A sodium-ion super-capacitor coupled lithium battery energy storage frequency modulation control method and system

Abstract

The application discloses a kind of sodium-ion super-capacitor coupling lithium battery energy storage frequency modulation control method and system, belong to energy storage technical field, including: obtaining the state of charge SOC value and temperature data of lithium battery in energy storage system, and the SOC value of all batteries in battery pack is sorted, equalization control is carried out, and the equalization current of charge and discharge is calculated;The SOC value correction degree of each battery is calculated, and the SOC value is adjusted to the equalization state;Using particle swarm optimization algorithm, the optimization objective function is constructed, the inertia weight is adjusted, and the equalization current distribution is optimized in combination with temperature disturbance factor, and the battery SOC value is updated;Calculate the power compensation, control the battery state of charge in the set range.The application optimizes SOC value equalization strategy, energy distribution method and frequency modulation control algorithm, improves the performance and response speed of energy storage system.Step-by-step equalization strategy is used, combined with battery temperature constraint, dynamically adjusts the charge and discharge equalization current, to ensure that the SOC value of battery pack is balanced.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of energy storage, and particularly relates to an energy storage frequency modulation control method and system of a sodium-ion super capacitor coupled with a lithium battery. BACKGROUND

[0002] With the rapid development of renewable energy, the power system is facing the problems of volatility and intermittency, which leads to power grid frequency fluctuation and voltage instability. As an important means of regulating power grid operation, the energy storage system can charge and discharge when the power supply and demand are unbalanced, and can provide functions of power grid frequency modulation, peak clipping and valley filling, and improvement of power quality. Among them, the energy storage system based on lithium-ion batteries has become one of the mainstream technologies due to its high energy density, high efficiency and long service life. However, the single lithium-ion battery energy storage system has defects such as insufficient power density, limited service life, and easy to cause safety problems due to overcharging and overdischarging. Therefore, in recent years, the hybrid energy storage system (HESS) coupled with super capacitors and lithium-ion batteries has attracted widespread attention. The super capacitor can make up for the deficiency of the lithium-ion battery in the instantaneous power response due to its high power density and fast charging and discharging capacity, and can improve the dynamic performance and service life of the energy storage system. As a new generation of energy storage technology, the sodium-ion battery has become an important alternative technology of the lithium-ion battery due to its abundant resources, low cost and environmental friendliness. In the sodium-ion super capacitor coupled with a lithium battery energy storage system, the sodium-ion battery is used to provide long-term energy storage, and the super capacitor is used to handle high-frequency power fluctuations to realize short-time power regulation. This hybrid energy storage system has obvious advantages in frequency modulation, short-time power support, dynamic response, etc., and is particularly suitable for power grid frequency modulation, microgrid, smart grid and other application scenarios. However, in actual application, how to effectively manage the power distribution of the super capacitor and the sodium-ion battery, optimize the SOC balancing strategy, and improve the frequency modulation performance of the system is still a key problem to be solved.

[0003] The existing lithium battery and super capacitor hybrid energy storage system still has technical challenges in SOC balancing, energy distribution optimization and frequency control. In terms of SOC balancing, due to the individual differences of the batteries, the SOC may be greatly unbalanced, affecting the overall performance of the system. The existing balancing methods are mostly based on simple current adjustment strategies, which do not fully consider the battery temperature, charging and discharging rate and dynamic load demand, resulting in inaccurate energy management. In terms of energy distribution optimization, traditional methods usually use fixed threshold switching or simple power shunting, which cannot dynamically adjust with intelligent optimization algorithms, resulting in power response lag or unreasonable distribution. In terms of frequency control strategy, the existing methods are mostly based on PI control or fuzzy control, which are difficult to realize the collaborative control of multiple energy storage elements, especially in dynamic working conditions, which do not fully consider the comprehensive optimization of temperature, SOC balancing and power demand, resulting in insufficient frequency control accuracy. In addition, some optimization algorithms (such as genetic algorithm, neural network) have high computational complexity, which is difficult to meet the demand of fast response of energy storage system, affecting the real-time control effect of the system. Therefore, for the energy storage system of sodium-ion super capacitor coupled with lithium battery, an efficient SOC balancing strategy, energy optimization distribution method and fast response frequency control algorithm are needed to improve the overall performance and application value of the system. SUMMARY

[0004] To solve the above technical problems, a sodium-ion super capacitor coupled with lithium battery energy storage frequency control method is proposed, which includes obtaining the state of charge (SOC) value and temperature data of the lithium battery in the energy storage system, sorting the SOC values of all batteries in the battery pack, determining the batteries with the maximum and minimum SOC values, and recording the corresponding temperature data;

[0005] According to the SOC value sorting result, the SOC value balancing control is carried out, the charging and discharging balancing current is calculated, and the battery temperature in the balancing process is monitored in real time. If the battery temperature exceeds the threshold, the balancing is stopped;

[0006] The average of the SOC values of all batteries after SOC value balancing control is calculated, the SOC value correction degree of each battery is calculated combined with the battery temperature information, and the balancing current is calculated according to the correction degree to adjust the SOC value to the balanced state;

[0007] The particle swarm optimization algorithm is used to construct the optimization objective function, adjust the inertia weight, and optimize the balancing current distribution combined with the temperature disturbance factor to update the battery SOC value;

[0008] According to the working range of the battery SOC value, the balanced SOC value is adjusted, the power compensation is calculated, and the battery state of charge is controlled within the set range;

[0009] The balanced SOC value and temperature data are output, and the SOC value balancing and frequency control of the energy storage system are completed.

[0010] As a preferred scheme of the energy storage frequency modulation control method of the sodium-ion super-capacity coupled lithium battery, in the formula, the charge and discharge balancing current is represented as,

[0011]

[0012] In the formula, I represents the charge and discharge balancing current, gelu represents the activation function, avg represents the average, e represents the exponential function, t1 represents the temperature data corresponding to the maximum value of the SOC value, t2 represents the temperature data corresponding to the minimum value of the SOC value, SOCmax represents the maximum value of the SOC value, and SOCmin represents the minimum value of the SOC value. max The maximum value of the SOC value is represented as SOCmax, and the minimum value of the SOC value is represented as SOCmin. min The maximum value of the SOC value is represented as SOCmax, and the minimum value of the SOC value is represented as SOCmin.

[0013] As a preferred scheme of the energy storage frequency modulation control method of the sodium-ion super-capacity coupled lithium battery, in the formula, the charge and discharge balancing current is represented as,

[0014] In the balancing process, the temperatures of the battery with the highest SOC value and the battery with the lowest SOC value are monitored in real time. If the difference between the SOC values of the battery with the highest SOC value and the battery with the lowest SOC value is less than a set threshold value, or the temperature of the battery with the highest SOC value or the battery with the lowest SOC value exceeds a temperature threshold value, the charge and discharge balancing of the battery with the highest SOC value to the battery with the lowest SOC value is terminated, and the SOC value and the temperature data after balancing are recorded.

[0015] After the SOC value balancing is terminated, the SOC values of all the batteries in the energy storage system are reordered according to the SOC values after balancing, the new battery with the highest SOC value and the new battery with the lowest SOC value are determined, and the temperature data of the batteries are obtained. The new balancing current is calculated to enable the battery with the highest SOC value to charge and discharge to the battery with the lowest SOC value. The calculation of the balancing current, the charge and discharge balancing, and the temperature monitoring are repeatedly performed until the balancing termination condition is met.

[0016] As a preferred scheme of the energy storage frequency modulation control method of the sodium-ion super-capacity coupled lithium battery, in the formula, the charge and discharge balancing current is represented as,

[0017] The correction degree of the battery is represented as,

[0018]

[0019] Wherein, D i represents the correction degree of the battery, SOC represents the average value of the SOC values of the n batteries, SOCi represents the SOC value of the i-th battery after the SOC value balancing control, and ti` represents the battery temperature after the SOC value balancing control of the i-th battery.

[0020] As a preferred scheme of the energy storage frequency modulation control method of the sodium ion super-capacity coupled lithium battery, the construction of the optimization objective function comprises the following steps: after excluding the batteries with the correction degree equal to zero or infinity, the correction degree set [Ds1, Ds2,..., Dsk] of the remaining batteries and the corresponding temperature data set [ts1, ts2,..., tsk] are obtained, the objective function is constructed based on the set data, and the constraint condition is set.

[0021] The construction of the optimization objective function is represented as,

[0022] F = D s1 + D s2 + … + D sk

[0023] The constraint condition is represented as,

[0024]

[0025] As a preferred scheme of the energy storage frequency modulation control method of the sodium ion super-capacity coupled lithium battery, the updating of the battery SOC value comprises the following steps: setting the number of population particles N, initializing the particle position x ik and the velocity v ik , setting the inertia weight W, the learning factor c1 and c2, and the maximum iteration number T max .

[0026] The inertia weight W is dynamically adjusted according to the particle search process, and the calculation method of the inertia weight is as follows:

[0027]

[0028] Wherein, W represents the inertia weight, sigmoid() is an activation function, and t represents the current iteration number of the particle swarm;

[0029] The fitness value of each particle is calculated based on the objective function, and the particle velocity is updated.

[0030] The updating of the particle velocity is represented as,

[0031]

[0032] wherein v ik (t) is the velocity of particle i in k dimension, is the historical optimal position of particle i in k dimension, is the global optimal position, c1 and c2 are learning factors, r1 and r2 are random numbers between [0, 1], x ik (t) represents the position of the i-th particle in the k-th dimension at time t;

[0033] The particle position is calculated based on the velocity update and is corrected in combination with the temperature data;

[0034] The correction in combination with the temperature data is represented as,

[0035] x ik (t+1) = βx ik (t) + v ik (t+1)

[0036]

[0037] wherein x ik (t+1) represents the position of the i-th particle in the k-th dimension at time t+1, β represents a correction parameter, t'1, t'2, …, t'k n are the temperature data of each battery in the balancing process;

[0038] The optimal solution of the optimization objective function F is calculated based on the updated particle position, and it is judged whether the convergence condition is met, if not, the inertia weight adjustment, the velocity update, the position correction are repeatedly executed until the set maximum iteration number T is reached max .

[0039] As a preferred scheme of the energy storage frequency modulation control method of the sodium ion super-capacitive coupled lithium battery, wherein: the adjustment of the balanced SOC value comprises obtaining the SOC values of all batteries in the energy storage system, and counting the number N1 of batteries whose SOC values are located in the first SOC value range [0.2, 0.3] and the number N2 of batteries whose SOC values are located in the second SOC value range [0.6, 0.8];

[0040] When the SOC value of the battery i is located in the first SOC value range [0.2, 0.3], the SOC value is adjusted to transition to the target SOC value range [0.3, 0.6], which is represented as,

[0041]

[0042] wherein Q1 irepresents the first SOC value adjustment electric quantity, n represents the number of battery packs, Q i represents the battery electric quantity before adjustment;

[0043] When the SOC value of the battery i is located in the second SOC value range [0.6, 0.8], the SOC value is adjusted to transition to the target SOC value range [0.3, 0.6], represented as,

[0044]

[0045] wherein Q2 i represents the second SOC value adjustment electric quantity.

[0046] Another object of the present application is to provide an energy storage frequency modulation control system of a sodium ion super-capacitive coupled lithium battery, which can effectively coordinate the power distribution of different energy storage units, optimize the SOC equalization control, and improve the frequency modulation accuracy and safety of the energy storage system in combination with the improved optimization algorithm.

[0047] As a preferred scheme of the energy storage frequency modulation control system of the sodium ion super-capacitive coupled lithium battery, characterized in that, comprising: a data acquisition module, used for acquiring the state of charge (SOC) value and temperature data of the lithium battery in the energy storage system, sorting the SOC values of all batteries in the battery pack, determining the batteries with the maximum and minimum SOC values, and recording the corresponding temperature data; an equalization control module, used for performing SOC value equalization control according to the SOC value sorting result, calculating the charge and discharge equalization current, and monitoring the battery temperature in real time during the equalization, and stopping the equalization if the battery temperature exceeds the threshold value; an SOC correction module, used for calculating the average value of the SOC values of all batteries after the SOC value equalization control, calculating the SOC value correction degree of each battery in combination with the battery temperature information, calculating the equalization current according to the correction degree, and adjusting the SOC value to the equalization state; an optimization calculation module, used for constructing an optimization objective function by using a particle swarm optimization algorithm, adjusting the inertia weight, and optimizing the equalization current distribution in combination with a temperature disturbance factor to update the battery SOC value; an SOC adjustment module, used for adjusting the SOC value after equalization according to the working range of the battery SOC value, calculating the electric quantity compensation, and controlling the state of charge of the battery within the set range; and a data output module, used for outputting the SOC value and temperature data after equalization to complete the SOC value equalization and frequency modulation control of the energy storage system.

[0048] A computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the energy storage frequency modulation control method of the sodium ion super-capacitive coupled lithium battery when executing the computer program.

[0049] A computer readable storage medium, having stored thereon a computer program, the computer program being executed by a processor to implement the steps of the energy storage frequency control method of the sodium-ion super-capacity coupled lithium battery.

[0050] The beneficial effects of the present application: by optimizing the SOC balancing strategy, energy distribution method and frequency control algorithm, the performance and response speed of the energy storage system are improved. In terms of SOC balancing, a step-by-step balancing strategy is adopted, combined with battery temperature constraints, the charging and discharging balancing current is dynamically adjusted to ensure the SOC balancing of the battery pack, while avoiding the performance degradation of the battery due to high temperature. In terms of energy distribution optimization, an optimization objective function is constructed based on power demand, SOC state and temperature constraints, and an improved particle swarm optimization algorithm is used to optimize the energy distribution of the super-capacity and sodium-ion battery, improve the response speed and frequency accuracy of the energy storage system. In terms of SOC range control, a reasonable SOC working range is set, and based on the dynamic compensation strategy, the battery whose SOC deviates from the target range is adjusted to ensure long-term stable operation of the system. In terms of optimization calculation, the inertia weight adjustment method is used to improve the global search ability of the particle swarm algorithm, improve the calculation efficiency of power optimization, reduce the calculation complexity, and make the optimization process meet the fast response demand of the energy storage system. Compared with the existing method, the present application can more accurately manage the sodium-ion super-capacity coupled lithium battery energy storage system, achieve better frequency control effect, and improve the stability and energy efficiency of the energy storage system. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0052] Figure 1 The overall flowchart of the energy storage frequency control method of the sodium-ion super-capacity coupled lithium battery provided by an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0054] Embodiment 1, refer to Figure 1For the first embodiment of the application, the embodiment provides a sodium-ion super-capacitive coupled lithium battery energy storage frequency modulation control method, comprising:

[0055] The state of charge SOC value and temperature data of the lithium battery in the energy storage system are obtained, and the SOC values of all batteries in the battery pack are sorted to determine the batteries with the maximum SOC value and the minimum SOC value, and the corresponding temperature data is recorded;

[0056] According to the SOC value sorting result, the SOC value balancing control is performed, the charging and discharging balancing current is calculated, and the battery temperature in the balancing process is monitored in real time, and if the battery temperature exceeds the threshold value, the balancing is stopped;

[0057] The average of the SOC values of all batteries after the SOC value balancing control is calculated, the SOC value correction degree of each battery is calculated in combination with the battery temperature information, and the balancing current is calculated according to the correction degree to adjust the SOC value to the balanced state;

[0058] The particle swarm optimization algorithm is adopted to construct an optimization objective function, adjust the inertia weight, and optimize the balancing current distribution in combination with the temperature disturbance factor to update the battery SOC value;

[0059] According to the working range of the battery SOC value, the balanced SOC value is adjusted, the power compensation is calculated, and the battery state of charge is controlled within the set range;

[0060] The balanced SOC value and temperature data are output, and the SOC value balancing frequency modulation control of the energy storage system is completed.

[0061] The charging and discharging balancing current is represented as,

[0062]

[0063] Wherein, I represents the charging and discharging balancing current, gelu represents the activation function, avg represents the average, e represents the exponential function, t1 represents the temperature data corresponding to the maximum SOC value, t2 represents the temperature data corresponding to the minimum SOC value, SOC max represents the maximum value of the SOC value, and SOC min represents the minimum value of the SOC value.

[0064] The SOC value balancing control includes obtaining the SOC values of n batteries connected in series in the energy storage system, sorting the SOC values from large to small, determining the battery with the highest SOC value and the battery with the lowest SOC value, obtaining the temperature data of the batteries, and calculating the balancing current to make the battery with the highest SOC value charge and discharge to the battery with the lowest SOC value;

[0065] In the balancing process, the temperature of the battery with the highest SOC value and the battery with the lowest SOC value is monitored in real time. If the difference between the SOC values of the battery with the highest SOC value and the battery with the lowest SOC value is less than a set threshold value, or the temperature of the battery with the highest SOC value or the battery with the lowest SOC value exceeds a temperature threshold value, the charging and discharging balancing of the battery with the highest SOC value to the battery with the lowest SOC value is terminated, and the SOC value and temperature data after balancing are recorded;

[0066] After the SOC value balancing is terminated, the SOC values of all batteries in the energy storage system are reordered according to the balanced SOC values, the new battery with the highest SOC value and the new battery with the lowest SOC value are determined, the temperature data of the batteries are obtained, the new balancing current is calculated, the charging and discharging balancing of the battery with the highest SOC value to the battery with the lowest SOC value is performed, and the balancing current calculation, charging and discharging balancing and temperature monitoring are repeatedly performed until the balancing termination condition is met.

[0067] The adjustment of the SOC value to the balanced state includes calculating the deviation of the SOC value of each battery from the average of the SOC values, and obtaining the correction degree of the battery in combination with the corresponding temperature data, measuring the degree of deviation of the SOC value from the average, and screening the correction degrees of all batteries, eliminating the batteries with correction degrees equal to zero or tending to infinity, and only retaining the batteries with correction degrees within a set range, and obtaining the temperature data of the retained batteries;

[0068] The correction degree of the battery is represented as,

[0069]

[0070] Wherein, D i represents the correction degree of the battery, SOC represents the average of the SOC values of the n batteries, SOCi represents the SOC value of the i-th battery after the SOC value balancing control, and ti` represents the battery temperature of the i-th battery after the SOC value balancing control.

[0071] The construction of the optimization objective function includes obtaining the correction degree set [Ds1, Ds2,..., Dsk] of the remaining batteries and the corresponding temperature data set [ts1, ts2,..., tsk] based on the elimination of the batteries with correction degrees equal to zero or infinity, constructing the objective function based on the set data, and setting the constraint condition;

[0072] The construction of the optimization objective function is represented as,

[0073] F = D s1 + D s2 + … + D sk

[0074] The constraint condition is represented as,

[0075]

[0076] The updating battery SOC value includes setting population particle number N, initializing particle position x ik and velocity v ik , setting inertia weight W, learning factor c1 and c2, and maximum iteration number T max ;

[0077] The inertia weight W is dynamically adjusted according to the particle search process, and the calculation method of the inertia weight is as follows:

[0078]

[0079] Wherein, W represents the inertia weight, sigmoid() is an activation function, and t represents the current iteration number of the particle swarm;

[0080] The fitness value of each particle is calculated based on the target function, and the particle velocity is updated;

[0081] The updating particle velocity is represented as,

[0082]

[0083] Wherein, v ik (t) is the velocity of particle i in k dimensions, is the historical optimal position of particle i in k dimensions, is the global optimal position, c1 and c2 are learning factors, r1 and r2 are random numbers between 0 and 1, and x ik (t) represents the position of the i-th particle in the k-th dimension at t time;

[0084] The particle position is calculated based on the velocity update and is corrected in combination with the temperature data;

[0085] The correction in combination with the temperature data is represented as,

[0086] x ik (t+1)=βx ik (t)+v ik (t+1)

[0087]

[0088] Wherein, x ik (t+1) represents the position of the i-th particle in the k-th dimension at t+1 time, β represents a correction parameter, and t'1, t'2, …, t'k represent the temperature data of each battery in the balancing process; n

[0089] ​The optimal solution of the optimization objective function F is calculated based on the updated particle position, and it is judged whether the convergence condition is met, if not, the inertia weight adjustment, speed update, position correction are repeatedly executed until the set maximum iteration number T is reached max .

[0090] The adjusted balanced SOC value includes obtaining the SOC values of all batteries in the energy storage system, counting the number N1 of batteries whose SOC values are between the first SOC value range [0.2, 0.3] and the number N2 of batteries whose SOC values are between the second SOC value range [0.6, 0.8];

[0091] When the SOC value of battery i is between the first SOC value range [0.2, 0.3], adjust its SOC value to transition to the target SOC value range [0.3, 0.6], which is represented as,

[0092]

[0093] Wherein, Q1 i represents the first SOC value adjustment electric quantity, n represents the number of battery groups, Q i represents the battery electric quantity before adjustment;

[0094] When the SOC value of battery i is between the second SOC value range [0.6, 0.8], adjust the SOC value to transition to the target SOC value range [0.3, 0.6], which is represented as,

[0095]

[0096] Wherein, Q2 i represents the second SOC value adjustment electric quantity.

[0097] It should be noted that in the particle swarm algorithm, the basic information of the battery group is first determined, such as the number of batteries and the initial electric quantity of each battery. These initial electric quantities may be uneven, and our goal is to achieve a relatively balanced state of the battery group through reasonable electric quantity transfer.

[0098] The related parameters of the particle swarm algorithm are set, including the number of particles (which can be determined according to the complexity of the problem and the computing resources, such as several times the number of batteries), the maximum iteration number (to limit the running time and calculation amount of the algorithm), the learning factor (to control the degree of learning of the particle to its own historical optimal position and the historical optimal position of the group), the inertia weight (to adjust the moving speed of the particle, affecting the search ability of the algorithm), etc.

[0099] Each particle is encoded as a vector, with the dimension of the vector being equal to the number of batteries. Each element in the vector represents the amount of power transfer for the corresponding battery (which can be positive if the battery is outputting power or negative if the battery is receiving power).

[0100] The position of each particle is randomly initialized (i.e., the initial guess for the amount of power transfer), and the velocity of each particle is also randomly initialized (the velocity vector has the same dimension as the number of batteries and determines the speed and direction of the particle's position update in the solution space).

[0101] For each particle, a fitness value is calculated to measure the quality of the power transfer scheme it represents. The fitness function can be designed based on the goal of balancing, for example, the standard deviation of the battery pack's power can be used as the basis for the fitness value, with a smaller standard deviation indicating more balanced battery power and a better fitness value. Other factors such as energy loss during power transfer can also be considered to design the fitness function.

[0102] The fitness value of each particle is calculated, and the current fitness value of the particle and the particle's position (i.e., the current power transfer scheme) are recorded.

[0103] For each particle, compare its current fitness value with its historical optimal fitness value. If the current fitness value is better, update the particle's individual optimal position (i.e., the best power transfer scheme found in history) and individual optimal fitness value.

[0104] Compare the current fitness values of all particles to find the best fitness value and the corresponding particle position. Set this position as the global optimal position (i.e., the best power transfer scheme found so far by the entire particle swarm) and record the global optimal fitness value.

[0105] According to the current velocity, current position, individual optimal position, and global optimal position of the particle, update the velocity of each particle according to the rules of the particle swarm algorithm. During the velocity update process, the particle is influenced by its historical optimal position (reflecting the particle's self-learning ability) and the historical optimal position of the group (reflecting the information sharing and cooperation between particles), while also being adjusted by the inertia weight.

[0106] According to the updated velocity, update the position of each particle (i.e., the new guess for the amount of power transfer). When updating the position, it is necessary to ensure that the amount of power transfer is within a reasonable range, such as not exceeding the current amount of power of a certain battery.

[0107] Repeat the steps and perform multiple iterations of calculations. In each iteration, the particle continuously adjusts its position (power transfer scheme) to find a better solution.

[0108] It is checked whether a termination condition is met, for example, a maximum number of iterations is reached, or the global optimal fitness value does not improve significantly in consecutive iterations, etc. If the termination condition is met, the iteration is stopped.

[0109] When the algorithm terminates, the power transfer scheme represented by the global optimal position is the optimal solution found by the particle swarm algorithm. According to this scheme, the equalization transfer power of each battery can be determined, so that the power of the battery pack is equalized.

[0110] Through the above iterative process of the particle swarm algorithm, continuous search and optimization can be performed, and a relatively optimal equalization transfer power scheme for each battery can be found, so that the power distribution of the battery pack is more balanced.

[0111] Further, the inertia weight of the traditional particle swarm algorithm is a fixed value. The disadvantage of the fixed value is that if the initial weight is too large, the particles may fly too fast in the search space, causing the local optimum to be missed. If the weight is too small, the particles may converge too early and fall into a local optimum. The inertia weight coefficient is related to the number of iterations and the SOC in the present application. The advantage of this is to balance global exploration and local development, avoid premature convergence, improve convergence speed and accuracy, enhance algorithm robustness and adaptability, and adjust the inertia weight in real time according to the change of the SOC value, so that the particle swarm algorithm can better optimize the power of each battery.

[0112] The position update formula of the traditional particle swarm algorithm has the following disadvantages: easy to fall into local optimum, position update mainly depends on the current position and speed, in the later search stage, particles may gather around the local optimal solution too early, making it difficult for the algorithm to jump out of the local optimum. Since the adjustment of the speed is affected by the individual optimal and global optimal, once the particle approaches the local optimum, the speed will gradually decrease, limiting the search ability of the particle in the local area, and making it difficult to explore a larger range to find a better global solution.

[0113] The position update formula is relatively smooth, and lacks an effective mechanism to introduce a larger disturbance to help the particle jump out of the local optimum. When the particle falls into the local optimum, it is difficult for the particle to escape from the attraction of the local optimum by relying only on the current position and speed update method, especially in complex multi-peak function optimization problems, this limitation is more obvious.

[0114] The initial position and speed of the particle are randomly generated, and different initial values may lead to different results of the algorithm. If the initial value distribution is unreasonable, the particle swarm may be biased towards certain local areas in the early search stage, affecting the global search ability and causing the algorithm to fail to find the true optimal solution.

[0115] Due to the randomness of initial values, the algorithm may start searching in a direction that is not conducive to finding the global optimal solution, and once the search direction deviates significantly, it is difficult to correct through the position update formula, which may cause the algorithm to take a large number of iterations to adjust to the correct search direction, or even may not be able to find the optimal solution.

[0116] In the later stage of algorithm iteration, as particles gradually approach the optimal solution, due to the characteristics of the velocity update formula, the velocity of the particles will gradually decrease, causing the position update amplitude of the particles to become smaller and the convergence speed to slow down. This makes the algorithm need a large number of iterations to reach a satisfactory accuracy when approaching the optimal solution, increasing the computational cost and time overhead.

[0117] As the iteration proceeds, the difference between particles gradually decreases, and the population diversity decreases. When the population diversity is too low, the particle swarm is prone to stagnation, unable to effectively search and optimize, further affecting the convergence speed and the final optimization effect.

[0118] The parameters in the position update formula, such as inertia weight and learning factor, have an important impact on the performance of the algorithm. Different problems require different parameter settings to achieve better optimization results, but there is currently no effective theoretical guidance to determine the optimal parameter value, and usually a large number of experiments are needed to adjust, which increases the difficulty and complexity of the algorithm application.

[0119] The algorithm is sensitive to changes in these parameters, and small parameter changes can cause significant differences in algorithm performance. For example, too large inertia weight may cause particles to rely too much on historical speed, leading to too random search and difficulty in convergence; too small inertia weight may limit the search ability of particles and easily fall into local optimum. Therefore, the present application adds a β value, which can change according to temperature, better improving the convergence of the algorithm in the later stage. Speed up the optimization process: The improved position update formula can make particles move faster to the optimal solution area. For example, by introducing adaptive inertia weight, particles have a larger inertia weight in the early stage of the algorithm, allowing them to search in a larger range and quickly locate the area where the optimal solution may exist; in the later stage of the algorithm, the inertia weight is reduced, allowing the particles to search in a local area, thereby speeding up the entire convergence process.

[0120] Taking the position update formula with a contraction factor as an example, it can more effectively control the search step of particles, avoid excessive oscillation or wandering of particles during the search process, and make particles converge more directly to the optimal solution, thereby reducing the number of iterations required to reach a satisfactory solution and saving computing time and resources.

[0121] Embodiment 2, the second embodiment of the application, provides a sodium-ion super-capacitive coupled lithium battery energy storage frequency modulation control system. In order to verify the beneficial effects of the application, scientific demonstration is carried out through experiments.

[0122] The data acquisition module is used for acquiring the state of charge (SOC) value and temperature data of the lithium battery in the energy storage system, sorting the SOC values of all the batteries in the battery pack, determining the batteries with the maximum SOC value and the minimum SOC value, and recording the corresponding temperature data.

[0123] The equalization control module is used for performing SOC value equalization control according to the SOC value sorting result, calculating the charge and discharge equalization current, and monitoring the battery temperature in real time during the equalization process. If the battery temperature exceeds the threshold value, the equalization is stopped.

[0124] The SOC correction module is used for calculating the average value of the SOC values of all the batteries after the SOC value equalization control, calculating the SOC value correction degree of each battery in combination with the battery temperature information, calculating the equalization current according to the correction degree, and adjusting the SOC value to the equalization state.

[0125] The optimization calculation module is used for constructing an optimization objective function by using a particle swarm optimization algorithm, adjusting an inertia weight, and optimizing the equalization current distribution in combination with a temperature disturbance factor to update the battery SOC value.

[0126] The SOC adjustment module is used for adjusting the SOC value after the equalization, calculating the power compensation, and controlling the battery state of charge to be within the set range according to the battery SOC value working range.

[0127] The data output module is used for outputting the SOC value and temperature data after the equalization, and completing the SOC value equalization frequency modulation control of the energy storage system.

[0128] Embodiment 3, the third embodiment of the application, is different from the first two embodiments in that:

[0129] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the application or the parts that essentially contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0130] The logic and / or steps represented in the flow diagrams or otherwise described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber (optical), and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by a computer. In this context, a "computer-readable medium" can be any means that can store the program for use by or in connection with the instruction execution system, apparatus, or device.

[0131] The computer-readable medium can also be, for example but not limited to, a computer- readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber (optical), and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable storage medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by a computer. In this context, a "computer-readable storage medium" can be any means that can store the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can have a direct or indirect coupling to the instruction execution system, apparatus, or device.

[0132] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be embodied in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals; an application specific integrated circuit having appropriate combinational logic gates; a programmable gate array (PGA), a field programmable gate array (FPGA), and / or the like.

[0133] Embodiment 4, a fourth embodiment of the present application, which provides a sodium-ion super-capacitive coupling lithium battery energy storage frequency control method, comprising.

[0134] In order to further verify the advantages of the present application, the present application uses the method of the present application and directly uses the particle swarm current equalization method to balance the battery pack.

[0135] The experimental objects were selected as the same specification battery group (lithium iron phosphate), and the battery group was configured as the same series-parallel structure (4S10P); the initial conditions were kept consistent in initial SOC, temperature, and capacity consistency.

[0136] The experimental conditions were controlled, and the environmental temperature was controlled by a thermostat (25±1°C).

[0137] The charge-discharge rate was 1C charge / 1C discharge, and the cycle number was at least 5 complete cycles. The cutoff condition was SOC 20%-80%.

[0138] The test equipment was selected as a high-precision temperature sensor (±0.5°C); a voltage / current acquisition system (precision 0.1%); and a thermal imager for auxiliary monitoring, and the results are shown in Table 1.

[0139] Table 1 Comparison table of experimental results

[0140] Method The present invention Directly using particle swarm algorithm Proportion of SOC value in 0.3-0.6 97％ 90％ Temperature qualification rate (not more than 50°) after equalization 99％ 92％

[0141] It should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A sodium-ion supercapacitor coupled with a lithium battery energy storage frequency modulation control method, characterized by: The application relates to a SOC value equalization control method for a storage system. The SOC value equalization control method comprises the following steps: The SOC value equalization control method comprises the following steps: The SOC value equalization control method comprises the following steps: The SOC value equalization control method comprises the following steps: The SOC value equalization control method comprises the following steps: The SOC value equalization control method comprises the following steps: The SOC value equalization control method comprises the following steps: The SOC value equalization control method comprises the following steps: wherein, The SOC value equalization control method comprises the following steps: a mean value of SOC values of the n batteries, an SOC value of the i-th battery after the SOC value equalization control, a battery temperature of the i-th battery after the SOC value equalization control.

2. The energy storage frequency modulation control method of the sodium-ion supercapacitor coupled lithium battery according to claim 1, characterized in that: The SOC value equalization control method comprises the following steps: wherein, gelu represents an activation function, avg represents a mean, e represents an exponential function, t1 represents temperature data corresponding to a maximum value of the SOC value, t2 represents temperature data corresponding to a minimum value of the SOC value, SOC max represents a maximum value of the SOC value, SOC min represents a minimum value of the SOC value.

3. 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The energy storage frequency modulation control method of a sodium-ion supercapacitor coupled lithium battery according to claim 3, characterized in that: The construction optimization objective function comprises: obtaining a correction degree set [Ds1, Ds2, …, Dsk] of the remaining batteries and a corresponding temperature data set [ts1, ts2, …, tsk] based on the battery after the elimination correction degree is equal to zero or infinity, constructing an objective function based on the set data, and setting a constraint condition; The construction optimization objective function is expressed as, F = D s1 + D s2 +... + D sk The constraint condition is expressed as, 5. The energy storage frequency modulation control method of a sodium-ion supercapacitor coupled lithium battery according to claim 4, characterized in that: The updating battery SOC value includes setting population particle number N, initializing particle position x ik and velocity v ik , setting inertia weight W, learning factor c1 and c2, and maximum iteration number T max ; The inertia weight W is dynamically adjusted according to the particle search process, and the calculation method of the inertia weight is: Wherein, sigmoid() is an activation function, and t represents the current iteration number of the particle group; The fitness value of each particle is calculated based on the objective function, and the particle speed is updated; The updated particle speed is expressed as, where v ik (t) is the velocity of particle i in k dimension, is the historical best position of particle i in k dimension, is the global best position, c1 and c2 are learning factors, r1 and r2 are random numbers between [0, 1], W is the inertia weight, x ik (t) represents the position of the i-th particle in the k-th dimension at time t; The particle position is calculated based on the speed update, and is corrected in combination with the temperature data; The correction in combination with the temperature data is expressed as, x ik(t+1) = βx ik(t) + v ik(t+1) wherein x ik (t+1) represents the position of the i-th particle in the k-th dimension at the time t+1, β represents a correction parameter, respectively represent the battery temperature after the SOC value of the i-th battery is balanced. The optimal solution of the optimization objective function F is calculated based on the updated particle position, and it is judged whether the convergence condition is met. If not, the inertia weight adjustment, speed update, position correction are repeatedly executed until the set maximum iteration number T is reached max .

6. The energy storage frequency modulation control method of a sodium-ion supercapacitor coupled lithium battery according to claim 5, characterized in that: The adjustment of the SOC value after the equalization comprises: obtaining the SOC values of all the batteries in the energy storage system, counting the number N1 of the batteries whose SOC values are located in a first SOC value range [0.2, 0.3] and the number N2 of the batteries whose SOC values are located in a second SOC value range [0.6, 0.8]; When the SOC value of the battery i is located in the first SOC value range [0.2, 0.3], the SOC value is adjusted to transition to a target SOC value range [0.3, 0.6], which is expressed as, wherein Q1 i represents the first SOC value adjustment electric quantity, n represents the number of battery packs, Q i represents the battery electric quantity before adjustment; When the SOC value of the battery i is located in the second SOC value range [0.6, 0.8], the SOC value is adjusted to transition to the target SOC value range [0.3, 0.6], which is expressed as, wherein Q2 i represents the second SOC value adjustment electric quantity.

7. A sodium-ion supercapacitor coupled lithium battery energy storage frequency modulation control system, applying a sodium-ion supercapacitor coupled lithium battery energy storage frequency modulation control method according to any one of claims 1-6, characterized in that, It comprises: A data acquisition module is configured to acquire the state of charge (SOC) value and temperature data of lithium batteries in an energy storage system, sort the SOC values of all the batteries in a battery pack, determine the batteries with the maximum and minimum SOC values, and record the corresponding temperature data; An equalization control module is configured to perform SOC value equalization control based on the SOC value sorting result, calculate the charging and discharging equalization current, and monitor the battery temperature in real time during the equalization. If the battery temperature exceeds a threshold value, the equalization is stopped. An SOC correction module is configured to calculate the mean value of the SOC values of all the batteries after the SOC value equalization control, calculate the SOC value correction degree of each battery in combination with the battery temperature information, calculate the equalization current based on the correction degree, and adjust the SOC value to an equalization state. An optimization calculation module is configured to use a particle swarm optimization algorithm to construct an optimization objective function, adjust an inertia weight, and optimize the equalization current distribution in combination with a temperature disturbance factor, and update the battery SOC value. An SOC adjustment module is configured to adjust the SOC value after the equalization, calculate the power compensation, and control the state of charge of the battery within a set range according to the working range of the battery SOC value. And, A data output module is configured to output the SOC value and temperature data after the equalization, and complete the SOC value equalization and frequency control of the energy storage system.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to realize the steps of the energy storage frequency control method of the sodium-ion super-capacitive coupled lithium battery in any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by a processor to implement the steps of the energy storage frequency modulation control method of the sodium-ion supercapacitor coupled lithium battery according to any one of claims 1 to 6.

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