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Citation: Klink, J.; Hebenbrock, A.;
Grabow, J.; Orazov, N.; Nylén, U.;
Benger, R.; Beck, H.-P. Comparison of
Model-Based and Sensor-Based
Detection of Thermal Runaway in
Li-Ion Battery Modules for
Automotive Application. Batteries
2022,8, 34. https://doi.org/10.3390/
Academic Editors: Binghe Liu,
Lubing Wang, Yuqi Huang, Yongjun
Pan and Carlos Ziebert
Received: 1 March 2022
Accepted: 7 April 2022
Published: 12 April 2022
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Comparison of Model-Based and Sensor-Based Detection of
Thermal Runaway in Li-Ion Battery Modules for
Jacob Klink 1,* , André Hebenbrock 1, Jens Grabow 1, Nury Orazov 1, Ulf Nylén 2, Ralf Benger 1
and Hans-Peter Beck 3
1Research Center Energy Storage Technologies, Clausthal University of Technology, Am Stollen 19A,
38640 Goslar, Germany; firstname.lastname@example.org (A.H.); email@example.com (J.G.);
firstname.lastname@example.org (N.O.); email@example.com (R.B.)
2Battery Performance & Cell Development, Scania CV AB, 151 87 Södertälje, Sweden; firstname.lastname@example.org
3Institute of Electrical Power Engineering and Electrical Energy Engineering, Clausthal University of
Technology, Leibnizstraße 28, 38678 Clausthal-Zellerfeld, Germany; email@example.com
In recent years, research on lithium–ion (Li-ion) battery safety and fault detection has
become an important topic, providing a broad range of methods for evaluating the cell state based
on voltage and temperature measurements. However, other measurement quantities and close-to-
application test setups have only been sparsely considered, and there has been no comparison in
between methods. In this work, the feasibility of a multi-sensor setup for the detection of Thermal
Runaway failure of automotive-size Li-ion battery modules have been investigated in comparison to
a model-based approach. For experimental validation, Thermal Runaway tests were conducted in a
close-to-application conﬁguration of module and battery case—triggered by external heating with
two different heating rates. By two repetitions of each experiment, a high accordance of characteristics
and results has been achieved and the signal feasibility for fault detection has been discussed. The
model-based method, that had previously been published, recognised the thermal fault in the fastest
way—signiﬁcantly prior to the required
pre-warning time. This requirement was also achieved
with smoke and gas sensors in most test runs. Additional criteria for evaluating detection approaches
besides detection time have been discussed to provide a good starting point for choosing a suitable
approach that is dependent on application deﬁned requirements, e.g., acceptable complexity.
lithium-ion battery module; fault detection; thermal runaway; thermal fault; external
heating; battery safety
The increasing demand on lithium–ion batteries in recent years—mainly spurred by
automotive applications [
]—is accompanied by rare ﬁre incidents with disproportionate
supra-regional attention with respect to the smaller incident frequency compared to vehicles
with combustion engines [
]. Besides the customers’ loss of conﬁdence in the safety of
electromobility and the risk of property damage and personal injury, these accidents
repeatedly lead to extensive recall and inspection campaigns—proactive or government-
imposed (Boeing 787 [
], Samsung Note 7 [
], Electric buses in Germany [
] or Chevrolet
])—causing ﬁnancial and reputative damage to the manufacturer [
]. In addition,
recent battery cells are developed under the need for high energy and power density,
which also results in more available energy in case of failure. Thus, battery safety is still
considered as one of the greatest challenges in battery technology [
] and a key aspect for
the safe operation of high-power battery storage systems.
The failure of lithium–ion cells is well-described [
] and characterised by extensive
exothermic side reactions of cell components that result in a self-accelerating loop until
Batteries 2022,8, 34. https://doi.org/10.3390/batteries8040034 https://www.mdpi.com/journal/batteries
Batteries 2022,8, 34 2 of 29
the remaining energy is released mostly in an explosion-like event. This failure process is
commonly named as Thermal Runaway [
]. However, the deﬁnition of Thermal Runaway
is not harmonised [
] and varies between the state of a thermal imbalanced system [
and precise process states. For the latter, e.g., Li et al. has deﬁned
] while either
dt ≥10 K min−1
has been utilised in [
a sudden voltage drop relative to a fault-free condition. Furthermore, Thermal Runaway
deﬁnitions based on the characteristic behaviour of self-heating, release of gases and
particles and explosion [
] are documented as well. Within this paper, the latter descriptive
deﬁnition is used.
As battery systems consist of hundreds to thousands of cells, such an explosive
energy release usually causes adjacent cells to fail as well. Thus, this phenomena of
propagating a single cell fault to further cells is labelled as Thermal Propagation in the
literature. Since the amount of released energy accumulates with each additional cell
failure, this process endangers bystanders and the environment much more than the
individual Thermal Runaway.
To reduce these risks, three approaches are mainly identiﬁed in the recent literature:
Increasing the thermal stability of cells by alternative active materials or additives, as
extensively summarised by Tidblad et al.  or Liu et al. .
Decreasing the heat transfer from cell to cell by constructive changes [
or passive cooling [
] and/or thermal isolation [
] to slow down or rather stop
Thermal Propagation and increase warning and evacuation times. This approach is
in agreement with the US Vehicle Battery Safety Roadmap Guidance that states Ther-
mal Propagation must not occur [
], acknowledging the imminent risk of one-cell
Early detection of battery faults to provide warning and evacuation time, which is also
the subject of this work. In this context, the Global Technical Regulation on Electrical
Vehicle Safety (GTR-EVS) speciﬁes at least a 5 min pre-warning time .
Based on the aforementioned description of Thermal Runaway, a variety of measured
variables with dependence on the fault condition are conceivable for detection, as listed in
Table 1. Due to the complexity of the electrochemical system, however, overlapping with
the other, non-safety-relevant processes and typical safety devices must also be taken into
account to minimise false-positive detection. In addition, the time of detection is highly
dependent on the underlying process, as indicated by the given temperature ranges.
Table 1. Selection of measurement variables to describe the presence of Thermal Runaway.
Process Variable Dependency of Thermal Runaway Processes Other Dependencies
After cell rupture: Electrolyte vaporisation and gas pro-
ducing reactions (see internal pressure)
Gas released approx.
Pre-cell rupture: Electrolyte leak-
SOC and chemistry 
Ageing (at different conditions) 
Atmosphere and oxygen availabil-
Type of trigger 
Increasing temperature by exothermic reactions cause de-
creasing impedance and change of phase [
behaviour for high temperatures 
Safety Device Shutdown-separator cause
of 1300% [
usually at 120 °C to 150 °C 
Temperature of operation
Cyclic Ageing [36,37]
Calendrical Ageing 
Batteries 2022,8, 34 3 of 29
Table 1. Cont.
Process Variable Dependency of Thermal Runaway Processes Other Dependencies
Electrolyte evaporation and gas producing reactions:
–CO2by SEI decomposition  at 90 °C to 120 °C 
–CxHyby SEI-reformation  at 120 °C to 218 °C 
Safety device: burst-disc rupture approx. at
for prismatic cells [
]; deviation with 18650 cells
σ≤5 % 
SOC and load [44,45]
Ambient pressure 
Temperature of operation
Gas formation during ageing 
After cell rupture: Vaporisation (White smoke [
combustion of cell components (Black smoke) .
Ejection of internal cell components (Black smoke) [15,47]
Increasing internal temperature due to exothermic reac-
] and cooling
Temperature of operation
Increasing temperature as OCV =f(T)
Melting of separator:
– PE = 120 °C to 135 °C, PP = 150 °C to 166°C [34,40,52]
Safety Device: CID, OSD, Shutdown-separator
150 °C 
Over-voltages during load
Despite the various signals, the vast majority of papers on the detection of cell faults,
as recently summarised by Hu et al. [
], mainly build their methods on voltage and
eventually surface temperature measurement—independently if the main principle comes
from statistical outlier detection [
], neural networks [
] or modelling [
]. Other signals,
such as strain and internal temperature, e.g., measured by optical ﬁbre, as proposed by
Zhu et al. 
and Nascimento et al. [
], have been published for describing the devel-
opment of Thermal Runaway in general instead of fast fault detection. In contrast, such
signal-based approaches are already documented in patents such as H
and CO monitoring
It has to be mentioned that there is extensive research on characterising the devel-
opment of Thermal Runaway by multiple quantities, e.g., by Finegan et al. in [
on the other hand, however, the results are neither investigated under the scope of fault
detection nor considered within detection-focused studies to give context for the achieved
results. Due to this development, a well-founded assessment of different measurement
parameters and detection methods for use in automotive applications is currently only
possible to a limited extent. In addition, current research is often limited to small lab-size
battery modules, which is signiﬁcantly differentiating from the size and quality of battery
modules used in automotive applications, and mainly focused on internal short circuits
(see review [
]). Thermal faults as the second type of failure, on the other hand, are
only investigated in few studies, such as [
]. Approaches like those presented in [
utilise real vehicle data from selected ﬁeld-failures, ensuring to investigate relevant faults;
however, the lab-advantage of controlling the boundary conditions is not given.
The ﬁrst approach on this gap was conducted by Koch et al. [
], comparing the
readings of a variety of different sensors during the Thermal Runaway of modules triggered
by multiple methods with respect to detection possibilities.
This study aims to build on that work by rerunning a similar sensor setup on an
automotive battery case in comparison to a model-based detection method from our
previous work [
]. By this, the change of impedance should be incorporated as well. The
test setup was designed to be as close as possible to the application, thereby using original
battery modules, battery housing and operation boundaries from an all electric (BEV) heavy
duty trucks by Scania. External heating at two rates was chosen as the trigger to represent
Batteries 2022,8, 34 4 of 29
an over-time-developing fault condition. To improve the signiﬁcance of the results, each
test was conducted twice.
The content of this paper is structured as follows: In Section 2, the experimental setup
of module and sensors are given, followed by the test setup and design of model-based
detection. The results of the experimental tests are described and discussed in Section 3
with a focus on the repeatability in between tests before analysing the individual sensors
and a comparative evaluation of detection feasibility. The main ﬁndings are summarised in
2. Materials and Methods
For the conduction of the abuse tests on a module/system level, both preparations of
the device under test (DUT) and of multiple sensors for monitoring the different quantities,
as described in the following, were performed.
2.1.1. Device under Test
As DUT, automotive battery modules based on prismatic cells of type PHEV2 [
were used (Please understand, some details on the cells are in conﬂict with conﬁdential
information due to the proximity to automotive production). The module is commercially
available and the cells contain NMC-based chemical constituents commonly employed in
BEV vehicles today. Each module consists of twelve cells that are conﬁgured in a side-by-
side 12s1p-setup, as pictured in the upper left section of Figure 1. The real appearance of the
module can be seen in the embedded photo also in Figure 1. According to the manufacturer,
the individual cells are equipped, among other things, with an overcharge-safety-device
(OSD), creating an internal short circuit between the cell terminals when the cell pressure
surpasses a (not published) threshold, as described in patent US 10.026.948 B2 [
disconnects the active parts from cell terminals. Thus, the cell is electrically bypassed
from the load current. Furthermore, a vent for the release of internal over-pressure is
implemented into the cell—hereinafter referred to as burst-disc. The position of the burst-
disc is indicated by the light-colour area behind the cell numbers in Figure 1.
Please note the nomenclature given in the schematic—numbering the cells from the
positive terminal (
01) towards the negative terminal (
12). For simpliﬁcation, this naming
convention is adopted to the associated measurements in the following; for example,
represents the temperature on top of cell C01.
For the individual tests, identical brand-new modules were utilised. The modules
were equipped with the original cell management controller (CMC) that is used in real
application as the responder within the whole battery management system (BMS). For a
detailed differentiation between CMC and BMS, please refer to [
]. In preparation for the
experiments, the modules were tempered at
and cycled two times using a constant
current–constant voltage (CC–CV) protocol for the charge and CC protocol for the discharge
direction. The cycles were performed within the given cutoff limits by the manufacturer
and at a maximal current of
3. In accordance with most similar test protocols, the CV
phase was terminated when the current fell below
20. Using this CC–CV protocol,
the DUT were charged, subsequently representing
state of charge (SOC), and after
a relaxation period of
, the initial state of
was reached by discharging
As displayed in the lower inset of Figure 1, the battery module consists of cells
encapsulated by an aluminium casing in an original state. For placement of the heating
device described below, the cover on the head-end at the side of the positive terminal of
this aluminium casing was removed. After the installation of the heater with direct contact
01, both the thermal contact between the heater and cell and the pre-tension of all
cells were guaranteed by two steel strapping tapes.
Batteries 2022,8, 34 5 of 29
Main ﬁgure: Position of module and placement of sensors within battery case (not to scale).
Sensors placed inside the battery case at the lid are marked by an arrow (
). For detailed sensor
information, please refer to Table 2.Photo inset: Prepared battery case (
) and modiﬁed module
with attached aluminium block (bottom).
Each DUT was placed and screwed in the top left of the original aluminium battery
system housing designed for eight modules in total, as visualised in the upper photo in
Figure 1. In the application, eight modules will be screwed to the battery system housing,
as indicated by the polished foundations in the photo. In this conﬁguration, the modules
are separated by a small distance. Through a combination of multiple battery systems, the
required electrical properties are achieved. The system case comes with an aluminium lid
and gasket screwed on top in addition to several openings in the side for items like cable
entries or pressure discs. In addition, channels for cooling ﬂuid from an external thermal
management are located in the bottom plate of the housing. For the experiments, a window
of polycarbonate-glass was added to the lid, providing vision towards the DUT during the
test. To restore the gas-tight original state, the window was properly sealed with silicon
and mounting adhesive. For the same reason, all openings were closed with polyurethane
foam after the installation of all cables.
The variety of sensors used in this study are summarised in Table 2with the respective
quantity. In addition, the utilised sample rate of each sensor is also given together with the
layer of measurement. Please refer to Figure 1for a visualisation of the sensor location in
relation to the module and heater placement inside the battery case. The external sensor set
is extended by the capabilities of the CMC to represent the close-to-application available
Batteries 2022,8, 34 6 of 29
Measured quantities, utilised sensors, sensor positions and corresponding sample rates. For
detailed information on locations please refer to Figure 1.
Quantity Sensor/ Logger Sample Rate Location
Voltage Gantner Q.station XB 10 Hz Cell-level
CMC 10 Hz Cell-level
EA Power Supply PSI 10000 10 Hz Module-level
Current Fluke i400s, Gantner Q.station XB 10 Hz Module-level
EA Power Supply PSI 10000 10 Hz Module-level
Temperature CMC 10 Hz C06
Pico TC-08 1 Hz 12×cell-level
2×ambient battery case
1×inlet and outlet of cooling ﬂuid
Vapour/particles MaximIntegrated MAX30101 ≈4 Hz System case
Gases Sensirion SGP40 ≈1 Hz System case
Dräger X-am 8000 1 Hz System environment
Pressure RS Pro IPS (7975043) 5 kHz System case
Strain Micro-Measurement 500UW 1.25 Hz Module-level
The data acquisition was performed using the data loggers and data processors suitable
for the sensors selected, with either analogue or digital signal outputs. The data loggers
were placed outside the test environment, as a result of which reliable data acquisition was
possible while sensors and cables were functional. All devices were synchronised to an
online time server for later time-synchronising on the logged timestamps. The achieved
synchronisation was validated by the redundant measurement of the current and voltage
across multiple loggers.
was logged on the cell level externally by a data logger and by using the
mounted CMC. For the latter, the data acquisition was realised by a custom debug func-
tionality that was implemented by Scania for CAN communication. Both external and
integrated voltage monitoring were screwed to the same point-of-contact on the cells. Ad-
ditional voltage monitoring and logging were conducted by the utilised power supply at
the module level. Since no separate sense cable was used, the actual value is regarded as
biased but provides an opportunity for veriﬁcation of the synchronisation.
was primarily logged by the power supply itself but was supplemented using
a current probe with the voltage data logger for redundancy and synchronising. Since the
cells of the DUT were connected in a serial connection, the module-current measured at the
power supply was equal to the cell current.
has been monitored on the cell level at the positive terminal of each cell
using type K thermocouples with welded tips. These sensors were electrically isolated from
the surface as well as glued to position with Kapton
tape. The same type of sensors was
used for the measurement points
. In contrast, both the heater temperature
and cooling ﬂuid temperature (
) were measured using type K metal sheath
thermocouples. For the ﬂuid temperature, the sensor tips were positioned approximately
centre in the tube. An additional sheath thermocouple was implemented close to
providing the actual temperature for the heater controller (see below). The cables of the
latter sensors were thermally protected by a high temperature-resistant furnace sealing strip.
In addition to the aforementioned external sensors, the module temperature monitored
internally by the CMC was logged as well. Please refer to Figure 1for the position of the
corresponding thermistor at C06.
detection, speciﬁcally vapour and particles in the atmosphere inside the battery
case, was implemented based on an integrated circuit board with infrared functionality.
Here, the measurement principle is based on the reﬂection time of an emitted infrared beam
that allows for the evaluation of the atmosphere properties in front of the diode. Due to
Batteries 2022,8, 34 7 of 29
the simple measurement setup, the evaluation is a qualitative rather than quantitative gas
assessment like gas amounts or further—lab-quality—analyses as particle size distribution.
The measurement signal is proportional to the particle-free distance in front of the sensor;
thus, the value can indicate the amount of smoke but is also inﬂuenced by the position of
the sensor relative to the smoke emitter. For the sake of simplicity, this measurement of
vapour and particles will be referred to as smoke sensor in the following sections.
Gases inside the battery case were also measured using an integrated circuit board—
in this case, based on a metal-oxide sensor for air quality. Thus, the sensor is sensible
that are summarised in the following sections
by volatile organic compounds (VOC). In addition to the raw signal gathered from the
metal-oxide sensor, the chip also calculates, internally, an air quality index. Both smoke and
gas sensors are similar to the setup described by Koch et al. [
]. Further gas measurement
was implemented outside the battery case using a mobile gas-sensing unit by Dräger to
assess the quality of the achieved case sealing. For this purpose, the air-intake was placed
directly beside the lid-sealing in the ﬂow-direction of the air inside the test chamber due to
the exhaust gas extraction. By this measurement position, the reading can also be utilised
as the worst-case estimation of the gas exposure for bystanders during Thermal Runaway
and potential Thermal Propagation.
that builds up inside the enclosed battery case relative to ambient atmosphere
is monitored using a piezo-based sensor. The sensor was screwed into the modiﬁed
aluminium casing close to the triggered battery module, as visualised in Figure 1. As a
result of the experience from previous abuse tests, the sample rate was set to
very short pressure peaks have used to be observed. The upper sensor range is
the reference pressure.
of the module was measured by a strain-gauge that was glued onto the two
metal strapping tape, as mentioned above. Here, the upper tape was prepared beforehand,
thereby guaranteeing at least 24 h curing time for a reliable connection.
Due to the energy release during each test, the destruction or damage of the sensors in-
side the battery case has been expected with the exception of the pressure sensor. Therefore,
each DUT was prepared using a new set of sensors.
The electrical load of the module during the abuse tests was provided by the above
mentioned power supply PSI 10000, controlled and monitored by a custom LabVIEW
program. With this setup, the power supply target value was actualised according to a
given current proﬁle at 10 Hz rate within the preset module safety limits.
During the operation, cooling ﬂuid was circulated within the cooling channels in
the bottom plate of the battery case. The ﬂuid temperature was controlled by the ECO E
10 S cooling unit from Lauda, capable of both heating and active cooling. In combination
with a separate pump, a ﬂuid velocity of
4 L min−1
was achieved. This value is within the
ﬂow–velocity range of the real automotive application of the battery case. At this ﬂow rate,
the area heat transfer coefﬁcient from cell to cooling ﬂuid was experimentally determined
from 100 W K−1to 130 W K−1.
For triggering the thermal-induced failure, a heating device was mounted at the head
end of cell
01 of each module. This heating block has the same dimensions as the cells and
was manufactured from aluminium with mounting positions for three heating cartridges, as
visualised in Figure 1. Heat cartridges with a nominal power of
each were connected
in parallel to
, providing a peak power of
in total. By a PID-controller that
was pre-tuned on this test-setup, phases of constant temperature as well as ramps with a
constant temperature rate were realised.
Batteries 2022,8, 34 8 of 29
The preconditioned and mounted modules (see above) were placed within the abuse
test-chamber that provides a safe test environment, with features such as active smoke
extraction and puriﬁcation. The battery housing was elevated from the ground to avoid
unrealistic heat loss. By activating the thermal management at least
before the abuse
test, a steady state at the beginning of the experiment was guaranteed. According to the
module data sheet, the thermal management was set to
as the operation temperature.
The ambient temperature outside of the battery case was highly dependent on the outside
temperature at the test site in northern Germany during October due to the active smoke
extraction. Thus, the thermal management regulates the module temperature during
pre-heating by heating and cooling during the thermal fault operation.
During the experiment, the module was electrically cycled with a dynamic drive
proﬁle, providing both charge and discharge periods. Instead of the often utilised synthetic
dynamic proﬁles, the data were collected at
rate beforehand on board of an electric
heavy duty truck, driving through a mixed urban–rural area, with a total duration of
. In comparison, the world harmonised vehicle cycle (WHVC) [
. A detailed comparison between the utilised cycle and the WHVC
or other popular test speciﬁcations is not performed as the characteristics of the synthetic
proﬁles highly depend on the used transformation from the velocity proﬁle to the power or
To increase the length of the load proﬁle, the measured data were concatenated twice
and a CC-charging phase was added at the end, resulting in a maximal test duration of
10,200 s, or rather,
. The full proﬁle relative to the module capacity is visualised
within Figure 2, as well as the corresponding SOC initialised at SOCup per .
Change of SOC of module by dynamic electrical test proﬁle with initial state
Synchronous thermal fault trigger with either 5 K min−1or 8 K min−1after 5 min of test.
To get a baseline of the voltage measurement and the implemented strain sensor, one
module was cycled using this proﬁle at a regulated 25 °C inside a climatic chamber.
For the investigation of the abuse behaviour, the attached heating device (see above)
was activated during dynamic load. The PID-controller was set to a
phase at a constant
followed by continuous heating with a constant temperature
slope. Besides a heating rate of
5 K min−1
that can be found in several test manuals,
Batteries 2022,8, 34 9 of 29
e.g., IEC 62600-2 and UL 2580 (see Ruiz et al. [
]), a faster heating rate with
8 K min−1
was investigated as well. The latter rate was chosen to consider the test requirements
by FreedomCAR of
5 K min−1
10 K min−1
]. Due to the faster heating, a smaller
pre-warning time was expected, thereby representing a more critical fault situation for the
investigated approaches. The described thermal trigger deﬁnition is also displayed within
Figure 2. With respect to the displayed ﬁnal temperature of the heating device for both
test variations, it was expected that the triggered cell
01 would suffer Thermal Runaway
signiﬁcantly before the end of the test cycle. Thus, the module-SOC at failure would be
within the operational range.
Previous studies, e.g., [
], have identiﬁed various critical boundary conditions and
disturbing inﬂuences altering the achieved results from abuse tests. To identify such dis-
turbing inﬂuences—if present—and for the evaluation of the result validity, both test setups
were repeated once, as proposed by [
]. An overview of the described test speciﬁcations is
given in Table 3.
Table 3. Test setup and nomenclature of individual abuse tests.
A B C D
Heating rate 5 K min−15 K min−18 K min−18 K min−1
The monitoring of the sensors listed in Table 2began before the start of the load with
the exception of the pressure sensor to minimise the ﬁle size. However, when the triggered
, the logging was manually started. In addition, the heating unit was
deactivated when either
or Thermal Runaway had been observed.
The electrical load was deactivated when the remaining module voltage fell below the
minimal operational voltage.
2.2.2. Model-Based Fault Detection
To deﬁne a baseline for detection time without additional sensors, the model-based
method, principally from our previous work [
], was employed. By modelling the battery
behaviour under normal conditions, the cell-level voltage measurement can be set in
contrast and potential deviations can be detected. The main structure of this fault detection
method using a parallel observer-like model can be found in Figure A2 in the
The feasibility of this method is made possible by the temperature-dependence of the cell
impedance and the resulting change of the internal over-voltages when heated, as indicated
in Table 1. To ensure early detection, a precise simulation of the normal behaviour at
different temperatures and SOC is mandatory.
The model structure was kept similar to the previous work, as described in the
following sections, and implemented in Matlab
]. Please ﬁnd additional
information on the model setup in the Appendix A.
As commonly chosen as the best compromise between accuracy and computational
], a second order equivalent circuit model was utilised for the electrical part,
see Figure A1. The dynamic voltage behaviour
of this model is described by
represents the open circuit voltage,
the load current (
are equivalent resistances and capacities, respectively. Current
direction, SOC and temperature were considered as inﬂuences for the cell parameters
was implemented as
is a correction factor to
incorporate the hysteresis of the open voltage behaviour, as described in .
Ucell(t) = OCV +I·"R0+
characteristics were deﬁned at
the static voltage measurement. In addition, the cell parameters were identiﬁed by ﬁtting
Batteries 2022,8, 34 10 of 29
the dynamic pulse test data onto Equation
for ranges of approximately
SOC for each current direction. Within the model, the parameters were
implemented as look-up-table.
To track the internal temperature of the cell jelly roll for electrical parameter identiﬁca-
tion, a simpliﬁed thermal model based on concentrated thermal masses was utilised. On
the basis of extensive simulation data by the involved automotive manufacturer Scania,
heat transfer mechanisms—except by conduction towards the cooling plate at the bottom—
were neglected. Based on these prerequisites, two system states
deﬁned representing the active cell parts (jelly roll) and the cell terminal as the location of
temperature measurement, respectively. The temperature of the cooling ﬂuid
Tf lui d
as the constant system boundary. During the load, the heating power
is deﬁned by the
electrical over-voltages, as shown in Equation
. Please ﬁnd the corresponding equivalent
circuit structure in Figure A1 in the Appendix A.
The thermal parameters were parameterised based on material dimensions and prop-
erties, as well as heat gradient tests performed internally by Scania.
For fault detection, the model output was continuously compared with the individual
cell voltages measured by the CMC. The difference was then subject to fault assessment.
At reference tests under normal conditions, this signal had very short but high peaks that
would have required to rise the detection threshold signiﬁcantly. As this behaviour is most
likely caused by measurement asynchronicities of the CMC or not sufﬁciently captured
dynamics of the model, a small delay was added to the signal evaluation. Therefore, the
threshold had to be exceeded for
to trigger the fault detection. Based on the
reference measurements with the same electrical load, the threshold was set to
3. Results and Discussion
With all four test setups of Table 3, the cell
01 was successfully triggered to Thermal
Runaway within the duration of the previously deﬁned test cycle. However, it has to be
mentioned that within Test
, the power of the electrical load was limited in charging
direction by accident. Due to this limitation, the maximum charging current was
instead of the given value of
in Figure 2. Nevertheless, in all cases, the cell failure of
the ﬁrst cell was propagated to the adjacent cells, resulting in full thermal propagation of
the battery module. However, a detailed discussion of the propagation characteristics was
outside the scope of this paper on fault detection before the ﬁrst Thermal Runaway.
As Thermal Runaway tests are often criticised for poor repeatability [
], the similarity
between the tests performed is brieﬂy discussed in the following paragraph. The develop-
ment of the different sensor readings are subsequently presented and approaches for fault
identiﬁcation are discussed. As mentioned above, all measurements were synchronised
based on the logged timestamps. For better comparability, all times are referred to as the
start time of the heating device in seconds, starting at zero (0 s) in the following analysis.
3.1. Development of Thermal Runaway
The temperature and voltage measurements of abuse Test
are displayed in Figure 3.
In contrast to the above-mentioned extensive sensor locations, only a few selected measure-
ments are presented for clarity. Here,
as well as
as the cells closest
to and most distanced to the heater, respectively, were chosen. In addition, the temperature
of the heating device
and the sensor of the CMC
are presented. Within the
tests, four characteristic events were identiﬁed:
1Begin of heating. Deﬁned by temperature THeater.
2Activation of OSD-device. Deﬁned by sudden loss of U01 readings.
3Rupture of burst-disc. Deﬁned by pressure measurement, visually validated.
Batteries 2022,8, 34 11 of 29
Thermal Runaway. Deﬁned by a sudden increase in temperature
The timings of these events during the experiment are also annotated in Figure 3
by vertical dashed lines. In addition, selected temperature readings at these events are
summarised in Table 4for all four tests conducted.
Development of Thermal Runaway due to external heating by characteristic events.
Start of heating.
Activation of OSD-device.
Rupture of burst-disc.
) Temperature measurements and module average
. Please refer to Figure 1for sensor position.
(b) Cell voltages C01 and C12 as the hottest and the coldest cells, respectively.
Batteries 2022,8, 34 12 of 29
Duration for reaching characteristic events by external heating with
5 K min−1
8 K min−1
). Externally measured temperatures
, as well as a reading from
CMC at time of event.
Start of heating.
Activation of OSD-device.
Rupture of burst-disc.
4 Thermal Runaway. Potential measurement error marked in red.
A B C D
1t0.00 s 0.00 s 0.00 s 0.00 s
TCMC 26.00 °C 25.00 °C 24.00 °C 24.50 °C
T01 26.26 °C 24.69 °C 23.97 °C 24.36 °C
T12 25.96 °C 25.27 °C 24.68 °C 24.89 °C
THeater 27.49 °C NaN °C 25.84 °C 25.68 °C
2t2076.00 s 2138.00 s 1449.00 s 1518.00 s
TCMC 33.50 °C 32.50 °C 29.50 °C 29.50 °C
T01 121.61 °C 120.75 °C 119.05 °C 123.27 °C
T12 29.13 °C 29.03 °C 26.95 °C 27.50 °C
THeater 215.82 °C NaN °C 236.16 °C 252.65 °C
3t2249.00 s 2297.00 s 1549.00 s 1611.00 s
TCMC 86.50 °C 27.56 °C 86.50 °C 86.50 °C
T01 118.41 °C 132.30 °C 129.47 °C 135.74 °C
T12 28.64 °C 29.78 °C 27.29 °C 28.80 °C
THeater 227.06 °C 105.10 °C 14.62 °C 248.58 °C
4t2608.00 s 2640.00 s 1748.00 s 1874.00 s
TCMC 32.09 °C 30.50 °C 30.54 °C 18.96 °C
T01 169.84 °C 30.06 °C 159.84 °C NaN °C
T12 31.11 °C 32.19 °C 30.27 °C 28.30 °C
THeater 281.79 °C NaN °C 282.19 °C 562.07 °C
into the dynamic proﬁle, the start of the linear heating ramp (
can be identiﬁed in the signal of
, causing a linear temperature increase in the adjacent
01. A slight offset and smaller rate is observed due to heat transfer resistance and thermal
mass. With more distance to the heating device, as valid for cells
thermal capacities and thermal transfer resistances are added along the module. Thus, the
heating of further cells is delayed, causing a temperature difference as visible between
. To display the great deviation in temperature, the mean module tempera-
is also given. During the heating of
01, the mean temperature is found to be
signiﬁcantly smaller than
. As the heating proceeds, an increasing deviation between the
voltage measurements of
12 is identiﬁed—especially visible within the zoomed
part of Figure 3. As inﬂuenced by the temperature the most,
shows greater values
than the approximately unaffected
. This observation is in accordance to previously
published experimental results on cell level (see [
]) and the above-described (Table 1)
inversely proportional temperature dependency of cell impedance—as the impedance
decreases, the internal over-voltage decreases as well.
No temperature development greater than normal ﬂuctuation was observed for both
the cooling ﬂuid temperatures
and ambient temperatures
experiment towards the ﬁrst Thermal Runaway. Therefore, these readings were excluded
from further evaluation.
Subsequently, a sudden loss of the voltage reading of
01 is identiﬁed in Figure 3
when the OSD safety device was triggered (
) due to the built up internal cell pressure,
creating a short circuit between both cell terminals. Shortly after this, the rupture of the
burst-disc is observable (
). This event causes a short cooling effect as visible in the
at the annotation of
in addition to a slight release of white smoke
into the battery case. Please refer to Figure A3b in the Appendix Afor a depiction of this
moment by photo. Besides the release of hot gases, this cooling effect is also caused by the
evaporation enthalpy, as identiﬁed by Qin et al. .
Batteries 2022,8, 34 13 of 29
Unfortunately, within all four tests, the temperature sensors were mounted close
to the heating device and the triggered cell temporally malfunctioned at the same time
of cell rupture. The readings jumped arbitrarily between higher (eventually plausible),
smaller (plausible due to cooling) and negative (not plausible) values, as well as reference
cold-junction temperature (
) and no reading at all. However, since the
measurements recover to reasonable values, the main description of the Thermal Runaway
is not affected. Please note that the measurements that are most likely to be inﬂuenced by
this sensor malfunction or due to sensor-destruction by Thermal Runaway are highlighted
in Table 4in red colour.
Shortly after the burst-disc rupture, signiﬁcant smoke development—starting white
and turning black—can be identiﬁed inside the battery case, as pictured in Figure A3c.
In combination with a sudden temperature increase and pressure pulse, this moment is
deﬁned as Thermal Runaway of the triggered cell (
). As pictured in Figure A3c, this
pressure pulse is accompanied by a signiﬁcant gas and smoke leakage that is also measured
outside the battery case by the mobile gas-sensing unit. The concentration of gases was
higher than the recommended human exposure, triggering the intern warning. Please note
that the detection of gases inside the passenger cabin is one criteria for passing the GTR
Thermal Propagation test [
]. It has to be mentioned that no ﬂames were visible at this
time due to the absence of an ignition source and displacement of oxygen by the released
gases inside the battery case. This changed when either the sealing or the polycarbonate lid
was thermally damaged, as shown in Figure A3d in Appendix A.
Comparing the described Test
with the remaining tests, using the characteristics
given in Table 4, the repeatability of the test setup is evaluated. With the exception of the
highlighted readings, a very high concordance of both timings and temperatures between
as well as
is observable. However, a greater difference between
is recognisable as all events of Test
were slightly delayed. Since the
accidental limitation of the charging current by factor
caused reduced ohmic losses
1/100 (see Equation
), the internal heat generation of the cell was reduced in
—thus, the observed delay is reasonable. Despite these variations, the experiments
are considered to be identical due to the achieved maximal variations of approximately
, the main development is unaltered and the characteristic events are consistent even
under the various inﬂuences, e.g., heat transfer resistances between heater and cell. Thus,
in the following, only ﬁgures of Test
are given and key characteristics of the other test
are listed in a tabular form.
3.2. Sensor Readings
The additional sensor readings of Test
are presented in Figure 4, relative to the same
four characteristic events mentioned above. Please note the signiﬁcant pressure impulse
used for deﬁning
, while all graphs represent raw data by the sensors, the pressure
was ﬁltered by a moving median ﬁlter with window-size
to decrease the noise. As
mentioned above, the utilised air quality sensor returns both raw measurement data and
an internal calculated air quality index. During the startup, this index is zeroed based on
the current environmental conditions and
was deﬁned as the baseline, as indicated in
Figure 4. The readings were then mapped to an interval of
. Due to this integrated
self-compensation of environmental inﬂuences, this index seems much more suitable for
both fault detection and comparison in between tests than the raw data—especially, since
sudden change in gas concentration relative to the normal operation are more of interest
than the actual concentration.
For better comparbility between sensors and tests, the readings from the smoke and
strain sensors were standardised each by a subtraction of the average reading at the start
up. Thus, the baseline for a normal operation is set at
, as shown in the corresponding
Batteries 2022,8, 34 14 of 29
Development of sensor reading during thermal triggered Thermal Runaway for test
5 K min−1
Start of heating.
Activation of OSD-device.
Rupture of burst-disc.
) Strain of module, (
) index for presence of VOC, (
) infrared-based smoke sensor,
(d) over-pressure of battery case; raw signal and ﬁltered by moving average ﬁlter.
3.2.1. Strain Sensor
With the start of the heating, the continuous growth of the strain reading is observable,
which is reasonable as the increasing temperature causes both thermal expansion and
internal gas generation (see Table 1) of the cells. Please refer to Figure 5for a more detailed
display of the data. Here, a subtle plateau can be identiﬁed at
reading drops signiﬁcantly after
. This behaviour is in accordance with the hypothesis
of gas generation inside the battery as this pressure is released with the opening of the
Batteries 2022,8, 34 15 of 29
The strain immediately increased with the start of the heating—that is, before gas
generation is plausible. Therefore, a superimposed effect by the thermal expansion of the
heating device is suspected. However, due to the relaxation of the strain towards the initial
baseline after the rupture of the cell—and release of internal pressure—the cell pressure
is most likely contributing to the increasing strain as well. This is also conﬁrmed by the
complete stress relief after the Thermal Runaway of the cell—please note the chronological
correspondence to the pressure pulses at the bottom of the Figure.
During the period between
, a difference
255 µm m−1
was observed. Please ﬁnd the values corresponding to the other tests in Table 5. Over three
tests, the burst-disc ruptured consistently at approximately
215 µm m−1
260 µm m−1
contrast, the strain sensor of Test
has not measured any signiﬁcant positive strain. Due
to the high accordance of the other tests, either a sensor malfunction or a fault of the glued
connection is suspected.
Module strain difference between start of heating
and rupture of burst-disc
external heating with
5 K min−1
8 K min−1
). Potential measurement error marked
A B C D
∆e255.16 µm m−1215.91 µm m−1259.33 µm m−166.85 µm m−1
The observed behaviour differs signiﬁcantly from the reference test without the exter-
nal heating mentioned above, as investigated separately in Figure 5relative to the module
SOC. Under normal conditions, a positive correlation between
and SOC is identiﬁed.
, the strain value drops as SOC drops. This behaviour is in good
accordance to previous investigations, such as [
], as the ions change the structure and
dimension of the active material during the intercalation. The correlation with the changes
of SOC is also found within the above-mentioned plateau at
in Figure 4.
Here, the thermally induced expansion of the module is counteracted by the shrinking
caused by the period of large discharge in the dynamic proﬁle at this moment.
Development of module strain during external heating with
5 K min−1eTR
to reference dynamic proﬁle
(see Section 2.2) and SOC. Characteristic events:
start of heating,
3 rupture of burst-disc and 4 Thermal Runaway.
Batteries 2022,8, 34 16 of 29
3.2.2. VOC Sensor
The air quality index left the baseline a signiﬁcant time before
. Since the cell should
be still encapsulated at this moment, the cause for this measurement is suspected to stem
from outside the cell, e.g., smouldering of plastic module parts or components of thermal
or electrical isolation. Since
has already reached temperatures above
moment, this seems plausible. However, this clear pre-
behaviour cannot be identiﬁed
for the remaining three tests. As the other tests are very comparable based on the other
signals described above, the pictured phenomenon is most likely not characteristic for
the Thermal Runaway behaviour and arbitrarily caused by the smouldering external test
equipment. For the other tests, a slight increase is detectable post-
—also most likely
caused by smouldering plastic parts. With the opening of the burst-disc
smoke release, a signiﬁcant peak is visible, reaching almost the sensor-range limit. As
the burst-disc rupture is accompanied by smoke, inter alia, from electrolyte evaporation
, this behaviour is highly expected. For the remaining duration
until Thermal Runaway, only a small recovery is observed.
During a normal operation, it is expected that the gas concentration within the module
does not ﬂuctuate so much, especially as the module is encapsulated in the battery case
and the vehicle. Thus, the inﬂuence of external conditions is considered low. Measuring
within the test environment supports the hypothesis, as approximately constant
interval were measured. Thus, the observed behaviour
during abuse differs signiﬁcantly from the readings in the normal operation.
3.2.3. Smoke Sensor
Within all four tests, the readings from the smoke sensor ﬁrst showed signiﬁcant devi-
ation from the baseline, with the opening of the burst-disc
and an increase continuously
until the cell failed thermally
. The latter event is indicated by a signiﬁcant peak up to
the full measurement range. However, due to the extreme nature of Thermal Runaway, it
cannot be determined whether this is a reasonable reading or caused by a thermal destruc-
tion of the sensor. As discussed above, event
is accompanied by smoke-release, causing
accumulating particles inside the module case, providing a reasonable explanation for the
behaviour. Similar to the VOC-sensor, small values—hidden by the large range—were
already measured before the cell opened. This effect is in accordance to the smouldering
suspected above, caused by the high temperatures of the heater and C01 at this period.
Within the same reference measurement as the VOC-sensor, highly constant behaviour
was identiﬁed as well, with a 1-σinterval of −14 ±32.
3.2.4. Pressure Sensor
As mentioned in Section 2.2, the pressure measurement was delayed due to the size of
the measurement data. While the signal is signiﬁcantly effected by the measurement noise,
the rupture of the burst-disc and release of built-up internal over-pressure can be clearly
identiﬁed—synchronous with readings from VOC and smoke sensors. The later Thermal
Runaway is also accompanied by signiﬁcant pressure-shocks greater than the ﬁrst one. This
behaviour is additionally observed with all four tests. Please note that the deviation from
the values reported in previous studies (see Section 1, Table 1) were signiﬁcantly greater
than the ﬁndings in these tests. Two causes were identiﬁed for this deviation:
Reduction in the pressure by the dead volume within the mainly empty battery case.
2. Leaks in the sealing of lid and cable bushings.
The later cause is indicated by signiﬁcant gas concentrations outside the battery case
measured by the mobile-gas-sensing unit, synchronous with pressure peaks. This leakage
is also visible in Figure A3c) in Appendix A. Thus, under standard conditions in the
application, e.g., more modules and proper cable bushings, larger pressure-shocks have to
be expected unless an over-pressure valve/relieve is provided for this range.
Batteries 2022,8, 34 17 of 29
Obviously, under a normal operation, the signal of such a relative pressure sensor is
mainly inﬂuenced by the measurement noise, as indicated by small and constant values
. Thus, the signal from the pressure sensor differs from normal behaviour as well.
3.3. Approaches for Early Fault Detection
In the previous sections, it was found that the appearance of a thermal fault is indi-
cated by all utilised quantities as the behaviour during abuse signiﬁcantly differs from
reference measurements. This observation is in high accordance to experiments on lab-level
referenced in the introduction and similar previous work [
]. In addition to the aforemen-
tioned usage in describing the process of the Thermal Runaway, the feasibility for early
fault detection should be discussed in the following sections.
Based on the raw strain measurement displayed in Figure 5, detection seems possible
approximately immediately with the start of heating. However, as the systematic measure-
ment deviation by the experimental setup itself—as discussed above—cannot be reasonably
corrected, any detection result would be biased. Therefore, the strain measurement is ex-
cluded from further investigation. However, the clear positive correlation between
SOC, shown in Figure 5, suggests a great approach for temperature monitoring that has
to be investigated further with a more robust experimental setup. Since the feasibility of
detection based on temperature measurement highly depends on the position of the sensor
relative to the triggered cell (see Figure 3) and the large deviation between
Tmean, it is also excluded from the analysis.
In Figure 6, the measured voltage is given together with the reference signal generated
by the employed model. As discussed in Section 2.2.2, one model is used as reference
for every cell. The deviation between model output and measurement can be identiﬁed
in the top subplot but becomes more obvious within when comparing the residuum of
, as pictured in the lower subplot. Due to the experimental setup
12 is approximately still within normal operation conditions, resulting
in a smaller residuum
. With continuous heating, the difference
approaches the lower threshold (
, as indicated in Figure by a red horizontal line.
However, the ﬁrst surpass, approximately at
, does not trigger a warning as the second
is not met. The usefulness of the implemented timing condition
can be seen approximately at
where an irregularity in both residua is observed.
Due to the very dynamic characteristic, a faulty reading is suspected—either the cell
voltage or the load current. However, this event would not have triggered a fault-positive
warning due to the threshold design. Further in the test, this condition is exceeded and
a fault detection is presumed—signiﬁcantly before
. The same results were achieved
for the other tests, as summarised in Table A1 in Appendix A. Thus, the proof-of-concept
described initially in [
] has been successfully transferred from cell level to module level,
and the feasibility has been conﬁrmed. As the achieved detection time is highly dependent
on the chosen thresholds, the selection of these provides an approach to increase the
pre-warning time. In the scope of this work, the thresholds were deﬁned based on the
measurement-model-deviation under normal conditions. Therefore, improving the overall
model accuracy would be necessary. In this context, a feedback loop of observation, e.g.,
as incorporated in Kalman Filters, would provide a long-term stable simulation as offsets
would be corrected. However, such Kalman Filter would also correct the fault-offset;
because of this, combined approaches such as [
] seem reasonable. Deﬁning adaptive
thresholds, e.g., dependent on the ambient temperature based on an extensive statistical
analysis of the model and module behaviour under different operational boundaries, is
another approach for narrower detection limits. In addition, the pictured deviation between
and the remaining cells, as shown in Figure 3, can also be evaluated by methods other
than model-based ones. However, these often referenced as signal-based [
] methods are
outside the scope of this paper.
Batteries 2022,8, 34 18 of 29
Model -based detection of external heating in Test
. Measured and simulated voltage of
); model deviation from
12 as fault signal (
of heating, 2 Activation of OSD-device, 3 rupture of burst-disc and 4 Thermal Runaway.
With the presented test setup, the method was only validated against thermal faults.
The faults of internal short circuits, however, were not experimentally investigated since
the application of such a trigger would have required a signiﬁcant modiﬁcation of the
battery module. Nevertheless, these faults are considered to pose a serious risk [
especially due to the spontaneous development and the little options for control from the
]. It is known that the practical faults caused by impurities or dendrites show
characteristic fusing phenomena [
], describing the behaviour of a short voltage drop that
recovers quickly when the point of contact melts due to resistive heating [
]. This short
voltage drop is often greater than the threshold chosen in Section 2.2.2 and develops into
a safety-critical hard short circuit over time [
]. Thus, it is expected that the method
presented detects such internal short circuits with a signiﬁcant voltage drop as well. In the
literature, a common test setup for representing internal short circuits is a sudden voltage
]. Since this voltage deviation is greater than the threshold
from Section 2.2.2, the method can pass these tests as well. Long-term high-ohmic short
circuits lead to a slow deviation of the SOC and
]. In addition, it depends on the
implemented balancing strategies whether and how fast this trend can be identiﬁed by the
As the modelling approach requires determination of the model parameters and
veriﬁcation of the model, the achieved result is compared to multiple, more simple sensor-
based detection methods. Although there are various methods from data science or statistics
for evaluating the measured quantities, a quite rudimentary approach based on pre-deﬁned
thresholds was chosen for the scope of this paper, as summarised in Table 6.
Using these detection approaches, the pre-discussed data of the abuse Tests
were evaluated. Here, the ﬁrst threshold-exceeding is considered as the time
of detection. The method-depending results for each test are summarised in Figure 7in
comparison to the four characteristic events that are repeatably addressed. Please also
note the above-mentioned
pre-warning time required by the GTR [
] indicated in
dashed-red relative to
of each test. The achieved detection times and corresponding
temperatures T01 and T12 are listed within Appendix Ain Table A1.
Batteries 2022,8, 34 19 of 29
Evaluation methods for detection of thermal fault. Thresholds deﬁned by lower cut-off
and by reference measurement (see Section 3.2).
represent a moving average with
window size t.
Index Quantity Evaluation
I Voltage Ui≤Umin
II Voltage Model residuum (see Section 2.2.2)
III Gas VOC(10 s)≥VOCre f +5·σVOC
IV Smoke IR(10 s)≥IRre f +5·σI R
V Pressure p(2 ms)≥0.01 bar
Time map of sensor-based fault detection relative to the start of heating and pre-warning
to Thermal Runaway (
) triggered by external heating with
5 K min−1
8 K min−1
Please ﬁnd method description in Table 6. Characteristic events
Start of heating,
Rupture of burst-disc are maked by grey dashed lines. The GTR-
marked by a red dashed line.
With all given detection approaches, the thermal abuse of the module can be identiﬁed
before Thermal Runaway of
01; however, the achieved pre-warning times differ slightly
in between tests and signiﬁcantly in between signals. The fact that detection times vary
between tests despite a deterministic deﬁnition and almost identical key characteristics
(see Table 4) underlines the importance of multiple reruns of abuse tests for reliable results.
Within the presented tests, the model-based approach is identiﬁed as the fastest
method over all four tests compared to the different sensor-based methods investigated
and—with the exception of Test
—quite constant with regard to the detection time as
well. It has to be underlined that the warning of the model-based method (II, grey line) is
signiﬁcantly earlier when compared to the other sensor-based approaches. In contrast, the
pressure-based detection (
) recognised the existing fault always last. However, with the
tests at a lower heating rate (
), the pressure signal still fulﬁls the GTR criteria due to the
. In addition, this criteria is achieved for each setup by all other
sensors with the exception of the voltage threshold (
) and VOC (
) in Test
, in which
detection is delayed by
, respectively. Since it is unclear whether a correct
Batteries 2022,8, 34 20 of 29
charging power in Test
would have accelerated the events, sensor-based detection of
faults might become difﬁcult with higher heating rates.
Thus, dependent on the desired pre-warning time, all detection approaches can be
utilised. This ﬁnding is also in accordance to the results from Koch et al. [
]. As the
sensor-based methods require an opening of the cell or at least smouldering of adjacent
components, detection is only possible relatively far into the process of Thermal Runaway.
In contrast, the model-based approach utilises inherent information on the cell temperature
from the dynamic behaviour and, therefore, detects the external heating early. Since the
detection based on smoke and air quality even before the opening of the cell is caused by
smouldering of components outside of the battery, the general feasibility is questionable.
On the one hand, the origin of the detected smoke and gases is unknown and eventually
caused by the manipulation of the original module; on the other hand, even unaltered
modules have various ﬂame- and smoulder-able components. Thus, in comparison to the
pressure sensor, both sensors provide the opportunity to detect fault-induced heating either
before the cell-opening by smouldering or approximately synchronous with the opening by
the vaporised electrolyte. This sensitivity to multiple failure cases is also underlined in [
In previous works such as [
], the authors have rated detection methods based
on criteria additional to the warning time itself. These criteria are summarised analogously
in Table 7and the methods are discussed before roughly evaluated.
Evaluation of the detection methods, taking into account the detectability as well as the
possibilities for integration into application-oriented battery systems on the basis of relevant criteria
from previous research. Rating scale ranges from positive (+) over neutral (o) to negative (-).
Method tDete cti on Certainty Localisation Monitoring Complexity Integration Scalabilty Transferability
Voltage threshold - + + + + + o o
Model-based + +/- + + - + + +
Cell temperature 1+ - - + - + - +
Strain 2+/o o o o + o/- o/- +
Gas o + o - + o + +
Smoke o + o - + o + +
Pressure - + - - o - + +
1Assumption: temperature sensors at cell level available. 2Please refer to Section 3.2.1 for limitations.
Besides the warning time itself, the certainty of the generated fault signal was adopted
from Koch et al. [
], representing the shape of fault signal where a step function is consid-
ered as optimum. As presented in Figure 4, the ambient sensors provide such behaviour as
well as the voltage due to the OSD. In this context, the simple sensor setup with approxi-
mately binary output signals has the advantage that evaluation of the signal concerning
the actual battery state is not complex. However, on the other hand the sensor setup is
not capable to differentiate the severity of the current battery state. Thus, smouldering
caused by a faulty chip (low severity) will cause the same fault signal as extensive venting
of electrolyte (high severity). Here, either the combination with other sensors, e.g., pres-
sure, might be advantageous or the implementation of a more advanced smoke sensor
that provides more insights like smoke density or a high sensitivity at certain particles
characteristic for Thermal Runaway. For the latter variant, however, a deep knowledge
of the module emissions during failure is required—potentially even at various states
of health. The model-based method output, however, depends on whether the reﬁned
detection threshold with a clear moment of detection is used or the generated fault signal
is used that is less certain. Because of this signal spread, the fault detection by
temperature differences is also considered to be a method of low certainty.
As fault location or fault isolation is a recurring aspect with fault detection, e.g., [
meaning the identiﬁcation of the faulty cell within the whole system, this is evaluated as
well. Obviously, the methods on cell level provide such functionality while the system-
based sensors, e.g., pressure, cannot. In a similar way, the external system-located envi-
Batteries 2022,8, 34 21 of 29
ronmental sensors are not well suited for evaluating the current cell or rather battery state.
In contrast, the generated fault signal by model or temperature sensor as well as voltage
monitoring can indicate the state, e.g., actual SOC, as usually performed by the BMS.
From an application-based viewpoint, the complexity is an important criterion that
evaluates the effort for incorporating the method into a battery system. Both model and cell
temperatures are rated with high complexity, as they require signiﬁcant work for validation
and parameterisation or sensor topology, respectively. However, most manufacturers or
OEMs already have detailed insights into the battery characteristics dependent on various
parameters like SOC, temperature, operation and SOH. Therefore, the effort for model
parameterisation might be reduced in reality. The pressure sensor was rated moderate
since it requires modiﬁcation of the battery case for the implementation of the sensor and
gas-tight sealing. Furthermore, the logging of the high-frequency data might become
The incorporation of the methods described into a battery system is evaluated by the
integration criteria, where voltage, model and the integrated-circuit-based environmental
sensors are suited best and good, respectively, since the quantities are either already
utilised in application or easy to implement due to the internally pre-calculated digital
output. In contrast, the cell-based temperature measurement increases the required data
acquisition relative to the actual common conﬁguration with one or only few temperature
measurement locations (see Figure 1). As the strain sensor has only worked in three of four
cases, the general evaluation is not clear. The high sample rate for pressure monitoring also
requires further effort for integration; however, as the chosen sample rate was based on
former experimental experience, there could be possibilities for optimisation. Based on the
observed pulse duration in this conﬁguration of approximately
, a smaller sample rate
might be feasible as well. Furthermore, long-term storage of high-frequency data is not
From an application-based viewpoint, scalability is a critical aspect since a battery
case as investigated holds eight individual modules in series connection that have to
be monitored during operation. As VOC, smoke and pressure were already measured
at battery case level (see Table 2), no adjustments have to be made to monitor multiple
modules—these methods are rated with great scalability. Due to the assumptions made
when designing the model, every cell is considered equal. Thus, the model just has to be
calculated for one reference cell and the output compared to each individual cell voltage.
Assuming that cell-level voltage monitoring is already required for other monitoring func-
tions, e.g., cut-off voltages or SOC tracking, besides calculation of
effort has to be made. Thus, the model-based approach is considered to be well scalable.
For the same reason the hard voltage threshold (
) is rated with moderate scalability. With
reference to Table 2, strain was measured on module-level. Therefore, an additional sensor
is needed for each module in such a battery case. As this is considered to be more complex
and costly than just calculation, the scalability is rated below the model-based approach. It
has to be mentioned that this evaluation is based on the assumption of a module made from
prismatic or pouch cells mounted compactly. The implementation of such a module-level
measurement topology on a module consisting of cylindrical cells can be difﬁcult or even
impossible. Due to this restriction, there is a high dependence on the scaleability of the
module and cell design.
Within this study, only one cell chemistry was investigated; thus, the transferability to
other cell types has to be evaluated as well. As discussed above, the clear failure indication
by the cell voltage was caused by the implemented OSD and, therefore, the feasibility is
mainly independent of the chemistry or cell size but dependent on the safety measures
by the manufacturer. However, the available safety devices and their behaviour during
different failure mechanisms have to be known in detail. Since this information is not ac-
cessible for a regular costumer in general, the transferability is rated neutral. The proposed
model-based approach utilises the temperature dependent change of impedance and the
slightly inﬂuenced open-circuit voltage of the considered cell. This parameter dependency
Batteries 2022,8, 34 22 of 29
is caused by the temperature inﬂuence on the reaction rate of the electrochemical processes
and valid in general. Hence, a similar failure characteristic is expected with all cathode
compositions such NMC as well as with LFP, LTO, etc. Please refer to Klink et al. [
] for a
brief comparison of cell impedance with variable shape and size as well to [
the transferability is considered as very good, especially since the same method was already
successfully implemented previously on another cell type. In a similar way, it is expected
to recognise cell faults by monitoring the cell temperature independent on the cell type or
chemistry. This can be caused either by decomposition reactions as well as by ohmic heating
in the case of internal or external short circuits. However, the location of the temperature
sensor relative to the fault location has a large inﬂuence on the achievable sensitivity as
indicated by simulations from Feng et al. [
]. Therefore, good transferability is considered
and mechanical properties such as the size and the thermal mass as well as the available
heat transfer paths are identiﬁed as most relevant for the sensitivity. Safety measures
like passive cooling systems will delay temperature-based detection since they smooth
thermal inhomogeneities [
]. As mentioned in Table 1, internal pressure is built up during
heating due to thermal expansion and electrolyte evaporation. As before, these processes
are mainly independent of the selected cell; therefore, thermal failure is expected to cause
mechanical stress over different cell types and sizes. However, the implementation of strain
measurement might be difﬁcult with alternating types such as pouch bags. Following the
above-mentioned reasoning in combination with Table 1, the transferability of gas, smoke
and pressure is rated with good since the evaporation of electrolyte is expected with all
cells. With exception of the smouldering, all three sensor types require the opening of the
cell, therefore, the availability of over-pressure safety devices as well as the mechanical
stability of the cell casing inﬂuence the achievable time of detection.
Consequently, based on the requirements of detection time and additional complexity,
battery monitoring can be extended by one or a combination of the investigated sensors.
By combining multiple methods, fault-positive signals from one method can be identi-
ﬁed and trigger-thresholds set more broadly to deal with disturbing factors in practice.
This approach can be also found in recent works but limited to voltage or temperature
Based on the state of knowledge of the Thermal Runaway process of lithium–ion
batteries, a multi-sensor monitoring setup was designed within the boundaries of a battery
case from automotive application. By means of a brief literature review, the main principles
of the individual fault signals were presented and ranked concerning the approximate
temperature ranges. The sensor setup was experimentally validated by Thermal Run-
away tests triggered by an external heating device using
5 K min−1
8 K min−1
It was found that, despite criticisms of the repeatability of the abuse tests, the devel-
opment of Thermal Runaway was highly constant based on activation of OSD, rupture
of burst-disc and the Thermal Runaway itself. Within all sensor readings, a deviation in
the reference behaviour or baseline was identiﬁed and discussed. With the exception of
the strain measurement that was affected by the test setup all readings were found to be
reasonable and suitable for further evaluation in a threshold-based thermal fault detection
approach. For comparability the results are set into contrast with a model-based approach
and the 5 min-requirement by GTR.
All sensors were capable of identifying the thermal fault before Thermal Runway;
however, especially with the slow heating rate, the model-based approach was by far the
fastest. Based on the known behaviour of internal short circuits, the feasibility for this fault
was discussed as well and positively evaluated. Dependent on the existence of smouldering
both smoke and VOC sensing registered the thermal fault even before opening of the cell,
at which moment the fault is detected by the the pressure-based method.
Batteries 2022,8, 34 23 of 29
Based on the identiﬁed detection times and, for example, requirements for up-scaling,
the model-based approach is the most convincing but the other methods are suitable as
well as compared in Table 7—at least as simple-to-implement redundant monitoring for
low false-positive rates. This result is further considered as transferable to other cells and
cell types with limitations due to mechanical design and implemented safety devices.
However, for implementing the model-based approach in application, a solution is still
required for the problem of dealing with ageing-induced parameter changes for long-term
functionality, as predicting ageing was outside of the scope of this paper. In addition, a
detailed evaluation of the identiﬁed increasing voltage deviation in between cells with the
methods other than the presented model-based approach seems promising and has to be
investigated in future work.
Conceptualisation, J.K., J.G. and A.H.; methodology, J.K. and A.H.; software,
A.H.; validation, A.H., N.O., J.G. and J.K.; formal analysis, J.K. and A.H.; investigation, J.K., A.H.,
N.O. and J.G.; resources, U.N.; data curation, J.K. and A.H.; writing—original draft preparation,
J.K.; writing—review and editing, J.K., J.G., A.H. and U.N.; visualisation, J.K.; supervision, R.B. and
H.-P.B.; project administration, U.N. and R.B.; funding acquisition, U.N. and R.B. All authors have
read and agreed to the published version of the manuscript.
Parts of this research were founded by the Federal Ministry for Economic Affairs and
Energy of Germany in the project RiskBatt (project number 03E13010A).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study is available on request from the
We thank Scania CV AB for providing battery modules and battery cases en-
abling tests close to application as well as the support in data, organisation and preparation of the
extensive experiments. In addition, we thank Draeger Safety AG & Co. KGaA for providing the
mobile gas-sensing unit. For conducting the high-energetic abuse tests we are grateful for having had
the opportunity to switch to a test ﬁeld of VoltaLabs, Goslar. The authors acknowledge the ﬁnancial
support by the Federal Ministry for Economic Affairs and Energy of Germany in the project RiskBatt
(project number 03E13010A). We acknowledge support by Open Access Publishing Fund of Clausthal
University of Technology.
Conﬂicts of Interest:
The authors declare no conﬂict of interest. The founders had no role in the
design of the study; in the collection, analyses, or interpretation of data; in the writing of the
manuscript, or in the decision to publish the results.
The following abbreviations are used in this manuscript:
BEV Battery Electric Vehicle
BMS Battery Management System
CC Constant Current
CID Current Interrupt Device
CMC Cell Management Controller
CV Constant Voltage
DUT Device Under Test
GTR-(EVS) Global Technical Regulation (on Electrical Vehicle Safety)
IR Infra Red
OCV Open Circuit Voltage
Batteries 2022,8, 34 24 of 29
OEM Original Equipment Manufacturer
OSD Over-charge Safety Device
PHEV Plug-In Hybrid Electric Vehicle
PID Proportional-Integral-Derivative Controller
SEI Solid Electrolyte Interface
SOC State of Charge
SOH State of Health
VOC Volatile Organic Compound
WHVC World Harmonised Vehicle Cycle
Appendix A.1. Model-Based Fault Detection
Figure A1 pictures the schematic of the two-layered model, coupling electrical and
thermal processes, as described in detail in [
]. The main purpose of the model—as pictured—
is providing a reference value for evaluating the measured cell voltages during operation.
With later application on a BMS in mind, a simplified approach based on equivalent circuit
models (ECM) was chosen. As proven in various previous studies (e.g., [
]) this setup
provides adequate accuracy for the comparison without the computational expense of
spatially resolved differential equations.
Due to the serial connection of cells every cell has the same load current and following
the same heat generation. In combination with the assumption of a simpliﬁed
heat transfer path from the active material to the cooling ﬂuid in the bottom plate that
is identical for each cell as well, all cells have the same thermal characteristic. Quality
assessment of the module under investigation has further found, that the cell parameters
are so close to each other that no difference can be identiﬁed between individual cells.
Thus, modelling the thermal behaviour of just one cell is sufﬁcient for describing the full
module. With this assumption calculating just one electrical model is sufﬁcient as well
since—in addition to the temperature—the current and SOC are also identical due to the
Structure of the equivalent circuit models (ECM). (
) Electrical ECM for modelling the cell
under the inﬂuence of the load current
. Both the parameters and the voltage source
were implemented as look-up tables based on data samples gathered beforehand. (
ECM for tracking the cell temperature during load and with heat dissipation to the cooling ﬂuid.
Besides the cell temperature
that is used to update the model parameters in the electrical model
is modelled, representing the position of the temperature sensors. Heat dissipation by
convection or radiation towards the ambient was neglected.
Based on the model-design described above the model-based fault detection is imple-
mented as pictured within the ﬂowchart in Figure A2.
Batteries 2022,8, 34 25 of 29
Structure of the implemented model-based fault detection as initially presented in [
For details on residuum assessment please refer to Section 2.2.2.
Appendix A.2. Results
The development of the thermally triggered failure of the battery module is docu-
mented by the screenshots presented in Figure A3. While the rupture of the burst-disc is
only slightly indicated by small amounts of white smoke in Figure A3b the massive smoke
generation during Thermal Runaway can be identiﬁed in Figure A3c. In addition to the
corresponding to the characteristic events mentioned before throughout the document, the
fast development can be identiﬁed by the given timings.
(a) 0 s, 00:00 min (b) 2249 s, 37:29 min
(c) 2616 s, 43:36 min (d) 2717 s, 45:17 min
Screenshots of Test
at characteristic events. Small time offsets relative to Table 4to
capture the smoke after the event. (
) Start of heating
, initial state. (
) Rupture of burst-disc
, start of slight white smoke release. (
) Thermal Runaway
, massive white smoke generation
and pressure-caused leakage of battery case. Signiﬁcant upwards leap of
, see Figure 3. (
Flames, ignition and black combustion products at crack in sealing.
In addition to the time map of the individual detection methods given in Figure 7a
summary of the detection times is displayed for all four test within Table A1. Besides the
detection times and pre-warning duration
the cell temperatures of the ﬁrst and last
cell are given. Due to the large deviation of the temperatures
of just one point of temperature measuremnet in the case of a local fault becomes clear.
Batteries 2022,8, 34 26 of 29
Thus, the indicrect measurment on cell level by the model-based approach provides more
Fault detection time relative to the start of heating and pre-warning to Thermal Runaway
) triggered by external heating with
5 K min−1
8 K min−1
). Adjacent and most
distanced cell temperatures
given at the moment of detection. Detailed information on
detection methods can be found in Table 6.
A B C D
It2076.00 s 2138.00 s 1449.00 s 1518.00 s
∆tTR 532.00 s 502.00 s 299.00 s 356.00 s
T01 121.61 °C 120.75 °C 119.05 °C 123.27 °C
T12 29.13 °C 29.03 °C 26.95 °C 27.50 °C
II t774.00 s 1231.00 s 809.00 s 556.00 s
∆tTR 1834.00 s 1409.00 s 939.00 s 1318.00 s
T01 48.73 °C 67.12 °C 62.65 °C 44.53 °C
T12 26.34 °C 26.48 °C 25.27 °C 25.54 °C
III t1754.00 s 2130.00 s 1478.00 s 1371.00 s
∆tTR 854.00 s 510.00 s 270.00 s 503.00 s
T01 101.70 °C 120.38 °C 123.04 °C 106.63 °C
T12 29.29 °C 29.11 °C 27.37 °C 26.72 °C
IV t1872.00 s 1794.00 s 1413.00 s 1046.00 s
∆tTR 736.00 s 846.00 s 335.00 s 828.00 s
T01 108.01 °C 99.60 °C 114.54 °C 77.84 °C
T12 28.57 °C 29.06 °C 26.45 °C 26.01 °C
Vt2249.00 s 2297.00 s 1549.00 s 1611.00 s
∆tTR 359.00 s 343.00 s 199.00 s 263.00 s
T01 118.41 °C 132.30 °C 129.47 °C 135.74 °C
T12 28.64 °C 29.78 °C 27.29 °C 28.80 °C
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