Abstract: This disclosure relates generally to method to determine an optimal set of atom centered symmetry functions. One or more parameters associated with one or more atom centered symmetry functions (ACSFs) are received. An initial set of ACSFs is generated by varying the one or more parameters. A histogram with a prespecified bin size is constructed to obtain a distribution of value of each of the initial set of ACSFs. A pruned list of ACSFs is obtained based on width and maximum value of the distribution of the value of initial set of ACSFs. The pruned list of ACSFs is sorted in decreasing order of spread to obtain a sorted list of ACSFs. An optimal set of one or more shortlisted ACSFs is determined by traversing through the sorted list of ACSFs. A high dimensional neural network potential is trained based on the optimal set of one or more shortlisted ACSFs.

Abstract: The present invention relates to the field of electrolyte design. Existing methods focus on optimizing the electrolyte composition on a stand-alone basis with respect to its properties and validating battery performance experimentally which is a time-consuming process. Thus, embodiments of present disclosure provide an automated method and system for identifying electrolyte composition for optimal battery performance. The system receives certain input parameters and computes transport properties using the input. Then, a feasible electrolyte composition is identified from a material database based on deviation index metric. The identified electrolyte composition is then optimized based on the input by considering the deviation index and battery performance metrics such as capacity fade and internal heat generation.

Abstract: Owing to complexity of the algorithms and tools very few attempts have been seen for usage of simulation methods in the development of new electrolytes. Moreover, the existing simulation methods focus on only one aspect of the electrolyte at a time and this limits accuracy of simulation results, and affects performance of electrolyte in real world, where multiple factors come into play simultaneously. The method disclosed provides method and system for in-silico optimization and design of electrolytes, enabling prediction of various properties of an electrolytic mixture of salts, solvents and various additives and its suitability for a given battery technology. The in-silico method shapes itself into an overall battery electrolyte property or component composition analyzer based on the user input.

Abstract: This disclosure relates generally to relates to the field of estimation of remaining useful life (RUL) in lithium based batteries, and, more particularly, to estimation of remaining useful life in lithium based batteries based on coupled estimation of a state of charge (SOC) and a state of health (SOH) during charging/discharging in constant current (CC) and constant voltage (CV) modes. The disclosed RUL estimation technique considers the inter-dependency of SOC-SOH and influence of internal-external parameters/factors during the coupled estimation of SOC-SOH. The coupled estimation of SOC and SOH is based on a reduced order physics based modelling technique and considers the influence real time environment obtained using real-time dynamic data obtained by several sensors during the coupled estimation of SOC-SOH.

Abstract: This disclosure relates generally to relates to the field of estimation of remaining useful life (RUL) in lithium based batteries, and, more particularly, to estimation of remaining useful life in lithium based batteries based on coupled estimation of a state of charge (SOC) and a state of health (SOH) during charging/discharging in constant current (CC) and constant voltage (CV) modes. The disclosed RUL estimation technique considers the inter-dependency of SOC-SOH and influence of internal-external parameters/factors during the coupled estimation of SOC-SOH. The coupled estimation of SOC and SOH is based on a reduced order physics based modelling technique and considers the influence real time environment obtained using real-time dynamic data obtained by several sensors during the coupled estimation of SOC-SOH.

Abstract: Owing to complexity of the algorithms and tools very few attempts have been seen for usage of simulation methods in the development of new electrolytes. Moreover, the existing simulation methods focus on only one aspect of the electrolyte at a time and this limits accuracy of simulation results, and affects performance of electrolyte in real world, where multiple factors come into play simultaneously. The method disclosed provides method and system for in-silico optimization and design of electrolytes, enabling prediction of various properties of an electrolytic mixture of salts, solvents and various additives and its suitability for a given battery technology. The in-silico method shapes itself into an overall battery electrolyte property or component composition analyzer based on the user input.

Abstract: A system and method for performing data-based optimization of performance indicators of process and manufacturing plants. The system consists of modules for collecting and merging data from industrial processing units, pre-processing the data to remove outliers and missingness. Further, the system generates customized outputs from data and identifies important variables that affect a given process performance indicator. The system also builds predictive models for key performance indicators comprising the important features and determines operating points for optimizing the key performance indicators with minimum user intervention. In particular, the system receives inputs from users on the key performance indicators to be optimized and notifies the users of outputs from various steps in the analysis that help the users to effectively manage the analysis and take appropriate operational decisions.

Abstract: A system and method for performing data-based optimization of performance indicators of process and manufacturing plants. The system consists of modules for collecting and merging data from industrial processing units, pre-processing the data to remove outliers and missingness. Further, the system generates customized outputs from data and identifies important variables that affect a given process performance indicator. The system also builds predictive models for key performance indicators comprising the important features and determines operating points for optimizing the key performance indicators with minimum user intervention. In particular, the system receives inputs from users on the key performance indicators to be optimized and notifies the users of outputs from various steps in the analysis that help the users to effectively manage the analysis and take appropriate operational decisions.