Description:
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION
A SYSTEM AND A METHOD FOR PREDICTION OF HEALTH OF A BATTERY
2. APPLICANT(S)
NAME NATIONALITY ADDRESS
MAHINDRA SUSTEN PRIVATE LIMITED INDIAN MAHINDRA SUSTEN PRIVATE LIMITED, 6TH FLOOR, TOWER B, EMBASSY 247 PARK, LAL BAHADUR SHASTRI ROAD, GANDHINAGAR, VIKHROLI (WEST), MUMBAI-400079
3. PREAMBLE TO THE DESCRIPTION

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE NATURE OF THIS INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED.

FIELD OF THE INVENTION
The present disclosure relates to prediction techniques of battery life cycle, and more particularly relates to a system and a method for prediction of health of a battery.
BACKGROUND OF THE INVENTION
With growing importance on green technology, battery to replace traditional energy systems is gaining popularity. Especially in the fields of transportation and utility, there is a fast development of extensive use of battery packs/containers tapping lithium-ion powered cells and lately sodium-ion powered cells as energy source. For satisfactory battery performance, it is essential to monitor the battery health by means of its capacity and internal resistance. For well-meaning/worthy operational efficiency of electric vehicles (EVs) and energy storage system (ESS), the battery pack/container used for this should at least exhibit capacity retention of 65%. However, over time the Round-trip-efficiency (RTE) of the battery pack/container decreases, thereby the capacity of the battery pack/container is reduced. Estimation of capacity fade is essential for determining the battery health. An ill estimated battery health results in modest use of portable electronic devices, EV range anxiety, interrupted power supplies and subsequent penalties for utility scale projects. Therefore, accurate prediction of the battery’s health over its life cycle is an essential part of Battery Management System (BMS).
In the above context, it is clear that the determination of battery health plays a crucial role in assessing cells under specific plant load profiles. The process of determination of battery health involves understanding the behavior of cells across varying operating temperatures, charge-discharge rates, and depth of discharge (DoD). The assessment of cell health primarily involves evaluating capacity fade and resistance rise. Capacity fade measures how a specific load profile affects the "energy delivery" throughout the cell's cycle life. Similarly, resistance rise gauges how the "power delivery" is influenced for a particular load profile over the cell's life cycle. Both parameters, i.e. the battery’s capacity fade and the resistance rise are critical factors to be considered in predicting the energy and power delivery of a cell over its cycle life.
In the context of energy storage applications, where C-rates (the rate of charging or discharging the battery) are typically below 0.5C, the impact of resistance rise on power delivery is minimal. Therefore, there is a pressing need for a robust and reliable method to assess capacity fade (battery health) deterioration, especially for utility kind solutions.
The traditional systems and methods are focused on estimating the battery health by considering only few of the related factors and not all the factors are modeled in a single estimation model.
In addition, the traditional systems and methods are not configured to extrapolate capacity fade prediction when limited data is available.
Therefore, there is a need for a system and a method to overcome the above mentioned drawbacks.
SUMMARY OF THE INVENTION
One or more embodiments of the present disclosure provide a system and a method for prediction of capacity deterioration with a chemistry-agnostic approach through battery modeling.
In one aspect of the present invention, a system for predicting health of a battery is disclosed. The system includes one or more processors operatively coupled with a memory. The said memory stored instructions are to be executed by the one or more processors. The system is configured to conduct a plurality of cell tests on the battery based on a plurality of predefined values. The plurality of predefined values includes at least one of different temperatures (T), discharge rate (C-rate) and depth of discharge (DoD). The system is further configured to determine Reference Performance Test (RPT) data and one or more parameters based on the plurality of cell tests conducted. The system is further configured to generate a cell prediction model based on the determined RPT data and one or more parameters and predict, the health of the battery using the cell prediction model.
In an embodiment, the system is configured to conduct the plurality of cell tests on the battery based on the plurality of predefined values. The system is configured to conduct a cell cyclic test for each of a plurality of cells of the battery for a predefined number of cycles. Afterwards, the system is configured to rest each of the plurality of cells upon completion of the predefined number of cycles. The system is configured to resume conducting, a RPT to gauge an overall capacity fade for each of a plurality of cells of the battery. Subsequent to conducting the RPT, the system is configured to resume the cell cyclic test until the cell health of the battery reaches End of Life (EoL). The system is then configured to generate the RPT data based on the plurality of predefined values and to estimate the parameters sequentially pertaining to the cell prediction model based on the RPT data using an adaptive filter logic. The parameters pertaining to the cell prediction model includes at least one of an activation energy (Ea), pre-activation energy coefficient (A), slope (z), ß1, ß2, Crate-base, and a. Each of the parameters pertaining to the cell prediction model are estimated sequentially in the order of, C-rate-base, slope (z), activation energy (Ea), pre-activation energy coefficient (A), ß1, ß2 and a.
In another embodiment, the health of the battery pertains to the capacity loss or State-of-Health (SoH) of the battery. The health of the battery is predicted based on at least one of, all the estimated parameters, predefined values, a cumulative discharge capacity of the battery over a period of time stored in Ah parameter, R parameter which is a gas constant, and the cell prediction model.
In another aspect of the present invention, a method for predicting health of a battery is disclosed. The method includes the step of conducting by one or more processors, a plurality of cell tests on the battery based on a plurality of predefined values. The plurality of predefined values includes at least one of different temperatures (T), discharge rate (C-rate) and depth of discharge (DoD). The method further includes the step of determining by the one or more processors, RPT data and one or more parameters based on the plurality of cell tests conducted. The method further includes the step of generating by the one or more processors, a cell prediction model based on the determined RPT data and one or more parameters. The method further includes the step of predicting, by the one or more processors, the health of the battery using the cell prediction model.
In an embodiment, for conducting by the one or more processors, the plurality of cell tests on the battery based on the plurality of predefined values, the method further includes the step of conducting, by one or more processors, a cell cyclic test for each of a plurality of cells of the battery for a predefined number of cycles. Afterwards, the method further includes the step of resting, by the one or more processors, each of the plurality of cells upon completion of the predefined number of cycles. Thereafter, the method further includes the step of conducting by the one or more processors, a Reference Performance Test (RPT) to gauge an overall capacity fade for each of a plurality of cells of the battery. The method further includes the step of generating by the one or more processors, the RPT data based on the plurality of predefined values. Subsequent to conducting the RPT, the method further includes the step of resuming by the one or more processors, the cell cyclic test until the cell health of the battery reaches End of Life (EoL). The method further includes the step of estimating, by the one or more processors, the parameters sequentially pertaining to the cell prediction model based on the test data using an adaptive filter logic.
In another embodiment, the parameters pertaining to the cell prediction model includes at least one of an activation energy (Ea), pre-activation energy coefficient (A), slope (z), ß1, ß2, C rate-base, and a. The parameters pertaining to the cell prediction model are estimated sequentially in the order of, Crate-base, slope (z), activation energy (Ea), pre-activation energy coefficient (A), ß1, ß2 and a.
Other features and aspects of this invention will be apparent from the following description and the accompanying drawings. The features and advantages described in this summary and in the following detailed description are not all-inclusive, and particularly, many additional features and advantages will be apparent to one of ordinary skill in the relevant art, in view of the drawings, specification, and claims hereof. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings further includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
FIG. 1 illustrates a block diagram of a system for prediction of capacity deterioration of a battery, according to various embodiments of the present invention;
FIG. 2 illustrates a flow diagram of a method for prediction of capacity deterioration of a battery, according to various embodiments of the present invention;
FIG. 3 illustrates a flow diagram of a method for conducting plurality of cell tests for prediction of capacity deterioration of a battery, according to various embodiments of the present invention;
FIG. 4 illustrates a flow diagram of a method for creating a cell prediction model of the system in FIG. 1, according to various embodiments of the present invention;
FIG. 5 illustrates a flow diagram of a method for determining values of C rate base, slope (z), A and Ea parameters for the system in FIG. 1, according to various embodiments of the present invention;
FIG. 6 illustrates a method for determining values of parameters ß1 and ß2 for the system in FIG. 1, according to various embodiments of the present invention;
FIG. 7 illustrates a method for determining value of parameter a for the system in FIG. 1, according to various embodiments of the present invention;
FIG. 8A, 8B and 8C illustrate graphical representation of results for the cell prediction model of the system in FIG. 1 for an exemplary embodiment, according to various embodiments of the present invention; and
FIG. 9A, 9B and 9C illustrate graphical representation of results for the prediction model of the system in FIG. 1 for yet another exemplary embodiment, according to various embodiments of the present invention.
The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.
Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
A person of ordinary skill in the art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
As per various embodiments depicted, the present invention discloses a system and a method for prediction of capacity deterioration with a chemistry-agnostic approach through mathematical modeling. The system and the method disclosed are further intended to provide an accurate estimation of the battery health. The system and the method are further configured to evaluate Original Equipment Manufacturer’s (OEM) cells for different applications/use cases such as electric transport vehicles, portable electronics, off-grid and hybrid power solutions. The system and method are further configured to provide an optimized estimation of battery sizing. The system and method are further configured to provide an extrapolated prediction of capacity fade of the battery even when available data is limited. The system and method are further configured to provide an optimized augmentation plan/strategy for the battery in different application/use cases. The system and the method are further configured to provide an optimized battery management system for battery health monitoring.
FIG. 1 illustrates a block diagram of a system 100 for prediction of capacity deterioration of battery, according to various embodiments of the present invention. The system 100 includes a plurality of battery packs 115, a User Interface (UI) 120 and a database 125. For further description, the plurality of battery packs 115 is referred to as the battery 115. The system 100 further includes one or more processors 105 coupled to memory 110 storing executable instructions for prediction of capacity of the battery 115. Operational and construction features of the system 100 will be explained in detail in following paragraphs.
In an embodiment, the battery 115 includes a plurality of cells such as a first cell 115A, a second cell 115B, and a third cell 115C. However, it is to be understood that, each of the plurality of battery 115 should not be construed to the first cell 115A, the second cell 115B, and the third cell 115C only. The battery 115 is one of, but not limited to, an alkaline battery composed of Zinc and manganese dioxide, a Lithium-Ion (Li-ion) battery and a Lithium Polymer (LiPo) battery composed of Lithium compounds (typically lithium cobalt oxide, lithium manganese oxide, or lithium iron phosphate), a Nickel-Metal Hydride (NiMH) battery composed of Nickel oxide hydroxide and a metal hydride (typically nickel, lanthanum, and aluminum), a Nickel Manganese Cobalt battery, a Zinc-Carbon battery composed of Zinc and manganese dioxide, a Sodium-Ion battery Composed of Sodium compounds (e.g., sodium-ion cathode, sodium anode ), a Silver Oxide battery composed of Silver oxide and zinc, a Zinc-Air battery composed of Zinc and oxygen from the air.
In an embodiment, the system 100 may be a standalone system and may be integrated with an application server for including one of laboratory information system (LIS) or a data acquisition system designed to support and streamline various scientific and experimental processes, for data collection and analysis, for experiment control and monitoring. The system 100 may further include Data Acquisition Interfaces (DAI) to connect with various sensors, instruments, and data sources for example include analog-to-digital converters (ADCs) for converting sensor signals into digital data and interfaces for connecting to scientific instruments, one or more experiment control modules employed to perform one of a user controlled and automate experiment, to set up experimental parameters, initiate data collection, and monitor the progress of experiments, one or more real-time monitoring modules, a data storage and management module for storing experimental data, and one or more analysis and visualization module to assist in data interpretation.
In an embodiment, the UI 120 is one of, but not limited to, an user equipment with an interface, a wired device, a wireless device including, by the way of example not limitation, a handheld wireless communication device such as a mobile phone, a smart phone, and a phablet device, an electrical, an electronic, an electro-mechanical or an equipment or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, any other computing device, any type of portable computer, a media playing device, a portable computer system, and/or any other type of computer device with wired and wireless communication capabilities, a computer system with one or more communication ports coupled to one or more communication bus.
The computer device may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as camera, audio aid, a microphone, a keyboard, input devices for receiving input from a user such as touch pad, touch enabled screen, electronic pen and the like.
In an embodiment, the one or more communication ports of the computer system may be, by the way of example not limitation, any of a FireWire (IEEE 1394), a PS/2 Port, Serial Port (RS-232), Thunderbolt, Audio Jacks (3.5 mm),a Display Port, a VGA (Video Graphics Array), a High-Definition Multimedia Interface (HDMI) port, a Ethernet Port (RJ45), a Gigabit using copper or fiber, a parallel port, or other existing or future ports.
In an embodiment, the one or more communication buses may be, but not limited to only the following examples, a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), universal serial bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects a processor to the computer system.
It would be appreciated that the UI 120 may not be restricted to the mentioned devices and various other devices may be used.
In an embodiment, the database 125 of the system 100 is configured to store various data pertaining to the capacity prediction of the battery 115 as a backup and the stored data can be retrieved by the system 100 if required. The database 125 is one of, but is not limited to, one of a centralized database, a cloud-based database, a commercial database, an open-source database, a distributed database, an end-user database, a graphical database, a No-Structured Query Language (NoSQL) database, an object-oriented database, a personal database, an in-memory database, a document-based database, a time series database, a wide column database, a key value database, a search database, a cache database and so forth. The given examples of database 125 types are non-limiting and may not be mutually exclusive e.g., a database can be both commercial and cloud-based, or both relational and open-source, etc.
In an embodiment, the one or more processors 105, hereinafter referred to as the processor 105 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions. As per the illustrated embodiment, among other capabilities, the processor 105 is configured to fetch and execute computer-readable instructions stored in the memory 110.
In an embodiment, the processor 105, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 105. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 105 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for processor 105 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 110 may store instructions that, when executed by the processing resource, implement the processor 105. In such examples, the system 100 may comprise the memory 110 storing the instructions and the processing resource to execute the instructions, or the memory 110 may be separate but accessible to the system 100 and the processing resource. In other examples, the processor 105 may be implemented by electronic circuitry.
In an embodiment, the memory 110 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 110 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
In an embodiment, the one or more processors 105 of the system 100 is configured to predict the capacity of the battery 115 and display the result on the UI 120. For the purpose of predicting the capacity of the battery 115, the one or more processors 105 includes a testing module 130, a determination module 135, a data generation module 140, a prediction module 145, a computation unit 155 and a UI module 150 communicably connected to each other as well as with the database 125 of the system 100.
The testing module 130 of the processor 105 is communicably coupled to the battery 115. The testing module 130 is configured to perform a plurality of cell tests on the battery 115 including the plurality of cells such as the first cell 115A, the second cell 115B, and the third cell 115C, for predicting the capacity of the battery 115. The plurality of tests includes one of the cell cyclic test, and the reference performance test (RPT). The testing module 130 is configured to conduct the plurality of cell tests based on a plurality of predefined values. The plurality of predefined values includes at least one of different temperatures (T), a discharge rate (C-rate) and a depth of discharge (DoD).
The cell cyclic test involves repeatedly charging and discharging the battery 115 in a controlled environment with an intention to develop a mathematical model that can emulate its real-world usage patterns and thus advantageously reduce the experimentation time and capital resource. The cell cyclic test is performed to assess the performance, capacity, and durability of the battery over time for evaluating the longevity and reliability of the battery 115. The cycling condition pertains to number of times the repetitive charging and discharging is to be performed. The cycling condition varies based on the type of the battery 115 and the intended application of the battery 115. For example, a lithium-ion battery used in electric vehicles may undergo more rigorous cycling compared to a battery used in a consumer electronic device. The cell cyclic test is continued until the battery 115 reaches a predefined End-of-Life (EoL) criteria, which is often based on a significant degradation in capacity. In an embodiment, the predefined EoL criteria is considered as about 65% of an original capacity of the battery 115.
The cell cyclic test is performed at different temperatures, (C-rate) discharge rates and temperatures. The cell cyclic test is performed in a sequence of charge-rest-discharge-rest-charge (C-R-D-R-C …) on the battery 115 and the sequence is repeated spanning over a predefined number of cycles set by the testing module 130 based on the battery 115 type and an area of application of the battery 115. The area of application may be one of, but not limited to an electric vehicle, grid storage, portable electronics, an electrical or electronic device, an electro-mechanical device and any such devices or instruments. After the cell cyclic test is performed for the predefined number of cycles, the battery 115 is rested, followed by the RPT. The RPT pertains to tests conducted on a battery 115 in the controlled environment for comparing different test results which enables to gauge overall capacity fade. After the RPT is conducted, the cyclic test resumes till the cell health touches EoL.
The testing module 130 is further configured to transmit information pertaining to the plurality of tests conducted to the determination module 135. The determination module 135 is configured to determine one or more parameters based on the information received from the testing module 130 pertaining to the plurality of cell tests conducted. The determination module 135 is further configured to compose test data based on the one or more parameters determined. The test data corresponds to the RPT data and is available at different temperatures, C-rate and DoD, chemical characteristics and composition of the battery 115, the current during charge-rate tests, and the current during discharge-rate tests. The one or more parameters pertains to at least one of an activation energy (Ea), a pre-activation energy coefficient (A), a slope (z), ß1, and ß2, a C rate-base, and a. The determination module 135 is configured to determine the one or more parameters sequentially in the order of the C rate-base initially, slope (z) next, followed by Ea, A, ß1, ß2 and a.
The activation energy (Ea) is the minimal energy required to initiate the chemical reaction in the battery measured in joules/mol. The Ea can be estimated by the following equation:
E_a=?R×ln???(k_1/k_2 )×(T_1 T_2/? T_1-T_2).
The T_1 and T_2 are temperatures taken at two different time. The k_1 and k_2 are rate constants at temperatures T_1 and T_2 respectively. The rate constant, k, is a proportionality constant that indicates the relationship between the molar concentration of reactants and the rate of a chemical reaction. The rate constant may be found experimentally, using the molar concentrations of the reactants and the order of reaction. The R is an ideal gas constant with a value of 8.3145 J/Kmol.
The C-rate is a measure of the rate at which a battery is charged or discharged relative to its maximum capacity. For example: a battery has a capacity of 100 Amp-hrs and a C-rate of C1 which means that the discharge current will discharge the entire battery in 1 hour. For the battery with a capacity of 100 Amp-hrs, this equates to a discharge current of 100 Amps. And if the C-rate is C20, then it means that the battery will completely discharge the battery in 20 hours and the discharge current is 5 Amps.
The slope (z) pertains to at least one of a voltage slope, a capacity fade slope, a temperature slope, a charge-discharge rate slope, an internal resistance slope, a cyclic stability slope, an activation over-potential slope. The slope is used to refer to the rate of change of the one or more parameters plotted against the plurality of predefined values with respect to throughput. Throughput refers to the cumulative capacity discharged by the battery 115 at any given point in its cycle life. In an embodiment, the slope pertains to the capacity fade slope with respect to the throughput.
The pre-activation energy coefficient (A), ß1, ß2, and a pertains to parameters which are derivable by conducting the plurality of tests on the battery 115 based on the battery 115 type.
In an embodiment, the determination module 135 is configured to determine the C rate base from the information received from the testing module 130 based on the RPT performed with varied temperature keeping discharge rate (C-rate) and DoD constant. The C rate base is computed when the DoD is kept at 100% at a fixed C rate and a varied temperature (T). Under aforementioned conditions, the C rate base value equates to the fixed C rate. Further, the determination module 135 is configured to compute a percentage of capacity loss (% Cap_loss) utilizing the following equation:
% Cap_loss=[ (N_Cap-RPT_Cap)/((N_Cap) ) ]*100
Where, N_Cap is Nominal Capacity, and
RPT_Cap is RPT Capacity
The Nominal capacity pertains to the initial capacity of the battery 115.
The determination module 135 is further configured to determine the natural logarithm of the throughput i.e., the parameter Ah as well as the natural logarithm of the % Cap_loss. The determination module 135 is further configured to compute the value of the slope (z) by linearly fitting the natural logarithm of the throughput i.e., the parameter Ah and the natural logarithm of the % Cap_loss and determining the line slope.
After obtaining the value of C rate base and the slope (z), the determination module is further configured to apply the modified Arrhenius equation using an adaptive filter logic iteratively to obtain the values of Ea, A, ß1, ß2, and a sequentially, in the following manner:
% Cap_loss=A*?exp?^(((-Ea)/(R*T)))??*?Ah?^z ?
By utilizing value of % Cap_loss, R, T and z, the determination module 135 is configured to find out values for Ea and A.
The determination module 135 is further configured to compute values of ß1, and ß2, by utilizing the RPT data with varied discharge rate (C-rate) keeping temperature and DoD constant as well as the determined values of C rate base, z, A, and Ea in the modified Arrhenius equation using an adaptive filter logic in the following equation:
% Cap_loss=(A*C_rate^ß1 )*?exp?^(((-(Ea- ß2*?C_rate?^(C_rate/C_(rate-base) ) ))/(R*T)) )?*?Ah?^z
The determination module 135 is further configured to compute value of alpha (a), by utilizing the RPT data obtained with varied DoD keeping Temperature and discharge rate (C-rate) constant as well as the computed values of C rate base, z, Ea, A, ß1, and ß2, in the modified Arrhenius equation using an adaptive filter logic, in the following equation:
% Cap_loss=(A*C_rate^ß1 )*?exp?^(((-(Ea + a*(100-DoD)^2- ß2*?C_rate?^(C_rate/C_(rate-base) ) ))/(R*T)) ) *?Ah?^z
Thus the determination module 135 computes information pertaining to the one or more parameters sequentially in the order of C rate-base initially, slope (z) next, followed by Ea, A, ß1, ß2 and a.
Upon determination of the one or more parameters, the determination module 135 is configured to transmit information pertaining to the one or more parameters and the test data received from the testing module 130 to the generation module 140. The generation module 140 is configured to generate a cell prediction model based on the determined test data and the one or more parameters. The cell prediction model is a chemistry agnostic model. The cell prediction model is devised based on a plurality of model parameters. The plurality of model parameters pertains to the one or more parameters extracted using an adaptive filter logic in a modified Arrhenius rate equation. The cell prediction model is devised based on the following equation:
Q_loss=(A*C_rate^ß1 )*?exp?^(((-(Ea + a*(100-DoD)^2- ß2*?C_rate?^(C_rate/C_(rate-base) ) ))/(R*T)) ) *?Ah?^z
The Q_loss pertains to the capacity fade determined for the plurality of predefined values. The Ah is defined as a parameter that stores cumulative discharge capacity of the battery 115 over a period of time. For example, for a battery of 10 Ah cycled for 100 cycles will have a cumulative throughput of 100*10 Ah i.e., 1000 Ah or 1 kAh.,.
The prediction module 145 is connected to the generation module 140 of the processor 105. The prediction module 145 is configured to receive the cell prediction model output from the generation module 140. Upon receiving the cell prediction model output, the prediction module is configured to compute the capacity fade for the battery 115 by utilizing a computation unit 155. The prediction module 145 is configured to store information pertaining to each of the computed capacity fade for the battery 115, the cell prediction model output and the one or more parameters in the database 125 of the system 100. The prediction module 145 is further configured to transmit the computed capacity fade for the battery 115 to the UI module 150 of the processor 105. The prediction module 145 is further configured to predict the health of the battery 115 based on at least one of, all the estimated parameters, the plurality of predefined values, a cumulative discharge capacity of the battery 115 over a period of time stored in Ah parameter, R parameter which is a gas constant, and the cell prediction model.
The UI module 150 is communicably coupled to the UI 120. The UI module 150 is configured to relay the computed capacity fade for the battery 115 to the UI 120 which displays the capacity fade for the battery 115.
Referring to FIG. 2, FIG. 2 illustrates a flow diagram of a method 200 for prediction of capacity deterioration of battery, according to various embodiments of the present invention. The method 200 is adapted to predict the capacity deterioration of the battery by utilizing the mathematical model. The mathematical model is a chemistry agnostic model which takes into account the variability of the one or more parameters relevant for accurate prediction. The one or more parameters pertains to at least one of the activation energy (Ea), the pre-activation energy coefficient (A), the slope (z), ß1, and ß2, C rate-base, and a. For the purpose of description, the method 200 is described with the embodiments as illustrated in FIG. 1 and should nowhere be construed as limiting the scope of the present disclosure.
At step 205, the method 200 includes the step of conducting by one or more processors 105, the plurality of cell tests on the battery 115 based on the plurality of predefined values. The plurality of cell tests includes one of the cell cyclic test, and the reference performance test (RPT). The plurality of predefined values pertains to at least one of different temperatures (T), discharge rate (C-rate) and depth of discharge (DoD). The plurality of cell tests are performed in a unique pattern where the cell cyclic test is performed for predefined number of cycles, then the battery 115 is rested, followed by the RPT. The RPT pertains to tests conducted on a battery 115 in the controlled environment for comparing different test results which enables to gauge overall capacity fade. After the RPT is conducted, the cell cyclic test resumes till the cell health touches EoL.
At step 210, the method 200 includes the step of determining by the one or more processors 105, the test data and one or more parameters based on the plurality of cell tests conducted. The test data pertains to the RPT data available at different temperatures, discharge rate (C-rate) and DoD, the chemical characteristics and composition of the battery, the current during charge-rate tests, and the current during discharge-rate tests. The one or more parameters pertains to the C rate-base, the slope (z), activation energy (Ea), the pre-activation energy coefficient (A), ß1, ß2 and a. The one or more parameters are determined sequentially in the order of C rate-base initially, slope (z) next, and followed by Ea, A, ß1, ß2 and a.
At step 215, the method 200 includes the step of generating, by the one or more processors 105, the cell prediction model based on the determined test data and the one or more parameters. The cell prediction model is a chemistry agnostic mathematical model. The cell prediction model is devised based on a plurality of model parameters. The plurality of model parameters pertains to the one or more parameters extracted using an adaptive filter logic in the modified Arrhenius rate equation.
At step 220, the method 200 includes the step of predicting, by utilizing the component unit 155, the capacity fade for the battery 115 using the cell prediction model. Thereby, the method 200 estimates the health of the battery 115.
FIG. 3 illustrates a flow diagram of a method 300 for conducting plurality of cell tests for prediction of capacity deterioration of battery, according to various embodiments of the present invention. The method 300 describes the cell cyclic test and the RPT test performed on the battery 115 in the controlled environment.
At step 305, the method 300 includes the step of conducting by the one or more processor 105, the cell cyclic test for each of the plurality of cells of the battery 115 for the predefined number of cycles. The predefined number of cycles is set by the system 100 based on the battery 115 compositions and the area of application.
At step 310, the method 300 includes the step of resting by the one or more processor 105, each of the plurality of cells upon completion of the predefined number of cycles.
At step 315, the method 300 includes the step of conducting by the one or more processors 105, the RPT to gauge an overall capacity fade for each of a plurality of cells of the battery 115.
At step 320, the method 300 includes the step of generating by the one or more processors 105, the RPT data based on the plurality of predefined values.
FIG. 4 illustrates a method 400 for creating the cell prediction model according to various embodiments of the present invention.
At step 405, the method 400 includes the step of collecting by the one or more processors 105, information pertaining to the test data. The test data includes the RPT data available at different temperatures, discharge rate (C-rate) and DoD.
At step 410, the method 400 includes the step of determining by the one or more processors 105, the model parameters based on the collected information by estimating the one or more parameters sequentially pertaining to the cell prediction model based on the test data using an adaptive filter logic. The parameters are further determined based on details pertaining to the chemical characteristics and composition of the battery 115, the current during charge-rate tests, and the current during discharge-rate tests as well as the one or more parameters pertains to the Crate-base, the slope (z), activation energy (Ea), the pre-activation energy coefficient (A), ß1, ß2 and a.
At step 415, the method 400 includes creating by the one or more processor 105, the cell prediction model based on the determined model parameters. The method 400 incorporates the determined model parameters in the modified Arrhenius rate equation:
Q_loss=(A*C_rate^ß1 )*?exp?^(((-(Ea + a*(100-DoD)^2- ß2*?C_rate?^(C_rate/C_(rate-base) ) ))/(R*T)) ) *?Ah?^z
The cell prediction model of the system 100 is a chemistry agnostic model which is configured to estimate the capacity fade of the battery 115. Additionally, the cell prediction model is configured to evaluate different OEM’s battery solutions for renewable energy tenders having more than 25 years life span while further de-risking capital intensive asset, by providing an accurate capacity fade prediction. The cell prediction model of the system 100 is further configured to optimize battery sizing for different use-cases i.e., EVs, portable electronics, energy storage systems etc.
The cell prediction model of the system 100 is further configured to aid in comprehending an augmentation plan/strategy for the battery 115 and to evaluate cycle-life of battery energy storage systems (BESS) including the battery 115, based on different use-cases and the estimated capacity fade of the battery 115.
For example, For the EVs use-case, the cell prediction model of the system 100 is configured to predict capacity fade for various drive cycle patterns given that the drive cycle pattern i.e., load pattern, DoD and temperature profile of the use-case is shared.
Similarly, for ESS use-case including the battery 115, the cell prediction model of the system 100 is configured to evaluate different OEMs. For ESS use-case, the cell prediction model is also configured to predict the optimum sizing of the battery 115 and thereby, comprehending the battery augmentation strategy.
In an embodiment, the system 100 is further configured to predict the capacity fade of the battery 115 by considering temperature, composition of battery 115 even when available data is limited. The system 100 is further configured to extrapolate the OEM evaluation, and augmentation plan/strategy preparation based limitedly available data.
In FIG. 5 a method 500 for determining values of C rate base, z, Ea and A, according to various embodiments of the present invention, is illustrated.
At step 505, the method 500 includes the step of determining by the one or more processor 105, the RPT data with varied temperature keeping discharge rate (C-rate) and DoD constant.
At step 510, the method 500 includes the step of determining by the one or more processor 105, the value of C rate base by utilizing the determined RPT data. The C rate base is computed when the DoD is kept at 100% at a fixed C rate and a varied temperature (T). The C rate base value equates to the fixed C rate.
At step 515, the method 500 includes the step of computing by the one or more processor 105 the value of the capacity fade slope (z). The value of the capacity fade slope (z) is computed by fitting the natural logarithm of the throughput i.e., the parameter Ah and the natural logarithm of the % Cap loss and determining the line slope.
At step 520, the method 500 includes the step of computing by the one or more processor 105, the modified Arrhenius equation using an adaptive filter logic iteratively to obtain the values of Ea, A sequentially, after obtaining the value of C rate base and z.
FIG. 6 illustrates a method 600 for determining values of ß1, and ß2, according to various embodiments of the present invention. The method 600 utilizes the values of C rate base, z, Ea and A determined at method 500 in the modified Arrhenius Equation to compute the values of ß1, and ß2.
At step 605, the method includes the step of determining the RPT data with varied discharge rate (C-rate) keeping Temperature and DoD constant.
At step 610, the method 600 includes the step of utilizing the values of C rate base, z, A, and Ea determined at method 500 in the modified Arrhenius equation using an adaptive filter logic as given below:
% Cap_loss=(A*C_rate^ß1 )*?exp?^(((-(Ea- ß2*?C_rate?^(C_rate/C_(rate-base) ) ))/(R*T)) )?*?Ah?^z
At step 615, the method includes the step of determining the values of ß1, and ß2.
FIG. 7 illustrates a method 700 for determining the value of a, according to various embodiments of the present invention.
At step 705, the method 700 includes the step of determining the RPT data with varied DoD keeping Temperature and discharge rate (C-rate) constant.
At step 710, the method 700 includes the step of utilizing the values of C rate base, z, A, and Ea determined at method 500 as well as ß1, and ß2 determined at method 600 in the modified Arrhenius equation using an adaptive filter logic as given below: .
% Cap_loss=(A*C_rate^ß1 )*?exp?^(((-(Ea + a*(100-DoD)^2- ß2*?C_rate?^(C_rate/C_(rate-base) ) ))/(R*T)) ) *?Ah?^z
The system 100 is configured to calculate the one or more parameters sequentially in the order of C rate-base at first, z next, followed by Ea, A, ß1, ß2 and a.
FIG. 8A, 8B and 8C illustrate graphical representation of results for the prediction model of the system in FIG. 1 for an exemplary embodiment, according to various embodiments of the present invention. According to the exemplary embodiment (EXAMPLE 1), the battery 115 is a Lithium Iron Phosphate battery and the battery 115 has the following specifications:
EXAMPLE 1:
Ampere-hour 1.1 Ah
Form-factor Cylindrical 18650
The temperature for the tests 15 degree C, 25 degree C and 35 degree C
The current charge-rate for the tests 0.5C
The current discharge-rate for the tests 0.5C, 1C, 2C and 3C
The depth of discharge (DoD) for the tests 0% - 100% , 20% - 80% and 40% - 60%
Based on the test performed, the capacity fade is observed for the Lithium Iron Phosphate battery against the aforesaid pre-defined values. The capacity fade is observed for change in temperature, the change in discharge rate (C-rate), and the change in DoD. The cell prediction model is configured to provide values corresponding to the capacity fade for change in temperature, the change in discharge rate (C-rate), and the change in DoD which is plotted in graphical manner in the FIG. 8A, 8B and 8C. It has been observed that with increase in temperature, C rate and DoD, the value of capacity fade also increases indicating deterioration of the battery 115 performance.
FIG. 9A, 9B and 9C illustrate graphical representation of results for the prediction model of the system in FIG. 1 for yet another exemplary embodiment (EXAMPLE 2), according to various embodiments of the present invention. According to the exemplary embodiment, the battery 115 is a Nickel Manganese Cobalt (NMC) battery and the battery 115 has the following specifications:
EXAMPLE 2:
Ampere-hour 2.95 Ah
Form-factor Cylindrical 18650
The temperature for the tests 15 degree C, 25 degree C and 35 degree C
The current charge-rate for the tests 0.5C
The current discharge-rate for the tests 0.5C, 1C, 2C and 3C
The depth of discharge (DoD) for the tests 0% - 100% , 20% - 80% and 40% - 60%
Based on the test performed, the capacity fade is observed for NMC battery corresponding to the aforesaid pre-defined values. The capacity fade is observed for change in temperature, the change in discharge rate (C-rate), and the change in DoD. The cell prediction model is configured to provide values corresponding to the capacity fade for the NMC battery against changes in temperature, the change in discharge rate (C-rate), and the change in DoD which is plotted in graphical manner in the FIG. 9A, 9B and 9C. It has been observed that with increase in temperature, discharge rate (C-rate) and DoD, the value of capacity fade also increases indicating deterioration of the battery 115 performance.
A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-9C) are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular function is performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
The present invention offers multiple advantages over the prior art and the above listed are a few examples to emphasize on some of the advantageous features. The listed advantages are to be read in a non-limiting manner.
, C , C , Claims:CLAIMS
We Claim:
1. A method (200) for predicting health of a battery, the method comprises the steps of:
conducting, by one or more processors (105), a plurality of cell tests on the battery based on a plurality of predefined values;
determining, by the one or more processors (105), Reference Performance Test (RPT) data and one or more parameters based on the plurality of cell tests conducted;
generating, by the one or more processors (105), a cell prediction model based on the determined RPT data and one or more parameters; and
predicting, by the one or more processors (105), the health of the battery using the cell prediction model.

2. The method (200) as claimed in claim 1, wherein the plurality of predefined values includes at least one of different temperatures (T), discharge rate (C-rate) and depth of discharge (DoD).

3. The method (200) as claimed in claim 1, wherein the step of conducting, by the one or more processors (105), the plurality of cell tests on the battery based on the plurality of predefined values comprises the steps of:
conducting, by one or more processors (105), a cell cyclic test for each of a plurality of cells (115A, 115B, 115C) of the battery (115) for a predefined number of cycles;
resting, by the one or more processors (105), each of the plurality of cells upon completion of the predefined number of cycles;
conducting, by the one or more processors (105), a Reference Performance Test (RPT) to gauge an overall capacity fade for each of a plurality of cells of the battery;
generating, by the one or more processors (105), the RPT data based on the plurality of predefined values; and
estimating, by the one or more processors (105), the parameters sequentially pertaining to the cell prediction model based on the test data using an adaptive filter logic .

4. The method (200) as claimed in claim 3, wherein the step of, generating, the RPT data based on the plurality of predefined values, includes the step of:
conducting, by the one or more processors, the cell cyclic test until the cell health of the battery reaches End of Life (EoL), subsequent to the RPT.

5. The method (200) as claimed in claim 3, wherein the parameters pertaining to the cell prediction model includes at least one of an activation energy (Ea), pre-activation energy coefficient (A), slope (z), ß1, ß2, C rate-base, and a.

6. The method (200) as claimed in claim 5, wherein each of the parameters pertaining to the cell prediction model are estimated sequentially in the order of, C rate-base, slope (z), activation energy (Ea), pre-activation energy coefficient (A), ß1, ß2 and a.

7. A system (100) for predicting health of a battery, the system (100) comprising:
one or more processors (105) operatively coupled with a memory (110), wherein said memory stores instructions which when executed by the one or more processors (105) causes the one or more processors (105) to:
conduct, a plurality of cell tests on the battery (115) based on a plurality of predefined values;
determine, RPT data and one or more parameters based on the plurality of cell tests conducted;
generate, a cell prediction model based on the determined RPT data and one or more parameters; and
predict, the health of the battery (115) using the cell prediction model.

8. The system (100) as claimed in claim 7, wherein the plurality of predefined values includes at least one of different temperatures (T), discharge rate (C-rate) and depth of discharge (DoD).

9. The system (100) as claimed in claim 7, wherein the one or more processors (105), conducts the plurality of cell tests on the battery based on the plurality of predefined values by:
conducting, a cell cyclic test for each of a plurality of cells (115A, 115B, 115C) of the battery (115) for a predefined number of cycles;
resting, each of the plurality of cells (115A, 115B, 115C) upon completion of the predefined number of cycles;
conducting, a Reference Performance Test (RPT) to gauge an overall capacity fade for each of a plurality of cells (115A, 115B, 115C) of the battery (115);
generating, the RPT data based on the plurality of predefined values; and
estimating, the parameters sequentially pertaining to the cell prediction model based on the test data using an adaptive filter logic.

10. The system (100) as claimed in claim 9, wherein the one or more processors (105), generates, the RPT data based on the plurality of predefined values, by:
conducting, the cell cyclic test until the cell health of the battery reaches End of Life (EoL), subsequent to the RPT.

11. The system (100) as claimed in claim 9, wherein the parameters pertaining to the cell prediction model includes at least one of an activation energy (Ea), pre-activation energy coefficient (A), slope (z), ß1, ß2, C rate-base, and a.

12. The system (100) as claimed in claim 11, wherein each of the parameters pertaining to the cell prediction model are estimated sequentially in the order of, C rate-base, slope (z), activation energy (Ea), pre-activation energy coefficient (A), ß1, ß2 and a.

13. The system (100) as claimed in claim 7, wherein the health of the battery (115) pertains to the capacity loss or State-of-Health (SoH) of the battery (115).

14. The system (100) as claimed in claim 7, wherein the health of the battery (115) is predicted based on at least one of, all the estimated parameters, predefined values, a cumulative discharge capacity of the battery (115) over a period of time stored in Ah parameter, R parameter which is a gas constant, and the cell prediction model.