DESC:CROSS REFERENCE TO RELATED APPLICATION
This application is based on and derives the benefit of Indian Provisional Application 202141026924, the contents of which are incorporated herein by reference.

TECHNICAL FIELD
Embodiments disclosed herein relate to battery packs, and more particularly to managing the discharge current from the battery pack.

BACKGROUND
Designing a traction battery system has to satisfy the power of a driver with the maximum performance capability of the vehicle to ensure reliable operation, safety and minimum degradation to the life of battery. The Battery Management System (BMS) accomplishes the task of designing the traction battery by setting constraints on the maximum allowable continuous pulse charge/discharge currents. The BMS can respond to the dynamic power and current demands of the drivetrain, which are dependent on the driving behavior, road conditions and auxiliary electrical equipment during a specific trip. The BMS can provide appropriate control limits to the higher-level application controller (for e.g., a Vehicle Control Unit (VCU)) to protect the battery from extreme conditions.
However, determining the appropriate limits is a complex task, wherein the lithium-ion batteries exhibit complex dynamics that can be difficult to have fixed threshold limits over the operating range. Under-predicting the threshold limits can lead to under-utilizing the capabilities of the battery, and hence can provide reduced performance. Also, over-predicting the threshold limits can potentially subject the batteries to the higher currents, leading to expedited degradation of the battery.
The control limits, that represent the magnitude of allowable currents, depend on multiple factors such as (i) current operating point (defined by the State-of-Charge (SoC), battery temperature and State-of-Health (SoH)),and (ii) history of exhaustion (duration and magnitude of current drawn from the beginning of complete relaxation). The cell manufacturer outlines the continuous and pulse current limits to be respected during the usage of cell, based on the above contributing factors.
Exceptions notwithstanding, during charging the vehicle, the current profiles (e.g., normal charging and fast charging) will be predefined for a given application, as specified by the OEM. Thereby making the implementation of control limits straightforward during charging. Look-up tables (LUTs) can be populated with control limits as a function for various operating conditions and appropriate limit can be retrieved by the BMS during operations and passed on to the VCU. On the contrary, during discharging of the battery (i.e., driving the vehicle), the battery system would be subjected to varied and unpredictable current profiles resulting in numerous permutations and combinations of unique driving behaviors with varied traffic and geographical conditions. Therefore, making the implementation of control limits complex, since it is not possible to understand the duration and the magnitude of current demand in advance.

OBJECTS
The principal object of embodiments herein is to disclose methods and systems for dynamically updating the battery current limits (control limits) during the discharge of current based on the operating state of the battery (SoC and temperature), previous history of usage and exhaustion.
These and other objects of embodiments herein will be better appreciated and understood when considered in conjunction with following description and accompanying drawings. It should be understood, however, that the following descriptions, while indicating embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF DRAWINGS
The embodiments are illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which:
FIG. 1 depicts a system, according to embodiments as disclosed herein;
FIG. 2 illustrates a flow chart for dynamically updating battery current limits (control limits)during the discharge of current based on operating state of the battery (SoC and temperature), previous history of usage and exhaustion, according to embodiments as disclosed herein;
FIGs. 3A, 3B and 3Cdepicts the effect of SoC, temperature and pulse duration on maximum allowed c-rate respectively for a lithium-ion cell, according to embodiments as disclosed herein;
FIG. 4depicts the selection of the first data point, according to embodiments as disclosed herein;
FIG. 5is an example diagram depicting the scenario of three data points selected for interpolation, according to embodiments as disclosed herein;
FIG. 6 is an example diagram depicting linear interpolation and the calculation of the denominator, according to embodiments as disclosed herein;
FIG. 7is an example diagram depicting the process of selecting battery current limits based on the flag value, according to an embodiment as disclosed herein; and
FIG. 8 is a flow diagram illustrating the de-rating core logic, according to embodiments as disclosed herein.

DETAILED DESCRIPTION
The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The embodiments herein disclose methods and systems for dynamically updating battery current limits (control limits) during discharge of current based on the operating state of the battery (SoC and temperature), previous history of usage and exhaustion. Referring now to the drawings, and more particularly to FIGS. 1 through 7, where similar reference characters denote corresponding features consistently throughout the figures, there are shown embodiments.
Embodiments herein measure the usage and exhaustion of battery using internal variables termed as fatigue fraction. The fatigue referred to herein may be a state of the battery which may no longer be fully charged after prolonged usage. The data points of the current and pulse duration can be selected to determine the exhaustion of the battery. A plurality of data points of the current, pulse duration can be selected for a plurality of C-rates from high to low of the look up tables (LUTs).The points selected for different C-rates can be based on the operating states of the battery, such as operating state of the battery (SOC), temperature and the history of exhaustion. Further, the allowable current limits can be calculated based on interpolating a plurality of data points. On determining that the current demand is within pre-defined allowable current limits, the demanded current value can be provided without any interruption until the battery gets fully exhausted. On the battery reaching the full exhaustion limit, the allowable C-rate of the battery will be de-rated. De-rated referred to herein may be a scenario where the device is operating at less than its rated maximum capability to prolong the life of the battery. On reaching the relaxation period, the battery may reach a zero-exhaustion point and can provide the maximum allowable current as per LUTs. Embodiments herein can simplify the development of controls and can ensure the controls which will be applicable to the full range of possible operating conditions and environments.
Embodiments herein can dynamically estimatethe battery pack current limit during discharge; i.e., whenthe vehicle is being operated. Embodiments herein disclose ameffective energy management/control strategy to decide when to de-rate the battery pack current during discharge.Embodiments herein can intelligently limit the time duration spent at higher currents (or c-rates). Embodiments herein achieve an optimized trade-off between the objectives of deriving maximum performance from the battery pack while subjecting it to minimum degradation.
FIG. 1 depicts a system, according to embodiments as disclosed herein. The system 150 comprises a battery 100 has a flexible architecture, to enable creation of groups, each group comprising a plurality of battery modules. Each group can be configured to behave like a single contiguous unit in both mechanical and electronic interface terms.
As depicted in FIG. 1, the system 150 comprises the battery 100 comprising one or more cells 104. The system 150 further comprises a battery management system (BMS) 102, a communication interface 108 and a power interface 110.
The BMS 102 can be configured to perform functions related to estimating the State of Charge (SOC) of the battery 100, temperature of the battery, previous history of usage, exhaustion of the battery, state of health of the battery 100, charging/balancing functions related to the battery 100, the diagnostic/prognostic functions related to the battery 100, data generation, data transmission and on-board analysis.
The battery 100 can comprise of one or more cells 104. A plurality of cells 104 can be connected together to store energy. In an embodiment herein, the cells 104 can be connected in a parallel or in series combination. In an embodiment herein, the cells 104 can be connected in a hybrid combination, wherein at least one of the cells 104can be connected in series and at least one of the cells is connected in parallel. The cells and cell formats can be standardized for adaptability to any required battery chemistry and also up gradation to newer formulations. The cells 104 can use at least one of different chemical formulations (such as Lithium cobalt oxide, NMC (nickel manganese cobalt oxide), Lithium Sulphur, titanate and other existing combinations), fuel cells, and super capacitors. The cells 104 can be modular, scalable and adaptable to different configurations and future upgradeability.
The system can further comprise of a memory 106, which can be at least one of a RAM (Random Access Memory) or a ROM (Read Only Memory). The memory 106 can comprise of information on the usage life of the batteries such as number of full/partial cycles of usage, temperature, current and voltage conditions during usage, depth of discharge and level of charge during discharge/charge cycles, DC (Direct Current) resistance and impedance values of the battery, and so on. The memory 106 can comprise of information received by the battery 100 and the BMS 102 from external modules, such as a host device, a user device, and so on. The host device can be a device, where the battery 100 has been installed. Examples of the host device can be, but not limited to, an electric vehicle, a UPS (Uninterruptible power supply), an inverter, an energy storage unit, a swapping station, an electronic device, and so on.
The memory 106 can further comprise of a means for the battery 100 and/or the BMS 102 and/or the host device to identify themselves to external entities, the type of battery module/cell being used, limits of operating parameters for the specific battery 100, state of charge at any given moment, ageing information of the battery 100 in real time terms (comprising of information such as dates of installation and commissioning, current date, and so on), cyclic ageing information (such as number of full and partial cycles of charging and discharging the module has been subjected to since commissioning/previous charging and so on), salient usage parameters (such as average and peak stresses of voltages, currents, temperatures, depth of discharge), external sensors (such as environmental sensors), battery performance parameters (such as State of Health, State of Function, Internal resistance, previous history of usage, exhaustion, and so on, which can be computed by the BMS 102 or received from external devices over the communication interface 108). The memory 106 can also comprise of additional information such as information related to warranty, second life, and so on.
The memory 107, in conjunction with the BMS 102 can also function as an aggregator of information received from different subsystems of the host system/application. Considering an example scenario, where the battery 100 is being used in a vehicle, the memory 106 can comprise of information received from the vehicle and systems/subsystems associated with the vehicle, wherein the information can be measured by the sensor(s) or can be received from the vehicle. Examples of the vehicle parameters can comprise of, but not limited to, acceleration, deceleration, velocity, distance traveled, information received from the drive unit such as speed, acceleration, braking patterns, terrain information, and so on.
The memory 106 can comprise a three-dimensional lookup table (3D LUT) which can be constructed offline and stored in the memory 106 to be accessed by the BMS 102in real-time. The three-dimensional lookup table can be configured with tabular representation of current limitation map for pulse currents provided by the cell manufacturer. Each axis (breakpoint vectors) of the 3D LUT can represent factors governing maximum allowable currents such as State of Charge (SOC), temperature and time duration.
The memory 106 can comprise one or more data points in the LUT which comprises maximum allowable currents at the intersection of these breakpoint values. The LUT block can map inputs to an output value by looking up or interpolating a table of values defined with block parameters.
m=Number of factors governing max.allowable current
where m is the dimension of the LUT. The State-of-Health figure of merit can be incorporated as a contributing factor.
Other steps performed are iterative in nature for the duration of operation that will be performed at every sample instant during the usage.
The communication interface 108can be configured to enable the battery 100 and/or the BMS 102 to interface with at least one external entity, such as the host device, a remote server (such as data server, the Cloud, and so on), vehicle systems, and so on. The communication interface 108 can comprise of at least one of wired communication interfaces or wireless communication interfaces. The communication interface 108 can use protocols such as CAN (Controller Area Network), Zigbee, Wi-Fi, Bluetooth, NFC (Near Field Communication), cellular, satellite, powerline, or any other suitable standards, which can be passive or active. The communication interface 108 can be flexible, so as to accommodate different data structures required by different users/applications/host devices.
The communication interface 108can also incorporate authentication means to ensure information security such as two-way authentication mechanisms. The communication interface 108 can communicate the received information to the BMS 102 and can receive information to be transmitted from the BMS 102.
The power interface 110 can be used for connecting the battery 100 and/or the cells 104 to an external entity, such as an energy source, a load, and so on. The power interface 110 can be bi-directional. In an example herein, the load can be an electric vehicle. In an embodiment herein, the power interface 110 can be an electronic/electromechanical switch.
FIG. 2 illustrates a flow chart for dynamically updating battery current limits (control limits) during the discharge of current based on operating state of the battery (SoC and temperature), previous history of usage and exhaustion, according to embodiments as disclosed herein. The data points in the LUT are the maximum allowable currents at the intersection of these breakpoint values. The LUT block maps inputs to an output value by looking up or interpolating a table of values defined with block parameters.
In step 202, initialization is performed with the initial values for the following inputs and parameters are provided such as input signals (SOC, temperature, battery current) and internal variables/parameters (fatigue fraction = 0 (Initial Value)).
In step 204, data points are dynamically picked with minimum three data-points to perform piecewise linear interpolation. The data-points can be selected on the availability of the data for different cells. The first data-point [x1, y1], where x1 is the C-rate corresponding to high-pulse current value and y1 is the time duration of that pulse, is going to be selected from the 3D LUT.
The pulse current limits for different time durations (p_1, p_2….p_nseconds) can be the same for a certain temperature above a certain SOC value, say, ?SOC?_l(As shown in FIGs.3A, 3B and 3C, ?SOC?_l= 25% ). To select the current limits for high current pulses for SOC lower than?SOC?_l, the history of exhaustioncan be considered. History of exhaustion of cell can be measured by an internal variable ‘fatigue fraction’. Fatigue fraction can have values in the range [0,1]. Low values for fatigue fraction indicate less usage and exhaustion of the cell in a particular trip, while high values are indicative of high exhaustion.
When the battery is in the less-exhaustion range, it can provide higher current pulses. If the SOC is lower than ?SOC?_l, depending on the fatigue fraction value, current limits will be chosen from 3D LUT. If there are n number of tables available for different time durations (p1, p2….pn seconds), the range of fatigue fraction can be segmented in n parts from 0 to 1 (0 to S1, S1 to S2 , …….S(n-2) to S(n-1) and S(n-1) to 1).
For example, there are three tables forp_1seconds, p_2seconds and p_3 secondsand therefore, the range of fatigue fraction will be divided into three segments; (i) 0 to 0.3 (ii) 0.3 to 0.7 and (iii) 0.7 to 1. The selection of first data point [x1, y1] is explained in the flow chart in FIG. 4.Let’s say, [x1, y1] = [C1, 30]. X1can be divided by nominal module capacity to convert it to C-rate from absolute current.
The second data-point [x2, y2] and third data-point [x3, y3] are for lower C-rates for continuous operation. The data-points are selected as [x2, y2] = [C2, 1800s] and [x3, y3] = [C3, 7200s]. Three data-points are plotted in a representative way in FIG. 5. The C-rate values for these two data-points can vary depending on the cell.
Selection of second and third data-points is based on the information provided by the cell manufacturer. In an embodiment herein, the second and third data-points are static. In an embodiment herein, the second and third data-points are dynamic, wherein second and third data-points can be selected in a manner similar to the first data-point [x1, y1].
Creating an interpolation function 206 can be performed in a piecewise linear interpolation between the three data-points [x1,y1], [x2,y2] and [x3,y3], selected in the previous step, is performed and plotted in FIG. 6. Thus, providing one-on-one relationship, time=f(c-rate) between each possible value of c-rate and maximum time duration for which it can be allowed to flow without any intervention.
The fatigue fraction 208 can be updated based on the supplier data, the lithium-ion cell can be discharged continuously at ‘C3’C-rate without any rest period until the cell discharges completely. This can indicate that ‘C3’C-rate is the baseline C-rate below which, if the cell is discharged, it would be in ‘relaxed’ mode. The discharging of cell below the baseline C-rate, will not make the cell exhausted, rather it would reduce the exhaustion. Above ‘C3’ C-rate, the operation needs to be de-rated based on the operating C-rate values and its duration. This baseline C-rate can be different for other cells.
To determine exhaustion of the battery, a variable ‘fatigue fraction’ at time ‘k’ is given by -
At C-rates higher than ‘C3’,
Fatigue fraction[k]=(TimeStep[k])/(Denom[k])+Fatigure fraction [k-1]
And, at C-rates lower than ‘C3’,
Fatigue fraction[k]=-(TimeStep[k])/(Denom[k])+Fatigure fraction [k-1]
The fatigue fraction will be same as before if the C-rate is same as baseline C-rate, here, ‘C3’.
The variable Denom[k] at time ‘k’ for any c_rate [k]is the maximum time for which crate[k] can be allowed without any intervention. Denom[k]will be computed using the one-on-one relationship ‘f’ constructed in the previous step (step 206) as
Denom[k]=f(c_rate [k])
For example, as shown in FIG.6, for ‘Ci’ current, the denominator will be the maximum time duration for which ‘Ci’ current is allowed; i.e,, ~1000 secs.
Embodiments herein use a fractional counter to enable the method to retain complete information about c-rate at each sample instant of time. Starting with a value of zero, fatigue fraction is incremented or reduced at each sample time with a value proportional to the impact caused by c-rate, during that sample time and unlike the traditional counter, is reset only when all necessary conditions have been met.
Decision to perform de-rating 208, de-rating is a well-established method in lithium-ion battery management, in which the battery is operated at less than its rate capability to ensure safe function and long operating life. The fatigue fraction on reaching the value of 1, the battery have reached maximum exhaustion and the battery needs the relaxation period, in which it would be operating in de-rated mode.
Along with the cut-off value for fatigue fraction, there may be several other conditions such as extreme temperatures, extreme voltages, extreme SoCs, fault conditions and the like, which can be combined using an OR logic to collectively contribute to the de-rating active/ inactive decision. The decision shall be updated at every sample time and stored in a flag (high for de-rate and low for no intervention).
The time-duration and target for this de-rating period should be decided in conjunction with the cell manufacturer recommendations as well as system requirements. Both these factors may be a fixed value or a varying value dependent on other parameters (using LUT based implementation).
If the de-rating flag in the previous step is low, it can indicate de-rating inactive; no intervention needed from the external factors, and the battery current limits shall be set to the maximum possible value. The BMS 102 can be configured to select the battery current limits based on de-rating flag as shown in FIG. 7.
However, if the flag is high, de-rating can be activated by the BMS 102 and the steps depicted in FIG. 8 are performed to get appropriate battery current limits. The target C-rate would be ‘C2’, if the operational C-rate is higher than ‘C2’ and it would be ‘C3’, if the operational C-rate lies in between ‘C3’ and ‘C2’. These two target C-rate values might be different for different cells. The target of lower C-rate value would be the baseline C-rate; at which C-rate the cell can be discharged continuously until termination.
The various actions in method 200 may be performed in the order presented, in a different order or simultaneously.
FIGs. 3A, 3B and 3C depicts the effect of SoC, temperature and pulse duration on maximum allowed C-rate respectively for a lithium-ion cell, according to embodiments as disclosed herein.
As depicted in the graph, the effect of SoC, temperature and pulse duration on maximum allowed C-rate respectively for a lithium-ion cell is illustrated.The time duration of allowable high current pulses can vary depending on the cell. There might be current values for time duration of p1, p2 ,….pn seconds (n number of tables available). The pulse-current limits for three (n=3)different time durations; p_1=2s, p_2= 10s and p_3= 30s. In Figure 3A, 3B and 3C the maximum allowable current values for NMC module are plotted as a function of SOC for different pulse durations at different temperatures.
Above 30% SoC, the pulse current limits for different time horizons i.e., 2s, 10s and 30s are the same. It is only below 30% SoC that we start to see notable differences - the allowable pulse current limits are higher for shorter pulse durations.
FIG. 4 depicts the selection of the first data point, according to embodiments as disclosed herein. As depicted in the flowchart, when the battery is in the less-exhaustion range, it can provide higher current pulses. If the SOC is lower than ?SOC?_l, depending on the fatigue fraction value, current limits will be chosen from 3D LUT. If there are total n number of tables available for different time durations (p1, p2….pn seconds), the range of fatigue fraction will be segmented in n parts from 0 to 1 (0 to S1, S1 to S2 , …….S(n-2) to S(n-1) and S(n-1) to 1).
For example, there are three tables for p_1seconds, p_2seconds and p_3 secondsand therefore, the range of fatigue fraction will be divided into three segments; (i) 0 to 0.3 (ii) 0.3 to 0.7 and (iii) 0.7 to 1. The selection of first data point [x1, y1] is explained in the flow chart in FIG. 4.Let’s say, [x1, y1] = [C1, 30]. X1can be divided by nominal module capacity to convert it to C-rate from absolute current.
FIG. 5 is an example diagram depicting the scenario of three data points selected for interpolation, according to embodiments as disclosed herein. As depicted in the graph, the data points are plotted on the graph with C-rate and time as the x-axis and y-axis respectively. [x1, y1]being dynamically selected data points while [x2, y2] and [x3, y3] are statically selected data points.
As depicted second data-point [x2, y2] and third data-point [x3, y3] are for lower C-rates for continuous operation. The data-points are selected as [x2, y2] = [C2, 1800s] and [x3, y3] = [C3, 7200s]. Three data-points are plotted in a representative way in the graph. The C-rate values for these two data-points can vary depending on the cell.
Selection of second and third data-points is based on the information provided by the cell manufacturer. In an embodiment herein, the second and third data-points are static. In an embodiment herein, the second and third data-points are dynamic, wherein second and third data-points can be selected in a manner similar to the first data-point [x1, y1].
FIG. 6 is an example diagram depicting linear interpolation and calculation of the denominator, according to embodiments as disclosed herein. As depicted in FIG. 6, the linear interpolation can be created in piecewise linear method between the three data points [x1, y1], [x2, y2] and [x3, y3], which were selected in the previous step and plotted in FIG. 6. Thus, providing a one- on- one relationship, with time – f ( c - rate )between each possible value of C-rate and maximum time duration for which it can be allowed to flow without any intervention. Also, additional safety margin of x% can be provided by linear reduction in order to be conservative with the estimation of safe operating area (SOA).
As depicted in FIG. 6, the denominator can be calculated based on the variable Denom[k]at time ‘k’ for any C-rate c rate[k] for the maximum time for which the crate[k] can be allowed. Denom[k] can be computed using one-on-one relationship ‘f’ constructed in the previous step asDenom[k] = f (crate [k]).
FIG. 7 is an example diagram depicting the process of selecting battery current limits based on the flag value, according to an embodiment as disclosed herein. Based on the decision of the previous step, BMS 102 can be configured to indicate de-rating is inactive without any manual intervention, and the battery current limits can be set to the maximum possible value.
The logic of selecting the battery current limits can be configured based on de-rating flag. As depicted in FIG. 7, the de-rating core logics can be configured to allow C-rate, de-rating activation flag can be configured to whether de-rate or to continue, the battery C rate limit can be configured obtain battery current limits.
FIG. 8 is a flow diagram illustrating the de-rating core logic, according to embodiments as disclosed herein. As illustrated, the flow diagram deals with de-rating the core logic in which the target is calculated and the rate of change (ROC) of the battery is determined. The BMS (102) on determining whether Crate[k]<= Target, then Crate[k] at the target for relaxation period can be maintained. Further the BMS (102) on detecting whether the relaxation period is complete, can reset de-rating flag to low else keep the de-rating flag to high.
The BMS (102) on determining that Crate[k] <= Targetis not satisfied, De-rate Crate[k] to target with rate = min (Desired ROC, Max allowed ROC) wherein the battery current limits can be de-rated.
Embodiments herein provide protection of battery pack from extreme use-cases, specifically, high current demand for prolonged periods of time, which ensures longevity of battery life. Embodiments herein mitigate the risk of incidents, due to unsafe battery operation. Embodiments herein prevent underutilizing the battery capabilities by having a fixed current limit for all operating range. Embodiments herein achieve an optimized trade-off between the conflicting objectives of deriving maximum performance from the battery pack while subjecting it to minimum degradation. Embodiments herein improve energy consumption while driving. Embodiments herein assist in thermal management by limiting higher C-rates to shorter time durations, which essentially keeps a check on the amount of heat generated.
The embodiment disclosed herein describes methods and systems to dynamically update battery current limits (control limits) during ‘discharge’ based on the operating state of the battery (SoC and temperature), previous history of usage and exhaustion. Therefore, it is understood that the scope of the protection is extended to such a program and in addition to a computer readable means having a message therein, such computer readable storage means contain program code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device.
The method is implemented in at least one embodiment through or together with a software program written in e.g. Very high speed integrated circuit Hardware Description Language (VHDL) another programming language, or implemented by one or more VHDL or several software modules being executed on at least one hardware device. The hardware device can be any kind of portable device that can be programmed. The device may also include means which could be e.g. hardware means like e.g. an ASIC, or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. The method embodiments described herein could be implemented partly in hardware and partly in software. Alternatively, the invention may be implemented on different hardware devices, e.g. using a plurality of CPUs.
The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the scope of the embodiments as described herein.

REFERENCE NUMERAL
100 - Battery
102 - Battery Management System
104 - Cell
106 - Memory
108 - Communication interface
110 - Power interface
,CLAIMS:1. A system (150) for dynamically updating current limits of a battery (100), the system (150) comprising:
a battery management system (BMS) (102) configured for:
computing State of Charge (SoC), temperature, State of Health (SoH), history of usage, exhaustion of the battery (100);
selecting a plurality of data points dynamically (204), wherein the plurality of data points is obtained based on high-pulse current, duration of pulse, history of exhaustion, fatigue fraction;
creating an interpolation function (206), based on linear interpolation performed on the selected data points and
updating a fatigue fraction (208), wherein a c-rate is determined based on at least one baseline c-rate.

2. The system (150) as claimed in claim 1, wherein the BMS (102) is configured to perform initialization (202) with the initial values for the computed parameters.

3. The system (150) as claimed in claim 1, wherein the BMS (102) is configured to determine the history of exhaustion based on at least one fatigue fraction that checks for usage and exhaustion of at least one cell (104) of the battery (100).

4. The system (150) as claimed in claim 1, wherein the interpolation function is performed on the selected data points based on (c-rate) the rate at which the battery (100) is discharged relative to the maximum capacity and maximum time duration of the battery (100) flow.

5. The system (150) as claimed in claim 1, wherein the BMS (102) is configured to de-rate the battery (100),on the fatigue fraction reaching a maximum value.

6. The system (150) as claimed in claim 1, wherein the BMS (102) is configured to perform interpolation function with the calculation of the denominator based on the maximum time for which the crate [k] is allowed and the denom[k] is computed based on f(crate[k]).

7. The system (150) as claimed in claim 1, wherein the cut-off value for fatigue fraction is determined based on temperature, voltage, State of Charge (SoC), fault conditions of the battery (100) combined to determine the de-rating of the battery (100).

8. The system (150) as claimed in claim 1, wherein the lookup table is configured to fetch limitations of the pulse current, maximum allowable currents, State of Charge (SoC), temperature and time duration of the battery (100).

9. The system (150) as claimed in claim 1, wherein the BMS (102) can be configured to dynamically update battery current limits during discharge of current based on operating state of battery (SoC), temperature, history of usage and exhaustion of battery (102).

10. A method for dynamically updating current limits during discharge of a battery (100), the method comprising:
computing, by a battery management system (BMS) (102), State of Charge (SoC), temperature, State of Health (SoH), history of usage, exhaustion of the battery (100);
selecting, by the (BMS) (102), a plurality of data points dynamically (204), wherein the plurality of data points is obtained based on high-pulse current, duration of pulse, history of exhaustion, fatigue fraction;
creating, by the (BMS) (102), based on linear interpolation performed on the selected data points and
updating, by the (BMS) (102), a fatigue fraction (208), wherein a c-rate is determined based on at least one baseline c-rate.

11. The method as claimed in claim 10, wherein the BMS (102) is configured to perform initialization (202) with the initial values for the computed parameters.

12. The method as claimed in claim 10, wherein the BMS (102) is configured to determine the history of exhaustion based on at least one fatigue fraction that checks for usage and exhaustion of at least one cell (104) of the battery (100).

13. The method as claimed in claim 10, wherein the interpolation function is performed on the selected data points based on (c-rate) the rate at which the battery (100) is discharged relative to the maximum capacity and maximum time duration of the battery (100) flow.

14. The method as claimed in claim 10, wherein the BMS (102) is configured to de-rate the battery (100), the fatigue fraction on reaching the maximum value with the maximum exhaustion of the battery.

15. The method as claimed in claim 10, wherein the BMS (102) is configured to perform interpolation function with the calculation of the denominator based on the maximum time for which the crate [k] is allowed and the denom[k] is computed based on f(crate[k]).

16. The method as claimed in claim 10, wherein the cut-off value for fatigue fraction is determined based on temperature, voltage, State of Charge (SoC), fault conditions of the battery (100) combined to determine the de-rating of the battery (100).

17. The method as claimed in claim 10, wherein the lookup table is configured to fetch limitations of the pulse current, maximum allowable currents, State of Charge (SoC), temperature and time duration of the battery (100).

18. The method as claimed in claim 10, wherein the BMS (102) can be configured to dynamically update battery current limits during discharge of current based on operating state of battery (SoC), temperature, history of usage and exhaustion of battery (102).