CN105759217A - Lead-acid battery pack online fault diagnosis method based on measurable data - Google Patents

Abstract

The invention discloses a lead-acid battery pack online fault diagnosis method based on measurable data. The lead-acid battery pack online fault diagnosis method comprises the steps of acquiring input and output history data of a lead-acid battery pack under test in a period of t0-tf in a working condition; calculating a nuclear Gram matrix K; solving the nuclear Gram matrix K by using a nonlinear iterative least square method, and obtaining a matrix T and a matrix U; calculating statistics T2 and statistics SPE of each sampling point and a confidence upper limit thereof; calculating a contribution value and an upper limit of each variable with respect to the statistics T2 and the statistics SPE; acquiring real-time data of the lead-acid battery pack under test; calculating a nuclear Gram matrix Knew; calculating statistics T2<new> and statistics SPEnew; determining whether the statistics T2<new> is greater than the statistics T2, or the statistics SPEnew is greater than the statistics SPE, and returning back to the step of acquiring the real-time data if not; and if so, determining the occurrence of a failure, and calculating the contribution values and relative contribution rates of the variables, among which, the variable with the relative contribution rate greater than 1 causes the system failure.

Description

A kind of based on the lead-acid batteries on-line fault diagnosis method that can survey data

Technical field

The present invention relates to lead-acid batteries method for diagnosing faults, be specifically related to a kind of based on the lead-acid batteries on-line fault diagnosis method that can survey data.

Background technology

Lead-acid accumulator is with the feature such as low price, technology maturation, internal resistance be little, it is widely used in UPS uninterrupted power source, controls the field such as switch and emerging electric vehicle, it it is currently used most commonly used accumulator, its electrode is mainly made by lead, electrolyte is sulfuric acid solution, is generally divided into open-type battery and two kinds of valve controlled type battery, owing to the former needs regular acid filling to safeguard, and the latter can realize non-maintaining, therefore valve controlled type battery receives increasing attention.

Owing to needing higher output voltage, electric motor car uses the lead-acid batteries being composed in series by more piece lead-acid accumulator, often there is complicated physical-chemical reaction in joint internal storage battery, therefore the lead-acid batteries for industry examples such as electric vehicles is a typical non-linear process system.Reliable and stable in order to ensure that lead-acid batteries is powered, it need to be monitored, but the feature such as coupling owing to having that data volume is big, between variable, lead-acid batteries is detected big with the difficulty of fault location by prior art in real time, degree of accuracy is low, cannot guaranteeing the stability of lead-acid batteries electric power system, system maintenance cost is high.

Summary of the invention

The technical problem to be solved in the present invention is to provide a kind of based on the lead-acid batteries on-line fault diagnosis method that can survey data, prior art can be solved and lead-acid batteries is detected in real time big with the difficulty of fault location, degree of accuracy is low, cause the stability that cannot guarantee lead-acid batteries electric power system, the problem that system maintenance cost is high.

The present invention is achieved through the following technical solutions:

A kind of based on the lead-acid batteries on-line fault diagnosis method that can survey data, comprise the following steps:

Step one: obtain under tested lead-acid batteries operating mode in time period t₀～t_fInput, output historical data；

Step 2: calculate core Gram matrix K according to input, output historical data；

Adopt nonlinear iteration method of least square to solve core Gram matrix K, obtain matrix T and matrix U；

The statistic T of each sampled point is calculated according to matrix T and matrix U²With statistic SPE and confidence upper limit thereof；

Calculate each variable to statistic T²With the contribution margin of statistic SPE and the upper limit；

Step 3: obtain the real time data of tested lead-acid batteries；

Core Gram matrix K is calculated according to real time data_new；

Counting statistics amountWith statistic SPE_new；

Judge statisticMore than statistic T², or statistic SPE_newMore than whether statistic SPE sets up, if the result is negative, then the real time data step obtaining tested lead-acid batteries is returned；

If above-mentioned judged result is yes, being then determined with fault and occur, calculate each variable contribution margin and Relative Contribution rate, the Relative Contribution rate variable more than 1 is judged to cause the system failure.

The further scheme of the present invention is, the input historical data in step one is form the voltage of single-unit lead-acid accumulator of tested lead-acid batteries, electric current, temperature data, and output historical data is tested lead-acid batteries voltage.

The further scheme of the present invention is, step 2 calculates core Gram matrix K according to input, output historical data is:

History inputted, output data are organized into the matrix of two 0 average variablees respectively:

X∈R^n×l, Y ∈ R^n×m, wherein n is sample number, and l, m be input/output variable dimension respectively；

Input, output matrix are decomposed into:

X=TP^T+ F, Y=UC^T+ G, wherein T ∈ R^n×p,U∈R^n×pComprising p the score vector being extracted, p is drawn by cross-validation method, P, and C is load matrix, F, and G is residual matrix；

Utilize Gaussian function K (x_i,x_j)=exp (-| | x_i-x_j||²/ σ), i, j=1,2 ... n calculates core Gram matrix K ∈ R^n×n, matrix K is carried out centralization and obtains:

Wherein I is that n ties up unit matrix, 1_nRepresent that element value is the n dimensional vector of 1.

The further scheme of the present invention is, step 2 adopts nonlinear iteration method of least square to solve core Gram matrix K, obtains matrix T and matrix U is:

Make i=1,Y₁=Y, performs following steps:

(1) random initializtion u_i；

$\left(2\right)---t_i=K_i{u}_i/\sqrt{u_i^T{K}_i{u}_i};$

$\left(3\right)---q_i=Y_i^T{t}_i/t_i^T{t}_i;$

$\left(4\right)---u_i=Y_i{q}_i/q_i^T{q}_i;$

(5) circulation (2)-(4) step, until u_iConvergence；

$\left(6\right)---K_{i+1}=(I-t_i{t}_i^T/t_i^T{t}_i)K_i(I-t_i{t}_i^T/t_i^T{t}_i);$

$\left(7\right)---Y_{i+1}=(I-t_i{t}_i^T/t_i^T{t}_i)Y_i;$

(8) i=i+1, if i < p returns step (1)；

Matrix T=[t is obtained after loop ends₁,t₂...t_p], matrix U=[u₁,u₂...u_p]。

The further scheme of the present invention is, step 2 calculates the statistic T of each sampled point according to matrix T and matrix U²With statistic SPE and confidence upper limit thereof it is:

The T of ith sample point²Statistic isWhereint_iI-th row of representing matrix T；The Q statistical magnitude of ith sample point is Representing matrixThe i-th row；

Counting statistics amount T²The confidence upper limit corresponding when confidence level is α with statistic SPE:

${T^2}_{UCL}\left(\mathrm{\&alpha;}\right)=\frac{h(n^2-1)}{n(n-h)}{F}_{\mathrm{\&alpha;}}(h,n-h),{\mathrm{SPE}}_{UCL}\left(\mathrm{\&alpha;}\right)={\mathrm{g\&chi;}}_{h,\mathrm{\&alpha;}}^2,$

Wherein F_α(h, n-h) is confidence level is α, and degree of freedom is the F-distribution of h and n-h, h=2 μ²/ S,Being then under confidence level α, proportionality coefficient is the χ of g=S/2 μ²Distribution, wherein μ and S represents sample average and the variance of statistic SPE respectively.

The further scheme of the present invention is, step 2 calculates each variable to statistic T²With the contribution margin of statistic SPE and the upper limit it is:

Introduce proportionality coefficient vector v=[v₁,v₂,...v_l]^T, wherein v_r=1, r=1,2 ... l,

Then have: $\frac{\&part;k(x_i,x_j)}{\&part;v_r}=\frac{\&part;k(v\&CenterDot;x_i,v\&CenterDot;x_j)}{\&part;v_r}=-\frac{1}{\mathrm{\&sigma;}}{(x_{i,r}-x_{j,r})}^2{k(x_i,x_j)|}_{v_r=1},$ Wherein x_i,rRepresent the r variable of ith sample point；

Have again: ${\frac{\&part;K}{\&part;v_r}|}_{ij}=\frac{\&part;K_{ij}}{\&part;v_r}=\frac{\&part;k(x_i,x_j)}{\&part;v_r},\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}=(I-\frac{1}{n}{1}_n{1}_n^T)\frac{\&part;K}{\&part;v_r}(I-\frac{1}{n}{1}_n{1}_n^T),$

Then the r variable of ith sample point is for T²The contribution of statistic and Q statistical magnitude is:

${\mathrm{cont}}_r^{T^2}=\left|{\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}|}_i{\mathrm{U\&Lambda;}}^{-1}{U}^T{\stackrel{\&OverBar;}{K}}_i^T+{\stackrel{\&OverBar;}{K}}_i{\mathrm{U\&Lambda;}}^{-1}{U}^T{\left({\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}|}_i\right)}^T\right|,$

${\mathrm{cont}}_r^{SPE}=\left|{\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}|}_{ii}-2\&CenterDot;{\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}|}_i{\mathrm{Tt}}_i^T+t_i{T}^T\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}{\mathrm{Tt}}_i^T\right|,$

WhereinRepresenting matrixThe i-th row；

The upper limit defining the r variable contribution margin is C_UCL,r=μ (cont_r)+2.58·s(cont_r), wherein cont_r∈R^{n ×1}Represent the contribution margin of r variable in whole sampled point, μ (cont_r)、s(cont_r) respectively cont_rAverage and standard deviation.

The further scheme of the present invention is, step 3 calculates core Gram matrix K according to real time data_newIt is:

For measuring the new sampled point x obtained in real time_new∈R^1×l, its core Gram matrix and centralization form thereof be:

$K_{new}=k(x_{new},x_j),{\stackrel{\&OverBar;}{K}}_{new}=(K_{new}-\frac{1}{n}{1}_n{1}_n^{T}K)(I-\frac{1}{n}{1}_n{1}_n^T),$

WhereinCorresponding score vector

CalculateFor v_rDerivative:

${\frac{\&part;K_{new}}{\&part;v_r}|}_i=-\frac{1}{\mathrm{\&sigma;}}{(x_{new,r}-x_{i,r})}^{2}k(x_{new},x_i),\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}=\frac{\&part;K_{new}}{\&part;v_r}-\frac{\&part;K_{new}}{\&part;v_r}\left(\frac{1}{n}{1}_n{1}_n^T\right),$

WhereinIt isI-th element.

The further scheme of the present invention is, step 3 counting statistics amountWith statistic SPE_newIt is:

$T_{new}^2=t_{new}{\mathrm{\&Lambda;}}^{-1}{t}_{new}^T,{\mathrm{SPE}}_{new}=k(x_{new},x_{new})-2{\stackrel{\&OverBar;}{K}}_{new}{\mathrm{Tt}}_{new}^T+t_{new}{T}^T\stackrel{\&OverBar;}{K}{\mathrm{Tt}}_{new}^T,$

Wherein $k(x_{new},x_{new})=1-\frac{2}{n}\underset{i=1}{\overset{n}{\&Sigma;}}{K}_{new,i}+\frac{1}{n^2}\underset{i=1}{\overset{n}{\&Sigma;}}\underset{j=1}{\overset{n}{\&Sigma;}}{K}_{ij},$ K_new,iIt is K_newI-th element.

The further scheme of the present invention is, step 3 calculates each variable contribution margin and is:

${\mathrm{cont}}_r^{T_{new}^2}=\left|\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}{\mathrm{U\&Lambda;}}^{-1}{U}^T{\stackrel{\&OverBar;}{K}}_{new}^T+{\stackrel{\&OverBar;}{K}}_{new}{\mathrm{U\&Lambda;}}^{-1}{U}^T{\left(\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}\right)}^T\right|,$

${\mathrm{cont}}_r^{{\mathrm{SPE}}_{new}}=\left|-\frac{2}{n}{\underset{i=1}{\overset{n}{\&Sigma;}}\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}|}_i-2\&CenterDot;\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}{\mathrm{Tt}}_{new}^T+\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}{\mathrm{UT}}^T\stackrel{\&OverBar;}{K}{\mathrm{Tt}}_{new}^T+t_{new}{T}^T\stackrel{\&OverBar;}{K}{\mathrm{TU}}^T{\left(\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}\right)}^T\right|$

, Relative Contribution rate is: cont_new,r/C_UCL,r。

It is an advantage of the current invention that:

The two kinds of statistics and the confidence upper limit that obtain each variable is first calculated according to the historical data of tested lead-acid batteries, again by the two of real time data kinds of statistics and confidence upper limit compared with historical data to judge whether to break down, determine whether to cause the variable of the system failure again through contribution margin and contribution rate when determining and breaking down, realize being accurately positioned of fault, guarantee the stability of lead-acid batteries electric power system, reduce system maintenance cost.

Accompanying drawing explanation

Fig. 1 is the flow chart of the present invention.

Fig. 2 is statistic T in embodiment²Monitoring figure.

Fig. 3 is statistic SPE monitoring figure in embodiment.

Detailed description of the invention

When the operating mode of lead-acid accumulator is defined as a certain specific environment temperature and discharge rate, lead-acid storage battery system enters state during normal operation after stable state, wherein discharge rate is defined as the ratio of battery capacity (ampere-hour) and standard discharge current (ampere), reflects battery discharge rates with the form of time.

The input of lead-acid batteries in actual condition, output state can be simulated in laboratory conditions and record relevant service data, these data generally also can directly obtain from battery supplier, input data are form the voltage of single-unit lead-acid accumulator of tested lead-acid batteries, electric current, temperature data, output data are the total voltage of tested lead-acid batteries, and they can reflect the duty of lead-acid batteries intuitively.

Adopt the service data of the PS-260 valve type lead-acid accumulator that U.S. Power-Sonic company provides in related description, utilize the PNGV model of this data construct lead-acid batteries to go forward side by side line parameter identification, in order to reflect battery discharge characteristic.MATLAB/Simulink builds this phantom and 5 lead acid storage battery pool unit tandem compounds are become emulation lead-acid accumulator group system.PS-260 battery rated output voltage 2V, capacity 6.0Ah, definition nominal situation is ambient temperature 20 DEG C, discharge rate 20h.

Performing step one: 2000 sampled points in obtaining 20 hours, using the voltage of 5 lead-acid accumulators, electric current, temperature data as input data, the voltage of lead-acid batteries, as output data, is organized into matrix X ∈ R^n×15, Y ∈ R^n×1Form, utilize data mean value and standard deviation to be standardized.

Perform step 2: off-line learning, structure monitoring algorithm model:

Calculate core Gram matrix and centralization obtain:

K(x_i,x_j)=exp (-| | x_i-x_j||²/ σ), $\stackrel{\&OverBar;}{K}=(I-\frac{1}{n}{1}_n{1}_n^T)K(I-\frac{1}{n}{1}_n{1}_n^T),$

Nonlinear iteration method of least square is utilized to calculate matrix T, matrix U；

Calculate the statistic T of whole service data point²With statistic SPE:

$T^2=t_i{\mathrm{\&Lambda;}}^{-1}{t}_i^T,\mathrm{\&Lambda;}=\frac{1}{n}{T}^{T}T,SPE={\stackrel{\&OverBar;}{K}}_{ii}-2{\stackrel{\&OverBar;}{K}}_i{\mathrm{Tt}}_i^T+t_i{T}^T\stackrel{\&OverBar;}{K}{\mathrm{Tt}}_i^T$

Counting statistics amount T²The confidence upper limit corresponding when confidence level is α=95% with statistic SPE:

${T^2}_{UCL}\left(\mathrm{\&alpha;}\right)=\frac{h(n^2-1)}{n(n-h)}{F}_{\mathrm{\&alpha;}}(h,n-h),{\mathrm{SPE}}_{UCL}\left(\mathrm{\&alpha;}\right)={\mathrm{g\&chi;}}_{h,\mathrm{\&alpha;}}^2$

Calculate each variate-value in each sampled point for statistic T²Contribution margin with statistic SPE:

${\mathrm{cont}}_r^{T^2}=\left|{\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}|}_i{\mathrm{U\&Lambda;}}^{-1}{U}^T{\stackrel{\&OverBar;}{K}}_i^T+{\stackrel{\&OverBar;}{K}}_i{\mathrm{U\&Lambda;}}^{-1}{U}^T{\left({\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}|}_i\right)}^T\right|,$

${\mathrm{cont}}_r^{SPE}=\left|{\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}|}_{ii}-2\&CenterDot;{\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}|}_i{\mathrm{Tt}}_i^T+t_i{T}^T\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}{\mathrm{Tt}}_i^T\right|,$

Calculate each variable upper limit C for statistic contribution margin_UCL,r=μ (cont_r)+2.58·s(cont_r)。

Perform step 3: on-line monitoring:

Artificial addition disturbance in simulation process, disturbs the voltage of wherein 2 monocells at the 601st data point place simultaneously, and concrete fluctuating margin differs so that process shows in corresponding time range abnormality occurs.

Using 0-1000 sampling point value after addition fault as the input of algorithm, simulate on-line monitoring.Obtain new sampled point x in real time_new∈R^1×15, and by the data mean value of step one and standard deviation by its standardization.

Calculate its core Gram matrix centralization and corresponding latent variable (score vector):

K_new=k (x_new,x_j), ${\stackrel{\&OverBar;}{K}}_{new}=(K_{new}-\frac{1}{n}{1}_n{1}_n^{T}K)(I-\frac{1}{n}{1}_n{1}_n^T),t_{new}={\stackrel{\&OverBar;}{K}}_{new}U,$

Calculate the statistic of new sampled point:

$T_{new}^2=t_{new}{\mathrm{\&Lambda;}}^{-1}{t}_{new}^T,{\mathrm{SPE}}_{new}=k(x_{new},x_{new})-2{\stackrel{\&OverBar;}{K}}_{new}{\mathrm{Tt}}_{new}^T+t_{new}{T}^T\stackrel{\&OverBar;}{K}{\mathrm{Tt}}_{new}^T,$

Judge: ifOr SPE_new> SPE_UCL(α), then it is determined with fault to occur；Otherwise it is assumed that system is working properly, returns and continue to gather new data point；

Each variable contribution margin to statistic in new sampled point is calculated when there being fault to occur:

${\mathrm{cont}}_r^{T_{new}^2}=\left|\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}{\mathrm{U\&Lambda;}}^{-1}{U}^T{\stackrel{\&OverBar;}{K}}_{new}^T+{\stackrel{\&OverBar;}{K}}_{new}{\mathrm{U\&Lambda;}}^{-1}{U}^T{\left(\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}\right)}^T\right|,$

${\mathrm{cont}}_r^{{\mathrm{SPE}}_{new}}=-\left|\frac{2}{n}{\underset{i=1}{\overset{n}{\&Sigma;}}\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}|}_i-2\&CenterDot;\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}{\mathrm{Tt}}_{new}^T+\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}{\mathrm{UT}}^T\stackrel{\&OverBar;}{K}{\mathrm{Tt}}_{new}^T+t_{new}{T}^T\stackrel{\&OverBar;}{K}{\mathrm{TU}}^T{\left(\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}\right)}^T\right|$

, calculate the Relative Contribution rate cont of each variable_new,r/C_UCL,r, all Relative Contribution rates variable more than 1, it is all considered to be in abnormal condition, and then causes the fault of system.

In the present embodiment, statistic T²With the monitoring figure of statistic SPE respectively as shown in Figures 2 and 3, it appeared that, this method can detect the generation of abnormality, when breaking down in certain or certain several battery places, when going wrong such as degradation failure, internal short-circuit, open circuit or voltage, current sensing means, this algorithm it appeared that and navigate to specific monocell place, for system maintenance provide effective information.

Claims (9)

1. one kind based on the lead-acid batteries on-line fault diagnosis method that can survey data, it is characterised in that comprise the following steps:

Step one: obtain under tested lead-acid batteries operating mode in time period t₀～t_fInput, output historical data；

Step 2: calculate core Gram matrix K according to input, output historical data；

Adopt nonlinear iteration method of least square to solve core Gram matrix K, obtain matrix T and matrix U；

The statistic T of each sampled point is calculated according to matrix T and matrix U²With statistic SPE and confidence upper limit thereof；

Calculate each variable to statistic T²With the contribution margin of statistic SPE and the upper limit；

Step 3: obtain the real time data of tested lead-acid batteries；

Core Gram matrix K is calculated according to real time data_new；

Counting statistics amountWith statistic SPE_new；

Judge statisticMore than statistic T², or statistic SPE_newMore than whether statistic SPE sets up, if the result is negative, then the real time data step obtaining tested lead-acid batteries is returned；

If above-mentioned judged result is yes, being then determined with fault and occur, calculate each variable contribution margin and Relative Contribution rate, the Relative Contribution rate variable more than 1 is judged to cause the system failure.

2. a kind of based on the lead-acid batteries on-line fault diagnosis method that can survey data as claimed in claim 1, it is characterized in that: the input historical data in step one is form the voltage of single-unit lead-acid accumulator of tested lead-acid batteries, electric current, temperature data, output historical data is tested lead-acid batteries voltage.

3. a kind of based on the lead-acid batteries on-line fault diagnosis method that can survey data as claimed in claim 1, it is characterised in that: step 2 calculates core Gram matrix K according to input, output historical data is:

History inputted, output data are organized into the matrix of two 0 average variablees respectively:

X∈R^n×l, Y ∈ R^n×m, wherein n is sample number, and l, m be input/output variable dimension respectively；

Input, output matrix are decomposed into:

X=TP^T+ F, Y=UC^T+ G, wherein T ∈ R^n×p,U∈R^n×pComprising p the score vector being extracted, p is drawn by cross-validation method, P, and C is load matrix, F, and G is residual matrix；

Utilize Gaussian function K (x_i,x_j)=exp (-| | x_i-x_j||²/ σ), i, j=1,2 ... n calculates core Gram matrix K ∈ R^n×n, matrix K is carried out centralization and obtains:

Wherein I is that n ties up unit matrix, 1_nRepresent that element value is the n dimensional vector of 1.

4. a kind of based on the lead-acid batteries on-line fault diagnosis method that can survey data as claimed in claim 1, it is characterised in that: step 2 adopts nonlinear iteration method of least square to solve core Gram matrix K, obtains matrix T and matrix U is:

Make i=1,Y₁=Y, performs following steps:

(1) random initializtion u_i；

(2) $t_i=K_i{u}_i/\sqrt{u_i^T{K}_i{u}_i};$

(3) $q_i=Y_i^T{t}_i/t_i^T{t}_i;$

(4) $u_i=Y_i{q}_i/q_i^T{q}_i;$

(5) circulation (2)-(4) step, until u_iConvergence；

(6) $K_{i+1}=(I-t_i{t}_i^T/t_i^T{t}_i)K_i(I-t_i{t}_i^T/t_i^T{t}_i);$

(7) $Y_{i+1}=(I-t_i{t}_i^T/t_i^T{t}_i)Y_i;$

(8) i=i+1, if i < p returns step (1)；

Matrix T=[t is obtained after loop ends₁,t₂...t_p], matrix U=[u₁,u₂...u_p]。

5. a kind of based on the lead-acid batteries on-line fault diagnosis method that can survey data as claimed in claim 1, it is characterised in that: step 2 calculates the statistic T of each sampled point according to matrix T and matrix U²With statistic SPE and confidence upper limit thereof it is:

The T of ith sample point²Statistic isWhereint_iI-th row of representing matrix T；

The Q statistical magnitude of ith sample point is Representing matrixThe i-th row；

Counting statistics amount T²The confidence upper limit corresponding when confidence level is α with statistic SPE:

${T^2}_{UCL}\left(\mathrm{\&alpha;}\right)=\frac{h(n^2-1)}{n(n-h)}{F}_{\mathrm{\&alpha;}}(h,n-h),{\mathrm{SPE}}_{UCL}\left(\mathrm{\&alpha;}\right)={\mathrm{g\&chi;}}_{h,\mathrm{\&alpha;}}^2,$

Wherein F_α(h, n-h) is confidence level is α, and degree of freedom is the F-distribution of h and n-h, h=2 μ²/ S,Being then under confidence level α, proportionality coefficient is the χ of g=S/2 μ²Distribution, wherein μ and S represents sample average and the variance of statistic SPE respectively.

6. a kind of based on the lead-acid batteries on-line fault diagnosis method that can survey data as claimed in claim 1, it is characterised in that: step 2 calculates each variable to statistic T²With the contribution margin of statistic SPE and the upper limit it is: introduce proportionality coefficient vector v=[v₁,v₂,...v_l]^T, wherein v_r=1, r=1,2 ... l,

Then have: $\frac{\&part;k(x_i,x_j)}{\&part;v_r}=\frac{\&part;k(v\&CenterDot;x_i,v\&CenterDot;x_j)}{\&part;v_r}=-\frac{1}{\mathrm{\&sigma;}}{(x_{i,r}-x_{j,r})}^{2}k(x_i,x_j){|}_{v_r=1},$ Wherein x_i,rRepresent the r variable of ith sample point；

Have again: $\frac{\&part;K}{\&part;v_r}{|}_{ij}=\frac{\&part;K_{ij}}{\&part;v_r}=\frac{\&part;k(x_i,x_j)}{\&part;v_r},\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}=(I-\frac{1}{n}{1}_n{1}_n^T)\frac{\&part;K}{\&part;v_r}(I-\frac{1}{n}{1}_n{1}_n^T),$

Then the r variable of ith sample point is for T²The contribution of statistic and Q statistical magnitude is:

${\mathrm{cont}}_r^{T^2}=\left|{\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}|}_i{\mathrm{U\&Lambda;}}^{-1}{U}^T{\stackrel{\&OverBar;}{K}}_i^T+{\stackrel{\&OverBar;}{K}}_i{\mathrm{U\&Lambda;}}^{-1}{U}^T{\left({\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}|}_i\right)}^T\right|,$

${\mathrm{cont}}_r^{SPE}=\left|{\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}|}_{ii}-2\&CenterDot;{\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}|}_i{\mathrm{Tt}}_i^T+t_i{T}^T\frac{\&part;\stackrel{\&OverBar;}{K}}{\&part;v_r}{\mathrm{Tt}}_i^T\right|,$

WhereinRepresenting matrixThe i-th row；

The upper limit defining the r variable contribution margin is C_UCL,r=μ (cont_r)+2.58·s(cont_r), wherein cont_r∈R^n×1Represent the contribution margin of r variable in whole sampled point, μ (cont_r)、s(cont_r) respectively cont_rAverage and standard deviation.

7. a kind of based on the lead-acid batteries on-line fault diagnosis method that can survey data as claimed in claim 1, it is characterised in that: step 3 calculates core Gram matrix K according to real time data_newIt is:

For measuring the new sampled point x obtained in real time_new∈R^1×l, its core Gram matrix and centralization form thereof be:

K_new=k (x_new,x_j), ${\stackrel{\&OverBar;}{K}}_{new}=(K_{new}-\frac{1}{n}{1}_n{1}_n^{T}K)(I-\frac{1}{n}{1}_n{1}_n^T),$

Wherein ${\stackrel{\&OverBar;}{K}}_{new}\&Element;R^{1\&times;n},$ Corresponding score vector $t_{new}={\stackrel{\&OverBar;}{K}}_{new}U;$

CalculateFor v_rDerivative:

${\frac{\&part;K_{new}}{\&part;v_r}|}_i=-\frac{1}{\mathrm{\&sigma;}}{(x_{new,r}-x_{i,r})}^{2}k(x_{new},x_i),\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}=\frac{\&part;K_{new}}{\&part;v_r}-\frac{\&part;K_{new}}{\&part;v_r}\left(\frac{1}{n}{1}_n{1}_n^T\right),$

WhereinIt isI-th element.

8. a kind of based on the lead-acid batteries on-line fault diagnosis method that can survey data as claimed in claim 1, it is characterised in that: step 3 counting statistics amountWith statistic SPE_newIt is:

$T_{new}^2=t_{new}{\mathrm{\&Lambda;}}^{-1}{t}_{new}^T,{\mathrm{SPE}}_{new}=k(x_{new},x_{new})-2{\stackrel{\&OverBar;}{K}}_{new}{\mathrm{Tt}}_{new}^T+t_{new}{T}^T\stackrel{\&OverBar;}{K}{\mathrm{Tt}}_{new}^T,$

Wherein $k(x_{new},x_{new})=1-\frac{2}{n}\underset{i=1}{\overset{n}{\&Sigma;}}{K}_{new,i}+\frac{1}{n^2}\underset{i=1}{\overset{n}{\&Sigma;}}\underset{j=1}{\overset{n}{\&Sigma;}}{K}_{ij},$ K_new,iIt is K_newI-th element.

9. a kind of based on the lead-acid batteries on-line fault diagnosis method that can survey data as claimed in claim 1, it is characterised in that: step 3 calculates each variable contribution margin and is:

${\mathrm{cont}}_r^{T_{new}^2}=\left|\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}{\mathrm{U\&Lambda;}}^{-1}{U}^T{\stackrel{\&OverBar;}{K}}_{new}^T+{\stackrel{\&OverBar;}{K}}_{new}{\mathrm{U\&Lambda;}}^{-1}{U}^T{\left(\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}\right)}^T\right|,$

${\mathrm{cont}}_r^{{\mathrm{SPE}}_{new}}=\left|-\frac{2}{n}\underset{i=1}{\overset{n}{\&Sigma;}}{\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}|}_i-2\&CenterDot;\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}{\mathrm{Tt}}_{new}^T+\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}{U}^T\stackrel{\&OverBar;}{K}{\mathrm{Tt}}_{new}^T+t_{new}{T}^T\stackrel{\&OverBar;}{K}{\mathrm{TU}}^T{\left(\frac{\&part;{\stackrel{\&OverBar;}{K}}_{new}}{\&part;v_r}\right)}^T\right|,$

Relative Contribution rate is: cont_new,r/C_UCL,r。