Systems and methods for detecting and/or identifying an attack on a battery management system (BMS) or a battery system. The voltage and/or state of charge (SOC) of the BMS or battery system can be monitored, and one or more datasets can be obtained. A principal component analysis (PCA) based unsupervised k-means approach can be applied on the one or more datasets to monitor for irregularities that indicate an attack.

An apparatus and method for testing a secondary battery. The apparatus includes a test path, test terminals provided at two ends of the test path and electrically connected to a positive terminal and a negative terminal of the secondary battery respectively, a sensor unit provided on the test path and configured to measure a discharge current of the secondary battery, a constant voltage generation unit provided on the test path and configured to maintain a constant voltage between the secondary battery and the sensor unit, and a control unit configured to receive data indicating the discharge current from the sensor unit, measure an inflection point on a profile of the discharge current, measure an intensity of the discharge current on the basis of the inflection point, and test a level of self-discharge of the secondary battery based on the intensity of the discharge current.

A battery state estimation method includes acquiring an initial value of a vector-type parameter for modeling an electrochemical-thermal (ECT) model of a battery, extracting a predetermined point from the vector-type parameter based on the initial value, generating a target parameter based on the predetermined point, to minimize an error between an actual state of the battery and a state of the battery acquired from the ECT model, and estimating the state of the battery based on the target parameter.

In a method for estimating a voltage of a battery a given electrochemical battery model is provided, wherein one parameter of the electrochemical battery model is an open circuit potential. The open circuit potential is linearized. The voltage of the battery is estimated by means of the electrochemical battery model with the linearized open circuit potential.

The present invention relates to a state of charge (SOC) management system of an energy storage device, the system comprising at least one energy storage device, wherein the SOC management system of the energy storage device manages SOC of the energy storage device by performing P-f (active power-frequency) droop control on the basis of a droop coefficient, a reference frequency, and a dead band, which determine the output of each energy storage device.

A battery diagnosing technology capable of effectively diagnosing a defective battery cell among a plurality of battery cells included in a battery system at an early stage. The battery system diagnosing apparatus diagnoses a battery system including a plurality of battery cells having electrode tabs therein, and includes a cell measuring unit for measuring a voltage or current for each of the plurality of battery cells; and a processor for calculating a state of health (SOH) for each battery cell multiple times over time by using the voltage or current measured by the cell measuring unit and detecting a battery cell having a defect in the electrode tab among the plurality of battery cells based on the SOH of each battery cell calculated multiple times.

A battery pack according to an embodiment of the present disclosure may include a plurality of battery cells and a BMS for controlling the battery pack. The BMS may include a state-of-charge (SOC) calculation module for calculating a SOC of each of the plurality of battery cells included in the battery pack and a faulty battery cell detection unit for detecting at least one faulty battery cell on the basis of the calculated SOC of each of the plurality of battery cells.

Various embodiments include a method for ascertaining a mean capacity loss of a battery storage device. The method may include: providing results of a measurement series on the battery storage device taken during a plurality of load cycles, each including a charging and a discharging phase; ascertaining a first and a second discharge capacity of the device using a first and a second calculation rule, wherein each calibration is different for correcting values of the current measurement; and carrying out an optimization process so a calibration of the current measurement is ascertained to achieve the greatest match of the ascertained first discharge capacity and the second discharge capacity.

A method for determining a full-charge capacity of a battery pack includes: obtaining first state of charge values of cells at a first state of charge correction moment; calculating a net cumulative charge capacity from the first state of charge correction moment to a full-charge moment; obtaining a voltage-capacity curve of a fully charged cell from start of a charge termination phase to the full-charge moment; obtaining a second voltage of a not fully charged cell at the full-charge moment; obtaining a capacity difference between the not fully charged cell at the full-charge moment and the not fully charged cell at the first full-charge voltage; calculating a state of charge difference; calculating a second state of charge value of the not fully charged cell at the full-charge moment; obtaining state of charge variations; calculating full-charge capacities; and obtaining a full-charge capacity based on the calculated full-charge capacities.

A method for determining a charging time includes obtaining a maximum monomer voltage of a cell of a battery, and under a condition that the maximum monomer voltage is determined to be greater than or equal to a value, determining, based on a charging voltage of the battery, the charging time of the battery.