WO2021075770A1

WO2021075770A1 - System and method for state estimation of battery of electronic device

Info

Publication number: WO2021075770A1
Application number: PCT/KR2020/013489
Authority: WO
Inventors: Sagar Bharathraj; Shashishekara Parampalli Adiga; Krishnan Seethalakshmy Hariharan
Original assignee: Samsung Electronics Co., Ltd.
Priority date: 2019-10-17
Filing date: 2020-10-05
Publication date: 2021-04-22

Abstract

Accordingly the embodiments herein disclose a system and method for state estimation of a battery of an electronic device. The method includes determining a partial charge profile of the battery. Further, the method includes constructing a full charge profile of the battery using the partial charge profile. Further, the method includes comparing the full charge profile of the battery with a full charge profile of a reference battery to give an accurate measure of the states. Further, the method also includes determining that the plurality of charging profile parameters of the battery are distinct from default charging parameters associated with the reference battery, identifying the battery is faulty and displaying a notification on the electronic device indicating that the battery is faulty.

Description

SYSTEM AND METHOD FOR STATE ESTIMATION OF BATTERY OF ELECTRONIC DEVICE

The present disclosure relates to state estimation of a battery and more particularly to a system and method for state estimation of the battery of an electronic device. The present application is based on, and claims priority from an Indian Application Number 201941042170 provisionally filed on 17th October, 2019, and filed on 4th August, 2020, the disclosure of which is hereby incorporated by reference herein.

Generally, there are a lot of consumers for electronic devices such as mobile phones, laptops, tablets, smart watches, etc. Usually a rechargeable battery (for example a lithium (Li) ion battery) provides portable electricity and powers most of the electronic devices. Also there are electric vehicles which are operated using electrical energy stored in the rechargeable battery. For all the electronic devices or the electric vehicles, etc. accurate state estimation of a battery helps a user to know the performance of the battery or a battery system accurately.

To know how the battery is performing in an electronic device such as a mobile phone, a state estimation of the battery of the electronic device is necessary. For example, for any information like a level of degradation of the battery, the user should know a State of Health (SOH) of the battery. The SOH basically measures health of the battery by measuring a degradation status of the battery with respect to a fresh battery. The State of health (SOH) is often used to quantify the extent of degradation, which includes capacity and power fade relative to the fresh battery. The user should also know about a State of Charge (SOC) of the battery. The SOC can be determined by measuring actual charge in the battery. The SOC is the level of charge of the battery relative to a maximum possible capacity of the battery.

In existing systems, the user determines behavior of the battery based on certain indications such as for example, if the electronic device displays available charge is 20%, then user assumes that the electronic device is capable of functioning for half an hour before automatically shutting down due to complete drainage of the charge from the electronic device. However, due to ageing of the battery resulting in deteriorations within the battery, the charge in the electronic device may suddenly reduce from 20 % to 0% within a short duration of time. The existing methods of state estimation is not able to accurately measure degradation mechanisms leading to deterioration in the battery performance and inform the user about a possible indications such as displaying a message on the electronic device that the electronic device may shut down within 5 minutes. This is because the existing system uses a method of Incremental Capacity Analysis (ICA) for state estimation of the battery which gives only resistive component of degradation (resistive losses) and detects only sudden loss of capacity (reversible) due to non-linear behavior of capacity fade due to resistive loss. Also conventional system focuses on battery shut down and end of life prediction of the battery using ICA as the core principle.

Two major degradation components mainly used in the battery system to quantify degradation are capacitive loss and resistive loss. Thus for reliable operation of the battery or for the user to be aware of the states of the battery, quantification of capacitive loss and resistive loss of the battery in an efficient manner and accurate determination of degradation mechanism in the battery is much needed.

Again, in conventional methods and systems, a full charge profile information (voltage with respect to time or the state of charge that is voltage information with respect to time for the full state of charge 0%-100%) is required to know the performance of the battery with respect to the fresh battery. But usually there are cases where one user might be charging the electronic device from 30% to 80% and there are users who might be charging from 0% to 90%. Again, there are some users who use the electronic device till the battery discharges fully and charges on a daily basis to 50%. So the charging habit of each user is different and there are multiple users who are doing partial charging of the electronic device. But the users who charge the phones from 0% to 100% are minimal and the charging habits of users are different. There are users who usually do partial charging of the electronic device and hence definitely there is a need to construct the full charge profile information of the battery or the battery system from a partial charge profile information (small chunks of information about charging pattern of the battery) for quantification of capacitive loss and resistive loss of the battery in an efficient manner and thus for the accurate state estimation of the battery.

Thus, it is desired to address the above mentioned disadvantages or other shortcomings or at least provide a useful alternative.

The principal object of the embodiments herein is to provide a system and method for state estimation of a battery.

Another object of the embodiments herein is to construct a full charge profile of the battery from a partial charge profile of the battery.

Another object of the embodiments herein is to use the full charge profile of the battery to generate a simple matrix of values associated with the full charge profile.

Another object of the embodiments herein is to describe the full charge profile of the battery using a combination of four Gaussian distributions.

Another object of the embodiments herein is to estimate the state of the battery with more than 99% accuracy.

Another object of the embodiments herein is to quantify capacitive loss and resistive loss of the battery for accurate estimation of state of the battery.

Accordingly the embodiments herein disclose a method for state estimation of a battery of an electronic device. The method includes determining a partial charge profile of the battery. Further, the method includes determining a plurality of charging profile parameters based on the partial charge profile. Further, the method includes constructing a full charge profile of the battery using the plurality of the charging profile parameters. Further, the method includes determining a state of the battery based on a comparison between the full charge profile of the battery with a full charge profile of a reference battery.

Accordingly the embodiments herein provide the electronic device for state estimation of the battery. The electronic device includes a memory, a processor, and a battery management system (BMS), where the BMS is coupled to the memory and the processor. The BMS is configured to determine a partial charge profile of the battery of the electronic device. The BMS is configured to determine a plurality of charging profile parameters based on the partial charge profile. The BMS is configured to construct a full charge profile of the battery using the plurality of the charging profile parameters. The BMS is configured to determine a state of the battery based on the comparison between the full charge profile of the battery with a full charge profile of a reference battery.

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 preferred 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.

This invention is 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 block diagram illustrating an electronic device for state estimation of a battery, according to an embodiment as disclosed herein;

FIG.2 is a flow diagram illustrating a method for state estimation of the battery, according to an embodiment as disclosed herein;

FIG.3 is a schematic diagram illustrating the method for state estimation of the battery, according to an embodiment as disclosed herein;

FIG.4 is an example diagram illustrating use of a partial charge profile to construct a full charge profile of the battery, according to an embodiment as disclosed herein;

FIG.5A is a graphical representation showing an accuracy of the proposed method in predicting a charging profile information of the battery using the partial charge profile of 0 - 33%, according to an embodiment as disclosed herein;

FIG.5B is a graphical representation showing the accuracy of the proposed method in predicting the charging profile information of the battery using the partial charge profile of 34% - 67%, according to an embodiment as disclosed herein;

FIG.5C is a graphical representation showing the accuracy of the proposed method in predicting the charging profile information of the battery using the partial charge profile of 68% - 100%, according to an embodiment as disclosed herein;

FIG.5D is a graphical representation showing the accuracy of the proposed method in predicting the charging profile information of the battery using the partial charge profile of 40% - 80%, according to an embodiment as disclosed herein;

FIG.5E is a graphical representation showing the accuracy of the proposed method in predicting the charging profile information of the battery using a full charge profile of 0% - 100%, according to an embodiment as disclosed herein;

FIG.6 is a graphical representation of the accuracy of the proposed method in predicting the full charging profile information of the battery using various charge profiles, according to an embodiment as disclosed herein; and

FIG.7 is a flow diagram illustrating an example scenario of detection of faulty battery using the proposed method, according to an embodiment as disclosed herein.

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. Also, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments can be combined with one or more other embodiments to form new embodiments. The term “or” as used herein, refers to a non-exclusive or, unless otherwise indicated. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those skilled in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

As is traditional in the field, embodiments may be described and illustrated in terms of blocks which carry out a described function or functions. These blocks, which may be referred to herein as units or modules or the like, are physically implemented by analog or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits and the like, and may optionally be driven by firmware and software. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like. The circuits constituting a block may be implemented by dedicated hardware, or by a processor (e.g., one or more programmed microprocessors and associated circuitry), or by a combination of dedicated hardware to perform some functions of the block and a processor to perform other functions of the block. Each block of the embodiments may be physically separated into two or more interacting and discrete blocks without departing from the scope of the disclosure. Likewise, the blocks of the embodiments may be physically combined into more complex blocks without departing from the scope of the disclosure.

The accompanying drawings are used to help easily understand various technical features and it should be understood that the embodiments presented herein are not limited by the accompanying drawings. As such, the present disclosure should be construed to extend to any alterations, equivalents and substitutes in addition to those which are particularly set out in the accompanying drawings. Although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are generally only used to distinguish one element from another.

Accordingly the embodiments herein disclose a system and method for state estimation of a battery of an electronic device. The method includes determining a partial charge profile of the battery. Further, the method includes determining a plurality of charging profile parameters based on the partial charge profile. Further, the method includes constructing a full charge profile of the battery using the plurality of the charging profile parameters. Further, the method includes determining a state of the battery based on a comparison between the full charge profile of the battery with a full charge profile of a reference battery.

In an embodiment, determining the partial charge profile of the battery includes recording a charging profile information of the battery during charging the battery of the electronic device, storing the charging profile information for a predetermined period of time and determining the partial charge profile using the charging profile information.

In an embodiment, determining the plurality of charging profile parameters from the partial charge profile includes determining a plurality of attributes of the full charge profile of the battery from the partial charge profile using a combination of a plurality of Gaussian distributions and determining the plurality of charging profile parameters using the plurality of attributes of the full charge profile of the battery.

In an embodiment, constructing the full charge profile of the battery using the plurality of the charging profile parameters includes predicting a missing charging profile information in the partial charge profile of the battery using the plurality of charging profile parameters and constructing the full charge profile of the battery using the missing charging profile information in the partial charge profile of the battery.

The embodiment further includes determining whether the plurality of charging profile parameters of the battery are distinct from default charging parameters associated with the reference battery. Further the embodiment includes identifying the battery is faulty. Further the embodiment includes displaying a notification on the electronic device indicating that the battery is faulty.

Referring now to the drawings and more particularly to FIGS.1 through 7, where similar reference characters denote corresponding features consistently throughout the figure, these are shown preferred embodiments.

FIG.1 is a block diagram illustrating an electronic device 100 for state estimation of a battery 150, according to an embodiment as disclosed herein. An example for the electronic device 100 is, but not limited to a mobile phone, a smart phone, a tablet computer, a handheld device, a laptop, a wearable computing device, an Internet of Things (IoT) device, a digital camera, a device or an apparatus for estimating state of a battery pack of an electric vehicle or a battery state estimation device for electric vehicles, or the like.

Referring to the FIG.1, the electronic device 100 includes a communicator 110, a memory 120, a processor 130, a battery management system (BMS) 140, and the battery 150.

The communicator 110 is configured for communicating internally between internal units and external devices via one or more networks. The memory 120 stores instructions to be executed by the processor 130. The processor 130 is configured to execute instructions stored in the memory 120 and to perform various operations.

The memory 120 may include one or more computer-readable storage media. The memory 120 may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard disc, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory 120 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 that the memory 120 is non-movable. In some examples, the memory 120 can be configured to store larger amounts of information than a memory 120. 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 BMS 140 is coupled to the memory 120, the processor 130 and the battery 150. The BMS 140 is an electronic system which manages the battery 150, for example, a rechargeable battery (a cell or the battery pack). The BMS 140 is configured to manage the charging and discharging of the battery 150, to provide notifications on a status of the battery 150 and also provide critical safeguards to protect the battery 150 from damage. The battery 150 may be the rechargeable battery. An example of the rechargeable battery may be a Lithium Ion Battery (LIB).

The BMS 140 is configured to determine a partial charge profile of the battery 150. For example, when a user charges the battery 150, the BMS 140 records a charging profile information of the battery 150 and the BMS 140 stores the charging profile information of the user for a certain period of time, for example, for one month or one year. Thus the BMS 140 determines the partial charge profile from the battery 150.

The BMS 140 is configured to determine a plurality of charging profile parameters based on the partial charge profile. The BMS 140 determines features or different attributes of a full charge profile of the battery 150 from the partial charge profile using a combination of four Gaussian distributions. The plurality of charging profile parameters from the partial charge profile is associated with the full charge profile of the battery 150. The combination of four Gaussian distributions or profiles is used to describe the full charge profile of the battery 150. The combination of four Gaussian distributions or profiles which is used to describe the full charge profile of the battery 150 creates the plurality of charging profile parameters (a matrix with elements which characterizes the full charge profile of the battery 150).

The BMS 140 is configured to construct the full charge profile of the battery 150 using the plurality of the charging profile parameters. The BMS 140 predicts a missing charging profile information in the partial charge profile of the battery 150 using the plurality of the charging profile parameters and constructs the full charge profile of the battery 150. For example, if the partial charge profile is from 20 % to 80%, since the full charge profile is from 0 % to 100%, the missing information in the partial charge profile that is from 0 to 20% and from 80 to 100% is constructed using the combination of four Gaussian profiles which is used to describe the full charge profile of the battery 150.

The BMS 140 is configured to compare the constructed full charge profile of the battery 150 with a full charge profile of a reference battery. The reference battery (a fresh battery) is the battery in its fresh condition. The manufacturer of the battery gives the full charge profile of the fresh battery. The full charge profile of the fresh battery is readily available on cloud or on the electronic device 100. The full charge profile of the battery 150 is compared with the full charge profile of the reference battery. The BMS 140 is further configured to determine a state of the battery 150. The BMS 140 includes a state estimation unit 142. The state estimation unit 142 estimates the state of the battery 150, wherein the state estimation unit 142 estimates a capacitive loss and a resistive loss of the battery 150.

In an embodiment, the BMS 140 is further configured to determine faulty cells (further explained in FIG.7) based on whether the plurality of charging profile parameters of the battery are completely distinct from default charging parameters associated with the reference battery. Further the BMS 140 may also prompt the display of the electronic device 100 for displaying a notification indicating that the battery is faulty. Therefore, unlike to the conventional methods and systems where the user gets to know of the faulty battery only after the battery malfunctions, in the proposed method the faulty battery can be identified and the user can be alerted in advance.

Although the FIG.1 shows the hardware elements of the electronic device 100 but it is to be understood that other embodiments are not limited thereon. In other embodiments, the electronic device 100 may include less or more number of elements. Further, the labels or names of the elements are used only for illustrative purpose and does not limit the scope of the invention. One or more components can be combined together to perform same or substantially similar function for state estimation of the battery 150 of the electronic device 100.

FIG.2 is a flow diagram 200 illustrating a method for the state estimation of the battery 150, according to an embodiment as disclosed herein.

Referring to the FIG.2, at step 202, the electronic device 100 determines the partial charge profile of the battery 150. In an embodiment, the BMS 140 of the electronic device 100 is configured to determine the partial charge profile of the battery 150.

At step 204, the electronic device 100 determines the plurality of charging profile parameters based on the partial charge profile. In an embodiment, the BMS 140 of the electronic device 100 is configured to determine the plurality of charging profile parameters based on the partial charge profile.

At step 206, the electronic device 100 constructs the full charge profile of the battery 150 using the plurality of the charging profile parameters. In an embodiment, the BMS 140 of the electronic device 100 is configured to construct the full charge profile of the battery 150 using the plurality of the charging profile parameters.

At step 208, the electronic device 100 determines the state of the battery 150 based on the comparison between the full charge profile of the battery 150 and the full charge profile of the reference battery. In an embodiment, the BMS 140 of the electronic device 100 is configured to determine the state of the battery 150 based on the comparison between the full charge profile of the battery 150 and the full charge profile of the reference battery.

The various actions, acts, blocks, steps, or the like in the method may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention.

FIG.3 is a schematic diagram illustrating the method for state estimation of the battery 150, according to an embodiment as disclosed herein.

Referring to the FIG.3, the method for state estimation of the battery 150 may be prompted by the user whenever the user of the electronic device 100 wants to know the state of the battery 150 (state of health (SOH) or state of charge (SOC) or state of power (SOP)) that is based on the user’s convenience the method is prompted. In an example, the electronic device 100 may be the smart phone. When the user charges the battery 150, the BMS 140 records the charging profile information of the battery 150 and the BMS 140 stores the recorded charging profile information of the user of a predefined period. In an example, the predefined period may be two weeks or one month or one year. A charging profile information means the variation of voltage of the battery 150 with respect to time or state of charge (SOC). As the battery 150 is charged, the voltage increases and saturates to a value after a certain point of time or state of charge. The information is called as a full charge profile or a full charging profile information, when voltage goes from its minimum value (depends on the battery chemistry) to a desired maximum value, as a function of time or state of charge (SOC).

In a practical scenario the user charges the electronic device 100, for example, from 30% to 80% for one day and from 0% to 90% the next day that is the user may not always charge the electronic device 100 from 0% to 100%. The user may be charging the electronic device 100 partially where by on a daily basis the electronic device 100 is charged from random percentage of say 30% to say 80%. The inbuilt BMS 140 records and stores a partial charge profile of charging which is done over the last couple of weeks or over the period of one month, or the like. The partial charge profile or the partial charging profile information includes the information of partial charging of the battery 150. In an example, if the user charges the battery 150 from 30% to 80%, then from 30% to 80% may be the partial charge profile. The BMS 140 also records and stores the full charge profile that is from 0% to 100%.

The reference battery (the fresh battery) is the battery in its fresh condition. A battery manufacturer gives the full charge profile of the fresh battery. The full charge profile of the fresh battery is used to generate a simple matrix of values associated with the full charge profile using a simple Gaussian expression. The Gaussian expression can be defined by the equation as below:

, wherein:

y = time

x = voltage

ai, bi, ci are parameters characterizing a specific battery chemistry.

The sum of four Gaussian expressions or distributions describes the full charge profile of the specific battery chemistry or the fresh battery. The full charge profile is modelled using the Gaussian expression. The Gaussian expression is used to generate a table of parameter values (a simple matrix of values associated with the full charge profile) specific to the battery chemistry or the fresh battery, which can be stored on board or on the cloud. Thus, a database of unique parameters relevant to the specific battery chemistry is available anytime.

When the user of the electronic device 100 wants to know the state of the battery 150, as shown in step 302, the BMS 140 takes lengthiest charge profile available, in an example, the BMS 140 takes 20% to 90%, if charge profile of 20% to 90% is available. The proposed method takes lengthiest charge profile available or the partial charging profile information which has a minimum of 25% information from the recent history and reconstructs the entire full charging profile information from the partial charging profile information.

The full charge profile is modelled using the Gaussian expression. In the proposed method, the full charge profile of the battery 150 is constructed from the partial charge profile of the battery 150 using the Gaussian expression. The Gaussian expression is used to model the full charge profile of the battery 150. So given the partial charge profile of the battery 150 which has a minimum of 25% information, the Gaussian expression constructs the full charge profile and generates charging profile parameters which is associated with the full charge profile. The combination of 4 Gaussian profiles is used to describe the nuances or different attributes of a charge profile.

In the proposed method of state estimation, the full charge profile of the battery 150 is fully characterized using the simple but novel profile that is sum of 4 Gaussian expressions or distributions. The combination of four Gaussian distributions or profiles is used to describe the full charge profile of the battery 150. The combination of four Gaussian distributions or profiles which is used to describe the full charge profile of the battery 150 also creates the plurality of charging profile parameters (a matrix with elements which characterizes the full charge profile of the battery 150 (battery parameters)). At step 304, the electronic device 100 constructs the full charge profile of the battery 150 from the partial charge profile of the battery 150 and generates the charging profile parameters of the battery 150.

As shown in FIG.3, at step 306, the full charge profile of the fresh battery is readily available on cloud or on the electronic device 100. As shown in FIG.3, at step 306 the unique parameters or the fresh battery parameters relevant to the specific battery chemistry or the fresh battery are A1, B1, C1, A2, B2, C2, A3, B3, C3, A4, B4, C4. These unique parameters are different for different battery chemistries and can be also used to identify different battery chemistries.

The unique parameters or the reference parameters act as reference values of the specific battery chemistry. The degradation of the battery 150 of the same family of the specific battery chemistry after usage over a period of one year may be found out by the comparing the charging profile parameters of the battery 150 generated using the Gaussian expression and the unique parameters or the reference parameters (fresh battery parameters) of the fresh battery. If the charging profile parameters of the battery 150 used for the period of one year is different from the unique parameters or the reference parameters of the fresh battery, then the battery 150 is degraded. At step 308, the electronic device 100 estimates the state of the battery 150 based on the comparison between the constructed full charge profile of the battery 150 with the full charge profile of the reference battery or the fresh battery.

Now for accurate estimation of the state of the battery 150, the full charge profile of the fresh battery (voltage level of the fresh battery in the x axis and time taken to charge the fresh battery in the y axis) is compared with the full charge profile of the battery 150 (voltage level of the battery 150 in the x axis and time taken to charge the battery 150 in the y axis) for which the state is to be estimated. While comparing the full charge profile of the battery 150 with the full charge profile of the reference battery or the fresh battery, shrinkage of time axis of the battery 150 with respect to the fresh battery and shrinkage of voltage axis of the battery 150 with respect to the fresh battery is accounted. Shrinkage of time axis gives capacitive loss of the battery 150 and shrinkage of voltage axis gives resistive loss of the battery 150.

Thus accurate quantification of capacitive loss and resistive loss accurately estimates the states of the battery 150. The quantification of capacitive loss and resistive loss gives the state of health (SOH) of the battery 150. For example, by knowing the resistive loss and capacitive loss, how much capacity the battery 150 is providing as compared to the fresh battery is determined, which is actually the state of health of the battery 150. In an example, if the fresh battery provides a capacity of 5000 mAH, now after quantifying the losses if the battery 150 is providing only 4000 mAH, then even if the battery 150 is charged to 100%, the battery 150 provides only the capacity of 4000 mAH. The state of health (SOH) in this case = (4000/5000) × 100 = 80% that is the state of health is 80% as compared to the fresh battery. Similarly the state of power (SOP) is found by comparing the power rating of the battery 150 with respect to the fresh battery. The SOH and SOP are used to quantify the degradations in the battery 150.

In an embodiment, resistive loss or shrinkage of voltage of the battery 150 with respect to the fresh battery may also be predicted using differential capacity analysis (dQ/dV) or by using dt/dV information.

FIG.4 is an example diagram illustrating use of the partial charge profile to construct the full charge profile of the battery 150, according to an embodiment as disclosed herein.

Referring to the FIG.4, the electronic device 100 obtains the partial charge profile from the battery 150. The combination of four Gaussian distributions or profiles is used to describe the full charge profile of the battery 150. The combination of 4 Gaussian profiles is used to describe the nuances or different attributes of the charge profile.

In the example shown, at step 402, partial charge profile of 68% - 100% is used (this can be 0-33%; 40-70% or any parcel of information which has a span of at least 25%).

The combination of four Gaussian distributions calculates the plurality of charging profile parameters from the obtained partial charge profile (matrix with elements which characterizes the full charge profile of the battery 150). The electronic device 100 predicts the missing charging profile information of the obtained partial charge profile of the battery 150 using the calculated plurality of the charging profile parameters. The partial charging profile information may not be the full charging profile information. For example, the partial charge profile may be from 20 % to 80% but the full charge profile is always from 0 to 100%. Thus the missing charge profile (missing information) in the partial charge profile, from 0% to 20% and from 80% to 100%, is constructed using the Gaussian expression, which is always followed by the full charge profile of the battery 150. At step 404, the electronic device 100 constructs the full charge profile (0% -100%) of the battery 150 using the calculated plurality of the charging profile parameters.

FIG.5A is a graphical representation showing an accuracy of the proposed method in predicting the charging profile information of the battery 150 using the partial charge profile of 0% - 33%, according to an embodiment as disclosed herein.

FIG.5B is a graphical representation showing the accuracy of the proposed method in predicting the charging profile information of the battery 150 using the partial charge profile of 34% - 67%, according to an embodiment as disclosed herein.

FIG.5C is a graphical representation showing the accuracy of the proposed method in predicting the charging profile information of the battery 150 using the partial charge profile of 68% - 100%, according to an embodiment as disclosed herein.

FIG.5D is a graphical representation showing the accuracy of the proposed method in predicting the charging profile information of the battery 150 using the partial charge profile of 40% - 80%, according to an embodiment as disclosed herein.

FIG.5E is a graphical representation showing the accuracy of the proposed method in predicting the charging profile information of the battery 150 using the full charge profile of 0% - 100%, according to an embodiment as disclosed herein.

Referring to the FIGS. 5A-5E, the charging profile information predicted with the proposed method is aligning with original data (charge profile) obtained from the battery manufacturer which shows the accuracy of the proposed method in predicting the charging profile information of the battery using the partial charge profile and the full charge profile.

FIG.6 is a graphical representation of the accuracy of the proposed method in predicting the full charging profile information of the battery 150 using various charge profiles, according to an embodiment as disclosed herein. Referring to the FIG.6, the proposed method works very well for different parcels of information (the partial charge profile or the full charge profile) spanning the entire charge profile (0-33%; 67-100% or any parcel of information which has a span of at least 25%). As seen in FIG.6, the proposed method of predicting the full charging profile information of the battery 150 using the partial charge profile is very accurate, since all the full charging profile information is aligning with the original data of the full charge profile given by the battery manufacturer. The proposed method also predicts SOH for the different parcels of information with more than 99% accuracy as shown in the Table 1.

Table 1 shows the prediction error for all the range of SOH and partial data (the partial charging profile information) available. For all the range of SOH and partial data available the accuracy is > 99%.

Referring to the FIG.7, the battery manufacturer gives the full charge profile of the fresh battery or the fresh cell. The method includes generating the fresh battery parameters or fresh cell parameters or healthy battery parameters using the Gaussian expression. At step 702, the method includes generating charging profile parameters or the battery parameters or cell parameters of the battery 150 using the Gaussian expression. In an embodiment, the method allows the BMS 140 to generate the charging profile parameters or the battery parameters or the cell parameters of the battery 150 using the Gaussian expression. At step 704, the method includes comparing the battery parameters with the fresh battery parameters. In an embodiment, the method allows the BMS 140 to compare the battery parameters with the fresh battery parameters. At step 706, based on the comparison of the battery parameters with the fresh battery parameters, the method includes determining whether the battery parameters of the battery 150 are entirely different from the fresh battery parameters. In an embodiment, the method allows the BMS 140 to determine whether the battery parameters of the battery 150 are entirely different from the fresh battery parameters. At step 708, on determining that the battery parameters of the battery 150 are entirely different from the fresh battery parameters, the battery 150 is identified as faulty. In an embodiment, the method allows the BMS 140 to identify the battery 150 as faulty, on determining that the battery parameters of the battery 150 are entirely different from the fresh battery parameters. At step 710, on determining that the battery parameters of the battery 150 are same as fresh battery parameters, the battery 150 is identified as a healthy battery. In an embodiment, the method allows the BMS 140 to identify the battery 150 as the healthy battery, on determining that the battery parameters of the battery 150 are same as fresh battery parameters. The identification of faulty battery using the proposed method also the helps the battery manufacturers in identifying the issues in the battery 150 at production stage which may also help the battery manufacturers in reducing the economic losses.

The proposed method is also used in comparison of similar battery chemistries or across similar cells. The battery parameters across similar battery chemistries are generated using the Gaussian expression, since the battery parameters generated for similar battery chemistries should ideally match. The battery parameters are then compared. If the battery parameters of one of them are different then using the database of fresh battery parameters which is available on the cloud or on the electronic device 100, fault in the battery 150 may be identified, if the battery parameters are different from fresh battery parameters.

The proposed method is also used in comparison of different battery chemistries or across different cells. The battery parameters are then compared. The battery parameters are different since the battery chemistries are different. The battery chemistry may also be identified based on the battery parameters. Comparison of power rating, capacity rating of different battery chemistries is also possible using the generated battery parameters.

In the proposed method, the database of fresh battery parameters for different battery chemistries is available on the cloud or on the electronic device 100 and can be used to identify whether the battery 150 is faulty or in identifying the battery chemistry.

In the proposed method, the availability of the database of fresh battery parameters for different battery chemistries helps the user in accessing the fresh battery parameters easily. With prior knowledge of the fresh battery parameters, which is downloadable from the cloud or available on the electronic device 100 for the concerned battery chemistry, the proposed method predicts the states of the battery 150 accurately.

The proposed method is computationally very efficient, very inexpensive, accurate and small on size, which can be implemented on board in the electronic device 100 for accurate estimation of state of the battery 150. The proposed method is chemistry independent method, and is physics based method with the base at the heart of battery dynamics.

Unlike existing systems, the proposed method for state estimation determines complete SOH during entire cycle life of the battery 150 (including prediction of end of life of the battery 150). The proposed method predicts both resistive and capacitive losses by fitting partial charge profiles or curves to Gaussian expressions and Incremental Capacity Analysis (ICA) which is only one of the methods to generate SOH information. The proposed method resolves both reversible and irreversible components of the capacity loss as well as the resistive losses, leading to complete SOH information and tracking (including end of life). The proposed method gives accurate state estimation irrespective of the battery 150 in linear or non-linear capacity fade region. The proposed method accurately estimates SOH during entire cycle life of the battery 150 using partial charging information.

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 elements. The elements shown in the FIGS. 1 to 7 include blocks, elements, actions, acts, steps, or the like 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.

Claims

A method for state estimation of a battery of an electronic device, the method comprising:

determining, by the electronic device, a partial charge profile of the battery;

determining, by the electronic device, a plurality of charging profile parameters based on the partial charge profile;

constructing, by the electronic device, a full charge profile of the battery using the plurality of charging profile parameters; and

determining, by the electronic device, a state of the battery based on a comparison between the full charge profile of the battery with a full charge profile of a reference battery.
The method as claimed in claim 1, wherein determining, by the electronic device, the partial charge profile of the battery comprises:

recording, by the electronic device, a charging profile information of the battery during charging the battery of the electronic device;

storing, by the electronic device, the charging profile information for a predetermined period of time; and

determining, by the electronic device, the partial charge profile using the charging profile information.
The method as claimed in claim 1, wherein determining, by the electronic device, the plurality of charging profile parameters from the partial charge profile comprises:

determining, by the electronic device, a plurality of attributes of the full charge profile of the battery based on the partial charge profile of the battery using a combination of a plurality of Gaussian distributions; and

determining, by the electronic device, the plurality of charging profile parameters using the plurality of attributes of the full charge profile of the battery.
The method as claimed in claim 1, wherein constructing, by the electronic device, the full charge profile of the battery using the plurality of charging profile parameters comprises:

predicting, by the electronic device, a missing charging profile information in the partial charge profile of the battery using the plurality of charging profile parameters; and

constructing, by the electronic device, the full charge profile of the battery using the missing charging profile information in the partial charge profile of the battery.
The method as claimed in claim 1, further comprising:

determining, by the electronic device, that the plurality of charging profile parameters of the battery are distinct from default charging parameters associated with the reference battery;

identifying, by the electronic device, the battery is faulty; and

displaying, by the electronic device, a notification on the electronic device indicating that the battery is faulty.
An electronic device for state estimation of a battery, the electronic device comprising:

a memory;

a processor; and

a battery management system (BMS), coupled to the memory and the processor, wherein the BMS configured to:

determine a partial charge profile of the battery of the electronic device;

determine a plurality of charging profile parameters based on the partial charge profile;

construct a full charge profile of the battery using the plurality of charging profile parameters; and

determine a state of the battery based on a comparison between the full charge profile of the battery with a full charge profile of a reference battery.
The electronic device as claimed in claim 6, wherein the BMS is configured to determine the partial charge profile of the battery by:

recording a charging profile information of the battery during charging the battery of the electronic device;

storing the charging profile information for a predetermined period of time; and

determining the partial charge profile using the charging profile information.
The electronic device as claimed in claim 6, wherein the BMS is configured to determine the plurality of charging profile parameters from the partial charge profile by:

determining a plurality of attributes of the full charge profile of the battery based on the partial charge profile of the battery using a combination of a plurality of Gaussian distributions; and

determining the plurality of charging profile parameters using the plurality of attributes of the full charge profile of the battery.
The electronic device as claimed in claim 6, wherein the BMS is configured to construct the full charge profile of the battery using the plurality of charging profile parameters by:

predicting a missing charging profile information in the partial charge profile of the battery using the plurality of charging profile parameters; and

constructing the full charge profile of the battery using the missing charging profile information in the partial charge profile of the battery.
The electronic device as claimed in claim 6, wherein the BMS is further configured to:

determine that the plurality of charging profile parameters of the battery are distinct from default charging parameters associated with the reference battery;

identify the battery is faulty; and

display a notification on the electronic device indicating that the battery is faulty.