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Aging of Lithium-Ion Batteries in Electric Vehicles: Impact of Regenerative Braking


Abstract and Figures

In an electric vehicle, energy recovery during regenerative braking causes recharge periods of high current rate, which might damage the Li-ion traction battery. To determine the impact of regenerative braking on battery aging, an experimental cycle life study has been performed: Driving load profiles with different magnitudes of regenerative braking have been applied to high-energy Li-ion cells at different temperatures and states of charge (SoC). An additional calendar life study has enabled an identification of usage-dependent and usage-independent battery aging. After five months of cycling, corresponding to a driven distance of 50,000 km, cell degradation has varied substantially with different operation conditions. Our paper provides valuable new insights on the impact of regenerative braking on battery aging: A higher level of regenerative braking has generally led to reduced battery aging. This can be attributed to a reduction of lithium plating, as the depth of discharge is reduced with an increased amount of charge recovered by regenerative braking. Our study has shown that it is not the short-time recharging with high current rates, but the long-lasting charging periods, even with only low current rates, that promotes lithium plating. Moreover, the comparison of usage-dependent and usage-independent battery aging has revealed that cyclic aging decreases with temperature, whereas calendar aging increases with temperature. Thus, battery life can be extended by optimized operating conditions. In this paper, we provide advice for optimizing the operating conditions for Li-ion battery systems in electric vehicles. Not only regenerative braking, but also temperature and SoC, is considered for optimal operating strategies maximizing battery life. Based on the results of our experimental study, achieving a driven distance of 100,000 km with only 10 % capacity fade appears to be possible. Such a low battery aging is essential to promote the spread of electric vehicles, as it reduces the total cost of ownership, which is a prerequisite for the long-term success of electric vehicles.
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EVS28 International Electric Vehicle Symposium and Exhibition
KINTEX, Korea, May 3-6, 2015
Aging of Lithium-Ion Batteries in Electric Vehicles:
Impact of Regenerative Braking
Peter Keil1, Andreas Jossen1
1Institute for Electrical Energy Storage Technology, Technische Universität München,
Arcisstr. 21, 80333 Munich, Germany,
In an electric vehicle, energy recovery during regenerative braking causes recharge periods of high current
rate, which might damage the Li-ion traction battery. To determine the impact of regenerative braking on
battery aging, an experimental cycle life study has been performed: Driving load profiles with different mag-
nitudes of regenerative braking have been applied to high-energy Li-ion cells at different temperatures and
states of charge (SoC). An additional calendar life study has enabled an identification of usage-dependent
and usage-independent battery aging.
After five months of cycling, corresponding to a driven distance of 50,000 km, cell degradation has varied
substantially with different operation conditions. Our paper provides valuable new insights on the impact of
regenerative braking on battery aging: A higher level of regenerative braking has generally led to reduced
battery aging. This can be attributed to a reduction of lithium plating, as the depth of discharge is reduced
with an increased amount of charge recovered by regenerative braking. Our study has shown that it is not the
short-time recharging with high current rates, but the long-lasting charging periods, even with only low cur-
rent rates, that promotes lithium plating. Moreover, the comparison of usage-dependent and usage-independ-
ent battery aging has revealed that cyclic aging decreases with temperature, whereas calendar aging increases
with temperature. Thus, battery life can be extended by optimized operating conditions.
In this paper, we provide advice for optimizing the operating conditions for Li-ion battery systems in electric
vehicles. Not only regenerative braking, but also temperature and SoC, is considered for optimal operating
strategies maximizing battery life. Based on the results of our experimental study, achieving a driven distance
of 100,000 km with only 10 % capacity fade appears to be possible. Such a low battery aging is essential to
promote the spread of electric vehicles, as it reduces the total cost of ownership, which is a prerequisite for
the long-term success of electric vehicles.
Keywords: Li-ion battery, electric vehicle, aging, cycle life, regenerative braking
EVS28 International Electric Vehicle Symposium and Exhibition
1 Introduction
An electric vehicle can recover energy during
braking by using its electric motor as an electric
generator. This regenerative braking leads to a
partial recharging of the vehicle’s traction battery,
thus, increasing range and efficiency. Braking ac-
tions in an automobile usually last only few sec-
onds. Hence, the traction battery is repeatedly re-
charged by short charging periods. The magnitude
of such a recharge period depends on the rate of
deceleration. Even during ‘moderate’ braking, the
battery has to cope with substantial current ampli-
tudes. In today’s electric vehicles, the traction bat-
tery is usually composed of Li-ion cells, which
have strict operational voltage limitations. Ex-
ceeding these limitations intensifies aging and can
lead to safety issues. Moreover, high charging cur-
rents can also damage the cells irreversibly, thus,
reducing cycle life. Obtaining a long cycle life for
the battery system is a prerequisite for the long-
term success of electric vehicles. Hence, a pro-
found knowledge of the determining factors for
battery aging is essential. Therefore, we present an
experimental cycle life study demonstrating the
effects of regenerative braking on the aging of Li-
ion cells under different operating conditions.
2 Fundamentals
For the design of a meaningful cycle life study, the
general limitations of charging Li-ion cells have
been considered. Moreover, an appropriate cell
has been selected and a representative load profile
has been defined.
2.1 Charging Li-Ion Cells
The charging process of Li-ion cells is mainly lim-
ited by two factors: lithium plating on the anode
and oxidation of the electrolyte solution due to
high potentials at the cathode [1][2]. Both unde-
sired side reactions lead to an irreversible loss of
cyclable lithium. Moreover, they promote the
growth of resistive surface layers. [1][3][4]
Lithium plating describes the reduction of Li+
ions, dissolved in the electrolyte, to metal lithium
at the anode’s surface, which takes place instead
of the regular intercalation as neutral lithium at-
oms into the host lattice structure of the active ma-
terial. It can originate from limitations in charge
transfer or lithium solid diffusion [6][7]. Lithium
plating can occur, when the anode potential drops
below the equilibrium potential of Li+/Li [6].
Some of the plated lithium later reacts irreversibly
with the electrolyte and forms insoluble side prod-
ucts [3][4]. Graphite anodes, which are used in
most Li-ion cells, are very prone to lithium plating
due to their low equilibrium potential, especially at
high states of charge (SoC) [8]. As a general trend,
lithium plating increases with higher SoC, higher
charging currents and reduced temperature [6][8].
All in all, charging currents for graphite-based Li-
ion cells are mainly limited by the intercalation ki-
netics at the anode [9].
The charging voltage is limited by the oxidation of
electrolyte solvents, which occurs at high cathode
(over)potentials [1][2]. Excessive overcharging can
entail gas evolution, overpressure inside the cell, an
opening of the cell’s safety vent, and leakage of
electrolyte. As the organic electrolytes of Li-ion
batteries are highly flammable, this can lead to a fire
or an explosion of the cell [10]. Thus, complying
with the maximum cell voltage, specified by the
manufacturer, is essential for safe operation and
long cycle life.
Whereas a maximum value for the cell voltage is
always defined in the datasheet, there are often only
recommended current values for continuous charg-
ing. No detailed information is provided, which cur-
rent rate can be applied to the cell for which period
of time without generating plated lithium, which de-
pends strongly on SoC and temperature. To investi-
gate the impact of recharging the battery during re-
generative braking on cycle life, an experimental
study is presented in this paper.
2.2 Cell Characterization
In our cycle life study, commercial 18650 cells from
a well-established manufacturer with mature pro-
duction processes have been examined. Stable pro-
duction processes guarantee reliable results without
substantial variations from production. As electric
vehicles have to provide a sufficient driving range,
high-energy cells are typically used for their trac-
tion battery systems. For our cycle life study, high-
energy Li-ion cells with NCA chemistry and a min-
imum nominal capacity of 2.8 Ah have been se-
lected (Panasonic NCR18650PD). These cells fea-
ture high specific energy of 220 Wh/kg, high energy
density of 570 Wh/l, and low internal resistance of
about 20 mΩ. Good capacity utilization at elevated
discharge currents, up to 10 A, and good discharge
performance at low temperature, even below 0 °C,
also qualify the cells for electric vehicle applica-
tions. [11]
The aging of Li-ion batteries generally comprises
usage-dependent and usage-independent degrada-
tion. Both lead to a loss of capacity and a rise of
internal resistances. The end of life is often defined
as a capacity loss of 20 % or a resistance rise by
100 %. [12][13]
EVS28 International Electric Vehicle Symposium and Exhibition
2.3 Vehicle Load Profiles
To investigate the impact of regenerative braking
on battery aging in an electric vehicle, an appro-
priate load profile has been determined. Moreo-
ver, it has been scaled down to cell level for our
experimental cycle life study. [14]
As there are no specific load or driving profiles for
electric vehicles, driving cycles for conventional
vehicles have been used to derive load profiles for
the traction battery of an electric vehicle.
The study by Neudorfer et al. has analyzed differ-
ent driving cycles used as standardized references
for fuel consumption measurements [15]. It is dis-
tinguished between ‘modal’ and ‘stylistic’ driving
cycles. Modal cycles are artificial driving cycles
that consist of several sections with constant ve-
locity. In contrast, stylistic driving cycles repre-
sent realistic driving scenarios with frequently
changing velocities. For our cycle life study, only
stylistic driving cycles have been considered, as
they produce a more realistic load scenario.
To generate a load profile for the battery of an
electric vehicle, a simplified vehicle model is
used. This simulation model considers different
driving resistances to compute the necessary driv-
ing power at each time step. Thus, the driving cy-
cle’s velocity profile is converted into a power
profile. As described in [16], driving power Pvehicle
can be calculated as
Pvehicle = Freq ∙ v = (FR + FA + FC + FI ) ∙ v
where Freq represents the sum of all driving re-
sistances and v is the velocity of the vehicle. Freq
consists of the following simplified driving re-
Rolling resistance: FR = (mV+mL) g froll ∙ cos()
Aerodynamic drag: FA = 1/2
A cW AA (v+vA
Climbing resistance: FA = (mV+mL) ∙ g ∙ sin()
Acceleration resistance: FI = (e mV + mL) ∙ a
where the acceleration a is the derivative of the
vehicle’s velocity and v+vA is the relative velocity
between air and vehicle. Table 1 provides a de-
scription of all symbols used and their values as-
sumed for the load profile calculation. The values
of vehicle A, a sub-compact urban vehicle for up
to two passengers, are used in this study. Vehicle
B represents a conventional compact car for four
To translate the driving power Pvehicle , required to
accelerate and decelerate the vehicle, into a bat-
tery load Pbattery , the drivetrain efficiency
the power consumption Paux of auxiliary consum-
ers, such as lighting, heating, or driving assistance
systems, have to be considered.
Table 1. Parameters for load profile computation
Description (Unit)
Mass of vehicle (kg)
Mass of load (kg)
Gravitational acceleration (m/s²)
Rolling resistance coefficient (-)
Angle of inclination (°)
Density of air (kg/m³)
Drag coefficient (-)
Cross-sectional area (m²)
Air velocity (m/s)
Factor for rotational masses (-)
Efficiency (-)
Auxiliary power consumption (W)
Number of cells (-)
Due to common sign conventions, positive values
of driving power indicate acceleration, whereas
positive battery currents represent a charging of the
battery. The following two equations, depending on
the direction of the power flow of the motor, ac-
count for these conventions:
acceleration: Pbattery = (Pvehicle /
+ Paux)
deceleration: Pbattery = (Pvehicle
+ Paux)
For simplicity reasons, a constant drivetrain effi-
ciency of
0.75 and a constant auxiliary power
consumption of Paux = 500 W are assumed. These
simplifications are tolerable, as for the investigation
of battery aging, the qualitative distribution of
charge and discharge loads is substantially more im-
portant than the precise quantitative values at each
time step. All presented calculations do not have the
aim to describe one specific electric vehicle as real-
istic and accurate as possible, but to generate a ge-
neric load profile suitable for the analysis of battery
aging related to regenerative braking.
As a next step, the load profile is converted from the
level of an entire traction battery to cell level by di-
viding the total power Pbattery by the number of cells
N in the battery pack:
Pcell = Pbattery / N
This yields the power profile Pcell for a single cell.
In order to obtain a demanding load profile, a small
battery pack of 1296 cylindrical 18650 cells with a
total energy content of about 13 kWh is assumed.
This amount of energy is sufficient for the regarded
sub-compact vehicle to provide a driving range of
at least 100 km.
Although the load profile has been computed for a
sub-compact car, comparable results can also be ob-
tained for a larger vehicle, which has, on the one
hand, a higher mass and more engine power, but, on
the other hand, a larger battery system. The param-
eter set of vehicle B, listed in Table 1, leads to a ra-
ther similar load profile as vehicle configuration A.
EVS28 International Electric Vehicle Symposium and Exhibition
Load profiles for battery aging experiments are
usually defined as current profiles instead of
power profiles. This guarantees the same charge
throughput for each cell, independent of its SoC
or terminal voltage. This leads to a better compa-
rability of the results. Therefore, the power profile
is divided by a reference voltage Uref to obtain a
current profile: Icell = Pcell / Uref
Since the investigations within this paper cover a
wide SoC range, Uref has been set to 3.6 V, which
is the nominal cell voltage, specified by the man-
For different European and American driving cy-
cles, velocity profiles from [17] have been trans-
formed into current profiles for a single Li-ion cell
with a numerical fixed-step solver and a step size
of 1 s (see Table 2). Fig. 1 depicts the current his-
tograms of several driving cycles, representing
different driving conditions. This serves as a basis
for the design of the cycle life study.
3 Experimental Aging Study
In this section, the design of the cycle life experi-
ment is presented. This comprises the selection of
an appropriate load profile, the definition of the
variables for parameter variations, the tracking of
cell aging, and the overall cycling process. Addi-
tional investigations on calendar life complete our
aging study.
Table 2. Comparision of eight European and American
driving cycles including the maximum amount of charge
recovered in the driving simulation with vehicle A.
hours (Ah)
25 %
17 %
Motorway 130
8 %
21 %
19 %
20 %
6 %
15 %
3.1 Load Profile
For investigating the impact of regenerative braking
on battery aging, highway driving cycles are bene-
ficial, as they feature a higher energy consumption
than urban and rural driving cycles. This leads to a
higher charge throughput and is, consequently, sim-
ulating a higher driving distance per time (see Ta-
ble 2). Moreover, this represents a worst-case usage
scenario, as electric vehicles are often used in urban
traffic conditions, where, despite of more frequent
stop-and-go driving conditions, the battery load is
considerably lower.
Table 2 also shows that the amount of energy that
can be recovered by regenerative braking in high-
way driving cycles amounts to 8 % for the Artemis
Motorway 130, 6 % for the HWFET, and 15 % for
the US06 driving cycle. Since the American US06
Fig. 1. Distribution of cell currents for different European and American driving cycles as percent of total driving
cycle duration. The parameters of vehicle configuration A, used in this driving simulation, are listed in Table 1.
EVS28 International Electric Vehicle Symposium and Exhibition
driving cycle recovers substantially more charge
than the other two highway driving cycles and
since it contains a considerable percentage of re-
generative braking events with a current magni-
tude above 2 A (see Fig. 1), it has been selected
for our cycle life study. The velocity profile of this
highway driving cycle is illustrated in Fig. 2.
Velocity profile of US06 highway driving cycle
3.2 Parameter Variations
In our cycle life study, different influencing fac-
tors are investigated: Temperature, SoC, and the
magnitude of regenerative braking have been var-
ied. For each factor, at least three values have been
tested. Different magnitudes of regenerative brak-
ing have been implemented by varying the maxi-
mum recharge current rate of the load profile.
3.2.1 Temperature
Three temperatures have been investigated: 25 °C
is considered as a standard operating temperature
for the Li-ion cells. To cover a realistic spectrum
of average operating temperatures for an electric
vehicle, an additional high and low temperature
level of 40 °C and 10 °C has been examined. This
can be interpreted as summer and winter condi-
tions, when the battery pack cannot be cooled
down or heated up to 25 °C. Three thermal cham-
bers have been used to establish the different en-
vironmental conditions.
3.2.2 Magnitude of Regenerative Braking
The cycle life study is conducted on a BaSyTec
CTS battery test system with 32 independent 5 A
test channels. Within the computed US06 load
profile, charge currents reach up to 4.5 A. Thus,
all recharge magnitudes can be covered by the bat-
tery test system. High discharge currents, how-
ever, have to be truncated at 5.5 A, which is the
limit of the test system. As variations in recharg-
ing during periods of regenerative braking are the
center of interest, the limitations in discharge di-
rection are negligible for this cycle life study.
To analyze the impact of regenerative braking on
battery aging, four different levels of recharge cur-
rents have been defined: The first level is ‘no re-
charging’ (no Ire), which corresponds to no regener-
ative braking at all. The second level limits recharge
currents Ire to 1 A (Ire 1 A) and is able to recover
8 % of the ampere-hours discharged during the driv-
ing cycle. This correlates with approximately half
of the maximum recoverable amount of charge of
15 %. The third level of regenerative braking has
been defined as Ire 2 A, covering more than 80 %
of the maximum recoverable amount of charge. The
fourth level, covering all recharge pulses, is labeled
as Ire 4 A. Although there are two short charge
pulses of 4.5 A, this designation is chosen, as less
than 0.5 % of the recovered amount of charge is
generated by these two current peaks. Fig. 3 illus-
trates the different current profiles, representing the
four magnitudes of regenerative braking examined.
US06 load profiles for a single Li-ion cell with dif-
ferent magnitudes of regenerative braking, represented
by different recharge current (Ire) limitations
3.2.3 State of Charge Levels
One run of the US06 driving cycle corresponds to a
driven distance of 13 km. To obtain a more repre-
sentative driving distance for the cycle life study,
two runs of the load profile are performed in series
before charging the cell again. As the total range of
the considered electric vehicle is at least 100 km, ap-
proximately one fourth of the battery’s capacity is
depleted by two consecutive runs of the highway
driving cycle.
Within our cycle life experiment, three SoC levels
are examined. The SoC levels are defined by the
maximum charging voltage for a constant-current
(CC) charging procedure with a charging current of
0.7 A (= 0.25 C). As shown in Fig. 4, the charging
voltages for ‘low SoC’, ‘medium SoC and ‘high
SoC’ are 3.7 V, 3.9 V, and 4.1 V, respectively. Fig.
4 also illustrates the cycling windows for the three
SoC levels. The remaining SoC safety margins pre-
vent overcharging during recharge pulses at high
SoC and allow to perform the cycling procedure
also on aged cells approaching the end-of-life crite-
rion of 20 % capacity loss.
A slight correction of charging voltages has been
conducted for the cells at 10 °C: As higher internal
EVS28 International Electric Vehicle Symposium and Exhibition
Voltage curve for constant-current-constant-
voltage charging at 25 °C. The double arrows illustrate
the cycling windows, when there is no regenerative
resistances at low temperatures lead to higher ter-
minal voltages during charging, the low SoC level
has been set to 3.75 V and the medium SoC has
been set to 3.925 V. The high SoC level has re-
mained at 4.1 V in order not to expose the cell to
higher voltage potentials, which might have in-
creased lithium plating or electrolyte decomposi-
3.3 Cycle Life Test Procedure
To track the aging of the Li-ion cells, a checkup
routine has been defined, which consists of capac-
ity and cell resistance measurements. This check-
up routine is identical for all cells: At 25 °C, the
cells are fully charged with a constant current of
0.7 A and a constant voltage of 4.2 V, until the cur-
rent drops below 0.1 A. After that, the cells are
discharged with a constant current of 3 A to 2.5 V,
followed by a constant voltage period with a cut-
off current of 0.1 A. The additional constant-volt-
age discharge period enables a more precise meas-
urement of the cell’s actual capacity.
All cells have performed a checkup at the begin-
ning of the cycle life experiment and after each
cycling sequence. The cycling sequence has been
defined as follows: After placing the cell in its
temperature chamber, it is charged with a constant
current of 0.7 A (= 0.25 C), until it reaches its ded-
icated charging voltage. After a pause of 5 min,
two consecutive driving cycle runs are performed
with a pause of 1 min after each run. This combi-
nation of constant-current charging and two driv-
ing cycle runs is repeated 400 times. As each of
the 400 cycles depletes about one fourth of the
cell’s capacity, each cell has been charged and dis-
charged approximately 100 times its nominal ca-
pacity (= 280 Ah) between two checkups.
As the Li-ion cells investigated in this aging study
exhibit considerably higher changes in capacity
than in internal resistance, this paper focuses on
the examination of capacity changes.
3.4 Calendar Life
In order to separate usage-dependent and usage-in-
dependent aging, a calendar life study has been con-
ducted in parallel: Additional cells have been stored
at various SoC and different temperatures. Fig. 5
shows the eight SoC levels, ranging from 0 % to
100 % SoC. The storage temperatures are identical
to the cycling temperatures: 10 °C, 25 °C, and 40 °C.
All stored cells are examined periodically at 25 °C
with the same checkup routine as the cycled cells.
After each checkup, the cells are charged to their
storage SoC and placed in the thermal chambers
SoC levels examined in the calendar life study
4 Results and Discussion
After five months of cycle life testing, the Li-ion
cells have been exposed to a charge throughput of
1,400 Ah, which corresponds to 500 nominal full
cycles and a driven distance of approximately
50,000 km. Variations in cell aging can be observed
for the different cycling conditions. In the following
sections, these results are presented and discussed
together with the results from the calendar life
study. Finally, optimized operational strategies are
derived from the findings.
4.1 Calendar Life
The SoC-dependent calendar aging has been re-
vealed by the stored Li-ion cells. Fig. 6 illustrates
the capacity fade after about five months of storage
for the eight different SoC and the three storage
temperatures. As expected, cell aging accelerates
with temperature: The capacity loss at 40 °C, rang-
ing between 2 % and 6 %, is almost twice as high as
at 25 °C. A further lowering of the temperature,
however, does not decelerate aging considerably. At
10 °C, there is still a capacity loss between 0.5 %
and 3 %, depending on SoC.
Fig. 6 also shows that cells stored in a discharged
state below 20 % SoC exhibit the least capacity
fade. Storage levels between 20 % and 50 % SoC
cause a medium degradation rate. The fastest capac-
ity fade occurs at a SoC interval between 60 % and
maximum cycling windows
EVS28 International Electric Vehicle Symposium and Exhibition
90 %. A fully charged cell, however, shows a
somewhat slower capacity fade again.
The data from the calendar life study serve as a
baseline for the following investigations on cycle
life under different operating conditions. For the
three cycling windows, depicted in Fig. 6, mean
curves for calendar aging have been computed
based on the capacity changes of the cells con-
tained in the respective window. For the compari-
son of calendar and cyclic aging, capacity curves
from calendar aging are scaled accordingly to the
about four weeks of cycling between two consec-
utive checkups in the cycle life study.
SoC-dependent capacity fade after five months
of storage. The double arrows illustrate the three oper-
ating windows at low, medium, and high SoC.
4.2 Cycle Life
The main focus of this paper is on investigating the
impact of regenerative braking on battery aging.
Fig. 7 shows the results of the experimental cycle
life study: Capacity losses are depicted separately
for the three temperatures and the three SoC levels.
In each subplot, the different magnitudes of regen-
erative braking are compared. Moreover, calendar
aging is included to distinguish between usage-de-
pendent and usage-independent capacity losses.
4.2.1 Capacity Fade at 40 °C
At 40 °C, the Li-ion cells cycled within the low and
medium SoC window show almost no dependency
on the level of regenerative braking. All curves lie
closely together. At the end of the cycling process,
cells at low SoC exhibit a capacity loss of almost
6 % and cells at medium SoC of almost 7 %. The
additional capacity loss at medium SoC can be at-
tributed to increased calendar aging. Thus, the ca-
pacity loss on top of calendar aging due to cycling
is considered identical for both SoC levels and ac-
counts for about 2.5 % after the total charge
throughput of 1,400 Ah.
cycling windows
Fig. 7. Cell degradation during the cycle life experiment for the four magnitudes of regenerative braking at different
temperatures and SoC. The checkup measurements have always been performed after a total charge throughput of about
280 Ah (=280 Ah charged + 280 Ah discharged), which represents a driven distance of 10,000 km. The calendar aging
curves are scaled accordingly to the about four weeks of cycling between two consecutive checkups.
EVS28 International Electric Vehicle Symposium and Exhibition
Regarding the high SoC level at 40 °C, Fig. 7
demonstrates a dependency on regenerative brak-
ing, as the capacity curves diverge. A trend be-
comes apparent: A higher level of regenerative
braking reduces the capacity loss. At the end of
the test, the capacity loss supplementary to calen-
dar aging is about 2.5 % for the cell with the max-
imum level of regenerative braking and more than
4 % for the cell with no regenerative braking.
4.2.2 Capacity Fade at 25 °C
Unlike at 40 °C, Fig. 7 shows a dependency of bat-
tery aging on the level of regenerative braking for
all three SoC levels at 25 °C: Cells with a higher
level of regenerative braking exhibit lower capac-
ity losses, which corresponds to a longer cycle
life. In analogy to 40 °C, capacity fade increases
with higher SoC level. At low and medium SoC,
capacity losses due to cycling are comparable for
the same levels of regenerative braking. At high
SoC, intensified aging can be observed again: Ca-
pacity losses due to cycling are more than 1 per-
centage point higher than at medium and low SoC.
Compared to 40 °C, calendar aging is about 2 per-
centage points lower and cyclic aging is about 1
percentage point higher at 25 °C.
4.2.3 Capacity Fade at 10 °C
At 10 °C, Fig. 7 presents again a dependency on
the level of regenerative braking for all SoC lev-
els. The most pronounced impact can be observed
at the high SoC level, where capacity curves di-
verge markedly. Although calendar aging is low
at 10 °C, the cells exhibit substantial capacity
losses. After the charge throughput of 1,400 Ah,
capacity losses due to calendar aging amount to
1 % for low SoC, 2 % for medium SoC, and 2.5 %
for high SoC. For the low and medium SoC level,
additional losses of 4-5 % can be attributed to cy-
clic aging. At the high SoC level, the cell with
maximum regenerative braking exhibits 6 % of
cyclic aging; for the cell with no regenerative
braking, elevated cyclic losses of 10 % are ob-
served, which represents the most severe aging of
all operating conditions. It can be assumed that the
reduced kinetics at low temperature increase inho-
mogeneity inside the cell and that an uneven po-
tential distribution intensifies aging effects.
4.3 Reduced Aging due to Regenera-
tive Braking
Analyzing the capacity fade with respect to regen-
erative braking at different temperatures has re-
vealed a common trend: A higher level of regenera-
tive braking usually reduces battery aging. This
trend can be observed most clearly for high SoC and
low temperature. These operating conditions are
known to aggravate lithium plating. Thus, the re-
duced aging is considered as a reduction of lithium
plating. Hence, the concern of regenerative braking
generating more lithium plating has been disproved
for all operating conditions examined in this cycle
life study. Moreover, our results lead to the conclu-
sion that regenerative braking helps to reduce lith-
ium plating.
This can be explained by a decreased depth of dis-
charge after the driving cycles: The more charge re-
covered by regenerative braking, the higher the SoC
remains at the end of the driving cycle. Thus, the
subsequent CC charging period becomes shorter.
As lithium plating is reduced, when the CC charg-
ing periods become shorter, the CC charging peri-
ods appear as the main driver of lithium plating.
Consequently, it is not the short recharging with
high current rates during regenerative braking that
promotes lithium plating, but the long-lasting
charging periods, when the vehicle is recharged by
the grid, although current rates are considerably
To verify this assertion, capacity degradation has
been plotted against the cumulated amount of
charge transferred solely in the CC charging peri-
ods. Fig. 8 shows that, at the end of the cycle life
experiment, the total ampere-hours charged during
the CC periods are considerably lower for cells with
higher levels of regenerative braking. Cells with
maximum regenerative braking have only been
charged with 1,200 Ah between the driving cycles,
whereas cells without regenerative braking have
been charged with the total 1,400 Ah during the CC
charging periods. Comparing Fig. 7 and Fig. 8, the
adjustments due to the different scale basis become
obvious: At 10 °C and 25 °C, capacity curves lie
closer together in Fig. 8 for all SoC levels. At 40 °C,
this effect can also be observed at high SoC. This
confirms the direct correlation between cell degra-
dation and the amount of charge transferred during
the CC charging periods under most operating con-
Only for low and medium SoC at 40 °C, the corre-
lation between cell degradation and the total charge
throughput is in better agreement. Under these op-
erating conditions, lithium plating is no determining
factor for cell aging. As Fig. 7 shows, even under
these operating conditions, regenerative braking
does not intensify battery aging.
Thus, a high level of regenerative braking is benefi-
cial for the cycle life of a Li-ion battery system in
EVS28 International Electric Vehicle Symposium and Exhibition
an electric vehicle. By reducing the depth of dis-
charge and the duration of the subsequent charg-
ing period, regenerative braking can reduce lith-
ium plating considerably.
4.4 Impact of Temperature
Our aging study also demonstrates that the contri-
butions of calendar aging and cyclic aging vary
with temperature. Fig. 9 compares calendar aging
with the aging results of one level of regenerative
braking for all three temperatures. Calendar aging
increases substantially with elevated temperature:
After five months at 40 °C, the capacity fade is
about 2 percentage points higher than at 25 °C and
about 2.5 percentage points higher than at 10 °C. In
contrast, cyclic aging decreases with temperature.
At low SoC, cyclic aging at 40 °C is approximately
1 percentage point lower than at 25 °C and 10 °C.
Cyclic aging at medium SoC is comparable to low
SoC for 40 °C and 25 °C, whereas it increases by
1 percentage point at 10 °C. At high SoC, lower
temperatures exhibit intensified aging. Fig. 9 illus-
trates that cyclic aging has increased by about 1 per-
centage point at 25 °C and by about 2 percentage
points at 10 °C compared to medium SoC.
Fig. 8. Cell degradation against cumulated charge throughput during constant-current (CC) charging periods. The same
magnitudes of regenerative braking, temperatures, and SoC levels are presented as in Fig. 7. The scaling of the calendar
aging curves is identical to Fig. 7.
Fig. 9. Comparison of calendar aging and cyclic aging for the three temperatures investigated. The scaling of the
calendar aging curves is identical to Fig. 7.
EVS28 International Electric Vehicle Symposium and Exhibition
4.5 Optimal Operating Conditions
Our study shows that cyclic aging increases with
lower temperature and calendar aging increases
with higher temperature. Thus, an optimization is
necessary to minimize the aging of a Li-ion bat-
tery. During storage periods, temperature should
be kept low to reduce calendar aging. When cy-
cling the battery, especially when charging the
battery, a higher temperature should be estab-
lished to minimize aging due to lithium plating.
When charging the battery for a longer time at low
temperature, current rates should be kept low to
reduce lithium plating.
In addition to temperature, the SoC level also in-
fluences aging substantially. High SoC levels
have been detrimental for calendar and cyclic life.
Thus, to minimize battery aging, a high SoC
should be avoided whenever possible.
Moreover, long-lasting charging periods have
been identified as main driver of lithium plating.
Thus, reducing the depth of discharge during cy-
cling can decrease battery aging. A high level of
regenerative braking should be implemented, as it
reduces the depth of discharge and has shown no
detrimental effects on cycle life.
In our study, cells cycled at 25 °C provide a good
compromise between calendar and cyclic aging.
Extrapolating their capacity degradation for max-
imum regenerative braking at low or medium SoC
predicts a capacity loss of only about 10 % after a
driven distance of 100,000 km. This highlights the
potential of optimized operating conditions.
5 Conclusion
Our cycle life study has provided valuable new in-
sights on the impact of regenerative braking on the
aging of a Li-ion traction battery in an electric ve-
hicle: Under all operating conditions investigated,
there have been no detrimental effects of regener-
ative braking on battery life. Moreover, regenera-
tive braking has prolonged cycle life by reducing
the depth of discharge.
Our study has shown that it is not the short-time
recharging with high current rates, but the long-
lasting charging periods, even with comparatively
low amperage, that leads to battery aging related
to lithium plating. By reducing the depth of dis-
charge and the duration of the subsequent recharg-
ing period, regenerative braking has decreased
lithium plating. This has been observed most pro-
nouncedly at high SoC and low temperature.
These conditions are known as the main drivers of
lithium plating.
Although our study provides clear insights, there are
still open questions for further research on battery
aging related to regenerative braking, such as deter-
mining the maximum current rates applicable with-
out generating plated lithium, the interdependencies
of cycle depth and aging, and the aging behavior be-
low 10 °C.
Our additional investigations on calendar aging
have revealed that cyclic aging decreases with tem-
perature, whereas calendar aging increases. Moreo-
ver, the results of the calendar life study have exhib-
ited an accelerated cell degradation at a SoC above
60 %. Thus, an optimization of operating conditions
is essential to maximize battery life.
First cycle life projections, based on an extrapola-
tion of the experimental data, show that under opti-
mized operational conditions, there is only a capac-
ity fade of about 10 % after a driven distance of
100,000 km. With such low degradation, battery re-
placements become redundant during the life of
most electric vehicles. Thus, optimized operational
strategies with a high magnitude of regenerative
braking can promote the spread of electric vehicles
by reducing the total cost of ownership.
The authors would like to thank the German Federal
Ministry of Education and Research for their finan-
cial support. [FKZ: 16N12101]
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Dipl.-Ing. Peter Keil earned his di-
ploma degree in mechanical engineer-
ing from the Technische Universität
München in 2010. Currently, he is
working as a research associate at the
Institute for Electrical Energy Storage
Technology at the Technische Univer-
sität München. His main research ac-
tivities are in the fields of electrical and
thermal characterization of Li-ion cells
for investigating cell aging.
Prof. Dr.-Ing. Andreas Jossen holds
a professorship at the Technische Uni-
versität München and he is the founder
and head of the Institute of Electrical
Energy Storage Technology. His re-
search activities are modeling, simula-
tion, and characterization of recharge-
able batteries and fundamental and ap-
plied topics in battery systems, such as
battery topologies, state determination,
and control of battery systems.
... Keil [51] additionally investigates the effect of dynamic versus static driving profiles. No influences can be observed at temperatures of 25 C and 40 C. At 10 C, however, the dynamically loaded cells age slightly faster. ...
... The starting temperatures correspond to the monthly average temperatures for Central Europe. The cyclic aging over one year is 2.6% and thus corresponds to the order of magnitude of Keil's experiments [51], from which a sufficient consideration of the dynamics by the stress factors can be concluded. ...
... The use of ETR allows for previously unrecoverable energy to be recuperated, and thus reduces the DOD, which has a positive effect on battery aging [51]. The FLC reduces the DOD by 3% at À10 C (Table 5), while at higher temperatures the difference from the reference decreases. ...
Short range remains a major disadvantage of battery electric vehicles compared to vehicles that have combustion engines. Range reductions also result from low ambient temperatures and from battery aging. By varying power of the heating system depending on the highly fluctuating propulsion power and using electrothermal recuperation, range losses can be minimized. This paper focuses on the development and comparison of strategies to control the vehicle's heating circuit. Rule-based, fuzzy logic, and optimization-based strategies are designed and implemented in a validated BMW i3 simulation model. The results show that fuzzy logic leads to the greatest improvement of range and battery lifetime. Compared to the standard strategy, range can be increased by 14% at −10 °C and by 6.5% at 0 °C. The current throughput during recuperation is reduced the most by a rule-based strategy that prioritizes the heater. For discharging, fuzzy logic can reduce the current throughput by a maximum of 11%, which leads to a capacity fade reduction of 4.3%. Since air mass is controlled separately, the cabin temperature remains almost constant, thus maintaining the comfort of the user.
... In preparation for the C/20 charge, we included a CC/CV discharge to ensure a more complete discharge as well as a pause. The C/20 charge step was mainly included for accurate calculation of ICA and DVA, peaks in the ICA show phase equilibria while active material phase transitions produce peaks in the DVA [18]. This step can however also be used to minimise the resistive limitation during charging and therefore as a way to determine a remaining capacity. ...
... However, the area C is usually linked to an NMC phase transition in the dQ/dU plot [20]. On the other hand, in the dU/dQ representation, 3 is commonly used as the central anode marker [18]. We believe that two separated peaks in Figure 3c relate to separated phase transition of the two layers in DL anode. ...
... For the differential voltage analysis in Figure 9 the information is contained in certain areas between the peaks. Instead of analysing the peaks itself, we look at the distance between them, Q a , Q b and Q c [18,22,26]. Q a represents a development in the anode, while Q c represents the cathode. ...
Full-text available
Incremental improvement to the current state-of-the-art lithium-ion technology, for example regarding the physical or electrochemical design, can bridge the gap until the next generation of cells are ready to take Li-ions place. Previously designed two-layered porosity-graded graphite anodes, together with LixNi0.6Mn0.2Co0.2O2 cathodes, were analysed in small pouch-cells with a capacity of around 1 Ah. For comparison, custom-made reference cells with the average properties of two-layered anodes were tested. Ten cells of each type were examined in total. Each cell pair, consisting of one double-layer and one single-layer (reference) cell, underwent the same test procedure. Besides regular charge and discharge cycles, electrochemical impedance spectroscopy, incremental capacity analysis, differential voltage analysis and current-pulse measurement are used to identify the differences in ageing behaviour between the two cell types. The results show similar behaviour and properties at beginning-of-life, but an astonishing improvement in capacity retention for the double-layer cells regardless of the cycling conditions. Additionally, the lifetime of the single-layer cells was strongly influenced by the cycling conditions, and the double-layer cells showed less difference in ageing behaviour.
... The batches differed in the resting times while cycling, as described in the Section 2. One observation that can be made is that the cells with the shortest cycle lives belonged to the batch with the longer resting times. Longer relaxation times have already been linked to higher impedance measurements in experiments [55]. A similar correlation seemed to happen in the data from Severson et al. [6]. ...
... It can be suggested that this is simply due to heating up the cells during operation. Nonetheless, the literature indicates that higher temperatures induce lower impedance measurements [55]. It must be noted that the temperature measurements are not perfectly reliable, as the thermal contact between the thermocouple and the cell may vary substantially, with contact sometimes even being lost in the course of the experiment [6]. ...
Full-text available
The prediction of the degradation of lithium-ion batteries is essential for various applications and optimized recycling schemes. In order to address this issue, this study aims to predict the cycle lives of lithium-ion batteries using only data from early cycles. To reach such an objective, experimental raw data for 121 commercial lithium iron phosphate/graphite cells are gathered from the literature. The data are analyzed, and suitable input features are generated for the use of different machine learning algorithms. A final accuracy of 99.81% for the cycle life is obtained with an extremely randomized trees model. This work shows that data-driven models are able to successfully predict the lifetimes of batteries using only early-cycle data. That aside, a considerable reduction in errors is seen by incorporating data management and physical and chemical understanding into the analysis.
... Batteries are the most well-known and marketed devices due to their high storage efficiency and high storage capacity compared to their competitors. However, batteries suffer from aging and low power densities, they cannot provide important charge rates in short periods since they rely entirely on chemical reactions [5]. In addition, the integration of micro electronic systems and miniaturized devices (bio-sensors, micro generators) into conventional textile is a recent and attractive strategy to satisfy the needs of modern society at a reasonable price [6]- [8]. ...
... The end of life is defined as 80% of the capacity or a doubling of the internal resistance. Because relaxation time has a significant impact on the results [34,35], two hours of relaxation time were maintained prior to EIS, in order to let the internal cell dynamics subside. Consequently, the SOC was 75%. ...
The number of electric vehicles is increasing worldwide. Due to the high energy density of their Li-ion batteries, these vehicles are gaining acceptance among customers. Nevertheless, Li-ion batteries experience calendric and cyclic aging over the lifetime of the vehicle, leading to a reduction in performance and range. For economical and sustainable vehicles, battery aging must be minimized. Numerous studies have focused on analyzing and influencing battery aging by performing tests on battery cells and packs with a DC current. However, the battery is exposed to high-frequency perturbations in electric vehicles. In recent years, more focus has been placed on the influence of the current ripples generated by the traction power electronics with contradictory results. High-voltage auxiliary loads also cause high-frequency loads due to pulse-width modulation. The scope of this study is to investigate whether pulse-width modeled auxiliary consumers have an impact on battery aging and which frequencies are detrimental. For this purpose, battery cells were loaded with a discharge frequency of 400 Hz of a serial heater. In addition, 10 Hz and 5,000 Hz were chosen as further test points and cells were discharged with direct current as a reference. The results show a significant acceleration of the capacity fade and the internal resistance increase under load with a 10 Hz discharge current. On the other hand, the aging of the cells discharged by high frequencies does not show any abnormalities compared to the DC cells. Differential voltage analysis demonstrates increased aging of the anode compared to the cathode for all cells. An influence of the frequencies on the aging of the different electrodes is not observed but suspected by the different corner frequencies of the double layer capacities. Low frequencies should therefore be avoided in the control design of auxiliary consumers in electric vehicles.
Full-text available
A hybrid Energy Storage Systems (ESS) consists of two or more energy storage technologies, with different power and energy characteristics. Using a hybrid ESS, both high-frequency and low-frequency power variations can be addressed at the same time. For an accurate sizing of a hybrid ESS, the use of high-resolution data is required. However, high-resolution data over long periods leads to large data sets, which are difficult to handle. In this paper, an improved motif discovery algorithm is introduced to find the most recurring daily consumption patterns within the time series of interest. The most recurring pattern is selected as the representative of the time series for sizing the hybrid ESS. Next, a simple optimization framework is proposed for selecting the cut-off frequency of a low-pass filter, used for allocating the power to different storage technologies. Finally, the proposed sizing approach is applied for sizing a hybrid battery-flywheel ESS at four different low voltage distribution grids in southern Germany using real measurement data. It is demonstrated that a hybrid ESS, with the characteristics derived from the most recurring patterns only, can effectively provide their intended grid services for most of the days during the whole period of the time series.
In real industrial electronic applications that involve batteries, the inevitable health degradation of batteries would result in both the shorter battery service life and decreased performance. In this article, an attention-based model is proposed for Li-ion battery calendar health prognostics, i.e., the capacity forecaster based on knowledge-data-driven attention (CFKDA), which will be the first work that applies attention mechanism to benefit battery calendar health monitor and management. By taking the battery empirical knowledge as the foundation of its crucial part, i.e., the knowledge-driven attention module, the CFKDA has realized a satisfactory combination of the complementary domain knowledge and data , which has improved both its theoretic strength and prognostic performance significantly. Experimental studies on practical battery calendar ageing demonstrate the superiority of CFKDA in forecasting and generalizing to unwitnessed conditions over both state-of-the-art knowledge-driven and data-driven calendar health prognostic models, implying that the introduction of domain knowledge in CFKDA has brought a significant performance improvement.
Electrode processing plays an important role in advancing lithium-ion battery technologies and has a significant impact on cell energy density, manufacturing cost, and throughput. Compared to the extensive research on materials development, however, there has been much less effort in this area. In this Review, we outline each step in the electrode processing of lithium-ion batteries from materials to cell assembly, summarize the recent progress in individual steps, deconvolute the interplays between those steps, discuss the underlying constraints, and share some prospective technologies. This Review aims to provide an overview of the whole process in lithium-ion battery fabrication from powder to cell formation and bridge the gap between academic development and industrial manufacturing.
Conference Paper
Since a laptop caught fire in 2006 at the latest, Li-ion cells were considered as more dangerous than other accumulators [1]. Recent incidents, such as the one involving a BYD e6 electric taxi [2] or the Boeing Dreamliner [3], give rise to questions concerning the safety of L#i-ion cells. This is a crucial point, since Li-ion cells are increasingly integrated in all kinds of (electric) vehicles. Therefore the economic success of hybrid electric vehicles (HEV) and battery electric vehicles (BEV) depends significantly on the safety of Li-ion cells. Lithium nickel manganese cobalt oxide (NMC) and lithium nickel cobalt aluminium oxide (NCA) are two standard Li-ion cathode chemistries, which are often used for today's HEVs and BEVs Li-ion batteries. Cells with this two cathode technologies are investigated in detail and compared to cells with the alleged save lithium iron phosphate (LFP) technology. Furthermore only commercially available and mass produced Li-ion cells were tested, in order to get as close to real end-user applications as possible. To ensure comparability, cells with the most common 18650 casing have been used. Furthermore all cells had no built-in resistor with positive temperature coefficient (PTC-device). For each abuse test at least 2 cells have been tested to get to know the statistical dispersion. The spread was in all tests for all measured values of each cell type lower than 11 %. Consequently it can be supposed, that mass produced cells show equal behaviour also in abusive test. The performed electrical safety tests on these cells, involve overcharge, overdischarge and short circuit tests. These tests represent real abuse scenarios and are geared to established standards [15], [16], [17], [18]. To complete these measurements an accelerated rate calorimetry (ARC) test has been carried out, to determine the thermal stability of the cells. As in the literature discussed, the investigated LFP/C cells show a higher thermal stability and are therefo- e safer, although they do not have any overcharge buffer as the investigated NCA/C and NMC/C cells.
This paper deals with occurrence of lithium plating on the negative electrode of lithium-ion batteries, a significant ageing phenomenon known to damage lithium-ion battery performances. Charge transfer process, one of the two different steps of the process of Li insertion in the negative active material being the cause of this ageing, was considered here to be the limiting process. This transfer occurs at short-time scales. The second process, the diffusion of lithium in the solid insertion compound, occurring at relatively long-time scales, has not been fully examined here. The aim of this paper was to develop a new method to evaluate the maximal rate of a charge pulse solicitation to prevent this ageing phenomenon. The approach relies on the use of a fundamental model of lithium ion battery with coupled mass and charge transfer. To validate the method, 2 s microcycles have been performed on a commercial VL41M SAFT cell. Theoretical and experimental works led to the maximum current density to be applied without undesired Li deposition, depending on the state of charge (SOC). The abacus established for the cell of interest can orient further specifications for suitable use of the battery.
The present study aims at establishing a methodology for a comprehensive calendar ageing predictive model development, focusing specially on validation procedures. A LFP-based Li-ion cell performance degradation was analysed under different temperature and SOC storage conditions. Five static calendar ageing conditions were used for understanding the ageing trends and modelling the dominant ageing phenomena (SEI growth and the resulting loss of active lithium). The validation process included an additional test under other constant operating conditions (static validation) and other four tests under non–constant impact factors operating schemes within the same experiment (dynamic validation), in response to battery stress conditions in real applications. Model predictions are in good agreement with experimental results as the residuals are always below 1% for experiments run for 300–650 days. The model is able to predict dynamic behaviour close to real operating conditions and the level of accuracy corresponds to a root-mean-square error of 0.93%.
Lithium plating in commercial LiNi1/3Mn1/3Co1/3O2/graphite cells at sub-ambient temperatures is studied by neutron diffraction at Stress-Spec, MLZ. Li plating uses part of the active lithium in the cell and competes with the intercalation of lithium into graphite. As a result, the degree of graphite lithiation during and after charge is lower. Comparison of graphite lithiation after a C/5 charging cycle fast enough to expect a considerable amount of Li plating with a much slower C/30 reference cycle reveals a lower degree of graphite lithiation in the first case; neutron diffraction shows less LiC6 and more LiC12 is present. If the cell is subjected to a 20 h rest period after charge, a gradual transformation of remaining LiC12 to LiC6 can be observed, indicating Li diffusion into the graphite. During the rest period after the C/5 charging cycle, the degree of graphite lithiation can be estimated to increase by 17%, indicating at least 17% of the active lithium is plated. Data collected during discharge immediately after C/5 charging give further evidence of the presence and amount of metallic lithium: in this case 19% of discharge capacity originates from the oxidation of metallic lithium. Also, lithium oxidation can be directly related to the high voltage plateau observed during discharge in case of lithium plating.
Major aspects related to lithium deposition in lithium-ion and lithium metal secondary batteries are reviewed. For lithium-ion batteries with carbonaceous anode, lithium deposition may occur under harsh charging conditions such as overcharging or charging at low temperatures. The major technical solutions include: (1) applying electrochemical models to predict the critical conditions for deposition initiation; (2) preventions by improved battery design and material modification; (3) applying adequate charging protocols to inhibit lithium deposition. For lithium metal secondary batteries, the lithium deposition is the inherent reaction during charging. The major technical solutions include: (1) the use of mechanistic models to elucidate and control dendrite initiation and growth; (2) engineering surface morphology of the lithium deposition to avoid dendrite formation via adjusting the composition and concentration of the electrolyte; (3) controlling battery working conditions. From a survey of the literature, the areas that require further study are proposed; e.g., refining the lithium deposition criteria, developing an effective AC self pre-heating method for low-temperature charging of lithium-ion batteries, and clarifying the role the solid electrolyte interphase (SEI) plays in determining the deposition morphology; to facilitate a refined control of the lithium deposition.
We have found ultrafast Li(+) intercalation into graphite in a superconcentrated ether electrolyte, even exceeding that in a currently used commercial electrolyte. This discovery is an important breakthrough toward fast-charging Li-ion batteries far beyond present technologies.
The effect of (dis)charge pulses on lithium-ion batteries is evaluated using an electronic network model. Simulations give insight into the effect of the pulses on the internal processes such as diffusion, migration, electrochemical reactions, heat generation, etc. on time scales from microseconds to hundreds of seconds. The simulated results are verified by experimental measurements on a commercial lithium-ion battery. The relevance of the results for battery charging with short pulses and for the occurrence of short circuit is discussed.
The capacity of a lithium‐ion battery decreases during cycling. This capacity loss or fade occurs due to several different mechanisms which are due to or are associated with unwanted side reactions that occur in these batteries. These reactions occur during overcharge or overdischarge and cause electrolyte decomposition, passive film formation, active material dissolution, and other phenomena. These capacity loss mechanisms are not included in the present lithium‐ion battery mathematical models available in the open literature. Consequently, these models cannot be used to predict cell performance during cycling and under abuse conditions. This article presents a review of the current literature on capacity fade mechanisms and attempts to describe the information needed and the directions that may be taken to include these mechanisms in advanced lithium‐ion battery models.
Lithium-ion batteries are typically charged using constant current that is applied until the cell voltage reaches about , at which time, charging continues at a constant voltage until the full battery capacity is attained. This process is slow, typically requiring . Empirically selected pulse-charging sequences have been shown to provide enhanced charging; however, no model exists to explain and optimize the pulse-charging protocols. Modeling the lithium diffusion into a homogeneous intercalant layer indicates that the lithium concentration reaches saturation at the graphite∕electrolyte interface after about under conventional constant current charging, mandating the shift to the lower rate constant voltage charging. It is shown here that charging the lithium battery using non-dc waveforms with properly selected parameters may circumvent this lithium saturation, enabling charging at significantly higher rates. A nonlinearly decreasing current density profile which conforms to the mass transfer coefficient variation was shown to provide complete charging in less than of an hour, faster than any other pulse-charging profile studied.
A three-electrode Li-ion cell with metallic lithium as the reference electrode was designed to study the charging process of Li-ion cells. The cell was connected to three independent testing channels, of which two channels shared the same lithium reference to measure the potentials of anode and cathode, respectively. A graphite/LiCoO2 cell with a C/A ratio, i.e., the reversible capacity ratio of the cathode to anode, of 0.985 was assembled and cycled using a normal constant-current/constant-voltage (CC/CV) charging procedure, during which the potentials of the anode and cathode were recorded. The results showed that lithium plating occurred under most of the charging conditions, especially at high currents and at low temperatures. Even in the region of CC charging, the potential of the graphite might drop below 0 V versus Li+/Li. As a result, lithium plating and re-intercalating of the plated lithium into the graphite coexist, which resulted in a low charging capacity. When the current exceeded a certain level (0.4C in the present case), increasing the current could not shorten the charging time significantly, instead it aggravated lithium plating and prolonged the CV charging time. In addition, we found that lowering the battery temperature significantly aggravated lithium plating. At −20 °C, for example, the CC charging became impossible and lithium plating accompanied the entire charging process. For an improved charging performance, an optimized C/A ratio of 0.85–0.90 is proposed for the graphite/LiCoO2 Li-ion cell. A high C/A ratio results in lithium plating onto the anode, while a low ratio results in overcharge of the cathode.