CN113625177A - SOC estimation method based on particle swarm optimization particle filter algorithm - Google Patents
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Abstract
The invention provides a particle swarm optimization particle filter algorithm-based SOC estimation method, which comprises the following steps of: 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 has the advantages that: the battery SOC estimation method for particle swarm optimization particle filtering continuously optimizes the positions of particles in iteration, and a new particle set is used for estimating the SOC value at the next moment, so that the problem of particle depletion is solved, and the estimation accuracy of SOC is improved.
Description
Technical Field
The invention relates to the technical field of battery state of charge prediction, in particular to an SOC estimation method based on a particle swarm optimization particle filter algorithm.
Background
As the whole station direct current system is also the most important guarantee finally, the storage battery plays an important role in the safe and stable operation of the transformer substation. However, in the practical application of the transformer substation alternating current and direct current integrated power supply system, the storage battery pack of the transformer substation is in a floating charge state for a long time, only charging and discharging can cause passivation of an anode plate of a storage battery, so that internal resistance is increased, capacity is reduced, the storage battery is prone to aging and capacity loss, thermal unbalance and other problems, and if the storage battery pack cannot be found and maintained in time, adverse effects can be caused on power supply safety and stability. The detection of the state of charge (SOC) of a battery is one of the important means for maintaining the battery of a substation.
However, the open-loop ampere-hour integration method in the prior art has the problem of accumulated errors, so that a Particle Filter (PF) is required to estimate the SOC of the battery, and a good effect is achieved. Particle filtering is a statistical filtering method based on a Monte Carlo method and recursive Bayes estimation, and the integral operation in Bayes estimation is solved by adopting the Monte Carlo method according to the majority theorem. In the process of particle weight correction, the variance of the importance weight is randomly increased along with time, so that the weight of the particles is concentrated on a few particles, even after several steps of recursion, only one particle is possibly a non-zero weight, the weights of other particles are very small and even can be ignored, and thus, a large amount of calculation work is used for the particles which hardly contribute to estimation, which is a degradation problem of a particle filter algorithm and affects the estimation accuracy in the estimation process which should be used for the SOC.
Based on the above, research is now conducted on an SOC estimation method based on a particle swarm optimization particle filter algorithm, which can effectively suppress the particle degradation problem of particle filtering and improve the estimation accuracy.
Disclosure of Invention
In order to solve the problems, the invention aims to provide an SOC estimation method based on a particle swarm optimization particle filter algorithm, which optimizes the sampling process by the Gaussian particle swarm optimization particle filter algorithm, and continuously updates the particle speed based on Gaussian distribution, so that the sampling distribution moves to an area with higher posterior probability, thereby reducing the particle degradation phenomenon and improving the state estimation precision.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a particle swarm optimization particle filter algorithm-based SOC estimation method 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.
Furthermore, the battery equivalent circuit model is used for performing offline parameter identification on experimental data by using a least square method, and respectively establishing a function equation between the open-circuit voltage and the SOC and a parameter equation of the equivalent circuit under different SOCs and temperatures.
Further, the state equation is: x is the number ofk+1＝f(xk，Ik，ωk) In the formula xk+1SOC State value and polarization Voltage value at System k +1 time, f (x)k，Ik，ωk) Is a state transition model.
Further, the state transition model f (x)k，Ik，ωk) The calculation process of (2) is as follows:
wherein ω iskFor process random noise, TsIs the sampling interval, CactualTo the actual capacity of the battery, IkFor discharge current, Uτ，kTerminal voltage, x, of an RC link for k incheskIs composed of
Further, the measurement equation is: u shapect，k+1＝g(Ik+1，υk) Wherein, U in the formulact，k+1The battery output voltage, g (I), obtained for the sampling at time k +1k+1，υk) Is a measurement model.
Further, the measurement model g (I)k+1，υk) The calculation process of (2) is as follows: g (I)k+1，υk)＝Uct，k+1＝Uocv(SOCk)+Uτ，k+IL，k R0+υkWherein upsilon iskFor random noise in the observation, R0The direct current internal resistance of the battery.
Further, the optimizing the positions of the particles in the particle filter by using the particle swarm optimization comprises the following steps:
s41: initializing an algorithm, and setting the number of particles, the iteration times and threshold related initial parameters;
s42: substituting the optimized N particle states at the previous moment into a state equation and a measurement equation of the lithium battery, so as to estimate the N particle states of the battery and the voltage at two ends of the battery at the next moment;
s43: calculating the difference value between the actually measured battery terminal voltage and the battery terminal voltage estimated through the model, and calculating the weights of different particles according to the difference value:
s44: calculating the speed of particle movement:
s45: calculating the position of the optimized particle:
has the advantages that: (1) the SOC estimation method provided by the invention has higher prediction accuracy in the nonlinear system of the battery under the actual working condition; (2) the invention can overcome the problem of low SOC estimation precision of the battery under complex working conditions, and effectively improves the accuracy of SOC estimation; (3) according to the method, the particle swarm optimization is used for optimizing the particle filter, so that the problems of low particle diversity and particle depletion when the SOC is estimated by the particle filter algorithm are solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flowchart of an SOC estimation method based on a particle swarm optimization particle filter algorithm according to an embodiment of the present invention;
FIG. 2 is an algorithm flowchart of the SOC estimation method based on particle swarm optimization particle filter algorithm according to the embodiment of the present invention;
FIG. 3 is a schematic diagram of an equivalent circuit model in the SOC estimation method based on particle swarm optimization particle filter algorithm according to the embodiment of the present invention;
fig. 4 is a schematic diagram of R0 parameters in equivalent circuit models at different temperatures and SOCs obtained by identification in the SOC estimation method based on the particle swarm optimization particle filter algorithm according to the embodiment of the present invention;
fig. 5 is a schematic diagram of R1 parameters in equivalent circuit models at different temperatures and SOCs obtained by identification in the SOC estimation method based on the particle swarm optimization particle filter algorithm according to the embodiment of the present invention;
fig. 6 is a schematic diagram of the C parameter in equivalent circuit models at different temperatures and under different SOCs obtained by identification in the SOC estimation method based on the particle swarm optimization particle filter algorithm according to the embodiment of the present invention;
fig. 7 is a schematic diagram of operating condition currents during pulse discharge in the SOC estimation method based on the particle swarm optimization particle filter algorithm according to the embodiment of the present invention;
FIG. 8 is a schematic diagram illustrating an estimation result of SOC of a battery according to an embodiment of the present invention during pulse discharge in an SOC estimation method based on particle swarm optimization particle filter algorithm according to an embodiment of the present invention;
FIG. 9 is a schematic diagram illustrating an estimation error of an embodiment of the present invention with respect to a battery SOC during pulse discharge in the SOC estimation method based on particle swarm optimization particle filter algorithm according to the embodiment of the present invention;
fig. 10 is a schematic diagram of an estimation error of the battery SOC according to the embodiment of the present invention when the SOC estimation method based on the particle swarm optimization particle filter algorithm is operated in a cycle.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Example 1
Referring to FIGS. 1-10: a particle swarm optimization particle filter algorithm-based SOC estimation method 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.
In the embodiment, a battery is subjected to a charge and discharge experiment, and an equivalent circuit model of the battery is constructed by analyzing and processing experimental data; constructing a state equation and a measurement equation for estimating the SOC of the battery according to the equivalent circuit model obtained by identification; estimating the change of the SOC of the battery by utilizing particle swarm optimization particle filtering; after the positions of the particles in the particle filter are optimized by using a particle swarm algorithm, estimating the SOC value of the next moment by using a new particle set; the particle swarm optimization particle filter battery SOC estimation method continuously optimizes the positions of particles in iteration, thereby solving the problem of particle depletion and improving the estimation precision of SOC.
In a specific example, the battery equivalent circuit model is obtained by performing off-line parameter identification on experimental data by using a least square method, and respectively establishing a function equation between open-circuit voltage and SOC and a parameter equation of equivalent circuits at different SOCs and temperatures.
In a specific example, the state equation is: x is the number ofk+1＝f(xk，Ik，ωk) In the formula xk+1SOC State value and polarization Voltage value at System k +1 time, f (x)k，Ik，ωk) Being a state transition model, the state transition model f (x)k，Ik，ωk) The calculation process of (2) is as follows:
wherein ω iskFor process random noise, TsIs the sampling interval, CactualTo the actual capacity of the battery, IkFor discharge current, Uτ，kTerminal voltage, x, of an RC link for k incheskIs composed of
In a specific example, the measurement equation is: u shapect，k+1＝g(Ik+1，υk) Wherein, U in the formulact，k+1The battery output voltage, g (I), obtained for the sampling at time k +1k+1，υk) For the measurement model, the measurement model g (I)k+1，υk) The calculation process of (2) is as follows: g (I)k+1，υk)＝Uct，k+1＝Uocv(SOCk)+Uτ，k+IL，k R0+υkWherein upsilon iskFor random noise in the observation, R0The direct current internal resistance of the battery.
In a specific example, the optimizing the positions of the particles in the particle filter by using the particle swarm optimization comprises the following steps:
s41: initializing an algorithm, and setting the number of particles, the iteration times and threshold related initial parameters;
s42: substituting the optimized N particle states at the previous moment into a state equation and a measurement equation of the lithium battery, so as to estimate the N particle states of the battery and the voltage at two ends of the battery at the next moment;
s43: calculating the difference value between the actually measured battery terminal voltage and the battery terminal voltage estimated through the model, and calculating the weights of different particles according to the difference value:
s44: calculating the speed of particle movement:
s45: calculating the position of the optimized particle:
FIG. 4 depicts R in an equivalent circuit0Fig. 5 and 6 show the polarization resistance and capacitance in the RC element, respectively: see figure 2 for details of Thevenin equivalent circuit model;
as can be seen in fig. 7-9: the SOC can be well estimated in the pulse discharge state;
as shown in fig. 10, the present embodiment still has better SOC estimation accuracy than the particle filter SOC estimation method under the cyclic condition.
In summary, in the embodiment, the sampling process is optimized by the gaussian particle swarm optimization particle filter algorithm, and the velocity of the particles is continuously updated based on gaussian distribution, so that the sampling distribution moves to an area with higher posterior probability, the particle degradation phenomenon is reduced, and the accuracy of state estimation is improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. An SOC estimation method based on a particle swarm optimization particle filter algorithm is characterized by comprising 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.
2. The particle swarm optimization particle filter algorithm-based SOC estimation method of claim 1, wherein the battery equivalent circuit model is to perform off-line parameter identification on experimental data by using a least square method, and respectively establish a function equation between an open-circuit voltage and an SOC and a parameter equation of an equivalent circuit at different SOCs and temperatures.
3. The particle swarm optimization particle filter algorithm-based SOC estimation method of claim 1, wherein the state equation is: x is the number ofk+1＝f(xk，Ik，ωk) In the formula xk+1SOC State value and polarization Voltage value at System k +1 time, f (x)k，Ik，ωk) Is a state transition model.
4. The particle swarm optimization particle filter algorithm-based SOC estimation method of claim 3, wherein the state transition model f (x)k，Ik，ωk) The calculation process of (2) is as follows:
wherein ω iskFor process random noise, TsIs the sampling interval, CactualTo the actual capacity of the battery, IkFor discharge current, Uτ，kTerminal voltage, x, of an RC link for k incheskIs composed of
5. The particle swarm optimization particle filter algorithm-based SOC estimation method of claim 1, wherein the measurement equation is: u shapect，k+1＝g(Ik+1，υk) Wherein, formulaMiddle Uct，k+1The battery output voltage, g (I), obtained for the sampling at time k +1k+1，υk) Is a measurement model.
6. The particle swarm optimization particle filter algorithm-based SOC estimation method of claim 5, wherein the measurement model g (I)k+1，υk) The calculation process of (2) is as follows: g (I)k+1，υk)＝Uct，k+1＝Uocv(SOCk)+Uτ，k+IL，kR0+υkWherein upsilon iskFor random noise in the observation, R0The direct current internal resistance of the battery.
7. The particle swarm optimization particle filter algorithm-based SOC estimation method according to claim 1, wherein the optimization of the positions of the particles in the particle filter by the particle swarm optimization comprises the following steps:
s41: initializing an algorithm, and setting the number of particles, the iteration times and threshold related initial parameters;
s42: substituting the optimized N particle states at the previous moment into a state equation and a measurement equation of the lithium battery, so as to estimate the N particle states of the battery and the voltage at two ends of the battery at the next moment;
s43: calculating the difference value between the actually measured battery terminal voltage and the battery terminal voltage estimated through the model, and calculating the weights of different particles according to the difference value:
s44: calculating the speed of particle movement:
s45: calculating the position of the optimized particle:
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