CN108845264B

CN108845264B - Wavelet-based battery health state estimation method

Info

Publication number: CN108845264B
Application number: CN201810557322.4A
Authority: CN
Inventors: 徐俊; 赵云飞; 王霄; 梅雪松
Original assignee: Xian Jiaotong University
Current assignee: Xian Jiaotong University
Priority date: 2018-06-01
Filing date: 2018-06-01
Publication date: 2020-07-28
Anticipated expiration: 2038-06-01
Also published as: CN108845264A

Abstract

The invention discloses a wavelet-based battery health state estimation method, which comprises the following steps of: firstly, combining multi-resolution analysis of wavelet transformation and electrochemical impedance spectrum frequency band analysis of batteries, determining the number of wavelet decomposition layers, then obtaining voltage signals of batteries with different aging degrees responding to the same simulation working condition at the same environmental temperature and the same SOC, carrying out MRA specific layer number decomposition based on DWT on the voltage signals, and estimating the SOH of the batteries by using the standard deviation statistical characteristics of high-frequency resolution approximate signals and detail signals of the required frequency band obtained by decomposition. Compared with a data-driven method, the method does not need to carry out a large number of experiments, can carry out SOH prediction on the battery on line, has high prediction accuracy, and does not influence the experimental life of the battery; compared with the SOH estimation method based on the model, the method has the advantages of simple algorithm and high calculation efficiency.

Description

Wavelet-based battery health state estimation method

Technical Field

The invention belongs to the technical field of battery health state estimation, and particularly relates to a wavelet-based battery health state estimation method.

Background

Due to the deterioration of the environment and the shortage of energy, compared with the traditional fuel oil type automobile, the electric automobile adopting the motor to drive the automobile to run has the advantages of energy conservation, waste emission reduction, low noise and the like, and has wide development prospect. The battery management system of the electric automobile is an important link for connecting a vehicle-mounted power battery and the electric automobile, and can monitor parameters of the battery in real time, estimate the state of charge (SOC) and the state of health (SOH) of the battery and provide effective vehicle information for a driver. An appropriate battery management system not only can fully exert the superiority of the battery, but also can give the optimal protection to the battery, thereby prolonging the service life of the battery.

The SOH of the battery, which is one of the key parameters of the battery, has been the core problem of the battery management system of the electric vehicle and the technical difficulty to be solved urgently. Accurate SOH estimation has a non-negligible effect in improving SOC estimation accuracy, preventing battery overcharge and overdischarge. At present, battery SOH is mainly predicted at home and abroad by two methods, the first method is a data-driven method, the relation between internal battery characteristic parameters such as ohmic internal resistance and polarization internal resistance and the SOH is fitted through a large amount of experimental data or the relation between external input parameters such as temperature, depth of discharge and battery open-circuit voltage and the SOH is established by a black box method such as artificial intelligence and machine learning through a large amount of experimental data training, the method consumes a large amount of time in experiments, and the established corresponding relation has no universality; the second is a model-based SOH estimation method, which is to firstly replace the actual capacity with the rated capacity to establish an observer to estimate the SOC and then estimate the actual capacity by using the SOC, so that the cyclic estimation has obvious error accumulation effect, complex algorithm and low calculation efficiency.

In summary, the data-driven method consumes a lot of time in the experiment, the established corresponding relation has no universality, the model-based SOH estimation method has obvious error accumulation effect, the algorithm is complex, and the calculation efficiency is low.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention aims to provide a wavelet-based battery state of health estimation method which does not need to carry out a large number of experiments, has low cost and high calculation efficiency and can be better applied to practical use.

The invention is realized by adopting the following technical scheme:

a wavelet-based battery state of health estimation method comprises the following steps:

firstly, determining the number of decomposition layers by combining multi-resolution analysis of discrete wavelet transform and electrochemical impedance spectroscopy frequency band analysis of a battery, wherein the discrete wavelet transform is abbreviated as DWT, and the multi-resolution analysis is abbreviated as MRA;

secondly, selecting a proper wavelet basis function, carrying out DWT analysis on response voltage signals under different simulation working conditions, and knowing that low-frequency approximate signals and high-frequency detail signals obtained after MRA decomposition based on DWT well represent the characteristics of the voltage signals;

thirdly, acquiring voltage signals of batteries with different aging degrees, which respond to the same simulation working condition under the same environmental temperature and the same SOC, and performing MRA specific layer number decomposition based on DWT on the acquired voltage signals to know that the change degrees of battery voltage approximate signals and detail signals are positively correlated with the aging degrees of the battery voltage approximate signals and the detail signals, so that the SOH of the batteries is diagnosed through the statistical property of the standard deviation of the battery voltage approximate signals and the detail signals of the response voltage signals to the same simulation working condition;

and fourthly, estimating the SOH of the battery by using the standard deviation statistical characteristics of the decomposed high-frequency resolution approximate signal of the required frequency band and the detail signal.

In a further improvement of the invention, the battery is a lead-acid battery, a nickel-hydrogen battery, a nickel-cadmium battery or a lithium ion battery.

The invention is further improved in that, in the first step, the process of determining the number of decomposition layers is as follows: testing a Nyquist diagram of the electrochemical impedance spectrum of the battery to obtain the frequency corresponding to the semicircle of the intermediate frequency region; determining a frequency band of the DWT according to the sampling frequency, wherein the frequency band is halved and the frequency resolution is doubled when the number of layers is increased by one; and determining the decomposition layer number according to the frequency of the semi-circular region in the electrochemical impedance spectrum and the correspondence based on the DWT decomposition frequency band.

A further improvement of the invention is that the wavelet basis functions are orthogonal wavelets or non-orthogonal wavelets.

The invention has the following beneficial technical effects:

the estimation method provided by the invention estimates the SOH of the battery by analyzing the frequency band information corresponding to the frequency band in the impedance spectrum in the voltage signal when the current is transient through the wavelet, compared with a data-driven method, the SOH of the battery can be predicted on line without carrying out a large number of experiments, the prediction accuracy is high, and the experimental life of the battery is not influenced; compared with the SOH estimation method based on the model, the method has the advantages of simple algorithm and high calculation efficiency.

Furthermore, the wavelet basis functions can be orthogonal wavelets or non-orthogonal wavelets, and different simulation conditions in the second step are mainly used for revealing the characteristic that the low-frequency approximate signals and the high-frequency detail signals obtained after the MRA decomposition based on the DWT can well represent the voltage signals.

Drawings

Fig. 1 is an overall framework diagram of the present invention.

Figure 2 is an electrochemical impedance spectrum of an NCR18650 cell employed in an example of the invention.

Fig. 3 illustrates the decomposition frequency range of the MRA signal based on DWT.

FIG. 4 shows simulated conditions for different time intervals, current levels, and sudden changes in the embodiments of the present invention.

FIG. 5 is a DWT analysis of voltage signals corresponding to three simulated conditions in an embodiment of the present invention, where FIG. 5(a) is a DWT analysis of voltage signals collected under simulated condition 1, FIG. 5(a1) is a schematic diagram of current signals under simulated conditions, and FIG. 5(a2) is a low-frequency approximate signal A₅FIG. 5(a3) is a diagram of voltage signals corresponding to the working condition, and FIG. 5(a4) is a diagram of high frequency detail signal D₅Partial diagram, fig. 5(b) is a DWT analysis of the voltage signal collected under the simulated condition 2, fig. 5(b1) is a schematic diagram of the condition current signal, and fig. 5(b2) is a low-frequency approximate signal A₅Partial diagram of voltage signals, fig. 5(b3) is a schematic diagram of voltage signals corresponding to the working condition, and fig. 5(b4) is a high-frequency detail signal D₅Partial diagram, fig. 5(c) is a DWT analysis of voltage signals collected under simulated condition 3, fig. 5(c1) is a schematic diagram of current signals under simulated condition, and fig. 5(c2) is a low-frequency approximate signal A₅FIG. 5(c3) is a diagram of voltage signals corresponding to the operating conditions, and FIG. 5(c4) is a diagram of high frequency detail signal D₅Partial view.

FIG. 6 is a DWT analysis of voltage signals of five batteries with different aging degrees in the embodiment of the present invention, in which FIG. 6(a) is a schematic diagram of a simulated condition, and FIG. 6(b) is a low-frequency approximate signal A₅FIG. 6(c) is a high frequency detail signal D₅Schematic representation.

FIG. 7 shows an approximate signal A of voltages of five batteries with different aging degrees in an embodiment of the present invention₅And a detail signal D₅Standard deviation (room temperature, initial SOC of 60%), and fig. 7(a) is an approximate signal a₅FIG. 7(b) is a detail signal D₅Standard deviation of (2).

FIG. 8 is a voltage approximation signal A of five batteries with different aging degrees under different initial SOC conditions in the embodiment of the present invention₅And a detail signal D₅Wherein A in FIG. 8(a1) is SOC 20%₅D at SOC 20% in FIG. 8(b1)₅FIG. 8(a2) shows A at SOC 30%₅D at SOC 30% in FIG. 8(b2)₅FIG. 8(a3) shows A at SOC 40%₅D at SOC 40% in FIG. 8(b3)₅Standard deviation of (2)FIG. 8(a4) shows A at SOC 50%₅D at SOC 50% in FIG. 8(b4)₅FIG. 8(a5) shows A at SOC 70%₅D at SOC 70% in FIG. 8(b5)₅FIG. 8(a6) shows A at SOC 80%₅D at SOC 80% in FIG. 8(b6)₅FIG. 8(a7) shows A at SOC 90%₅D at SOC 90% in FIG. 8(b7)₅Standard deviation of (2).

Detailed Description

The invention is described in detail below with reference to the figures and examples.

Referring to fig. 1, the method for estimating the state of health of a battery based on wavelets according to the present invention includes the following steps:

in the first step, the number of decomposition layers is determined by combining the multiresolution analysis (MRA) of Discrete Wavelet Transform (DWT) and the electrochemical impedance spectroscopy frequency band analysis of the cell.

And secondly, selecting a proper wavelet basis function, and carrying out DWT analysis on the response voltage signals under different simulation working conditions, so that the low-frequency approximate signals and the high-frequency detail signals obtained after MRA decomposition based on DWT can well represent the characteristics of the voltage signals.

And thirdly, acquiring voltage signals of batteries with different aging degrees, which respond to the same simulation working condition under the same environmental temperature and the same SOC, and performing DWT-based MRA specific layer number decomposition on the acquired voltage signals to know that the change degrees of the battery voltage approximate signals and the detail signals are positively correlated with the aging degrees of the batteries, so that the SOH of the batteries can be diagnosed through the standard deviation statistical characteristics of the voltage approximate signals and the detail signals of the batteries responding to the voltage signals of the same simulation working condition.

The battery is a lead-acid battery, a nickel-hydrogen battery, a nickel-cadmium battery or a lithium ion battery.

The process of determining the number of decomposition layers by combining the multiresolution analysis (MRA) of Discrete Wavelet Transform (DWT) and the electrochemical impedance spectroscopy (ec) of the cell is as follows: testing a Nyquist diagram of the electrochemical impedance spectrum of the battery to obtain the frequency corresponding to the semicircle of the intermediate frequency region; determining a frequency band of the DWT according to the sampling frequency, wherein the frequency band is halved and the frequency resolution is doubled when the number of layers is increased by one; and determining the decomposition layer number according to the frequency of the semi-circular region in the electrochemical impedance spectrum and the correspondence based on the DWT decomposition frequency band.

The wavelet basis functions can be orthogonal wavelets or non-orthogonal wavelets, different simulation working conditions in the second step are mainly used for revealing the characteristic that low-frequency approximate signals and high-frequency detail signals obtained after the MRA decomposition based on the DWT can well represent voltage signals, and the low-frequency approximate signals and the high-frequency detail signals can be selected according to needs.

The same simulation working condition of the third step should contain more current pulses, so that the characteristic extraction is easy when the DWT is adopted to analyze the voltage signal.

Example (b):

the battery adopted in the embodiment is an NCR18650 ternary lithium battery, a nyquist diagram of an electrochemical impedance spectrum of which the SOC of the battery at room temperature is 60% is measured, and referring to fig. 2, wherein the horizontal axis is an impedance real part, and the vertical axis is a negative value of an impedance imaginary part, and the battery is divided into a high-frequency (5Hz-5kHz), a medium-frequency (0.1Hz-3.5Hz) and a low-frequency part (below 0.1 Hz) from left to right in sequence, wherein a flattened semicircle in a medium-frequency region reflects a charge transfer process in the electrochemical reaction of the battery, and is represented by polarization internal resistance and electric double layer capacitance, the semicircle part is susceptible to the aging of the battery, and the cross-axis intercept, namely the polarization. Due to the internal resistance characteristic of the battery, when the battery is subjected to current mutation, the output voltage of the battery can jump obviously, so that the SOH of the battery can be estimated by analyzing corresponding frequency band information in a voltage signal during current transient.

The DWT analysis method can enable the signal to have higher frequency resolution in a low-frequency part, is suitable for detecting a sudden change part in a voltage signal, decomposes the voltage signal of a battery under the current transient condition through the MRA based on DWT, and doubles the frequency resolution when the number of layers is increased by one and the frequency range is halved, referring to FIG. 3, wherein D₁、D₂To decompose the detail signals at

layers

1 and 2, A_n、D_nFor decomposing the approximation signal and detail signal in n layers, f_sIs the sampling frequency. Since the frequency band corresponding to the middle frequency band of the impedance spectrum in the voltage signal to be obtained is 0.1Hz to 3.5Hz, and the lower the frequency band, the higher the frequency resolution is, the sampling frequency is 0.01 seconds, when the number of layers is 5, the corresponding frequency band is calculated as follows:

the frequency bands corresponding to the 5 th layer of approximate signals and the detail signals of each layer are shown in table 1, and it can be known from the table that the frequency bands where the fifth layer of approximate signals and the detail signals are located are already in the middle frequency band of the electrochemical impedance spectrum, and the higher the number of decomposition layers is, the more complicated the calculation is, so that the 5-layer decomposition of the voltage signals is finally selected.

TABLE 1 frequency band of the fifth layer approximate signal and detail signal of each layer

Three simulation working conditions with different time intervals, current intensities and abrupt changes are set to obtain different voltage signals, and the specific flow of the working conditions is shown in fig. 4, wherein the discharge current of the battery is a negative value, the charge current is a positive value, and all the current intensities are between-6A and 6A. In the simulated condition 1, constant discharging and charging current is applied to the battery from 1s to 10s at a time interval of 1s, which is a cycle, and then the current intensity is increased from 1A to 6A for cycling, wherein no time interval exists between discharging and next charging; compared with the simulation working condition 1, the holding time of 3s is increased between the discharging and the next charging in the simulation working condition 2, namely the current is zero; the simulation working condition 3 is slightly different from the

simulation working conditions

1 and 2, the standing time of 10s is increased between discharging and next charging under the working condition relative to the simulation working condition 1, the discharging current is enabled to be from 1A to 6A under the working condition relative to the simulation working condition 2, after the standing is carried out for 10s, the charging current is enabled to be from 1A to 6A, a cycle is calculated, and the charging and discharging time is increased to be repeatedly cycled from 1s to 10 s.

The three simulation working conditions with different time intervals, current intensities and abrupt changes are used as excitation signals of the battery, corresponding voltage signals are acquired by taking 0.01 second as sampling frequency, 5-layer decomposition is carried out on the three voltage signals by adopting a DB4 orthogonal wavelet basis function to obtain a 5-layer low-frequency voltage approximate signal and a high-frequency detail signal, and the results are analyzed by referring to FIG. 5. As can be seen from fig. 5, the low-frequency approximate signal a obtained by performing MRA decomposition of the voltage signal by DWT is obtained₅And a high-frequency detail signal D₅The frequency has high frequency resolution, and a plurality of sharp peaks appear in the transient period, so that transient sudden change can be better detected; for discharge/charge currents that are unchanged over time, the low frequency approximates signal A₅There is little difference from the voltage signal; when the discharging/charging current suddenly changes (from the discharging state to the charging state or from the charging state to the discharging state), the high-frequency detail signal D₅The sharp peak of the wave energy converter is instantly and obviously increased, and the larger the mutation amplitude and the mutation frequency are, the larger the sharp peak is; high-frequency detail signal D₅The amplitude of (a) fluctuates around zero, which in practice means that the characteristics of the voltage signal can be approximated almost from a low frequency to signal a₅And (4) obtaining.

In summary, the combination of the low frequency approximation signal and the high frequency detail signal obtained after the DWT-based MRA decomposition can well represent the characteristics of the voltage signal, namely the voltage approximation signal A₅And a detail signal D₅The sudden change caused by internal resistance in the voltage signal can be well highlighted, and the polarization internal resistance is greatly influenced by the SOH of the battery, so that the voltage approximation signal A can be analyzed₅And a detail signal D₅And SOH to establish an estimated battery SOH estimation method.

The same simulation working condition is applied to five NCR18650 ternary lithium batteries with different aging degrees and the same initial SOC (60%) at room temperature, more current pulses are contained in the simulation working condition, so that the characteristic extraction is easy when voltage signals are analyzed by DWT, the specific capacity of the batteries is determined by experiments, and the specific result is shown in Table 2.

TABLE 2 measured capacities of five aged different batteries

Then, DB4 orthogonal wavelet basis functions are adopted to carry out 5-layer decomposition on voltage signals of the five batteries responding to the same simulation working condition, and the simulation working condition and the decomposed voltage approximate signals A of the five batteries with different aging degrees are obtained₅And a detail signal D₅As shown in FIG. 6, it can be seen that the No. 1 battery has the minimum measured capacity, the highest aging degree, the minimum SOH, and the voltage approximate signal A₅And a detail signal D₅The maximum variation of the voltage, the maximum measured capacity of the No. 5 battery, the minimum aging degree and the maximum SOH, and the voltage of the approximate signal A5 and the detail signal D₅With the least variation, the remaining cell voltage approximates signal A₅And a detail signal D₅The degree of change of (a) also increases with the degree of aging of the battery; that is to say, five cell voltage approximation signals A₅And a detail signal D₅Has a positive correlation with its aging degree, and has a voltage approximate signal A₅And a detail signal D₅The larger the variation, the higher the degree of battery aging, the smaller the battery actual capacity, and the smaller the battery SOH. The amplitude of the signal change can be represented by the standard deviation statistical characteristic of the signal, so that the voltage approximation signal A of the response voltage signal of the battery to the same simulation working condition under the same temperature and the same initial SOC can be calculated₅And a detail signal D₅The standard deviation statistic of the battery to diagnose the SOH of the battery.

FIG. 7 shows an approximate voltage signal A for five batteries with different aging degrees at room temperature and an initial SOC of 60%₅And a detail signal D₅Standard deviation of (1), voltage approximation signal A₅And a detail signal D₅All decrease with increasing SOH of the battery, and the voltage approximation signal a with the maximum value₅And a detail signal D₅Corresponding to the cell with the lowest SOH, with a voltage approximation of the minimum valueSignal A₅And a detail signal D₅Corresponds to the cell with the largest SOH. Because SOC has influence on SOH estimation, the initial SOC of the battery is 20% -90% at room temperature, voltage signals of five batteries with different aging degrees responding to the same simulation working condition are collected, and voltage approximate signal A is obtained after DWT analysis₅And a detail signal D₅As shown in fig. 8, it is understood that the voltage approximation signal a is obtained when the temperature is the same and the initial SOC of the battery is the same₅And a detail signal D₅All decrease with increasing SOH of the battery, and the voltage approximation signal a with the maximum value₅And a detail signal D₅Corresponds to the cell with the lowest SOH, and has the voltage approximate signal A with the lowest value₅And a detail signal D₅Corresponds to the cell with the largest SOH.

In conclusion, the SOH diagnosis method of the battery based on DWT is provided: in a battery pack, a voltage approximation signal A₅And a detail signal D₅The battery with the largest standard deviation is the battery with the smallest SOH, the SOH is set to be zero, and the voltage approximates to the signal A₅And a detail signal D₅The battery with the minimum standard deviation is the battery with the maximum SOH, the SOH of the battery is set to be 1, and the SOH calculation method of the rest batteries is as follows:

in the formula

Approximating the voltage of the currently estimated battery by a signal A₅The standard deviation of (a) is determined,

is the voltage detail signal D of the currently estimated battery₅The standard deviation of (a) is determined,

the voltage approximation signal A of the most aged, i.e. the lowest SOH, cell of the cells₅Standard deviation of (2) and detail signal D₅The standard deviation of (a) is determined,

approximate voltage signals A of the batteries with the lowest aging, namely the battery with the highest SOH₅Standard deviation of (2) and detail signal D₅Standard deviation of (2).

SOH estimation is carried out on five batteries with different aging degrees according to the SOH diagnosis method of the DWT-based battery, and the SOH estimation is carried out on the five batteries, wherein the SOH estimation is carried out on the No. 1 battery approximate signal A, taking the state of charge at room temperature as an example₅And a detail signal D₅The standard deviation is maximum, namely the SOH is minimum, and the set value is zero, namely the SOH₁＝0，

And

i.e. the approximate signals a for the respective battery No. 1₅And a detail signal D₅Standard deviation, approximate signal a for battery voltage No. 5₅And a detail signal D₅The standard deviation is minimum, namely the SOH is maximum, and the set value is SOH₅＝1，

And

i.e. the approximate signals a for the 5 th cell, respectively₅And a detail signal D₅Standard deviation, state of health SOH of No. 2, 3, 4 battery₂SOH₃SOH₄Calculated according to equation (4-2) as follows:

expanding the estimation to the whole SOC interval, and carrying out SOH estimation on five blocks with different aging degrees according to the proposed SOH estimation method adopting DWT, wherein the SOH estimation result and the error with the actual SOH are shown in Table 3;

TABLE 3 Battery SOH estimation results based on DWT SOH estimation method

The actual SOH of the five batteries was calculated from their measured capacities using the proposed SOH calculation formula and is shown in table 4.

TABLE 4 actual SOH of Battery based on DWT SOH estimation method

The SOH estimation results for each cell at different initial SOCs were compared to the actual SOH error for each cell, as shown in table 5.

TABLE 5 DWT-based SOH estimation method error

All batteries have SOH estimation errors not exceeding 5 percent and high estimation precision, the accuracy of the DWT-based SOH estimation method is verified, a large number of experiments are not needed, only the voltage signals acquired under the multi-pulse working condition need to be analyzed and calculated, and the method is convenient and low in cost.

Claims

1. A wavelet-based battery state of health estimation method is characterized by comprising the following steps:

firstly, determining the number of decomposition layers by combining multi-resolution analysis of discrete wavelet transform and electrochemical impedance spectroscopy frequency band analysis of a battery, wherein the discrete wavelet transform is abbreviated as DWT, and the multi-resolution analysis is abbreviated as MRA; the process of determining the number of decomposition layers is as follows: testing a Nyquist diagram of the electrochemical impedance spectrum of the battery to obtain the frequency corresponding to the semicircle of the intermediate frequency region; determining a frequency band of the DWT according to the sampling frequency, wherein the frequency band is halved and the frequency resolution is doubled when the number of layers is increased by one; determining the number of decomposition layers according to the frequency of the intermediate frequency semicircular region in the electrochemical impedance spectrum and the correspondence based on the DWT decomposition frequency band;

2. The wavelet-based battery state of health estimation method of claim 1, wherein the battery is a lead-acid battery, a nickel-metal hydride battery, a nickel-cadmium battery, or a lithium ion battery.

3. The wavelet-based battery state of health estimation method of claim 1, wherein the wavelet basis functions are orthogonal wavelets or non-orthogonal wavelets.