A battery architecture optimization method based on a many-to-many allocation model

Abstract

The application discloses a battery architecture optimization method based on a many-to-many distribution model, relates to the technical field of optimization scheduling, and comprises the following steps: constructing a many-to-many distribution model; constructing a battery attribute evaluation matrix Q according to the many-to-many distribution model; establishing an optimization objective function and constraint conditions related to the battery attribute evaluation matrix Q and an optimal architecture scheme matrix T, with the overall demand satisfaction degree sigma as the maximum optimization objective function; solving the optimization objective function to obtain the optimal architecture scheme matrix T; and obtaining the optimized battery architecture according to the optimal architecture scheme matrix T. The application improves the flexibility of the battery architecture scheme distribution scheme, comprehensively evaluates the demand and performance demand of components, and is simple in process.

Description

Technical Field

[0001] This invention relates to the field of optimization scheduling technology, and in particular to a battery architecture optimization method based on a many-to-many allocation model. Background Technology

[0002] When funds are limited, demand-supply systems can implement intelligent architectures for distributed power sources. By leveraging coordinated optimization models, they can fully utilize the regulatory role of controllable resources, rationally control total energy consumption, improve the balance between power supply and demand, and promote energy conservation and emission reduction. This is of great significance to both power-consuming enterprises and grid companies.

[0003] Traditional battery architecture coordination optimization models are mostly nonlinear models that employ a series of intelligent algorithms. They are prone to convergence problems and getting trapped in local optima. When dealing with large amounts of data and complex constraints, it is difficult to find a solution in a short time. Therefore, it is necessary to establish a coordinated optimization linear model for battery architecture coordination optimization.

[0004] The determination of the battery architecture scheme is inevitably related to the evaluation of battery performance. The effectiveness of battery performance evaluation depends on the scientific nature of the indicator system, requiring the establishment of an independent and comprehensive evaluation indicator system. Based on the evaluation indicator system, a reasonable evaluation model for battery allocation in the architecture system should be established, and then appropriate evaluation methods should be adopted to find a reasonable allocation decision. In addition, when data changes, it is also necessary to flexibly provide the optimal solution after the change in a short period of time.

[0005] Therefore, it is necessary to evaluate based on battery attribute data, propose effective battery architecture methods, achieve scientific performance analysis, provide accurate and high-performance decision-making solutions, and improve the service quality of intelligent battery architecture.

[0006] Existing publicly available battery architecture solutions are inflexible in their allocation, lack comprehensive assessment of component and performance requirements, are complex in their processes, and require high-end hardware. Summary of the Invention

[0007] To address the issues of inflexible battery architecture allocation and incomplete assessment of component and performance requirements, this invention proposes a battery architecture optimization method based on a many-to-many allocation model. This method improves the flexibility of battery architecture allocation and provides a more comprehensive assessment of component and performance requirements.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0009] A battery architecture optimization method based on a many-to-many allocation model includes:

[0010] Construct a many-to-many allocation model.

[0011] Based on the many-to-many allocation model, construct the battery attribute evaluation matrix Q.

[0012] Establish an optimization objective function and its constraints that are related to the battery attribute evaluation matrix Q and the optimal architecture scheme matrix T, with the overall demand satisfaction σ as the maximum.

[0013] Solving the objective function yields the optimal architecture matrix T.

[0014] The optimized battery architecture is obtained based on the optimal architecture scheme matrix T.

[0015] The working principle of this invention is as follows:

[0016] A many-to-many allocation model is constructed, and a battery attribute evaluation matrix Q is constructed. The objective function is determined based on the constructed battery attribute evaluation matrix Q. The optimal architecture scheme matrix T is obtained by solving the optimization objective function. The optimized battery architecture is obtained based on the optimal architecture scheme matrix T.

[0017] Preferably, the method for constructing the many-to-many allocation model is as follows:

[0018] The task is to assign n known battery architecture requirement parts, the assignment objects are m batteries with known attributes, and the assignment target is the battery pack with the highest overall architecture requirement satisfaction. Each requirement part is powered by several different batteries, and each battery powers several different parts.

[0019] Furthermore, a multi-objective quantity constraint vector is used to illustrate the relationship between demand components and batteries; where the multi-objective quantity constraint vector L represents the quantity constraint on demand components, and each demand component, based on its importance, must have L[j] batteries to power it; the multi-objective quantity constraint vector L... a [i] represents a limit on the number of batteries; each battery can supply a maximum of L. a [i] parts are powered.

[0020] Furthermore, a battery attribute evaluation matrix Q is constructed using a fuzzy comprehensive hierarchical evaluation method, as follows:

[0021] Determine the set of battery attribute evaluation factors {u1, u2, ..., u a}, determine the known battery attribute matrix P and the demand attribute matrix X of each part, determine the membership function R(x) of each factor, and determine the weight coefficient vector W.

[0022] The battery attribute evaluation factor set is selected based on the battery architecture requirements and expert opinions, using a indicators for battery evaluation; the battery attribute matrix P is an n×a matrix, and the requirement attribute matrix X for each part is an m×a matrix; expert opinions are solicited through expert surveys and then fed back to the experts until a consensus is reached, finally obtaining the weight coefficient vector W={w1,w2,…,w a}

[0023] Furthermore, the battery attribute evaluation factor set {u1,u2,…,u b The evaluation factors in the data are divided into three categories: large-scale, medium-scale, and small-scale variables; and corresponding membership functions R(x) are constructed for different factor indicators.

[0024] Furthermore, the method for constructing the membership function R(x) is as follows:

[0025] When the battery value exceeds the requirement of the part, the membership function outputs 1, indicating that the requirement is met.

[0026] If the battery value is lower than the requirement, a penalty score will be imposed for the difference; the larger the difference, the larger the penalty score, and the output will be equal to 0 when the difference is lower than the threshold; set the threshold t to construct the membership function.

[0027] Furthermore, the membership function expressions constructed for different factor indicators are as follows:

[0028]

[0029]

[0030]

[0031] Where R1(x) is the membership function for larger variables; R2(x) is the membership function for intermediate variables; R3(x) is the membership function for smaller variables; x is the value of the requirement attribute matrix X for each part; and p is the value of the battery attribute matrix P.

[0032] Furthermore, the method for constructing the battery attribute evaluation matrix Q is as follows:

[0033] By using membership functions, the scores of all factors in the combination of each part and battery are calculated. Then, the weight vector W obtained by expert evaluation is weighted and summed to obtain the corresponding m×n matrix, and the battery attribute evaluation matrix Q is constructed.

[0034] Furthermore, the method for obtaining the optimal architecture scheme matrix T is as follows:

[0035] The battery attribute evaluation matrix Q is optimized by combining L and L2. aSubstituting these values, we can calculate the optimal architecture matrix T, which maximizes the overall requirement satisfaction.

[0036] The optimization package mentioned is the CPLEX toolkit; the solve method of the CPLEX toolkit is used to connect the battery attribute evaluation matrix Q with L and L a Substitute; T is defined as an m×n matrix.

[0037] Furthermore, the expression for the objective function is as follows:

[0038]

[0039] In the formula, Q[i,j] represents the rating of part i for battery j; T[i,j] represents whether part i is allocated to battery j for power supply; σ is the overall demand satisfaction; the larger σ is, the higher the degree of demand satisfaction.

[0040] Furthermore, the constraints of the objective function are expressed as follows:

[0041] T[i,j]∈{0,1}(0≤i <m,0≤j<n) (1)

[0042]

[0043]

[0044] When optimizing the objective function, equations (1), (2), and (3) must be satisfied simultaneously.

[0045] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it performs the steps of the method described above.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] 1. By optimizing the objective function obtained from the many-to-many allocation model, the model construction can be flexibly changed according to changes in battery properties and part requirements, making the allocation scheme highly flexible.

[0048] 2. Construct a battery attribute evaluation matrix Q to evaluate several independent and fuzzy factors, making the battery attribute evaluation comprehensive and intuitive, and ensuring strong adaptability of the battery architecture.

[0049] 3. The steps are simple, the solution process is short, the amount of calculation is small, and the results can be flexibly changed as the battery attribute evaluation matrix Q changes, thus reducing costs. Attached Figure Description

[0050] Figure 1This is a flowchart of a battery architecture optimization method based on a many-to-many allocation model according to the present invention. Detailed Implementation

[0051] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0052] Example 1

[0053] In this embodiment, a battery architecture optimization method based on a many-to-many allocation model includes:

[0054] Construct a many-to-many allocation model.

[0055] Based on the many-to-many allocation model, construct the battery attribute evaluation matrix Q.

[0056] Establish an optimization objective function and its constraints that are related to the battery attribute evaluation matrix Q and the optimal architecture scheme matrix T, with the overall demand satisfaction σ as the maximum.

[0057] Solving the objective function yields the optimal architecture matrix T.

[0058] The optimized battery architecture is obtained based on the optimal architecture scheme matrix T.

[0059] Example 2

[0060] In this embodiment, a battery architecture optimization method based on a many-to-many allocation model includes:

[0061] Construct a many-to-many allocation model.

[0062] Based on the many-to-many allocation model, construct the battery attribute evaluation matrix Q.

[0063] Establish an optimization objective function and its constraints that are related to the battery attribute evaluation matrix Q and the optimal architecture scheme matrix T, with the overall demand satisfaction σ as the maximum.

[0064] Solving the objective function yields the optimal architecture matrix T.

[0065] The optimized battery architecture is obtained based on the optimal architecture scheme matrix T.

[0066] In this embodiment, the method for constructing the many-to-many allocation model is as follows:

[0067] The task is to assign n known battery architecture requirement parts, the assignment objects are m batteries with known attributes, and the assignment target is the battery pack with the highest architecture requirement satisfaction. Each requirement part is powered by several different batteries, and each battery powers several different parts.

[0068] More specifically, the relationship between demand components and batteries is illustrated using a multi-objective quantity constraint vector; where the multi-objective quantity constraint vector L represents the quantity constraint on demand components, and each demand component, based on its importance, must have L[j] batteries to power it; the multi-objective quantity constraint vector L... a [i] represents a limit on the number of batteries; each battery can supply a maximum of L. a [i] parts are powered.

[0069] More specifically, the battery attribute evaluation matrix Q is constructed using the fuzzy comprehensive hierarchical evaluation method, which is as follows:

[0070] Determine the set of battery attribute evaluation factors {u1, u2, ..., u a}, determine the known battery attribute matrix P and the demand attribute matrix X of each part, determine the membership function R(x) of each factor, and determine the weight coefficient vector W.

[0071] The battery attribute evaluation factor set is selected based on the battery architecture requirements and expert opinions, using a indicators for battery evaluation; the battery attribute matrix P is an n×a matrix, and the requirement attribute matrix X for each part is an m×a matrix; expert opinions are solicited through expert surveys and then fed back to the experts until a consensus is reached, finally obtaining the weight coefficient vector W={w1,w2,…,w a}

[0072] More specifically, the battery attribute evaluation factor set {u1,u2,…,u b The evaluation factors in the data are divided into three categories: large-scale, medium-scale, and small-scale variables; a membership function R(x) is constructed for each of the different factor indicators.

[0073] The method for constructing the membership function R(x) is as follows:

[0074] When the battery value exceeds the requirement of the part, the membership function outputs 1, indicating that the requirement is met.

[0075] If the battery value is lower than the requirement, a penalty score will be imposed for the difference; the larger the difference, the larger the penalty score, and the output will be equal to 0 when the difference is lower than the threshold; set the threshold t to construct the membership function.

[0076] More specifically, the membership function expressions constructed for different factor indicators are as follows:

[0077]

[0078]

[0079]

[0080] Where R1(x) is the membership function for larger variables; R2(x) is the membership function for intermediate variables; R3(x) is the membership function for smaller variables; x is the value of the requirement attribute matrix X for each part; and p is the value of the battery attribute matrix P.

[0081] More specifically, the method for constructing the battery attribute evaluation matrix Q is as follows:

[0082] By using membership functions, the scores of all factors in the combination of each part and battery are calculated. Then, the weight vector W obtained by expert evaluation is weighted and summed to obtain the corresponding m×n matrix, and the battery attribute evaluation matrix Q is constructed.

[0083] More specifically, the method for obtaining the optimal architecture scheme matrix T is as follows:

[0084] The battery attribute evaluation matrix Q is optimized by combining L and L2. a Substituting these values, we can calculate the optimal architecture matrix T, which maximizes the overall requirement satisfaction.

[0085] The optimization package mentioned is the CPLEX toolkit; the solve method of the CPLEX toolkit is used to connect the battery attribute evaluation matrix Q with L and L a Substitute; T is defined as an m×n matrix.

[0086] More specifically, the expression for the objective function is as follows:

[0087]

[0088] In the formula, Q[i,j] represents the rating of part i for battery j; T[i,j] represents whether part i is allocated to battery j for power supply; σ is the overall demand satisfaction; the larger σ is, the higher the degree of demand satisfaction.

[0089] More specifically, the constraints of the objective function are expressed as follows:

[0090] T[i,j]∈{0,1}(0≤i <m,0≤j<n) (1)

[0091]

[0092]

[0093] When optimizing the objective function, equations (1), (2), and (3) must be satisfied simultaneously.

[0094] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it performs the steps of the method described above.

[0095] Example 3

[0096] In this embodiment, based on the battery architecture optimization method based on a many-to-many allocation model described in Embodiment 2, a manufacturer needs to produce a large electromechanical equipment. The allocation task is determined to be 8 known battery architecture requirement parts, the allocation object is 20 batteries with known attributes, and the allocation target is the battery pack whose architecture best meets the requirements.

[0097] Considering the physical volume and importance of the required components, a multi-objective quantity constraint vector L[j] (0≤j<8) is determined. Considering the performance differences of different batteries, a multi-objective quantity constraint vector L is also determined. a [i](0≤i<20).

[0098] First, the set of battery attribute evaluation factors was determined. Based on expert opinions, six evaluation factors {u1, u2, ..., u6} were selected, and according to the requirements of the membership function, these factors need to be divided into three types:

[0099] Price u1: The market price of this type of battery. When it exceeds the manufacturer's budget price, the score will be reduced. It is a relatively small variable.

[0100] Power u2: The amount of electricity supplied by this type of battery per unit time. If it is lower than the requirements of the part, the score will be reduced. It is a relatively large variable.

[0101] Capacity u3: This is an indicator of how much electrical energy this type of battery stores; it is a relatively large variable.

[0102] Average lifespan u4: The average lifespan of this type of battery determines its practicality and is a relatively large variable.

[0103] Battery volume u5: The space occupied by the battery. If it is too large or too small, the volume will not meet the production requirements. It is an intermediate variable.

[0104] Failure rate u6: The failure rate of this type of battery within five years, which is a relatively small variable.

[0105] More specifically, by constructing the known battery attribute matrix P and the required attribute matrix X for each part from the known data, and then substituting each attribute into its respective membership function, the output value can be obtained to construct an m×n battery attribute evaluation matrix Q.

[0106] The expressions for the three membership functions are as follows:

[0107]

[0108]

[0109]

[0110] The threshold t can be specifically selected according to the characteristics and requirements of each factor, so that the function satisfies the condition that the score is 0 when the difference is lower than the threshold t.

[0111] The following battery attribute evaluation matrix Q is constructed:

[0112]

[0113] More specifically, the objective function is optimized, and its expression is as follows:

[0114]

[0115] The constraint expressions are as follows:

[0116] T[i,j]∈{0,1}(0≤i <m,0≤j<n) (1)

[0117]

[0118]

[0119] Combining L, L a Using the solve method of the CPLEX toolkit, the optimal architecture matrix T can be obtained as follows:

[0120]

[0121] Based on the optimal architecture scheme matrix T, detailed battery architecture decision schemes can be obtained, such as: Part 1 should use batteries 4, 10, and 12 to complete the architecture, and battery 1 should be used for the architecture of Parts 5 and 8. Decision-makers can quickly realize battery architecture planning using the intelligent battery architecture construction method based on a many-to-many allocation model according to the present invention.

[0122] The manufacturer added three more components to its construction plan, resulting in dynamic evolution of demand. The intelligent battery architecture construction method based on a many-to-many allocation model described in this invention can quickly respond to changes in demand. By expanding the demand attribute matrix X of each component by three columns, the changed battery attribute evaluation matrix Q can be obtained in a short time. The optimal architecture scheme matrix T can then be replanned, enabling the manufacturer to arrive at the most reasonable battery architecture scheme.

[0123] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A battery architecture optimization method based on a many-to-many allocation model, characterized in that, include: Construct a many-to-many assignment model by limiting the number of multi-objective vectors. L Explain the relationship between the required components and the battery; The multi-objective number constraint vector L To limit the number of required parts, each required part must be allocated according to its importance. L [ j One battery supplies power to the parts that require it; L a [ i To limit the number of batteries, each battery can only supply a maximum of [number missing] [units missing]. L a [ i Power supply to 10 parts; among them, For location, For batteries; Based on the many-to-many allocation model, determine the set of battery attribute evaluation factors { The evaluation factors are categorized into three types, and corresponding membership functions are constructed for each factor indicator. R ( x ); Determine the known battery property matrix P Attribute matrix of requirements for each part X Determine the membership function for each factor. R ( x Determine the weight coefficient vector W By using membership functions, scores for all factors in each combination of part and battery are calculated, and then weight vectors are obtained through expert evaluation. W Perform a weighted summation to obtain the corresponding m×n A matrix for evaluating battery properties is constructed using the fuzzy comprehensive hierarchical evaluation method. Q ; Solving the objective function yields the matrix of the optimal architecture scheme. T ; Based on the optimal architecture scheme matrix T An optimized battery architecture has been achieved.

2. The battery architecture optimization method based on a many-to-many allocation model according to claim 1, characterized in that, The method for constructing the many-to-many allocation model is as follows: Assign tasks as n A known battery architecture requirement, assigned to the following objects. m Given a battery with known attributes, the allocation target is the battery pack that best satisfies the overall requirements of the architecture. Each requirement area is powered by several different batteries, and each battery powers several different areas.

3. The battery architecture optimization method based on a many-to-many allocation model according to claim 2, characterized in that, The fuzzy comprehensive hierarchical evaluation method is as follows: The aforementioned set of battery attribute evaluation factors was selected based on expert opinions and the requirements of the battery architecture. a A set of indicators for battery evaluation; battery attribute matrix P for n×a The matrix, the requirement attribute matrix for each part X for m×a Matrix; After soliciting expert opinions through expert surveys and providing feedback to the experts until a consensus is reached, the final weighted coefficient vector is obtained. W= { } 4. The battery architecture optimization method based on a many-to-many allocation model according to claim 3, characterized in that, Construct corresponding membership functions for different factor indicators. R ( x The method is as follows: When the battery value exceeds the requirement of the part, the membership function outputs 1, indicating that the requirement is met; If the battery value is lower than the requirement, a penalty score will be imposed for the difference; the larger the difference, the larger the penalty score, and the output will be equal to 0 when the difference is lower than the threshold; set the threshold t to construct the membership function.

5. The battery architecture optimization method based on a many-to-many allocation model according to claim 4, characterized in that, Obtain the optimal architecture scheme matrix T The method is as follows: The battery attribute evaluation matrix is ​​optimized using the package. Q and L [ j ]、 L a [ i Substituting the values, we can calculate the optimal architecture matrix that maximizes overall requirement satisfaction. T ; The optimization package mentioned is the CPLEX toolkit; the battery attribute evaluation matrix is ​​obtained through the solve method of the CPLEX toolkit. Q and L [ j ]、 L a [ i Substitute; T Defined as m×n The matrix.

6. The battery architecture optimization method based on a many-to-many allocation model according to claim 5, characterized in that, The objective function is expressed as follows: max σ = In the formula, Q [ i, j [Indicates location] i For batteries j The rating value; T [ i, j [Indicates location] i Whether it is allocated to the battery j To provide power; σ To satisfy the overall demand; σ The larger the value, the higher the degree to which the demand is met; The optimization of the objective function needs to satisfy constraints, which are expressed as follows: {0, 1} （1） （2） （3） When optimizing the objective function, it is necessary to satisfy equations (1), (2), and (3) simultaneously.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6.

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