CN114236401B - Battery state estimation method based on self-adaptive particle swarm algorithm - Google Patents
Battery state estimation method based on self-adaptive particle swarm algorithm Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 11
- 229910052744 lithium Inorganic materials 0.000 description 11
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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-state of charge 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 charge state of the battery and recording state data of the battery in operation; and step 3, after the recorded state data reaches a preset threshold value, starting a new round of parameter self-correction, including updating the characteristic parameter and the battery capacity parameter of the battery, and using the updated characteristic parameter and battery capacity parameter to update 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 lithium ion battery State management. The SOC of the battery is accurately estimated, so that the service efficiency of the battery can be guaranteed, the service safety is improved, and the method has important significance.
Ma Yan et al in China patent application No. CN202110902979.1, a SOC estimation method based on a particle swarm optimization particle filter algorithm is provided, which comprises the following steps: s1, carrying out a charge-discharge experiment on a battery under a 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 through identification; s3, optimizing particle filtering by using a particle swarm to estimate the change of the SOC of the battery; s4, optimizing the positions of particles in the particle filtering by using a particle swarm algorithm; s5, estimating the battery SOC at the next moment again through S3 until the estimation process is finished.
Sun Zheng et al in China patent application No. (application No. CN 201910414377.4) disclose a self-correction SOC estimation method for a lithium battery for mines, wherein the self-correction is carried out on the SOC prediction of the lithium battery according to the daily charge and discharge conditions of the lithium battery, and the self-correction is carried out on a battery model according to the different charge and discharge states of the battery pack, and meanwhile, the realizability of the 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 adopting a recursive least square recognition mode of the battery model, and the model inaccuracy caused by overlarge discharge current change can be avoided. But this approach only increases the accuracy of the SOC estimation for non-gaussian noise conditions.
Liao Kai et al in Chinese patent application "a method and system for estimating state of lithium battery based on random fragment data" (application number: CN 202111031237.2), disclose a method and system for estimating state of lithium battery based on random fragment data, the steps of the estimation method include: acquiring first data and second data, and matching the first data with the second data by adopting a particle swarm algorithm to acquire 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 obtaining fourth data based on the first model in an identification mode; based on the third data and the fourth data, estimating the state of charge of the lithium battery to be estimated by adopting extended Kalman filtering, and obtaining state information of the lithium battery to be estimated.
In the prior art, the method for identifying the model parameters by adopting the particle swarm algorithm has the problem that the particle swarm converges to the local optimum, and because the relation between the selected state of charge and the open-circuit voltage only adopts one curve to simulate the whole section of data, the particle swarm algorithm is always in the optimizing process under the condition of the system error of the extreme state of charge (the state of charge is close to 1 or 0) or the open-circuit voltage in multiple platform stages.
Accordingly, those skilled in the art have been directed to developing a battery state estimation method based on an adaptive particle swarm algorithm, which solves the above-mentioned problems of the prior art.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the present invention aims to solve the technical problem of how to converge the particle swarm to the global optimum in the process of estimating the battery state by using 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 steps of:
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-state of charge 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 charge state of the battery and recording state data of the battery in operation;
and step 3, after the recorded state data reaches a preset threshold value, starting a new round of parameter self-correction, including updating the characteristic parameter and the battery capacity parameter of the battery, and using the updated characteristic parameter and battery capacity parameter to update the state of charge of the battery.
Further, the step 1 includes the following substeps:
step 1.1, determining different temperatures according to a temperature range of operation of the battery and a preset temperature step; sequentially carrying out constant current pulse experiments on the battery for a plurality of times 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 end voltage of the standing segment of the constant current pulse experiment and the ratio of the discharged quantity of the constant current pulse experiment to the total discharged quantity of the test, and recording as (OCV) 1 ，SOC 1 )，(OCV 2 ，SOC 2 )，...，(OCV n ，SOC n )；
Step 1.3, adopting piecewise cubic spline interpolation to fit each open circuit voltage-state of charge data point to obtain a fit curve, and obtaining the open circuit voltage-state of charge characteristic curve fit parameter [ K ] 11 ，K 12 ，K 13 ，K 14 ，K 21 ，...，K (n-1)4 ]Wherein K is 11 ，K 12 ，K 13 ，K 14 Correspondence (OCV) 1 ，SOC 1 ) Sum (OCV) 2 ，SOC 2 ) Fitting parameters of the connecting lines; k (K) 21 ，K 22 ，K 23 ，K 24 Correspondence (OCV) 2 ，SOC 2 ) Sum (OCV) 3 ，SOC 3 ) Fitting parameters of the connecting lines; ..; k (K) (n-1)1 ，K (n-1)2 ，K (n-1)3 ，K (n-1)4 Correspondence (OCV) (n-1) ，SOC (n-1) ) Sum (OCV) n ，SOC n ) Fitting parameters of the connecting lines;
step 1.4, calibrating the ohmic internal resistance R of the battery by using current and voltage data at the moment of current pulse dc The method comprises the steps of carrying out a first treatment on the surface of the Calibrating the electrochemical polarized internal resistance, the electrochemical polarized capacitance, the concentration polarized internal resistance and the concentration polarized capacitance of the battery by using a double-exponential fitting mode by using two sections of zero input and zero state response processes in and after a current pulse process, and sequentially marking as R ep 、C ep 、R cp And C cp 。
Further, in the step 2, the method includes the following steps:
step 2.1, initializing a state of charge estimation particle filter, including selecting an observed noise variance v according to a field environment to which the battery is applied, in the vicinity of a corresponding initial state of charge value before the battery is put into the actual operation 1 Setting a convergence threshold epsilon 1 And sampling in Gaussian distribution to generate N 1 First random particles, the N 1 The first random particles are positioned 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 v 1 Calculating a first weight of the first random particle;
step 2.4, normalizing 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, 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 epsilon 1 And obtaining the state of charge of the battery.
Further, in the step 3, the method includes the following steps:
step 3.1, selecting an estimated change proportion eta based on the parameter self-correction of the previous round, multiplying the characteristic parameter of the battery by a random number within the range of the characteristic parameter obtained by the parameter self-correction of the previous round to initialize and normalize the random number, and reducing the characteristic parameter to between-1 and 1 so as to ensure that singular occurrence of each characteristic parameter is avoided when matrix operation is carried out;
step 3.2, calculating a moderate value of the first random particles, and sequencing the first random particles according to the level of the moderate value; if the high moderate value particle count reaches the required cluster precision or the iteration number is reached, outputting the characteristic parameter of the battery corresponding to the highest moderate value particle as the latest characteristic parameter of the battery, otherwise, entering the next step;
step 3.3, judging whether the high-moderate value particle count reaches a given distribution proportion delta, 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 so, keeping the distribution proportion delta unchanged, otherwise, adjusting the distribution proportion delta according to the step 3.3;
step 3.5, distributing different first random particles according to the distribution proportion delta after adjustment, directly adopting a particle swarm algorithm for the first random particles higher than the distribution proportion delta, updating the particle position and speed, firstly obtaining moderate values of particles around the first random particles for the first random particles lower than the distribution proportion delta, and then carrying out next step judgment;
step 3.6, updating particle parameters according to the highest moderate value which can be obtained by the first random particles;
step 3.7, updating the moderate values of all the first random particles, sorting, and returning to the step 3.2.
Further, the step 3.6 is to select one of the following 4 steps according to the highest moderate value that the first random particle can obtain, and update the particle parameters;
step 3.6.1, randomly selecting a random state in a changing radius according to the current state of the first random particle, if the moderate value of the random state is larger than the current moderate value of the first random particle, the first random particle is further before the selected random state, otherwise, the state is randomly selected again; if randomly select N try After that, if the moderate value of the random state is not higher than the current moderate value of the first random particle, step 3.6.4 is executed;
step 3.6.2, if a particle aggregation center exists in the change radius of the first random particle, and the ratio of the moderate value of the particle aggregation center to the number of particles existing in the change radius of the first random particle is lower than a set threshold value, the first random particle moves to the particle aggregation center by one step;
step 3.6.3, if a maximum moderate value particle exists in the changing radius of the first random particle, and the ratio of the maximum moderate value particle to the number of particles existing in the changing radius of the first random particle is lower than a set threshold, the first random particle moves to the maximum moderate value particle by one step;
step 3.6.4, if steps 3.6.2 and 3.6.3 are not satisfied, randomly selecting a state within the particle variation range.
Further, the step 3 further includes the following steps:
step 3.8, updating the battery capacity parameter by using an adaptive battery capacity estimation particle filter;
step 3.9, dividing the updated ohmic internal resistance by the ohmic internal resistance at the initial standard to obtain the internal resistance health of the battery; and 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 observed noise variance v based on the initial battery capacity of the battery and the applied field environment of the battery 2 Generating N in a Gaussian distribution 2 A second random number of particles;
step 3.8.2, setting the iteration times of the battery capacity estimation particle filter;
step 3.8.3, according to the observed noise variance v 2 Calculating a second weight of the second random particle;
step 3.8.4: normalizing the second weight;
step 3.8.5: judging whether the second random particles are effective or not, and determining whether resampling is needed or not;
step 3.8.6, updating the state data of the battery according to the state data of the battery including current, voltage, temperature and state of charge at the last moment, and repeating the steps 3.8.2-3.8.6 until the battery capacity estimating particle filter converges to the preset convergence threshold ε 2 And obtaining the battery capacity parameter of the battery.
Further, the N is 2 The second random particles are second random particles having a value between 0 and the initial battery capacity.
Further, the numerical range of the estimated variation ratio eta is 0< eta < 1.
Further, the threshold range of the distribution ratio delta is 0.3< delta <0.7.
The invention provides a battery state estimation method based on a self-adaptive particle swarm algorithm, which has at least the following technical effects:
1. the technical scheme provided by the invention firstly utilizes an initial constant current pulse test to determine initial system parameters of the battery pack, adopts piecewise cubic spline interpolation to realize fitting of open-circuit voltage-state-of-charge relation, has excellent parameter fitting characteristics under extreme state-of-charge, is not easy to diverge due to polynomial fitting, and is more suitable for parameter identification by adopting a group intelligent algorithm;
2. in order to avoid the particle swarm to finally fall into local optimum, the technical scheme provided by the invention modifies the optimizing process of the particle swarm algorithm, designs a self-adaptive threshold value, so that particles lower than the threshold value are globally optimized in a manner similar to artificial fish swarm, and particles higher than the threshold value continue to perform the optimizing process of the particle swarm algorithm so as to converge to an extreme point as soon as possible, and continuously adjusts the threshold value according to the moderate value of each particle in the process, thereby realizing the acceleration of the optimizing process;
3. the model provided by the invention is a second-order RC equivalent circuit model based on batteries, has strong parameter interpretation and strong algorithm global optimizing capability, and can realize the joint estimation of the states of charge, the internal resistance and the capacity health of different types of lithium batteries.
The conception, specific structure, and technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, features, and effects of the present invention.
Drawings
FIG. 1 is a schematic overall flow diagram of a preferred embodiment of the present invention.
Detailed Description
The following description of the preferred embodiments of the present invention refers to the accompanying drawings, which make the technical contents thereof more clear and easy to understand. The present invention may be embodied in many different forms of embodiments and the scope of the present invention is not limited to only the embodiments described herein.
In order to solve the problems that a particle swarm converges to a local optimum or can not converge in the prior art by adopting a method for identifying model parameters by only adopting a particle swarm algorithm, the invention discloses a battery state estimation strategy based on a self-adaptive particle swarm algorithm, which comprises the following steps: firstly, performing constant current pulse experiments at different temperatures, and performing battery model parameter calibration; then, putting the battery into actual operation, and updating the charge state of the battery on line in real time by utilizing 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 function and the like according to a given operation period by using a self-adaptive particle swarm algorithm, determining the health of the battery internal resistance by using the updated battery ohmic internal resistance, updating battery capacity parameters by using a particle filter algorithm to further determine the battery capacity health, and using the updated parameters for subsequent battery state of charge updating. The model adopted by the invention is based on a second-order RC equivalent circuit model of the battery, has strong parameter interpretation and strong global optimization capability of an algorithm, and can realize the joint estimation of the state of charge, the internal resistance and the capacity health of different types of lithium batteries.
Referring to fig. 1, an overall flow chart of a preferred embodiment of the present invention is shown, wherein a particle swarm algorithm with an adaptive threshold is designed to update battery characteristic parameters to further realize 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, performing constant current pulse experiments at different temperatures, and calibrating initial characteristic parameters of a battery, wherein the characteristic parameters comprise: open circuit voltage-state of charge 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 charge state of the battery and recording the state data of the battery in operation;
and step 3, after the recorded state data reaches a preset threshold value, starting a new round of parameter self-correction, including updating the characteristic parameters and the battery capacity parameters of the battery, and using the updated characteristic parameters and battery capacity parameters for updating the state of charge of the battery.
Wherein step 1 comprises the following sub-steps:
step 1.1, determining different temperatures according to a temperature range of operation of a battery and a preset temperature step; sequentially carrying out constant current pulse experiments on the battery for a plurality of times 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 end voltage of the static stage of the constant current pulse experiment and the ratio of the discharged quantity of the constant current pulse experiment to the total discharged quantity of the test, and recording as (OCV) 1 ，SOC 1 )，(OCV 2 ，SOC 2 )，...，(OCV n ，SOC n )；
Step 1.3, adopting piecewise cubic spline interpolation to fit each open-circuit voltage-state-of-charge data point to obtain a fit curve, and obtaining an open-circuit voltage-state-of-charge characteristic curve fit parameter [ K ] 11 ，K 12 ，K 13 ，K 14 ，K 21 ，...，K (n-1)4 ]Wherein K is 11 ，K 12 ，K 13 ，K 14 Correspondence (OCV) 1 ，SOC 1 ) Sum (OCV) 2 ，SOC 2 ) Fitting parameters of the connecting lines; k (K) 21 ，K 22 ，K 23 ，K 24 Correspondence (OCV) 2 ，SOC 2 ) Sum (OCV) 3 ，SOC 3 ) Fitting parameters of the connecting lines; ..; k (K) (n-1)1 ，K (n-1)2 ，K (n-1)3 ，K (n-1)4 Correspondence (OCV) (n-1) ，SOC (n-1) ) Sum (OCV) n ，SOC n ) Fitting parameters of the connecting lines;
step 1.4, calibrating the ohmic internal resistance R of the battery by utilizing current and voltage data at the moment of current pulse dc The method comprises the steps of carrying out a first treatment on the surface of the The electrochemical polarized internal resistance, the electrochemical polarized capacitance, the concentration polarized internal resistance and the concentration polarized capacitance of the battery are calibrated by utilizing a double-exponential fitting mode through two sections of zero input and zero state response processes in and after the current pulse process and are sequentially marked as R ep 、C ep 、R cp And C cp 。
Wherein, in step 2, the method comprises the following steps:
step 2.1, initializing a state of charge estimation particle filter including, before the battery is put into actual operation, a corresponding initial state of charge value, based on the batteryThe applied field environment is selected to observe the noise variance v 1 Setting a convergence threshold epsilon 1 And sampling in Gaussian distribution to generate N 1 First random particles, N 1 The first random particles are positioned 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 v 1 Calculating 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, 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 epsilon 1 And obtaining the charge state of the battery.
Wherein, in step 3, the method comprises the following steps:
step 3.1, selecting an estimated change proportion eta based on the self-correction of the previous round of parameters, initializing and standardizing the characteristic parameters of the battery within the range of (1-eta, 1+eta) multiplied by the characteristic parameters obtained by the self-correction of the previous round of parameters, and reducing the characteristic parameters to between-1 and 1 so as to ensure that the singular of each characteristic parameter is avoided when matrix operation is carried out; the numerical range of the estimated change proportion eta is 0< eta <1;
step 3.2, calculating a moderate value of the first random particles, and sequencing the first random particles according to the level of the moderate value; if the high moderate value particle count reaches the required cluster precision or the iteration number is reached, outputting the characteristic parameter of the battery corresponding to the highest moderate value particle as the latest characteristic parameter of the battery, otherwise, entering the next step;
step 3.3, judging whether the particle number with the high moderate value reaches a given distribution proportion delta, 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 proportion delta is 0.3< delta < 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;
step 3.5, distributing different first random particles according to the distribution proportion delta after adjustment, directly adopting a particle swarm algorithm for the first random particles higher than the distribution proportion delta, updating the particle position and speed, firstly obtaining moderate values of 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 moderate value which can be obtained by the first random particles;
step 3.7, updating the moderate values of all the first random particles, sorting, and returning to step 3.2.
Step 3.6, selecting one of the following 4 steps according to the highest moderate value which can be obtained by the first random particles, and updating the particle parameters;
step 3.6.1, randomly selecting a random state in the changing radius according to the state of the current 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 is further before moving to the selected random state, otherwise, the state is randomly selected again; if randomly select N try After that, if the moderate value of the random state is not higher than the current moderate value of the first random particle, step 3.6.4 is executed;
step 3.6.2, if a particle aggregation center exists in the changing radius of the first random particle, and the ratio of the moderate value of the particle aggregation center to the number of particles existing in the changing radius of the first random particle is lower than a set threshold value, the first random particle moves to the particle aggregation center by one step;
step 3.6.3, if the maximum moderate value particle exists in the changing radius of the first random particle, and the ratio of the maximum moderate value particle to the number of particles existing in the changing radius of the first random particle is lower than the set threshold, the first random particle moves to the maximum moderate value particle by one step;
step 3.6.4, if steps 3.6.2 and 3.6.3 are not satisfied, randomly selecting a state within the particle variation range.
Wherein, step 3 further comprises the following steps:
step 3.8, updating battery capacity parameters by using the self-adaptive battery capacity estimation particle filter;
step 3.9, dividing the updated ohmic internal resistance by the ohmic internal resistance at the initial calibration to obtain the internal resistance health of the battery; and 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 sub-steps are included:
step 3.8.1, initializing a battery capacity estimation particle filter, taking the initial battery capacity of the battery as a reference, and selecting an observed noise variance v according to the field environment to which the battery is applied 2 Generating N in a Gaussian distribution 2 A second random number of particles; n (N) 2 The 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 a battery capacity estimation particle filter;
step 3.8.3, according to the observed noise variance v 2 Calculating a second weight of the second random particle;
step 3.8.4: normalizing the second weight;
step 3.8.5: judging whether the second random particles are effective or not, and determining whether resampling is needed or not;
step 3.8.6, updating the state data of the battery according to the state data of the battery, including the current, the voltage, the temperature and the state of charge at the previous time, and repeating steps 3.8.2 to 3.8.6 until the battery capacity estimating particle filter converges to a preset convergence threshold epsilon 2 And obtaining the battery capacity parameter of the battery.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention without requiring creative effort by one of ordinary skill in the art. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (5)
1. A battery state estimation method based on an adaptive particle swarm algorithm, the method comprising the steps of:
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-state of charge 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 charge state of the battery and recording state data of the battery in operation;
step 3, after the recorded state data reaches a preset threshold value, starting a new round of parameter self-correction, including updating the characteristic parameter and the battery capacity parameter of the battery, and using the updated characteristic parameter and battery capacity parameter to update the state of charge of the battery;
said step 1 comprises the sub-steps of:
step 1.1, determining different temperatures according to a temperature range of operation of the battery and a preset temperature step; sequentially carrying out constant current pulse experiments on the battery for a plurality of times 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 states at different temperatures according to the end voltage of the standing section of the constant current pulse experiment and the ratio of the discharged quantity of the constant current pulse experiment to the total discharged quantity of the testData points, and are noted as (OCV) 1 ，SOC 1 )，(OCV 2 ，SOC 2 )，…，(OCV n ，SOC n )；
Step 1.3, adopting piecewise cubic spline interpolation to fit each open circuit voltage-state of charge data point to obtain a fit curve, and obtaining the open circuit voltage-state of charge characteristic curve fit parameter [ K ] 11 ，K 12 ，K 13 ，K 14 ，K 21 ，…，K (n-1)4 ]Wherein K is 11 ，K 12 ，K 13 ，K 14 Correspondence (OCV) 1 ，SOC 1 ) Sum (OCV) 2 ，SOC 2 ) Fitting parameters of the connecting lines; k (K) 21 ，K 22 ，K 23 ，K 24 Correspondence (OCV) 2 ，SOC 2 ) Sum (OCV) 3 ，SOC 3 ) Fitting parameters of the connecting lines; …; k (K) (n-1)1 ，K (n-1)2 ，K (n-1)3 ，K (n-1)4 Correspondence (OCV) (n-1) ，SOC (n-1) ) Sum (OCV) n ，SOC n ) Fitting parameters of the connecting lines;
step 1.4, calibrating the ohmic internal resistance R of the battery by using current and voltage data at the moment of current pulse dc The method comprises the steps of carrying out a first treatment on the surface of the Calibrating the electrochemical polarized internal resistance, the electrochemical polarized capacitance, the concentration polarized internal resistance and the concentration polarized capacitance of the battery by using a double-exponential fitting mode by using two sections of zero input and zero state response processes in and after a current pulse process, and sequentially marking as R ep 、C ep 、R cp And C cp ；
In the step 2, the method comprises the following steps:
step 2.1, initializing a state of charge estimation particle filter, including selecting an observed noise variance v according to a field environment to which the battery is applied, in the vicinity of a corresponding initial state of charge value before the battery is put into the actual operation 1 Setting a convergence threshold epsilon 1 And sampling in Gaussian distribution to generate N 1 First random particles, the N 1 The first random particles are positioned 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 v 1 Calculating a first weight of the first random particle;
step 2.4, normalizing 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, 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 epsilon 1 Obtaining the state of charge of the battery;
in the step 3, the method comprises the following steps:
step 3.1, selecting an estimated change proportion eta based on the parameter self-correction of the previous round, multiplying the characteristic parameter of the battery by a random number within the range of the characteristic parameter obtained by the parameter self-correction of the previous round to initialize and normalize the random number, and reducing the characteristic parameter to between-1 and 1 so as to ensure that singular occurrence of each characteristic parameter is avoided when matrix operation is carried out;
step 3.2, calculating a moderate value of the first random particles, and sequencing the first random particles according to the level of the moderate value; if the high moderate value particle count reaches the required cluster precision or the iteration number is reached, outputting the characteristic parameter of the battery corresponding to the highest moderate value particle as the latest characteristic parameter of the battery, otherwise, entering the next step;
step 3.3, judging whether the high-moderate value particle count reaches a given distribution proportion delta, 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 so, keeping the distribution proportion delta unchanged, otherwise, adjusting the distribution proportion delta according to the step 3.3;
step 3.5, distributing different first random particles according to the distribution proportion delta after adjustment, directly adopting a particle swarm algorithm for the first random particles higher than the distribution proportion delta, updating the particle position and speed, firstly obtaining moderate values of particles around the first random particles for the first random particles lower than the distribution proportion delta, and then carrying out next step judgment;
step 3.6, updating particle parameters according to the highest moderate value which can be obtained by the first random particles;
step 3.7, updating the moderate values of all the first random particles, sorting, and returning to the step 3.2;
the numerical range of the estimated variation ratio eta is 0< eta <1; the threshold range of the distribution ratio delta is 0.3< delta <0.7.
2. The battery state estimation method based on the adaptive particle swarm algorithm according to claim 1, wherein said step 3.6 is to select one of the following 4 steps according to the highest fitness value that the first random particle can obtain, and to update the particle parameter;
step 3.6.1, randomly selecting a random state in a changing radius according to the current state of the first random particle, if the moderate value of the random state is larger than the current moderate value of the first random particle, the first random particle is further before the selected random state, otherwise, the state is randomly selected again; if randomly select N try After that, if the moderate value of the random state is not higher than the current moderate value of the first random particle, step 3.6.4 is executed;
step 3.6.2, if a particle aggregation center exists in the change radius of the first random particle, and the ratio of the moderate value of the particle aggregation center to the number of particles existing in the change radius of the first random particle is lower than a set threshold value, the first random particle moves to the particle aggregation center by one step;
step 3.6.3, if a maximum moderate value particle exists in the changing radius of the first random particle, and the ratio of the maximum moderate value particle to the number of particles existing in the changing radius of the first random particle is lower than a set threshold, the first random particle moves to the maximum moderate value particle by one step;
step 3.6.4, if steps 3.6.2 and 3.6.3 are not satisfied, randomly selecting a state within the particle variation range.
3. The method for estimating a battery state based on an adaptive particle swarm algorithm according to claim 1, wherein said step 3 further comprises the steps of:
step 3.8, updating the battery capacity parameter by using an adaptive battery capacity estimation particle filter;
step 3.9, dividing the updated ohmic internal resistance by the ohmic internal resistance at the initial standard to obtain the internal resistance health of the battery; and dividing the updated battery capacity parameter by the initial battery capacity parameter to obtain the capacity health degree of the battery.
4. A battery state estimation method based on an adaptive particle swarm algorithm according to claim 3, wherein in said step 3.8, comprising the sub-steps of:
step 3.8.1, initializing the battery capacity estimation particle filter, and selecting an observed noise variance v based on the initial battery capacity of the battery and the applied field environment of the battery 2 Generating N in a Gaussian distribution 2 A second random number of particles;
step 3.8.2, setting the iteration times of the battery capacity estimation particle filter;
step 3.8.3, according to the observed noise variance v 2 Calculating a second weight of the second random particle;
step 3.8.4: normalizing the second weight;
step 3.8.5: judging whether the second random particles are effective or not, and determining whether resampling is needed or not;
step 3.8.6, updating the state data of the battery according to the state data of the battery including current, voltage, temperature and state of charge at the last moment, and repeating the steps 3.8.2-3.8.6 until the battery capacity estimating particle filter converges to the preset convergence threshold ε 2 And obtaining the battery capacity parameter of the battery.
5. The method for estimating a battery state based on an adaptive particle swarm algorithm according to claim 4, wherein said N 2 The second random particles are second random particles having a value between 0 and the initial battery capacity.
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