CN111123112A - Lithium ion battery model parameter identification method based on artificial bee colony algorithm - Google Patents

Lithium ion battery model parameter identification method based on artificial bee colony algorithm Download PDF

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CN111123112A
CN111123112A CN202010024696.7A CN202010024696A CN111123112A CN 111123112 A CN111123112 A CN 111123112A CN 202010024696 A CN202010024696 A CN 202010024696A CN 111123112 A CN111123112 A CN 111123112A
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lithium ion
solution
ion battery
artificial bee
colony algorithm
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聂晓华
刘意期
万晓凤
余运俊
王淳
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Lattice Power Jiangxi Corp
Nanchang University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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
    • G01R31/005—Testing of electric installations on transport means
    • G01R31/006—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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]
    • G01R31/385—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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Abstract

The invention discloses a lithium ion battery model parameter identification method based on an artificial bee colony algorithm, which relates to the technical field of electric power, wherein the parameter identification is carried out on a lithium ion battery model by introducing the artificial bee colony algorithm, the artificial bee colony algorithm does not need to know special information of problems, only needs to compare the advantages and the disadvantages of the problems, finally enables a global optimum value to emerge in a colony through the local optimization behavior of each artificial bee individual, and has higher convergence speed; and the artificial bee colony algorithm has few control parameters, is easy to realize and simple to calculate, and greatly improves the convergence rate of the global optimal solution.

Description

Lithium ion battery model parameter identification method based on artificial bee colony algorithm
Technical Field
The invention relates to the field of electric power, in particular to a lithium ion battery model parameter identification method based on an artificial bee colony algorithm.
Background
The lithium ion battery has the characteristics of large capacity, moderate voltage, wide sources, long cycle service life, good performance, no pollution to the environment and the like, and is more and more widely applied to new energy electric vehicles which are rapidly developed. The SOC of the lithium ion battery represents the ratio of the residual dischargeable electric quantity to the electric quantity in a fully charged state after the battery is used for a period of time or is left unused for a long time, the SOC is accurately estimated, the remaining mileage which can be traveled by an electric automobile can be provided for a user, the lithium ion battery is ensured to work within a reasonable voltage range, the damage to the battery due to overcharge and overdischarge can be effectively prevented, the service life of the battery is prolonged, the utilization rate of energy is improved, and the use cost is reduced. Therefore, accurate estimation of the battery SOC is particularly important for electric vehicles. Establishing a battery model and identifying the parameters of the battery model are the key points for accurately estimating the SOC of the battery. The existing method for identifying the battery model parameters has low convergence speed.
Disclosure of Invention
In order to solve the problems in the prior art, the parameter identification is carried out on the lithium ion battery model by introducing the artificial bee colony algorithm, the artificial bee colony algorithm does not need to know special information of the problems, only needs to compare the advantages and the disadvantages of the problems, finally enables the global optimum value to be highlighted in the colony through the local optimization searching action of each artificial bee individual, and has higher convergence speed.
The invention specifically adopts the following technical scheme:
a lithium ion battery model parameter identification method based on an artificial bee colony algorithm is characterized by comprising the following steps: the method comprises the following steps:
s1: randomly generating an initial population, corresponding one half of the initial population to the honey bees, calculating the fitness value of each solution, and recording the optimal solution;
s2: setting Cycle as 1;
s3: the bee is collected according to the formula: v. ofij＝xij+φij(xij-xkj) (wherein phi)ijIs [ -1,1 [ ]]Random number in between), perform a neighborhood searchNew form of the solution vijCalculating its fitness value, and for xijAnd vijGreedy selection is carried out;
s4: according to the formula
Figure BDA0002362023810000021
Calculation of and xiAssociated selection probability Pi；
S5: observing the roulette selection method with a probability PiSelecting food source and according to formula vij＝xij+φij(xij-xkj) Performing neighborhood search to generate new solution, calculating fitness value, and calculating fitness value for xijAnd viGreedy selection is carried out;
s6: the scout bee judges whether a solution to be abandoned exists, if so, the formula x is adoptedi j＝xmin j+rand(0,1)(xmax j-xmin j) Random search is carried out to generate a new solution to replace the old solution;
s7: record the best solution so far;
s8: if Cycle is equal to Cycle +1, if Cycle < number of initial population, go to S3; if Cycle is larger than the number of the initial population, outputting an optimal result;
s9: respectively solving U in a second-order RC circuit model according to the obtained optimal resultocv、R1、R2、C1And C2。
Further, the generation of the initial population in S1 is based on the voltage rebound characteristic curve of the lithium ion battery and a zero-input response equation of the second-order RC circuit model after the discharge is completed: u (t) ═ UOCV-U(R1)*e-t/τ1-U(R2)*e-t/τ2And 5 corresponding parameters related to initialization, wherein the 5 parameters respectively represent: x is the number of1＝Uocv、x2＝U(R1)、x3＝τ1、x4＝U(R2)、x5τ 2; for formula u (t) ═ x1-x2*e-t/x3-x4*e-t/x5At any solution x ═ x1x2x3x4x5]Now, at any time, there is a uniquely determined y (t) corresponding thereto. That is, the unique terminal voltage value can be determined from the power battery model parameters at any time, so the following objective function can be established:
Figure BDA0002362023810000022
further, U in the second order RC circuit model described in S9ocv、R1、R2、C1And C2Is according to x1＝Uocv，x2＝U(R1)＝I*R1，x3＝τ1＝R1*C1，x4＝U(R2)＝I*R2，x5＝τ2＝R2*C2To derive the calculated.
The invention has the beneficial effects that:
parameter identification is carried out on the lithium ion battery model by introducing an artificial bee colony algorithm, and as the artificial bee colony algorithm does not need to know special information of problems, only the advantages and disadvantages of the problems need to be compared, and finally, the global optimum value is highlighted in the colony through the local optimization action of each artificial bee individual, so that the convergence rate is high;
because the artificial bee colony algorithm has few control parameters, is easy to realize and simple to calculate, the convergence rate of the global optimal solution is greatly improved.
Drawings
Fig. 1 is a flowchart of a method for identifying parameters of a lithium ion battery model based on an artificial bee colony algorithm according to an embodiment of the present invention;
FIG. 2 is a graph comparing a test value of a voltage rebound characteristic curve of a lithium ion battery with an estimated value of an artificial bee colony algorithm in an embodiment of the invention;
FIG. 3 is a diagram of the variable current operating conditions of US06 for simulation verification according to an embodiment of the present invention;
FIG. 4 is a comparison graph of simulated voltage values and actual voltage values obtained under the US06 variable current condition by obtaining the optimal solution of each parameter of the second-order RC circuit in the embodiment of the present invention;
fig. 5 is a diagram of the percentage error between the simulated voltage value and the actual voltage value obtained under the US06 variable current condition by obtaining the optimal solution of each parameter of the second-order RC circuit in the embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
As shown in fig. 1-4, an embodiment of the present invention discloses a method for identifying parameters of a lithium ion battery model based on an artificial bee colony algorithm, which is characterized in that: the method comprises the following steps:
s1: randomly generating an initial population, corresponding one half of the initial population to the honey bees, calculating the fitness value of each solution, and recording the optimal solution;
s2: setting Cycle as 1;
s3: the bee is collected according to the formula: v. ofij＝xij+φij(xij-xkj) (wherein phi)ijIs [ -1,1 [ ]]Random number in between), a neighborhood search is performed to generate a new solution vijCalculating its fitness value, and for xijAnd vijGreedy selection is carried out;
s4: according to the formula
Figure BDA0002362023810000031
Calculation of and xiAssociated selection probability Pi；
S5: observing the roulette selection method with a probability PiSelecting food source and according to formula vij＝xij+φij(xij-xkj) Performing neighborhood search to generate new solution, calculating fitness value, and calculating fitness value for xijAnd viGreedy selection is carried out;
s6: the scout bee judges whether a solution to be abandoned exists, if so, the formula x is adoptedi j＝xmin j+rand(0,1)(xmax j-xmin j) Random search is carried out to generate a new solution to replace the old solution;
s7: record the best solution so far;
s8: if Cycle is equal to Cycle +1, if Cycle < number of initial population, go to S3; if Cycle is larger than the number of the initial population, outputting an optimal result;
s9: respectively solving U in a second-order RC circuit model according to the obtained optimal resultocv、R1、R2、C1And C2。
In this embodiment, the initial population in S1 is generated according to the voltage rebound characteristic curve of the lithium ion battery and the zero-input response equation of the second-order RC circuit model after the end of discharge: u (t) ═ UOCV-U(R1)*e-t/τ1-U(R2)*e-t/τ2And 5 corresponding parameters related to initialization, wherein the 5 parameters respectively represent: x is the number of1＝Uocv、x2＝U(R1)、x3＝τ1、x4＝U(R2)、x5τ 2; for formula u (t) ═ x1-x2*e-t/x3-x4*e-t/x5At any solution x ═ x1x2x3x4x5]Now, at any time, there is a uniquely determined y (t) corresponding thereto. That is, the unique terminal voltage value can be determined from the power battery model parameters at any time, so the following objective function can be established:
Figure BDA0002362023810000041
in the present embodiment, U in the second order RC circuit model in S9ocv、R1、R2、C1And C2Is according to x1＝Uocv，x2＝U(R1)＝I*R1，x3＝τ1＝R1*C1，x4＝U(R2)＝I*R2，x5＝τ2＝R2*C2To derive the calculated.
The battery types used in this example were: INR 18650-20R, rated capacity 2000 mAh. The parameters in the algorithm take the following values: the artificial bee colony size is NP which is 20, and the number of bee colony food sources is FoodNumber NP/2.
The simulation test is carried out under the working condition of the variable current of the US06, the percentage of the error between the test result, namely the simulation voltage value and the actual voltage value, is shown in figure 5, the error of the test result is in a smaller range, and the algorithm convergence speed is higher.
Finally, only specific embodiments of the present invention have been described in detail above. The invention is not limited to the specific embodiments described above. Equivalent modifications and substitutions by those skilled in the art are also within the scope of the present invention. Accordingly, equivalent alterations and modifications are intended to be included within the scope of the invention, without departing from the spirit and scope of the invention.

Claims (3)

1. A lithium ion battery model parameter identification method based on an artificial bee colony algorithm is characterized by comprising the following steps: the method comprises the following steps:
s1: randomly generating an initial population, corresponding one half of the initial population to the honey bees, calculating the fitness value of each solution, and recording the optimal solution;
s2: setting Cycle as 1;
s3: the bee is collected according to the formula: v. ofij＝xij+φij(xij-xkj) (wherein phi)ijIs [ -1,1 [ ]]Random number in between), a neighborhood search is performed to generate a new solution vijCalculating its fitness value, and for xijAnd vijGreedy selection is carried out;
s4: according to the formula
Figure FDA0002362023800000011
Calculation of and xiAssociated selection probability Pi；
S5: observing the roulette selection method with a probability PiSelecting food source and according to formula vij＝xij+φij(xij-xkj) Performing neighborhood search to generate new solution, calculating fitness value, and calculating fitness value for xijAnd viGreedy selection is carried out;
s6: the scout bee judges whether there is a solution to be abandoned, if soIf present, then the formula x is adoptedi j＝xmin j+rand(0,1)(xmax j-xmin j) Random search is carried out to generate a new solution to replace the old solution;
s7: record the best solution so far;
s8: if Cycle is equal to Cycle +1, if Cycle < number of initial population, go to S3; if Cycle is larger than the number of the initial population, outputting an optimal result;
s9: respectively solving U in a second-order RC circuit model according to the obtained optimal resultocv、R1、R2、C1And C2。
2. The method for identifying parameters of a lithium ion battery model based on an artificial bee colony algorithm according to claim 1, wherein the method comprises the following steps:
the generation of the initial population in S1 is according to the voltage rebound characteristic curve of the lithium ion battery and the zero-input response equation of the second-order RC circuit model after the discharge is finished: u (t) ═ UOCV-U(R1)*e-t/τ1-U(R2)*e-t/τ2And 5 corresponding parameters related to initialization, wherein the 5 parameters respectively represent: x is the number of1＝Uocv、x2＝U(R1)、x3＝τ1、x4＝U(R2)、x5τ 2; for formula u (t) ═ x1-x2*e-t/x3-x4*e-t/x5At any solution x ═ x1x2x3x4x5]Now, at any time, there is a uniquely determined y (t) corresponding thereto. That is, the unique terminal voltage value can be determined from the power battery model parameters at any time, so the following objective function can be established:
Figure FDA0002362023800000012
3. the method for identifying parameters of a lithium ion battery model based on an artificial bee colony algorithm according to claim 1, wherein the method comprises the following steps:
u in the second order RC circuit model described in S9ocv、R1、R2、C1And C2Is according to x1＝Uocv，x2＝U(R1)＝I*R1，x3＝τ1＝R1*C1，x4＝U(R2)＝I*R2，x5＝τ2＝R2*C2To derive the calculated.
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CN113410865A (en) * 2021-05-08 2021-09-17 南昌大学 Double parallel inverter control parameter setting method based on improved artificial bee colony algorithm
CN116646568A (en) * 2023-06-02 2023-08-25 陕西旭氢时代科技有限公司 Fuel cell stack parameter optimizing method based on meta heuristic
CN117686919A (en) * 2024-02-01 2024-03-12 昆明理工大学 Lithium battery SOC and SOH estimation method based on optimized electrochemical model

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CN111898726B (en) * 2020-07-30 2024-01-26 长安大学 Parameter optimization method, equipment and storage medium for electric automobile control system
CN113410865A (en) * 2021-05-08 2021-09-17 南昌大学 Double parallel inverter control parameter setting method based on improved artificial bee colony algorithm
CN116646568A (en) * 2023-06-02 2023-08-25 陕西旭氢时代科技有限公司 Fuel cell stack parameter optimizing method based on meta heuristic
CN116646568B (en) * 2023-06-02 2024-02-02 陕西旭氢时代科技有限公司 Fuel cell stack parameter optimizing method based on meta heuristic
CN117686919A (en) * 2024-02-01 2024-03-12 昆明理工大学 Lithium battery SOC and SOH estimation method based on optimized electrochemical model
CN117686919B (en) * 2024-02-01 2024-04-19 昆明理工大学 Lithium battery SOC and SOH estimation method based on optimized electrochemical model

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