CN115494400B - Lithium battery lithium separation state online monitoring method based on ensemble learning - Google Patents

Lithium battery lithium separation state online monitoring method based on ensemble learning Download PDF

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CN115494400B
CN115494400B CN202211381818.3A CN202211381818A CN115494400B CN 115494400 B CN115494400 B CN 115494400B CN 202211381818 A CN202211381818 A CN 202211381818A CN 115494400 B CN115494400 B CN 115494400B
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范玉千
左现刚
金松林
李倩
杨书廷
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Henan Power Battery Innovation Center Co ltd
Henan Institute of Science and Technology
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Abstract

The invention provides an integrated learning-based lithium battery lithium analysis state online monitoring method, which comprises the steps of modeling a lithium battery through an SP + model, determining importance scores of identification parameters according to a degradation experiment, finally adjusting the SP + model in real time through a fitting function, calculating the current negative electrode potential of the lithium battery, and judging whether the lithium battery is close to or in a lithium analysis state according to the negative electrode potential. According to the method, the importance of each identification parameter is sequenced through a degradation experiment, so that the accuracy of lithium analysis state prediction of the lithium battery is improved on one hand; on the other hand, the calculation amount is greatly reduced, the timeliness of lithium analysis state prediction of the lithium battery is improved, and online monitoring of the lithium analysis state of the lithium battery is really realized.

Description

Lithium battery lithium separation state online monitoring method based on ensemble learning
Technical Field
The invention relates to the field of lithium batteries, in particular to a lithium battery lithium analysis state online monitoring method based on ensemble learning.
Background
In practical use, an excessively high charging current or an excessively high ambient temperature may cause an irreversible lithium precipitation phenomenon in the lithium battery. The occurrence of the phenomenon of lithium precipitation not only can reduce the service life of the lithium battery, but also can cause irreversible damage to the lithium battery. In addition, the lithium battery is in a lithium analysis state for a long time, so that internal short circuit can be caused, and thermal runaway can occur in serious people, and further, fire accidents are caused. Therefore, if the lithium analysis condition of the lithium battery can be monitored on line, the safety of the lithium battery, vehicles and passengers is guaranteed.
Usually, need to disassemble the lithium cell and can learn through manual observation whether the lithium cell takes place to analyse the lithium phenomenon, however the way is difficult to in time obtain the lithium condition of analysing of lithium cell like this, moreover also inadvisable in practical application. At present, a method for calculating the potential of a negative electrode of a lithium battery through an RC equivalent model so as to judge whether the lithium battery is in a lithium analysis state exists, but the method is large in calculation amount and low in efficiency, and if the accuracy of lithium analysis state prediction is guaranteed, the timeliness of prediction cannot be guaranteed, so that the method cannot be really used in a vehicle-mounted scene.
Disclosure of Invention
In order to overcome the defects in the background art, the invention discloses an integrated learning-based lithium battery lithium separation state online monitoring method, which aims to: the method improves the accuracy of lithium battery lithium analysis state prediction, considers the operation efficiency and really realizes the online monitoring of the lithium battery lithium analysis state.
In order to achieve the purpose, the invention adopts the following technical scheme:
a lithium battery lithium analysis state online monitoring method based on ensemble learning comprises the following steps:
the method comprises the following steps: modeling the lithium battery by using an SP + model, and determining identification parameters required by modeling;
step two: setting pulse working conditions under various different charging and discharging multiplying powers, carrying out degradation experiments on the lithium battery at different temperatures, identifying and obtaining identification parameters under different SOH, different SOC, different temperatures and different charging and discharging multiplying powers according to the pulse working conditions set for the lithium battery at different degradation stages, and carrying out sensitivity analysis on the identification parameters respectively to obtain importance scores of the SOH, the SOC, the temperatures and the charging and discharging multiplying powers on the identification parameters;
step three: according to the variation ranges of the identification parameters in different SOH and SOC stages and under the variation of temperature and charge-discharge multiplying power, respectively carrying out sensitivity analysis on the identification parameters to obtain the importance scores of the identification parameters to terminal voltage;
step four: the method comprises the steps of conducting importance sorting on importance scores of SOH, SOC, temperature and charge-discharge multiplying power for identification parameters and importance scores of the identification parameters for terminal voltage to obtain important identification parameters, and fitting the important identification parameters into fitting functions relevant to the SOH, the SOC, the temperature and the charge-discharge multiplying power;
step five: and taking the real-time temperature and the real-time current of the current lithium battery as input, adjusting the SP + model in real time through a fitting function, calculating the current negative electrode potential of the lithium battery, and judging whether the lithium battery is close to or in a lithium analysis state according to the negative electrode potential.
Further improving the technical scheme, in the step one, the SP + model includes the following formula:
the basic process of the electrochemical reaction is described by the following formula:
Figure 691750DEST_PATH_IMAGE001
wherein, the first and the second end of the pipe are connected with each other,
Figure 521166DEST_PATH_IMAGE002
for positive open circuit potential
Figure 908285DEST_PATH_IMAGE003
About
Figure 481349DEST_PATH_IMAGE004
As a function of (a) or (b),
Figure 854168DEST_PATH_IMAGE005
is negative open circuit potential
Figure 916802DEST_PATH_IMAGE006
About
Figure 728900DEST_PATH_IMAGE007
As a function of (a) or (b),
Figure 902392DEST_PATH_IMAGE004
、
Figure 116336DEST_PATH_IMAGE007
respectively the lithium concentration fractions embedded on the surfaces of the positive electrode active particles and the negative electrode active particles of the lithium battery,
Figure 553134DEST_PATH_IMAGE008
、
Figure 914845DEST_PATH_IMAGE009
respectively, the average lithium intercalation concentration fractions in the positive and negative electrode active particles,
Figure 78979DEST_PATH_IMAGE010
、
Figure 475325DEST_PATH_IMAGE011
respectively the maximum variation range of the lithium intercalation concentration fraction of the positive electrode and the negative electrode,
Figure 83024DEST_PATH_IMAGE012
for the load current, the discharge is specified to be positive,
Figure 604135DEST_PATH_IMAGE013
the total capacity (C) of the lithium battery.
The solid phase diffusion process is described by the following equation:
Figure 385009DEST_PATH_IMAGE014
wherein the content of the first and second substances,
Figure 573545DEST_PATH_IMAGE015
、
Figure 148883DEST_PATH_IMAGE016
respectively, positive and negative electrode solid phase diffusion time constants.
The liquid phase concentration polarization process is described by the following equation:
Figure 908023DEST_PATH_IMAGE017
wherein the content of the first and second substances,
Figure 227009DEST_PATH_IMAGE019
the gas constant is an ideal gas constant,
Figure 535630DEST_PATH_IMAGE021
the internal temperature (K) of the lithium battery,
Figure 219552DEST_PATH_IMAGE023
is a function of the faraday constant and is,
Figure 43152DEST_PATH_IMAGE025
the initial lithium ion concentration of the electrolyte is generally set to 1000,
Figure 103512DEST_PATH_IMAGE026
the cation transport number is generally 0.3 to 0.4,
Figure 515907DEST_PATH_IMAGE028
is the time constant of the diffusion of the liquid phase,
Figure 433048DEST_PATH_IMAGE029
is the liquid phase diffusion proportionality coefficient.
The reactive polarization process is described by the following equation:
Figure 416047DEST_PATH_IMAGE030
in the formula, the reaction polarization overpotential of the negative electrode is as follows:
Figure 76836DEST_PATH_IMAGE031
wherein, the first and the second end of the pipe are connected with each other,
Figure 94470DEST_PATH_IMAGE032
、
Figure 385774DEST_PATH_IMAGE033
positive and negative reaction polarization coefficients, respectively.
The ohmic polarization process is described by the following equation: a
Figure 918387DEST_PATH_IMAGE034
Wherein, the first and the second end of the pipe are connected with each other,
Figure 68352DEST_PATH_IMAGE035
equivalent ohmic internal resistance.
Terminal voltage
Figure 2810DEST_PATH_IMAGE036
Described by the following formula:
Figure 199436DEST_PATH_IMAGE037
wherein, the identification parameters required by modeling are 13, which are respectively the positive and negative active particles of the batterySub-initial lithium intercalation concentration
Figure 219345DEST_PATH_IMAGE038
、
Figure 425198DEST_PATH_IMAGE039
Maximum variation range of positive and negative electrode lithium intercalation concentration fractions
Figure 417425DEST_PATH_IMAGE011
、
Figure 847269DEST_PATH_IMAGE010
Positive and negative electrode capacity
Figure 541425DEST_PATH_IMAGE040
、
Figure 347707DEST_PATH_IMAGE041
Time constant of solid phase diffusion of positive and negative electrodes
Figure 928861DEST_PATH_IMAGE015
、
Figure 998448DEST_PATH_IMAGE016
Time constant of liquid phase diffusion
Figure 727370DEST_PATH_IMAGE028
Coefficient of liquid phase diffusion ratio
Figure 275026DEST_PATH_IMAGE029
Positive and negative reaction polarization coefficients
Figure 773003DEST_PATH_IMAGE032
、
Figure 498645DEST_PATH_IMAGE033
Equivalent ohmic internal resistance
Figure 714862DEST_PATH_IMAGE035
。
Further improving the technical scheme, in the second step, after the battery is fully charged, the battery is subjected to cross discharge charging with the magnification from small to large and with the discharge first and the charge later according to the actually acceptable charge-discharge magnification of the battery, the discharge and charge duration time is minutes, and the battery needs to be placed for minutes after each charge or discharge is finished; each charging multiplying factor and each discharging multiplying factor are subjected to 1 cycle from small to large, and the discharging cut-off voltage is reached after a plurality of cycles.
And in the second step, performing the degradation experiment on the battery under the working conditions that constant current and constant voltage charging with fixed multiplying power is carried out to the charging cut-off voltage and constant current discharging with fixed multiplying power is carried out to the discharging cut-off voltage, performing pulse working conditions in each cycle, and recording the current corresponding SOH of the battery.
And in the second step, performing sensitivity analysis on the identification parameters by using an AdaBoost algorithm, a decision tree algorithm and a random forest algorithm to obtain the importance of SOH, SOC, temperature and charge-discharge multiplying power on the identification parameters, and obtaining the importance scores of the identification parameters by weighting.
And in the third step, performing sensitivity analysis on the identification parameters by using an AdaBoost algorithm, a decision tree algorithm and a random forest algorithm to obtain the importance of the identification parameters to the terminal voltage, and obtaining the importance scores of the identification parameters by weighting.
The technical scheme is further improved, and the importance scores of identification parameters which are sensitive to temperature and charge-discharge multiplying power and have important influence on terminal voltage are increased.
According to the further improved technical scheme, in the fifth step, the negative electrode potential
Figure 800630DEST_PATH_IMAGE043
Described by the following formula:
Figure 621956DEST_PATH_IMAGE044
wherein the content of the first and second substances,
Figure 830083DEST_PATH_IMAGE045
is a negative open circuit potential.
The technical scheme is further improved, the UKF is used for updating in real time to obtain the accurate SOC, the battery simulation terminal voltage obtained by the SP + model is used as an estimated value, the actually measured terminal voltage is used as an observed value, the relative error between the estimated value and the observed value is calculated, and the relative error is updated; when the relative error is <1%, the SOC is considered accurate.
Further improve the technical scheme when
Figure 471280DEST_PATH_IMAGE046
Judging that the battery is in a normal state; when the temperature is higher than the set temperature
Figure 423055DEST_PATH_IMAGE047
Judging that the battery is close to a lithium separation state; when the temperature is higher than the set temperature
Figure 82576DEST_PATH_IMAGE048
And judging that the battery is in a lithium separation state.
Due to the adoption of the technical scheme, compared with the background technology, the invention has the following beneficial effects:
the invention provides an integrated learning-based lithium battery lithium analysis state online monitoring method, which is characterized in that a lithium battery is modeled through an SP + model, the importance scores of identification parameters are determined according to a degradation experiment, finally, the SP + model is adjusted in real time through a fitting function, the current negative pole potential of the lithium battery is calculated, and whether the lithium battery is close to or in a lithium analysis state is judged according to the negative pole potential.
According to the method, the importance of each identification parameter is sequenced through a degradation experiment, so that the accuracy of lithium analysis state prediction of the lithium battery is improved, the calculated amount is greatly reduced, the timeliness of lithium analysis state prediction of the lithium battery is improved, and the online monitoring of the lithium analysis state of the lithium battery is really realized.
Drawings
Fig. 1 shows a flow chart of the lithium battery lithium analysis state online monitoring method.
FIG. 2 is a schematic diagram illustrating the analysis and ranking of the sensitivity of the algorithms to identifying parameters.
FIG. 3 is a flow chart showing the updating of SOC by UKF according to the present invention.
Fig. 4 is a graph showing a comparison of a simulated voltage and a measured voltage obtained by using the existing RC equivalent model.
Fig. 5 shows a comparison of the simulated voltage and the measured voltage obtained by the method.
Detailed Description
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and are not intended to limit the scope of the present invention.
The invention provides an integrated learning-based lithium battery lithium analysis state online monitoring method, which comprises the following steps of:
the method comprises the following steps: and modeling the lithium battery by using the SP + model, and determining identification parameters required by modeling.
The SP + model comprises five processes of a lithium battery in the charging and discharging processes, namely an electrochemical reaction basic process, a particle internal solid phase diffusion process, a liquid phase concentration polarization process, reaction polarization and ohmic polarization. The basic process of the electrochemical reaction is described by the following formula:
Figure 664867DEST_PATH_IMAGE049
wherein, the first and the second end of the pipe are connected with each other,
Figure 855677DEST_PATH_IMAGE002
for positive open circuit potential
Figure 283247DEST_PATH_IMAGE003
About
Figure 875902DEST_PATH_IMAGE004
As a function of (a) or (b),
Figure 97936DEST_PATH_IMAGE005
is negative open circuit potential
Figure 776042DEST_PATH_IMAGE006
About
Figure 20685DEST_PATH_IMAGE007
Is a function of (a) a function of (b),
Figure 405530DEST_PATH_IMAGE004
、
Figure 126362DEST_PATH_IMAGE007
respectively the lithium concentration fractions embedded on the surfaces of the positive electrode active particles and the negative electrode active particles of the lithium battery,
Figure 963868DEST_PATH_IMAGE008
、
Figure 998820DEST_PATH_IMAGE009
respectively, the average lithium intercalation concentration fractions in the positive and negative electrode active particles,
Figure 566067DEST_PATH_IMAGE010
、
Figure 644751DEST_PATH_IMAGE011
respectively the maximum variation range of the lithium intercalation concentration fraction of the positive electrode and the negative electrode,
Figure 31870DEST_PATH_IMAGE012
for the load current, the discharge is specified to be positive,
Figure 870513DEST_PATH_IMAGE013
the total capacity (C) of the lithium battery.
The solid phase diffusion process is described by the following formula:
Figure 229950DEST_PATH_IMAGE050
wherein, the first and the second end of the pipe are connected with each other,
Figure 292584DEST_PATH_IMAGE015
、
Figure 104682DEST_PATH_IMAGE016
respectively, positive and negative electrode solid phase diffusion time constants.
The liquid phase concentration polarization process is described by the following equation:
Figure 232169DEST_PATH_IMAGE051
wherein, the first and the second end of the pipe are connected with each other,
Figure 508430DEST_PATH_IMAGE019
the gas constant is an ideal gas constant,
Figure 679648DEST_PATH_IMAGE021
the internal temperature (K) of the lithium battery,
Figure 41359DEST_PATH_IMAGE023
is a function of the faraday constant and is,
Figure 956226DEST_PATH_IMAGE025
the initial lithium ion concentration of the electrolyte is generally set to 1000,
Figure 290255DEST_PATH_IMAGE052
the cation transference number is generally 0.3 to 0.4,
Figure 694692DEST_PATH_IMAGE028
is the time constant of the diffusion of the liquid phase,
Figure 730650DEST_PATH_IMAGE029
is the liquid phase diffusion proportionality coefficient.
The reactive polarization process is described by the following equation:
Figure DEST_PATH_IMAGE053
the negative electrode reaction polarization overpotential in the above formula is:
Figure 714786DEST_PATH_IMAGE054
wherein the content of the first and second substances,
Figure 700060DEST_PATH_IMAGE032
、
Figure 478660DEST_PATH_IMAGE033
positive and negative reaction polarization coefficients, respectively.
The ohmic polarization process is described by the following equation:
Figure DEST_PATH_IMAGE055
wherein the content of the first and second substances,
Figure 18225DEST_PATH_IMAGE035
equivalent ohmic internal resistance.
Terminal voltage
Figure 22697DEST_PATH_IMAGE036
Described by the following formula:
Figure 128056DEST_PATH_IMAGE056
wherein, the identification parameters required by modeling are 13, and the identification parameters are respectively the initial lithium intercalation concentration of the positive and negative active particles of the lithium battery
Figure 811979DEST_PATH_IMAGE038
、
Figure 838841DEST_PATH_IMAGE039
Maximum variation range of positive and negative electrode lithium intercalation concentration fraction
Figure 695938DEST_PATH_IMAGE011
、
Figure 593487DEST_PATH_IMAGE010
Positive and negative electrode capacity
Figure 776207DEST_PATH_IMAGE040
、
Figure 8474DEST_PATH_IMAGE041
Time constant of solid phase diffusion of positive and negative electrodes
Figure 669262DEST_PATH_IMAGE015
、
Figure 952476DEST_PATH_IMAGE016
Time constant of liquid phase diffusion
Figure 978201DEST_PATH_IMAGE028
Coefficient of liquid phase diffusion ratio
Figure 510813DEST_PATH_IMAGE029
Positive and negative reaction polarization coefficients
Figure 912976DEST_PATH_IMAGE032
、
Figure 801428DEST_PATH_IMAGE033
Equivalent ohmic internal resistance
Figure 60372DEST_PATH_IMAGE035
。
Step two: setting pulse working conditions under various different charging and discharging multiplying powers, carrying out degradation experiments on the battery at different temperatures, identifying and obtaining identification parameters under different SOH, different SOC, different temperatures and different charging and discharging multiplying powers according to the pulse working conditions set for the battery at different degradation stages, and carrying out sensitivity analysis on the identification parameters respectively to obtain the importance scores of the SOH, the SOC, the temperatures and the charging and discharging multiplying powers on the identification parameters.
Specifically, the pulse working condition is set according to the actually acceptable charge-discharge multiplying power of the lithium battery. In this embodiment, the maximum acceptable charge rate of the lithium battery is 4C, the maximum acceptable discharge rate is 4C, four charge rates are set to be 1C, 2C, 3C, and 4C, and four discharge rates are set to be 1C, 2C, 3C, and 4C. And then carrying out pulse discharge charging working condition on the lithium battery in a full charging state. The lithium battery is subjected to cross discharging charging with the multiplying power from small to large and charging after discharging, the discharging and charging duration time is 1 minute each time, and the lithium battery needs to be placed for 30 minutes after charging or discharging is completed each time. After the four kinds of charge rates and discharge rates were performed one round from small to large, 1 cycle was considered to be completed, and the cycle was continued until the discharge cutoff voltage was reached.
And performing degradation experiments on the lithium battery at different temperatures, and setting the pulse working conditions for the lithium battery at different degradation stages. The degradation experiment refers to the degradation experiment carried out in a constant temperature box with different temperatures (5 ℃, 10 ℃, 25 ℃, 30 ℃ and 40 ℃) under the working conditions that 1C constant current and constant voltage charging is carried out till the charging cut-off voltage and 1C constant current discharging is carried out till the discharging cut-off voltage, the pulse working condition is carried out every 50 times of circulation, and the SOH corresponding to the lithium battery at present is recorded.
Based on a pulse excitation response method and according to the following sequence, identification parameters under different SOH, different SOC, different temperatures and different multiplying power are obtained through identification:
(1) preparing a three-electrode lithium battery, fitting an inherent characteristic curve of a positive electrode and a negative electrode, and identifying to obtain basic characteristic parameters of the lithium battery, namely the initial lithium intercalation concentration of active particles of the positive electrode and the negative electrode by combining with an electrochemical reaction basic equation of the lithium battery
Figure 17963DEST_PATH_IMAGE038
、
Figure 20554DEST_PATH_IMAGE039
And maximum variation range of lithium intercalation concentration fractions of positive and negative electrodes
Figure 747202DEST_PATH_IMAGE011
、
Figure 645888DEST_PATH_IMAGE010
；
(2) Measuring the AC impedance spectrum of the lithium battery by using an electrochemical workstation, and calculating to obtain the equivalent ohmic internal resistance
Figure 153092DEST_PATH_IMAGE035
；
(3) Measuring and calculating reaction polarization overpotential at pulse rising edge, and identifying to obtain positive and negative reaction polarization coefficients
Figure 880746DEST_PATH_IMAGE032
、
Figure 789796DEST_PATH_IMAGE033
；
(4) Calculating concentration polarization overpotential at each steady-state working point, and identifying to obtain the time constants of the solid phase diffusion of the positive electrode and the negative electrode
Figure 328225DEST_PATH_IMAGE015
、
Figure 322726DEST_PATH_IMAGE016
Time constant of liquid phase diffusion
Figure 870382DEST_PATH_IMAGE028
And liquid phase diffusion proportionality coefficient
Figure 571621DEST_PATH_IMAGE029
；
As shown in the attached figure 2, sensitivity analysis is carried out on the identification parameters by using AdaBoost, a decision tree and a random forest algorithm, and the importance of SOH, SOC, temperature and charge-discharge multiplying power on 13 identification parameters is obtained. And giving equal weight weighting calculation for the three algorithms, wherein the sum of the weights is 1, and comprehensively obtaining importance scores of different identification parameters.
Step three: and respectively carrying out sensitivity analysis on the identification parameters according to the variation ranges of the identification parameters in different SOH and SOC stages under the variation of temperature and charge-discharge multiplying power, and obtaining the importance scores of the identification parameters to the terminal voltage.
Specifically, when sensitivity analysis is performed on a certain identification parameter, the other identification parameters are kept as identification values under the conditions of current SOH, current SOC, current temperature and current multiplying power of 0.5C, the identification parameter to be analyzed is changed from low temperature to high temperature and from low multiplying power to high multiplying power by taking a 1% change interval as a change value in a change interval of the identification parameter, a corresponding simulation terminal voltage curve is calculated through an SP + model and compared with an actual terminal voltage curve under the conditions of current SOH, current SOC, current temperature and current multiplying power of 0.5C, and the maximum error in the curves corresponding to different change values and the average error of the curves are obtained through calculation. Then, sensitivity analysis is carried out on the identification parameters by using AdaBoost, a decision tree and a random forest algorithm to obtain the importance of the identification parameters to terminal voltage, three methods are given for equal weight weighted calculation, wherein the total weight is 1, and importance scores of different parameters are obtained comprehensively.
Step four: and carrying out importance sorting on the importance scores of the SOH, the SOC, the temperature and the charge-discharge multiplying factor for the identification parameters and the importance scores of the identification parameters for the terminal voltage to obtain important identification parameters, and fitting the important identification parameters into a fitting function related to the SOH, the SOC, the temperature and the charge-discharge multiplying factor.
Specifically, the weight of each identification parameter is calculated according to the importance scores of the SOH, the SOC, the temperature and the charge-discharge multiplying power on the identification parameters and the importance scores of the identification parameters on the terminal voltage obtained in the step 2 and the step 3 to obtain the importance ranking. Wherein the weight of the importance in the range of 0.8 to 1.0 is 0.7, the weight of the importance in the range of 0.5 to 0.8 is 0.2, the weight of the importance below 0.5 is 0.1, and then the identification parameters are classified according to the importance scores and the result of importance ranking.
It is worth noting that for identification parameters which are sensitive to temperature and charge-discharge multiplying power and have important influence on terminal voltage, importance scores of the identification parameters are properly increased according to specific conditions in actual working conditions, and therefore model accuracy is improved. And the identification parameters which are sensitive to temperature and charge-discharge multiplying power but have no important influence on terminal voltage, or have important influence on terminal voltage but are insensitive to temperature and charge-discharge multiplying power are set to be identification values at the room temperature of 25 ℃ and the charge-discharge multiplying power of 0.5C, so that the parameter calculation amount in the actual working condition is reduced.
After obtaining the important identification parameters, the important identification parameters are fit to a fitting function related to SOH, SOC, temperature and charge-discharge multiplying power. For example, a high-order polynomial is used to fit the important identification parameters into a function related to SOH, SOC, temperature, and charge/discharge rate.
Step five: and taking the real-time temperature and the real-time current of the current battery as input, adjusting the SP + model in real time through a fitting function, calculating the current negative electrode potential of the battery, and judging whether the battery is close to or in a lithium analysis state according to the negative electrode potential.
FIG. 3 is a flow chart showing the updating of SOC by UKF according to the present invention. And (4) in an actual working condition, taking the real-time temperature and the real-time current of the lithium battery measured by the sensor as input, and adjusting the identification parameters in real time according to the function obtained in the step (4). Because lithium cell SOC is great to electrochemical model precision influence, consequently usable UKF discerns in real time and obtains accurate SOC. Meanwhile, battery simulation terminal voltage obtained by the SP + model is used as an estimation value, actual terminal voltage is used as an observation value, a relative error between the estimation value and the observation value is calculated, and the relative error is updated. When the relative error is <1%, the SOC is considered accurate.
And then, calculating the current negative electrode potential through an SP + model, and judging whether the lithium battery is close to or in a lithium separation state at the current moment according to the negative electrode potential. Wherein the negative pole potential
Figure DEST_PATH_IMAGE057
Described by the following formula:
Figure 825492DEST_PATH_IMAGE058
in the above formula, the first and second carbon atoms are,
Figure 979393DEST_PATH_IMAGE045
is a negative open circuit potential. If it is not
Figure 127477DEST_PATH_IMAGE046
If so, judging that the battery is in a normal state; if it is used
Figure 683223DEST_PATH_IMAGE047
If so, judging that the battery is close to a lithium separation state; if it is used
Figure 891351DEST_PATH_IMAGE048
If so, the battery is judged to be in a lithium separation state. When the lithium battery is in a lithium analysis state or is close to the lithium analysis state, an alarm is triggered, and at the moment, the current needs to be reduced or the temperature of the lithium battery needs to be reduced through thermal management.
Fig. 4 is a graph showing a comparison of a simulated voltage and a measured voltage obtained by using a conventional RC equivalent model. Fig. 5 shows a comparison of the simulated voltage and the measured voltage obtained by the method. Comparing fig. 4 and 5, it can be seen that the simulated voltage obtained by the present invention is closer to the measured voltage. Moreover, the method greatly reduces the calculated amount, improves the timeliness of the lithium analysis state prediction of the lithium battery, and really realizes the online monitoring of the lithium analysis state of the lithium battery.
The details of which are not described in the prior art. Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (7)

1. An integrated learning-based lithium battery lithium analysis state online monitoring method is characterized by comprising the following steps:
the method comprises the following steps: modeling the lithium battery by using an SP + model, and determining identification parameters required by modeling;
the SP + model includes the following formula:
the basic process of the electrochemical reaction is described by the following formula:
E ocv (t)＝U p [y surf (t)]-U n [x surf (t)]
y surf (t)＝y avg (t)+Δy(t)＝y 0 +D y (1-SoC(t))
x surf (t)＝x avg (t)-Δx(t)＝x 0 -D x (1-SoC(t))
Figure FDA0004058074960000011
Figure FDA0004058074960000012
Figure FDA0004058074960000013
wherein, U p [y surf (t)]For positive pole open circuit potential U p About y surf Function of (t), U n [x surf (t)]Is negative open circuit potential U n With respect to x surf Function of (t), y surf (t)、x surf (t) the concentration fractions of lithium intercalated into the surfaces of positive and negative active particles of a lithium battery, y avg (t)、x avg (t) average embedded lithium concentration fractions in positive and negative electrode active particles, respectively, D y 、D x The maximum variation ranges of the lithium intercalation concentration fractions of the positive and negative electrodes, I is the load current, the discharge is defined as positive, and Q all The total capacity (C) of the lithium battery;
the solid phase diffusion process is described by the following formula:
Figure FDA0004058074960000014
Figure FDA0004058074960000015
Figure FDA0004058074960000021
Figure FDA0004058074960000022
wherein the content of the first and second substances,
Figure FDA0004058074960000023
respectively positive and negative solid phase diffusion time constants;
the liquid phase concentration polarization process is described by the following equation:
Figure FDA0004058074960000024
Figure FDA0004058074960000025
wherein R is an ideal gas constant, T is the internal temperature (K) of the lithium battery, F is a Faraday constant, c 0 The initial lithium ion concentration of the electrolyte is generally set to 1000,t + The cation transport number is generally 0.3 to 0.4, τ e Is the liquid phase diffusion time constant, P con Is the liquid phase diffusion proportionality coefficient;
the reactive polarization process is described by the following equation:
Figure FDA0004058074960000026
Figure FDA0004058074960000027
Figure FDA0004058074960000028
in the formula, the reaction polarization overpotential of the negative electrode is as follows:
Figure FDA0004058074960000029
wherein, P actp 、P actn Positive and negative reaction polarization coefficients, respectively;
the ohmic polarization process is described by the following equation:
η ohm (t)＝I(t)R ohm (t)
wherein R is ohm Equivalent ohmic internal resistance;
terminal voltage U app Described by the following formula:
U app (t)＝E ocv (t)-η con (t)-η act (t)-η ohm (t)
the identification parameters required by modeling are 13, namely the initial lithium intercalation concentration x of the positive and negative active particles of the lithium battery 0 、y 0 Maximum variation range D of positive and negative electrode lithium intercalation concentration fractions x 、D y Positive and negative electrode capacity Q p 、Q n Time constant of solid phase diffusion of positive and negative electrodes
Figure FDA0004058074960000031
Liquid phase diffusion time constant τ e Liquid phase diffusion proportionality coefficient P con Positive and negative reaction polarization coefficient P actp 、P actn Equivalent ohmic internal resistance R ohm ；
Step two: setting pulse working conditions under various different charging and discharging multiplying powers, carrying out degradation experiments on the lithium battery at different temperatures, identifying and obtaining identification parameters under different SOH, different SOC, different temperatures and different charging and discharging multiplying powers according to the pulse working conditions set for the lithium battery at different degradation stages, and carrying out sensitivity analysis on the identification parameters respectively to obtain the importance scores of the SOH, the SOC, the temperatures and the charging and discharging multiplying powers on the identification parameters;
step three: according to the variation ranges of the identification parameters in different SOH and SOC stages and under the variation of temperature and charge-discharge multiplying power, respectively carrying out sensitivity analysis on the identification parameters to obtain the importance scores of the identification parameters to terminal voltage;
step four: the method comprises the steps of conducting importance sorting on importance scores of SOH, SOC, temperature and charge-discharge multiplying power for identification parameters and importance scores of the identification parameters for terminal voltage to obtain important identification parameters, and fitting the important identification parameters into fitting functions relevant to the SOH, the SOC, the temperature and the charge-discharge multiplying power;
step five: taking the real-time temperature and the real-time current of the current lithium battery as input, adjusting the SP + model in real time through a fitting function, calculating the current negative electrode potential of the lithium battery, and judging whether the lithium battery is close to or in a lithium analysis state according to the negative electrode potential;
the negative pole potential U appn (t) is described by the following formula:
Figure FDA0004058074960000032
/>
wherein, U n (t) is the negative open circuit potential.
2. The integrated learning-based lithium battery lithium analysis state online monitoring method according to claim 1, wherein in the second step, after the lithium battery is fully charged, the lithium battery is charged by cross discharge with the magnification from small to large and the lithium battery is charged first and then charged according to the actually acceptable charging and discharging magnification of the lithium battery, the discharging and charging duration time of each time is 1 minute, and the lithium battery needs to be left for 30 minutes after each charging or discharging is completed; each charge multiplying factor and discharge multiplying factor are subjected to 1 cycle from small to large, and the discharge cut-off voltage is reached after a plurality of cycles.
3. The lithium battery lithium analysis state online monitoring method based on integrated learning of claim 1, wherein in the second step, the degradation experiment is performed in a thermostat, the degradation experiment is performed on the lithium battery under the working conditions that constant-current constant-voltage charging at a fixed rate is performed to a charging cut-off voltage, and constant-current discharging at the fixed rate is performed to a discharging cut-off voltage, a pulse working condition is performed every cycle, and the SOH corresponding to the lithium battery at present is recorded.
4. The lithium battery lithium analysis state online monitoring method based on ensemble learning of claim 1, wherein in the second step, an AdaBoost algorithm, a decision tree algorithm and a random forest algorithm are used for carrying out sensitivity analysis on identification parameters to obtain the importance of SOH, SOC, temperature and charge-discharge rate on the identification parameters, and importance scores of the identification parameters are obtained through weighting.
5. The lithium battery lithium analysis state online monitoring method based on ensemble learning according to claim 1, wherein in the third step, an AdaBoost algorithm, a decision tree algorithm and a random forest algorithm are used for carrying out sensitivity analysis on the identification parameters to obtain the importance of the identification parameters to terminal voltage, and the importance scores of the identification parameters are obtained through weighting.
6. The lithium battery lithium analysis state online monitoring method based on ensemble learning as claimed in claim 1, wherein UKF is used to update in real time to obtain accurate SOC, simulated terminal voltage of lithium battery obtained by SP + model is used as estimated value, measured terminal voltage is used as observed value, relative error between the estimated value and the observed value is calculated, and the relative error is updated; when the relative error is <1%, the SOC is considered accurate.
7. The integrated learning-based lithium battery lithium analysis state online monitoring method as claimed in claim 1, wherein when U is detected, U is detected appn If the (t) is more than 0.2, judging that the lithium battery is in a normal state; when 0.2 > U appn When the (t) > 0, judging that the lithium battery is close to a lithium separation state; when U is turned appn And (t) < 0, judging that the lithium battery is in a lithium separation state.
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