Generator and battery power distribution method and device based on model predictive control

Abstract

The invention provides a generator and battery power distribution method and device based on model prediction control, and belongs to the technical field of power distribution. The method provided by the invention comprises the following steps: loading a preset dynamic model library; generating and calibrating a total load power prediction sequence based on the flight plan and the rotor aerodynamic power model; calling the dynamic model library to predict and control the rolling optimization to output the optimal power instruction sequence of the generator; in the rolling optimization execution process, bus voltage dynamic characteristics are monitored in real time, a graded response mechanism is started based on a turboshaft engine dynamic model when load sudden change occurs, and the power of a generator and the power of a battery are adjusted; in the load abrupt change response process, rolling optimization is executed again based on the adjusted power and the corresponding system state, meanwhile, feed-forward compensation is carried out on the bus voltage deviation, and the voltage is controlled to be within a stable interval set by rolling optimization by adjusting the power of the generator; and circularly executing until the flight task is finished, and determining the generator power and the battery power.

Description

Technical Field

[0001] This application relates to the field of power distribution technology, and in particular to a generator and battery power distribution method and apparatus based on model predictive control. Background Technology

[0002] In the vortex-electric hybrid system of a vertical takeoff and landing (VTOL) aircraft, the power distribution between the generator (driven by a turboshaft engine) and the battery is a core element ensuring the system's efficient and stable operation. On one hand, this hybrid system aims to combine the advantages of the high power density of the turboshaft engine and the flexible response of the battery. The turboshaft engine can provide continuous power to meet the needs of long flight time, while the battery can quickly replenish energy or absorb excess power to cope with load fluctuations. On the other hand, the omission of the DC / DC converter at the battery end in the architecture results in strong coupling between the generator output voltage and the battery voltage, causing the engine to be unable to operate at its optimal speed / power point independently of real-time load requirements. Therefore, precise power distribution is needed to coordinate the operating states of the two, ensuring that the engine operates in its most efficient range to improve fuel economy, while utilizing the battery's rapid response characteristics to smooth out load fluctuations, and avoiding overcharging and over-discharging of the battery to ensure its lifespan, ultimately achieving comprehensive optimization of system performance.

[0003] Currently, power distribution in eddy electric hybrid systems mostly employs rule-based logic threshold control. This method establishes fixed control logic (e.g., battery power for low loads, engine start-up for high loads) by presetting thresholds for parameters such as load power and battery state of charge (SOC), simplifying the control process and reducing system complexity. However, this method has shortcomings: rule-based control relies on pre-set logic and cannot anticipate changes in power demand during future flight missions (e.g., sudden loads during takeoff and landing), leading to frequent engine start-stops or prolonged operation under undesigned low-power conditions, resulting in a sharp deterioration in fuel efficiency; simultaneously, due to the lack of dynamic adaptation to voltage coupling characteristics, it exhibits lag in response to sudden load changes, easily causing bus voltage fluctuations and even damaging battery life due to exceeding charging and discharging power limits.

[0004] Therefore, there is an urgent need for a method that can predict future power demands by combining flight missions to achieve optimal, rapid, and coordinated allocation of generator and battery power. Summary of the Invention

[0005] In view of this, this application provides a generator and battery power allocation method and apparatus based on model predictive control, which can achieve optimal, rapid and coordinated allocation of generator and battery power by combining the prediction of future power demand in conjunction with flight mission.

[0006] Specifically, this application is implemented through the following technical solution:

[0007] The first aspect of this application provides a generator-battery power distribution method based on model predictive control, the method comprising:

[0008] During system initialization, a preset dynamic model library is loaded, which includes a dynamic model of the torque-speed-power of a turboshaft engine, a second-order RC equivalent circuit model of a battery, and a rotor aerodynamic power model.

[0009] Based on the flight plan and the rotor aerodynamic power model, a total load power prediction sequence for a future preset time domain is generated, and the total load power prediction sequence is calibrated using real-time data.

[0010] Based on the total load power prediction sequence, the turboshaft engine torque-speed-power dynamic model and the battery second-order RC equivalent circuit model are invoked to predict and control the rolling optimization output of the generator's optimal power command sequence in the future preset time domain; wherein, the rolling optimization takes a preset multi-objective function as the optimization objective and satisfies the constraints set based on the dynamic model library;

[0011] During the rolling optimization process, the dynamic characteristics of the bus voltage are monitored in real time. If a sudden load change is detected, within the constraints, a graded response mechanism is initiated based on the dynamic model of the turboshaft engine torque-speed-power to temporarily adjust the generator power and battery power.

[0012] During the load change response process, rolling optimization is re-executed based on the adjusted generator power, battery power and corresponding system state, while feedforward compensation is performed on the bus voltage deviation, and the generator power control voltage is adjusted to be within the stable range set by the rolling optimization.

[0013] The process of generating a total load power prediction sequence and adjusting the power is repeated until the flight mission ends, at which point the generator power and battery power are determined.

[0014] A second aspect of this application provides a generator and battery power distribution device based on model predictive control, the device comprising a loading module, a generation module, a prediction module, a processing module, and a determination module;

[0015] The loading module is used to load a preset dynamic model library during system initialization. The dynamic model library includes a dynamic model of torque-speed-power of a turboshaft engine, a second-order RC equivalent circuit model of a battery, and a rotor aerodynamic power model.

[0016] The generation module is used to generate a total load power prediction sequence within a preset time domain based on the flight plan and the rotor aerodynamic power model, and to calibrate the total load power prediction sequence using real-time data.

[0017] The prediction module is used to predict and control the rolling optimization output of the generator's optimal power command sequence in the future preset time domain based on the total load power prediction sequence and by calling the turboshaft engine torque-speed-power dynamic model and the battery second-order RC equivalent circuit model; wherein the rolling optimization takes a preset multi-objective function as the optimization objective and satisfies the constraints set based on the dynamic model library;

[0018] The processing module is used to monitor the dynamic characteristics of the bus voltage in real time during the rolling optimization process. If a sudden load change is detected, within the constraints, a graded response mechanism is initiated based on the dynamic model of the turboshaft engine torque-speed-power to temporarily adjust the generator power and battery power.

[0019] The processing module is also used to re-execute rolling optimization based on the adjusted generator power, battery power and corresponding system state during the load change response process, and to perform feedforward compensation for the bus voltage deviation, and to control the voltage within the stable range set by the rolling optimization by adjusting the generator power.

[0020] The determining module is used to repeatedly execute the process from generating the total load power prediction sequence to adjusting the power until the flight mission ends, and to determine the generator power and battery power.

[0021] The generator and battery power allocation method and apparatus based on model predictive control provided in this application, in the first aspect, determines the sudden increase or decrease of load by real-time monitoring of the dynamic characteristics of the bus voltage, and temporarily adjusts the generator power and battery power by initiating a graded response mechanism based on the dynamic model of the torque-speed-power of the turboshaft engine within the constraints, and re-executes rolling optimization based on the adjusted system state during the load change response process. This effectively solves the problem of the impact of sudden load changes on the original predicted optimal sequence, ensuring that power allocation can still maintain stability and optimization after the change. Secondly, by loading a dynamic model library containing information related to the turboshaft engine, battery, and rotor aerodynamics during system initialization, a total load power prediction sequence is generated and calibrated based on the flight plan and rotor aerodynamic power model. The dynamic model library is called to output the optimal power command sequence of the generator through model prediction control rolling optimization. Combined with load change response adjustment and full-process cyclic execution, the dynamic model library provides accurate physical model support for power allocation. The total load power prediction sequence clarifies the power allocation requirement benchmark. Rolling optimization is guided by multi-objective functions and meets equipment constraints, ensuring that power allocation takes into account core objectives such as fuel economy, battery life, and voltage stability. The load change response mechanism ensures the rationality of allocation under special operating conditions, and full-process cyclic execution realizes dynamic adaptation of power allocation. Ultimately, it can accurately and efficiently complete the power allocation between the generator and battery throughout the entire flight process, ensuring stable system operation and supporting the successful completion of flight missions. Attached Figure Description

[0022] Figure 1 A flowchart of the generator and battery power allocation method based on model predictive control provided in Embodiment 1 of this application;

[0023] Figure 2 This is a schematic diagram of the generator and battery power distribution device based on model predictive control provided in Embodiment 2 of this application. Detailed Implementation

[0024] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0025] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used herein are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0026] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0027] The following specific embodiments are given to illustrate the technical solution of this application in detail.

[0028] Figure 1 This is a flowchart illustrating the generator and battery power allocation method based on model predictive control provided in Embodiment 1 of this application. Please refer to... Figure 1 The method provided in this embodiment may include:

[0029] S101. Load the preset dynamic model library during system initialization.

[0030] The dynamic model library includes a dynamic model of torque-speed-power for turboshaft engines, a second-order RC equivalent circuit model for batteries, and a rotor aerodynamic power model.

[0031] Optionally, the turboshaft engine torque-speed-power dynamic model is used to describe the dynamic correlation between the turboshaft engine's output torque, operating speed, and corresponding generator output power, including engine fuel consumption characteristics and speed change rate limits; the battery second-order RC equivalent circuit model is based on a second-order RC circuit structure and is used to describe the dynamic correlation between the battery's open-circuit voltage, internal resistance, charge / discharge efficiency, SOC, and temperature; the rotor aerodynamic power model is used to describe the correlation between rotor aerodynamic torque, rotor speed, and airspeed, and is used to calculate the motor's electromagnetic power.

[0032] Specifically, the dynamic model library is a set of pre-set models used in the vortex-electric hybrid system of vertical takeoff and landing aircraft to support power distribution calculation and control, and is the core foundation for realizing coordinated power distribution between the generator and battery. The dynamic model library contains three types of models: the turboshaft engine torque-speed-power dynamic model, which accurately describes the core dynamic characteristics of the turboshaft engine. On the one hand, it establishes the correlation between output torque, operating speed, and generator output power, clarifying how speed changes affect power output; on the other hand, it includes engine fuel consumption characteristics (such as fuel consumption rate at different power levels) and speed change rate limits (such as maximum acceleration / deceleration rate). Its core function is to provide engine-side constraints and performance parameters for the optimized allocation of generator power, supporting the achievement of fuel economy goals.

[0033] For example, in one embodiment, the torque-speed-power dynamic model of a turboshaft engine can be expressed as:

[0034] ;

[0035] in, This refers to the generator's output power. Operating speed; This is the output torque.

[0036] The second-order RC equivalent circuit model of a battery is constructed based on a second-order RC circuit structure and is used to quantify the dynamic electrical characteristics of the battery. Specifically, it describes the relationship between open-circuit voltage, internal resistance, charge / discharge efficiency, and the battery's state of charge (SOC) and temperature (e.g., how internal resistance changes when SOC decreases, and the impact of temperature on charge / discharge efficiency). Its function is to provide battery-side constraints and state parameters for the dynamic adjustment of battery power, supporting the achievement of battery protection (avoiding overcharging, over-discharging, and abnormal temperature) and bus voltage stability goals.

[0037] Furthermore, the second-order RC equivalent circuit model of the battery is an equivalent circuit model describing the dynamic response of the aircraft's power battery. For multi-rotor aircraft that frequently undergo high-power acceleration and deceleration, this model can accurately characterize the transient changes in terminal voltage during load abrupt changes. Its discrete state-space equation can be expressed as:

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] in, , The voltage across the two RC circuits; , The resistor and capacitor are the components of the first RC circuit. , The resistor and capacitor are in the second RC circuit. , These are the battery open-circuit voltage and the battery internal resistance in ohms, respectively. , , , , , All are in battery state of charge. and temperature The functions are obtained through standard battery testing. In practical applications, these parameters are typically obtained using a two-dimensional lookup table (using...). and It is stored in the controller in the form of an index for real-time retrieval; This refers to the battery current. Battery voltage; This refers to the battery's rated capacity. To control the time step of the cycle; For a specific moment; This indicates the battery's state of charge.

[0043] The rotor aerodynamic power model is used to describe the relationship between rotor aerodynamic torque and rotor speed and airspeed (such as the change law of aerodynamic torque as the speed increases, the influence of airspeed on rotor load, etc.). Its core purpose is to calculate the electromagnetic power of the propulsion motor. By correlating aerodynamic torque with speed and combining the motor efficiency characteristics, the rotor load in the mechanical domain is transformed into the power demand in the electrical domain, providing key input for total load power prediction.

[0044] Furthermore, the core of the rotor aerodynamic power model is based on the propeller aerodynamic characteristics and motor dynamics. For the first... The power requirement of a propulsion unit (motor + propeller) can be modeled as follows:

[0045] ;

[0046] in, For the first The power required for each propulsion unit; These are the inherent aerodynamic characteristic parameters of the propeller, which can be obtained through wind tunnel testing or CFD simulation. air density; The diameter of the propeller; This refers to the motor speed; This refers to the output torque of the motor. For motor efficiency.

[0047] Total propulsion power , For the first The power required for each propulsion unit The number of propulsion units. The sequence of motor speed commands calculated by the Flight Control System (FCS). , is generation Direct input to the predicted sequence.

[0048] It should be noted that the dynamic model library is pre-existing in a module of the system and can be directly loaded when the system is powered on.

[0049] S102. Based on the flight plan and the rotor aerodynamic power model, generate a total load power prediction sequence within a preset time domain in the future, and calibrate the total load power prediction sequence using real-time data.

[0050] Specifically, the total load power prediction sequence is a continuous set of values ​​for the total electrical power required by a vertical takeoff and landing (VTOL) aircraft at each moment within a predetermined time domain (e.g., 5 seconds or 10 seconds in the future). In other words, it predicts in advance the total electrical power consumed by the aircraft at each moment over a future period. The total load power prediction sequence includes two core loads: first, the electromagnetic power of the propulsion motor (calculated using a rotor aerodynamic power model, supporting the core power requirements for takeoff, landing, and flight); and second, the power consumption of onboard equipment (such as the stable power requirements of navigation, communication, and sensing equipment).

[0051] Furthermore, real-time data refers to physical quantity data directly related to load power calculation, collected in real time by aircraft sensors during flight. Its core purpose is to calibrate the errors of the initial prediction sequence and ensure prediction accuracy. Real-time data specifically includes two categories: core measurement data, including real-time rotor speed (reflecting the actual operating state of the rotor) and real-time propulsion motor current (directly related to the actual output power of the motor); and auxiliary calibration data, which, depending on the system's sensing capabilities, may also include real-time airspeed (affecting rotor aerodynamic torque calculation) and real-time battery voltage (indirectly reflecting load matching).

[0052] In specific implementation, the following steps are taken: First, a propulsion motor torque command sequence for multiple moments within a future preset time domain is obtained based on flight plan calculations. Second, based on the propulsion motor torque command sequence, the rotor aerodynamic power model is invoked, and the rotor and motor rotational inertia and dynamic balance relationship are combined to solve for the rotor speed dynamic change sequence and the corresponding motor output torque sequence for multiple moments within the future preset time domain. Third, based on the motor output torque sequence, rotor speed dynamic change sequence, and propulsion motor efficiency characteristics, the propulsion motor electromagnetic power sequence for multiple moments within the future preset time domain is calculated. Fourth, the propulsion motor electromagnetic power sequence is superimposed with other airborne equipment power consumption sequences to obtain an initial prediction sequence of total load power. Fifth, by fusing real-time measured rotor speed and motor current data for corresponding moments, the initial prediction sequence of total load power is calibrated to generate a total load power prediction sequence for the future preset time domain.

[0053] Specifically, firstly, the propulsion motor torque command sequence is obtained from the flight management system based on the preset trajectory and mission parameters of the flight plan (such as takeoff, cruise, hovering, and landing phases). This sequence contains the target torque value for each moment in a preset future time domain (e.g., 100 moments divided into 0.1-second intervals within the next 10 seconds), which guides the propulsion motor to output the corresponding mechanical torque to drive the rotor. Next, based on this propulsion motor torque command sequence, the rotor aerodynamic power model in the dynamic model library is called, and the total rotational inertia parameter of the rotor and motor (reflecting the inertial characteristics of the rotating system) is introduced. According to the dynamic balance relationship (i.e., the resultant force of the motor output torque, rotor aerodynamic drag torque, and transmission loss torque determines the rate of change of rotational speed), the dynamic change of rotor speed at each moment in the preset future time domain is calculated by solving the torque balance equation (forming a speed sequence), and the actual torque value output by the motor at the corresponding moment is calculated (forming a motor output torque sequence). Then, based on the obtained motor output torque sequence and rotor speed dynamic change sequence, and combined with the efficiency characteristics of the propulsion motor (energy conversion efficiency curves at different speeds and torques), the electromagnetic power of the propulsion motor at each moment in the future preset time domain (i.e., the electrical power obtained by the motor from the bus) is obtained through the calculation method of "motor output torque × rotor speed ÷ motor efficiency", forming the propulsion motor electromagnetic power sequence. Next, this propulsion motor electromagnetic power sequence is superimposed moment-by-moment with the power consumption sequences of other pre-set airborne equipment (such as the power consumption of navigation equipment, communication systems, sensors, etc., divided into equal time intervals) to obtain the initial prediction sequence of total load power. Finally, the actual rotor speed and actual motor current data at corresponding moments are collected in real time by the speed sensors and current sensors on the aircraft. Data fusion algorithms (such as Kalman filtering) are used to compare and correct these real-time measurement data with the initial prediction sequence of total load power. When there is a deviation between the real-time speed and the predicted speed, the predicted value of the propulsion motor electromagnetic power at the corresponding moment is adjusted, ultimately generating the total load power prediction sequence for the future preset time domain that conforms to the actual operating conditions.

[0054] For example, in one embodiment, the flight management computer calculates the propulsion motor torque command sequence {T_cmd(k)} based on the timestamp within the next 0-10 seconds according to the flight plan.

[0055] The propulsion motor torque command sequence {T_cmd(k)} is input into the rotor aerodynamic power model to predict the future total load power prediction sequence:

[0056] J×dω / dt=T_motor(T_cmd(t))-T_aero(ω, V_air)-T_loss;

[0057] Where J is the total moment of inertia of the motor and rotor, T_motor is the motor output torque sequence, which is obtained from T_cmd through the motor MAP diagram, T_aero is the aerodynamic torque, which is a function of rotational speed ω and airspeed V_air (T_aero∝ω²), and T_loss is the transmission system loss torque.

[0058] Subsequently, the electromagnetic power sequence of the propulsion motor was calculated:

[0059] P_elec(k)=T_motor(k)×ω(k) / η_motor(ω, T);

[0060] Where P_elec(k) is the electromagnetic power sequence of the propulsion motor; T_motor(k) is the output torque sequence of the motor; and η_motor is the efficiency of the propulsion motor.

[0061] Finally, taking into account the power consumption sequences of other airborne equipment, the total load power prediction sequence is obtained:

[0062] P_load(k)=P_elec(k)+P_avionics;

[0063] Wherein, P_load(k) is the total load power prediction sequence; P_elec(k) is the propulsion motor electromagnetic power sequence; and P_avionics is the power consumption sequence of other airborne equipment.

[0064] S103. Based on the total load power prediction sequence, the turboshaft engine torque-speed-power dynamic model and the battery second-order RC equivalent circuit model are called, and the model predicts and controls the rolling optimization to output the generator optimal power command sequence in the future preset time domain.

[0065] The rolling optimization takes a preset multi-objective function as the optimization objective and satisfies the constraints set based on the dynamic model library.

[0066] Specifically, rolling optimization in model predictive control is the core execution logic within the model predictive control (MPC) framework, comprising two key stages: model prediction and rolling optimization. Model prediction, based on the total load power prediction sequence, invokes the dynamic model of the turboshaft engine's torque-speed-power and the battery's second-order RC equivalent circuit model to predict the dynamic changes in system states such as generator power, battery power, bus voltage, and battery SOC within a preset future time domain. Rolling optimization uses a preset multi-objective function as the optimization objective. Based on the above predictions and combined with constraints set by the dynamic model library, it solves for the optimal generator power allocation scheme within a preset future time domain. The core characteristic of rolling optimization in model predictive control is rolling; that is, each optimization only executes the power command at the current moment. The next control cycle will re-execute the "prediction-optimization" process based on the updated total load prediction and real-time system state, continuously correcting the commands at subsequent moments to adapt to dynamically changing operating conditions.

[0067] Optionally, the multi-objective function is a weighted integration of multiple control objectives, including minimizing the fuel consumption of the turboshaft engine, minimizing the deviation between the battery SOC and the preset target value, minimizing the fluctuation range of the bus voltage, minimizing the deviation between the battery temperature and the preset target temperature, suppressing sudden changes in generator power, and suppressing sudden changes in battery power.

[0068] Specifically, the multi-objective function can be expressed as:

[0069] J=Σ[α×mfuel(k)+β×(SOC(k)-SOCref)²+γ×ΔU(k)²+δ×(T_batt(k)-45)²+c×(Pgen(k)-Pgen(k-1))²+λ×(P_batt(k)-P_batt(k-1))²];

[0070] Where J is a multi-objective function; α is the weighting coefficient of fuel consumption; mfuel(k) is the fuel consumption of the engine at time k; β is the weighting coefficient of SOC deviation; SOC(k) is the SOC of the battery at time k; SOCref is the preset target value; γ is the weighting coefficient of bus voltage deviation; ΔU(k) ​​is the bus voltage fluctuation; δ is the weighting coefficient of battery temperature deviation; T_batt(k) is the battery temperature; c is the weighting coefficient of generator power change rate; Pgen(k) and Pgen(k-1) are the generator power at adjacent times; λ is the weighting coefficient of battery power change rate; P_batt(k) and P_batt(k-1) are the battery power at adjacent times.

[0071] It should be noted that the weighting coefficients in the multi-objective function J are used to balance multiple objectives under the typical rapidly changing load conditions of a pure multi-rotor aircraft. A systematic simulation tuning process is employed.

[0072] Voltage stability priority (γ): To ensure reliable operation of airborne equipment and prevent power surges, the weight (γ) of the bus voltage fluctuation term should be set to the highest.

[0073] Battery life protection is secondary (β, δ): To avoid overcharging and over-discharging of the battery during frequent load changes, the weights of the state of charge (SOC) tracking term (β) and the temperature term (δ) are set to medium.

[0074] Balancing economy and smoothness (α, c, λ): After meeting the above objectives, fuel economy is optimized by adjusting the weight (α), and power surges are suppressed by using smaller weights (c, λ) to ensure smooth system operation.

[0075] Based on the above principles, each weight coefficient is assigned a basic dimension (for example, setting the highest priority γ as a baseline value of 1.0), and the proportions of other coefficients are determined according to their relative importance. The adjusted relative weight proportions can be set as follows: This reflects a greater focus on voltage stability and battery life (due to frequent charge and discharge) in multi-rotor applications.

[0076] Optionally, the constraints include upper and lower limits of generator power and generator speed change rate constraints based on the dynamic model of torque-speed-power of turboshaft engine, and upper and lower limits of battery charging and discharging power and battery SOC based on the second-order RC equivalent circuit model of battery.

[0077] Specifically, the constraints of the dynamic model library can be expressed as:

[0078] Generator power upper and lower limit constraints: P_gen_min≤P_gen(k)≤P_gen_max;

[0079] Where P_gen(k) is the generator power; P_gen_min and P_gen_max are the upper and lower limits of the generator power, respectively.

[0080] Battery charging and discharging power upper and lower limit constraints: -P_chg_max≤P_batt(k)≤P_dis_max;

[0081] Where P_batt(k) is the battery charging and discharging power; -P_chg_max and P_dis_max are the upper and lower limits of the battery charging and discharging power, respectively.

[0082] Battery SOC upper and lower limit constraints: SOC_min≤SOC(k)≤SOC_max;

[0083] Where SOC(k) is the battery SOC; SOC_min and SOC_max are the upper and lower limits of the battery SOC, respectively.

[0084] Engine speed change rate constraint: |N_gen(k)-N_gen(k-1)| / Δt≤R_max;

[0085] Where Δt is the rate of change of generator speed; R_max is the upper limit of generator speed; N_gen(k) and N_gen(k-1) are the generator speeds at adjacent times.

[0086] In specific implementation, the system receives a total load power prediction sequence within a preset future time domain, using this sequence as the demand benchmark for power allocation. It then invokes the turboshaft engine torque-speed-power dynamic model to analyze the relationship between engine output power and fuel consumption, the feasible range of generator power, and the speed change rate limit at each moment, based on the power demand in the total load power prediction sequence. Next, it invokes the battery second-order RC equivalent circuit model, combining it with the total load power prediction sequence to analyze the dynamic relationship between battery power and SOC, bus voltage, and temperature at each moment, determining the feasible range of battery charging and discharging power. Based on the characteristics of the engine and battery sides, and combined with a multi-objective function, a rolling optimization problem is constructed within the preset future time domain. The generator power and battery power at each moment must satisfy the total load power balance relationship. Solving the rolling optimization problem yields candidate sequences of generator power at each moment within the preset future time domain. From these candidate sequences, the sequence that satisfies all constraints and optimizes the multi-objective function is selected as the optimal generator power command sequence, and the command for the current moment is output for execution. Commands for the remaining moments are updated in the next rolling optimization.

[0087] Specifically, the system receives a predicted sequence of total load power within a preset time domain. The power value at each moment in this sequence serves as the benchmark for the total electricity demand to be matched in subsequent power allocation. It then invokes a dynamic model of the turboshaft engine's torque-speed-power output to analyze the quantitative correlation between engine output power and fuel consumption at each moment in the predicted total load power sequence, using built-in characteristic curves (such as the power-fuel consumption rate mapping relationship at different speeds). Simultaneously, based on engine hardware parameters and safety thresholds, it determines the feasible range of generator power at that moment (including minimum stable output power and maximum allowable output power) and the required power output. Rate of change limits (e.g., maximum acceleration / deceleration per minute); a second-order RC equivalent circuit model of the battery is invoked, combined with the demand at each moment in the total load power prediction sequence, to calculate the dynamic trend of SOC change under different battery power (e.g., SOC decrease rate during discharge), bus voltage fluctuation amplitude (calculated based on voltage drop due to internal resistance and current), and battery temperature change (combined with the charging / discharging power and heat loss model), and the feasible range of battery charging / discharging power at each moment is determined based on battery safety characteristics (e.g., maximum discharge power, maximum charging power, upper and lower limits of SOC); based on the above analysis, the starting... Based on the characteristics of the engine side (fuel consumption correlation, power range, speed limit) and the battery side (power correlation with SOC / voltage / temperature, charge / discharge range), a multi-objective function (including sub-items such as fuel consumption, SOC deviation, and voltage fluctuation) is used as the optimization objective. A rolling optimization problem is constructed within a future preset time domain, where the generator power and battery power at each moment must satisfy the balance relationship of "generator power + battery power = total load power at that moment", while also meeting various constraints of the engine and battery. An optimization algorithm (such as quadratic programming) is used to solve this rolling optimization problem to obtain the results within the future preset time domain. Multiple generator power candidate values ​​at each time moment are combined to form multiple generator power candidate sequences. From these candidate sequences, the sequence that satisfies the constraints such as engine power range, speed change rate limit, and battery charge / discharge range at all times, and minimizes the sum of multiple objective functions, is selected as the optimal generator power command sequence. Then, the power command at the current time moment in this sequence is extracted and sent to the actuator to control the generator operation. The power commands at the remaining times in the sequence are temporarily stored and updated in the next control cycle when rolling optimization is re-executed based on the updated total load power prediction sequence and the real-time system status.

[0088] For example, in one embodiment, power balance can be expressed as:

[0089] P_batt(k)=P_load(k)-P_gen(k);

[0090] Where P_batt(k) is the power of the battery at time k; P_load(k) is the total load power at time k; and P_gen(k) is the output power of the generator at time k.

[0091] Battery SOC dynamics can be expressed as:

[0092] SOC(k+1)=SOC(k)-(η(P_batt(k))×P_batt(k)×Δt) / E_batt_max;

[0093] Where SOC(k+1) and SOC(k) are the SOC of the battery at times k+1 and k, respectively; η is the power-related charge and discharge efficiency; P_batt(k) is the power of the battery at time k; Δt is the time step of the control cycle; and E_batt_max is the maximum capacity of the battery.

[0094] The dynamics of the bus voltage can be expressed as:

[0095] I_batt(k)=P_batt(k) / U_bus(k);

[0096] U(k)=U_ocv(SOC(k))-I_batt(k)×R_internal(SOC(k),T_batt(k));

[0097] Where I_batt(k) is the battery current at time k; P_batt(k) is the battery power at time k; U_bus(k) is the bus voltage at time k; U(k) is the actual output voltage of the battery in the working state; U_ocv(SOC(k)) is the open circuit voltage of the battery; R_internal(SOC(k),T_batt(k)) is the time-varying internal resistance of the battery; and T_batt(k) is the battery temperature at time k.

[0098] The engine fuel consumption model can be expressed as:

[0099] m_fuel(t) = f(P_gen(t));

[0100] Where m_fuel(t) is the engine fuel consumption at time t; P_gen(t) is the generator output power at time t; and f(·) is a lookup function or fitting function based on the given fuel consumption data.

[0101] It should be noted that in a pure multi-rotor hybrid power system, the engine fuel consumption function m_fuel(t) = f(P_gen(t)) characterizes the static mapping from generator output power to fuel consumption. Since the engine is typically directly connected to the generator and operates within a narrow optimized speed range to simplify control, this mapping can often be simplified to primarily be related to P_gen(t). Its specific form is obtained through bench calibration tests of the engine-generator set at the expected operating speeds.

[0102] ;

[0103] in, For the first Engine fuel consumption at any given time; The fuel consumption rate mapping function represents the fuel consumption per unit power and per unit time. It is obtained through bench steady-state characteristic tests and stored as a two-dimensional lookup table with engine speed and generator output power as indexes. For the first Engine speed at any given moment; For the first The generator's output electrical power at any given time; This is the runtime step.

[0104] S104. During the rolling optimization process, the dynamic characteristics of the bus voltage are monitored in real time. If a sudden load change is detected, within the constraints, a graded response mechanism is initiated based on the dynamic model of the turboshaft engine torque-speed-power to temporarily adjust the generator power and battery power.

[0105] Specifically, the dynamic characteristics of bus voltage refer to the real-time changes in DC bus voltage over time, including the instantaneous voltage value, rate of change (the amount of voltage change per unit time), and voltage deviation (the difference between the actual voltage and the target stable voltage). These characteristics directly reflect the power matching status between the generator, battery, and total load: when the load is stable, the bus voltage usually fluctuates slightly within the target range; when the load changes abruptly, the voltage will experience a rapid surge (when the load is suddenly unloaded) or a sudden drop (when the load is suddenly increased), and its rate of change and deviation will exceed the normal range.

[0106] Furthermore, load mutation refers to a situation where the total load power of a vertical takeoff and landing aircraft changes drastically and rapidly within a short period of time (e.g., milliseconds to seconds). It mainly includes two types: load surge and load deceleration. Load surge refers to a sudden and significant increase in total load power (e.g., a sudden increase in rotor load due to a sudden maneuver or the temporary start-up of high-power onboard equipment). In this case, it is necessary to quickly replenish the power to avoid a sharp drop in bus voltage. Load deceleration refers to a sudden and significant decrease in total load power (e.g., a sudden drop in rotor load due to a sudden change in airflow or an emergency shutdown of onboard equipment). In this case, it is necessary to quickly absorb the excess power to avoid a sharp rise in bus voltage.

[0107] In practice, the bus voltage change rate and voltage deviation are monitored in real time. If the voltage change rate is greater than the first preset threshold and the voltage deviation is greater than the second preset threshold, it is determined to be a sudden load shedding. If the voltage change rate is less than the third preset threshold and the voltage deviation is less than the fourth preset threshold, it is determined to be a sudden load increase. Different adjustment methods are matched based on the different levels of response to sudden load shedding and sudden load increase, and the adjusted generator power and battery power are obtained respectively.

[0108] Optionally, different adjustment methods are matched based on different levels of response to load unloading and load surge, respectively, to obtain the adjusted generator power and battery power. These methods include: for the first level of load unloading response, limiting the battery charging power to no more than a preset charging upper limit, calculating the excess power after load unloading, and instantaneously reducing the generator power based on the constraint conditions; for the second level of load unloading response, invoking the turboshaft engine torque-speed-power dynamic model, adjusting the engine throttle control speed back to the target value based on the dynamic correlation between engine speed and power, and controlling the generator power to smoothly transition to a state matching the load after unloading; for the first level of load surge response, controlling the battery to replenish energy with a preset maximum discharge power; for the second level of load surge response, based on the maximum engine power increase rate limited by the turboshaft engine torque-speed-power dynamic model, gradually increasing the generator power, while simultaneously smoothly reducing the battery discharge power as the generator power increases until it exits the state.

[0109] Specifically, the system monitors the real-time changes in bus voltage and calculates the bus voltage change rate (the amount of voltage change per unit time) and voltage deviation (the difference between the actual bus voltage and the preset target stable voltage). The monitored voltage change rate is compared with the first and third preset thresholds, and the voltage deviation is compared with the second and fourth preset thresholds. If the voltage change rate is greater than the first preset threshold and the voltage deviation is greater than the second preset threshold, a sudden load shedding is determined to have occurred. If the voltage change rate is less than the third preset threshold and the voltage deviation is less than the fourth preset threshold, a sudden load increase is determined to have occurred. For the identified load shedding, a two-stage response is executed: The first stage (within 0-100ms) first limits the battery charging power according to the preset battery safety charging parameters to ensure that it does not exceed the preset charging upper limit. At the same time, the excess power after the load shedding (i.e., the difference between the total output power before the shedding and the actual load power after the shedding) is calculated. Then, based on the constraints set by the dynamic model library (such as the minimum output power of the engine, the speed change rate limit, etc.), the generator output power is instantaneously reduced to quickly absorb the excess power. The second stage (within 100-500ms) calls the dynamic model of the turboshaft engine torque-speed-power. Through the model, the dynamic correlation between engine speed and output power (such as the influence of speed change on power output) is analyzed. The engine throttle is adjusted to control the speed to smoothly return to the target value, so that the generator output power gradually and smoothly transitions to a state that matches the load power after the shedding. For a detected load surge, a two-stage response is implemented: The first stage (0-100ms) determines the preset maximum discharge power based on the battery's current state (e.g., SOC, temperature), controlling the battery to rapidly output electrical energy at this power to fill the power gap caused by the surge. The second stage (100-1000ms) gradually increases the generator output power based on the maximum engine power increase rate (e.g., the maximum allowable power increase per unit time) defined in the turboshaft engine torque-speed-power dynamic model. Simultaneously, as the generator power increases, the battery discharge power is smoothly reduced proportionally (e.g., linearly reduced or adjusted according to the generator power ratio) until the battery discharge power drops to zero and recharging ceases. The final result is the temporarily adjusted generator and battery power. It should be noted that the battery charging power, generator power, and engine speed are five different values ​​for load relief and load surge.

[0110] S105. During the load change response process, rolling optimization is re-executed based on the adjusted generator power, battery power and corresponding system state. At the same time, feedforward compensation is performed on the bus voltage deviation, and the generator power control voltage is adjusted to be within the stable range set by the rolling optimization.

[0111] Specifically, rolling optimization is re-executed after a load mutation response because the load mutation causes a deviation between the actual system state (adjusted generator power, battery power, battery SOC, bus voltage, engine speed, etc.) and the initial state and load prediction sequence of the original rolling optimization. The original optimal generator power command sequence is no longer suitable for the post-mutation operating conditions, and the temporary adjustment only focuses on quickly suppressing disturbances without taking into account global multi-objective optimization (such as fuel economy and battery life protection). By re-executing rolling optimization, the optimization problem can be reconstructed and solved based on the updated system state and the actual load demand after the mutation, combined with the constraints and multi-objective functions of the dynamic model library. This ensures that subsequent power allocation not only connects with the temporary adjustment actions but also returns to the global optimal objective, avoiding uncoordinated power allocation, inefficient equipment operation, or damage caused by state deviations in subsequent operating conditions.

[0112] It should be noted that the voltage is controlled within the stable range because the vortex-electric hybrid system of the vertical takeoff and landing aircraft adopts an architecture in which the battery and generator are directly connected in parallel to the DC bus. The bus voltage is a core indicator of power quality, and the voltage is strongly coupled with the operating state of the battery and generator. If the voltage exceeds the stable range, on the one hand, it will directly affect the normal operation of airborne electronic equipment such as navigation, communication, and sensing, and in severe cases, it may lead to equipment failure or performance failure, threatening flight safety. On the other hand, overvoltage may cause battery overcharging and generator power exceeding the limit, while undervoltage may cause battery over-discharging and insufficient propulsion motor power, all of which will damage the life of core equipment and disrupt the system power balance. At the same time, voltage stability is one of the key objectives of multi-objective optimization. Controlling it within the stable range can ensure the effectiveness of power distribution and the stability and reliability of system operation, laying the foundation for coordinated control under subsequent operating conditions.

[0113] In specific implementation, the adjusted real-time generator power, real-time battery power, and corresponding system state parameters (including battery SOC, bus voltage, engine speed, and battery temperature) in the load surge response are synchronized to the model predictive control rolling optimization module as new initial conditions. The rolling optimization module, based on the turboshaft engine torque-speed-power dynamic model and the battery second-order RC equivalent circuit model, uses the updated state parameters as a starting point and combines them with the latest generated total load power prediction sequence to reconstruct the rolling optimization problem. This problem uses a preset multi-objective function as the optimization objective and satisfies constraints set based on the dynamic model library. Solving the reconstructed rolling optimization problem outputs an updated sequence of optimal generator power commands for the future preset time domain, ensuring seamless integration between this sequence and the temporary adjustment actions in the load surge response. The specific working principle of the rolling optimization module can be found in the description of the above embodiments, and will not be repeated here.

[0114] Optionally, feedforward compensation is also performed on the bus voltage deviation by adjusting the generator power control voltage within the stable range set by rolling optimization. This includes: real-time monitoring of the deviation between the bus voltage and the target voltage set by rolling optimization; if the voltage deviation exceeds the stable range set by rolling optimization, calculating the power compensation amount to offset the deviation based on the dynamic correlation between voltage and power in the second-order RC equivalent circuit model of the battery; and adding the power compensation amount to the currently executed generator power command to pull the bus voltage back into the stable range by adjusting the generator output power in real time.

[0115] Specifically, the actual voltage value of the current bus and the target voltage value set by rolling optimization are monitored in real time. The difference between the actual voltage value and the target voltage value is calculated to obtain the bus voltage deviation. This voltage deviation is compared with the stable range set by rolling optimization (such as ±5% of the target voltage) to determine whether the voltage deviation exceeds the range. If it is determined that the voltage deviation has exceeded the stable range, the second-order RC equivalent circuit model of the battery is called. Based on the dynamic correlation between the bus voltage, battery power, and generator power described in the model (such as the voltage deviation being caused by current changes due to power imbalance through internal resistance voltage division), combined with the current battery voltage S... Using state parameters such as OC and temperature, the power compensation amount that can offset the voltage deviation (i.e., the additional generator power value that needs to be increased or decreased) is calculated. The calculated power compensation amount is superimposed on the currently executed generator power command (such as the current moment's optimal generator power command output by rolling optimization) to form a new generator power adjustment command. By executing this new command, the generator output power is adjusted in real time (such as by controlling the turboshaft engine throttle to change the speed, thereby adjusting the generator power output), so that the bus voltage gradually changes with the correction of the power balance relationship and is eventually pulled back into the stable range set by rolling optimization.

[0116] For example, in one embodiment, the voltage compensator is triggered when the predicted voltage deviation ΔU(k) ​​> 5%.

[0117] Calculate the power compensation amount based on the sensitivity coefficient identified online:

[0118] ΔP_comp = K_p × ΔU_desired;

[0119] Where ΔP_comp is the power compensation amount; K_p is the sensitivity coefficient of online identification; and ΔU_desired is the desired voltage deviation adjustment amount.

[0120] The power compensation amount is superimposed on the currently executed generator power command to form the final generator power command, which is then sent to the engine FADEC system for execution.

[0121] In addition, the system uses the generator power command as the main control variable, and the battery power is passively determined according to the real-time power balance equation, but is always limited by the dynamic power constraints set by the battery management system.

[0122] S106. Repeatedly execute the process from generating the total load power prediction sequence to adjusting the power until the flight mission ends, and determine the generator power and battery power.

[0123] Specifically, the following process is executed cyclically: A total load power prediction sequence for the future preset time domain is generated based on the flight plan and rotor aerodynamic power model, and calibrated using real-time data; based on this total load power prediction sequence, the turboshaft engine torque-speed-power dynamic model and the battery second-order RC equivalent circuit model are invoked, and the model predicts and controls the rolling optimization to output the optimal generator power command sequence for the future preset time domain; during the rolling optimization process, the dynamic characteristics of the bus voltage are monitored in real time. If a load mutation is detected, a graded response mechanism is initiated based on the turboshaft engine torque-speed-power dynamic model within constraints to temporarily adjust the generator power and battery power; during the load mutation response process, the rolling optimization is re-executed based on the adjusted generator power, battery power, and corresponding system state, while feedforward compensation is performed on the bus voltage deviation, adjusting the generator power control voltage within the stable range set by the rolling optimization; until the flight mission ends, the final generator power and battery power are determined based on the results of this optimization.

[0124] The method provided in this embodiment, in the first aspect, uses the prediction window of model predictive control to predict the power demand in advance for a period of time in the future, and at the same time combines rolling optimization to output the optimal power command sequence of the generator. Since the turboshaft engine has a natural response lag characteristic, the predicted power demand can reserve reaction time for the engine power adjustment. When the load changes, the system already has a preset power allocation scheme to adapt to it, which effectively solves the dynamic mismatch problem between engine response lag and sudden load changes, and ensures timely matching of power supply and demand.

[0125] Secondly, by using the second-order RC equivalent circuit model of the battery to analyze the dynamic relationship between voltage, power, SOC, and temperature, and by performing feedforward compensation for bus voltage deviation during load change response, the second-order RC equivalent circuit model of the battery can accurately depict the dynamic change law of bus voltage, making the analysis of the source of voltage deviation more accurate. The feedforward compensation mechanism calculates the targeted power compensation amount and adds it to the generator power command to offset the voltage deviation in real time. The synergistic effect of the two effectively suppresses bus voltage fluctuations and ensures the stable power quality required by airborne electronic equipment.

[0126] Thirdly, by adopting a total load power prediction sequence, calling the dynamic model of turboshaft engine torque-speed-power and the second-order RC equivalent circuit model of battery, and using a preset multi-objective function as the optimization objective, rolling optimization is carried out under the constraints set by the dynamic model library. The multi-objective function takes into account multiple core performance indicators such as fuel consumption, battery SOC deviation, and voltage fluctuation. The constraints clearly define the safe operation boundaries of the equipment, such as engine power range, speed change rate limit, and battery charging and discharging range. The rolling optimization continuously combines the latest system status and load prediction correction instructions through a "prediction-optimization-execution-update" cycle mode. The final generator optimal power instruction sequence can achieve global optimization of multiple objectives and ensure that the engine and battery operate within a safe and feasible range, thus taking into account both operating efficiency and equipment safety.

[0127] Fourthly, considering load surge response, load surges are determined by real-time monitoring of the dynamic characteristics of the bus voltage. A tiered response mechanism is then activated to temporarily adjust the generator and battery power. During the response process, rolling optimization is re-executed based on the adjusted generator power, battery power, and corresponding system state. The first level of the tiered response mechanism quickly suppresses the severe disturbances in the early stages of the surge by rapidly replenishing or limiting battery power and instantaneously adjusting generator power, thus avoiding significant voltage deviations or equipment impacts. The second level smoothly adjusts power through the engine dynamic model to achieve a transition. Re-executing rolling optimization corrects the system state deviations caused by load surges, allowing power allocation to return from temporary emergency adjustments to the global optimum. Simultaneously, feedforward compensation stabilizes the voltage, achieving both a rapid and stable response to load surges and ensuring the stability and optimization of subsequent system operation, avoiding operational inefficiencies or equipment damage caused by state deviations after a surge.

[0128] Corresponding to the aforementioned embodiment of a generator and battery power distribution method based on model predictive control, this application also provides an embodiment of a generator and battery power distribution device based on model predictive control.

[0129] Figure 2 This is a schematic diagram of the generator and battery power distribution device based on model predictive control provided in Embodiment 2 of this application. Please refer to... Figure 2 The apparatus provided in this embodiment includes a loading module 210, a generation module 220, a prediction module 230, a processing module 240, and a determination module 250.

[0130] The loading module 210 is used to load a preset dynamic model library during system initialization. The dynamic model library includes a dynamic model of the torque-speed-power of a turboshaft engine, a second-order RC equivalent circuit model of a battery, and a rotor aerodynamic power model.

[0131] The generation module 220 is used to generate a total load power prediction sequence within a preset time domain based on the flight plan and the rotor aerodynamic power model, and to calibrate the total load power prediction sequence using real-time data.

[0132] The prediction module 230 is used to predict and control the rolling optimization output of the generator's optimal power command sequence in the future preset time domain based on the total load power prediction sequence and by calling the turboshaft engine torque-speed-power dynamic model and the battery second-order RC equivalent circuit model; wherein the rolling optimization takes a preset multi-objective function as the optimization objective and satisfies the constraints set based on the dynamic model library;

[0133] The processing module 240 is used to monitor the dynamic characteristics of the bus voltage in real time during the rolling optimization process. If a sudden load change is determined, within the constraints, a graded response mechanism is initiated based on the dynamic model of the turboshaft engine torque-speed-power to temporarily adjust the generator power and battery power.

[0134] The processing module 240 is also used to re-execute rolling optimization based on the adjusted generator power, battery power and corresponding system state during the load change response process, and to perform feedforward compensation for the bus voltage deviation, and to control the voltage within the stable range set by the rolling optimization by adjusting the generator power.

[0135] The determining module 250 is used to repeatedly execute the process from generating the total load power prediction sequence to adjusting the power until the flight mission ends, and to determine the generator power and battery power.

[0136] The apparatus of this embodiment can be used to perform... Figure 1 The steps of the method embodiment shown are similar in principle and process, and will not be repeated here.

[0137] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0138] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0139] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for generator and battery power distribution based on model predictive control, characterized in that, The method comprises: The system initializes to load a preset dynamic model library, and the dynamic model library comprises a turboshaft engine torque-speed-power dynamic model, a battery second-order RC equivalent circuit model, and a rotor aerodynamic power model; Based on the flight plan and the rotor aerodynamic power model, a total load power prediction sequence in a future preset time domain is generated, and the total load power prediction sequence is calibrated through real-time data; Based on the total load power prediction sequence, the turboshaft engine torque-speed-power dynamic model and the battery second-order RC equivalent circuit model are called to output a generator optimal power instruction sequence in the future preset time domain through model predictive control rolling optimization; wherein the rolling optimization takes a preset multi-objective function as an optimization objective, and satisfies a constraint condition set based on the dynamic model library; During the execution of the rolling optimization, the bus voltage dynamic characteristics are monitored in real time, and if it is determined that a load mutation occurs, a hierarchical response mechanism is started based on the turboshaft engine torque-speed-power dynamic model within the constraint condition to temporarily adjust the generator power and the battery power; During the load mutation response process, the rolling optimization is re-executed based on the adjusted generator power, battery power and corresponding system state, and the bus voltage deviation is fed forwardly compensated, so that the voltage is controlled in the stable interval set by the rolling optimization through adjusting the generator power. The process from generating the total load power prediction sequence to adjusting the power is cyclically executed until the flight task is completed, and the generator power and the battery power are determined.

2. The method of claim 1, wherein, Based on the total load power prediction sequence, the turboshaft engine torque-speed-power dynamic model and the battery second-order RC equivalent circuit model are called to output a generator optimal power instruction sequence in the future preset time domain through model predictive control rolling optimization, comprising: Receiving a total load power prediction sequence in a future preset time domain, and taking the total load power prediction sequence as a demand reference for power distribution; Calling the turboshaft engine torque-speed-power dynamic model, according to the power demand at each time in the total load power prediction sequence, analyzing the correlation between the output power and fuel consumption of the engine at the corresponding time, the feasible range of the generator power and the speed change rate limit; Calling the battery second-order RC equivalent circuit model, combining the total load power prediction sequence, analyzing the dynamic correlation between the battery power at the corresponding time and the SOC, bus voltage and temperature, and determining the feasible range of the battery charging and discharging power; Based on the characteristics of the engine side and the battery side, a rolling optimization problem is constructed in the future preset time domain in combination with the multi-objective function; wherein the generator power and the battery power at each time need to satisfy the total load power balance relationship; Solving the rolling optimization problem to obtain a generator power candidate sequence at each time in the future preset time domain, selecting a sequence that satisfies all constraint conditions and makes the multi-objective function optimal from the generator power candidate sequence as the generator optimal power instruction sequence, and outputting the instruction of the current time for execution, and the instructions of the remaining times are updated by the next rolling optimization.

3. The method of claim 1, wherein, The multi-objective function is a weighted integration of multiple control objectives, including minimizing the fuel consumption of the turboshaft engine, minimizing the deviation of the battery SOC from a preset target value, minimizing the fluctuation amplitude of the bus voltage, minimizing the deviation of the battery temperature from a preset target temperature, suppressing the abrupt change of the generator power, and suppressing the abrupt change of the battery power.

4. The method of claim 1, wherein, The constraint conditions include generator power upper and lower limit constraints and generator speed change rate constraints based on a turboshaft engine torque-speed-power dynamic model, battery charge and discharge power upper and lower limit constraints and battery SOC upper and lower limit constraints based on a battery second-order RC equivalent circuit model.

5. The method of claim 1, wherein, Based on the flight plan and the rotor aerodynamic power model, a total load power prediction sequence in a future preset time domain is generated, including: Obtaining a propeller motor torque instruction sequence at multiple time points in the future preset time domain calculated based on the flight plan; Based on the propeller motor torque instruction sequence, the rotor aerodynamic power model is called, and the rotor inertia and dynamic balance relationship of the motor are combined to obtain a rotor speed dynamic change sequence at multiple time points in the future preset time domain and a motor output torque sequence at the corresponding time points; According to the motor output torque sequence, the rotor speed dynamic change sequence and the efficiency characteristics of the propeller motor, a propeller motor electromagnetic power sequence at multiple time points in the future preset time domain is calculated; Superimposing the propeller motor electromagnetic power sequence and other on-board device power consumption sequences, a total load power initial prediction sequence is obtained; By fusing real-time measured rotor speed and motor current data at the corresponding time points, the total load power initial prediction sequence is calibrated to generate a total load power prediction sequence in the future preset time domain.

6. The method of claim 1, wherein, The turboshaft engine torque-speed-power dynamic model is used to describe the dynamic correlation between the output torque, operating speed and corresponding generator output power of the turboshaft engine, including engine fuel consumption characteristics and speed change rate limits; the battery second-order RC equivalent circuit model is based on a second-order RC circuit structure and is used to describe the dynamic correlation between the open-circuit voltage, internal resistance, charge and discharge efficiency, SOC and temperature of the battery; the rotor aerodynamic power model is used to describe the correlation between the rotor aerodynamic torque and the rotor speed, airspeed, and is used to calculate the motor electromagnetic power.

7. The method of claim 1, wherein, During the rolling optimization execution process, the dynamic characteristics of the bus voltage are monitored in real time, and if it is determined that a load mutation occurs, the hierarchical response mechanism is started based on the turboshaft engine torque-speed-power dynamic model within the constraint conditions to temporarily adjust the generator power and the battery power, including: The bus voltage change rate and voltage deviation are monitored in real time, and if the voltage change rate is greater than a first preset threshold and the voltage deviation is greater than a second preset threshold, it is determined that the load is suddenly unloaded; If the voltage change rate is less than a third preset threshold and the voltage deviation is less than a fourth preset threshold, it is determined that the load is suddenly increased; Different adjustment methods are matched based on different levels of response to load sudden unloading and load sudden increase, respectively, to obtain adjusted generator power and battery power.

8. The method of claim 7, wherein, Different adjustment methods are matched based on different levels of response to load sudden unloading and load sudden increase, respectively, to obtain adjusted generator power and battery power, including: For the first level response to load sudden drop, limit the battery charging power not to exceed the preset upper limit of charging, calculate the excess power after load sudden drop, and instantaneously reduce the generator power based on the constraint condition; For the second level response to load sudden drop, call the torque-speed-power dynamic model of the turboshaft engine, adjust the engine throttle control speed regression target value according to the dynamic correlation between engine speed and power, and control the generator power to smoothly transition to the state matching the load after sudden drop; For the first level response to load sudden increase, control the battery to supplement energy at the preset maximum discharge power; For the second level response to load sudden increase, gradually increase the generator power based on the maximum engine power increase rate defined by the torque-speed-power dynamic model of the turboshaft engine, and simultaneously smoothly reduce the battery discharge power as the generator power increases until it is turned off.

9. The method of claim 1, wherein, At the same time, the bus voltage deviation is fed forwardly compensated, the voltage is controlled in the stable interval set by the rolling optimization through adjusting the generator power, including: Real-time monitoring of the deviation between the bus voltage and the target voltage set by the rolling optimization; If the voltage deviation exceeds the stable interval set by the rolling optimization, based on the dynamic correlation between voltage and power in the battery second-order RC equivalent circuit model, calculate the power compensation amount for offsetting the deviation; The power compensation amount is added to the currently executed generator power instruction, and the bus voltage is pulled back into the stable interval by adjusting the generator output power in real time.

10. A model predictive control based generator and battery power distribution apparatus, characterized by, The device comprises a loading module, a generating module, a predicting module, a processing module and a determining module; The loading module is configured to load a preset dynamic model library when the system is initialized, wherein the dynamic model library comprises a turboshaft engine torque-speed-power dynamic model, a battery second-order RC equivalent circuit model and a rotor aerodynamic power model; The generating module is configured to generate a total load power prediction sequence in a future preset time domain based on a flight plan and the rotor aerodynamic power model, and calibrate the total load power prediction sequence in real time; The predicting module is configured to predict a generator optimal power instruction sequence in a future preset time domain by calling the turboshaft engine torque-speed-power dynamic model and the battery second-order RC equivalent circuit model based on the total load power prediction sequence, wherein the rolling optimization takes a preset multi-objective function as an optimization objective and meets the constraint condition set based on the dynamic model library; The processing module is configured to monitor the dynamic characteristics of the bus voltage in real time during the execution of the rolling optimization, and if it is determined that a load mutation occurs, the generator power and the battery power are temporarily adjusted based on the turboshaft engine torque-speed-power dynamic model within the constraint condition; The processing module is further configured to re-execute the rolling optimization based on the adjusted generator power, battery power and corresponding system state during the load mutation response process, and feed forwardly compensate the bus voltage deviation by adjusting the generator power to control the voltage within the stable interval set by the rolling optimization. The determining module is configured to cyclically execute the process from generating the total load power prediction sequence to adjusting the power until the end of the flight task, and determine the power of the generator and the power of the battery.

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