A battery characterization system includes a drive-sense circuit (DSC), memory that stores operational instructions, and processing module(s) operably coupled to the DSC and the memory. Based on a reference signal, the DSC generates a charge signal, which includes an AC (alternating current) component, and provides the charge signal to a terminal of a battery via a single line and simultaneously to senses the charge signal via the single line to detect an electrical characteristic of the battery based on a response of the battery. The DSC generates a digital signal representative of the electrical characteristic of the battery. The processing module(s), based on the operational instructions, generate the reference signal to include a frequency sweep of the AC component of the charge signal (e.g., different frequencies at different times or multiple frequencies simultaneously) and processes the digital signal to characterize the battery across the different respective frequencies and generate spectrum analysis (SA) information of the battery.

A battery characterization system includes a drive-sense circuit (DSC), memory that stores operational instructions, and processing module(s) operably coupled to the DSC and the memory. Based on a reference signal, the DSC generates a charge signal, which includes an AC (alternating current) component, and provides the charge signal to a terminal of a battery via a single line and simultaneously to senses the charge signal via the single line to detect an electrical characteristic of the battery based on a response of the battery. The DSC generates a digital signal representative of the electrical characteristic of the battery. The processing module(s), based on the operational instructions, generate the reference signal to include a frequency sweep of the AC component of the charge signal (e.g., different frequencies at different times or multiple frequencies simultaneously) and processes the digital signal to characterize the battery across the different respective frequencies and generate spectrum analysis (SA) information of the battery.

A method of calibration is described that simplifies the measurement of battery impedance conducted in-situ while determining battery state-of-health. A single shunt measurement with a known Sum of Sines (SOS) current, at the desired frequency spread and known root mean squared (RMS) current is used to create a calibration archive. A calibration selected from this archive is used to calibrate an impedance measurement made on the battery.

A method of calibration is described that simplifies the measurement of battery impedance conducted in-situ while determining battery state-of-health. A single shunt measurement with a known Sum of Sines (SOS) current, at the desired frequency spread and known root mean squared (RMS) current is used to create a calibration archive. A calibration selected from this archive is used to calibrate an impedance measurement made on the battery.

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.

In an arithmetic system, a data acquisition unit acquires operation data at least including a voltage of a battery mounted on an identical product (for example, an electrically-driven vehicle of an identical vehicle type) and a state of charge (SOC) from a plurality of individuals of the product via a network. A detector statistically processes the plurality of pieces of acquired operation data to detect an individual on which a battery different from specifications of the product is mounted.

In an arithmetic system, a data acquisition unit acquires operation data at least including a voltage of a battery mounted on an identical product (for example, an electrically-driven vehicle of an identical vehicle type) and a state of charge (SOC) from a plurality of individuals of the product via a network. A detector statistically processes the plurality of pieces of acquired operation data to detect an individual on which a battery different from specifications of the product is mounted.

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 acquiring a battery capacity, includes: acquiring multiple initial charging parameters of a battery when the battery is charged during a current charging process, where state of charge (SOC) of the battery in the current charging process changes for a range covering an SOC range, or a minimum charging temperature of the battery in the current charging process is greater than or equal to a temperature threshold; periodically acquiring multiple actual charging parameters of the battery during the current charging process and a current number of charging times corresponding to the current charging process; and acquiring, according to the multiple initial charging parameters, the multiple actual charging parameters, and the current number of charging times, a predicted battery capacity of the battery in a next charging process.