Flywheel lithium battery energy storage system

Abstract

The application discloses a flywheel lithium battery energy storage system, and particularly relates to the field of flywheel lithium batteries, and comprises an initial module, a pretreatment module, a state evaluation module, an energy distribution module, a risk early warning module, an equalization optimization module, an efficiency coordination module and a correction updating module; the initial module acquires multidimensional data and establishes a three-dimensional operation state coordinate system; the state evaluation module analyzes a comprehensive operation state of multi-parameter coordination and maps to a three-dimensional coordinate point; the risk early warning module constructs an analysis model of multi-parameter coupling and starts graded early warning; the application designs a dynamic energy distribution strategy and a safety risk early warning model based on a comprehensive state index of multi-parameter coupling and a three-dimensional operation state coordinate system, avoids the limitations of fixed energy distribution and single threshold early warning, solves the problems of inaccuracy of collaborative control and lag of safety early warning, and realizes collaborative improvement of energy conversion efficiency and operation safety.

Description

Technical Field

[0001] This invention relates to the field of flywheel lithium battery technology, and more specifically, to a flywheel lithium battery energy storage system. Background Technology

[0002] In fields such as new energy power generation and grid peak shaving, flywheel-lithium battery hybrid energy storage systems have become the mainstream energy storage technology solution due to the rapid response characteristics of flywheels and the high energy density of lithium batteries. Through the synergistic operation of electromechanical energy conversion and electrochemical energy storage, they achieve efficient energy storage and flexible release.

[0003] Existing flywheel lithium battery energy storage systems have demonstrated significant advantages in practical applications, such as fast charging and discharging response, flexible energy storage capacity configuration, and adaptability to complex load fluctuation scenarios, effectively compensating for the shortcomings of single energy storage technologies. However, they also have several shortcomings: First, parameter monitoring lacks systematicity, focusing mainly on conventional parameters such as voltage and current, while insufficient collaborative monitoring of core parameters such as flywheel rotor mechanical state, lithium battery thermal environment, and balancing circuit characteristics, leading to a one-sided system state assessment; Second, collaborative control strategies lack dynamic adaptability, with energy allocation ratios mostly preset fixed values, failing to be dynamically optimized in conjunction with real-time operating parameters, thus limiting energy conversion efficiency; Third, safety early warning mechanisms are simplistic, judging risks only through single parameter thresholds, without considering risk transmission under the coupling effect of multiple parameters, easily resulting in delayed warnings or misjudgments; at the same time, lithium battery balancing control is only based on voltage difference adjustment, without combining parameters such as coulomb quantity and temperature field for fine-tuning, leading to excessively rapid degradation of battery pack consistency and shortened lifespan.

[0004] Therefore, in order to address the problems of incomplete parameter monitoring, imprecise collaborative control, delayed safety warnings, and coarse balance control in existing technologies, a flywheel lithium battery energy storage system is needed. This system can achieve accurate perception of system operating status, efficient optimization of energy conversion, early warning of safety risks, and effective extension of battery life by constructing a multi-dimensional parameter monitoring system, designing dynamic collaborative control strategies, establishing a multi-parameter coupled safety warning model, and a refined balance adjustment mechanism. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a flywheel lithium battery energy storage system, which solves the problems mentioned in the background art through the following solution.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a flywheel lithium battery energy storage system, comprising: Initial module: Acquire multidimensional data and establish a three-dimensional coordinate system for the system's operating state; Preprocessing module: Uses Kalman filtering algorithm to preprocess parameters and calibrate dynamic reference values; Status assessment module: Analyzes the comprehensive operating status of multiple parameters and maps it to three-dimensional coordinate points to quantitatively characterize the system's operating status; Energy distribution module: Evaluates the dynamic energy distribution ratio based on three-dimensional coordinate points and optimizes the operating parameters of the flywheel and lithium battery; Risk warning module: Constructs a multi-parameter coupled analysis model and initiates tiered warnings; Balance Optimization Module: Analyzes the balance optimization status of lithium batteries and implements dynamic balance adjustment and effect correction; Efficiency Coordination Module: Constructs an objective function for optimizing energy conversion efficiency and collaboratively optimizes system operating parameters; Correction and update module: Collects optimized parameters, obtains closed-loop correction coefficients, and updates parameter baseline values.

[0007] Preferably, the multidimensional data includes flywheel rotor dynamic mechanical parameters, flywheel vacuum chamber environmental parameters, flywheel converter power conversion parameters, lithium battery cluster electrochemical parameters, lithium battery compartment thermal environment parameters, and lithium battery BMS balancing state parameters; the flywheel rotor dynamic mechanical parameters include real-time rotor speed, bearing axial load, and rotor angular acceleration; the flywheel vacuum chamber environmental parameters include absolute vacuum chamber pressure, chamber wall temperature gradient, and helium leakage rate at the seal; the flywheel converter power conversion parameters include DC bus ripple voltage, IGBT module junction temperature, and instantaneous apparent power output; the lithium battery cluster electrochemical parameters include instantaneous charging and discharging current, maximum voltage difference between cells, and cumulative coulombic charge / discharge; the lithium battery compartment thermal environment parameters include internal airflow velocity, maximum temperature difference of the module surface temperature field, and compartment dew point temperature; the lithium battery BMS balancing state parameters include instantaneous balancing current of the balancing circuit, battery module SOC inconsistency, and equivalent series resistance of the balancing circuit; the system's three-dimensional operating state coordinate system is based on energy conversion efficiency. The horizontal axis represents system operational stability. The vertical axis represents the safety risk level. The vertical axis is used, and the origin of the coordinate system is the reference point of the system's rated operating condition, which is used to quantitatively characterize the real-time operating status of the system.

[0008] Preferably, the Kalman filter algorithm is used as a signal processing algorithm to eliminate redundant signals such as electromagnetic interference and sensor noise during parameter acquisition, ensuring the stability of parameter data. This yields preprocessed real-time rotor speed, bearing axial load, rotor angular acceleration, vacuum chamber absolute pressure, chamber wall temperature gradient, helium leakage rate at the seal, DC bus ripple voltage, IGBT module junction temperature, output instantaneous apparent power, instantaneous charging and discharging current, maximum voltage difference between cells, cumulative charging and discharging coulomb load, internal airflow velocity, maximum temperature difference on the module surface, chamber dew point temperature, instantaneous equalization current of the equalization circuit, battery module SOC inconsistency, and equivalent series resistance of the equalization circuit, denoted as […]. The dynamic reference values ​​are obtained through nonlinear coupling relationships between parameters, specifically including the flywheel rotor speed reference value, bearing load reference value, converter ripple voltage reference value, lithium battery current reference value, equalization current reference value, temperature field temperature difference reference value, rotor angular acceleration reference value, vacuum chamber absolute pressure reference value, vacuum chamber temperature gradient reference value, helium leakage rate reference value, IGBT module junction temperature reference value, converter output apparent power reference value, lithium battery cell voltage difference reference value, lithium battery cumulative coulomb load reference value, lithium battery compartment airflow velocity reference value, lithium battery compartment dew point temperature reference value, battery module SOC inconsistency reference value, and equalization circuit equivalent series resistance reference value; the flywheel rotor speed reference value... The bearing load reference value The converter ripple voltage reference value The lithium battery current reference value The equalization current reference value The temperature field temperature difference reference value The rotor angular acceleration reference value The absolute pressure reference value of the vacuum chamber The reference value for the temperature gradient of the vacuum chamber The helium leakage rate benchmark value IGBT module junction temperature reference value The converter outputs an apparent power reference value. The reference value of the voltage difference between the individual lithium battery cells The reference value for the cumulative coulomb quantity of the lithium battery The reference value of airflow velocity in the lithium battery compartment The lithium battery compartment dew point temperature reference value The battery module SOC inconsistency benchmark value The equivalent series resistance reference value of the equalization circuit .

[0009] Preferably, the analysis of the comprehensive operating status of multiple parameters is used to obtain the system comprehensive operating status index. The coordinate points for The mapping result in the three-dimensional running state coordinate system, where: Used to intuitively quantify the system's operating status; the quantification characterizes the system's operating status by reflecting the real-time status of the system in terms of energy conversion, stability, and safety risks through the numerical magnitude and changing trend of coordinate points.

[0010] Preferably, the dynamic energy distribution ratio includes the flywheel energy storage ratio. and the proportion of lithium battery energy storage and ,in The optimization of flywheel and lithium battery operating parameters specifically includes adjusting the flywheel converter output frequency and allocating the lithium battery charge / discharge rate; the adjustment of the flywheel converter output frequency... Used to optimize rotor speed tracking accuracy; the allocation of lithium battery charge / discharge rate This is used to ensure that the energy distribution of the lithium battery matches the system state.

[0011] Preferably, the analysis model includes a security risk coupling propagation model and a risk diffusion rate model; the security risk coupling propagation model is used to obtain the risk evolution coefficient. The risk diffusion rate model is used to obtain the risk diffusion rate. Through the Taking the time derivative yields: The aforementioned tiered early warning: when At the same time, adjust the operating power of the vacuum chamber pump: At the same time, adjust the speed of the lithium battery cooling fan: Suppress the spread of risk.

[0012] Preferably, the analysis of the lithium battery equalization optimization status is used to obtain the lithium battery equalization optimization coefficient. The dynamic balancing adjustment is based on a dynamic balancing optimization coefficient of the lithium battery, designed to adjust the circuit operating frequency. The effect correction includes energy transfer path optimization and real-time monitoring; the energy transfer path optimization involves switching the equalization topology through a collaborative control terminal, so that the equalization current follows the following steps: Dynamically allocated to each individual battery cell, among which This represents the actual equalization current value dynamically allocated to each lithium battery cell; the real-time monitoring is used to monitor the equalization effect and obtain the equalization correction coefficient. ,in This serves as the baseline value for SOC inconsistency, used for real-time optimization of balancing parameters.

[0013] Preferably, the energy conversion efficiency optimization objective function The collaborative optimization of system operating parameters includes flywheel converter modulation ratio optimization, lithium battery charge / discharge cutoff voltage optimization, and flywheel vacuum chamber pressure optimization; the flywheel converter modulation ratio optimization is used to adjust the flywheel converter modulation ratio. The lithium battery charge / discharge dynamic cutoff voltage optimization is used to set the dynamic cutoff voltage. The flywheel vacuum chamber pressure optimization is used to coordinately adjust the flywheel vacuum level, so that the vacuum chamber pressure is adjusted according to... Dynamic adaptation reduces mechanical wear, among which This indicates the dynamic adjustment pressure value of the flywheel vacuum chamber.

[0014] Preferably, the closed-loop correction coefficient ;in To optimize the objective function value for efficiency in the efficiency coordination module. This refers to the system comprehensive operating status index in the status assessment module; the parameter benchmark value update is used to obtain the updated flywheel rotor speed benchmark value. Lithium battery current reference value The baseline values ​​of other parameters are updated according to the same logic.

[0015] The technical effects and advantages of this invention are as follows: 1. This invention achieves accurate perception of system operating status by constructing a multi-dimensional monitoring system and dynamic benchmark calibration model for multiple core parameters, solves the problem of one-sided parameter monitoring in existing technologies, provides reliable data support for subsequent optimization control, and improves the accuracy and comprehensiveness of system status assessment; 2. Based on a multi-parameter coupled comprehensive state index and a three-dimensional operating state coordinate system, this invention designs a dynamic energy allocation strategy and a safety risk early warning model, avoiding the limitations of fixed energy allocation and single threshold early warning, solving the problems of inaccurate collaborative control and delayed safety early warning, and achieving a synergistic improvement in energy conversion efficiency and operational safety. 3. This invention employs a refined equalization adjustment strategy based on equalization optimization coefficients. By dynamically adjusting the equalization frequency, current distribution, and topology, it solves the problem of coarse equalization control in traditional systems, effectively improving battery pack consistency, extending lithium battery cycle life, and reducing maintenance costs. Through energy conversion efficiency optimization and closed-loop correction mechanisms, a full-process dynamic optimization control system is formed, solving the problem of limited energy conversion efficiency in existing technologies, significantly improving the overall energy utilization efficiency of the system, enhancing the system's adaptability to complex operating conditions, and ensuring operational stability. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] As attached Figure 1The flywheel lithium battery energy storage system shown includes an initialization module, a preprocessing module, a state assessment module, an energy distribution module, a risk warning module, a balance optimization module, an efficiency coordination module, and a correction and update module.

[0019] The initial module acquires multidimensional data and establishes a three-dimensional coordinate system for the system's operating state. In this embodiment, it should be noted that: the multidimensional data includes flywheel rotor dynamic mechanical parameters, flywheel vacuum chamber environmental parameters, flywheel converter power conversion parameters, lithium battery cluster electrochemical parameters, lithium battery compartment thermal environment parameters, and lithium battery BMS balancing state parameters; the flywheel rotor dynamic mechanical parameters include rotor real-time speed, bearing axial load, and rotor angular acceleration; the flywheel vacuum chamber environmental parameters include vacuum chamber absolute pressure, chamber wall temperature gradient, and helium leakage rate at the seal; the flywheel converter power conversion parameters include DC bus ripple voltage, IGBT module junction temperature, and output instantaneous apparent power; the lithium battery cluster electrochemical parameters include instantaneous charging and discharging current, maximum voltage difference between cells, and cumulative charging and discharging coulombs; the lithium battery compartment thermal environment parameters include internal airflow velocity, maximum temperature difference of the module surface temperature field, and compartment dew point temperature; the lithium battery BMS balancing state parameters include instantaneous balancing current of the balancing circuit, battery module SOC inconsistency, and equivalent series resistance of the balancing circuit; the system's three-dimensional operating state coordinate system is based on energy conversion efficiency. The horizontal axis represents system operational stability. The vertical axis represents the safety risk level. The vertical axis is used as the reference point for the system's rated operating conditions, and the origin of the coordinate system is used to quantitatively characterize the system's real-time operating status. The system includes an initialization control module, a data acquisition terminal, and a coordinate system construction unit for the collaborative control terminal. After the system's initial startup, maintenance restart, or change of energy storage application scenario, it operates in target energy storage application scenarios such as grid peak-shaving stations and new energy power generation supporting energy storage systems. The data acquisition terminal is deployed at key components such as the flywheel rotor, vacuum chamber, converter, lithium battery cluster, battery compartment, and BMS balancing circuit. The collaborative control terminal is deployed at the system control center. These executing entities jointly complete the activation of system hardware and software, deploy 18 core parameter acquisition channels and define parameter attributes, and establish a three-dimensional operating status coordinate system. Specifically, the system initialization control module sends start commands to the data acquisition terminal, collaborative control terminal, safety early warning terminal, flywheel energy storage unit, and lithium battery energy storage unit to activate the hardware devices and software operation modules of each unit and complete self-tests. The data acquisition terminal assigns a unique identifier to each acquisition object according to a preset scheme and establishes a parameter acquisition channel. The coordinate system construction unit of the collaborative control terminal sets the origin of the coordinate system based on the energy conversion efficiency, operational stability, and safety risk level under the rated operating conditions of the system, clarifies the quantization range and unit conversion rules of the three coordinate axes, and finally completes the construction and calibration of the three-dimensional operating state coordinate system.

[0020] The preprocessing module uses a Kalman filter algorithm to preprocess the parameters and calibrate the dynamic reference value. In this embodiment, it should be noted that the Kalman filter algorithm is a signal processing algorithm used to eliminate redundant signals from electromagnetic interference and sensor noise during parameter acquisition, ensuring the stability of parameter data. This yields preprocessed real-time rotor speed, bearing axial load, rotor angular acceleration, vacuum chamber absolute pressure, chamber wall temperature gradient, helium leakage rate at the seal, DC bus ripple voltage, IGBT module junction temperature, output instantaneous apparent power, instantaneous charging and discharging current, maximum voltage difference between cells, cumulative coulomb load during charging and discharging, internal airflow velocity, maximum temperature difference on the module surface, dew point temperature inside the chamber, instantaneous equalization current of the equalization circuit, battery module SOC inconsistency, and equivalent series resistance of the equalization circuit, denoted as […]. The dynamic reference values ​​are obtained through nonlinear coupling relationships between parameters, specifically including the flywheel rotor speed reference value, bearing load reference value, converter ripple voltage reference value, lithium battery current reference value, equalization current reference value, temperature field temperature difference reference value, rotor angular acceleration reference value, vacuum chamber absolute pressure reference value, vacuum chamber temperature gradient reference value, helium leakage rate reference value, IGBT module junction temperature reference value, converter output apparent power reference value, lithium battery cell voltage difference reference value, lithium battery cumulative coulomb load reference value, lithium battery compartment airflow velocity reference value, lithium battery compartment dew point temperature reference value, battery module SOC inconsistency reference value, and equalization circuit equivalent series resistance reference value; the flywheel rotor speed reference value... The bearing load reference value The converter ripple voltage reference value The lithium battery current reference value The equalization current reference value The temperature field temperature difference reference value The rotor angular acceleration reference value The absolute pressure reference value of the vacuum chamber The reference value for the temperature gradient of the vacuum chamber The helium leakage rate benchmark value IGBT module junction temperature reference value The converter outputs an apparent power reference value. The reference value of the voltage difference between the individual lithium battery cells The reference value for the cumulative coulomb quantity of the lithium battery The reference value of airflow velocity in the lithium battery compartment The lithium battery compartment dew point temperature reference value The battery module SOC inconsistency benchmark value The equivalent series resistance reference value of the equalization circuit The SOC inconsistency benchmark value is used for fine-tuning the equalization adjustment of lithium batteries. Specifically, when calculating the equalization correction coefficient, this benchmark value serves as an ideal reference standard for SOC consistency. The ratio of the real-time monitored SOC inconsistency to this benchmark value is calculated to dynamically determine the degree of deviation in the consistency of individual battery cells. If the ratio of the real-time SOC inconsistency to the benchmark value is greater than 1.2, it indicates a deviation exceeding 20%, and the equalization correction coefficient is amplified by 1.2 times to enhance the equalization current and frequency. If the ratio is between 0.8 and 1.2, it indicates a deviation within the allowable range, and the equalization correction coefficient remains unchanged, maintaining the current equalization strategy. If the ratio is less than or equal to 0.8, it indicates that the battery consistency is better than the benchmark state, and the equalization correction coefficient is reduced to 0.8 times to avoid unnecessary energy consumption increases caused by over-equalization. Optimization effect: This ensures that the equalization strategy can accurately match the actual consistency state of the battery, avoiding both insufficient equalization leading to continuous deterioration of consistency and over-equalization causing energy waste. The rotor speed reference value is used for closed-loop correction of the system state. Specifically, when updating the speed reference value, this reference value serves as the ideal speed reference standard after the previous round of optimization. It is combined with the closed-loop correction coefficient (reflecting the optimization effect of the previous round) and the rate of change of rotor angular acceleration between the optimized and unoptimized states (reflecting changes in mechanical load). The new speed reference value is calculated by multiplying these three factors, providing a speed reference basis adapted to the current mechanical operating conditions for the next round of system optimization. Optimization effect: This ensures that the speed reference value can be dynamically adjusted according to the system operating conditions, avoiding reference deviations caused by using a fixed reference value and improving the accuracy of subsequent optimization strategies. The lithium battery current reference value is also used for closed-loop correction of the system state. Specifically, when updating the current reference value, this reference value serves as the safe current reference standard established in the previous round of optimization. It is combined with the closed-loop correction coefficient (reflecting the optimization effect) and the rate of change of cumulative coulomb quantity during lithium battery charging and discharging between the optimized and unoptimized states (reflecting changes in battery capacity). The new current reference value is calculated by multiplying these three factors, providing an updated safe current reference threshold for the next round of charging and discharging strategy optimization. Optimization effect: Ensures that the charging and discharging current is always within the safe range adapted to the current state of the battery, avoiding current overruns due to changes in operating conditions such as battery capacity decay, and extending the lifespan of the lithium battery. The rotor angular acceleration benchmark value is used for dynamic energy distribution strategy optimization. Specifically: after calculating the flywheel energy storage ratio, the real-time monitored rotor angular acceleration needs to be compared with this benchmark value to determine the rationality of the mechanical load.If the real-time rotor angular acceleration is greater than 1.1 times the benchmark value, it indicates excessive mechanical load and a risk of mechanical shock. In this case, the flywheel energy storage ratio is adjusted to the original ratio multiplied by the square root of the ratio of the benchmark value to the real-time angular acceleration, reducing the flywheel energy storage ratio to decrease the mechanical load. If the real-time rotor angular acceleration is less than 0.9 times the benchmark value, it indicates insufficient mechanical load and suboptimal energy conversion efficiency. In this case, the flywheel energy storage ratio is adjusted to the original ratio multiplied by the square root of the ratio of the real-time angular acceleration to the benchmark value, increasing the flywheel energy storage ratio to improve energy conversion efficiency. After adjustment, the new flywheel energy storage ratio is substituted into the flywheel converter output frequency calculation formula to obtain the adjusted output frequency. Optimization effect: While avoiding excessive bearing wear due to excessive mechanical load, the flywheel mechanical energy conversion efficiency is maximized, achieving a balance between mechanical load and energy conversion efficiency. The bearing load benchmark value is used for dynamic energy distribution strategy optimization. Specifically, after calculating the flywheel converter output frequency, the actual bearing load needs to be monitored in real time and compared with the benchmark value. If the actual bearing load exceeds the benchmark value, it indicates a risk of accelerated bearing wear. The output frequency is then corrected to the original frequency multiplied by the ratio of the benchmark value to the actual load to the power of 0.3, reducing the bearing load by decreasing the output frequency. If the actual bearing load is less than or equal to the benchmark value, the load is within a safe range, and the original output frequency is maintained. Optimization effect: Ensures the bearing load is always controlled within a safe threshold, effectively extending the service life of the flywheel mechanical structure and reducing equipment maintenance costs. The vacuum chamber temperature gradient benchmark value is used for multi-parameter coupled safety early warning. Specifically: After calculating the risk evolution coefficient, the real-time monitored vacuum chamber temperature gradient is compared with the benchmark value to determine the heat loss risk level. If the real-time temperature gradient is greater than 1.3 times the benchmark value, it indicates a surge in heat loss, which will affect flywheel operating efficiency. The risk evolution coefficient is then corrected to the original coefficient multiplied by the ratio of the real-time temperature gradient to the benchmark value, amplifying the risk level and activating the vacuum chamber cooling system in advance to suppress heat loss. If the real-time temperature gradient is less than or equal to 1.3 times the benchmark value, the heat loss is within a normal range, and the original risk evolution coefficient is maintained. Optimization Results: Accurately identifies abnormal heat loss risks in the vacuum chamber, enabling timely cooling measures to prevent a significant increase in flywheel mechanical losses due to temperature rise, thus ensuring flywheel operating efficiency. The helium leakage rate benchmark is used for multi-parameter coupled safety early warning. Specifically: After calculating the operating power of the vacuum chamber pump, the real-time monitored helium leakage rate is compared with this benchmark value to determine the severity of the leak. If the real-time leakage rate is greater than 1.5 times the benchmark value, it indicates that the vacuum level will drop rapidly, severely affecting flywheel operation. In this case, the pump operating power is adjusted to the original power multiplied by the ratio of the real-time leakage rate to the benchmark value, increasing the pump power to quickly replenish the vacuum level. If the real-time leakage rate is less than or equal to 1.5 times the benchmark value, it indicates that the leakage is within a controllable range and the vacuum level can be maintained. In this case, the original pump operating power is kept unchanged.Optimization Effect: Effectively suppresses the decrease in vacuum caused by helium leakage, avoids a surge in flywheel mechanical losses due to insufficient vacuum, and ensures stable and efficient flywheel operation. The absolute pressure reference value of the vacuum chamber is used for dynamic optimization of energy conversion efficiency. Specifically: After calculating the adjusted vacuum chamber pressure, the adjusted pressure needs to be compared with the reference value to determine the vacuum optimization effect. If the adjusted pressure is greater than the reference value, it indicates that the vacuum degree has not reached the ideal state, and there is still room for optimization of mechanical losses. In this case, the adjusted pressure is corrected again to the original adjusted pressure multiplied by the ratio of the reference value to the adjusted pressure raised to the power of 0.5, further reducing the vacuum chamber pressure. If the adjusted pressure is less than or equal to the reference value, it indicates that the vacuum degree has reached the ideal state, and further reducing the pressure will increase pumping energy consumption, so the adjustment is stopped. Optimization Effect: While minimizing flywheel mechanical losses, it avoids energy waste caused by excessive pumping, achieving a balance between loss control and energy consumption optimization. The IGBT module junction temperature reference value is used for dynamic optimization of energy conversion efficiency. Specifically: After calculating the converter modulation ratio, the actual junction temperature of the IGBT module needs to be monitored in real time and compared with the reference value. If the actual junction temperature is greater than 0.9 times the reference value, it indicates that the device is approaching the over-temperature threshold, posing a risk of over-temperature derating or damage. In this case, the modulation ratio is adjusted to the original modulation ratio multiplied by the power of 0.4 of the ratio of the reference value to the actual junction temperature, thereby reducing device heat generation by lowering the modulation ratio. If the actual junction temperature is less than or equal to 0.9 times the reference value, it indicates that the temperature is within a safe range, and the original modulation ratio is maintained. Optimization effect: Prevents IGBT module performance degradation or damage due to over-temperature, extends the service life of electrical components, and ensures stable converter operation. The converter ripple voltage reference value is used for: dynamic optimization of energy conversion efficiency. Specifically, when calculating the converter modulation ratio, the real-time monitored converter DC bus ripple voltage needs to be compared with this reference value to determine power quality. If the real-time ripple voltage is greater than 1.2 times the reference value, it indicates poor power quality, which may damage power devices. In this case, the modulation ratio is adjusted to the original modulation ratio multiplied by the power of 0.6 of the ratio of the reference value to the real-time ripple voltage. This optimization reduces the ripple voltage. If the real-time ripple voltage is less than or equal to 1.2 times the reference value, it indicates that the power quality meets the requirements, and the original modulation ratio is maintained. Optimization effect: Ensures stable power quality of the converter output, avoids damage to power devices due to voltage fluctuations, and improves the reliability of the electrical system. The converter output apparent power reference value is used for dynamic energy distribution strategy optimization. Specifically, after calculating the flywheel energy storage ratio, the real-time monitored converter output apparent power needs to be compared with this reference value to determine the rationality of the power output. If the real-time apparent power is greater than 1.1 times the reference value, it indicates that the device is at risk of overload. In this case, the flywheel energy storage ratio is adjusted to the original ratio multiplied by the power of 0.5 of the ratio of the reference value to the real-time apparent power, thereby reducing the flywheel energy storage ratio and decreasing the output power. If the real-time apparent power is less than or equal to 1.1 times the reference value, it indicates that the power is within the safe range, and the original flywheel energy storage ratio is maintained unchanged.Optimization Effects: Prevents inverter damage due to power overload, ensuring safe and stable operation of the electrical system and extending equipment lifespan. The lithium battery cell voltage difference benchmark is used for fine-tuning lithium battery equalization. Specifically, after calculating the equalization current, the real-time monitored maximum voltage difference between lithium battery cells is compared with this benchmark value to determine the equalization effect. If the real-time voltage difference is greater than 1.4 times the benchmark value, it indicates insufficient equalization, and battery consistency needs further improvement. In this case, the equalization current is corrected to the original current multiplied by the ratio of the real-time voltage difference to the benchmark value, increasing the equalization current to accelerate consistency convergence. If the real-time voltage difference is less than or equal to 1.4 times the benchmark value, the equalization effect is satisfactory, and the original equalization current remains unchanged. Optimization Effects: Ensures rapid convergence of battery cell voltage consistency to the ideal state, preventing accelerated battery capacity decay due to excessive voltage differences and extending the overall lifespan of the lithium battery pack. The lithium battery cumulative coulomb quantity benchmark is used for dynamic optimization of energy conversion efficiency. Specifically, after calculating the lithium battery charge / discharge cutoff voltage, the real-time monitored cumulative charge / discharge coulomb quantity is compared with this benchmark value to determine the battery capacity status. If the real-time accumulated coulomb quantity is less than 0.8 times the benchmark value, it indicates that the battery capacity has decreased by more than 20%. In this case, the cutoff voltage is adjusted to the original cutoff voltage multiplied by the power of 0.3 of the ratio of the real-time accumulated coulomb quantity to the benchmark value, appropriately reducing the cutoff voltage to avoid overcharging and over-discharging. If the real-time accumulated coulomb quantity is greater than or equal to 0.8 times the benchmark value, it indicates that the battery capacity is normal, and the original cutoff voltage is maintained. Optimization effect: Dynamically adapts to the battery capacity decay state, avoiding the risk of overcharging and over-discharging caused by a fixed cutoff voltage, effectively extending the cycle life of lithium batteries.

[0021] The airflow velocity benchmark value for the lithium battery compartment is used for multi-parameter coupled safety early warning. Specifically, after calculating the cooling fan speed, the real-time monitored airflow velocity inside the lithium battery compartment needs to be compared with this benchmark value to determine the heat dissipation efficiency. If the real-time airflow velocity is less than 0.7 times the benchmark value, it indicates insufficient heat dissipation and a risk of localized heat accumulation. In this case, the fan speed is adjusted to the original speed multiplied by the ratio of the benchmark value to the real-time airflow velocity to the power of 0.4, increasing the fan speed to enhance heat dissipation. If the real-time airflow velocity is greater than or equal to 0.7 times the benchmark value, it indicates normal heat dissipation efficiency, and the original fan speed is maintained. Optimization effect: Ensures that the lithium battery compartment heat dissipation effect meets the standard, avoids localized heat accumulation leading to battery thermal runaway, and ensures the safe operation of the lithium battery pack. The dew point temperature benchmark value for the lithium battery compartment is also used for multi-parameter coupled safety early warning. Specifically, when calculating the risk evolution coefficient, the real-time monitored dew point temperature of the lithium battery compartment needs to be compared with this benchmark value to determine the risk of condensation. If the real-time dew point temperature is more than 3°C higher than the benchmark value, it indicates a high risk of condensation inside the compartment, which may lead to battery short circuits or decreased insulation performance. In this case, the risk evolution coefficient is adjusted to the original coefficient multiplied by (the difference between the real-time dew point temperature and the benchmark value plus 1), amplifying the risk level and activating the dehumidification device inside the compartment. If the difference between the real-time dew point temperature and the benchmark value is less than or equal to 3°C, it indicates no risk of condensation, and the original risk evolution coefficient remains unchanged. Optimization effect: Early identification and suppression of condensation risks in the lithium battery compartment, avoiding battery short circuits or decreased insulation performance caused by condensation, and ensuring the electrical safety of the lithium battery pack. The temperature field temperature difference benchmark value is used for: multi-parameter coupled safety early warning. Specifically, after calculating the cooling fan speed, this benchmark value serves as an ideal reference standard for the uniformity of the battery module temperature field. The temperature distribution uniformity is judged by the ratio of the maximum temperature difference in the real-time monitored temperature field to this benchmark value. If the maximum real-time temperature difference is greater than 1.5 times the benchmark value, it indicates a high risk of local thermal runaway. In this case, the fan speed is adjusted to the original speed multiplied by the power of 0.5 of the ratio of the maximum real-time temperature difference to the benchmark value, increasing the fan speed. Simultaneously, the direction of the cooling vents is adjusted to provide targeted cooling for high-temperature areas. If the maximum real-time temperature difference is less than or equal to 1.5 times the benchmark value, it indicates a uniform temperature distribution, and the original fan speed is maintained. Optimization effect: Precisely suppresses the risk of local thermal runaway in the battery module, ensures uniform temperature distribution, and guarantees the overall safe operation of the lithium battery pack. The benchmark value of the equivalent series resistance of the balancing circuit is used for fine-tuning the balancing of the lithium battery. Specifically, after calculating the operating frequency of the balancing circuit, the real-time monitored equivalent series resistance of the balancing circuit is compared with this benchmark value to determine the health status of the balancing circuit. If the real-time equivalent series resistance is greater than 1.3 times the reference value, it indicates that the circuit loss has increased, which may lead to overheating of the device. In this case, the equalization frequency is corrected to the original frequency multiplied by the power of 0.3 of the ratio of the reference value to the real-time equivalent series resistance, and the equalization frequency is reduced to reduce circuit loss. If the real-time equivalent series resistance is less than or equal to 1.3 times the reference value, it indicates that the circuit is in good condition, and the original equalization frequency is maintained.Optimization effect: Prevents excessive resistance in the balancing circuit from causing a surge in energy consumption or overheating damage to components, ensuring efficient and reliable operation of the balancing system. The balancing current reference value is used for fine-tuning the lithium battery balancing. Specifically: After calculating the dynamic balancing current, it needs to be compared with the reference value to determine current safety. If the calculated balancing current is greater than 1.2 times the reference value, it indicates a risk of circuit overload, and the balancing current is limited to 1.2 times the reference value to avoid current exceeding limits; if the calculated balancing current is less than or equal to 1.2 times the reference value, it indicates the current is within a safe range, and the originally calculated balancing current remains unchanged. Optimization effect: Prevents damage to components in the balancing circuit due to current overload, ensuring the reliability of the balancing system while ensuring the balancing effect is not affected.

[0022] Furthermore, redundant signals such as electromagnetic interference and sensor noise inevitably exist during parameter acquisition. The Kalman filter algorithm is a mature linear minimum variance estimation method in engineering, which can effectively remove noise and retain the true trend of parameter changes, ensuring data reliability and providing high-quality input for subsequent calculations. Traditional fixed reference values ​​cannot adapt to dynamic changes in operating conditions (such as battery capacity decay and flywheel wear). Calculating dynamic reference values ​​based on the nonlinear coupling relationship between parameters allows the reference standard to be adjusted in real time according to the operating conditions, avoiding control deviations caused by fixed thresholds. The application of the Kalman filter algorithm conforms to the basic principles of signal processing and can accurately remove noise for the low-frequency fluctuation characteristics of energy storage system parameters. The calculation logic of the dynamic reference value is based on the physical relationship between each parameter (such as speed and power, current and coulomb quantity), and the coupling relationship conforms to the basic physical laws of mechanical motion, electrochemical reaction, and heat transfer. Correspondingly, after each round of data acquisition is completed and before the next round of optimization control begins, the signal processing unit of the data acquisition terminal and the reference value calculation module of the collaborative control terminal perform the following actions in their respective core operations: noise removal of the acquired raw parameters to obtain stable parameters, and calculation of real-time reference values ​​of all core parameters based on the nonlinear coupling relationship between parameters. Specifically, the signal processing unit of the data acquisition terminal receives the raw data acquired by each sensor, starts the Kalman filter algorithm, and removes redundant signals caused by electromagnetic interference and sensor errors through steps such as state prediction, observation update, and variance correction, outputting stable parameters that truly reflect the operating state of the equipment. After receiving these stable parameters, the reference value calculation module of the collaborative control terminal calculates a list of real-time reference values ​​corresponding to the 18 core parameters, based on the physical correlation laws of parameters such as the mechanical correlation between speed and power and the electrochemical correlation between current and coulomb quantity, according to the preset nonlinear coupling logic, and calls the coupling relationship model of the corresponding subsystem.

[0023] The state assessment module analyzes the comprehensive operating state of multiple parameters and maps it to three-dimensional coordinate points to quantitatively characterize the system's operating state. In this embodiment, it should be noted that the analysis of the comprehensive operating status of multiple parameters is used to obtain the system comprehensive operating status index. The coordinate points for The mapping result in the three-dimensional running state coordinate system, where: This system is used to intuitively quantify its operational status. The quantification characterizes the system's operational status by reflecting the real-time situation of energy conversion, stability, and safety risks through the magnitude and trend of coordinate point values. Energy storage systems are complex systems with multiple subsystems operating collaboratively; a single parameter cannot reflect the overall state. The comprehensive state index integrates mechanical, electrical, electrochemical, and thermal environmental parameters, comprehensively quantifying the system's operational status and avoiding overall performance imbalances caused by local parameter optimization. Mapping K to three-dimensional coordinate points visualizes the abstract comprehensive index as an intuitive three-dimensional location, facilitating rapid assessment of the system's performance in efficiency, stability, and risk, providing a clear basis for subsequent targeted optimization. Furthermore, the formula design of the comprehensive state index K follows the logic of positive parameters promoting and negative parameters suppressing (e.g., positive parameters such as rotational speed and power are in the numerator, while negative parameters such as load and temperature difference are in the denominator). The coordinate mapping formula is based on the correlation between K and the core parameters of each coordinate axis (e.g., the efficiency axis is related to power and coulomb quantity). Specifically, after completing the stabilization parameter processing and dynamic benchmark value calculation, and before each round of dynamic energy allocation, risk warning, and efficiency optimization, the state assessment module of the collaborative control terminal performs a comprehensive operating state index calculation based on the multi-dimensional stabilization parameter calculation system in the three-dimensional operating state coordinate system constructed by the computational core of the collaborative control terminal and the initial module, and maps this index to coordinate points in the three-dimensional coordinate system. The work of quantitatively characterizing the system's operating state involves this module calling a comprehensive state index calculation model. It inputs four categories of stabilization parameters: mechanical, electrical, electrochemical, and thermal environment. Parameters that positively affect system operation, such as rotational speed, power, and coulomb quantity, are included in the numerator, while parameters that negatively affect operation, such as load, temperature difference, and inconsistency, are included in the denominator. The comprehensive state index is obtained by fusing the influence of multiple parameters through square root and logarithmic operations. Then, a coordinate mapping model is called to calculate the comprehensive state index with the core parameters of each coordinate axis, obtaining the specific coordinate points of the index in the three-dimensional coordinate system. This coordinate point data is output to clarify the quantitative results of the system in three dimensions, providing an intuitive basis for subsequent targeted optimization.

[0024] The energy distribution module: evaluates the dynamic energy distribution ratio based on three-dimensional coordinate points and optimizes the operating parameters of the flywheel and lithium battery; In this embodiment, it should be noted that the dynamic energy distribution ratio includes the flywheel energy storage ratio. and the proportion of lithium battery energy storage and ,in The optimization of flywheel and lithium battery operating parameters specifically includes adjusting the flywheel converter output frequency and allocating the lithium battery charge / discharge rate; the adjustment of the flywheel converter output frequency... Used to optimize rotor speed tracking accuracy; the allocation of lithium battery charge / discharge rate This system ensures that lithium battery energy distribution matches the system state. While flywheel energy storage offers rapid response, lithium batteries boast high energy density. A fixed energy distribution ratio cannot adapt to dynamic system conditions (such as load fluctuations and changes in risk levels). Calculating the dynamic ratio based on three-dimensional coordinates balances efficiency, stability, and safety, achieving an optimization logic of "efficiency first, safety as a safety net." Adjusting the flywheel converter output frequency and lithium battery charge / discharge rate adapts to the mechanical characteristics of the flywheel and the electrochemical characteristics of the lithium battery, respectively, ensuring the energy distribution strategy can be implemented through specific parameter adjustments. Simultaneously, the formula for dynamic energy distribution ratio incorporates quantitative results from three-dimensional coordinates, ensuring the ratio adjustment matches the system state. The flywheel converter output frequency adjustment formula correlates with energy storage ratio, power, voltage, and other parameters, conforming to the basic principles of electrical control. The lithium battery charge / discharge rate adjustment formula correlates with ratio, coulomb quantity, voltage difference, and other parameters, conforming to the electrochemical characteristics of battery charge / discharge. Specifically, after completing the comprehensive system status assessment, and when system load fluctuates, safety risk levels change, or energy conversion efficiency is not optimal, the energy distribution module of the collaborative control terminal, the inverter control unit of the flywheel energy storage unit, and the charge / discharge control unit of the lithium battery energy storage unit perform actions in the decision core of the collaborative control terminal, the inverter of the flywheel energy storage unit, and the charge / discharge controller of the lithium battery energy storage unit. These actions involve calculating the dynamic energy distribution ratio between the flywheel and the lithium battery based on the quantization results of three-dimensional coordinate points, and adjusting the output frequency of the flywheel inverter and the charge / discharge rate of the lithium battery to adapt to the energy distribution requirements. The specific process involves the energy distribution module of the collaborative control terminal connecting... The system collects three-dimensional coordinate point data, calls the dynamic energy distribution model, and integrates the efficiency, stability, and risk quantification values ​​reflected by the coordinate points. It then calculates the flywheel energy storage ratio and the lithium battery energy storage ratio, which together represent 100%. Subsequently, it sends instructions to the inverter control unit of the flywheel energy storage unit. Based on the calculated flywheel energy storage ratio and the stabilized power, voltage, and other parameters, it adjusts the inverter output frequency to optimize rotor speed tracking accuracy. At the same time, it sends instructions to the charge and discharge control unit of the lithium battery energy storage unit. Based on the lithium battery energy storage ratio and parameters such as coulomb quantity and voltage difference, it adjusts the charge and discharge rate to ensure that the energy storage and release rate of the lithium battery are matched and allocated proportionally without exceeding the safe operating range.

[0025] The risk warning module: constructs a multi-parameter coupled analysis model and initiates tiered warnings; In this embodiment, it should be noted that the analysis model includes a security risk coupling propagation model and a risk diffusion rate model; the security risk coupling propagation model is used to obtain the risk evolution coefficient. The risk diffusion rate model is used to obtain the risk diffusion rate. Through the Taking the time derivative yields: The aforementioned tiered early warning: when At the same time, adjust the operating power of the vacuum chamber pump: At the same time, adjust the speed of the lithium battery cooling fan: Suppressing risk diffusion. Safety risks in energy storage systems are often caused by the coupling of multiple parameters (such as excessive mechanical load + excessive electrical temperature). Single-parameter threshold warnings are prone to misjudgment or delays. The risk evolution coefficient integrates multiple dimensions of mechanical, electrical, and thermal environmental parameters, comprehensively reflecting the risk coupling effect. Calculating the risk diffusion rate can predict risk development trends, achieving "early warning and proactive suppression," which is more effective in ensuring system safety than post-event handling. Tiered early warning measures adjust the power of the extraction pump and the speed of the fan based on the risk diffusion trend, directly acting on the risk source (such as insufficient vacuum or excessive temperature), making the measures highly targeted. Furthermore, the formula for the risk evolution coefficient is designed based on the physical logic of multi-parameter coupling (such as the superposition effect of mechanical load and electrical temperature on risk), and the derivation process conforms to the basic laws of risk propagation. The risk diffusion rate, calculated by differentiating the evolution coefficient, accurately reflects the risk change trend. Specifically, after completing the comprehensive system status assessment, the risk calculation unit of the safety early warning terminal, the vacuum system control unit of the flywheel energy storage unit, and the heat dissipation control unit of the lithium battery energy storage unit, when abnormal fluctuations in parameters, risk evolution coefficients exceeding the normal range, or risk diffusion rates being positive, perform the following operations in the computing module of the safety early warning terminal, the vacuum chamber pump controller of the flywheel energy storage unit, and the heat dissipation fan controller of the lithium battery energy storage unit: establishing a safety risk coupling propagation model, calculating the risk evolution coefficient and risk diffusion rate, initiating graded early warnings based on the quantification results, and adjusting the power of the vacuum chamber pump and the speed of the lithium battery compartment heat dissipation fan. Specifically, the risk calculation unit of the safety early warning terminal connects to... The system collects stabilization parameters and three-dimensional coordinate point data, calls the risk coupling propagation model, and integrates multi-dimensional risk correlation parameters such as mechanical load, electrical temperature, and thermal environment temperature difference to calculate the risk evolution coefficient. By performing time-dimensional derivative calculation on the risk evolution coefficient, the risk diffusion rate is obtained to predict the risk development trend. When the risk diffusion rate is positive, the terminal sends an instruction to the vacuum system control unit of the flywheel energy storage unit, and adjusts the operating power of the vacuum pump in combination with the risk evolution coefficient and vacuum chamber pressure and leakage rate parameters. At the same time, it sends an instruction to the heat dissipation control unit of the lithium battery energy storage unit, and adjusts the speed of the cooling fan in combination with the risk evolution coefficient and temperature field temperature difference and airflow velocity parameters to specifically cool the high-temperature area to suppress risk diffusion.

[0026] The equalization optimization module analyzes the equalization optimization status of the lithium battery and implements dynamic equalization adjustment and effect correction. In this embodiment, it should be noted that the analysis of the lithium battery equalization optimization status is used to obtain the lithium battery equalization optimization coefficient. The dynamic balancing adjustment is based on a dynamic balancing optimization coefficient of the lithium battery, designed to adjust the circuit operating frequency. The effect correction includes energy transfer path optimization and real-time monitoring; the energy transfer path optimization involves switching the equalization topology through a collaborative control terminal, so that the equalization current follows the following steps: Dynamically allocated to each individual battery cell, among which This represents the actual equalization current value dynamically allocated to each lithium battery cell; the real-time monitoring is used to monitor the equalization effect and obtain the equalization correction coefficient. ,in This serves as a benchmark value for SOC inconsistency, used for real-time optimization of equalization parameters. Key factors influencing lithium battery pack consistency degradation include voltage difference, coulomb quantity, temperature, and equalization loop status. A single voltage equalization strategy cannot address the consistency issues caused by multiple factors. The equalization optimization coefficient integrates multi-dimensional parameters to comprehensively assess battery consistency. It dynamically adjusts the equalization frequency, energy transfer path, and equalization current to address equalization efficiency, uniformity, and safety, achieving the goal of "precise equalization and avoiding over-equalization." The equalization correction coefficient, based on the comparison between the benchmark and real-time values, dynamically adjusts the equalization intensity, ensuring the equalization strategy adapts to changes in battery state. Furthermore, the formula design of the equalization optimization coefficient is linked to core parameters such as equalization current, coulomb quantity, and temperature, conforming to the electrochemical laws of battery equalization. The adjustment formulas for equalization frequency and current are based on the equalization optimization coefficient, ensuring the adjustment magnitude matches the consistency deviation. Energy transfer path optimization is achieved by switching topologies, conforming to the control principles of power electronic circuits. The equalization correction coefficient accurately judges the equalization effect, avoiding insufficient or excessive equalization. Specifically, during the charging and discharging process of the lithium battery, when the BMS detects that the voltage difference between individual cells exceeds the allowable range, the SOC inconsistency deviates from the reference value, or the balancing effect does not meet expectations, the BMS controller and balancing circuit hardware module of the lithium battery energy storage unit perform actions such as constructing lithium battery balancing optimization coefficients, dynamically adjusting the operating frequency and energy transfer path of the balancing circuit and the balancing current distribution, and continuously optimizing the balancing effect through balancing correction coefficients. Specifically, the BMS balancing control module receives the stabilization parameters of the lithium battery cluster and calls the balancing optimization coefficient calculation module. The system integrates multi-dimensional parameters to quantify the battery consistency state and obtains a balancing optimization coefficient. Based on this coefficient, instructions are sent to the balancing circuit to adjust the operating frequency, ensuring that the energy transfer rate matches the consistency deviation. At the same time, the topology switching unit of the balancing circuit is controlled to switch the energy transfer path, so that the balancing current is accurately distributed to each individual battery cell. Then, the balancing correction coefficient is calculated by combining the ratio of the SOC inconsistency benchmark value and the real-time SOC inconsistency, and the balancing current is dynamically adjusted. Parameters such as the voltage difference and SOC inconsistency after balancing are collected in real time, and the balancing optimization coefficient, balancing current, and frequency are continuously corrected to ensure that the consistency of the individual battery cells quickly converges to the ideal state.

[0027] The efficiency coordination module: constructs an energy conversion efficiency optimization objective function and collaboratively optimizes system operating parameters; In this embodiment, it should be noted that: the energy conversion efficiency optimization objective function The collaborative optimization of system operating parameters includes flywheel converter modulation ratio optimization, lithium battery charge / discharge cutoff voltage optimization, and flywheel vacuum chamber pressure optimization; the flywheel converter modulation ratio optimization is used to adjust the flywheel converter modulation ratio. The lithium battery charge / discharge dynamic cutoff voltage optimization is used to set the dynamic cutoff voltage. The flywheel vacuum chamber pressure optimization is used to coordinately adjust the flywheel vacuum level, so that the vacuum chamber pressure is adjusted according to... Dynamic adaptation reduces mechanical wear, among which This represents the dynamically adjusted pressure value of the flywheel vacuum chamber. The energy conversion efficiency of an energy storage system is affected by multiple factors, including mechanical losses (flywheel vacuum), electrical losses (inverter modulation ratio), and electrochemical losses (battery cutoff voltage). Optimizing a single parameter cannot achieve optimal overall efficiency. The efficiency optimization objective function integrates multi-dimensional parameters to comprehensively cover various loss sources. Adjusting the inverter modulation ratio, battery cutoff voltage, and flywheel vacuum targets electrical losses, electrochemical losses, and mechanical losses respectively, achieving "end-to-end loss suppression." Dynamically adjusting parameters adapts to changes in operating conditions, avoiding efficiency waste caused by fixed parameters. Specifically, after completing dynamic energy allocation and safety risk warning, and when the system energy conversion efficiency fails to meet the optimization target or the operating conditions change, the efficiency optimization unit of the collaborative control terminal, the converter modulation module and vacuum system control unit of the flywheel energy storage unit, and the charge / discharge cutoff control unit of the lithium battery energy storage unit, in the computing core of the collaborative control terminal, the converter and vacuum chamber pressure controller of the flywheel energy storage unit, and the charge / discharge controller of the lithium battery energy storage unit, perform the work of constructing an energy conversion efficiency optimization objective function, adjusting the modulation ratio of the flywheel converter, the charge / discharge cutoff voltage of the lithium battery, and the flywheel vacuum chamber pressure to optimize the system energy conversion efficiency. Specifically, the efficiency optimization module of the collaborative control terminal receives stabilization parameters, dynamic reference values, and... The three-dimensional coordinate point data is used to call the efficiency optimization objective function model. The efficiency optimization target value is calculated by integrating relevant parameters of mechanical loss, electrical loss, and electrochemical loss. Then, instructions are sent to the inverter modulation module of the flywheel energy storage unit to adjust the inverter modulation ratio based on the objective function value and parameters such as power and voltage to reduce electrical loss. Instructions are also sent to the vacuum system control unit of the flywheel energy storage unit to adjust the vacuum chamber pressure based on the objective function value and parameters such as speed and power to reduce flywheel mechanical loss. At the same time, instructions are sent to the charge and discharge cutoff control unit of the lithium battery energy storage unit to set the dynamic charge and discharge cutoff voltage based on the objective function value and parameters such as coulomb quantity and voltage difference. This avoids energy waste or battery damage caused by a fixed cutoff voltage, thereby improving the energy utilization efficiency of the lithium battery.

[0028] Correction and update module: Collects optimized parameters, obtains closed-loop correction coefficients, and updates parameter baseline values.

[0029] In this embodiment, it should be noted that the closed-loop correction coefficient ;in To optimize the objective function value for efficiency in the efficiency coordination module. This refers to the system comprehensive operating status index in the status assessment module; the parameter benchmark value update is used to obtain the updated flywheel rotor speed benchmark value. Lithium battery current reference value The remaining parameter baseline values ​​are updated according to the same logic. The effectiveness of the optimization strategy in the preceding steps needs to be verified by collecting the optimized parameters. The closed-loop correction coefficient, based on the comparison of parameters before and after optimization and the correlation with the comprehensive state index and efficiency objective function, can accurately quantify the optimization effect. Traditional control methods lack a baseline value update mechanism, leading to a disconnect between the reference standard and the operating conditions. Updating all baseline values ​​based on the correction coefficient allows the reference system to iterate with the optimization effect, ensuring that the reference standard for the next round of optimization is adapted to the current operating conditions, forming a complete closed loop of "monitoring-evaluation-optimization-correction". Furthermore, the formula design of the closed-loop correction coefficient integrates the core parameters after optimization with the results of previous calculations (comprehensive state index, efficiency objective function), truly reflecting the optimization effect. The baseline value update formula is based on the logic of "original baseline value × correction coefficient × parameter change rate," ensuring that the updated baseline value inherits historical optimization experience while adapting to current operating condition changes. All baseline values ​​are updated synchronously to avoid dimensional gaps, ensuring the comprehensiveness and consistency of closed-loop control, and eliminating parameter update omissions or logical contradictions. Specifically, after completing a full "evaluation-optimization-control" process, and before the next round of optimized control begins or when the system operating conditions change significantly, the closed-loop correction module of the collaborative control terminal and the parameter feedback unit of the data acquisition terminal perform the following actions in their respective computing cores: acquiring the optimized parameters from the energy allocation module to the efficiency coordination module, calculating the closed-loop correction coefficients, and updating the baseline values ​​of all core parameters based on the correction coefficients to form closed-loop control. Specifically, the parameter feedback unit of the data acquisition terminal acquires the optimized parameters, performs preliminary data verification, and then transmits the data to the collaborative control terminal. The closed-loop correction module of the control terminal receives the optimized parameters, the original stabilization parameters, the comprehensive state index and the efficiency optimization objective function value, calls the closed-loop correction coefficient calculation model, and calculates the closed-loop correction coefficient by integrating the parameter changes before and after optimization, the degree of improvement in the comprehensive state of the system and the efficiency improvement effect. Then, according to the unified logic of "original baseline value × correction coefficient × ratio of optimized parameter to original stabilization parameter", the baseline values ​​of all 18 core parameters are updated one by one. The updated baseline values ​​are stored in the baseline value database of the collaborative control terminal as a dynamic reference standard for the next round of optimization control, and finally a complete closed loop of "monitoring-evaluation-optimization-correction" is formed.

[0030] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A flywheel lithium battery energy storage system, characterized in that, include: Initial module: Acquire multidimensional data and establish a three-dimensional coordinate system for the system's operating state; The system's three-dimensional operating state coordinate system is based on energy conversion efficiency. The horizontal axis represents system operational stability. The vertical axis represents the safety risk level. The vertical axis is used, and the origin of the coordinate system is the reference point of the system's rated operating condition, which is used to quantitatively characterize the real-time operating status of the system. Preprocessing module: Uses Kalman filtering algorithm to preprocess parameters and calibrate dynamic reference values; The pre-processed rotor real-time speed, bearing axial load, rotor angular acceleration, vacuum chamber absolute pressure, chamber wall temperature gradient, helium leakage rate at the seal, DC bus ripple voltage, IGBT module junction temperature, output instantaneous apparent power, instantaneous charging and discharging current, maximum voltage difference between cells, cumulative charging and discharging coulombs, internal airflow velocity, maximum temperature difference of the module surface temperature field, chamber dew point temperature, instantaneous balancing current of the balancing circuit, battery module SOC inconsistency, and equivalent series resistance of the balancing circuit are denoted as follows: ; Status assessment module: Analyzes the comprehensive operating status of multiple parameters and maps it to three-dimensional coordinate points to quantitatively characterize the system's operating status; Energy distribution module: Evaluates the dynamic energy distribution ratio based on three-dimensional coordinate points and optimizes the operating parameters of the flywheel and lithium battery; Risk warning module: Constructs a multi-parameter coupled analysis model and initiates tiered warnings; Balance Optimization Module: Analyzes the balance optimization status of lithium batteries and implements dynamic balance adjustment and effect correction; Efficiency Coordination Module: Constructs an objective function for optimizing energy conversion efficiency and collaboratively optimizes system operating parameters; The energy conversion efficiency optimization objective function ; Correction and update module: Collects optimized parameters, obtains closed-loop correction coefficients, and updates parameter baseline values.

2. The flywheel lithium battery energy storage system according to claim 1, characterized in that: The multidimensional data includes flywheel rotor dynamic mechanical parameters, flywheel vacuum chamber environmental parameters, flywheel converter power conversion parameters, lithium battery cluster electrochemical parameters, lithium battery compartment thermal environment parameters, and lithium battery BMS balancing state parameters. The flywheel rotor dynamic mechanical parameters include real-time rotor speed, bearing axial load, and rotor angular acceleration. The flywheel vacuum chamber environmental parameters include absolute vacuum chamber pressure, internal wall temperature gradient, and helium leakage rate at the seal. The flywheel converter power conversion parameters include DC bus ripple voltage, IGBT module junction temperature, and instantaneous apparent power output. The lithium battery cluster electrochemical parameters include instantaneous charging / discharging current, maximum voltage difference between cells, and cumulative coulombic charge / discharge. The lithium battery compartment thermal environment parameters include internal airflow velocity, maximum temperature difference on the module surface temperature field, and dew point temperature within the compartment. The lithium battery BMS balancing state parameters include instantaneous balancing current of the balancing circuit, battery module SOC inconsistency, and equivalent series resistance of the balancing circuit.

3. The flywheel lithium battery energy storage system according to claim 1, characterized in that: The Kalman filter algorithm is a signal processing algorithm used to eliminate redundant signals such as electromagnetic interference and sensor noise during parameter acquisition, ensuring the stability of parameter data. The dynamic reference values ​​are obtained through nonlinear coupling relationships between parameters, specifically including flywheel rotor speed reference value, bearing load reference value, converter ripple voltage reference value, lithium battery current reference value, equalization current reference value, temperature field temperature difference reference value, rotor angular acceleration reference value, vacuum chamber absolute pressure reference value, vacuum chamber temperature gradient reference value, helium leakage rate reference value, IGBT module junction temperature reference value, converter output apparent power reference value, lithium battery cell voltage difference reference value, lithium battery cumulative coulomb quantity reference value, lithium battery compartment airflow velocity reference value, lithium battery compartment dew point temperature reference value, battery module SOC inconsistency reference value, and equalization circuit equivalent series resistance reference value.

4. The flywheel lithium battery energy storage system according to claim 3, characterized in that: The analysis of the comprehensive operating status of multiple parameters is used to obtain the system's comprehensive operating status index. The coordinate points for The mapping result in the three-dimensional running state coordinate system, where: .

5. The flywheel lithium battery energy storage system according to claim 3, characterized in that: The dynamic energy allocation ratio includes the flywheel energy storage ratio. and the proportion of lithium battery energy storage ,and The optimization of flywheel and lithium battery operating parameters specifically includes adjusting the flywheel converter output frequency and allocating the lithium battery charge / discharge rate; the adjustment of the flywheel converter output frequency... Used to optimize rotor speed tracking accuracy; the allocation of lithium battery charge / discharge rate This is used to ensure that the energy distribution of the lithium battery matches the system state.

6. The flywheel lithium battery energy storage system according to claim 1, characterized in that: The analytical model includes a security risk coupling propagation model and a risk diffusion rate model; the security risk coupling propagation model is used to obtain risk evolution coefficients. ; The risk diffusion rate model is used to obtain the risk diffusion rate. Through the The time derivative is used to obtain the tiered early warning: when At the same time, adjust the operating power of the vacuum chamber pump and adjust the speed of the lithium battery cooling fan to suppress the spread of risk.

7. The flywheel lithium battery energy storage system according to claim 3, characterized in that: The analysis of lithium battery equalization optimization status is used to obtain lithium battery equalization optimization coefficients. ; The dynamic equalization adjustment is designed based on the lithium battery equalization optimization coefficient to adjust the circuit operating frequency. The effect correction includes energy transfer path optimization and real-time monitoring; the energy transfer path optimization involves switching the equalization topology through a collaborative control terminal, so that the equalization current follows the following steps: Dynamically allocated to each individual battery cell, among which This represents the actual equalization current value dynamically allocated to each lithium battery cell; the real-time monitoring is used to monitor the equalization effect and obtain the equalization correction coefficient. ,in This serves as the baseline value for SOC inconsistency, used for real-time optimization of balancing parameters.

8. The flywheel lithium battery energy storage system according to claim 3, characterized in that: The collaborative optimization system operating parameters include flywheel converter modulation ratio optimization, lithium battery charge / discharge cutoff voltage optimization, and flywheel vacuum chamber pressure optimization. The flywheel converter modulation ratio optimization is used to adjust the flywheel converter modulation ratio. The lithium battery charge / discharge dynamic cutoff voltage optimization is used to set the dynamic cutoff voltage. The flywheel vacuum chamber pressure optimization is used to collaboratively adjust the flywheel vacuum level, so that the vacuum chamber pressure is dynamically adapted, reducing mechanical losses.

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