Dynamic reconfigurable battery network scheduling method and system based on in-module monomer state aggregation

Abstract

The invention discloses a dynamic reconfigurable battery network scheduling method and system based on state polymerization of monomers in a module, and relates to the technical field of electrochemical energy storage systems. In order to solve the problem of how to fully utilize single body state information to realize better system scheduling under the hardware constraint that only a module can be controlled to be switched in / switched out, operation state parameters of all battery single bodies in a battery network are collected; for each battery module, calculating at least one module-level aggregation state index based on the state parameters of all monomers in the battery module; calculating the scheduling emergency degree of each module according to the module-level aggregation state index; obtaining the historical scheduling frequency of each module, and calculating the scheduling fairness of each module; fusing the scheduling emergency degree and the scheduling fairness to obtain a comprehensive scheduling priority of each module; and according to the comprehensive scheduling priority, selecting the target module to carry out cut-in or cut-out operation to complete the reconstruction of the battery network topology. The method is mainly used for scheduling and optimizing the single batteries of the electrochemical energy storage system.

Description

Technical Field

[0001] This invention relates to the field of electrochemical energy storage system technology, and in particular to a dynamic reconfigurable battery network scheduling method and system based on the aggregation of individual cell states within a module. Background Technology

[0002] Current Dynamic Reconfigurable Battery Network (DRBN) technology revolutionizes traditional fixed series-parallel battery pack assembly through millisecond-level digital scheduling. However, limited by system complexity and cost, most existing DRBN systems can only control module-level access and disconnection, unable to achieve direct control at the individual cell level. Current scheduling strategies typically only consider the average state of the module (such as average voltage and average temperature), and this coarse-grained scheduling approach has significant drawbacks: The "bucket effect" is invisible internally: inconsistencies among individual components within a module are a key factor affecting system performance and safety. A module may be at risk due to a single "weak link" component (such as low or high voltage, or high temperature), but this risk is masked by the module's average value, preventing the scheduling system from identifying and addressing it in a timely manner.

[0003] Safety hazard: Early faults in a single unit within the module (such as temperature rise caused by the initial stage of an internal short circuit) cannot be reflected in the module-level average value in a timely manner, causing the fault to be ignored and continue to deteriorate, which may eventually lead to thermal runaway.

[0004] Capacity loss: Because the scheduling system cannot perceive the specific SOC distribution of individual cells within the module, in order to avoid overcharging and over-discharging, it can only perform conservative charging and discharging control based on the worst-performing cell, resulting in the inability to fully utilize the capacity of other normal cells within the module.

[0005] Lifespan reduction: The fastest aging individual unit within a module limits the usable lifespan of the entire module. The lack of awareness of individual unit aging status prevents the system from proactively balancing aging rates between and within modules through scheduling strategies.

[0006] Therefore, under the hardware constraint of only being able to perform module-level control, there is an urgent need for a dynamic reconfigurable battery network scheduling method and system based on the aggregation of individual unit states within a module, which can accurately identify the state of individual units within a module and make module-level scheduling decisions accordingly. Summary of the Invention

[0007] This invention aims to solve the problem of how to make full use of individual unit state information to achieve better system scheduling under the hardware constraint of "only being able to control module entry / exit". It provides a dynamic reconfigurable battery network scheduling method and system based on the aggregation of individual unit states within the module, which can accurately identify the state of individual units within the module and make module-level scheduling decisions accordingly.

[0008] The present invention discloses a dynamic reconfigurable battery network scheduling method based on intra-module single-unit state aggregation, comprising the following steps: S1. Collect the operating status parameters of all battery cells in the battery network; for each battery module m, calculate at least one module-level aggregated status index based on the status parameters of all its internal cells. S2. Calculate the scheduling urgency of each module based on the module-level aggregated status index. ; S3. Obtain the historical scheduling frequency of each module and calculate its scheduling fairness. S4. Combine the scheduling urgency and scheduling fairness to obtain the comprehensive scheduling priority of each module; S5. Based on the comprehensive scheduling priority, select the target module for entry or exit operation to complete the reconstruction of the battery network topology.

[0009] Furthermore: the module-level aggregation status index includes: the minimum value of the individual unit voltage within module m. and maximum value The maximum value of the unit temperature within module m Minimum State of Charge (SOC) of individual cells within module m and maximum value And the average value of the state of health (SOH) of individual cells within module m. At least one of them.

[0010] Furthermore: the scheduling urgency Including the urgency of scheduling during the charging process and the urgency of scheduling during the discharge process : The calculation formula during the charging process is as follows: ; The calculation formula during the discharge process is: ; In the formula, , and All are weighting coefficients, satisfying ; This is the highest temperature allowed by the system. To prevent small quantities from being excluded.

[0011] Furthermore: the scheduling fairness Historical scheduling frequency of the module Negative correlation, specifically: ; in, It is a smoothing factor; The historical scheduling frequency The exponentially weighted moving average method is used for updating, and the update formula is as follows: ; in, Forgetting factor, This is a scheduling indicator function; its value is 1 when module m is scheduled at time t, and 0 otherwise. The comprehensive scheduling priority The calculation formula is: .

[0012] Furthermore: the specific method for selecting a target module for inbound or outbound operations based on the aforementioned comprehensive scheduling priority is as follows: During the charging process, the module with the highest priority in the overall charging scheduling is selected to enter the charging circuit; During the discharge process, the module with the highest priority in the overall discharge scheduling is selected to enter the discharge circuit.

[0013] Furthermore, it also includes a safety forced cut-out procedure: When the state parameters of any single unit are detected to exceed the preset safety hard threshold, the module is immediately disconnected from the network, regardless of the overall scheduling priority of the module to which it belongs; the safety hard threshold includes at least one of the upper voltage limit, lower voltage limit, and upper temperature limit.

[0014] Furthermore, it also includes a model update step: Periodically recalculate the module-level aggregated status indicators and / or update the parameters in the scheduling urgency calculation model based on newly collected normal operation data.

[0015] The network scheduling system described in this invention for implementing the dynamic reconfigurable battery network scheduling method based on intra-module single-unit state aggregation includes: The data acquisition module is used to collect the status parameters of all individual battery cells; The state aggregation module is used to calculate the aggregated state index for each module; The scheduling calculation module is used to calculate the scheduling urgency, scheduling fairness, and overall scheduling priority of each module; The scheduling decision module is used to generate module control commands based on the overall scheduling priority. The network reconstruction module is used to perform module entry and exit operations.

[0016] Furthermore: the data acquisition module includes a digital energy network card, and the network reconstruction module includes a digital energy hub and a digital energy switch.

[0017] The battery management system described in this invention integrates the aforementioned dynamic reconfigurable battery network scheduling system.

[0018] The beneficial effects of this invention are: The method described in this invention constructs a module-level aggregated comprehensive scheduling priority index by sensing the status of all individual units within the module in real time, and makes scheduling decisions between modules under a proportional fairness framework, thereby achieving the optimization of system-level security, fairness and efficiency under the constraint of only being able to control module access and cut-out.

[0019] First, under the hardware constraints of module-level control, this invention achieves effective utilization of the individual unit state, solving the "black box" problem of the internal state of the module. By defining the module aggregation index, the discrete state of the individual units within the module is transformed into a continuous decision basis that can be used for module-level scheduling, thus achieving penetrating perception.

[0020] Secondly, this invention also enhances system safety by identifying the "worst" individual modules within a module (e.g., lowest voltage, highest temperature) and prioritizing intervention for risky modules (e.g., charging low-voltage modules, or cutting off heat dissipation for high-temperature modules). By focusing on the "worst" individual modules, risky modules can be identified and isolated earlier and more accurately, preventing problems before they occur and thus fundamentally improving safety.

[0021] Third, this invention maximizes the effective capacity of the system. Based on the internal individual unit status, it accurately assesses the real-time charge / discharge capability of each module, avoiding the limitation of the entire system performance by individual units. Scheduling is based on the actual available SOC range within the module, optimizing capacity utilization and avoiding capacity waste caused by conservative estimations, thereby improving system throughput.

[0022] Finally, this invention achieves refined lifespan management: by considering the historical scheduling frequency and health status of individual modules during scheduling, it proactively balances aging and extends the overall lifespan of the system. The adoption of a proportional fairness mechanism avoids some modules from working under fatigue for extended periods due to "excellent" status, or some modules from being idle for extended periods due to "poor" status, thus balancing system aging.

[0023] In summary, this invention addresses the core contradiction in dynamically reconfigurable battery networks (DRBNs) where the control granularity (module level) and state awareness granularity (cell level) are mismatched through the core idea of ​​"observing individual cells and deciding on modules." By innovating algorithms within hardware constraints, this invention "translates" fine-grained individual cell state information into efficient module scheduling instructions. This enables early identification of individual cell-level risks and global optimization of system-level performance in systems where individual cells cannot be directly controlled. The computational load of this invention is concentrated in the data center scheduler, with low requirements for edge control units, making it easy to upgrade and deploy in existing DRBN systems. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention. Figure 2 These are the working steps of the method of the present invention. Detailed Implementation

[0025] The following are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. The embodiments described below are only for explaining the present invention and should not be construed as limiting the present invention. The scope of protection of the present invention should be determined by the scope of the claims. The embodiments of the present invention are described in detail below. In order to facilitate the description of the present invention and simplify the description, the technical terms used in the specification of the present invention should be interpreted broadly, including but not limited to conventional alternatives not mentioned in this application, and including both direct and indirect implementation methods.

[0026] Example 1 Combination Figure 1 and Figure 2 This embodiment discloses a dynamic reconfigurable battery network scheduling method based on the aggregation of individual unit states within a module. Its overall technical approach involves a multi-stage information processing and decision-making process, as detailed below: Input Phase: The data acquisition module includes digital energy network interface cards (NICs) and digital energy terminal units (DTUs), while the network reconfiguration module includes a digital energy hub and a digital energy switch. The digital energy hub is the control core of the dynamically reconfigurable battery network system, responsible for managing the NICs and DTUs, executing online dynamic reconfiguration of battery modules, data acquisition, and forwarding. Based on real-time status information, it enables flexible switching between series and parallel topologies of battery modules, improving the system's tolerance to inconsistent batteries, and performing overvoltage and overtemperature protection actions to ensure the system's safety and efficiency during dynamic operation. The digital energy switch is the management center of the dynamically reconfigurable battery network system, responsible for energy scheduling, strategy execution, and multi-protocol communication. It collects system-wide status data, performs analysis and decision-making, coordinates the operation of subsystems such as the PCS, and supports dynamic topology reconfiguration strategies. Through a high-speed communication interface, it achieves microsecond-level control and fault isolation, improving system response speed and reliability, and is a key control unit for realizing software-defined energy systems. The digital energy network interface card (DAC) serves as the real-time control and digital interface for the battery module. It possesses local computing power, is responsible for collecting voltage / temperature data from the battery module, and responding to hub commands to drive the charging and discharging switches of the battery module. It achieves microsecond-level topology switching and fault isolation through high-speed power electronic switches, and is the lowest-level execution unit in the dynamically reconfigurable network, directly ensuring precise management of the battery module and system safety. The digital energy terminal unit (DTU) is the precision sensing unit of the battery module, responsible for collecting voltage and temperature data from each individual battery cell within the module. Each battery module is equipped with one DAC and one DTU. The DAC collects voltage and current data at the battery module level, while the DTU collects voltage and temperature data from individual battery cells. Through deployed DACs, DTUs, and other sensing units, the system collects multi-dimensional state parameters of all battery cells in real time at millisecond-level frequencies, including voltage, temperature, and current, and further estimates the state of charge (SOC) and state of health (SOH). Simultaneously, the system maintains data recording the historical scheduling frequency of each module.

[0027] The processing phase includes four key steps: Aggregated State Data: For each module, the state data of all its individual units are aggregated and analyzed. This not only calculates the module's average state (such as average voltage), but more importantly, extracts its "worst-performing unit" state, such as lowest voltage, highest temperature, and lowest state of charge (SOC). These "bottleneck" indicators are key to assessing the module's immediate risks and performance limits. Simultaneously, statistics such as the average state of harmonics (SOH) are calculated to assess the module's long-term health.

[0028] Quantifying Scheduling Urgency: Based on the aggregated results, particularly the "worst-case" state, the scheduling urgency of each module is calculated. This metric dynamically characterizes the module's urgent need for charging / discharging operations at the current moment. For example, during charging, a module containing a cell with extremely low voltage has a high charging urgency to prevent over-discharging of that cell; a module containing a high-temperature cell has a low discharging urgency (or high cut-off urgency) to avoid the risk of thermal runaway. This step transforms discrete cell alarm signals into continuous, comparable module scheduling requirements.

[0029] Quantitative Scheduling Fairness: To address the balance issue in long-term scheduling, the proportional fairness principle from the communications field is introduced to calculate the scheduling fairness of each module. This indicator is inversely proportional to the weighted historical scheduling frequency of the module. A module that has not been scheduled for a long time will have a significantly higher fairness, thus gaining priority in scheduling competition. This prevents some modules from working excessively due to "good" status or being idle for extended periods due to "slightly poor" status, effectively balancing system aging.

[0030] The fusion strategy combines the aforementioned scheduling urgency and scheduling fairness to form the final comprehensive scheduling priority for each module. The fusion strategy employs the core formula of "proportional fairness," namely, Priority = Urgency / Fairness. This design achieves an optimal balance between the two objectives of "meeting the most pressing current needs" and "maintaining long-term system fairness."

[0031] Output and Execution Phase: The system sorts all online modules according to their calculated overall scheduling priority. During charging, the highest-priority module is switched into the charging circuit; during discharging, the highest-priority module is switched into the discharging circuit. Simultaneously, modules that trigger absolute safety thresholds (such as hard upper temperature limits) are forcibly switched out. This process is executed periodically, achieving millisecond-level intelligent dynamic reconfiguration of the battery network topology, ultimately achieving the core goals of improving system safety, efficiency, and lifespan.

[0032] The dynamic reconfigurable battery network scheduling method based on intra-module single-unit state aggregation described in this embodiment includes the following steps: S1. Define the module aggregation state vector; For each battery module Define a vector of its module-level aggregated state indices. for: .

[0033] In the formula, It is the minimum value of the individual unit voltage within module m. It is the maximum value of the individual unit voltage within module m; It is the maximum value of the unit temperature within module m; It is the minimum SOC of a single unit within module m. The maximum SOC of a single unit within module m; It is the average value of monomer SOH within module m.

[0034] S2, Calculation module scheduling urgency ; Dispatch urgency It is a measure of the module The urgency at which it needs to be scheduled at the current moment; Regarding the charging process: the goal is to prioritize charging modules with low voltage and low SOC, while avoiding charging high-temperature modules. This represents the maximum temperature of each module.

[0035] ; For the discharge process: the goal is to prioritize discharging from modules with high voltage and high SOC, while avoiding discharging from high-temperature modules.

[0036] ; In the formula, , , These are weighting coefficients, all of which take values ​​greater than or equal to 0 and less than or equal to 1, and satisfy the following conditions: ; This is the highest temperature allowed by the system. To prevent small quantities from being excluded.

[0037] S3, Fairness of Computing Module Scheduling ; The historical scheduling frequency of each module is updated using an exponentially weighted moving average (EWMA), and its reciprocal is the scheduling fairness.

[0038] ; ; in, It is a module At the time Weighted historical scheduling frequency It is the scheduling instruction function, i.e., the module. At any moment The value is 1 if the event is scheduled, and 0 otherwise. It is the forgetting factor ( ), used to control the decay rate of historical records; It is a smoothing factor used to prevent division by zero.

[0039] S4. Computing Module Integrated Scheduling Priority ; Combining scheduling urgency with scheduling fairness forms the final priority under the proportional fairness scheduling framework.

[0040] ; Scheduling decision: In each scheduling cycle, the module with the highest priority is selected for operation.

[0041] When charging: Select The largest module is integrated into the charging circuit; During discharge: Select The largest module is switched into the discharge circuit.

[0042] When cutting out: In emergency situations such as abnormal temperature, you can directly cut out The module was forcibly cut out. As a hard threshold for temperature safety, when the battery management system detects that the temperature of any battery cell reaches or exceeds this critical value, the system will immediately and unconditionally perform a safety operation, regardless of the current computing priority, scheduling status or system operating mode of the module to which the cell belongs.

[0043] S5, Scheduling process; Data acquisition: The voltage and temperature of all cells are acquired in milliseconds, and their state of charge (SOC) and state of health (SOH) are estimated.

[0044] State aggregation: Calculate the aggregated state vector for each module. .

[0045] Dispatch urgency calculation: Calculate the urgency of each module based on whether the system is in charging or discharging mode. or .

[0046] Fairness calculation: Query and update the historical scheduling frequency of each module. Calculate scheduling fairness .

[0047] Priority sorting: Calculate the overall scheduling priority of all online (or standby) modules. Then sort them in descending order.

[0048] Scheduling execution: Based on the sorting results, the module with the highest priority is switched into the current charging and discharging circuit, or the module with the lowest priority (or the module that triggers the safety threshold) is switched out.

[0049] Record update: Update the scheduled module and .

[0050] Fault handling: If the status of a single unit exceeds the safety hard threshold, the module to which it belongs will be immediately forcibly disconnected and an alarm will be triggered.

[0051] Example 2 Combination Figure 1 and Figure 2 This embodiment describes a dynamic reconfigurable battery network scheduling method based on the aggregation of individual cell states within a module. The implementation environment is a DRBN system in a 100MWh energy storage power station. This system can control module-level access and disconnection and has the capability to collect individual cell voltage and temperature data. The monitoring objective is to optimize the module scheduling strategy based on individual cell states, thereby improving system performance and safety.

[0052] The system consists of a cluster of 200 modules, each composed of 16 lithium-ion battery cells connected in series. The scheduling cycle is 100ms. Algorithm parameters: , , , , , ℃, ℃.

[0053] Implementation process: System initialization: Loading the initial state of all modules. .

[0054] Real-time data acquisition: Assuming that the lowest voltage of a single unit within module #105 is 3.45V ( The highest temperature is 38℃. The minimum SOC is 15% ( ).

[0055] Aggregation and computation (system is in charging mode): Calculate urgency: .

[0056] Query history: .

[0057] Calculation priority: .

[0058] Scheduling decision: The system calculates all modules Assuming module #105 has the highest priority, it will be switched into the charging circuit.

[0059] Log Update: Settings And updated: .

[0060] Safety monitoring: During charging, the temperature of all individual cells within module #105 is continuously monitored. If the temperature of any individual cell instantly reaches 50°C, module #105 will be forcibly disconnected immediately, and an alarm will be triggered.

Claims

1. A dynamic reconfigurable battery network scheduling method based on intra-module single-unit state aggregation, characterized in that, Includes the following steps: S1. Collect the operating status parameters of all battery cells in the battery network; for each battery module m, calculate at least one module-level aggregated status index based on the status parameters of all its internal cells. S2. Calculate the scheduling urgency of each module based on the module-level aggregated status index. ; S3. Obtain the historical scheduling frequency of each module and calculate its scheduling fairness. S4. Combine the scheduling urgency and scheduling fairness to obtain the comprehensive scheduling priority of each module; S5. Based on the comprehensive scheduling priority, select the target module for entry or exit operation to complete the reconstruction of the battery network topology.

2. The dynamic reconfigurable battery network scheduling method based on intra-module single-unit state aggregation according to claim 1, characterized in that, The module-level aggregation status indicators include: the minimum value of the individual unit voltage within module m. and maximum value The maximum value of the unit temperature within module m Minimum State of Charge (SOC) of individual cells within module m and maximum value And the average value of the state of health (SOH) of individual cells within module m. At least one of them.

3. A dynamic reconfigurable battery network scheduling method based on intra-module single-unit state aggregation as described in claim 1 or 2, characterized in that, The scheduling urgency Including the urgency of scheduling during the charging process and the urgency of scheduling during the discharge process : The calculation formula during the charging process is as follows: ； The calculation formula during the discharge process is: ； In the formula, , and All are weighting coefficients, satisfying ; This is the highest temperature allowed by the system. To prevent small quantities from being excluded.

4. The dynamic reconfigurable battery network scheduling method based on intra-module single-unit state aggregation according to claim 1, characterized in that, The scheduling fairness Historical scheduling frequency of the module Negative correlation, specifically: ； in, It is a smoothing factor; The historical scheduling frequency The exponentially weighted moving average method is used for updating, and the update formula is as follows: ； in, Forgetting factor, This is a scheduling indicator function; its value is 1 when module m is scheduled at time t, and 0 otherwise. The comprehensive scheduling priority The calculation formula is: 。 5. The dynamic reconfigurable battery network scheduling method based on intra-module single-unit state aggregation according to claim 1, characterized in that, Based on the comprehensive scheduling priority, the specific method for selecting the target module for inbound or outbound operations is as follows: During the charging process, the module with the highest priority in the overall charging scheduling is selected to enter the charging circuit; During the discharge process, the module with the highest priority in the overall discharge scheduling is selected to enter the discharge circuit.

6. The dynamic reconfigurable battery network scheduling method based on intra-module single-unit state aggregation according to claim 1, characterized in that, It also includes a safety forced cut-out procedure: When the state parameters of any single unit are detected to exceed the preset safety hard threshold, the module is immediately disconnected from the network, regardless of the overall scheduling priority of the module to which it belongs; the safety hard threshold includes at least one of the upper voltage limit, lower voltage limit, and upper temperature limit.

7. The dynamic reconfigurable battery network scheduling method based on intra-module single-unit state aggregation according to claim 1, characterized in that, It also includes the model update step: Periodically recalculate the module-level aggregated status indicators and / or update the parameters in the scheduling urgency calculation model based on newly collected normal operation data.

8. A network scheduling system for implementing the dynamic reconfigurable battery network scheduling method based on intra-module single-unit state aggregation as described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to collect the status parameters of all individual battery cells; The state aggregation module is used to calculate the aggregated state index for each module; The scheduling calculation module is used to calculate the scheduling urgency, scheduling fairness, and overall scheduling priority of each module; The scheduling decision module is used to generate module control commands based on the overall scheduling priority. The network reconstruction module is used to perform module entry and exit operations.

9. The system according to claim 8, characterized in that, The data acquisition module includes a digital energy network card, and the network reconstruction module includes a digital energy hub and a digital energy switch.

10. A battery management system, characterized in that, It integrates the dynamic reconfigurable battery network scheduling system as described in claim 8 or 9.

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