Large-scale battery active equalization system optimization design method

Abstract

The invention relates to an optimization design method for a large-scale battery active equalization system. According to the optimization design method, optimality exploration of different equalization topology circuits is realized based on equalization topology connectivity evaluation. The method mainly comprises the following steps: firstly, establishing general mathematical models of different equalization systems to describe energy flow characteristics of a battery pack equalization process; secondly, designing a connectivity evaluation method considering different equalization topologies, and constructing a control matrix reconstruction rule representing equalization performance; and finally, comprehensively constructing a multi-objective optimization problem based on an optimization algorithm and considering the complexity, the cost and the connectivity of the equalization topology circuit, and realizing the optimal design of the large-scale battery equalization system. The invention designs an equalization system optimization method by taking an adjacent equalization topology as an example, and the method comprises the processes of equalization system state modeling, topology connectivity calculation and equalization topology optimization algorithm. According to the method, the optimal design efficiency of the equalization topology is improved, the working efficiency of a large-scale equalization system is further improved, and the development of a large-scale energy storage system is facilitated.

Description

Technical Field

[0001] This invention relates to the field of battery energy storage technology and management systems, and is mainly applied to the state balancing control of multi-series battery packs to solve inconsistencies in voltage, state of charge, and / or state of energy. In particular, it relates to a fast and efficient balancing application for large-scale battery systems, specifically an optimization design method for a large-scale active battery balancing system. Background Technology

[0002] As a typical representative of electrochemical energy storage, lithium batteries are widely used in electric vehicles and energy storage systems due to their advantages such as high energy efficiency, low pollution, and long lifespan. To meet the high-voltage and high-power requirements of the system, individual cells need to be connected in series and / or parallel to form a battery pack. However, due to manufacturing errors and inconsistent operating conditions, battery packs will inevitably tend to become unbalanced. This characteristic will lead to a continuous decrease in battery pack performance, a shortened lifespan, and even safety risks such as internal short circuits and thermal runaway. Therefore, a balance management system to suppress battery pack imbalance is essential. Current balance management schemes can be divided into dissipative balance and non-dissipative balance based on energy flow characteristics. Dissipative balance discharges high-energy batteries through resistors and releases the energy as heat. It has the advantages of simple structure, low cost, good stability, and ease of implementation, but this method has low energy utilization efficiency and thermal safety hazards. Non-dissipative balance is based on an equalizer to suppress inconsistencies by transferring energy. The equalizer is an external circuit composed of devices such as inductors, capacitors, transformers, and MOSFETs, and has the advantages of short time and high energy efficiency. Existing methods have improved the performance of equalization systems, but there is limited research on the optimal configuration of equalization systems for battery packs with different numbers of batteries connected in series, which fails to achieve efficient utilization of the battery packs.

[0003] Therefore, this invention proposes a large-scale active battery balancing system optimization design method to optimize battery pack balancing systems and further improve battery pack energy utilization. The proposed optimization design method combines general mathematical modeling of balancing systems, evaluation of balancing topology connectivity, and multi-objective optimization solutions considering circuit complexity and performance. This achieves optimal design for active balancing systems of different battery packs and significantly improves the operating efficiency of the balancing system. Summary of the Invention

[0004] In view of this, this invention proposes an optimization design method for a large-scale battery active balancing system to obtain the optimal balancing system configuration. Considering that the time efficiency and energy efficiency of the balancing system can be expressed by the connectivity of the balancing topology circuit, a connectivity modeling method for different balancing topologies is designed based on graph theory, namely, calculating the second minimum eigenvalue of the Laplace matrix of the balancing topology graph. Firstly, a general mathematical model is established considering the energy flow characteristics during the operation of different balancing systems, and the balancing topology control matrix used for system optimization is derived. Finally, based on the optimization algorithm and considering the complexity, cost, and connectivity of the balancing topology circuit, a multi-objective optimization problem is constructed, ultimately achieving the optimal design of the large-scale battery balancing system.

[0005] This invention is implemented using the following scheme: an optimization design method for a large-scale battery active balancing system, specifically including the following steps:

[0006] Step S1: Establish a general mathematical model for the battery balancing system, which specifically includes constructing a description of the balancing energy flow characteristics and deriving the balancing system control matrix;

[0007] The general mathematical model obtained to describe the equilibrium energy flow characteristics is shown below:

[0008] x(k+1)=x(k)+Ηu(k)+v d d k

[0009] In the formula, H and u represent the control matrix and input vector of the general mathematical model of the equalization system, respectively, and depend on the equalization topology characteristics such as the number of batteries connected in series, the number of equalization module layers, and the grouping configuration.

[0010] The derived control matrix design for the equilibrium system is as follows:

[0011]

[0012] Step S2: Establish the connectivity description formula and solution process of the equalization system to achieve a compatible expression of the performance of most traditional equalization systems, and provide a foundation for the rolling optimization of the equalization system;

[0013] L=HH T

[0014] Subsequently, the characteristic polynomial |L-λE| is constructed and set to zero. The second minimum eigenvalue can then be used to characterize the connectivity performance of the equilibrium system.

[0015] Step S3: Design the optimization process for the balancing system and select the best balancing topology configuration:

[0016]

[0017] st |HH T -λ2E|=0

[0018] λ max ≥…≥λ2≥λ min

[0019] The above formulas comprehensively consider the circuit complexity, cost, and connectivity of the equalization system, providing a foundation for optimizing the equalization system.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The present invention is used to evaluate the performance of a pre-designed equalization system and can quickly select the best equalization system from several schemes, thereby achieving a significant improvement in the performance of large-scale battery systems and reducing the low energy utilization rate and safety risks caused by battery inconsistency. Attached Figure Description

[0021] Figure 1 This is a flowchart of the optimized design of the battery active balancing system;

[0022] Figure 2 This is a schematic diagram of a two-layer equalization system based on an adjacent equalizer.

[0023] Figure 3 It is the connectivity of a balanced system with different topology circuit configurations. Detailed Implementation

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that the following detailed description is exemplary and intended to provide further explanation of this application. Obviously, the drawings described below are only some embodiments of the present invention, and other similar drawings can be obtained by other researchers in the art without creative effort.

[0025] This embodiment proposes an optimization design method for a large-scale battery active balancing system. The flowchart of this optimization design method is shown below. Figure 1 As shown, the work includes establishing a general mathematical model for battery equalization systems, constructing a multi-objective optimization problem for circuit complexity and connectivity, and solving the equalization topology connectivity problem based on graph theory. The specific working process is as follows: First, a general mathematical model for different equalization systems is established. Then, an optimization algorithm is used to evaluate the connectivity of different equalization systems, and the equalization topology circuit configuration corresponding to the maximum connectivity is selected. Next, the possibility of having the same high connectivity but different equalization topology configurations is considered; that is, the optimal equalization system configuration is further selected by combining circuit complexity.

[0026] Specifically, the implementation process of this embodiment is divided into the following steps:

[0027] (a) Establish a general mathematical model for the battery balancing system, specifically including constructing a description of the balancing energy flow characteristics and deriving the balancing system control matrix;

[0028] (a) includes the following specific processes:

[0029] a1. Obtain the mathematical expression to describe the energy flow characteristics during the battery balancing process: According to the analysis, the energy of each series-connected battery cell can be exchanged through different balancing topology circuits, and its operating current includes discharge current, charging current and balancing current.

[0030] Firstly Figure 2 Taking the two-layer equalization system based on an adjacent equalizer as an example, the current of the j-th (2≤j≤n) battery in the i-th (2≤i≤m) equalization module is expressed as follows:

[0031]

[0032] In the formula, I i,j Battery current; I C,i,j I M,i,1 and I P These represent the battery equalization current of the j-th equalizer, the current of the i-th module in the first module layer, and the battery pack current, respectively. The battery pack current can be directly calculated based on the vehicle's longitudinal dynamics and speed prediction estimator. For the first equalization module, the battery current is calculated as follows:

[0033]

[0034] a2. Establish a dynamic model of the battery balancing system: First, based on the ampere-hour integral method, the state of charge of the j-th battery in the i-th balancing module is obtained as follows:

[0035]

[0036] In the formula, η represents the battery coulombic efficiency; Δt is the sampling time; based on the mapping function between open-circuit voltage and state of charge, the initial state of charge of the battery is obtained by fitting a nonlinear function:

[0037]

[0038] In the formula, α g V represents the polynomial coefficients; i,j Let be the open-circuit voltage of the j-th battery in the i-th balancing module.

[0039] Combining formulas (1) and (2), the state-space model of the two-layer equalization system based on the adjacent equalizer can be obtained as follows:

[0040] x(k+1)=x(k)+Ηu(k)+v d dk (5)

[0041] In the formula, the state vector This represents the battery's state of charge; for the i-th equalization module, the state vector is x. i =[z i,1 z i,2 …z i,n ] T The input vector u integrates the equalizers from different equalization modules, representing the standardized equalization current of the equalizer. d = I P =[I P,1 ;I P,2 ;…;I P,m ] represents the external current when the battery pack is operating, where I P,i =[I P,i,1 ;I P,i,2 ;…;I P,i,n (i = 1, 2, ..., m); other matrices in formula (5) are defined as follows:

[0042] u = [u M,1,1 ;u C,1 ;u M,1,2 ;u C,2 ;…;u M,1,m ;u C,m ] (mn+m)×1 ,

[0043] The input vector in the above formula is specifically defined as: u C =[u C,1 ;u C,2 ;…;u C,m ] and u M,1 =[u M,1,1 u M,1,2 …u M,1,m ] T , where u C,i =[u C,i,1 ;u C,i,2 ;…;u C,i,n (i = 1, 2, ..., m); I max This is the maximum equalization current of the equalizer; 1 n This represents a column vector consisting of n 1s.

[0044] Similarly, equalization systems with different topology circuit structures can be modeled to obtain a general mathematical model in the form of formula (5).

[0045] (b) Establish a connectivity description formula and solution process for the equilibrium system to achieve a compatible expression of the performance of most traditional equilibrium systems, providing a foundation for the rolling optimization of the equilibrium system. Based on the analysis of formula (5), it can be seen that how to configure the number and topology of equilibrium modules is the key to the optimization of the equilibrium system. Among them, the control matrix of the established general model of the equilibrium system is the key factor affecting the equilibrium performance. According to graph theory, the second smallest eigenvalue of the Laplace matrix represents the algebraic connectivity of the topology graph, so algebraic connectivity can be used to represent the connectivity of the equilibrium system.

[0046] In a specific embodiment of the present invention, the control matrix in formula (5) is regarded as the incidence matrix in graph theory. Therefore, the corresponding Laplace matrix can be solved using the control matrix H, and is defined as follows:

[0047] L=HH T (6)

[0048] Subsequently, the characteristic polynomial |L-λE| is constructed and set to zero. The second minimum eigenvalue can then be used to characterize the connectivity performance of the equalization system. Therefore, equalization systems with different topology circuit configurations can be evaluated and optimized.

[0049] In a specific embodiment (c) of the present invention, the proposed battery active balancing system optimization design method was designed and tested. The specific process is as follows:

[0050] c1. Taking into account the circuit complexity, cost, and connectivity of the equalization system, this invention defines the following objective function for the equalization system optimization problem:

[0051]

[0052] In the formula, a represents the weight of the eigenvalue λ2; n C n L n T and n M These represent the quantities of capacitors, inductors, transformers, and MOSFETs in the equalization circuit, respectively, which are determined by the equalization circuit configuration and equalizer structure; C C C L C T and C M These represent the costs of the capacitor, inductor, transformer, and MOSFET in the circuit, respectively.

[0053] c2, according to Figure 1 The flowchart of the equalization system optimization shown uses a two-layer equalization system based on an adjacent equalizer as an example for experimental analysis. First, the connectivity variation trend of a battery pack composed of 96 individual cells is plotted. Then, the optimized design results of the active equalization system for this battery pack are presented, as follows: Figure 3 As shown.

[0054] In summary, this implementation case presents a large-scale battery active equalization system optimization design method that employs generalized mathematical modeling of equalization systems, derivation and calculation of connectivity to characterize equalization performance, and optimization design considering the complexity of equalization topology circuits and optimal connectivity. An example test was conducted on a two-layer equalization system based on an adjacent equalizer. The method achieves optimized configuration and performance enhancement of different equalization systems, further improving the design efficiency and operational effectiveness of large-scale equalization systems.

[0055] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0056] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0057] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0058] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0059] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for optimizing the design of a large-scale battery active balancing system, characterized in that, Includes the following steps: Step S1: Establish a general mathematical model for the battery balancing system, which specifically includes constructing a description of the balancing energy flow characteristics and deriving the balancing system control matrix; Step S2: Establish the connectivity description formula and solution process of the equalization system to achieve a compatible expression of the performance of most traditional equalization systems, and provide a foundation for the rolling optimization of the equalization system; Step S3: Taking into account the circuit complexity, cost, and connectivity of the equalization system, define the multi-objective optimization problem of the equalization system.

2. The large-scale battery active balancing system optimization design method according to claim 1, characterized in that, A mathematical expression describing the energy flow characteristics during battery equalization is established based on Kirchhoff's laws. Analysis shows that the energy of each series-connected battery cell can be exchanged through different equalization topologies, and its operating current includes discharge current, charging current, and equalization current. The established general state-space model of the equalization system is as follows: x(k+1)=x(k)+Ηu(k)+v d d k In the formula, the state vector This represents the battery's state of charge; for the i-th equalization module, the state vector is x. i =[z i,1 z i,2 …z i,n ] T ; The input vector u integrates the equalizers from different equalization modules, representing the standardized equalization current of the equalizer; d = I P =[I P,1 ;I P,2 ;…;I P,m ] represents the external current when the battery pack is operating, where I P,i =[I P,i,1 ;I P,i,2 ;…;I P,i,n (i = 1, 2, ..., m); other matrices in formula (5) are defined as follows: in=[in M,1,1 ;in C,1 ;in M,1,2 ;in C,2 ;…;in M,1,m ;in C,m ] (mn+m)×1 , The input vector in the above formula is specifically defined as: u C =[u C,1 ;u C,2 ;…;u C,m ] and u M,1 =[u M,1,1 u M,1,2 …u M,1,m ] T , where u C,i =[u C,i,1 ;u C,i,2 ;…;u C,i,n (i = 1, 2, ..., m); I max This is the maximum equalization current of the equalizer; 1 n This represents a column vector consisting of n 1s.

3. The large-scale battery active balancing system optimization design method according to claim 1, characterized in that, Modeling equalization systems with different topological circuit structures yields general mathematical models that are similar to the mathematical formulas in claim 2.

4. The large-scale battery active balancing system optimization design method according to claim 2, characterized in that, The configuration of the number and topology of equalization modules is the key to optimizing the equalization system. The control matrix of the general mathematical model of the battery equalization system is a key factor affecting the equalization performance. According to graph theory, the second smallest eigenvalue of the Laplace matrix represents the algebraic connectivity of the topological graph, and thus algebraic connectivity can be used to represent the connectivity quality of an equilibrium system.

5. The large-scale battery active balancing system optimization design method according to claim 4, characterized in that, The control matrix H is considered as the incidence matrix in graph theory; therefore, the corresponding Laplace matrix can be solved using the control matrix H and is defined as follows: L=HH T Subsequently, the characteristic polynomial |L-λE| is constructed and set to zero. The second minimum eigenvalue can then be used to characterize the connectivity performance of the equalization system. Therefore, equalization systems with different topology circuit configurations can be evaluated and optimized.

6. The large-scale battery active balancing system optimization design method according to claim 1, characterized in that, Taking into account the circuit complexity, cost, and connectivity of the equalization system, the following objective function is defined for the equalization system optimization problem: s.t.|HH T -λ2E|=0 l max ≥…≥λ2≥λ min In the formula, a represents the weight of the eigenvalue λ2; n C n L n T and n M These represent the quantities of capacitors, inductors, transformers, and MOSFETs in the equalization circuit, respectively, which are determined by the equalization circuit configuration and equalizer structure; C C C L C T and C M These represent the costs of the capacitor, inductor, transformer, and MOSFET in the circuit, respectively.

7. The large-scale battery active balancing system optimization design method according to claim 1, characterized in that, The method includes establishing a general mathematical model for battery balancing systems, constructing multi-objective optimization problems for circuit complexity and connectivity, and solving the balancing topology connectivity problem based on graph theory.

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