CN104316879B - A kind of prediction technique in lead-acid batteries service life - Google Patents
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Abstract
The invention discloses a kind of prediction techniques in lead-acid batteries service life, periodically activate accumulator group, obtain it and activate charging capacity, and obtain the time t periodically activated every time and corresponding charging capacity S with thiscVariable point (t, Sc)；By variable point (t, the S of gained after each activationc) naturalization is to corresponding normal capacity point (ti, Sci), wherein i is the corresponding number periodically activated；Least square method is called, according to normal capacity point (ti, Sci) fit a charging capacity ScWith the parabolic function S of time tc=at2+ bt+c, using the prediction curve as battery life, wherein a, b, c is parabolical related coefficient.The present invention improves the accuracy of prediction, and is suitable for various accumulator groups, and forecast cost is relatively low；The present invention is predicted using single parameter, reduces computation complexity.
Description
Technical Field
The invention relates to the field of power distribution networks, in particular to a method for predicting the service life of a lead-acid storage battery pack.
Background
The lead-acid storage battery is used as an independent direct-current power supply for transmission, protection, control and communication, is widely applied to an automatic system of a power distribution network, plays a role of an independent power supply when alternating-current power supply faults or normal commercial power interruption occurs, and provides electric energy for switching on and off of a switching-on operation mechanism and operation of power distribution terminal equipment. Therefore, the storage battery is stable, reliable and safe in operation, and has very important significance for power distribution network automation.
The storage battery installation is in the open air in many places in the distribution network, and the operational environment is abominable, in addition to the storage battery misuse, leads to inside water loss of storage battery, polar plate corruption easily, causes the storage battery capacity to descend, the performance reduction. Along with the distribution of the storage batteries is wider and more, the number of the storage batteries is more and more, the number of the storage batteries is less, the number of the relative operation and maintenance personnel is small, the operation and maintenance mode is simple, the storage batteries cannot be detected in time, and the serious degradation of the battery capacity is found only after the power failure of an accident occurs in many cases, so that the personal safety is seriously endangered, and huge economic and property losses are caused.
Therefore, on-line prediction of the deterioration degree of the lead-acid storage battery becomes increasingly important, however, the capacity detection is influenced by a large number of factors (temperature, voltage, charging and discharging current and frequency, discharging depth, internal resistance, solution density, service time, self-discharge and the like), so that accurate on-line prediction is difficult to perform, and the only accurate method for obtaining the residual capacity of the battery at present is an off-line checking discharge experiment, and the discharging test time is long, so that much inconvenience is brought to the detection.
The prior art battery failure prediction methods currently focus mainly on three directions:
1. single parameter internal resistance analysis:
according to the fact that the SOH of the battery is reduced after the battery is aged, the internal resistance is increased, namely the service life of the battery is predicted through the high nonlinearity of the SOH and the internal resistance.
Here, soh (state of health), i.e., the degree of health of the battery, is represented by the following formula:
SOH is current maximum battery capacity/nominal battery capacity × 100%.
The SOH reflects the current capacity of the battery as a percentage, which for a new battery is often 100% or more, and gradually decreases as the battery ages, IEEE standard 1188.1996 specifies that the battery should be replaced when the battery capacity can decrease to 80%, i.e., SOH < 80%.
EIS (electrochemical impedance spectroscopy) analysis:
the detection method is the most complex detection method at the present stage, and the idea is to inject a plurality of sinusoidal signals with different frequencies into the battery, and analyze the collected data by using fuzzy logic to estimate the performance of the battery. The method needs to perform a large amount of data acquisition and analysis in advance to obtain the characteristics of the battery. The method has relatively ideal accuracy for estimating the performance of the battery with the known model.
3. The multi-input parameter estimation method comprises the following steps:
the method comprises the steps of finding out characteristic quantities from a failure mechanism for analysis and estimation, carrying out prediction analysis through different voltage characteristics of a steep drop and rise section of a discharge characteristic curve, and mining an SOH rule from data of a plurality of influence factors (such as temperature, discharge current, internal resistance, last discharge point depth, floating charge voltage, floating charge holding time, current discharge depth and the like) in the discharge process of a storage battery. The method comprises various optimized neural network methods (fuzzy neural network, GA-Elman neural network), support vector machine methods and other advanced methods, and the algorithms have the advantages of short prediction time, high accuracy and the like.
The service life prediction based on the single internal resistance parameter has the following defects: the internal resistance is obviously changed after the battery capacity is reduced by 25-30%, and the internal resistance is judged to be invalid when 80% of the internal resistance is invalid at present, so that the deviation in the precision is large through an internal resistance measuring mode.
The EIS analysis method, while having relatively perfect accuracy in estimating the performance of batteries of known types, is only suitable for portable equipment and is also expensive.
The prediction based on data driving does not need mechanism knowledge of an object system, and on the basis of the acquired data, the implicit information in the data is mined for prediction through various data analysis and learning methods, so that the complexity of model acquisition is avoided, and the prediction method is practical. However, the multi-input parameter variable neural network needs to perform strict system identification on a specific battery model before being applied to practice, and a large amount of sample data is needed to support in advance.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a method for predicting the service life of a lead-acid storage battery pack, which can predict the performance trend of the storage battery in time by single parameter online prediction and calculate the residual service life of the storage battery.
The technical scheme adopted by the invention for solving the problems is as follows:
a method for predicting the service life of a lead-acid storage battery pack comprises the following steps:
periodically activating the storage battery to obtain the activated charging capacity, and obtaining the time t of each periodic activation and the corresponding charging capacity ScPoint of variable (t, S)c)；
The variable points (t, S) obtained after each activationc) Normalized to the corresponding normal capacity point (t)i，Sci) Wherein i is the number of corresponding periodic activations;
calling the least square method, according to the normal capacity point (t)i，Sci) A charging capacity S is fittedcParabolic function S with time tc＝at2And + bt + c as a prediction curve of the service life of the storage battery pack, wherein a, b and c are correlation coefficients of parabolas.
Further, still include:
according to Scinvalid＝Scmax80% calculation of predicted time to failure tinvalidI.e. ScinvalidSubstitution of a parabolic function Sc＝at2+ bt + c calculates the failure time tinvalidWhereinto the maximum activation charge capacity value, ScinvalidFor the limit of the failure of the accumulator, tinvalidAnd subtracting the current used time of the storage battery to calculate the residual life of the storage battery.
Further, the number of times of periodically activating the secondary battery pack is more than 4.
Further, the variable points (t, S) obtained after each activation are describedc) Normalized to the corresponding normal capacity point (t)i，Sci) When the temperature is 25 ℃, the discharge rate is 0.1Ca, and the normalization is carried out by adopting the following formula:wherein, Δ T is interval sampling time, I represents 1,2,3 … … times of activation, j represents j time interval sampling time, k is the influence coefficient of temperature on capacity, T is temperature, and I is current.
Further, the point (t) according to the normal capacityi，Sci) A charging capacity S is fittedcParabolic function S with time tc＝at2+ bt + c:
firstly, a group of orthogonal polynomial function systems { Qj (t) (j ═ 0,1,2) } at given points is constructed and is used as a basis function to carry out least square curve fitting, and the obtained curve is Sc＝q0Q0(t)+q1Q1(t)+q2Q2(t) wherein the coefficient qj(j ═ 0,1,2) is:and j is 0,1, 2;
constructing an orthogonal polynomial Q constructed at a given pointj(t) (j ═ 0,1,2) recursion:
wherein,
sequentially construct Q0(t)、Q1(t) and Q2And (t) calculating the values of a, b and c.
The invention has the beneficial effects that:
the invention adopts a method for predicting the service life of a lead-acid storage battery pack, adopts a periodic activation mode, acquires the activation charging capacity of the storage battery pack, replaces the rated discharge capacity of the storage battery in an approximate mode, adopts a least square method to fit a parabola for predicting the service life of the storage battery, and reasonably and accurately predicts the residual service life of the storage battery. Compared with a single-parameter internal resistance analysis method, the method has the advantages that the activation charging capacity is acquired in a periodic activation mode, the accuracy of data is improved, and the prediction precision is improved; the method is suitable for various storage battery packs, can perform real-time online prediction, and is low in cost; the invention adopts a single parameter (activated charge capacity) for prediction, reduces the calculation complexity and effectively improves the accuracy of predicting the service life.
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The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a flow chart of a method of the present invention;
fig. 2 is a schematic view of a parabola according to the present invention.
Detailed Description
The storage battery health prediction directly determines the speed and cost of enterprise operation and maintenance management. The battery degradation trend is found in time, effective measures are taken, the battery is properly used and replaced, the large-area power failure accident rate caused by the battery is reduced and stopped, the accident is prevented from happening in the bud, passive fire-rescue type management after the accident is converted into active evasive type management before the accident occurs, the automatic safe and stable operation of the power distribution network is guaranteed, the maintenance difficulty is reduced, and the practical value and the management benefit are very high.
The timely and effective storage battery performance prediction can change the maintenance mode of a user, the original large-scale, blind and regular maintenance is changed into planned, purposeful and object-determined maintenance, the reasonable arrangement of power grid operation and unit maintenance is facilitated, network nodes are more robust, the smooth implementation of functions of power distribution network automation system fault location, automatic power supply switching, reasonable scheduling and the like is ensured, the economic and property loss caused by abnormal power failure of accidents is reduced, and the method can play a great role in improving the service life and reliability of power distribution equipment, reducing the workload of maintenance personnel and the like.
To this end, referring to fig. 1, the present invention provides a method for predicting the life of a lead-acid battery pack, including:
step 100, periodically activating the storage battery to obtain the activated charging capacity, and thus obtaining the time t of each periodic activation and the corresponding charging capacity ScPoint of variable (t, S)c)；
Step 101, obtaining variable points (t, S) after each activationc) Normalized to the corresponding normal capacity point (t)i，Sci) Wherein i is the number of corresponding periodic activations;
step 102, calling a least square method, and according to the normal capacity point (t)i，Sci) A charging capacity S is fittedcParabolic function S with time tc＝at2+ bt + c as the prediction curve of the service life of the storage battery pack, wherein a, b and c are correlation coefficients of parabolas;
step 103, according to Scinvalid＝Scmax80% calculation of predicted time to failure tinvalidI.e. ScinvalidSubstitution of a parabolic function Sc＝at2+ bt + c calculates the failure time tinvalidWhereinto the maximum activation charge capacity value, ScinvalidFor the limit of the failure of the accumulator, tinvalidAnd subtracting the current used time of the storage battery to calculate the residual life of the storage battery.
It should be noted that, in the present invention, when estimating the SOH of the current storage battery, the reduced activated charge capacity is used as the only input judgment parameter for predicting the battery failure. For the storage battery, the activation charge capacity after the reduction of the relative discharge rated capacity Sxfn ≈ Sci is met, the current 0.1Ca discharge capacity of the storage battery is in direct proportion to the activation charge capacity after the reduction from rated voltage charge to floating charge voltage, and according to the basic condition, the invention realizes reasonable and accurate prediction of the service life of the storage battery by adopting a regular activation mode.
When the storage battery pack is activated periodically in the step 100, the specific method refers to the application number of 201410377997.2, and the name of the online judgment system for the failure of the valve-regulated lead-acid storage battery comprises the following contents:
a periodic automatic activation of the valve regulated lead acid battery pack comprises a complete charging and discharging process, the process comprising the steps of:
the storage battery management module closes the charging power supply and starts the storage battery pack to discharge;
collecting discharge current Ifi and voltage U;
when the voltage U of the storage battery pack is smaller than the set activation termination voltage, stopping discharging, and calculating the discharge capacity Sfi and the discharge time t;
turning on a charging power supply to convert into a charging state, and charging according to a set charging control strategy;
collecting current Ici and voltage U in the charging process;
when the charging current Ici is smaller than the set floating charging current value, setting a charging completion flag, and calculating the charging capacity Sci and the charging time t;
the letter i indicates the number of automatic activations.
The activation charging capacity of each time of periodically activating the storage battery pack can be obtained by the method, and the time t of each time of periodically activating and the corresponding charging capacity S are obtained according to the activation charging capacitycPoint of variable (t, S)c)。
Obtaining a variable point (t, S)c) Then, considering that the discharge capacity of the secondary battery is affected by various factors such as temperature and discharge rate, the variable point must be reduced to the normal capacity under the normal condition of 25 ℃ and 0.1Ca discharge rate. Thus, step 101 will obtain the variable point (t, S) after each activationc) Normalized to the corresponding normal capacity point (t)i，Sci) Wherein i is the number of corresponding regular activations, i is 1,2 and 3 … …, and the following formula is adopted for classification when the classification is carried out specifically:wherein, Δ T is interval sampling time, I represents 1,2,3 … … times of activation, j represents j time interval sampling time, k is the influence coefficient of temperature on capacity, T is temperature, and I is current.
The above-obtained normal capacity point (t)i，Sci) All are discrete points, and in order to obtain the variation trend of the activation capacity, two variables t need to be searched according to the normal capacity pointiAnd SciApproximate expressions of the functional relationships between the two, such that the resulting approximation function as a whole deviates from the known function by some measure to a minimum but not necessarily to over all points. Therefore, the invention introduces a fitting algorithm and adopts a regression analysis method to determine tiAnd SciFunction of betweenAnd (4) relationship.
When the external conditions are substantially the same, the life of the battery tends to increase at the initial stage of use, and starts to decrease after a certain period of time reaches a maximum value. It can be thus judged that the check capacity of the lead acid storage battery is attenuated in the form of an opening-down parabola, and therefore, the activated discharge capacity after the reduction thereof is also approximated to an opening-down parabola. The invention can obtain two variables t by using a regression model in statisticsiAnd SciA parabolic relationship therebetween.
Since the activation capacity curve is approximated as a downward opening parabola, a fitted polynomial can be used as: sc＝at2+bt + c, where t is the number of months of operation, ScThe value of the activated charge capacity after the reduction (the temperature is reduced to 25 ℃ and the discharge rate is 0.1 Ca). Due to (t, S)c) The number of sample data is in direct proportion to the fitting effect, therefore, curve fitting calculation can be carried out after certain statistical times are met, the invention provides that the number of times of periodically activating the storage battery pack is more than 4 times, and then the statistical result (t) of the activation charging capacity after the reduction calculated after the previous activation is carried out for a plurality of timesi，Sci) An array of (i ═ 0,1,2, 3.. n) is calculated as input parameters.
To obtain a parabola Sc＝at2+The coefficients a, b and c of bt + c are obtained by adopting a least square method, and the fitting principle is as follows:
for an nth order polynomialLet xj-xj(j-0, 1,2, 3.., n), then converted to linear formThis is a curved line. For the i ═ 1,2, … m experimental pointsSubstituting positive of multivariate linear fitThe rule equation is as follows:the polynomial least squares normal equation can be derived directly:the base matrix form is:
in which ∑ representsWhich is a vector with n +1 parameters a0,a1,a2,…,anAnd n +1 equations, and solving the unknown parameters by a Gaussian iteration method to obtain a regression equation.
Based on the above principle, the step 102 of the present invention is based on the normal capacity point (t)i，Sci) A charging capacity S is fittedcParabolic function S with time tc＝at2+ bt + c:
firstly, a group of orthogonal polynomial function systems { Qj (t) (j ═ 0,1,2) } at given points is constructed and is used as a basis function to carry out least square curve fitting, and the obtained curve is Sc＝q0Q0(t)+q1Q1(t)+q2Q2(t) wherein the coefficient qj(j ═ 0,1,2) is:and j is 0,1, 2;
constructing an orthogonal polynomial Q constructed at a given pointj(t) (j ═ 0,1,2) recursion:
wherein,
structure Q0(t) gives dj＝n+1，Is equivalent to the array SciAre accumulated to obtain q0＝>c, continuously accumulating the parameter c;
structure Q1(t), then Q1(t)＝t-α0Substituting into the calculation to obtain Finally, q is1Q1The (t) term is expanded and then added to the fitting polynomial to obtain c- α0q1＝>c，q1＝>b. By analogy, finally, Q is constructed2And (t), obtaining a, b and c parameters so as to obtain a parabolic function.
Finally, step 103 is according to Scinvalid＝Scmax80% calculation of predicted time to failure tinvalidI.e. ScinvalidSubstitution of a parabolic function Sc＝at2+ bt + c calculates the failure time tinvalidWhereinto the maximum activation charge capacity value, ScinvalidFor the limit of the failure of the accumulator, tinvalidAnd subtracting the current used time of the storage battery to calculate the residual life of the storage battery.
Referring to FIG. 2, the predicted parabola according to the present invention is shown, wherein the point A is the maximum activation charge capacity value of the parabolaCorresponding time point isTotally divided into three periods, wherein the first period is a data collection period and is used for collecting statistical data and obtaining a parabola; the second is a prediction period for predicting the life period of the storage battery according to the parabola, and the last is a failure period, when the specified judgment condition is exceeded, the storage battery is failed. The final failure determination condition has various determination methods, such as satisfying a certain number of activation periods beyond the limit of a parabola, and the like, and is within the protection scope of the present invention.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and the present invention shall fall within the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means.
Claims (4)
1. A method for predicting the service life of a lead-acid storage battery pack is characterized by comprising the following steps:
periodically activating the storage battery to obtain the activated charging capacity, and obtaining the time t of each periodic activation and the corresponding charging capacity ScPoint of variable (t, S)c)；
The variable points (t, S) obtained after each activationc) Normalized to the corresponding normal capacity point (t)i，Sci) Wherein i is the number of corresponding periodic activations;
calling the least square method to be normalCapacity point (t)i，Sci) A charging capacity S is fittedcParabolic function S with time tc＝at2+ bt + c as the prediction curve of the service life of the storage battery pack, wherein a, b and c are correlation coefficients of parabolas;
the variable point (t, S) obtained after each activationc) Normalized to the corresponding normal capacity point (t)i，Sci) When the temperature is 25 ℃, the discharge rate is 0.1Ca, and the normalization is carried out by adopting the following formula:wherein, Δ T is interval sampling time, I represents 1,2,3 … … times of activation, j represents j time interval sampling time, k is the influence coefficient of temperature on capacity, T is temperature, and I is current.
2. The prediction method according to claim 1, further comprising:
according to Scinvalid＝Scmax80% calculation of predicted time to failure tinvalidI.e. ScinvalidSubstitution of a parabolic function Sc＝at2+ bt + c calculates the failure time tinvalidWhereinto the maximum activation charge capacity value, ScinvalidFor the limit of the failure of the accumulator, tinvalidAnd subtracting the current used time of the storage battery to calculate the residual life of the storage battery.
3. The prediction method according to claim 1, wherein the number of times of periodically activating the secondary battery pack is greater than 4 times.
4. Prediction method according to claim 1, characterized in that said point (t) according to the normal capacity is determinedi，Sci) A charging capacity S is fittedcWith timeParabolic function S of time tc＝at2+ bt + c:
first, a set of orthogonal polynomial function systems { Q } at a given point is constructedj(t) }, where j is 0,1,2, which is subjected to least squares curve fitting as a basis function, resulting in a curve Sc＝q0Q0(t)+q1Q1(t)+q2Q2(t) wherein the coefficient qjWhere j is 0,1,2,and j is 0,1, 2;
constructing an orthogonal polynomial Q at a given pointj(t) (j ═ 0,1,2) recursion:
wherein,
sequentially construct Q0(t)、Q1(t) and Q2And (t) calculating the values of a, b and c.
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| CN106324518B (en) * | 2016-08-31 | 2019-09-20 | 浙江长兴笛卡尔科技有限公司 | A kind of electric automobile power battery SOH evaluation method |
| CN107703452B (en) * | 2016-12-01 | 2020-08-25 | 国网辽宁省电力有限公司电力科学研究院 | Lead-acid battery application life prediction system |
| WO2018112818A1 (en) * | 2016-12-22 | 2018-06-28 | 深圳中兴力维技术有限公司 | Rapid prediction method for cycle life of battery and rapid prediction device therefor |
| CN111143973B (en) * | 2019-12-05 | 2021-01-26 | 云南电网有限责任公司玉溪供电局 | Valve-regulated lead-acid storage battery degradation trend prediction method based on Gauss process regression |
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| CN114361621B (en) * | 2022-03-18 | 2022-07-08 | 广东电网有限责任公司东莞供电局 | Battery management module of storage battery, method and device thereof, and storage medium |
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