thereby the complexity of the experimentalsetup was reduced, without any limitation for evaluation andtesting. However, in an integrated converter, the IR sensorsmust be replaced by one the discussed temperature extractionapproaches.
III. THERMAL OBSERVER STRUCTURE
The electrothermal real-time model and the junction tem-perature measurement are combined in a thermal observerstructure depicted in Fig. 4. This section describes the op-eration of the observer that estimates 3-D distributed temper-atures as well as device losses with minimal error. Conse-quently, it is explained how the observer can detect modelerrors that can either be used for calibration or trackingelectrothermal parameters that change due to degradation.
A. Functional Principle
During basic operation of the observer, the adaptive lossmodel identification (red) and the excitation temperature esti-mation (blue) can be neglected, i.e. the indicated switchesA and B in Fig. 4 are open. First, the thermal observercomputes instantaneous and averaged losses P loss and P
avg
loss
based on the operation vector Xconv. In a next step, theaverage losses are subtracted from the instantaneous lossesto determine the transient losses P
t
loss, which exhibit onlyhigh bandwidth loss information. The transient losses andthe averaged losses are passed to two identical instances ofthe reduced-order thermal model to compute the transientand the averaged temperatures within the power module. Thetemperature estimates are summarized in the vectors T
t
out andT
avg
out and its locations are illustrated in Fig. 3.The transient temperature vector T
t
out only exhibits the highbandwidth temperature information, whereas the averagedtemperature vector T
avg
out exhibits only low bandwidth temper-ature information. If both temperature vectors are summed, theinstantaneous temperatures T out are obtained. The junctiontemperature estimates T j, which are embedded in T out, aredelayed by the measurement delay n to ensure consistency and
are compared with the measured junction temperatures T j. Ifthere is an error between the junction temperature predictionand the measurement, this error ∆T j is multiplied by aproportional feedback gain Kp to correct the transient temper-ature estimation. The error is also passed to an integrator withthe gain Ki such that the accumulated error can correct theaverage loss prediction. Thereby, the high bandwidth error isused to correct the transient temperature estimation, whereasany error at low bandwidth including dc is used to correctthe average temperature estimation. This technique, referredto as bandwidth partitioning, has been developed based on theconceptual background of [32], [86].
The maximum bandwidth at which the proportional statefeedback path can correct the temperature and loss estimatesof the model can be adjusted with the feedback gain Kp.By adjusting the ratio between integral and proportionalfeedback gain Kp/Ki, the bandwidth at which the separationof averaged and transient estimates occurs can be changed.Fig. 5 shows that the observer can estimate instantaneous andaverage junction temperature within the integral bandwidthwithout error even if the loss model has errors. Above theintegral bandwidth, model errors lead to small magnitudeestimation errors that moderately grow to the bandwidth fo theproportional feedback path. Above the proportional feedbackpath the magnitude accuracy only depends on the accuracyof the electrothermal model as the observer cannot correctestimates above its feedback bandwidth. Most importantly,over the entire bandwidth the observer estimates temperatureswith nearly zero lag in phase independent of modeling errors.This zero phase-lag property is critical for active thermalmanagement and control [14]. A more detailed analysis anddiscussion of the observer, in particular of its feedback designis provided in [29] and [18].
B. Utilizing Correction Variables for Calibration and Degra-dation Detection
The correction variables, ∆Pavg
loss and ∆Pt
loss of the ob-server support three major tasks:
Firstly, they correct the temperature estimates within thefeedback bandwidth of the observer. This basic task wasdescribed in the previous paragraph.
Secondly, the correction variables allow closed loop lossestimation for power modules whose thermal interface is accu-rately modeled. This is the case for liquid cooled power mod-ules, whose structure and convection interface can be modeledwith good accuracy if it has not experienced degradation.Thus, transient and averaged feedback corrections ∆P
avg
loss
and ∆Pt
loss are almost exclusively caused by inaccuraciesof the device loss model. The closed loop correction of thedevice loss model enables highly accurate loss estimation ofindividual power devices, i.e. IGBTs and diodes Consequently,it can even detect hard- and soft-switched operation of powerdevices.
Finally, the the correction variables can be used to calibratethe device loss model that is used for loss estimation. A cali-bration is desirable as it enables accurate estimation of devicelosses and thus temperatures even above the observer band-width. Thus, in the following section, a method is introduced
0
0.5
1
1.5
2
10-2 10-1 10
Frequency in Hz
0 101 102
-90
-45
0
45
90
-20% Loss Estimation Error+20% Loss Estimation Error
Perfect Loss Estimation
Proprtional bandwidth
Ob
se
rve
rD
esi
gn
Integral bandwidth
Resonator frequency f = 10 Hzex
f = 1Hzb,i
f = 10 Hzb,p
Excitation TemperatureAveraged Temperature
Instantaneous Temperature0.5
1
8 9 10 11 12
8 9 10 11 12
-90-45
04590
Fig. 5. Observer estimation accuracy showing that independent of potentialmodeling errors: A) Instantaneous and averaged junction temperatures areestimated over wide bandwidth with minimized magnitude error and zerophase lag B) Temperature response to loss excitation is separated fromremaining temperature profile and estimated precisely in magnitude and phase
Fig. 6. Observer-based model calibration and degradation detection
to calibrate a parametric loss model and drive the correctionvariables to zero. In Fig. 6 it is illustrated how this calibrationleads to a strong reduction of the correction variables ∆P
avg
loss
and ∆Pt
loss at initial operation. If ∆Pavg
loss and ∆Pt
loss riseagain after long-term operation of the power module, this canbe used to identify fatigue-related degradation of the packageor the power device that are reflected by the electrical orthermal behavior of the power module, e.g. bond wire lift-offs or solder delamination.
IV. ADAPTIVE SWITCHING LOSS MODEL CALIBRATION
This paper adds a loss injection unit and a model referenceadaptive system (MRAS) [87] to the observer for automaticswitching loss model calibration. This is necessary, due tovariation of the switching loss characteristics for differentdevices, gate drivers and modules parasitics. Conduction lossprediction is less critical, as it depends exclusively on thedevice such that off-line calibration or using data sheet valuesare effective options.
Typically, the gate resistance Rg and the dc-link voltage Vdcare not changed during the operation of the power module. Asa consequence, the averaged switching losses of the IGBT andthe diode are approximated, based on (10).
P IGBTsw ≈ K0
π· I · fpwm and PDiode
sw ≈ 0 (10)
The diode switching losses are approximated to zero, asthey are small compared to its conduction losses. Also, it istolerable assumption to neglect temperature dependencies aswell as current independent offsets in the IGBT switchinglosses.
PlossPloss
Ploss Tj
ReZ (w )th 0
ImZ (w )th 0
P sin(w t)0 0
P0 Ploss
Excitation and
Filtering
ThermalImpedance
Analysis
State of Health
Detection
Die Attach Solder
DCB Solder
Convection Process
w0
ex
ex ex
Rth,1
Cth,1
Rth,2
Cth,2
Rth,3
Cth,3
TaTj
Tj
T
Fig. 7. In-situ thermal impedance spectroscopy (ITIS)
The resulting expression (10) has a single tunable parameterK0. For identification of K0, switch A of the observer struc-ture in Fig. 4 is activated. This adds a rectangular excitationsignal ∆fsw(t) at a frequency of f1ex with an amplitude of∆fpwm to the operation switching frequency f0sw:
fsw = f0sw + ∆fsw(t) (11)
Consequently, any error of the estimated loss prediction∆P
avg
loss at this particular frequency f1ex can only occur dueto and incorrect K0. Thus, by correlation of the average lossprediction error ∆P
avg
loss with the excitation signal ∆fsw(t)and subsequent integration, a correction signal is generated.It automatically corrects K0, as it intrinsically drives theestimate of K0 to zero.
This self-calibration of the switching loss model is apowerful tool to obtain more accurate loss models duringinitial operation of the power converter avoiding sophisticatedoff-line calibrations. It makes the thermal monitoring moreaccurate at high bandwidth were the magnitude accuracy ofthe temperature and loss estimates depends on the loss model.
V. MONITORING LIFE-TIME VARYING THERMALPARAMETERS FOR DEGRADATION DIAGNOSIS
A. In-situ Thermal Impedance Spectroscopy (ITIS)
This section introduces in-situ thermal impedance spec-troscopy (ITIS) that extracts the thermal impedance of thepower module Zth(jω) over multiple frequency decades dur-ing normal converter operation. For ITIS, the observer isequipped with a resonant feedback path and operated togetherwith a device loss manipulation unit and a system identifica-tion algorithm.
The principle of the ITIS is illustrated in Fig. 7. In additionto the operation loss Ploss a small sinusoidal loss excitationP exex is injected in the devices that excites a temperature T ex
j .
P exloss = P0 · sin(ω0t) T ex
j = T0 · sin(ω0t+ ϕ) (12)
The load-independent loss excitation can be either generatedby modulation schemes that manipulate the zero sequenceduty cycle to control devices losses. Alternatively, an adaptivegate driver can be used that controls the switching processto manipulate losses. This work used an adaptive gate driverdeveloped in [63]. It uses multiple paralleled gate resistancesthat can be activated and deactivated every PWM period.A model-based loss manipulation algorithm ensures that thecommanded losses are realized by activating a suitable gateresistance combination.
T (k)j
P (k)0
P0
P0
2
2
ReZ (jw )(k)th 0
ImZ (jw )(k)th 0
2
2
P (k)0
Ploss
Ploss
eex
eexQ
w (k)0
sin(w t)0
cos(w t)0
ex
S
S
e (n)ex
e (n)ex
n = k-N
n = k-N
k
k
N
N
1
1
ex
ex Q
(k)
(k)
Fig. 8. Thermal impedance estimation via orthogonal correlation
The junction temperature response Tj that occurs due tothe load profile Ploss and the small signal loss excitationP exloss must be measured. In this work, the measurement is
realized with IR sensor that are placed above the devices of adecapsulated power module. However, in future applicationsTSEP sensing or integrated temperature sensors need to beappled for junction temperature measurement.
In a next step, the small-signal thermal response T exj must
be separated from the junction temperature caused by theoperational loss profile Ploss. This filtering is realized by anextension of the thermal observer structure in Fig. 4 thatis activated by the switch B. The activated third instanceof the thermal model (blue) is fed with the estimated lossexcitation P
ex
loss as a feedforward command, such that itideally estimates the resulting temperature excitation T
ex
j . Aresonant feedback loop, which is frequently used for currentcontrol of converters [67], [88], is tuned to the excitationfrequency ω0. It corrects any deviations of the temperatureestimation at the excitation frequency ω0 with zero amplitudeand phase error, as illustrated in the estimation accuracy plotin Fig. 5, even if the electrothermal model deviates from thephysical system. This observer-based filtering approach hasbeen developed by the authors in a strong analogy to observer-based filters for position self-sensing of electrical machinesthat were published in [89].
Finally, the isolated small-signal temperature T exj and its
excitation P exloss are processed via orthogonal correlation to
obtain the real and imaginary component of the thermalimpedance Zth(jω). The orthogonal correlation method is astandard tool for system identification that is illustrate in Fig.8 and discussed in [90]. By repeating this process for severaldiscrete values of ω0, the frequency response function (FRF)of the thermal impedance Zth(jω) is estimated over multiplefrequency decades. Different from state-of-the-art approachesthat require to interrupt normal converter operation for thermalimpedance extraction [91], this approach extracts the thermalimpedance without interrupting of converter operation. Theresulting information is very important for the state-of-healthdiagnosis of power modules, as it reveals localized degra-dation of the thermal interface of the power module, e.g.delamination or reduced convection [56], [92].
B. Utilizing Zth(jω)) for Degradation Diagnosis
The thermal impedance frequency response functionZth(jω)), which is extracted by ITIS, contains informationabout structural degradation of the power module, i.e. delam-inations of the device and substrate as well as deteriorationof the convection process. This intrinsically embedded infor-mation is illustrated in Fig. 9 showing the thermal-impedanceFRF of a IGBT in a healthy and differently degraded power
|Z(jw
)| in
K/W
th,j
∠(Z
(jw
)) in
°th
,j
Frequency in Hz
Thermal impedance of IGBTA
10-3
10-2
10-1
10-2 100 102 104
-45
0
-90
Normal
Convection Reduction
Die Delamination
DCB Solder Delamination
Fig. 9. Thermal impedance frequency response function plot representingthe thermal characteristics of the HP2 at different stages of degradation
modules. The thermal-impedance FRF is obtained followingthe modeling approach introduced in Section II. For a thermalmodel that includes delaminations, the thermal conductivity ofthe respective solder layer is reduced by 50 %. Similarly, forthe model with a deteriorated convection, the convection heattransfer coefficient is also reduced by 50 %. Both, magnitudeinformation |(Zth(jω))| and phase information ∠(Zth(jω))of the FRF plot reveal individual signatures of differentdegradation mechanisms. A detailed analysis and discussionof these degradation signatures has been conducted in [60]and [62].
Using the degradation signatures of thermal impedanceFRF for degradation diagnosis provides a key advantage.ITIS extracts the phase information arg(Zth(jω)) with zeroerror. This is because the precision of the phase extrac-tion only depends on accurate timing and is not influencedby moderate scaling errors of the measurement. Fig. 9, aswell as [22] show that phase information effectively allowsidentifying and separating different degradation mechanisms.Consequently, future monitoring system can strongly benefitfrom extracting phase information arg(Zth(jω)) with ITIScontinuously during normal converter operation. This enablesaccurate degradation diagnosis that shows minimal sensitivityto model errors.
VI. SIMULATION RESULTS
A. Adpative Switching Loss Model Calibration
First, instantaneous and averaged estimation of the devicelosses and the junction temperatures during adaptive calibra-tion of the loss model are investigated based on the simulationresults in Fig. 10. Initially, the switching loss parameter K0 isset to zero. The loss model calibration is activated at t = 22 s.This adds a rectangular excitation signal with a frequency of8 Hz and an amplitude of ∆fsw = 200 Hz to the nominalPWM frequency of fsw = 7 kHz. Without adaption, theswitching loss model is not accurate and loss deviations occur
T in
KJ
0 10 20 30 40 50 60 70 80-500 -50
0 0
500 50
1000 100
Lo
sse
s in
W
Lo
ss e
rro
r in
W
Losses
Loss Error
0 10 20 30 40 50 60 70 800
50
100
Instantaneous Temperature
Averaged Temperature
0 10 20 30 40 50 60 70 80
Time in s
-0.1
0
0.1
0.2
Ksw
in m
J/A
Estimated Switching loss gain of IGBTA
Fig. 10. Simulation of observer based IGBT junction temperature and deviceloss estimation with adaptive loss model calibration
that are estimated as a loss error by the observer. The averageloss error ∆Ploss corrects the estimation process within theobserver bandwidth and ensures the accurate estimation ofjunction temperature even before the loss model is calibrated.At its activation at t = 22 s, the adaption process startsusing the average loss estimation error ∆P avg
loss to adaptivelydetermine the correct switching loss gain K0. After a timehorizontal of 15 s the calibration process has converged andthe loss estimation error has decayed to zero.
B. In-situ Thermal Impedance Spectroscopy (ITIS)
In a second simulation, illustrated in Fig. 11, ITIS isevaluated during a mission load of an electric vehicle. Duringtransient converter operation, a periodic loss excitation withan amplitude of 30 W is generated by manipulation of thegate resistance [63] of IGBTA. The excitation frequencyis step-wise increased from 0.1 − 10 Hz to excite junctiontemperature response in this frequency range of the IGBT.The resulting instantaneous and averaged junction temperatureestimates are depicted in Fig. 11. They reflect dominantlythe mission load but also exhibit traces of the periodic lossexcitation. At low excitation frequencies one can identifythe temperature response to the excitation signal. At higherexcitations frequencies, the excitation losses are damped toostrongly by the thermal capacitance of the power module,such that the temperature response cannot be distinguishedby eye from the thermal profile that is caused by the missionprofile. However, the observer with its resonant feedbackpath is able to extract the thermal response T ex
j exclusivelyat the excitation frequency with great precision and zerophase lag. Based on this extracted thermal response T ex
j ,which is separated from the thermal response of the powermodule to the mission load, the orthogonal correlation unit canaccurately estimate magnitude and phase of IGBTA’s thermalimpedance at one excitation frequency after another.
0 20 40 60 80 100 120 140 160 180 200
0.1 Hz 0.2 Hz 0.5 Hz 1 Hz 2 Hz 5 Hz 10 Hz
-10
0
10
20F
reqeuncy
in H
z
-100
0
100
0 20 40 60 80 100 120 140 160 180 200-50
0
50
ex
ex
P in W
, T
in K
lo
ss
J
Estimated Losses
Estimated Temperature
0 20 40 60 80 100 120 140 160 180 20050
100
150
T in K
J
Time in s
Instantaneous Temperature
Averaged Temperature
0 20 40 60 80 100 120 140 160 180 2000
0.05
0.1
|Zth
| in
K/W
arg
(Zth
) in
°Magnitude
Phase
Fig. 11. Simulation of observer based in-situ thermal impedance spectroscopy(TIS) with 0.1 − 10 Hz excitation during transient converter operation
aidut
udut
Ri
Cdc Cdut
Lf
bidut
cidut
uidc
didc
Load Emualtor Device under Test (DUT)
udc
*Udc
3 Inductors (0.5mH/0.3mH) Driver Board
XCS 2000 Control Platform Hybridpack2 Power Module
Load emulator converter LT25F IR Sensors
Fig. 12. Experimental setup showing the decapsulated HP2 power modulewith adaptive gate drivers developed in [63] interfaced to a 3 phase loademulator [30]
VII. EXPERIMENTAL EVALUATION
The feasibility of the thermal monitoring concept is demon-strated for a HP2 power module on a three-phase load-emulator test bench, which is depicted in Fig. 12 and in-depth introduced in [30]. The power module was decapsu-lated such that the surface temperature at the center of thepower device could be measured with LT25F IR sensorsfrom Optris. This device surface temperature is used as avirtual junction temperature for the experimental evaluation.The power converter is operated with a gate driver developedin [63], that can dynamically adjust the gate resistances Rg
between 1.8 Ω and 18 Ω The monitoring, system excitationand identification algorithm are implemented in C++ on anXCS2005 rapid control prototyping platform from AixControland are operated with sampling frequency of fs = 1 kHz.
A. Thermal Monitoring
First, the basic functionality of the observer, i.e. monitoringdevice losses and temperatures within the power module, isevaluated over a realistic mission profile that is applied bythe load emulator and depicted in Fig. 13. The observer
Time in s
Tem
pe
ratu
re in
°C
Tem
pe
ratu
re in
°C
Ave
rag
ed
Lo
ss E
rro
r in
WL
osse
s in
WI
in
A, V
in V
, f in
°p
p
f in
Hz
e
0 10 20 30 40 50 60
0 10 20 30 40 50 60
0
200
400
0 10 20 30 40 50 60
20
25
30
0 1 2 3 4 5 6 7 8 9 10
20
25
30
0 10 20 30 40 50 60-20
0
20
40
IGBTA
Instantaneous
DiodeA
Averaged
IGBTA
DiodeA
IGBTA
DiodeA
IGBTA
DiodeA
Lateral Layer
Upper Sd
Lower Sd
Base Junction
Junction Temperature
Measurement
Averaged
Instantaneous
-100 -10
0 0
100 10
200 20
300 30Phase Current
Phase Voltage
Load angleExcitation frequency
Fig. 13. Experimental instanteneous and averaged loss and temperatureestimtion over a realistic load profile applied by a load emulator
Lo
sse
s in
W
Time in s Time in sK
/ K
sw
sw0 0
0 0
50 0.5
1001
20 2040 4060 60
Loss Estimation Error
Averaged Losses
Estimated gain
Low pass filtered gain
Fig. 14. Experimental response of the automatic loss model calibration
provides estimates of the instantaneous and averaged junc-tion temperature with excellent accuracy while rejecting thehigh bandwidth measurement noise. Furthermore, it providesspatial temperature estimates of the upper and lower solderlayer temperature and the temperature of the base plate. Inaddition, the observer provides closed-loop estimates of thedevice losses. The averaged loss-error reflects the part of thelosses that were not accurately predicted by the device lossesmodel, which exhibits deviations of up to 5−15 % dependentof the operation point.
B. Adaptive Loss Model Calibration
The device loss model can be calibrated by adaptive esti-mation of the switching loss parameter K0 in Fig. 14. Duringthe adaptive parameter estimation, the power module wasoperated with a current of 300 A, a constatn dc-link voltageof 100 V, an AC voltage amplitude of 10 V, a load angle of10 °, a frequency of fbase = 20 Hz and an average switchingfrequency of f0sw = 7 kHz. The excitation signal had a fre-quency of f1ex = 4 Hz and an amplitude of ∆fpwm = 500 Hz.The feedback gain of the adaptive parameter estimation wasset to km = 0.3 ns/A2. Initially, the loss parameter K0 wasset to 50 % of its true value. It can be seen in Fig. 14 that
8.5 9 9.5 10 10.5 11 11.5 12 12.5
-10
0
10
20
Loss
in W
, R
esi
stance
in
Excitation
Quadrature Excitation
Rgate
0 5 10 15 20 25 30 35 40 45 50
0
20
40
Tem
pera
ture
in °
C
Instantaneous temperature estimate
Averaged temperature estimate
Estimated Tj response
0 5 10 15 20 25 30 35 40 45 500
0.02
0.04
0.06
|Zth
| in
K/W
0 5 10 15 20 25 30 35 40 45 50
Time in s
-100
-50
0
50
arg
(Zth
) in
K/W
48
44
40
19.8 20 20.2
j
Fig. 15. Thermal impedance estimation of a IGBT during normal converteroperation at a current of 300 A, a dc-link voltage of 100 V, an AC voltageamplitude of 20 V, a load angle of 10 °, a frequency of fbase = 20 Hz anda PWM frequency of f0sw = 7000 Hz.
over a time horizon of 70 s the switching loss parameter K0
is adaptively corrected to its original value. The averaged lossestimation error simultaneously decays to zero.
C. Thermal Impedance Spectroscopy
Finally, ITIS is experimentally evaluated during converteroperation with a current of 350 A, a dc-link voltage of 100 V,a phase voltage of 20 V, a load angle of 15 °, a fundamentalfrequency of 50 Hz and a switching frequency of the 5 kHz.The thermal impedance extraction process at an electrothermalexcitation frequency of 1 Hz is illustrated in Fig. 15. Att = 8 s, the IGBT losses are excited with an amplitude of15 W via gate resistance manipulation between Rg = 3 Ω andRg = 12 Ω. This causes a very small temperature responseT exj whose ∆T = 1.5 K is a factor of two smaller than the
device temperature variation caused by the current alternation.The observer is able to separate this response T ex
j from theremaining junction temperature signal, such that it can beprocessed by the orthogonal correlation unit to compute thethermal impedance Zth(jω0) in magnitude and phase. Alluncorrelated noise gets rejected by the averaging processwithin the orthogonal correlation unit resulting in a smoothestimate of the thermal impedance.
This process has been repeated for excitation frequenciesfrom 50 mHz to 5 Hz to determine the FRF of the thermalimpedance Zth(jω) that is plotted in Fig. 16 in comparison tothe thermal model developed in II. The extracted FRF of theZth exhibits a maximum deviation of 13 % in magnitude and2 ° in phase from the model. This shows that the proposed ITISconcept is able to extract the Zth FRF over a wide frequency
10-2
10-1
|Zth
(jw
)| in K
/W
On-line extracted Zth
Model based Zth
10-1 100 101
Frequency in Hz
-80
-60
-40
-20
0
arg
(Zth
(jw
)) in K
/W
Fig. 16. Frequency response function (FRF) of the thermal impedanceZth(jω) extracted from a accurate model that was calibrated based onexperimental Zth(t) measurements [41] and FRF extracted via ITIS byrepetition of the impedance estimation illustrated in Fig. 15 at a wide rangeof excitation frequencies
range with excellent precision without interrupting converteroperation.
The frequency range in which the proposed ITIS conceptworks is limited by the bandwidth, signal-to-noise ratio (SNR)and the resolution of the utilized temperature sensing tech-nology. In the presented experimental work, the frequencyrange was limited to 10 Hz, as the IR sensors provided noisydata at higher bandwidth. Consequently, there is a strong de-mand integrated high-bandwidth junction temperature sensingtechnologies, e.g. based on temperature sensitive electrical[34], [75], [77], [79] or optical parameters [80], [93].Thetechnologies to be used should be selected based on theirbandwidth, SNR, temperature resolution and insensitivity todisturbances.
A second limit to the frequency range of the proposed ITISconcept lies in the decaying characteristics of the thermalimpedance at higher excitation frequencies. This requires toincrease the loss excitation amplitude for extracting thermalimpedances at higher bandwidth, such that the resulting tem-perature response has an amplitude that is sufficiently large tobe measured. As the loss manipulation ability of active gatedrivers, which were used in this work, but also of other lossmanipulation techniques is limited, this results in a maximalbandwidth above which the thermal impedance cannot bedetected. However, this is not only a limit of the proposedtechnology, but a general limit for all thermal impedanceextraction technologies.
VIII. CONCLUSIONS
The contributions and key associated conclusions of thispaper are summarized as follows:
• Real-time calibration and extraction of life-time varyingparameters of the power module without interruptingnormal converter operation
– Observer correction variables allow detecting elec-trothermal model errors as well as changes of theelectrothermal behavior, e.g. due to degradation
– Self-correction of errors in the loss model with amodel reference adaptive system
– In-situ thermal impedance spectroscopy extractsthermal impedance with minimal magnitude andzero phase error over multiple frequency decades
• Thermal monitoring, i. e. 3-D temperature and lossestimation, with an observer that uses a self-calibratingdevice loss model yields improved accuracy even at highbandwidth and provides:
– Thermal overload protection of the power module atrapidly changing loading
– Closed-loop loss estimation of individual devices– Detection of hard- and soft switching operation
• Identification of frequency-dependent thermal impedanceZth(jω) as key indicator of power module state-of-health
– Phase information is highly sensitive to structuralchanges of the power module and can be extractedwith superior accuracy compared to magnitude
– Strong opportunity for future diagnosis systems thatevaluate phase information instead of magnitude
• Experimental evaluation of the thermal monitoring sys-tem on a state-of-the-art IGBT power module duringrealistic converter operation accomplishes
– Closed loop monitoring of instantaneous and aver-aged spatial temperatures and device losses
– Successful loss model parameter identification– Accurate extraction of thermal impedance transfer
function Zth(jω) over multiple frequency decades
ACKNOWLEDGMENT
The authors would like to thank the German AcademicExchange Service (DAAD), the Institute of Power Electronicsand Electrical Drives (ISEA) at RWTH Aachen University andthe Wisconsin Electric Machines and Power Electronics Con-sortium (WEMPEC) at the University of Wisconsin-Madisonfor supporting this research.
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