A hierarchical control method based on a hybrid electric propulsion system and related devices

Abstract

The application discloses a layered control method based on a hybrid electric propulsion system and related devices, and the method comprises the following steps: obtaining real-time data of the hybrid electric propulsion system; based on the real-time data, constructing a target function containing a fuel consumption term and a battery state of charge maintenance term in predictive control with the minimum fuel consumption as the target, and calculating the output power instruction of each energy storage battery group and the output power instruction of a generator set at the next moment; obtaining the droop coefficient corresponding to each DAB converter; calculating the voltage reference value of each DAB converter at the next moment, taking the minimum voltage deviation value as the optimization target, combining the constraint condition, and obtaining the optimal phase shift ratio at the next moment; and adjusting and controlling each DAB converter based on the optimal phase shift ratio at the next moment. In the embodiment of the application, the joint optimization of the fuel economy of the hybrid electric propulsion energy system, the power quality and the reliability of the converter is realized.

Description

Technical Field

[0001] This invention relates to the field of propulsion control technology for hybrid unmanned aerial vehicles (UAVs), and more particularly to a hierarchical control method and related apparatus based on a hybrid electric propulsion system. Background Technology

[0002] The hybrid electric propulsion energy system of UAVs consists of multiple power sources. How to coordinate and control multiple energy sources through reasonable energy management and control strategies, and give full play to their respective advantages to achieve the best overall performance is the key and difficult point of the current hybrid electric propulsion system.

[0003] Current research primarily focuses on designing control strategies to coordinate energy distribution among various energy sources to minimize fuel consumption. However, it often overlooks the impact of time-scale differences between different loads on the system's dynamic response. For hybrid electric propulsion systems, the load includes flight propulsion loads, mission loads, and flight control actuation loads. These loads have different time scales, leading to varying demands on energy distribution and power response. The time scale difference manifests in the fact that propulsion loads change slowly and have a larger time scale, requiring steady-state energy optimization to reduce fuel consumption. In contrast, mission and actuation loads change rapidly and have a smaller time scale, requiring rapid dynamic power adjustment to ensure flight safety and mission execution. This contradiction is a problem that traditional energy management strategies have failed to consider.

[0004] Current control strategies for hybrid electric propulsion systems include rule-based control, optimization-based control, and learning-based control. Rule-based control is simple to implement and computationally inexpensive, but its rules typically rely on expert experience or fixed thresholds, making it difficult to adapt to the time scale differences of different loads during flight missions and unable to dynamically coordinate the power allocation of multiple energy sources to achieve optimal overall performance. In contrast, model predictive control in optimization-based control strategies can achieve finite-time rolling optimization, taking into account the load characteristics at different time scales, solving in real time and dynamically coordinating power, thus approximating the global optimum while meeting the system's rapid response requirements. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a hierarchical control method and related device based on a hybrid electric propulsion system, which achieves the combined optimization of fuel economy, power quality and converter reliability of the hybrid electric propulsion energy system.

[0006] To address the aforementioned technical problems, this invention provides a hierarchical control method based on a hybrid electric propulsion system. The hybrid electric propulsion system comprises a generator set, at least two energy storage battery packs, at least two DAB converters, several drive motors, and a DC bus. The at least two energy storage battery packs are connected in series and parallel with the generator set on the DC bus via at least two DAB converters. The several drive motors are connected to the load side of the DC bus. The method includes:

[0007] Obtain real-time data of the hybrid electric propulsion system, the real-time data including at least load power, energy storage battery status information of each of the at least two energy storage battery packs, real-time output voltage of the DC bus, and real-time output current of each DAB converter;

[0008] Based on the load power and energy storage battery status information of each energy storage battery pack in the real-time data, in predictive control, an objective function containing fuel consumption and battery state of charge maintenance is constructed with the goal of minimizing fuel consumption. The objective function is then used to calculate the output power command corresponding to each energy storage battery pack and the output power command of the generator set at the next moment.

[0009] When the generator set controls the generator set with the output power command at the next moment, the droop coefficient corresponding to each DAB converter is obtained based on the output power command corresponding to each energy storage battery pack at the next moment.

[0010] Based on the reference voltage of the DC bus, the droop coefficient of each DAB converter, and the output current of each DAB converter, the voltage reference value of each DAB converter at the next moment is calculated. The optimal shift ratio at the next moment is obtained by minimizing the voltage deviation value as the optimization objective and combining the constraints.

[0011] The optimal shift ratio for the next time step is used to adjust the control of each DAB converter.

[0012] Optionally, the predictive control is established based on discrete time, as follows:

[0013] ;

[0014] ;

[0015] In the formula, The current moment; For the next moment; The sampling period; For the first One energy storage battery pack The state of charge at any given moment; For the first One energy storage battery pack The state of charge at any given moment; For the first Rated capacity of each energy storage battery pack; For the first The charge / discharge efficiency function of an energy storage battery pack; For the first The output power of the energy storage battery pack.

[0016] Optionally, the specific process of constructing the objective function, which includes a fuel consumption term and a battery state-of-charge maintenance term, with the goal of minimizing fuel consumption includes:

[0017] The characteristic curve of the generator set is fitted using a cubic polynomial, and the fitting coefficients are obtained using the least squares method. The cubic polynomial is as follows:

[0018] ;

[0019] The instantaneous fuel consumption is then expressed as:

[0020] ;

[0021] Based on the instantaneous fuel consumption of the generator set, an objective function is established with the goal of minimizing fuel consumption, comprising a fuel consumption term and a battery state-of-charge maintenance term, as follows:

[0022] ;

[0023] ;

[0024] In the formula, The output power of the generator set. In order to be in Fuel consumption rate per kWh (g / kWh); The fitting coefficients are obtained by fitting the fuel consumption rate curve with experimental data. This refers to instantaneous fuel consumption. For finite prediction time domain; At any moment The instantaneous fuel consumption at any given time is related to the power of the generator set; For the first SOC reference value for an energy storage battery pack; For the first One energy storage battery pack The state of charge at any given moment; and These are weighting coefficients, used to adjust fuel economy and battery SOC, respectively.

[0025] Optionally, the step of calculating the output power command corresponding to each energy storage battery pack and the output power command of the generator set at the next moment using the objective function includes:

[0026] Introducing constraints, specifically the power balance equation constraint, whereby the sum of the power of the generator set and at least two energy storage battery packs must satisfy the load demand, as follows:

[0027] ;

[0028] Limit the upper and lower limits of the generator set's output power to keep the generator set at its optimal operating point or optimal operating range, as follows:

[0029] ;

[0030] For each energy storage battery pack, the battery power is limited to ensure that the charging and discharging power is within the acceptable range as follows:

[0031] ;

[0032] The state of charge (SOC) of each energy storage battery pack is strictly limited to a set safe range, as follows:

[0033]

[0034] Under the above constraints, the output power command of each energy storage battery pack and the output power command of the generator set at the next moment are calculated using the objective function.

[0035] in, In order to be in Load power at any given moment; For generator sets in Output power at any given moment; For the first energy storage battery pack in Output power at any given moment; For the second energy storage battery pack in Output power at any given moment; For the m-th energy storage battery pack in Output power at any given moment; For generator sets in The optimal output power at any given time; This is the lower limit of the engine set's output power; This is the upper limit of the generator set's output power; The optimal charge / discharge power for the m-th energy storage battery pack; This is the upper limit of the charging power of the energy storage battery pack; This is the upper limit of the discharge power of the energy storage battery pack; Let m be the SOC value of the m-th energy storage battery pack; This is the lower limit of the SOC value; This represents the upper limit of the SOC value.

[0036] Optionally, obtaining the droop coefficient corresponding to each DAB converter based on the output power command corresponding to each energy storage battery pack at the next moment includes:

[0037] A reference value for the DC voltage of the m-th DAB converter is generated based on the current value output by the m-th DAB converter. as follows:

[0038] ;

[0039] In parallel operation, if the DC voltage of each DAB converter is equal, then:

[0040] ;

[0041] Then we have:

[0042] ;

[0043] Therefore, the relationship between the output power and the droop coefficient of each energy storage battery pack is as follows:

[0044] ;

[0045] Then we have:

[0046] ;

[0047] When calculating the droop factor corresponding to the m-th DAB converter based on the allowable DC bus voltage deviation and the top-level power allocation result, let the allowable DC bus voltage droop be... Then the reference current of the m-th DAB converter is:

[0048] ;

[0049] The droop coefficient of the m-th DAB converter is as follows:

[0050] ;

[0051] in, The given reference voltage for the DC bus; Let be the droop coefficient of the m-th DAB converter; Let m be the output current of the m-th DAB converter; , ; Let be the output power of the m-th energy storage battery pack.

[0052] Optionally, based on the reference voltage of the DC bus, the droop coefficient corresponding to each DAB converter, and the output current of each DAB converter, the voltage reference value of each DAB converter at the next moment is calculated as follows:

[0053] ;

[0054] In the formula, This represents the droop coefficient corresponding to the m-th DAB converter; This is the reference voltage for the DC bus. For the m-th DAB converter in Voltage reference value at any given time; For the m-th DAB converter in The output current at any given time.

[0055] Optionally, the step of minimizing the voltage deviation value as the optimization objective, combined with constraints, to obtain the optimal shift ratio at the next time step includes:

[0056] The discrete state equations for each DAB converter are established as follows:

[0057] ;

[0058] Then, an objective function is constructed with minimizing the voltage deviation as the optimization objective to obtain the optimal shift ratio at the next time step, as follows:

[0059] ;

[0060] The constraints on the control variables are as follows:

[0061] ;

[0062] The DC bus output voltage must also not exceed the set variation range as required by the standard, under the following constraints:

[0063] ;

[0064] Each DAB converter in The constraint on the output current at any given time is:

[0065] ;

[0066] in, For DC bus in Output voltage at any given moment; DC bus at Output voltage at any given moment; For the m-th DAB converter in Voltage reference value at any given time; For the m-th DAB converter in Output voltage at any given moment; The m-th DAB converter in Output current at any given moment; and For the corresponding weight values, ; For the m-th DAB converter in Output voltage at any given moment; This refers to the input voltage of the DAB converter; This refers to the turns ratio of the high-frequency isolation transformer in the DAB converter from the primary side to the secondary side. The leakage inductance of the transformer is referred to the primary side of the high-frequency isolation transformer; The output capacitor of the DAB converter; The sampling period; For the m-th DAB converter in Compared to the shift in time; For finite prediction time domain.

[0067] In addition, this invention also provides a hierarchical control device based on a hybrid electric propulsion system, applied to a hybrid electric propulsion system. The hybrid electric propulsion system comprises a generator set, at least two energy storage battery packs, at least two DAB converters, several drive motors, and a DC bus. The at least two energy storage battery packs are connected in series and parallel with the generator set on the DC bus using at least two DAB converters. The several drive motors are connected to the load side of the DC bus. The device includes:

[0068] Data acquisition module: used to acquire real-time data of the hybrid electric propulsion system, the real-time data including at least load power, energy storage battery status information of each of the at least two energy storage battery packs, real-time output voltage of the DC bus, and real-time output current of each DAB converter;

[0069] Output power command calculation module: Based on the load power and energy storage battery status information of each energy storage battery pack in the real-time data, in predictive control, construct an objective function containing fuel consumption and battery state of charge maintenance with the goal of minimizing fuel consumption, and use the objective function to calculate the output power command corresponding to each energy storage battery pack and the output power command of the generator set at the next moment.

[0070] Droop coefficient calculation module: used to obtain the droop coefficient corresponding to each DAB converter based on the output power command corresponding to each energy storage battery pack at the next moment when the generator set's output power command is used to control the generator set.

[0071] The optimal shift ratio calculation module is used to calculate the voltage reference value of each DAB converter at the next moment based on the reference voltage of the DC bus, the droop coefficient corresponding to each DAB converter, and the output current of each DAB converter. It uses minimizing the voltage deviation value as the optimization objective and combines it with the constraints to obtain the optimal shift ratio at the next moment.

[0072] Adjustment control module: Used to adjust and control each DAB converter based on the optimal shift ratio at the next time step.

[0073] In addition, embodiments of the present invention also provide an electronic device, including a processor and a memory, wherein the processor runs a computer program or code stored in the memory to implement the hierarchical control method as described in any of the above.

[0074] In addition, embodiments of the present invention also provide a computer-readable storage medium for storing a computer program or code, which, when executed by a processor, implements the hierarchical control method as described above.

[0075] In this embodiment of the invention, a hierarchical model predictive control framework is constructed. At the energy management layer, fuel consumption and battery state of charge are simultaneously optimized through a multi-objective cost function, outputting engine power setpoint and droop coefficient to achieve globally optimal energy allocation. At the power control layer, a distributed multi-objective MPC (model predictive control) strategy for DAB converters is proposed. Based on its nonlinear power transfer model, a cost function containing fast voltage tracking and shift ratio smoothness is constructed to solve for the optimal shift ratio. Power regulation and bus voltage stability are achieved based on the top-level droop coefficient. This realizes the joint optimization of fuel economy, power quality, and converter reliability in the hybrid electric propulsion energy system. Attached Figure Description

[0076] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0077] Figure 1 This is a flowchart illustrating the hierarchical control method based on a hybrid electric propulsion system in an embodiment of the present invention.

[0078] Figure 2 This is a schematic diagram of the structural composition of the hierarchical control device based on a hybrid electric propulsion system in an embodiment of the present invention;

[0079] Figure 3This is a schematic diagram of the structural composition of the electronic device in an embodiment of the present invention;

[0080] Figure 4 This is a schematic diagram of the structural composition of the hybrid electric propulsion system in an embodiment of the present invention;

[0081] Figure 5 This is a schematic diagram of the structural composition of the DAB converter in an embodiment of the present invention. Detailed Implementation

[0082] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0083] Example 1, please refer to Figure 1 , Figure 1 This is a flowchart illustrating the hierarchical control method based on a hybrid electric propulsion system in an embodiment of the present invention.

[0084] like Figure 1 As shown, a hierarchical control method based on a hybrid electric propulsion system is applied to the hybrid electric propulsion system, which comprises a generator set, at least two energy storage battery packs, at least two DAB converters, several drive motors, and a DC bus. The at least two energy storage battery packs are connected in series and parallel with the generator set on the DC bus via at least two DAB converters, and the several drive motors are connected to the load side of the DC bus. The method includes:

[0085] S101: Obtain real-time data of the hybrid electric propulsion system, the real-time data including at least load power, energy storage battery status information of each of the at least two energy storage battery packs, real-time output voltage of the DC bus, and real-time output current of each DAB converter.

[0086] In the specific implementation of this invention, such as Figure 4 As shown, the hybrid electric propulsion system comprises a generator set, at least two energy storage battery packs, at least two DAB converters, several drive motors, and a DC bus. At least two energy storage battery packs are connected in series and parallel to the generator set on the DC bus via at least two DAB converters. Several drive motors are connected to the load side of the DC bus. The DC bus and energy storage batteries are connected via DAB converters. DAB converters, with their advantages of electrical isolation, bidirectional power transmission, high power density, and ease of soft switching, have become the preferred choice for meeting aerospace application requirements. The structure of the DAB converter is as follows: Figure 5 As shown.

[0087] Then, real-time data of the hybrid electric propulsion system can be obtained. The real-time data includes at least the load power, the energy storage status information of each of the at least two energy storage battery packs, the real-time output voltage of the DC bus, and the real-time output current of each DAB converter.

[0088] S102: Based on the load power and energy storage battery status information of each energy storage battery pack in the real-time data, in predictive control, a target function containing fuel consumption and battery state of charge maintenance is constructed with the goal of minimizing fuel consumption. The target function is used to calculate the output power command corresponding to each energy storage battery pack and the output power command of the generator set at the next moment.

[0089] In the specific implementation of this invention, the predictive control is established based on discrete time, as follows:

[0090] ;

[0091] ;

[0092] In the formula, The current moment; For the next moment; The sampling period; For the first One energy storage battery pack The state of charge at any given moment; For the first One energy storage battery pack The state of charge at any given moment; For the first Rated capacity of each energy storage battery pack; For the first The charge / discharge efficiency function of an energy storage battery pack; For the first The output power of the energy storage battery pack.

[0093] Furthermore, the specific process of constructing the objective function, which includes a fuel consumption term and a battery state-of-charge maintenance term, with the goal of minimizing fuel consumption includes:

[0094] The characteristic curve of the generator set is fitted using a cubic polynomial, and the fitting coefficients are obtained using the least squares method. The cubic polynomial is as follows:

[0095] ;

[0096] The instantaneous fuel consumption is then expressed as:

[0097] ;

[0098] Based on the instantaneous fuel consumption of the generator set, an objective function is established with the goal of minimizing fuel consumption, comprising a fuel consumption term and a battery state-of-charge maintenance term, as follows:

[0099] ;

[0100] ;

[0101] In the formula, The output power of the generator set. In order to be in Fuel consumption rate per kWh (g / kWh); The fitting coefficients are obtained by fitting the fuel consumption rate curve with experimental data. This refers to instantaneous fuel consumption. For finite prediction time domain; At any moment The instantaneous fuel consumption at any given time is related to the power of the generator set; For the first SOC reference value for an energy storage battery pack; For the first One energy storage battery pack The state of charge at any given moment; and These are weighting coefficients used to adjust fuel economy and battery SOC, respectively.

[0102] Furthermore, the step of calculating the output power command corresponding to each energy storage battery pack and the output power command of the generator set at the next moment using the objective function includes:

[0103] Introducing constraints, specifically the power balance equation constraint, whereby the sum of the power of the generator set and at least two energy storage battery packs must satisfy the load demand, as follows:

[0104] ;

[0105] Limit the upper and lower limits of the generator set's output power to keep the generator set at its optimal operating point or optimal operating range, as follows:

[0106] ;

[0107] For each energy storage battery pack, the battery power is limited to ensure that the charging and discharging power is within the acceptable range as follows:

[0108] ;

[0109] The state of charge (SOC) of each energy storage battery pack is strictly limited to a set safe range, as follows:

[0110]

[0111] Under the above constraints, the output power command of each energy storage battery pack and the output power command of the generator set at the next moment are calculated using the objective function.

[0112] in, In order to be in Load power at any given moment; For generator sets in Output power at any given moment; For the first energy storage battery pack in Output power at any given moment; For the second energy storage battery pack in Output power at any given moment; For the m-th energy storage battery pack in Output power at any given moment; For generator sets in The optimal output power at any given time; This is the lower limit of the engine set's output power; This is the upper limit of the generator set's output power; The optimal charge / discharge power for the m-th energy storage battery pack; This is the upper limit of the charging power of the energy storage battery pack; This is the upper limit of the discharge power of the energy storage battery pack; Let m be the SOC value of the m-th energy storage battery pack; This is the lower limit of the SOC value; This represents the upper limit of the SOC value.

[0113] Specifically, in each control cycle, the goal of the top-level MPC (predictive control) is to dynamically allocate the output power of the generator set and at least two energy storage battery packs while satisfying the charge constraints of the energy storage battery packs and the power balance of the system, so as to make the generator set operate in the optimal operating range and minimize fuel consumption.

[0114] In the top-level predictive control, the state of charge of at least two sets of energy storage battery packs is used as the state variable, and the load power is... The flight profile is predetermined as known feedforward information; then, discrete-time predictive control is established for the state of charge of the energy storage battery pack, as follows:

[0115] ;

[0116] ;

[0117] In the formula, The current moment; For the next moment; The sampling period; For the first One energy storage battery pack The state of charge at any given moment; For the first One energy storage battery pack The state of charge at any given moment; For the first Rated capacity of each energy storage battery pack; For the first The charge / discharge efficiency function of an energy storage battery pack; For the first The output power of the energy storage battery pack.

[0118] A fuel consumption model for the generator set was established. To meet the requirements of cruise operation, the generator set's operating point was adjusted to its most efficient operating range. To accurately describe the relationship between fuel consumption rate and output power, a cubic polynomial model was used to fit the generator set's characteristic curve, and the fitting coefficients were obtained using the least squares method. The cubic polynomial is as follows:

[0119] ;

[0120] The instantaneous fuel consumption is then expressed as:

[0121] ;

[0122] Based on the instantaneous fuel consumption of the generator set, an objective function is established with the goal of minimizing fuel consumption, comprising a fuel consumption term and a battery state-of-charge maintenance term, as follows:

[0123] ;

[0124] ;

[0125] In the formula, The output power of the generator set. In order to be in Fuel consumption rate per kWh (g / kWh); The fitting coefficients are obtained by fitting the fuel consumption rate curve with experimental data. This refers to instantaneous fuel consumption. For finite prediction time domain; At any moment The instantaneous fuel consumption at any given time is related to the power of the generator set; For the first SOC reference value for an energy storage battery pack; For the first One energy storage battery pack The state of charge at any given moment; and These are weighting coefficients used to adjust fuel economy and battery SOC, respectively.

[0126] To ensure the safe operation of the hybrid electric propulsion system, the following constraints need to be introduced: First, the output power of the generator set and the sum of the output power of at least two energy storage battery sets should meet the load requirements, as follows:

[0127] ;

[0128] Secondly, limit the upper and lower limits of the generator set's output power to keep the generator set at its optimal operating point or optimal operating range, as follows:

[0129] ;

[0130] For each energy storage battery pack, the battery power is limited to ensure that the charging and discharging power is within the acceptable range as follows:

[0131] ;

[0132] To ensure safety redundancy, the SOC of each energy storage battery pack is strictly limited to a set safety range, as follows:

[0133]

[0134] Under the above constraints, the output power command of each energy storage battery pack and the output power command of the generator set at the next moment are calculated using the objective function.

[0135] in, In order to be in Load power at any given moment; For generator sets in Output power at any given moment; For the first energy storage battery pack in Output power at any given moment; For the second energy storage battery pack in Output power at any given moment; For the m-th energy storage battery pack in Output power at any given moment; For generator sets in The optimal output power at any given time; This is the lower limit of the engine set's output power; This is the upper limit of the generator set's output power; The optimal charge / discharge power for the m-th energy storage battery pack; This is the upper limit of the charging power of the energy storage battery pack; This is the upper limit of the discharge power of the energy storage battery pack; Let m be the SOC value of the m-th energy storage battery pack; This is the lower limit of the SOC value; This represents the upper limit of the SOC value.

[0136] At each sampling time, MPC solves the above optimization problem to obtain the optimal control sequence, and uses the first element as the engine power command at the current time. At the next time, it is re-solved based on the updated SOC measurement value to achieve rolling optimization.

[0137] S103: When the generator set is controlled by the output power command of the generator set at the next moment, the droop coefficient corresponding to each DAB converter is obtained based on the output power command corresponding to each energy storage battery pack at the next moment.

[0138] In a specific implementation of this invention, obtaining the droop coefficient corresponding to each DAB converter based on the output power command corresponding to each energy storage battery pack at the next moment includes: generating a reference value of the DC voltage of the m-th DAB converter based on the current value output by the m-th DAB converter. as follows:

[0139] ;

[0140] In parallel operation, if the DC voltage of each DAB converter is equal, then:

[0141] ;

[0142] Then we have:

[0143] ;

[0144] Therefore, the relationship between the output power and the droop coefficient of each energy storage battery pack is as follows:

[0145] ;

[0146] Then we have:

[0147] ;

[0148] When calculating the droop factor corresponding to the m-th DAB converter based on the allowable DC bus voltage deviation and the top-level power allocation result, let the allowable DC bus voltage droop be... Then the reference current of the m-th DAB converter is:

[0149] ;

[0150] The droop coefficient of the m-th DAB converter is as follows:

[0151] ;

[0152] in, The given reference voltage for the DC bus; Let be the droop coefficient of the m-th DAB converter; Let m be the output current of the m-th DAB converter; , ; Let be the output power of the m-th energy storage battery pack.

[0153] Specifically, by establishing the optimal power obtained from the MPC solution... The droop coefficient of multiple DAB converters Based on the mathematical relationship, and utilizing the VI characteristic, a reference voltage value for the DC voltage is generated according to the DC circuit of the branch output. ,as follows:

[0154] ;

[0155] in, For a given voltage value, Let m be the droop coefficient of the m-th DAB converter. Let be the magnitude of the DC current output by the m-th DAB converter; therefore, the bus voltage... Represented as:

[0156]

[0157] In parallel operation, the DC voltage of each DAB converter is equal, therefore:

[0158] ;

[0159] Further results were obtained:

[0160] ;

[0161] Therefore, the relationship between the output power and the droop coefficient of each energy storage battery pack is as follows:

[0162] ;

[0163] Then we have:

[0164] ;

[0165] When calculating the droop factor corresponding to the m-th DAB converter based on the allowable DC bus voltage deviation and the top-level power allocation result, let the allowable DC bus voltage droop be... Then the reference current of the m-th DAB converter is:

[0166] ;

[0167] The droop coefficient of the m-th DAB converter is as follows:

[0168] ;

[0169] in, The given reference voltage for the DC bus; Let be the droop coefficient of the m-th DAB converter; Let m be the output current of the m-th DAB converter; , ; Let be the output power of the m-th energy storage battery pack.

[0170] Through the above calculation process, the droop coefficient corresponding to each DAB converter can be accurately calculated.

[0171] S104: Based on the reference voltage of the DC bus, the droop coefficient of each DAB converter and the output current of each DAB converter, calculate the voltage reference value of each DAB converter at the next moment, and take minimizing the voltage deviation value as the optimization objective. Combined with the constraints, obtain the optimal shift ratio at the next moment.

[0172] In the specific implementation of this invention, the voltage reference value of each DAB converter at the next moment is calculated based on the reference voltage of the DC bus, the droop coefficient corresponding to each DAB converter, and the output current of each DAB converter, as follows:

[0173] ;

[0174] In the formula, This represents the droop coefficient corresponding to the m-th DAB converter; This is the reference voltage for the DC bus. For the m-th DAB converter in Voltage reference value at any given time; For the m-th DAB converter in Output current at any given time.

[0175] Furthermore, the step of minimizing the voltage deviation value as the optimization objective, combined with constraints, to obtain the optimal shift ratio at the next time step includes:

[0176] The discrete state equations for each DAB converter are established as follows:

[0177] ;

[0178] Then, an objective function is constructed with minimizing the voltage deviation as the optimization objective to obtain the optimal shift ratio at the next time step, as follows:

[0179] ;

[0180] The constraints on the control variables are as follows:

[0181] ;

[0182] The DC bus output voltage must also not exceed the set variation range as required by the standard, under the following constraints:

[0183] ;

[0184] Each DAB converter in The constraint on the output current at any given time is:

[0185] ;

[0186] in, For DC bus in Output voltage at any given moment; DC bus at Output voltage at any given moment; For the m-th DAB converter in Voltage reference value at any given time; For the m-th DAB converter in Output voltage at any given moment; The m-th DAB converter in Output current at any given moment; and For the corresponding weight values, ; For the m-th DAB converter in Output voltage at any given moment; This refers to the input voltage of the DAB converter; This refers to the turns ratio of the high-frequency isolation transformer in the DAB converter from the primary side to the secondary side. The leakage inductance of the transformer is referred to the primary side of the high-frequency isolation transformer; The output capacitor of the DAB converter; The sampling period; For the m-th DAB converter in Compared to the shift in time; For finite prediction time domain.

[0187] Specifically, the underlying MPC uses the nonlinear model of the DAB converter to solve for the optimal shift ratio based on the droop coefficient given by the top layer, thereby achieving power regulation and bus voltage stability.

[0188] The bottom layer receives the droop control command (formed by the droop coefficient) from the top layer and calculates the voltage reference value for each DAB converter at the next moment, as follows:

[0189] ;

[0190] In the formula, This represents the droop coefficient corresponding to the m-th DAB converter; This is the reference voltage for the DC bus. For the m-th DAB converter in Voltage reference value at any given time; For the m-th DAB converter in Output current at any given time.

[0191] The discrete state equations of the DAB converter are shown below:

[0192] ;

[0193] The underlying layer employs distributed multi-objective MPC, aiming to: rapidly track the top-level power command, stabilize the command bus, and suppress drastic shift ratio jumps. To achieve this objective, a target function is constructed using minimizing the voltage deviation as the optimization objective to obtain the optimal shift ratio at the next time step, as follows:

[0194] ;

[0195] Then, the constraints of the underlying MPC controller are given; among them, the constraints of the control variables are as follows:

[0196] ;

[0197] The DC bus output voltage must also not exceed the set variation range as required by the standard, under the following constraints:

[0198] ;

[0199] Each DAB converter in The constraint on the output current at any given time is:

[0200] ;

[0201] in, For DC bus in Output voltage at any given moment; DC bus at Output voltage at any given moment; For the m-th DAB converter in Voltage reference value at any given time; For the m-th DAB converter in Output voltage at any given moment; The m-th DAB converter in Output current at any given moment; and For the corresponding weight values, ; For the m-th DAB converter in Output voltage at any given moment; This refers to the input voltage of the DAB converter; This refers to the turns ratio of the high-frequency isolation transformer in the DAB converter from the primary side to the secondary side. The leakage inductance of the transformer is referred to the primary side of the high-frequency isolation transformer; The output capacitor of the DAB converter; The sampling period; For the m-th DAB converter in Compared to the shift in time; For finite prediction time domain.

[0202] S105: Adjust and control each DAB converter based on the optimal shift ratio at the next time step.

[0203] In the specific implementation of this invention, after obtaining the optimal shift ratio at the next time step by taking the minimization of voltage deviation as the optimization objective and combining the constraint conditions, the optimal shift ratio at the next time step is used to adjust and control each DAB converter.

[0204] In this embodiment of the invention, a hierarchical model predictive control framework is constructed. At the energy management layer, fuel consumption and battery state of charge are simultaneously optimized through a multi-objective cost function, outputting engine power setpoint and droop coefficient to achieve globally optimal energy allocation. At the power control layer, a distributed multi-objective MPC (model predictive control) strategy for DAB converters is proposed. Based on its nonlinear power transfer model, a cost function containing fast voltage tracking and shift ratio smoothness is constructed to solve for the optimal shift ratio. Power regulation and bus voltage stability are achieved based on the top-level droop coefficient. This realizes the joint optimization of fuel economy, power quality, and converter reliability in the hybrid electric propulsion energy system.

[0205] Example 2, please refer to Figure 2 , Figure 2 This is a schematic diagram of the structural composition of a hierarchical control device based on a hybrid electric propulsion system in an embodiment of the present invention.

[0206] like Figure 2As shown, a hierarchical control device based on a hybrid electric propulsion system is applied to the hybrid electric propulsion system, which comprises a generator set, at least two energy storage battery packs, at least two DAB converters, several drive motors, and a DC bus. The at least two energy storage battery packs are connected in series and parallel with the generator set on the DC bus via at least two DAB converters, and the several drive motors are connected to the load side of the DC bus. The device includes:

[0207] Data acquisition module 201: used to acquire real-time data of the hybrid electric propulsion system, the real-time data including at least load power, energy storage battery status information of each of the at least two energy storage battery packs, real-time output voltage of the DC bus, and real-time output current of each DAB converter;

[0208] Output power command calculation module 202: Based on the load power in the real-time data and the energy storage battery status information of each energy storage battery pack, in predictive control, construct an objective function containing a fuel consumption term and a battery state of charge maintenance term with the goal of minimizing fuel consumption, and use the objective function to calculate the output power command corresponding to each energy storage battery pack and the output power command of the generator set at the next moment.

[0209] Droop coefficient calculation module 203: used to obtain the droop coefficient corresponding to each DAB converter based on the output power command corresponding to each energy storage battery pack at the next moment when the generator set is controlled by the output power command of the generator set at the next moment.

[0210] Optimal shift ratio calculation module 204: Based on the reference voltage of the DC bus, the droop coefficient corresponding to each DAB converter and the output current of each DAB converter, it calculates the voltage reference value of each DAB converter at the next moment, and obtains the optimal shift ratio at the next moment by minimizing the voltage deviation value as the optimization objective and combining the constraints.

[0211] Adjustment control module 205: used to adjust and control each DAB converter based on the optimal shift ratio at the next time step.

[0212] The specific implementation method of Embodiment 2 can be found in Embodiment 1, and will not be repeated here.

[0213] This invention provides a computer-readable storage medium storing a computer program. When executed by a processor, this program implements the hierarchical control method of any of the above embodiments. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic cards, or optical cards. In other words, the storage device includes any medium that stores or transmits information in a readable form by a device (e.g., a computer, a mobile phone), and can be a read-only memory, a disk, or an optical disk, etc.

[0214] This invention also provides a computer application running on a computer, which is used to execute the hierarchical control method of any of the above embodiments.

[0215] also, Figure 3 This is a schematic diagram of the structural composition of the electronic device in an embodiment of the present invention.

[0216] This invention also provides an electronic device, such as... Figure 3 As shown. The electronic device includes a processor 302, a memory 303, an input unit 304, and a display unit 305, among other devices. Those skilled in the art will understand that... Figure 3 The structural components of the illustrated electronic device do not constitute a limitation on all devices and may include more or fewer components than illustrated, or combine certain components. Memory 303 can be used to store application program 301 and various functional modules. Processor 302 runs application program 301 stored in memory 303, thereby performing various functional applications and data processing of the device. Memory can be internal memory or external memory, or both. Internal memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or random access memory. External memory may include hard disks, floppy disks, ZIP disks, USB flash drives, magnetic tapes, etc. The memory disclosed in this invention includes, but is not limited to, these types of memory. The memory disclosed in this invention is only an example and not a limitation.

[0217] Input unit 304 is used to receive signal input and user-input keywords. Input unit 304 may include a touch panel and other input devices. The touch panel can collect user touch operations on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel) and drive the corresponding connection device according to a pre-set program; other input devices may include, but are not limited to, one or more of physical keyboards, function keys (such as play control buttons, power buttons, etc.), trackballs, mice, joysticks, etc. Display unit 305 can be used to display user-input information or information provided to the user, as well as various menus of the terminal device. Display unit 305 may be in the form of a liquid crystal display, organic light-emitting diode, etc. Processor 302 is the control center of the terminal device, connecting various parts of the entire device through various interfaces and lines, and performing various functions and processing data by running or executing software programs and / or modules stored in memory 303, and calling data stored in memory.

[0218] As one embodiment, the electronic device includes: one or more processors 302, a memory 303, and one or more application programs 301, wherein the one or more application programs 301 are stored in the memory 303 and configured to be executed by the one or more processors 302, and the one or more application programs 301 are configured to execute the hierarchical control method corresponding to any of the embodiments described above.

[0219] In this embodiment of the invention, a hierarchical model predictive control framework is constructed. At the energy management layer, fuel consumption and battery state of charge are simultaneously optimized through a multi-objective cost function, outputting engine power setpoint and droop coefficient to achieve globally optimal energy allocation. At the power control layer, a distributed multi-objective MPC (model predictive control) strategy for DAB converters is proposed. Based on its nonlinear power transfer model, a cost function containing fast voltage tracking and shift ratio smoothness is constructed to solve for the optimal shift ratio. Power regulation and bus voltage stability are achieved based on the top-level droop coefficient. This realizes the joint optimization of fuel economy, power quality, and converter reliability in the hybrid electric propulsion energy system.

[0220] Furthermore, the above provides a detailed description of the hierarchical control method and related apparatus based on a hybrid electric propulsion system provided by the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A hierarchical control method based on a hybrid electric propulsion system, characterized in that, An application is made to a hybrid electric propulsion system, the hybrid electric propulsion system comprising a generator set, at least two energy storage battery packs, at least two DAB converters, several drive motors, and a DC bus, wherein at least two energy storage battery packs are connected in series and parallel with the generator set on the DC bus via at least two DAB converters, and several drive motors are connected to the load side of the DC bus, the method comprising: Obtain real-time data of the hybrid electric propulsion system, the real-time data including at least load power, energy storage battery status information of each of the at least two energy storage battery packs, real-time output voltage of the DC bus, and real-time output current of each DAB converter; Based on the load power and energy storage battery status information of each energy storage battery pack in the real-time data, in predictive control, an objective function containing fuel consumption and battery state of charge maintenance is constructed with the goal of minimizing fuel consumption. The objective function is then used to calculate the output power command corresponding to each energy storage battery pack and the output power command of the generator set at the next moment. When the generator set controls the generator set with the output power command at the next moment, the droop coefficient corresponding to each DAB converter is obtained based on the output power command corresponding to each energy storage battery pack at the next moment. Based on the reference voltage of the DC bus, the droop coefficient of each DAB converter, and the output current of each DAB converter, the voltage reference value of each DAB converter at the next moment is calculated. The optimal shift ratio at the next moment is obtained by minimizing the voltage deviation value as the optimization objective and combining the constraints. The optimal shift ratio for the next time step is used to adjust the control of each DAB converter.

2. The hierarchical control method according to claim 1, characterized in that, The predictive control is based on discrete time, as detailed below: ； ； In the formula, The current moment; For the next moment; The sampling period; For the first One energy storage battery pack The state of charge at any given moment; For the first One energy storage battery pack The state of charge at any given moment; For the first Rated capacity of each energy storage battery pack; For the first The charge / discharge efficiency function of an energy storage battery pack; For the first The output power of the energy storage battery pack.

3. The hierarchical control method according to claim 1, characterized in that, The specific process of constructing an objective function that includes a fuel consumption term and a battery state-of-charge maintenance term with the goal of minimizing fuel consumption includes: The characteristic curve of the generator set is fitted using a cubic polynomial, and the fitting coefficients are obtained using the least squares method. The cubic polynomial is as follows: ； The instantaneous fuel consumption is then expressed as: ； Based on the instantaneous fuel consumption of the generator set, an objective function is established with the goal of minimizing fuel consumption, comprising a fuel consumption term and a battery state-of-charge maintenance term, as follows: ； ； In the formula, The output power of the generator set. In order to be in Fuel consumption rate per kWh (g / kWh); The fitting coefficients are obtained by fitting the fuel consumption rate curve with experimental data. This refers to instantaneous fuel consumption. For finite prediction time domain; At any moment The instantaneous fuel consumption at any given time is related to the power of the generator set; For the first SOC reference value for an individual energy storage battery pack; For the first One energy storage battery pack The state of charge at any given moment; and These are weighting coefficients used to adjust fuel economy and battery SOC, respectively.

4. The hierarchical control method according to claim 1, characterized in that, The step of calculating the output power command for each energy storage battery pack and the output power command for the generator set at the next moment using the objective function includes: Introducing constraints, specifically the power balance equation constraint, whereby the sum of the power of the generator set and at least two energy storage battery packs must satisfy the load demand, as follows: ； Limit the upper and lower limits of the generator set's output power to keep the generator set at its optimal operating point or optimal operating range, as follows: ； For each energy storage battery pack, the battery power is limited to ensure that the charging and discharging power is within the acceptable range as follows: ； The state of charge (SOC) of each energy storage battery pack is strictly limited to a set safe range, as follows: Under the above constraints, the output power command of each energy storage battery pack and the output power command of the generator set at the next moment are calculated using the objective function. in, In order to be in Load power at any given moment; For generator sets in Output power at any given moment; For the first energy storage battery pack in Output power at any given moment; For the second energy storage battery pack in Output power at any given moment; For the m-th energy storage battery pack in Output power at any given moment; For generator sets in The optimal output power at any given time; This is the lower limit of the engine set's output power; This is the upper limit of the generator set's output power; The optimal charge / discharge power for the m-th energy storage battery pack; This is the upper limit of the charging power of the energy storage battery pack; This is the upper limit of the discharge power of the energy storage battery pack; Let m be the SOC value of the m-th energy storage battery pack; This is the lower limit of the SOC value; This represents the upper limit of the SOC value.

5. The hierarchical control method according to claim 1, characterized in that, The process of obtaining the droop coefficient for each DAB converter based on the output power command corresponding to each energy storage battery pack at the next time step includes: A reference value for the DC voltage of the m-th DAB converter is generated based on the current value output by the m-th DAB converter. as follows: ； In parallel operation, if the DC voltage of each DAB converter is equal, then: ； Then we have: ； Therefore, the relationship between the output power and the droop coefficient of each energy storage battery pack is as follows: ； Then we have: ; When calculating the droop factor corresponding to the m-th DAB converter based on the allowable DC bus voltage deviation and the top-level power allocation result, let the allowable DC bus voltage droop be... Then the reference current of the m-th DAB converter is: ； The droop coefficient of the m-th DAB converter is as follows: ； in, The given reference voltage for the DC bus; Let be the droop coefficient of the m-th DAB converter; Let m be the output current of the m-th DAB converter; , ; Let be the output power of the m-th energy storage battery pack.

6. The hierarchical control method according to claim 1, characterized in that, Based on the reference voltage of the DC bus, the droop coefficient corresponding to each DAB converter, and the output current of each DAB converter, the voltage reference value of each DAB converter at the next moment is calculated as follows: ； In the formula, This represents the droop coefficient corresponding to the m-th DAB converter; This is the reference voltage for the DC bus. For the m-th DAB converter in Voltage reference value at any given time; For the m-th DAB converter in The output current at any given time.

7. The hierarchical control method according to claim 1, characterized in that, The process of minimizing the voltage deviation as the optimization objective, combined with constraints, to obtain the optimal shift ratio at the next time step includes: The discrete state equations for each DAB converter are established as follows: ； Then, an objective function is constructed with minimizing the voltage deviation as the optimization objective to obtain the optimal shift ratio at the next time step, as follows: ； The constraints on the control variables are as follows: ; The DC bus output voltage must also not exceed the set variation range as required by the standard, under the following constraints: ； Each DAB converter in The constraint on the output current at any given time is: ； in, For DC bus in Output voltage at any given moment; DC bus at Output voltage at any given moment; For the m-th DAB converter in Voltage reference value at any given time; For the m-th DAB converter in Output voltage at any given moment; The m-th DAB converter in Output current at any given moment; and For the corresponding weight values, ; For the m-th DAB converter in Output voltage at any given moment; This refers to the input voltage of the DAB converter; This refers to the turns ratio of the high-frequency isolation transformer in the DAB converter from the primary side to the secondary side. The leakage inductance of the transformer is referred to the primary side of the high-frequency isolation transformer; The output capacitor of the DAB converter; The sampling period; For the m-th DAB converter in Compared to the shift in time; For finite prediction time domain.

8. A hierarchical control device based on a hybrid electric propulsion system, characterized in that, The hierarchical control method as described in any one of claims 1-7 is used and applied to a hybrid electric propulsion system; the hybrid electric propulsion system comprises a generator set, at least two energy storage battery packs, at least two DAB converters, a plurality of drive motors, and a DC bus, wherein at least two energy storage battery packs are connected in series and parallel with the generator set on the DC bus using at least two DAB converters, and the plurality of drive motors are connected to the load side of the DC bus; the hierarchical control device includes: Data acquisition module: used to acquire real-time data of the hybrid electric propulsion system, the real-time data including at least load power, energy storage battery status information of each of the at least two energy storage battery packs, real-time output voltage of the DC bus, and real-time output current of each DAB converter; Output power command calculation module: Based on the load power and energy storage battery status information of each energy storage battery pack in the real-time data, in predictive control, construct an objective function containing fuel consumption and battery state of charge maintenance with the goal of minimizing fuel consumption, and use the objective function to calculate the output power command corresponding to each energy storage battery pack and the output power command of the generator set at the next moment. Droop coefficient calculation module: used to obtain the droop coefficient corresponding to each DAB converter based on the output power command corresponding to each energy storage battery pack at the next moment when the generator set's output power command is used to control the generator set. The optimal shift ratio calculation module is used to calculate the voltage reference value of each DAB converter at the next moment based on the reference voltage of the DC bus, the droop coefficient corresponding to each DAB converter, and the output current of each DAB converter. It uses minimizing the voltage deviation value as the optimization objective and combines it with the constraints to obtain the optimal shift ratio at the next moment. Adjustment control module: Used to adjust and control each DAB converter based on the optimal shift ratio at the next time step.

9. An electronic device comprising a processor and a memory, characterized in that, The processor runs computer programs or code stored in the memory to implement the hierarchical control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium for storing computer programs or code, characterized in that, When the computer program or code is executed by a processor, the hierarchical control method as described in any one of claims 1 to 7 is implemented.

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