Hybrid energy storage converter predictive control method

Abstract

The invention discloses a prediction control method for a hybrid energy storage converter, solves the problems of complex control structure and control delay when a storage battery-super capacitor hybrid energy storage converter is controlled in the prior art, and belongs to the technical field of hybrid energy storage converter control. According to the method, a frequency weighting coefficient is added into an evaluation function of the storage battery-super capacitor hybrid energy storage converter, and a frequency weighting evaluation function is constructed. Frequency weighting evaluation is carried out on the output power of the storage battery and the super capacitor at the next moment, automatic power distribution is achieved, no extra filter needs to be arranged, the storage battery bears the low-frequency power of the direct-current bus, power impact can be reduced, the service life is prolonged, and the super capacitor responds to the high-frequency power; the advantage of high power density can be brought into full play to improve the dynamic response capability of direct current bus voltage control, the control structure of the hybrid energy storage converter is simplified, and the control delay is reduced.

Description

TECHNICAL FIELD

[0001] The application relates to a hybrid energy storage converter predictive control method and belongs to the technical field of hybrid energy storage converter control. BACKGROUND

[0002] With the increase of the proportion of direct-current power sources and loads represented by photovoltaic cells and electric vehicles, the power grid architecture is developing towards a hybrid AC / DC form. The hybrid AC / DC system with direct-current, alternating-current power sources and loads is considered to be the most likely power distribution and even power transmission structure in the future. The hybrid AC / DC system has good inclusiveness for new energy power generation access, can promote the improvement of local consumption rate, and helps to reduce the number of power conversions, thereby improving the overall efficiency of the system. Under the architecture of the AC / DC hybrid power system, the DC microgrid has become a reliable way to gather various new energy, energy storage and loads, and has developed rapidly in recent years. In the DC microgrid, due to the intermittency of new energy generation and the influence of load switching, there is a risk of imbalance in the supply and demand of energy in the steady state and dynamic state, which is manifested as short-term fluctuation or even instability of the DC bus voltage. Adding energy storage is an effective means to solve this problem. By coordinating the system energy in both directions, the overall energy balance of the microgrid is ensured to achieve the effect of "peak shaving and valley filling", and the stable and reliable operation of the system is promoted.

[0003] The energy storage converter is a key equipment connecting energy storage and microgrid, and its performance directly affects the response capability of the energy storage link to the system requirements, which will involve both steady state and dynamic state. The energy storage capacity is the primary indicator to ensure the steady state power balance in the system. The battery has a significant advantage in capacity, which is determined by its high energy density. The installation and expansion of energy storage equipment based on the battery are relatively flexible, and the battery has become the first large energy storage way in the power system. Whether it is an ion battery or a fuel cell, it belongs to an electrochemical energy storage method, and the storage and release of electric energy are realized through internal chemical reactions. Therefore, the power dynamic response speed of the battery is often slow. With the increasing degree of power electronics, the factors of rapid disturbance and transient switching in the power grid are gradually increasing, and the demand for fast power response of energy storage is increasing. It is difficult to cope with power mutations, irregular rapid changes and other situations in the power grid by using a single battery energy storage. The power density of energy storage determines its dynamic response capability to the power grid. As a physical energy storage method, the super capacitor has high power density and can realize fast charging and discharging to meet the buffering demand of short-term power mutation of the system. However, due to its small capacity, it is difficult to be used independently as an energy storage unit. Considering that the super capacitor and the battery have complementary energy and power characteristics, the hybrid energy storage system composed of the super capacitor and the battery can flexibly provide the DC microgrid with energy buffering services with large-capacity steady state and fast dynamic state.

[0004] The hybrid energy storage converter is a key equipment connecting the battery, super capacitor and direct current microgrid, and reasonable design of the control strategy is the key to determine the characteristics of the energy storage medium. The existing scheme is to use a voltage-current double closed loop control method based on a PI regulator, the total output current of the hybrid energy storage system is calculated through the voltage outer loop, and then the current reference signals of the battery and super capacitor ports are generated by frequency division, and the output currents of the two are adjusted by the inner loop controller, so as to achieve the effect that the battery bears the low frequency power and the super capacitor bears the high frequency power.

[0005] The existing control technology of the battery-super capacitor hybrid energy storage converter is realized based on a PI regulator, and two controllers are needed to form a voltage-current double closed loop structure. As a linear controller, the PI regulator has inherent problems such as complicated parameter tuning, difficult to balance speed and overshoot in dynamic response, and poor robustness.

[0006] In the prior art, for the hybrid energy storage converter of the battery and the super capacitor, one method is to first obtain the power reference of the battery and the super capacitor through a power frequency divider, and then to determine the state of the switch tube in the hybrid energy storage converter by predicting the output power of the two, which can alleviate the problems of the PI control method to some extent, but in the implementation, an additional filter needs to be set to determine the power reference of the battery and the super capacitor, which will cause the problems of complex structure and control delay. SUMMARY

[0007] In view of the problems of complex control structure and control delay in the existing control of the battery-super capacitor hybrid energy storage converter, the application provides a frequency-weighted hybrid energy storage converter predictive control method.

[0008] The predictive control method of the hybrid energy storage converter provided by the application comprises:

[0009] A frequency-weighted evaluation function of the battery port is constructed by introducing a frequency-weighted coefficient, the battery output power under the action of all possible switch signals of the battery port at the next moment is evaluated by using the frequency-weighted evaluation function, and the best switch signal and the optimal output power of the battery port at the next moment are determined according to the evaluation value;

[0010] The difference between the total power demand of the hybrid energy storage at the next moment and the optimal output power of the battery port is taken as the power reference value of the super capacitor, an evaluation function of the super capacitor port is constructed according to the power reference value, the super capacitor output power under the action of all possible switch signals of the super capacitor port at the next moment is evaluated by using the evaluation function of the super capacitor port, and the best switch signal of the super capacitor port at the next moment is determined according to the evaluation value;

[0011] The switch tubes in the hybrid energy storage converter are controlled by the optimal switch signals of the battery port and the super capacitor port at the next moment.

[0012] As preferred, the frequency-weighted evaluation function of the battery port is constructed by introducing a frequency-weighted coefficient, which is:

[0013]

[0014] wherein, Pbat is the total power demand of the hybrid energy storage at the next moment, Pbat is the battery output power at the next moment, is the frequency-weighted coefficient, is the evaluation value of the battery output power at the current moment under the action of the corresponding switch signal, is the evaluation value of the battery output power at the next moment under the action of the corresponding switch signal, and the switch signal corresponding to the minimum evaluation value is the optimal switch signal of the battery port at the next moment, and the optimal output power of the battery port under the action of the optimal switch signal is determined. .

[0015] As preferred, the evaluation function of the super capacitor port is:

[0016]

[0017] wherein, Pcap is the super capacitor output power at the next moment; is the evaluation value of the super capacitor output power at the next moment under the action of the corresponding switch signal, and the switch signal corresponding to the minimum evaluation value is the optimal switch signal of the super capacitor port.

[0018] As preferred, according to the hybrid energy storage converter, the output power prediction models of the battery port and the super capacitor port are respectively established, and the battery output power under the action of all possible switch signals of the battery port at the next moment is predicted, and the method comprises:

[0019] The continuous mathematical models of the output currents of the battery port and the super capacitor port in the hybrid energy storage converter are respectively established;

[0020] The continuous mathematical models of the output currents of the battery port and the super capacitor port are discretized by using the forward Euler method to obtain the discretized mathematical model of the output current of the battery port at the next moment and the discretized mathematical model of the output current of the super capacitor port, and then the output power prediction models of the battery port and the super capacitor port are respectively established in combination with the port voltages.

[0021] As preferred, in the discretization mathematical model of the battery port output current and the discretization mathematical model of the super capacitor port output current of the next moment, the battery port output current of the next moment is obtained according to the switch signal, the port output current, the port voltage and the DC bus voltage of the current moment.

[0022] As preferred, the total power demand of the hybrid energy storage to the stable DC bus voltage of the next moment is calculated according to the dynamic characteristics of the DC bus capacitor, and the method comprises:

[0023] In the case of meeting the current of the filter capacitor in the hybrid energy storage converter, the DC bus voltage reaches the expected value, and then the current of the filter capacitor of the next moment is obtained.

[0024] Based on the obtained current of the filter capacitor of the next moment, the total power demand of the hybrid energy storage of the next moment is obtained according to the relationship among the total current of the battery and the super capacitor flowing to the DC bus, the current of the renewable energy injected into the DC bus, the current of the filter capacitor and the DC load current.

[0025] As preferred, the hybrid energy storage converter is composed of two parallel bidirectional DC / DC converters.

[0026] The application has the beneficial effects that the frequency weighted evaluation function can automatically realize the power frequency division of the battery and the super capacitor in the hybrid energy storage system. The battery bears the low-frequency power required for stabilizing the DC bus voltage, which helps to reduce the power impact and prolong the service life. The super capacitor is responsible for the high-frequency power required for stabilizing the DC bus voltage, which can fully exert the advantages of high power density to improve the dynamic response ability of the DC bus voltage control. The prediction control technology has the advantages of simple structure, strong robustness, fast dynamic response, convenient parameter setting and the like compared with the linear control method.

[0027] Meanwhile, the frequency weighted evaluation function is used to automatically realize the distribution of the output power of the battery and the super capacitor, without setting an additional filter, which further simplifies the control structure of the hybrid energy storage converter and reduces the control delay, and the power reference of the super capacitor is calculated based on the best prediction value of the battery output power of the next moment, which can further reduce the control delay and improve the response speed of the hybrid energy storage converter. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The flow chart of the prediction control method of the application;

[0029] Figure 2 The topology structure of the hybrid energy storage converter;

[0030] Figure 3 The block diagram of the DC micro-grid system with hybrid energy storage;

[0031] Figure 4 Fig. 2 is a power response waveform of a battery and a super capacitor when a photovoltaic power changes, wherein (a) is a power response waveform of the battery, and (b) is a power response waveform of the super capacitor;

[0032] Figure 5 Fig. 4 is a comparison of effects of the application and a PID control method when a renewable energy power changes. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the application will be apparently and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the application.

[0034] It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict.

[0035] The application will be further described below with reference to the drawings and specific embodiments, but is not limited to the application.

[0036] The prediction control method of the hybrid energy storage converter in the embodiment includes:

[0037] Step 1, an output power prediction model of a battery and a super capacitor port is established from a hybrid energy storage converter main circuit model, and the output power prediction model is used to predict the output power of the battery and the super capacitor under all possible switch signals of the battery port at the next moment.

[0038] Step 2, the total power demand of the hybrid energy storage at the next moment is calculated according to the dynamic characteristics of the DC bus capacitor to stabilize the DC bus voltage.

[0039] Step 3, a frequency weighted evaluation function of the battery port is constructed by introducing a frequency weighting coefficient, the output power of the battery under all possible switch signals of the battery port at the next moment is evaluated by using the frequency weighted evaluation function, and the optimal switch signal and the optimal output power of the battery port at the next moment are determined.

[0040] Step 4, the difference between the total power demand of the hybrid energy storage at the next moment and the optimal output power of the battery port is taken as a power reference value of the super capacitor, an evaluation function of the super capacitor port is constructed according to the power reference value, the output power of the super capacitor under all possible switch signals of the super capacitor port at the next moment is evaluated by using the evaluation function of the super capacitor port, and the optimal switch signal of the super capacitor port at the next moment is determined.

[0041] Step 5, control the switch tube in the hybrid energy storage converter with the optimal switch signal of the next moment battery port and super capacitor port.

[0042] The embodiment adds a frequency weighting coefficient to the evaluation function of the battery-super capacitor hybrid energy storage converter model predictive control, and constructs a frequency weighted evaluation function. The frequency weighted evaluation is performed on the predicted values of the battery and super capacitor output power, so as to realize the automatic distribution of power, without setting an additional filter, so as to make the battery bear the low-frequency power required for stabilizing the DC bus voltage, reduce the power impact, and prolong the service life. The super capacitor is responsible for the high-frequency power required for stabilizing the DC bus voltage, and can fully exert the advantage of high power density to improve the dynamic response capability of the DC bus voltage control.

[0043] The embodiment is described for the hybrid energy storage converter composed of two parallel bidirectional DC / DC converters. Of course, the control method of the embodiment is also applicable to other types of hybrid energy storage converters, such as hybrid energy storage converters composed of bidirectional dual active bridge, bidirectional LLC, etc. The main circuit of the hybrid energy storage converter is shown in Figure 2 The two bidirectional DC / DC converters provide interfaces for the battery and the super capacitor, respectively. Figure 2 L1 and L2 are inductors, C B is a DC bus filter capacitor, K is a DC bus common connection point. S1, S2, S3, and S4 are switch tubes, wherein S1 and S2 are complementary on, S3 and S4 are complementary on. V BAT is a battery port voltage, V SC is a super capacitor port voltage, V DC is a DC bus voltage, I BAT is a battery port output current, I SC is a super capacitor port output current, I DC is the total current of the battery and the super capacitor flowing to the DC bus, I LOAD is a DC load current, I C is the current of the filter capacitor C B , I RE is the current of the renewable energy injected into the DC bus.

[0044] In the preferred embodiment, the method for establishing the output power prediction model of the battery and the super capacitor port includes:

[0045] According to Figure 2, respectively, the continuous mathematical model of the battery and super capacitor port output current in the hybrid energy storage converter is established:

[0046] (1)

[0047] where S w1 is the switching signal of the switch S1, S w3 is the switching signal of the switch S3, 1 represents on, and 0 represents off. When the sampling period is T s , the above continuous mathematical model is discretized by using the forward Euler method, and the discretized mathematical model of the battery and super capacitor port output current at the next time can be obtained. According to formula (1), the battery port output current at the next time can be obtained according to the switching signal, the port output current, the port voltage, and the DC bus voltage at the current time. Specifically, after the discretization of formula (1), the discretized mathematical model is obtained as:

[0048] (2)

[0049] wherein, represents the current time, represents the next time; in combination with the port voltage and current, the output power prediction models of the battery and the super capacitor are respectively:

[0050] (3)

[0051] (4)

[0052] wherein, P BAT (k+1) is the battery output power at the next time, and P SC (k+1) is the super capacitor output power at the next time. The battery output power under the action of all possible switching signals of S1 at the next time can be predicted by using formula (3), and the super capacitor output power under the action of all possible switching signals of S3 at the next time can be predicted by using formula (4).

[0053] In the preferred embodiment, the next time stable DC bus voltage total power demand of the hybrid energy storage is calculated according to the dynamic characteristics of the DC bus capacitor, and specifically can include:

[0054] Maintaining the stability of the DC bus voltage is the control target of the hybrid energy storage converter, and the dynamic characteristics of the DC bus voltage are determined by the filter capacitor, which satisfies

[0055] (5)

[0056] It is assumed that after N steps of adjustment, the DC bus voltage reaches the expected value V REFThe current of the filter capacitor at the next moment can be obtained as follows:

[0057] (6)

[0058] In the Figure 2 , the total current flowing from the battery and the super capacitor to the DC bus is , the current injected to the DC bus by the renewable energy source is , the current of the filter capacitor is , and the DC load current is The relationship between the above currents is that Figure 2 In the

[0059] (7)

[0060] The total power demand of the hybrid energy storage at the next moment can be obtained by discretization as follows:

[0061] (8)

[0062] The total power demand of the hybrid energy storage at the next moment for satisfying the DC bus voltage stability can be obtained as follows:

[0063] (9)

[0064] In the formula, P HESS (k+1) is the total power demand of the hybrid energy storage at the next moment, which is provided by the battery and the super capacitor.

[0065] (3) Frequency-weighted evaluation function

[0066] In order to realize the model predictive control of the hybrid energy storage converter, the evaluation function is needed to evaluate the output power of the battery and the super capacitor port under all possible switch signals predicted by formula (3) and (4), so as to determine the optimal switch signal. In order to make the two input ports of the hybrid energy storage converter meet the power characteristics of the battery and the super capacitor, the application constructs an evaluation function based on frequency weighting. The evaluation function of the battery port can be obtained by: calculating the difference between the total power demand of the hybrid energy storage at the next moment and the output power of the battery at the next moment, respectively weighting the evaluation value of the output power of the battery at the current moment under the corresponding switch signal and the difference, and summing, wherein the normalized weight is used for weighting, and the value of the frequency weighting coefficient is set to limit the response speed of the battery power; the result after summation is the evaluation value of the output power of the battery at the next moment under the corresponding switch signal. Specifically, for the battery port in Figure 2 , the evaluation function is used to determine the specific value of the switch signal S w1 , which can be expressed as

[0067] (10)

[0068] In the formula, This is the evaluation value of the battery output power at the current moment under the action of the corresponding switch signal. q is the evaluation value of the battery output power under the action of the corresponding switching signal at the next moment. q is the frequency weighting coefficient. By setting the value of q, the speed of battery power response can be limited, so as to realize the role of battery in responding to the low-frequency power demand of DC bus and reduce the power surge of battery.

[0069] By evaluating the predicted battery output power under all possible switching signals of S1 using formula (10), the next moment S can be determined. w1 The optimal value S w1opt (k+1): That is, S is obtained using formula (2). w1 When taking different states (0 or 1) ,Will Substituting into formula (3) yields S w1 P takes different states (0 or 1) BAT (k+1) will P BAT Substituting (k+1) into formula (10) yields S w1 When taking different states (0 or 1) , S corresponding to the minimum value w1 The state (0 or 1) serves as the optimal state S for the next moment. w1opt (k+1), and obtain the corresponding optimal output power P of the battery. BATopt (k+1).

[0070] Because supercapacitors have fast power response characteristics and can handle the high-frequency power demands of DC buses, the evaluation function for supercapacitor ports can be designed as follows:

[0071] (11)

[0072] in, The evaluation value of the supercapacitor's output power under the action of the corresponding switching signal at the next moment is given by equation (11). It can be seen from equation (11) that the power reference value of the supercapacitor is the total power demand of the DC bus minus the optimal output power P of the battery at the next moment. BAToptThe value determined by (k+1) represents the high-frequency portion of the DC bus power demand. The power reference of the supercapacitor is calculated based on the optimal predicted value of the battery output power at the next moment, which can further reduce control delay and improve the response speed of the hybrid energy storage converter. The supercapacitor output power under all possible switching signals of S3 is evaluated using formula (11). The switching signal corresponding to the minimum evaluation value is the next moment S. w3 The optimal value S w3opt (k+1).

[0073] Finally, using the determined S w1opt (k+1) controls the switching transistor S1 in the main circuit of the hybrid energy storage converter, and uses its complementary signal to control the switching transistor S2, thus achieving power control of the battery port. Similarly, using S... w3opt By controlling switch S3 with (k+1) and using its complementary signal to control S4, power control of the supercapacitor port can be achieved.

[0074] Example:

[0075] (1) System composition and simulation parameters

[0076] To verify the effectiveness of the predictive control method of this application, a simulation system was constructed in the simulation software as follows: Figure 3 The DC microgrid system shown consists of photovoltaic batteries, supercapacitors, a hybrid energy storage converter, photovoltaic cells, and DC loads. The internal structure of the hybrid energy storage converter is as follows: Figure 2 As shown.

[0077] exist Figure 3 In the DC microgrid shown, the system's power conservation is jointly determined by the battery, supercapacitor, photovoltaic cells, and DC load. The hybrid energy storage converter responds to changes in system power conditions by controlling the output power of the battery and supercapacitor. In the simulation, the effectiveness of the hybrid energy storage converter and the proposed frequency-weighted model predictive control is analyzed and verified by setting different power conditions. The system simulation parameters are shown in Table 1.

[0078]

[0079] (2) Simulation results

[0080] Figure 4 The waveforms showing the response of the battery and supercapacitor output power to changes in photovoltaic power are illustrated. Figure 4It can be seen that when the load power suddenly changes at 1.5s, and the photovoltaic cell output power suddenly changes at 3s, the super capacitor output power appears a short-term rapid dynamic response, and the battery output power realizes a slow transition. In the power stable stage, the battery bears long-time power output, and the super capacitor output power is basically zero. The simulation results show that the prediction control method of the application effectively realizes the power distribution of the battery and the super capacitor.

[0081] In order to verify the advantages of the prediction control method of the application in dynamic response compared with the PID control method, Figure 5 The simulation waveforms of the DC bus voltage under the two control methods are compared. It can be seen that when the load power or the photovoltaic power suddenly changes (1.5s, 3s), the prediction control method of the application has a significant advantage in voltage overshoot and regulation time. The simulation results prove that compared with the PID method, the prediction control method of the application has better dynamic response capability, and can effectively improve the support capability of the hybrid energy storage converter for the DC bus voltage.

[0082] Although the application is described herein with reference to particular embodiments, it should be understood that these examples are merely illustrative of the principles and applications of the present application. It should therefore be understood that numerous modifications can be made to the illustrative embodiments, and that other arrangements can be devised without departing from the spirit and scope of the present application as defined by the appended claims. It should be understood that the features described in connection with one embodiment can be used in conjunction with other embodiments described herein. It should also be understood that features described in connection with separate embodiments can be used in combination with each other.

Claims

1. A predictive control method for a hybrid energy storage converter, characterized in that, The method comprises: A frequency weighting evaluation function of the battery port is constructed by introducing a frequency weighting coefficient, the frequency weighting evaluation function is used to evaluate the battery output power under all possible switch signals of the battery port at the next time, and the optimal switch signal and the optimal output power of the battery port at the next time are determined according to the evaluation value; A difference between the total power demand of the hybrid energy storage at the next time and the optimal output power of the battery port is taken as a power reference value of the super capacitor, an evaluation function of the super capacitor port is constructed according to the power reference value, the evaluation function of the super capacitor port is used to evaluate the super capacitor output power under all possible switch signals of the super capacitor port at the next time, and the optimal switch signal of the super capacitor port at the next time is determined according to the evaluation value; The switch tubes in the hybrid energy storage converter are controlled by using the optimal switch signals of the battery port and the super capacitor port at the next time.

2. The predictive control method of a hybrid energy storage inverter according to claim 1, wherein, The frequency weighting evaluation function of the battery port constructed by introducing the frequency weighting coefficient is: wherein, is the total power demand of the hybrid energy storage for the next time instant, is the battery output power for the next time instant, is the frequency weighting coefficient, is the evaluation value of the battery output power at the current time instant under the action of the corresponding switch signal, is the evaluation value of the battery output power for the next time instant under the action of the corresponding switch signal, and the switch signal corresponding to the minimum evaluation value is the optimal switch signal of the battery port for the next time instant, and the optimal output power of the battery port under the action of the optimal switch signal is determined .

3. The predictive control method of a hybrid energy storage inverter according to claim 2, wherein, The evaluation function of the super capacitor port is: wherein, is the output power of the super capacitor at the next moment; is the evaluation value of the output power of the super capacitor under the corresponding switching signal at the next moment, and the switching signal corresponding to the minimum evaluation value is the optimal switching signal of the super capacitor port.

4. The predictive control method of a hybrid energy storage inverter according to claim 1, wherein, According to the hybrid energy storage converter, the output power prediction models of the battery port and the super capacitor port are respectively established, and the battery output power under all possible switch signals of the battery port at the next time is predicted, and the method comprises: Continuous mathematical models of output currents of the battery port and the super capacitor port in the hybrid energy storage converter are respectively established; The continuous mathematical models of the output currents of the battery port and the super capacitor port are discretized by using the forward Euler method to obtain the discretized mathematical model of the battery port output current at the next time and the discretized mathematical model of the super capacitor port output current, and then the output power prediction models of the battery port and the super capacitor port are respectively established in combination with the port voltage.

5. The predictive control method of a hybrid energy storage inverter according to claim 4, wherein, In the discretized mathematical model of the battery port output current at the next time and the discretized mathematical model of the super capacitor port output current, the battery port output current at the next time is obtained according to the switch signal at the current time, the port output current, the port voltage and the DC bus voltage.

6. The predictive control method of a hybrid energy storage inverter according to claim 1, wherein, The total power demand of the hybrid energy storage at the next time is calculated according to the dynamic characteristics of the DC bus capacitor, and the method comprises: In the case that the current of the filter capacitor in the hybrid energy storage converter is met, the DC bus voltage reaches the expected value, and then the current of the filter capacitor at the next time is obtained; Based on the obtained current of the filter capacitor at the next time, the total power demand of the hybrid energy storage at the next time is obtained according to the relationship among the total current of the battery and the super capacitor flowing to the DC bus, the current of the renewable energy injected into the DC bus, the current of the filter capacitor and the DC load current.

7. The predictive control method of a hybrid energy storage inverter according to claim 6, characterized in that, The hybrid energy storage converter is composed of two parallel bidirectional DC / DC converters.

8. A storage device readable by a computer, the storage device storing a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the prediction control method of the hybrid energy storage converter according to any one of claims 1 to 7.

9. A device for predictive control of a hybrid energy storage converter, comprising a storage device, a processor, and a computer program stored in the storage device and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the prediction control method of the hybrid energy storage converter according to any one of claims 1 to 7.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the prediction control method of the hybrid energy storage converter according to any one of claims 1 to 7.

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