Hybrid energy storage system energy management strategy based on active disturbance rejection control and related device

Abstract

The invention belongs to the technical field of energy storage, and particularly relates to a hybrid energy storage system energy management strategy based on active disturbance rejection control and a related device. The active disturbance rejection control comprises a trajectory tracker, an expansion state observer, a feedback link and an interference compensation link. The trajectory tracker performs interpolation and filtering on discrete sampling prediction of the model prediction control module to obtain a continuous tracking trajectory and a differential trajectory. And the extended state observer outputs corresponding tracking trajectory, differential trajectory and disturbance state estimation. In a feedback link, corresponding state estimation output by the extended state observer is subtracted from a tracking trajectory and a differential trajectory generated by the trajectory tracker to obtain two tracking errors, and summation is performed through a proportional-integral controller corresponding to a feedback loop. In the interference compensation link, the disturbance is compensated according to the summation result and the disturbance state estimation, and a reference value for system control is obtained to control system operation. The current change rate of the fuel cell can be effectively reduced, and aging of the fuel cell is relieved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage technology, and in particular to an energy management strategy and related devices of a hybrid energy storage system based on active disturbance rejection control. Background Art

[0002] Under the background of carbon neutrality, the fields related to energy storage urgently need to explore new energy fuels to reduce dependence on traditional fossil fuels. Hydrogen energy is becoming more and more important as a clean, pollution-free and widely available new energy source. Fuel cells (FC) have become the main form of hydrogen energy application due to their high energy density and clean operation, and have attracted much attention. However, due to the poor dynamic response of fuel cells, it is difficult to meet the instantaneous power demand alone, so hybrid energy storage systems (HESS) that combine fuel cells with electrical energy storage devices have emerged. Among them, hybrid energy storage systems are applied to vehicles and / or renewable energy systems, vehicles include but are not limited to passenger cars, trucks, buses, ships, and airplanes, renewable energy systems include but are not limited to power grids and microgrids, hybrid energy storage systems include at least electrical energy storage devices and fuel cells, electrical energy storage devices include but are not limited to supercapacitors, lithium-ion batteries and sodium-ion batteries, and fuel cells include but are not limited to proton exchange membrane fuel cells, alkaline fuel cells, phosphoric acid fuel cells, solid oxide fuel cells and molten carbonate fuel cells.

[0003] Model Predictive Control (MPC) is a control method based on optimization. It can predict the future trend of model state changes and input control variables into the closed-loop controller to achieve system control. For commonly used closed-loop controllers, the traditional proportional integral (PI) controller is prone to cause system overshoot or steady-state error. Therefore, it is necessary to provide an energy management strategy and related devices for a hybrid energy storage system based on active disturbance rejection control. Summary of the invention

[0004] The present invention provides an energy management strategy and related devices for a hybrid energy storage system based on active disturbance rejection control, which improves the problem of poor control performance caused by overshoot of a PI controller in the prior art hybrid energy storage system.

[0005] The present invention provides an energy management strategy for a hybrid energy storage system based on active disturbance rejection control, which is applied to a hybrid energy storage system. The energy management strategy includes: obtaining the output current of the fuel cell at a current sampling moment, and the current change of the fuel cell between the current sampling moment and the next sampling moment; generating a current prediction trajectory and a current differential trajectory between the current sampling moment and the next sampling moment based on the output current and the current change; dividing the period between the current sampling moment and the next sampling moment into a plurality of sub-sampling moments, and performing an extended state observation on the output current to generate a first state estimate, a second state estimate, and a third state estimate at each sub-sampling moment; calculating a current prediction error and a differential error at each sub-sampling moment according to the current prediction trajectory and the corresponding first state estimate, the current differential trajectory and the corresponding second state estimate; calculating a control voltage at each sub-sampling moment according to the corresponding current prediction error, the differential error, and the third state estimate; adjusting the duty cycle of the boost converter in the hybrid energy storage system according to the control voltage at each sub-sampling moment, and allocating the energy of the fuel cell and the electrical energy storage device at the corresponding sub-sampling moment based on the adjusted duty cycle.

[0006] In one embodiment of the present invention, the current prediction trajectory and the current differential trajectory between the current sampling moment and the next sampling moment are generated based on the output current and the current change, including: calculating the reference current at the current sampling moment and the reference current at the next sampling moment based on the output current and the current change; performing interpolation processing between the reference current at the current sampling moment and the reference current at the next sampling moment to generate an initial current prediction trajectory between the current sampling moment and the next sampling moment; performing low-pass filtering on the initial current prediction trajectory to generate a final current prediction trajectory between the current sampling moment and the next sampling moment; performing differential processing on the final current prediction trajectory to obtain a current differential trajectory.

[0007] In one embodiment of the present invention, the current sampling moment to the next sampling moment is divided into a plurality of sub-sampling moments, and the output current is subjected to extended state observation to generate a first state estimate, a second state estimate, and a third state estimate at each sub-sampling moment, including: based on a preset sub-sampling interval, the current sampling moment to the next sampling moment is divided into a plurality of sub-sampling moments; for each sub-sampling moment, based on the output current of the fuel cell at the previous sub-sampling moment and the first state estimate, the second state estimate, and the third state estimate, the first state estimate, the second state estimate, and the third state estimate are generated at the sub-sampling moment through extended state observation; wherein, when the sub-sampling moment is the current sampling moment, the first state estimate, the second state estimate, and the third state estimate have initial preset values.

[0008] In one embodiment of the present invention, for each sub-sampling moment, the current prediction error is calculated as follows: searching the current prediction value corresponding to the sub-sampling moment from the current prediction trajectory; and calculating the current prediction error based on the current prediction value and the corresponding first state estimation.

[0009] In one embodiment of the present invention, for each sub-sampling moment, the differential error calculation process is as follows: searching the differential value corresponding to the sub-sampling moment from the current differential trajectory; and calculating the differential error according to the differential value and the corresponding second state estimation.

[0010] In one embodiment of the present invention, for each sub-sampling moment, the control voltage is calculated according to the corresponding current prediction error, differential error and third state estimation, including: proportionally integrating the current prediction error and the differential error respectively, and weighted summing the integration results to obtain the feedback control voltage at the sub-sampling moment; determining the disturbance compensation at the sub-sampling moment according to the third state estimation; and calculating the control voltage at the sub-sampling moment according to the feedback control voltage and the disturbance compensation.

[0011] In one embodiment of the present invention, for each sub-sampling moment, the duty cycle of the boost converter in the hybrid energy storage system is adjusted according to the control input at the sampling moment, and the energy of the fuel cell and the electrical energy storage device at the corresponding sampling moment is distributed based on the adjusted duty cycle, including: based on a proportional-integral algorithm, obtaining the duty cycle of the boost converter according to the control voltage at the sub-sampling moment; regulating the output current of the fuel cell at the sub-sampling moment based on the duty cycle; and adjusting the energy distribution of the fuel cell and the electrical energy storage device at the corresponding sub-sampling moment based on the regulated output current.

[0012] In one embodiment of the present invention, there is also provided an energy management device for a hybrid energy storage system based on active disturbance rejection control, which is applied to a hybrid energy storage system. The energy management device includes: a data acquisition module, which is used to obtain the output current of the fuel cell at the current sampling moment, and the current change of the fuel cell between the current sampling moment and the next sampling moment; a trajectory generation module, which is used to generate a current prediction trajectory and a current differential trajectory between the current sampling moment and the next sampling moment based on the output current and the current change; a state estimation module, which is used to divide the period from the current sampling moment to the next sampling moment into multiple sub-sampling moments, and perform an extended state observation on the output current to generate each sub-sampling moment. a first state estimate, a second state estimate and a third state estimate; a feedback module, for calculating a current prediction error and a differential error according to the current prediction trajectory and the corresponding first state estimate, the current differential trajectory and the corresponding second state estimate at each sub-sampling moment; a disturbance compensation module, for calculating a control voltage according to the corresponding current prediction error, the differential error and the third state estimate at each sub-sampling moment; an energy management module, for adjusting the duty cycle of the boost converter in the hybrid energy storage system according to the control voltage at each sub-sampling moment at each sub-sampling moment, and distributing the energy of the fuel cell and the electrical energy storage device at the corresponding sub-sampling moment based on the adjusted duty cycle.

[0013] In one embodiment of the present invention, an electronic device is also provided, one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the electronic device implements any of the above-mentioned hybrid energy storage system energy management strategies based on active anti-disturbance control.

[0014] In one embodiment of the present invention, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor of a computer, the computer executes any of the above-mentioned hybrid energy storage system energy management strategies based on active disturbance rejection control.

[0015] As described above, the energy management strategy and related device of a hybrid energy storage system based on self-disturbance rejection control proposed by the present invention have the following beneficial effects: by obtaining the output current and current change of the fuel cell at the current sampling moment, generating the current prediction trajectory and differential trajectory from the current sampling moment to the next sampling moment, and dividing the interval into multiple sub-sampling moments, using extended state observation to estimate the output current at each sub-sampling moment in real time, and generating the first state estimation, the second state estimation and the third state estimation. The current prediction error and the differential error at each sub-sampling moment are calculated according to the first state estimation and the second state estimation, and the control voltage is dynamically generated in combination with the third state estimation to adjust the duty cycle of the boost converter in real time. By adjusting the duty cycle, not only can the output current of the fuel cell at each high-frequency sub-sampling moment be accurately controlled, but also the energy between the fuel cell and the electrical energy storage device can be more accurately allocated. The present invention can quickly respond to high-frequency dynamic requirements under complex working conditions, and effectively improves the problem of poor control performance of the prior art due to the overshoot of the PI controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the structure of a fuel cell hybrid vehicle provided by an embodiment of the present invention;

[0017] Figure 2 A schematic flow chart of an energy management strategy for a hybrid energy storage system based on active disturbance rejection control provided in an embodiment of the present invention;

[0018] Figure 3 Shown is a schematic diagram of the ADRC process of an embodiment of the present invention;

[0019] Figure 4 Shown is a schematic diagram of a process of predicting a trajectory according to an embodiment of the present invention;

[0020] Figure 5 Shown is a structural block diagram of a hybrid energy storage system energy management device based on active disturbance rejection control provided by an embodiment of the present invention;

[0021] Figure 6 Shown is a structural schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0023] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0024] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0025] The present invention belongs to the field of energy storage technology, and specifically relates to a hybrid energy storage system composed of a fuel cell and an electric energy storage device. The present invention discloses an energy management strategy and related devices for a hybrid energy storage system based on self-disturbance rejection control. Self-disturbance rejection control includes a trajectory tracker, an extended state observer, a feedback link, and an interference compensation link. The trajectory tracker interpolates and filters the discrete sampling prediction of the model predictive control module to obtain a continuous tracking trajectory and a differential trajectory. The extended state observer outputs the corresponding tracking trajectory, differential trajectory, and state estimation of the disturbance. In the feedback link, the tracking trajectory and differential trajectory generated by the trajectory tracker are respectively subtracted from the corresponding state estimation output by the extended state observer to obtain two tracking errors, which are summed by the proportional integral controller corresponding to the feedback loop. In the interference compensation link, the disturbance is compensated based on the summation result and the state estimation of the disturbance, and a reference value for system control is obtained to control the operation of the system. The present invention can effectively reduce the current change rate of the fuel cell and alleviate its aging.

[0026] The energy management strategy described in the present invention is applicable to any hybrid energy storage system including a fuel cell, an electric energy storage device, and a boost converter. For ease of description, the present invention takes a hybrid energy storage system including a semi-automatic fuel cell and a supercapacitor in the context of a fuel cell hybrid electric vehicle power system as an example. Figure 1 As shown in the figure, the fuel cell is connected in series with the boost converter as the main power source, the supercapacitor is connected in parallel with the boost converter as an energy buffer, and the load includes components such as DC / AC inverter, motor and transmission. In this topology, L fc Simulating the input inductance of the boost converter, R fc Indicates L fc The parasitic resistance, D fcIt is the duty cycle signal generated by the PI controller and is affected by the real-time Active Disturbance Rejection Control (ADRC) in the present invention. The MPC predicts the current change of the fuel cell at the future sampling time and generates a reference current trajectory for use by ADRC. ADRC adjusts the output current i of the fuel cell based on the predicted reference trajectory. fc , real-time change of the duty cycle signal D of the boost converter fc , so as to more accurately control the output current i of the fuel cell at each ADRC sampling moment fc , thereby changing the output current i of the supercapacitor at the sampling moment sc , to achieve energy distribution between the supercapacitor and the fuel cell. So that the output current i of the fuel cell after final distribution sc The output current i of the supercapacitor sc After merging, a load current i that meets the load requirements is formed dc .

[0027] See also Figure 2 The present invention provides an energy management strategy for a hybrid energy storage system based on self-disturbance rejection control. The energy management strategy or the hybrid energy storage system in other embodiments can be applied to a variety of scenarios, including but not limited to energy storage scenarios such as vehicles, renewable energy systems, vehicles including but not limited to passenger cars, trucks, buses, ships, airplanes, renewable energy systems including but not limited to power grids, microgrids, hybrid energy storage systems including at least electrical energy storage devices and fuel cells, electrical energy storage devices including but not limited to supercapacitors, lithium-ion batteries and sodium-ion batteries, the fuel cells including but not limited to proton exchange membrane fuel cells, alkaline fuel cells, phosphoric acid fuel cells, solid oxide fuel cells and molten carbonate fuel cells. The energy management strategy for a hybrid energy storage system based on self-disturbance rejection control includes the following steps:

[0028] S1. Obtain the output current of the fuel cell at a current sampling time, and the current change of the fuel cell between the current sampling time and the next sampling time.

[0029] The output current of the fuel cell at the current sampling time is collected in real time, and the system state is optimized and predicted through model predictive control to obtain the current change at the current sampling time and the current change at the next sampling time. Among them, fuel cells include but are not limited to proton exchange membrane fuel cells, alkaline fuel cells, phosphoric acid fuel cells, solid oxide fuel cells and molten carbonate fuel cells.

[0030] S2. Based on the output current and the current variation, generate a current prediction trajectory and a current differential trajectory between the current sampling moment and the next sampling moment.

[0031] The current prediction trajectory refers to the period from the current sampling time k to the next sampling time k+1, based on the output current i at the current sampling time k. fc (k) and the corresponding current change Δi fc (k|k), combined with the current change Δi at the next sampling moment fc The current differential trajectory refers to the trajectory curve after the current prediction trajectory is differentially processed, which is used to characterize the dynamic change characteristics of the current during this period of time.

[0032] In one embodiment of the present invention, generating a current prediction trajectory and a current differential trajectory between a current sampling moment and a next sampling moment based on the output current and the current variation includes:

[0033] Based on the output current and the current variation, calculating the reference current at the current sampling moment and the reference current at the next sampling moment;

[0034] Perform interpolation processing between the reference current at the current sampling moment and the reference current at the next sampling moment to generate an initial current prediction trajectory between the current sampling moment and the next sampling moment;

[0035] Performing low-pass filtering on the initial current prediction trajectory to generate a final current prediction trajectory between the current sampling moment and the next sampling moment;

[0036] The final current prediction trajectory is subjected to differential processing to obtain a current differential trajectory.

[0037] like Figure 4 As shown, the output current i of the fuel cell at the current sampling time k is fc (k) and the current change Δi at the current sampling time k generated by MPC fc (k|k) is superimposed to obtain the reference current i at the current sampling time k. fc In the same way, the output current i of the fuel cell at the current sampling time k can be fc (k) and the current change Δi at the next sampling time k+1 generated by MPC fc (k+1|k) is superimposed to obtain the reference current i at the next sampling time k+1 fc After obtaining the reference currents at two adjacent sampling moments, the current between the two sampling moments is interpolated to generate the initial current prediction trajectory F between the two adjacent sampling moments. k(t). The interpolation method includes but is not limited to linear interpolation, Lagrange interpolation or bilinear interpolation. Considering that this interpolation method can make the trajectory maintain continuity between two sampling moments, the trajectory between adjacent sampling moments may jump. In order to improve this problem, the present invention also smoothes the interpolation result after interpolation. Specifically, the initial current prediction trajectory is low-pass filtered by a second-order low-pass filter, and the transfer function of the filter is shown in formula (1):

[0038]

[0039] Among them, r f is the preset parameter of the filter, which is used to control the filtering strength and smoothness. Through low-pass filtering, a smoother trajectory can be generated, and the trajectory after low-pass filtering is used as the final current prediction trajectory L 1 The final current prediction trajectory is differentially calculated to obtain the current differential trajectory L 2 It can be understood that the present invention can generate the current prediction trajectory and current differential trajectory within two adjacent MPC sampling intervals each time, and can also generate the current prediction trajectory and current differential trajectory for the next N sampling moments after the MPC generates the current change at the next N sampling moments, and perform low-pass filtering on these current prediction trajectories to generate a smooth current prediction trajectory curve.

[0040] S3, dividing the period from the current sampling moment to the next sampling moment into a plurality of sub-sampling moments, and performing extended state observation on the output current to generate a first state estimate, a second state estimate, and a third state estimate at each sub-sampling moment.

[0041] like Figure 3As shown, according to the ADRC sampling interval h, the current sampling moment k to the next sampling moment k+1 is divided into multiple sub-sampling moments. For each sub-sampling moment: the state of the output current at the previous sub-sampling moment is estimated by the extended state observer (ESO), and a first estimated state, a second estimated state and a third estimated state are generated. Among them, the first estimated state represents the estimated value of the output current at the current sub-sampling moment, the second estimated state represents the dynamic change of the output current at the current sub-sampling moment, and the third estimated state represents the estimated value of the disturbance at the current sub-sampling moment. By estimating the state of the output current at each ADRC sampling moment, the output current of the fuel cell at the corresponding ADRC sampling moment can be more accurately regulated. Compared with the existing method of regulating the output current of the fuel cell by the current change amount output by the MPC, the method of the present invention continuously adjusts and optimizes the output current of the fuel cell in a near real-time manner through the high-frequency control mechanism of the self-disturbance rejection control, which significantly improves the dynamic response capability of the entire hybrid energy storage system and improves the system's adaptability to external disturbances.

[0042] In one embodiment of the present invention, the step of dividing the period from the current sampling moment to the next sampling moment into a plurality of sub-sampling moments, performing extended state observation on the output current, and generating a first state estimate, a second state estimate, and a third state estimate at each sub-sampling moment includes:

[0043] Based on a preset sub-sampling interval, the time from the current sampling moment to the next sampling moment is divided into a plurality of sub-sampling moments;

[0044] For each sub-sampling moment, based on the output current of the fuel cell at the previous sub-sampling moment and the first state estimate, the second state estimate and the third state estimate, the first state estimate, the second state estimate and the third state estimate at the sub-sampling moment are generated through extended state observation; wherein, when the sub-sampling moment is the current sampling moment, the first state estimate, the second state estimate and the third state estimate have initial preset values.

[0045] According to the sampling frequency of ADRC, the period from the current sampling moment to the next sampling moment is divided into N sub-sampling moments. At the first sub-sampling moment (i.e., the current sampling moment), the first state estimate, the second state estimate, and the third state estimate are initialized to preset values ​​(e.g., zero value). 1 (0), z 2 (0), z 3(0) are initialized to zero. For each sub-sampling time, the output current of the fuel cell (such as the fuel cell) at that time is passed through the ESO, and the state estimate at the current sub-sampling time is obtained based on the output current and the state estimate at the previous sub-sampling time. Specifically, based on the first state estimate and the second state estimate at the previous sub-sampling time, the first state estimate at the current sub-sampling time is updated, as shown in formulas (2) and (3):

[0046] e=z 1 (t-1)-i fc (t-1) (2)

[0047] z 1 (t) = z 1 (t-1)+h(z 2 (t-1)-β 01 e) (3)

[0048] Where e is the tracking error, z 1 (t-1) is the first state estimate of the previous sub-sampling time, i fc (t-1) is the output current of the fuel cell at the previous sub-sampling moment, z 1 (t) is the first state estimate at the current sub-sampling time, h is the sampling time of ADRC, z 2 (t-1) is the second state estimate of the previous sub-sampling time, β 01 =3ω 0 ,ω 0 is the preset weight. According to the second state estimate at the previous sub-sampling moment and the current change of the fuel cell, the second state estimate at the current sub-sampling moment is updated, as shown in formulas (4) and (5):

[0049] z 2 (t) = z 2 (t-1)+h(z 3 (t-1)-h[z 3 (t-1)+bu(t-1)-β 02 fal(e,0.5,h))] (4)

[0050]

[0051] Among them, z 2 (t) is the second state estimate at the current sub-sampling time, z 3 (t-1) is the third state estimate at the previous sub-sampling moment, u(t-1) is the change in the fuel cell output current generated by the MPC at the previous sub-sampling moment (i.e., the previous sampling moment), fal can be regarded as the differential of the disturbance, α is the preset disturbance coefficient, According to the third state estimation and error feedback at the previous sub-sampling time, the third state estimation at the current sub-sampling time is updated, as shown in formula (6):

[0052] z 3 (t) = z 3 (t-1)-hβ 03 fal(e,α 2 ,h) (6)

[0053] Among them, z 3 (t) is the third state estimate at the current sub-sampling time,

[0054] S4. Calculate the current prediction error and the differential error according to the current prediction trajectory and the corresponding first state estimation, the current differential trajectory and the corresponding second state estimation at each sub-sampling time.

[0055] Specifically, in one embodiment of the present invention, for each sub-sampling moment, the current prediction error is calculated as follows:

[0056] Find the current prediction value corresponding to the sub-sampling moment from the current prediction trajectory;

[0057] A current prediction error is calculated based on the current prediction value and the corresponding first state estimate.

[0058] like Figure 3 As shown, for each sub-sampling moment, the following processing is performed: the current value at the sub-sampling moment is extracted from the current prediction trajectory. The current value at the sub-sampling moment is subtracted from the corresponding first state estimate to obtain the prediction error e at the sub-sampling moment. 1 Among them, the prediction error is used to characterize the current tracking accuracy at the current sub-sampling moment.

[0059] In one embodiment of the present invention, for each sub-sampling moment, the calculation process of the differential error is:

[0060] Find the differential value corresponding to the sub-sampling moment from the current differential trajectory;

[0061] A differential error is calculated based on the differential value and the corresponding second state estimate.

[0062] like Figure 3 As shown, for each sub-sampling moment, the following processing is performed: the differential value of the current sub-sampling moment is extracted from the current differential trajectory, and the differential value of the sub-sampling moment is subtracted from the corresponding second state estimate to obtain the differential error e at the sub-sampling moment. 2 .

[0063] S5. Calculate the control voltage at each sub-sampling time according to the corresponding current prediction error, differential error and third state estimation.

[0064] Specifically, in one embodiment of the present invention, for each sub-sampling moment, the control voltage is calculated according to the corresponding current prediction error, differential error and third state estimation, including:

[0065] Proportional integration is performed on the current prediction error and the differential error respectively, and the integration results are weighted summed to obtain the feedback control voltage at the sub-sampling moment;

[0066] Determining disturbance compensation at the sub-sampling moment according to the third state estimate;

[0067] The control voltage at the sub-sampling moment is calculated according to the feedback control voltage and the disturbance compensation.

[0068] The current prediction error and differential error are proportionally integrated to obtain a control signal reflecting the system deviation. The two control signals are weighted and summed to obtain the feedback control voltage u at the current sub-sampling moment. oin , and the third state estimate is calculated according to 1 / b 0 Weighted processing is performed to compensate for the disturbance at the sub-sampling moment, where b 0 It is the compensation coefficient related to the controlled object model, so as to compensate for disturbance. oin Subtract the weighted third state estimate to obtain the control voltage u at the sub-sampling moment in The output current of the subsequent fuel cell is controlled by controlling the voltage.

[0069] S6. According to each sub-sampling moment, the duty cycle of the boost converter in the hybrid energy storage system is adjusted according to the control voltage at each sub-sampling moment, and the energy of the fuel cell and the electrical energy storage device at the corresponding sub-sampling moment is distributed based on the adjusted duty cycle.

[0070] Specifically, in one embodiment of the present invention, for each sub-sampling moment, the duty cycle of the boost converter in the hybrid energy storage system is adjusted according to the control voltage at the sub-sampling moment, and the energy of the fuel cell and the electrical energy storage device at the corresponding sub-sampling moment is distributed based on the adjusted duty cycle, including:

[0071] Based on a proportional-integral algorithm, obtaining a duty cycle of the boost converter according to the control voltage at the sub-sampling moment;

[0072] Regulating the output current of the fuel cell at the sub-sampling time based on the duty cycle;

[0073] The energy distribution between the fuel cell and the electrical energy storage device at the corresponding sub-sampling time is adjusted based on the regulated output current.

[0074] The duty cycle data D of the boost converter can be obtained at the current sub-sampling moment according to the control voltage through the proportional integral algorithm. fc (t s ) and outputs it as an adjustment signal. By adjusting the duty cycle of the boost converter, the current flowing through the fuel cell and the current flowing through the supercapacitor are changed, thereby realizing power regulation of the hybrid energy storage device at the next moment.

[0075] See also Figure 5 The hybrid energy storage system in this embodiment or other embodiments can be applied to a variety of scenarios, including but not limited to energy storage scenarios such as vehicles, renewable energy systems, vehicles including but not limited to passenger cars, trucks, buses, ships, and airplanes, renewable energy systems including but not limited to power grids and microgrids, and hybrid energy storage systems including at least electrical energy storage devices and fuel cells, electrical energy storage devices including but not limited to supercapacitors, lithium-ion batteries, and sodium-ion batteries, and fuel cells including but not limited to proton exchange membrane fuel cells, alkaline fuel cells, phosphoric acid fuel cells, solid oxide fuel cells, and molten carbonate fuel cells. The energy management device 100 of the hybrid energy storage system based on self-disturbance rejection control includes: a data acquisition module 110, a trajectory generation module 120, a state estimation module 130, a feedback module 140, a disturbance compensation module 150, and an energy management module 160. The data acquisition module 110 is used to obtain the output current of the fuel cell at the current sampling time, and the current change of the fuel cell at the current sampling time and the next sampling time. The trajectory generation module 120 is used to generate a current prediction trajectory and a current differential trajectory between the current sampling time and the next sampling time based on the output current and the current change. The state estimation module 130 is used to divide the period from the current sampling moment to the next sampling moment into multiple sub-sampling moments, and perform extended state observation on the output current to generate the first state estimate, the second state estimate and the third state estimate at each sub-sampling moment. The feedback module 140 is used to calculate the current prediction error and the differential error according to the current prediction trajectory and the corresponding first state estimate, the current differential trajectory and the corresponding second state estimate at each sub-sampling moment. The disturbance compensation module 150 is used to calculate the control voltage according to the corresponding current prediction error, the differential error and the third state estimate at each sub-sampling moment. The energy management module 160 is used to adjust the duty cycle of the boost converter in the hybrid energy storage system according to the control voltage at each sub-sampling moment, and allocate the energy of the fuel cell and the electrical energy storage device at the corresponding sub-sampling moment based on the adjusted duty cycle.

[0076] For the specific limitations of the energy management device of the hybrid energy storage system based on active disturbance rejection control, please refer to the limitations of the energy management strategy of the hybrid energy storage system based on active disturbance rejection control in the above text, which will not be repeated here. Each module in the above-mentioned energy management device of the hybrid energy storage system based on active disturbance rejection control can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware format, or can be stored in the memory of the computer device in software format, so that the processor can call the operations corresponding to the above modules.

[0077] It should be noted that, in order to highlight the innovative part of the present invention, the present embodiment does not introduce modules that are not closely related to solving the technical problem proposed by the present invention, but this does not mean that there are no other modules in the present embodiment.

[0078] See also Figure 6 The electronic device 1 may include a memory 12, a processor 13 and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as an energy management program for a hybrid energy storage system based on active disturbance rejection control.

[0079] Among them, the memory 12 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 12 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 12 can also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 12 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of energy management of the hybrid energy storage system based on the self-disturbance rejection control, but also can be used to temporarily store data that has been output or is to be output.

[0080] In some embodiments, the processor 13 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 13 is the control core (Control Unit) of the electronic device 1, and uses various interfaces and lines to connect the various components of the entire electronic device 1, and executes or executes the programs or modules stored in the memory 12 (for example, the energy management program of the hybrid energy storage system based on the self-disturbance rejection control, etc.), and calls the data stored in the memory 12 to execute various functions of the electronic device 1 and process data.

[0081] The processor 13 executes the operating system of the electronic device 1 and various installed application programs. The processor 13 executes the application programs to implement the steps in the above-mentioned hybrid energy storage system energy management strategy based on active disturbance rejection control.

[0082] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory 12 and executed by the processor 13 to complete the present application. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into a data acquisition module 110, a trajectory generation module 120, a state estimation module 130, a feedback module 140, a disturbance compensation module 150, and an energy management module 160.

[0083] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium, and the computer-readable storage medium can be non-volatile or volatile. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a computer device, or a network device, etc.) or a processor to perform part of the functions of the energy management strategy of the hybrid energy storage system based on the self-disturbance rejection control described in each embodiment of the present application.

[0084] In summary, the present invention discloses an energy management strategy and related device for a hybrid energy storage system based on self-disturbance rejection control, which obtains the output current and current change of the fuel cell at the current sampling moment, generates a current prediction trajectory and a differential trajectory from the current sampling moment to the next sampling moment, and divides the interval into multiple sub-sampling moments, and uses extended state observation to estimate the output current at each sub-sampling moment in real time, and generates a first state estimate, a second state estimate, and a third state estimate. The current prediction error and the differential error at each sub-sampling moment are calculated according to the first state estimate and the second state estimate, and the control voltage is dynamically generated in combination with the third state estimate to adjust the duty cycle of the boost converter in real time. By adjusting the duty cycle, not only can the output current of the fuel cell at each high-frequency sub-sampling moment be accurately controlled, but also the energy between the fuel cell and the electrical energy storage device can be more accurately allocated. The present invention can quickly respond to high-frequency dynamic requirements under complex working conditions, and effectively improves the problem that the prior art has poor control performance due to the overshoot of the PI controller. Therefore, the present invention effectively overcomes the various shortcomings of the prior art and has a high industrial utilization value.

[0085] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. An energy management strategy for a hybrid energy storage system based on active disturbance rejection control, characterized in that: Applied to a hybrid energy storage system, the energy management strategy includes: Obtaining the output current of the fuel cell at the current sampling moment, and the current change of the fuel cell between the current sampling moment and the next sampling moment; Based on the output current and the current variation, a current prediction trajectory and a current differential trajectory between the current sampling moment and the next sampling moment are generated; Divide the period from the current sampling moment to the next sampling moment into a plurality of sub-sampling moments, and perform extended state observation on the output current to generate a first state estimate, a second state estimate, and a third state estimate at each sub-sampling moment; Calculating a current prediction error and a differential error according to the current prediction trajectory and the corresponding first state estimate, the current differential trajectory and the corresponding second state estimate at each sub-sampling time; Calculating the control voltage according to the corresponding current prediction error, differential error and third state estimation at each sub-sampling time; According to each sub-sampling moment, the duty cycle of the boost converter in the hybrid energy storage system is adjusted according to the control voltage at each sub-sampling moment, and the energy of the fuel cell and the electrical energy storage device at the corresponding sub-sampling moment is distributed based on the adjusted duty cycle.

2. The energy management strategy of the hybrid energy storage system based on active disturbance rejection control according to claim 1 is characterized in that: The generating of a current prediction trajectory and a current differential trajectory between a current sampling moment and a next sampling moment based on the output current and the current variation comprises: Based on the output current and the current variation, calculating the reference current at the current sampling moment and the reference current at the next sampling moment; Perform interpolation processing between the reference current at the current sampling moment and the reference current at the next sampling moment to generate an initial current prediction trajectory between the current sampling moment and the next sampling moment; Performing low-pass filtering on the initial current prediction trajectory to generate a final current prediction trajectory between the current sampling moment and the next sampling moment; The final current prediction trajectory is subjected to differential processing to obtain a current differential trajectory.

3. The energy management strategy of the hybrid energy storage system based on active disturbance rejection control according to claim 1 is characterized in that: The step of dividing the time period from the current sampling moment to the next sampling moment into a plurality of sub-sampling moments, performing extended state observation on the output current, and generating a first state estimate, a second state estimate, and a third state estimate at each sub-sampling moment includes: Based on a preset sub-sampling interval, the time from the current sampling moment to the next sampling moment is divided into a plurality of sub-sampling moments; For each sub-sampling moment, based on the output current of the fuel cell at the previous sub-sampling moment and the first state estimate, the second state estimate and the third state estimate, the first state estimate, the second state estimate and the third state estimate at the sub-sampling moment are generated through extended state observation; wherein, when the sub-sampling moment is the current sampling moment, the first state estimate, the second state estimate and the third state estimate have initial preset values.

4. The energy management strategy of a hybrid energy storage system based on active disturbance rejection control according to claim 1 is characterized in that: For each sub-sampling moment, the calculation process of the current prediction error is: Find the current prediction value corresponding to the sub-sampling moment from the current prediction trajectory; A current prediction error is calculated based on the current prediction value and the corresponding first state estimate.

5. The energy management strategy of a hybrid energy storage system based on active disturbance rejection control according to claim 1 is characterized in that: For each sub-sampling moment, the calculation process of the differential error is: Find the differential value corresponding to the sub-sampling moment from the current differential trajectory; A differential error is calculated based on the differential value and the corresponding second state estimate.

6. The energy management strategy of a hybrid energy storage system based on active disturbance rejection control according to claim 1 is characterized in that: For each sub-sampling moment, the control voltage is calculated according to the corresponding current prediction error, differential error and third state estimation, including: Proportional integration is performed on the current prediction error and the differential error respectively, and the integration results are weighted summed to obtain the feedback control voltage at the sub-sampling moment; Determining disturbance compensation at the sub-sampling moment according to the third state estimate; The control voltage at the sub-sampling moment is calculated according to the feedback control voltage and the disturbance compensation.

7. The energy management strategy of a hybrid energy storage system based on active disturbance rejection control according to claim 1 is characterized in that: For each sub-sampling moment, adjusting the duty cycle of the boost converter in the hybrid energy storage system according to the control voltage at the sub-sampling moment, and allocating the energy of the fuel cell and the electrical energy storage device at the corresponding sub-sampling moment based on the adjusted duty cycle, including: Based on a proportional-integral algorithm, obtaining a duty cycle of the boost converter according to the control voltage at the sub-sampling moment; Regulating the output current of the fuel cell at the sub-sampling time based on the duty cycle; The energy distribution between the fuel cell and the electrical energy storage device at the corresponding sub-sampling time is adjusted based on the regulated output current.

8. An energy management device for a hybrid energy storage system based on active disturbance rejection control, characterized in that: Applied to a hybrid energy storage system, the energy management device comprises: A data acquisition module, used to acquire the output current of the fuel cell at the current sampling moment, and the current change of the fuel cell between the current sampling moment and the next sampling moment; A trajectory generation module, used to generate a current prediction trajectory and a current differential trajectory between a current sampling moment and a next sampling moment based on the output current and the current variation; A state estimation module, used to divide the period from the current sampling moment to the next sampling moment into a plurality of sub-sampling moments, and perform extended state observation on the output current to generate a first state estimation, a second state estimation and a third state estimation at each sub-sampling moment; A feedback module, configured to calculate a current prediction error and a differential error according to the current prediction trajectory and the corresponding first state estimate, the current differential trajectory and the corresponding second state estimate at each sub-sampling moment; A disturbance compensation module, for calculating a control voltage according to the corresponding current prediction error, differential error and third state estimation at each sub-sampling moment; The energy management module is used to adjust the duty cycle of the boost converter in the hybrid energy storage system according to the control voltage at each sub-sampling moment, and distribute the energy of the fuel cell and the electrical energy storage device at the corresponding sub-sampling moment based on the adjusted duty cycle.

9. An electronic device, characterized in that: The electronic device comprises: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the energy management strategy of the hybrid energy storage system based on active anti-disturbance control as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is enabled to execute the energy management strategy of the hybrid energy storage system based on active disturbance rejection control as described in any one of claims 1 to 7.

Images