Abstract:
A method for managing a battery includes obtaining a first plurality of battery parameters with respect to a first charging or discharging cycle of the battery; obtaining a second plurality of battery parameters with respect to a second charging or discharging cycle of the battery; determining a relative entropy by comparing the first plurality of battery parameters measured during the first charging or discharging cycle and the second plurality of battery parameters measured during the second charging or discharging cycle; and estimating a relative entropy value to predict a number of cycles after which a battery capacity is predicted to drop.
Abstract:
A method for forecasting remaining useful life (RUL) of a battery by an electronic device is provided. The method includes forecasting the RUL of the battery based on at least one capacity value of the battery estimated by at least one of a battery capacity estimation model and a data driven model, determining whether the at least one capacity value for the charging cycle and the discharging cycle is lower than the at least one capacity value estimated by at least one of the battery capacity estimation model and the data driven model and correcting the forecasting RUL of the battery by feeding back the at least one capacity value to at least one of the battery capacity estimation model and the data driven model.
Abstract:
A method and apparatus for predicting a state of charge (SoC) of a battery includes estimating an open circuit voltage of the battery based on an open current voltage model, where the open current voltage model is defined by internal parameters that are derived from a battery voltage of the battery, a load current of the battery for a load, or a first SoC of the battery, or any combination thereof; and predicting a second SoC based on the estimated open current voltage.
Abstract:
A method for on-device real-time customization of charge profiles for a battery in an electronic device, and/or a corresponding device. The method may include determining at least one charging behavior parameter of the battery during every charging cycle. Further, the method may include determining at least one discharging behavior parameter of the battery subsequent to every charging cycle. Further, the method may include generating a charging profile for charging the battery based on the at least one charging behavior parameter and the at least one discharging behavior parameter. Further, the method may include charging the battery using the generated charging profile for the subsequent charging-discharging cycles.
Abstract:
The present disclosure provides a method and a system for improving the state of health (SoH) of rechargeable batteries. The method comprises receiving a plurality of battery parameters during charging of a battery and estimating model parameters of the battery using a mathematical model. The method also comprises comparing the estimated model parameters of the battery and the received battery parameters to determine degradation parameters of the battery in real-time, determining new charging current profile of the battery based on the degradation parameters of the battery, and applying the determined new charging current profile to the battery for improving the SoH of the battery.
Abstract:
The present subject refers to a method for battery fault diagnosis and prevention of hazardous conditions. The method comprises determining a plurality of parameters defined as one or more of current, voltage, or state of charge during operation of a battery-powered device. Further, one or more likelihood ratios related to malfunctioning of the battery are evaluated based on determined parameters. At least one of: a current battery-state or a type of current battery state are determined based on the one or more likelihood ratios as evaluated.
Abstract:
Embodiments herein disclose an apparatus and methods for identifying an anomaly in at least one of a re-chargeable battery and at least one component(s) connected to the re-chargeable battery. Embodiments herein relates to the field of battery management systems, and more particularly to apparatus and methods for identification of anomalies in batteries and loads/component(s) connected to the re-chargeable battery. The embodiments herein includes outputting a severity level of the anomaly in the at least one of the re-chargeable battery and at least one component connected to the re-chargeable battery, based on the analyzed plurality of the threshold values of the determined plurality of the characteristic data corresponding to the voltage data and current data, wherein the severity level of anomaly comprise at least one of, a negligent level, a medium level and a critical level.
Abstract:
A method and apparatus for predicting a capacity fade rate of a battery are provided. The method includes collecting capacity degradation data of a battery based on a current and a state of charge (SOC) for a predefined number of cycles, generating a capacity fade model based on the capacity degradation data, and estimating a capacity fade rate of the battery using the capacity fade model.