Abstract: An open-circuit potential (OCP) estimation method includes measuring a set of operating parameters of a battery cell of a battery system, the battery cell being a lithium-ion type battery cell and comprising two electrodes, and performing an OCP estimation process based on the measured set of operating parameters, the OCP estimation process further including obtaining known information relating to the two electrodes, identifying one of the two electrodes as a known electrode based on the known information, applying a physics-based model to reconstruct an OCP curve for the known electrode, determining lithiation ranges of the other of the two electrodes based on experimental test results for the battery cell, reconstructing an OCP curve for the other of the two electrodes based on its lithiation ranges, and generating a final estimate of the OCP of the two electrodes of the battery cell based on the reconstructed OCP curves.

Abstract: A method for designing an accelerated battery aging testing protocol from battery electric vehicle usage data is provided. An initial search space database is created based on a collection of vehicle usage data. The usage data includes current demand over a first timeframe. Data compression is performed including classifying the database into specific segments representing use events. A synthetic profile is generated including a sequence of elements having a battery current and a battery state of charge (SOC) for selected segments of the specific segments. An optimization for accelerated aging of the battery is defined. A genetic algorithm (GA) is executed that generates the accelerated battery aging testing protocol requiring a second timeframe, shorter than the first timeframe, based on the optimization.

Abstract: A system for issue prediction based on multidimensional data analysis includes a model generator that receives a resolved data item relating to a service issue. The resolved data item includes different attributes corresponding to multiple data dimensions and adjusts a population of attributes based on a statistical data model and a deep learning data model operating independent of each other. The statistical data model operates on the attributes for providing a predictive feature and the deep learning data model operates on the attributes for providing a predictive label based on performance metrics related to the data dimensions. The predictive feature and the predictive label collectively define training data. The model generator also trains a classification model based on the training data for predicting a potential issue related to an unresolved data item. The trained data model provides a trigger based on the potential issue being related to the performance metrics.

Abstract: A system for issue prediction based on multidimensional data analysis includes a model generator that receives a resolved data item relating to a service issue. The resolved data item includes different attributes corresponding to multiple data dimensions and adjusts a population of attributes based on a statistical data model and a deep learning data model operating independent of each other. The statistical data model operates on the attributes for providing a predictive feature and the deep learning data model operates on the attributes for providing a predictive label based on performance metrics related to the data dimensions. The predictive feature and the predictive label collectively define training data. The model generator also trains a classification model based on the training data for predicting a potential issue related to an unresolved data item. The trained data model provides a trigger based on the potential issue being related to the performance metrics.