CN110275114B - Storage battery internal resistance on-line monitoring method based on combined filtering algorithm - Google Patents
Storage battery internal resistance on-line monitoring method based on combined filtering algorithm Download PDFInfo
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- G01—MEASURING; TESTING
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The invention discloses an online monitoring method for internal resistance of a storage battery based on a combined filtering algorithm. The online monitoring method for the internal resistance of the storage battery filters transient pulse noise components in alternating current response signals by adopting a mathematical morphology alternating mixing filtering method, and extracts characteristic quantities of approximate signals by utilizing a wavelet threshold denoising algorithm, thereby not only filtering high-frequency noise and transient pulse interference in the alternating current response signals, but also overcoming the influence of line resistance on a detection result, and remarkably improving the online measurement precision of the internal resistance of the storage battery by an alternating current injection method.
Description
Technical Field
The invention relates to a storage battery internal resistance online monitoring method based on a combined filtering algorithm, in particular to a storage battery internal resistance online monitoring method based on a combined filtering algorithm, which is suitable for UPS power storage batteries and electric automobile power batteries in the field of green buildings.
Background
At present, the evaluation of the health state of the storage battery generally takes the change of the internal resistance of the storage battery as an important basis of the capacity and the residual life of the storage battery, and when the internal resistance value is increased by 20 percent compared with the standard internal resistance value and the capacity of the storage battery is less than 80 percent of the rated capacity, the storage battery generally has a fault and needs to be replaced immediately to ensure the normal operation of a direct current power supply system.
At present, a direct current instantaneous large current discharge method and an alternating current impedance method are generally adopted for internal resistance detection. Patent No. CN105092977A 'method and circuit for measuring internal resistance of accumulator, method and system for detecting health status', adopts direct current instantaneous heavy current discharge method to calculate internal resistance of accumulator by measuring instantaneous change of terminal voltage of accumulator and volt-ampere characteristic curve of accumulator when load is switched on or removed. The large load current is instantly connected, so that the influence of capacitance effect between the electrode plates of the storage battery on internal resistance measurement can be effectively avoided, but the service life and safety of the storage battery are adversely affected by the instant large current, and the storage battery is not suitable for the storage batteries with medium and small capacities. Patent No. CN104950180A 'EIS-based accurate measurement method of internal resistance of single storage battery' applies sinusoidal excitation current with fixed frequency to the storage battery by AC impedance method, and AC voltage drop signals are formed at two ends of the storage battery under the action of equivalent internal resistance of the storage battery, and the signals are used as AC response signals of the system, and the internal resistance value of the storage battery can be calculated by voltammetry after wavelet transformation. The method is not limited by the capacity of the storage battery, has a wide application range and supports online monitoring. Because the amplitude of the excitation signal is small, the generated ripple has small influence on a direct current load and a storage battery, but is influenced by interference such as high-frequency noise and transient pulse generated by loads such as an electromagnetic switch, a charger and an inverter, so that an alternating current response signal is subjected to wavelet transformation to cause local distortion, and the measurement precision is reduced. The simple wavelet transformation noise reduction method cannot meet the precision requirement of internal resistance measurement when the storage battery outputs power under the complex working condition. Therefore, how to effectively avoid the distortion influence of the high-strength pulse interference on the signal processing and improve the measurement precision of the internal resistance of the storage battery is a problem to be solved urgently at present. .
Disclosure of Invention
In order to overcome the defects of the technical problems, the invention provides an online monitoring method for the internal resistance of a storage battery based on a combined filtering algorithm.
The invention relates to a storage battery internal resistance on-line monitoring method based on a combined filtering algorithm1A sinusoidal excitation signal with amplitude A; a contrast sampling resistor R is connected in series on a loop between the excitation source and the storage battery1(ii) a The method is characterized in that the online monitoring method of the internal resistance of the storage battery comprises the following steps: firstly, collecting alternating current response signals at two ends of a storage battery and two ends of a comparison resistor, then respectively determining the scale expansion range of structural elements of two pairs of alternating current response signals, and then carrying out multi-scale alternate mixed filtering based on open-close operation on the alternating current response signals by utilizing the scale expansion range of the structural elements so as to eliminate transient pulse noise components in the alternating current response signals; then, after wavelet decomposition, threshold filtering processing of soft-hard compromise is carried out, effective values of the storage battery and the comparison resistance response signals are calculated, and finally, the internal resistance of the storage battery is calculated by adopting a comparison method.
The invention discloses a storage battery internal resistance online monitoring method based on a combined filtering algorithm, which is characterized by comprising the following steps of: the online monitoring method for the internal resistance of the storage battery is realized by the following steps:
a) AC signal acquisition at the same sampling frequency f under the condition that the excitation source generates a continuous and stable sinusoidal excitation signal2Collecting alternating current response signals f at two ends of storage batterybsw1(n) and a comparative sampling resistor R1Ac response signal f at both endsdsw2(N), counting the number of sampling points of the two alternating current response signals to be N;
b) determining the scale expansion range of the structural element, and collecting the AC discrete signal fbsw1(n)、fdsw2(n) performing the processes of the steps b-1) to b-3), respectively:
b-1), determining a maximum position sequence, determining a maximum in the sampling signal by searching and comparing the numerical values of sampling points in the discrete sampling signal, and determining a maximum position point set according to the sampling sequence:
Post-max＝{postmaxi|i＝1，2，...，Nnum-max} (1)
in the formula, Post-maxIs a set of maximum location points, post-maxiIs the abscissa of the maximum value with the number i, Nost-maxThe number of maximum points;
b-2) calculating the distance between adjacent maximum values, and calculating the horizontal distance set D of the adjacent maximum values according to the abscissa of each maximum value obtained in the step b-1)p：
Dp＝{dpm|dpm＝post-max(m+1)-post-maxm，m＝1，2，...，Nnum-max-1} (2)
In the formula (d)pmIs the horizontal distance between the maximum value with the serial number m +1 and the maximum value with the serial number m;
b-3) calculating the scale of the structural element, firstly, calculating the maximum value K of the scale of the structural element by using a formula (3)maxAnd a minimum value Kmin：
In the formula, ceil [ ] is rounded up, floor [ ] is rounded down; from this, the set sg of dimensions of the structural elements is:
sg＝{sgi|sgi＝Kmin，Kmin+1，…，Kmax} (4)
c) multi-scale alternate hybrid filtering, setting discrete sampling signal fbsw1(n)、fdsw2(n) after the processing of step b), the obtained scale sets of the structural elements are sg-sg respectivelyi|sgi＝Kmin，Kmin+1，...，Kmax}、sg′＝{sg′i|gg′i＝K′min，K′min+1，...，K′max}; adopting the multi-scale alternative mixed filtering algorithm based on the open-close operation given by the formula (5) to the sampling signal fbsw1(n)、fdsw2(n) filtering:
in the formula (f)bs(n)、fds(n) are each fbsw1(n)、fdsw2(n) represents a closed operation,
represents an on operation; wherein, the open operation mainly eliminates the positive pulse interference, and the close operation mainly eliminates the negative pulse interference;
d) wavelet decomposition with DB10Wavelet basis function pair morphological filtering signal fbs(n)、fds(n) performing 4-layer wavelet decomposition, wherein the expression after decomposition is as follows:
in the formula (I), the compound is shown in the specification,
for the i-th layer detail signal,
approximating the signal for the ith layer;
then using formula (7) to respectively process four layers of detail signals
Carrying out threshold filtering processing of soft and hard compromise:
in the formula, σ ∈ (0, 1)]In order to be the weight coefficient,
is the standard deviation of the detail signal of layer i, sgn [ [ alpha ] ]]Is a sign function, i is 1, 2, 3, 4, N' is Di(n) totality of discrete pointsCounting; di(n) is
Or
e) Signal reconstruction, setting four layers of detail signals
The signals processed by the soft and hard compromise threshold function of formula (7) are respectively marked as Wi、Wi', using WiAnd approximate signal
Reconstructing to obtain a response voltage signal U of the storage battery1(n) using Wi' sum approximation signal
Reconstructing to obtain a contrast voltage signal U2(n), i ═ 1, 2, 3, 4; then, the response voltage signal and the comparison voltage signal are subjected to effective value calculation using equation (8):
in the formula, N is the number of sampling points;
f) calculating internal resistance of the battery by using the effective value U of the battery response voltage signalp1Comparing the effective value U of the voltage signalp2Calculating equivalent internal resistance R of the storage battery by adopting a comparison methodac：
In the formula, R1The resistance value of the comparison resistor is shown.
The method for monitoring the internal resistance of the storage battery on line based on the combined filtering algorithm comprises the steps of obtaining the internal resistance of the storage battery, judging whether the current internal resistance increment of the storage battery exceeds 20% of the standard internal resistance of the storage battery, giving a warning and a prompt for replacing the storage battery if the current internal resistance increment of the storage battery exceeds 20% of the standard internal resistance of the storage battery, and giving the internal resistance of the storage battery and the percentage exceeding the standard internal resistance if the current internal resistance increment of the.
The invention has the beneficial effects that: the invention relates to an on-line monitoring method for internal resistance of a storage battery, which comprises the steps of firstly collecting alternating current response signals at two ends of the storage battery and a comparison resistor under the condition of adding alternating current and sine excitation signals, then filtering transient pulse noise components in the alternating current response signals by adopting a mathematical morphology alternating mixing filtering method, extracting characteristic quantities of approximate signals by utilizing a wavelet threshold noise reduction algorithm, and finally calculating the internal resistance of the storage battery by adopting a comparison method.
Drawings
FIG. 1 is a circuit diagram of the online monitoring of internal resistance of a storage battery according to the present invention;
FIG. 2 is a signal processing flow diagram of the combined filtering algorithm of the present invention;
FIG. 3 is a flow chart of the method for on-line monitoring the internal resistance of the storage battery according to the present invention;
in fig. 4, a is a diagram of an ac response signal at two ends of an unprocessed storage battery, b is a diagram of a waveform of an ac signal at two ends of a storage battery after being processed by a simple wavelet threshold filtering process, and c is a diagram of a waveform of an ac signal at two ends of a storage battery after being processed by a combined filtering algorithm according to the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
As shown in figure 1, the circuit diagram for on-line monitoring of internal resistance of the storage battery is provided, the lead-acid storage battery is a battery with internal resistance to be detected, the alternating current excitation signal generator is used for injecting alternating current sinusoidal signals into two ends of the storage battery, and a comparison sampling resistor R is connected in series on a line between the alternating current excitation signal generator and the storage battery1AC excitation signal generationThe equivalent resistance of the line between the device and the accumulator is R2. While injecting the AC excitation signal, the AC response signal collected from the two ends of the storage battery is fbsw1(n) the AC response signal collected from both ends of the comparison sampling resistor is fdsw2(n) of (a). Since the internal resistance of the battery is in milliohm level under normal conditions, the specific sampling resistance R is1Also in the milliohm range, e.g. 20 milliohms. The processing flow of the collected alternating current response signals at the two ends of the storage battery and the comparison resistor is as follows: firstly, according to the frequency and peak value self-setting structural element parameters of a sampling signal, a mathematical morphology alternating hybrid filter is used for conducting pre-filtering processing on the sampling signal, a wavelet threshold noise reduction function is used for conducting characteristic quantity extraction on an alternating current response signal of a monitoring system, and online accurate measurement on internal resistance of a lead-acid storage battery is achieved.
As shown in fig. 2, a signal processing flow chart of the combined filtering algorithm of the present invention is given, and fig. 3 is a flow chart of the online monitoring method for internal resistance of storage battery of the present invention, which is specifically realized by the following steps:
a) AC signal acquisition at the same sampling frequency f under the condition that the excitation source generates a continuous and stable sinusoidal excitation signal2Collecting alternating current response signals f at two ends of storage batterybsw1(n) and a comparative sampling resistor R1Ac response signal f at both endsdsw2(n), wherein n is the number of sampling points;
b) determining the scale expansion range of the structural element, and collecting the AC discrete signal fbsw1(n)、fdsw2(n) performing the treatments of the steps b-1) to b-3), respectively:
b-1), determining a maximum position sequence, determining a maximum in the sampling signal by searching and comparing the numerical values of sampling points in the discrete sampling signal, and determining a maximum position point set according to the sampling sequence:
Post-max＝{post-maxi|i＝1，2，...，Nnum-max} (1)
in the formula, Post-maxIs a set of maximum location points, post-maxiIs a serial number iAbscissa of maximum value of, Nost-maxThe number of maximum points;
b-2) calculating the distance between adjacent maximum values, and calculating the horizontal distance set D of the adjacent maximum values according to the abscissa of each maximum value obtained in the step b-1)p：
Dp＝{dpm|dpm＝post-max(m+1)-post-maxm，m＝1，2，...，Nnum-max-1} (2)
In the formula (d)pmIs the horizontal distance between the maximum value with the serial number m +1 and the maximum value with the serial number m;
b-3) calculating the scale of the structural element, firstly, calculating the maximum value K of the scale of the structural element by using a formula (3)maxAnd a minimum value Kmin：
In the formula, ceil [ ] is rounded up, floor [ ] is rounded down; from this, the set sg of dimensions of the structural elements is:
sg＝{sgi|sgi＝Kmin，Kmin+1，...，Kmax} (4)
c) multi-scale alternate hybrid filtering, setting discrete sampling signal fbsw1(n)、fdsw2(n) after the processing of step b), the obtained scale sets of the structural elements are sg-sg respectivelyi|sgi＝Kmin，Kmin+1，...，Kmax}、sg′＝{sg′i|gg′i＝K′min，K′min+1，...，K′max}; adopting the multi-scale alternative mixed filtering algorithm based on the open-close operation given by the formula (5) to the sampling signal fbsw1(n)、fdsw2(n) filtering:
in the formula (f)bs(n)、fds(n) are each fbsw1(n)、fdsw2(n) represents a closed operation,
represents an on operation; wherein, the open operation mainly eliminates the positive pulse interference, and the close operation mainly eliminates the negative pulse interference;
d) wavelet decomposition with DB10Wavelet basis function pair morphological filtering signal fbs(n)、fds(n) performing 4-layer wavelet decomposition, wherein the expression after decomposition is as follows:
in the formula (I), the compound is shown in the specification,
for the i-th layer detail signal,
approximating the signal for the ith layer;
then using formula (7) to respectively process four layers of detail signals
Carrying out threshold filtering processing of soft and hard compromise:
in the formula, σ ∈ (0, 1)]In order to be the weight coefficient,
is the standard deviation of the detail signal at layer j, sgn [ [ deg. ] ]]Is a sign function, i is 1, 2, 3, 4; di(n) is
Or
e) Signal reconstruction, setting four layers of detail signals
The signals processed by the soft and hard compromise threshold function of formula (7) are respectively marked as Wi、Wi', using WiAnd approximate signal
Reconstructing to obtain a response voltage signal U of the storage battery1(n) using Wi' sum approximation signal
Reconstructing to obtain a contrast voltage signal U2(n), i ═ 1, 2, 3, 4; then, the response voltage signal and the comparison voltage signal are subjected to effective value calculation using equation (8):
in the formula, N1Is U1(N) number of discrete voltage values contained, N2Is U2(n) the number of discrete voltage values contained;
f) calculating internal resistance of the battery by using the effective value U of the battery response voltage signalp1Comparing the effective value U of the voltage signalp2Calculating equivalent internal resistance R of the storage battery by adopting a comparison methodac：
In the formula, R1The resistance value of the comparison resistor is shown.
After the internal resistance of the storage battery is obtained, whether the current internal resistance increment of the storage battery exceeds 20% of the standard internal resistance is judged, if yes, an alarm and a prompt for replacing the storage battery are given, and if not, the internal resistance of the storage battery and the percentage exceeding the standard internal resistance are given.
As shown in fig. 4, a is an unprocessed ac response signal at two ends of the storage battery, b is a waveform diagram of the ac signal at two ends of the storage battery after being processed only by the simple wavelet threshold filtering, and c is a waveform diagram of the ac signal at two ends of the storage battery after being processed by the combined filtering algorithm of the present invention. By comparing the waveform diagrams in the graph b and the graph a, it is found that the simple wavelet threshold filtering can only filter small positive and negative impulse noise in the signal, and is difficult to filter large transient impulse noise components in the alternating current response signal. By comparing the graph c with the graph a, the combined filtering algorithm can filter out high-frequency noise and transient impulse noise in the alternating current response signal, and is favorable for calculating more accurate internal resistance of the storage battery.
The storage battery internal resistance online monitoring method based on the combined filtering algorithm not only ensures the filtering capability of the morphological filter on transient pulse interference, but also ensures the filtering capability of the wavelet threshold algorithm on high-frequency noise, and fully exerts the respective characteristics of the morphological filter and the wavelet threshold algorithm. Compared with the wavelet threshold denoising algorithm for directly processing the alternating current response signal, the combined method reduces the influence of the transient pulse component on the peak value of the alternating current response signal and effectively improves the signal-to-noise ratio of the filtering signal. Meanwhile, the method effectively avoids measurement deviation caused by factors such as electromagnetic interference and the like under the background of a complex environment, and has important significance for detecting the internal resistance of the storage battery.
Claims (2)
1. A storage battery internal resistance on-line monitoring method based on a combined filtering algorithm is characterized in that excitation sources are connected to two ends of a storage battery, and the frequency generated by the excitation sources is f1A sinusoidal excitation signal with amplitude A; a contrast sampling resistor R is connected in series on a loop between the excitation source and the storage battery1(ii) a The method is characterized in that the online monitoring method of the internal resistance of the storage battery comprises the following steps: firstly, alternating current response signals at two ends of a storage battery and two ends of a comparison resistor are collected, then the scale expansion range of structural elements of two pairs of alternating current response signals is respectively determined, and then the scale expansion range of the structural elements is utilized to conduct opening-based operation on the alternating current response signals-closed-operation multiscale alternating mixing filtering to eliminate transient impulse noise components in the ac response signal; then, after wavelet decomposition, threshold filtering processing of soft-hard compromise is carried out, effective values of the storage battery and the comparison resistance response signals are calculated, and finally, the internal resistance of the storage battery is calculated by adopting a comparison method;
the online monitoring method for the internal resistance of the storage battery is realized by the following steps:
a) AC signal acquisition at the same sampling frequency f under the condition that the excitation source generates a continuous and stable sinusoidal excitation signal2Collecting alternating current response signals f at two ends of storage batterybsw1(n) and a comparative sampling resistor R1Ac response signal f at both endsdsw2(N), counting the number of sampling points of the two alternating current response signals to be N;
b) determining the scale expansion range of the structural element, and collecting the AC discrete signal fbsw1(n)、fdsw2(n) performing the treatments of the steps b-1) to b-3), respectively:
b-1), determining a maximum position sequence, determining a maximum in the sampling signal by searching and comparing the numerical values of sampling points in the discrete sampling signal, and determining a maximum position point set according to the sampling sequence:
Post-max＝{post-maxi|i＝1，2，...，Nnum-max} (1)
in the formula, Post-maxIs a set of maximum location points, post-maxiIs the abscissa of the maximum value with the number i, Nnum-maxThe number of maximum points;
b-2) calculating the distance between adjacent maximum values, and calculating the horizontal distance set D of the adjacent maximum values according to the abscissa of each maximum value obtained in the step b-1)p：
Dp＝{dpm|dpm＝post-max(m+1)-post-maxm，m＝1，2，...，Nnum-max-1} (2)
In the formula (d)pmIs the horizontal distance between the maximum value with the serial number m +1 and the maximum value with the serial number m;
b-3) solving structureThe element scale is obtained by first calculating the maximum value K of the structural element scale by using formula (3)maxAnd a minimum value Kmin：
In the formula, ceil [ ] is rounded up, floor [ ] is rounded down; from this, the set sg of dimensions of the structural elements is:
sg＝{sgi|sgi＝Kmin，Kmin+1，...，Kmax} (4)
c) multi-scale alternate hybrid filtering, setting discrete sampling signal fbsw1(n)、fdsw2(n) after the processing of step b), the obtained scale sets of the structural elements are sg-sg respectivelyi|sgi＝Kmin，Kmin+1，...，Kmax}、sg′＝{sg′i|sg′i＝K′min，K′min+1，...，K′max}; adopting the multi-scale alternative mixed filtering algorithm based on the open-close operation given by the formula (5) to the sampling signal fbsw1(n)、fdsw2(n) filtering:
in the formula (f)bs(n)、fds(n) are each fbsw1(n)、fdsw2(n) represents a closed operation,
represents an on operation; wherein, the open operation mainly eliminates the positive pulse interference, and the close operation mainly eliminates the negative pulse interference;
d) wavelet decomposition with DB10Wavelet basis function pair morphological filtering signal fbs(n)、fds(n) performing 4-layer wavelet decomposition, wherein the expression after decomposition is as follows:
in the formula (I), the compound is shown in the specification,
for the i-th layer detail signal,
approximating the signal for the ith layer;
then using formula (7) to respectively process four layers of detail signals
Carrying out threshold filtering processing of soft and hard compromise:
in the formula, σ ∈ (0, 1)]In order to be the weight coefficient,
is the standard deviation of the detail signal of layer i, sgn [ [ alpha ] ]]Is a sign function, i is 1, 2, 3, 4, N' is Di(n) total number of discrete points; di(n) is
Or
e) Signal reconstruction, setting four layers of detail signals
The signals processed by the soft and hard compromise threshold function of formula (7) are respectively marked as Wi、Wi', using WiAnd approximate signal
Reconstructing to obtain a response voltage signal U of the storage battery1(n) using Wi' sum approximation signal
Reconstructing to obtain a contrast voltage signal U2(n), i ═ 1, 2, 3, 4; then, the response voltage signal and the comparison voltage signal are subjected to effective value calculation using equation (8):
in the formula, N is the number of sampling points;
f) calculating internal resistance of the battery by using the effective value U of the battery response voltage signalp1Comparing the effective value U of the voltage signalp2Calculating equivalent internal resistance R of the storage battery by adopting a comparison methodac：
In the formula, R1The resistance value of the comparison resistor is shown.
2. The combined filtering algorithm-based storage battery internal resistance online monitoring method according to claim 1, characterized in that: after the internal resistance of the storage battery is obtained, whether the current internal resistance increment of the storage battery exceeds 20% of the standard internal resistance is judged, if yes, an alarm and a prompt for replacing the storage battery are given, and if not, the internal resistance of the storage battery and the percentage exceeding the standard internal resistance are given.
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