Power correction and control method for hybrid energy storage system

Abstract

The invention discloses a power correction and control method for a hybrid energy storage system, and the method comprises the steps: monitoring the current SOC values of a lithium iron phosphate battery and a super capacitor, and carrying out the SOC interval division of the lithium iron phosphate battery and the super capacitor in a power correction controller according to a preset threshold value; correcting the original power instruction according to the SOC interval of the lithium iron phosphate battery and the super capacitor; sampling regularly by using a black-wing plinary algorithm, and optimizing PID parameters in real time; the real-time optimized PID acts on the current tracking error, a PWM modulation module outputs a duty ratio instruction signal, and then the hybrid energy storage system is controlled to work. Through cooperative control of dynamic power correction and parameter self-adaption, sudden power change is suppressed, the dynamic response capability of the system is improved, the risk of over-charging and over-discharging of equipment is reduced, and technical support is provided for hybrid energy storage.

Description

Technical Field

[0001] This invention belongs to the field of energy storage control technology, specifically relating to a power correction and control method for hybrid energy storage systems. Background Technology

[0002] With the continuous increase in the penetration rate of renewable energy generation, the power fluctuation problem caused by the grid connection of fluctuating power sources such as photovoltaic and wind power is becoming increasingly prominent, posing a severe challenge to grid frequency stability and power quality. Hybrid energy storage systems, by integrating high-energy-density lithium iron phosphate batteries with high-power-density supercapacitors, can effectively mitigate power fluctuations and improve system regulation capabilities. However, existing hybrid energy storage control strategies still face multiple technical bottlenecks in practical applications: the real-time changes in the state of charge (SOC) of energy storage devices and power allocation commands lack a dynamic coordination mechanism; traditional fixed threshold protection is prone to sudden changes in charging and discharging power, causing energy storage devices to age; and control parameter tuning relies on manual experience, making it difficult to adapt to the dynamic response requirements under complex operating conditions. Summary of the Invention

[0003] This invention is proposed to solve the problems existing in the prior art, and its purpose is to provide a power correction and control method for hybrid energy storage systems.

[0004] This invention is achieved through the following technical solution: A power correction and control method for hybrid energy storage systems includes the following steps: S1. Monitor the current SOC value of lithium iron phosphate battery and supercapacitor in real time, and divide the SOC range of lithium iron phosphate battery and supercapacitor into different ranges in the power correction controller according to the preset threshold. S2. Correct the original power command based on the SOC range of the lithium iron phosphate battery and the supercapacitor; S3. Use the Black-winged Kite algorithm to sample periodically and optimize PID parameters in real time; S4. The real-time optimized PID is applied to the current tracking error, and the duty cycle command signal is output by the PWM modulation module to control the operation of the hybrid energy storage system.

[0005] In the above technical solution, the SOC range division rule of the lithium iron phosphate battery is as follows: SOC < 20%, discharge prohibited zone; 20% ≤ SOC < 25%, linear discharge decay region; 25% ≤ SOC ≤ 75%, normal working range; 75% < SOC ≤ 80%, linear charging decay zone; SOC > 80%, charging prohibited zone.

[0006] In the above technical solution, the SOC range division rule of the supercapacitor is as follows: SOC < 20%, discharge prohibited zone; 20% ≤ SOC < 30%, linear discharge decay region; 30% ≤ SOC ≤ 70%, normal working range; 70% < SOC ≤ 80%, linear charging decay zone; SOC > 80%, charging prohibited zone.

[0007] In the above technical solution, the power correction formula for each SOC range of the lithium iron phosphate battery is as follows: Discharge prohibited zone: Linear decay region of discharge: Normal working area: Linear decay region of charging: No charging zone: In the above formula: The corrected power rating for lithium iron phosphate batteries is expressed in kWh. This refers to the original power of the lithium iron phosphate battery, in kWh. This represents the state of charge (SOC) value of a lithium iron phosphate battery, which is dimensionless.

[0008] In the above technical solution, the power correction formula for each SOC range of the supercapacitor is as follows: Discharge prohibited zone: Linear decay region of discharge: Normal working area: Linear decay region of charging: No charging zone: In the above formula: The corrected supercapacitor power is expressed in kWh. This is the original power of the supercapacitor, in kWh. This is the state of charge value of the supercapacitor, which is dimensionless.

[0009] In the above technical solution, the specific method for optimizing PID parameters using the Black-winged Kite algorithm in step S3 includes the following steps: S31. Initialize the black-winged kite population, setting the population size to 15 and the maximum number of iterations T=30. The initial position of each black-winged kite is uniformly generated according to the following formula: In the formula: is the position of the i-th black-winged kite in the b-th dimension, where b = 1, 2, 3, representing a set of possible PID parameter values; is the lower bound of the b-th dimensional search space; is the upper bound of the b-th dimensional search space; is a random number uniformly distributed in [0, 1]; is the parameter number of the PID, , 1, 2, 3 respectively correspond to , , ; S32. Calculate the fitness value and select the leader. After one iteration, calculate and store the fitness values corresponding to the black-winged kite group respectively, and find the optimal position of the black-winged kite in the current iteration according to the size of the fitness value corresponding to each black-winged kite; S33. Determine whether the set iteration accuracy is satisfied through the fitness value corresponding to the black-winged kite. If the iteration accuracy is satisfied, jump out of the iteration loop and output the PID parameters at this time. If the iteration accuracy is not satisfied, enter the main loop iteration; S34. Initialize and assign values to the dynamically adjusted parameters; S35. Update the positions of the black-winged kites in stages; S36. Complete a single iteration. After all 30 black-winged kites complete the position update, update the iteration counter parameter t, and reset the dynamic parameter hunting probability threshold and the vibration intensity factor , and return to step S32 for the next iteration; when the termination condition is satisfied, output the global optimal PID parameters. <00OO082>

[0010] In the above technical solution, the dynamically adjusted parameters in step S34 include the hunting probability threshold and the vibration intensity factor ; the assignment of the dynamically adjusted parameters is specifically: set the initial hunting probability threshold p(1) = 0.7, which increases linearly with the iteration to p(30) = 0.95; the vibration intensity factor n(t) decays exponentially with the increase of the iteration times.

[0011] In the above technical solution, the stages of updating the positions of the black-winged kites in step S35 include the hunting stage and the migration stage; In the hunting stage, a random number r ∈ [0, 1] is generated for each black-winged kite. If r < p(t), the position of the black-winged kite is updated according to formula (2): In the formula: is the position at the (t + 1)-th iteration; is the position of the i-th black-winged kite in the j-th dimension at the t-th iteration; The current global leader position; Uniformly random numbers, ; This is the hunting probability threshold for the current iteration number, and its value is dynamically adjusted according to the iteration process. The formula for calculating the hunting probability threshold is: in: This represents the current iteration number; Total number of iterations; initial stage Gradually increase to The approach involves rapid, comprehensive exploration in the early stages, followed by localized development in the later stages. Vibration intensity factor The calculation formula is: in: This represents the current iteration number; This represents the total number of iterations. The location of the black-winged kite during the migration phase is updated using the following Cauchy mutation formula: In the formula: This represents the position in the (t+1)th iteration. Let j be the j-th dimension position of the i-th black-winged kite in the t-th iteration; The current global leader position; For the position of the previous generation of leaders; These are standard Cauchy distribution random numbers; For the current individual fitness; This is the fitness threshold for a random population. Migration intensity factor; The formula for calculating the migration intensity factor is: in: Uniformly random numbers, .

[0012] In the above technical solution, the control of the hybrid energy storage device specifically includes PWM modulation module regulation and DC / DC converter regulation; The PWM modulation module specifically controls the following: it generates a duty cycle command based on the optimized PID parameters, outputs a high-frequency PWM signal through the triangular carrier comparison method, converts it into the gate drive level of the power device through the isolation drive circuit, and controls the switching action of the DC / DC energy storage converter. The DC / DC converter regulation specifically adopts a bidirectional Buck-Boost topology structure based on a dual closed-loop control architecture. The outer loop maintains the DC bus voltage stability, while the inner loop tracks the current command in real time by optimizing PID parameters.

[0013] The beneficial effects of this invention are: This invention provides a method for secondary power allocation and control of hybrid energy storage systems based on SOC partitioning dynamic correction and Blackwing Kite algorithm to optimize PID parameters. It is applicable to the dynamic power allocation and adaptive parameter tuning of hybrid energy storage systems composed of lithium iron phosphate batteries and supercapacitors. It can effectively suppress power mutations, improve dynamic response speed and steady-state accuracy, and effectively prevent overcharging and over-discharging of energy storage devices.

[0014] The core innovation of the collaborative control method for hybrid energy storage systems proposed in this invention lies in the integration of a dual-dimensional SOC dynamic correction mechanism and an improved intelligent optimization algorithm. Firstly, a differentiated five-range SOC partitioning strategy is designed for energy-type lithium iron phosphate batteries and power-type supercapacitors, with a wider linear decay zone specifically set for supercapacitors. A linear power correction algorithm is used to achieve a smooth transition of charging and discharging power, effectively preventing overcharging and over-discharging. Simultaneously, an improved Black-winged Kite optimization algorithm is introduced, which, through a linearly increasing hunting threshold and an exponentially decaying vibration factor, combined with a Cauchy mutation strategy, achieves efficient self-tuning of PID parameters within a population size of only 15 and 30 iterations. Finally, through the synergy of power feedforward correction and parameter feedback optimization, the dynamic response speed and control accuracy of the system are significantly improved, suppressing power surges while extending the lifespan of the energy storage device.

[0015] This invention successfully controls the SOC fluctuation range of lithium iron phosphate batteries and supercapacitors within a safe range of 20% to 80%, thereby ensuring the service life of energy storage devices by minimizing overcharging and over-discharging of lithium iron phosphate batteries and supercapacitors. Through the improved Blackwing PID control, the system response speed can be accelerated, and the overshoot and steady-state error can be significantly reduced. Attached Figure Description

[0016] Figure 1 This is a block diagram illustrating the control principle of the present invention; Figure 2 This is a flowchart of the method of the present invention; Figure 3 The flowchart of the PID control algorithm of the Black-winged Kite of the present invention is shown below; Figure 4 This is a comparison chart of the effects of the BKA-PID of the present invention and the traditional PID control; Figure 5 This is a simulation diagram of the SOC protection effect of lithium iron phosphate battery under the operating conditions of this invention. Figure 6 This is a simulation diagram of the supercapacitor's SOC over-limit protection effect under the operating conditions of this invention.

[0017] For those skilled in the art, other related figures can be obtained from the above figures without any creative effort. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0019] Example 1 like Figures 1-3 As shown, a power correction and control method for a hybrid energy storage system includes the following steps: S1, SOC partition detection: Real-time monitoring of the current SOC values ​​of lithium iron phosphate batteries and supercapacitors; and division of SOC ranges for lithium iron phosphate batteries and supercapacitors in the power correction controller according to preset thresholds. The SOC range division rule for the lithium iron phosphate battery is as follows: SOC < 20%, discharge prohibited zone (discharge prohibited); 20% ≤ SOC < 25%, linear decay region of discharge (linear slow discharge); 25% ≤ SOC ≤ 75%, normal operating range (normal charge and discharge); 75% < SOC ≤ 80%, linear charging decay zone (linear slow charging); SOC > 80%, charging prohibited zone (charging prohibited); Because supercapacitors have relatively low energy density, they are more prone to overcharging and over-discharging. Therefore, when dividing the SOC range, the discharge linear decay region and the charge linear decay region are widened to reduce the possibility of overcharging and over-discharging. The SOC range division rule for the supercapacitor is as follows: SOC < 20%, discharge prohibited zone (discharge prohibited); 20% ≤ SOC < 30%, linear discharge decay region (linear slow discharge); 30% ≤ SOC ≤ 70%, normal operating range (normal charging and discharging); 70% < SOC ≤ 80%, linear charging decay zone (linear slow charging); SOC > 80%, charging prohibited zone (charging prohibited).

[0020] S2. Dynamic Power Command Correction: The original power command is corrected based on the SOC range of the lithium iron phosphate battery and the supercapacitor. The power during HESS discharge is defined as negative, and the power during charging is defined as positive. Based on the SOC range of the lithium iron phosphate battery and the supercapacitor, the original power command is linearly attenuated or hard-limited to generate a corrected (charging / discharging / prohibiting) command. The power correction formula for each SOC range of the lithium iron phosphate battery is as follows: Discharge prohibition zone (interval 1): Linear discharge decay region (interval 2): Normal working area (interval 3): Linear charging attenuation region (interval 4): Charging Prohibited Zone (Zone 5): In the above formula: The corrected power rating for lithium iron phosphate batteries is expressed in kWh. This refers to the original power of the lithium iron phosphate battery, in kWh. This represents the state of charge (SOC) value of a lithium iron phosphate battery, which is dimensionless. The power correction formula for each SOC range of the supercapacitor is as follows: Discharge prohibition zone (interval 1): Linear discharge decay region (interval 2): Normal working area (interval 3): Linear charging attenuation region (interval 4): Charging Prohibited Zone (Zone 5): In the above formula: The corrected supercapacitor power is expressed in kWh. This is the original power of the supercapacitor, in kWh. This is the state of charge value of the supercapacitor, which is dimensionless. S3. Black-winged Kite Algorithm Optimization of PID Parameters: Initialize the black-winged kite population, generate PID parameter combinations, convert the corrected power command into a current command and calculate the tracking error, update the individual position and optimize the parameters by calculating the fitness value, and optimize the PID parameters in real time to achieve better control effect. After completing their respective power correction, their respective power commands will change significantly. For example, if the SOC of the lithium iron phosphate battery cannot support its continued discharge, the power command will change from positive to zero. Faced with sudden changes in input, the control may lose stability. Therefore, the Blackwing Kite algorithm is used to sample periodically and optimize PID parameters in real time to ensure the controller's stability and fast tracking of the working state. The specific method for optimizing PID parameters using the Black-winged Kite algorithm includes the following steps: S31. Initialize the black-winged kite population, setting the population size to 15 and the maximum number of iterations T=30. The initial position of each black-winged kite is uniformly generated according to the following formula: In the formula: For the first Only the black-winged kite in the first The position of the dimension represents a set of possible PID parameter values; For the first The lower bound of the dimensional search space; For the first The upper bound of the dimensional search space; These are random numbers uniformly distributed in [0,1]. For the parameter number of the PID, 1, 2, 3 correspond to , , ; S32. Calculate the fitness value and select the leader. After one iteration, calculate and store the fitness values ​​corresponding to the black-winged kite flock. Find the optimal position of the black-winged kite in the current iteration based on the fitness value of each black-winged kite. S33. Based on the fitness value corresponding to the Black-winged Kite, determine whether the set iteration precision is met. If the iteration precision is met, exit the iteration loop and output the PID parameters at this time. If the iteration precision is not met, enter the main loop iteration. The main loop is the loop process of PID parameter iteration. S34. Initialize and assign values ​​to the dynamically adjusted parameters: Set the hunting probability threshold p(t), with the initial hunting probability threshold p(1)=0.7, and linearly increase it to p(30)=0.95 with iteration, realizing the transition from global search to local development; The vibration intensity factor n(t) decays exponentially with the number of iterations to control the local search step size; S35. Phased update of Black-winged Kite location: S351. During the hunting phase, generate a random number r∈[0,1] for each black-winged kite. If r< p(t), update the position of the black-winged kite according to equation (2): In the formula: For the first The position of the next iteration; For the first In the nth iteration The first black-winged kite Dimensional position; The current global leader position; Uniformly random numbers, , Similar to rand, it is a random number generated by a computer; This is the hunting probability threshold for the current iteration number, and its value is dynamically adjusted according to the iteration process. The formula for calculating the hunting probability threshold is: in: This represents the current iteration number; Total number of iterations; initial stage Gradually increase to The approach involves rapid, comprehensive exploration in the early stages, followed by localized development in the later stages. Vibration intensity factor The calculation formula is: in: This represents the current iteration number; This represents the total number of iterations. S352. During the migration phase, the position of the Black-winged Kite is updated using the following Cauchy mutation formula: In the formula: This represents the position in the (t+1)th iteration. Let j be the j-th dimension position of the i-th black-winged kite in the t-th iteration; The current global leader position; For the position of the previous generation of leaders; These are standard Cauchy distribution random numbers; For the current individual fitness; This is the fitness threshold for a random population. The migration intensity factor is calculated using the following formula: in: Uniformly random numbers, ; S36. After completing a single iteration and all 30 black-winged kites have updated their positions, update the iteration counter parameter t and reset the dynamic parameter hunting probability threshold. and Return to step S32 for the next iteration; when the termination condition is met, output the globally optimal PID parameters. The termination condition is that the fitness value meets the iteration accuracy or reaches the maximum number of iterations. S4. Apply the real-time optimized PID (the globally optimal PID parameter output in step S3) to the current tracking error, and the PWM modulation module outputs the duty cycle command signal to control the operation of the hybrid energy storage system.

[0021] The implementation of the control of the hybrid energy storage device specifically includes: S41, PWM modulation module: The duty cycle command is generated based on the optimized PID parameters. A high-frequency PWM signal is output through the triangular carrier comparison method. The signal is converted into the gate drive level of the power device through the isolation drive circuit to control the switching action of the DC / DC energy storage converter. S42, DC / DC converter regulation: adopts a bidirectional Buck-Boost topology and is based on a dual closed-loop control architecture. The outer loop maintains the DC bus voltage stability, while the inner loop tracks the current command in real time by optimizing PID parameters.

[0022] The technical solution of this invention was built and verified in the MATLAB / Simulink simulation environment. The specific steps are as follows: A power correction and control simulation system for a hybrid energy storage system was built in the MATLAB / Simulink simulation platform. The lithium iron phosphate battery was modeled as Lead-Acid, with a rated voltage of 48V and a capacity of 20Ah. An internal resistance of 0.02Ω was configured to simulate the charging and discharging losses of a real battery. The supercapacitor module was modeled as Supercapacitor, with a total capacitance of 300F, an initial voltage of 24V, and a self-discharge rate parameter adjusted to 0.002% / s to reflect its natural energy dissipation characteristics.

[0023] To simulate real-world operating conditions, the bidirectional DC / DC converter is configured with a 10kHz switching frequency and a dead time of 1 microsecond. The power corrector divides the lithium battery into SOC ranges: when the SOC is below 20%, it enters a discharge prohibition zone, forcibly cutting off the discharge circuit to protect battery life; when the SOC is between 20% and 25%, the discharge power decreases linearly to avoid damage to the electrode materials from deep discharge; when the SOC is in the normal operating range of 25% to 75%, the system responds to commands at full power; when the SOC exceeds 75%, the charging power begins to decrease linearly until the SOC reaches 80%, at which point charging is completely prohibited. The zoning strategy for supercapacitors is similar to that of lithium batteries, but considering their lower energy density, the linear discharge decay zone is extended to 20% to 30%, and the linear charging decay zone is set to 70% to 80%.

[0024] The power during discharge of lithium iron phosphate batteries and supercapacitors is defined as negative, and the power during charging is defined as positive. The system collects the SOC data of the energy storage device every 5 seconds. Based on the actual SOC range of the lithium iron phosphate batteries and supercapacitors, the power corrector dynamically corrects the original power command issued by the upper level: Taking lithium iron phosphate batteries as an example, according to the predetermined SOC range, when the detected actual SOC value of the lithium iron phosphate battery is greater than 80%, and the system's power correction command is positive, the power corrector adjusts the power command of the lithium iron phosphate battery to zero according to the program; if the system's power correction command is negative, no intervention is performed; when the detected actual SOC value of the lithium iron phosphate battery is between 75% and 80%, regardless of whether the system's power correction command is positive or negative, the power corrector adjusts the power command according to the program. The power command of the lithium iron phosphate battery is adjusted to a linear decay state, so that the closer the SOC of the lithium iron phosphate battery is to 80%, the closer the power command is to zero. When the actual SOC value of the lithium iron phosphate battery is detected to 25%~75%, the lithium iron phosphate battery maintains normal operation and smooths the power required by the system. When the actual SOC value of the lithium iron phosphate battery is detected to 20%~25%, regardless of whether the system's smoothing power is positive or negative, the power corrector adjusts the power command of the lithium iron phosphate battery to a linear decay state to provide a buffer. When the actual SOC of the lithium iron phosphate battery is detected to be below 20%, and the system's smoothing power command is negative (no intervention if positive), the power corrector adjusts the power command of the lithium iron phosphate battery to zero according to the program to avoid deep discharge of the lithium iron phosphate battery.

[0025] The power correction formulas for the linear decay region of lithium iron phosphate batteries during charging and discharging are as follows: , The management strategy for supercapacitors is similar to that for lithium iron phosphate batteries. However, due to the lower energy density and storage capacity of supercapacitors, they are more prone to overcharging and over-discharging. Therefore, the linear decay region of their State of Charge (SOC) is widened, with 20%~30% and 70%~80% designated as linear decay regions. The power correction formulas for the charging and discharging linear decay regions of supercapacitors are as follows: , ; SOC partitioning logic: Based on the real-time SOC status of the lithium iron phosphate battery and the supercapacitor, the acceptable charge and discharge power commands are dynamically adjusted to avoid overcharging (SOC>80%) or over-discharging (SOC<20%). At the same time, a smooth transition is achieved through the linear decay region, protecting the electrode materials and extending the life of the energy storage device.

[0026] The SOC range of lithium iron phosphate batteries is divided as follows: When SOC < 20%, it is the discharge prohibition zone, the discharge circuit is forcibly cut off, and only charging is allowed; when 20% ≤ SOC < 25%, it is the discharge linear decay zone, the discharge power decays linearly to avoid damage to the electrode materials caused by deep discharge; when 25% ≤ SOC ≤ 75%, it is the normal operation zone, the system can respond to the smoothing command at full power, and there are no charging and discharging restrictions; when 75% < SOC ≤ 80%, it is the charging linear decay zone, the charging power decays linearly to suppress overcharging; when SOC > 80%, it is the charging prohibition zone, the charging circuit is forcibly cut off, and only discharging is allowed.

[0027] Due to their lower energy density and higher risk of overcharging and over-discharging, supercapacitors have an adjusted SOC zoning strategy based on lithium iron phosphate batteries: the linear decay zone for discharge is extended to 20% ≤ SOC < 30%, the linear decay zone for charging is set to 70% < SOC ≤ 80%, and the remaining zones (discharge prohibition zone SOC < 20%, normal operation zone 25% ≤ SOC ≤ 75%, charging prohibition zone SOC > 80%) are consistent with those of lithium iron phosphate batteries.

[0028] Dynamic correction logic for power commands: The system collects real-time SOC data of the lithium iron phosphate battery and supercapacitor every 5 seconds, and dynamically corrects the original power command (negative for discharging, positive for charging) issued by the upper level based on the current SOC range. Taking the lithium iron phosphate battery as an example, the specific correction rules are as follows: When SOC < 20%, if the power smoothing command is negative (discharging), it is corrected to 0 (forced discharging is prohibited); if it is positive (charging), no intervention is made. When 20% ≤ SOC < 25%, the discharging power decreases linearly (providing a buffer), and the charging power is not intervened. When 25% ≤ SOC ≤ 75%, the system is in the normal operating range and responds to the original command at full power (no correction). When 75% < SOC ≤ 80%, the charging power decreases linearly (suppressing overcharging), and the discharging power is not intervened. When SOC > 80%, if the power smoothing command is positive (charging), it is corrected to 0 (forced charging is prohibited); if it is negative, no intervention is made.

[0029] The correction rules for supercapacitors are similar to those for lithium iron phosphate batteries, but their linear discharge decay region is extended to 20% ≤ SOC < 30%, and their linear charge decay region is extended to 70% < SOC ≤ 80%. The remaining threshold ranges are consistent with those for lithium iron phosphate batteries. Through these correction strategies, the SOC of both lithium iron phosphate batteries and supercapacitors is consistently maintained within the safe range of 20% to 80%, avoiding performance degradation under extreme conditions.

[0030] Dynamic control of PID parameters optimized by the Black Kite Algorithm (BKA): The core objective is to improve the system's dynamic response speed to power commands, reduce overshoot, shorten settling time, and enhance steady-state accuracy by adjusting PID control parameters (proportional coefficient Kp, integral coefficient Ki, and derivative coefficient KD) in real time using intelligent optimization algorithms. The Blackwing Kite algorithm achieves closed-loop optimization of PID parameters through the following steps: First, the black-winged kite population is initialized, with a population size of 15 and a maximum number of iterations of 30. The initial position of each black-winged kite is generated uniformly according to the following formula: Calculate the fitness value and select an initial leader. After one iteration, calculate and store the fitness values ​​corresponding to each black-winged kite flock. Find the optimal position for the current iteration based on the fitness value of each black-winged kite. Determine whether the set iteration precision is met. If the iteration precision is met, exit the iteration loop and output the PID parameters at this time. If not, enter the main loop for iterative optimization. Initialize the dynamic adjustment parameters, set the hunting probability threshold p(t), with the initial p(1) = 0.7, linearly increasing to p(30) = 0.95 with iteration to achieve the transition from global search to local development, and the vibration intensity factor n(t), which decays exponentially with the increase of the iteration number to control the local search step size; Update the position of the black-winged kite in stages. During the hunting stage, generate a random number r ∈ [0, 1] for each black-winged kite. If r < p(t), update it according to the following formula: During the migration stage, perform Cauchy mutation update according to the following formula: ; After completing the iteration, when all 30 black-winged kites have completed the position update, update the iteration counter t = t + 1, reset the dynamic parameters p(t) and n(t), and return to step 2 for the next iteration. When the termination condition is met, output the global optimal PID parameters.

[0031] The optimized PID parameters are directly applied to the current inner loop control of the bidirectional Buck-Boost converter. The system adopts a triangular wave modulation method with a carrier frequency of 10 kHz, converts the error signal output by the PID into a PWM duty cycle command to control the operation of the energy storage converter.

[0032] Comparison of the dynamic performance between BKA-PID and traditional PID: The test scenario is to apply a step power command with an initial amplitude of 1 at 0.5 s and reduce the command amplitude to 0.8 after 0.5 s (simulating a sudden change in input power). By comparing the response curves of BKA-PID and traditional PID (as Figure 4 shown), it can be seen that the overshoot of BKA-PID is reduced by about 40%, the adjustment time is shortened by about 35%, the steady-state error is reduced by about 60%, and the dynamic response speed and steady-state accuracy are significantly better than those of traditional PID.

[0033] Long-term verification of the SOC over-limit prevention effect: The test scenario is to apply sufficient charge and discharge cycles to the hybrid energy storage system within a 360-second simulation period (simulating large-scale power fluctuations in actual working conditions), and record the SOC values of the lithium iron phosphate battery and the supercapacitor every 5 seconds. By Figure 5 and Figure 6 it can be seen that the SOC of both remains stable within the safe range of 20% - 80%, and there is no situation of exceeding 80% (overcharge) or being lower than 20% (overdischarge), proving that the power correction control strategy can effectively protect the overcharge and overdischarge risks of energy storage devices.

[0034] This invention establishes a two-dimensional dynamic state of charge (SOC) correction mechanism for HESS, dividing the SOC of the energy storage device into five zones: discharge prohibition, linear discharge decay, normal operation, linear charging decay, and charging prohibition. A differentiated protection strategy is constructed by extending the linear decay zone of the supercapacitor. Power allocation is dynamically corrected in real time based on the SOC state, and PID parameter self-tuning is achieved by combining an improved Blackwing Kite algorithm. The algorithm of this invention, through a linearly increasing hunting probability threshold and an exponentially decaying vibration factor, integrates a Cauchy mutation strategy to complete parameter optimization within 30 iterations and a population size of 15. The optimized parameters are used by a PWM module to generate a drive signal to control the power output of the energy storage converter.

[0035] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for power modification and control of a hybrid energy storage system, characterized by: Includes the following steps: S1. Monitor the current SOC value of lithium iron phosphate battery and supercapacitor in real time, and divide the SOC range of lithium iron phosphate battery and supercapacitor into different ranges in the power correction controller according to the preset threshold. S2. Correct the original power command based on the SOC range of the lithium iron phosphate battery and the supercapacitor; S3. Use the Black-winged Kite algorithm to sample periodically and optimize PID parameters in real time; S4. The real-time optimized PID is applied to the current tracking error, and the duty cycle command signal is output by the PWM modulation module to control the operation of the hybrid energy storage system.

2. The method for power conditioning and control of a hybrid energy storage system of claim 1, wherein: The SOC range division rule for the lithium iron phosphate battery is as follows: SOC < 20%, discharge prohibited zone; 20% ≤ SOC < 25%, linear discharge decay region; 25% ≤ SOC ≤ 75%, normal working range; 75% < SOC ≤ 80%, linear charging degradation region; SOC > 80%, charging prohibited zone.

3. The method for power conditioning and control of a hybrid energy storage system of claim 1, wherein: The SOC range division rule for the supercapacitor is as follows: SOC < 20%, discharge prohibited zone; 20% ≤ SOC < 30%, linear discharge decay region; 30% ≤ SOC ≤ 70%, normal working range; 70% < SOC ≤ 80%, linear charging degradation region; SOC > 80%, charging prohibited zone.

4. The method for power conditioning and control of hybrid energy storage system according to claim 1, wherein: The power correction formula for each SOC range of the lithium iron phosphate battery is as follows: Discharge Inhibition Zone: Discharge linear decay region: Normal work area: Charging linear decay region: Charging prohibited zone: In the above formula: is the corrected power of the lithium iron phosphate battery, in kWh; is the original power of the lithium iron phosphate battery, in kWh; is the state of charge value of the lithium iron phosphate battery, dimensionless.

5. The method for power conditioning and control of hybrid energy storage system of claim 1, wherein: The power correction formula for each SOC range of the supercapacitor is as follows: Discharge Inhibition Zone: Discharge linear decay region: Normal work area: Charging linear decay region: Charging prohibited zone: In the above formulae: is the corrected supercapacitor power in kWh; is the supercapacitor raw power in kWh; is the state of charge value of the supercapacitor, dimensionless.

6. The method for power conditioning and control of hybrid energy storage system of claim 1, wherein: The specific method for optimizing PID parameters using the Black-winged Kite algorithm in step S3 includes the following steps: S31. Initialize the black-winged kite population, setting the population size to 15 and the maximum number of iterations T=30. The initial position of each black-winged kite is uniformly generated according to the following formula: In the formula: Let i be the position of the i-th black-winged kite in dimension b. This is the lower bound of the b-th dimension search space; This is the upper bound of the b-th dimension of the search space; These are random numbers uniformly distributed in [0,1]. For the parameter number of the PID, ; 1, 2, 3 respectively correspond to , , ; S32. Calculate the fitness value and select the leader. After one iteration, calculate and store the fitness values ​​corresponding to the black-winged kite flock. Find the optimal position of the black-winged kite in the current iteration based on the fitness value of each black-winged kite. S33. Based on the fitness value corresponding to the Black-winged Kite, determine whether the set iteration precision is met. If the iteration precision is met, exit the iteration loop and output the PID parameters at this time. If the iteration precision is not met, enter the main loop iteration. S34. Initialize and assign values ​​to the dynamically adjusted parameters; S35. Update the location of the Black-winged Kite in stages; S36. After completing a single iteration and all 30 black-winged kites have updated their positions, update the iteration counter parameter t and reset the dynamic parameter hunting probability threshold. and vibration intensity factor Return to step S32 for the next iteration; when the termination condition is met, output the globally optimal PID parameters.

7. The power correction and control method for a hybrid energy storage system according to claim 6, characterized in that: The dynamically adjusted parameters in step S34 include the hunting probability threshold. and vibration intensity factor The specific values ​​of the dynamically adjusted parameters are as follows: the initial hunting probability threshold is set to p(1)=0.7, which increases linearly with iteration to p(30)=0.95; the vibration intensity factor n(t) decreases exponentially with the number of iterations.

8. The power correction and control method for a hybrid energy storage system according to claim 1, characterized in that: The phased updating of the black-winged kite's location in step S35 includes the hunting phase and the migration phase. During the hunting phase, a random number r∈[0,1] is generated for each black-winged kite. If r< p(t), the position of the black-winged kite is updated according to equation (2): In the formula: This represents the position in the (t+1)th iteration. Let j be the j-th dimension position of the i-th black-winged kite in the t-th iteration; The current global leader position; Uniformly random numbers, ; This is the hunting probability threshold for the current iteration number, and its value is dynamically adjusted according to the iteration process. The formula for calculating the hunting probability threshold is: in: This represents the current iteration number; Total number of iterations; initial stage Gradually increase to The approach involves rapid, comprehensive exploration in the early stages, followed by localized development in the later stages. Vibration intensity factor The calculation formula is: in: This represents the current iteration number; This represents the total number of iterations. The location of the black-winged kite during the migration phase is updated using the following Cauchy mutation formula: In the formula: This represents the position in the (t+1)th iteration. Let j be the j-th dimension position of the i-th black-winged kite in the t-th iteration; The current global leader position; For the position of the previous generation of leaders; These are random numbers distributed according to the standard Cauchy distribution. For the current individual fitness; This is the fitness threshold for a random population. Migration intensity factor; The formula for calculating the migration intensity factor is: in: Uniformly random numbers, .

9. The power correction and control method for a hybrid energy storage system according to claim 1, characterized in that: The control of the hybrid energy storage device specifically includes PWM modulation module regulation and DC / DC converter regulation; The PWM modulation module specifically controls the following: it generates a duty cycle command based on the optimized PID parameters, outputs a high-frequency PWM signal through the triangular carrier comparison method, converts it into the gate drive level of the power device through the isolation drive circuit, and controls the switching action of the DC / DC energy storage converter. The DC / DC converter regulation specifically adopts a bidirectional Buck-Boost topology structure based on a dual closed-loop control architecture. The outer loop maintains the DC bus voltage stability, while the inner loop tracks the current command in real time by optimizing PID parameters.

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