Battery energy storage (BES) asset management to facilitate transition from battery first life to second life using physics-based modeling

Abstract

A method comprises obtaining historical aging data of a battery that has aged in a first life use; determining, based on the historical aging data, parameters of a physics-based model that represents a first life use aging of the battery; for respective ones of multiple candidate second life uses of the battery, determining parameters of a predictive physics-based model that predicts a second life use aging of the battery, the determining based on the first life use aging of the battery and second life use operating conditions associated with the respective candidate second life use; and selecting a second life use for the battery based on the predicted second life use agings of the battery. A computer-readable medium includes computer-executable instructions, which when executed on one or more processors, perform operations of the method.

Description

[0001]BATTERY ENERGY STORAGE ASSET MANAGEMENT TO FACILITATE TRANSITION FROM FIRST LIFE TO SECOND LIFE USING PHYSICS-BASED MODELING PRIORITY CLAIM This application claims the benefit of the filing date of United States Provisional Patent Application Serial No.63 / 509,201, filed June 20, 2023, for “BATTERY ENERGY STORAGE (BES) ASSET MANAGEMENT TO FACILITATE TRANSITION FROM BATTERY FIRST LIFE TO SECOND LIFE USING PHYSICS-BASED MODELING,” the disclosure of which is hereby incorporated herein in its entirety by this reference. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT This invention was made with government support under Contract Number DE- AC07-05-ID14517 awarded by the United States Department of Energy. The government has certain rights in the invention. TECHNICAL FIELD Examples of the disclosure relate to facilitating transition of a battery from a first service life to a second service life using physics-based modeling. BACKGROUND A battery is to convert stored chemical energy into electrical energy which may be conveyed as a voltage potential. A rechargeable battery may be charged and discharged a relatively large number of times over its lifetime. As a rechargeable battery ages, the storage capacity and conductance of the battery may decrease (e.g., fade) from a Beginning of Life (BOL) to an End of Life (EOL) of the battery. Observations of battery parameters, such as cycle rate (magnitude of current), cumulative cycling time, and storage capacity, may be helpful to determine an overall State of Health (SOH) of the battery over its service life. Certain performance characteristics, such as capacity fade and power loss, may experience losses over the battery’s service life. Such losses may be caused by degradation mechanisms, which are initiated and accelerated by aging conditions imposed on the battery. Aging conditions may include environmental factors, characteristics of the battery itself, and usage conditions of the battery. DISCLOSURE A method comprises obtaining historical aging data of a battery that has aged in a first life use; determining, based on the historical aging data, parameters of a physics-based model that represents a first life use aging of the battery; for respective ones of multiple candidate second life uses of the battery, determining parameters of a predictive physics-based model that predicts a second life use aging of the battery, the determining based on the first life use aging of the battery and second life use operating conditions associated with the respective candidate second life use; and selecting a second life use for the battery based on the predicted second life use agings of the battery. A computer-readable medium includes computer-executable instructions, which when executed on one or more processors, perform operations to obtain historical aging data of a battery that has aged in a first life use; determine, based on the historical aging data, parameters of a physics-based model that represents a first life use aging of the battery; for respective ones of multiple candidate second life uses of the battery, determine parameters of a predictive physics-based model that predicts a second life use aging of the battery, the determining based on the first life use aging of the battery and second life use operating conditions associated with the respective candidate second life use; and select a second life use for the battery based on the predicted second life use agings of the battery. BRIEF DESCRIPTION OF THE DRAWINGS FIG.1 is an overview of a system or process to facilitate a transition of one or more batteries from a first life to a second life, or materials recycling, according to one or more examples of the disclosure; FIG.2 is a process flow diagram of a computational method to support a transition of one or more batteries from a first life to a second life, or materials recycling, according to one or more examples; FIG.3 depicts a process for time synchronization of physics-based aging rate equations from a first life to a second life, demonstrated with use of sigmoidal rate expressions (SREs), according to one or more examples; FIG.4 is a timeline diagram for describing time synchronization which may be used when aging conditions of a battery change over time, according to one or more examples; FIG.5 is a graph of capacity loss versus time for a first life and a second life of a battery according to a first example, illustrating a time-synchronized continuity between the first life and the second life; FIG.6 is a graph of capacity loss versus time for a first life and a second life of a battery according to a second example, illustrating a time-synchronized continuity between the first life and the second life; FIG.7 is a block diagram of a sorting scheme for sorting the batteries within a SOH category, according to one or more examples; FIG.8 is a table which shows example state of health (SOH) categories within which batteries may be sorted or classified, according to one or more examples; FIG.9 is a flowchart of a method of facilitating a transition of one or more batteries from a first life to a second life, or materials recycling, according to one or more examples; and FIG.10 illustrates a block diagram of an example device that may be used to implement various functions, operations, acts, processes, and / or methods, according to one or more examples. MODE(S) FOR CARRYING OUT THE INVENTION In the following detailed description, reference is made to the accompanying drawings, which form a part hereof, and in which are shown, by way of illustration, specific embodiments of the disclosure that may be practiced. These embodiments are described in sufficient detail to enable those of ordinary skill in the art to practice the invention, and it is to be understood that other embodiments may be utilized, and that structural, logical, and electrical changes may be made within the scope of the disclosure. In this description, specific implementations are shown and described only as examples and should not be construed as the only way to implement the disclosure unless specified otherwise herein. It will be readily apparent to one of ordinary skill in the art that the various embodiments of the disclosure may be practiced by numerous other partitioning solutions. For the most part, details concerning timing considerations and the like have been omitted where such details are not necessary to obtain a complete understanding of the disclosure and are within the abilities of persons of ordinary skill in the relevant art. Referring in general to the following description and accompanying drawings, various embodiments of the disclosure are illustrated to show their structure and method of operation. Common elements of the illustrated embodiments may be designated with similar reference numerals. It should be understood that the figures presented are not meant to be illustrative of actual views of any particular portion of the actual structure or method, but are merely idealized representations employed to more clearly and fully depict the disclosure defined by the claims below. It should be further appreciated and understood that the various illustrative logical blocks, modules, circuits, and algorithm acts described in connection with embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and acts are described generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. One of ordinary skill in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the embodiments of the disclosure described herein. The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general-purpose processor, a special-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the general-purpose processor may be any conventional processor, controller, microcontroller, or state machine. A general-purpose processor may be considered a special-purpose processor while the general-purpose processor executes instructions (e.g., software code) stored on a computer-readable medium. A processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Widespread proliferation of battery energy storage (BES) into growing applications has created the need for cradle-to-grave management of BES assets, to ensure that the valuation and economic benefit of these assets will persist beyond their initial first life application (e.g., in terms of battery cell reuse, materials recapture, or both). Accordingly, methods, computer-readable media, and systems to facilitate transition of a battery from its first service life to a second service life using physics-based modeling are disclosed. In one or more examples, diagnostic data markers of aged cells are coupled with physics-based model (PBM) mechanistic interpretations, and then predictive PBM within an algorithmic platform is employed to examine large matrices for second use scenarios. When the batteries complete their primary mission, they may embark on their next mission or be identified for materials recycling. In one example application, fleet Electric Vehicle (EV) batteries are harvested and analyzed for use in vehicle charging stations or grid storage. In one or more examples, the methods, computer-readable media, and systems of the disclosure utilize physics-based lifecycle models to support mechanistic health diagnostics and predict aging within an assortment of second-life application options. In one or more examples, aging path dependence over first and second life uses or applications may be a central consideration. In one or more examples, state of health (SOH) categories are used to guide the placement of used cells to their second use. In one or more examples, used cells are directed to materials recycle if second use is not feasible. In one or more examples, physics-based models may be used to simulate virtual testing and diagnostics for new cell chemistries that have not been sufficiently tested. In one or more examples, the methods, computer-readable media, and systems of the disclosure may be applied to battery strings and packs. In one or more examples, techno-economic analysis is utilized for selecting second life options. In one or more specific examples, a parallel consideration of battery cells, strings, and packs is employed to permit more technical and economic options for second life use. In one or more examples, the methods, computer-readable media, and systems of the disclosure use Sigmoidal Rate Expression (SRE) diagnostic and predictive engines. SRE diagnostic and predictive engines are suitable for closed “batch reactor” systems, like batteries. SRE parameters may provide a unique thumbprint of thermodynamic and kinetic aging information corresponding to the prevailing aging conditions of battery testing or arbitrary use. SRE parameters include a rate constant (sometimes referred to herein as “a”), an order of a reaction (sometimes referred to herein as “b”), and a maximum extent of aging (sometimes referred to herein as . The SRE parameters may be referred to herein collectively as “(a, b, and M),” “(a, b, M),” or “(a,b,M).” As an example, an SRE for a single aging mechanism may be expressed as follows: ,^ ൌ ^ ^൫1 െ ^θt ^,^i* ൯ ^ൌ! 1 1^^^^,^i* ^ ^ ^ ^^^^ᇱ ^^ 2^^^^െ^^^ᇱ^ ^2െ1^ exp^^^^^^^^^^^൨ൠfollows: ^^^^^,^i*, ^^ ൌ ^ ^^൫1 െ ^θt^^,^i*^൯^ൌ!^^^൬^^ᇱ ^^ 2^^^^െ^^^ᇱ^ ^2െ1^ exp^^^^^^^^^^^൨^ ൡof management. The methods, computer-readable media, and systems of the disclosure may be used as an independent, yet complementary, route to existing physics-based tools to diagnose and predict aging processes in battery applications. For example, the approach may leverage the computational methods in the CellSage software suite of tools (including modeling tools), which may facilitate the prediction of battery aging for the primary-to- secondary life transition. The CellSage platform is provided by Idaho National Labs, of Idaho Falls, Idaho, USA. At least some computational methods of the CellSage software suite of tools are described in the following documents: U.S. Application No.12 / 793,224 filed on June 3, 2010, issued as U.S. Patent No.8,521,497 on August 27, 2013; U.S. Application No.12 / 765,384 filed on April 22, 2010, issued as U.S. Patent No.8,346,495 on January 1, 2013; U.S. Application No.12 / 571,253 filed on September 30, 2009, issued as U.S. Patent No.8,467,984 on June 18, 2013; U.S. Application No.13 / 598,290 filed on August 29, 2012, issued as U.S. Patent No.9,625,532 on April 18, 2017; U.S. Application No.17 / 654,931 filed on March 15, 2022; U.S. Application No.18 / 001,304 filed on December 9, 2022; and U.S. Application No.18 / 170,471 filed on February 16, 2023. Each one of the disclosures of the foregoing documents is hereby incorporated herein by reference in its entirety. FIG.1 is an overview of a system 100 or process to facilitate a transition of one or more batteries from a first life to a second life, or materials recycling, according to one or more examples of the disclosure. One or more batteries are utilized in a first life (FL). Data associated with candidate chemistries of a battery are obtained. For example, at a block 102, data associated with known cell chemistries may be obtained. Known cell chemistries include historical aging data, and involve either monolithic test conditions (e.g., constant temperature, fixed duty cycle, and so on), real application aging conditions (e.g., actual EV duty cycles with random deviations, temperature variations, and so on), or cell versus string versus pack. At an end of test (EOT) or an end of life (EOL) of the battery, State of Health (SOH) diagnostics on the battery may be performed. Specifically, at a block 104, the SOH of the battery may be determined via standardized diagnostic markers based on deviation from a beginning of life (BOL) baseline. Considerations may include capacity loss, capacity versus rate, voltage relaxation, impedance growth, and so on. A physics-based model (PBM) may be applied to the data to identify aging mechanisms and chief stress factors. In one or more examples, SREs are utilized as the PBM for application to the data. At a block 106, data associated with new cell chemistries may be obtained. New cell chemistries are “prototypical” in chemistry design, new application usage, or both. New cell chemistries may have little or no aging data associated with them. Upfront materials characterization (e.g., electrode design) is suggested to support physics models for aging in one or more primary uses. In one or more examples, aging may be projected using physics-based models. In one or more examples, the aging may be (e.g., entirely) projected by physics-based models using SRE (e.g., using the CellSage tools), detailed cycling conditions (DCC), and transient active agent (TAA) analyses that view materials reactivity and degradation through a catalysis basis. In one or more examples, the aging projection may be performed with use of ML and / or AI. A precise capture of cycling conditions. Note that the assigned primary use of the battery may be completely arbitrary. At the EOT or the EOL of the battery, SOH diagnostics may be performed. Specifically, at a block 108, a virtual reference performance test (RPT) may be performed, for example, for key metrics of capacity, conductance loss, and battery polarization. One or more secondary life options for a battery are determined. Specifically, at a block 110, remaining useful life projections for multiple secondary life options may be generated. PBM-assisted sorting for cell, string, and pack may be performed. Techno- Economic Analyses (TEA) for secondary use options and materials recycling may also be performed. In one or more examples, Machine Learning (ML) and / or other artificial intelligence (AI) may be utilized in the projections. At a block 112, classification and decision algorithms may be performed. At a block 114, a secondary use of a battery may be selected or determined based on the classification and decision algorithms. In one or more examples, the secondary use may be a best or optimal secondary use of the battery. A secondary use period versus point of recycle may also be projected. At a block 116, a determination may be made for materials recycling of the battery. After the secondary use of a battery is selected or determined in block 114, the battery is utilized in a second life (SL) according to the selected secondary use. At the EOT or the EOL of the battery (i.e., its secondary use or second life), a SOH diagnostics may be performed with respect to a battery. In one or more examples, the process is repeated for multiple uses or lives of a battery (e.g., Nthuse determinations). FIG.2 is a process flow diagram of a computational method 200 to support a transition of one or more batteries from a first life to a second life, or materials recycling, according to one or more examples. At a block 202, diagnostic computational processes associated with a first life of a battery are performed. At a block 204, prognostic computational processes associated with second life options of the battery are performed. The diagnostic computational processes of block 202 are now described. At a block 206, battery historical data and related test and / or use condition data are gathered or obtained. These data may include one or more of cell chemistry, BOL condition, capacity loss, conductance loss, power loss, temperature, cycling rates (e.g., for charge and discharge), cycling frequency, percent time at cal-life, conditions of post-first life storage, and so on. At a block 208, regression analyses are performed on first life battery aging data to establish parameters. In one or more examples, regression analyses on first life battery aging data is performed using mechanism-wise SRE to establish parameters (a, b, M)1 for each SRE. The parameters may serve as a first life thumbprint for aging or SOH. In one or more examples, the mechanisms include a loss of lithium inventory (LLI), a loss of active (electrode) material (LAM), and / or other mechanisms. In one or more other examples, the mechanisms include conductance loss and power fade. In one or more examples, the goal is to establish the progression of LLI, LAM, and so on, as well as possible signs of damage, by the end of the first life. At a block 210, for complex first life aging profiles, a module or process to imitate field use conditions is utilized. In one or more examples, complex first life aging profiles utilize a DCC module of the CellSage to mimic field use conditions and to assign strength of aging stress factors to those conditions. At a block 212, disparate cell aging categories or SOH categories associated with different second use scenarios are identified. Analysis of several cells might reveal disparate cell aging categories or SOH categories that call for different second use scenarios to be identified. The prognostic computational processes of block 204 are now described. At a block 214, one or more scenarios for second battery operating conditions and second use operational life expectancy (SUOLE) are defined based on cell aging categories. Using SRE basis, (a, b) parameters are derived at the conditions, setting the starting M2from the value derived from block 208 (M1) at the corresponding starting time for each (a, b, M)2. Such “time synchronization” of equations is described in more detail in relation to FIGS.3 and 4. In one or more examples, the CellSage platform is utilized for these processes. For more complex use conditions at block 214, inputs to a DCC module or process may be populated with the second use operating conditions. At a block 216, it is determined whether the second use operational life expectancy is met under the requested life conditions. This determination may be based on one or more of capacity, conductance, or power metrics. If the second use operational life expectancy is not met, simulations of a matrix of possible simulations that will survey most-likely conditions (and combinations thereof) to modify in order to extend cell life may be determined. In one or more examples, the above achieves life optimization of batteries. At a block 218, a final fate of the SOH at the end of life is determined. This will aid in decisions for ultimate materials reuse or recycling. In one or more examples of method 200, regression parameter data (e.g., SRE (a, b, M)) may be stored in relation to chemistry type and aging conditions, and thereafter retrieved for use in subsequent determination steps for increased efficiency in processing. FIG.3 depicts a process 300 for time synchronization of physics-based aging rate equations from a first life 302 to a second life 306, demonstrated with use of sigmoidal rate expressions (SRE), according to one or more examples. For the first life, 11^^^^^൯^. Here, (a, b, M) are SRE parameters of mechanism j for arbitrary battery first life (FL) or previous secondary life. For battery first life, ^^^will typically be zero. At a determination point 304, the end-of-first-life SOH loss may be determined for each jthmechanism, . 11^^^^^^, i*, ^^ ൌ ^2^^^ ^^2െ^^^^ ^^^^^^ . Here, (a, b, M)j projected battery second life (SL) or life. In the above equation, set ^ௌ^^^j,^i*,^t^ = ^ி^^j,^i*,^EOL^ and solve for time (t). This time will be the time at which the jthmechanism for SL will begin, as enacted by a unique timeframe for each mechanism. Using the established timescale, set ^^^to ^ி^^j,^i*,^EOL^and solve for ^ௌ^^j,^i*,^t^.^ Perform this task for each mechanism involved in aging of the chosen cell chemistry. ^^ ^ ^^^ ^^ ^2^൫^^ െ^^^^ 1 1^ ^ ^, i*, ^^ ൌ ^ ൯ ^^ െ ^^^^ In one or LAM mechanisms, with possible subsets represented by auxiliary SRE. In one or more examples, the above process involves conductance loss and power fade mechanisms. Note that, in the above, there will be a global aging timeframe as well as underlying mechanism-wise timeframes. Note that if {^ி^^^j,^i*,^EOL^ >^^^}, then it is assumed the jthaging mechanism cannot progress past ^ி^^^j,^i*,^EOL^ for the given SL battery use conditions. In one or more alternative examples, rate equations other than SRE may be utilized to achieve the functions, provided they are properly bounded in ^^. FIG.4 is a timeline diagram 400 for describing time synchronization which may be used when aging conditions of a battery change over time, according to one or more examples. Changing aging conditions using sigmoidal rate expressions may be properly navigated as follows. Starting with a sigmoidal rate expression that covers a single aging mechanism, SOH loss is given by ^ where j is an arbitrary aging that may by loss of capacity, conductance or power. Rearranging for time, ^ 1^ 1 ^^^^, ^∗ ,^^ െ^^ᇱ ି^^^ೕ^^ ൌ ^^ ^ln൭^^ ^^ െ 1^^൩^^^^^^^^^^^2^ When the occurs (e.g., as would . time at the beginning of the (k+1)thtime interval is the same as the time at the end of the kthtime interval. At the conditions found in ^^∗ା^, SRE parameters (a, b, M)j,k+1may be estimated. In one or more examples, the SRE parameter estimation may be performed using a deviation-from- baseline approach, for example, using at least some of the computational methods of the CellSage software suite described above. Solving for time, using the SOH loss value carried over from the kthperiod, ^ 11 ^^^^, ^∗^ , ^^ െ^^ᇱ ି^^^ ^ೕ,ೖశభ^ ^ Note that ^^ା^is not the continuation of the aging time, rather, it is a different scale that captures where in time the changed condition at ^^∗ା^would begin with a SOH value of ^^^^, ^^∗, ^^. This is referred to as time synchronization. In one or more examples, the process may be performed for respective ones of all j mechanisms and all related SOH metrics. With reference to FIG.4, for any k+1 aging condition, there will be the following timelines: a true aging timeline 402 (global), and jthaging timelines 404 (local). FIG.5 is a graph 500 of capacity loss versus time over a first life and a second life of a battery, according to a first example. The first life and the second life profiles are shown on the same time scale for comparison. These aging profiles were generated using the CellSage modeling tools. In general, curves 502 illustrate a time-synchronized continuity between the first life and the second life. A time reference 504 indicates a transition point between the first life and the second life, and indicates where time syncing occurs (i.e., at 110 weeks, relative to a total of 400 weeks of battery life usage). More specifically, curves 502 of graph 500 include a curve associated with the first life and the LLI aging mechanism, a curve associated with the first life and the LAM aging mechanism, a curve associated with the second life and the LLI aging mechanism, a curve associated with the second life and the LAM aging mechanism, and a curve associated with the first life and the second life. Note that graph 500 of FIG.5 specifically illustrates an aging progression for a relatively aggressive aging under the first life (e.g., a temperature or an SOC higher than that of the second life), followed by a milder aging under second life. FIG.6 is a graph 600 of capacity loss versus time over a first life and a second life of a battery, according to a second example. Individual LLI and LAM are not shown in FIG.6 for increased clarity. Again, the aging profiles were generated using the CellSage modeling tools. In general, a curve 602 illustrates a time-synchronized continuity between a first life 604 and a second life 606. A time reference 608 indicates a transition point between first life 604 and second life 606, and indicates where time syncing occurs (i.e., at about 160 weeks, relative to a total of 400 weeks of battery life usage). First life 604 involved cells operating in Oklahoma City, Oklahoma, under moderate cycling conditions with battery thermal management. Seasonal variations in temperature cause the subtle stair- steps as indicated in the capacity loss profile. Second life 606 represents a condition at a mildly elevated temperature that remains fairly consistent over the duration of battery lifecycle. FIG.7 is a block diagram 700 of a sorting scheme for sorting the batteries within a SOH category, according to one or more examples. FIG.8 is a table 800 which shows example SOH categories within which batteries may be sorted or classified according to one or more examples. At a block 702 of FIG.7, cell-wise SOH evaluations may be performed. Here, standard diagnostic analyses may be invoked at the cell level, which may involve combinations of lab and model techniques. Each cell may undergo diagnostic steps to establish SOH, which may be used to form cell SOH categories (FIG.8) from which second use scenarios 704 may be supported. Different performance and aging thresholds may be used to define the boundaries of the SOH categories. Cells that are too aged to fit within a reuse category will be designated for materials recycling. At a block 706 of FIG.7, similar may be performed at the battery string and pack level, for example, provided there is adequate definition of string (assembly of cells in series and / or parallel configuration) and pack (total autonomous assembly of cells and strings) BOL, SOH, and aging thresholds. Based on the above cell, string, and pack evaluations, a determination can be made if strings and packs can be re-used “as-is” without the added expense of string or pack disassembly. This may be only feasible if the constituent cells group well (per small tolerances) within the same SOH category, with no outliers in terms of capacity or resistance (i.e., the cell-to-cell variation in SOH should be small). Strings or packs that are not suitable for secondary use “as-is” will require disassembly and cell harvest for one or more secondary use scenarios. In one or more examples, optimization of sorting within a particular category may involve a techno-economic analysis of the secondary use options tied to that category. Considering cell level versus pack level decision protocol, the following practical options are recognized for extended capture of battery value: whole pack re-use, pack refurbishment, selected cell harvesting, and materials recycle. FIG.9 is a flowchart of a method 900 of facilitating a transition of one or more batteries from a first life to a second life, or materials recycling, according to one or more examples. In one or more examples, method 900 of FIG.9 may be embodied in a non- transitory computer-readable medium including computer-executable instructions, which when executed on one or more processors (e.g., of one or more computing devices), perform the operations of method 900. At an act 902 of FIG.9, historical aging data of a battery that has aged in a first life use may be obtained. At an act 904, parameters of a physics-based model that represents a first life use aging of the battery may be determined based on the historical aging data. At an act 906, for respective ones of multiple candidate second life uses of the battery, parameters of a predictive physics-based model that predicts a second life use aging of the battery may be determined. The determination may be made based on the first life use aging of the battery and second life use operating conditions associated with the respective candidate second life use. At an act 908, a second life use for the battery may be selected based on the predicted second life use agings of the battery. In one or more examples, at the act 904, the parameters of the physics-based model that represents the first life use aging may be determined for respective ones of multiple aging mechanisms of the battery. In one or more examples, the parameters of the predictive physics- based model that predicts the second life use aging may be determined for respective ones of the multiple aging mechanisms of the battery. In one or more examples, the multiple aging mechanisms include a LLI, a LAM, or both. In one or more examples, the multiple aging mechanisms include conductance loss, power fade, or both. In one or more examples, for respective ones of multiple candidate second life uses of the battery, projections of the predicted second life use agings of the battery are performed according to the predictive physics-based model. In one or more examples, classification and decision processes are performed for selecting the second life use for the battery. In one or more examples, techno-economic analyses may be performed on the predicted second life use agings for selecting the second life use for the battery. In one or more examples, it is determined whether respective ones of the predicted second life use agings satisfy a second life use expectancy associated with the respective candidate second life use. In one or more examples, cell aging categories or SOH categories are determined, and it is determined whether respective ones of the predicted second life use agings satisfy the second life use expectancy associated with the respective candidate second life use based on the cell aging categories or the SOH categories. In one or more examples, the second life use for the battery may be selected by selecting, from the multiple candidate second life uses, an optimal second life use for the battery. In one or more examples, the physics-based model and the predictive physics-based model are based on sigmoidal rate expressions. In one or more examples, the sigmoidal rate expressions may be time-synchronized. In one or more examples, the sigmoidal rate expressions of the physics-based model that represents the first life use aging of the battery and the predictive physics-based model that predicts the predicted second life use agings are time-synchronized. When executed as firmware or software, the instructions for performing the operations of method 900 may be stored on a computer-readable medium. A computer- readable medium includes, but is not limited to, non-transitory storage media, such as magnetic and optical storage devices such as disk drives, magnetic tape, CDs (compact disks), DVDs (digital versatile discs or digital video discs), and semiconductor devices such as RAM, DRAM, ROM, EPROM, and Flash memory. Note that conventional techniques may utilize empirical modeling, which can be inaccurate and / or prolong testing, which may be expensive. In one or more examples, the methods, computer-readable media, and systems of the disclosure may quickly and accurately tie battery use conditions to aging outcomes. In one or more examples, the methods, computer-readable media, and systems of the disclosure may greatly reduce the test time needed to provide baseline information for PBM learning, and use much less data than machine learning methods. Thus, answers for issues of battery aging at arbitrary conditions of first and secondary uses may be obtained within a highly accelerated timeframe, allowing a competitive advantage in decision-making toward optimal battery deployment. Key information and metrics that support CellSage modeling tools for second-life evaluations according to the disclosure are now described. In one or more examples, the key information and metrics may be categorized into three (3) different categories, including Category (1) associated with upfront characterization information (e.g., for a new cell), Category (2) associated with battery conditions and data during baseline (BL) testing, and Category (3) associated with projected battery use conditions during arbitrary second use operation. In Category (1), upfront characterization information (e.g., for a new cell) may include Cref which is a rated capacity of the new battery, usually at a stated voltage range and cycling rate (e.g., C / 3); Vmax which is a maximum operating voltage (sometimes the manufacturer’s recommended Vmaxis less than the true Vmaxto avoid undue aging that is more likely at the true value); Vmin which is a minimum operating voltage (sometimes the manufacturer’s recommended Vminis greater than the true Vminto avoid the possibility of discharge to excessive levels); cell design (e.g., flat, prismatic, cylindrical, or other); string configuration (e.g., which will determine the interplay of cell-to-cell aging effects); cell impedance at standardized temperature, voltage, or state of charge (SOC) (direct current (DC) ohmic value is acceptable); string impedance at standardized temperature, voltage, or SOC (DC ohmic value is acceptable); and cell chemistry. Cell chemistry is typically expressed by choice of electrode couple and attributes thereof (particle sizes, porosity, and so on). This is desirable since each chemistry carries with it unique attributes of performance and aging. One might be able to make use of archived data for the chosen chemistry to have an early thumb-printing. Some cell chemistry details might be proprietary, but at least the type of cathode and anode materials may be known. In Category (2), battery conditions and data during baseline (BL) testing may include Thigh, Tlow, Taveover typical baseline cycle (TBC) and mode of thermal management (if any); Vhigh, Vlow, Vave over TBC, and corresponding SOC values; Ihigh, Ilow, Iave over TBC, or corresponding power metrics; duration and frequency of TBC and rest periods in between; and noteworthy TBC variance in any of the above. Battery conditions during non-use (cal-life conditions) may include voltage or SOC and temperature; open- circuit voltage (OCV) can be monitored over time to check for excessive self-discharging. Charging conditions may include starting voltage or SOC, charging rate and temperature; charging mode (typically constant current “CC” to Vmax, then constant voltage “CV” at Vmaxuntil a limiting low current is reached). To maintain a clear baseline, the charging mode may follow the manufacturer’s recommended procedure, but deviations are anticipated as long as it is known what those conditions are. Here, baseline (BL) may be or include first life aging conditions. In Category (3), projected battery use conditions during arbitrary second use operation may include Thigh, Tlow, Tave over typical duty cycle (TDC), and mode of thermal management (if any); Vhigh, Vlow, Vaveover TDC, and corresponding SOC values; Ihigh, Ilow, Iave over TDC, or corresponding power metrics; duration and frequency of TDC and rest periods in between; and noteworthy TDC variance in any of the above (e.g., daily thermal cycling). Battery conditions during non-use (calendar-life conditions that might vary from BL) may include temperature, voltage, and SOC. OCV may be estimated over time to approximate typical self-discharging. Charging conditions may include starting voltage or SOC, charging rate and temperature; charging mode (typically CC to Vmax, then CV at Vmax until a limiting low current is reached). Charging mode may be more highly customized to product requirements, capturing a step-wise charging procedure whose parameters can be customized to investigate aging response. In one or more examples, the information and metrics may include recommended metrics to track (e.g., data capture) over the first life aging and the second life aging, periodically evaluated over standard conditions of temperature and voltage. The information and metrics may include battery capacity; DC and alternating current (AC) (Electrochemical Impedance Spectroscopy or EIS) impedance; self-discharge under storage; and representative power pulse. Much of this information may be obtained through periodic (e.g., monthly) RPT, typically done at 25-30⁰ C, for example: static and residual capacity from Vminto Vmax, covering two each of C1 / 10, C1 / 3, C1, 2 C1 discharge, C1 / 2 or C1 / 3 charge (CC only), and one-hour rest between; Beginning of Test (BOT) and EOT EIS impedance over 100 kHz-10 mHz at 0.9 - 0.95 * Vmax; BOT / EOT self-discharge at starting open circuit voltage (OCV) of (0.975 to 1.0) * Vmax or other target (3+ days); data at key duty cycle steps can be captured (e.g., cell resistance and temperature when each charge event reaches Vmax); and other information can be extracted from data (e.g., differential capacity analysis, dQ / dV). In one or more examples, historical data on when and / or how batteries failed to perform at one or more predetermined temperatures (e.g., low and / or high) would also be informative and useful for analysis. FIG.10 is a block diagram of an example device 1000 that, in various examples, may be used to implement various functions, operations, acts, processes, and / or methods disclosed herein. Device 1000 includes one or more processors 1002 (sometimes referred to herein as “processors 1002”) operably coupled to one or more apparatuses such as data storage devices (sometimes referred to herein as “storage 1004”), without limitation. Storage 1004 includes machine-executable code 1006 stored thereon (e.g., stored on a computer-readable memory) and processors 1002 include logic circuitry 1008. Machine- executable code 1006 includes information describing functional elements that may be implemented by (e.g., performed by) logic circuitry 1008. Logic circuitry 1008 is adapted to implement (e.g., perform) the functional elements described by machine-executable code 1006. Device 1000, when executing the functional elements described by machine- executable code 1006, should be considered as special purpose hardware configured for carrying out the functional elements disclosed herein. In various examples, processors 1002 may be configured to perform the functional elements described by machine-executable code 1006 sequentially, concurrently (e.g., on one or more different hardware platforms), or in one or more parallel process streams. When implemented by logic circuitry 1008 of processors 1002, machine-executable code 1006 is configured to adapt processors 1002 to perform operations of one or more examples disclosed herein. For example, machine-executable code 1006 may be configured to adapt processors 1002 to perform at least a portion or a totality of the system, process, and / or methods of FIGS.1, 2, 3, 4, 8 and 9. Processors 1002 may include a general purpose processor, a special purpose processor, a central processing unit (CPU), a microcontroller, a programmable logic controller (PLC), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, other programmable device, or any combination thereof designed to perform the functions disclosed herein. A general-purpose computer including a is considered a special-purpose computer while the general-purpose computer is configured to execute computing instructions (e.g., software code) related to examples of the disclosure. It is noted that a general-purpose processor (may also be referred to herein as a host processor or simply a host) may be a microprocessor, but in the alternative, processors 1002 may include any conventional processor, controller, microcontroller, or state machine. Processors 1002 may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In one or more examples, storage 1004 includes volatile data storage (e.g., random- access memory (RAM)), non-volatile data storage (e.g., Flash memory, a hard disc drive, a solid-state drive, erasable programmable read-only memory (EPROM), without limitation). In one or more examples, processors 1002 and storage 1004 may be implemented into a single device (e.g., a semiconductor device product, a system on chip, without limitation). In one or more examples, processors 1002 and storage 1004 may be implemented into separate devices. In one or more examples, machine-executable code 1006 may include computer- readable instructions (e.g., software code, firmware code). By way of non-limiting example, the computer-readable instructions may be stored by storage 1004, accessed directly by processors 1002, and executed by processors 1002 using at least logic circuitry 1008. Also by way of non-limiting example, the computer-readable instructions may be stored on storage 1004, transmitted to a memory device (not shown) for execution, and executed by processors 1002 using at least logic circuitry 1008. Accordingly, in one or more examples, logic circuitry 1008 includes electrically configurable logic circuitry. In one or more examples, machine-executable code 1006 may describe hardware (e.g., circuitry) to be implemented in logic circuitry 1008 to perform the functional elements. This hardware may be described at any of a variety of levels of abstraction, from low-level transistor layouts to high-level description languages. At a high-level of abstraction, a hardware description language (HDL) such as an Institute of Electrical and Electronics Engineers (IEEE) Standard hardware description language (HDL) may be used, without limitation. By way of non-limiting examples, VERILOG™, SYSTEMVERILOG™ or very large-scale integration (VLSI) hardware description language (VHDL™) may be used. HDL descriptions may be converted into descriptions at any of numerous other levels of abstraction as desired. As a non-limiting example, a high-level description can be converted to a logic-level description such as a register-transfer language (RTL), a gate- level (GL) description, a layout-level description, or a mask-level description. As a non- limiting example, micro-operations to be performed by hardware logic circuits (e.g., gates, flip-flops, registers, without limitation) of logic circuitry 1008 may be described in a RTL and then converted by a synthesis tool into a GL description, and the GL description may be converted by a placement and routing tool into a layout-level description that corresponds to a physical layout of an integrated circuit of a programmable logic device, discrete gate or transistor logic, discrete hardware components, or combinations thereof. Accordingly, in one or more examples, machine-executable code 1006 may include an HDL, an RTL, a GL description, a mask level description, other hardware description, or any combination thereof. In one or more examples, where machine-executable code 1006 includes a hardware description (at any level of abstraction), a system (not shown, but including storage 1004) may be configured to implement the hardware description described by machine-executable code 1006. By way of non-limiting example, processors 1002 may include a programmable logic device (e.g., an FPGA or a PLC) and the logic circuitry 1008 may be electrically controlled to implement circuitry corresponding to the hardware description into logic circuitry 1008. Also by way of non-limiting example, logic circuitry 1008 may include hard-wired logic manufactured by a manufacturing system (not shown, but including storage 1004) according to the hardware description of machine- executable code 1006. Regardless of whether machine-executable code 1006 includes computer-readable instructions or a hardware description, logic circuitry 1008 is adapted to perform the functional elements described by machine-executable code 1006 when implementing the functional elements of machine-executable code 1006. It is noted that although a hardware description may not directly describe functional elements, a hardware description indirectly describes functional elements that the hardware elements described by the hardware description are capable of performing. As used herein, the term “substantially” in reference to a given parameter, property, or condition means and includes to a degree that one of ordinary skill in the art would understand that the given parameter, property, or condition is met with a small degree of variance, such as within acceptable tolerances. For example, a parameter that is substantially met may be at least about 90% met, at least about 95% met, or even at least about 99% met. As used in the disclosure, the term “combination” with reference to a plurality of elements may include a combination of all the elements or any of various different subcombinations of some of the elements. For example, the phrase “A, B, C, D, or combinations thereof” may refer to any one of A, B, C, or D; the combination of each of A, B, C, and D; and any subcombination of A, B, C, or D such as A, B, and C; A, B, and D; A, C, and D; B, C, and D; A and B; A and C; A and D; B and C; B and D; or C and D. Terms used in the disclosure and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including, but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes, but is not limited to,” and so on). Additionally, if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to examples containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and / or “an” should be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those of ordinary skill in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.,” or “one or more of A, B, and C, etc.,” is used, in general such a construction is intended to include A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B, and C together, etc. Any disjunctive word or two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” should be understood to include the possibilities of “A” or “B” or “A and B.” Various examples herein are disclosed with reference to SRE. However, the disclosure is not restricted to SRE-type equations, as there are other forms of rate equations that may be surrogates for SRE, provided they encompass the same type of kinetic and thermodynamic information within their parameters. The same methods may still apply toward accelerated determination of parameters for such non-SRE forms. In the disclosure, the terms “battery,” “cell,” and “cells” include one or more cells that produces electric energy including e.g., a lithium-ion-battery cell and / or a lithium- metal-battery cell. In one or more examples, a battery may include rechargeable cells, fuel cells, and other cells that use an electrochemical reaction to produce electric energy, and combinations thereof. In addition, although batteries having lithium-ion cells are primarily discussed herein, other types of batteries may be used and analyzed according to one or more examples of the disclosure. Although the context of the examples described herein generally apply to batteries (e.g., lithium-ion batteries and / or lithium-metal batteries), the disclosure is not to be viewed as limited in this way. For example, the methodology described herein may be used in estimating the aging consequences of arbitrary aging conditions for objects in the field of electronics or electrochemistry. A non-exhaustive, non-limiting list of examples follows. Not each of the examples listed below is explicitly and individually indicated as being combinable with all others of the examples listed below and examples discussed above. It is intended, however, that these examples are combinable with all other examples unless it would be apparent to one of ordinary skill in the art that the examples are not combinable. Additional non-limiting examples of the disclosure include: Example 1: A method comprising: obtaining historical aging data of a battery that has aged in a first life use; determining, based on the historical aging data, parameters of a physics-based model that represents a first life use aging of the battery; for respective ones of multiple candidate second life uses of the battery: determining parameters of a predictive physics-based model that predicts a second life use aging of the battery, the determining based on the first life use aging of the and second life use operating conditions associated with the respective candidate second life use; and selecting a second life use for the battery based on the predicted second life use agings of the battery. Example 2: The method according to Example 1, wherein determining the parameters of the physics-based model that represents the first life use aging is performed for respective ones of multiple aging mechanisms of the battery. Example 3: The method according to any of Examples 1 and 2, wherein determining the parameters of the predictive physics-based model that predicts the second life use aging is performed for the respective ones of the multiple aging mechanisms of the battery. Example 4: The method according to any of Examples 1 through 3, wherein the multiple aging mechanisms include a loss of lithium inventory (LLI) and a loss of active material (LAM). Example 5: The method according to any of Examples 1 through 4, wherein the multiple aging mechanisms include a conductive loss and a power fade. Example 6: The method according to any of Examples 1 through 5, wherein the physics-based model and the predictive physics-based model are based on sigmoidal rate expressions. Example 7: The method according to any of Examples 1 through 6, comprising: time-synchronizing the sigmoidal rate expressions of the physics-based model that represents the first life use aging of the battery and the predictive physics-based model that predicts the predicted second life use agings. Example 8: The method according to any of Examples 1 through 7, further comprising: performing techno-economic analyses on the predicted second life use agings for selecting the second life use for the battery. Example 9: The method according to any of Examples 1 through 8, further comprising: determining whether respective ones of the predicted second life use agings satisfy a second life use expectancy associated with the respective candidate second life use. Example 10: The method according to any of Examples 1 through 9, wherein selecting the second life use for the battery comprises selecting, from the multiple candidate second life uses, an optimal second life use for the battery. Example 11: A non-transitory readable medium including computer- executable instructions, which when executed on one or more processors, perform operations to: obtain historical aging data of a battery that has aged in a first life use; determine, based on the historical aging data, parameters of a physics-based model that represents a first life use aging of the battery; for respective ones of multiple candidate second life uses of the battery: determine parameters of a predictive physics-based model that predicts a second life use aging of the battery, the determining based on the first life use aging of the battery and second life use operating conditions associated with the respective candidate second life use; and select a second life use for the battery based on the predicted second life use agings of the battery. Example 12: The non-transitory computer-readable medium according to Example 11, wherein the operation to determine the parameters of the physics-based model that represents the first life use aging is performed for respective ones of multiple aging mechanisms of the battery. Example 13: The non-transitory computer-readable medium according to any of Examples 11 and 12, wherein the operation to determine the parameters of the predictive physics-based model that predicts the second life use aging is performed for the respective ones of the multiple aging mechanisms of the battery. Example 14: The non-transitory computer-readable medium according to any of Examples 11 through 13, wherein the multiple aging mechanisms include one or more of a loss of lithium inventory (LLI), a loss of active material (LAM), a conductive loss, and a power fade. Example 15: The non-transitory computer-readable medium according to any of Examples 11 through 14, for respective ones of multiple candidate second life uses of the battery, perform projections of the predicted second life use agings of the battery according to the predictive physics-based model. Example 16: The non-transitory computer-readable medium according to any of Examples 11 through 15, wherein the physics-based model and the predictive physics- based model are based on sigmoidal rate expressions. Example 17: The non-transitory computer-readable medium according to any of Examples 11 through 16, wherein the computer-executable instructions perform the operations to: time-synchronize the sigmoidal rate expressions. Example 18: The non- readable medium according to any of Examples 11 through 17, wherein the computer-executable instructions perform the operations to: perform classification and decision processes for selecting the second life use for the battery. Example 19: The non-transitory computer-readable medium according to any of Examples 11 through 18, wherein the computer-executable instructions perform the operations to: determine state of health (SOH) categories; and determine, based on the SOH categories, whether respective ones of the predicted second life use agings satisfy a second life use expectancy associated with the respective candidate second life use. Example 20: The non-transitory computer-readable medium according to any of Examples 11 through 19, wherein the operation to select the second life use for the battery comprises selecting, from the multiple candidate second life uses, an optimal second life use for the battery. While the disclosure has been described herein with respect to certain illustrated embodiments, those of ordinary skill in the art will recognize and appreciate that the present invention is not so limited. Rather, many additions, deletions, and modifications to the illustrated and described embodiments may be made without departing from the scope of the invention as hereinafter claimed along with their legal equivalents. In addition, features from one embodiment may be combined with features of another embodiment while still being encompassed within the scope of the invention as contemplated by the inventor.

Claims

CLAIMS What is claimed is:

1. A method comprising: obtaining historical aging data of a battery that has aged in a first life use; determining, based on the historical aging data, parameters of a physics-based model that represents a first life use aging of the battery; for respective ones of multiple candidate second life uses of the battery: determining parameters of a predictive physics-based model that predicts a second life use aging of the battery, the determining based on the first life use aging of the battery and second life use operating conditions associated with the respective candidate second life use; and selecting a second life use for the battery based on the predicted second life use agings of the battery.

2. The method of claim 1, wherein determining the parameters of the physics- based model that represents the first life use aging is performed for respective ones of multiple aging mechanisms of the battery.

3. The method of claim 2, wherein determining the parameters of the predictive physics-based model that predicts the second life use aging is performed for the respective ones of the multiple aging mechanisms of the battery.

4. The method of claim 3, wherein the multiple aging mechanisms include a loss of lithium inventory (LLI) and a loss of active material (LAM).

5. The method of claim 3, wherein the multiple aging mechanisms include a conductive loss and a power fade.

6. The method of claim 3, wherein the physics-based model and the predictive physics-based model are based on sigmoidal rate expressions.

7. The method of claim time-synchronizing the sigmoidal rate expressions of the physics-based model that represents the first life use aging of the battery and the predictive physics-based model that predicts the predicted second life use agings.

8. The method of claim 1, further comprising: performing techno-economic analyses on the predicted second life use agings for selecting the second life use for the battery.

9. The method of claim 1, further comprising: determining whether respective ones of the predicted second life use agings satisfy a second life use expectancy associated with the respective candidate second life use.

10. The method of claim 1, wherein selecting the second life use for the battery comprises selecting, from the multiple candidate second life uses, an optimal second life use for the battery.

11. A non-transitory computer-readable medium including computer-executable instructions, which when executed on one or more processors, perform operations to: obtain historical aging data of a battery that has aged in a first life use; determine, based on the historical aging data, parameters of a physics-based model that represents a first life use aging of the battery; for respective ones of multiple candidate second life uses of the battery: determine parameters of a predictive physics-based model that predicts a second life use aging of the battery, the determining based on the first life use aging of the battery and second life use operating conditions associated with the respective candidate second life use; and select a second life use for the battery based on the predicted second life use agings of the battery.

12. The non-transitory computer-readable medium of claim 11, wherein the operation to determine the parameters of the physics-based model that represents the first life use aging is performed for respective ones of multiple aging mechanisms of the battery.

13. The non-transitory computer-readable medium of claim 12, wherein the operation to determine the parameters of the predictive physics-based model that predicts the second life use aging is performed for the respective ones of the multiple aging mechanisms of the battery.

14. The non-transitory computer-readable medium of claim 13, wherein the multiple aging mechanisms include one or more of a loss of lithium inventory (LLI), a loss of active material (LAM), a conductive loss, and a power fade.

15. The non-transitory computer-readable medium of claim 12, for respective ones of multiple candidate second life uses of the battery, perform projections of the predicted second life use agings of the battery according to the predictive physics-based model.

16. The non-transitory computer-readable medium of claim 12, wherein the physics-based model and the predictive physics-based model are based on sigmoidal rate expressions.

17. The non-transitory computer-readable medium of claim 16, wherein the computer-executable instructions perform the operations to: time-synchronize the sigmoidal rate expressions.

18. The non-transitory computer-readable medium of claim 11, wherein the computer-executable instructions perform the operations to: perform classification and decision processes for selecting the second life use for the battery.

19. The non-transitory readable medium of claim 11, wherein the computer-executable instructions perform the operations to: determine state of health (SOH) categories; and determine, based on the SOH categories, whether respective ones of the predicted second life use agings satisfy a second life use expectancy associated with the respective candidate second life use.

20. The non-transitory computer-readable medium of claim 11, wherein the operation to select the second life use for the battery comprises selecting, from the multiple candidate second life uses, an optimal second life use for the battery.

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