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 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: A method, for intelligent management of a battery is provided. The method includes detecting at least one anomaly associated with the battery. The at least one anomaly impacts one or more operations of the battery. The method includes identifying at least one portion of data from reference charging data to include the at least one anomaly for managing the one or more operations of the battery. The method further includes modifying at least one portion of data from the reference charging data based on a pre-determined logic to include the at least one anomaly. The method also includes retraining an Artificial Intelligence (AI) model based on the reference charging data upon modification for managing the one or more operations of the battery.

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 predicting an onset of a capacity fading in a battery includes measuring, over a period of time, a plurality of parameters related to charging and discharging cycles of the battery; detecting, based on the measured plurality of parameters, the onset of the capacity fade in the battery; and providing a notification on the electronic device indicating the detected onset of the capacity fade.

Abstract: Provided are a battery state measuring method and battery management system, which predict a time point when charging capacity of a battery is to be relatively abruptly reduced. The battery state measuring method includes: monitoring a change of at least one precursor related to the charging capacity of the battery with respect to a number of charging cycles undergone by the battery; and predicting that an abrupt reduction in the charging capacity of the battery is imminent when the change of the at least one precursor follows at least one pre-configured pattern of the battery that has undergone a critical number of charging cycles.

Abstract: Provided are a method and system for determining State of Health (SOH) of a battery. The method includes: detecting a charging state of the battery that is being charged by a Constant Current (CC) or a Constant Voltage (CV); adding a low frequency current signal to the CC that is charging the battery to obtain a low frequency charging current, or adding a low frequency voltage signal to the CV, that is charging the battery to obtain a low frequency charging voltage; determining one or more parameters relating to the battery during the charging state of the battery; calculating an internal impedance and Open Circuit Voltage (OCV) values based on the one or more parameters; and determining SOH of the battery based on the calculated internal impedance and the OCV values.

Abstract: Disclosed are methods and apparatuses for charging a hybrid battery pack, the method including determining, by a battery management system (BMS), a current split ratio for allocating charging current to an energy cell and a power cell in a hybrid battery pack, based on any one or any combination of a state of charge (SoC) level of the energy cell at an instance of initiation of charging, a SoC level of the power cell at the instance of the initiation of the charging, a wattage of an adapter for charging the hybrid battery pack, a capacity of the hybrid battery pack, and a charging time period, and charging, by the BMS, the hybrid battery pack by allocating the charging current to the energy cell and the power cell based on the current split ratio.

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: Provided are a battery state measuring method and battery management system, which predict a time point when charging capacity of a battery is to be relatively abruptly reduced. The battery state measuring method includes: monitoring a change of at least one precursor related to the charging capacity of the battery with respect to a number of charging cycles undergone by the battery; and predicting that an abrupt reduction in the charging capacity of the battery is imminent when the change of the at least one precursor follows at least one pre-configured pattern of the battery that has undergone a critical number of charging cycles.

Abstract: Provided are a method and system for determining State of Health (SOH) of a battery. The method includes: detecting a charging state of the battery that is being charged by a Constant Current (CC) or a Constant Voltage (CV); adding a low frequency current signal to the CC that is charging the battery to obtain a low frequency charging current, or adding a low frequency voltage signal to the CV, that is charging the battery to obtain a low frequency charging voltage; determining one or more parameters relating to the battery during the charging state of the battery; calculating an internal impedance and Open Circuit Voltage (OCV) values based on the one or more parameters; and determining SOH of the battery based on the calculated internal impedance and the OCV values.

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.

Abstract: Disclosed is a battery state of charge (SOC) determining method and a battery managing apparatus that acquires a stochastic reduced order model (SROM) and a mean using battery conservation equations acquired based on a stochastic Pseudo 2-dimensional electrochemical thermal (P2D-ECT) model, measures state information, and assimilates the state information with the SROM and the mean to determine an accurate SOC at a relatively low calculation cost.

Abstract: A method for predicting an onset of a capacity fading in a battery includes measuring, over a period of time, a plurality of parameters related to charging and discharging cycles of the battery; detecting, based on the measured plurality of parameters, the onset of the capacity fade in the battery; and providing a notification on the electronic device indicating the detected onset of the capacity fade.

Abstract: A method of estimating an available power of a battery may include: receiving discharge data associated with the battery and a minimum voltage cut-off of the battery; determining one or more discharge curves based on the discharge data; estimating a model representing the one or more discharge curves; calculating a plurality of parameter values of the model at a plurality of time intervals; and estimating the available power of the battery based on the plurality of parameter values and the minimum voltage cut-off of the battery.

Abstract: A method and apparatus for estimating a state of health (SOH) of a battery are provided. The method includes measuring a voltage and a partial discharge time of the battery, acquiring a normalized partial discharge time using a battery model of a reference battery normalized with respect to a time, calculating a full discharge time of the battery based on the partial discharge time and the normalized partial discharge time, and estimating the SOH based on a ratio between a stored full discharge time of the reference battery and the full discharge time of the battery.

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: 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.