A vehicle includes a vehicle battery; a vehicle sensor configured to detect a current, a voltage and a temperature of the vehicle battery; and an alternator configured to output a target voltage to the vehicle battery. A controller is configured to calculate state of charge (SOC) estimation based on the current, voltage and temperature of the vehicle battery, calculate an initial SOC based on a direct current internal resistance (DCIR) map and apply the initial SOC to the SOC estimation, when an open circuit voltage (OCV) is maintained in a predetermined range after engine-off, and adjust an available SOC range based on a difference between an actual battery charge current amount, to which the initial SOC is applied, and the calculated SOC estimation.

A vehicle includes a vehicle battery; a vehicle sensor configured to detect a current, a voltage and a temperature of the vehicle battery; and an alternator configured to output a target voltage to the vehicle battery. A controller is configured to calculate state of charge (SOC) estimation based on the current, voltage and temperature of the vehicle battery, calculate an initial SOC based on a direct current internal resistance (DCIR) map and apply the initial SOC to the SOC estimation, when an open circuit voltage (OCV) is maintained in a predetermined range after engine-off, and adjust an available SOC range based on a difference between an actual battery charge current amount, to which the initial SOC is applied, and the calculated SOC estimation.

Provided are a battery management system, a battery management method, a battery pack and an electric vehicle. The battery management system includes a sensing unit to generate battery information indicating a current, a voltage and a temperature of a battery, and a control unit. The control unit determines a temporary estimate for a SOC in a current cycle using a time update process of an extended Kalman filter based on a previous estimate indicating a SOC in a previous cycle and the battery information. The control unit determines open circuit voltage (OCV) information based on the temporary estimate. The control unit determines a definitive estimate indicating the SOC in the current cycle using a measurement update process of the extended Kalman filter based on the temporary estimate, the OCV information and the battery information.

Systems, methods, and devices are provided for estimating a state-of-health (SOH) in an energy storage system, where the estimate accounts for both power fade and capacity fade. The SOH estimation system comprises a SOH estimation module configured to receive, and to determine the estimate of the SOH based on: a nominal capacity input; a voltage input; a current input; a nominal open circuit voltage curve; an operational resistance model; a nominal resistance model; an operational dynamic model; and a rated discharge current. The SOH estimation module may further comprise a SOH aggregation module configured to receive a power rating estimate, PR.sub.n, and a normalized capacity estimate, NC.sub.n, and may be configured to determine the estimate of the SOH based on the power rating estimate, PR.sub.n, and the normalized capacity estimate, NC.sub.n.

Systems, methods, and devices are provided for estimating a state-of-health (SOH) in an energy storage system, where the estimate accounts for both power fade and capacity fade. The SOH estimation system comprises a SOH estimation module configured to receive, and to determine the estimate of the SOH based on: a nominal capacity input; a voltage input; a current input; a nominal open circuit voltage curve; an operational resistance model; a nominal resistance model; an operational dynamic model; and a rated discharge current. The SOH estimation module may further comprise a SOH aggregation module configured to receive a power rating estimate, PR.sub.n, and a normalized capacity estimate, NC.sub.n, and may be configured to determine the estimate of the SOH based on the power rating estimate, PR.sub.n, and the normalized capacity estimate, NC.sub.n.

The invention provides methods, systems and terminal devices for analyzing a state of a battery pack based on cell parameters thereof. The method comprises acquiring cell voltages of each cell and total voltages of the battery pack within a preset time; performing data cleaning on the cell voltages and the total voltages of the battery pack; calculating a voltage standard score of the cells based on the cell voltages after data cleaning, and calculating a voltage standard score of the battery pack based on the total voltages after data cleaning; and performing a time-domain analysis on the voltage standard score of each cell and/or the voltage standard score of the battery pack.

The invention provides methods, systems and terminal devices for analyzing a state of a battery pack based on cell parameters thereof. The method comprises acquiring cell voltages of each cell and total voltages of the battery pack within a preset time; performing data cleaning on the cell voltages and the total voltages of the battery pack; calculating a voltage standard score of the cells based on the cell voltages after data cleaning, and calculating a voltage standard score of the battery pack based on the total voltages after data cleaning; and performing a time-domain analysis on the voltage standard score of each cell and/or the voltage standard score of the battery pack.

An apparatus and a method for predicting a state of a battery are provided. The apparatus includes a data measuring unit that measures information about the battery and outputs first data, a data producing unit that reflects a change in available capacity of the battery based on at least a portion of the first data to calculate a corrected state of charge and processes the first data based on the corrected state of charge to generate second data, and outputs the second data, and a battery state estimating unit that estimates state information of the battery based on the second data.

An apparatus and a method for predicting a state of a battery are provided. The apparatus includes a data measuring unit that measures information about the battery and outputs first data, a data producing unit that reflects a change in available capacity of the battery based on at least a portion of the first data to calculate a corrected state of charge and processes the first data based on the corrected state of charge to generate second data, and outputs the second data, and a battery state estimating unit that estimates state information of the battery based on the second data.

A battery internal temperature information processing method, a computer device, and a storage medium that first acquire off-line testing data for off-line testing a battery module and construct an equivalent thermal network model from the off-line testing data, determine optimal model parameters of the equivalent thermal network model based on a multi-objective function fitting method; thereafter, a first battery internal temperature estimate of the battery of the vehicle at a first moment in actual operation of the vehicle is determined, in turn, based on the acquired initial state vector values of the battery of the vehicle, first operational data at a first moment in actual operation of the vehicle, and an equivalent thermal network model including the optimal model parameters.