Method and system for improving energy efficiency of a mwh-level manganese-based energy storage battery system

Abstract

The application discloses a method and system for improving the energy efficiency of a MWh-level manganese-based energy storage battery system. The method includes: obtaining the operating data of each battery cluster in the MWh-level manganese-based energy storage battery system; based on the operating data, estimating the health status of each battery cluster; obtaining the ambient temperature of the energy storage system; based on the ambient temperature and the health status of each battery cluster, determining the corresponding relationship between the charge-discharge efficiency of the battery cluster and the ambient temperature and the health status; based on the health status of each battery cluster, the corresponding relationship, and the grid power instruction, determining the charge-discharge power distribution scheme of each battery cluster in the energy storage system through an optimization algorithm; and controlling each battery cluster in the energy storage system to perform charge-discharge operation according to the charge-discharge power distribution scheme. The application can improve the overall charge-discharge energy efficiency of the energy storage system considering the differences in the health status of the battery clusters and the influence of the ambient temperature.

Description

Technical Field

[0001] This application relates to the field of power energy storage technology, and in particular to a method and system for improving the energy efficiency of an MWh-level manganese-based energy storage battery system. Background Technology

[0002] With the widespread application of electrochemical energy storage systems in power grids, improving their operational efficiency and reliability has become a key technical issue. Existing energy storage battery systems typically consist of multiple parallel battery clusters, with the system distributing total power among these clusters according to grid power commands. However, after long-term operation, the health of battery clusters deteriorates to varying degrees, and their charge / discharge efficiency is significantly affected by ambient temperature. Current technologies often fail to adequately consider the differences in health status between different battery clusters when allocating power, and also fail to incorporate the impact of ambient temperature on the real-time charge / discharge efficiency of battery clusters into the decision-making model. This results in low overall charge / discharge efficiency of energy storage systems during actual operation, especially in scenarios with significant temperature variations or severe aging of some battery clusters, leading to unnecessary energy losses.

[0003] For example, manganese-based energy storage batteries, with their abundant raw material reserves, low cost, environmental friendliness, and excellent safety performance, have become one of the core technologies for large-scale grid energy storage scenarios. However, current engineering applications of MWh-level manganese-based energy storage systems still suffer from the following core technological shortcomings: 1. The system monitoring topology is not adaptable enough. The existing centralized management and control architecture is difficult to adapt to the distributed and refined management and control of large-scale manganese-based battery clusters. The inconsistency of cell SOH (state of health) between clusters leads to significant energy loss in system charging and discharging, and a high usable capacity reduction rate. 2. Insufficient energy management capabilities in wide temperature range scenarios. The charge and discharge efficiency of manganese-based batteries is highly sensitive to temperature. Existing solutions have not established a coupled optimization model between SOH and temperature. Capacity decay is rapid at low temperatures and energy efficiency deteriorates severely at high temperatures. The system's usable capacity loss can reach up to 20% under extreme temperatures. 3. The lack of a quantitative correlation between battery intrinsic performance, system operating characteristics, and grid application effects, and the absence of a comprehensive life-cycle evaluation system for MWh-level manganese-based energy storage systems, results in low energy efficiency and economic performance throughout the system's life cycle. Summary of the Invention

[0004] The technical problem to be solved by this application is to provide a method and system for improving the energy efficiency of an MWh-level manganese-based energy storage battery system, which aims to improve the overall charging and discharging energy efficiency of the energy storage system considering the differences in the health status of battery clusters and the influence of ambient temperature.

[0005] To address the aforementioned technical problems, this application provides a method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system.

[0006] The method includes: acquiring operational data of each battery cluster in an MWh-level manganese-based energy storage battery system; estimating the health status of each battery cluster based on the operational data; acquiring the ambient temperature of the energy storage system; determining the correspondence between the charge / discharge efficiency of the battery cluster and the ambient temperature and the health status based on the ambient temperature and the health status of each battery cluster; determining a charge / discharge power allocation scheme for each battery cluster in the energy storage system using an optimization algorithm based on the health status of each battery cluster, the correspondence, and the grid power command; and controlling each battery cluster in the energy storage system to perform charge / discharge operations according to the charge / discharge power allocation scheme.

[0007] Furthermore, the acquisition of operational data for each battery cluster in the MWh-level manganese-based energy storage battery system includes: A three-layer distributed monitoring topology for an energy storage system is constructed, comprising a cell-level acquisition layer, a cluster-level control layer, and a station-level monitoring layer. The station-level monitoring layer is communicatively connected to the power grid dispatching system, the energy conversion system, and the thermal management system, respectively. The three-layer distributed monitoring topology is used to collect the operating data of each battery cluster in the energy storage system.

[0008] Furthermore, the operational data includes the voltage, temperature, internal resistance, and charging / discharging current of the battery clusters; the collection of operational data for each battery cluster in the energy storage system through the three-layer distributed monitoring topology specifically includes: The raw operating data of each battery cell is collected through the cell-level acquisition layer; The cluster-level control layer aggregates the original operating data of all cells in each battery cluster and calculates the cluster-level operating data of each battery cluster. The cluster-level operational data is standardized through the station-level monitoring layer.

[0009] Furthermore, the estimation of the health status of each battery cluster specifically includes: For any battery cluster, the health status of the battery cluster is obtained by weighted calculation based on the ratio of its actual usable capacity to its nominal capacity and the ratio of its DC internal resistance to its nominal internal resistance.

[0010] Furthermore, the health status of the battery cluster is calculated using the following method:

[0011] In the formula: This represents the real-time health status of the k-th battery cluster, with a value ranging from 0 to 1. This represents the actual usable capacity of the k-th battery cluster. The nominal capacity of the battery cluster; Let be the DC internal resistance of the k-th battery cluster. The nominal internal resistance of the battery cluster; Let be the weighting coefficient, satisfying .

[0012] Furthermore, the relationship between the charge / discharge efficiency of the battery cluster and the ambient temperature and the health state is a temperature-SOH-energy efficiency coupling model obtained by fitting a wide-temperature-range charge / discharge cycle test. The temperature-SOH-energy efficiency coupling model characterizes the functional relationship between the real-time charge / discharge efficiency of the k-th battery cluster and the ambient temperature and the health state.

[0013] Furthermore, the step of determining the charging and discharging power allocation scheme for each battery cluster within the energy storage system through an optimization algorithm specifically includes: With maximizing the overall charging and discharging energy efficiency of the energy storage system as the optimization objective, an objective function is constructed; Under the premise of satisfying the preset constraints, the objective function is solved to obtain the charging and discharging power allocation scheme.

[0014] Furthermore, the objective function is:

[0015] In the formula: Improve the overall charging and discharging energy efficiency of the system; This represents the total number of battery clusters within the system. This represents the allocated charging and discharging power for the k-th battery cluster, with charging being positive and discharging being negative. Let be the real-time charge / discharge efficiency of the k-th battery cluster, and let be the temperature. and The coupling function; This refers to the total charging and discharging power command issued by the power grid dispatch center.

[0016] Furthermore, the preset constraints include at least one of power balance constraints, temperature safety constraints, and health state equilibrium constraints.

[0017] Furthermore, the power balance constraint is: Power balance constraints: ,and ,in The minimum / maximum allowable charge / discharge power of the k-th battery cluster is given by... Real-time calibration with temperature threshold (e.g.) , hour, , ); Temperature safety constraints: Among them, the low temperature protection threshold High temperature protection threshold When the threshold is exceeded, the power of the cluster is automatically adjusted to a safe range, and the temperature is adjusted in conjunction with the thermal management system. SOH equilibrium constraint: ,in The system average SOH and the inconsistency threshold are given. This avoids excessive cycling of low-SOH clusters, which accelerates their decay.

[0018] Furthermore, after controlling each battery cluster in the energy storage system to perform charging and discharging operations, the method further includes: Collect actual charge and discharge efficiency data for each battery cluster; Based on the deviation between the actual charge / discharge efficiency data and the expected efficiency data, the parameters or constraints of the optimization algorithm are adjusted.

[0019] Furthermore, it also includes: Establish a quantitative correlation model between battery performance parameters and system operating parameters or power grid application parameters; A comprehensive evaluation system for the entire life cycle of an energy storage system is constructed based on the analytic hierarchy process (AHP). The comprehensive evaluation system includes energy efficiency indicators, life cycle indicators, grid adaptability indicators, and safety and reliability indicators. Based on the quantitative correlation model and the comprehensive evaluation system, the performance of the energy storage system throughout its entire life cycle is evaluated and optimized.

[0020] Furthermore, the quantitative correlation model quantifies the correlation between parameter X and parameter Y by calculating the Pearson correlation coefficient, specifically as follows:

[0021] In the formula: These are the intrinsic performance parameters of the battery (SOH, capacity decay rate, internal resistance growth rate, etc.). These are system operating parameters (charge and discharge efficiency, temperature fluctuation rate, etc.) or power grid application parameters (frequency regulation response accuracy, peak shaving completion rate, renewable energy consumption rate, etc.). Let X be the covariance of Y. This represents the standard deviation of the corresponding parameter.

[0022] This application also provides an energy efficiency improvement device for an MWh-level manganese-based energy storage battery system. The device includes: a data acquisition module for acquiring operating data of each battery cluster in the MWh-level manganese-based energy storage battery system and the ambient temperature of the energy storage system; a state estimation module for estimating the health state of each battery cluster based on the operating data; a relationship determination module for determining the correspondence between the charge / discharge efficiency of each battery cluster and the ambient temperature and health state based on the ambient temperature and the health state of each battery cluster; an optimization allocation module for determining a charge / discharge power allocation scheme for each battery cluster in the energy storage system using an optimization algorithm based on the health state of each battery cluster, the correspondence, and grid power commands; and a control execution module for controlling each battery cluster in the energy storage system to perform charge / discharge operations according to the charge / discharge power allocation scheme.

[0023] This application also provides an electronic device, including: a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the energy efficiency improvement method for the MWh-level manganese-based energy storage battery system described in any of the above claims.

[0024] This application also provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the energy efficiency improvement method for the MWh-level manganese-based energy storage battery system described in any of the above claims.

[0025] Compared to the closest existing technology, the advantages of this application are as follows: For MWh-level manganese-based energy storage battery systems, this application acquires the health status of each battery cluster and the system ambient temperature, and determines the correlation between charge / discharge efficiency and these two parameters. When allocating power, it incorporates the differences in the health status of battery clusters and the impact of ambient temperature on efficiency into the decision-making process. Based on this, an optimization algorithm is used to determine the power allocation scheme for each battery cluster with the goal of maximizing the overall energy efficiency of the system. Power can be preferentially allocated to battery clusters with better health status and higher efficiency at specific temperatures, while power is constrained to protect battery clusters with poorer health status. This allocation method allows the energy storage system to maximize the utilization of high-efficiency battery clusters when executing grid commands, thereby effectively improving the overall charge / discharge efficiency of the system. Furthermore, by including temperature safety constraints, it can prevent battery clusters from operating at unfavorable temperatures, ensuring the operational safety of the system. By setting health status balancing constraints, it helps to prevent individual battery clusters from aging faster due to overuse, which is beneficial for maintaining consistency among battery clusters and extending the overall lifespan of the system. In addition, the closed-loop feedback correction mechanism can continuously adjust the optimization strategy based on actual operating data, enabling the system's energy efficiency management to have adaptive capabilities. A comprehensive performance evaluation throughout the entire lifecycle provides a quantitative basis for long-term system optimization and operation and maintenance decisions.

[0026] Furthermore, unlike existing technologies where the charge / discharge efficiency function does not consider differences in battery material systems, this application addresses the inherent temperature sensitivity of manganese-based battery material systems. Through wide-temperature-range charge / discharge cycle tests (covering the full operating temperature range of practical manganese-based batteries, such as -20℃ to 55℃), and combined with different state-of-health (SOH) levels of manganese-based batteries, a temperature-SOH-efficiency coupling model specifically for manganese-based batteries is pre-constructed. This model accurately characterizes the nonlinear characteristics of manganese-based batteries, such as a sharp increase in internal resistance and a drop in capacity at low temperatures, and an exacerbation of side reactions and a sharp decrease in efficiency at high temperatures. This provides a precise performance prediction basis for the power distribution of manganese-based energy storage systems in wide-temperature-range scenarios.

[0027] Furthermore, charging manganese-based batteries below -10°C may lead to lithium plating, while cycle life decreases sharply above 45°C. Unlike existing technologies that rely solely on SOC grouping and power limits for allocation, this application adds low-temperature and high-temperature protection constraints to the power optimization model specifically targeting the temperature-sensitive thresholds of manganese-based batteries. When the temperature of any battery cluster reaches the threshold, the system automatically limits the charge and discharge power of that cluster to a safe range and coordinates with the thermal management system to regulate the temperature. Simultaneously, when the temperature is in a sub-optimal range (e.g., 0°C~10°C or 35°C~45°C), the optimization algorithm dynamically adjusts the power allocation weight of that cluster, prioritizing power allocation to battery clusters in the optimal temperature range (20°C~30°C). This mechanism effectively solves the engineering challenge of manganese-based batteries being "sensitive to both cold and heat."

[0028] Furthermore, the degradation of manganese-based batteries exhibits a coupled characteristic of capacity decay and internal resistance growth. Unlike existing technologies that primarily rely on State of Charge (SOC) or historical charge-discharge capacity for allocation, this application proposes a two-parameter fusion SOH estimation model for manganese-based batteries. Based on this, an SOH equalization constraint is further introduced, and a power penalty term for low-SOH clusters is added to the optimization objective. This prevents severely aged manganese-based battery clusters from being over-utilized and accelerating their failure, thereby significantly improving the SOH consistency between clusters and reducing the usable capacity reduction caused by inconsistency.

[0029] Furthermore, existing technologies (such as prior art) only focus on the power distribution effect in a single instance or short period. This application further establishes a quantitative correlation model for manganese-based batteries, linking battery intrinsic performance, system operation, and grid application. For example, it uses the Pearson correlation coefficient to quantify the correlation between the SOH decay rate of manganese-based batteries and the system's annual average charge-discharge efficiency and frequency regulation response accuracy. Based on this, a comprehensive lifecycle evaluation system for manganese-based energy storage systems is constructed using the Analytic Hierarchy Process (AHP), including energy efficiency indicators (such as wide-temperature-range average efficiency), lifespan indicators (such as equivalent cycle count), grid adaptability indicators (such as peak shaving completion rate and renewable energy absorption rate), and safety and reliability indicators. Through periodic evaluation, the long-term adjustment of power optimization strategies is guided, achieving full-link quantitative management and control of manganese-based energy storage systems from cell intrinsic performance to grid application effects. Attached Figure Description

[0030] Figure 1 A flowchart illustrating the method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system provided in this application is shown.

[0031] Figure 2 A flowchart illustrating an embodiment of the present application provides a method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0033] Example 1 The energy efficiency improvement method for MWh-level manganese-based energy storage battery systems provided in this application is particularly applicable to large-scale energy storage power stations containing multiple parallel battery clusters, especially energy storage battery systems represented by manganese-based material systems. In such systems operating over a wide temperature range, the combined impact of differences in the health status of battery clusters and ambient temperature on charge and discharge efficiency is more significant. This method maximizes the overall charge and discharge efficiency of the energy storage system while meeting grid power commands, and also considers system operational safety and battery lifespan.

[0034] Figure 1 A flowchart illustrating a method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system is shown. Figure 1 As shown, embodiments of this application provide a method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system, the method comprising steps S1 to S6. Specific steps are described below: S1, acquire the operating data of each battery cluster in the MWh-level manganese-based energy storage battery system; In this embodiment, step S1 can be specifically executed as acquiring the operating data of each battery cluster in an MWh-level manganese-based energy storage battery system. The operating data includes the voltage, temperature, and / or internal resistance of the cells within each battery cluster. In practical applications, this raw data can be collected in real time using sensors deployed at the cell and battery cluster levels. To ensure the accuracy of subsequent calculations, the collected raw data typically needs to be preprocessed, such as data cleaning (removing outliers) and data standardization (unifying dimensions). The preprocessed cluster-level operating data provides reliable input for subsequent SOH estimation.

[0035] S2, Based on the operating data, the health status of each battery cluster is estimated; In this embodiment, step S2 can be specifically executed as estimating the health status of each battery cluster based on the operating data. Step S2 can be specifically executed as follows: for any battery cluster, the health status of the battery cluster is obtained by weighted calculation based on the ratio of its actual usable capacity to its nominal capacity and the ratio of its DC internal resistance to its nominal internal resistance.

[0036] Specifically, the health status of the battery cluster It can be calculated using the formula:

[0037] In the formula: This represents the real-time health status of the k-th battery cluster, with a value ranging from 0 to 1. This represents the actual usable capacity of the k-th battery cluster. The nominal capacity of the battery cluster; Let be the DC internal resistance of the k-th battery cluster. The nominal internal resistance of the battery cluster; Let be the weighting coefficient, satisfying The characteristics of manganese-based batteries can be calibrated as follows: , ; 3.4 SOH Anomaly Judgment: If a certain cluster (i.e., health status below 80%), are marked as low SOH clusters, and their charge and discharge power is limited in subsequent power allocation to avoid excessive cycling and accelerated decay.

[0038] In actual operation, the actual usable capacity of the battery cluster The DC internal resistance can be measured through a constant current charge-discharge test. It can be measured using methods known in the art, such as the AC impedance method. As a preferred embodiment, a weighting coefficient is used, taking into account the characteristics of manganese-based batteries. The value can range from 0.3 to 0.7, and correspondingly, the weighting coefficient... The value range is from 0.7 to 0.3. For example, it can be set to... , To better reflect the impact of internal resistance growth on the health status of manganese-based batteries. If the calculated value of a certain battery cluster... If the percentage is below a preset threshold (e.g., 80%), it can be marked as a low-health cluster and restricted in subsequent power allocation.

[0039] S3, Obtain the ambient temperature of the energy storage system; In this embodiment, step S3 can specifically be performed by acquiring the ambient temperature of the energy storage system. The ambient temperature can be the average temperature inside the energy storage container or battery compartment, acquired through one or more temperature sensors deployed at key locations. This ambient temperature serves as an input variable for subsequently determining the correlation between the battery cluster's charge and discharge efficiencies.

[0040] S4, based on the ambient temperature and the health status of each battery cluster, determine the correspondence between the charging and discharging efficiency of the battery cluster and the ambient temperature and the health status; In this embodiment, step S4 can be specifically executed as determining the correspondence between the charge / discharge efficiency of the battery cluster and the ambient temperature and health status, based on the ambient temperature and the health status of each battery cluster. This correspondence is specifically a temperature-SOH-energy efficiency coupling model obtained by fitting a wide-temperature-range charge / discharge cycle test. This model characterizes the real-time charge / discharge efficiency of the k-th battery cluster. With the ambient temperature and the health status The functional relationship, that is In the actual modeling process, different temperature points can be simulated in a laboratory environment chamber, and charge-discharge cycle tests can be conducted on battery clusters with different SOH levels to obtain a large number of efficiency data samples. Subsequently, data fitting algorithms such as least squares method are used to perform regression analysis on the sample data to obtain a model that can characterize... Follow and The model represents the mathematical function or lookup table illustrating the changing patterns. It covers typical temperatures and State of Harm (SOH) ranges likely encountered in actual operation, providing crucial performance mapping relationships for optimized power allocation.

[0041] S5, determined by an optimization algorithm based on the health status of each battery cluster, the corresponding relationship, and the power grid command; In this embodiment, step S5 can be specifically executed as follows: based on the health status of each battery cluster, the corresponding relationship, and the power grid command, an optimization algorithm is used to determine the charging and discharging power allocation scheme for each battery cluster in the energy storage system. Specifically, step S5 includes: constructing an objective function with the goal of maximizing the overall charging and discharging energy efficiency of the energy storage system; and solving the objective function under the premise of satisfying preset constraints to obtain the charging and discharging power allocation scheme.

[0042]

[0043] In the formula: Improve the overall charging and discharging energy efficiency of the system; This represents the total number of battery clusters within the system. This represents the allocated charging and discharging power for the k-th battery cluster, with charging being positive and discharging being negative. Let be the real-time charge / discharge efficiency of the k-th battery cluster, and let be the temperature. and The coupling function; The total charging and discharging power command issued by the power grid dispatch center; The preset constraints include at least one of power balance constraints, temperature safety constraints, and health state equilibrium constraints.

[0044] Power balance constraints: ,and ,in The minimum / maximum allowable charge / discharge power of the k-th battery cluster is given by... Real-time calibration with temperature threshold; For example: , hour, , .

[0045] Temperature safety constraints: Among them, the low temperature protection threshold High temperature protection threshold When the threshold is exceeded, the power of the cluster is automatically adjusted to a safe range, and the temperature is adjusted in conjunction with the thermal management system. SOH equilibrium constraint: ,in The system average SOH and the inconsistency threshold are given. This avoids excessive cycling of low-SOH clusters, which accelerates their decay.

[0046] S6, the charging and discharging power allocation scheme of each battery cluster in the energy storage system; In this embodiment, step S6 can be specifically executed as controlling each battery cluster in the energy storage system to perform charging and discharging operations according to the charging and discharging power allocation scheme. Specifically, the station-level monitoring system will calculate the power commands of each battery cluster. The command is then sent to the corresponding cluster-level control unit or power conversion system (PCS), which performs precise power control to respond to the power command from the grid.

[0047] S7, according to the charging and discharging power allocation scheme, control each battery cluster in the energy storage system to perform charging and discharging operations; In this embodiment, the method may further include step S7. Adding step S7 after controlling each battery cluster in the energy storage system to perform charging and discharging operations helps to achieve closed-loop optimization, improving the long-term energy efficiency stability and algorithm adaptability of the system. Figure 1 As shown, step S7 can be specifically executed as follows: Collect actual charge / discharge efficiency data for each battery cluster; adjust the parameters or constraints of the optimization algorithm based on the deviation between the actual charge / discharge efficiency data and the expected efficiency data. For example, if the actual efficiency is consistently lower than the model's expected value by more than a preset threshold (e.g., 5%), it may indicate a deviation in the temperature-SOH-energy efficiency coupling model or an unexpected degradation in battery performance. In this case, model parameter recalibration, adjustment of the weights of certain terms in the optimization objective function, or relaxation / tightening of certain safety constraint thresholds can be triggered to make the next round of optimization decisions more closely match the actual system state.

[0048] In this embodiment, the method may further include step S8. Adding step S8 enables the management and evaluation of the energy storage system from a more macroscopic and long-term perspective, providing data support for operation and maintenance decisions and system upgrades. Step S8 can be specifically executed as follows: collecting the entire lifecycle operation data of the energy storage system, including battery intrinsic performance data, system operation data, and grid application data; establishing a quantitative correlation model between battery performance parameters, system operation parameters, and grid application parameters based on the entire lifecycle operation data; and comprehensively evaluating the entire lifecycle performance of the energy storage system based on the quantitative correlation model and a preset evaluation index system.

[0049] Specifically, the quantitative correlation model can be achieved by calculating the Pearson correlation coefficient:

[0050] In the formula: These are the intrinsic performance parameters of the battery (SOH, capacity decay rate, internal resistance growth rate, etc.). These are system operating parameters (charge and discharge efficiency, temperature fluctuation rate, etc.) or power grid application parameters (frequency regulation response accuracy, peak shaving completion rate, renewable energy consumption rate, etc.). Let X be the covariance of Y. This represents the standard deviation of the corresponding parameter.

[0051] For example, the correlation between battery SOH degradation rate and system annual average energy efficiency can be analyzed, or the correlation between system frequency regulation response accuracy and grid evaluation indicators can be analyzed. The evaluation indicator system can be constructed based on the analytic hierarchy process (AHP) and includes quantitative indicators across multiple dimensions such as energy efficiency, lifespan, safety, and grid adaptability. Periodic (e.g., monthly or quarterly) comprehensive evaluation results can be used to guide the long-term adjustment of the optimization strategy in step S5 and the optimization of the feedback correction mechanism in step S7.

[0052] In summary, the energy efficiency improvement method for MWh-level manganese-based energy storage battery systems in this embodiment obtains the State of Health (SOH) and ambient temperature of each battery cluster and establishes a coupling relationship between charge / discharge efficiency and these two parameters, accurately reflecting the actual performance of different battery clusters under current operating conditions. Based on this, an optimization model aimed at maximizing the overall energy efficiency of the system is constructed, considering multiple constraints such as power balance, temperature safety, and balanced state of health, to obtain the optimal power allocation scheme and ultimately control the execution of each battery cluster. This closed-loop process can dynamically adapt to changes in battery state and environment, effectively solving the problem of low overall system energy efficiency caused by neglecting SOH differences and temperature effects in existing technologies.

[0053] Example 2 This embodiment addresses the shortcomings of existing technologies and aims to solve the following technical problems to achieve full-scenario and full-lifecycle energy efficiency improvement for MWh-level manganese-based energy storage systems: It resolves the energy efficiency loss and capacity reduction issues caused by poor monitoring topology adaptability and inter-cluster SOH inconsistency in existing MWh-level manganese-based energy storage systems by constructing a distributed management and control architecture adapted to large-scale manganese-based energy storage systems; it addresses the energy management mismatch and energy efficiency degradation caused by temperature and SOH coupling in wide temperature range scenarios by proposing a temperature-adaptive energy management method considering SOH to achieve dual improvement in energy efficiency and capacity under extreme temperatures; and it addresses the lack of full-link performance correlation by establishing a quantitative correlation model of battery performance, system operation, and grid application, constructing a comprehensive lifecycle evaluation method for MWh-level manganese-based energy storage systems, and improving the system's full-lifecycle economy and reliability.

[0054] like Figure 2 As shown, the energy efficiency improvement method of this application achieves full-process energy efficiency improvement through a series of steps: "architecture construction → data collection → model building → optimization execution → evaluation and optimization". Each step is interconnected, and the specific operational details, formulas and requirements are as follows: Step 1: Build a three-tier distributed monitoring topology and supporting system Based on the above system architecture, complete the hardware deployment and debugging of the entire system to ensure normal linkage between all levels and supporting systems. Specific operations are as follows: 1.1 Deployment of cell-level acquisition units: Install voltage, temperature, internal resistance, and current acquisition modules at the end of each cell, adjust the acquisition accuracy (e.g., voltage ±0.01V, temperature ±0.5℃, internal resistance ±0.01mΩ, current ±0.1A), and configure a CAN bus communication module to ensure real-time data upload; 1.2 Cluster-level management and control unit deployment: One cluster-level management and control unit is configured for each battery cluster to debug data aggregation, preliminary SOH estimation, and equalization control functions, and to ensure smooth communication with the cell-level acquisition unit and the station-level monitoring layer; 1.3 Deployment of station-level monitoring layer: Install a station-level monitoring platform in the central control room of the energy storage power station, complete the docking and debugging with the power grid dispatching system, PCS system and thermal management system, configure energy efficiency optimization algorithm and data storage module, and set the control cycle to 1 second; 1.4 System integration and commissioning: Start the entire system, simulate power grid charging and discharging commands, test the accuracy of data transmission and command execution at each level, ensure no communication failures, control delay ≤100ms, and overall system operation is stable.

[0055] Step 2: Low-level data acquisition and preprocessing After the system integration and testing are successful, the data acquisition function is activated to complete the collection and preprocessing of basic data for the entire system, providing accurate data support for subsequent SOH estimation and energy efficiency modeling. Specific operations are as follows: 2.1 Cell-level data acquisition: Real-time acquisition of voltage, temperature, internal resistance, and charging / discharging current of each cell, and real-time uploading to the corresponding cluster-level control unit via CAN bus; 2.2 Cluster-level data aggregation: The cluster-level control unit aggregates all cell data in its cluster every 500ms, calculates the cluster-level average temperature, average voltage, total internal resistance, and total charging and discharging current, removes abnormal data (such as data with voltage or temperature exceeding the range), retains valid data, and uploads it to the station-level monitoring layer. 2.3 Data Standardization Processing: The station-level monitoring layer standardizes all received cluster-level data, converting parameters such as voltage, current, and temperature into uniform units to eliminate data deviations and obtain a standardized cluster-level operating data matrix for subsequent model calculations.

[0056] Step 3: Real-time estimation of SOH for battery clusters using dual-parameter fusion To address the coupling characteristics of capacity decay and internal resistance growth in manganese-based batteries, a two-parameter fusion SOH estimation model is constructed based on the cluster-level data preprocessed in step 2. This model accurately obtains the real-time health status of each battery cluster, providing a core basis for energy efficiency optimization. Specific operations and formulas are as follows: 3.1 Actual usable capacity at the cluster level C kCalculation: Through constant current charge-discharge tests, record the total charge amount of each battery cluster from the cutoff voltage of 3.0V to 4.2V. This total charge amount is the actual usable capacity C of the cluster. k Simultaneously record the cluster-level nominal capacity C0 (factory calibration value); 3.2 Cluster-level DC internal resistance R k Measurement: The DC internal resistance R of each cell cluster was measured using the AC impedance method at a normal temperature of 25℃. k Record the nominal internal resistance R0 of the cluster level (factory calibration value); 3.3 Real-time SOH Calculation: Substituting the parameters into the two-parameter fusion SOH estimation model, the formula is as follows:

[0057] In the formula: This represents the real-time health status of the k-th battery cluster, with a value ranging from 0 to 1. This represents the actual usable capacity of the k-th battery cluster. The nominal capacity of the battery cluster; Let be the DC internal resistance of the k-th battery cluster. The nominal internal resistance of the battery cluster; Let be the weighting coefficient, satisfying The characteristics of manganese-based batteries can be calibrated as follows: , ; 3.4 SOH Anomaly Judgment: If a certain cluster (i.e., health status below 80%), are marked as low SOH clusters, and their charge and discharge power is limited in subsequent power allocation to avoid excessive cycling and accelerated decay.

[0058] Step 4: Construction of the temperature-energy efficiency coupling model for manganese-based batteries Through wide-temperature-range charge-discharge cycle tests, combined with different states of health (SOH) of the battery cluster, a coupled model of charge-discharge efficiency of manganese-based batteries with ambient temperature and SOH of the battery cluster was obtained. This model covers the entire operating temperature range and the entire SOH range for practical applications of manganese-based energy storage batteries, providing theoretical support for temperature-adaptive and SOH-adaptive energy management. The specific operation is as follows: 4.1 Wide Temperature Range, Full SOH Test: Several typical temperature points covering the full operating range are simulated in an environmental chamber. At the same time, manganese-based battery clusters with different states of health (SOH) are selected, and constant current charge-discharge tests are carried out on battery clusters of each SOH level. The test is repeated multiple times for each temperature point and each SOH level. The charge-discharge efficiency of battery clusters under different temperatures and different SOH is fully recorded to ensure that the test data covers various combinations of operating conditions in actual operation.

[0059] 4.2 Data Fitting: The least squares method was used to fit the experimental data to obtain a temperature-SOH-energy efficiency coupled model. This model simultaneously relates to the two core influencing factors, temperature and SOH, and can reflect the variation law of battery cluster charge and discharge efficiency under different temperatures and different SOH combinations. The model form can be flexibly selected according to the actual characteristics of the manganese-based battery experimental data, without limiting the specific function type. It is only necessary to ensure that the deviation between the calculated value of the model and the measured value meets the accuracy requirements, so as to ensure that the model can accurately represent... (The real-time charge / discharge efficiency of the k-th battery cluster, given by temperature) and The intrinsic relationship of the coupling function.

[0060] 4.3 Model Validation: Several intermediate temperature points were selected within the full operating temperature range, and battery clusters with different SOH levels were selected for charge-discharge tests. The deviations between the model's calculated values ​​and the actual experimental values ​​were verified to meet the accuracy requirements, ensuring that the model can accurately output values ​​at different temperatures. ,different The corresponding real-time charge and discharge efficiency of the battery cluster This provides reliable model support for subsequent power optimization and allocation.

[0061] Step 5: Energy efficiency maximization power allocation under multiple constraints With the goal of maximizing the overall charging and discharging energy efficiency of the system, and combining the SOH data obtained in step 3 and the temperature-energy efficiency coupled model constructed in step 4, a constrained optimization objective function is constructed. Model predictive control (MPC) is used to achieve rolling optimization solution for each control cycle (1s), and power commands are issued and executed. Specific operations are as follows: 5.1 Determine the optimization objective: Construct the objective function for maximizing the overall charging and discharging energy efficiency of the system, as shown in the following formula:

[0062] In the formula: Improve the overall charging and discharging energy efficiency of the system; This represents the total number of battery clusters within the system. This represents the allocated charging and discharging power for the k-th battery cluster, with charging being positive and discharging being negative. Let be the real-time charge / discharge efficiency of the k-th battery cluster, and let be the temperature. and The coupling function; The total charging and discharging power command issued by the power grid dispatch center; 5.2 Define Constraints: Based on the characteristics of manganese-based batteries and the requirements of power grid operation, three types of constraints are defined: 5.2.1 Power balance constraints: ,and ,in The minimum / maximum allowable charge / discharge power of the k-th battery cluster is given by... Real-time calibration with temperature threshold (e.g.) , hour, , ); 5.2.2 Temperature safety constraints: Among them, the low temperature protection threshold High temperature protection threshold When the threshold is exceeded, the power of the cluster is automatically adjusted to a safe range, and the temperature is adjusted in conjunction with the thermal management system. 5.2.3 SOH Equilibrium Constraint: ,in The system average SOH and the inconsistency threshold are given. To avoid excessive cycling of low-SOH clusters that accelerate their decay; 5.3 Optimization and Command Execution: The MPC algorithm is adopted, with the next 5 seconds as the prediction time domain. The objective function is solved in a rolling manner in each control cycle (1 second) to obtain the optimal charging and discharging power allocation scheme for each battery cluster. The scheme is then sent from the station-level monitoring layer to the cluster-level control unit. The cluster-level control unit executes the power command to complete the cluster-level charging and discharging control.

[0063] Step 6: Closed-loop feedback correction to continuously optimize energy efficiency. After the cluster-level control unit executes the power allocation command, it collects real-time data on the actual charge / discharge efficiency, temperature, and SOH changes of each cluster and uploads it to the station-level monitoring layer. The data is then compared with the optimization target value. If the deviation is ≥ a%, the weight coefficients of the optimization objective function and the constraint threshold are readjusted, and steps 3-5 are repeated to achieve continuous optimization of energy efficiency and ensure that the system is in the optimal operating state for a long time. At the same time, the parameters and optimization effects of each adjustment are recorded to form an optimization log, which provides a basis for subsequent strategy optimization.

[0064] Step 7: Establish a performance correlation model and conduct a comprehensive lifecycle evaluation. Collect system lifecycle operational data (including battery intrinsic performance data, system operation data, and power grid application data), establish a quantitative correlation model, construct a comprehensive evaluation system, and achieve full lifecycle management of the system. Specific operations include: 7.1 Constructing a correlation model between battery performance, system operation, and grid application: The Pearson correlation coefficient is used to quantify the correlation between parameters of each dimension, as shown in the following formula:

[0065] In the formula: These are the intrinsic performance parameters of the battery (SOH, capacity decay rate, internal resistance growth rate, etc.). These are system operating parameters (charge and discharge efficiency, temperature fluctuation rate, etc.) or power grid application parameters (frequency regulation response accuracy, peak shaving completion rate, renewable energy consumption rate, etc.). Let X be the covariance of Y. This represents the standard deviation of the corresponding parameter.

[0066] 7.2 Establish a comprehensive life-cycle evaluation system: A three-level evaluation system is constructed using the Analytic Hierarchy Process (AHP): The target layer is the system's comprehensive energy efficiency and life-cycle performance; the criteria layer includes four dimensions: energy efficiency indicators, lifespan indicators, grid adaptability indicators, and safety and reliability indicators; the indicator layer includes several quantitative sub-indicators; the weight of each indicator is determined by expert scoring, and the comprehensive system evaluation score is calculated. 7.3 Evaluation and Optimization: Based on the comprehensive evaluation score each month, and combined with the correlation model analysis of energy efficiency influencing factors, adjust the optimization parameters in step 5 and the feedback correction threshold in step 6 to continuously improve the energy efficiency and economy of the system throughout its entire life cycle.

[0067] Compared with existing technologies, the method in this embodiment adopts a three-layer distributed monitoring topology and a SOH-temperature coupled adaptive energy management method, which effectively improves the overall charging and discharging energy efficiency of the system, improves the SOH consistency between battery clusters, reduces the reduction of available system capacity, extends the cycle life of the system, and achieves a significant improvement in system energy efficiency and capacity utilization. It can significantly improve the system's operating performance in extreme temperature scenarios, effectively improve the available system capacity and charging and discharging energy efficiency in low-temperature environments, reduce the risk of thermal runaway in high-temperature environments, reduce energy efficiency decay in high-temperature conditions, achieve stable and efficient operation across the entire temperature range, and greatly enhance wide-temperature adaptability. It can be well adapted to typical power grid application scenarios such as peak shaving, primary frequency regulation, and renewable energy grid integration and consumption, improve power grid response performance, meet power grid connection technical requirements, improve renewable energy consumption levels, enhance system operating economy, and achieve significant optimization of power grid adaptability and economy. The multi-dimensional parameter correlation model and comprehensive evaluation system established in this application realize the full-link quantitative control of MWh-level manganese-based energy storage systems, from the intrinsic performance of the cells to the grid application effect. It has full life-cycle control capabilities and provides a standardized evaluation and control basis for the large-scale engineering application of manganese-based energy storage systems.

[0068] In summary, this embodiment adopts a three-layer distributed monitoring topology—cell-level acquisition layer, cluster-level control layer, and station-level monitoring layer—linking the power grid dispatching system, PCS energy conversion system, and thermal management system to achieve end-to-end distributed refined control from the cell level to the station level, providing hardware support for energy efficiency improvement. The process sequentially includes system architecture construction, underlying data acquisition and preprocessing, real-time estimation of SOH through dual-parameter fusion, construction of a temperature-energy efficiency coupling model, multi-constraint power optimization allocation, closed-loop feedback correction, performance correlation, and comprehensive evaluation. Through these steps, simultaneous improvement of SOH balance and system energy efficiency is achieved across a wide temperature range. A quantitative correlation model of battery performance, system operation, and grid application is established, and a three-level evaluation system is constructed based on the analytic hierarchy process (AHP), realizing quantitative control and performance evaluation of the entire lifecycle of a large-scale manganese-based energy storage system.

[0069] This application embodiment also provides an energy efficiency improvement device for an MWh-level manganese-based energy storage battery system, the device comprising: The data acquisition module is used to acquire the operating data of each battery cluster in the MWh-level manganese-based energy storage battery system and the ambient temperature of the energy storage system. The status estimation module is used to estimate the health status of each battery cluster based on the operating data. The relationship determination module is used to determine the correspondence between the charging and discharging efficiency of the battery cluster and the ambient temperature and the health status of each battery cluster based on the ambient temperature and the health status of each battery cluster. An optimization allocation module is used to determine the charging and discharging power allocation scheme of each battery cluster in the energy storage system based on the health status of each battery cluster, the corresponding relationship, and the power grid command, through an optimization algorithm. The control execution module is used to control each battery cluster in the energy storage system to perform charging and discharging operations according to the charging and discharging power allocation scheme.

[0070] The functions of each module in this device correspond one-to-one with the steps in the aforementioned method embodiments, and will not be repeated here.

[0071] This application also provides an electronic device, which includes a processor and a memory. The memory stores computer programs. The memory can be random access memory (RAM), read-only memory (ROM), etc. The processor executes the computer program stored in the memory to implement the steps in the above-described method for improving the energy efficiency of a MWh-level manganese-based energy storage battery system. The processor can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor (e.g., CPU), digital signal processor (DSP), application-specific integrated circuit (ASIC), etc. The processor and memory can be connected via a bus, and the electronic device may also include necessary components such as communication interfaces and input / output devices.

[0072] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method for improving the energy efficiency of the MWh-level manganese-based energy storage battery system described above.

[0073] The computer-readable storage medium can be any medium capable of storing program code, such as cloud storage, USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0074] 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.

[0075] 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.

[0076] This application may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, readable storage media, optical storage, etc.) containing computer-usable program code.

[0077] 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.

[0078] Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system, characterized in that, include: Obtain operational data of each battery cluster in an MWh-level manganese-based energy storage battery system; Based on the aforementioned operational data, the health status of each battery cluster is estimated. Obtain the ambient temperature of the energy storage system; Based on the ambient temperature and the health status of each battery cluster, the correspondence between the charging and discharging efficiency of the battery cluster and the ambient temperature and the health status is determined. Based on the health status of each battery cluster, the corresponding relationship, and the power grid command, an optimization algorithm is used to determine the charging and discharging power allocation scheme of each battery cluster in the energy storage system. According to the charging and discharging power allocation scheme, each battery cluster in the energy storage system is controlled to perform charging and discharging operations.

2. The method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system according to claim 1, characterized in that, The acquisition of operational data for each battery cluster in the MWh-level manganese-based energy storage battery system includes: A three-layer distributed monitoring topology for an energy storage system is constructed, comprising a cell-level acquisition layer, a cluster-level control layer, and a station-level monitoring layer. The station-level monitoring layer is communicatively connected to the power grid dispatching system, the energy conversion system, and the thermal management system, respectively. The three-layer distributed monitoring topology is used to collect the operating data of each battery cluster in the energy storage system.

3. The method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system according to claim 2, characterized in that, The operational data includes the voltage, temperature, internal resistance, and charging / discharging current of the battery clusters; the acquisition of operational data for each battery cluster in the energy storage system through the three-layer distributed monitoring topology specifically includes: The raw operating data of each battery cell is collected through the cell-level acquisition layer; The cluster-level control layer aggregates the original operating data of all cells within each battery cluster and calculates the cluster-level operating data of each battery cluster. The cluster-level operational data is standardized through the station-level monitoring layer.

4. The method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system according to claim 1, characterized in that, The estimation of the health status of each battery cluster specifically includes: For any battery cluster, the health status of the battery cluster is obtained by weighted calculation based on the ratio of its actual usable capacity to its nominal capacity and the ratio of its DC internal resistance to its nominal internal resistance.

5. The method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system according to claim 4, characterized in that, The health status of the battery cluster is calculated using the following method: In the formula: This represents the real-time health status of the k-th battery cluster, with a value ranging from 0 to 1. This represents the actual usable capacity of the k-th battery cluster. The nominal capacity of the battery cluster; Let be the DC internal resistance of the k-th battery cluster. The nominal internal resistance of the battery cluster; For the weighting coefficients, satisfying .

6. The method for improving the energy efficiency of a MWh-level manganese-based energy storage battery system according to claim 1, characterized in that, The relationship between the charge / discharge efficiency of the battery cluster and the ambient temperature and the health state is a temperature-SOH-energy efficiency coupling model obtained by fitting a wide-temperature-range charge / discharge cycle test. The temperature-SOH-energy efficiency coupling model characterizes the real-time charge / discharge efficiency of the k-th battery cluster as a function of the ambient temperature and the health state.

7. The method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system according to claim 1, characterized in that, The step of determining the charging and discharging power allocation scheme for each battery cluster in the energy storage system through an optimization algorithm specifically includes: With maximizing the overall charging and discharging energy efficiency of the energy storage system as the optimization objective, an objective function is constructed; Under the premise of satisfying the preset constraints, the objective function is solved to obtain the charging and discharging power allocation scheme.

8. The method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system according to claim 7, characterized in that, The objective function is: In the formula: Improve the overall charging and discharging energy efficiency of the system; This represents the total number of battery clusters within the system. This represents the allocated charging and discharging power for the k-th battery cluster, with charging being positive and discharging being negative. Let be the real-time charge / discharge efficiency of the k-th battery cluster, and let be the temperature. and The coupling function; This refers to the total charging and discharging power command issued by the power grid dispatch center.

9. The method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system according to claim 8, characterized in that, The preset constraints include at least one of power balance constraints, temperature safety constraints, and health state equilibrium constraints.

10. The method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system according to claim 9, characterized in that, The power balance constraint is: Power balance constraints: ,and ,in The minimum / maximum allowable charge / discharge power of the k-th battery cluster is given by... Real-time calibration with temperature threshold; Temperature safety constraints: Among them, the low temperature protection threshold High temperature protection threshold ; SOH equilibrium constraint: ,in For the system average SOH, This is the inconsistency threshold.

11. The method for improving the energy efficiency of a MWh-level manganese-based energy storage battery system according to claim 1, characterized in that, After controlling each battery cluster in the energy storage system to perform charging and discharging operations, the system further includes: Collect actual charge and discharge efficiency data for each battery cluster; Based on the deviation between the actual charge / discharge efficiency data and the expected efficiency data, the parameters or constraints of the optimization algorithm are adjusted.

12. The method for improving the energy efficiency of an MWh-level manganese-based energy storage battery system according to claim 1, characterized in that, Also includes: Establish a quantitative correlation model between battery performance parameters and system operating parameters or power grid application parameters; A comprehensive evaluation system for the entire life cycle of an energy storage system is constructed based on the analytic hierarchy process (AHP). The comprehensive evaluation system includes energy efficiency indicators, life cycle indicators, grid adaptability indicators, and safety and reliability indicators. Based on the quantitative correlation model and the comprehensive evaluation system, the performance of the energy storage system throughout its entire life cycle is evaluated and optimized.

13. The method for improving the energy efficiency of a MWh-level manganese-based energy storage battery system according to claim 12, characterized in that, The quantitative correlation model quantifies the correlation between parameter X and parameter Y by calculating the Pearson correlation coefficient, specifically: In the formula: These are the intrinsic performance parameters of the battery, including SOH, capacity decay rate, and internal resistance growth rate. These are system operating parameters, including charging and discharging efficiency, temperature fluctuation rate, etc., or grid application parameters, including frequency regulation response accuracy, peak shaving completion rate, and renewable energy consumption rate. Let X be the covariance of Y. This represents the standard deviation of the corresponding parameter.

14. An energy efficiency improvement device for an MWh-level manganese-based energy storage battery system, characterized in that, include: The data acquisition module is used to acquire the operating data of each battery cluster in the MWh-level manganese-based energy storage battery system and the ambient temperature of the energy storage system. The status estimation module is used to estimate the health status of each battery cluster based on the operating data. The relationship determination module is used to determine the correspondence between the charging and discharging efficiency of the battery cluster and the ambient temperature and the health status of each battery cluster based on the ambient temperature and the health status of each battery cluster. An optimization allocation module is used to determine the charging and discharging power allocation scheme of each battery cluster in the energy storage system based on the health status of each battery cluster, the corresponding relationship, and the power grid command, through an optimization algorithm. The control execution module is used to control each battery cluster in the energy storage system to perform charging and discharging operations according to the charging and discharging power allocation scheme.

15. An electronic device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method for improving the energy efficiency of a MWh-level manganese-based energy storage battery system as described in any one of claims 1 to 13.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for improving the energy efficiency of the MWh-level manganese-based energy storage battery system as described in any one of claims 1 to 13.

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