Claims:1. A method (600) for determining the State of Health (SOH) of a battery (202) in a vehicle, the method comprising:
determining (601), by a power management Integrated Circuit (IC) (201a) in a Battery Management System (201) of the vehicle, an initial available capacity in the battery (202);
identifying (602), by the power management IC (201a), desired reference voltage limits of charging strips from a charging curve of the battery (202);
determining (603), by the power management IC (201a), a partial charge capacity (Ah) corresponding to the identified charging strips at any time of the charging; and
determining (604), by the power management IC (201a), a relative partial charge capacity (Ah) and the SOH of the battery (202) for a distance run in real-time, based on the partial charge capacity.

2. The method, as claimed in claim 1, wherein the initial available capacity in the battery (202) is determined by completely discharging the battery (202) and charging the battery (202) with a specified C-rate at a plurality of ambient temperatures.

3. The method, as claimed in claim 1, wherein the desired reference voltage limits of the charging strips are determined from a charging curve of the battery (202) based on a plurality of factors comprising of cell chemistry, and temperature.

4. The method, as claimed in claim 1, wherein distance run in real-time is determined using at least one of inputs from the odometer; and a geo-location tracking means.

5. The method, as claimed in claim 1, wherein the relative partial Ah can be determined, when the partial Ah at a temperature is obtained less than a partial Ah maximum.

6. The method, as claimed in claim 1, wherein the method further comprises of at least one of,
displaying the determined SOH of the battery (202); and
storing the determined SOH of the battery (202).

7. The method, as claimed in claim 1, wherein the method comprises of using a weighted average method to update the SOH.

8. The method, as claimed in claim 7, wherein the method further comprises of at least one of,
displaying the updated SOH of the battery (202); and
storing the updated SOH of the battery (202).

9. A Battery Management System (BMS) (201) in a vehicle, the BMS comprising a Power Management Integrated Circuit (IC) (201a) configured for
determining an initial available capacity in the battery (202);
identifying desired reference voltage limits of charging strips from a charging curve of the battery (202);
determining a partial charge capacity (Ah) corresponding to the identified charging strips at any time of the charging; and
determining a relative partial charge capacity (Ah) and the SOH of the battery (202) for a distance run in real-time, based on the partial charge capacity.

10. The BMS, as claimed in claim 9, wherein the Power Management Integrated Circuit (IC) (201a) is configured to determine the initial available capacity in the battery (202) by completely discharging the battery (202) and charging the battery (202) with a specified C-rate at a plurality of ambient temperatures.

11. The BMS, as claimed in claim 9, wherein the Power Management Integrated Circuit (IC) (201a) is configured to determine the desired reference voltage limits of the charging strips from a charging curve of the battery (202) based on a plurality of factors comprising of cell chemistry, and temperature.

12. The BMS, as claimed in claim 9, wherein the Power Management Integrated Circuit (IC) (201a) is configured to determine the distance run in real-time using at least one of inputs from the odometer; and a geo-location tracking means.

13. The BMS, as claimed in claim 9, wherein the Power Management Integrated Circuit (IC) (201a) is configured to determine the relative partial Ah, when the partial Ah at a temperature is obtained less than a partial Ah maximum.

14. The BMS, as claimed in claim 9, wherein the Power Management Integrated Circuit (IC) (201a) is configured to,
display the determined SOH of the battery (202); and
store the determined SOH of the battery (202).

15. The BMS, as claimed in claim 9, wherein the Power Management Integrated Circuit (IC) (201a) is configured to use a weighted average method to update the SOH.

16. The BMS, as claimed in claim 15, wherein the Power Management Integrated Circuit (IC) (201a) is configured to,
display the updated SOH of the battery (202); and
store the updated SOH of the battery (202).
, Description:TECHNICAL FIELD
Embodiments disclosed herein relate to managing Lithium-ion (Li-ion) batteries and more particularly to determining the State of Health (SOH) of a battery using a capacity fade model.

BACKGROUND
Li-ion batteries are one of the key preferred choices for energy source especially for automobile applications and others. The lithium ion battery degrades in the course of usage. To avoid expensive battery replacements, it is very important to have a sufficiently longer life for the battery packs and thus determination of battery degradation or ageing accurately becomes a necessary requirement. Battery lifetime prognosis is a key requirement for successful market introduction of electric and hybrid vehicles. One of the most important tasks of a Battery Management System (BMS) is to estimate the battery states, such as, but not limited to, State of Charge (SOC), State of Health (SOH), and so on.
State-of-Health (SOH) is a parameter that helps one to determine the battery pack’s time to time performances. It gives a measure of how much the battery has degraded with respect to its initial conditions. Compared with the SOC estimation techniques, SOH prediction techniques are not as developed and/or accurate. Current SOH prediction techniques cannot be performed online, and cannot be implemented in the BMS, without using any artificial intelligence. Current SOH prediction techniques require the battery to be bought to a service station for measuring the SOH of the battery, which can be time consuming and costly from a consumer perspective.
Current SOH prediction techniques are also not accurate. Inaccurate prediction of the SOH of the battery in a vehicle can lead to inaccurate range estimation and range anxiety. Inaccurate prediction of the SOH of the battery can also lead to unwanted warranty claims.

OBJECTS
The principal object of embodiments herein is to disclose methods and systems for predicting the State of Health (SOH) of a Lithium-ion (Li-ion) battery, based on the partial Ampere-Hours (Ah) delivered during the charging of the battery and considering a plurality of factors influencing the battery degradation and from an analysis of a detailed series of cycle life test experiments, wherein the impact of parameters, such as, but not limited to temperature, charging rates, storage losses on the charging curve of the battery are tested and quantified.
These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating at least one embodiment 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 FIGURES
Embodiments herein are illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which:
FIG. 1 is a flowchart depicting a method for predicting the State of Health (SOH) of a Lithium-ion (Li-ion) battery, according to embodiments as disclosed herein;
FIG. 2 depicts a system in a vehicle for predicting the SOH of a battery, according to embodiments as disclosed herein;
FIG. 3 depicts the process of determining and displaying the SOH, according to embodiments as disclosed herein;
FIGs. 4A and 4B depict the process of determining the SOHi corresponding to the partial Ah, according to embodiments as disclosed herein;
FIG. 5 is an example flowchart depicting the process of determining the initial parameters, according to embodiments as disclosed herein;
FIG. 6 is a flowchart depicting the process of determining the SOH using a capacity fade model and displaying the SOH, according to embodiments as disclosed herein;
FIG. 7 depicts the process of calculating the partial charge Ah, according to embodiments as disclosed herein; and
FIGs. 8A and 8B depict an example process for estimating SOH in a running vehicle, 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 achieve methods and systems for predicting the State of Health (SOH) of a Lithium-ion (Li-ion) battery. Referring now to the drawings, and more particularly to FIGS. 1 through 8B, where similar reference characters denote corresponding features consistently throughout the figures, there are shown embodiments.
Embodiments herein utilize the way the charging performance of the li-ion battery with ageing changes. As the battery degrades, the charging curve shrinks and the voltage shifts towards upward. Embodiments herein determine the loss in capacity or degradation of the battery during anytime in its lifetime; i.e., till it reaches its end of life (EOL), using a charge strip or strips from a lower State of Charge (SOC) to an upper SOC which can be a considered as representation of full charging curve behavior of the battery.
Embodiments herein disclose methods and systems for predicting the State of Health (SOH) of a Lithium-ion (Li-ion) battery, based on the partial Ampere-Hours (Ah) delivered during the charging of the battery and considering a plurality of factors influencing the battery degradation and from an analysis of a detailed series of cycle life test experiments, wherein the impact of parameters, such as, but not limited to temperature, charging rates, storage losses on the charging curve of the battery are tested and quantified.
The term ‘battery’ as referred to herein can refer to a single battery or a battery pack. The battery can be a Lithium-ion (Li-ion) battery, or a battery that can use any other suitable chemistry.
FIG. 1 is a flowchart depicting a method for predicting the State of Health (SOH) of a Lithium-ion (Li-ion) battery. In step 101, the initial performance parameters of the battery pack, such as the full charge capacity, the desired reference voltage limits of the charging strips are identified for multiple ambient temperatures and C-rates (as depicted in FIG. 5). In step 102, the initial performance parameters of the battery pack, such as the full charge capacity, the desired reference voltage limits of the charging strips are stored in a Battery Management System (BMS) present in the vehicle for multiple ambient temperatures and C-rates. In step 103, the Amp hours (Ah) charge corresponding to the initial reference strips are calculated and compared to the initial value at any time of the charging of the battery. In step 104, the relative Ah obtained is corelated to the loss in capacity of the battery using an empirical equation. In step 105, the SOH corresponding to the distance travelled by a vehicle equipped with the battery is updated using a weighted average method. The various actions in method 100 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 1 may be omitted.
FIG. 2 depicts a system in a vehicle for predicting the SOH of a battery. The system 200, as depicted, comprises of the BMS 201. The BMS 201 is present in a vehicle, wherein the vehicle comprises at least one Li-ion battery and/or a Li-ion battery pack 202. The BMS 201 may be connected to at least one Li-ion battery and/or a Li-ion battery pack present in the vehicle. In an embodiment herein, the BMS 201 may be connected to at least one display means, wherein the display can be present in a location in the vehicle, so as to be visible to the user (such as the instrument console, the dashboard, and so on). In an embodiment herein, the BMS 201 may be connected to a user device, wherein the user device can present data from the BMS 201. In an embodiment herein, the BMS 201 may be connected to an external data storage means, such as a data server, a file server, a Cloud storage, and so on, wherein the external data storage means can store data from the BMS 201.
The BMS 201 may comprise a power management Integrated Circuit (IC) 201a. The power management IC 201a can receive information from the battery pack 202. The information received from the battery pack 202 can comprise of temperature, charging C rate, battery voltage, and so on.
The BMS 201 can comprise a data storage means 201b, which can be used to store the information received from the battery pack 202. Examples of the data storage means 201b may be, but are not limited to, NAND, embedded Multimedia Card (eMMC), Secure Digital (SD) cards, Universal Serial Bus (USB), Serial Advanced Technology Attachment (SATA), solid-state drive (SSD), and so on. Further, the data storage means 201b may include one or more computer-readable storage media. The data storage means 201b may include one or more non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the data storage means 201b may, in some examples, be considered a non-transitory storage medium. The term “non-transitory” may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term “non-transitory” should not be interpreted to mean that the memory is non-movable. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache).
The power management IC 201a can perform initial measurements, and estimate usage loss and weighted average incorporating distance travelled for updating the SOH. In an embodiment herein, the distance travelled can be determined using odometer readings of the vehicle. In an embodiment herein, the distance travelled can be determined using a geo-location tracking means (such as, but not limited to, Global Positioning System (GPS)). In an embodiment herein, the distance travelled can be determined using odometer readings of the vehicle and the geo-location tracking means (such as, but not limited to, Global Positioning System (GPS)).
The BMS 201 completely discharges the battery 202, wherein the battery 202 was fully charged. The BMS 201 charges the battery 202 to the full charge with a pre-defined C rate at multiple ambient temperatures. Based on this, the BMS 201 determines the initial available capacity in the battery. The BMS 201 stores the determined initial available capacity in the data storage means 201b.
During the charging stage, the power management IC 201a evaluates, at multiple temperatures and C rates, the charge Ah values corresponding to single or multiple charge strips from the charging curve of the battery 202 that have been identified to represent the entire charging curve. The power management IC 201a stores the evaluated charge Ah values corresponding to single or multiple charge strips that have been identified to represent the entire charging curve in the data storage means 201b.
The power management IC 201a can identify a selected voltage strip from a lower SOC (LSOC) to an upper or full charge SOC (USOC). The power management IC 201a can identify the selected voltage strip based on factors such as cell chemistry of the battery 202, temperature of the battery 202, and so on. These characteristic voltages corresponding to the LSOC and the USOC at different conditions like temperature C-rate etc., wherein the characteristic voltages are obtained from detailed experiments performed at different cyclic ageing conditions.
At the time of first charging when charging Ah reaches the identified LSOC of constant current (CC) stage, the power management IC 201a can store the corresponding voltage as Vref in the data storage means 201b. The capacity degradation is estimated using this partial charge data (as depicted in FIG. 7, which depicts the process of calculating the partial charge Ah).
During charging, when the cell voltage crosses the Vref, the power management IC 201a can calculate the charging Ah from Vref up to voltage corresponding to USOC (hereinafter referred to as relative partial Ah). The calculated charging Ah is hereinafter referred to as partial charge capacity and is denoted by PCapi. During operation if PCapi, at temperature T is less than PCapmax (maximum charge capacity), the power management IC 201a can calculate ??Cap?_(i,T) as
??Cap?_(i,T)=(P?Cap?_(max,T)-P?Cap?_(i,T) ) (1)
Rel??Cap?_(i,T)= (??Cap?_(i,T))/(P?Cap?_(max,T) ) (2)
The power management IC 201a considers the initial SOH value corresponding to the initial capacity stored (Capmax) as 100%. The power management IC 201a can determine the SOHi corresponding to the Rel??Cap?_(i,T) from an empirical correlation obtained from experimental and simulations studies for different cell chemistries at various operating conditions (as depicted in FIG. 4). An example equation for determining the SOHi is given below:
?SOH?_i=IC-(A*exp?((-B)/T_SOH ) )*Rel?P (3a)
The constant IC, A and B can be calculated from experimental results.
The equivalent operating temperature for the SOH calculation is termed as TSOH.
T_SOH=(?_0^i¦T_i )/i (3b)
Which is the moving average of ambient temperature of the battery pack 202. This temperature gives a measurement of the average temperature that the battery is subjected to. To calculate TSOH, the ambient temperature of the battery pack is identified at regular intervals in a pre-defined time period (such as a 24 hour period) and is monitored and sored continuously till the next SOH is updated. The total number of such temperature points obtained is given in equation (3b) as i. The power management IC 201a can calculate the updated SOH (SOHui) for the distance run Di as
?SOH?_ui={?(D?_(i-1)*?SOH?_(ui-1))+(D_i-?(D?_(i-1))*SOH}/D_i (4)
The SOH weighted averaging method with distance travelled can help in minimizing error(s) and sudden spikes or lower values in SOH. ?SOH?_(ui-1 ) is the corresponding SOH value obtained in the previous SOH updating when the distance covered as ?(D?_(i-1) and is the time, when the previously identified charging strip is obtained during the battery charging operation. The accuracy of SOH estimation using embodiments disclosed herein is 1% for measurement of voltage with in ±5mV and current measurement accuracy of ±2A.
FIG. 3 depicts the process of determining and displaying the SOH. In step 301, the power management IC 201a monitors the charge strip, Vref and T, based on data received from the battery pack 202. In step 302, the power management IC 201a estimates the partial Ah at temperatures T and TSOH, using equation (3b). In step 303, the power management IC 201a calculates the relative partial Ah and SOH using equations (2) and (3a). In step 304, the power management IC 201a updates the SOH with the distance run Di of the vehicle using equation (4). In step 305, the power management IC 201a displays the updated SOH using at least one of a vehicle infotainment system, the vehicle console, the vehicle dashboard, a wearable device, a user device, and so on. In step 306, the power management IC 201a stores the estimated partial Ah at temperatures T and TSOH, the calculated relative partial Ah and SOH, the updated SOH in the data storage means 201b or a remote entity (such as storage present in the vehicle, the wearable device, the user device, a data server, a file server, the Cloud, and so on). The various actions in method 300 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 3 may be omitted.
FIGs. 4A and 4B depict the process of determining the SOH corresponding to the partial Ah. Consider that the battery pack is being charged. The battery pack 202 is current being charged. In step 401, the power management IC 201a checks if the ambient temperature (Tamb) is within a pre-defined temperature range. In an example herein, the pre-defined ambient temperature range can be between 20OC and 45OC. In step 402, if the ambient temperature is not within the pre-defined temperature range, the power management IC 201a stops the process. If the ambient temperature is within the pre-defined temperature range, in step 403, the power management IC 201a checks if the voltage of at least one cell present in the battery pack is equal to Vref,T (i.e., identified LSOC of constant current (CC) stage at temperature T). If the voltage of at least one cell present in the battery pack is equal to Vref,T, the power management IC 201a starts monitoring the ambient temperature, the voltage of the battery pack 202 and its cells, and the distance travelled by the vehicle (step 404). The power management IC 201a monitors the voltage of each cell present in the battery pack 202. In step 405, the voltage of at least one cell in the battery pack 202 has reached the maximum voltage (Vmax) at temperature Ti. If the voltage of at least one cell in the battery pack 202 has not reached the maximum voltage, in step 406, the power management IC 201a does not update the stored SOH value or distance travelled and/or disable the process, wherein the SOH value will be the value corresponding to the previous updated SOH value till the next updation.
If the voltage of at least one cell in the battery pack 202 has reached the maximum voltage, in step 407, the power management IC 201a calculates the Ah charged till the maximum voltage and the ambient temperature. In step 408, the power management IC 201a stores the PCapi, Ti and the total distance travelled (Di). In step 409, the power management IC 201a checks if PCapi is less than PCapmax at Ti (wherein PCapmax is determined from end results of a line test and/or a look up table). If PCapi is not less than PCapmax at Ti, the power management IC 201a does not update the stored SOH value or distance travelled and/or disable the process, wherein the SOH value will be the value corresponding to the previous updated SOH value till the next updation (step 406). If PCapi is less than PCapmax at Ti, in step 410, the power management IC 201a determines the RelPCapi and SOHi as follows:
Rel??Cap?_i= (Pcapmax-PCapi)/PCapmax
?SOH?_i=IC-((A*exp?((-B)/T_SOH ) )*Rel??Cap?_i)
In step 411, the power management IC 201a stores the determined SOHi in the data storage means. The various actions in method 400 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIGs. 4A and 4B may be omitted.
FIG. 5 is an example flowchart depicting the process of determining the initial parameters. Consider that the discharge tests have started from a 100% SOC at a temperature of 25OC. The power management IC 201a determines if the voltage of at least one cell in the battery pack has reached a minimum voltage. If the voltage of at least one cell in the battery pack has reached a minimum voltage, the power management IC 201a measures the Ah delivered and stores the Ah as Capmax (step 501). In step 502, the power management IC 201a starts the charging process, wherein the charge (1C) starts from an SOC of 0% at 25OC. The power management IC 201a checks if LSOC has been reached. If LSOC has been reached, in step 503, the power management IC 201a stores the corresponding highest cell voltage as Vref,25 (wherein 25OC is the current temperature). On USOC being reached, the power management IC 201a stores the Ah charged as PCapmax,25 (i.e., the partial capacity of the battery 202 at 25OC) (step 504). The various actions in method 500 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 5 may be omitted.
FIG. 6 is a flowchart depicting the process of determining the SOH using a capacity fade model and displaying the SOH. In step 601, an initial available capacity in a battery is determined by the power management IC 201a by completely discharging the battery and charging the battery with a specified C-rate at multiple ambient temperatures (as depicted in FIG. 5). In step 602, the desired reference voltage limits of the charging strips are identified by the power management IC 201a from a battery charging curve based on the factors like cell chemistry, temperature and so on. In step 603, the partial charge capacity (i.e., Partial Ah) corresponding to the identified charging strips at any time of the charging are determined by the power management IC 201a. In step 604, the relative partial charge capacity (i.e., relative partial Ah) and a State-Of-Health (SOH) of the battery for a distance run in real-time are determined by the power management IC 201a, based on the partial charge capacity. The Relative partial Ah can be calculated by the power management IC 201a, when the partial Ah at a temperature is obtained less than a partial Ah maximum. In step 605, the SOH is updated by the power management IC 201a with distance run using a weighted average method, to avoid sudden changes in SOH (which may be caused due to sudden operating environment change(s)). In step 606, the final SOH of the battery is displayed and/or stored by the power management IC 201a in at least one location. The various actions in method 600 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 6 may be omitted.
FIGs. 8A and 8B depict an example process for estimating SOH in a running vehicle. The SOH values are calculated based on the nominal capacity of the cells. Based on sorting, 3 groups of cells are being used in a battery pack. In a single pack there could be difference of 2 Ah between cells. This can add around 2% difference may SOH by capacity calculation.
Embodiments herein have been simulated using batteries with a LiFePO4 (LFP) chemistry, but it may be obvious to a person of ordinary skill in the art to extend the embodiments herein to batteries with any lithium ion chemistry. Embodiments herein are validated for test vehicles and shows that the concept is feasible to predict SOH online accurately.
The embodiment disclosed herein describes methods and systems for predicting the State of Health (SOH) of a Lithium-ion (Li-ion) battery. 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 and examples, those skilled in the art will recognize that the embodiments and examples disclosed herein can be practiced with modification within the scope of the embodiments as described herein.