FORM 2
THE PATENTS ACT 1970
[39 OF 1970]
&
The Patents Rules, 2003
COMPLETE SPECIFICATION
[See Section 10 and Rule 13]
TITLE: “METHOD, APPARATUS AND SYSTEM FOR ESTIMATING REMAINING
USEFUL LIFE (RUL) OF A BATTERY OF A VEHICLE”
Name and address of the Applicant:
TATA MOTORS LIMITED, an Indian company having its registered office at Bombay
house, 24 Homi Mody Street, Hutatma Chowk, Mumbai 400 001, Maharashtra, India.
Nationality: INDIAN
The following specification particularly describes the nature of the invention and the manner
in which it is to be performed.
2
TECHNICAL FIELD
5 [001] Present disclosure generally relates to field of automobile engineering. Particularly but
not exclusively, the present disclosure relates to a method, apparatus and a system for
estimating Remaining Useful Life (RUL) of a battery of a vehicle.
BACKGROUND OF THE DISCLOSURE
10 [002] Some vehicles, such as an Electric Vehicle (EV) and a hybrid vehicle, use one or more
electric motors for propulsion. Such vehicles may include a battery for powering the electric
motors. The battery may be charged from an external energy source. The battery stores the
electric power and provides electricity to run the electric motor of the vehicle. The vehicles
require battery monitoring for maintaining health of the battery. Though the existing techniques
15 discuss multiple methods for monitoring the battery, determining state of health of battery,
state of charge of the battery and the like, lack of accuracy and reliability of such techniques
lead to incorrect estimation regarding life of the battery. This may lead to situations wherein
users of the vehicles may underutilize their battery and replace the battery due to incorrect
estimation of life of the battery. In some other scenarios, the users of battery powered vehicles
20 may not replace the battery assuming that the estimation is accurate, which may lead to
functional issues in the vehicle.
[003] Some of the existing techniques determine SoH of the battery by monitoring rise and
fall of the current and voltage output of the battery.
25
[004] However, the existing techniques do not consider all the factors that impact the State of
Health (SoH) and the RUL of the battery, which leads to the inaccurate estimation of the RUL
of the battery. Thus, there is a need for an improved method to accurately estimate RUL of the
battery of the EVs.
30
[005] The information disclosed in this background of the disclosure section is only for
enhancement of understanding of the general background of the disclosure and should not be
taken as an acknowledgement or any form of suggestion that this information forms prior art
already known to a person skilled in the art.
35
SUMMARY OF THE DISCLOSURE
3
[006] One or more shortcomings of the conventional systems are overcome by system and
method as claimed and additional advantages are provided through the provision of system and
method as claimed in the present disclosure. Additional features and advantages are realized
5 through the techniques of the present disclosure. Other embodiments and aspects of the
disclosure are described in detail herein and are considered a part of the claimed disclosure.
[007] In one non-limiting embodiment, present disclosure discloses a method for estimating
Remaining Useful Life (RUL) of a battery of a vehicle. The method includes, receiving, by a
10 controller of a vehicle, sensor data corresponding to a plurality of battery parameters and a
plurality of vehicle parameters from one or more sensors associated with the vehicle. Further,
the method includes determining, by the controller, a current internal resistance of a battery
based on the plurality of battery parameters and the plurality of vehicle parameters. Thereafter,
the method includes predicting, by the controller, an increase in the current internal resistance
15 based on simulation of a future battery usage using the plurality of battery parameters and the
plurality of vehicle parameters. Finally, the method includes, estimating, by the controller, a
RUL based on the predicted increase in the internal resistance.
[008] In an embodiment of the disclosure, the plurality of battery parameters comprises
20 voltage output of the battery, current discharge from the battery, temperature of the battery and
current state of charge of the battery.
[009] In an embodiment of the disclosure, the plurality of vehicle parameters comprises
acceleration of the vehicle, braking pattern of the vehicle, and speed of the vehicle. In an
25 embodiment, the method includes determining a weighted drive cycle indicative of a vehicle
driver behaviour based on the plurality of vehicle parameters. Particularly, the standard drive
cycle of the vehicle is modified with respect to the plurality of vehicle parameters to determine
the weighted drive cycle of the vehicle.
30 [0010] In an embodiment of the disclosure, the current internal resistance of the battery is
determined as a function of a weighted drive cycle and the plurality of battery parameters, using
an Equivalent Circuit Model (ECM) of the battery.
[0011] In an embodiment of the disclosure, the method further includes determining, by the
controller, a current State of Health (SoH) value of the battery based on the current internal
35 resistance of the battery.
4
[0012] In an embodiment of the disclosure, the method of predicting the increase in the current
internal resistance of the battery includes, simulating, by the controller, the future battery usage
of the vehicle using a battery model.
5
[0013] In an embodiment of the disclosure, simulating the future battery usage of the vehicle
using a model includes, predicting, by the controller, a future drive cycle and future battery
parameters of the vehicle based on the weighted drive cycle, the plurality of battery parameters
of the vehicle and predicting, by the controller, a future internal resistance indicative of the
10 increase in the current internal resistance of the battery based on the predicted future drive
cycle and the future battery parameters.
[0014] In an embodiment of the disclosure, estimating the RUL of the battery includes,
estimating, by the controller, a total number of charging cycles remaining until a current SoH
15 value of the battery drops to a predefined threshold SoH value, based on the predicted increase
in the current internal resistance and estimating, by the controller, the RUL of the battery based
on the total number of charging cycles.
[0015] In an embodiment of the disclosure, the method discloses using a battery model to
20 determine SoH of the battery and estimate the RUL of the battery.
[0016] In another non-limiting embodiment, the present disclosure discloses a controller for
estimating Remaining Useful Life (RUL) of a Battery in a vehicle. The controller is
communicatively coupled to a memory for storing instructions, which on execution, causes the
25 controller to receive sensor data corresponding to a plurality of battery parameters and a
plurality of vehicle parameters from one or more sensors associated with the vehicle. Further,
the controller determines a current internal resistance of a battery based on the plurality of
battery parameters and the plurality of vehicle parameters. Thereafter, the controller predicts
an increase in the current internal resistance based on simulation of a future battery usage using
30 the plurality of battery parameters and the plurality of vehicle parameters. Finally, the
controller estimates a RUL based on the predicted increase in the internal resistance.
[0017] In another non-limiting embodiment, the present disclosure discloses a system for
estimating Remaining Useful Life (RUL) of a Battery in a vehicle. The system includes one or
more sensors associated with a controller of the vehicle and the controller of the vehicle. The
5
controller of the vehicle is configured to estimate Remaining Useful Life (RUL) of a Battery
in the vehicle in accordance with the embodiments of the present disclosure.
[0018] It is to be understood that aspects and embodiments of the disclosure described above
5 may be used in any combination with each other. Several aspects and embodiments may be
combined together to form a further embodiment of the disclosure.
[0019] The foregoing summary is illustrative only and is not intended to be in any way limiting.
In addition to illustrative aspects, embodiments, and features described above, further aspects,
10 embodiments, and features will become apparent by reference to drawings and the following
detailed description.
BRIEF DESCRIPTION OF THE ACCOMPANYING FIGURES
15
[0020] The accompanying drawings, which are incorporated in and constitute a part of this
disclosure, illustrate exemplary embodiments and, together with the description, serve to
explain the disclosed principles. In the figures, the left-most digit(s) of a reference number
identifies the figure in which the reference number first appears. The same numbers are used
20 throughout the figures to reference like features and components. Some embodiments of system
and/or methods in accordance with embodiments of the present subject matter are now
described, by way of example only, and with reference to the accompanying figures, in which:
[0021] FIGURE.1 shows an exemplary system architecture for estimating Remaining Useful
25 Life (RUL) of a battery of a vehicle, in accordance with some embodiments of the present
disclosure;
[0022] FIGURE. 2A shows a detailed block diagram of a controller for estimating Remaining
Useful Life (RUL) of a battery of a vehicle, in accordance with some embodiments of the
30 present disclosure;
[0023] FIGURE. 2B shows an exemplary representation of an Equivalent Circuit Model
(ECM), in accordance with some embodiments of the present disclosure;
6
[0024] FIGURE.3A-FIGURE-3C show flowcharts illustrating a method of estimating
Remaining Useful Life (RUL) of a battery of a vehicle, in accordance with some embodiments
of the present disclosure; and
5 [0025] FIGURE. 4 is a block diagram of an exemplary computer system for implementing
embodiments consistent with the present disclosure.
[0026] It should be appreciated by those skilled in the art that any block diagrams herein
represent conceptual views of illustrative systems embodying the principles of the present
10 subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state
transition diagrams, pseudo code, and the like represent various processes which may be
substantially represented in computer readable medium and executed by a computer or
processor, whether or not such computer or processor is explicitly shown.
15 [0027] The figures depict embodiments of the disclosure for purposes of illustration only. One
skilled in the art will readily recognize from the following description that alternative
embodiments of the system illustrated herein may be employed without departing from the
principles of the disclosure described herein.
20 DETAILED DESCRIPTION
[0028] In the present document, the word “exemplary” is used herein to mean “serving as an
example, instance, or illustration.” Any embodiment or implementation of the present subject
matter described herein as “exemplary” is not necessarily be construed as preferred or
25 advantageous over other embodiments.
[0029] While the disclosure is susceptible to various modifications and alternative forms,
specific embodiment thereof has been shown by way of example in the drawings and will be
described in detail below. It should be understood, however that it is not intended to limit the
30 disclosure to the forms disclosed, but on the contrary, the disclosure is to cover all
modifications, equivalents, and alternative falling within the scope of the disclosure.
[0030] The terms “comprises”, “comprising”, “includes” or any other variations thereof, are
intended to cover a non-exclusive inclusion, such that a setup, device or method that includes
35 a list of components or steps does not include only those components or steps but may include
other components or steps not expressly listed or inherent to such setup or device or method.
7
In other words, one or more elements in a system or apparatus proceeded by “comprises… a”
does not, without more constraints, preclude the existence of other elements or additional
elements in the system or method.
5 [0031] Disclosed herein are a method, apparatus and a system for estimating Remaining Useful
Life (RUL) of a battery of a vehicle. As an example, the vehicle may be a battery powered
vehicle such as electric vehicles, hybrid vehicles and the like. In some embodiments, in the
context of the claimed invention, apparatus may be a controller configured to perform the
method disclosed in the present disclosure. Initially, a controller may receive sensor data from
10 one or more sensors associated with the vehicle. For example, the one or more sensors
associated with the vehicle may include, but not limited to, temperature sensor, current sensors,
accelerometer, the pressure sensor and the like which provide a measure of vehicle and battery
parameters. The one or more sensors may be associated with the components of the vehicle
such as battery, brakes, steering wheel, accelerator and the like. The data captured by the one
15 or more sensors may be referred to as the sensor data in the present disclosure. The sensor data
may include, but not limited to, a plurality of battery parameters, and a plurality of vehicle
parameters. The battery parameters may represent the parameters associated with battery such
as voltage output of the battery, current discharge from the battery, temperature of the battery,
State of Charge (SoC) and the like. The plurality of vehicle parameters may include, but not
20 limited to, acceleration of the vehicle, braking pattern, and speed of the vehicle 101. Further,
the controller may determine a weighted drive cycle which is indicative of vehicle driver
behaviour based on the plurality of vehicle parameters. Further, the controller may determine
a current internal resistance of the battery as a function of the plurality of battery parameters
and the weighted drive cycle determined based on the plurality of vehicle parameters. The
25 internal resistance may indicate the ability of the battery to carry current. The current internal
resistance may be further used to determine the current State of Health (SoH) of the battery.
Thereafter, the controller may predict an increase in the current internal resistance based on the
simulation of future battery usage using the plurality of battery parameters and the plurality of
vehicle parameters. The controller may predict the increase in the current internal resistance
30 based on simulation of future battery usage using the plurality of vehicle parameters and the
plurality of battery parameters. Subsequently, the controller may estimate the Remaining
Useful Life (RUL) of the battery based on the predicted increase in the internal resistance.
8
[0032] In the present disclosure, the controller determines the current internal resistance based
on a combination of plurality of battery parameters and the plurality of the vehicle parameters.
Particularly, the current internal resistance is determined as a function of the plurality of battery
parameters and a weighted drive cycle indicative of driver behaviour using the Equivalent
5 Circuit Model (ECM) of the battery. The weighted drive cycle is determined by modifying a
standard drive cycle based on the plurality of vehicle parameters. Such determination of the
current internal resistance using a physics based calibrated ECM of battery enhances accuracy
in the determination of the current internal resistance, unlike conventional data driven models.
Further, the controller performs simulation of battery usage based on the determined current
10 internal resistance to predict the future internal resistance, which in turn may be used to
estimate the RUL of the battery. Therefore, accurate determination of the current internal
resistance and the future internal resistance helps in accurate and reliable estimation of the RUL
of the battery. This in turn helps in maintaining and planning service requirements for the
vehicle as necessary, and thus enhances the user experience with respect to electric vehicles.
15
[0033] A description of an embodiment with several components in communication with each
other does not imply that all such components are required. On the contrary, a variety of
optional components are described to illustrate the wide variety of possible embodiments of
the disclosure.
20
[0034] In the following detailed description of the embodiments of the disclosure, reference
is made to the accompanying drawings that form a part hereof, and in which are shown by way
of illustration specific embodiments in which the disclosure may be practiced. These
embodiments are described in sufficient detail to enable those skilled in the art to practice the
25 disclosure, and it is to be understood that other embodiments may be utilized and that changes
may be made without departing from the scope of the present disclosure. The following
description is, therefore, not to be taken in a limiting sense.
30 [0035] FIGURE.1 illustrates an exemplary system architecture 100 for estimating Remaining
Useful Life (RUL) of a battery of a vehicle, in accordance with some embodiments of the
present disclosure.
[0036] The system architecture 100 includes a vehicle 101, sensor 103A to sensor 103N (also
35 referred as one or more sensors 103), and a controller 105. The vehicle 101 may be a vehicle
9
that may use one or more electric motors for propulsion. For example, the vehicle may be an
electric vehicle (EV). In some embodiments, the vehicle 101 may be a vehicle that may operate
in a hybrid mode which is powered by an internal combustion engine and electric one or more
electric motors for the propulsion. The vehicle 101 may include one or more components such
5 as battery 109, electric motor, charging ports, brakes, and the like. The one or more components
of the vehicle 101 may be associated with one or more sensors 103. As an example, the one or
more sensors 103 may include, but not limited to, temperature sensor, current sensor, voltage
sensor, pressure sensor, accelerometer, speed sensors and the like. As an example, the
temperature sensor may detect the temperature of the battery 109, the current sensor may
10 measure the current discharge from the battery 109, accelerometer may measure the
acceleration of the vehicle, speed sensors may measure the speed of the vehicle and the like.
The one or more sensors 103 may communicate with the controller 105 through a
communication network (not shown in the Figure.1). The communication network may be at
least one of a wired communication network and a wireless communication network. In some
15 embodiments, the communication network can be implemented as one of the different types of
networks, such as intranet or Local Area Network (LAN), Controller Area Network (CAN) and
such within the vehicle. The communication network may either be a dedicated network or a
shared network, which represents an association of the different types of networks that use a
variety of protocols, for example, Hypertext Transfer Protocol (HTTP), CAN Protocol,
20 Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol
(WAP), etc., to communicate with each other. As an example, in a scenario where the controller
105 is configured in the vehicle, the communication network may be, without a limitation,
CAN that enables communication between the one or more sensors and the controller 105 via
CAN protocol. In some embodiments, when the controller 105 is remotely connected to the
25 one or more sensors 103, the communication network may be a wireless communication
network that communicates via TCP/IP or WAP.
[0037] In some embodiments, the controller 105 may be a single Electronic Control Unit
(ECU) or a combination of ECUs in the vehicle 101. In some other embodiments, the controller
30 105 may be configured in a remote server or a cloud infrastructure (Not shown in FIGURE. 1),
and may communicate with at least one of the one or more ECUs of the vehicle 101 to receive
sensor data.
10
[0038] The controller 105 may include a memory 107 that stores data necessary for the
controller 105. The controller 105 may receive the sensor data from the one or more sensors
103 through the communication network. The sensor data may correspond to a plurality of
battery parameters and a plurality of vehicle parameters. In some embodiments, the plurality
5 of battery parameters may include, but not limited to, voltage output of the battery 109, current
discharge from the battery 109, temperature of the battery 109, and state of charge (SoC) of the
battery 109. In some embodiments, the plurality of vehicle parameters may include, but not
limited to, acceleration of the vehicle, braking pattern and speed of the vehicle. In some
embodiments, the memory 107 may be configured to store the sensor data received from the
10 one or more sensors 103. The memory 107 may also be configured to store the one or more
instructions executed by the controller 105. Upon receiving the sensor data, the controller 105
may determine the current internal resistance of the battery 109 based on the plurality of battery
parameters and the plurality of vehicle parameters, using an Equivalent Circuit Model (ECM).
The method of determining the current internal resistance of the battery 109 using the ECM of
15 the battery 109 is described in greater detail in FIG.2B of the present disclosure. To determine
the current internal resistance, the controller 105 may initially determine a weighted drive cycle
which is indicative of a vehicle driver behaviour based on the plurality of vehicle parameters.
Thereafter, the controller 105 may determine a current internal resistance of the battery 109 as
a function of the plurality of battery parameters and the weighted drive cycle determined based
20 on the plurality of vehicle parameters. The internal resistance indicates the ability of the battery
109 to carry the current.
[0039] In some embodiments, the controller 105 may update the internal resistance of the
battery 109 periodically. Further, the controller 105 may determine the current State of Health
25 (SoH) of the battery 109 based on the determined current internal resistance using a battery
model. As the internal resistance of the battery 109 is inversely proportional to SoH of the
battery 109, an increase in the internal resistance indicates a degradation in the SoH of the
battery 109. In some embodiments, the controller 105 may predict an increase in the current
internal resistance by simulating a future battery usage of the vehicle 101 based on the plurality
30 of battery parameters and the plurality of vehicle parameters. In some embodiments, the
controller 105 may simulate the future battery usage of the vehicle 101 using the battery model.
11
[0040] Thereafter, the controller 105 may estimate the Remaining Useful Life (RUL) of the
battery 109 of the vehicle 101 based on the increase in the current internal resistance. In some
embodiments, through simulation, the controller 105 may estimate a total number of charging
cycles remaining until the current SoH value of the battery 109 drops to a predefined threshold
5 SoH value based on the determined increase in the current internal resistance. The controller
105 may estimate the RUL of the battery 109 based on the estimated total number of charging
cycles. This will be explained in greater detail with reference to FIG.2A in the present
disclosure.
10 [0041] FIGURE.2A shows a detailed block diagram of a controller for estimating Remaining
Useful Life (RUL) of a battery 109 of a vehicle, in accordance with some embodiments of the
present disclosure.
[0042] In some embodiments, the data 203 is stored in a memory 107 of the controller 105 as
shown in the FIGURE.2A. In one embodiment, the data 203 may include sensor data 207,
15 internal resistance data 209, weighted drive cycle data 211, simulated and predicted data 213,
estimated data 215 and other data 217. In the illustrated FIGURE.2A, modules 205 are
described herein in detail.
[0043] In some embodiments, the data 203 may be stored in the memory 107 in form of various
data structures. Additionally, the data 203 can be organized using data models, such as
20 relational or hierarchical data models. The other data 217 may store data, including temporary
data and temporary files, generated by the modules 205 for performing the various functions
of the controller 105.
[0044] In some embodiments, the sensor data 207 may include, the data measured by one or
more sensors 103 associated with the vehicle 101. The sensor data 207 may include plurality
25 of battery parameters and plurality of vehicle parameters. As an example, the plurality of
battery parameters may include, but not limited to, temperature of the one or more components
within the vehicle 101, current discharge from the battery 109, voltage output of the battery
109, and state of charge of the battery 109. Similarly, plurality of vehicle parameters may
include, but not limited to, acceleration of the vehicle, braking pattern, and speed of the vehicle
30 101. As an example, braking patterns may include, but not limited to, sudden braking, slow
braking, number of times the brakes are applied and the like.
12
[0045] In some embodiments, the internal resistance data 209 may comprise values associated
with the current internal resistance of the battery 109 and an initial internal resistance of the
battery 109. Generally, internal resistance of the battery 109 may indicate ability of the battery
109 to carry the current and is inversely proportional to the discharge rate of the current from
5 the battery 109. In some embodiments, the initial internal resistance may be the internal
resistance measured at the time of manufacturing the vehicle 101. The current internal
resistance of the battery 109 may be the internal resistance determined in real-time based on
the vehicle usage, using an Equivalent Circuit Model (ECM) model as shown in FIGURE. 2B.
[0046] In some embodiments, the weighted drive cycle data 211 may include a weighted drive
10 cycle indicative of the vehicle driver behaviour. In some embodiments, the weighted drive
cycle may indicate whether the vehicle driver behaviour is one of gentle, normal, or aggressive.
The classification indicated in terms of gentle, normal and aggressive is only exemplary and
should not be construed as a limitation of the present disclosure, as the classification of the
vehicle driver behaviour may be predefined as per requirement. The weighted drive cycle data
15 211 may be determined based on the plurality of vehicle parameters. Particularly, the weighted
drive cycle data 211 may be measured based on the modification of the standard drive cycle of
the vehicle 101 with respect to the plurality of vehicle parameters. The standard drive cycle is
generally a specific standard based on which the vehicles are tested in terms of speed of the
vehicle, acceleration of the vehicle and the like. However, such standard drive cycle may
20 change based on the manner in which the vehicle 101 is driven or in other words, vehicle driver
behaviour. In some embodiments, deviation in the standard drive cycle may be measured based
on comparative analysis of a drive cycle resulting due to the vehicle driver behaviour with the
standard drive cycle. This in turn results in the modification of the standard drive cycle with
respect to the vehicle parameters such as speed of the vehicle 101, acceleration of the vehicle
25 101, braking pattern of the vehicle 101 and the like.
[0047] In some embodiments, simulated and predicted data 213 may indicate increase in the
current internal resistance of the battery 109 associated with vehicle 101. In some
embodiments, the simulated and predicted data 213 may include the future drive cycle, future
battery parameters, and future internal resistance. The simulated and predicted data 213 may
30 be obtained by performing simulation of the battery usage using the battery model. The
weighted drive cycle of the vehicle 101 and plurality of battery parameters may be provided as
13
an input for the battery model to perform the simulation and predict future internal resistance
of the battery 109 associated with the vehicle 101.
[0048] In some embodiments, the estimated data 215 may comprise the estimated Remaining
Useful Life (RUL) of the battery 109. The RUL of the battery 109 may be estimated based on
5 the predicted increase in current internal resistance, as will be explained in detail with reference
to a predicting module 223 and an estimating module 225 in the FIGURE.2A in the present
disclosure.
[0049] In some embodiments, the data 203 stored in the memory 107 may be processed by the
modules 205 of the controller 105. In some embodiments, the modules 205 may be stored in
10 the memory 107 (not shown in the FIGURE.2A) and implemented as software. In some other
embodiments, the modules 205 may be outside the memory 107 as shown in the FIGURE.2A
and implemented as software. In yet other embodiments, the modules 205 may be
communicatively coupled to the controller 105 and implemented as hardware. In yet other
embodiments, a combination of software and hardware may be implemented i.e., some
15 modules may be included in the controller 105 and implemented as a software and some other
modules may be communicatively coupled to the controller 105 and implemented as a
hardware. As used herein, the term modules may refer to an application specific integrated
circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that
execute one or more software or firmware programs, a combinational logic circuit, and/or other
20 suitable components that provide the described functionality.
[0050] In some embodiments, the modules 205 may include, for example, a receiving module
219, a determining module 221, a predicting module 223, an estimating module 225 and other
modules 227. The other modules 227 may be used to perform one or more miscellaneous
functionalities of the controller 105.
25 [0051] In some embodiments, the receiving module 219 may receive the sensor data 207 from
the one or more sensors 103. The sensor data 207 may include, but not limited to, plurality of
battery parameters and plurality of vehicle parameters. The plurality of battery parameters and
the plurality of vehicle parameters are related to various components of the vehicle, for
instance, plurality of battery parameters are related to battery 109 of the vehicle 101 and the
30 plurality of vehicle parameters are related to drive system components of the vehicle 101 such
as brakes, wheels, steering, accelerator, clutch and the like. The one or more sensors 103
14
associated with the vehicle 101 may include, but not limited to, a temperature sensor, current
sensor, voltage sensor, pressure sensor, accelerometer, and speed sensors.
[0052] In some embodiments, the determining module 221 may determine the current internal
resistance of the vehicle 101 based on the plurality of battery parameters and the plurality of
5 vehicle parameters. Particularly, the determining module 221 may determine the current
internal resistance as a function of the plurality of battery parameters and a weighted drive
cycle which is determined based on the plurality of vehicle parameters, using the Equivalent
Circuit Model (ECM) of the battery 109. In some embodiments, the ECM of the battery 109
may be implemented as part of a battery model associated with the controller 105. The ECM
10 of the battery 109 is explained in greater detail in the upcoming paragraph of the present
disclosure. In some embodiments, the weighted drive cycle is indicative of the driver behaviour
of the vehicle 101 and is a modification of a standard drive cycle of the vehicle 101 with respect
to the plurality of vehicle parameters measured in real-time. In some embodiments, the
modification of the standard drive cycle of the vehicle 101 may include, but not limited to,
15 applying weights to the standard drive cycle of the vehicle 101 based on the vehicle driver
behaviour i.e., based on the plurality of vehicle parameters, to obtain the weighted drive cycle.
[0053] An exemplary representation of the ECM of the battery 109 used for determining the
current internal resistance is as shown in the FIGURE.2B.
20
[0054] ECM shown in the FIGURE.2B comprises two capacitors (C1 and C2) that are
connected in parallel with resistors (R1 and R2) respectively. Further the pair of parallel
resistors and capacitors circuit is connected in series with a resistor R0. R0 indicates an initial
internal resistance that may be computed at the time of manufacturing the vehicle 101. battery
25 109In some embodiments, determining module 221 may use values corresponding to change
in temperature of the battery 109 and vehicle driver behaviour indicated by the weighted drive
cycle, and may determine a value of a combination resistance value using a battery model. The
battery model indicates dependence of the combination resistance value on the change in
temperature of the battery 109 and vehicle driver behaviour. Therefore, based on the
30 determined combination resistance value, the determining module 221 may determine the
individual increment values i.e., the amount of increase in the values of R0, R1, R2, C1 and C2.
Further, the determined increment value of R0 when added with the internal resistance value
R0 gives the updated internal resistance, or in other words, the current internal resistance of the
battery 109.
15
[0055] Therefore, the current internal resistance determined based on the vehicle driver
behaviour and the temperature of the battery 109 may be depicted using the equation 1 as
shown below:
5
Current Internal Resistance (R0) = Internal resistance (R0) + increment value of the internal
resistance ---- Equation 1
Thereafter, the determining module 221 may determine the current State of Health (SoH) of
10 the battery 109 based on the determined current internal resistance. In some embodiments, the
determining module 221 may determine the current SoH of the battery 109 by mapping the
current internal resistance with a pre-stored look-up table, which indicates values of SoH
corresponding to a plurality of values of internal resistance. The pre-stored look-up table may
be specific to the make and model of the battery.
15 [0056] In some embodiments, the predicting module 223 may predict the increase in current
internal resistance based on simulation of a future battery usage using the plurality of battery
parameters and the plurality of vehicle parameters. For instance, the predicting module 225
helps in predicting how the internal resistance of the battery 109 may increase, if the vehicle
driver behaviour and the change in temperature of the battery 109 of the vehicle continue as
20 per the current trend for a given time period. For example, if the time period is considered to
be 1 year, the predicting module 225 may predict the increase in the internal resistance of the
battery 109 at the end of 1 year, assuming that currently observed trend of the vehicle
parameters and the battery parameters continue for the period of 1 year. Therefore, based on
such assumption, the predicting module 223 may extrapolate the future drive cycle and future
25 battery parameters for a given time period, from the weighted drive cycle and the plurality of
battery parameters of the current scenario. In some embodiments, the predicting module 223
may extrapolate the future drive cycle and future battery parameters based on a simulation
mechanism implemented using the battery model. Based on the extrapolated values of future
drive cycle and the future battery parameters, the predicting module 223 may predict a future
30 internal resistance based on the predicted future drive cycle and future battery parameters. The
future internal resistance of the battery 109 thus predicted may be indicative of the increase in
the current internal resistance of the battery 109.
16
[0057] In some embodiments, estimating module 225 may estimate the Remaining Useful Life
(RUL) of the battery 109 based on the determined increase in the current internal resistance.
To this end, the estimating module 225 may estimate the total number of charging cycles
remaining until a current SoH value of the battery 109 drops to a predefined threshold SoH
5 value, based on the predicted increase in the current internal resistance. For example, consider
the current SoH of the battery 109 is determined to be 90% and the predefined threshold SoH
of the battery 109 is set to be 80%. Based on simulation of the future battery usage, the trained
battery model may estimate total number of charging cycles that are remaining for the battery
109 until the current SoH of the battery 109 drops to the predefined threshold SoH of 80%.
10 Based on the estimated total number of charging cycles, the estimating module 225 may
estimate the RUL of the battery 109. In some embodiments, the estimating module 225 may
use a predefined technique for estimating the RUL based on the estimated total number of
charging cycles.
[0058] Henceforth, the present disclosure is explained with the help of an exemplary scenario.
15 However, such exemplary scenario is only for the purpose of illustration and should not be
construed as a limitation of the present disclosure.
[0059] Consider an exemplary standard drive cycle of a vehicle “X” of a certain make and
model, is as shown in the below Table 1.
Sl.no Vehicle and/or battery
parameters
Parameter
value
1. Battery C rating 0.5 C
2. Voltage 2.4-3.65
3. BOL Internal Resistance (R0) 0.5 mohm
4. EOL Internal Resistance (R0) 1 mohm
5. BOL Sate of Health 100%
6. EOL State of Health 80%
7. EOL Cycles @ 0.5 C 3400
Table 1
20 [0060] In the above Table 1, as per the standard drive cycle for the vehicle “X”, consider the
battery C rating of the battery used in the vehicle is 0.5C. Battery C rating is the measurement
of current in which a battery is charged and discharged at. Therefore, considering that the
17
battery capacity of the battery used in the vehicle is 100 Ah, a battery C rating of 0.5C indicates
that, average current drawn from the battery is 0.5C x 100Ah = 50 Amps. In other words, a
fully charged battery of 100Ah and 0.5C rating, discharges 50Amps of current over a period of
2 hours. Further, the voltage range observed is in the range of 2.4-3.65V. Further, the internal
5 resistance at the Beginning of Life (BOL) of the battery obtained based on cell testing data is
0.5 mohm, which is equivalent to the battery State of Health (SOH) of 100%. Similarly, internal
resistance at the End of Life (EOL) of the battery which is determined based on
parameterization of EOL voltage response of battery in standard drive cycle is 1 mohm, which
is equivalent to the battery SoH of 80%. Therefore, based on the EOL, the total charging cycles
10 computed for the given standard drive cycle is 3400 cycles.
[0061] As explained in the module section of the present disclosure, the standard drive cycle
of the vehicle “X” is modified in accordance with the current vehicle driver behaviour, which
is evaluated based on the vehicle parameters and the battery parameters, to obtain the weighted
drive cycle. In this exemplary scenario, the weighted drive cycle is determined based on the
15 vehicle driver behaviour observed for 100 charging cycles, i.e., based on the vehicle and battery
parameters collected for 100 charging cycles. Consider the weighted drive cycle of the vehicle
“X” based on the current vehicle driver behaviour as the shown in below Table 2.
Sl.no Actual vehicle usage
parameters
Parameter value
1. Speed, Acceleration and
braking pattern observed for
100 drive cycles
S, A, and B
2. Weighted drive cycle
determined after 100 cycles
(calculated current C rate
based on the parameters
speed S, acceleration A and
braking pattern B)
1C
3. New EOL estimated based
on weighted drive cycle at 1C 2900 cycles
4. RUL with weighted drive
cycle 2800 cycles
Table 2
18
[0062] In the above Table 2, vehicle driver behaviour is estimated based on the vehicle
parameter such as speed of the vehicle, acceleration of the vehicle and braking pattern of the
vehicle observed for the period of 100 cycles. For the estimated vehicle driver behaviour, the
average current is computed to be 1C. Thereafter, weights are applied to the standard drive
5 cycle of the vehicle “X” based on the estimated vehicle driver behaviour to obtain the weighted
drive cycle. This may be referred to as modifying the standard drive cycle based on the vehicle
driver behaviour estimated using the vehicle parameters. Using the weighted drive cycle and
the battery parameters, consider the controller 105 estimates the new EoL of the battery to be
2900 cycles. Therefore, the Remaining Useful Life (RUL) of the battery is estimated by the
10 below Equation 1.
New EoL cycles determined based on weighted drive cycle – currently driven cycles -
----------- Equation 1
[0063] Therefore, the RUL of the battery is estimated to be equivalent to (2900 cycles – 100
cycles) i.e., 2800 cycles.
15 [0064] FIGURE.3A-FIGURE-3C shows a flowchart illustrating a method of estimating
Remaining Useful Life (RUL) of a battery 109 of a vehicle, in accordance with some
embodiments of the present disclosure.
[0065] As illustrated in FIGURE.3A, the method 300A includes one or more blocks
20 illustrating a method of estimating RUL of a battery 109 of a vehicle 101. The method 300A
may be described in the general context of computer executable instructions. Generally,
computer executable instructions can include routines, programs, objects, components, data
structures, procedures, modules, and functions, which perform functions or implement abstract
data types.
25
[0066] The order in which the method 300A is described is not intended to be construed as a
limitation, and any number of the described method blocks can be combined in any order to
implement the method 300A. Additionally, individual blocks may be deleted from the methods
without departing from the spirit and scope of the subject matter described herein. Furthermore,
30 the method 300A can be implemented in any suitable hardware, software, firmware, or
combination thereof.
19
[0067] At block 301, the method 300A may include receiving, by a controller 105, sensor data
207 corresponding to a plurality of battery parameters and a plurality of vehicle parameters
from one or more sensors 103 associated with the vehicle 101.
5 [0068] At block 303, the method 300A may include determining, by the controller 105, a
current internal resistance of a battery 109 based on the plurality of battery parameters and the
plurality of vehicle parameters. The current internal resistance of the battery 109 is determined
using an Equivalent Circuit Model (ECM) of the battery 109. Particularly, controller 105 may
determine the current internal resistance of the battery 109 as a function of a weighted drive
10 cycle indicative of the vehicle driver behaviour and plurality of battery parameters, using the
ECM of the battery 109. The weighted drive cycle of the vehicle 101 may be determined based
on a modification of a standard drive cycle of the vehicle 101 with respect to the plurality of
vehicle parameters measured in real-time. Further, the controller 105 may determine the State
of Health (SoH) of the battery 109 based on the current internal resistance using a trained
15 battery model.
[0069] At block 305, the method 300A may include, predicting, by the controller 105, an
increase in the current internal resistance based on simulation of a future battery usage using
the plurality of battery parameters and the plurality of vehicle parameters. In some
20 embodiments, the simulation of the future battery usage of the vehicle 101 may be performed
using a battery model. The method 300B as illustrated in FIGURE. 3B shows the steps
involved in simulation of the future battery usage for predicting the increase in the current
internal resistance.
25 [0070] At block 311, the method 300B may include, predicting, by the controller 105, a future
drive cycle and future battery parameters of the vehicle 101 based on the weighted drive cycle
and the plurality of battery parameters of the vehicle 101.
[0071] At block 313, the method 300B may include, predicting, by the controller 105, a future
30 internal resistance indicative of the increase in the current internal resistance of the battery 109
based on the predicted future drive cycle and the future battery parameters.
[0072] Returning back to FIGURE.3A, at block 307 the method 300A may include estimating,
by the controller 105, RUL based on the predicted increase in the internal resistance. Further,
20
the method 300C as illustrated in FIGURE. 3C shows the steps involved in estimating the RUL
of the battery 109.
[0073] At block 315, the method 300C may include estimating, by the controller 105, the total
5 number of charging cycles remaining until the current SoH value of the battery 109 drops to
the predefined threshold SoH value, based on the predicted increase in the current internal
resistance of the battery 109 of the vehicle 101.
[0074] At block 317, the method 300C may include, estimating, by the controller 105, the RUL
10 of the battery 109 based on the total number of charging cycles.
[0075] FIGURE. 4 is a block diagram of an exemplary computer system for implementing
embodiments consistent with the present disclosure.
15 [0076] In some embodiments, FIGURE. 4 illustrates a block diagram of an exemplary
computer system 400 for implementing embodiments consistent with the present invention. In
some embodiments, the computer system 400 can be a controller 105 that is used for estimating
Remaining Useful Life (RUL) of a battery 109 of a vehicle 101. The computer system 400 may
include a central processing unit (“CPU” or “processor”) 402. The processor 402 may include
20 at least one data processor for executing program components for executing user or systemgenerated business processes. A user may include a person, a person using a device such as
those included in this invention, or such a device itself. The processor 402 may include
specialized processing units such as integrated system (bus) controllers, memory management
control units, floating point units, graphics processing units, digital signal processing units, etc.
25
[0077] The processor 402 may be disposed in communication with input devices 411 and
output devices 412 via I/O interface 401. The I/O interface 401 may employ communication
protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-1394, serial
bus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial, component, composite, Digital
30 Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF)
antennas, S-Video, Video Graphics Array (VGA), IEEE 802.n /b/g/n/x, Bluetooth, cellular
(e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global
System For Mobile Communications (GSM), Long-Term Evolution (LTE), WiMax, or the
like), etc.
35
21
[0078] Using the I/O interface 401, computer system 400 may communicate with input devices
411 and output devices 412.
[0079] In some embodiments, the processor 402 may be disposed in communication with a
5 communication network 409 via a network interface 403. The network interface 403 may
communicate with one or more sensors 103 through the communication network 409. The
network interface 403 may employ connection protocols including, without limitation, direct
connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control
Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. Using the network
10 interface 403 and the communication network 409, the computer system 400 may communicate
with one or more sensors 103. Further, the communication network 409 can be implemented
as one of the different types of networks, such as intranet or Local Area Network (LAN),
Closed Area Network (CAN) and such within the vehicle. The communication network 409
may either be a dedicated network or a shared network, which represents an association of the
15 different types of networks that use a variety of protocols, for example, Hypertext Transfer
Protocol (HTTP), CAN Protocol, Transmission Control Protocol/Internet Protocol (TCP/IP),
Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the
communication network 409 may include a variety of network devices, including routers,
bridges, servers, computing devices, storage devices, etc. In some embodiments, the processor
20 402 may be disposed in communication with a memory 405 (e.g., RAM, ROM, etc. not shown
in FIGURE.4) via a storage interface 404. The storage interface 404 may connect to memory
405 including, without limitation, memory drives, removable disc drives, etc., employing
connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated
Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fibre channel, Small
25 Computer Systems Interface (SCSI), etc. The memory drives may further include a drum,
magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent
Discs (RAID), solid-state memory devices, solid-state drives, etc.
[0080] The memory 405 may store a collection of program or database components, including,
30 without limitation, a user interface 406, an operating system 407, a web browser 408 etc. In
some embodiments, the computer system 400 may store user/application data, such as the data,
variables, records, etc. as described in this invention. Such databases may be implemented as
fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase.
22
[0081] The operating system 407 may facilitate resource management and operation of the
computer system 400. The User interface 406 may facilitate display, execution, interaction,
manipulation, or operation of program components through textual or graphical facilities. For
example, user interfaces may provide computer interaction interface elements on a display
5 system operatively connected to the computer system 400, such as cursors, icons, checkboxes,
menus, scrollers, windows, widgets, etc. Graphical User Interfaces (GUIs) may be employed,
including, without limitation, Apple® Macintosh® operating systems’ Aqua®, IBM® OS/2®,
Microsoft® Windows® (e.g., Aero, Metro, etc.), web interface libraries (e.g., ActiveX®,
Java®, Javascript®, AJAX, HTML, Adobe® Flash®, etc.), or the like.
10
[0082] In some embodiments, the computer system 400 may implement the web browser 408
stored program components. The web browser 408 may be a hypertext viewing application,
such as MICROSOFT® INTERNET EXPLORER®, GOOGLETM CHROMETM,
MOZILLA® FIREFOX®, APPLE® SAFARI®, etc. Secure web browsing may be provided
15 using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport
Layer Security (TLS), etc. Web browsers 408 may utilize facilities such as AJAX, DHTML,
ADOBE® FLASH®, JAVASCRIPT®, JAVA®, Application Programming Interfaces (APIs),
etc.
20 [0083] Furthermore, one or more computer-readable storage media may be utilized in
implementing embodiments consistent with the present invention. A computer-readable
storage medium refers to any type of physical memory on which information or data readable
by a processor may be stored. Thus, a computer-readable storage medium may store
instructions for execution by one or more processors, including instructions for causing the
25 processor(s) to perform steps or stages consistent with the embodiments described herein. The
term “computer-readable medium” should be understood to include tangible items and exclude
carrier waves and transient signals, i.e., non-transitory. Examples include Random Access
Memory (RAM), Read-Only Memory (ROM), volatile memory, non-volatile memory, hard
drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any
30 other known physical storage media.
[0084] Thus, in the present disclosure discloses that discloses the method of estimating the
RUL of the battery 109 the controller determines the current internal resistance based on a
combination of plurality of battery parameters and the plurality of the vehicle parameters.
35 Particularly, the current internal resistance is determined as a function of the plurality of battery
23
parameters and a weighted drive cycle indicative of driver behaviour using the Equivalent
Circuit Model (ECM) of the battery 109. The weighted drive cycle is determined by modifying
a standard drive cycle based on the plurality of vehicle parameters. Such determination of the
current internal resistance using a physics based calibrated ECM of battery 109 enhances
5 accuracy in the determination of the current internal resistance, unlike conventional data driven
models. Further, the controller performs simulation of battery usage based on the determined
current internal resistance to predict the future internal resistance, which in turn may be used
to estimate the RUL of the battery 109. Therefore, accurate determination of the current internal
resistance and the future internal resistance helps in accurate and reliable estimation of the RUL
10 of the battery 109 which in turn helps in maintaining and planning service requirements for the
vehicle as necessary, and thus enhances the user experience with respect to electric vehicles.
[0085] A description of an embodiment with several components in communication with each
other does not imply that all such components are required. On the contrary, a variety of
15 optional components are described to illustrate the wide variety of possible embodiments of
the invention. When a single device or article is described herein, it will be apparent that more
than one device/article (whether or not they cooperate) may be used in place of a single
device/article. Similarly, where more than one device or article is described herein (whether or
not they cooperate), it will be apparent that a single device/article may be used in place of the
20 more than one device or article or a different number of devices/articles may be used instead
of the shown number of devices or programs. The functionality and/or the features of a device
may be alternatively embodied by one or more other devices which are not explicitly described
as having such functionality/features. Thus, other embodiments of the invention need not
include the device itself.
25
[0086] The specification has described a method, apparatus and system for estimating
Remaining Useful Life (RUL) of a battery 109 of a vehicle 101. The illustrated steps are set
out to explain the exemplary embodiments shown, and it should be anticipated that on-going
technological development will change the manner in which particular functions are
30 performed. These examples are presented herein for purposes of illustration, and not limitation.
Further, the boundaries of the functional building blocks have been arbitrarily defined herein
for the convenience of the description. Alternative boundaries can be defined so long as the
specified functions and relationships thereof are appropriately performed. Alternatives
(including equivalents, extensions, variations, deviations, etc., of those described herein) will
24
be apparent to persons skilled in the relevant art(s) based on the teachings contained herein.
Such alternatives fall within the scope of the disclosed embodiments. Also, the words
“comprising,” “having,” “containing,” and “including,” and other similar forms are intended
to be equivalent in meaning and be open-ended in that an item or items following any one of
5 these words is not meant to be an exhaustive listing of such item or items, or meant to be limited
to only the listed item or items. It must also be noted that as used herein and in the appended
claims, the singular forms “a,” “an,” and “the” include plural references unless the context
clearly dictates otherwise.
10 [0087] Finally, the language used in the specification has been principally selected for
readability and instructional purposes, and it may not have been selected to delineate or
circumscribe the inventive subject matter. It is therefore intended that the scope of the invention
be limited not by this detailed description, but rather by any claims that issue on an application
based here on. Accordingly, the embodiments of the present invention are intended to be
15 illustrative, but not limiting, of the scope of the invention, which is set forth in the following
claims.
20
25
30
25
Referral Numerals:
Reference
Number
Description
100 Architecture
101 Vehicle
103 One or more sensors
105 Controller
107 Memory
203 Data
205 Modules
207 Sensor data
209 Internal resistance data
211 Weighted drive cycle data
213 Simulated and predicted data
215 Estimated data
217 Other data
219 Receiving Module
221 Determining module
223 Predicting Module
225 Estimating module
227 Other modules
400 Exemplary computer system
401 I/O Interface of the exemplary computer system
402 Processor of the exemplary computer system
403 Network interface
404 Storage interface
405 Memory of the exemplary computer system
406 User interface
407 Operating system
408 Web browser
409 Communication network
411 Input devices
412 Output devices

Claims:1. A method for estimating Remaining Useful Life (RUL) of a battery (109) of a vehicle (101), the method comprising:
receiving, by a controller (105) of a vehicle (101), sensor data corresponding to a plurality of battery parameters and a plurality of vehicle parameters from one or more sensors (103) associated with the vehicle (101);
determining, by the controller (105), a current internal resistance of a battery (109) based on the plurality of battery parameters and the plurality of vehicle parameters;
predicting, by the controller (105), an increase in the current internal resistance based on simulation of a future battery usage using the plurality of battery parameters and the plurality of vehicle parameters; and
estimating, by the controller (105), a RUL based on the predicted increase in the current internal resistance.

2. The method as claimed in claim 1, wherein the plurality of battery parameters comprises voltage output of the battery (109), current discharge from the battery (109), temperature of the battery (109) and current state of charge of the battery (109).

3. The method as claimed in claim 1, wherein the plurality of vehicle parameters comprises acceleration of the vehicle, braking pattern of the vehicle, and speed of the vehicle, wherein the method comprises:

determining, by the controller (105), a weighted drive cycle indicative of a vehicle driver behaviour based on the plurality of vehicle parameters.

4. The method as claimed in claim 3, comprising determining, by the controller (105), the weighted drive cycle of the vehicle (101) based on a modification of a standard drive cycle of the vehicle (101) with respect to the plurality of vehicle parameters.

5. The method as claimed in claim 1, wherein the current internal resistance of the battery (109) is determined as a function of a weighted drive cycle and the plurality of battery parameters, using an Equivalent Circuit Model (ECM) of the battery (109).
6. The method as claimed in claim 1 comprises determining, by the controller (105), a current State of Health (SoH) value of the battery (109) based on the current internal resistance of the battery (109).

7. The method as claimed in claim 1, wherein predicting the increase in the current internal resistance of the battery (109) comprises:

simulating, by the controller (105), the future battery usage of the vehicle (101) using a battery model, wherein simulation comprises:
predicting, by the controller (105), a future drive cycle and future battery parameters of the vehicle (101) based on the weighted drive cycle, the plurality of battery parameters of the vehicle (101); and
predicting, by the controller (105), a future internal resistance indicative of the increase in the current internal resistance of the battery (109) based on the predicted future drive cycle and the future battery parameters.

8. The method as claimed in claim 7, wherein estimating the RUL of the battery (109) comprises:
estimating, by the controller (105), a total number of charging cycles remaining until a current SoH value of the battery (109) drops to a predefined threshold SoH value, based on the predicted increase in the current internal resistance; and
estimating, by the controller (105), the RUL of the battery (109) based on the total number of charging cycles.

9. The method as claimed in claim 1 comprises using a battery model to determine SoH of the battery (109) and estimate the RUL of the battery (109).

10. A controller (105) for estimating Remaining Useful Life (RUL) of a Battery (109) in a vehicle (101), the controller (105) is communicatively coupled to a memory (107) storing instructions, which, on execution, causes the controller (105) to:

receive sensor data corresponding to a plurality of battery parameters and a plurality of vehicle parameters from one or more sensors (103) associated with the vehicle;
determine a current internal resistance of a battery (109) based on the plurality of battery parameters and the plurality of vehicle parameters;
predict an increase in the current internal resistance based on simulation of a future battery usage using the plurality of battery parameters and the plurality of vehicle parameters; and
estimate a RUL based on the predicted increase in the current internal resistance.

11. The controller (105) as claimed in claim 10, wherein the plurality of battery parameters comprises voltage output of the battery (109), current discharge from the battery (109), temperature of the battery (109) and current state of charge of the battery (109).

12. The controller (105) as claimed in claim 10, wherein the plurality of vehicle parameters comprises acceleration of the vehicle, braking pattern of the vehicle, and speed of the vehicle, wherein the controller (105) is further configured to:
determine a weighted drive cycle indicative of a vehicle driver behaviour based on the plurality of vehicle parameters.

13. The controller (105) as claimed in claim 12, wherein the controller (105) is further configured to determine the weighted drive cycle of the vehicle (101) based on a modification of a standard drive cycle of the vehicle (101) with respect to the plurality of vehicle parameters.

14. The controller (105) as claimed in claim 10, wherein the controller (105) determines the current internal resistance of the battery (109) as a function of a weighted drive cycle and the plurality of battery parameters, using an Equivalent Circuit Model (ECM) of the battery (109).

15. The controller (105) as claimed in claim 10, wherein the controller (105) is further configured to determine a current State of Health (SoH) value of the battery (109) based on the current internal resistance of the battery (109).

16. The controller (105) as claimed in claim 10, wherein to predict the increase in current internal resistance of the battery (109), the controller (105) is further configured to:
simulate the future battery usage of the vehicle (101) using a battery model, wherein simulation comprises, wherein to simulate the controller (105) is further configured to:
predict a future drive cycle and future battery parameters of the vehicle (101) based on the weighted drive cycle, the plurality of battery parameters of the vehicle (101); and
predict a future internal resistance indicative of the increase in the current internal resistance of the battery (109) based on the predicted future drive cycle and the future battery parameters.

17. The controller (105) as claimed in claim 16, wherein to estimate the RUL of the battery (109) the controller (105) is further configured to:
estimate a total number of charging cycles remaining until a current SoH value of the battery (109) drops to a predefined threshold SoH value, based on the predicted increase in the current internal resistance; and
estimate the RUL of the battery (109) based on the total number of charging cycles.
18. The controller (105) as claimed in claim 10, comprises using a battery model to determine SoH of the battery (109) and estimate the RUL of the battery (109).

19. A system (100) for estimating Remaining Useful Life (RUL) of a battery (109) in a vehicle (101), the system comprising:
one or more sensors (103) associated with a controller (105) of the vehicle (101); and
the controller (105) of the vehicle (101) configured to perform the method as claimed in the claims 1-9.