Methods and apparatuses for accurately estimating an internal temperature of a secondary battery with a charging rate SOC and a charge/discharge state taken into account are provided. A method may include a method of estimating an internal temperature (T) of a secondary battery for a vehicle, which includes a change determination process to be repeatedly performed and an internal temperature estimation process to be repeatedly performed.

The invention relates to an energy storage comprising a plurality of series connectable energy modules connected to a string via a plurality of switches. Wherein a string controller controls which of the energy modules that are part of a current path through the string by control of the status of the switches. An energy storage monitoring system is monitoring an energy storage element operating parameter of an energy module, the energy storage monitoring system comprises: a current sensor and a plurality of energy module print. The plurality of energy module prints establishes an energy module operating parameter of the associated energy module. The current sensor establishes the current in the current path. The string controller is configured for by-passing an energy module based on information of status of the switches, the measured current in the current path and the battery operating parameter measured at the energy modules.

A computer-implemented method for improved determination of state of health (SoH) of a battery of a device can include determining that the device is in a charging state such that a charge of the battery is increased over a charge procedure; determining that a state of charge (SoC) metric of the battery reported by a battery gauge system has increased by a fixed SoC interval; determining a charge time interval over which the SoC metric of the battery has increased by the fixed SoC interval; and determining a SoH metric indicative of the SoH of the battery based at least in part on the charge time interval; wherein determining the SoH metric is based at least in part on a known relationship between a reference time interval representative of time required to increase a reference battery at full SoH by the fixed SoC interval and the charge time interval.

A computer-implemented method for improved determination of state of health (SoH) of a battery of a device can include determining that the device is in a charging state such that a charge of the battery is increased over a charge procedure; determining that a state of charge (SoC) metric of the battery reported by a battery gauge system has increased by a fixed SoC interval; determining a charge time interval over which the SoC metric of the battery has increased by the fixed SoC interval; and determining a SoH metric indicative of the SoH of the battery based at least in part on the charge time interval; wherein determining the SoH metric is based at least in part on a known relationship between a reference time interval representative of time required to increase a reference battery at full SoH by the fixed SoC interval and the charge time interval.

In one aspect, computer-implemented method may include receiving, from a cloud-based computing system, one or more machine learning model parameters that are configured to enable predicting a remaining useful life of each cell of a battery pack of a vehicle. The method may include loading, into memory of a processing device at the vehicle, the one or more machine learning model parameters, receiving data comprising one or more measurements and one or more user battery usage profiles, and based on the data, executing a trained machine learning model with the one or more parameters to input the data and to output the remaining useful life of each cell of the battery pack.

In one aspect, computer-implemented method may include receiving, from a cloud-based computing system, one or more machine learning model parameters that are configured to enable predicting a remaining useful life of each cell of a battery pack of a vehicle. The method may include loading, into memory of a processing device at the vehicle, the one or more machine learning model parameters, receiving data comprising one or more measurements and one or more user battery usage profiles, and based on the data, executing a trained machine learning model with the one or more parameters to input the data and to output the remaining useful life of each cell of the battery pack.

The battery inspection method includes the following. Self-discharging the battery to be inspected until a predetermined time elapses from the start of self-discharging. Detecting a voltage, a current, a temperature of the battery and an ambient temperature when the predetermined time elapses. Perform a preliminary step prior to self-discharge of the battery. Obtain preliminary step information representing the execution conditions of the preliminary step. Calculating the current convergence value from the acquired preliminary step information, the detected voltage, current, and temperature, and the detected ambient temperature by referring to a predetermined correspondence between the preliminary step information, the voltage, current, and temperature of the battery at the time when the predetermined time elapses, the ambient temperature, and the current convergence value. Determining whether a short circuit failure of the battery has occurred based on the calculated current convergence value.

The battery inspection method includes the following. Self-discharging the battery to be inspected until a predetermined time elapses from the start of self-discharging. Detecting a voltage, a current, a temperature of the battery and an ambient temperature when the predetermined time elapses. Perform a preliminary step prior to self-discharge of the battery. Obtain preliminary step information representing the execution conditions of the preliminary step. Calculating the current convergence value from the acquired preliminary step information, the detected voltage, current, and temperature, and the detected ambient temperature by referring to a predetermined correspondence between the preliminary step information, the voltage, current, and temperature of the battery at the time when the predetermined time elapses, the ambient temperature, and the current convergence value. Determining whether a short circuit failure of the battery has occurred based on the calculated current convergence value.

A cell sampling circuit includes a plurality of target units sequentially connected in a series connection in a daisy-chain communication manner and one or more transformer units each connected between two successive target units of the plurality of target units. Each of the target units is configured to acquire an impedance of at least one transformer unit connected to the target unit, and judge, based on the acquired impedance, whether to perform circuit abnormality early warning.

A cell sampling circuit includes a plurality of target units sequentially connected in a series connection in a daisy-chain communication manner and one or more transformer units each connected between two successive target units of the plurality of target units. Each of the target units is configured to acquire an impedance of at least one transformer unit connected to the target unit, and judge, based on the acquired impedance, whether to perform circuit abnormality early warning.