CN113011082B - Lithium battery SOC prediction method for optimizing particle filtering by improving ant colony algorithm - Google Patents

Lithium battery SOC prediction method for optimizing particle filtering by improving ant colony algorithm Download PDF

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CN113011082B
CN113011082B CN202110176036.5A CN202110176036A CN113011082B CN 113011082 B CN113011082 B CN 113011082B CN 202110176036 A CN202110176036 A CN 202110176036A CN 113011082 B CN113011082 B CN 113011082B
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李立伟
张承慧
段彬
商云龙
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Abstract

The invention particularly relates to a lithium battery SOC prediction method for optimizing particle filtering by improving an ant colony algorithm, which comprises the following steps of: carrying out discharge tests on the lithium battery under different working condition currents, and preprocessing experimental data; performing parameter identification according to the preprocessed experimental data, and constructing a state equation according to an ampere-hour integration method and SOC prediction influence factors; establishing a measurement equation of a theoretical prediction model of the battery according to a second-order Thevenin equivalent model; optimizing particle filtering by using an improved ant colony algorithm; and predicting the SOC change of the battery through the optimized particle filter. The prediction method provided by the invention improves the condition that the traditional ant colony algorithm is easy to fall into the local optimal solution; the particle filter is optimized by using the improved ant colony algorithm, the problems of low particle diversity and poor particles when the particle filter algorithm is used for estimating the SOC are solved, the problems of complexity and low accuracy of the lithium battery SOC estimation method are solved, and the estimation precision is effectively improved.

Description

Lithium battery SOC prediction method for optimizing particle filtering by improving ant colony algorithm
Technical Field
The invention belongs to the field of battery energy management systems, relates to a lithium battery state of charge prediction technology, and particularly relates to a lithium battery SOC prediction method for optimizing particle filtering by improving an ant colony algorithm.
Background
With the increasing severity of the problems of energy shortage, environmental pollution and the like, people gradually turn to the technical field of research on low energy consumption and environmental protection, wherein the research and the use of electric vehicles are favored by people and are supported by the strong force of national policies, so that the electric vehicles are rapidly developed. The lithium ion battery has the advantages of high energy ratio, long service life, low self-discharge rate and the like, and is widely applied to the field of electric automobiles. As the lithium battery ages, the capacity and stability of the battery are gradually reduced, and therefore, in order to ensure safe use of the battery, the Battery Management System (BMS) provided in the electric vehicle is required to monitor, predict, control, etc. the battery state in real time.
Battery state of charge (SOC) is an important parameter of the BMS, represents the internal state of the battery, and thus cannot be directly measured. At present, in the existing method, a particle filter algorithm (PF) is used for research on lithium battery SOC prediction, the particle filter algorithm approximates posterior distribution of the system by sampling a group of weighted particles using a monte carlo method, and there is no clear assumption about the distribution form, and the method is a high-precision probability-based estimation algorithm. The algorithm has certain advantages when a complex nonlinear battery model is provided under the condition of processing non-Gaussian distribution system noise, but the traditional PF algorithm cannot update the latest observation information, all weights tend to zero except some important weights, the particle diversity is reduced, the particle depletion problem of the PF algorithm is solved, and the accuracy of SOC estimation can be reduced when the PF algorithm is applied to SOC estimation.
Disclosure of Invention
The invention aims to solve the problems in the prior art, provides the lithium battery SOC prediction method for optimizing the particle filter by improving the ant colony algorithm, can overcome the problems of complexity and low accuracy of the lithium battery SOC estimation method, and effectively improves the estimation precision.
The technical scheme of the invention is as follows:
a lithium battery SOC prediction method for optimizing particle filtering by improving an ant colony algorithm comprises the following steps:
step 1, carrying out discharge tests on lithium batteries under currents under different working conditions, and preprocessing experimental data;
step 2, performing parameter identification according to the preprocessed experimental data, and constructing a state equation according to an ampere-hour integral method;
step 3, establishing a measurement equation of a theoretical prediction model of the battery according to a second-order Thevenin equivalent model;
step 4, optimizing particle filtering by using an improved ant colony algorithm;
and 5, predicting the SOC change of the battery through the optimized particle filter.
Further, the equation of state in step 2 is xk+1＝fk(xk，ωk) Wherein, x in the formulak+1Is the battery SOC state value at the time of system k +1, fk(xk，ωk) Is a state transition model; the state transition model formula is as follows:
Figure BDA0002940740600000021
wherein, ω iskProcess random noise; x is the number ofkIs the SOC state value of the battery at the moment k of the system, eta is the coulombic efficiency, the discharging condition is generally 1, delta t is the sampling interval, C is the rated capacity of the battery, i is the rated capacity of the batterykIs a discharge current.
Further, the measurement equation in step 3 is: y isk＝hk(xk，vk)，ykIs the output voltage value at the time k of the system, hk(xk，vk) For the measurement model, the formula of the measurement model is as follows: h isk(xk，vk)＝uOC(xk)-u1k-u2k-r0ik+vk，vkTo observe random noise, uOC(xk) Is a relation of open circuit voltage and SOC, u1k，u2kTerminal voltages r of two RC links0Is the internal resistance of the battery.
Further, the optimization in step 4 includes the following steps:
4.1 initializing an algorithm, and setting initial relevant parameters;
4.2 initializing particles, and optimizing particle state distribution according to an ant colony algorithm;
4.3, resampling by the particle filter and outputting the state.
Further, the initial relevant parameters in step 4.1 include the number of particles, the number of iterations, and a threshold.
Further, the step 4.2 comprises the following steps:
4.2.1 Ant substituted particle, transition probability PijRepresenting the probability of selecting a particle j as a moving direction for a particle i, wherein the particle j is the N-1 particles left by dividing i;
4.2.2 improving transfer probability parameters in the ant colony algorithm and updating the pheromone;
4.2.3 setting the probability threshold λPAnd a distance threshold λLWhen the distance to the target particle is not in lambdaLWithin, and without exceeding the maximum number of iterations, is selected at λLAccording to transition probability PijSelecting a moving object when Pij＜λPWhen P is equal, the particle position remains unchangedij＞λPThen, the particles are transferred to the target particles, and then the parameters are updated;
4.2.4 outputting the updated particle state.
Further, the step 2 comprises: the pheromone and heuristic interaction parameters are improved, the pheromone evaporation rate in pheromone updating is improved, and the ant colony algorithm is prevented from falling into local optimization.
The invention has the beneficial effects that:
(1) according to the lithium battery SOC prediction method for optimizing the particle filter by the improved ant colony algorithm, the particle filter is optimized by the improved ant colony algorithm, and the problems of low particle diversity and poor particles when the SOC is estimated by the particle filter algorithm are solved; the method is improved aiming at the ant colony algorithm, and the situation that the traditional ant colony algorithm is easy to fall into the local optimal solution is improved.
(2) The prediction method provided by the invention has good prediction accuracy on the non-Gaussian and non-linear system of the battery in the actual working condition operation process of the vehicle; the method can solve the problems of complexity and low accuracy of the lithium battery SOC estimation method, and effectively improves the estimation precision.
Drawings
FIG. 1 is a schematic diagram of a lithium battery SOC prediction model building process provided by the invention;
FIG. 2 is a graph of the terminal voltage of the present invention at a pulse current of 1.5A;
FIG. 3 is a graph of the open circuit voltage of the present invention at a pulse current of 1.5A;
FIG. 4 is a lithium battery SOC prediction curve diagram under a pulse discharge condition according to the present invention;
FIG. 5 is a diagram of a lithium battery SOC prediction error curve under a pulse discharge condition according to the present invention;
FIG. 6 is a DST condition current curve of the present invention;
FIG. 7 is a diagram of a lithium battery SOC prediction curve under a DST condition according to the present invention;
FIG. 8 is a diagram of a lithium battery SOC prediction error curve under a DST condition according to the present invention;
fig. 9 is a diagram of a second-order Thevenin equivalent model.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
For a further understanding of the invention, reference will now be made to the following description taken in conjunction with the accompanying drawings and examples.
As shown in fig. 1, the invention relates to a lithium battery SOC prediction method for optimizing particle filtering by improving ant colony algorithm, comprising the following steps:
step 1, performing a discharge test on a lithium battery at room temperature (about 25 ℃) under different working condition current conditions, obtaining battery discharge voltage test data under certain temperature and current conditions, and preprocessing the test data;
step 2, as shown in fig. 2, voltage experimental data under the condition of the pulse current of 1.5A at room temperature is adopted to perform offline parameter identification by using a least square method to obtain the results shown in the following table 1, and the equation of state constructed according to the ampere-hour integration method is xk+1＝fk(xk，ωk) Wherein, x in the formulak+1Is the battery SOC state value at the time of system k +1, fk(xk，ωk) Is a state transition model; the state transition model formula is:
Figure BDA0002940740600000031
TABLE 1 parameter identification results
Figure BDA0002940740600000041
In Table 1 above, R0Is the internal resistance of the battery; r1C1And R2C2Corresponding to parameters in two RC links; see in particular the second order Thevenin equivalent model diagram shown in fig. 9.
Step 3, establishing a measurement equation of the theoretical prediction model of the battery according to the second-order Thevenin equivalent model, wherein the measurement equation is as follows: y isk＝hk(xk，vk)，ykIs the output voltage value at the moment k of the system, hk(xk，vk) For the measurement model, the measurement model formula is: h isk(xk，vk)＝uOC(xk)-u1k-u2k-r0ik+vk. U is determined by the open circuit voltage profile as shown in FIG. 3OC(xk) In the formula
Figure BDA0002940740600000042
Wherein R is1R2C1C2The parameter identification results in table 1 are substituted for the parameters of the RC link in fig. 9.
Step 4, optimizing particle filtering through an improved ant colony algorithm; the method comprises the following specific steps:
4.1, initializing an algorithm, setting initial relevant parameters, and setting relevant initial parameters such as particle number, iteration times, threshold values and the like;
4.2 initializing particles, and optimizing particle state distribution according to an ant colony algorithm; the method specifically comprises the following steps:
4.2.1 Ant substituted particles, transition probability
Figure BDA0002940740600000043
Expressed as the probability of selecting particle j (N-1 particles left by dividing i) as the moving direction for particle i, and updating the formula according to the pheromone
Figure BDA0002940740600000044
Updating parameters;
4.2.2 improving transfer probability parameters in the ant colony algorithm and updating the pheromone; the method comprises the following specific steps:
(1) the improved pheromone and heuristic interaction parameters alpha and beta are as follows: α ═ 1+ e-0.1N_c，
Figure BDA0002940740600000051
Wherein N _ c is the current iteration number;
(2) improving pheromone evaporation rate in pheromone updates: ρ ═ ρ0+(0.9-ρ0)×(N_c/N_cMAX)2Where ρ is0For the initial definition of the pheromone volatilization rate, N _ cMAXIs the maximum number of iterations.
4.2.3 setting the probability threshold λPAnd a distance threshold λLWhen the distance to the target particle is not in lambdaLWithin, and without exceeding the maximum number of iterations, is selected at λLWithin particle according to transition probability PijSelecting a moving object when Pij＜λPWhen P is equal, the particle position remains unchangedij＞λPThen, the particles are transferred to the target particles, and then the parameters are updated;
4.2.4 outputting the updated particle state.
And 5, predicting the SOC change of the battery through the optimized particle filter.
As shown in FIG. 4 and FIG. 5, the present invention can predict the SOC value well under the pulse discharge condition, and the error is controlled within 1%.
As shown in FIG. 7 and FIG. 8, the present invention can predict the SOC value well under the DST condition, and the error is controlled within 1.5%.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described above, or equivalents may be substituted for elements thereof. Any modification, equivalent replacement, or modification made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (4)

1. A lithium battery SOC prediction method for optimizing particle filtering by improving an ant colony algorithm is characterized by comprising the following steps:
step 1, carrying out discharge tests on lithium batteries under currents of different working conditions, and preprocessing experimental data;
step 2, performing parameter identification according to the preprocessed experimental data, and constructing a state equation according to an ampere-hour integral method;
step 3, establishing a measurement equation of a theoretical prediction model of the battery according to a second-order Thevenin equivalent model;
step 4, optimizing particle filtering by using an improved ant colony algorithm; the method comprises the following steps:
4.1 initializing an algorithm, and setting initial relevant parameters;
4.2 initializing particles, and optimizing particle state distribution according to an ant colony algorithm;
4.2.1 Ant substituted particle, transition probability PijThe probability that the particle j is selected as the moving direction for the particle i is expressed, and the parameter is updated according to the pheromone updating formula; wherein the particle j is N-1 particles left after the step of i;
4.2.2 improving transfer probability parameters in the ant colony algorithm and updating pheromones; the method comprises the following specific steps:
(1) the improved pheromone and heuristic interaction parameters alpha and beta are as follows: α ═ 1+ e-0.1N_c，
Figure FDA0003583677400000011
Wherein N _ c is the current iteration number;
(2) improving pheromone evaporation rate in pheromone updates: ρ ═ ρ0+(0.9-ρ0)×(N_c/N_cMAX)2Where ρ is0For the initial definition of the pheromone volatilization rate, N _ cMAXIs the maximum iteration number;
4.2.3 setting the probability threshold λPAnd a distance threshold λLWhen the distance to the target particle is not in lambdaLIn and not overMaximum number of iterations, selected at λLAccording to transition probability PijSelecting a moving object when Pij＜λPWhen P is equal, the particle position remains unchangedij＞λPThen, the particles are transferred to the target particles, and then the parameters are updated;
4.2.4 outputting the updated particle state;
4.3 resampling the particle filter and outputting the state;
and 5, predicting the SOC change of the battery through the optimized particle filter.
2. The lithium battery SOC prediction method for improving ant colony algorithm optimized particle filtering as claimed in claim 1, wherein the state equation in the step 2 is xk+1＝fk(xk，ωk) Wherein, x in the formulak+1Is the battery SOC state value at the time of system k +1, fk(xk，ωk) Is a state transition model; the state transition model formula is:
Figure FDA0003583677400000012
wherein, ω iskIs process random noise, xkIs the SOC state value of the battery at the moment k of the system, eta is the coulombic efficiency, delta t is the sampling interval, C is the rated capacity of the battery, ikIs a discharge current.
3. The lithium battery SOC prediction method for improving ant colony algorithm optimized particle filtering according to claim 1, wherein the measurement equation in the step 3 is as follows: y isk＝hk(xk，vk)，ykIs the output voltage value at the moment k of the system, hk(xk，vk) For the measurement model, the formula of the measurement model is as follows: h isk(xk，vk)＝uOC(xk)-u1k-u2k-r0ik+vk，vkTo observe random noise, uOC(xk) Is a relation of open circuit voltage and SOC，u1k，u2kTerminal voltages r of two RC links0Is the internal resistance of the battery, ikIs a discharge current.
4. The lithium battery SOC prediction method based on improved ant colony algorithm optimized particle filtering as claimed in claim 1, wherein the initial relevant parameters in step 4.1 include number of particles, iteration number, and threshold.
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