Abstract: For each cell in a plurality of cells from a same manufacturing run, a first and a second cell characteristic are received in order to obtain a plurality of cell characteristics. For each cell, a batch compatibility number that is associated with a number of compatible cells that that cell is compatible with is determined based at least in part on the plurality of cell characteristics. The plurality of cells is sorted according to the batch compatibility numbers to obtain a sorted list of cells. A plurality of compatible cells to include in a battery is selected from the plurality of cells, including by evaluating the plurality of cells according to the order of the sorted list of cells and beginning with the lowest batch compatibility number.

Abstract: System, methods, and other embodiments described herein relate to improving the estimation of battery life. In one embodiment, a method includes measuring electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle. The method also includes determining a feature associated with the degradation of the battery cell from the electrochemical data. The method also includes predicting an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.

Abstract: For each cell in a plurality of cells from a same manufacturing run, a first and a second cell characteristic are received in order to obtain a plurality of cell characteristics. For each cell, a batch compatibility number that is associated with a number of compatible cells that that cell is compatible with is determined based at least in part on the plurality of cell characteristics. The plurality of cells is sorted according to the batch compatibility numbers to obtain a sorted list of cells. A plurality of compatible cells to include in a battery is selected from the plurality of cells, including by evaluating the plurality of cells according to the order of the sorted list of cells and beginning with the lowest batch compatibility number.

Abstract: System, methods, and other embodiments described herein relate to improving the cycling of batteries by using data and a hierarchical Bayesian model (HBM) for predicting the cycle life of a cycling protocol. In one embodiment, a method includes classifying cycle life of a battery into a class using battery data from cycling with a protocol, wherein the class represents cycle life distributions of cycling protocols. The method also includes quantifying, using the class in a HBM, variability for the battery induced by the protocol. The method also includes predicting, using the HBM, an adjusted cycle life for the protocol according to the variability. The method also includes communicating the adjusted cycle life to operate the battery.

Abstract: For each cell in a plurality of cells from a same manufacturing run, a first and a second cell characteristic are received in order to obtain a plurality of cell characteristics. For each cell, a batch compatibility number that is associated with a number of compatible cells that that cell is compatible with is determined based at least in part on the plurality of cell characteristics. The plurality of cells is sorted according to the batch compatibility numbers to obtain a sorted list of cells. A plurality of compatible cells to include in a battery is selected from the plurality of cells, including by evaluating the plurality of cells according to the order of the sorted list of cells and beginning with the lowest batch compatibility number.

Abstract: Systems, methods, and storage media for generating a predicted discharge profile of a vehicle battery pack are disclosed. A method includes receiving, by a processing device, data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles operating under a plurality of conditions, the data received from at least one of each vehicle in the fleet of vehicles, providing, by the processing device, the data to a machine learning server, directing, by the processing device, the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, generating, by the processing device, the predicted discharge profile of the vehicle battery pack from the predictive model, and providing the discharge profile to an external device.

Abstract: In one embodiment, a vehicle battery diagnostics system forecasts a future state for a battery by monitoring, over a period of time, one or more of voltage, current or temperature signals from at least one battery of the vehicle, storing information from the voltage, current or temperature signals as time-series data, obtaining a forecasting model from a server, the forecasting model indicating at least one shapelet feature that corresponds to a forecast categorization, identifying, in the time-series data, a shapelet that matches the at least one shapelet feature to a degree exceeding a predetermined similarity threshold, and providing a notification indicating the forecast categorization.

Abstract: A battery management system includes one or more processors, a battery comprising a plurality of cells, an output device, an input device, and a memory having an input module, a battery characteristic prediction module, and an output module. The input module includes instructions that cause the one or more processors to receive a mode selection from a user via the input device. The battery characteristic prediction module includes instructions that cause the one or more processors to predict a characteristic of the battery based on the mode selection using an active machine learning model to predict the characteristic of the battery. The output module includes instructions that cause the one or more processors to output an estimated cost to the output device based on the characteristic of the battery determined by the active machine learning model.

Abstract: A solid electrolyte interface is formed on a silicon monoxide electrode in a battery cell. While the solid electrolyte interface is being formed on the silicon monoxide electrode, the battery cell is charged for one or more initial cycles.

Abstract: System, methods, and other embodiments described herein relate to improving the cycling of batteries by using data and a hierarchical Bayesian model (HBM) for predicting the cycle life of a cycling protocol. In one embodiment, a method includes classifying cycle life of a battery into a class using battery data from cycling with a protocol, wherein the class represents cycle life distributions of cycling protocols. The method also includes quantifying, using the class in a HBM, variability for the battery induced by the protocol. The method also includes predicting, using the HBM, an adjusted cycle life for the protocol according to the variability. The method also includes communicating the adjusted cycle life to operate the battery.

Abstract: For each cell in a plurality of cells from a same manufacturing run, a first and a second cell characteristic are received in order to obtain a plurality of cell characteristics. For each cell, a batch compatibility number that is associated with a number of compatible cells that that cell is compatible with is determined based at least in part on the plurality of cell characteristics. The plurality of cells is sorted according to the batch compatibility numbers to obtain a sorted list of cells. A plurality of compatible cells to include in a battery is selected from the plurality of cells, including by evaluating the plurality of cells according to the order of the sorted list of cells and beginning with the lowest batch compatibility number.

Abstract: System, methods, and other embodiments described herein relate to predicting effects of a stimulus on a particle or other material structure. In one embodiment, a method includes receiving a segmented image of a particle that identifies at least semantics of the particle and associated characteristics according to subregions of the particle. The method includes analyzing, using a stimulus model, the segmented image to predict changes in the particle associated with applying the stimulus to the particle. Analyzing the segmented image includes generating a predicted image identifying characteristics, semantics and other properties of the particle according to the changes. The method includes providing the predicted image as an electronic output.

Abstract: Systems, methods, and storage media for determining a target charging level of a battery pack for a drive route are disclosed. A method includes receiving data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle in the fleet of vehicles, providing the data to a machine learning server, directing the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, receiving a vehicle route request from the vehicle, the vehicle route request corresponding to the drive route, estimating travel conditions of the vehicle based on the route request, determining a temperature of the battery pack in the vehicle, determining a target battery charging level based on the predictive model, the travel conditions, and the temperature, and providing the target battery charging level to the vehicle.

Abstract: System, methods, and other embodiments described herein relate to improving the estimation of battery life. In one embodiment, a method includes measuring electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle. The method also includes determining a feature associated with the degradation of the battery cell from the electrochemical data. The method also includes predicting an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.

Abstract: In one embodiment, a vehicle battery diagnostics system forecasts a future state for a battery by monitoring, over a period of time, one or more of voltage, current or temperature signals from at least one battery of the vehicle, storing information from the voltage, current or temperature signals as time-series data, obtaining a forecasting model from a server, the forecasting model indicating at least one shapelet feature that corresponds to a forecast categorization, identifying, in the time-series data, a shapelet that matches the at least one shapelet feature to a degree exceeding a predetermined similarity threshold, and providing a notification indicating the forecast categorization.

Abstract: Systems, methods, and storage media for arranging a plurality of cells in a vehicle battery pack are disclosed. A method includes receiving, by a processing device, data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle in the fleet and one or more battery testing devices, providing, by the processing device, the data to a machine learning server, directing, by the processing device, the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, estimating, by the processing device, one or more electrical characteristics of each cell to be included in the vehicle battery pack based on the predictive model, and directing, by the processing device, an arrangement of the cells within the battery pack based on the electrical characteristics.

Abstract: A battery management system includes one or more processors, a battery comprising a plurality of cells, an output device, an input device, and a memory having an input module, a battery characteristic prediction module, and an output module. The input module includes instructions that cause the one or more processors to receive a mode selection from a user via the input device. The battery characteristic prediction module includes instructions that cause the one or more processors to predict a characteristic of the battery based on the mode selection using an active machine learning model to predict the characteristic of the battery. The output module includes instructions that cause the one or more processors to output an estimated cost to the output device based on the characteristic of the battery determined by the active machine learning model.

Abstract: Systems, methods, and storage media for optimizing performance of a vehicle battery pack are disclosed. A method includes receiving data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle, providing the data to a machine learning server, and directing the machine learning server to generate a predictive model. The predictive model is based on machine learning of the data. The method further includes providing the predictive model to each vehicle, the predictive model providing instructions for adjusting configuration parameters for each of the cells in the battery pack such that the battery pack is optimized for a particular use, and directing each vehicle to optimize performance of the vehicle battery pack based on the predictive model.

Abstract: System, methods, and other embodiments described herein relate to classifying semantics of a particle or other material component. In one embodiment, a method includes, in response to receiving a particle image, analyzing the particle image to identify characteristics of the particle represented in respective pixels of the particle image to produce a segmented image that groups the pixels into subregions. The method includes identifying semantics of the particle according to at least boundaries between the subregions. The semantics define expected behaviors of the particle in relation to material physics. The method includes providing the segmented image including the semantics as an electronic output.

Abstract: Systems, methods, and storage media for optimizing performance of a vehicle battery pack are disclosed. A method includes receiving data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle, providing the data to a machine learning server, and directing the machine learning server to generate a predictive model. The predictive model is based on machine learning of the data. The method further includes providing the predictive model to each vehicle, the predictive model providing instructions for adjusting configuration parameters for each of the cells in the battery pack such that the battery pack is optimized for a particular use, and directing each vehicle to optimize performance of the vehicle battery pack based on the predictive model.