Capacity evaluation method for flywheel energy storage and battery hybrid ups configuration

Abstract

The application discloses a capacity evaluation method for a flywheel energy storage and battery hybrid UPS configuration, and belongs to the technical field of capacitor application and hybrid energy storage system, which comprises the following steps: obtaining preset load fluctuation parameters, flywheel rotating speed parameters and real-time state of charge parameters of the battery, and generating a dynamic load feature vector; calculating dynamic coupling parameters of flywheel mechanical inertia and battery chemical energy attenuation according to the dynamic load feature vector; generating flywheel and battery collaborative capacity distribution weights based on the dynamic coupling parameters; adjusting the preset rectifier duty cycle parameters according to the collaborative capacity distribution weights to obtain an energy storage charging and discharging strategy; and simulating and verifying the energy storage charging and discharging strategy to output a final capacity evaluation result. The application adopts dynamic coupling parameters and a closed-loop simulation verification mechanism, can accurately adapt to flywheel inertia and battery attenuation characteristics, optimizes the energy distribution ratio to improve power supply quality and equipment life.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of capacitor applications and hybrid energy storage systems, and in particular to a capacity evaluation method for a flywheel energy storage and battery hybrid UPS configuration. BACKGROUND

[0002] A flywheel energy storage and battery hybrid UPS system realizes energy buffering and transient power compensation through a capacitor, and is a key power guarantee device in fields such as data centers and medical facilities. The existing technology generally adopts a fixed capacity ratio mode, that is, static parameters are set according to the rated speed of the flywheel and the nominal capacity of the battery, and double-way energy output is realized through a rectifier. Some improved schemes judge the switching time of the flywheel and the battery based on a load current threshold, or add a capacitor in the battery pack to smooth the discharge curve.

[0003] The current mainstream scheme allocates energy through a preset capacity ratio of the flywheel and the battery, triggers fixed switching logic according to load power fluctuation, and uses the instantaneous charging and discharging characteristics of the capacitor to make up for the power gap. However, the existing technology regards the mechanical inertia of the flywheel and the chemical energy decay of the battery as independent parameters, and does not fully consider the nonlinear relationship between the mutual influence of the two in the dynamic process.

[0004] However, inertia compensation lags behind the critical point of the flywheel speed, resulting in excessive consumption of mechanical energy; the characteristics of the capacitor are not linked to the battery temperature and life decay parameters, causing the capacity redundancy to be set too large; the rectifier duty cycle adjustment lacks real-time feedback on the coupling of the remaining mechanical energy of the flywheel and the electrical stress of the capacitor, resulting in insufficient robustness of the hybrid energy storage strategy. SUMMARY

[0005] To solve the above problems, the application provides a capacity evaluation method for a flywheel energy storage and battery hybrid UPS configuration, which adopts dynamic coupling parameters and a closed-loop simulation verification mechanism, can accurately adapt to the inertia of the flywheel and the decay characteristics of the battery, and optimizes the energy distribution ratio to improve power supply quality and equipment life.

[0006] The above object can be achieved by the following scheme:

[0007] A capacity evaluation method for a flywheel energy storage and battery hybrid UPS configuration, comprising: obtaining preset load fluctuation parameters, flywheel speed parameters and real-time state of charge parameters of the battery, and generating a dynamic load feature vector; calculating dynamic coupling parameters of the mechanical inertia of the flywheel and the chemical energy decay of the battery according to the dynamic load feature vector; generating a collaborative capacity distribution weight of the flywheel and the battery based on the dynamic coupling parameters; adjusting a preset rectifier duty cycle parameter according to the collaborative capacity distribution weight to obtain an energy storage charging and discharging strategy; and simulating and verifying the energy storage charging and discharging strategy to output a final capacity evaluation result.

[0008] Optionally, the calculating the dynamic coupling parameter of the flywheel mechanical inertia and the battery chemical energy attenuation according to the dynamic load feature vector comprises: constructing a virtual inertia attenuation curve according to the flywheel rotating speed parameter, and identifying a flywheel rotating speed critical point; obtaining a preset battery temperature parameter and a life attenuation coefficient, and fitting a mapping relationship between the temperature and the available capacity through a nonlinear function; generating a flywheel mechanical energy and a battery chemical energy complementary weight based on the virtual inertia attenuation curve and the mapping relationship; and updating the complementary weight in real time according to the dynamic load feature vector to obtain the dynamic coupling parameter.

[0009] Optionally, the method further comprises: when detecting that the load power exceeds a preset fluctuation threshold, extracting flywheel mechanical energy and generating a residual mechanical energy parameter; generating a battery supplementary power supply instruction according to the residual mechanical energy parameter; and correcting the complementary weight by using the supplementary power supply instruction and a preset weighting factor to obtain an optimized dynamic coupling parameter.

[0010] Optionally, the method further comprises: obtaining a rectifier efficiency parameter and a preset topology switching threshold; when an output power ratio of the flywheel and the battery exceeds the topology switching threshold, generating a rectifier mode switching signal; and correcting the weighting factor by using the rectifier mode switching signal.

[0011] Optionally, the adjusting a preset rectifier duty cycle parameter according to the collaborative capacity allocation weight to obtain an energy storage charge and discharge strategy comprises: calculating a flywheel residual mechanical energy pulse feedback frequency according to the collaborative capacity allocation weight; determining a rectifier duty cycle interval based on the pulse feedback frequency and a preset electric energy conversion efficiency matrix; and dynamically adjusting a rectifier topology structure in the duty cycle interval to obtain the energy storage charge and discharge strategy.

[0012] Optionally, the dynamically adjusting a rectifier topology structure in the duty cycle interval to obtain an energy storage charge and discharge strategy comprises: generating a heat dissipation compensation coefficient according to a rectifier temperature rise parameter; weighting and fusing the heat dissipation compensation coefficient and the duty cycle interval to obtain a dynamic duty cycle parameter; and adjusting an output power ratio of the flywheel and the battery by using the dynamic duty cycle parameter to obtain the energy storage charge and discharge strategy.

[0013] Optionally, the simulating and verifying the energy storage charge and discharge strategy to output a final capacity evaluation result comprises: inputting the energy storage charge and discharge strategy into a preset virtual simulation platform to generate an initial system response time parameter; applying a preset disturbance load to the initial system response time parameter to calculate a capacity redundancy deviation value; and if the capacity redundancy deviation value exceeds a preset deviation threshold, re-distributing the collaborative capacity allocation weight of the flywheel and the battery.

[0014] Optionally, the re-distribution of the flywheel and the battery capacity weight to obtain the final capacity evaluation result comprises: generating a capacity adjustment proportion parameter according to the capacity redundancy deviation value; linearly superimposing the capacity adjustment proportion parameter and the collaborative capacity distribution weight of the flywheel and the battery to obtain a modified collaborative capacity distribution weight of the flywheel and the battery; adjusting a preset rectifier duty cycle parameter by using the modified collaborative capacity distribution weight 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 a current energy storage charging and discharging strategy to obtain the final capacity evaluation result.

[0015] Optionally, the method further comprises: generating a life prediction curve of the hybrid energy storage device according to a preset maintenance period parameter and a historical capacity evaluation result; and triggering a preset alarm threshold based on comparison between the life prediction curve and the current final capacity evaluation result.

[0016] Based on the same inventive concept, the application also provides a capacity evaluation system for a flywheel energy storage and battery hybrid UPS configuration, which comprises: a data acquisition module for acquiring preset load fluctuation parameters, flywheel speed parameters and real-time state of charge parameters of the battery, and generating a dynamic load feature vector; a dynamic coupling module for calculating dynamic coupling parameters of flywheel mechanical inertia and battery chemical energy attenuation according to the dynamic load feature vector; a collaborative distribution module for generating a collaborative capacity distribution weight of the flywheel and the battery based on the dynamic coupling parameters; an energy consumption optimization module for adjusting a preset rectifier duty cycle parameter according to the collaborative capacity distribution weight to obtain an energy storage charging and discharging strategy; and a verification execution module for simulating and verifying the energy storage charging and discharging strategy and outputting a final capacity evaluation result.

[0017] Compared with the prior art, the application has the following advantages:

[0018] 1. The application can significantly improve the response speed of the hybrid energy storage system in the load mutation scene and reduce the efficiency loss in the energy conversion process by dynamically coupling the flywheel mechanical inertia and the battery chemical energy attenuation characteristics and optimizing the collaborative capacity distribution weight in real time.

[0019] 2. The application uses a dynamic load feature vector and a rectifier duty cycle interval adjustment strategy to effectively avoid the flywheel speed critical point and the risk of over-discharge of the battery, prolong the service life of the flywheel bearing and the battery, and reduce the equipment maintenance cost.

[0020] 3. The application combines a virtual simulation platform and a capacity redundancy deviation value feedback mechanism to realize multi-level iterative verification of the charging and discharging strategy, ensure the capacity configuration accuracy to adapt to different working conditions, and improve the system power supply reliability.

[0021] 4、The application synchronously optimizes the rectifier topology operation state and the energy storage device health management through monitoring the heat dissipation compensation coefficient and the dynamic correction of the life prediction curve, and guarantees the economy of the hybrid UPS system in the whole life cycle.

[0022] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and attained by the structure particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0024] Figure 1 is a flowchart of a capacity evaluation method of a flywheel energy storage and battery hybrid UPS configuration according to an embodiment of the present application.

[0025] Figure 2 is a load power fluctuation curve according to an embodiment of the present application.

[0026] Figure 3 is a flywheel mechanical inertia contribution factor and battery chemical compensation factor change diagram according to an embodiment of the present application.

[0027] Figure 4 is a structural diagram of a capacity evaluation system of a flywheel energy storage and battery hybrid UPS configuration according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the objects, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0029] With reference to Figure 1 , one embodiment of the present application proposes a capacity evaluation method of a flywheel energy storage and battery hybrid UPS configuration, which adopts dynamic coupling parameters and closed-loop simulation verification mechanism, can accurately adapt to flywheel inertia and battery attenuation characteristics, and optimizes energy distribution ratio to improve power supply quality and equipment life.

[0030] The method described in this embodiment specifically includes:

[0031] 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;

[0032] 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.

[0033] Based on the dynamic load characteristic vector, calculate the dynamic coupling parameters between the flywheel mechanical inertia and the battery chemical energy decay.

[0034] Based on the aforementioned dynamic coupling parameters, a collaborative capacity allocation weight between the flywheel and the battery is generated.

[0035] 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 weights. for:

[0036] ;

[0037] 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 greater than the preset threshold, k is automatically increased by 5% to preferentially allocate flywheel output. The acquisition method of each parameter is as follows: the flywheel speed is measured by an optical encoder, the moment of inertia is based on the theoretical calculation value of the flywheel material density and geometric structure, the available capacity of the battery is obtained through the ampere-hour integral model fitted by the charge and discharge cycle test data, and the environmental temperature attenuation coefficient is determined by the exponential function established by the accelerated aging experiment. The physical meaning of weight calculation is to dynamically allocate capacity according to the real-time mechanical energy and chemical energy effective reserve ratio. The generated cooperative capacity allocation weight And high-frequency oscillation is eliminated by second-order filtering.

[0038] According to the cooperative capacity allocation weight, the preset rectifier duty cycle parameter is adjusted to obtain an energy storage charge and discharge strategy;

[0039] The energy storage charge and discharge strategy is simulated and verified, and a final capacity evaluation result is output.

[0040] Specifically, the precise evaluation of hybrid energy storage capacity is realized through the cooperative modeling and real-time coupling analysis of multiple source dynamic parameters. The method constructs a dynamic load feature vector by collecting real-time data of load fluctuation, flywheel speed and battery state of charge, which comprehensively represents the space-time correlation characteristics of power demand and energy storage element state. The dynamic coupling parameters reflecting the transient response capability of flywheel and the sustained power supply capability of battery are generated by dynamically quantifying the energy complementary relationship between the virtual attenuation curve of flywheel mechanical inertia and the nonlinear attenuation model of battery chemical energy with temperature. Based on the cooperative capacity allocation weight calculated by the coupling parameters, the duty cycle interval of the rectifier is adjusted, and the efficient cooperative output of flywheel kinetic energy and battery chemical energy is realized through the dynamic adjustment of the power conversion topology, forming an optimized charge and discharge strategy adapting to load transients. The core lies in establishing a real-time mapping mechanism of mechanical energy and chemical energy dynamic attenuation, and realizing the time scale matching of the two through weight distribution.

[0041] Optionally, the calculation of the dynamic coupling parameters of flywheel mechanical inertia and battery chemical energy attenuation according to the dynamic load feature vector comprises:

[0042] A virtual inertia attenuation curve is constructed according to the flywheel speed parameter, and a flywheel speed critical point is identified;

[0043] A preset battery temperature parameter and a life attenuation coefficient are obtained, and the mapping relationship between temperature and available capacity is fitted by a nonlinear function;

[0044] Based on the virtual inertia attenuation curve and the mapping relationship, a complementary weight of flywheel mechanical energy and battery chemical energy is generated;

[0045] The complementary weight is updated in real time according to the dynamic load feature vector, and the dynamic coupling parameters are obtained.

[0046] Specifically, first, a virtual inertia attenuation curve is constructed based on the flywheel speed parameter, which represents the rate at which the flywheel kinetic energy is lost as the speed decreases, wherein the speed critical point is defined as the lowest speed value when the flywheel output torque cannot meet the load requirement. Second, the temperature parameter and the life attenuation coefficient of the battery are collected, and a nonlinear function is constructed to describe the nonlinear relationship between the capacity reduction and the temperature rise, and the temperature coefficient in the function corresponds to the physical properties of the electrolyte activity reduction. Then the slope of the virtual inertia attenuation curve and the slope of the battery capacity temperature response curve are normalized, and the combined value after adding them is used as the initial reference value of the complementary weight. The reference value is multiplied by the power demand component in the dynamic load feature vector to obtain the real-time adjustment value of the dynamic coupling parameter. In the process of calculating the complementary weight, if the flywheel speed is close to the critical point, the weight distribution mechanism is triggered to dynamically increase the power sharing proportion of the battery.

[0047] For example, when a UPS system in a data center detects a sudden increase in load power demand, causing the flywheel speed to drop from 6000 revolutions to the critical value of 4200 revolutions, the system immediately constructs a virtual inertia attenuation curve, and the slope of the curve is measured to be 25 joules of energy lost per 100 revolutions. At the same time, the battery temperature rises to 45 degrees Celsius, triggering the temperature and available capacity mapping function calculation, resulting in a decrease in available capacity to 83% of the nominal value. The flywheel inertia slope of 25 units and the battery capacity loss ratio of 17 units are added together, giving the flywheel a weight coefficient of 0.6 and the battery a weight coefficient of 0.4. At this time, the dynamic load feature vector detects that the instantaneous power demand is 150kW, and the flywheel needs to bear 90kW and the battery needs to bear 60kW according to the weight distribution. Through physical mechanism modeling, dynamic energy distribution is realized, and the output proportion of the battery is automatically increased when the flywheel energy storage capacity is weakened, which can avoid the risk of low flywheel speed and prevent the battery from overloading, ensuring that the hybrid energy storage system maintains optimal state under different working conditions. Especially in high-power impact events, this method effectively improves the system power supply reliability by predicting the trend of energy element state changes in advance.

[0048] Optionally, the method further comprises:

[0049] When detecting that the load power exceeds the preset fluctuation threshold, extracting the flywheel mechanical energy and generating a residual mechanical energy parameter;

[0050] According to the residual mechanical energy parameter, generating a battery supplementary power supply instruction;

[0051] Using the supplementary power supply instruction and a preset weighting factor to correct the complementary weight to obtain an optimized dynamic coupling parameter.

[0052] Specifically, first, the power sensor is used to collect the load current value in real time and compare it with the preset threshold value. If the current power value exceeds the threshold value for three seconds, an alarm signal is triggered. Then, the real-time angular velocity is obtained through the flywheel speed sensor, and the residual mechanical energy parameter is calculated using the product of the flywheel moment of inertia and the square root of the angular velocity. The moment of inertia is determined in advance by the flywheel material density and geometric structure. Then, the residual mechanical energy parameter and the current load power are input into the dynamic mapping table, and the energy difference that the battery needs to supplement is calculated according to the principle of energy conservation. The difference is equal to the product of the load demand power and the residual mechanical energy with respect to the integral of the discharge time, and finally the pulse width modulation battery supplement power supply instruction is generated. According to the preset weighting factor, the factor is determined by the ratio of the historical cycle number of the battery and the real-time state of charge SOC. The specific acquisition method is to divide the cycle number by the designed maximum cycle number to obtain a first ratio, and at the same time, the second ratio of the current value and the rated value of the SOC is extracted. The multiplication result of the first ratio and the second ratio is normalized to the interval of zero to one to obtain the weighting factor. Multiply the power requirement of the supplement power supply instruction by the weighting factor to generate a correction amount, and then add the correction amount to the original complementary weight to finally obtain the optimized dynamic coupling parameter, and the flywheel weight is adjusted to the original weight minus the correction amount, ensuring that the total weight value remains one.

[0053] For example, the load power of a certain hospital UPS system suddenly increases to 180% of the nominal value when the elevator group starts simultaneously. At this time, the power sensor detects that the fluctuation exceeds the preset threshold of 150%. The system immediately reads the current speed of the flywheel as 3500 revolutions, and calculates the residual mechanical energy as 82 MJ based on the known moment of inertia. Combined with the load demand power, the dynamic mapping table determines that 420 kW of battery output needs to be supplemented. At this time, the cycle number of the battery is 70% of the designed maximum value, and the state of charge is 85%. The calculated weighting factor is 0.7 times 0.85, which is 0.595. The battery proportion in the original complementary weight is 35%, which is increased to 35% plus 59.5% times the supplement power proportion coefficient 0.15 after adding the correction amount. The final corrected battery weight is adjusted to 42.9%. Through real-time mechanical energy monitoring and dynamic weight correction mechanism, the output proportion of energy storage equipment is accurately allocated under sudden load, which not only avoids the flywheel from losing stability due to instantaneous energy release too fast, but also prevents the battery from affecting the service life due to excessive discharge. This method quantitatively evaluates the state decay factor of energy storage units, realizes flexible power allocation in multiple time scales, ensures uninterrupted power supply for critical loads, and improves the overall economic efficiency of the system. Especially for short-time high-power impact scenarios, energy source switching decisions can be made within milliseconds, significantly enhancing the system's anti-disturbance ability.

[0054] Optionally, the method further comprises:

[0055] Obtain the rectifier efficiency parameter and the preset topology switching threshold value.

[0056] generating a rectifier mode switching signal when the ratio of the flywheel output power to the battery output power exceeds the topology switching threshold value;

[0057] correcting the weighting factor using the rectifier mode switching signal.

[0058] Specifically, first, the input and output voltages are collected by the rectifier current sensor, and the rectifier efficiency is calculated, which is equal to the ratio of the output power to the input power, wherein the output power is obtained by multiplying the output voltage by the output current, and the input power is obtained by multiplying the input voltage by the input current. This rectifier efficiency reflects the effectiveness of the current working mode of the power conversion, and the topology switching threshold value is obtained by experimental test. When the ratio of the flywheel output power to the battery output power exceeds the topology switching threshold value for five consecutive sampling periods, the logic judgment is triggered. When the ratio of the flywheel output power to the battery output power is greater than the topology switching threshold value and the duration meets the condition, a mode switching signal is generated. The signal contains an identifier of the boost or buck mode, and its generation is based on the relationship between the rectifier efficiency and the target working interval. If the rectifier efficiency is less than the preset minimum efficiency, the boost mode signal is forced to trigger. 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, and the corrected weighting factor is equal to the original weighting factor multiplied by the mode switching coefficient, wherein the mode switching coefficient is valued according to the signal type, such as 1.2 for the boost mode and 0.8 for the buck mode.

[0059] For example, a sudden increase in the load of a frequency converter in a certain factory causes the flywheel output power to reach 550kW, while the battery output power is 200kW. At this time, the power ratio is 2.75. When the preset topology switching threshold value is 2.5, the system immediately detects the overrun and checks the efficiency parameter rectifier efficiency, which is 88%, lower than the minimum efficiency of 90% under this working condition. After determining that it needs to switch to the boost mode, a mode switching signal is generated, and the original weighting factor is multiplied by the boost coefficient 1.2 to update to 0.78. The corrected weighting factor value is input into the dynamic coupling parameter calculation module, so that the flywheel weight is increased by 12%, and the rectifier enters the boost working state. Through the dual judgment mechanism of real-time monitoring of the power ratio and the efficiency parameter, the running state is actively adjusted when the system tends to be in the low efficiency area, which not only guarantees the economy of the power conversion process, but also avoids the risk of overheating caused by long-term operation of the equipment in the non-optimal working condition. Especially in the scene of frequent load fluctuation, this method realizes the adaptive optimization of the energy distribution strategy by dynamically correcting the weighting factor, effectively prevents the overload of a single energy storage unit and prolongs the service life of the equipment, while ensuring that the power supply quality meets the demand of sensitive loads.

[0060] Optionally, the adjusting the preset rectifier duty cycle parameter according to the cooperative capacity distribution weight to obtain the energy storage charging and discharging strategy comprises:

[0061] calculate a pulse feedback frequency of the flywheel residual mechanical energy according to the cooperative capacity allocation weight;

[0062] determine a duty cycle interval of the rectifier based on the pulse feedback frequency and a preset electric energy conversion efficiency matrix;

[0063] obtain the energy storage charging and discharging strategy based on dynamically adjusting the rectifier topology structure in the duty cycle interval.

[0064] Optionally, the obtaining the energy storage charging and discharging strategy based on dynamically adjusting the rectifier topology structure in the duty cycle interval comprises:

[0065] generating a heat dissipation compensation coefficient according to a rectifier temperature rise parameter;

[0066] weighting and fusing the heat dissipation compensation coefficient and the duty cycle interval to obtain a dynamic duty cycle parameter;

[0067] 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.

[0068] Specifically, first, based on the value of the cooperative capacity allocation weight, the pulse feedback frequency of the flywheel residual mechanical energy is obtained by multiplying the weight value by the flywheel speed, and then dividing by the system reference frequency. The specific formula is that the pulse feedback frequency is equal to the cooperative capacity allocation weight multiplied by the square value of the real-time flywheel speed, and then divided by the product of the square value of the system rated speed and the electric energy conversion base frequency. The system base frequency is the power frequency or the circuit design frequency. Then, a preset electric energy conversion efficiency matrix is combined. The matrix is a two-dimensional table. The row index corresponds to the discretization interval of the pulse feedback frequency, and the column index is the working temperature classification. Each cell stores the rectifier efficiency under the corresponding working condition. By looking up the interval and temperature level of the current pulse feedback frequency, the maximum and minimum efficiency values corresponding to the interval and temperature level are determined. According to the positive correlation between the efficiency value and the duty cycle, the upper and lower limits of the duty cycle interval are linearly mapped. Then, the temperature change gradient of the rectifier during operation is collected as the temperature rise parameter. The heat dissipation compensation coefficient is calculated by an exponential function. The value is the ratio of the real-time temperature rise rate to the designed maximum allowable temperature rise rate, and the complement of the natural logarithm is calculated. The value of the heat dissipation compensation coefficient is ensured to automatically reduce the duty cycle when the temperature is too high. Finally, the heat dissipation compensation coefficient and the upper and lower limits of the duty cycle interval are weighted and fused. The weight distribution method is to dynamically adjust 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 priority. The result after weighting is the dynamic duty cycle parameter, which is input into the pulse width modulation controller of the rectifier to control the power output ratio of the flywheel and the battery. In this process, it is necessary to ensure that the sampling period of the temperature rise parameter and the duty cycle adjustment period are synchronized to avoid parameter mismatch caused by time delay.

[0069] 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.

[0070] Optionally, the simulation verification of the energy storage charging and discharging strategy, and the output of the final capacity evaluation results, include:

[0071] The energy storage charging and discharging strategy is input into a preset virtual simulation platform to generate initial system response time parameters;

[0072] A preset disturbance load is applied to the initial system response time parameter, and the capacity redundancy deviation value is calculated;

[0073] 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.

[0074] Optionally, the reallocation of capacity weights between the flywheel and the battery to obtain the final capacity assessment result includes:

[0075] Generate capacity adjustment ratio parameters based on the capacity redundancy deviation value;

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] Optionally, the method further includes:

[0082] Based on preset maintenance cycle parameters and historical capacity assessment results, a life prediction curve for hybrid energy storage equipment is generated.

[0083] Based on the comparison between the predicted lifetime curve and the current final capacity assessment result, a preset alarm threshold is triggered.

[0084] 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.

[0085] 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.

[0086] 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:

[0087] 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.

[0088] 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.

[0089] The collaborative allocation module is used to generate collaborative capacity allocation weights between the flywheel and the battery based on the dynamic coupling parameters.

[0090] 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.

[0091] The verification execution module is used to simulate and verify the energy storage charging and discharging strategy and output the final capacity evaluation result.

[0092] 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.

[0093] 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 feature vector, the dynamic coupling parameters of flywheel mechanical inertia and battery chemical energy decay are calculated; this 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 based on the dynamic load feature vector to obtain the dynamic coupling parameters. 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 weights, a preset rectifier duty cycle parameter is adjusted to obtain an energy storage charging and discharging strategy. This includes: calculating the pulse feedback frequency of the flywheel's remaining mechanical energy based on the collaborative capacity allocation weights; determining the rectifier's duty cycle range based on the pulse feedback frequency and a preset energy conversion efficiency matrix; generating a heat dissipation compensation coefficient based on the rectifier's 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 using the dynamic duty cycle parameter to obtain the energy storage charging and discharging strategy. The energy storage charging and discharging strategy is simulated and verified, and the final capacity evaluation result is output. 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. The method further includes: acquiring rectifier efficiency parameters and a preset topology switching threshold; generating a rectifier mode switching signal when the output power ratio of the flywheel to the battery exceeds the topology switching threshold; and correcting the weighting factor using the rectifier mode switching signal.

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 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.

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 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.

4. 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.

5. 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 1-4, 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 of flywheel mechanical inertia and battery chemical energy decay based on the dynamic load feature vector. This 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 lifespan decay coefficients, and fitting the mapping relationship between temperature and available capacity using 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. The collaborative allocation module is used to generate collaborative capacity allocation weights between the flywheel and the battery based on the dynamic coupling parameters. An energy consumption optimization module is used to adjust a preset rectifier duty cycle parameter according to the collaborative capacity allocation weight to obtain an energy storage charging and discharging strategy. This 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 a preset energy conversion efficiency matrix; generating a heat dissipation compensation coefficient based on the rectifier's 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 using the dynamic duty cycle parameter 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. This also includes: when the load power is detected to exceed a preset fluctuation threshold, extracting flywheel mechanical energy and generating remaining mechanical energy parameters; generating a battery supplementary power supply command based on the remaining 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. This also 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 using the rectifier mode switching signal to correct the weighting factor.

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