Capacity evaluation method for flywheel energy storage and storage battery mixed UPS configuration

Abstract

The invention discloses a capacity evaluation method for flywheel energy storage and storage battery hybrid UPS configuration, and belongs to the technical field of capacitor application and hybrid energy storage systems, and the method comprises the steps: obtaining a preset load fluctuation parameter, a flywheel rotating speed parameter and a real-time charge state parameter of a storage battery, and generating a dynamic load feature vector; according to the dynamic load feature vector, calculating a dynamic coupling parameter of flywheel mechanical inertia and storage battery chemical energy attenuation; based on the dynamic coupling parameter, generating a cooperative capacity distribution weight of the flywheel and the storage battery; according to the cooperative capacity distribution weight, adjusting a preset rectifier duty ratio parameter to obtain an energy storage charging and discharging strategy; and performing simulation verification on the energy storage charging and discharging strategy, and outputting a final capacity evaluation result. A dynamic coupling parameter and a closed-loop simulation verification mechanism are adopted, flywheel inertia and storage battery attenuation characteristics can be accurately adapted, and the energy distribution proportion is optimized to improve power supply quality and prolong the service life of equipment.

Description

Technical Field

[0001] This invention relates to the technical field of capacitor applications and hybrid energy storage systems, and in particular to a capacity assessment method for a hybrid UPS configuration of flywheel energy storage and battery. Background Technology

[0002] Hybrid UPS systems combining flywheel energy storage and batteries utilize capacitors for energy buffering and transient power compensation, making them critical power backup devices for data centers, medical facilities, and other fields. Existing technologies generally employ a fixed capacity ratio mode, where static parameters are set based on the flywheel's rated speed and the battery's nominal capacity, achieving dual-path energy output through a rectifier. Some improved solutions determine the switching timing between the flywheel and battery based on load current thresholds, or add capacitors to the battery bank to smooth the discharge curve.

[0003] Current mainstream solutions allocate energy by pre-setting the capacity ratio of the flywheel and the battery, triggering fixed switching logic based on load power fluctuations, and utilizing the instantaneous charging and discharging characteristics of capacitors to compensate for power gaps. However, existing technologies often treat the mechanical inertia of the flywheel and the chemical energy decay of the battery as independent parameters, failing to fully consider the nonlinear relationship between the two in dynamic processes.

[0004] However, the inertia compensation lag at the flywheel speed critical point leads to excessive consumption of mechanical energy; the capacitor characteristics are not linked with the battery temperature and life decay parameters, resulting in excessive capacity redundancy; the rectifier duty cycle adjustment lacks real-time feedback on the coupling between the flywheel's remaining mechanical energy and the capacitor's electrical stress, resulting in insufficient robustness of the hybrid energy storage strategy. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a capacity assessment method for a hybrid UPS configuration combining flywheel energy storage and batteries. Employing dynamic coupling parameters and a closed-loop simulation verification mechanism, it can accurately adapt to the flywheel inertia and battery degradation characteristics, optimizing energy distribution ratios to improve power quality and equipment lifespan.

[0006] The above objectives can be achieved through the following approach: A capacity assessment method for a hybrid UPS configuration combining flywheel energy storage and battery includes: acquiring preset load fluctuation parameters, flywheel speed parameters, and real-time state-of-charge parameters of the battery to generate a dynamic load feature vector; calculating dynamic coupling parameters between the flywheel mechanical inertia and the battery chemical energy decay based on the dynamic load feature vector; generating a collaborative capacity allocation weight between the flywheel and the battery based on the dynamic coupling parameters; adjusting preset rectifier duty cycle parameters according to the collaborative capacity allocation weight to obtain an energy storage charging and discharging strategy; simulating and verifying the energy storage charging and discharging strategy to output the final capacity assessment result.

[0007] Optionally, calculating the dynamic coupling parameters of flywheel mechanical inertia and battery chemical energy decay based on the dynamic load feature vector includes: constructing a virtual inertia decay curve based on the flywheel speed parameters and identifying the flywheel speed critical point; obtaining preset battery temperature parameters and life decay coefficients, and fitting the mapping relationship between temperature and available capacity through a nonlinear function; generating complementary weights for flywheel mechanical energy and battery chemical energy based on the virtual inertia decay curve and the mapping relationship; and updating the complementary weights in real time according to the dynamic load feature vector to obtain the dynamic coupling parameters.

[0008] Optionally, the method further includes: when the load power is detected to exceed a preset fluctuation threshold, extracting flywheel mechanical energy and generating residual mechanical energy parameters; generating a battery supplementary power supply command based on the residual mechanical energy parameters; and using the supplementary power supply command and a preset weighting factor to correct the complementary weights to obtain optimized dynamic coupling parameters.

[0009] Optionally, the method further includes: acquiring rectifier efficiency parameters and a preset topology switching threshold; generating a rectifier mode switching signal when the ratio of the output power of the flywheel to the battery exceeds the topology switching threshold; and correcting the weighting factor using the rectifier mode switching signal.

[0010] Optionally, adjusting the preset rectifier duty cycle parameters according to the collaborative capacity allocation weight to obtain the energy storage charging and discharging strategy includes: calculating the pulse feedback frequency of the flywheel's remaining mechanical energy according to the collaborative capacity allocation weight; determining the rectifier's duty cycle range based on the pulse feedback frequency and the preset power conversion efficiency matrix; and dynamically adjusting the rectifier topology within the duty cycle range to obtain the energy storage charging and discharging strategy.

[0011] Optionally, the step of dynamically adjusting the rectifier topology within the duty cycle range to obtain the energy storage charging and discharging strategy includes: generating a heat dissipation compensation coefficient based on the rectifier temperature rise parameter; weighting and fusing the heat dissipation compensation coefficient with the duty cycle range to obtain a dynamic duty cycle parameter; and adjusting the output power ratio of the flywheel and the battery through the dynamic duty cycle parameter to obtain the energy storage charging and discharging strategy.

[0012] Optionally, the step of simulating and verifying the energy storage charging and discharging strategy and outputting the final capacity evaluation result includes: inputting the energy storage charging and discharging strategy into a preset virtual simulation platform to generate initial system response time parameters; applying a preset disturbance load to the initial system response time parameters and calculating the capacity redundancy deviation value; if the capacity redundancy deviation value exceeds a preset deviation threshold, then reallocating the collaborative capacity allocation weights of the flywheel and the battery.

[0013] Optionally, the reallocation of the capacity weights of the flywheel and the battery to obtain the final capacity assessment result includes: generating a capacity adjustment ratio parameter based on the capacity redundancy deviation value; linearly superimposing the capacity adjustment ratio parameter with the collaborative capacity allocation weight to obtain the corrected collaborative capacity allocation weights of the flywheel and the battery; adjusting the preset rectifier duty cycle parameter using the corrected collaborative capacity allocation weights of the flywheel and the battery to obtain a new energy storage charging and discharging strategy, and performing simulation verification; when the capacity redundancy deviation value is less than or equal to a preset deviation threshold, outputting the current energy storage charging and discharging strategy to obtain the final capacity assessment result.

[0014] Optionally, the method further includes: generating a life prediction curve for the hybrid energy storage device based on preset maintenance cycle parameters and historical capacity assessment results; and triggering a preset alarm threshold based on a comparison between the life prediction curve and the current final capacity assessment results.

[0015] Based on the same inventive concept, this invention also provides a capacity assessment system for a hybrid UPS configuration of flywheel energy storage and battery. The system includes: a data acquisition module for acquiring preset load fluctuation parameters, flywheel speed parameters, and real-time state-of-charge parameters of the battery, generating a dynamic load feature vector; a dynamic coupling module for calculating dynamic coupling parameters between the flywheel mechanical inertia and the battery chemical energy decay based on the dynamic load feature vector; a collaborative allocation module for generating collaborative capacity allocation weights between the flywheel and the battery based on the dynamic coupling parameters; an energy consumption optimization module for adjusting preset rectifier duty cycle parameters according to the collaborative capacity allocation weights to obtain an energy storage charging and discharging strategy; and a verification and execution module for simulating and verifying the energy storage charging and discharging strategy and outputting the final capacity assessment result.

[0016] Compared with the prior art, the present invention has the following advantages: 1. This invention optimizes the collaborative capacity allocation weight in real time by dynamically coupling the mechanical inertia of the flywheel and the chemical energy decay characteristics of the battery, which can significantly improve the response speed of the hybrid energy storage system under load change scenarios and reduce efficiency loss in the energy conversion process. 2. This invention adopts a dynamic load characteristic vector and rectifier duty cycle range adjustment strategy to effectively avoid the risk of flywheel speed critical point and battery over-discharge, extend the cycle life of flywheel bearings and batteries, and reduce equipment maintenance costs; 3. This invention combines a virtual simulation platform with a capacity redundancy deviation value feedback mechanism to achieve multi-level iterative verification of the charging and discharging strategy, ensuring that the capacity configuration accuracy adapts to different operating conditions and improving the reliability of the system power supply. 4. This invention monitors the heat dissipation compensation coefficient and life prediction curve and dynamically corrects them, simultaneously optimizing the rectifier topology operation status and energy storage device health management, thus ensuring the economic efficiency of the hybrid UPS system throughout its entire life cycle.

[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a capacity assessment method for a hybrid UPS configuration combining flywheel energy storage and battery, according to an embodiment of the present invention.

[0020] Figure 2 This is the load power fluctuation curve of an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram illustrating the changes in the flywheel mechanical inertia contribution factor and the battery chemical compensation factor in an embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram of the structure of a capacity assessment system for a hybrid UPS configuration of flywheel energy storage and battery, according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0024] Reference Figure 1 One embodiment of the present invention proposes a capacity evaluation method for a hybrid UPS configuration of flywheel energy storage and battery. It adopts dynamic coupling parameters and closed-loop simulation verification mechanism, which can accurately adapt to the flywheel inertia and battery degradation characteristics, optimize the energy distribution ratio to improve power supply quality and equipment life.

[0025] The method described in this embodiment specifically includes: Obtain preset load fluctuation parameters, flywheel speed parameters, and real-time state of charge parameters of the battery to generate a dynamic load feature vector; Specifically, the dynamic load feature vector generation method is achieved through multi-sensor collaborative operation. First, a Hall current sensor is used to collect the amplitude and frequency of current changes at the UPS output in real time, and this data is converted into load power fluctuation parameters according to a fixed sampling period, such as... Figure 2 As shown. This parameter is processed by low-pass filtering to eliminate high-frequency noise and is updated and stored in the dynamic database at a rate of ten times per second. Next, the angular position signal is read by an optical encoder installed on the flywheel shaft, and the real-time rotational speed is calculated by combining it with the flywheel rotor mass and radius parameters. The inflection point of the flywheel rotational speed parameter is identified based on a rotational speed threshold algorithm. Simultaneously, voltage and temperature sensors are deployed between the individual cells of the battery pack, and the state-of-charge parameters are calculated using a multi-cycle coulomb counting method. After normalizing the above three parameters to the zero-to-one interval, they are weighted and fused according to preset weighting coefficients to generate a dynamic load feature vector.

[0026] Based on the dynamic load characteristic vector, calculate the dynamic coupling parameters between the flywheel mechanical inertia and the battery chemical energy decay. Based on the aforementioned dynamic coupling parameters, a collaborative capacity allocation weight for the flywheel and the battery is generated. Specifically, such as Figure 3 As shown, the flywheel mechanical inertia contribution factor and the battery chemical energy degradation compensation factor (i.e., the battery chemical compensation factor) are first extracted through dynamic coupling parameters. The contribution factor is the ratio of the flywheel's current kinetic energy to its rated energy storage, and the compensation factor is the difference between the battery's current usable capacity and its reciprocal of its rated capacity. Dynamic coupling parameters Includes sub-parameters and ,in It equals the product of the square of the flywheel speed and the moment of inertia, divided by the system's base kinetic energy. This equals the product of the predicted usable battery capacity and the current ambient temperature degradation coefficient. Coordinated capacity allocation weight. for: ; Where k is an adjustment coefficient, determined by the system's preset transient response priority, with a value ranging from 0.8 to 1.2. When the dynamic coupling parameters... When the value exceeds a preset threshold, k automatically increases by 5% to prioritize flywheel output. The parameters are obtained as follows: flywheel speed is measured by a photoelectric encoder; moment of inertia is calculated based on the theoretical value of flywheel material density and geometry; battery capacity is obtained through an ampere-hour integral model fitted from charge-discharge cycle test data; and the environmental temperature degradation coefficient is determined by an exponential function established through accelerated aging experiments. The physical meaning of the weight calculation is to dynamically allocate capacity based on the effective reserve ratio of real-time mechanical energy to chemical energy. The final generated collaborative capacity allocation weights are... And high-frequency oscillations are eliminated through second-order filtering.

[0027] Based on the collaborative capacity allocation weight, the preset rectifier duty cycle parameter is adjusted to obtain the energy storage charging and discharging strategy. The energy storage charging and discharging strategy is simulated and verified, and the final capacity assessment result is output.

[0028] Specifically, the method achieves accurate assessment of hybrid energy storage capacity through collaborative modeling and real-time coupling analysis of multi-source dynamic parameters. The method constructs a dynamic load feature vector by collecting real-time data on load fluctuations, flywheel speed, and battery state of charge. This vector comprehensively characterizes the spatiotemporal correlation between power demand and the state of energy storage components. Utilizing the virtual decay curve of flywheel mechanical inertia and the nonlinear decay model of battery chemical energy with temperature, the method dynamically quantifies the energy complementarity between the two, generating dynamic coupling parameters reflecting the flywheel's transient response capability and the battery's continuous power supply capability. Based on the collaborative capacity allocation weights calculated using these coupling parameters, the method adjusts in conjunction with the rectifier's duty cycle range. Through dynamic adjustment of the power conversion topology, it achieves efficient collaborative output of flywheel kinetic energy and battery chemical energy, forming an optimized charging and discharging strategy adapted to load transients. The core of this method lies in establishing a real-time mapping mechanism for the dynamic decay of mechanical and chemical energy, achieving time-scale matching between the two through weight allocation.

[0029] Optionally, calculating the dynamic coupling parameters between the flywheel mechanical inertia and the battery chemical energy decay based on the dynamic load characteristic vector includes: A virtual inertia decay curve is constructed based on the flywheel speed parameters, and the critical point of flywheel speed is identified; Obtain preset battery temperature parameters and lifespan degradation coefficient, and fit the mapping relationship between temperature and usable capacity using a nonlinear function; Based on the virtual inertia decay curve and the mapping relationship, complementary weights for flywheel mechanical energy and battery chemical energy are generated. The complementary weights are updated in real time based on the dynamic load feature vector to obtain the dynamic coupling parameters.

[0030] Specifically, firstly, a virtual inertia decay curve is constructed based on the flywheel speed parameters. This curve characterizes the rate at which the flywheel's kinetic energy is lost as the speed decreases. The critical speed point is defined as the minimum speed at which the flywheel's output torque cannot meet the load requirements. Secondly, the battery's temperature parameters and lifespan degradation coefficient are collected. A nonlinear function is constructed to describe the nonlinear relationship between usable capacity and temperature increase. The temperature coefficient in this function corresponds to the physical characteristic of reduced electrolyte activity. Then, the slopes of the virtual inertia decay curve and the battery capacity temperature response curve are normalized. The combined value of these two is used as the initial benchmark value for complementary weights. This benchmark value is multiplied by the power demand component in the dynamic load characteristic vector to obtain the real-time adjustment value of the dynamic coupling parameters. During the complementary weight calculation process, if the flywheel speed approaches the critical point, the weight allocation mechanism is triggered to dynamically increase the battery's power sharing ratio.

[0031] For example, when a data center UPS system uses this method, if a sudden increase in load power demand is detected, causing the flywheel speed to drop from 6000 rpm to a critical value of 4200 rpm, the system immediately constructs a virtual inertia decay curve. The measured curve slope shows a loss of 25 joules of energy for every 100 rpm decrease. Simultaneously, the battery temperature rises to 45 degrees Celsius, triggering the temperature-available capacity mapping function calculation, which shows the available capacity has decreased to 83% of the nominal value. The flywheel inertia slope of 25 units and the battery capacity loss ratio of 17 units are superimposed, assigning a weighting coefficient of 0.6 to the flywheel and 0.4 to the battery. At this point, the dynamic load characteristic vector detects an instantaneous power demand of 150kW. Based on the weighted allocation, the flywheel is assigned 90kW of power, and the battery 60kW. Dynamic energy allocation is achieved through physical mechanism modeling, automatically increasing the battery output ratio when the flywheel's energy storage capacity weakens. This avoids the risk of excessively low flywheel speed and prevents battery overload, ensuring the hybrid energy storage system maintains optimal performance under different operating conditions. Especially in high-power surge events, this method effectively improves the reliability of system power supply by predicting the state change trend of energy components in advance.

[0032] Optionally, the method further includes: When the load power is detected to exceed the preset fluctuation threshold, the flywheel mechanical energy is extracted and the remaining mechanical energy parameters are generated. Based on the remaining mechanical energy parameters, a command to replenish battery power is generated; The complementary weights are corrected using the supplementary power supply command and a preset weighting factor to obtain optimized dynamic coupling parameters.

[0033] Specifically, firstly, a power sensor collects the load current value in real time and compares it with a preset threshold. If the current power value exceeds the limit for three consecutive seconds, an alarm signal is triggered. Then, a flywheel speed sensor acquires the real-time angular velocity. The remaining mechanical energy parameter is calculated by multiplying the flywheel's moment of inertia by the square of the angular velocity. The moment of inertia is pre-determined based on the flywheel's material density and geometry. Next, the remaining mechanical energy parameter and the current load power are input into a dynamic mapping table. Based on the principle of energy conservation, the energy difference that the battery needs to replenish is calculated. This difference is equal to the integral of the product of the load demand power and the remaining mechanical energy relative to the discharge time. Finally, a pulse width modulation (PWM) battery replenishment command is generated. A preset weighting factor is used, determined by the ratio of the battery's historical cycle count to the real-time state of charge (SOC). Specifically, the first ratio is obtained by dividing the cycle count by the maximum designed cycle count, and the second ratio is extracted by comparing the current SOC value with the rated value. The product of the first and second ratios is normalized to the zero-to-one range to obtain the weighting factor. The power requirement of the supplementary power supply command is multiplied by the weighting factor to generate a correction amount. This correction amount is then added to the original complementary weights to obtain the optimized dynamic coupling parameters. Meanwhile, the flywheel weights are simultaneously adjusted to the original weights minus the correction amount to ensure that the total weight value remains one.

[0034] For example, when a group of elevators in a hospital's UPS system simultaneously starts, the load power suddenly increases to 180% of the nominal value. At this point, the power sensor detects that the fluctuation exceeds the preset 150% threshold. The system immediately reads the current flywheel speed as 3500 rpm and calculates the remaining mechanical energy as 82 MJ based on the known moment of inertia. Combined with the load demand power, the dynamic mapping table determines that an additional 420 kW of battery output is needed. At this time, the battery cycle count is 70% of the design maximum, and the state of charge is 85%. The calculated weighting factor is 0.7 multiplied by 0.85, which equals 0.595. In the original complementary weighting, the battery's proportion was 35%. After the adjustment, it is increased to 35% plus 59.5% multiplied by the supplementary power proportion coefficient of 0.15, and the final adjusted battery weight is 42.9%. Through real-time mechanical energy monitoring and dynamic weighting correction mechanism, the output ratio of energy storage equipment is accurately allocated when the load suddenly increases. This avoids the flywheel from becoming unstable due to a sudden drop in speed caused by excessive instantaneous energy release, and also prevents the battery from affecting its service life due to over-discharge. This method quantifies the state decay factors of energy storage units, enabling flexible power allocation across multiple time scales. This ensures uninterrupted power supply to critical loads while improving the overall economic efficiency of the system. Particularly for short-duration, high-power surge scenarios, it can complete energy source switching decisions within milliseconds, significantly enhancing the system's resilience to disturbances.

[0035] Optionally, the method further includes: Obtain rectifier efficiency parameters and preset topology switching thresholds; When the ratio of the output power of the flywheel to that of the battery exceeds the topology switching threshold, a rectifier mode switching signal is generated. The weighting factor is corrected using the rectifier mode switching signal.

[0036] Specifically, the input and output voltages are first collected using a rectifier current sensor to calculate the rectifier efficiency. Rectifier efficiency is equal to the ratio of output power to input power, where output power is obtained by multiplying the output voltage by the output current, and input power is obtained by multiplying the input voltage by the input current. This rectifier efficiency reflects the effectiveness of power conversion in the current operating mode. The topology switching threshold is obtained through experimental testing. A logic check is triggered when the ratio of flywheel output power to battery output power exceeds the topology switching threshold for five consecutive sampling cycles. If the ratio of flywheel output power to battery output power is detected to be greater than the topology switching threshold, and the duration meets the condition, a mode switching signal is generated. This signal contains an identifier for either boost or buck mode, and its generation is based on the relationship between rectifier efficiency and the target operating range. If the rectifier efficiency is less than the preset minimum efficiency, a boost mode signal is forcibly triggered. After the signal is sent to the controller, the calculation method of the weighting factor is corrected. The original weighting factor is the ratio of the flywheel speed to the rated speed. The corrected weighting factor is equal to the original weighting factor multiplied by the mode switching coefficient. The mode switching coefficient is determined according to the signal type, such as 1.2 for boost mode and 0.8 for buck mode.

[0037] For example, a sudden increase in the load on a factory's frequency converter caused the flywheel output power to reach 550kW, while the battery output power was 200kW, resulting in a power ratio of 2.75. When the preset topology switching threshold is 2.5, the system immediately detected the over-limit and checked the efficiency parameter. The rectifier efficiency was 88%, lower than the minimum efficiency of 90% under this condition. Determining that a switch to boost mode was necessary, a mode switching signal was generated. Simultaneously, the original weighting factor was updated from 0.65 multiplied by the boost coefficient of 1.2 to 0.78. This corrected weighting factor value was input to the dynamic coupling parameter calculation module, increasing the flywheel weight by 12%, and the rectifier entered boost operation. Through a dual judgment mechanism that monitors both the power ratio and efficiency parameters in real time, the system proactively adjusts its operating state when it approaches an inefficient region. This ensures the economy of the power conversion process and avoids the risk of overheating caused by prolonged operation under suboptimal conditions. Especially in scenarios with frequent load fluctuations, this method achieves adaptive optimization of energy allocation strategy by dynamically adjusting the weighting factor, effectively preventing overload of a single energy storage unit and extending equipment life, while ensuring that power supply quality meets the needs of sensitive loads.

[0038] Optionally, adjusting the preset rectifier duty cycle parameter according to the coordinated capacity allocation weight to obtain the energy storage charging and discharging strategy includes: The pulse feedback frequency of the flywheel's remaining mechanical energy is calculated based on the cooperative capacity allocation weight. Based on the pulse feedback frequency and the preset power conversion efficiency matrix, the duty cycle range of the rectifier is determined; Based on the dynamic adjustment of the rectifier topology within the duty cycle range, an energy storage charging and discharging strategy is obtained.

[0039] Optionally, the energy storage charging and discharging strategy obtained by dynamically adjusting the rectifier topology within the duty cycle range includes: A heat dissipation compensation coefficient is generated based on the rectifier temperature rise parameters; The heat dissipation compensation coefficient is weighted and fused with the duty cycle range to obtain the dynamic duty cycle parameter; By adjusting the output power ratio of the flywheel and the battery using the dynamic duty cycle parameter, an energy storage charging and discharging strategy is obtained.

[0040] Specifically, firstly, based on the weighted value of the collaborative capacity allocation, the pulse feedback frequency of the flywheel's remaining mechanical energy is obtained by multiplying this weighted value by the flywheel speed and then dividing by the system base frequency. The specific formula is: pulse feedback frequency equals the collaborative capacity allocation weight multiplied by the square of the flywheel's real-time speed, then divided by the square of the system's rated speed and the power conversion base frequency. The system base frequency is either the power grid frequency or the circuit design frequency. Next, a preset power conversion efficiency matrix is ​​used. This matrix is ​​a two-dimensional table where row indices correspond to the discretized intervals of the pulse feedback frequency, column indices represent operating temperature levels, and each cell stores the rectification efficiency under the corresponding operating condition. By finding the interval and temperature level where the current pulse feedback frequency falls, the corresponding maximum and minimum efficiency values ​​are determined. Based on the positive correlation between efficiency values ​​and duty cycle, the upper and lower limits of the duty cycle interval are linearly mapped. Then, the temperature gradient during rectifier operation is collected as a temperature rise parameter. A heat dissipation compensation coefficient is calculated using an exponential function. Its value is the complement of the natural logarithm of the ratio of the real-time temperature rise rate to the design maximum allowable temperature rise rate, ensuring that the duty cycle is automatically reduced when the temperature is too high. Finally, the heat dissipation compensation coefficient and the upper and lower limits of the duty cycle range are weighted and integrated. The weighting method is dynamically adjusted according to the ratio of the current load power to the rated power. If the power ratio exceeds the threshold, the heat dissipation compensation coefficient is given more weight. The weighted result is the dynamic duty cycle parameter, which is input to the rectifier's pulse width modulation controller to control the power output ratio of the flywheel and the battery. During this process, it is necessary to ensure that the sampling period of the temperature rise parameter is synchronized with the duty cycle adjustment period to avoid parameter mismatch caused by time delay.

[0041] For example, in a semiconductor factory's UPS system, the load power fluctuation rate during the startup of precision equipment is 30kW per second, and the collaborative capacity allocation weight, calculated from previous steps, is 0.7. The system measures a flywheel speed of 4500 rpm. Substituting this into the formula, the pulse feedback frequency is calculated as 0.7 multiplied by the square of 4500, then divided by the square of 5000 rpm and the product of 50Hz, yielding a pulse frequency of 43Hz. The power conversion efficiency matrix is ​​used to find the corresponding mid-temperature range for 43Hz, resulting in an efficiency range of 88%-92%. At this time, the temperature rise parameter shows that the rectifier heats up by 4 degrees Celsius every ten seconds. The heat dissipation compensation coefficient is 1 minus the natural logarithm of the ratio of the current temperature rise rate of 4 to the maximum allowable rate of 5, resulting in a compensation coefficient of 0.8. The duty cycle range of 65%-75% is weighted and integrated with the compensation coefficient of 0.8, selecting a 30% heat dissipation weight and a 70% power weight, ultimately determining the dynamic duty cycle as 70% multiplied by 0.8 plus 65% multiplied by 0.2, which equals 69%. The rectifier adjusts its output accordingly, allowing the flywheel to handle 70% of the power while the battery supplements the remaining demand. In scenarios with rapidly fluctuating loads, the dynamic duty cycle mechanism maintains the flywheel's efficient discharge characteristics while keeping the rectifier's temperature rise within a safe range. Simultaneously, it utilizes an efficiency matrix to select the optimal operating point, avoiding the risk of protection shutdowns due to equipment overheating. In practical applications, this method effectively balances equipment operating conditions and system response speed, making it particularly suitable for precision manufacturing scenarios with stringent power quality requirements.

[0042] Optionally, the simulation verification of the energy storage charging and discharging strategy, and the output of the final capacity evaluation results, include: The energy storage charging and discharging strategy is input into a preset virtual simulation platform to generate initial system response time parameters; A preset disturbance load is applied to the initial system response time parameter, and the capacity redundancy deviation value is calculated; If the capacity redundancy deviation value exceeds the preset deviation threshold, the collaborative capacity allocation weights of the flywheel and the battery are reallocated to obtain the final capacity assessment result.

[0043] Optionally, the reallocation of capacity weights between the flywheel and the battery to obtain the final capacity assessment result includes: Generate capacity adjustment ratio parameters based on the capacity redundancy deviation value; The capacity adjustment ratio parameter and the collaborative capacity allocation weight are linearly superimposed to obtain the corrected collaborative capacity allocation weight between the flywheel and the battery. By adjusting the preset rectifier duty cycle parameters using the modified collaborative capacity allocation weights of the flywheel and battery, a new energy storage charging and discharging strategy is obtained and verified through simulation. When the capacity redundancy deviation value is less than or equal to the preset deviation threshold, the current energy storage charging and discharging strategy is output to obtain the final capacity evaluation result.

[0044] Specifically, key parameters of the energy storage charging and discharging strategy, including the flywheel speed control curve, battery discharge depth limit, and rectifier duty cycle sequence, are first imported into a virtual simulation platform. This platform is built based on a power electronic system model and mechanical dynamics equations. The method for generating initial system response time parameters involves simulating a standard load change scenario in a virtual environment and recording the time from load change to the system output voltage recovering to 90% of its rated value. Subsequently, a preset disturbance load is applied, including composite disturbance modes such as voltage spikes and drops, frequency shifts, and impulsive loads. The duration and amplitude of each disturbance cycle are preset according to actual operating conditions. When calculating the capacity redundancy deviation, the time integral of the difference between instantaneous power demand and the system's actual support capacity is used, and the ratio of the integral result to the rated capacity is defined as the capacity redundancy deviation. If this deviation exceeds a preset threshold, the system triggers an optimization mechanism, multiplying the capacity redundancy deviation by a proportional tuning coefficient to generate a capacity adjustment proportional parameter. The proportional tuning coefficient is determined by the ratio of the system's rated capacity to the total flywheel and battery capacity in the initial strategy. Before linearly superimposing the adjusted proportional parameter with the original collaborative capacity allocation weights, the absolute value of the adjusted proportional parameter must be limited to no more than 30% of the initial weight value to avoid strategy oscillation. The superposition process uses a weighted average method; the new collaborative weight is equal to a linear combination of the original weight and the adjusted parameter. The combination coefficient is adjusted inversely according to the current simulation iteration number to prevent over-correction. The corrected weight values ​​are then re-input into the dynamic coupling module for flywheel mechanical inertia and battery chemical energy decay for secondary optimization. Iteration through closed-loop feedback continues until the capacity redundancy deviation is less than or equal to a preset deviation threshold.

[0045] For example, when a rail transit power supply system adopts this method, the initial strategy assigns weights of 0.65 to the flywheel and 0.35 to the battery capacity. The simulation platform simulates a scenario where a train accelerates and suddenly receives an 800kW load, measuring a system response time of 3.2 seconds. After applying a voltage fluctuation disturbance of 15%, the calculated capacity redundancy deviation is 18%, exceeding the preset threshold of 10%. The system retrieves the ratio of the rated capacity of 10MWh to the current total capacity of 8.5MWh (1.176) as the tuning coefficient, generating an adjustment ratio parameter of 18% multiplied by 1.176, which equals 21.2%. Since the original flywheel weight of 0.65 is limited to 0.195 by 30%, an actual adjustment of 21.2% is used. The new weight is obtained by subtracting 0.212 from the original flywheel weight of 0.65, resulting in 0.438, and the battery weight is correspondingly increased to 0.562. After three closed-loop optimizations, the system response time is shortened to 2.7 seconds, and the deviation value is reduced to 8%. By employing virtual simulation and a closed-loop correction mechanism to assess the effectiveness of strategy execution, capacity configuration defects can be predicted before deployment. This dynamically balances the rapid response advantage of the flywheel with the long-term endurance of the battery, making it particularly suitable for complex power supply scenarios that present both mechanical delay risks and chemical energy decay characteristics. Through an iterative process of approximating the optimal solution, this method effectively prevents capacity waste or equipment overload issues caused by static parameter settings in traditional strategies, improving the economic efficiency of energy storage resource allocation while ensuring system dynamic stability.

[0046] Optionally, the method further includes: Based on preset maintenance cycle parameters and historical capacity assessment results, a life prediction curve for hybrid energy storage equipment is generated. Based on the comparison between the predicted lifetime curve and the current final capacity assessment result, a preset alarm threshold is triggered.

[0047] Specifically, the data collection time points are first determined by preset maintenance cycle parameters, including the number of days between routine equipment inspections and key component replacement cycle indicators. Real-time collection of historical capacity assessment results, including flywheel speed decay rate, battery capacity retention rate, and rectifier efficiency degradation values, is stored in a time-series database. A hybrid energy storage lifespan degradation model is constructed based on the Arrhenius equation. The calculation formula is that the predicted lifespan value equals the initial lifespan parameter multiplied by the base of the natural logarithm (negative thermal stress factor) and the square of the battery cycle count. The thermal stress factor is obtained by multiplying the ambient temperature monitoring value by the battery electrolyte activation energy parameter, which is obtained by fitting charge-discharge cycle test data provided by the battery manufacturer. Historical capacity data of each energy storage unit is input into the degradation model to calculate the predicted curves for the next three maintenance cycles. The horizontal axis of the curve represents time, and the vertical axis represents the capacity retention percentage. The system continuously compares the final capacity assessment result obtained after current simulation optimization with the values ​​at the corresponding time points of the predicted curve. If the actual value is lower than the predicted value and the deviation exceeds the alarm threshold percentage three times consecutively, a three-level progressive alarm is triggered. The alarm thresholds are dynamically set. The first-level threshold is 95% of the predicted value, and only logs are recorded when the limit is exceeded for the first time. The second-level threshold is 90% of the predicted value, triggering a maintenance work order. The third-level threshold is 85%, executing a forced shutdown protection command. The threshold setting logic is based on the equipment's minimum safe operating capacity to ensure that unplanned shutdowns are not caused by misjudgments.

[0048] For example, the flywheel energy storage device of an airport UPS system has a maintenance cycle set at 90 days, and the battery pack automatically initiates capacity verification after 200 cycles. Historical data shows that the flywheel speed decreases by 1.2% per quarter, and the battery capacity decreases by 0.05% per cycle. Based on the current ambient temperature of 28 degrees Celsius and the cumulative number of cycles of 150, the system calculates a thermal stress factor of 0.0083. Substituting the initial lifespan of 8 years into the formula, the predicted capacity retention rate for the next quarter is 97.6%. The final capacity assessment based on actual simulation optimization shows that the capacity retention rate at the end of the current quarter has dropped to 94.5%, which is below the allowable range of 90% for the second-level threshold but triggers the first-level warning. The system automatically generates a flywheel bearing lubrication inspection work order and prompts for enhanced monitoring of the battery pack temperature. By integrating the predictive model of equipment physical characteristics and operating environment parameters, a forward-looking judgment standard is established before actual capacity decay, which avoids the resource waste that may occur with traditional periodic maintenance and can promptly capture abnormal degradation trends. Especially in scenarios involving the collaborative operation of multiple devices, this method can differentiate the degradation rate of each energy storage unit, enabling precise preventive maintenance, effectively reducing the risk of operational interruption of critical infrastructure due to sudden failures of energy storage equipment, and extending the service life of high-value energy storage devices.

[0049] Based on the same inventive concept, such as Figure 4As shown, the present invention also provides a capacity assessment system for a hybrid UPS configuration of flywheel energy storage and battery, the system comprising: The data acquisition module is used to acquire preset load fluctuation parameters, flywheel speed parameters, and real-time state of charge parameters of the battery, and generate a dynamic load feature vector. The dynamic coupling module is used to calculate the dynamic coupling parameters between the flywheel mechanical inertia and the battery chemical energy decay based on the dynamic load characteristic vector. The collaborative allocation module is used to generate collaborative capacity allocation weights between the flywheel and the battery based on the dynamic coupling parameters. The energy consumption optimization module is used to adjust the preset rectifier duty cycle parameters according to the collaborative capacity allocation weight to obtain the energy storage charging and discharging strategy. The verification execution module is used to simulate and verify the energy storage charging and discharging strategy and output the final capacity evaluation result.

[0050] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0051] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A capacity assessment method for a hybrid UPS configuration combining flywheel energy storage and battery, characterized in that, The method includes: Obtain preset load fluctuation parameters, flywheel speed parameters, and real-time state of charge parameters of the battery to generate a dynamic load feature vector; Based on the dynamic load characteristic vector, calculate the dynamic coupling parameters between the flywheel mechanical inertia and the battery chemical energy decay. Based on the aforementioned dynamic coupling parameters, a collaborative capacity allocation weight between the flywheel and the battery is generated. Based on the collaborative capacity allocation weight, the preset rectifier duty cycle parameter is adjusted to obtain the energy storage charging and discharging strategy; The energy storage charging and discharging strategy is simulated and verified, and the final capacity assessment result is output.

2. The capacity assessment method for a hybrid UPS configuration combining flywheel energy storage and battery as described in claim 1, characterized in that, The calculation of the dynamic coupling parameters between the flywheel mechanical inertia and the battery chemical energy decay based on the dynamic load characteristic vector includes: A virtual inertia decay curve is constructed based on the flywheel speed parameters, and the critical point of flywheel speed is identified; Obtain preset battery temperature parameters and lifespan degradation coefficient, and fit the mapping relationship between temperature and usable capacity using a nonlinear function; Based on the virtual inertia decay curve and the mapping relationship, complementary weights for flywheel mechanical energy and battery chemical energy are generated. The complementary weights are updated in real time based on the dynamic load feature vector to obtain the dynamic coupling parameters.

3. The capacity assessment method for a hybrid UPS configuration combining flywheel energy storage and battery as described in claim 2, characterized in that, The method further includes: When the load power is detected to exceed the preset fluctuation threshold, the flywheel mechanical energy is extracted and the remaining mechanical energy parameters are generated. Based on the remaining mechanical energy parameters, a command to replenish battery power is generated; The complementary weights are corrected using the supplementary power supply command and a preset weighting factor to obtain optimized dynamic coupling parameters.

4. The capacity assessment method for a hybrid UPS configuration combining flywheel energy storage and battery as described in claim 3, characterized in that, The method further includes: Obtain rectifier efficiency parameters and preset topology switching thresholds; When the ratio of the output power of the flywheel to that of the battery exceeds the topology switching threshold, a rectifier mode switching signal is generated. The weighting factor is corrected using the rectifier mode switching signal.

5. The capacity assessment method for a hybrid UPS configuration combining flywheel energy storage and battery as described in claim 1, characterized in that, The step of adjusting the preset rectifier duty cycle parameter according to the collaborative capacity allocation weight to obtain the energy storage charging and discharging strategy includes: The pulse feedback frequency of the flywheel's remaining mechanical energy is calculated based on the cooperative capacity allocation weight. Based on the pulse feedback frequency and the preset power conversion efficiency matrix, the duty cycle range of the rectifier is determined; Based on the dynamic adjustment of the rectifier topology within the duty cycle range, an energy storage charging and discharging strategy is obtained.

6. The capacity assessment method for a hybrid UPS configuration combining flywheel energy storage and battery as described in claim 5, characterized in that, The energy storage charging and discharging strategy obtained by dynamically adjusting the rectifier topology within the duty cycle range includes: A heat dissipation compensation coefficient is generated based on the rectifier temperature rise parameters; The heat dissipation compensation coefficient is weighted and fused with the duty cycle range to obtain the dynamic duty cycle parameter; By adjusting the output power ratio of the flywheel and the battery using the dynamic duty cycle parameter, an energy storage charging and discharging strategy is obtained.

7. The capacity assessment method for a hybrid UPS configuration combining flywheel energy storage and battery as described in claim 1, characterized in that, The simulation verification of the energy storage charging and discharging strategy, and the output of the final capacity evaluation results, include: The energy storage charging and discharging strategy is input into a preset virtual simulation platform to generate initial system response time parameters; A preset disturbance load is applied to the initial system response time parameter, and the capacity redundancy deviation value is calculated; If the capacity redundancy deviation value exceeds the preset deviation threshold, the collaborative capacity allocation weight of the flywheel and the battery will be reallocated.

8. The capacity assessment method for a hybrid UPS configuration combining flywheel energy storage and battery as described in claim 7, characterized in that, The reassignment of capacity weights between the flywheel and the battery to obtain the final capacity assessment result includes: Generate capacity adjustment ratio parameters based on the capacity redundancy deviation value; The capacity adjustment ratio parameter and the collaborative capacity allocation weight are linearly superimposed to obtain the corrected collaborative capacity allocation weight between the flywheel and the battery. By adjusting the preset rectifier duty cycle parameters using the modified collaborative capacity allocation weights of the flywheel and battery, a new energy storage charging and discharging strategy is obtained and verified through simulation. When the capacity redundancy deviation value is less than or equal to the preset deviation threshold, the current energy storage charging and discharging strategy is output to obtain the final capacity evaluation result.

9. The capacity assessment method for a hybrid UPS configuration combining flywheel energy storage and battery as described in claim 1, characterized in that, The method further includes: Based on preset maintenance cycle parameters and historical capacity assessment results, a life prediction curve for hybrid energy storage equipment is generated. Based on the comparison between the predicted lifetime curve and the current final capacity assessment result, a preset alarm threshold is triggered.

10. A capacity assessment system for a hybrid UPS configuration of flywheel energy storage and battery, applied to the capacity assessment method for a hybrid UPS configuration of flywheel energy storage and battery as described in any one of claims 1-9, characterized in that, The system includes: The data acquisition module is used to acquire preset load fluctuation parameters, flywheel speed parameters, and real-time state of charge parameters of the battery, and generate a dynamic load feature vector. The dynamic coupling module is used to calculate the dynamic coupling parameters between the flywheel mechanical inertia and the battery chemical energy decay based on the dynamic load characteristic vector. The collaborative allocation module is used to generate collaborative capacity allocation weights between the flywheel and the battery based on the dynamic coupling parameters. The energy consumption optimization module is used to adjust the preset rectifier duty cycle parameters according to the collaborative capacity allocation weight to obtain the energy storage charging and discharging strategy. The verification execution module is used to simulate and verify the energy storage charging and discharging strategy and output the final capacity evaluation result.

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