An energy storage battery active equalization system based on multi-stage energy scheduling

Abstract

The application discloses a kind of energy storage battery active equalization systems based on multistage energy scheduling.The application realizes multilayer collaborative equalization, by establishing the multilayer structure of battery monomer level, module level, battery cluster level and DC bus level, overcome the problem that only monomer level or module level equalization is limited in the prior art;Support cross-level energy flow, improve the equalization efficiency, realize the dynamic transmission of energy between different levels by path selection module, so that energy can be redistributed between monomer, module, battery cluster and system.At the same time, the application also realizes the equalization decision of multi-parameter fusion, based on the comprehensive evaluation of state of charge, health state, internal resistance and temperature and other multi-dimensional parameters, improve the accuracy and adaptability of equalization strategy.The application realizes the collaborative control of battery management system, power conversion system and energy management system through collaborative control module.

Description

Technical Field

[0001] This invention relates to the field of electrochemical energy storage and battery management technology, and in particular to an active balancing system for energy storage batteries based on multi-level energy dispatch. Background Technology

[0002] With the rapid growth of new energy power generation and the advancement of new power system construction, electrochemical energy storage systems play an important role in power peak shaving, frequency regulation, and new energy consumption. In large-scale energy storage systems, batteries are usually composed of a hierarchical structure of single cells, modules, and battery clusters, resulting in large-scale and complex systems.

[0003] Due to differences in the manufacturing, use, and aging processes of batteries, inconsistencies exist between individual battery cells, modules, and battery clusters in terms of state of charge (SOC), state of health (SOH), and internal resistance. This affects the system's capacity utilization and lifespan, and in severe cases, may even lead to safety issues.

[0004] In existing technologies, balancing is mainly achieved by the battery management system, including passive balancing and active balancing methods. Passive balancing is inefficient; while active balancing is more efficient, it is usually limited to the cell or module level and has limited balancing capabilities.

[0005] Furthermore, existing equalization strategies are typically based on a single parameter and lack multi-dimensional information fusion; the equalization process lacks coordinated control with PCS and EMS, making it impossible to participate in energy redistribution during system operation; at the same time, existing technologies lack cross-level energy scheduling mechanisms, making it difficult to achieve system-level energy optimization.

[0006] Therefore, it is necessary to propose an active balancing technology that supports multi-level energy scheduling, cross-level energy flow, and system collaborative control. Summary of the Invention

[0007] To address the technical problems mentioned above, this invention provides an active balancing system for energy storage batteries based on multi-level energy dispatch, comprising: The battery system is constructed using a multi-level structure, specifically including: the individual battery cell level, the module level, and the battery cluster level. DC bus, used to connect the battery system and the power conversion system; Battery management system is used to collect battery status information; A power conversion system is used to enable energy exchange between the battery system and the external power grid; An energy management system is used to generate scheduling instructions based on system operation requirements. The energy scheduling module is used to obtain the system state vector based on the battery state information collected by the battery management system; obtain the inconsistency evaluation index based on the system state vector; generate the balance target based on the inconsistency evaluation index; and make energy scheduling decisions. The path selection module is used to obtain a set of energy paths based on the energy scheduling decisions of the energy scheduling module; and to determine the energy transmission path based on the set of energy paths. The collaborative control module is used to control the battery management system, power conversion system and energy management system to perform collaborative control based on the energy transmission path determined by the path selection module, and to perform equalization control at different levels.

[0008] Preferably, the system state vector includes: a battery cell level state vector, a module level state vector, a battery cluster level state vector, and a DC bus level state vector; Among them, the battery cell level state vector is obtained based on the battery cell number, number of battery cells, state of charge, state of health, internal resistance and temperature; The module-level state vector is obtained based on the module number, the average state of charge of the module, and the standard deviation of the state of charge within the module. The battery cluster hierarchical state vector is obtained based on the battery cluster number, battery cluster state of charge, and battery cluster power. The DC bus level state vector is obtained based on the DC bus voltage and the total system power.

[0009] Preferably, the inconsistency evaluation indicators include: inconsistency at the individual battery cell level, inconsistency at the module level, inconsistency at the battery cluster level, and overall system inconsistency. Among them, the inconsistency of the battery cell level is obtained based on the average state of charge of all battery cells and the state of charge of each battery cell. The module hierarchy inconsistency is obtained based on the average state of charge of all modules and the average state of charge of each module. The inconsistency of battery cluster hierarchy is obtained based on the average state of charge of all battery clusters and the state of charge of each battery cluster. The overall system inconsistency is determined based on the inconsistency at the individual battery cell level, the module level, and the battery cluster level, along with their corresponding weighting coefficients.

[0010] Preferably, the energy scheduling module makes energy scheduling decisions by constructing a scheduling optimization objective function based on the inconsistency evaluation index. The scheduling optimization objective function is obtained based on the energy loss during the equilibrium process, the time required for equilibrium, and the weight coefficients.

[0011] Preferably, the energy path set includes: energy paths between individual battery cells, paths from individual cells to modules, paths from modules to battery clusters, and paths from battery clusters to the DC bus.

[0012] Preferably, the path selection module selects the optimal path that satisfies the optimization relationship from the energy path set based on path loss, path transmission time, path constraint cost and corresponding weight coefficients.

[0013] Preferably, the collaborative control module controls the battery management system to perform active balancing control at the battery cell level and module level according to the energy transmission path, and obtains the balancing current according to the balancing gain coefficient. Furthermore, the collaborative control module controls the power conversion system to participate in equalization at the battery cluster level, and obtains the actual power of the battery cluster based on the reference power and the power adjustment coefficient.

[0014] Preferably, the collaborative control module controls the battery management system, power conversion system, and energy management system to perform collaborative control based on the system power balance constraints and the number of battery clusters, so that the sum of the actual power of the battery clusters meets the total system power.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention achieves multi-level collaborative balancing by establishing a multi-level structure at the battery cell level, module level, battery cluster level, and DC bus level, overcoming the problem that existing technologies are limited to balancing at only the cell or module level. It supports cross-level energy flow, improves balancing efficiency, and realizes dynamic energy transfer between different levels through a path selection module, enabling energy to be redistributed among cells, modules, battery clusters, and the system.

[0016] Simultaneously, this invention achieves multi-parameter fusion-based equalization decision-making, comprehensively evaluating multiple dimensions such as state of charge, state of health, internal resistance, and temperature to improve the accuracy and adaptability of the equalization strategy. This invention utilizes a collaborative control module to achieve coordinated control of the battery management system, power conversion system, and energy management system; it also organically combines these three systems, enabling the equalization process to coordinate with the system's charging and discharging processes, thereby improving system energy utilization efficiency. This invention is applicable to large-scale energy storage systems, improving system consistency and operational performance, adapting to complex hierarchical structures, and optimizing battery consistency through system-level energy scheduling; it also possesses good scalability and engineering applicability, adapting to energy storage systems of different sizes and topologies. Attached Figure Description

[0017] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1This is a schematic diagram of the structure of a multi-level energy dispatching battery active balancing system according to an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Example: This embodiment provides an active balancing system for energy storage batteries based on multi-level energy dispatch, such as... Figure 1 As shown, the system includes a battery system, a DC bus, a battery management system, a power conversion system, and an energy management system. The battery system employs a multi-level structure, specifically including cell-level, module-level, and cell cluster-level components. The DC bus connects the battery system and the power conversion system, enabling system-level energy collection and distribution. The battery management system collects battery status information and performs local equalization control. The power conversion system facilitates energy interaction between the battery system and the external power grid. The energy management system generates scheduling commands based on system operational requirements. Furthermore, the system also includes an energy scheduling module, a path selection module, and a collaborative control module to achieve multi-level energy scheduling and cross-level equalization control.

[0022] To achieve multi-level energy dispatch, this embodiment first performs state modeling on the energy storage system. Let the system state vector be... ,in These represent the state vectors at the individual battery cell level, module level, battery cluster level, and DC bus level, respectively. Specifically, the individual battery cell level state vector is defined as: Among them, it means i Battery cell serial number, and , N This indicates the number of individual battery cells, while Then they represent the first i The state of charge, health status, internal resistance, and temperature of each individual battery cell.

[0023] The module-level state vector is defined as: in, m Indicates the module number. Indicates the first m The average state of charge of each module This represents the standard deviation of the state of charge within the module.

[0024] The battery cluster-level state vector is defined as follows: in, k Indicates the battery cluster number, Indicates the first k The state of charge of each battery cluster, Pk This indicates the power of the battery cluster.

[0025] The DC bus level state vector is defined as follows: in, Indicates the DC bus voltage. This represents the total power of the system.

[0026] To characterize the degree of consistency between different battery levels, this embodiment constructs an inconsistency evaluation index. Inconsistency at the battery cell level is defined as follows: in, Indicates an inconsistency index for individual layers. This represents the average state of charge of all individual battery cells.

[0027] Module hierarchy inconsistency is defined as: in, This indicates an inconsistency index at the module level.

[0028] Battery cluster hierarchy inconsistency is defined as: in, This indicates an inconsistency index in the battery cluster layer.

[0029] Furthermore, system-wide inconsistency is defined as: in, The weighting coefficients are and satisfy the following conditions: .

[0030] After obtaining system status information, the energy scheduling module generates an equilibrium target based on inconsistency indicators and makes energy scheduling decisions. Preferably, a scheduling optimization objective function is constructed: in, This represents the objective function to be optimized. This represents the energy loss during the equilibrium process. This indicates the time required for equilibrium to be reached. These are the weighting coefficients.

[0031] In this embodiment, the path selection module is used to determine the energy transmission path. Let the set of energy paths be: in, P cc Indicates the energy path between individual battery cells. P cm Indicates the path from unit to module. P mk Indicates the path from the module to the battery cluster. P kb This indicates the path from the battery cluster to the DC bus.

[0032] Preferably, the path selection satisfies the following optimization relationship: in, Indicates the optimal path. Indicates path loss. Indicates path transmission time. Represents the cost of path constraints. These are the weighting coefficients.

[0033] Different balancing methods are used at different levels. At the individual battery cell and module levels, the battery management system performs active balancing control, and the balancing current can be expressed as: in, Indicates the first i The equal current of each battery cell k This is the equalization gain coefficient.

[0034] At the battery cluster level, the power conversion system participates in equalization under the control of the collaborative control module, and its power regulation method is as follows: in, P k Indicates the first k The actual power of each battery cluster P ref Indicates reference power. Indicating the adjustment amount, further: in, η This is the power regulation coefficient.

[0035] At the system level, power balance constraints are satisfied: in, M This indicates the number of battery clusters.

[0036] This embodiment also constructs a collaborative control mechanism for the battery management system, power conversion system, and energy management system. The battery management system is used to perform local equalization control, the power conversion system is used to perform energy regulation, and the energy management system is used to perform system-level scheduling optimization. The above collaborative control achieves the integration of equalization control and system operation process.

[0037] Based on the above implementation methods, optimization algorithms can be further introduced to optimize the scheduling strategy. For example, in a reinforcement learning framework, the optimization objective can be defined as: in, t Indicates a time step. T Indicates the total duration. γ Indicates the discount factor. R t Indicates the first t Momentary reward value.

[0038] Preferably, the reward function can be expressed as: in, These are the weighting coefficients.

[0039] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An active balancing system for energy storage batteries based on multi-level energy dispatch, characterized in that, include: The battery system is constructed using a multi-level structure, specifically including: the individual battery cell level, the module level, and the battery cluster level. DC bus, used to connect the battery system and the power conversion system; Battery management system is used to collect battery status information; A power conversion system is used to enable energy exchange between the battery system and the external power grid; An energy management system is used to generate scheduling instructions based on system operation requirements. The energy scheduling module is used to obtain the system state vector based on the battery state information collected by the battery management system; obtain the inconsistency evaluation index based on the system state vector; generate the balance target based on the inconsistency evaluation index; and make energy scheduling decisions. The path selection module is used to obtain a set of energy paths based on the energy scheduling decisions of the energy scheduling module; and to determine the energy transmission path based on the set of energy paths. The collaborative control module is used to control the battery management system, power conversion system and energy management system to perform collaborative control based on the energy transmission path determined by the path selection module, and to perform equalization control at different levels.

2. The active balancing system for energy storage batteries based on multi-level energy dispatch according to claim 1, characterized in that, The system state vector includes: battery cell level state vector, module level state vector, battery cluster level state vector, and DC bus level state vector; Among them, the battery cell level state vector is obtained based on the battery cell number, number of battery cells, state of charge, state of health, internal resistance and temperature; The module-level state vector is obtained based on the module number, the average state of charge of the module, and the standard deviation of the state of charge within the module. The battery cluster hierarchical state vector is obtained based on the battery cluster number, battery cluster state of charge, and battery cluster power. The DC bus level state vector is obtained based on the DC bus voltage and the total system power.

3. The active balancing system for energy storage batteries based on multi-level energy dispatch according to claim 2, characterized in that, Inconsistency evaluation indicators include: inconsistency at the individual battery cell level, inconsistency at the module level, inconsistency at the battery cluster level, and overall system inconsistency; Among them, the inconsistency of the battery cell level is obtained based on the average state of charge of all battery cells and the state of charge of each battery cell. The module hierarchy inconsistency is obtained based on the average state of charge of all modules and the average state of charge of each module. The inconsistency of battery cluster hierarchy is obtained based on the average state of charge of all battery clusters and the state of charge of each battery cluster. The overall system inconsistency is determined based on the inconsistency at the individual battery cell level, the module level, and the battery cluster level, along with their corresponding weighting coefficients.

4. The active balancing system for energy storage batteries based on multi-level energy dispatch according to claim 3, characterized in that, The energy scheduling module makes energy scheduling decisions by constructing a scheduling optimization objective function based on inconsistency evaluation indicators. The scheduling optimization objective function is obtained based on energy loss during the equilibrium process, the time required for equilibrium, and weight coefficients.

5. The active balancing system for energy storage batteries based on multi-level energy dispatch according to claim 1, characterized in that, The energy path set includes: energy paths between individual battery cells, paths from individual cells to modules, paths from modules to battery clusters, and paths from battery clusters to the DC bus.

6. The active balancing system for energy storage batteries based on multi-level energy dispatch according to claim 5, characterized in that, The path selection module selects the optimal path that satisfies the optimization relationship from the energy path set based on path loss, path transmission time, path constraint cost and corresponding weight coefficients.

7. The active balancing system for energy storage batteries based on multi-level energy dispatch according to claim 1, characterized in that, The collaborative control module controls the battery management system to perform active balancing control at the battery cell level and module level according to the energy transmission path, and obtains the balancing current according to the balancing gain coefficient. Furthermore, the collaborative control module controls the power conversion system to participate in equalization at the battery cluster level, and obtains the actual power of the battery cluster based on the reference power and the power adjustment coefficient.

8. The active balancing system for energy storage batteries based on multi-level energy dispatch according to claim 7, characterized in that, The collaborative control module controls the battery management system, power conversion system, and energy management system to perform collaborative control based on the system power balance constraints and the number of battery clusters, so that the sum of the actual power of the battery clusters meets the total system power.

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