A method of determining battery degradation retroactively using historical data is disclosed. The method includes the steps of collecting state of charge (SOC) and DC ampere data for a predetermined time period; determining a delta (Δ) SOC based on the data collected; creating a set of SOC regimes having a size based on ΔSOC; filtering the SOC data and determining a set of points which indicate a charging or discharging event; and calculating overall Coulombs associated with each charging or discharging event and for each event, producing a timestamp and Coulombs associated with each event.

A method for battery capacity estimation is provided. The method includes monitoring a sensor, collecting a plurality of data points including a voltage-based state of charge value and an integrated current value, defining within the data points a first data set collected during a first time period and a second data set collected during a second time period, determining an integrated current error related to the second data set, comparing the integrated current error related to the second data set to a threshold integrated current error. When the error related to the second data set exceeds the threshold, the method further includes resetting the second data set based upon an integrated current value from the first time period. The method further includes combining the data sets to create a combined data set and determining a voltage slope capacity estimate as a change in integrated current versus voltage-based state of charge.

A battery management system for tracking a state of charge of a battery including multiple cells including a coulomb counter (CC) estimator and an artificial intelligence (AI) estimator. The CC estimator determines a charge difference over time for tracking changes of a state of charge of each cell and updates a corresponding state of charge value. The AI estimator converts a set of sample values of a cell into an estimated state of charge for calibrating the cell. A controller may periodically invoke the AI estimator to calibrate the state of charge of each cell and to update a corresponding state of charge value. Processing by the AI estimator may be minimized by being used only to calibrate each cell before combined error of the CC estimator reaches a predetermined threshold. AI based calibration may use Long Short-Term Memories and may further incorporate voltage mean and voltage variance over time.

A method and system of counting coulombs drawn from a power source by a load. A reference current source regulates the current drawn by the load at any given time to be either zero or a predetermined fixed amount. A comparator controls a time the switch is closed and open. When the switch is closed, coulombs are allowed to be drawn from the power source, and prevented to be drawn when the switch is open. An oscillator generates a clock signal during the time the switch is closed. A counter counts the number of clock cycles from the clock signal during the time the switch is closed. The count is provided as a signal at a second output of the coulomb counter.

In an example, a battery management system may include a host microcontroller, which may be operated in accordance with a first clock signal; and a first analog front end (AFE) circuit. The first AFE circuit may be operated in accordance with a second clock signal that may be unsynchronized with the first clock signal. The first AFE circuit may also include first digital circuitry to (1) accumulate a first value corresponding to a number of ADC sample cycles of the first ADC, and to (2) accumulate a second value corresponding to the digital output representative of the first battery current for the ADC sample cycles accumulated in the first value. The first AFE circuit may transfer a representation of the first value and a representation of the second value to the host microcontroller in response to a request from the host microcontroller.

A battery charge management method and device operable to perform that method. The battery charge management method comprises the steps of determining charge delivered to or from a battery based on Coulomb counting and updating an estimated battery state of charge based on the amount of delivered charge and measuring the battery current. Then comparing the updated estimated battery state of charge with a predetermined state of charge value of less than 100%, such that each predetermined state of charge value being associated with an expected battery current for that predetermined state of charge value. The method also includes modifying the updated estimated battery state of charge when the updated estimated state of charge is greater than the predetermined state of charge value and the measured battery current is greater than the expected battery current associated with the predetermined state of charge.

A signal processing circuit includes: a plurality of A/D conversion units of a plurality of channels, each of plurality of the A/D conversion units including an amplifier configured to amplify an input analog signal and an A/D converter configured to convert an output signal from the amplifier into a digital signal, wherein at least one of operation parameters of the amplifier and the A/D converter is set individually for each of the plurality of channels.

In an analog-to-frequency converting circuit, a set of switches receive a first sense signal indicative of a current and provides a second sense signal that alternates between an original version of the first sense signal and a reversed version of the first sense signal, under control of a switching signal. An integral comparing circuit integrates the second sense signal to generate an integral value and generates a train of trigger signals. Each trigger signal is generated when the integral value reaches a preset reference. A compensation circuit compensates for the integral value with a predetermined value in response to each trigger signal. A control circuit generates the switching signal such that a time interval during which the second sense signal is the original version and a time interval during which the second sense signal is the reversed version are substantially the same.

An apparatus for battery state of charge (SOC) estimation, including: a pre-processing unit (200), configured for detecting operating parameters of a batters, and filtering at least part of the parameters to generate filtered parameters; an SOC estimating unit (300), configured for calculating nominal SOC by using a first neural network based on the operating parameters and the filtered parameters; a validity estimating unit (400), configured for estimating a validity value based on the operating parameters and the filtered parameters, the validity value indicating validity of the nominal SOC outputted by the SOC estimating unit (300); an SOC Kalman filter (500), configured for performing a Kalman filtering algorithm based on the operating parameters, the nominal SOC transmitted and the validity value, thereby outputting an estimated SOC of the battery.

A battery management unit for a battery module, the battery management unit including a sensing unit electrically connected to a plurality of battery cells, the sensing unit detecting voltage of each battery cell of the plurality of battery cells and outputting a detection signal including voltage information representing the detected voltage, a first power supply unit generating a first operating voltage using a module voltage of the battery module, and a communication unit which operates using the first operating voltage, the communication unit including an antenna, a wireless communication circuit and a first input port, the communication unit receiving the detection signal from the sensing unit through the first input port, testing at least one preset item based on the detection signal, and outputting a RF signal representing a result of the testing through the antenna and the wireless communication circuit.