CN114236401A - Battery state estimation method based on adaptive particle swarm optimization - Google Patents
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Abstract
The invention discloses a battery state estimation method based on a self-adaptive particle swarm algorithm, which relates to the field of battery state management and comprises the following steps: step 1, performing constant current pulse experiments at different temperatures, and calibrating initial characteristic parameters of a battery, wherein the characteristic parameters comprise: open circuit voltage-charge state characteristic curve fitting parameters, ohmic internal resistance, concentration polarization capacitance, electrochemical polarization internal resistance and electrochemical polarization capacitance; step 2, putting the battery into actual operation, updating the state of charge of the battery and recording the state data of the battery in operation; and 3, starting a new round of parameter self-correction after the recorded state data reaches a preset threshold value, wherein the new round of parameter self-correction comprises updating the characteristic parameters and the battery capacity parameters of the battery, and the updated characteristic parameters and the updated battery capacity parameters are used for updating the state of charge of the battery.
Description
Technical Field
The invention relates to the field of battery state management, in particular to a battery state estimation method based on a self-adaptive particle swarm algorithm.
Background
The State of Charge (SOC) of a battery is a key parameter in the State management of a lithium ion battery. The SOC of the battery is accurately estimated, so that the service efficiency of the battery can be guaranteed, the service safety can be improved, and the method has important significance.
In the Chinese patent application "an SOC estimation method based on particle swarm optimization particle filter algorithm" (application number: CN202110902979.1), Malayan et al provides an SOC estimation method based on particle swarm optimization particle filter algorithm, which comprises the following steps: s1, performing charge and discharge experiments on the battery under the specified working condition, and constructing a battery equivalent circuit model by analyzing and processing experimental data; s2, constructing a state equation and a measurement equation for estimating the SOC of the battery according to the equivalent circuit model obtained by identification; s3, estimating the change of the SOC of the battery by utilizing particle swarm optimization particle filtering; s4, optimizing the positions of the particles in the particle filter by utilizing a particle swarm algorithm; and S5, estimating the SOC of the battery at the next moment again through S3 until the estimation process is finished.
The invention discloses a self-correction SOC estimation method for a mining lithium battery in Chinese patent application No. CN201910414377.4, which carries out self-correction on the SOC prediction of the lithium battery according to the daily charge and discharge conditions of the lithium battery, wherein the self-correction of the system is to correct a battery pack model according to different charge and discharge states of the battery pack, and meanwhile, the realizability of an SOC estimation result is enhanced due to the randomness of particle generation; due to the universality of the battery model, the accuracy of the model can be continuously improved by a recursive least square identification mode of the battery model, and the model inaccuracy caused by overlarge discharge current change can be avoided. But this method only increases the accuracy of SOC estimation for non-gaussian noise scenarios.
Liakyjen et al, in the Chinese patent application "a lithium battery state estimation method and system based on random fragment data" (application number: CN202111031237.2), discloses a lithium battery state estimation method and system based on random fragment data, the estimation method steps include: acquiring first data and second data, and matching the first data with the second data by adopting a particle swarm algorithm to obtain third data of the lithium battery to be estimated, wherein the third data are initial SOC data and SOH data; constructing a first model of the lithium battery to be estimated based on a second-order Thevenin equivalent circuit model, and identifying and obtaining fourth data based on the first model; and estimating the state of charge of the lithium battery to be estimated by adopting extended Kalman filtering based on the third data and the fourth data to obtain the state information of the lithium battery to be estimated.
In the prior art, the method for identifying model parameters by using the particle swarm algorithm has the problem that the particle swarm converges to local optimum, and because the relation between the selected charge state and the open-circuit voltage only adopts one curve to simulate the whole-segment data, the particle swarm algorithm is always in the optimization process under the condition of system errors of extreme charge states (the charge state is close to 1 or close to 0) or multi-platform open-circuit voltages.
Therefore, those skilled in the art have been devoted to developing a battery state estimation method based on an adaptive particle swarm algorithm to solve the above-mentioned problems in the prior art.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the technical problem to be solved by the present invention is how to converge the particle swarm to the global optimum in the process of estimating the battery state by the particle swarm algorithm, so as to accurately estimate the battery state.
In order to achieve the above object, the present invention provides a battery state estimation method based on an adaptive particle swarm algorithm, the method comprising the following steps:
step 1, performing constant current pulse experiments at different temperatures, and calibrating initial characteristic parameters of a battery, wherein the characteristic parameters comprise: open circuit voltage-charge state characteristic curve fitting parameters, ohmic internal resistance, concentration polarization capacitance, electrochemical polarization internal resistance and electrochemical polarization capacitance;
step 2, putting the battery into actual operation, updating the state of charge of the battery and recording the state data of the battery in operation;
and 3, starting a new round of parameter self-correction after the recorded state data reaches a preset threshold value, wherein the new round of parameter self-correction comprises updating the characteristic parameters and the battery capacity parameters of the battery, and the updated characteristic parameters and the updated battery capacity parameters are used for updating the state of charge of the battery.
Further, the step 1 comprises the following substeps:
step 1.1, determining the different temperatures according to the temperature range of the operation work of the battery and the preset temperature step length; sequentially carrying out a plurality of times of constant current pulse experiments on the battery at different temperatures, and recording current data and voltage data of the battery at different charge states;
step 1.2, according to the tail voltage of the standing section of the constant current pulse experiment and the ratio of the discharged capacity of the constant current pulse experiment to the total discharged capacity of the testSeveral open circuit voltage-state of charge data points at different temperatures are determined and noted as (OCV)1，SOC1)，(OCV2，SOC2)，...，(OCVn，SOCn)；
Step 1.3, fitting each open-circuit voltage-charge state data point by adopting segmented cubic spline interpolation to obtain a fitting curve, and obtaining a fitting parameter [ K ] of the open-circuit voltage-charge state characteristic curve11，K12，K13，K14，K21，...，K(n-1)4]Wherein, K is11，K12，K13，K14Correspondence (OCV)1，SOC1) And (OCV)2，SOC2) Fitting parameters of the connecting lines between the two; k21，K22，K23，K24Correspondence (OCV)2，SOC2) And (OCV)3，SOC3) Fitting parameters of the connecting lines between the two; ...; k(n-1)1，K(n-1)2，K(n-1)3，K(n-1)4Correspondence (OCV)(n-1)，SOC(n-1)) And (OCV)n，SOCn) Fitting parameters of the connecting lines between the two;
step 1.4, calibrating the ohmic internal resistance R of the battery by using instantaneous current and voltage data of current pulsedc(ii) a Calibrating the electrochemical polarization internal resistance, the electrochemical polarization capacitance, the concentration polarization internal resistance and the concentration polarization capacitance of the battery by using two sections of zero input and zero state response processes in and after the current pulse process and a double-exponential fitting mode, and sequentially recording as Rep、Cep、RcpAnd Ccp。
Further, in the step 2, the following steps are included:
step 2.1, initializing a state of charge estimation particle filter, including selecting an observation noise variance v according to the field environment to which the battery is applied near a corresponding initial state of charge value before the battery is put into actual operation1Setting a convergence threshold ε1And sampling according to a Gaussian distribution to generate N1A first random particle, said N1The first random particles are located between 0 and 1;
step 2.2, setting the iteration times of the first random particles;
step 2.3, according to the observed noise variance v1Calculating a first weight of the first random particle;
step 2.4, carrying out normalization processing on the first weight;
step 2.5, judging whether the first random particles are effective or not, and determining whether resampling is needed or not;
step 2.6, updating the state data of the battery according to the state data of the battery, including current, voltage, temperature, the state of charge at the last moment and the characteristic parameters, and repeating the steps 2.2-2.6 until the state of charge estimation particle filter converges to the preset convergence threshold epsilon1And obtaining the state of charge of the battery.
Further, in the step 3, the following steps are included:
step 3.1, selecting an estimated change proportion eta on the basis of the previous round of parameter self-correction, initializing and standardizing the characteristic parameters of the battery by taking random numbers in a range of (1-eta, 1+ eta) times the characteristic parameters obtained by the previous round of parameter self-correction, and reducing the characteristic parameters to be between-1 and 1 so as to ensure that singularity does not occur when each characteristic parameter is subjected to matrix operation;
step 3.2, calculating a moderate value of the first random particles, and sequencing the first random particles according to the degree of the moderate value; if the number of the high moderate value particles reaches the required cluster precision or reaches the iteration times, outputting the characteristic parameter of the battery corresponding to the highest moderate value particles as the latest characteristic parameter of the battery, otherwise, entering the next step;
step 3.3, judging whether the number of the particles with the high moderate value reaches a given distribution proportion delta or not, if not, reducing the distribution proportion delta, enabling more first random particles to enter a particle swarm algorithm estimation process, and accelerating algorithm convergence; if the given distribution proportion delta is reached, the next step is carried out;
step 3.4, judging whether the distribution proportion delta reaches a set threshold, if the distribution proportion delta reaches the set threshold, keeping the distribution proportion delta unchanged, otherwise, adjusting the distribution proportion delta according to the step 3.3;
3.5, distributing different first random particles according to the adjusted distribution proportion delta, directly adopting a particle swarm algorithm to update the positions and the speeds of the particles of the first random particles higher than the distribution proportion delta, firstly obtaining fitness values of the particles around the first random particles for the first random particles lower than the distribution proportion delta, and then carrying out next judgment;
step 3.6, updating particle parameters according to the highest fitness value which can be obtained by the first random particle;
and 3.7, updating the fitness values of all the first random particles, sequencing and then returning to the step 3.2.
Further, the step 3.6 selects one of the following 4 steps according to the highest fitness value that can be obtained by the first random particle, and updates the particle parameter;
step 3.6.1, randomly selecting a random state within a variable radius according to the current state of the first random particle, if the moderate value of the random state is greater than the current moderate value of the first random particle, the first random particle advances one step towards the selected random state, otherwise, the state is randomly selected again; if N is chosen randomlytryIf the fitness value of the random state is not higher than the current fitness value of the first random particle, go to step 3.6.4;
step 3.6.2, if a particle aggregation center exists in the variation radius of the first random particle, and the ratio of the fitness value of the particle aggregation center to the number of particles existing in the variation radius of the first random particle is lower than a set threshold, moving the first random particle to the particle aggregation center by one step;
3.6.3, if the maximum fitness value particle exists in the variation radius of the first random particle, and the ratio of the number of the particles existing in the variation radius of the maximum fitness value particle to the number of the particles existing in the variation radius of the first random particle is lower than a set threshold, moving the first random particle to the maximum fitness value particle by one step;
at step 3.6.4, if neither of steps 3.6.2 and 3.6.3 can meet the requirement, then a state within the range of particle variation is randomly selected.
Further, the step 3 further comprises the following steps:
step 3.8, updating the battery capacity parameter by utilizing a self-adaptive battery capacity estimation particle filter;
step 3.9, dividing the ohmic internal resistance obtained by updating by the ohmic internal resistance in the initial calibration to obtain the internal resistance health degree of the battery; and according to the updated battery capacity parameter, dividing the updated battery capacity parameter by the initial battery capacity parameter to obtain the capacity health degree of the battery.
Further, in the step 3.8, the following sub-steps are included:
step 3.8.1, initializing the battery capacity estimation particle filter, and selecting an observation noise variance v according to the field environment to which the battery is applied by taking the initial battery capacity of the battery as a reference2Generating N in a Gaussian distribution2A second plurality of random particles;
step 3.8.2, setting the iteration number of the battery capacity estimation particle filter;
step 3.8.3, based on the observed noise variance v2Calculating a second weight of the second random particle;
step 3.8.4: normalizing the second weight value;
step 3.8.5: judging whether the second random particles are effective or not, and determining whether resampling is needed or not;
3.8.6, obtaining the state of charge of the battery according to the state data of the battery, including current, voltage, temperature, and last timeUpdating the state data of the battery and repeating the steps 3.8.2-3.8.6 until the battery capacity estimation particle filter converges to the predetermined convergence threshold epsilon2And obtaining the battery capacity parameter of the battery.
Further, said N2The second random particles are second random particles having a value between 0 and the initial battery capacity.
Further, the estimated variation ratio η has a value range of 0 < η < 1.
Further, the threshold range of the distribution ratio δ is 0.3 < δ < 0.7.
The invention provides a battery state estimation method based on a self-adaptive particle swarm algorithm, which at least has the following technical effects:
1. the technical scheme provided by the invention firstly determines the initial system parameters of the battery pack by utilizing an initial constant current pulse test, and realizes the fitting of the open circuit voltage-charge state relation by adopting segmented cubic spline interpolation, the parameter fitting characteristic is excellent under the extreme charge state, and the parameters are not easy to diverge due to polynomial fitting, so that the method is more suitable for parameter identification by adopting a group intelligent algorithm;
2. in order to avoid the particle swarm from finally falling into local optimization, the technical scheme provided by the invention modifies the optimization process of the particle swarm algorithm and designs the self-adaptive threshold, so that particles below the threshold are globally optimized in a manner similar to an artificial fish swarm, particles above the threshold are continuously optimized by the particle swarm algorithm to converge to an extreme point as soon as possible, and the threshold is continuously adjusted according to the moderate value of each particle in the process, thereby realizing the acceleration of the optimization process;
3. the model provided by the invention is a second-order RC equivalent circuit model based on the battery, has strong parameter interpretability and strong algorithm global optimization capability, and can realize the joint estimation of the charge states, the battery internal resistances and the battery capacity health degrees of different types of lithium batteries.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 is a schematic overall flow chart of a preferred embodiment of the present invention.
Detailed Description
The technical contents of the preferred embodiments of the present invention will be more clearly and easily understood by referring to the drawings attached to the specification. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.
In order to solve the problem that the particle swarm converges to local optimum or can not converge in the method for identifying the model parameters by simply adopting the particle swarm algorithm in the prior art, the invention discloses a battery state estimation strategy based on a self-adaptive particle swarm algorithm, which comprises the following steps: firstly, constant current pulse experiments at different temperatures are carried out, and battery model parameter calibration is carried out; then, putting the battery into actual operation, and updating the state of charge of the battery in real time on line by using self-adaptive particle filtering; and finally, updating parameters such as battery polarization internal resistance, polarization capacitance, ohmic internal resistance, open-circuit voltage and state-of-charge relation functions and the like by using a self-adaptive particle swarm optimization according to a given operation cycle, determining the health degree of the battery internal resistance by using the updated ohmic internal resistance of the battery, updating battery capacity parameters by using a particle filter optimization to further determine the health degree of the battery capacity, and using the updated parameters for subsequent updating of the state-of-charge of the battery. The model adopted by the invention is based on a second-order RC equivalent circuit model of the battery, has strong parameter interpretability and strong algorithm global optimization capability, and can realize the joint estimation of the charge states, the internal resistances and the capacity health degrees of different types of lithium batteries.
As shown in fig. 1, which is a schematic overall flow chart of a preferred embodiment of the present invention, a particle swarm algorithm with adaptive thresholds is used to update battery characteristic parameters so as to achieve battery state estimation.
Specifically, the battery state estimation method based on the adaptive particle swarm algorithm provided by the embodiment of the invention comprises the following steps:
step 1, constant current pulse experiments at different temperatures are carried out, and initial characteristic parameters of the battery are calibrated, wherein the characteristic parameters comprise: open circuit voltage-charge state characteristic curve fitting parameters, ohmic internal resistance, concentration polarization capacitance, electrochemical polarization internal resistance and electrochemical polarization capacitance;
step 2, putting the battery into actual operation, updating the state of charge of the battery and recording the state data of the battery in operation;
and 3, starting a new round of parameter self-correction after the recorded state data reaches a preset threshold value, wherein the new round of parameter self-correction comprises updating the characteristic parameters and the battery capacity parameters of the battery, and the updated characteristic parameters and the updated battery capacity parameters are used for updating the charge state of the battery.
Wherein, step 1 comprises the following substeps:
step 1.1, determining different temperatures according to the temperature range of the operation work of the battery and the preset temperature step length; sequentially carrying out a plurality of constant current pulse experiments on the battery at different temperatures, and recording current data and voltage data of the battery at different charge states;
step 1.2, determining a plurality of open circuit voltage-charge state data points at different temperatures according to the tail voltage of the standing section of the constant current pulse experiment and the ratio of the discharged amount of the constant current pulse experiment to the total discharged amount of the test, and recording the data points as (OCV)1，SOC1)，(OCV2，SOC2)，...，(OCVn，SOCn)；
Step 1.3, fitting each open-circuit voltage-charge state data point by adopting segmented cubic spline interpolation to obtain a fitting curve, and obtaining an open-circuit voltage-charge state characteristic curve fitting parameter [ K ]11，K12，K13，K14，K21，...，K(n-1)4]Wherein, K is11，K12，K13，K14Correspondence (OCV)1，SOC1) And (OCV)2，SOC2) Fitting parameters of the connecting lines between the two; k21，K22，K23，K24Correspondence (OCV)2，SOC2) And (OCV)3，SOC3) BetweenFitting parameters of the connecting line; ...; k(n-1)1，K(n-1)2，K(n-1)3，K(n-1)4Correspondence (OCV)(n-1)，SOC(n-1)) And (OCV)n，SOCn) Fitting parameters of the connecting lines between the two;
step 1.4, calibrating ohmic internal resistance R of the battery by using instantaneous current and voltage data of current pulsedc(ii) a Calibrating the electrochemical polarization internal resistance, the electrochemical polarization capacitance, the concentration polarization internal resistance and the concentration polarization capacitance of the battery by using two sections of zero input and zero state response processes in and after the current pulse process and using a double-exponential fitting mode, and sequentially recording as Rep、Cep、RcpAnd Ccp。
Wherein, in step 2, the method comprises the following steps:
step 2.1, initializing the state of charge estimation particle filter, including selecting observation noise variance v according to the field environment of the battery before the battery is put into practical operation and corresponding to the vicinity of the initial state of charge value1Setting a convergence threshold ε1And sampling according to a Gaussian distribution to generate N1A first random particle, N1The first random particles are located between 0 and 1;
step 2.2, setting the iteration times of the first random particles;
step 2.3, according to the observed noise variance v1Calculating a first weight of the first random particle;
step 2.4, carrying out normalization processing on the first weight;
step 2.5, judging whether the first random particles are effective or not, and determining whether resampling is needed or not;
step 2.6, updating the state data of the battery according to the state data of the battery, including current, voltage, temperature, the state of charge at the last moment and characteristic parameters, and repeating the steps 2.2-2.6 until the state of charge estimation particle filter converges to a preset convergence threshold epsilon1And obtaining the charge state of the battery.
Wherein, in step 3, the method comprises the following steps:
3.1, selecting an estimated change proportion eta on the basis of the previous round of parameter self-correction, initializing and standardizing the characteristic parameters of the battery by taking random numbers in a range of (1-eta, 1+ eta) multiplied by the characteristic parameters obtained by the previous round of parameter self-correction, and reducing the characteristic parameters to be-1 to ensure that singularity does not occur when each characteristic parameter is subjected to matrix operation; estimating the numerical range of the variation ratio eta to be 0 < eta < 1;
step 3.2, calculating a proper value of the first random particles, and sequencing the first random particles according to the height of the proper value; if the number of the particles with the high moderate value reaches the required cluster precision or reaches the iteration times, outputting the characteristic parameters of the battery corresponding to the particles with the highest moderate value as the latest characteristic parameters of the battery, otherwise, entering the next step;
step 3.3, judging whether the number of the particles with the high fitness value reaches a given distribution proportion delta or not, if not, reducing the distribution proportion delta, enabling more first random particles to enter a particle swarm algorithm estimation process, and accelerating algorithm convergence; if the given distribution proportion delta is reached, the next step is carried out; the threshold range of the distribution ratio delta is more than 0.3 and less than 0.7;
step 3.4, judging whether the distribution proportion delta reaches a set threshold, if so, keeping the distribution proportion delta unchanged, otherwise, adjusting the distribution proportion delta according to the step 3.3;
3.5, distributing different first random particles according to the adjusted distribution proportion delta, updating the positions and the speeds of the particles of the first random particles higher than the distribution proportion delta by directly adopting a particle swarm algorithm, firstly obtaining fitness values of the particles around the first random particles for the first random particles lower than the distribution proportion delta, and then carrying out next judgment;
step 3.6, updating the particle parameters according to the highest fitness value which can be obtained by the first random particles;
and 3.7, updating the fitness values of all the first random particles, sequencing, and returning to the step 3.2.
Step 3.6, according to the highest fitness value which can be obtained by the first random particle, selecting one of the following 4 steps to update the particle parameter;
step 3.6.1, randomly selecting a random state within the variable radius according to the current state of the first random particle, if the moderate value of the random state is greater than the current moderate value of the first random particle, the first random particle advances one step towards the selected random state, otherwise, the state is randomly selected again; if N is chosen randomlytryAfter that, if the fitness value of the random state is not higher than the current fitness value of the first random particle, go to step 3.6.4;
step 3.6.2, if a particle aggregation center exists in the variation radius of the first random particle, and the ratio of the fitness value of the particle aggregation center to the number of particles existing in the variation radius of the first random particle is lower than a set threshold, moving the first random particle to the particle aggregation center by one step;
3.6.3, if the maximum fitness value particle exists in the variation radius of the first random particle, and the ratio of the number of the particles existing in the variation radius of the maximum fitness value particle to the number of the particles existing in the variation radius of the first random particle is lower than a set threshold value, moving the first random particle to the maximum fitness value particle by one step;
at step 3.6.4, if neither of steps 3.6.2 and 3.6.3 can meet the requirement, then a state within the range of particle variation is randomly selected.
Wherein, step 3 also includes the following steps:
step 3.8, updating the battery capacity parameter by utilizing a self-adaptive battery capacity estimation particle filter;
3.9, dividing the ohmic internal resistance obtained by updating by the ohmic internal resistance in the initial calibration to obtain the internal resistance health degree of the battery; and according to the updated battery capacity parameter, dividing the updated battery capacity parameter by the initial battery capacity parameter to obtain the capacity health degree of the battery.
Wherein, in step 3.8, the following substeps are included:
step 3.8.1, initializing the battery capacity estimation particle filter, selecting the observation noise variance v according to the field environment of the battery with the initial battery capacity of the battery as the reference2Generating N in a Gaussian distribution2A second plurality of random particles; n is a radical of2The second random particles are second random particles having a value between 0 and the initial battery capacity.
Step 3.8.2, setting the iteration times of the battery capacity estimation particle filter;
step 3.8.3, based on the observed noise variance v2Calculating a second weight of the second random particle;
step 3.8.4: normalizing the second weight value;
step 3.8.5: judging whether the second random particles are effective or not, and determining whether resampling is needed or not;
3.8.6, updating the state data of the battery according to the state data of the battery, including current, voltage, temperature and the state of charge at the last time, and repeating the steps 3.8.2-3.8.6 until the battery capacity estimation particle filter converges to the preset convergence threshold epsilon2And obtaining the battery capacity parameter of the battery.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. A battery state estimation method based on an adaptive particle swarm algorithm is characterized by comprising the following steps:
step 1, performing constant current pulse experiments at different temperatures, and calibrating initial characteristic parameters of a battery, wherein the characteristic parameters comprise: open circuit voltage-charge state characteristic curve fitting parameters, ohmic internal resistance, concentration polarization capacitance, electrochemical polarization internal resistance and electrochemical polarization capacitance;
step 2, putting the battery into actual operation, updating the state of charge of the battery and recording the state data of the battery in operation;
and 3, starting a new round of parameter self-correction after the recorded state data reaches a preset threshold value, wherein the new round of parameter self-correction comprises updating the characteristic parameters and the battery capacity parameters of the battery, and the updated characteristic parameters and the updated battery capacity parameters are used for updating the state of charge of the battery.
2. The adaptive particle swarm algorithm-based battery state estimation method according to claim 1, wherein the step 1 comprises the sub-steps of:
step 1.1, determining the different temperatures according to the temperature range of the operation work of the battery and the preset temperature step length; sequentially carrying out a plurality of times of constant current pulse experiments on the battery at different temperatures, and recording current data and voltage data of the battery at different charge states;
step 1.2, determining a plurality of open circuit voltage-charge state data points at different temperatures according to the tail voltage of the standing section of the constant current pulse experiment and the ratio of the discharged amount of the constant current pulse experiment to the total discharged electric quantity to be tested, and recording as (OCV)1，SOC1)，(OCV2，SOC2)，...，(OCVn，SOCn)；
Step 1.3, fitting each open-circuit voltage-charge state data point by adopting segmented cubic spline interpolation to obtain a fitting curve, and obtaining a fitting parameter [ K ] of the open-circuit voltage-charge state characteristic curve11，K12，K13，K14，K21，...，K(n-1)4]Wherein, K is11，K12，K13，K14Correspondence (OCV)1，SOC1) And (OCV)2，SOC2) Fitting parameters of the connecting lines between the two; k21，K22，K23，K24Correspondence (OCV)2，SOC2) And (OCV)3，SOC3) Fitting parameters of the connecting lines between the two; ...; k(n-1)1，K(n-1)2，K(n-1)3，K(n-1)4Correspondence (OCV)(n-1)，SOC(n-1)) And (OCV)n，SOCn) Fitting parameters of the connecting lines between the two;
step 1.4, calibrating the ohmic internal resistance R of the battery by using instantaneous current and voltage data of current pulsedc(ii) a Calibrating the electrochemical polarization internal resistance, the electrochemical polarization capacitance, the concentration polarization internal resistance and the concentration polarization capacitance of the battery by using two sections of zero input and zero state response processes in and after the current pulse process and a double-exponential fitting mode, and sequentially recording as Rep、Cep、RcpAnd Ccp。
3. The adaptive particle swarm algorithm-based battery state estimation method according to claim 2, wherein in the step 2, the following steps are included:
step 2.1, initializing a state of charge estimation particle filter, including selecting an observation noise variance v according to the field environment to which the battery is applied near a corresponding initial state of charge value before the battery is put into actual operation1Setting a convergence threshold ε1And sampling according to a Gaussian distribution to generate N1A first random particle, said N1The first random particles are located between 0 and 1;
step 2.2, setting the iteration times of the first random particles;
step 2.3, according to the observed noise variance v1Calculating a first weight of the first random particle;
step 2.4, carrying out normalization processing on the first weight;
step 2.5, judging whether the first random particles are effective or not, and determining whether resampling is needed or not;
step 2.6, updating the state data of the battery according to the state data of the battery, including current, voltage, temperature, the state of charge at the last moment and the characteristic parameters, and repeating the steps 2.2-2.6 until the state of charge estimation particle filter converges to the preset convergence threshold epsilon1In order to obtain the electricityThe state of charge of the pool.
4. The adaptive particle swarm algorithm-based battery state estimation method according to claim 3, wherein in the step 3, the following steps are included:
step 3.1, selecting an estimated change proportion eta on the basis of the previous round of parameter self-correction, initializing and standardizing the characteristic parameters of the battery by taking random numbers in a range of (1-eta, 1+ eta) times the characteristic parameters obtained by the previous round of parameter self-correction, and reducing the characteristic parameters to be between-1 and 1 so as to ensure that singularity does not occur when each characteristic parameter is subjected to matrix operation;
step 3.2, calculating a moderate value of the first random particles, and sequencing the first random particles according to the degree of the moderate value; if the number of the high moderate value particles reaches the required cluster precision or reaches the iteration times, outputting the characteristic parameter of the battery corresponding to the highest moderate value particles as the latest characteristic parameter of the battery, otherwise, entering the next step;
step 3.3, judging whether the number of the particles with the high moderate value reaches a given distribution proportion delta or not, if not, reducing the distribution proportion delta, enabling more first random particles to enter a particle swarm algorithm estimation process, and accelerating algorithm convergence; if the given distribution proportion delta is reached, the next step is carried out;
step 3.4, judging whether the distribution proportion delta reaches a set threshold, if the distribution proportion delta reaches the set threshold, keeping the distribution proportion delta unchanged, otherwise, adjusting the distribution proportion delta according to the step 3.3;
3.5, distributing different first random particles according to the adjusted distribution proportion delta, directly adopting a particle swarm algorithm to update the positions and the speeds of the particles of the first random particles higher than the distribution proportion delta, firstly obtaining fitness values of the particles around the first random particles for the first random particles lower than the distribution proportion delta, and then carrying out next judgment;
step 3.6, updating particle parameters according to the highest fitness value which can be obtained by the first random particle;
and 3.7, updating the fitness values of all the first random particles, sequencing and then returning to the step 3.2.
5. The adaptive particle swarm optimization-based battery state estimation method according to claim 4, wherein the step 3.6 selects one of the following 4 steps according to the highest fitness value that the first random particle can obtain to update the particle parameters;
step 3.6.1, randomly selecting a random state within a variable radius according to the current state of the first random particle, if the moderate value of the random state is greater than the current moderate value of the first random particle, the first random particle advances one step towards the selected random state, otherwise, the state is randomly selected again; if N is chosen randomlytryIf the fitness value of the random state is not higher than the current fitness value of the first random particle, go to step 3.6.4;
step 3.6.2, if a particle aggregation center exists in the variation radius of the first random particle, and the ratio of the fitness value of the particle aggregation center to the number of particles existing in the variation radius of the first random particle is lower than a set threshold, moving the first random particle to the particle aggregation center by one step;
3.6.3, if the maximum fitness value particle exists in the variation radius of the first random particle, and the ratio of the number of the particles existing in the variation radius of the maximum fitness value particle to the number of the particles existing in the variation radius of the first random particle is lower than a set threshold, moving the first random particle to the maximum fitness value particle by one step;
at step 3.6.4, if neither of steps 3.6.2 and 3.6.3 can meet the requirement, then a state within the range of particle variation is randomly selected.
6. The adaptive particle swarm algorithm-based battery state estimation method according to claim 4, wherein the step 3 further comprises the steps of:
step 3.8, updating the battery capacity parameter by utilizing a self-adaptive battery capacity estimation particle filter;
step 3.9, dividing the ohmic internal resistance obtained by updating by the ohmic internal resistance in the initial calibration to obtain the internal resistance health degree of the battery; and according to the updated battery capacity parameter, dividing the updated battery capacity parameter by the initial battery capacity parameter to obtain the capacity health degree of the battery.
7. The adaptive particle swarm algorithm based battery state estimation method according to claim 6, characterized in that in the step 3.8, it comprises the following sub-steps:
step 3.8.1, initializing the battery capacity estimation particle filter, and selecting an observation noise variance v according to the field environment to which the battery is applied by taking the initial battery capacity of the battery as a reference2Generating N in a Gaussian distribution2A second plurality of random particles;
step 3.8.2, setting the iteration number of the battery capacity estimation particle filter;
step 3.8.3, based on the observed noise variance v2Calculating a second weight of the second random particle;
step 3.8.4: normalizing the second weight value;
step 3.8.5: judging whether the second random particles are effective or not, and determining whether resampling is needed or not;
3.8.6, updating the state data of the battery according to the state data of the battery, including current, voltage, temperature and the state of charge at the last moment, and repeating the steps 3.8.2-3.8.6 until the battery capacity estimation particle filter converges to the preset convergence threshold epsilon2And obtaining the battery capacity parameter of the battery.
8. The method of claim 7 based on selfThe battery state estimation method adapting to the particle swarm optimization is characterized in that N is2The second random particles are second random particles having a value between 0 and the initial battery capacity.
9. The adaptive-particle-swarm-algorithm-based battery state estimation method of claim 4, wherein the estimated variation ratio η has a numerical range of 0 < η < 1.
10. The adaptive-particle-swarm-algorithm-based battery state estimation method according to claim 4, wherein the threshold range of the distribution ratio δ is 0.3 < δ < 0.7.
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| CN116542118A (en) * | 2023-06-08 | 2023-08-04 | 上海玫克生储能科技有限公司 | Electrochemical parameter identification system and method of thermal coupling electrochemical model |
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