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Development of battery management system for cell monitoring and
protection
Conference Paper · December 2014
DOI: 10.1109/ICEECS.2014.7045246
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2014 IEEE International Conference on Electrical Engineering and Computer Science
24-25 November 2014, Bali, Indonesia
Development of Battery Management System for
Cell Monitoring and Protection
Irsyad Nashirul Haq#1, Edi Leksono*2, Muhammad Iqbal*3, FX Nugroho Soelami*4, Nugraha*5, Deddy Kurniadi*6,
Brian Yuliarto*7
#
Doctorate Student at Department of Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung
Jalan Ganesha 10, Bandung 40132, Indonesia
1
inhprop@gmail.com
*
Department of Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung
Jalan Ganesha 10, Bandung 40132, Indonesia
2
edi@tf.itb.ac.id, 3m.iqbal@tf.itb.ac.id, 4nugroho@tf.itb.ac.id, 5nugraha@tf.itb.ac.id,
6
kurniadi@tf.itb.ac.id, 7brian@tf.itb.ac.id
Abstract— Battery has an important role as energy storage in
electricity system utilization such as in electric vehicle and in
smart microgrid system. Battery Management System (BMS) is
needed to treat the dynamics of energy storage process in the
battery in order to improve the performance and extend the life
time of battery. In this paper, BMS cell monitoring and
protection has been designed and tested for Lithium Ferro
Phosphate (LFP) battery cells. The BMS cell monitoring function
has been able to measure the battery parameters such as the
voltage and current dynamics of each cell. The data taken from
the BMS cell monitoring experiment is used to estimate the state
of charge (SOC) of battery which is based on coulomb counting
with coulomb efficiency ratios. The BMS cell monitoring
function has successfully demonstrated the presence of
unbalanced cell voltages during both processes of charging and
discharging as well. From the analysis, the existence of capacity
and energy fades was also investigated for every discharging and
charging cycles. Based on the BMS cell protection experiment
results, overcharged and over discharged protections have
successfully been demonstrated for the battery cells. The
charging process is disabled when the voltage of the
corresponding battery cell exceeds its high limit (HLIM) at 3.65V,
and the battery will be available for charging when all of the cell
voltages are below their boundary limits (CAVL) at 3.3V. The
discharging process will be disabled when the battery cell voltage
is lower than the corresponding low limit (LLIM) at 2.5 V. The
battery will be available again when all battery cell voltages are
above their discharge available (DAVL) voltage at 2.8V. The
proposed BMS cell monitoring and protection has shown its
function as a data acquisition system, safety protection, ability to
determine and predict the state of charge of the battery, and
ability to control the battery charging and discharging.
Keywords—
Monitoring,
Counting
Battery Management System (BMS), Cell
Cell Protection, SOC Estimation, Coulomb
I. INTRODUCTION
Battery has an important role as energy storage in
electricity system utilization, such as portable electronic
devices, electric vehicle, and in renewable energy power plant
such as in smart microgrid system. Battery with good
performance would provide optimal support for the operation
of the corresponding system.
Battery useful life will be longer if the battery operation is
maintained in safety operating area (SOA), either when the
battery is charged or discharged. Improper charging and
discharging processes could decrease the performance and
shorten the battery useful life.
Battery Management System (BMS) is needed to treat the
dynamics of energy storage process in the battery in order to
improve the performance and extend the life of battery. BMS
has two operational aspects: monitoring and control.
Monitoring aspect cannot be separated from the control aspect.
To run proper control of battery charging and discharging
processes, a fast, precise and accurate monitoring system is
required [1]. An ideal BMS will be energy efficient with low
power consumption in achieving the full capacity of battery.
The BMS ensures that the battery will not damage due to
overcharging, over discharging or over load power
consumption [2]. The BMS will examine the operational
parameters of the battery e.g. voltage, current, the internal
temperature during charging and discharging and estimate the
battery state e.g. state of charge (SOC) and state of health
(SOH).
A BMS which flexible enough to protect different types of
batteries and can provide all the safety features, has been a
recent topic of development and research in electric vehicle
and alternative energy systems [3]. As described in [2], a
comprehensive BMS should include functions for data
acquisition, safety protection, ability to determine and predict
the state of the battery, ability to control battery charging and
discharging, cell balancing, thermal management, delivery of
battery status and the authentication to a user interface,
communication with all BMS components and the most
important thing is to prolong battery life.
The model of battery is needed to relate the input current
rate and its SOC estimation, whereby the coulomb counting
methods can be ideal for this purpose. One of SOC estimation
algorithm which is based on coulomb counting and taking into
account the coulomb efficiency for monitoring system in
discrete time, can be expressed as in Equation (1) [4] [5].
978-1-4799-8478-7/14/$31.00 ©2014 IEEE
203
SOC k = SOC k −1 +
η i ik −1Δt
Cn
(1)
where k is time variable, and η i is coulombic efficiency of
battery during charging and discharging. This factor describes
the ratio between the consumed over its corresponding
available electrons in charging or discharging processes. This
ratio is assumed to be 0.992 during charging period and 1.0
during discharging period [5].
II. BATTERY MANAGEMENT SYSTEM DEVELOPMENT
A. BMS Specification
In this paper, the BMS is designed on a modular basis
which consists of two main sections, which are called the
Local and Central Module respectively. Local Module has the
function as data acquisition device that measures the amount
of voltage, current and temperature of the battery and for the
implementation of the control algorithms for battery cell
protection. Central Module is the main controller for data
logging where the collected data will be used to estimate the
battery SOC. Central Module also has a communication
interface of serial USB and TCP/IP to transfer the monitored
data to and from Local Module.
This initial version of BMS is designed for 12 volt battery
module nominal voltage which consists of 4 battery cells,
where in the further work this configuration will be scaled up.
In Figure 1, the schematic diagram of the BMS for cell
monitoring and protection is illustrated. Meanwhile, Figure 2
shows the algorithm for BMS cell protection.
installed on cell board. The communication between Local
and Central Module use USB serial and TCP / IP as well. The
Central Module can be connected directly to an HDMI display
and it is also possible to access the display unit via remote
desktop communication protocol as user interface.
To get the actual voltage value in ADC measurement
system, the calibrated relationships for each ADC channel are
y = 4.5577x, y = 4.5514x, y = 4.5956x and y = 4.7265x,
where y is the actual value of voltage and x is bit value of the
ADC. Meanwhile, for the bi-directional current measurement,
the calibrated relationship is y = -0.0489x + 24.743, where y
is current value in Ampere and x is the bits value of ADC
measurement. Because the temperature measurement uses a
digital onewire communication sensor, the actual value of the
temperature can directly be obtained in Celcius value for each
Local Module. Meanwhile, in Local Module, the sampling
rate of the data acquisition system is set at 5 seconds, where at
each sampling time interval there are 25 measurement data
that are filtered using Equation (2).
_
y=
∑
k = 25
yk
(2)
25
_
where y represents filtered monitoring data from 25
measurement in Local Module. And to measure the current
rate It , Equation (3) is presented as per IEC 61434 standard[1]:
It =
Cn
(3)
1h
where Cn represents battery reference capacity in (Ah) with 1
hour rate.
Because the BMS is based on digital data acquisition, this
paper also proposes SOC estimation of battery based on
discrete time function of current rate It , and sampling time of
data acquisition τ r in BMS, such that the corresponding SOC
estimation can be represented as (4).
SOCk = SOCk −1 + ηi I t ( k )τ r
(4)
where I t (k ) is the current rate value implemented to the
battery at sampling rate of data acquisition of
τr
hour. I t (k )
has negative value in discharging period, and has the positive
value during its charging period.
Fig. 1 Schematic diagram of BMS Cell Monitoring & Protection
The design specification of the BMS cell monitoring and
protection can be explained as follows. The Local Module is
implemented using Xboard microcontroller with 10 bit ADC,
the Central Module is implemented using Raspberry pie Rev.
B microprocessor, the current sensor is realized using bidirectional ACS512-20A, while the temperature sensor is
designed using onewire DS18B20 and LM324 which are
For the battery cell protection algorithm, as Figure 2, there
are 4 boundary limits where it uses to protect each of battery
cell so that the battery operation will remains in SOA. The
SOA boundary values for the proposed BMS are high limit
values (HLIM) in order to avoid overcharged voltage at 3.65
V and low limit values (LLIM) to prevent over discharged at
2.5 V, such that the operating cell voltage values should be
higher than that of the cut-off voltages of the corresponding
battery cell.
204
Four battery cells have been used and are set up in series
connection so that the battery module has its nominal voltage
of 12.8V as shown in Fig 3. Each battery cell is equipped with
the proposed BMS cell monitoring board that measures the
voltage, and in addition it is also completed with bidirectional
current sensor for discharging / charging current rate
measurement.
B. BMS Experiment Setup
The lithium battery charger, batteries, and load are
connected in parallel circuit. Figure 1 shows the schematic
diagram while Figure 3 shows the BMS experiment setup. The
lithium battery charger used in this experiment is GW Dual
Tracking GPC-3030 power supply, and a dummy load is used
for constant current process. The experiment that has been
conducted uses constant current 0.05 It for charging and
discharging process.
Fig. 2 Cell Protection Algorithm
The Charge available value (CAVL) at 3.3 V can indicate
the battery standby voltage when the battery is not in use or in
rest time, and the discharge available value (DAVL) at 2.8
Volts can indicate the minimum voltage that battery can be
discharged.
The detailed experiment steps are described as follows.
Firstly, the battery cells are electrified until fully charged with
current rate 0.05 It until all battery cells get their balanced
voltages and waiting for 30 minutes of rest time so that all the
cells reach 3.33 V. After the rest time, the battery cells are
discharged with current rate 0.05 It until one of the battery
cell hits the cut off voltage of 2.2 V., which indicates that the
batteries is fully discharged at its normal capacity. The
experiment was conducted for 3 cycles of charging and
discharging with 30 minutes rest time for every period. The
measured dynamic voltage and load current data are
monitored using the proposed BMS cell monitoring and log
the data in Central Module or PC using serial USB data
communication.
III. BATTERY MANAGEMENT SYSTEM TESTING
A. Battery Specification
The battery used in this work is Lithium Ferro Phosphate
(LFP) type with nominal voltage of 3.2 V and nominal
capacity of 30Ah. The recommended rest time after charging
or discharging is over 10 minutes, and the other characteristics
are shown in Table I.
TABLE I
LFP BATTERY CELL CHARACTERISTICS
Parameter
Manufacturer
Model
Nominal capacity
Nominal voltage
Charging Voltage
Charging Method
Discharge Cut-off Voltage
Maximum charging current
Maximum discharge current / peak
Operating temperature (charge) Operating
temperature (discharge)
Case/Tube material
Weight
Value
Lyno Power
LYS4882160S
30.000 mAh
3.2 V
3.65 V
CC/CV
2.2 V
0.5 C
3 C / 20 C
0 - 45 oC
(-20) - 60 oC
SUS/ PVC
1.175 gram
Fig. 3 BMS cell monitoring & protection experiment setup
IV. RESULTS AND DISCUSSIONS
A. BMS Cell Monitoring
Figure 4 and 5 show the discharge characteristics of the
battery cell voltage as a function of its discharge capacity
when the current rate is 0.05 It.
205
From Figure 4, we observed that every battery cell has
nearly linear voltage to discharge capacity relationship when
the voltage of the battery cell is around 3.3 to 3.4 V, but
outside that range, the relationship is highly non-linear.
Based on Figure 4, the BMS has been able to detect any
voltage difference for every battery cell. There is one battery
cell discharged faster compared to the others, where it can be
seen by its voltage that decreases faster than the others when it
is about to reach its normal capacity.
After the experiment for the discharging period is done, the
battery is left for 30 minutes of rest time. Then, the charging
process experiment was done by providing 0.05 current rates,
where Figure 6 shows the dynamic voltage of battery cell
characteristics as a function of charge capacity. Based on the
experimental data and analysis that have been conducted from
the first charging period, the charged capacity of battery is
29.46 Ah when it stops. The SOC estimation relationship for
each cell voltage dynamics can be seen in Figure 7.
Fig. 4 Discharging cells voltage (V) as a function of discharge capacity (Ah)
Fig. 6 Charging cells voltage (V) as a function of charge capacity (Ah)
It also was found that battery cell 3 was earlier reaches its
cut-off voltage at 2.2 volt as per Table IV (Vfinal), which means
the end point for the discharge capacity. From the
experimental data and analysis, battery cell 3 empties faster
than the others and then stops the discharging period when it
reached 28.28 Ah capacity. This means that 1.72 Ah capacity
fade in battery was already happened when the experiment
was conducted.
The SOC estimation in Figure 7 is done using Equation (6)
with columbic efficiency of 95.98% for charging period based
on data obtained from the ratio of discharging and charging in
the first cycles. It can be seen that the charging process for
every battery cell has different effect, and it was also known
that battery cell 4 was the most rapidly affected by the
charging process and reach high limit voltage at 3.65 V as per
Table IV (Vfinal).
Fig. 5 Discharging cells voltage (V) as a function of State of Charge (%)
Fig. 7 Charging cells voltage (V) as a function of State of Charge (%)
The SOC estimation in Figure 5 is done using Equation (4)
with columbic efficiency of 1 for discharging period. As we
can see in Figure 5, the discharging process was stop when
SOC reach 5.73%, because there is one battery has reached its
cut-off voltage, so that the discharging process is stopped to
prevent over discharge.
The experiment was continued for cycles 2 and 3 by the
same mechanism as in cycle 1. The detailed monitoring data
and analytical results for each cycle can be seen from Table II
- V, and Figure 8 illustrates the discharge/charge efficiency
for each cycle.
206
TABLE II
BATTERY CELL 1 MONITORING DATA
Period
Vi
Vf
ΔV
C(Ah)
E(Wh)
Discharge 1
3.34
2.92
0.42
-28.28
-92.00
Charge 1
3.00
3.46
-0.46
29.46
98.02
Discharge 2
3.34
2.90
0.44
-27.93
-90.83
Charge 2
2.98
3.45
-0.47
29.43
97.86
Discharge 3
3.34
2.88
0.46
-27.76
-90.26
Charge 3
2.96
3.43
-0.47
29.38
97.67
TABLE III
BATTERY CELL 2 MONITORING DATA
Period
Fig. 8 Battery Cell Efficiency as a function of Cycles (Discharge/Charge)
Vi
Vf
ΔV
C(Ah)
E(Wh)
Discharge 1
3.35
2.94
0.41
-28.28
-92.38
Charge 1
3.04
3.48
-0.44
29.46
98.14
Discharge 2
3.34
2.95
0.39
-27.93
-91.26
Charge 2
3.02
3.46
-0.44
29.43
98.00
Discharge 3
3.34
2.95
0.39
-27.76
-90.72
Charge 3
3.02
3.45
-0.43
29.38
97.83
B. BMS Cell Protection
The BMS cell protection experiments and testing that are
performed during the charging and discharging of the battery
are to see the dynamics of the overcharged and over
discharged. During the charging process, the boundary values
are HLIM and CAVL that are designed to enable or disable
the charger, and during the discharging process, the DAVL
and LLIM are designed to enable or disable the discharging
process. The charging process or the charger relay connection
is enable when the status is 1, and disable when the status is 0.
The discharging process or load relay connection is enable
when the status is 0, and disable when the status is 1.
TABLE IV
BATTERY CELL 3 MONITORING DATA
Period
Discharge 1
Vi
Vf
ΔV
C(Ah)
E(Wh)
3.36
2.20
1.16
-28.28
-91.78
Charge 1
2.74
3.52
-0.78
29.46
97.72
Discharge 2
3.33
2.20
1.13
-27.93
-90.81
Charge 2
2.71
3.48
-0.77
29.43
97.58
Discharge 3
3.34
2.20
1.14
-27.76
-90.30
Charge 3
2.69
3.46
-0.77
29.38
97.50
TABLE V
BATTERY CELL 4 MONITORING DATA
Period
Vi
Vf
Discharge 1
3.34
Charge 1
2.86
Discharge 2
ΔV
C(Ah)
E(Wh)
2.73
0.61
-28.28
-91.41
3.65
-0.79
29.46
97.72
3.34
2.78
0.56
-27.93
-90.38
Charge 2
2.87
3.65
-0.78
29.43
97.64
Discharge 3
3.34
2.81
0.53
-27.76
-89.89
Fig. 9 Cells protection process at HLIM and CAVL
Charge 3
2.89
3.65
-0.76
29.38
97.50
The charging experiment was performed during 25000
second. The overall HLIM and CAVL for the battery cell
protection in charging process can be seen in Figure 9. The
test mechanism for HLIM has been successfully performed for
overcharged protection for each cell. From Figure 10 we
known that battery cell 4 is the one which triggered the
protection, it occurred when battery cell 4 voltage exceeded
3.65V at 3355 second, and it triggered the relay or charging
status to become disable, so the charging stopped. The
charging process to the battery cell will be available when all
of the cell voltages are below their boundary CAVL of 3.3V,
and as shown in Figure 11, the CAVL mechanism has
successfully been performed during the charging experiment
that was conducted in the time interval of 23490 second.
From Figure 8 that shows discharging/charging efficiency, it
is clear that each cell had different efficiency, and we can see
that there are energy fade for every cycle they experience.
And from analytical results, the coulomb efficiencies (nC) that
were obtained for each cycle are 95.98%, 94.88% and 94.47%
respectively. This means that for every cycles of experiment
was performed, the battery cell experience the same amount of
capacity fades.
Combining analytical results from energy and capacity fade
are very useful to estimate the State Of Health (SOH) of
battery cells, which will be the future work of this research.
207
Figure 12 also shows the discharging process when all the
battery cell voltages are above their DAVL boundary limit of
2.8 V, where the discharging can be enable again, and from
the experiment result, it occurred at 365 second.
V. CONCLUSIONS
The BMS cell monitoring and protection has been designed
and tested for LFP battery cells. The BMS cell monitoring
function has been able to measure the battery parameters such
as the voltage dynamics of each cell. The data from the BMS
cell monitoring experiment has been used to estimate the SOC
of battery based on coulomb counting with coulomb
efficiency ratios. The BMS cell monitoring function has
successfully demonstrated the presence of unbalanced cell
voltage both during charging as well as discharging processes.
From analysis, the existence of capacity and energy fades
were also investigated for every discharge and charge cycles.
Based on experiment results, the BMS cell protection
mechanisms for HLIM, CAVL, LLIM and DAVL have
successfully been performed for overcharging and over
discharging protection of each cell. The charging process will
be disabled when the voltage of battery cell 4 exceeds its
HLIM 3.65 V, and it will be available for charging or
discharging when all of the cell voltages are below the CAVL
boundary of 3.3V. The discharging process will be disabled
when the battery cell 3 voltage value is lower than the LLIM
at 2.5 V and the battery will be available again when all of the
battery cell voltages are above DAVL at 2.8 V.
Fig. 10 Cells protection process at HLIM (detailed)
The proposed BMS cell monitoring and protection has
proven its function as a data acquisition system, safety
protection, determination and prediction the state of charge
(SOC) of the battery, and the ability to control the battery
charging and discharging. The future work for this research
will be the implementation of SOC and SOH estimation
algorithm directly in Central Module, and as a solution for the
existence of unbalance voltage on each cell, the active cell
balancing control algorithm will also be implemented.
Fig. 11 Cells protection process at CAVL (detailed)
ACKNOWLEDGMENT
This work is fully supported by the Program Bantuan Dana
Riset Inovatif-Produktif (RISPRO) which is funded by the
Lembaga Pengelola Dana Pendidikan (LPDP), Republic of
Indonesia, and as a part of MOLINA ITB – SPE research.
REFERENCES
Fig. 12 Cells protection process at LLIM and DAVL
The over discharged protection mechanism for each cell is
limited by LLIM and DAVL boundary voltages during
discharging experiment that was conducted in 500 second as
seen in Figure 12. The cell protection mechanisms test for
LLIM has successfully been performed for over discharged
protection for each cell when the battery cell 3 triggers the
protection. It was occurred when the battery cell 3 voltage
value was lower than 2.5 V at 280 second, and it triggered the
relay or discharging status to become disable. This also means
that the battery cell 3 was depleted faster than the others and
the one that must be protected first for over discharged.
[1] E. Leksono, I. N. Haq, M. Iqbal, F. X. N. Soelami, and I. G. N.
Merthayasa, “State of charge (SoC) estimation on LiFePO4 battery
module using Coulomb counting methods with modified Peukert,” 2013
Jt. Int. Conf. Rural Inf. Commun. Technol. Electr. Technol. (rICT ICeVT), pp. 1–4, Nov. 2013.
[2] Y. Xing, E. W. M. Ma, K. L. Tsui, and M. Pecht, “Battery Management
Systems in Electric and Hybrid Vehicles,” Energies, vol. 4, no. 12, pp.
1840–1857, Oct. 2011.
[3] K. W. E. Cheng, S. Member, B. P. Divakar, H. Wu, K. Ding, and H. F.
Ho, “Battery-Management System ( BMS ) and SOC Development for
Electrical Vehicles,” IEEE Transactions On Vehicular Technology, vol.
60, no. 1, pp. 76–88, 2011.
[4] A. Singh, A. Izadian, and S. Anwar, “Model Based Condition Monitoring
in Lithium-Ion Batteries,” J. Power Sources, no. 9 June 2014, Jun. 2014.
[5] M. Mastali, J. Vazquez-Arenas, R. Fraser, M. Fowler, S. Afshar, and M.
Stevens, “Battery state of the charge estimation using Kalman filtering,”
J. Power Sources, vol. 239, pp. 294–307, Oct. 2013.
208
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