Adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters

Abstract

The invention discloses a self-adaptive SOC balancing and current-sharing control method for parallel operation of multiple energy storage converters. The method comprises the following steps: calculating a self-adaptive current compensation item; a self-adaptive droop coefficient containing an exponential function is introduced in the control process of the energy storage converter; and constructing a closed-loop small-signal model comprising the adaptive current compensation item in the step 1 and the adaptive droop coefficient in the step 2, and realizing adaptive SOC equalization and current-sharing control of the energy storage system. Rapid dynamic response is realized through an adaptive current compensation item, steady-state precision is ensured through an adaptive droop coefficient, and dynamic adjustment of compensation amount along with SOC difference is realized by adopting a nonlinear function. The adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters has the advantages that the energy storage system can keep the bus voltage stable so as to realize automatic optimization of power distribution, any energy storage converter is prevented from entering an overcharge / overdischarge state, and the dynamic performance and robustness of the energy storage system are improved.

Description

Technical Field

[0001] This invention relates to a stability control technology for microgrid systems, and more particularly to an adaptive SOC balancing and current sharing control method for parallel operation of multiple energy storage converters. Background Technology

[0002] A microgrid is a small-scale power generation and distribution system composed of distributed power sources, energy storage systems, energy conversion devices, loads, monitoring and protection devices, etc. The power sources in a microgrid are mostly small-capacity distributed power sources, i.e., small units with power electronic interfaces, including micro gas turbines, fuel cells, photovoltaic cells, small wind turbines, and energy storage devices such as supercapacitors, flywheels, and batteries. The aim is to achieve flexible and efficient application of distributed power sources and solve the grid connection problem. Microgrids enable large-scale integration of distributed power sources and renewable energy sources and have islanded operation capabilities. Connected to the user side, microgrids are characterized by low cost, low voltage, and low pollution.

[0003] Ensuring system stability is a critical technical challenge during the operation of microgrid systems. As a key component of the microgrid architecture, distributed energy storage systems not only play an important role in maintaining the stability of the DC bus voltage, but also provide necessary power support for the system. Distributed energy storage systems include multiple parallel energy storage converters. In the coordinated operation of multiple parallel energy storage converters, the difference in the State of Charge (SOC) between the energy storage converters will cause a series of operational problems: (1) SOC imbalance will cause some energy storage converters to be forced to shut down due to premature overcharging or over-discharging; (2) SOC imbalance will significantly reduce the utilization efficiency of the entire energy storage system and shorten the service life of the energy storage converters.

[0004] Traditional SOC equalization methods based on droop control have revealed two main drawbacks in practical applications: (1) the equalization adjustment process has a slow response speed, making it difficult to meet the needs of rapid dynamic response in energy storage systems; (2) there is a risk that the converter output power will exceed the rated upper limit, which will not only affect the safe operation of the energy storage system, but may also accelerate the aging of the energy storage converter. The existence of these problems highlights the necessity and urgency of developing new energy storage control methods. Summary of the Invention

[0005] To overcome the shortcomings of the existing technologies, this invention provides an adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters, so as to achieve rapid dynamic response in the SOC equalization process, effectively prevent any energy storage converter from entering the overcharge / over-discharge state, and improve the dynamic performance and robustness of the energy storage system.

[0006] The present invention adopts the following technical solution to solve the technical problem.

[0007] This invention provides an adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters, comprising the following steps:

[0008] Step 1: Calculate the adaptive current compensation term δ of the energy storage converter. i ;

[0009] Step 2: Introduce an adaptive droop coefficient R containing an exponential function into the control process of the energy storage converter. di ;

[0010] Step 3: Construct the adaptive current compensation term δ from Step 1 i And the adaptive droop coefficient R in step 2 di The closed-loop small-signal model;

[0011] Step 4: Through the closed-loop small-signal model, the stability of the energy storage system is verified and the dynamic characteristics are analyzed, thereby realizing the adaptive adjustment of the system parameters of the energy storage system; the adaptively adjusted system parameters are fed back to the energy storage system to form a closed-loop feedback control of the energy storage system, realizing the adaptive SOC balancing and current sharing control of the energy storage system.

[0012] The adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters in this invention is also characterized by:

[0013] Further, step 1 includes the following steps:

[0014] Step 11: Calculate the SOC and its rate of change SOC′ of the energy storage converter;

[0015] Step 12: Determine the adaptive current compensation term δ i The basic formula;

[0016] Step 13: Confirm the adaptive current compensation term δ i Calculation parameters;

[0017] Step 14: Determine the adaptive current compensation term δ i The complete expression.

[0018] Furthermore, in step 11, the SOC calculation of the energy storage converter is shown in the following formula (1).

[0019] (1);

[0020] In formula (1), SOC i (t) represents the state of charge (SOC) of the i-th energy storage converter at time t; i0The initial state of charge of the i-th energy storage converter; u dc This is the output voltage of the energy storage converter (also the DC bus voltage). C is the reference value for the output voltage of the energy storage converter. bati R is the DC capacitor capacity of the i-th energy storage converter; di Let be the droop coefficient of the i-th energy storage converter during the droop control process.

[0021] Furthermore, the rate of change SOC′ is SOC i The first derivative of (t).

[0022] Furthermore, in step 12, the adaptive current compensation term δ i The basic formula is formula (3);

[0023] (3);

[0024] In formula (3), n is the equalization coefficient among the various energy storage converters, λ is the gain coefficient, μ is the gain damping, and the current difference is... Let I be the output current of the i-th energy storage converter. dci and average current I dcave The difference; SOC i The state of charge (SOC) of the i-th energy storage converter. ave This represents the average state of charge.

[0025] Furthermore, substituting equations (6) and (7) into equation (3), we obtain the adaptive current compensation term δ. i The complete expression is shown in formula (8) below;

[0026] (8).

[0027] In formula (8), n is the equalization coefficient among the various energy storage converters; λ is the gain coefficient; λ0 is the basic gain coefficient, and k λ k is the gain adjustment coefficient; μ0 is the initial damping coefficient; k μ The damping attenuation coefficient; SOC i This represents the state of charge of the i-th energy storage converter; The SOC deviation of the i-th energy storage converter; Let I be the output current of the i-th energy storage converter. dci and average current I dcave difference.

[0028] Furthermore, in step 2, a SOC safety boundary weight factor w is introduced to optimize the adaptive droop coefficient R. di .

[0029] Furthermore, step 2 includes the following steps:

[0030] Step 21: Determine the adaptive droop coefficient R di The calculation formula;

[0031] Adaptive droop coefficient R di The calculation formula is shown in the following formula (9);

[0032] (9);

[0033] In formula (9), R 0i β is the initial droop coefficient of the i-th energy storage converter, and β is the convergence coefficient; SOC i The state of charge (SOC) of the i-th energy storage converter. ave The average value of the state of charge; δ i For adaptive current compensation; I dci Let be the output current of the i-th energy storage converter.

[0034] Step 22: The adaptive droop coefficient R di Optimization steps;

[0035] Step 23: Adaptive droop coefficient R di Optimized calculation.

[0036] Furthermore, the closed-loop small-signal model is constructed using the system state-space equations.

[0037] Furthermore, the system state-space equation of the energy storage system is shown in the following formula (12).

[0038] (12);

[0039] In formula (12), the state variable ; This refers to the SOC deviation of the energy storage converter; The output current I of the energy storage converter dc and average current I dcave difference; y represents the disturbance value experienced by the DC bus voltage itself; A is the system matrix of the closed-loop small-signal model; B is the input matrix of the closed-loop small-signal model, i.e., the control matrix; and y is the external disturbance input vector. ;in, This indicates a step change or fluctuation in the load current.

[0040] Compared with existing technologies, the beneficial effects of this invention are reflected in:

[0041] This invention discloses an adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters, including: calculating the adaptive current compensation term δ. i In the control process of the energy storage converter, an adaptive droop coefficient R containing an exponential function is introduced. di A closed-loop small-signal model is constructed, including the adaptive current compensation term from step 1 and the adaptive droop coefficient from step 2. The adaptive current compensation term enables fast dynamic response, while the variable adaptive droop coefficient, incorporating an exponential function, ensures steady-state accuracy. A nonlinear function is used to dynamically adjust the compensation amount according to the SOC difference.

[0042] The adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters of the present invention has the advantages of enabling the energy storage system to maintain the bus voltage stability to achieve automatic optimization of power distribution, preventing any energy storage converter from entering the overcharge / over-discharge state, and improving the dynamic performance and robustness of the energy storage system. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of an adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters according to the present invention.

[0044] Figure 2 The diagram shows the SOC simulation waveform of the converter under traditional droop control in the adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters according to the present invention.

[0045] Figure 3 This is a simulation waveform of the converter's SOC under the proposed control method of the adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters in this invention.

[0046] Figure 4 This is a simulation waveform diagram of the dynamic response of the bus voltage under the control method of the present invention.

[0047] Figure 5 This is a bar chart comparing the bus voltage fluctuation values ​​when the SOC difference between the energy storage converters of the present invention and the traditional control method is between 0.2 and 0.5.

[0048] Figure 6 This is a comparison chart showing the change process of ΔSOC between the traditional method and the method of this invention when ΔSOCmax is 0.4.

[0049] Figure 7 A bar chart comparing the convergence time of the equilibrium process under different initial SOC differences.

[0050] The present invention will be further described below through specific embodiments and in conjunction with the accompanying drawings. Detailed Implementation

[0051] See Figures 1 to 7 This invention provides an adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters, comprising the following steps:

[0052] Step 1: Calculate the adaptive current compensation term δ of the energy storage converter. i ;

[0053] Step 2: Introduce an adaptive droop coefficient R containing an exponential function into the control process of the energy storage converter. di ;

[0054] In the control of energy storage converters in DC microgrid systems, an exponential function is introduced to design the adaptive droop coefficient R. di By leveraging the nonlinear characteristics of the exponential function, the charging and discharging efficiency curves of the battery in the energy storage system and the dynamic response characteristics of the energy storage converter are accurately reflected. Combining the classic mathematical model of the exponential droop coefficient, an adaptive droop coefficient R is designed that can be dynamically adjusted based on the real-time SOC value. di .

[0055] Step 3: Construct the adaptive current compensation term δ from Step 1 i And the adaptive droop coefficient R in step 2 di The closed-loop small-signal model;

[0056] Step 4: Through a closed-loop small-signal model, the stability of the energy storage system is verified and its dynamic characteristics are analyzed. This enables the adaptive adjustment of the system parameters of the energy storage system. The adjusted system parameters are then fed back to the energy storage system to form a closed-loop feedback control, thereby achieving adaptive SOC balancing and current sharing control of the energy storage system.

[0057] After completing the construction of the closed-loop small-signal model, the specific implementation steps for adaptive SOC equalization and current sharing control based on the closed-loop small-signal model are as follows:

[0058] Step 41: System stability verification and dynamic characteristic analysis of the energy storage system;

[0059] Based on the system state-space equation of the closed-loop small-signal model, i.e., formula (12), the eigenvalues ​​of the system matrix A are calculated. By analyzing the distribution of the eigenvalues ​​on the complex plane, it is verified whether the energy storage system meets the stability conditions at typical operating points. Furthermore, the dynamic response speed of the system is evaluated by the magnitude of the real part of the eigenvalues, and the oscillation trend of the energy storage system is analyzed by the imaginary part, providing a basis for controller parameter tuning.

[0060] Step 42: Design and implementation of the parameter adaptive adjustment mechanism;

[0061] Based on the system dynamic characteristics revealed by the closed-loop small-signal model, an adaptive adjustment mechanism for the following system parameters of the energy storage system is designed and implemented:

[0062] (1) Gain coefficient λ i With damping coefficient μ i Online adjustments: with | | is the input, and λ is updated in real time according to formulas (6) and (7). i With μ i To ensure a strong compensation effect to accelerate the equilibrium process when the SOC difference is large in the early stage of equilibrium, and to enhance damping to suppress overshoot and oscillation in the later stage of equilibrium when the SOC difference is small, thus achieving fast and smooth convergence.

[0063] (2) Sag coefficient R di Security boundary optimization: combining SOC security boundary weight factor w i (t), the optimized adaptive droop coefficient R is calculated in real time using formula (11). di This design allows the droop coefficient to adjust normally when the SOC is in the middle of the safe range, and suppresses its growth trend when the SOC value approaches the upper or lower threshold, preventing overcharging / over-discharging of the energy storage converter, while avoiding R... di Dramatic changes caused large fluctuations in bus voltage.

[0064] Step 43: Integration and operation of the closed-loop feedback control system;

[0065] The adaptive current compensation term δ obtained in step 1 i The adaptive droop coefficient R optimized in step 2 di It is integrated into the local control loop of each energy storage converter to form a complete distributed closed-loop feedback control system for the energy storage system. The specific operating logic is as follows:

[0066] (1) Data acquisition and processing. Each energy storage converter acquires local information in real time, including its own output current and DC bus voltage, and calculates the local SOC value using formula (1).

[0067] (2) Global Information Estimation. Each energy storage converter estimates the average current based on the local current value using formula (4), and calculates the average state of charge (SOC) of the energy storage system using the local SOC value. ave .

[0068] (3) Calculation of compensation amount and droop coefficient. The adaptive current compensation term δ is calculated using formula (8). i The adaptive droop coefficient R is calculated using formula (11). di .

[0069] (4) Generate control commands and execute them. (The last part, "δ", appears to be a typo and can be omitted.) i As a correction factor, a current reference value is introduced, and R di A droop control law is introduced to generate the final PWM drive signal, which controls the switching action of each energy storage converter, thereby adjusting the output power of each energy storage converter.

[0070] Step 44: Simulation verification and parameter tuning of energy storage system performance.

[0071] The performance of the energy storage system was simulated and verified using the closed-loop small-signal model described above.

[0072] (1) Stability verification. Verify the system stability under typical operating conditions such as different initial SOC and different load step disturbances.

[0073] (2) Dynamic performance testing. Compared with traditional fixed droop control methods, the proposed method is evaluated in terms of SOC equalization speed, such as... Figure 3 and Figure 7 Bus voltage fluctuation range, such as Figure 4 and Figure 5 It also has advantages in terms of resistance to load disturbances.

[0074] (3) Parameter sensitivity analysis and tuning. Based on model analysis, the key parameter k λ k μ The influence of β and α on system stability, response speed and steady-state accuracy is investigated to ensure that the system has good dynamic and static performance over a wide operating range;

[0075] like Figure 1 The adaptive SOC balancing and current sharing control method for parallel operation of multiple energy storage converters in this invention aims to solve the problems of slow SOC balancing speed and insufficient power distribution accuracy in traditional fixed-coefficient droop control, and to prevent energy storage converters from prematurely shutting down due to overcharging / over-discharging caused by SOC imbalance, thereby achieving distributed and precise current sharing control without relying on centralized communication. First, the adaptive SOC balancing and current sharing control method for parallel operation of multiple energy storage converters in this invention employs a current compensation coefficient based on SOC deviation, utilizing dynamic compensation characteristics to achieve automatic optimization of power distribution while maintaining stable bus voltage. Second, this invention establishes a relationship between SOC and droop coefficient R... diThe mathematical mapping relationship is used to express the droop coefficient as a function of the real-time SOC. Larger / smaller droop coefficients are obtained based on different SOC values ​​to form a protective regulation, preventing the energy storage converter from entering overcharge / over-discharge states. Finally, this invention further introduces an adaptive mechanism for gain and damping, a SOC safety boundary weighting factor, and verifies the system stability through a small-signal model. Simulation results show that the proposed method can significantly shorten the SOC equalization time, reduce bus voltage fluctuations, avoid excessive reliance on the communication system in the control process, achieve precise current sharing control of multiple parallel energy storage converters, and improve the system's dynamic performance and robustness.

[0076] In practice, step 1 includes the following steps:

[0077] Step 11: Calculate the SOC and its rate of change SOC′ of the energy storage converter;

[0078] Step 12: Determine the adaptive current compensation term δ i The basic formula;

[0079] Step 13: Confirm the adaptive current compensation term δ i Calculation parameters;

[0080] Step 14: Determine the adaptive current compensation term δ i The complete expression.

[0081] In specific implementation, the SOC calculation of the energy storage converter in step 11 is shown in the following formula (1).

[0082] (1);

[0083] In formula (1), SOC i (t) represents the state of charge (SOC) of the i-th energy storage converter at time t; i0 The initial state of charge of the i-th energy storage converter; u dc This is the output voltage of the energy storage converter (also the DC bus voltage). C is the reference value for the output voltage of the energy storage converter. bati R is the DC capacitor capacity of the i-th energy storage converter; di Let be the droop coefficient of the i-th energy storage converter during the droop control process.

[0084] In specific implementation, in step 11, the rate of change SOC′ is SOC. i The first derivative of (t).

[0085] Differentiating formula (1) yields SOC. i rate of change of (t) See formula (2) below;

[0086] (2);

[0087] As can be seen from formula (2), SOC i rate of change of (t) It mainly depends on the DC capacitor capacity C of the energy storage converter. bati and the droop coefficient R di However, the capacity of the energy storage converter is given when the system is built, so it mainly depends on the droop factor R. di .

[0088] In specific implementation, in step 12, the adaptive current compensation term δ i The basic formula is formula (3);

[0089] (3);

[0090] In formula (3), n is the equalization coefficient among the various energy storage converters, λ is the gain coefficient, μ is the gain damping, and the current difference is... Let I be the output current of the i-th energy storage converter. dci and average current I dcave The difference; SOC ave The average state of charge (SOC) i This represents the state of charge of the i-th energy storage converter.

[0091] In the coordinated operation of multiple parallel energy storage converters, traditional droop control achieves power distribution through virtual resistors. However, a fixed droop coefficient can lead to a vicious cycle where high-SOC energy storage converters discharge less and low-SOC energy storage converters charge less due to differences in State of Charge (SOC). A fixed droop coefficient is insufficient to simultaneously meet the requirements of static accuracy and rapid balancing. Therefore, the first consideration is to design a current compensation term that compares the SOC of each energy storage converter with the average SOC of the state of charge. ave The difference | SOC i -SOC ave | This is converted into compensation current, which allows the compensation amount to be dynamically adjusted according to the SOC difference, enabling the energy storage converter to intelligently allocate power when the SOC is high (more discharge / less charging) and low (less discharge / more charging).

[0092] Therefore, the adaptive current compensation term δ of the i-th energy storage converter is calculated. i In the process, it is necessary to consider not only the SOC of the energy storage converter and the average SOC of the state of charge. ave The difference | SOC i -SOC ave | In addition, the gain coefficient λ and gain damping μ are also introduced.

[0093] The output current I of the i-th energy storage converterdci and average current I dcave difference SOC deviation ΔSOC i The calculation process for several parameters, including (t), gain coefficient λ, damping coefficient μ, and n, is as follows; n is the equalization coefficient among the various energy storage converters.

[0094] 1. ΔI i The calculation formula is shown in the following formula (4);

[0095] (4);

[0096] 2. To enhance the dynamic performance and adaptive capability of current compensation, a method based on SOC deviation ΔSOC is introduced. i The gain adaptive adjustment term of (t). The SOC deviation ΔSOC of the i-th energy storage converter at time t. i The formula for calculating (t) is shown in the following formula (5);

[0097] ΔSOC i (t)=SOC i (t)-SOC ave (t) (5);

[0098] In formula (5), SOC ave (t) represents the average SOC of all energy storage converters at time t; SOC i (t) represents the state of charge of the i-th energy storage converter at time t. This deviation directly reflects the difference between the i-th energy storage converter and the system average level.

[0099] 3. The gain coefficient λ is about |ΔSOC i | is a monotonically increasing function to ensure stronger regulation when the SOC difference is large, as shown in the following formula (6);

[0100] (6);

[0101] In formula (6), λ0 is the basic gain coefficient, and k λ This is the gain adjustment coefficient. When ΔSOC i As λ increases, i This increases accordingly, strengthening the regulatory intensity of the compensation term.

[0102] 4. To avoid oscillations caused by excessive compensation in the later stages of equilibrium, the damping coefficient μ is also designed as an adaptive term that varies with the SOC deviation. The damping coefficient μ of the i-th energy storage converter... i The calculation is shown in the following formula (7);

[0103] (7);

[0104] In formula (7), μ0 is the initial damping coefficient, and k μ This represents the damping attenuation coefficient. When the SOC deviation is large, μ of the i-th energy storage converter... i Decreasing μ allows for stronger compensation; when SOC approaches equilibrium, μ i Increase the value to suppress overshoot and smooth the convergence process.

[0105] In practice, substitute formulas (6) and (7) into formula (3) to obtain the complete expression of the adaptive current compensation term, as shown in formula (8) below.

[0106] (8).

[0107] In formula (8), n is the equalization coefficient among the various energy storage converters; λ is the gain coefficient; λ0 is the basic gain coefficient, and k λ k is the gain adjustment coefficient; μ0 is the initial damping coefficient; k μ The damping attenuation coefficient; SOC i Let ΔSOC represent the state of charge of the i-th energy storage converter. i Let ΔI be the SOC deviation of the i-th energy storage converter. i Let I be the output current of the i-th energy storage converter. dci and average current I dcave difference.

[0108] Due to the adaptive current compensation term δ i It has an automatic enhancement and adjustment function when there is a large difference in SOC, and it decays smoothly when it approaches equilibrium, which helps to improve the convergence speed and suppress overshoot.

[0109] In specific implementation, step 2 introduces a SOC safety boundary weight factor w to optimize the adaptive droop coefficient R. di .

[0110] In practice, step 2 includes the following steps:

[0111] Step 21: Determine the adaptive droop coefficient R di The calculation formula;

[0112] Adaptive droop coefficient R di The calculation formula is shown in the following formula (9);

[0113] (9);

[0114] In formula (9), R 0i β is the initial droop coefficient of the i-th energy storage converter, and β is the convergence coefficient; SOCi The state of charge (SOC) of the i-th energy storage converter. ave The average value of the state of charge; δ i For adaptive current compensation; I dci Let be the output current of the i-th energy storage converter.

[0115] The two formulas in formula (9) are applied to the discharge state and the charging state of the energy storage converter, respectively, and can adjust R according to the SOC difference index. di This invention avoids the voltage deviation problem of traditional linear methods. At the same time, it constructs an adaptive droop control strategy based on local SOC. By embedding the global equalization target into the local control law of each energy storage converter through a nonlinear function, each energy storage converter can autonomously adjust its output characteristics based only on its own SOC and local electrical quantities. Without relying on any real-time communication between energy storage converters, it achieves fast SOC equalization and accurate current sharing, reduces the cost and complexity of the energy storage system, and avoids excessive reliance on the communication system.

[0116] Step 22: The adaptive droop coefficient R di Optimization steps;

[0117] To further optimize the adjustment characteristics of the droop coefficient near the SOC boundary, a SOC safety boundary weighting factor w is introduced. Simultaneously, the safety upper limit of SOC is defined as SOC. max The safety lower limit is SOC min Then the boundary proximity weighting factor w of the i-th energy storage converter at time t. i The formula for calculating (t) is shown in the following formula (10);

[0118] (10);

[0119] In formula (10), SOC mid =(SOC max +SOC min ) / 2, which is the SOC safe interval [SOC min SOC max The median of ]; α is the boundary sensitivity coefficient; SOC i (t) represents the state of charge of the i-th energy storage converter at time t. Weighting factor w i (t) When SOC is close to the safe range [SOC min SOC max The median SOC mid The time is approximately 1, near the safe range [SOC] min SOC maxThe droop coefficient decreases significantly at the boundary, which helps to suppress drastic changes in the droop coefficient near the boundary.

[0120] Step 23: Adaptive droop coefficient R di Optimized calculation.

[0121] Incorporate the SOC safety boundary weight factor w into the adaptive droop coefficient R di In the calculation, the expression for the final adaptive droop coefficient is obtained, as shown in the following formula (11).

[0122] (11);

[0123] In formula (11), R 0i β is the initial droop coefficient of the i-th energy storage converter, and β is the convergence coefficient; SOC i The state of charge (SOC) of the i-th energy storage converter. ave The average value of the state of charge; δ i For adaptive current compensation; I dci w is the output current of the i-th energy storage converter. i (t) is the boundary proximity weight factor of the i-th energy storage converter at time t. Formula (11) introduces the SOC safety boundary weight factor w, which suppresses the growth trend of the droop coefficient when the SOC is close to the safety boundary, avoids the bus voltage from fluctuating drastically due to the rapid change of the coefficient, and can still effectively limit the power of the extreme SOC unit.

[0124] In practice, the closed-loop small-signal model is constructed using the system state-space equation.

[0125] To further analyze the system stability of the energy storage system, an adaptive current compensation term δ is established. i With the adaptive droop coefficient R di The closed-loop small-signal model is given. State variables are defined as x = [ΔSOC, ΔI, ΔU]. dc ] T , where ΔU dc This represents the disturbance value experienced by the DC bus voltage itself. Linearization is performed near the equilibrium point to obtain the system state-space equation of the closed-loop small-signal model, as shown in formula (12) below.

[0126] (12);

[0127] In formula (12), the state variable x = [ΔSOC, ΔI, ΔU] dc ] T ΔSOC is the SOC deviation of the energy storage converter; ΔI is the output current I of the energy storage converter. dc and average current I dcaveThe difference; ΔU dc A represents the disturbance value experienced by the DC bus voltage itself; B represents the system matrix of the closed-loop small-signal model; C represents the input matrix of the closed-loop small-signal model, i.e., the control matrix; D represents the external disturbance input vector, y = [ΔI]. load ,ΔU dc ] T ; where ΔI load This indicates a step change or fluctuation in the load current.

[0128] The expressions for the system matrix A and the input matrix B are shown in the following formula (13);

[0129] (13);

[0130] In formula (13), C dc Let f1 be the total equivalent capacitance of the DC bus; f2 and f3 are the dynamic equations of SOC, current, and bus voltage, respectively. The specific expressions of the equations f1, f2, and f3 are shown in formulas (14) to (16) below:

[0131] (14);

[0132] In formula (14), k SOCi The sensitivity coefficient reflects the sensitivity of the droop coefficient to changes in SOC.

[0133] (15);

[0134] In formula (15), Let k be the equivalent time constant of the current loop of the i-th energy storage converter; Ii The overall current sensitivity coefficient reflects the dynamic adjustment effect of changes in the output current itself. The overall SOC sensitivity coefficient includes the sensitivity of the droop coefficient change and the compensation term to SOC, reflecting the comprehensive regulatory effect of a unit change in SOC on the current reference value, and thus on the dynamic generation of the current.

[0135] (16);

[0136] In formula (16), ΔI load For load current disturbance, C dc The total equivalent capacitance of the DC bus is given by , and j represents the total number of energy storage converters operating in parallel in the system; the current difference ΔI i Let I be the output current of the i-th energy storage converter. dci and average current I dcave The difference, 1≤i≤j.

[0137] By calculating the eigenvalues ​​of the system matrix A, it can be verified that the system satisfies the Hurwitz stability condition within a reasonable range of parameters, thus ensuring the closed-loop stability of the proposed control method.

[0138] Figure 1 This is a schematic diagram of the control method of the present invention. The voltage and current dual closed-loop control is a traditional control method. The voltage and current dual closed-loop output trigger pulses of the switching transistors in the energy storage converter control the on and off of the circuit.

[0139] Figure 2 This describes the SOC equalization process of three parallel converters under traditional droop control. Due to the fixed droop coefficient, the SOC convergence to equilibrium takes a long time, and equilibrium has not been reached even after 120 seconds, indicating an excessively long equalization time.

[0140] Figure 3 This describes the SOC equalization process under the control method proposed in this invention. The control method proposed in this invention employs dynamic current compensation and adaptive droop coefficient, enabling the SOC of each unit to quickly approach uniformity, reducing the equalization time to approximately 70 seconds, and improving the equalization speed by approximately 41.7%.

[0141] Figure 4 The simulation waveform of the dynamic response of the DC bus voltage under the control method of this invention is shown. Under the condition of a step change in load power from 4kW to 6kW to 3kW, the dynamic response process of the DC bus voltage using the control method of this invention is shown. Throughout the equalization process, the bus voltage is consistently controlled between 398V and 402V with a deviation of ±0.5%, meeting the requirements for stable system operation. This verifies that the proposed method improves the equalization speed without sacrificing voltage stability.

[0142] Figure 5 The bus voltage fluctuation values ​​are given when the SOC difference between each energy storage converter is between 0.2 and 0.5. The voltage fluctuation of the method described in this invention is significantly lower than that of traditional control methods, indicating that this invention not only improves the balancing speed but also further enhances voltage stability.

[0143] Figure 6 Comparison of initial ΔSOC max When the ΔSOC is 0.4, the changes in ΔSOC between the traditional method and the method of this invention are shown. After 80 seconds, the traditional method still has a certain difference in ΔSOC, approximately 0.15, while the method of this invention reaches the allowable error range in ΔSOC after 60 seconds, and its equalization speed is far superior to that of the traditional method.

[0144] Figure 7A bar chart comparing the convergence time of the equilibrium process under different initial SOC differences is presented. The chart compares the equilibrium convergence time of the traditional control method and the control method of this invention under four initial SOC maximum differences of 0.2, 0.3, 0.4, and 0.5. The bar chart shows that the traditional control has a long convergence time, failing to converge within 120 seconds, while the control of this invention converges quickly, within 65.9-79.9 seconds, representing an average improvement in equilibrium speed of over 40%. This verifies the superiority and robustness of the adaptive control method of this invention in dealing with different SOC differences.

[0145] The present invention provides an adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters, which has the following technical features.

[0146] 1. An adaptive current compensation term δ based on SOC deviation was designed. i When the SOC difference increases, the compensation amount automatically increases according to a preset function, forming an intelligent adjustment mechanism that increases discharge / reduces charging for energy storage converters with high SOC and reduces discharge / increases charging for energy storage converters with low SOC. This dynamic compensation characteristic enables the system to achieve automatic optimization of power distribution while maintaining stable bus voltage.

[0147] 2. The relationship between SOC and droop coefficient R was established. di The mathematical mapping relationship expresses the droop coefficient as a real-time function of SOC. This ensures that: energy storage converters with higher SOC automatically acquire a larger droop coefficient, naturally increasing discharge power; energy storage converters with lower SOC automatically acquire a smaller droop coefficient, actively limiting discharge power. When SOC approaches its limit, the droop coefficient changes drastically, forming a protective regulation that effectively prevents any energy storage converter from entering an overcharge / over-discharge state.

[0148] In summary, in this invention, the adaptive current compensation term δ... i This allows the compensation amount to dynamically adjust according to the SOC difference, achieving a fast dynamic response and solving the problems of slow SOC balancing speed and insufficient power distribution accuracy in traditional fixed coefficient droop control. Simultaneously, SOC is incorporated into the calculation of the droop coefficient, and a design is made that integrates SOC with the adaptive droop coefficient R. di The relationship between the quantities allows the design of the droop coefficient based on the real-time value of the SOC in the regulation and control process, ensuring steady-state accuracy. The nonlinear function is used to realize the dynamic adjustment of the compensation quantity with the difference of SOC, avoiding the premature shutdown of the energy storage converter due to overcharging / over-discharging caused by SOC imbalance, and realizing distributed precise current sharing control without relying on centralized communication.

[0149] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0150] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. An adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters, characterized in that, Includes the following steps: Step 1: Calculate the adaptive current compensation term δ of the energy storage converter. i ; Step 2: Introduce an adaptive droop coefficient R containing an exponential function into the control process of the energy storage converter. di ; Step 3: Construct the adaptive current compensation term δ from Step 1 i And the adaptive droop coefficient R in step 2 di The closed-loop small-signal model; Step 4: Through the closed-loop small-signal model, the stability of the energy storage system is verified and the dynamic characteristics are analyzed, thereby realizing the adaptive adjustment of the system parameters of the energy storage system; the adaptively adjusted system parameters are fed back to the energy storage system to form a closed-loop feedback control of the energy storage system, realizing the adaptive SOC balancing and current sharing control of the energy storage system.

2. The adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters according to claim 1, characterized in that, Step 1 includes the following steps: Step 11: Calculate the SOC and its rate of change SOC′ of the energy storage converter; Step 12: Determine the adaptive current compensation term δ i The basic formula; Step 13: Confirm the adaptive current compensation term δ i Calculation parameters; Step 14: Determine the adaptive current compensation term δ i The complete expression.

3. The adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters according to claim 2, characterized in that, In step 11, the SOC calculation of the energy storage converter is shown in the following formula (1). （1）； In formula (1), SOC i (t) represents the state of charge (SOC) of the i-th energy storage converter at time t; i0 The initial state of charge of the i-th energy storage converter; u dc This is the output voltage of the energy storage converter; C is the reference value for the output voltage of the energy storage converter. bati R is the DC capacitor capacity of the i-th energy storage converter; di For the first The droop coefficient of an energy storage converter during the droop control process.

4. The adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters according to claim 3, characterized in that, In step 11, the rate of change SOC′ is SOC i The first derivative of (t).

5. The adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters according to claim 4, characterized in that, In step 12, the adaptive current compensation term δ i The basic formula is formula (3); （3）； In formula (3), n is the equalization coefficient among the various energy storage converters, λ is the gain coefficient, μ is the gain damping, and the current difference is... Let I be the output current of the i-th energy storage converter. dci and average current I dcave The difference; SOC i The state of charge (SOC) of the i-th energy storage converter. ave This represents the average state of charge.

6. The adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters according to claim 5, characterized in that, The adaptive current compensation term δ i The complete expression is shown in formula (8) below; (8)； In formula (8), n is the equalization coefficient among the various energy storage converters; λ is the gain coefficient; λ0 is the basic gain coefficient, and k λ k is the gain adjustment coefficient; μ0 is the initial damping coefficient; k μ The damping attenuation coefficient; SOC i Let ΔSOC represent the state of charge of the i-th energy storage converter. i Let ΔI be the SOC deviation of the i-th energy storage converter. i Let I be the output current of the i-th energy storage converter. dci and average current I dcave difference.

7. The adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters according to claim 1, characterized in that, In step 2, a SOC safety boundary weight factor w is introduced to optimize the adaptive droop coefficient R. di .

8. The adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters according to claim 7, characterized in that, Step 2 includes the following steps: Step 21: Determine the adaptive droop coefficient R di The calculation formula; Adaptive droop coefficient R di The calculation formula is shown in the following formula (9); （9）； In formula (9), R 0i β is the initial droop coefficient of the i-th energy storage converter, and β is the convergence coefficient; SOC i The state of charge (SOC) of the i-th energy storage converter. ave The average value of the state of charge; δ i For adaptive current compensation; I dci Let i be the output current of the i-th energy storage converter; Step 22: The adaptive droop coefficient R di Optimization steps; Step 23: Adaptive droop coefficient R di Optimized calculation.

9. The adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters according to claim 1, characterized in that, The closed-loop small-signal model is constructed using the system state-space equations.

10. The adaptive SOC equalization and current sharing control method for parallel operation of multiple energy storage converters according to claim 9, characterized in that, The system state-space equation is shown in the following formula (12); （12）； In formula (12), the state variable ; C represents the SOC deviation of the energy storage converter; The output current I of the energy storage converter dc and average current I dcave difference; y represents the disturbance value experienced by the DC bus voltage itself; A is the system matrix of the closed-loop small-signal model; B is the input matrix of the closed-loop small-signal model; and y is the external disturbance input vector. ; where ΔI load This indicates a step change or fluctuation in the load current.

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