Description:FIELD OF INVENTION 5
[0001]
The present subject matter generally relates to batteries, and specifically to systems and methods for analysis of candidate materials for use in manufacturing of the batteries.
BACKGROUND 10
[0002]
Energy storage devices, such as such as batteries, are widely used across a range of electrical and mechanical systems to provide power to their various components. The energy storage devices are found in numerous applications, including but not limited to, mobile phones, digital cameras, wearable electronics, electric vehicles, power tools, and medical 15 devices. Widespread of these devices has made the energy storage devices an important technology in both consumer and industrial domains.
[0003]
Typically, an energy storage device includes a variety of structural and electrochemical components, including a metal shell, cathode and anode assemblies, separators, an electrolyte, an insulating plate, and 20 sealing elements such as gaskets. The electrochemical behaviour of the energy storage devices is governed by the interactions between the anode, cathode, and electrolyte materials, each of which plays a significant role in determining the overall capacity, efficiency, safety, and lifecycle of the energy storage device. 25
[0004]
With the growing demand for high-performance, long-lasting, and safe energy storage systems, for example, in the context of electric mobility and renewable energy integration, greater emphasis is being placed on the selection and optimization of materials used in manufacturing of the energy storage devices. The materials used in an energy storage device directly 30 impact key characteristics, such as energy density, life cycle, rate capability, thermal stability, and manufacturability, of the energy storage device. 2
[0005]
Therefore, careful evaluation and comparison of materials for 5 use in the manufacturing of the components of the energy storage devices has become essential.
BRIEF DESCRIPTION OF DRAWINGS
[0006]
The detailed description is provided with reference to the 10 accompanying figures, wherein:
[0007]
FIG. 1 illustrates a network environment for implementing example techniques for analysing of candidate materials to be used in the manufacturing of a battery, in accordance with an example of the present subject matter. 15
[0008]
FIG. 2 illustrates a system for analysis of the candidate materials to be used in the manufacturing of the battery, in accordance with an example of the present subject matter.
[0009]
FIG. 3 illustrates an exemplary dashboard for comparing the candidate materials to be used in the manufacturing of the battery, in 20 accordance with an example of the present subject matter.
[0010]
FIG. 4 illustrates a method for analysis of candidate materials to be used in the manufacturing of a battery, in accordance with an example of the present subject matter.
[0011]
It may be noted that throughout the drawings, identical reference 25 numbers designate similar, but not necessarily identical, elements. The figures are not necessarily to scale, and the size of some parts may be exaggerated to more clearly illustrate the example shown. Moreover, the drawings provide examples and/or implementations consistent with the description; however, the description is not limited to the examples and/or 30 implementations provided in the drawings.
3
DETAILED DESCRIPTION 5
[0012]
Energy storage devices, such as batteries, are essential for storing and delivering electrical energy across a wide range of applications. These applications may include, but are not limited to, consumer electronics like smartphones and cameras, electric vehicles, wearable devices, industrial machineries, and the like. The batteries are typically composed of 10 components such as an anode, a cathode, an electrolyte, separators, and casings, each of which plays a crucial role in determining the overall performance, efficiency, and safety of the batteries.
[0013]
As the demand for high-performance and application-specific batteries continues to grow, there is an increasing need to carefully select 15 materials that may be used to construct the key components, such as the anode, cathode, and electrolyte, of the batteries. Electrochemical parameters of these materials directly influence critical performance metrics of a battery, such as energy density, charge/discharge efficiency, life cycle, thermal stability, resistance characteristics, or the like. Therefore, selecting 20 the right material is crucial in design and development of the efficient batteries.
[0014]
To evaluate the suitability of candidate materials intended to be used in the manufacturing of the components of the batteries, numerous experimental tests for the candidate materials are typically conducted under 25 varied conditions. In such tests, the performance of the various candidate materials is assessed to determine their electrochemical behavior under predefined testing protocols. These tests yield electrochemical data that includes values corresponding to various electrochemical parameters, such as initial capacity, irreversible capacity loss, rate capability, coulombic 30 efficiency, internal resistance, life cycle, and thermal stability, among others, of the candidate materials. For example, charge-discharge cycling may be performed to observe how a candidate material retains capacity over multiple cycles, or impedance measurements may be used to understand 4
the internal resistance characteristics of an electrolyte formulation. The 5 electrochemical data generated from such testing reflects the behavior of the different candidate materials in conditions simulating real-world application scenarios.
[0015]
The electrochemical data is then analyzed to assess the comparative suitability of each candidate material for use in a battery as 10 anode, cathode, or electrolyte. This analysis is critical to identifying which materials demonstrate superior performance characteristics and are therefore more likely to contribute to the development of high-efficiency, durable, and application-specific batteries.
[0016]
However, current approaches to analyzing this electrochemical 15 data are fragmented and manual. Conventionally, the electrochemical data is often processed and visualized using general-purpose tools such as spreadsheets, which require extensive user intervention. Experts may be required to manually extract relevant metrics from the electrochemical data, generate plots, and compare behavior of the candidate materials across 20 multiple files and formats. This process is not only time-consuming and prone to human error, but also highly dependent on the expertise of an expert to extract meaningful insights from complex sets of the electrochemical data.
[0017]
Furthermore, these conventional tools do not offer the capability 25 to systematically compare and rank multiple candidate materials in a unified environment. The lack of automation and standardization in processing of the electrochemical data adds to the difficulty, more so when scaling up to screen a large number of the candidate materials. As a result, selection of the materials for use in the batteries often becomes inefficient, inconsistent, 30 and inaccessible to users without deep domain expertise in analysis of the electrochemical data.
[0018]
Accordingly, it is desirable to provide a new technique for analysing the electrochemical test data of the candidate materials, which 5
may overcome the inefficiencies of the conventional techniques used for 5 analysing such data.
[0019]
According to example embodiments of the present subject matter, techniques for analysing candidate materials to be used in the manufacturing of a battery are described.
[0020]
In an embodiment, a plurality of key performance indicators 10 (KPIs) are defined for each of a plurality of candidate materials that are intended to be used in manufacturing of an energy storage device, such as a battery. In one example, the plurality of candidate materials are intended to be used as at least one of an anode, cathode, and electrolyte of the battery. In one example, the KPIs for the plurality of candidate materials 15 intended to be used as the electrolyte include one or more of: initial irreversible capacity loss, self-discharge, life cycle, Coulombic efficiency, constant voltage (CV) time, direct current internal resistance (DCIR), rate performance, low-temperature capacity, and differential capacity (dQ/dV) behaviour. In one example, the KPIs for the plurality of candidate materials 20 intended to be used as at least one of the anode and the cathode include one or more of: initial capacity (by weight or volume), initial irreversible capacity loss, life cycle at 80% state-of-health after a predefined number of cycles, average coulombic efficiency, temperature rise during cycling, rate performance at high charge/discharge rates, growth of direct current 25 internal resistance (DCIR) over time, voltage profile and plateau shift, self-discharge rate during open-circuit storage, and constant voltage (CV) time evolution.
[0021]
In one example, each of the plurality of KPIs is indicative of an electrochemical parameter of the plurality of candidate materials relevant to 30 performance of the battery. Further, a weight is assigned to each of the plurality of KPIs based on relevance of the KPI to the performance of the battery. Furthermore, the plurality of KPIs are integrated into a dashboard that is configured to provide real-time comparison of each of the plurality of 6
candidate materials. Further, a value corresponding to each 5 electrochemical parameter of the plurality of candidate materials is determined, and, for each of the plurality of candidate materials, a composite score is determined. In one example, the value of each of the electrochemical parameters is determined based on experimental data collected in respect of the plurality of candidate materials under predefined 10 testing conditions.
[0022]
In one example, the composite score is determined by evaluating the value of each electrochemical parameters of the respective candidate materials against the weight assigned to the corresponding KPI. In one example, computing the composite score includes normalizing, for each of 15 the plurality of candidate materials, the value of the electrochemical parameters corresponding to each of the plurality of KPIs. Further, the composite score for each of the plurality of candidate materials is computed using a weighted summation function. In one example, the weighted summation function is given by: 20
Σ𝑛𝑖=1𝑤𝑖∗ 𝑥𝑖
wherein, 𝑤𝑖 is indicative of the weight assigned to an 𝑖𝑡ℎ KPI, and 𝑥𝑖 is indicative of the normalized value of the electrochemical parameters associated with the 𝑖𝑡ℎ KPI, and 𝑛 is the total number of the KPIs. 25
[0023]
Proceeding further, the plurality of candidate materials are ranked on the dashboard based on the composite score of the respective candidate materials.
[0024]
By implementing the KPIs for evaluating the electrochemical performance of the candidate materials, the present subject matter enables 30 quantitative analysis of the candidate materials intended to be used in the manufacturing of batteries. Each KPI corresponds to a specific electrochemical parameter that directly impacts performance of the battery. 7
By assigning appropriate weights to these KPIs based on their relevance to 5 specific applications or performance goals, the present subject matter allows for computing a composite score for each candidate material. This allows for a more objective, data-driven comparison of candidate materials, moving beyond manual interpretation of raw test data. Thus, the present subject matter facilitates benchmarking across different chemistries and 10 formulations by normalizing and scoring the performance data, thus providing clearer insights into candidate material suitability for components such as anodes, cathodes, or electrolytes.
[0025]
It may be noted that the examples as described above have been described in the context of a cylindrical battery. The same should not be 15 construed as a limitation and the approaches as described above may be implemented with any other shapes as well without deviating from the scope of the present subject matter.
[0026]
The above techniques are further described with reference to FIG. 1 to FIG. 4. It should be noted that the description and the Figures 20 merely illustrate the principles of the present subject matter along with examples described herein and should not be construed as a limitation to the present subject matter. It is thus understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present subject matter. Moreover, all 25 statements herein reciting principles, aspects, and implementations of the present subject matter, as well as specific examples thereof, are intended to encompass equivalents thereof.
[0027]
FIG. 1 illustrates a network environment 100 for implementing example techniques for analysing candidate materials to be used in the 30 manufacturing of a battery, in accordance with an example of the present subject matter.
[0028]
In an embodiment, the network environment 100 may include a system 102 for the analysis of the candidate materials. In one example, the 8
candidate materials may be understood as substances or compounds under 5 consideration for use in manufacturing of components of the battery. In one example, the components may include at least one of anode, cathode, or electrolyte of the battery. These candidate materials may be newly synthesized, modified, or known compounds being re-evaluated under different performance requirements or design specifications. The system 10 102 may enable the evaluation and ranking of various candidate materials based on their performance with respect to various electrochemical parameters. This may allow a user, such as a materials scientist, engineer, or the like, to assess the suitability of each candidate material for the manufacturing of batteries, thereby facilitating informed material selection 15 and optimizing performance and reliability for the battery to be designed with the predefined specifications.
[0029]
In one example, the system 102 may be implemented by a battery manufacturer or a research organization engaged in discovery of the candidate materials or optimization for battery technologies, and may be 20 configured to operate within an on-premises computing infrastructure of the manufacturer or on a cloud-based architecture.
[0030]
In accordance with an embodiment of the present subject matter, the system 102 may be implemented as any of variety of computing devices, including, but not limited to, a desktop computer, personal computer, 25 notebook, portable computer, workstation, mainframe computer, laptop, or a cloud-based computing platform. In one example, if the system 102 is implemented as a cloud-based computing platform, functionality of the system 102 may be accessed by a user 104 via a client device 106. The client device 106 may interact with the system 102, for example, over a 30 network 108. In one example, the client device 106 may be configured to receive inputs from the user 104 and communicate these inputs to the system 102 or its components. The client device 106 may be any suitable computing device capable of establishing a communication link with the 9
system 102 through the network 108. Examples of such client devices 5 include, but are not limited to, desktops, laptops, tablet devices, smartphones, or other internet-enabled devices.
[0031]
In one example, to facilitate access, a client application (not illustrated) may be installed and executed on the client device 106, allowing the user 104 to interact with the system 102. In some implementations, 10 instead of a dedicated client application, a web browser on the client device 106 may be used to access a web-based interface hosted by the system 102. In an alternative embodiment, the functionality of the system 102 may be locally available on the client device 106 itself, such that the user 104 may operate and interact with the system 102 without requiring a continuous 15 network connection. In such cases, the system 102 may support local data processing and synchronization with external databases or servers whenever network connectivity becomes available.
[0032]
In one example, the network 108 may be a single network or a combination of multiple networks and may use a variety of different 20 communication protocols. The network 108 may be a wireless or a wired network, or a combination thereof. Examples of such individual networks include, but are not limited to, Global System for Mobile Communication (GSM) network, Universal Mobile Telecommunications System (UMTS) network, Personal Communications Service (PCS) network, Time Division 25 Multiple Access (TDMA) network, Code Division Multiple Access (CDMA) network, Next Generation Network (NON), Public Switched Telephone Network (PSTN). Depending on the technology, the network 108 may include various network entities, such as gateways, and routers; however, such details have been omitted for the sake of brevity of the present 30 description.
[0033]
In an embodiment, the network environment 100 may further include a central database 110. In one example, the central database 110 may be configured to store various information that may be received, 10
exchanged, generated, or stored by the system 102 for the purposes of 5 analysing the candidate materials. Although the central database 110 is shown to be external to the system 102, in some embodiments, the central database 110 may be internal to the system 102. Alternatively, in embodiments where the central database 110 is external, it may be implemented as a separate server or a cloud-based database, accessible 10 to the system 102, for example, over the network 108.
[0034]
As may be understood, a starting point in the process of identifying the candidate materials to be used in the manufacturing of the battery may involve defining specification of the battery to be designed. These specifications may capture the intended application and performance 15 requirements of the battery, such as whether the battery is to support ultra-fast charging, prioritize energy density, minimize weight, or operate efficiently under extreme temperatures. For example, an industrial battery may be designed to emphasize rapid charging, even at a cost of increased weight, whereas a battery for wearable electronics may prioritize 20 compactness and lightness, accepting slower charging speeds.
[0035]
Based on these specifications, a set of evaluation criteria, referred to as key performance indicators (KPIs), may be derived. These KPIs may serve as measurable benchmarks to assess the suitability of each candidate material. In one example, each KPI may correspond to an 25 electrochemical parameter, such as life cycle, rate performance, coulombic efficiency, low temperature capacity, or the like, which are relevant to the defined performance requirements of the battery. In an embodiment, the system 102 may assess the performance of the candidate materials against this predefined set of KPIs. In an example, the KPIs may be defined by 30 subject matter experts that may include, but is not limited to, researchers, engineers, materials scientists, and battery design specialists, based on factors such as industry standards, application-specific goals, manufacturing constraints, research objectives, or market demand. 11
[0036]
In an embodiment, each KPI may be assigned a weight based 5 on its relevance to the overall performance of the battery. These weights may reflect relative importance of different electrochemical parameters in determining the suitability of a candidate material for manufacturing of the battery. In one example, the weighting may be implemented as a numerical scale, such as 0 to 1, or as percentage values totalling 100% across all 10 KPIs. For example, in a lithium-ion battery for electric vehicles, the initial capacity KPI may be assigned a weight of 20%, while the life cycle at 80% state-of-health may carry a 25% weight. In one example, the weights assigned to the KPIs may be adjustable based on specific application requirements or as new insights into battery performance emerge. This may 15 allow the system 102 to adapt to different use cases and evolving industry standards. For example, in a scenario where fast charging becomes a priority, the weight assigned to the rate performance KPI may be increased to reflect its heightened importance.
[0037]
In one embodiment, the KPIs may be integrated into a dashboard 20 of the system 102 that may be configured to provide real-time comparison of the candidate materials. In one example, integration of the KPIs into the dashboard may mean that the KPIs and their corresponding weights are visually presented on a single interactive interface of the system 102 to allow the user 104 to easily view, compare, and analyse the performance of 25 different candidate materials based on the selected KPIs and their relative importance.
[0038]
To assess the candidate materials against the defined KPIs, it is necessary to first determine the performance of the candidate materials across relevant electrochemical parameters. These electrochemical 30 parameters, such as capacity, coulombic efficiency, life cycle, rate capability, voltage stability, self-discharge rate, temperature performance, or the like, may be obtained by subjecting the candidate materials to electrochemical testing under multiple predefined testing conditions. In one 12
example, these testing conditions may include, but are not limited to, 5 specific charge and discharge rates, voltage ranges, temperature profiles, and cycling protocols. Experimental data, which may include values of the electrochemical parameters collected during testing for each candidate material, may be stored in data sources 112-1, 112-2, ..., and 112-N for later reference and analysis. 10
[0039]
In some embodiments, in addition to the values of the electrochemical parameters, the experimental data may also include metadata tagging for each candidate material, such as supplier identification, batch number, synthesis method, or the like. This metadata may be used for tracking, comparison, and reproducibility of results, as well 15 as facilitating data management and traceability throughout the analysis process of the candidate materials.
[0040]
In one example, the data sources 112-1, 112-2, …, and 112-N may include, but are not limited to, public or private databases, or the like, that may be accessed by the system 102 over the network 108. In some 20 examples, the data sources 112-1, 112-2, …, and 112-N may be on-premises data sources implemented by the manufacturer that may be accessible to the system 102 through a secure local network. In one example, the system 102 may have direct access to these on-premises data sources via a dedicated connection or any other suitable communication 25 channel. Although more than one data sources 112-1, 112-2, …, and 112-N are depicted in FIG. 1, in some embodiments, it is possible that the experimental data corresponding to the performance of the candidate materials on various electrochemical parameters may be stored in a single data source. 30
[0041]
In an embodiment, in a process to perform the analysis of the candidate materials to assess suitability of the candidate in the manufacturing of the battery, the system 102 may interact, for example, over the network 108, with the data sources 112-1, 112-2, …, and 112-N to 13
retrieve the experimental data collected in respect of the candidate 5 materials. In one example, the system 102 may store the experimental data received from the data sources 112-1, 112-2, …, and 112-N to the central database 110 for further processing.
[0042]
Once the experimental data corresponding to the performance of each candidate material on various electrochemical parameters is acquired 10 by the system 102, the system 102 may initiate a series of analytical steps to evaluate the performance of the candidate materials against a predefined set of KPIs, in order to determine the suitability of each candidate material for battery manufacturing. In doing so, the system 102 may extract a value corresponding to each electrochemical parameter of the candidate 15 materials from the experimental data. For example, the system 102 may extract the initial capacity value from cycling test data, calculate the average Coulombic efficiency over a specified number of cycles, or determine the rate performance by comparing capacity retention at different charge/discharge rates. Further, in an embodiment, the system 102 may 20 compute, for each of the candidate materials, a composite score by evaluating the value of each electrochemical parameter of the respective candidate materials against the weight assigned to the corresponding KPI.
[0043]
Further, in an embodiment, the system 102 may rank the candidate materials on the dashboard based on the composite score of the 25 respective candidate materials. For example, the dashboard may display a sortable list of the candidate materials, with a highest composite score at a top, representing a most promising material based on the weighted KPI evaluation. The dashboard may also provide visual representations such as bar charts or heat maps to illustrate the relative performance of different 30 materials across various KPIs. This ranking may allow researchers and engineers to quickly identify top-performing materials and understand their strengths and weaknesses across different performance criteria. 14
[0044]
Accordingly, by evaluating the suitability of the candidate 5 materials based on the predefined KPIs, the present subject matter allows for a comprehensive and objective assessment of the performance of the candidate materials. This enables researchers and engineers to make data-driven decisions in the selection of materials for manufacturing of the batteries. The use of weighted KPIs ensures that the evaluation process 10 takes into account the relative importance of different electrochemical parameters, aligning the assessment with specific application requirements and industry standards. Furthermore, the real-time dashboard facilitates rapid comparison and analysis of the candidate materials, thereby accelerating the material selection process and reducing development time. 15
[0045]
FIG. 2 illustrates the system 102 for analysis of the candidate materials to be used in the manufacturing of the battery, in accordance with an example embodiment of the present subject matter. The system 102 of the present subject matter enables evaluation and comparison of various candidate materials for battery components, including anodes, cathodes, 20 and electrolytes.
[0046]
In an embodiment, the system 102 may include a processor 202 and a memory 204 coupled to the processor 202. In one example, the processor 202 may be implemented as microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state 25 machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. In one example, the memory 204 may include any computer-readable medium known in the art including, for example, volatile memory (e.g., RAM), and/or non-volatile memory (e.g., EPROM, flash memory, etc.). The memory 204 may also be an external 30 memory unit, such as a flash drive, a compact disk drive, an external hard disk drive, or the like.
[0047]
Also, in an embodiment, the system 102 may include interface(s) 206 that may be coupled to the processor 202. The interface(s) 206 may 15
include a variety of software and hardware interfaces that allow interaction 5 of the system 102 with other communication and computing devices, such as network entities, web servers, external repositories, and peripheral devices, such as input/output (I/O) devices. For example, the interface(s) 206 may couple the system 102 with the data sources 112-1, 112-2, …, and 112-N, the central database 110, and the client device 106 of the user 104. 10 The interface(s) 206 may also enable coupling of internal components, if any, of the system 102 with each other. The system 102 may also include module(s) 208 and data 210 coupled to the processor 202. In one example, the module(s) 208 and data 210 may reside in the memory 204.
[0048]
In one example, the data 210 may include key performance 15 indicator (KPI) data 222, experimental performance data 224, and other data 226. In one example, the module(s) 208 may include routines, programs, objects, components, data structures, and the like, which perform particular tasks or implement particular abstract data types. The module(s) 208 may further includes modules that supplement applications on the 20 system 102, for example, modules of an operating system. The data 210 serves, amongst other things, as a repository for storing data that may be fetched, processed, received, or generated by one or more of the module(s) 208.
[0049]
In one example, the module(s) 208 may include a user input 25 module 212, a data acquisition module 214, a scoring module 216, a visualization module 218, and other module(s) 220. The other module(s) 220 may include programs or coded instructions that supplement applications and functions, for example, programs in the operating system of the system 102. 30
[0050]
In an embodiment, the user inputs module 212 may allow the user 104 to provide inputs corresponding to the KPIs based on which the candidate materials are to be evaluated and ranked. In one example, these 16
inputs may include the selection of relevant KPIs, and assignment of 5 weights to each KPI.
[0051]
In one example, to receive input from the user 104 corresponding to the KPIs, the user input module 212 may provide a user interface that may be displayable on the client device 106. Through this user interface, the user 104 may enter information such as the selection of relevant KPIs 10 and assign a weight to each KPI to indicate its relative importance in the selection of the candidate materials. In one example, each KPI may be indicative of an electrochemical parameter of the one or more candidate materials that is relevant to the performance of the battery. Further, the weight assigned to each KPI may be based on the relevance of the 15 corresponding KPI to a particular performance goal or application-specific requirement of the battery. For example, in high-energy applications, the user 104 may assign a higher weight to initial capacity or energy density, while in applications requiring long life cycle, metrics such as coulombic efficiency or life cycle at 80% state-of-health may be given greater 20 importance. This weightage input allows the system 102 to prioritize the candidate materials based on performance objectives defined by the user 104, thereby enabling a more targeted and optimized candidate material selection process. In one example, the user interface may present options such as dropdown menus, checkboxes, or input fields to facilitate entry of 25 the information corresponding to the KPIs. For example, the user 104 may select "Life Cycle" as a KPI and assign this KPI a weight of 30%, or specify that a minimum Coulombic efficiency of 99% is required.
[0052]
As explained previously, the user 104, who may be responsible for providing data corresponding to the KPIs, may include, but is not limited 30 to, researchers, engineers, materials scientists, battery manufacturing specialists, or other domain experts involved in development of the batteries and material selection. In one example, the KPIs defined by the user 104 for the selection of the candidate materials intended to be used as the anode 17
or the cathode may include, but are not limited to, initial capacity (by weight 5 or volume), initial irreversible capacity loss, life cycle at 80% state-of-health after a predefined number of cycles, average coulombic efficiency, temperature rise during cycling, rate performance at high charge/discharge rates, growth of direct current internal resistance (DCIR) over time, voltage profile and plateau shift, self-discharge rate during open-circuit storage, and 10 constant voltage (CV) time evolution. Similarly, the KPIs defined by the user 104 for the selection of candidate materials intended to be used as the electrolyte may include, but are not limited to, initial irreversible capacity loss, self-discharge, life cycle, coulombic efficiency, constant voltage (CV) time, direct current internal resistance (DCIR), rate performance, low-15 temperature capacity, and differential capacity (dQ/dV) behaviour. These KPIs serve as critical metrics for evaluating the electrochemical performance of the candidate materials under different conditions, thereby enabling informed material selection decisions during the battery design process. 20
[0053]
In some alternative embodiments, the system 102 may include a set of standard KPIs based on industry best practices or prior configurations. These standard KPIs may be reviewed and modified by the user 104 as needed. Additionally, the user may have the option to add custom KPIs to address specific project requirements or unique evaluation 25 criteria. For example, a user may add a KPI for "Thermal Stability" if it is critical for a particular battery application. In one example, the data corresponding the KPIs received from the user 104 may be stored in the memory 204 as the KPI data 222 for use during analysis of the candidate materials. 30
[0054]
As explained previously, to assess the suitability of the candidate materials with respect to the KPIs defined by the user 104, it may be necessary to have experimental data corresponding to the performance of the candidate materials on the relevant electrochemical parameters. This
18
experimental data may enable the system 102 to evaluate how well each 5 candidate material aligns with the user-defined KPIs.
[0055]
In an embodiment, the data acquisition module 214 may retrieve the experimental data from the data sources 112-1, 112-2, ..., and 112-N. In one example, the data sources 112-1, 112-2, ..., and 112-N may include, but are not limited to, on-premises databases, laboratory information 10 management systems (LIMS), electronic lab notebooks (ELNs), simulation result repositories, research paper databases, proprietary datasets generated by battery manufacturers, government or academic research data portals, online materials science databases such as Materials Project or PubChem, or the like. These data sources 112-1, 112-2, ..., and 112-N 15 may store the experimental data relevant to electrochemical testing, material characterization, and battery performance metrics, which are required for assessing the candidate materials based on user-defined KPIs.
[0056]
In an alternative embodiment, the data acquisition module 214 may provide an interface that allows the user 104 to enter data or upload a 20 file containing the experimental data from a local storage of the client device 106 through which the system 102 is being accessed. In one example, file formats that may be supported by the system 102 may include, but are not limited to, Comma-Separated Values (CSV), Electrochemical Impedance Spectroscopy (EIS) data files, Arbin Instruments data files, Biologic Science 25 Instruments data files, Gamry Instruments data files, or the like.
[0057]
As explained previously, the experimental data may include, but is not limited to, values corresponding to the electrochemical parameters of the candidate materials, such as charge/discharge capacity values, cycle life values, coulombic efficiency values, rate capability values, voltage 30 profile characteristics, impedance values, thermal response values during cycling, and values reflecting degradation behaviour over time. In one example, the experimental data retrieved from the data sources 112-1, 112-
19
2, ..., and 112-N may be stored in the memory 204 as the experimental 5 performance data 224.
[0058]
In an embodiment, the scoring module 216 may compute, for each of the candidate materials, a composite score by evaluating the value of each of the electrochemical parameters included in the experimental data against the weight assigned to the corresponding KPI. To do so, in one 10 example, the scoring module 216 may include a machine learning (ML) model that may first normalize, for each of the plurality of candidate materials, the values of the electrochemical parameters corresponding to each of the plurality of KPIs. Further, ML model may compute the composite score for each of the plurality of candidate materials using a weighted 15 summation function. In one example, the weighted summation function may be given by:
Σ𝑛𝑖=1𝑤𝑖∗ 𝑥𝑖
[0059]
In an example, in the above indicated weighted summation function, 𝑤𝑖 may be indicative of the weight assigned to an 𝑖𝑡ℎ KPI, and 20 𝑥𝑖 may be indicative of the normalized value of the electrochemical parameters associated with the 𝑖𝑡ℎ KPI, and 𝑛 may be the total number of the KPIs. For example, the user 104 may have define three KPIs, initial irreversible capacity loss with a weight of 20%, self-discharge with a weight of 5%, and life cycle with a weight of 25% for evaluating the candidate 25 materials. If, for a particular candidate material, the normalized values of these KPIs are 0.75, 0.60, and 0.85 respectively, the scoring module 216 may compute the composite score as: (0.20 × 0.75) + (0.05 × 0.60) + (0.25 × 0.85) = 0.15 + 0.03 + 0.2125 = 0.3925.
[0060]
In an embodiment, the visualization module 218 may rank the 30 candidate materials on a dashboard based on the composite scores of the respective candidate materials. The composite scores computed by the scoring module 216, which reflect the relative suitability of each candidate 20
material based on the weighted KPIs, may be used to sort the candidate 5 materials in descending order of performance. For example, for the anode, a candidate material "Graphite-A" may receive a composite score of 0.85, another candidate material "Silicon-B" may receive a score of 0.78, and yet another candidate material "LTO-C" may receive a score of 0.73, and thus, each of these candidate materials may appear in that ranked order. For the 10 cathode, a candidate material "NMC811-X" may be ranked highest with a score of 0.88, followed by candidate materials "LFP-Y" and "NCA-Z." For the electrolyte, a candidate material "Electrolyte-E1" may top the list with a score of 0.81, ahead of candidate materials "Electrolyte-E2" and "Electrolyte-E3." 15
[0061]
In one example, the dashboard may present the ranking of the candidate materials in a visually intuitive format such as a sortable table, bar chart, or heat map to enable easy comparison. In one example, the dashboard may be displayed on the client device 106 of the user 104, either via a dedicated client application or through a web-based interface 20 accessed via a browser. In one example, the dashboard may include multiple panels or views to provide both high-level summaries and detailed drill-down capabilities. For example, each candidate material entry may be accompanied by its composite score, a breakdown of individual KPI values, normalized scores, and assigned weights. Tooltips, hover interactions, or 25 expandable sections may be used to offer contextual insights such as source of data, statistical confidence levels, or recent trends in performance.
[0062]
Accordingly, by using the KPIs defined by the user 104, the present subject matter enables a structured and objective assessment of the suitability of the candidate materials for various components of a battery, 30 such as the anode, cathode, or electrolyte. Each KPI represents a critical electrochemical performance metric that is relevant to the functional requirements of the target application. By assigning weights to these KPIs and evaluating how each candidate material performs against them, the 21
present subject matter facilitates a comparative analysis that highlights the 5 candidate materials best aligned with performance priorities. Hence, by using the KPIs, the present allows for the identification of the most suitable candidate materials from a large pool, ensuring that the selected candidate materials meet key benchmarks for efficiency, stability, and overall battery performance. 10
[0063]
Further, the visualization module 218 may support dynamic filtering, allowing the user 104 to refine the list of the candidate materials based on specific KPI thresholds, data quality metrics, or material properties (e.g., chemical composition or cost). The visualization module 218 may allow the user 104 to adjust weight assigned to the KPIs in real-time and 15 observe how the ranking of materials changes accordingly. In some implementations, visual indicators such as colour coding, star ratings, or performance tags, such as “high longevity,” “low self-discharge”, etc, may be employed to highlight standout candidate materials or those candidate materials that meet certain predefined performance benchmarks. 20
[0064]
In some example embodiments, a summary report corresponding to the ranked candidate materials, their composite scores, and associated KPI values may be downloadable in various formats such as PDF or CSV, so as to allow the user 104 to store, review, or share the results offline. In one example, the summary report may include, but is not 25 limited to, detailed tabulations of performance of each candidate material, visualizations such as charts and graphs for comparative analysis, and metadata regarding the sources of the experimental data used in the evaluation.
[0065]
In some other embodiments, the dashboard may include an 30 integrated sharing interface to allow the user 104 to generate secure links or export data snapshots for collaboration. This may enable remote access to the results for teams engaged in collaborative research or industrial 22
applications, such as joint battery development efforts, academic-industry 5 partnerships, or inter-departmental review processes.
[0066]
In some embodiments, the ML model of the scoring module 216 may also perform tasks such as peak identification in electrochemical signals, equivalent circuit fitting from impedance spectroscopy data, capacity retention tracking over multiple charge-discharge cycles, and 10 extraction of impedance parameters including charge transfer resistance and double-layer capacitance. These capabilities may enable more accurate and automated interpretation of complex experimental data. In some other embodiments, the ML model may also be configured to perform predictive analytics, such as cycle life estimation based on early-cycle 15 behaviour, degradation pattern recognition by analysing trends in performance decay, and electrolyte stability forecasting under various operational conditions. This may support proactive decision-making in selection of the candidate material and design, aiding researchers and engineers in identifying promising candidate materials that meet both short-20 term performance targets and long-term reliability goals.
[0067]
FIG. 3 illustrates an exemplary dashboard 300 for real-time display and evaluation of the candidate materials intended for use in the manufacturing of the battery, in accordance with an example embodiment of the present subject matter. 25
[0068]
According to an example implementation of the present subject matter, the dashboard 300 may provide a digital workspace for a user, such as the user 104, to view the performance of the candidate materials in respect of various KPIs defined by the user 104. The dashboard 300 may enable integration of the experimental data, configurable KPIs, real-time 30 performance visualization, and intelligent ranking of the candidate materials. In one example, the user 104 may interact with the dashboard 300 to select the experimental from the data sources 112-1, 112-2, …, and 112-N or locally upload a file containing the experimental data, define or edit 23
KPI weights, and initiate automated analysis routines. This may allow for 5 scalable and collaborative candidate material screening workflows for components, such as anode, cathode and electrolyte, of the battery.
[0069]
As shown in FIG. 3, in one example, the dashboard 300 may include a KPI input panel 302, where the user 104 may add, remove, or adjust the weight of various KPIs such as DCIR, dQ/dV behaviour, CV time, 10 life cycle, or the like. In some examples, the dashboard 300 may provide an option to adjust the weights interactively, for example, via sliders or numeric fields, to influence how the composite score of each candidate material is calculated.
[0070]
In one example, once the file corresponding the experimental 15 data is uploaded by the user 104 or fetched, for example, from the data sources 112-1, 112-2…, and 112-N, the back-end ML model of the scoring module 216 may automatically extract value of the relevant electrochemical parameters of the candidate materials and normalize them against KPI criteria to calculate the composite score for each of the candidate materials. 20
[0071]
As shown in FIG. 3, the dashboard 300 may include a performance chart 304, which may provide a comparative visualization of the composite score of the candidate materials in respect of the selected KPIs. In some examples, the user 104 may select specific KPIs to highlight on this performance chart 302, filter candidate materials based on 25 performance thresholds, and switch between chart types (bar, radar, scatter) for improved visual analysis.
[0072]
In addition to performance visualization, in one example, the dashboard 300 may include a material ranking table 306, which may list the candidate materials in descending order based on a calculated composite 30 score. In one example, three materials, Material 1, Material 2, and Material 3 may be evaluated for their suitability for use as the electrolyte of the battery and ranked based on their composite scores. Material 2, having the highest composite score of 0.85, may be ranked first in the material ranking 24
table 304, indicating that Material 2 offers the best balance across the 5 selected KPIs. Material 1 may be ranked second with a score of 0.78, and Material 3 may be ranked third with a score of 0.73. This ranked output may provide a quick reference to the user 104 for identifying the most promising candidate materials for use in the electrolyte. In some examples, when a candidate material is selected in the material ranking table 304, a contextual 10 panel may appear displaying formulation details, test conditions, and links to raw data corresponding to the selected candidate material, thereby providing the user 104 with immediate access to critical metadata required for validation and decision-making.
[0073]
In some examples, alerts or annotations may also be triggered 15 based on the composite score. For example, if composite score of a candidate material exceeds a configurable benchmark (e.g., 0.80), the dashboard 300 may visually flag it as a high-potential candidate material. Similarly, if a candidate material has a strong performance in one KPI but poor in another (e.g., excellent dQ/dV profile but poor life cycle), the 20 dashboard 300 may surface that insight via tooltips or annotation markers. This may allow the user 104 to make informed material selection decisions with enhanced contextual understanding.
[0074]
In some examples, the client device 106 used by the user 104 to access the system 102 may also facilitate synchronized viewing of the 25 dashboard 300 in distributed settings. For example, if multiple users are using the system 102 from different client devices, updates to the KPIs, file uploads, or composite score recalculations may be reflected in real time across all sessions. This ensures that all the users have access to the most current insights and supports collaborative review sessions, such as during 30 candidate material selection workshops or technical design reviews.
[0075]
In one example, the dashboard 300 may also include a download panel 308 to allow downloading of a summary report of the ranking and analysis. In one example, the summary report may include, but is not limited 25
to, metadata corresponding to the composite score of the candidate 5 materials, visual plots, and commentary generated by the user 104. Additionally, in one example, the dashboard may include a share panel 310 that may allow the user 104 to generate unique access links or grant permission-based remote access to other users. This may allow access to the dashboard 300 for distributed research environments and cross-10 functional material screening teams.
[0076]
The scope of the present disclosure should not be construed as being restricted to the example dashboard 300 illustrated in FIG. 3, and it may encompass a variety of alternative dashboards that support the functionalities of analysing the candidate materials. 15
[0077]
Thus, by integrating the KPIs into the dashboard 300, the present subject matter enables real-time visualization and comparison of the candidate materials. By linking raw experimental data to the KPIs, the dashboard 300 may provide a structured, data-driven approach for ranking and selecting top-performing candidate materials, thereby streamlining 20 decision-making regarding selection of the candidate materials for manufacturing the batteries.
[0078]
FIG. 4 illustrates a method 400 for analysis of candidate materials to be used in the manufacturing of a battery, in accordance with an example of the present subject matter. The order in which the method 25 400 is described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method 400, or an alternative method. Furthermore, the method 400 may be implemented by processor(s) or computing device(s) through any suitable hardware, non-transitory machine-readable 30 instructions, or a combination thereof.
[0079]
It may be understood that steps of the method 400 may be performed by programmed computing devices and may be executed based on instructions stored in a non-transitory computer-readable medium. The 26
non-transitory computer-readable medium may include, for example, digital 5 memories, magnetic storage media, such as magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media. In an example, the method 400 may be performed by the system 102.
[0080]
Referring to FIG. 4, at block 402, the method 400 may include defining a plurality of key performance indicators (KPIs) for a plurality of 10 candidate materials intended for use in the battery. As explained previously, each of the plurality of KPIs may be indicative of an electrochemical parameter of the candidate materials relevant to performance of the battery. In one example, the candidate materials are intended to be used as an anode, cathode, and electrolyte of the battery. In one example, the KPIs 15 may be defined by a user, such as the user 104, using the user input module 212.
[0081]
As explained previously, the KPIs for the candidate materials that are intended to be used as the electrolyte may include, but are not limited to, initial irreversible capacity loss, self-discharge, life cycle, Coulombic 20 efficiency, constant voltage (CV) time, direct current internal resistance (DCIR), rate performance, low-temperature capacity, and differential capacity (dQ/dV) behaviour.
[0082]
As also explained previously, the KPIs for the candidate materials that are intended to be used as the anode and the cathode of the 25 battery may include, but are not limited to, initial capacity (by weight or volume), initial irreversible capacity loss, life cycle at 80% state-of-health after a predefined number of cycles, average coulombic efficiency, temperature rise during cycling, rate performance at high charge/discharge rates, growth of direct current internal resistance (DCIR) over time, voltage 30 profile and plateau shift, self-discharge rate during open-circuit storage, and constant voltage (CV) time evolution.
[0083]
At block 404, the method 400 may include assigning a weight to each of the plurality of KPIs based on relevance of the KPI to the 27
performance of the battery. As explained previously, the user 104 may 5 provide weight to the KPIs using the user input module 212.
[0084]
At block 406, the method 400 may include integrating the plurality of KPIs into a dashboard, such as the dashboard 300, that is configured to provide real-time comparison of the candidate materials.
[0085]
At block 408, the method 400 may include determining a value 10 corresponding to each electrochemical parameter of the candidate materials. As previously explained, the data acquisition module 214 may access the experimental data collected for the candidate materials under predefined testing conditions, for example, from the data sources 112-1, 112-2, …, and 112-N over the network 108. Based on the retrieved 15 experimental data, the scoring module 216 may extract the values of relevant electrochemical parameters, such as internal resistance, dQ/dV behaviour, etc, associated with the candidate materials.
[0086]
At block 410, the method 400 may include computing, for each of the plurality of candidate materials, a composite score. As explained 20 previously, the scoring module 216 may calculate the composite score by evaluating the value of each electrochemical parameters of the respective candidate materials against the weight assigned to the corresponding KPI. In one example, to calculate the composite score, the scoring module 216 may first normalize, for each of the plurality of candidate materials, the value 25 of the electrochemical parameters corresponding to each of the plurality of KPIs. Further, the scoring module 216 may compute the composite score for each of the plurality of candidate materials by using a weighted summation function. In one example, the weighted summation function may be given by: 30
Σ𝑛𝑖=1𝑤𝑖∗ 𝑥𝑖
[0087]
In one example, in the weighted summation function, 𝑤𝑖 may be indicative of the weight assigned to an 𝑖𝑡ℎ KPI, and 𝑥𝑖 may be indicative of
28
the normalized value of the electrochemical parameters associated with the 5 𝑖𝑡ℎ KPI, and 𝑛 is the total number of the KPIs.
[0088]
At block 412, the method 400 may include ranking the candidate materials on the dashboard 300 based on the composite score of the respective candidate materials. As explained previously, the visualization module 218 may present on the dashboard 300, a ranked table or chart that 10 displays each candidate material along with its corresponding composite score.
[0089]
Thus, by associating the candidate materials with the KPIs, the present subject matter allows for a quantifiable evaluation of the performance of the candidate material performance based on the 15 experimental data. This enables an objective comparison between multiple candidate materials by mapping raw experimental data to standardized performance KPIs. As a result, the present subject matter not only enables visualisation of the performance of the candidate materials on various performance KPIs in real time, but also facilitates data-driven decision-20 making by highlighting the strengths and weaknesses of each candidate material. This may accelerate candidate material screening, optimize formulation strategies, and reduce trial-and-error in the research and development workflow.
[0090]
Although examples for the present disclosure have been 25 described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed and explained as examples of the present disclosure. 30
29
I/We Claim: 5
1. A method (400) for analysis of candidate materials to be used in the manufacturing of a battery, the method comprising:
defining (402) a plurality of key performance indicators (KPIs) for a plurality of candidate materials intended for use in the battery, wherein each of the plurality of KPIs is indicative of an electrochemical parameter of the 10 plurality of candidate materials relevant to performance of the battery;
assigning (404) a weight to each of the plurality of KPIs based on relevance of the KPI to the performance of the battery;
integrating (406) the plurality of KPIs into a dashboard configured to provide real-time comparison of the plurality of candidate materials; 15
determining (408) a value corresponding to each electrochemical parameter of the plurality of candidate materials;
computing (410), for each of the plurality of candidate materials, a composite score by evaluating the value of each electrochemical parameters of the respective candidate materials against the weight 20 assigned to the corresponding KPI; and
ranking (412) the plurality of candidate materials on the dashboard based on the composite score of the respective candidate materials.
2. The method (400) as claimed in claim 1, wherein computing the 25 composite score comprises:
normalizing, for each of the plurality of candidate materials, the value of the electrochemical parameters corresponding to each of the plurality of KPIs; and
computing the composite score for each of the plurality of candidate 30 materials using a weighted summation function, wherein the weighted summation function is given by:
Σ𝑛𝑖=1𝑤𝑖∗ 𝑥𝑖 30
wherein, 𝑤𝑖 is indicative of the weight assigned to an 𝑖𝑡ℎ KPI, and 𝑥𝑖 is 5 indicative of the normalized value of the electrochemical parameters associated with the 𝑖𝑡ℎ KPI, and 𝑛 is the total number of the KPIs.
3. The method (400) as claimed in claim 1, wherein the plurality of candidate materials are intended to be used as at least one of an anode, 10 cathode, and electrolyte of the battery.
4. The method (400) as claimed in claim 3, wherein the KPIs for the plurality of candidate materials intended to be used as the electrolyte include one or more of: initial irreversible capacity loss, self-discharge, life 15 cycle, Coulombic efficiency, constant voltage (CV) time, direct current internal resistance (DCIR), rate performance, low-temperature capacity, and differential capacity (dQ/dV) behaviour.
5. The method (400) as claimed in claim 3, wherein the KPIs for the 20 plurality of candidate materials intended to be used as at least one of the anode and the cathode include one or more of: initial capacity (by weight or volume), initial irreversible capacity loss, life cycle at 80% state-of-health after a predefined number of cycles, average coulombic efficiency, temperature rise during cycling, rate performance at high charge/discharge 25 rates, growth of direct current internal resistance (DCIR) over time, voltage profile and plateau shift, self-discharge rate during open-circuit storage, and constant voltage (CV) time evolution.
6. The method (400) as claimed in claim 1, wherein the value of each of 30 the electrochemical parameters is determined based on experimental data collected in respect of the plurality of candidate materials under predefined testing conditions.
31
7. A system (102) for analysis of candidate materials to be used in the 5 manufacturing of a battery, the system (102) comprising:
at least one processor (202);
a user input module (212), coupled to the at least one processor (202), to receive user inputs in respect of:
a plurality of key performance indicators (KPIs) corresponding to a 10 plurality of candidate materials intended for use in the battery, wherein each KPI is indicative of an electrochemical parameter of the one or more candidate materials relevant to performance of the battery; and
a weight to be assigned to each of the KPIs, the weight being based on a relevance of the corresponding KPI to the performance of the battery; 15
a data acquisition module (214), coupled to the at least one processor (202), to access data corresponding to value of each electrochemical parameter associated with the plurality of candidate materials;
a scoring module (216), coupled to the at least one processor (202), 20 to compute, for each of the plurality of candidate materials, a composite score by evaluating the value of each of the electrochemical parameters against the weight assigned to the corresponding KPI; and
a visualization module (218), coupled to the at least one processor (202), to display, on a dashboard (300), a comparative ranking of the 25 plurality of candidate materials based on the composite score of the respective candidate materials.
8. The system (102) as claimed in claim 7, wherein, to compute the composite score, the scoring module (216) is to: 30
normalize, for each of the plurality of candidate materials, the values of the electrochemical parameters corresponding to each of the plurality of KPIs; and 32
compute the composite score for each of the plurality of candidate 5 materials using a weighted summation function, wherein the weighted summation function is given by:
Σ𝑛𝑖=1𝑤𝑖∗ 𝑥𝑖
wherein, 𝑤𝑖 is indicative of the weight assigned to an 𝑖𝑡ℎ KPI, and 𝑥𝑖 is indicative of the normalized value of the electrochemical parameters 10 associated with the 𝑖𝑡ℎ KPI, and 𝑛 is the total number of the KPIs.
9. The system (102) as claimed in claim 7, wherein the plurality of candidate materials are intended to be used as at least one of an anode, cathode, and electrolyte of the battery. 15
10. The system (102) as claimed in claim 7, wherein the value of each of the electrochemical parameters is determined based on experimental data collected in respect of the plurality of candidate materials under predefined testing conditions. 20
33
ABSTRACT 5
ANALYSIS OF CANDIDATE MATERIALS FOR BATTERIES
Approaches for analysing candidate materials for use in manufacturing a battery are described. In one example, a user input module (212) receives user inputs corresponding to a plurality of key performance 10 indicators (KPIs) for the candidate materials. Each KPI represents an electrochemical parameter relevant to battery performance. Inputs specifying the weight assigned to each KPI are also received, where the weight reflects the relevance of the KPI to performance of the battery. A data acquisition module (214) accesses experimental data corresponding 15 to values of the electrochemical parameters for each candidate material. Based on this data, a scoring module (216) computes a composite score for each candidate material by evaluating the parameter values against their respective KPI weights. A visualization module (218) presents on a dashboard (300) ranking of the candidate materials based on their 20 computed composite scores.
FIG. 2 34 , Claims:I/We Claim: 5
1. A method (400) for analysis of candidate materials to be used in the manufacturing of a battery, the method comprising:
defining (402) a plurality of key performance indicators (KPIs) for a plurality of candidate materials intended for use in the battery, wherein each of the plurality of KPIs is indicative of an electrochemical parameter of the 10 plurality of candidate materials relevant to performance of the battery;
assigning (404) a weight to each of the plurality of KPIs based on relevance of the KPI to the performance of the battery;
integrating (406) the plurality of KPIs into a dashboard configured to provide real-time comparison of the plurality of candidate materials; 15
determining (408) a value corresponding to each electrochemical parameter of the plurality of candidate materials;
computing (410), for each of the plurality of candidate materials, a composite score by evaluating the value of each electrochemical parameters of the respective candidate materials against the weight 20 assigned to the corresponding KPI; and
ranking (412) the plurality of candidate materials on the dashboard based on the composite score of the respective candidate materials.
2. The method (400) as claimed in claim 1, wherein computing the 25 composite score comprises:
normalizing, for each of the plurality of candidate materials, the value of the electrochemical parameters corresponding to each of the plurality of KPIs; and
computing the composite score for each of the plurality of candidate 30 materials using a weighted summation function, wherein the weighted summation function is given by:
Σ𝑛𝑖=1𝑤𝑖∗ 𝑥𝑖 30
wherein, 𝑤𝑖 is indicative of the weight assigned to an 𝑖𝑡ℎ KPI, and 𝑥𝑖 is 5 indicative of the normalized value of the electrochemical parameters associated with the 𝑖𝑡ℎ KPI, and 𝑛 is the total number of the KPIs.
3. The method (400) as claimed in claim 1, wherein the plurality of candidate materials are intended to be used as at least one of an anode, 10 cathode, and electrolyte of the battery.
4. The method (400) as claimed in claim 3, wherein the KPIs for the plurality of candidate materials intended to be used as the electrolyte include one or more of: initial irreversible capacity loss, self-discharge, life 15 cycle, Coulombic efficiency, constant voltage (CV) time, direct current internal resistance (DCIR), rate performance, low-temperature capacity, and differential capacity (dQ/dV) behaviour.
5. The method (400) as claimed in claim 3, wherein the KPIs for the 20 plurality of candidate materials intended to be used as at least one of the anode and the cathode include one or more of: initial capacity (by weight or volume), initial irreversible capacity loss, life cycle at 80% state-of-health after a predefined number of cycles, average coulombic efficiency, temperature rise during cycling, rate performance at high charge/discharge 25 rates, growth of direct current internal resistance (DCIR) over time, voltage profile and plateau shift, self-discharge rate during open-circuit storage, and constant voltage (CV) time evolution.
6. The method (400) as claimed in claim 1, wherein the value of each of 30 the electrochemical parameters is determined based on experimental data collected in respect of the plurality of candidate materials under predefined testing conditions.
31
7. A system (102) for analysis of candidate materials to be used in the 5 manufacturing of a battery, the system (102) comprising:
at least one processor (202);
a user input module (212), coupled to the at least one processor (202), to receive user inputs in respect of:
a plurality of key performance indicators (KPIs) corresponding to a 10 plurality of candidate materials intended for use in the battery, wherein each KPI is indicative of an electrochemical parameter of the one or more candidate materials relevant to performance of the battery; and
a weight to be assigned to each of the KPIs, the weight being based on a relevance of the corresponding KPI to the performance of the battery; 15
a data acquisition module (214), coupled to the at least one processor (202), to access data corresponding to value of each electrochemical parameter associated with the plurality of candidate materials;
a scoring module (216), coupled to the at least one processor (202), 20 to compute, for each of the plurality of candidate materials, a composite score by evaluating the value of each of the electrochemical parameters against the weight assigned to the corresponding KPI; and
a visualization module (218), coupled to the at least one processor (202), to display, on a dashboard (300), a comparative ranking of the 25 plurality of candidate materials based on the composite score of the respective candidate materials.
8. The system (102) as claimed in claim 7, wherein, to compute the composite score, the scoring module (216) is to: 30
normalize, for each of the plurality of candidate materials, the values of the electrochemical parameters corresponding to each of the plurality of KPIs; and 32
compute the composite score for each of the plurality of candidate 5 materials using a weighted summation function, wherein the weighted summation function is given by:
Σ𝑛𝑖=1𝑤𝑖∗ 𝑥𝑖
wherein, 𝑤𝑖 is indicative of the weight assigned to an 𝑖𝑡ℎ KPI, and 𝑥𝑖 is indicative of the normalized value of the electrochemical parameters 10 associated with the 𝑖𝑡ℎ KPI, and 𝑛 is the total number of the KPIs.
9. The system (102) as claimed in claim 7, wherein the plurality of candidate materials are intended to be used as at least one of an anode, cathode, and electrolyte of the battery. 15
10. The system (102) as claimed in claim 7, wherein the value of each of the electrochemical parameters is determined based on experimental data collected in respect of the plurality of candidate materials under predefined testing conditions. 20
33