Claims:STATEMENT OF CLAIMS
We claim:
1. A method for predicting a State of Charge (SoC) of a battery, the method comprising:
estimating, by a battery management module, an open circuit voltage (OCV) of said battery based on a OCV model defined by a plurality of internal parameters, wherein said plurality of internal parameters are derived from at least one of an output voltage of said battery, a load current supplied by said battery to a load, and an initial SoC of said battery; and
predicting, by a battery management module, said SoC from said OCV based on functional dependence of said SoC on said OCV.
2. The method as claimed in claim 1, wherein said OCV is estimated when said battery is in use to provide effect of aging of said battery on said OCV.
3. The method as claimed in claim 1, wherein said initial SoC is an initial surface concentration of ions in one of a positive electrode and a negative electrode of said battery.
4. The method as claimed in claim 1, wherein said plurality of internal parameters used for estimating said OCV comprise at least one of reaction fluxes at said positive electrode and said negative electrode, interfacial potentials in an electrolyte phase at said positive electrode and said negative electrode, reaction rates at said positive electrode and said negative electrode, an open circuit potential of said positive electrode, a side reaction current density of said battery, and a resistance of a solid-electrolyte interphase (SEI) film formed at said negative electrode.
5. The method as claimed in claim 4, wherein said interfacial potentials in said electrolyte phase at said positive electrode and said negative electrode are dependent on interfacial electrolyte concentrations at said positive electrode and said negative electrode, and interfacial mass fluxes at said positive electrode and said negative electrode, wherein said interfacial electrolyte concentrations and interfacial mass fluxes are dependent on said load current.
6. The method as claimed in claim 4, wherein said reaction fluxes at said positive electrode and said negative electrode are obtained from said load current.
7. The method as claimed in claim 4, wherein said side reaction rate and said SEI film resistance are obtained from said output voltage and said load current.
8. The method as claimed in claim 4, wherein said reaction rates at said positive electrode and said negative electrode, and said open circuit potential of said positive electrode is obtained from a surface concentration of ions in said positive electrode and said negative electrode, wherein said surface concentration of ions in said negative electrode is derived from one of said initial surface concentration of ions in said negative electrode, and a current surface concentration ions in said negative electrode.
9. The method as claimed in claim 1, wherein said SoC is predicted based on said OCV using said open circuit potential of said negative electrode, wherein said open circuit potential of said negative electrode is used to obtain said current surface concentration of said negative electrode.
10. The method as claimed in claim 1, wherein said SOC and a remaining battery life of said battery is displayed on a user interface.
11. A battery system for predicting a State of Charge (SoC) of a battery, the battery system comprising:
said battery connected to a load and a battery management module, wherein said battery management module is configured to:
estimate an open circuit voltage (OCV) of said battery based on a OCV model defined by a plurality of internal parameters, wherein said plurality of internal parameters are derived from at least one of an output voltage of said battery, a load current supplied by said battery to said load, and an initial SoC of said battery; and
predict said SoC from said OCV based on functional dependence of said SoC on said OCV.
12. The battery system as claimed in claim 11, wherein said battery management module is configured to estimate said OCV when said battery is in use to provide effect of aging of said battery on said OCV.
13. The battery system as claimed in claim 11, wherein said initial SoC is an initial surface concentration of ions in one of a positive electrode and a negative electrode of said battery.
14. The battery system as claimed in claim 11, wherein said plurality of internal parameters used for estimating said OCV comprise at least one of reaction fluxes at said positive electrode and said negative electrode, interfacial potentials in an electrolyte phase at said positive electrode and said negative electrode, reaction rates at said positive electrode and said negative electrode, an open circuit potential of said positive electrode, a side reaction current density of said battery, and a resistance of a solid-electrolyte interphase (SEI) film formed at said negative electrode.
15. The battery system as claimed in claim 14, wherein said interfacial potentials in said electrolyte phase at said positive electrode and said negative electrode are dependent on interfacial electrolyte concentrations at said positive electrode and said negative electrode, and interfacial mass fluxes at said positive electrode and said negative electrode, wherein said interfacial electrolyte concentrations and interfacial mass fluxes are dependent on said load current.
16. The battery system as claimed in claim 14, wherein said battery management module is configured to obtain said reaction fluxes at said positive electrode and said negative electrode from said load current.
17. The battery system as claimed in claim 14, wherein said battery management module is configured to obtain said side reaction rate and said SEI film resistance from said output voltage and said load current.
18. The battery system as claimed in claim 14, wherein said battery management module is configured to obtain said reaction rates at said positive electrode and said negative electrode, and said open circuit potential of said positive electrode from a surface concentration of ions in said positive electrode and said negative electrode, wherein said surface concentration of ions in said negative electrode is derived from one of said initial surface concentration of ions in said negative electrode, and a current surface concentration ions in said negative electrode.
19. The battery system as claimed in claim 11, wherein said battery management module is configured to predict said SoC based on said OCV using said open circuit potential of said negative electrode, wherein said open circuit potential of said negative electrode is used to obtain said current surface concentration of said negative electrode.
20. The battery system as claimed in claim 11, wherein said battery management module is configured to display said SOC and a remaining battery life of said battery on a user interface.
Dated this 20th August 2015

Signature:
Name: Kalyan Chakravarthy
, Description:FORM 2
The Patent Act 1970
(39 of 1970)
&
The Patent Rules, 2005

COMPLETE SPECIFICATION
(SEE SECTION 10 AND RULE 13)

TITLE OF THE INVENTION

“A method and a battery system for predicting State of Charge (SoC) of a battery”

APPLICANTS:
Name Nationality Address
SAMSUNG R&D Institute India - Bangalore Private Limited India # 2870, Orion Building, Bagmane Constellation Business Park, Outer Ring Road, Doddanekundi Circle, Marathahalli Post,Bangalore-560 037, India

The following specification particularly describes and ascertains the nature of this invention and the manner in which it is to be performed:-

TECHNICAL FIELD
The embodiments herein generally relate to field of battery management systems and more particularly to a battery management system for predicting State of Charge (SoC) of a battery.

BACKGROUND
Lithium ion (Li-ion) batteries are used in wide variety of applications due to their low weight, high energy density and slow discharge rate. A Li-ion battery (battery) is generally used in devices such as mobile phones, digital cameras, robotic vacuum cleaners, lawn movers and electric vehicles. The operation of these devices is majorly dependent on battery power derived from the battery. Thus, updating a user of a device with current status of the battery is critical for enabling the user to seamlessly operate the device.
One of the parameter that may be indicated to the user to indicate the current battery status may be a state of charge (SOC) of the battery. For a lithium ion cell or a Li-ion battery, the SoC is a measure of the lithium available in electrodes of the battery for chemical reaction to produce electricity. However, the lithium inside the cell is not a measurable quantity. State-of-art techniques equate cell information such as current as an estimate of SOC. The estimation of SOC requires knowledge of battery capacity. However, the total capacity of the battery decreases while in operation, due to repeated cycling. Capacity fade can be due to side reactions like solid-electrolyte interphase (SEI) film formation. The effects of the side reactions on capacity loss are well understood in the art and may be considered in SoC estimation. However, due to ageing, an Open Circuit Voltage (OCV) of the battery can alter and needs to be considered during SoC estimation. Another existing method estimates the SoC based on a charge current, a discharge current, and an output voltage using a nonlinear optimization that expresses a relationship between the SoC and an open circuit voltage (OCV). However, the existing method utilizes a combination a non linear adaptive filter, along with a single particle model (SPM) that well-known in art. This filter introduces artificial parameter dependencies and physics. Thus, the filter alters values of parameters of the OCV and the SOC, for matching the measured voltage that may not provide reliable accuracy. Further, the existing method describes the SoC estimation for fresh cells or batteries and does not mention consideration of the aging effects on the battery OCV. Thus, the existing methods may not provide true SoC estimation.
OBJECTS
The principal object of the embodiments herein is to provide a method and a battery system for predicting a State of Charge (SoC) of a battery based an Open Circuit Voltage (OCV) model, where the OCV model provides estimation of the SoC in terms of actual ion concentration present in the electrodes of the battery.
Another object of the embodiments herein is to provide a method for predicting the SoC of the battery by estimating an OCV of the battery based on the OCV model, wherein the OCV model is a physics based model defined by a plurality of internal parameters that are derived from one or more input parameters including an output voltage of the battery, a load current supplied by the battery to a load, and an initial SoC.

SUMMARY
In view of the foregoing, an embodiment herein provides a method for predicting a State of Charge (SoC) of a battery. The method includes estimating an open circuit voltage (OCV) of the battery based on an OCV model. Further, the method includes predicting the SoC from the OCV based on functional dependence of the SoC on the OCV.
Embodiments further disclose a battery system for predicting a State of Charge (SoC) of a battery. The battery system comprises the battery connected to a load and a battery management module. Further, the battery management module is configured to estimate an open circuit voltage (OCV) of the battery based on an OCV model defined by a plurality of internal parameters. The plurality of internal parameters are derived from at least one of an output voltage of the battery, a load current supplied by the battery to the load, and an initial SoC of the battery. Further, the battery system is configured to predict the SoC from the OCV based on functional dependence of the SoC on the OCV.
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 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 FIGURES
The embodiments of this invention 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 illustrates a battery system for predicting a State of Charge (SoC) of a battery based an Open Circuit Voltage (OCV) model, according to embodiments as disclosed herein;
FIG. 2a through 2d illustrates graphical analysis of sample results for the battery system for a LCO/C (Lithium Cobalt) battery, according to embodiments as disclosed herein; and
FIG. 3 is a flow diagram illustrating a method for predicting a State of Charge (SoC) of a battery based an Open Circuit Voltage (OCV) model, 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 a method and a battery system for predicting a State of Charge (SoC) of a battery based an Open Circuit Voltage (OCV) model. The OCV model provides estimation of the SoC in terms of actual ion concentration present in the electrodes of the battery, thus providing a more accurate prediction of the SoC. The battery system includes a battery management model that can be configured to predict the SoC of the battery by estimating an OCV of the battery based on the OCV model. The prediction of the SoC from the OCV is based on the functional dependence of the SoC on the OCV. The OCV model proposed by the battery system is defined by a plurality of internal parameters that are derived from one or more internal parameters including an output voltage of the battery, a load current supplied by the battery to a load, and an initial SoC. Since the internal parameters derived are based on real time values of the output voltage, the load current along with the initial SoC, the battery system provides the a real time prediction of the SoC of the battery. Since the output voltage is considered for SoC computation along with the load current, the battery system includes the cell (battery) aging effects during the SoC prediction and provides more accurate prediction of the SoC with aging of the battery.
The OCV model based SoC prediction is semi-analytical, which is computationally economical and hence may consume less computational power. The reduced computational power usage enables the usage of the battery system onboard (for example, on board of an electric vehicle) where reduced power consumption is an advantage. The OCV model considers all electrochemical processes during the OCV estimation; hence the battery system based on the OCV model can be applied for batteries that undergo higher charging and discharging rates. Thus, any changes in the electrochemical processes due to higher charging or discharging rates are taken into account by the OCV model, effectively providing accurate, real time SoC prediction.
Further, the battery system can be configured to indicate health of the battery to a user through a User Interface (UI). The battery health can be indicated in terms of real time SoC of the battery, and change in initial capacity of the battery with cycling. Thus, the battery system enhances the user experience by keeping the user updated about the status of the battery.
In an embodiment, the battery is a lithium-ion (Li-ion) battery such as a Li NCA (Lithium Nickel Cobalt Aluminum positive electrode) battery or the like. Further, the battery may include any secondary battery like Ni Metal Hydride, Sodium ion, or Li-air battery or the like.
Referring now to the drawings, and more particularly to FIGS. 1 through 3, where similar reference characters denote corresponding features consistently throughout the figures, there are shown embodiments.
FIG. 1 illustrates a battery system 100 for predicting a State of Charge (SoC) of a battery 102 based an Open Circuit Voltage (OCV) model, according to embodiments as disclosed herein. In an embodiment, the battery system 100 includes a battery 102 connected to a battery management controller 104 and a load 106. Further, the battery management controller 104 is connected to a User Interface (UI) 108 that provides an interface to indicate the battery health to the user in terms of real time SoC of the battery 102, and change in initial capacity of the battery 102 with cycling. The battery management controller 104 can be configured to predict the SoC of the battery 102 based on the OCV model. The OCV model is defined by plurality of internal parameters corresponding to the battery 102. The battery management module 104 can be configured to derive the plurality of internal parameters from the input parameters that include the output voltage (Vcell), the load current (I) supplied by the battery 102 to the load 106, and an initial SoC of the battery 102. The initial SoC is an initial surface concentration of ions (for example, Li-ions in a Li-ion battery) in a positive electrode (Cn0), or the initial surface concentration of ions in a negative electrode (Cp0) of the battery 102. The initial SoC is an indication of the Soc of battery 102 when unused or fresh. However, with aging of the battery, the battery management module provides the prediction of the real time SoC by including the internal parameters such as Vcell during estimating OCV that are dependent on aging of the battery 102. The plurality of internal parameters include reaction fluxes at the positive electrode (jp)and the negative electrode (jn), interfacial potentials in an electrolyte phase at the positive electrode(?2p) and the negative electrode(?2n), reaction rates at the positive electrode (jp0)and the negative electrode(jn0). Further, the plurality of parameters include an open circuit potential of the positive electrode (Up), a side reaction current density of the battery 102 (js), and a resistance (Rf) of a solid-electrolyte interphase (SEI) film formed at the negative electrode
The battery management controller 104 is configured to derive the plurality of internal parameters based one or more input parameters including Vcell, I, and initial SoC (cn0 and cp0). The mathematical analysis for the derivation of the plurality of internal parameters based on at least one of input parameters including Vcell, I, and initial SoC (cn0 and cp0) is explained based on the OCV model obtained from the equations provide below.
Input quantities measured include Vcell, I, cn0 and cp0
Using I the reaction fluxes at the positive electrode (jp)and the negative electrode (jn) are calculated
j_n=I/(a_n Fl_n ), j_p=(-I)/(a_p Fl_p ) (1)
Further, interfacial mass fluxes at the positive electrode (q2ip) and the negative electrode (q2in) and interfacial electrolyte concentrations at the positive electrode (c2ip) and the negative electrode (c2in) are calculated using state of art techniques. These q2ip, q2in, c2in, c2ip are used to obtain electrolyte phase potentials or the interfacial potential in electrolyte phase at the positive electrode (?2p) and the negative electrode(?2n) as given by equation 2 and 3:
?_2n=?_2in+2T log??((c_2n (x=0))/c_2in )+(Il_n)/(2K_2n )? (2)
?_2p=?_2ip+2T log??((c_2p (x=L))/c_2ip )-(Il_p)/(2K_2p )? (3)
This is used to calculate the side reaction current density (js) as provided in equation 4 and 5 below, assuming an initial value of (?1n). (Rf) is the resistance of the SEI film (df) formed on the negative electrode.
j_s=?-j?_s0/F×exp(-(a_c F)/RT (?_1n-?_2n-U_sr-j_n R_f F) ) (4)
(dd_f)/dt=-(j_sr M_f)/?_f ? R?_f=R_f0+d_f/?_f (5)
Based on equation: jint = jn - js (6)
the concentration gradients within solid phase is calculated as in equation 7 below:
(dc_1rn)/dt=-(45j_int)/(2R_n^2 )-(30D_1n c_1rn)/(R_n^2 ),(dc_1rp)/dt=-(45j_p)/(2R_p^2 )-(30D_1p c_1rp)/(R_p^2 ) (7)
From the input values of cn0 and cp0 the reaction rates at the positive electrode (jp0)and the negative electrode(jn0) and the surface concentration of ions in the electrodes (For example, Li-ions in electrodes of the Li-ion battery) are calculated as provided in equation 8, 9 and 10 below:
c_1n=c_sn+r_n/(35D_1n ) j_n-(8r_n)/35 c_1nr, c_1p=c_1p0+(l_n e_1n)/(l_p e_1p ) (c_1n0-c_1n ) (8)
c_sp=c_1p-r_p/(35D_1p ) j_p+(8r_p)/35 c_1pr (9)
j_n0=k_n (c_smaxn-c_sn )^0.5 c_sn^0.5 c_2^0.5, j_p0=k_p (c_smaxp-c_sp )^0.5 c_sp^0.5 c_2^0.5
(10)
Upon deriving the plurality of internal parameters, the battery management module 104 is configured to use these internal parameters to estimate the OCV using the OCV model provided in equation 11 below. All the quantities obtained through equations 1 to 10 are substituted in the equation 11 for the OCV model. The OCV model, which provides a physics model based OCV computation is as below:
OCV=-[-V_cell+?_2p+2RT/F sinh^(-1)??(j_p/(2j_p0 ))-? ?_2n-j_n R_f F-2RT/F sinh^(-1)?((j_n-j_s)/(2j_n0 )) ]
(11)
The Eq. 11 explains estimation of the OCV for any measured value of current and voltage. The applicability of this method is manifested during cycling of batteries. The signatures in voltage and current on aging are mapped to change in OCV on degradation. Thus, the battery management module is configured to provide direct method of estimation of OCV while the battery is in use onboard.
Upon estimating the OCV, the battery management controller 104 can be configured to predict the SoC using the functional dependence of the SoC on the OCV. The functional dependence of the SoC on the OCV is given in equations below.

U_n=U_p-OCV where, U_p=f_p (c_sp ) (12)

Where, Un is an open circuit potential of the negative electrode and Up is an open circuit potential of the positive electrode. Further, the Up is derived from a surface concentration of ions in the positive electrode (csp) and the negative electrode (csn). Further, the surface concentration of ions in the negative electrode (csn) is derived from one of the initial surface concentration of ions in the negative electrode (cn0), and a current surface concentration of ions in the negative electrode (csn) as provided in equation 13 below.
c_sn=f_n^(-1) (U_n ) (13)
Where,f_p and f_n is the functional dependence of equilibrium potential of electrodes on the lithium concentration in the positive and negative electrode. With aging, OCV changes, this leads to change in U_n. This U_n is used to calculate c_sn. In the next step, this c_sn is used to obtain new value of c_sp and in turn U_p.
Further, the battery management controller 104 can be configured to indicate the battery health to the user through the user interface 108. The battery health can be indicated in terms of real time SoC of the battery 102, a remaining capacity of the battery, and change in initial capacity of the battery with cycling. The user interface 108 for indicating the battery health can be a display that may be at least one of a dashboard of the vehicle, an instrument cluster of the vehicle, a Multi-function display means in the vehicle (in the instrument cluster and/or the dashboard), a device connected to the vehicle 101 using a suitable means (such as WiFi Direct, Bluetooth, wired means and so on), the infotainment system of the vehicle, a display screen in the vehicle, a Heads-up-Display (HUD) in the vehicle and so on. Thus, the battery system 100 enhances the user experience by keeping the user updated about the status of the battery 102
FIG. 1 shows a limited overview of the battery system 100. The battery system 100 may include plurality of other components or modules or units that directly or indirectly interact with the components or modules shown in FIG. 1. However, other components are not described here for brevity. Further, the names of the other components of the battery system are illustrative and need not be construed as a limitation.
For simplicity and ease of understanding FIG.1 describes the battery system 100 for a single battery module. However, in an embodiment, the battery 102 can be a battery pack that includes plurality of battery modules.
FIG. 2a through 2d illustrates graphical analysis of sample results for the example battery system 100 with a LCO/C (Lithium Cobalt) battery, according to embodiments as disclosed herein.
FIG. 2a illustrates validation of V (potential) v/s t (time) for fresh and aged LCO/C (Lithium Cobalt) cell with experimental data from [2]. The re-calculated potential shows a good match with the input experimental data at n=1 and n=1968.
FIG. 2b illustrates equilibrium potential (OCV) for fresh and aged LCO/C cell calculated from experimental current and voltage data. The OCV is observed to be modified upon aging.
FIG. 2c illustrates electrode equilibrium voltage Un obtained from calculated OCV data. This data also shows the capacity loss (reduction in SoC) upon aging. This can be used to estimate SoC using experimental data interpolation.
FIG. 2d illustrates the SOC varying with time for fresh and aged LCO/C cell. Upon onboard use during cell cycling the battery management module 104 can provide the loss of useful cell capacity.
FIG. 3 is a flow diagram illustrating a method 300 for predicting a State of Charge (SoC) of the battery 102 based an Open Circuit Voltage (OCV) model, according to embodiments as disclosed herein. In an embodiment, the method 300 allows the battery management controller 104 to perform the steps as described in step 302 through 308.
At step 302, the method 300 includes obtaining the output voltage (Vcell) , the load current (I) , and the initial SoC of the battery 102 (also referred as input parameters) for estimating the OCV. The Vcell and I can be measured using the state of art techniques. The initial SOC, which is the initial surface concentration of ions (for example, Li-ions in a Li-ion battery) in the positive electrode (Cpo) or the initial surface concentration of ions in the negative electrode (Cn0) of the battery 102 can be obtained from the Vcell that is measured when the battery 102 is fresh with maximum capacity.
Once the input parameters are obtained, at step 304, the method 300 includes deriving the plurality of internal parameters based on the output voltage of the battery 102, the load current supplied to the load 106 by the battery 102, and the initial SoC of the battery 102. The plurality of internal parameters include the reaction fluxes at the positive electrode (jp) and the negative electrode (jn), the interfacial potentials in an electrolyte phase at the positive electrode(?2p) and the negative electrode(?2n), and the reaction rates at the positive electrode (jp0)and the negative electrode(jn0). Further, the plurality of parameters include the open circuit potential of the positive electrode (Up), the side reaction current density of the battery 102 (js), and the resistance (Rf) of the solid-electrolyte interphase (SEI) film formed at the negative electrode. The mathematical analysis for deriving the internal parameters is explained in conjunction with FIG. 1 and is not repeated for brevity. Pluralities of other intermediate parameters are derived to derive the internal parameters as explained in mathematical analysis of Fig. 1. The interfacial potentials in the electrolyte phase at the positive electrode and the negative electrode ( ?_2n,?_2p) are dependent on the intermediate parameters such the interfacial electrolyte concentrations (c_2in,c_2ip) at the positive electrode and the negative electrode, and the interfacial mass fluxes at the positive electrode and the negative electrode (q_2in,q_2ip). Further, the interfacial electrolyte concentrations and interfacial mass fluxes are dependent on the load current (i). The method 300 includes obtaining the reaction fluxes at the positive electrode and the negative electrode from the load current. The method 300 includes obtaining the intermediate parameters such as the side reaction rate and the SEI film resistance (j_s, R_f) from the output voltage and the load current. The method 300 includes obtaining the reaction rates at the positive electrode and the negative electrode (j_n0, j_p0), and the open circuit potential of the positive electrode (Up) from the surface concentration of ions in the positive electrode and the negative electrode (c_sn, c_sp). The surface concentration of ions in the negative electrode is derived from the initial surface concentration of ions in the negative electrode cn0 for the battery 102, when fresh (also referred as the initial or first charge/discharge cycle). However, the surface concentration of ions in the negative electrode is derived from the current surface concentration ions in the negative electrode (csn) for the battery 102, when the battery 102 is aging (also referred as the second and subsequent charge/discharge cycles).
At step 306, the method 300 includes estimating the OCV of the battery 102 based the internal parameters using the OCV model in equation 11. The method 300 includes estimating the OCV even during usage of the battery to include effect of aging of the battery on the OCV.
At step 308, the method 300 includes predicting the SoC from the OCV using the functional dependence of the SoC on the OCV of the battery 102. The SoC is predicted based on the OCV using the open circuit potential of the negative electrode Un. This open circuit potential of the negative electrode is used to obtain the current surface concentration of the negative electrode(csn ). Since, the OCV is estimated both for fresh battery and for the battery when in use, the method 300 includes predicting the SoC for the fresh and the aging battery.
Further, the method 300 includes indicating the battery health to the user through the user interface 108. The battery health can be indicated in terms of real time SoC of the battery 102, a remaining capacity of the battery, and change in initial capacity of the battery with cycling.
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.
The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the network elements. The network elements shown in Fig. 1 include blocks which can be at least one of a hardware device, or a combination of hardware device and software module.
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 preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

STATEMENT OF CLAIMS
We claim:
A method for predicting a State of Charge (SoC) of a battery, the method comprising:
estimating, by a battery management module, an open circuit voltage (OCV) of said battery based on a OCV model defined by a plurality of internal parameters, wherein said plurality of internal parameters are derived from at least one of an output voltage of said battery, a load current supplied by said battery to a load, and an initial SoC of said battery; and
predicting, by a battery management module, said SoC from said OCV based on functional dependence of said SoC on said OCV.
The method as claimed in claim 1, wherein said OCV is estimated when said battery is in use to provide effect of aging of said battery on said OCV.
The method as claimed in claim 1, wherein said initial SoC is an initial surface concentration of ions in one of a positive electrode and a negative electrode of said battery.
The method as claimed in claim 1, wherein said plurality of internal parameters used for estimating said OCV comprise at least one of reaction fluxes at said positive electrode and said negative electrode, interfacial potentials in an electrolyte phase at said positive electrode and said negative electrode, reaction rates at said positive electrode and said negative electrode, an open circuit potential of said positive electrode, a side reaction current density of said battery, and a resistance of a solid-electrolyte interphase (SEI) film formed at said negative electrode.
The method as claimed in claim 4, wherein said interfacial potentials in said electrolyte phase at said positive electrode and said negative electrode are dependent on interfacial electrolyte concentrations at said positive electrode and said negative electrode, and interfacial mass fluxes at said positive electrode and said negative electrode, wherein said interfacial electrolyte concentrations and interfacial mass fluxes are dependent on said load current.
The method as claimed in claim 4, wherein said reaction fluxes at said positive electrode and said negative electrode are obtained from said load current.
The method as claimed in claim 4, wherein said side reaction rate and said SEI film resistance are obtained from said output voltage and said load current.
The method as claimed in claim 4, wherein said reaction rates at said positive electrode and said negative electrode, and said open circuit potential of said positive electrode is obtained from a surface concentration of ions in said positive electrode and said negative electrode, wherein said surface concentration of ions in said negative electrode is derived from one of said initial surface concentration of ions in said negative electrode, and a current surface concentration ions in said negative electrode.
The method as claimed in claim 1, wherein said SoC is predicted based on said OCV using said open circuit potential of said negative electrode, wherein said open circuit potential of said negative electrode is used to obtain said current surface concentration of said negative electrode.
The method as claimed in claim 1, wherein said SOC and a remaining battery life of said battery is displayed on a user interface.
A battery system for predicting a State of Charge (SoC) of a battery, the battery system comprising:
said battery connected to a load and a battery management module, wherein said battery management module is configured to:
estimate an open circuit voltage (OCV) of said battery based on a OCV model defined by a plurality of internal parameters, wherein said plurality of internal parameters are derived from at least one of an output voltage of said battery, a load current supplied by said battery to said load, and an initial SoC of said battery; and
predict said SoC from said OCV based on functional dependence of said SoC on said OCV.
The battery system as claimed in claim 11, wherein said battery management module is configured to estimate said OCV when said battery is in use to provide effect of aging of said battery on said OCV.
The battery system as claimed in claim 11, wherein said initial SoC is an initial surface concentration of ions in one of a positive electrode and a negative electrode of said battery.
The battery system as claimed in claim 11, wherein said plurality of internal parameters used for estimating said OCV comprise at least one of reaction fluxes at said positive electrode and said negative electrode, interfacial potentials in an electrolyte phase at said positive electrode and said negative electrode, reaction rates at said positive electrode and said negative electrode, an open circuit potential of said positive electrode, a side reaction current density of said battery, and a resistance of a solid-electrolyte interphase (SEI) film formed at said negative electrode.
The battery system as claimed in claim 14, wherein said interfacial potentials in said electrolyte phase at said positive electrode and said negative electrode are dependent on interfacial electrolyte concentrations at said positive electrode and said negative electrode, and interfacial mass fluxes at said positive electrode and said negative electrode, wherein said interfacial electrolyte concentrations and interfacial mass fluxes are dependent on said load current.
The battery system as claimed in claim 14, wherein said battery management module is configured to obtain said reaction fluxes at said positive electrode and said negative electrode from said load current.
The battery system as claimed in claim 14, wherein said battery management module is configured to obtain said side reaction rate and said SEI film resistance from said output voltage and said load current.
The battery system as claimed in claim 14, wherein said battery management module is configured to obtain said reaction rates at said positive electrode and said negative electrode, and said open circuit potential of said positive electrode from a surface concentration of ions in said positive electrode and said negative electrode, wherein said surface concentration of ions in said negative electrode is derived from one of said initial surface concentration of ions in said negative electrode, and a current surface concentration ions in said negative electrode.
The battery system as claimed in claim 11, wherein said battery management module is configured to predict said SoC based on said OCV using said open circuit potential of said negative electrode, wherein said open circuit potential of said negative electrode is used to obtain said current surface concentration of said negative electrode.
The battery system as claimed in claim 11, wherein said battery management module is configured to display said SOC and a remaining battery life of said battery on a user interface.
Dated this 20th August 2015

Signature:
Name: Kalyan Chakravarthy

ABSTRACT
A method for predicting a State of Charge (SoC) of a battery is provided. The method includes estimating an open circuit voltage (OCV) of the battery based on an OCV model. Further, the method includes predicting the SoC from the OCV based on functional dependence of the SoC on the OCV. The method identifies and models all processes that lead to capacity loss of continued cycling of batteries. From the current and voltage signals, the method to estimate OCV that results due to cycling is provided. The SOC on aging is re-calibrated from this estimated OCV.
FIG. 1