Method, system, device and medium for detecting battery state of health

1 . A method for detecting a battery state of health, comprising:

extracting a target curve segment from a first charging characteristic curve of a battery under test; the first charging characteristic curve being determined according to first charging data obtained by charging the battery under test according to target charging parameters, and the target curve segment being at least one curve segment with highest correlation to the battery state of health; and

determining, according to a corresponding relation between characteristic parameters of the target curve segment and the battery state of health, the battery state of health matching the characteristic parameters of the target curve segment, and determining the battery state of health as the battery state of health of the battery under test; wherein the corresponding relation is determined according to second charging data obtained by charging a test battery of the same type as the battery under test using the target charging parameters, wherein the characteristic parameters include a charging duration,

wherein the step of extracting the target curve segment from the first charging characteristic curve of the battery under test comprises:

dividing the first charging characteristic curve into a plurality of first curve segments;

calculating a correlation coefficient of the charging duration and the battery state of health of each first curve segment; and

selecting at least one of the first curve segments with a highest correlation coefficient as the target curve segment of the first charging characteristic curve.

2 . The method of claim 1 , wherein the corresponding relation is established by:

charging the test battery for M times to obtain the second charging data corresponding to each time; the second charging data including a second charging characteristic curve and a battery state of health; M≥1 and M being an integer;

for each second charging characteristic curve, dividing the second charging characteristic curve into a plurality of second curve segments according to a same division rule, calculating a correlation coefficient between the charging duration and the battery state of health of each second curve segment, and selecting at least one second curve segment with a highest correlation coefficient as a target curve segment of the second charging characteristic curve; and

fitting the target curve segments corresponding to the M second charging characteristic curves and the M battery states of health, and determining the fitting result as the corresponding relation.

3 . The method of claim 1 , wherein before the step of extracting the target curve segment from the first charging characteristic curve of the battery under test, the method further comprises:

assessing whether a number of charging times of the battery under test in abusive operating conditions meets a frequency threshold;

if an assessment result is yes, executing the step of extracting the target curve segment from the first charging characteristic curve of the battery under test.

4 . The method of claim 3 , further comprising:

if the assessment result is no, determining a capacity and a direct current internal resistance of the battery under test during a current charging time and at least one past charging time;

calculating a relative change rate of current internal resistance capacity of the battery under test according to the capacity and the direct current internal resistance; and

detecting the abusive operating conditions of the battery under test according to the relative change rate of current internal resistance capacity and the corresponding relation between relative change rates of internal resistance capacity and abnormal types.

5 . An electronic device comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for detecting a battery state of health according to claim 1 when executing the computer program.

6 . A system for detecting a battery state of health, comprising:

an extraction module, configured to extract a target curve segment from a first charging characteristic curve of a battery under test; the first charging characteristic curve being determined according to first charging data obtained by charging the battery under test according to target charging parameters, and the target curve segment being at least one curve segment with highest correlation to the battery state of health; and

a first determination module, configured to determine, according to a corresponding relation between characteristic parameters of the target curve segment and the battery state of health, the battery state of health matching the characteristic parameters of the target curve segment, and determining the battery state of health as the battery state of health of the battery under test; wherein the corresponding relation is determined according to second charging data obtained by charging a test battery of the same type as the battery under test using the target charging parameters, wherein the characteristic parameters include a charging duration,

wherein the extraction module comprises:

a first dividing unit, configured to divide the first charging characteristic curve into a plurality of first curve segments;

a calculation unit, configured to calculate a correlation coefficient of the charging duration and the battery state of health of each first curve segment; and

a selection unit, configured to select at least one of the first curve segments with a highest correlation coefficient as the target curve segment of the first charging characteristic curve.

7 . The system of claim 6 , wherein the corresponding relation comprises is established by:

an acquisition unit, configured to charge the test battery for M times to obtain the second charging data corresponding to each time; the second charging data including a second charging characteristic curve and a battery state of health; M≥1 and M being an integer;

a second dividing unit, configured to divide each second charging characteristic curve into a plurality of second curve segments according to a same division rule, calculate a correlation coefficient between the charging duration and the battery state of health of each second curve segment, and select at least one second curve segment with a highest correlation coefficient as a target curve segment of the second charging characteristic curve; and

a fitting unit, configured to fit the target curve segments corresponding to the M second charging characteristic curves and the M battery states of health and determine the fitting result as the corresponding relation.

8 . The system of claim 6 , further comprising:

an assessment module, configured to assess whether a number of charging times of the battery under test in abusive operating conditions meets a frequency threshold; if the assessment result is yes, invoke the extraction module, and if the assessment result is no, invoke a second determination module.

9 . The system of claim 8 , further comprising:

the second determination module, configured to determine a capacity and a direct current internal resistance of the battery under test during a current charging time and at least one past charging time;

a calculation module, configured to calculate a relative change rate of current internal resistance capacity of the battery under test according to the capacity and the direct current internal resistance; and

a detection module, configured to detect abusive operating conditions of the battery under test according to the relative change rate of current internal resistance capacity and the corresponding relation between relative change rates of internal resistance capacity and abnormal types.