A power allocation method for hybrid energy storage system based on dynamic virtual impedance network

Abstract

The present invention relates to a power distribution method for a hybrid energy storage system based on a dynamic virtual impedance network, and belongs to the field of power electronics. In the prior art, most power distribution methods for battery-supercapacitor hybrid energy storage systems have fixed power frequency division points, which cannot be dynamically adjusted according to the state of energy sources with different characteristics, and there are problems such as the DC bus voltage cannot be maintained stable and the supercapacitor SOC is easy to exceed the working range. The method described in the present invention takes multiple controlled targets as constraints, optimizes the structure of the R-C virtual impedance network, and combines the dynamic virtual impedance parameter strategy to form a dynamic virtual impedance network, thereby optimizing the system operation. The method described in the present invention can be used to adjust the corresponding virtual impedance parameters according to the battery SOC state and the supercapacitor capacitance, dynamically and reasonably divide the load power, so that the battery responds to low-frequency power and the supercapacitor responds to high-frequency power, further complete the power distribution of each branch, and maintain the DC bus voltage stable, realize the supercapacitor SOC self-recovery, and greatly improve the flexibility and efficiency of system operation.

Description

Technical Field

[0001] The present invention belongs to the technical field of power electronics, and in particular relates to a power distribution method for a hybrid energy storage system based on a dynamic virtual impedance network. Background Art

[0002] With the rapid development of society, the demand for electricity continues to grow, and non-renewable energy such as oil is in short supply, which has caused global environmental problems. The large-scale development and utilization of renewable energy is accelerating worldwide. However, whether it is renewable energy generation represented by photovoltaics or new energy vehicles, it is necessary to store, convert and use energy, which is inseparable from a variety of energy storage devices and corresponding energy storage technologies. In photovoltaic DC microgrids, the photovoltaic output power is random and volatile, which will cause system power imbalance, so energy storage devices need to be configured. For new energy vehicles, the single energy structure based on batteries that has been used for a long time is difficult to meet the performance requirements of electric vehicles at the same time. To solve the above problems, the hybrid energy storage system composed of batteries and supercapacitors has been widely used because of its high power density and high energy density characteristics.

[0003] However, in actual use, the load power demand needs to be divided to obtain high-frequency and low-frequency power, and then distributed to supercapacitors and batteries respectively. At the same time, due to the system capacity requirements, hybrid energy storage systems usually use battery energy storage systems composed of multiple battery branches and capacitor energy storage systems composed of multiple supercapacitor branches. Therefore, the power of each frequency must be distributed between the same energy source branches. In addition, during the operation of the system, there are often problems such as the DC bus voltage cannot remain stable and the supercapacitor SOC is prone to exceed the working range. Therefore, optimizing the power allocation strategy of the hybrid energy storage system plays a vital role in maintaining the stable operation of the entire energy system.

[0004] H. Zhou et al. used batteries as high energy density storage and supercapacitors as high power density storage to connect to the DC bus, and adopted modular converter topology and corresponding energy management scheme to achieve energy distribution between different batteries and supercapacitors [H. Zhou et al. Composite Energy Storage System Involving Battery and Ultracapacitor With Dynamic Energy Management in Microgrid Applications. IEEE Trans. Power Electron., Mar. 2011, 26 (3): 923-930]. However, this method only studies the case where there is a single supercapacitor group, and the converter topology it adopts is relatively complex. MC Joshi et al. proposed an improved frequency sharing control for average current mode control of battery / supercapacitor hybrid energy storage system to meet load demand [MC Joshi et al. Frequency Sharing Based Control of Battery / Ultracapacitor Hybrid Energy System in the Presence of Delay. IEEE Trans. Veh. Technol., Nov. 2019, 68 (11): 10571-10584]. However, the control logic of this method is relatively complex, and only considers the situation of a single battery group and a single supercapacitor group, and the operation mode is not reasonable. T. Wu et al. adopted a low-pass filter control strategy with a variable filter time constant, and used an improved particle swarm optimization algorithm to solve the optimal output power, which can effectively reduce the power fluctuation of the DC bus [T. Wu et al. A Study on Use of Hybrid Energy Storage System Along with Variable Filter Time Constant to Smooth DC Power Fluctuation in Microgrid. IEEE Access, 2019, 7: 175377-175385]. However, as a centralized control, it is not suitable for working conditions with multiple energy sources, and it is too dependent on the controller. Single-point failure will reduce the reliability of the system. As the number of power supplies in the system increases, the complexity of the algorithm will increase while it decreases.Q.Xu et al. adopted a distributed droop control strategy based on virtual resistors and virtual capacitors to perform dynamic current sharing in hybrid energy storage systems, and can be used in multi-battery and multi-supercapacitor systems [Q.Xu et al. A Decentralized Dynamic Power Sharing Strategy for Hybrid Energy Storage System in Autonomous DC Microgrid. IEEE Trans. Ind Electron., July. 2017, 64(7): 5930-5941]. However, it did not focus on the stability of the DC bus voltage under different load conditions. In addition, many power distribution methods for hybrid energy storage systems have also been proposed.

[0005] Although the above mentioned methods have been verified by simulation or experiment, they have a series of shortcomings, such as only studying the single battery and single supercapacitor system, the DC bus voltage is not easy to stabilize, and the supercapacitor SOC self-recovery strategy is complicated. Therefore, it is necessary to study a more flexible and practical power allocation method for hybrid energy storage systems. Summary of the invention

[0006] In view of the defects existing in the prior art, the purpose of the present invention is to propose a power distribution method for a hybrid energy storage system based on a dynamic virtual impedance network, so that the battery energy storage system responds to the low-frequency component in the load power, and the supercapacitor energy storage system responds to the high-frequency component, and reasonable power distribution is performed in each branch within each energy storage system, while maintaining the stability of the DC bus voltage, realizing supercapacitor SOC self-recovery, and improving the operating stability, flexibility and efficiency of the hybrid energy storage system.

[0007] To achieve the above purpose, the technical solution adopted by the present invention is: a power distribution method of a hybrid energy storage system based on a dynamic virtual impedance network, wherein the hybrid energy storage system is composed of multiple groups of batteries and multiple groups of supercapacitors, which are respectively connected to the DC bus through a bidirectional DC converter, comprising the following steps:

[0008] (1) An RC virtual impedance network is abstracted from the traditional first-order filter, and optimized for a multi-battery and multi-supercapacitor hybrid energy storage system to construct an R-RC virtual impedance network;

[0009] (2) Optimize the R-RC impedance network based on the variable DC bus voltage setting strategy;

[0010] (3) Optimize the R-RC impedance network based on the supercapacitor SOC self-recovery strategy;

[0011] (4) Based on the optimized virtual impedance network, the virtual impedance parameters corresponding to each energy source branch are determined in combination with the multi-source dynamic virtual impedance power allocation strategy, so as to perform power allocation during operation.

[0012] Furthermore, the R-RC virtual impedance network is composed of a virtual resistance branch and a virtual resistance and virtual capacitance series branch in parallel, wherein the battery branch corresponds to the virtual resistance branch, the supercapacitor branch corresponds to the virtual resistance and virtual capacitance series branch, and multiple battery branches and multiple supercapacitor branches are allowed to exist.

[0013] Furthermore, the network optimization process described in step (2) is as follows: a DC bus voltage correction link is added to the droop control link of each branch, the input of which is the difference between the given value and the actual value of the DC bus voltage, and proportional integral control is adopted. The output is a DC bus voltage correction value, which is a positive feedback value and is equivalent to adding a virtual voltage source in each branch of the R-RC network, and its value is the DC bus voltage correction value.

[0014] Furthermore, the network optimization process described in step (3) is as follows: a capacitor voltage correction link is added to the droop control link of each supercapacitor branch, the input of which is the difference between the given value and the actual value of the capacitor voltage of each branch, and proportional integral control is adopted. The output is a capacitor voltage correction value, which is a negative feedback value, and the given value of the capacitor voltage of each branch can be different, which is equivalent to connecting a virtual voltage source in series in the virtual impedance network RC branch, and its value is the capacitor voltage correction value, and then the network is optimized for the second time.

[0015] Furthermore, the secondary optimization process is: changing the capacitor-corrected virtual voltage source and the RC branch direct series structure into a structure in which the capacitor is connected in series with the virtual inductor and then in parallel with the RC branch, and then changing the capacitor-corrected virtual voltage source and the virtual inductor in series and then in parallel with the RC branch to an equivalent structure in which the capacitor is connected in series with the original RC branch with a low-pass filtering link.

[0016] Furthermore, the multi-source dynamic virtual impedance power allocation strategy is that during the operation of the system, the virtual resistance corresponding to the battery branch is dynamically determined by the real-time SOC of the battery, combined with the battery branch virtual parameter piecewise function designed according to the system parameters; before the system is operated, the virtual capacitance corresponding to each supercapacitor branch is dynamically determined by the supercapacitor capacitance, combined with the supercapacitor branch virtual parameter piecewise function designed according to the system parameters, and the virtual resistance corresponding to each supercapacitor branch does not need to be changed after the reasonable value is determined by the system parameters.

[0017] The effects of the present invention are as follows: by adopting the method described in the present invention, the load power can be dynamically and reasonably divided, and the working characteristics of the two different energy sources, the battery and the supercapacitor, can be fully utilized; a dynamic virtual impedance power distribution strategy is adopted to adjust the virtual resistance corresponding to the battery branch and the virtual capacitance corresponding to the supercapacitor branch, so as to perform reasonable power distribution; an impedance network optimized for the DC bus voltage correction link is used to maintain the stability of the DC bus voltage, and eliminate the DC bus voltage drop caused by droop control; an impedance network optimized for the supercapacitor voltage correction link is used to achieve self-recovery of the supercapacitor SOC, avoid overcharging and over-discharging of the supercapacitor, and simplify the parameter design process; in summary, by adopting the method described in the present invention, during the operation of the hybrid energy storage system, the virtual impedance parameters can be automatically adjusted while achieving reasonable power division, and the power distribution of each branch can be dynamically controlled, which greatly improves the stability and flexibility of the system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a diagram of the equivalent circuit optimization process based on the dynamic virtual impedance network power allocation strategy;

[0019] Figure 2 It is the evolution diagram of the filter and virtual impedance network corresponding to the distributed droop control;

[0020] Figure 3 It is an RC virtual impedance network;

[0021] Figure 4 It is an R-RC virtual impedance network;

[0022] Figure 5 This is the distributed control structure diagram corresponding to the R-RC virtual impedance network;

[0023] Figure 6 It is a control structure diagram of variable DC bus voltage given strategy;

[0024] Figure 7 It is the equivalent circuit optimization process diagram of the variable DC bus voltage setting strategy;

[0025] Figure 8 This is the control structure diagram of the supercapacitor branch after adding the supercapacitor SOC self-recovery strategy;

[0026] Fig. 9 It is the equivalent circuit optimization process after adding the supercapacitor SOC self-recovery strategy;

[0027] Fig.10 It is a diagram of the equivalent circuit optimization process after using inductance to transform the impedance network;

[0028] Fig.11 It is an equivalent circuit optimized with a low-pass filter;

[0029] Fig.12 It is a control structure diagram of supercapacitor branch based on multi-objective optimization virtual impedance network power division strategy;

[0030] Fig.13 It is the virtual parameter value of each branch in the virtual impedance network power distribution experiment based on multi-objective optimization;

[0031] Fig.14 It is the power distribution waveform of the R-RC network hybrid energy storage system based on multi-objective optimization;

[0032] Fig.15 It is the battery SOC change setting and the corresponding virtual parameter change;

[0033] Fig.16 It is the state switching experimental waveform based on the dynamic virtual impedance power allocation strategy;

[0034] Fig.17 It is the experimental waveform based on the dynamic virtual impedance power allocation strategy after changing the supercapacitor capacitance. DETAILED DESCRIPTION

[0035] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0036] The present invention is designed to provide a power distribution method for a hybrid energy storage system based on a dynamic virtual impedance network, wherein the hybrid energy storage system is composed of multiple groups of batteries and multiple groups of supercapacitors, which are respectively connected to a DC bus through a bidirectional DC converter, and each energy source branch corresponds to its own virtual impedance branch to form a virtual impedance network. Therefore, on the basis of optimizing the virtual impedance network for multiple control objectives, combined with a dynamic virtual impedance power distribution strategy, the virtual impedance parameters are determined according to the battery group SOC and the supercapacitor group capacitance, thereby performing system power division and distribution, thereby improving the flexibility, stability and efficiency of system operation. Figure 1 This is the overall multi-source equivalent circuit optimization process of the present invention. The specific implementation steps are as follows:

[0037] Step 1: A virtual impedance network is evolved from the first-order filter corresponding to the traditional distributed droop control. The evolution process is as follows: Figure 2 As shown in the figure, the battery branch and the supercapacitor branch are equivalent to voltage sources, which are connected in series with their own virtual resistors and virtual capacitors to form droop control. Since the voltage values ​​of the two voltage sources are generally equal to the bus voltage set value and have the same zero potential point, they can be simplified to the same voltage source. At this time, the virtual impedance network composed of the virtual resistor and the virtual capacitor can play the role of a filter, so that the current of the two energy sources is divided in the internal branch of the virtual impedance network. The abstracted RC impedance network is shown in Figure 3Then, a virtual resistor is connected in series to the virtual capacitor branch of the RC impedance network to avoid energy oscillation between the capacitor branches. The resulting R-RC impedance network is as follows: Figure 4 As shown. Take the example of a system with only one battery branch and one supercapacitor branch, R d_bat The battery response corresponding to the branch is the low-frequency component, R d_SC With C d_SC The supercapacitor corresponding to the series branch responds to high-frequency components, and the number of parallel branches in the impedance network increases with the increase of each battery and supercapacitor branch. The corresponding control system structure is as follows: Figure 5 , is a distributed control method, in which each current loop controls the current of each energy source. When using this R-RC impedance network, the corresponding equivalent circuit can be used to obtain equation (1), thereby obtaining the output current I of the converter side of the battery branch and the supercapacitor branch out_bat and I out_SC It is formula (2).

[0038]

[0039]

[0040] Step 2: Optimize the R-RC impedance network based on the variable DC bus voltage setting strategy. The specific process is: Use the PI controller to process the DC bus voltage deviation ΔU dc , thus generating the DC bus correction voltage value U modofy , whose expression is

[0041]

[0042] where k p_dc , k i_dc It is the PI parameter of DC bus voltage correction loop.

[0043] The correction voltage U modofy As positive feedback, it is added to the original DC bus voltage given value U dc_ref The new DC bus voltage given value is obtained. Its meaning is that when the bus voltage sags, a positive voltage correction is generated to increase the voltage given value, thereby compensating for the voltage sag. The correction loop is added to the corresponding control loop of each branch. Its control structure is as follows: Figure 6 As shown. The virtual impedance network is improved in this way, and the correction voltage is equivalent to a virtual voltage source connected in series with the original voltage source. The equivalent circuit optimization process is shown in Figure 7 As shown. The following formula can be obtained from the equivalent circuit model.

[0044]

[0045] When the load current is large, the bus voltage drop in the original network will be greater, and the corresponding corrected voltage value U modofy The larger the U dc_ref The larger it is, the higher the output capacity of the system.

[0046] Step 3: Optimize the R-RC impedance network based on the supercapacitor SOC self-recovery strategy. The specific process is: Use PI controller to eliminate U SC The static error that exists generates a capacitance correction voltage value U modofy_SC , whose expression is

[0047]

[0048] where k p_SC , k i_SC is the capacitor voltage correction loop PI parameter.

[0049] The capacitor correction voltage U modofy_SC As negative feedback, the original bus voltage given value U dc_ref The meaning is that when the supercapacitor voltage is too low, the capacitor branch should reduce the output energy, so the voltage setting is reduced, which is equivalent to reducing the current setting output by the voltage loop to achieve the purpose of reducing energy output, and vice versa. Add the correction loop to the control loop corresponding to the supercapacitor branch, and its control structure is as follows Figure 8 As shown. The virtual impedance network is optimized, and the supercapacitor correction voltage is equivalent to a virtual voltage source connected in series on a capacitor branch. The equivalent circuit optimization process is shown in Fig. 9 The corresponding expression is as follows

[0050]

[0051] However, the virtual voltage source always affects the output branch of the supercapacitor, that is, when the supercapacitor responds to high-frequency components, the instantaneous voltage drop will also cause the correction loop to work, and the negative feedback causes the given voltage of the branch to decrease, thereby reducing the given current. It can be seen that although it realizes the self-recovery of the supercapacitor SOC, it affects the response of the energy source to the change of system load. Therefore, the "passing DC and blocking AC" characteristics of the inductor are further utilized, that is, it plays a role similar to a "switch" to transform the impedance network, and perform equivalent simplification as follows: Fig.10 As shown. When the system load changes, the supercapacitor branch responds to the high-frequency component. d_SC Approximately open circuit, U modofy_SC The corresponding virtual voltage source does not work, and C d_SC It is similar to a short circuit and outputs normal energy. After entering the low frequency band, L d_SC Approximately short circuit, C d_SCIt is close to an open circuit, and the supercapacitor voltage correction link plays a role, thereby achieving the supercapacitor SOC self-recovery effect without affecting the system's high-frequency power response. Fig.10 Middle U d_SC The Thevenin equivalent gives

[0052]

[0053] From formula (7), we can know that U d_SC Equivalent to the original correction voltage U modofy_SC A low-pass filter link is connected in series on the basis of the supercapacitor. This link is realized by adding an equivalent impedance network after adding virtual inductance. In order to simplify the system virtual parameter design, the method of adding a first-order low-pass filter to the supercapacitor voltage correction loop is used in the control to replace the implementation. By changing the filtering time constant to change the filtering effect, L d_SC The addition of reduces the complexity of the virtual impedance network, but has a similar control effect. The equivalent circuit at this time is as follows Fig.11 . Among them, U d_SC The expression of is as follows (8), T SC is the low-pass filter time constant. From this equivalent circuit, the following equation (9) is obtained. The control structure of the optimized supercapacitor self-recovery strategy is as follows: Fig.12 .

[0054]

[0055]

[0056] Step 4: Use a dynamic virtual impedance power allocation strategy to determine the virtual impedance parameters by using the real-time SOC of the battery and the capacitance of the supercapacitor. The parameters on the battery side are determined dynamically during the operation of the system, and the virtual parameters on the supercapacitor side are determined based on the capacitance of the supercapacitor when it is connected before the system is operated. First, before establishing the relationship between the battery SOC, the capacitance of the supercapacitor and their respective virtual parameters, the virtual impedance parameters need to be designed.

[0057] When there are only two branches, R d_bat , R d_SC and C d_SC Together they determine the crossover frequency of the filter. In order to avoid frequent charging and discharging of the battery and shortening its life, the crossover frequency should not be set too high, nor too low, which would cause the supercapacitor to output too much energy. It should be reasonably selected based on system parameters. d_SC It only acts as a current limiting resistor, so it is set to a reasonable value. Then, after determining the appropriate frequency division point, determine R d_bat and C d_SCTake the hybrid energy storage system with i battery groups and j supercapacitor groups in parallel as an example. Combined with the system parameters in this example, when i=j=2, take R d_SCn =1Ω, where 1≤n≤2, R d_batn and C d_SCn The range is

[0058] 1Ω≤R d_batn ≤3Ω (10)

[0059] 2mF≤C d_SCn ≤8mF (11)

[0060] The virtual parameters are determined by the piecewise function method. For the virtual resistance on the battery side and the virtual capacitance on the supercapacitor side that need to be changed, taking the dual-battery dual-supercapacitor system as an example, with a certain margin, the functional relationship between the battery SOC and the corresponding virtual resistance is established as follows

[0061]

[0062] Where R d_batn Refers to the virtual resistance corresponding to the nth battery branch, 1≤n≤2, SOC n Refers to the state of charge of the nth battery pack. The virtual resistance of each of the two battery branches is dynamically determined by the above function.

[0063] For the supercapacitor branch, since the supercapacitor voltage can be controlled to maintain stability, its stored energy is proportional to the capacitance value. Therefore, when the supercapacitor voltage is controlled, the branch with a larger supercapacitor capacitance should bear more high-frequency current, that is, a larger virtual capacitor should be used. The reasonable value range is as follows: m The value range is 14.5F ~ 58F. With a certain margin, the functional relationship between the supercapacitor capacitance and the corresponding virtual capacitor is established as follows

[0064]

[0065] Among them C d_SCm Refers to the virtual capacitance corresponding to the mth supercapacitor branch, 1≤m≤2, C m Refers to the capacitance of the mth supercapacitor. The above function is used to determine the virtual capacitance of each of the two supercapacitor branches before the system is running.

[0066] Finally, for a hybrid energy storage system with i battery groups and j supercapacitor groups in parallel, ignoring the line resistance, the following equation can be obtained:

[0067]

[0068] Where subscript i represents i battery packs, subscript j represents j supercapacitor packs, and U modify Represents the DC bus correction voltage, U modify_SC1,…,j Represents the correction voltage of each supercapacitor, R d_bat1,…,i Represents the virtual resistance on the battery side, R d_SC1,…,j Represents the virtual resistance on the SC side, C d_SC1,…,j represents the virtual capacitance on the SC side, T SC Represents the supercapacitor correction voltage filter time constant.

[0069] The current distribution relationship between different energy source branches conforms to the following equations (15) and (16).

[0070]

[0071]

[0072] In summary, the core of this control strategy is to regard the battery energy storage system and the supercapacitor system as a whole. Therefore, the power division problem between the two systems is first studied. When the whole is considered, the total output current of the battery energy storage system and the total output current of the supercapacitor energy storage system are

[0073]

[0074] Then, the virtual impedance parameters of each energy source branch are determined by the dynamic virtual impedance strategy to complete the power distribution among the branches.

[0075] In order to show the remarkable effect of the present invention, this embodiment gives some experimental results obtained by using the embodiment. First, under the condition that the DC bus voltage is given as 24V and the voltage of each branch supercapacitor is given as 11V, for a hybrid energy storage system consisting of two 12V battery groups and two supercapacitor groups, the following is adopted: Fig.13 The fixed virtual impedance parameters shown in the experiment are shown in the experimental waveform. Fig.14 , the measured current is the output current of the low-voltage side energy source. At time t1, the system load R L Switched from 24Ω to 6Ω, at t2, the load resistance is switched from 6Ω to 24Ω, the power frequency division effect between the two battery groups and the two supercapacitor groups is good, and because the same energy source uses the same virtual impedance parameters in each branch, the output current I bat1 and I bat2 And the output current I of the two supercapacitor groups SC1 and I SC2 Basically the same, achieving an even distribution of power between energy sources of the same type. Fig.14(b) The waveform also shows that the bus voltage can remain stable before and after the load switching. At the same time, it can be seen that the supercapacitor voltage can gradually recover to a given value of 11V after two load switchings, that is, this strategy realizes the self-recovery of the supercapacitor SOC.

[0076] Then, based on the above experiments, a multi-source dynamic virtual impedance power distribution strategy is adopted. The virtual parameters corresponding to the battery branch are determined by the battery SOC of each branch according to the functional relationship of formula (12). The virtual parameters corresponding to the supercapacitor branch are determined by the capacitance of each supercapacitor before the system starts working according to the functional relationship of formula (13). Since the battery SOC will not mutate during the normal operation of the system, in order to observe obvious experimental effects, the battery SOC is dynamically changed to a large extent during the operation of the system to observe the changes in the power distribution of the system, which can also prove the effectiveness of the control strategy. The battery SOC change setting and the corresponding virtual parameter change are as follows: Fig.15 , the load resistance is fixed at 12Ω. Fig.15 The changing waveform of the power distribution of each branch switched in the process is as follows: Fig.16 It can be seen that at time t1, when the SOC of battery pack 2 is artificially set from 70% to 30%, the corresponding virtual resistance automatically changes from 1.5Ω to 3Ω, and the power distribution of battery packs 1 and 2 changes from 1:1 to 2:1; at time t2, the SOC of battery pack 1 is also artificially set from 70% to 30%, and the corresponding virtual resistance automatically changes from 1.5Ω to 3Ω, and battery packs 1 and 2 are again distributed with the same current. And the DC bus voltage and supercapacitor voltage can be maintained stable, which verifies that the strategy can dynamically adjust the low-frequency power distribution ratio of each battery branch according to the SOC of each battery pack.

[0077] When the battery SOC state is the above state 2, the capacitance of the two supercapacitors is changed before the system is operated. Different capacitances are selected, 14.5F and 58F respectively. According to formula (13), the virtual capacitance of the two branches is changed to 3mF and 6mF respectively. The power distribution experiment is carried out. The waveform is as follows: Fig.17 , the two battery groups bear low-frequency power in a ratio of 2:1, and the two supercapacitor groups distribute high-frequency power in different proportions. At the same time, the DC bus voltage and supercapacitor voltage can be maintained stable. It can be seen that the multi-source dynamic virtual impedance power allocation strategy can adjust the high-frequency power allocation status of the corresponding branch according to the change of the capacitance of each supercapacitor.

[0078] The experimental results obtained in this embodiment can illustrate that the method described in the present invention can realize the reasonable dynamic frequency distribution of power in a multi-battery and multi-supercapacitor system, overcome many defects of the original network, realize the stability of the DC bus voltage and the self-recovery of the supercapacitor SOC, and greatly improve the flexibility, stability and efficiency of the operation of the hybrid energy storage system.

[0079] The present invention can be implemented in other specific forms without departing from its spirit or essential characteristics. The described embodiments are considered to be illustrative and non-restrictive in all aspects, for example:

[0080] 1) The number of energy sources used in the hybrid energy storage system is not limited to the configuration in the embodiment;

[0081] 2) The parameters of the battery pack and the supercapacitor are not limited to those used in the embodiments;

[0082] 3) The virtual impedance parameters of each branch are not limited to those used in the embodiments. Therefore, the scope of the present invention is indicated by the appended claims rather than the above description. All changes within the meaning and scope of the equivalent technical solutions of the claims are included in their scope.

Claims

1. A power allocation method for a hybrid energy storage system based on a dynamic virtual impedance network, wherein: The hybrid energy storage system consists of multiple groups of batteries and multiple groups of supercapacitors, which are connected to the DC bus through bidirectional DC converters, including the following steps: (1) An RC virtual impedance network is abstracted from the traditional first-order filter, and optimized for a multi-battery and multi-supercapacitor hybrid energy storage system to construct an R-RC virtual impedance network. The R-RC virtual impedance network consists of a virtual resistance branch and a virtual resistance and virtual capacitance series branch in parallel. The battery branch corresponds to the virtual resistance branch, and the supercapacitor branch corresponds to the virtual resistance and virtual capacitance series branch. Multiple battery branches and multiple supercapacitor branches are allowed to exist. (2) The R-RC impedance network is optimized based on the variable DC bus voltage setting strategy. The network optimization process is as follows: a DC bus voltage correction link is added to each branch droop control link. The input of this link is the difference between the DC bus voltage setting value and the actual value. Proportional integral control is adopted, and the output is the DC bus voltage correction value, which is a positive feedback value. It is equivalent to adding a virtual voltage source in each branch of the R-RC network, and its value is the DC bus voltage correction value. (3) Optimizing the R-RC impedance network based on the supercapacitor SOC self-recovery strategy. The network optimization process is as follows: adding a capacitor voltage correction link to the droop control link of each supercapacitor branch. The input of this link is the difference between the given value and the actual value of the capacitor voltage of each branch. Proportional integral control is adopted, and the output is the capacitor voltage correction value, which is a negative feedback value. The given value of the capacitor voltage of each branch can be different, which is equivalent to connecting a virtual voltage source in series in the virtual impedance network RC branch. Its value is the capacitor voltage correction value, and then the network is optimized twice. (4) Based on the optimized virtual impedance network, the virtual impedance parameters corresponding to each energy source branch are determined in combination with a multi-source dynamic virtual impedance power allocation strategy, so as to perform power allocation during operation. The multi-source dynamic virtual impedance power allocation strategy is as follows: during system operation, the virtual resistance corresponding to the battery branch is dynamically determined by the real-time SOC of the battery in combination with a piecewise function of a virtual parameter of the battery branch designed according to system parameters; before the system is operated, the virtual capacitance corresponding to each supercapacitor branch is dynamically determined by the capacitance of the supercapacitor in combination with a piecewise function of a virtual parameter of the supercapacitor branch designed according to system parameters, and the virtual resistance corresponding to each supercapacitor branch does not need to be changed after a reasonable value is determined by the system parameters.

2. A hybrid energy storage system power distribution method based on a dynamic virtual impedance network as claimed in claim 1, characterized in that: The secondary optimization process is: changing the capacitor-corrected virtual voltage source and the RC branch direct series structure into a structure in which the capacitor is connected in series with the virtual inductor and then in parallel with the RC branch, and then changing the capacitor-corrected virtual voltage source and the virtual inductor in series with the RC branch and then in parallel with the RC branch to an equivalent structure in which the capacitor-corrected virtual voltage source with a low-pass filtering link is added and connected in series with the original RC branch.

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