CN114418465A - Data-driven power battery use behavior quantitative evaluation method - Google Patents
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Abstract
The invention relates to a quantitative evaluation method for use behaviors Of a data-driven power battery, which comprises the steps Of constructing membership function based on SOC (State Of Charge) health intervals and current health intervals, constructing a use behavior evaluation matrix, calculating an equivalent charging (discharging) matrix and calculating grades based on the evaluation matrix and the equivalent charging (discharging) matrix. The cloud big data platform can provide effective support for power battery fault diagnosis for quantitative evaluation of each charge-discharge cycle, and the power-assisted power battery is safe to use. The invention has the beneficial effects that: (1) the using behavior of each charging (discharging) cycle of the power battery is evaluated based on cloud historical data, the requirement on data quality is low, and the robustness is strong; (2) and in the face of batteries with different models, the membership function can be flexibly adjusted according to the self-defined health interval, and the transportability is strong.
Description
Technical Field
The invention relates to a quantitative evaluation method for use behaviors of a power battery, in particular to a quantitative evaluation method for use behaviors of a data-driven power battery.
Background
The battery safety of the electric automobile is very important for the automobile using experience of an automobile owner and the brand influence of an automobile enterprise. Therefore, the fault diagnosis of the power battery is of great significance to the safe use and popularization of the battery system. Most current techniques for battery fault diagnosis are directed to laboratory measurement data. The fault diagnosis method for the actual operation data of the power battery of the electric automobile mainly comprises the step of identifying an internal short circuit based on a battery mechanism, namely, designing an identification algorithm according to the voltage data characteristics of the battery in an internal short circuit state to realize the identification of the internal short circuit fault. The method only analyzes fault data exceeding a fixed threshold value, and lacks analysis and calculation of data within the threshold value, so that fault diagnosis results seriously depend on an expert system. Therefore, a method for quantitatively evaluating historical data of a battery is urgently needed, and effective support is provided for a fault diagnosis result.
At present, most of evaluation methods for battery use behaviors are to directly perform basic statistical analysis on specific behaviors in a battery full cycle, and quantitative evaluation on charge and discharge data in a single cycle is lacked.
Disclosure of Invention
The technical problem of the invention is mainly solved by the following technical scheme:
a quantitative evaluation method for the use behavior of a data-driven power battery comprises
Calculating corresponding membership degrees of each SOC subinterval and each current subinterval based on the divided SOC subinterval and the divided current subinterval and the constructed membership degree function to form a use behavior evaluation matrix;
reading battery data to judge the charging or discharging state of the battery, and calculating an equivalent charging or discharging matrix according to the charging or discharging;
and calculating the evaluation score of each charging or discharging cycle according to the equivalent charging or discharging matrix and the established evaluation matrix.
The quantitative evaluation method for the use behaviors of the data-driven power battery further comprises the step of judging a fault occurrence part according to the evaluation score corresponding to the fault occurrence battery when the battery fails.
In the above quantitative evaluation method for the use behavior of the data-driven power battery, the SOC subinterval and the current subinterval are defined based on the following divisions:
SOC subinterval as
{(0,100/N],(100/N,200/N],…,(100(N-2)/N,100(N-1)/N],(100(N-1)/N,100]}，
The current subinterval is noted
{ (0,200/M ], (200/M,400/M ], …, (200(M-2)/M,200(M-1)/M ], (200(M-1)/M,200 }, wherein N is an even number and M is a positive integer greater than 2.
In the quantitative evaluation method for the use behavior of the data-driven power battery, the SOC membership function A is constructedsoc(x) And current membership function Ac(x) Averaging points according to the number of subintervals in the independent variable range, and calculating the membership degree of each subinterval; wherein A issoc(x) Is a one-dimensional axisymmetric function, Ac(x) Is a monotonically decreasing function; function independent variable range, namely SOC and current interval range; obtaining SOC membership degree sequence { Asoc1,…,Asoci,…,AsocNAnd the sequence of current membership AI1,…,AIi,…,AIM}。
In the quantitative evaluation method for the use behaviors of the data-driven power battery, the SOC membership degree sequence is taken as a column vector, the current membership degree sequence is taken as a row vector, and the vector product of the two vectors is taken as a battery state evaluation matrix AM×N。
In the above quantitative evaluation method for the use behavior of the data-driven power battery, the specific steps of calculating the equivalent charging or discharging matrix are as follows:
step 1, judging the charging or discharging state of the battery system based on the read data, if the charging or discharging state is identified as the discharging state, turning to step 2, and if the charging or discharging state is identified as the charging state, turning to step 5;
step 2, caching discharge state related variables including current timeSOC value SOC at moment ttCurrent value I at the present timetThe current charge or discharge state; eliminating data with a current value of 0 in the data, and splicing the data again; turning to the step 3;
step 3, accumulating time, and turning to step 4 after t + 1;
step 4, caching the current time state, comparing the current time state with the previous time state, if the state changes, turning to step 6, if the state does not change, turning to step 1, and repeating the steps;
step 5, caching variables related to the charging state, including the current time SOC value SOCtAbsolute value of current at presenttCurrent charge-discharge state; turning to the step 4;
and 6, constructing an equivalent charging or discharging matrix according to the cached SOC data and the current data.
In the method for quantitatively evaluating the use behaviors of the data-driven power battery, in step 1, the charge and discharge judgment basis is that the starting point of a window is judged to be in a discharge state when the current of L sampling points in the window is identified to be positive according to a sliding window with the length of L; and when the current of L sampling points in the window is identified to be a negative value, judging that the starting point of the window is in a charging state, wherein L is a positive integer greater than 10.
In the above quantitative evaluation method for the use behavior of the data-driven power battery, the equivalent charging or discharging matrix is constructed based on the following definitions:
constructing a blank matrix with M rows and N columns, and recording the blank matrix as SM×N(ii) a Judging a subinterval where the SOC is located based on the cached SOC data, and recording the current subinterval as i; SOC _ min is a data start value, and SOC _ max is a data end value; from the beginning of SOC _ min execution to the end of SOC _ max, accumulating and summing the current value corresponding to each moment in the judgment process of each subinterval, recording the result as I _ sum, and simultaneously recording the number t of sampling pointsiUntil the SOC is judged to belong to the next subinterval; calculating the equivalent current IdxJudgment of IdxRecording the current subinterval as j in the current interval; calculating the equivalent charging Times Times _ (i) (j) belonging to the SOC subinterval and the current subinterval, and inputting Times _ (i) (j) to SM×NRow j +1 and column i + 1.
In the above quantitative evaluation method for the use behavior of the data-driven power battery,
the SOC interval judgment rule is as follows: if (100/N) i<SOCt<(100/N) (i +1), the SOC at the moment belongs to the (i +1) th subinterval; equivalent current Idx=I_sum/ti
The current interval judgment rule is as follows: if (200/M) j<Idx<(200/M) (j +1), the equivalent current at the moment belongs to the j +1 th subinterval;
the equivalent charging times calculation rule is as follows:
if the SOC _ min belongs to the ith subinterval;
Times_(i)(j)= ((100/N)(i+1)-SOC_min)/(100/N)；
if the SOC _ max belongs to the ith sub-interval;
Times_(i)(j)= (SOC_max-(100/N)i)/(100/N)；
otherwise Times _ (i) (j) = 1.
In the above quantitative evaluation method for the use behavior of the data-driven power battery, the dot product result A of the equivalent charging or discharging matrix and the evaluation matrixM×N·SM×NScore, which is an evaluation Score of the charge or discharge cycle; taking the median of each cycle score as the historical score of the battery pack for the full-period data;
the evaluation results for each cycle are divided into three grades:
score assessment Score >2/3 Score ceiling: the using behavior is excellent;
evaluation Score 1/3 max Score <2/3 Score upper limit: the use behavior is good;
score evaluation Score <1/3 upper Score limit: poor use behavior;
when a fault is detected, the using behavior is evaluated to be excellent, and the fault probability of the battery monomer connecting line or the sensor is high;
when a fault is detected, the using behavior is evaluated to be good, and the probability of the abnormal monomer capacity or the abnormal monomer internal resistance is high;
when a fault is detected, the use behavior is evaluated to be poor, and the probability of internal short circuit fault is high.
Therefore, the invention has the following advantages: 1. the using behavior of each charging (discharging) cycle of the power battery is evaluated based on cloud historical data, the requirement on data quality is low, and the robustness is strong; 2. and in the face of batteries with different models, the membership function can be flexibly adjusted according to the self-defined health interval, and the transportability is strong. The calculation of the equivalent charge and discharge matrix and the calculation of the evaluation matrix have low requirement on data quality.
Drawings
FIG. 1 is a schematic flow diagram of a method of the present invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b):
as shown in fig. 1, the method according to the present invention first calculates the membership degree corresponding to each SOC subinterval and current subinterval based on the divided SOC subinterval and current subinterval and the constructed membership degree function, and forms a usage behavior evaluation matrix. And then reading the battery data to judge the charging (discharging) state of the battery, and calculating an equivalent charging (discharging) matrix according to the charging (discharging) state. And calculating the evaluation score of each charging (discharging) cycle according to the equivalent charging (discharging) matrix and the evaluation matrix.
The technical scheme comprises the following specific implementation steps:
(a) the SOC interval was defined as [0%,100% ], and the current interval was defined as [0,200A ].
N SOC subintervals (N is an even number) and M current subintervals are divided.
SOC subinterval as
{(0,100/N],(100/N,200/N],…,(100(N-2)/N,100(N-1)/N],(100(N-1)/N,100]}；
The current subinterval is noted
{ (0,200/M ], (200/M,400/M ], …, (200(M-2)/M,200(M-1)/M ], (200(M-1)/M,200] }, go to step (b).
(b) Constructing an SOC membership function A according to a self-defined health intervalsoc(x) And current membership function Ac(x) Taking points according to the average number of subintervals in the independent variable range, and countingAnd calculating the membership degree of each subinterval. Wherein A issoc(x) Is a one-dimensional axisymmetric function, Ac(x) Is a monotonically decreasing function;
The function independent variable range is the SOC and the current interval range. Obtaining SOC membership degree sequence { Asoc1,…,Asoci,…,AsocNAnd the sequence of current membership AI1,…,AIi,…,AIMAnd (c) turning to the step (c).
(c) A battery state evaluation matrix is constructed, the SOC membership degree sequence is taken as a column vector, the current membership degree sequence is taken as a row vector, and the vector product of the two vectors is taken as a battery state evaluation matrix AM×NAnd (d) turning to the step (d).
(d) And (e) reading input data of the battery system, including SOC, current and battery charging (discharging) state, and turning to the step (e).
(e) And (e) judging the charging (discharging) state of the battery system based on the read data, and if the battery system is identified as the discharging state, turning to the step (f), and if the battery system is identified as the charging state, turning to the step (i).
The charging and discharging judgment basis is that according to a sliding window with the length of 10, if the current magnitude of 10 sampling points in the window is identified to be positive, the starting point of the window is judged to be in a discharging state; and when the current of 10 sampling points in the window is identified to be a negative value, judging that the starting point of the window is in a charging state.
(f) Buffering discharge state related variables including current time SOC value SOCtCurrent value I at the present timetThe current charge (discharge) state. Eliminating data with a current value of 0 in the data, and splicing the data again; go to step (g).
(g) And (5) accumulating the time, t + +, and turning to the step (h).
(h) And (4) caching the current time state, comparing the current time state with the previous time state, if the state changes, turning to the step (j), if the state does not change, turning to the step (e), and repeating the steps.
(i) Caching state-of-charge related variables, including the current time SOC valuetAbsolute value of current at presenttCurrent charge and discharge state. Go to step (h).
(j) And constructing an equivalent charging (discharging) matrix according to the cached SOC data and the current data. Go to step (k).
The matrix construction rules are as follows:
the matrix is an equivalent charge-discharge matrix and is used for judging the distribution state of currents with different magnitudes in each SOC interval.
The evaluation matrix constructed in the step c is used for scoring the current distribution condition; in other words, step c is to establish a scoring criterion to score the matrix constructed in step j.
Constructing a blank matrix with M rows and N columns, and recording the blank matrix as SM×N. Based on cachingJudging the subinterval of the SOC, and recording the current subinterval as i. From SOC _ min, accumulating and summing current values corresponding to each moment, recording the result as I _ sum, and simultaneously recording the number t of sampling pointsiUntil the SOC is judged to belong to the next subinterval. Calculating the equivalent current IdxJudgment of IdxAnd recording the current subinterval as j in the current interval. The equivalent number of Times Times _ (i) (j) of charging belonging to the SOC subinterval and the current subinterval is calculated, and Times _ (i) (j) is input to the j +1 th row and the i +1 th column in S.
The SOC interval judgment rule is as follows: if (100/N) i<SOCt<(100/N) (i +1), the time SOC belongs to the (i +1) th subinterval.
Equivalent current Idx=I_sum/ti；
The current interval judgment rule is as follows: if (200/M) j<Idx<(200/M) (j +1), the equivalent current at the moment belongs to the j +1 th subinterval.
The equivalent charging times calculation rule is as follows:
if the SOC _ min belongs to the ith subinterval;
Times_(i)(j)= ((100/N)(i+1)-SOC_min)/(100/N)；
if the SOC _ max belongs to the ith sub-interval;
Times_(i)(j)= (SOC_max-(100/N)i)/(100/N)；
otherwise Times _ (i) (j) = 1;
(k) dot product result A of equivalent charging matrix and evaluation matrixM×N·SM×NAn evaluation Score for the charge (discharge) cycle is Score.
And taking the median of each cycle score as the historical score of the battery pack for the full-period data.
The evaluation results for each cycle are divided into three grades:
score assessment Score >2/3 Score ceiling: the using behavior is excellent;
evaluation Score 1/3 max Score <2/3 Score upper limit: the use behavior is good;
score evaluation Score <1/3 upper Score limit: the use behavior is poor.
When a fault is detected, the using behavior is evaluated to be excellent, and the fault probability of the battery monomer connecting line or the sensor is higher;
when a fault is detected, the using behavior is evaluated to be good, and the probability of the abnormal monomer capacity or the abnormal monomer internal resistance is higher;
when a fault is detected, the use behavior is evaluated as poor, and the probability of internal short circuit fault is higher.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (10)
1. A quantitative evaluation method for use behaviors of a data-driven power battery is characterized by comprising the following steps
Calculating corresponding membership degrees of each SOC subinterval and each current subinterval based on the divided SOC subinterval and the divided current subinterval and the constructed membership degree function to form a use behavior evaluation matrix;
reading battery data to judge the charging or discharging state of the battery, and calculating an equivalent charging or discharging matrix according to the charging or discharging;
and calculating the evaluation score of each charging or discharging cycle according to the equivalent charging or discharging matrix and the established evaluation matrix.
2. The quantitative evaluation method for the use behaviors of the data-driven power battery as claimed in claim 1, further comprising a step of judging a fault occurrence part according to the evaluation score corresponding to the fault occurrence battery when the battery fails.
3. The quantitative evaluation method for the use behavior of the data-driven power battery according to claim 2, characterized in that the SOC subinterval and the current subinterval are defined based on the following divisions:
SOC subinterval as
{(0,100/N],(100/N,200/N],…,(100(N-2)/N,100(N-1)/N],(100(N-1)/N,100]}，
The current subinterval is noted
{ (0,200/M ], (200/M,400/M ], …, (200(M-2)/M,200(M-1)/M ], (200(M-1)/M,200 }, wherein N is an even number and M is a positive integer greater than 2.
4. The quantitative evaluation method for the use behaviors of the data-driven power battery as claimed in claim 3, characterized in that an SOC membership function A is constructedsoc(x) And current membership function Ac(x) Averaging points according to the number of subintervals in the independent variable range, and calculating the membership degree of each subinterval; wherein A issoc(x) Is a one-dimensional axisymmetric function, Ac(x) Is a monotonically decreasing function; function independent variable range, namely SOC and current interval range; obtaining SOC membership degree sequence { Asoc1,…,Asoci,…,AsocNAnd the sequence of current membership AI1,…,AIi,…,AIM}。
5. The quantitative evaluation method for the use behaviors of the data-driven power battery as claimed in claim 4, wherein the SOC membership degree sequence is used as a column vector, the current membership degree sequence is used as a row vector, and the vector product of the two vectors is used as a battery state evaluation matrix AM×N。
6. The quantitative evaluation method for the use behavior of the data-driven power battery according to claim 5, characterized in that the specific steps of calculating the equivalent charging or discharging matrix are as follows:
step 1, judging the charging or discharging state of the battery system based on the read data, if the charging or discharging state is identified as the discharging state, turning to step 2, and if the charging or discharging state is identified as the charging state, turning to step 5;
step 2, caching discharge state related variables including SOC value SOC of current time ttCurrent value I at the present timetThe current charge or discharge state; eliminating data with a current value of 0 in the data, and splicing the data again; turning to the step 3;
step 3, accumulating time, and turning to step 4 after t + 1;
step 4, caching the current time state, comparing the current time state with the previous time state, if the state changes, turning to step 6, if the state does not change, turning to step 1, and repeating the steps;
step 5, caching variables related to the charging state, including the current time SOC value SOCtAbsolute value of current at presenttCurrent charge-discharge state; turning to the step 4;
and 6, constructing an equivalent charging or discharging matrix according to the cached SOC data and the current data.
7. The quantitative evaluation method for the use behaviors of the data-driven power battery according to claim 6, characterized in that in the step 1, the charge and discharge judgment basis is that according to a sliding window with the length of L, if the current magnitudes of L sampling points in the window are recognized to be positive, the starting point of the window is judged to be in a discharge state; and when the current of L sampling points in the window is identified to be a negative value, judging that the starting point of the window is in a charging state, wherein L is a positive integer greater than 10.
8. The quantitative evaluation method for the use behavior of the data-driven power battery according to claim 7, characterized in that the construction of the equivalent charging or discharging matrix is based on the following definitions:
constructing a blank matrix with M rows and N columns, and recording the blank matrix as SM×N(ii) a Judging a subinterval where the SOC is located based on the cached SOC data, and recording the current subinterval as i; SOC _ min is a data start value, and SOC _ max is a data end value; from the beginning of SOC _ min execution to the end of SOC _ max, accumulating and summing the current value corresponding to each moment in the judgment process of each subinterval, recording the result as I _ sum, and simultaneously recording the number t of sampling pointsiUntil the SOC is judged to belong to the next subinterval; calculating the equivalent current IdxJudgment of IdxRecording the current subinterval as j in the current interval; calculating the equivalent charging Times Times _ (i) (j) belonging to the SOC subinterval and the current subinterval, and inputting Times _ (i) (j) to SM×NRow j +1 and column i + 1.
9. The quantitative evaluation method for the use behavior of the data-driven power battery according to claim 8,
the SOC interval judgment rule is as follows: if (100/N) i<SOCt<(100/N) (i +1), the SOC at the moment belongs to the (i +1) th subinterval; equivalent current Idx=I_sum/ti
The current interval judgment rule is as follows: if (200/M) j<Idx<(200/M) (j +1), the equivalent current at the moment belongs to the j +1 th subinterval;
the equivalent charging times calculation rule is as follows:
if the SOC _ min belongs to the ith subinterval;
Times_(i)(j)= ((100/N)(i+1)-SOC_min)/(100/N)；
if the SOC _ max belongs to the ith sub-interval;
Times_(i)(j)= (SOC_max-(100/N)i)/(100/N)；
otherwise Times _ (i) (j) = 1.
10. The quantitative evaluation method for the use behavior of the data-driven power battery as claimed in claim 9, wherein the dot product of the equivalent charging or discharging matrix and the evaluation matrix is AM×N·SM×NScore, which is an evaluation Score of the charge or discharge cycle; taking the median of each cycle score as the historical score of the battery pack for the full-period data;
the evaluation results for each cycle are divided into three grades:
score assessment Score >2/3 Score ceiling: the using behavior is excellent;
evaluation Score 1/3 max Score <2/3 Score upper limit: the use behavior is good;
score evaluation Score <1/3 upper Score limit: poor use behavior;
when a fault is detected, the using behavior is evaluated to be excellent, and the fault probability of the battery monomer connecting line or the sensor is high;
when a fault is detected, the using behavior is evaluated to be good, and the probability of the abnormal monomer capacity or the abnormal monomer internal resistance is high;
when a fault is detected, the use behavior is evaluated to be poor, and the probability of internal short circuit fault is high.
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