] The invention relates generally to a system for monitoring state of charge (SOC) and state
of health (SOH) of electric vehicle (EV) Lithium-ion battery pack under real-time operation, more
particularly it relates to the battery’s monitoring of the lithium-ion batteries and improving the
overall performance of the battery management system to increase the life cycles of the lithiumion batteries. Said system comprises Measurement Sensors, Battery Modeling unit, SOC
Estimation Module, Cycle Counter Module, SOH estimation Module, IoT and Remote Monitoring
Module, EV Dashboard; wherein the method for SOC and SOH estimation using the system of the
instant invention is characterized in the fact that it considers the effect of aging on the SOC
estimation and effect of power fade and capacity fade for SOH estimation.
BACKGROUND
[0002] In Electric Vehicles, energy storage is an important part that defines the applications and
requirements. To fulfill the need for EVs a long driving range, safety, and fast charging, the
Lithium-ion battery is widely used by the OEMs. However, to operate the Lithium-ion batteries
within safe operating limits, it is necessary to estimate the EV Lithium-ion battery states such as
state of charge (SOC) and state of health (SOH) accurately. The SOC helps to protect the battery
from overcharging, deep discharging and control the C-rate. Whereas SOH provides the idea about
the battery remaining useful life and ageing level. Further, to improve the overall life cycle of the
Lithium-ion batteries, the operating SOC range for charging and discharging of the battery can be
varied based on the estimated SOC and SOH of the EV Lithium-ion battery. For the SOC and SOH
estimation, different methods have been developed by the OEMs with their properties.
[0003] US 2014/0316728A1 disclose about the system and method of SOC estimation of a battery.
It employs the Ampere-hour method to estimate the SOC along with determining the initial SOC
value. Further, the Extended Kalman Filter (EKF) is considered to optimize the performance of
SOC estimation.
[0004] CN107526037B discloses about the battery state estimating device and
a battery state estimating method. This employs the Ah-OCV map to estimate the state of charge
of the battery. The OCV is calculated based on the detected battery pack voltage and current. A
correction unit is used to update the Ah-OCV map to achieve high estimation accuracy.
[0005] CN108445406B discloses about the health state estimation of the power battery. This
employs the V-Q relation curve and acquires the peak position information of a capacity increment
3
curve during constant-current charging. Further, the RBF neural network model is trained to
estimate the health state of the battery for real-time application.
[0005] US 2014/0350877A1 disclose the co-estimation of battery state of charge and state of health
estimation method. The battery model employs to describe the relationship between the battery
parameters, current, and voltage. The capacity estimation is done by using estimated SOC, by an
observer, and the coulomb counting method.
[0006] As per the invention disclosed above, the conventional systems are only focused on a
complex observer method for SOC and SOH estimation. In addition, conventionally the effect of
aging on the SOC estimation is not considered, which may affect the accuracy significantly. In the
disclosed reference documents, the capacity fading-based SOH estimation method is mainly
focused.
[0007] Therefore, there exists a need to develop an accurate state of charge and health monitoring
method and system to control the operations of Lithium-ion batteries used in EVs. It is required to
timely update the value of capacity in the coulomb counting based SOC estimation method to high
accuracy throughout the life cycle. Further, both the capacity fade SOH and power fade SOH need
to monitor to optimize battery usage to achieve improved performance of the EV Lithium-ion
battery pack in use. As the complexity of the monitoring method directly influences the
implementation cost. Hence, the low overall complexity of the SOC and SOH estimation method
is required to maintain the cost of the BMS chip.
OBJECTIVE OF THE INVENTION
[0008] The principal objective of the present invention is the provide the accurate state of charge
and state of health monitoring method system for an EV Lithium-ion battery pack in use.
[0009] An object is to improve the performance of the EV Lithium-ion battery pack in use by
accurate estimation of SOC along with the feature to update the battery capacity.
[0010] Another object is to simultaneously monitor and update the power fade SOH and capacity
fade SOH of the EV Lithium-ion battery pack in use.
[0011] Another object is to keep the EV Lithium-ion battery pack in safe operating limits under
real-time conditions by accurate monitoring of SOC and SOH.
[0012] An object to remotely monitor SOC and SOH of the EV Lithium-ion battery pack in use
with the help of Internet of thing device and internet cloud.
4
[0012] Final object is to reduce the computational cost of the SOC and SOH monitoring system to
implement in the low cost and less memory capacity BMS.
[0014] The foregoing and other objects of the present invention will become readily apparent upon
further review of the following detailed description of the preferred embodiment as illustrated in
the accompanying drawings.
SUMMARY
[0015] This summary provided a describe the invention in the simplified form that is explained
below in the detailed description section. The summary is not intended to identify the key features
or the essential features of the claimed subject matter.
Parameters relating to SOC: Real-time charge level related to battery pack capacity.
Parameters relating to SOH: Capacity fade (Degradation of actual capacity) and Power fade
(Increase in internal resistance).
[0016] The system for monitoring SOC and SOH of EV Lithium-ion battery comprises the
following components:-
 Measurement sensor(s), (battery pack current, voltage, and temperature)
 Battery Modeling Unit;
 State of Charge (SOC) and State of Health (SOH) monitoring module;
 Cycle Counter module;
 EV dashboard (SOC and SOH display),
 Remote Monitoring Module (Remote Geographic Locations and Platforms (such as Mobile
apps and Screens)).
[0017] During the real-time operation, the monitoring and controlling of Lithium-ion batteries are
mandatory because of their dynamic and nonlinear behavior. Generally, the battery management
system (BMS) is employed to perform the different functions that help to control the Lithium-ion
batteries operations. In which, EV Lithium-ion battery states such as SOC and SOH are required
to monitor accurately in real-time application. The EV Lithium-ion battery SOC refers to the
available charge in the battery pack related to its capacity and SOH defines the degree of health
compared to the new EV Lithium-ion battery. Two important factors to represent the SOH of the
5
battery pack are power fade and capacity fade. An increase in thickness of solid electrolytic
interference and loss of active material is the main cause of power fade and capacity fade,
respectively. Accurate SOC and SOH monitoring is always needed to improve the EV Lithiumion battery performance with the optimization of the power and energy management system.
However, the accurate EV Lithium-ion battery state estimation is challenging as it cannot be
monitored directly with the use of direct measurement signals. To perform the accurate EV
Lithium-ion battery SOC and SOH estimation, advanced microcontroller-based BMS is designed
and customized for real-time estimation and data processing. Further, it is always essential to
maintain the complexity and computational cost of the SOC and SOH estimation to implement in
the low-cost BMS chip.
[0018] In the more preferred embodiment of the present invention, the SOC estimation is
performed by using the improved coulomb counting method. In which, the EV Lithium-ion battery
aging effect on the EV Lithium-ion battery capacity is incorporated by updating the actual capacity
of the battery after every Nth number of cycle counts.
[0019] In the more preferred embodiment of the present invention, the actual SOH estimation of
the EV Lithium-ion battery pack is done by the SOH estimation module after every Nth number of
cycle counts. Where both power fade SOH and capacity fade SOH is considered to achieve high
accuracy of SOH estimation.
[0020] In the more preferred embodiment of the present invention, the SOC estimation module
and SOH estimation module are implemented in the low computational cost microcontroller.
Further, the estimated SOC and SOH information based on capacity fade and power fade are
displayed on the EV dashboard and remotely monitor by using a remote monitoring module. In a
remote monitoring module, the data collected through connected IoT devices is transferred to the
internet cloud for remote monitoring from different geographic locations and platforms such as
mobile applications or screens.
BRIEF DESCRIPTION OF THE DRAWINGS
[0021] Fig 1 presents an example of the schematic of the system for monitoring the state of charge
(SOC) and state of health (SOH) of electric vehicle lithium-ion battery pack and the method
thereof.
6
[0022] Fig 2 shows an example of the function of the battery modeling unit. Under this, an
electrical equivalent circuit battery model parameter is identified by using an identification
algorithm in real-time.
[0023] Fig 3 shows the first-order RC equivalent circuit simulating the EV Lithium-ion battery
pack.
[0024] Fig 4 shows the flowchart of the evaluation of actual capacity during charging of EV battery
pack in use at every Nth cycle count according to a preferred embodiment of the present disclosure.
[0025] Fig 5 shows the flowchart of the SOC and SOH estimation of the EV battery pack in use
according to a preferred embodiment of the present disclosure.
[0026] Fig 6 shows the remote monitoring module that combines the Internet of Thing (IoT)
device, and Internet Cloud to monitor the SOC and SOH from a remote location.
DETAILED DESCRIPTION OF THE INVENTION
[0027] The present disclosure is about a novel system for monitoring SOC and SOH of EV
Lithium-ion battery, which comprises of the following major components:-
 Measurement sensor(s), (battery pack current, voltage, and temperature)
 Battery Modeling Unit;
 State of Charge (SOC) and State of Health (SOH) monitoring module;
 Cycle Counter module;
 EV dashboard (SOC and SOH display),
 Remote Monitoring Module (Remote Geographic Locations and Platforms (such as Mobile
apps and Screens))
Real-time charge level related to battery pack capacity is considered as the major parameter
relating to the monitoring of SOC of EV Lithium-ion battery, as provisioned in the said system of
the present invention. Similarly, Capacity fade ( Degradation of actual capacity) and Power fade
(Increase in internal resistance) are provisioned as the major parameters for monitoring the SOH
of EV Lithium-ion batteries.
The method for monitoring (SOC) and (SOH) of the battery pack (100) of EV is characterized in:
7
 operating the said battery pack (100) in continuous connection, using electric wires, with
the said battery management system (200) comprising low-cost and less memory capacity
microcontroller, provided with the low complexity estimation algorithms; and
 accurate monitoring of the state of charge (SOC) and state of health (SOH) of the EV
Lithium-ion battery pack on the EV dashboard and from a remote geographic location and
platforms.
Different embodiments of the present disclosure will be described more fully hereinafter
concerning the enclosed drawings. However, the method and system disclosed herein can be
realized in many different forms and should not be construed as being limited to the aspects set
forth herein.
[0028] With the reference of Fig 1, the real-time parameters of the EV battery pack 100 connected
with the battery management system hardware 200, are measured by the highly accurate
measurement sensors 201. The battery management system hardware (200) is mainly referred to
herein as the system for monitoring SOC and SOH of EV Lithium-ion battery; wherein it is directly
integrated with the battery pack (100) of the electric vehicle using electric wires, so that it is
capable sensing, recording SOC and SOH related data and supplying the corresponding
information on the EV dashboard as well as on remote locations through IoT.
The measured battery parameters send to the electrical battery modeling unit 202 to mimic the
battery physics and electromechanical behavior in the form of electrical components like
resistance, capacitance, and controlled voltage source. To control the functioning and improve the
performance of the EV lithium-ion battery pack, the SOC and SOH estimation module 203 are
used to estimate the accurate SOC and SOH. For the capacity update after every Nth cycle count,
the cycle counter module 204 is used. N can be any integer number, for example, 5 or 10. The
estimated SOC and SOH are remotely monitored by the battery OEMs, charging stations, and
battery swapping stations with the help of IoT devices connected with the battery management
system chip and Internet connection300. In addition, the estimated SOC and SOH information are
displayed on EV dashboard 400. With the accurate SOC and SOH, EV users can schedule their
trip accordingly and know the status of the battery health.
[0029] A cycle counter unit 204 is used to get the overall cycle count of the EV Lithium-ion battery
pack. In which, the rate of change is positive and negative of estimated SOC is monitored to decide
8
whether the battery is under charge and discharge mode, respectively. If the battery is in
undercharging mode, the charge counter will be increased by one when the estimated SOC reached
95 %. On the other hand, if the battery is under discharge mode, the discharge counter will be
increased by one when the estimated SOC reached 5 %. After the update of both the counters, the
average charge/discharge counter information is utilized to update the overall cycle count of the
EV Lithium-ion battery pack.
[0030] To achieve the high estimation accuracy of SOC and SOH of EV Lithium-ion battery pack
in use, it is crucial to identify the accurate electrical equivalent circuit battery model parameters. As
the battery model parameters changes with the change in the battery operating conditions like
temperature, SOC level, charging/discharging rate, and SOH. To outperform this issue, it is required
to use online model parameters identification algorithm. Referring to Fig 2, the electrical equivalent
circuit battery model 201A is utilized to simulate the behavior of the EV Lithium-ion battery pack
in use. As shown in Fig 3, the constructed first-order electrical equivalent circuit battery model
201Aa consists of series internal or ohmic resistance (R0), electrochemical polarization/diffusion
resistance (R1), and polarization/diffusion capacitance (C1) connected in parallel to form a
resistance-capacitor branch, and series-connected controller voltage source (Vocv) equivalent to
open-circuit voltage (OCV).
[0031] By using Kirchhoff’s voltage law and applying the Laplace transformation on the first order
battery model 201Aa, the continuous-time voltage equations can be written as:
𝑉ை஼௏ሺ𝑡ሻ ൌ ൬ 𝑅ଵ
𝐶ଵ𝑅ଵ𝑠 ൅ 1 ൅ 𝑅଴൰ 𝐼ሺ𝑡ሻ ൅ 𝑉௧ሺ𝑡ሻ
Further, the above equation can be written in the discrete form as:
𝑉௧,௞ ൌ ሺ1 െ 𝑎ଵሻ𝑉ை஼௏,௞ ൅ 𝑎ଵ𝑉௧,௞ିଵ ൅ 𝑎ଶ𝐼௞ ൅ 𝑎ଷ𝐼௞ିଵ
Where, 𝑎ଵ, 𝑎ଶ, and 𝑎ଷ are the coefficients. 𝑘 is the discrete-time instant.
[0032] For the real-time parameter identification of first-order electrical equivalent circuit
Lithium-ion battery model 201A parameters such as (R0,k-1), (R1,k-1), (C1,k-1), and (Vocv,k-1) 202A,
the forgetting factor recursive least square (FFRLS) algorithm 202B can be used. Under which the
last recorded value of the measured battery parameters such as current (Ik) and voltage (Vt,k) are
utilized.
9
[0033] Forgetting Factor Recursive Least Square can be described by the equation as
൞
𝜃௥
෡ ሺ𝑘ሻ ൌ 𝜃௥
෡ ሺ𝑘 െ 1ሻ ൅ Κሺ𝑘ሻሾyሺ𝑘ሻ െ 𝜙෠்ሺ𝑘ሻ𝜃௥
෡ ሺ𝑘 െ 1ሻሿ
Κሺ𝑘ሻ ൌ ൫Pሺ𝑘 െ 1ሻ𝜃௥ሺ𝑘ሻ൯ ሺ𝜆 ൅ 𝜙் ⁄ ሺ𝑘ሻ Pሺ𝑘 െ 1ሻ𝜙ሺ𝑘ሻሻ
𝑃ሺ𝑘ሻ ൌ ሾ൫𝐼 െ Κሺ𝑘ሻ𝜙்ሺ𝑘ሻ൯Pሺ𝑘 െ 1ሻሿ 𝜆⁄
Where, measurement vector 𝜙ሺ𝑘ሻ ൌ ሾ1 െ𝐼௞ െ𝑉௧,௞ െ𝑉௧.௞ିଵሿ்,𝐾ሺ𝑘ሻ refers to the gain matrix
and model parameter vector 𝜃ሺ𝑘ሻ ൌ ሾሺ1 െ 𝑎ଵሻ𝑉ை஼௏,௞ 𝑎ଵ 𝑎ଶ 𝑎ଷሿ்.𝑃ሺ𝑘ሻ is the noise
covariance matrix. yሺ𝑘ሻ is the battery model terminal voltage output. The forgetting factor (𝜆ሻis
set to 0.99. 𝑘 is the sample time.
[0034] Based on the evaluated 𝜃ሺ𝑘ሻ using FFRLS, the battery model parameters can be identified
by using the expression given below:
⎣
⎢
⎢
⎡
𝑉ை஼௏,௞
𝑅଴,௞
𝑅ଵ,௞
𝐶ଵ,௞ ⎦
⎥
⎥
⎤
ൌ
⎣
⎢
⎢
⎡ 𝑎ଵ⁄ሺ1 െ 𝑎ଵሻ
െሺ𝑎ଶ െ 𝑎ଷሻ ሺ ⁄ 1 ൅ 𝑎ଵሻ
2ሺ𝑎ଵ𝑎ଶ ൅ 𝑎ଷሻ ሺ1 ൅ 𝑎ଵ
ଶ ⁄ ሻ
ሺ1 ൅ 𝑎ଵሻଶ⁄4ሺሺ𝑎ଵ𝑎ଶ ൅ 𝑎ଷሻ⎦
⎥
⎥
⎤
[0035] The identified battery model parameters 202C values are sent to the SOC and SOH
estimation module 203.
[0036] To evaluate and update of the 𝑄஺ሺ𝑡ሻ value, the charging profile of every Nth cycle count is
considered. Refer to Fig 4, in Step S201, the real-time charging current (𝐼) and identified OCV
using the FFRLS algorithm is recorded for the period 𝑇ଵ to 𝑇ଶ. In the next step S201, the value of
𝑆𝑂𝐶ሺ𝑇ଵሻ and 𝑆𝑂𝐶ሺ𝑇ଶሻ are computed based on identified 𝑂𝐶𝑉ሺ𝑇ଵሻ and 𝑂𝐶𝑉ሺ𝑇ଶሻ using OCV-SOC
lookup. Meantime, the value of current integration is evaluated for the same period (𝑇ଵto𝑇ଶሻ in
step S203. The value of 𝑄஺ሺ𝑡ሻ is evaluated in step S204 based on the expression given below:
𝑄஺ሺ𝑡ሻ ൌ ׬் �𝑑� �� మ
்భ
𝑆𝑂𝐶ሺ𝑇ଵሻ െ 𝑆𝑂𝐶ሺ𝑇ଶሻ
[0037] Now referring to Fig 5, based on the measured current, voltage, and temperature of the EV
Lithium-ion battery pack in use, in Step S101, the working status is acknowledged in step S103.
If the EV Lithium-ion battery pack in use, is in ideal condition for more than 1 Hour then the value
of the initial SOC ((𝑆𝑂𝐶ሺ𝑡଴ሻሻevaluated based on the OCV-SOC lookup given on the manufacturer
datasheet in Step S104 based on the identified OCV in Step S102. Otherwise, in step S105, the
previous estimated SOC is considered as initial SOCሺ𝑆𝑂𝐶ሺ𝑡଴ሻሻ for SOC estimation.
10
[0038] As the actual capacity ሺ𝑄஺ሺ𝑡ሻሻdecrease with the increase in the cycle counts hence it is
mandatory to update it after every Nth cycle count. Otherwise, it would be hard to achieve accurate
SOC during the overall life cycle of EV Lithium-ion battery. A cycle count refers to the full chargedischarge cycle of the EV Lithium-ion battery pack.
[0039] To achieve high accuracy SOC estimation of EV Lithium-ion battery pack in use, the SOC
estimation is performed by using the improved coulomb counting method, in Step S106. It
combines the coulomb counting method and the feature of actual capacity update with battery
ageing. The coulomb counting method can be expressed by the equation given below:
𝑆𝑂𝐶ሺ𝑡ሻ ൌ 𝑆𝑂𝐶ሺ𝑡଴ሻ െ ׬�� ሺ𝑡ሻ 𝑑𝑡 ௧
௧బ
𝑄஺ሺ𝑡ሻ ൈ 100 %
Where the 𝑆𝑂𝐶ሺ𝑡ሻ is estimated SOC at time 𝑡. 𝐼ሺ𝑡ሻ and 𝑄஺ሺ𝑡ሻ are the measured current and actual
capacity at time 𝑡.
[0040] As the EV Lithium-ion battery pack ages, the battery internal resistance increases called
power fade due to electrolyte decomposition and solid electrolyte interface deterioration at the
electrode surface. On the other hand, due to the low participation of active material in the chargingdischarging process in the aged battery, the actual capacity of the battery is significantly reduced
i.e., capacity fade. Thus, the accurate monitoring of both the effects of EV lithium-ion battery pack
in use is important to identify the optimal usage profile e.g., charge-discharge C-rate.
[0041] In Step S107, for estimation of SOH based on the power fade (𝑆𝑂𝐻௉௢௪௘௥) of the EV
lithium-ion battery pack in use, an equation can be expressed as;
𝑆𝑂𝐻௉௢௪௘௥ ൌ 𝑅௘௢௟ െ 𝑅଴ሺ𝑡ሻ
𝑅௘௢௟ െ 𝑅௡௘௪
ൈ 100 %
Where the (𝑅଴ሺ𝑡ሻ) refers to the identified internal Resistance using the FFRLS algorithm at time
𝑡,𝑅௡௘௪is the internal resistance of the new EV lithium-ion battery pack in use and 𝑅௘௢௟ refers to
the end-of-life internal resistance of the EV lithium-ion battery pack in use. Generally, the value
of 𝑅௘௢௟is set to 130 % of the 𝑅௡௘௪. The value of 𝑅଴ሺ𝑡ሻ is updated in 𝑆𝑂𝐻௉௢௪௘௥ formula at 50 %
SOC during discharging;
𝑆𝑂𝐻஼௔௣௔௖௜௧௬ ൌ 𝑄஺ሺ𝑡ሻ
𝑄௡௘௪
ൈ 100 %
11
Where the (𝑄஺ሺ𝑡ሻ) refers to the evaluated at time 𝑡 and 𝑄௡௘௪is the capacity of the new EV lithiumion battery pack in use. The estimated 𝑆𝑂𝐻஼௔௣௔௖௜௧௬ of the EV Lithium-ion battery pack in use is
updated after every Nth cycle count.
[0042] In step S108, the average of the past few estimated 𝑆𝑂𝐶 and 𝑆𝑂𝐻௉௢௪௘௥ is evaluated to
reduce the unwanted values and improve the data visibility.
[0043] Based on the number of cycle counts, the decision on an estimation of SOH based on the
capacity fade (𝑆𝑂𝐻஼௔௣௔௖௜௧௬) of the EV lithium-ion battery pack is made, in step S109. If the
number of cycle count is equal to N then 𝑆𝑂𝐻஼௔௣௔௖௜௧௬ estimation of the EV lithium-ion battery
pack in use is performed in Step S110. Otherwise, the estimate 𝑆𝑂𝐻஼௔௣௔௖௜௧௬ remains same as
previous estimated 𝑆𝑂𝐻஼௔௣௔௖௜௧௬.
[0044] In step S111, the evaluated actual capacity is considered to update the actual capacity value
in the SOC estimation formula to achieve high SOC estimation accuracy through the battery
lifetime.
[0045] In step S112, estimated SOC, 𝑆𝑂𝐻௉௢௪௘௥ and 𝑆𝑂𝐻஼௔௣௔௖௜௧௬ EV lithium-ion battery pack in
use are recorded and updated.
[0046] Now referring to Fig 6, the estimated SOC, SOC, 𝑆𝑂𝐻௉௢௪௘௥ and 𝑆𝑂𝐻஼௔௣௔௖௜௧௬ EV lithiumion battery pack in use remotely monitor from the remote location 303 with the help of IoT device
301 connected with the BMS chip and internet cloud 302.
[0047] Moreover, estimated SOC, SOC, 𝑆𝑂𝐻௉௢௪௘௥ and 𝑆𝑂𝐻஼௔௣௔௖௜௧௬ EV lithium-ion battery pack
in use is displayed on the EV dashboard 400.
[0048] Although a preferred embodiment of the invention has been illustrated and
described/illustrated, it will at once be apparent to those skilled in the art that the invention includes
advantages and features over and beyond the specific illustrated construction. Accordingly, it is
intended that the scope of the invention be limited solely by the scope of the claims, and not by
the foregoing specification, when interpreted in light of the relevant prior arts.

WE CLAIM
1. A system for monitoring the state of charge (SOC) and state of health (SOH) of electric
vehicle lithium-ion battery pack and the method thereof, wherein said system (200)
comprises of:
 measurement sensors (201), to monitor the battery pack and cells voltage, battery
pack temperature, and battery pack current;
 battery modeling unit (202), to develop the electrical equivalent circuit model using
online battery model parameters identification algorithm;
 SOC and SOH estimation module (203), to estimate the SOC, capacity fade SOH,
and power fade SOH of the EV Lithium-ion battery pack in use;
 Cycle counter module (204), to evaluate the cycle count based on the considered
full charge-discharge SOC range;
 Remote monitoring module (300), to remotely monitor the estimated EV Lithiumion battery in use SOC and SOH;
 and dashboard EVs (400), to display the real-time estimated EV Lithium-ion
battery SOC and SOH to the EV users;
wherein the method for monitoring (SOC) and (SOH) of the battery pack (100) of EV is
characterized in:
 operating the said battery pack (100) in continuous connection, using electric wires,
with the said battery management system (200) comprising low-cost and less
memory capacity microcontroller, provided with the low complexity estimation
algorithms; and
 accurate monitoring of the state of charge (SOC) and state of health (SOH) of the
EV Lithium-ion battery pack on the EV dashboard and from a remote geographic
location and platforms.
2. The system (200) and the method for monitoring (SOC) and (SOH) of EV battery pack
(100); as claimed in claim 1, wherein the said SOC and SOH estimation module is used to
real-time estimate the SOC, capacity fade SOH and power fade SOH of the EV Lithiumion battery pack (100) in use.
14
3. The system (200) and the method for monitoring (SOC) and (SOH) of EV battery pack
(100), as claimed in claim 1, wherein the SOC estimation is performed at low
computational cost by using coulomb counting method in step S106 with the consideration
of actual capacity degradation to achieve high accuracy during the complete life cycle of
the EV lithium-ion battery pack in use.
4. The system (200) and the method for monitoring (SOC) and (SOH) of EV battery pack
(100), as claimed in claim 1, wherein the actual capacity evaluation is performed in step
after every Nth cycle count S204; under which, the SOC interpolated using a lookup based
on identified OCV at the start and end time instant of the charging profile and the
accumulated current between the respective time instants after every Nth cycle count is
employed.
5. The system (200) and the method for monitoring (SOC) and (SOH) of EV battery pack
(100), as claimed in claim 1, wherein said cycle counter module (204) is employed to
produce the accurate cycle count of the EV Lithium-ion battery pack in use under partial
as well as full charge-discharge condition.
6. The system (200) and the method for monitoring (SOC) and (SOH) of EV battery pack
(100), as claimed in claim 1, wherein said SOC and SOH estimation module (203) is used
to simultaneously estimate the power fade SOH and capacity fade SOH of EV Lithium-ion
battery pack in use based on the identified electrical equivalent circuit battery model
parameters and actual capacity, respectively.
7. The system (200) and the method for monitoring (SOC) and (SOH) of EV battery pack
(100), as claimed in claim 1, said SOC and SOH estimation module (203) can update the
estimated SOC every second and estimated SOH power fade and SOH capacity fade after
every Nth cycle count, respectively.
8. The system (200) and the method for monitoring (SOC) and (SOH) of EV battery pack
(100), as claimed in claim 1, wherein said SOC and SOH estimation module (203) is
implemented in the hardware with the low-cost and less memory capacity microcontroller.
9. The system (200) and the method for monitoring (SOC) and (SOH) of EV battery pack
(100), as claimed in claim 1, wherein the SOC and SOH estimation method and the system
is configurable for the different chemistries EV Lithium-ion battery pack.
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10. The system (200) and the method for monitoring (SOC) and (SOH) of EV battery pack
(100), as claimed in claim 1, wherein the system and method is configurable to monitor the
estimated SOC and SOH from the EV dashboard (400) as well as from the remote
geographic location and platforms through Remote Monitoring Module (300).