Battery grouping arrangement equalization strategy optimization method based on qta-tlbo double-layer multi-objective algorithm

Abstract

The application provides a battery group arrangement equalization strategy optimization method based on a QTA-TLBO double-layer multi-objective algorithm, which comprises the following steps: establishing an upper-layer battery group optimization model comprising an upper-layer optimization objective function and an upper-layer optimization constraint condition, and obtaining an optimal single cell arrangement structure of the battery group; establishing a lower-layer battery group optimization energy scheduling model comprising a lower-layer optimization objective function and a lower-layer optimization constraint condition, and obtaining an optimal equalization strategy of the battery group; establishing a coupling optimization model between the upper-layer battery group single cell arrangement structure and the lower-layer battery group equalization strategy; and using the proposed quantum tunneling annealing-teaching and learning double-layer multi-objective optimization algorithm to optimize and solve the model, so as to obtain an optimal grouping scheme of the battery group and an optimal equalization strategy of each cycle. Through the application, the reasonable arrangement of the single cells of the battery group can be obtained, the long-term reliable operation of the battery group can be realized, and the energy scheduling strategy of the battery group between the single cells can be planned, so that the maximization of battery energy utilization is realized.

Description

Technical Field

[0001] This invention relates to the field of battery pack control technology, and in particular to an optimization method for battery pack arrangement balancing strategy based on the QTA-TLBO two-layer multi-objective algorithm. Background Technology

[0002] Lithium-ion batteries are widely used in power and energy storage systems for equipment in various fields. Because the energy and power output of a single lithium-ion battery is limited, individual cells need to be connected in series and parallel to form battery packs. However, due to manufacturing limitations, individual cells inevitably exhibit performance differences during the complex production process, and these differences further arise in capacity, internal resistance, and state of charge (SOC) during later production and use. This difference is known as cell inconsistency. The arrangement of inconsistent cells has a significant impact on the capacity and performance of the battery pack. Furthermore, with the charging and discharging process, cell degradation exacerbates this inconsistency, affecting the degradation pattern of the battery pack. In actual production, if the initial parameter data of individual cells can be used to optimize the battery pack arrangement and plan balancing strategies, the performance and lifespan of the battery pack can be effectively improved, bringing significant economic benefits to enterprises.

[0003] Optimizing battery pack assembly schemes and balancing strategies requires an accurate and efficient optimization algorithm. Quantum tunneling annealing (QTA), as a metaheuristic algorithm, outperforms many intelligent optimization algorithms due to its simple operating principle, fewer and easier-to-implement parameters, fast convergence speed, and resistance to getting trapped in local optima. Combining QTA with the swarm intelligence optimization algorithm—Teach-and-Learn (TLBO)—for searching integer variables in discrete space is highly suitable for solving mixed-integer multi-objective optimization control problems in the context of battery pack assembly. Furthermore, it expands the swarm optimization space, enhances local search capabilities, and avoids the problem of premature convergence.

[0004] In view of this, the present invention proposes a battery pack arrangement balancing strategy optimization method based on the QTA-TLBO two-layer multi-objective algorithm. The purpose is to use the initial parameter data of the battery cells, based on integer programming and heuristic algorithms, to achieve multi-objective and two-layer optimization model optimization, so as to realize the optimization of lithium battery pack scheme and balancing strategy planning. Summary of the Invention

[0005] The purpose of this invention is to provide a battery pack arrangement balancing strategy optimization method based on a QTA-TLBO two-layer multi-objective algorithm. The upper layer aims to maximize the battery pack capacity and minimize the total battery pack balancing flow after a specified number of charge-discharge cycles, rationally formulating the battery pack arrangement structure to achieve long-term reliable operation. The lower layer aims to minimize the energy loss of a single battery pack balancing cycle, planning energy balancing strategies for individual battery cells, improving SOC inconsistency, and leaving a certain margin to maximize energy utilization. To address the problem of premature convergence in the teaching and learning optimization algorithm, the quantum tunneling annealing concept is introduced to improve the algorithm's search speed, expand the local search space, and enhance its optimization ability at different search stages.

[0006] To address the above problems, this invention provides a battery pack arrangement balancing strategy optimization method based on the QTA-TLBO two-layer multi-objective algorithm, which includes the following steps:

[0007] S1. Establish an upper-level battery pack optimization model that includes the upper-level optimization objective function and upper-level optimization constraints. When establishing the upper-level optimization objective function, take the maximum capacity and minimum total balanced flow after the upper-level battery pack has a given target number of charge and discharge cycles as the optimization objective to obtain the individual cell arrangement structure of the battery pack.

[0008] S2. Establish a lower-level battery pack optimization scheduling model that includes the lower-level optimization objective function and the lower-level optimization constraints. When establishing the lower-level optimization objective function, the goal is to minimize the energy loss in the single cycle balancing process of the lower-level battery pack, and obtain the balancing strategy of the battery pack.

[0009] S3. Establish a coupled optimization model between the upper battery pack cell arrangement structure and the lower battery pack balancing strategy; input the initial parameter values ​​and degradation model parameters of each cell in the battery pack to improve the model;

[0010] S4. A quantum tunneling annealing strategy is introduced to enhance the search capability of the teaching and learning optimization algorithm. The quantum tunneling annealing-teaching and learning two-layer multi-objective algorithm is used to solve the battery pack two-layer optimization model and obtain the battery pack single cell arrangement structure and balancing strategy.

[0011] S41. Set the parameters of the quantum tunneling annealing-teaching and learning dual-layer multi-objective algorithm. Randomly generate an initial population P with a population size of N and a population dimension D = n. The maximum number of iterations is maxiter, and the current iteration number is it. The set of all points in the search space is called the class, and a point in the class is called a student X. i X i It is an integer sequence, which is the decision variable in the upper-level optimization model: the individual cell arrangement structure of the battery pack;

[0012] S42. Based on the optimization objective function value, give the individual scores, and calculate the scores of each student in the initial population P based on the input raw data. Select the individual with the highest score as the teacher.

[0013] Score=ω1C pack,TargetCycle -ω2Q sum

[0014] Wherein, ω1 and ω2 are two weights of the upper-level multi-objective function value, which are adjusted according to the actual effect of the algorithm;

[0015] S43. Conduct the "teaching" and "self-study" phases separately, and update the individual grades in the class.

[0016] S5. Determine whether the iteration termination condition is met. If not, update the solution set according to the update rules for teachers and individual students in step S4.

[0017] S6. If the iteration termination condition is met, output the Pareto solution set of the upper layer and the solution of the lower layer.

[0018] Furthermore, in step S1, the decision variable of the upper-level optimization model is X, which is an integer sequence representing the arrangement position of individual battery cells in the battery pack, and the solution space is the permutation and combination of the natural number sequence {1, 2, ..., n}; the upper-level battery pack optimization objective function is:

[0019] f up =[max(C pack,TargetCycle ),min(Q sum )]

[0020] Among them, f up The overall performance-cost objective function for the upper-layer battery pack is defined after specifying the target number of charge-discharge cycles, including maximizing the battery pack capacity and minimizing the total balanced flow rate of the battery pack after specifying the target number of charge-discharge cycles; C pack,TargetCycle Q sum These are the battery pack capacity and the total equalization flow rate after the specified target charge-discharge cycles, respectively, and we have:

[0021] C pack,TargetCycle =mean(C i,TargetCycle )

[0022]

[0023] In the formula, C i,TargetCycle =f TargetCycle (C i,0 (x) represents the capacity of the i-th battery cell after a specified target number of charge-discharge cycles, which is related to the initial capacity of the battery cell and its position in the battery pack; where f TargetCycle() represents the function for calculating the capacity of a single battery cell, C i,0 This represents the initial capacity value of the i-th cell; the capacity C of the battery pack. pack,TargetCycle Q is the average capacity of all individual cells in the battery pack after a specified target number of charge-discharge cycles. i,k The equilibrium flow rate generated by the i-th battery cell in the k-th cycle is derived from the lower-level optimization model; n and TargetCycle represent the total number of battery cells and the specified target total number of charge-discharge cycles, respectively; and the battery pack layout constraints are also satisfied.

[0024]

[0025] Where x1, x2, ..., x n These represent the order of 1, 2, ..., n individuals.

[0026] Furthermore, in step S2, the decision variable of the lower-level optimization model is an n×n matrix Y. n×n Y ij,k This represents the balanced flow rate transferred from the i-th battery cell to the j-th battery cell in the k-th iteration. The objective function for optimizing the lower-level battery pack is:

[0027] f low,k =min(Q) loss,k k = 1, 2, ... Targetcycle

[0028] After the upper-layer battery pack structure is given by the upper-layer battery pack optimization model, the lower-layer optimization model is optimized once in each cycle; where: H is the sum of the products of the balanced flow rate transferred from the i-th battery cell to the j-th battery cell in the k-th cycle and the energy loss rate, where H ij The energy loss rate matrix H n×n The elements in the model are given by the known structure and rules of the battery pack; at the same time, the lower battery pack optimization model satisfies constraints such as single-cell output constraints, single-cell self-constraints, energy conservation constraints, and state-of-charge balance constraints.

[0029]

[0030] Equation ① is the single-cell output constraint, ensuring that the flow rate from cell i to cell j in the battery pack is greater than or equal to zero; Equation ② is the single-cell self-constraint, ensuring that there is no energy exchange between the individual cells in the battery pack; Equation ③ is the energy conservation constraint, ensuring that the flow rate out of cell i in the k-th cycle is less than the sum of the remaining flow rate of cell i and the flow rate flowing into cell i after losses; Equation ④ is the state-of-charge (SOC) balance constraint, ensuring that the sum of the flow rate flowing into cell i after losses minus the sum of the flow rate out of cell i in the k-th cycle is the flow rate required for balancing cell i; Since estimation and measurement may introduce certain errors, the difference ΔSOC between cell i and the mean SOC of all cells in the battery pack is defined as follows at the k-th cycle. i,k Equilibrium should only be initiated when the ΔSOC is not less than 0.5%. i,k Represented as:

[0031] ΔSOC i,k =mean(SOC) k,init )-SOC i,k,init

[0032] Among them, SOC i,k,init This represents the unbalanced SOC value of the i-th battery cell in the k-th cycle; mean(SOC) k,init ) represents the average SOC of all individual cells in the battery pack at the k-th cycle; C i,k This represents the capacity of the i-th battery cell in the k-th cycle.

[0033] Furthermore, in step S3, the optimization logic of the coupled optimization model between the upper-layer battery pack cell arrangement structure and the lower-layer battery pack balancing strategy is as follows: In the upper-layer model, the optimized cell arrangement structure of the battery pack determines the capacity, internal resistance, temperature, and unbalanced SOC of all cells after each cycle, and these variables are passed to the lower-layer battery pack balancing model as constraints; In the lower-layer model, the flow generated in a single balancing process is returned to the upper-layer model as part of the upper-layer objective function; At the same time, the SOC of each cell after balancing is also calculated by the lower-layer model and returned to the upper-layer model as the initial SOC value for the previous cycle number; Finally, through iterative optimization under the specified target number of charge-discharge cycles, the optimal arrangement structure of the battery pack and the optimal balancing strategy for each cycle are obtained.

[0034] Furthermore, step S42 specifically includes the following steps:

[0035] S421, During the "Teaching" phase, the student update formula is:

[0036]

[0037] in, f represents a student individual who has learned from an individual teacher. teach () represents the teaching function, which determines the individual student's condition after instruction by the teacher, r. i Represents the learning rate, X teacher Indicates individual teachers, The learning rate r represents the number of students before they begin learning from the teacher. i The value is given by the algorithm's performance;

[0038] The teaching rule is as follows: The product of the learning rate, the difference between teacher performance and individual student performance, rounded up and denoted as an integer, is used to randomly select students. The int elements are updated to the elements of the teacher model, and the corresponding elements are found in... Replace the element at its current position;

[0039] S422, During the "Self-Study" phase, the student's update formula is:

[0040]

[0041]

[0042] The teacher update formula is:

[0043]

[0044] S423. A quantum tunneling annealing strategy is introduced to enhance the search capability of the teaching and learning optimization algorithm. When updating individual students and teachers, after each "teaching" and "self-study" phase, the simulated quantum tunneling effect allows for a certain probability of PR (Progression-Revised) annealing. i PR m The rule for replacing an old individual with a new individual whose performance is inferior to the current individual, and the probability of accepting the second solution, is as follows:

[0045]

[0046]

[0047]

[0048] Where it represents the current iteration number, Γ(it) represents the kinetic energy of the current individual, score is the potential energy of the individual at the current solution defined by the objective function, and w is the width of the tunneled potential barrier, which gradually widens with the number of iterations.

[0049] Preferably, the equilibrium flow value Q generated by the i-th battery cell in the k-th cycle is... i,k It is obtained through the following formula:

[0050]

[0051] Preferably, the capacity C of the i-th battery cell in the k-th cycle is... i,k It can be obtained through the following formula:

[0052] C i,k =f capacity (T i,k ,X,k)k=1,2,…Targetcycle

[0053] In the formula, f capacity () represents the single-cell capacity calculation function, given by the known battery capacity degradation law, where k represents the number of cycles, and T i,k This represents the temperature of the current i-th battery cell, and we have:

[0054] T i,k =f temperature (X,k)k=1,2,…Targetcycle

[0055] In the formula, f temperature () represents the function for calculating the temperature of a single cell in the battery pack, given by known thermal performance laws, where the single cell temperature T is... i,k It is related to the number of cycles k and the battery pack cell arrangement structure X.

[0056] Preferably, the State of Charge (SOC) of the i-th battery cell before equalization during the k-th cycle is... i,k,init Obtained through the following formula:

[0057] SOC i,k,init =f SOC (SOC i,k-1,ulti ,T i,k ,X,k)k=1,2,…Targetcycle

[0058] In the formula, f SOC () represents the single-cell state of charge (SOC) calculation function, given by the known charge-discharge characteristics of the battery pack. i,k-1,ulti This represents the state of charge (SOC) of the i-th battery cell after the previous cycle's equalization process. The SOC of the i-th battery cell after the k-th cycle's equalization process is:

[0059]

[0060] Preferably, the Pareto solution set in step S6 is the set of all non-dominated solutions in the feasible region, and the solution in the lower layer is the equilibrium strategy for each cycle within a specified number of iterations.

[0061] Compared with existing technologies, the technical effects of this solution are as follows:

[0062] (1) The present invention utilizes the initial parameter data of lithium battery cells and the given battery pack degradation model to construct a two-layer multi-objective optimization model of battery pack cell arrangement structure and balancing strategy. This model can support the planning of energy scheduling strategy among battery cells, maximize energy utilization in each single charge-discharge cycle, and plan the battery pack arrangement structure scheme, thereby effectively improving the long-term economic and practicality of the battery pack.

[0063] (2) This invention addresses the problem of mixed integer programming where parameters are passed between two-layer models and the upper-layer model is nonlinear. Based on the quantum tunneling annealing-teaching and learning two-layer multi-objective algorithm, this invention uses the idea of ​​heuristic algorithm to reduce the solution complexity of the two-layer model and improve the search speed of Pareto solution.

[0064] (3) In view of the problem that the teaching and learning optimization algorithm is prone to premature convergence, the present invention introduces the idea of ​​quantum tunneling annealing to improve it, expands the local search space, and enhances the optimization ability of the algorithm in different search stages. Attached Figure Description

[0065] Figure 1 This is a flowchart of the battery pack arrangement balancing strategy optimization method based on the quantum tunneling annealing-teaching and learning two-layer multi-objective algorithm of the present invention;

[0066] Figure 2 This is a multi-objective convergence curve of the battery pack arrangement balancing strategy optimization method according to an embodiment of the present invention.

[0067] Figure 3 In order to select the solution with the largest battery pack capacity after a specified number of charge-discharge cycles in the upper-level optimization model, the capacity value of each cell is selected at the 100th cycle.

[0068] Figure 4 In order to select the solution with the largest battery pack capacity after a specified number of charge-discharge cycles in the upper-level optimization model, the state of charge before equalization at the 100th cycle is selected.

[0069] Figure 5 In order to select the solution with the largest battery pack capacity after a specified number of charge-discharge cycles in the upper-level optimization model, a lower-level equalization optimization strategy based on integer programming is selected for the 100th cycle.

[0070] Figure 6 In order to select the solution with the largest battery pack capacity after a specified number of charge-discharge cycles in the upper-level optimization model, the state of charge after equalization at the 100th cycle is selected. Detailed Implementation

[0071] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in the present application can be combined with each other.

[0072] This embodiment provides an optimization method for battery pack arrangement balancing strategy based on the QTA-TLBO two-layer multi-objective algorithm, and verifies its effectiveness. The selected battery pack is an 8-cell battery pack, and the parameters of the 8 lithium battery cells that make up it are shown in Table 1 below:

[0073] Table 1

[0074] 1 2.46 70.2％ 2 3.18 41.2％ 3 3.38 54.9％ 4 2.65 35.8％ 5 2.44 41.2％ 6 3.15 64.1％ 7 2.80 57.5％ 8 2.65 41.8％

[0075] The flowchart of the battery pack arrangement balancing strategy optimization method based on the quantum tunneling annealing-teaching and learning two-layer multi-objective algorithm is as follows: Figure 1 As shown, the method includes:

[0076] S1. Establish an optimization model for the upper-level battery pack, including the upper-level optimization objective function and upper-level optimization constraints, with targets for capacity and equilibrium flow rate after 1000 cycles. When establishing the upper-level optimization objective function, the objective is to maximize the capacity and minimize the total equilibrium flow rate after a specified number of charge-discharge cycles for the upper-level battery pack. In this example, the specified number of charge-discharge cycles is TargetCycle = 1000. The decision variable of the upper-level optimization model is X, which is an integer sequence representing the arrangement position of individual cells in the battery pack. The solution space is the permutation and combination of the natural number sequence {1, 2, ..., 8}.

[0077] The upper-level optimization objective function is:

[0078] f up =[max(C pack,TargetCycle ),min(Q sum )]

[0079] Among them, f up The objective function for the overall performance cost of the upper battery pack structure after 1,000 charge-discharge cycles includes maximizing the battery pack capacity and minimizing the total balanced flow rate of the battery pack after 1,000 charge-discharge cycles; C pack,TargetCycle Q sum These are the battery pack capacity and the total balanced flow rate of the battery pack after the specified target number of charge-discharge cycles, respectively, and we have:

[0080] C pack,TargetCycle =mean(C i,TargetCycle )

[0081]

[0082] In the formula, C i,1000 =f 1000 (C i,0 (f, X) represents the capacity of the i-th battery cell after 1000 charge-discharge cycles, which is related to the initial capacity of the battery cell and its position in the battery pack; where f 1000 () represents the function for calculating the capacity of a single battery cell after 1000 cycles, C i,0 This represents the initial capacity value of the i-th cell; the capacity C of the battery pack. pack,1000 Q represents the average capacity of all individual cells in the battery pack after 1,000 charge-discharge cycles. i,k This represents the balanced flow rate generated by the i-th battery cell in the k-th cycle, derived from the lower-level optimization model, while also satisfying the battery pack layout constraints.

[0083]

[0084] Where x1, x2, ..., x8 represent the order of 1, 2, ..., 8 individual units, respectively.

[0085] S2. Establish a battery pack single-cycle equalization energy loss target optimization scheduling model, including the lower-level optimization objective function and lower-level optimization constraints. When establishing the lower-level optimization objective function, the goal is to minimize the energy loss during the lower-level battery pack single-cycle equalization process. The lower-level decision variable is Y. 8×8 , is an 8×8 matrix, Y ij,k This represents the balanced flow rate transmitted from the i-th battery cell to the j-th battery cell in the k-th iteration. The lower-level optimization objective function is:

[0086] f low,k =min(Q) loss,k k = 1, 2, ... 1000

[0087] After the upper-layer battery pack structure is given by the upper-layer battery pack optimization model, the lower-layer optimization model is optimized once in each cycle. Where: H is the sum of the products of the balanced flow rate transferred from the i-th battery cell to the j-th battery cell in the k-th cycle and the energy loss rate, where H ij The energy loss rate matrix H 8×8 The elements in the model are given by the known structure and rules of the battery pack; at the same time, the lower battery pack optimization model satisfies constraints such as single-cell output constraints, single-cell self-constraints, energy conservation constraints, and state-of-charge balance constraints.

[0088]

[0089] Equation ① is the single-cell output constraint, ensuring that the flow rate from cell i to cell j in the battery pack is greater than or equal to zero; Equation ② is the single-cell self-constraint, ensuring that there is no energy exchange between the individual cells in the battery pack; Equation ③ is the energy conservation constraint, ensuring that the flow rate out of cell i in the k-th cycle is less than the sum of the remaining flow rate of cell i and the flow rate flowing into cell i after losses; Equation ④ is the state-of-charge (SOC) balance constraint, ensuring that the sum of the flow rate flowing into cell i after losses minus the sum of the flow rate out of cell i in the k-th cycle is the flow rate required for balancing cell i; Since estimation and measurement may introduce certain errors, the difference ΔSOC between cell i and the mean SOC of all cells in the battery pack is defined as follows at the k-th cycle. i,k Equilibrium should only be initiated when the ΔSOC is not less than 0.5%; i,k Represented as:

[0090] ΔSOC i,k =mean(SOC) k,init )-SOC i,k,init

[0091] Among them, SOC i,k,init This represents the unbalanced SOC value of the i-th battery cell in the k-th cycle; mean(SOC) k,init ) represents the average SOC of all individual cells in the battery pack at the k-th cycle; C i,k This represents the capacity of the i-th battery cell in the k-th cycle.

[0092] The equilibrium flow value Q generated by the i-th unit in the k-th cycle i,k Obtained through the following formula:

[0093]

[0094] The capacity C of the i-th monomer in the k-th cycle i,k Obtained through the following formula:

[0095] C i,k =f capacity (T i,k (X,k) k=1,2,…1000

[0096] In the formula, f capacity () represents the single-cell capacity calculation function, given by the known battery capacity degradation law, where k represents the number of cycles, and T i,k This represents the temperature of the current i-th battery cell, and we have:

[0097] T i,k =f temperature (X,k) k=1,2,…1000

[0098] In the formula, f temperature () represents the function for calculating the temperature of a single cell in the battery pack, given by known thermal performance laws, where the single cell temperature T is... i,k It is related to the number of cycles k and the battery pack cell arrangement structure X.

[0099] The state of charge (SOC) of the i-th cell during the k-th cycle i,k,init Obtained through the following formula:

[0100] SOC i,k,init =f SOC (SOC i,k-1,ulti ,T i,k (X,k) k=1,2,…1000

[0101] In the formula, f SOC () represents the single-cell state of charge (SOC) calculation function, given by the known charge-discharge characteristics of the battery pack. i,k-1,ulti This represents the state of charge (SOC) of the i-th battery cell after the previous cycle's equalization process. The SOC of the i-th battery cell after the k-th cycle's equalization process is:

[0102]

[0103] Their values ​​are all determined by the lower-level optimization model.

[0104] S3. Establish a coupling model between the upper battery pack cell arrangement structure and the lower battery pack balancing strategy. Input the initial parameter values ​​of each cell and the degradation model parameters to improve the model, and give the algorithm iteration convergence accuracy value.

[0105] In the established two-layer model, the two-layer coupling model between the upper battery pack cell layout structure and the lower battery pack balancing strategy is based on the temperature change law, capacity degradation law and SOC change law determined by the physical information of the battery pack. As a whole, the current state of the cells (SOC and capacity after a single cycle) determined by the cell layout structure of the upper battery pack and the balancing path loss rate are uniformly passed to the lower battery pack balancing model as constraints. The lower balancing model is based on an integer programming model and returns the flow generated in a single balancing process as part of the upper objective function to the upper battery pack cell layout structure model. Finally, the two-layer multi-objective optimization algorithm gives the final layout structure of the battery pack and the balancing strategy for each cycle.

[0106] S4. The quantum tunneling annealing-teaching and learning two-layer multi-objective algorithm is used to solve the above two-layer optimization model. The specific steps are as follows:

[0107] S41. Set the parameters of the quantum tunneling annealing-teaching and learning dual-layer multi-objective algorithm. Randomly generate an initial population P with a population size of N and a population dimension D = n. The maximum number of iterations is maxiter, and the current iteration number is it. The set of all points in the search space is called the class, and a point in the class is called a student X. i X i It is an integer sequence, which is the decision variable in the upper-level optimization model: the individual cell arrangement structure of the battery pack.

[0108] S42. Based on the optimization objective function value, give the individual scores. Calculate the scores of each student in the initial population P based on the input raw data, and select the optimal individual as the teacher.

[0109] Score=ω1C pack,TargetCycle -ω2Q sum

[0110] Here, ω1 and ω2 are two weights of the upper-level multi-objective function value, which are adjusted according to the actual effect of the algorithm.

[0111] S421, During the "Teaching" phase, the student update formula is:

[0112]

[0113] in, f represents a student individual who has learned from an individual teacher. teach () represents the teaching function, which determines the individual student's condition after instruction by the teacher, r. i Represents the learning rate, X teacher Indicates individual teachers, The learning rate r represents the number of students before they begin learning from the teacher. i The value is given by the effect of the algorithm.

[0114] The teaching rule is as follows: The product of the learning rate, the difference between teacher performance and individual student performance, rounded up and denoted as an integer, is used to randomly select students. The int elements are updated to the elements of the teacher model, and the corresponding elements are found in... Replace the element at its current location.

[0115] S422, During the "Self-Study" phase, the student's update formula is:

[0116]

[0117]

[0118] The teacher update formula is:

[0119]

[0120] S423. A quantum tunneling annealing strategy is introduced to enhance the search capability of the teaching and learning optimization algorithm. When updating individual students and teachers, after each "teaching" and "self-study" phase, the simulated quantum tunneling effect allows for a certain probability of PR (Progression-Revised) annealing. i PR m The rule for calculating the probability of accepting a second-best solution is as follows: When replacing an old individual with a newer individual whose performance is inferior to the current individual, the probability of accepting the second-best solution is:

[0121]

[0122]

[0123]

[0124] Where it represents the current iteration number, Γ(it) is the kinetic energy of the current individual, score is the potential energy of the individual at the current solution as defined by the objective function, and w is the width of the tunneled potential barrier, which gradually widens with the number of iterations.

[0125] S43. Conduct the "teaching" and "self-study" phases separately, and update the individual grades in the class.

[0126] S5. Determine whether the iteration termination condition is met. If not, update the solution set according to the update rules for individual teachers and students in S4.

[0127] S6. If the iteration termination condition is met, and the iteration count reaches 20, output the Pareto solution set of the upper layer and the solution of the lower layer. The Pareto solution set is the set of all non-dominated solutions in the feasible region, and the solution of the lower layer is the equilibrium strategy for each of the specified number of iterations.

[0128] The upper-level multi-objective convergence curve of the battery pack optimization and equalization strategy method based on the quantum tunneling annealing-teaching and learning two-layer multi-objective algorithm is shown in the figure below. Figure 2 As shown. In the upper-level model, the solution with the largest battery pack capacity after 1000 cycles is selected. Due to space limitations, the capacity values ​​of each cell at the 100th cycle are shown below. Figure 3 As shown, the charge state before equilibrium is as follows Figure 4 As shown, the lower-level equilibrium optimization strategy based on integer programming is as follows: Figure 5 As shown, the equilibrium state of charge is as follows Figure 6 As shown.

[0129] This invention can utilize the initial parameter data of lithium battery cells and a given battery pack degradation model. Based on the quantum tunneling annealing-teaching and learning dual-layer multi-objective algorithm, it can plan the battery pack layout structure to achieve the long-term economic and practicality of the battery pack. On the other hand, it can support the planning of energy scheduling strategies among battery cells to maximize energy utilization in each charge-discharge cycle.

[0130] Finally, it should be noted that the above embodiments are for illustration only and not for limiting the technical solutions of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention without departing from the spirit and scope of the present invention. Any modifications or partial substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A battery pack arrangement balancing strategy optimization method based on QTA-TLBO two-layer multi-objective algorithm, characterized in that, It includes the following steps: S1. Establish an upper-level battery pack optimization model that includes the upper-level optimization objective function and upper-level optimization constraints. When establishing the upper-level optimization objective function, take the maximum capacity and the minimum total balanced flow after the upper-level battery pack has a given target number of charge and discharge cycles as the optimization objective, and obtain the individual cell arrangement structure of the battery pack. S2. Establish a lower-level battery pack optimization scheduling model that includes the lower-level optimization objective function and the lower-level optimization constraints. When establishing the lower-level optimization objective function, the goal is to minimize the energy loss in the single cycle balancing process of the lower-level battery pack, and obtain the balancing strategy of the battery pack. S3. Establish a coupled optimization model between the upper battery pack's individual cell arrangement structure and the lower battery pack's balancing strategy; Input the initial parameter values ​​and degradation model parameters of each cell in the battery pack to improve the model; S4. A quantum tunneling annealing strategy is introduced to enhance the search capability of the teaching and learning optimization algorithm. The quantum tunneling annealing-teaching and learning two-layer multi-objective algorithm is used to solve the battery pack two-layer optimization model and obtain the battery pack single cell arrangement structure and balancing strategy. S41. Set the parameters of the quantum tunneling annealing-teaching and learning two-layer multi-objective algorithm. Randomly generate an initial population P with a population size of N and a population dimension D = n. The maximum number of iterations is maxiter, and the current iteration number is it. The set of all points in the search space is called the class, and a point in the class is called a student. , It is an integer sequence, which is the decision variable in the upper-level optimization model: the individual cell arrangement structure of the battery pack; S42. Based on the optimization objective function value, give the individual scores, and calculate the scores of each student in the initial population P based on the input raw data. Select the individual with the highest score as the teacher. ； in, , These are two weights for the upper-level multi-objective function value, which are adjusted based on the actual performance of the algorithm. , These are the battery pack capacity and the total equalization flow rate after the specified target number of charge-discharge cycles, respectively. S43. Conduct "teaching" and "self-study" phases separately, and update the individual grades in the class; S5. Determine whether the iteration termination condition is met. If not, update the solution set according to the update rules for teachers and individual students in step S4. S6. If the iteration termination condition is met, output the Pareto solution set of the upper layer and the solution of the lower layer.

2. The battery pack arrangement balancing strategy optimization method based on the QTA-TLBO two-layer multi-objective algorithm according to claim 1, characterized in that, In step S1, the decision variable of the upper-level optimization model is X, which is an integer sequence representing the arrangement position of individual battery cells in the battery pack, and the solution space is a sequence of natural numbers. The permutations and combinations; the optimization objective function for the upper battery pack is: ； in, The overall performance cost objective function after specifying the target number of charge-discharge cycles for the upper battery pack includes maximizing the battery pack capacity and minimizing the total balanced flow rate of the battery pack after specifying the target number of charge-discharge cycles. , for: ； ； In the formula This represents the capacity of the i-th battery cell after a specified target number of charge-discharge cycles, which is related to the initial capacity of the battery cell and its position within the battery pack; where, () represents the function for calculating the capacity of a single battery cell. This represents the initial capacity value of the i-th cell; the capacity of the battery pack. The average capacity of all individual battery cells after a specified target number of charge-discharge cycles. This represents the balanced flow generated by the i-th battery cell in the k-th cycle, which is derived from the lower-level optimization model. TargetCycle represents the total number of individual battery cells in the battery pack and the specified target total number of charge-discharge cycles, respectively; the battery pack layout constraints must also be met. ； Where x1, x2, ..., x n These represent the order of 1, 2, ..., n individuals.

3. The battery pack arrangement balancing strategy optimization method based on the QTA-TLBO two-layer multi-objective algorithm according to claim 2, characterized in that, In step S2, the decision variables of the lower-level optimization model are: Matrix , This represents the balanced flow rate transferred from the i-th battery cell to the j-th battery cell in the k-th iteration. The objective function for optimizing the lower-level battery pack is: ； After the upper-layer battery pack structure is given by the upper-layer battery pack optimization model, the lower-layer optimization model is optimized once in each cycle; where: , Let be the sum of the products of the balanced flow rate transferred from the i-th battery cell to the j-th battery cell in the k-th cycle and the energy loss rate, where Energy loss rate matrix The elements in The known structure and characteristics of the battery pack are given; at the same time, the optimization model of the lower battery pack satisfies the constraints of single-cell output, single-cell self-constraint, energy conservation constraint, and state of charge balance constraint. ； Equation ① is the single-cell output constraint, ensuring that the flow rate from cell i to cell j in the battery pack is greater than or equal to zero; Equation ② is the single-cell self-constraint, ensuring that there is no energy exchange between the individual cells in the battery pack; Equation ③ is the energy conservation constraint, ensuring that the flow rate out of cell i in the k-th cycle is less than the sum of the remaining flow rate of cell i and the flow rate flowing into cell i after losses; Equation ④ is the state-of-charge (SOC) balance constraint, ensuring that the sum of the flow rate flowing into cell i after losses minus the sum of the flow rate out of cell i in the k-th cycle is the flow rate required for balancing cell i; Since estimation and measurement may introduce certain errors, the difference ΔSOC between cell i and the mean SOC of all cells in the battery pack is defined as follows at the k-th cycle. i,k Equilibrium should only be initiated when the SOC is not less than 0.5%. i,k Represented as: ； in, This represents the unbalanced SOC value of the i-th battery cell during the k-th cycle. This represents the average SOC of all individual cells in the battery pack at the k-th cycle. This represents the capacity of the i-th battery cell in the k-th cycle.

4. The battery pack arrangement balancing strategy optimization method based on the QTA-TLBO two-layer multi-objective algorithm according to claim 1, characterized in that, In step S3, the optimization logic of the coupled optimization model between the upper battery pack cell arrangement structure and the lower battery pack balancing strategy is as follows: In the upper model, the optimized cell arrangement structure of the battery pack determines the capacity, internal resistance, temperature, and unbalanced SOC of all cells after each cycle, and these variables are passed to the lower battery pack balancing model as constraints; In the lower model, the flow generated in a single balancing process is returned to the upper model as part of the upper objective function; At the same time, the SOC of each cell after balancing is also calculated by the lower model and returned to the upper model as the initial SOC value for the previous cycle. Finally, through iterative optimization under the specified target number of charge-discharge cycles, the optimal battery pack layout and the optimal balancing strategy for each cycle were obtained.

5. The battery pack arrangement balancing strategy optimization method based on the QTA-TLBO two-layer multi-objective algorithm according to claim 1, characterized in that, Step S42 specifically includes the following steps: S421, During the "Teaching" phase, the student update formula is: ； in, This refers to the individual student who has learned from an individual teacher. ( ) represents the teaching function, which determines the individual student's progress after being taught by the teacher. Indicates learning rate, Indicates individual teachers, This refers to the individual student before learning from the individual teacher; the learning rate. The value is given by the algorithm's performance; The teaching rule is as follows: The product of the learning rate, the difference between teacher performance and individual student performance, rounded up and denoted as an integer, is used to randomly select students. The int elements are updated to the elements of the teacher model, and the corresponding elements are found in... Replace the element at its current position; S422, During the "self-study" phase, the student's update formula is: ； ； The teacher update formula is: ； S423. A quantum tunneling annealing strategy is introduced to enhance the search capability of the teaching and learning optimization algorithm. When updating individual students and teachers, after each "teaching" and "self-study" phase, the simulated quantum tunneling effect allows for a certain probability. , The rule for replacing an old individual with a new individual whose performance is inferior to the current individual, and the probability of accepting the second solution, is as follows: ； in, Represents the current iteration number. The value represents the kinetic energy of the current individual, the score is the potential energy of the individual at the current solution as defined by the objective function, and w is the width of the tunneled potential barrier, which gradually widens with the number of iterations.

6. The battery pack arrangement balancing strategy optimization method based on the QTA-TLBO two-layer multi-objective algorithm according to claim 3, characterized in that, The equilibrium flow value generated by the i-th battery cell in the k-th cycle It is obtained through the following formula: 。 7. The battery pack arrangement balancing strategy optimization method based on the QTA-TLBO two-layer multi-objective algorithm according to claim 3, characterized in that, The capacity of the i-th battery cell in the k-th cycle It is obtained through the following formula: ； In the formula, ( ) represents the single-cell capacity calculation function, given by the known battery capacity degradation law, where k represents the number of cycles. This represents the temperature of the current i-th battery cell, and we have: ； In the formula, ( ) represents the function for calculating the temperature of individual cells in the battery pack, given by known thermal performance laws, and the individual cell temperature. It is related to the number of cycles k and the battery pack cell arrangement structure X.

8. The battery pack arrangement balancing strategy optimization method based on the QTA-TLBO two-layer multi-objective algorithm according to claim 3, characterized in that, The state of charge of the i-th battery pack before cell balancing during the k-th cycle. It is obtained through the following formula: ； In the formula, ( ) represents the single-cell state of charge (SOC) calculation function, given by the known charge-discharge characteristics of the battery pack. This represents the state of charge (SOC) of the i-th battery cell after the previous cycle after equalization; where the SOC of the i-th battery cell after the k-th cycle after equalization is: 。 9. The battery pack arrangement balancing strategy optimization method based on the QTA-TLBO two-layer multi-objective algorithm according to claim 1, characterized in that, The Pareto solution set mentioned in step S6 is the set of all non-dominated solutions in the feasible region, and the solution in the lower layer is the equilibrium strategy for each cycle within a specified number of iterations.

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