Method provides accurate state-of-health (SOH) diagnostics and prognostics during the whole-life-service of a lithium-ion battery by considering the effects of state- of-charge (SOC) and SOH on certain parameters (such as consideration of nonlinearity of the terminal voltage) during the process of SOC diagnostics and prognostics. The method integrates Lebesgue sampling and equivalent circuit model (ECM) analysis, which greatly decreases computation cost and uncertainty accumulation to provide efficient acquisition of open circuit voltage (OCV) determinations for the ECM process. The OCV curve of the battery was obtained during Hybrid Pulse Power Characterization testing by fitting a series of selected OCV points after enough rest of the subject battery. Identified parameters of ECM are updated according to terminal voltage measurement to enable accurate SOC estimation and prediction during the period from full charge to full discharge of the battery. Parameter identification is re-conducted and an initial condition for SOC estimation is updated according to SOH to enable accurate SOC estimation during the whole-life-service of battery.

Various embodiments of the present technology may comprise methods and apparatus to determine an RSOC value of a battery. The methods and apparatus may comprise utilizing various parameters, such as voltage and/or current, to calculate the RSOC of the battery. In various embodiments, the methods and apparatus may display one of a first RSOC and a second RSOC. In various embodiments, the methods and apparatus may further detect changes in the relevant parameter(s), adjust a previously-reported RSOC of the battery accordingly, and report the adjusted RSOC.

Various embodiments of the present technology may comprise methods and apparatus to determine an RSOC value of a battery. The methods and apparatus may comprise utilizing various parameters, such as voltage and/or current, to calculate the RSOC of the battery. In various embodiments, the methods and apparatus may display one of a first RSOC and a second RSOC. In various embodiments, the methods and apparatus may further detect changes in the relevant parameter(s), adjust a previously-reported RSOC of the battery accordingly, and report the adjusted RSOC.

A power prediction system includes a battery removably mounted on an electric power device using electric power, a charging device configured to charge the battery, and a power prediction device configured to predict an amount of electric power capable of being supplied by the charging device to outside of the charging device through machine learning on the basis of usage information indicating at least one of the usage state and the usage environment of the charging device.

A power prediction system includes a battery removably mounted on an electric power device using electric power, a charging device configured to charge the battery, and a power prediction device configured to predict an amount of electric power capable of being supplied by the charging device to outside of the charging device through machine learning on the basis of usage information indicating at least one of the usage state and the usage environment of the charging device.

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.

The present disclosure provides systems and methods for managing a temperature of a battery energy storage system (“BESS”). A method may comprise identifying operating temperature limitations of the BESS; obtaining a forecast horizon comprising a forecast of external environmental conditions for a time period; identifying a charging/discharging schedule of the BESS; simulating operation of the BESS for the time period for each of a plurality of sequences of thermal management modes according to the charging/discharging schedule and the forecast horizon, the simulating generating an energy consumption and an operating temperature forecast of for each of the plurality of sequences of thermal management modes; selecting a sequence of thermal management modes of the plurality of sequences; and operating the equipment according to the selected sequence of thermal management modes.

The present disclosure provides systems and methods for managing a temperature of a battery energy storage system (“BESS”). A method may comprise identifying operating temperature limitations of the BESS; obtaining a forecast horizon comprising a forecast of external environmental conditions for a time period; identifying a charging/discharging schedule of the BESS; simulating operation of the BESS for the time period for each of a plurality of sequences of thermal management modes according to the charging/discharging schedule and the forecast horizon, the simulating generating an energy consumption and an operating temperature forecast of for each of the plurality of sequences of thermal management modes; selecting a sequence of thermal management modes of the plurality of sequences; and operating the equipment according to the selected sequence of thermal management modes.

A method of estimating the remaining driving distance available from a current charge of a battery of an electrically powered mobility device, wherein the method comprises: a) determining a state of charge of the battery based on a measured battery-related parameter, b) determining a state of health of the battery based on the state of charge, and c) estimating the remaining driving distance that the mobility device can travel with the current charge based on the state of charge and the state of health, wherein step b) involves determining the state of health based on actual consumed ampere hours of the battery.

A method of estimating the remaining driving distance available from a current charge of a battery of an electrically powered mobility device, wherein the method comprises: a) determining a state of charge of the battery based on a measured battery-related parameter, b) determining a state of health of the battery based on the state of charge, and c) estimating the remaining driving distance that the mobility device can travel with the current charge based on the state of charge and the state of health, wherein step b) involves determining the state of health based on actual consumed ampere hours of the battery.