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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 5 min 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.
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.
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
Automotive Application
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; (A.H.); (J.G.); (N.O.); (R.B.)
2Battery Performance & Cell Development, Scania CV AB, 151 87 Södertälje, Sweden;
3Institute of Electrical Power Engineering and Electrical Energy Engineering, Clausthal University of
Technology, Leibnizstraße 28, 38678 Clausthal-Zellerfeld, Germany;
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
5 min
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
1. Introduction
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
Bolt [
])—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.
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
90 °C
] while either
T350 °C
U0.5 V
dt 10 K min1
has been utilised in [
], where
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. [16] or Liu et al. [17].
Decreasing the heat transfer from cell to cell by constructive changes [
], active
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
faults [7].
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 [24].
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.
0.9 L/Ah
3.5 L/Ah
] at
25 °C
1 bar
Pre-cell rupture: Electrolyte leak-
age [26]
SOC and chemistry [27]
Ageing (at different conditions) [28]
Atmosphere and oxygen availabil-
ity [29]
Type of trigger [30]
Increasing temperature by exothermic reactions cause de-
creasing impedance and change of phase [
]; inverse
behaviour for high temperatures [32]
Safety Device Shutdown-separator cause
of 1300% [
usually at 120 °C to 150 °C [34]
SOC [31,35]
Temperature of operation
Cyclic Ageing [36,37]
Calendrical Ageing [7]
Batteries 2022,8, 34 3 of 29
Table 1. Cont.
Process Variable Dependency of Thermal Runaway Processes Other Dependencies
Internal pres-
Thermal expansion
Electrolyte evaporation and gas producing reactions[38]:
CO2by SEI decomposition [39] at 90 °C to 120 °C [40]
CxHyby SEI-reformation [41] at 120 °C to 218 °C [34]
Safety device: burst-disc rupture approx. at
10 bar
12 bar
for prismatic cells [
]; deviation with 18650 cells
σ5 % [43]
SOC and load [44,45]
Ambient pressure [46]
Temperature of operation
Gas formation during ageing [7]
After cell rupture: Vaporisation (White smoke [
]) and
combustion of cell components (Black smoke) [38].
Ejection of internal cell components (Black smoke) [15,47]
Ambient atmosphere
Increasing internal temperature due to exothermic reac-
Load [
] and cooling
situation [49,50]
Temperature of operation
Increasing temperature as OCV =f(T)[51]
Melting of separator:
– PE = 120 °C to 135 °C, PP = 150 °C to 166°C [34,40,52]
Safety Device: CID, OSD, Shutdown-separator
120 °C
150 °C [34]
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. [57]
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
in [58].
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
Section 4.
2. Materials and Methods
2.1. Material
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 [
], and
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
25 °C
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
100 %
state of charge (SOC), and after
a relaxation period of
30 min
, the initial state of
was reached by discharging
with C/3.
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
to cell
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
Figure 1.
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.
2.1.2. Sensors
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
data set.
Batteries 2022,8, 34 6 of 29
Table 2.
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
1×heating device
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 (
TInl et
) 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
for hydrogen
and hydrocarbons
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
5 kHz
very short pressure peaks have used to be observed. The upper sensor range is
1 bar
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.
2.1.3. Load
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 min1
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 K1to 130 W K1.
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
400 W
each were connected
in parallel to
230 V
, providing a peak power of
1200 W
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
2.2. Method
2.2.1. Experimental
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
30 min
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
25 °C
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
10 Hz
rate beforehand on board of an electric
heavy duty truck, driving through a mixed urban–rural area, with a total duration of
3600 s
. In comparison, the world harmonised vehicle cycle (WHVC) [
application lasts
1800 s
. 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
current cycle.
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,
170 min
. The full profile relative to the module capacity is visualised
within Figure 2, as well as the corresponding SOC initialised at SOCup per .
Figure 2.
Change of SOC of module by dynamic electrical test profile with initial state
SOCup per
Synchronous thermal fault trigger with either 5 K min1or 8 K min1after 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
5 min
phase at a constant
25 °C
followed by continuous heating with a constant temperature
slope. Besides a heating rate of
5 K min1
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 min1
was investigated as well. The latter rate was chosen to consider the test requirements
by FreedomCAR of
5 K min1
10 K min1
]. 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.
Heating rate 5 K min15 K min18 K min18 K min1
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
cell (
) reached
80 °C
, the logging was manually started. In addition, the heating unit was
deactivated when either
had surpassed
150 °C
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
Appendix A
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
/Simulink [
]. Please find additional
information on the model setup in the Appendix A.
As commonly chosen as the best compromise between accuracy and computational
effort [
], 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 (
charging) and
are equivalent resistances and capacities, respectively. Current
direction, SOC and temperature were considered as influences for the cell parameters
, whereas
was implemented as
. Here,
is a correction factor to
incorporate the hysteresis of the open voltage behaviour, as described in [77].
Ucell(t) = OCV +I·"R0+
Ri·1exp t
characteristics were defined at
25 °C
in between
100 %
0 %
by averaging
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
20 °C
40 °C
10 %
90 %
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
was set
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.
Q=I·(Ucell OCV)(2)
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
t0.5 s
to trigger the fault detection. Based on the
reference measurements with the same electrical load, the threshold was set to
20 mV
60 mV.
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
0.25 C
instead of the given value of
2.4 C
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
visually validated.
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.
Figure 3.
Development of Thermal Runaway due to external heating by characteristic events.
Start of heating.
Activation of OSD-device.
Rupture of burst-disc.
Thermal Runaway.
) 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
Table 4.
Duration for reaching characteristic events by external heating with
5 K min1
) and
8 K min1
). 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.
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
300 s
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
12, additional
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
01 and
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
TInl et
and ambient temperatures
during the
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
measurement of
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. [78].
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 (
10 °C
20 °C
) 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
120 s
, 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
0; 500
. 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
Figure 4.
Development of sensor reading during thermal triggered Thermal Runaway for test
5 K min1
Start of heating.
Activation of OSD-device.
Rupture of burst-disc.
Runaway. (
) 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
1000 s
1500 s
before the
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
of approximately
255 µm m1
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 m1
260 µm m1
. In
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.
Table 5.
Module strain difference between start of heating
and rupture of burst-disc
external heating with
5 K min1
) and
8 K min1
). Potential measurement error marked
in red.
e255.16 µm m1215.91 µm m1259.33 µm m166.85 µm m1
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.
Thus, at
300 s
1400 s
, 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
1000 s
1500 s
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.
Figure 5.
Development of module strain during external heating with
5 K min1eTR
in comparison
to reference dynamic profile
eRe f
(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
200 °C
at this
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
and subsequent
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
45 min
within the test environment supports the hypothesis, as approximately constant
values within
102.71 ±2.04
as 1-
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
the cells
, as pictured in the lower subplot. Due to the experimental setup
of heating
12 is approximately still within normal operation conditions, resulting
in a smaller residuum
. With continuous heating, the difference
approaches the lower threshold (
20 mV
, as indicated in Figure by a red horizontal line.
However, the first surpass, approximately at
450 s
, does not trigger a warning as the second
condition of
t0.5 s
is not met. The usefulness of the implemented timing condition
can be seen approximately at
1700 s
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
Figure 6.
Model -based detection of external heating in Test
. Measured and simulated voltage of
01 (
); model deviation from
01 and
12 as fault signal (
).Characteristic events:
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
outside [
]. 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
drop of
U0.1 V
]. 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
5 min
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
Table 6.
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 UiUmin
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
Figure 7.
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 min1
) and
8 K min1
Please find method description in Table 6. Characteristic events
Start of heating,
Activation of
OSD-device and
Rupture of burst-disc are maked by grey dashed lines. The GTR-
5 min
-criteria is
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
duration between
. 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
130 s
330 s
, 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.
Table 7.
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
2 s
, 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
USim Ui
no additional
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 [
]. Therefore,
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
readings [92].
4. Conclusions
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 min1
8 K min1
in two
repetitions each.
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.
Author Contributions:
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
corresponding author.
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
LFP Lithium-Iron-Phosphate
LTO Lithium-Titanium-Oxide
NMC Nickel-Manganese-Cobalt
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
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
series connection.
Figure A1.
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. (
) Thermal
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
Figure A2.
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
Figure A3.
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. (
) First
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
the disadvantage
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
detailed insights.
Table A1.
Fault detection time relative to the start of heating and pre-warning to Thermal Runaway
) triggered by external heating with
5 K min1
) and
8 K min1
). Adjacent and most
distanced cell temperatures
given at the moment of detection. Detailed information on
detection methods can be found in Table 6.
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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... Most failures for these kinds of systems are due chilling and thermal runaway [6]. Additionally, crash/shock results, charging conditions at low temperature and cell stress and aging effects might be the reason for failures [7,8]. However, the technology of Li-ion batteries is under development and still progressing, which means it has not settled yet. ...
... Therefore, the maximum allowed torque and power load should satisfy Condition (7). T continuous 0.7 ≤ T max_required , P continuous 0.7 ≤ P max_required (7) In other words, the maximum continuous torque required by the vehicle T max_required should never exceed the maximum continuous torque allowed by the electric motors T continuous . At the same time, the electric motors should satisfy the same requirement for the power maximum allowed power P continuous . ...
... The sprocket shaft is usually driven by the final reduction gears that, in our case, have a maximum reduction ratio of ratiofinal_drive = 19.5. If the electric motors run at maximum speed (3500 rpm), the vehicle will run at a maximum of about 20 kmh (7). ...
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The performance of modern, new generation-armored vehicles would greatly benefit from overall engineering, optimization, and integration techniques of advanced diesel engines-electrified transmissions. Modern axial flux electric motors and controllers are perfectly able to replace the classical automatic gearbox and complex steering system of traditional Main Battle Tanks. This study shows a possible design of a serial hybrid electric power pack for very heavy tanks with a weight well over 50 tons. The result is a hybrid power system that improves the overall performance of armored vehicles off-road and on-road, improving the acceleration and the smoothness of the ride. In addition, fuel consumption will be reduced because the internal combustion engine operates at fixed rpm. The electric motors will outperform the traditional engines due to their very high torque output even at “zero speed”. The weight of a hybrid system has also been calculated. In fact, in many cases, it is possible to use all off-the-shelf components. The on-board diagnosis of the subsystems in the hybrid powertrain makes it possible to achieve a Time Between Overhaul (TBO) of 4500 h with a failure probability inferior to one in 10,000.
... The voltage and temperature of the battery were used to develop fault diagnosis models using the data-driven methods described above. Despite this, few studies have been conducted on the diagnosis of abnormal charging capacity, particularly those based on actual vehicle data [26]. Zheng et al. [27] proposed a method based on an Extended Kalman Filter (EKF) for the diagnosis of capacity anomalies. ...
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Overcharging due to an abnormal charging capacity is one of the most common causes of thermal runaway (TR). This study proposes a method for diagnosing abnormal battery charging capacity based on electric vehicle (EV) data. The proposed method can obtain the fault frequency and output the corresponding state of charge (SOC) when a fault occurs. First, a machine-learning-based data cleaning framework is developed to overcome the limitations of the interpolation method. Then, offline training is implemented, based on big vehicle operation data and an improved Gaussian process regression (GPR). Thereafter, online monitoring of the discrete capacity increment (DCI) is used to identify the abnormal charging capacity. The abnormal charging capacity fault is identified by the absolute error between the GPR outputs and the true DCI, and the thresholds are determined using a Box–Cox transformation with a value of 3σ. The diagnostic results indicate that the abnormal charging capacity of the TR vehicle is identified two months in advance, and the fault frequency of the abnormal and normal vehicles is 0.5221 and 0.0311, respectively. EV operation data and various methods are used to validate the robustness and applicability of the proposed method.
... It is considered that the stresses that may appear in the exploitation of EV energy sources ( Figure 1) are multiple from the point of view of physical phenomena. In various research, the reactions to the safety incidents that occur in the energy sources after overloading have been analysed in detail, and based on this, the following classification, the external stresses on the energy sources have been determined as in [20][21][22] and as follows: ...
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The exponential development and successful application of systems-related technologies that can put electric vehicles on a level playing field in direct competition with vehicles powered by internal combustion engines mean that the foreseeable future of the automobile (at least) will be dominated by vehicles that have electric current stored in batteries as a source of energy. The problem at the European level related to the dependence on battery suppliers from Asia directly correlates with the need to use batteries as energy storage media for energy from renewable sources (photovoltaic and wind), and leads to the need for research into the possibilities for their reuse, remanufacturing or recycling (at the end of their life or purpose of use), and reintroduction, either fully or partially, back into the economy. This article presents possibilities for increasing the protection of the integrity of the cells that form a battery in the event of an impact/road accident, by the numerical analysis of a topographically optimized battery module case. The proposed solution/method is innovative and offers a cell protection efficiency of between 16.6–60% (19.7% to 40.7% if the mean values for all three impact velocities are considered). The efficiency of a cell’s protection decreases with the increase in impact velocity and provides the premise for a greater part of the saved cells to be reintegrated into other energy storage systems (photovoltaic and/or wind), avoiding future problems relating to environmental pollution.
... The onset of a safety-critical condition can be triggered by mechanical, electrical or thermal failures [11,12]. With the addition of passive protection devices [13], a battery management system (BMS) with intelligent fault detection [14,15], a robust mechanical design [16] or with appropriate cooling solutions [17] on system level, the risk of many faults can be reduced to a lower and acceptable level. One of these faults, which is suspected to be responsible for up to 90% of field failures [18], is the internal short circuit (ISC). ...
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A possible contamination with impurities or material weak points generated in cell production of lithium-ion batteries increases the risk of spontaneous internal short circuits (ISC). An ISC can lead to a sudden thermal runaway (TR) of the cell, thereby making these faults especially dangerous. Evaluation regarding the criticality of an ISC, the development of detection methods for timely fault warning and possible protection concepts require a realistic failure replication for general validation. Various trigger methods are currently discussed to reproduce these ISC failure cases, but without considering a valid basis for the practice-relevant particle properties. In order to provide such a basis for the evaluation and further development of trigger methods, in this paper, the possibilities of detecting impurity particles in production were reviewed and real particles from pouch cells of an established cell manufacturer were analysed. The results indicate that several metallic particles with a significant size up to 1 mm × 1.7 mm could be found between the cell layers. This evidence shows that contamination with impurity particles cannot be completely prevented in cell production, as a result of which particle-induced ISC must be expected and the need for an application-oriented triggering method currently exists. The cause of TR events in the field often cannot be identified. However, it is noticeable that such faults often occur during the charging process. A new interesting hypothesis for this so-far unexplained phenomenon is presented here. Based on all findings, the current trigger methods for replicating an external particle-induced ISC were evaluated in significant detail and specific improvements are identified. Here, it is shown that all current trigger methods for ISC replication exhibit weaknesses regarding reproducibility, which results mainly from the scattering random ISC contact resistance.
... Therefore, energy saving and environmental protection have become priorities in the development of various products [1][2][3][4]. As an efficient energy storage device, Li-ion batteries are widely used because of their high energy and power density, long cycle life, and high environmental friendliness [5][6][7]. However, in recent years, cases of explosion or fire due to li-ion batteries have commonly occurred in cell phones, laptops, electric vehicles, energy storage plants, and airplanes worldwide. ...
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Thermal runaway (TR) and the thermal runaway propagation (TRP) of Li-ion batteries can lead to safety incidents and cause explosion or fire accidents. Therefore, TR is a critical issue for the thermal safety of Li-ion batteries. In this study, the TR and TRP behavior of Li-ion batteries using different abuse methods (nail penetration, side heating, and overcharge) was investigated experimentally. First, the Extended Volume Accelerating Rate Calorimetry (EV-ARC) test was performed using the cell with an internal implantation thermocouple for a comparative study. Three abuse methods were used to induce TR and TRP for the cells and modules. At the cell level, the maximum temperature inside the cell under the EV-ARC test, nail penetration, and side-heating abuse was 994.8 °C, 964.3 °C, and 1020 °C, respectively. The thermocouple inside the cell under the overcharge abuse test was broken, and the experimental phenomenon indicated that the cell was most severely damaged under the overcharging abuse test. At the module level, the TRP behavior using the three abuse methods was different than in the first two TR cells, while the behavior of the other cells was similar. It was evidenced that TRP triggered by the overcharge abuse was the most hazardous, followed by the side-heating abuse, and lastly, the nail-penetration abuse was the least.
... In the 21 st century, energy and the environment are fundamental issues for human survival and social development [1]. Several industrial applications require rechargeable batteries working at elevated temperatures, such as the power supply of electric vehicles [2]. For such particular applications, batteries are exposed to an operating temperature range of 20 to 50 °C and should be able to undergo only moderate degradations at those temperatures. ...
In this study, the effect of operating temperature at 55 °C on xLi2MnO3-(1-x)LiMn0.5Ni0.5O2 electrodes during the charge/discharge process at different current densities was investigated. X-ray diffraction (XRD) and scanning electron microscopy (SEM) were used for structural and morphological analysis of the fabricated cathode materials, while charge-discharge curves and differential capacity were used to study the electrochemical behavior. Results confirm the formation of the structures with two phases associated with the components of the layered material. It was found that at 55 °C, a capacity higher than 357 mAh g-1 could be achieved at a voltage of 2.5-4.8 V vs. Li/Li+, which was larger than the capacity achieved at room temperature. At 55 °C, a change in valence could be observed during charging and discharging due to the change in the position of the peaks associated with Mn and Ni, highlighting cathodic material with x = 0.5 as the material that retains the layered structure at this temperature. This work confirms the good performance of electrodes made with this material at elevated temperatures and gives a better understanding of its electrochemical behavior.
... The use of these energy systems is closely related to the development of increasingly efficient storage systems; hence, much research is focused on improving their performance. The characteristics of lithium batteries, such as their high specific capacity, energy density, and the possibility of using non-toxic reagents in their manufacture [6][7][8][9], have enabled them to be used in various technological devices, especially in the automotive area, such as electric vehicles and hybrid electric vehicles [10,11], where energy density plays an important role. In this aspect, lithium batteries surpass other energy storage systems. ...
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In our study, the cathodic material xLi2MnO3–(1􀀀x)LiNi0.5Mn0.5O2 was synthesized by means of the co-precipitation technique. The effect of x (proportion of components Li2MnO3 and LiNi0.5Mn0.5O2) on the structural, morphological, and electrochemical performance of the material was evaluated. Materials were structurally characterized using X-ray diffraction (XRD), and the morphological analysis was performed using the scanning electron microscopy (SEM) technique, while charge–discharge curves and differential capacity and impedance spectroscopy (EIS) were used to study the electrochemical behavior. The results confirm the formation of the structures with two phases corresponding to the rhombohedral space group R3m and the monoclinic space group C2/m, which was associated to the components of the layered material. Very dense agglomerations of particles between 10 and 20 �m were also observed. In addition, the increase in the proportion of the LiNi0.5Mn0.5O2 component affected the initial irreversible capacity and the Li2MnO3 layer’s activation and cycling performance, suggesting an optimal chemical ratio of the material’s component layers to ensure high energy density and long-term durability.
... Klink et al. [50] compared the thermal runaway detection effectiveness of a thresholdscheme multi-sensor system with that of a model-based system in an EV battery case. The model was based on an electrical equivalent circuit model (ECM) for cell voltage and a thermal ECM for temperature. ...
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Lithium-ion (Li-ion) batteries have been utilized increasingly in recent years in various applications, such as electric vehicles (EVs), electronics, and large energy storage systems due to their long lifespan, high energy density, and high-power density, among other qualities. However, there can be faults that occur internally or externally that affect battery performance which can potentially lead to serious safety concerns, such as thermal runaway. Thermal runaway is a major challenge in the Li-ion battery field due to its uncontrollable and irreversible nature, which can lead to fires and explosions, threatening the safety of the public. Therefore, thermal runaway prognosis and diagnosis are significant topics of research. To efficiently study and develop thermal runaway prognosis and diagnosis algorithms, thermal runaway modeling is also important. Li-ion battery thermal runaway modeling, prediction, and detection can help in the development of prevention and mitigation approaches to ensure the safety of the battery system. This paper provides a comprehensive review of Li-ion battery thermal runaway modeling. Various prognostic and diagnostic approaches for thermal runaway are also discussed.
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Nowadays, batteries for electric vehicles are expected to have a high energy density, allow fast charging and maintain long cycle life, while providing affordable traction, and complying with stringent safety and environmental standards. Extensive research on novel materials at cell level is hence needed for the continuous improvement of the batteries coupled towards achieving these requirements. This article firstly delves into future developments in electric vehicles from a technology perspective, and the perspective of changing end-user demands. After these end-user needs are defined, their translation into future battery requirements is described. A detailed review of expected material developments follows, to address these dynamic and changing needs. Developments on anodes, cathodes, electrolyte and cell level will be discussed. Finally, a special section will discuss the safety aspects with these increasing end-user demands and how to overcome these issues.
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Safety for automotive lithium-ion battery (LIB) applications is of crucial importance, especially for electric vehicle applications using batteries with high capacity and high energy density. In case of a defect inside or outside the cell, serious safety risks are possible including extensive heat generation, toxic and flammable gas generation, and consequently fire and explosion. New regulations (GB 38031-2020) require a warning for passengers at least five minutes before serious incidents. This regulation can hardly be fulfilled with state-of-the-art battery monitoring. In this study, gases produced during battery failure before and during a thermal runaway (TR) are investigated in detail and the use of different gas sensors as early detectors of battery incidents is tested and proposed. The response of several commercially available gas sensors is tested in four battery failure cases: unwanted electrolysis of voltage carrying parts, electrolyte vapor, first venting of the cell and the TR. The experiments show that battery failure detection with gas sensors is possible but depends highly on the failure case. The chosen gas sensor can detect H2 produced by unwanted electrolysis and electrolyte vapor and gases produced by degassing of state-of-the-art LIBs. The results may contribute significantly to failure detection and improvement of battery safety.
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With this paper a method to detect faults of lithium-ion cells during operation is first presented and later validated by experiment. Since every cell fault will increase the cell temperature towards its process until thermal runaway the method uses the temperature-dependent change of the cell impedance as fault feature. Using a 46 Ah pouch cell the model was parameterised by electrochemical impedance spectroscopy and then validated during dynamic load. For this purpose the Worldwide harmonised Light vehicles Test Procedure (WLTP) was chosen. The presence of a fault was simulated by heating the cell once uniformly and once locally and the progression of the chosen fault feature analysed. For both test cases the method proposed was able to detect the present heat source before the thermal runaway was triggered and venting or voltage drop were observed.
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Efficient and reliable energy storage systems are crucial for our modern society. Lithium-ion batteries (LIBs) with excellent performance are widely used in portable electronics and electric vehicles (EVs), but frequent fires and explosions limit their further and more widespread applications. This review summarizes aspects of LIB safety and discusses the related issues, strategies, and testing standards. Specifically, it begins with a brief introduction to LIB working principles and cell structures, and then provides an overview of the notorious thermal runaway, with an emphasis on the effects of mechanical, electrical, and thermal abuse. The following sections examine strategies for improving cell safety, including approaches through cell chemistry, cooling, and balancing, afterwards describing current safety standards and corresponding tests. The review concludes with insights into potential future developments and the prospects for safer LIBs.
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Lithium (Li)-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles (EVs) and smart grids. However, various faults in a Li-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This article provides a comprehensive review of the mechanisms, features, and diagnosis of various faults in LIBSs, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed.
Battery fault diagnosis is essential to ensure the safe and reliable operation of electric vehicles. Early detection of battery faults can reduce battery incidents and property losses. However, early warning of battery thermal runaway is still a challenging task. This paper proposes a novel data-driven method for lithium-ion battery pack fault diagnosis and thermal runaway warning based on state representation methodology. The normalized battery voltages are used to achieve accurate identification of battery early faults. The proposed method calculates the real-time state of each cell to characterize the internal characteristics of the battery cell, and the state changes are recorded to achieve battery fault diagnosis. The fault detection time is compared with the alarm time of real vehicles to verify the effectiveness of the proposed method. The real-world operation data of four electric vehicles with different specifications are used to verify the feasibility, robustness, and reliability of the proposed method. The results show that the method can achieve not only the accurate identification of the faulty cells and accurate determination of the voltage fault type but also the early detection of faults and early warning of thermal runaway.
Along with the popularization of the lithium-ion battery (LIB), much more attention is attracted to its problem of thermal runaway. This paper proposes a simple but novel method to investigate the gas generation throughout the process of thermal runaway. The novelty from the previous study lies in that the battery cells without the cap are used as the test samples. The naked battery offers a key to decouple the pressure caused by the vapor of electrolyte and that of the gases generated in the chemical reaction. This study makes up the knowledge gap of measuring internal pressure before the venting behavior and provides a more accurate method to describe the gas generation of thermal runaway. Three 100% state of charge LIBs with the cathode of LiNi0·8Co0·1Mn0·1O2 are used as tested samples to illustrate the application of this method. Two important results are withdrawn from the samples’ test: (I) the relationship between temperature increasing rate and gas generation rate is not linear until battery temperature approaches the uncontrollable temperature; (II) the partial pressure of gas generated due to the redox reactions bears the major responsibility for the safety valve opening.
The study on the thermal flow characteristics of temperature and pressure is helpful to understand the thermal runway mechanism of batteries. In this study, a 50 Ah, 3.65 V commercial prismatic battery with an Li(Ni0.6Co0.2Mn0.2)O2 cathode is tested through thermal abusive experiments. The thermal flow characteristics of thermal runaway behaviors including temperature, pressure and gas quality have studied comprehensively by the proposed method of a dual-sensor, which are buried into battery. The experimental results indicate that the thermal runaway test can be divided into five stages. The internal pressure reaches to its maximum value of 1230 kPa and the first venting of the battery is appeared during Stage Ⅲ. During Stage Ⅴ, the battery internal temperature reaches to the maximum value of 655℃, and the second venting happens. The maximum pressure appears 290s earlier than temperature in average, which can be used for warming signal. Additionally, before battery safety valve bursting, its internal pressure variation has interactions with changings in both temperature and the amount of chemical reactant. To analyze this relationship, the P-T coefficient is introduced. The P-T coefficient is positive in the temperature ranges of 30 ℃ to 65 ℃, 95℃∼ 115 ℃and 130 ℃∼ 180 ℃. This means that the internal pressure of battery increases with temperature rising, which is mainly due to the reaction gas production. In the temperature of 65 ℃∼95℃, the P-T coefficient is negative. It can be deduced that the produced reactants, such as alkanes and alkenes, are re-dissolved in the organic electrolyte solvents. In summary, this novel study will be a guidance for thermal hazards early warming and battery internal thermal state analysis, which guides the thermal safety design of batteries.
Electric Vehicles (EVs) have emerged as most promising means of transport owing to the low operational costs, high speed, and energy-efficient battery technologies, where battery thermal management system (BTMS) is possibly the most crucial element of an EV. During the charging/discharging mode of EVs, a major focused area for the researcher is to maintain the optimal working temperature range of the batteries and reduce both the maximum temperature and temperature difference. Suitable and effective cooling methods can significantly reduce the adverse effect of the high surface temperature of battery cells and efficiently augments the battery thermal efficiency, improves the safety of EVs, and increase the service life. In this context, this work presents a detailed state of the art review of different BTMS technologies, including natural and forced air-cooling techniques, direct and indirect liquid cooling methods, and cooling by heat pipes. It is found that the air-cooled BTMS possesses advantageous features such as safe, consistent, and simple design, but the lower heat capacity and thermal efficiency of the air as a cooling medium restricts its application to a low capacity battery. This leads to employment of forced air-cooled BTMS under high charging/discharging rate, in which air flows through the channels inside the battery packs to provide the optimum cooling. Liquid-cooled BTMS is also emerging as one of the most promising cooling technologies, which requires attention to the sealing cover during the design stage to avoid leakages. The integration of metal plates with the mini channel can effectively improve the cooling performance, but the weight of the system is a major concern. Liquid metals, nanofluids, and boiling liquids are considered as the most prominent battery cooling methods owing to their higher thermal conductivity. The advancement in hybrid cooling using fins, nanofluids, PCM along with micro channels-based cooling will significantly improve the battery performance under high charging/discharging rate and attention should be given to compact design with a cheaper cost.
Lithium ion batteries (LIBs) are booming due to their high energy density, low maintenance, low self-discharge, quick charging and longevity advantages. However, the thermal stability of LIBs is relatively poor and their failure may cause fire and, under certain circumstances, explosion. The fire risk hinders the large scale application of LIBs in electric vehicles and energy storage systems. This manuscript provides a comprehensive review of the thermal runaway phenomenon and related fire dynamics in singe LIB cells as well as in multi-cell battery packs. Potential fire prevention measures are also discussed. Mitigating the hazards associated with a growing number of LIB applications represents a significant new challenge for the fire safety engineering community. Some perspectives and outlooks on the future of LIB fire safety research and safety engineering are given.