A secondary battery management device is connected with electronic equipment using a secondary battery as a driving power supply via the Internet. The secondary battery management device acquires performance information of the secondary battery from the electronic equipment, and measures a degradation level of performance of the secondary battery during a predetermined unit period. The secondary battery management device estimates a degrading speed of the secondary battery on the basis of the degradation level. The secondary battery management device may notify a user of the electronic equipment of the replacement timing of the secondary battery, use fees of the secondary battery, and the like.

In one aspect, a method comprises receiving first data pertaining to a battery pack of a vehicle, wherein the first data is received from sensors associated with the vehicle, and the first data pertains to a battery pack current, a cell voltage, a cell current, a cell temperature, or some combination thereof; receiving second data pertaining to simulation of the battery pack; receiving third data from a manufacturer; receiving historical data on a fleet of vehicles that use the battery pack; predicting a remaining useful life of the battery pack of the vehicle by using a hybrid model comprising a physics-based model that receives the based on the first, second, third, and historical data, and generates properties pertaining to the battery pack; a machine learning model that uses the properties to predict the remaining useful life of each cell of the battery pack; and transmitting the remaining useful life.

In one aspect, a method comprises receiving first data pertaining to a battery pack of a vehicle, wherein the first data is received from sensors associated with the vehicle, and the first data pertains to a battery pack current, a cell voltage, a cell current, a cell temperature, or some combination thereof; receiving second data pertaining to simulation of the battery pack; receiving third data from a manufacturer; receiving historical data on a fleet of vehicles that use the battery pack; predicting a remaining useful life of the battery pack of the vehicle by using a hybrid model comprising a physics-based model that receives the based on the first, second, third, and historical data, and generates properties pertaining to the battery pack; a machine learning model that uses the properties to predict the remaining useful life of each cell of the battery pack; and transmitting the remaining useful life.

To provide a storage battery state estimation device that can accurately perform deterioration diagnosis even when there is no characteristic information of a storage battery.

The storage battery state estimation device includes: a baseline function estimation unit for separating a data point sequence including a capacity and differential voltage of a storage battery into a baseline point sequence and a peak point sequence, and estimating a baseline function on the basis of the baseline point sequence; a peak function estimation unit for estimating a peak function on the basis of the peak point sequence; a model function estimation unit for estimating a model function on the basis of the baseline function and the peak function; an error peak detection unit for detecting presence or absence of an error peak.

To provide a storage battery state estimation device that can accurately perform deterioration diagnosis even when there is no characteristic information of a storage battery.

The storage battery state estimation device includes: a baseline function estimation unit for separating a data point sequence including a capacity and differential voltage of a storage battery into a baseline point sequence and a peak point sequence, and estimating a baseline function on the basis of the baseline point sequence; a peak function estimation unit for estimating a peak function on the basis of the peak point sequence; a model function estimation unit for estimating a model function on the basis of the baseline function and the peak function; an error peak detection unit for detecting presence or absence of an error peak.

There is provided an apparatus for diagnosing whether or not an error has occurred in battery cells, the apparatus including a voltage measurement circuit for measuring a voltage of a battery cell, a data processing circuit for calculating a target statistical value indicating a state of the battery cell based on a voltage measured by the voltage measurement circuit, and calculates a cumulative statistical value, and a diagnosis circuit that determines whether or not an error has occurred in the battery cell through a cumulative determination operation of comparing the cumulative statistical value with a cumulative reference value, and counts a number of times of cumulative error when it is determined that the error has occurred in the battery cell in the cumulative determination operation.

There is provided an apparatus for diagnosing whether or not an error has occurred in battery cells, the apparatus including a voltage measurement circuit for measuring a voltage of a battery cell, a data processing circuit for calculating a target statistical value indicating a state of the battery cell based on a voltage measured by the voltage measurement circuit, and calculates a cumulative statistical value, and a diagnosis circuit that determines whether or not an error has occurred in the battery cell through a cumulative determination operation of comparing the cumulative statistical value with a cumulative reference value, and counts a number of times of cumulative error when it is determined that the error has occurred in the battery cell in the cumulative determination operation.

A diagnosis information generating apparatus according to an embodiment of the present disclosure includes: an input unit configured to receive diagnosis request information including data identification information for at least one of a plurality of diagnosis items of a diagnosis target and diagnosis data corresponding to the data identification information; a control unit configured to receive the diagnosis request information from the input unit, calculate a diagnosis data amount for the data identification information and the diagnosis data included in the diagnosis request information, select a packet structure corresponding to the calculated diagnosis data amount among a plurality of packet structures, and generate a communication packet including the diagnosis request information based on the selected packet structure; and a communication unit configured to output the communication packet generated by the control unit to an external diagnosing device as diagnosis information for the diagnosis target.

A diagnosis information generating apparatus according to an embodiment of the present disclosure includes: an input unit configured to receive diagnosis request information including data identification information for at least one of a plurality of diagnosis items of a diagnosis target and diagnosis data corresponding to the data identification information; a control unit configured to receive the diagnosis request information from the input unit, calculate a diagnosis data amount for the data identification information and the diagnosis data included in the diagnosis request information, select a packet structure corresponding to the calculated diagnosis data amount among a plurality of packet structures, and generate a communication packet including the diagnosis request information based on the selected packet structure; and a communication unit configured to output the communication packet generated by the control unit to an external diagnosing device as diagnosis information for the diagnosis target.

A battery system includes: a battery pack including a plurality of battery cells; and a battery management system for setting numbers to the battery cells by measuring cell voltages of the respective battery cells at a wake-up time, detecting a maximum cell voltage and a minimum cell voltage by periodically measuring the cell voltage of the respective battery cells after the wake-up, comparing the minimum cell voltage and the cell voltage of at least one first diagnosis target battery cell, comparing the maximum cell voltage and the cell voltage of at least one second diagnosis target battery cell, and diagnosing whether the diagnosis target battery cell is abnormal according to results of the comparison.