A method for detecting internal short circuit of a battery, includes: discharging a battery with a first current I.sub.1 at a moment t.sub.1; calculating a first discharge voltage drop ΔV.sub.1 of the battery at a moment t.sub.1+dt; discharging the battery with a second current I.sub.2 at a moment t.sub.2, where I.sub.1≠I.sub.2; calculating a second discharge voltage drop ΔV.sub.2 of the battery at a moment t.sub.2+dt; and determining, based on the first current I.sub.1, the first discharge voltage drop ΔV.sub.1, the second current I.sub.2, and the second discharge voltage drop ΔV.sub.2, whether the battery has an internal short circuit. In this application, whether the battery has an internal short circuit can be accurately determined, thereby ensuring safety of an electronic apparatus and a user.

A method for detecting internal short circuit of a battery, includes: discharging a battery with a first current I.sub.1 at a moment t.sub.1; calculating a first discharge voltage drop ΔV.sub.1 of the battery at a moment t.sub.1+dt; discharging the battery with a second current I.sub.2 at a moment t.sub.2, where I.sub.1≠I.sub.2; calculating a second discharge voltage drop ΔV.sub.2 of the battery at a moment t.sub.2+dt; and determining, based on the first current I.sub.1, the first discharge voltage drop ΔV.sub.1, the second current I.sub.2, and the second discharge voltage drop ΔV.sub.2, whether the battery has an internal short circuit. In this application, whether the battery has an internal short circuit can be accurately determined, thereby ensuring safety of an electronic apparatus and a user.

Systems and apparatus may carry out analysis of battery physical phenomena, and characterize batteries based on phenomena occurring in particular time and/or frequency domains. These systems may be additionally responsible for charging and/or monitoring a rechargeable battery. Examples of battery physical phenomena include mass transport (e.g., diffusion and/or migration) in battery electrolytes, mass transport in battery electrodes, and reactions on battery electrodes.

The invention relates to a method for estimating the state of an energy store comprising at least one electrochemical battery cell (12, 14, 16, 18, 20, 22, 24, 26, 28) using a battery management system (BMS) which comprises an impedance spectroscopy chip, having at least the following steps: a) determining the frequency-dependent impedance of the at least one electrochemical battery cell (12, 14, 16, 18, 20, 22, 24, 26, 28) using a data set recording taken in real-time, b) training an artificial neural network (60) with temperature-based training spectra as the input and a specification for temperature values belonging to each training spectrum as the output, c) taking into consideration a battery cell-to-battery cell variance (30) between the electrochemical battery cells (12, 14, 16, 18, 20, 22, 24, 26, 28) when testing the artificial neural network (60) using weighting functions ascertained during step b) and test spectra and estimating the temperature values belonging to the test spectra according to the weighting functions ascertained in step b), and d) estimating at least one internal state (SoC, SoH, T.sub.int) of the at least one electrochemical battery cell (12, 14, 16, 18, 20, 22, 24, 26, 28) of the energy store using the trained artificial neural network (6).

The invention relates to a method for estimating the state of an energy store comprising at least one electrochemical battery cell (12, 14, 16, 18, 20, 22, 24, 26, 28) using a battery management system (BMS) which comprises an impedance spectroscopy chip, having at least the following steps: a) determining the frequency-dependent impedance of the at least one electrochemical battery cell (12, 14, 16, 18, 20, 22, 24, 26, 28) using a data set recording taken in real-time, b) training an artificial neural network (60) with temperature-based training spectra as the input and a specification for temperature values belonging to each training spectrum as the output, c) taking into consideration a battery cell-to-battery cell variance (30) between the electrochemical battery cells (12, 14, 16, 18, 20, 22, 24, 26, 28) when testing the artificial neural network (60) using weighting functions ascertained during step b) and test spectra and estimating the temperature values belonging to the test spectra according to the weighting functions ascertained in step b), and d) estimating at least one internal state (SoC, SoH, T.sub.int) of the at least one electrochemical battery cell (12, 14, 16, 18, 20, 22, 24, 26, 28) of the energy store using the trained artificial neural network (6).

A method for generating an electrochemical impedance spectrum for a battery includes: collecting, in a discharge state of a battery, battery discharge information of the battery periodically according to a preset collection interval, where the battery discharge information includes collection time, and current information and voltage information associated with the collection time; performing Fourier transform according to the collection interval and battery discharge information, to obtain multiple frequency-based first battery signals; determining a second battery signal from the multiple first battery signals, where the second battery signal includes a voltage signal greater than or equal to a preset voltage threshold; and determining an electrochemical impedance at a corresponding frequency according to the second battery signal, and constructing an electrochemical impedance spectrum according to all the electrochemical impedance.

A method for generating an electrochemical impedance spectrum for a battery includes: collecting, in a discharge state of a battery, battery discharge information of the battery periodically according to a preset collection interval, where the battery discharge information includes collection time, and current information and voltage information associated with the collection time; performing Fourier transform according to the collection interval and battery discharge information, to obtain multiple frequency-based first battery signals; determining a second battery signal from the multiple first battery signals, where the second battery signal includes a voltage signal greater than or equal to a preset voltage threshold; and determining an electrochemical impedance at a corresponding frequency according to the second battery signal, and constructing an electrochemical impedance spectrum according to all the electrochemical impedance.

A battery management apparatus includes a charging unit configured to charge a battery cell, a measuring unit configured to measure voltage and current of the battery cell, and a control unit configured to receive battery information including the voltage and current from the measuring unit, estimate a SOC of the battery cell based on the received battery information, calculate an internal resistance of the battery cell based on the battery information whenever the SOC of the battery cell increases by a criterion amount, compare a change pattern of the calculated internal resistance with a preset criterion pattern, and set a negative electrode capacity for the battery cell based on the comparison result.

A secondary battery diagnosing technology capable of effectively diagnosing a state of a secondary battery using a charge and discharge signal of the secondary battery, including a memory unit configured to store a positive electrode reference profile and a negative electrode reference profile for charge or discharge of a reference battery, a voltage measuring unit configured to measure a voltage of a target battery during a charge or discharge process, and a processor configured to generate a charge and discharge measurement profile based on the voltage, compare a simulation profile obtained from the positive electrode reference profile and the negative electrode reference profile with the generated charge and discharge measurement profile, and determine a positive electrode adjustment profile and a negative electrode adjustment profile so that an error between the simulation profile and the charge and discharge measurement profile is within a predetermined level.

A deterioration estimation device (1) includes: a discharge control unit (11) configured to discharge a lead-acid battery (3) or a lead-acid battery module (4) that includes a plurality of lead-acid batteries until the lead-acid battery (3) or the lead-acid battery module (4) reaches a predetermined SOC; and a first estimation unit (11) configured to estimate a rate of deterioration of the lead-acid battery (3) or the lead-acid battery module (4) based on internal resistance or conductance derived when the lead-acid battery (3) or the lead-acid battery module (4) is discharged.