CN114802774A - Hybrid power system energy self-adaptive control method and system of unmanned aerial vehicle - Google Patents

Hybrid power system energy self-adaptive control method and system of unmanned aerial vehicle Download PDF

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CN114802774A
CN114802774A CN202210439401.1A CN202210439401A CN114802774A CN 114802774 A CN114802774 A CN 114802774A CN 202210439401 A CN202210439401 A CN 202210439401A CN 114802774 A CN114802774 A CN 114802774A
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power
fuel engine
lithium battery
optimization
load power
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孙健
李建奇
李建英
李泽文
齐庭庭
鲁建全
万晶莹
张斌
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Hunan University of Arts and Science
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
    • B64D27/00—Arrangement or mounting of power plants in aircraft; Aircraft characterised by the type or position of power plants
    • B64D27/02—Aircraft characterised by the type or position of power plants
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
    • B64D27/00—Arrangement or mounting of power plants in aircraft; Aircraft characterised by the type or position of power plants
    • B64D27/02—Aircraft characterised by the type or position of power plants
    • B64D27/026—Aircraft characterised by the type or position of power plants comprising different types of power plants, e.g. combination of a piston engine and a gas-turbine
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
    • B64D31/00—Power plant control systems; Arrangement of power plant control systems in aircraft
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02B—INTERNAL-COMBUSTION PISTON ENGINES; COMBUSTION ENGINES IN GENERAL
    • F02B63/00—Adaptations of engines for driving pumps, hand-held tools or electric generators; Portable combinations of engines with engine-driven devices
    • F02B63/04—Adaptations of engines for driving pumps, hand-held tools or electric generators; Portable combinations of engines with engine-driven devices for electric generators
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D43/00—Conjoint electrical control of two or more functions, e.g. ignition, fuel-air mixture, recirculation, supercharging or exhaust-gas treatment
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D45/00—Electrical control not provided for in groups F02D41/00 - F02D43/00

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  • Aviation & Aerospace Engineering (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Control Of Vehicle Engines Or Engines For Specific Uses (AREA)

Abstract

The invention discloses an energy self-adaptive control method and system for a hybrid power system of an unmanned aerial vehicle, wherein the method comprises the following steps: obtaining real-time signal data of a control object; performing power optimization search processing in the optimal power search model of the fuel engine to obtain an optimal working point of the fuel engine; estimating in a load power estimation model to obtain estimated load power of the next time period; performing adaptive adjustment processing in an adaptive adjustment model to obtain a lithium battery state of charge optimization weight and a fuel engine mechanical weight; performing rolling optimization design processing in the multi-target capacity management model to generate lithium battery power of a control object at the next moment and fuel engine power of the control object at the next moment; and adjusting and controlling the control object by using the power of the lithium battery at the next moment and the power of the fuel engine at the next moment. In the embodiment of the invention, the fuel economy in the hybrid power system is realized, and the balance of energy consumption and the dynamic performance of the switching process are considered.

Description

Hybrid power system energy self-adaptive control method and system of unmanned aerial vehicle
Technical Field
The invention relates to the technical field of unmanned aerial vehicle control, in particular to an energy self-adaptive control method and system for a hybrid power system of an unmanned aerial vehicle.
Background
At present, in general unmanned aerial vehicles such as unmanned aerial vehicles, the energy sources of the general unmanned aerial vehicles mainly comprise full electric drive, direct drive of fuel engines and hybrid power drive. The all-electric driven unmanned aerial vehicle is limited by the capacity of a power battery, the endurance mileage and the flight time of the all-electric driven unmanned aerial vehicle are limited, the unmanned aerial vehicle directly driven by the fuel engine works in a low energy efficiency ratio working state in many times at different power demand stages of lift-off, descent, flight and the like, and the model selection of an engine system needs to be carried out according to the maximum demand power. Hybrid unmanned aerial vehicle moves with oil and electronic combination is in the same place, can promote the efficiency of the single power supply of unmanned aerial vehicle under wide operating mode, therefore can improve unmanned aerial vehicle's duration greatly, and then satisfy the needs of the long time of navigating in the actual work.
The energy management system is a core technology of hybrid unmanned aerial vehicle research, and directly influences the running performance of the whole machine. The energy management control strategy reasonably controls the running states of an engine, a battery pack and a motor according to different working modes of the airplane and by combining the characteristics of various power sources, dynamically distributes the output power of various energy sources, enables the power sources to work in an efficient area, and meets the requirements of system dynamic property and economy. Currently, energy management control strategies are mainly divided into rule-based energy management strategies and optimal control-based energy management strategies in terms of form.
A rule-based energy management policy includes a deterministic rule and a fuzzy rule. And the rule determining strategy changes the output power distribution proportion among the multiple energy sources by controlling the switching of the working modes, so that the fuel economy is improved. To small-size low latitude electric unmanned aerial vehicle power demand during long voyage, designed a hybrid unmanned aerial vehicle among the prior art to divide the flight task into 7 kinds of mode according to rule strategy, this unmanned aerial vehicle's the time of navigating has improved 1.2 times under the same weight. The fuzzy rule energy management strategy sets rules by using an expert knowledge base, fuzzifies signals such as battery residual charge (SOC) and load power in the operation process of the hybrid power system, establishes a fuzzy rule base for decision making, and finally obtains the actual output power of each energy source. The rule-based energy management strategy algorithm is simple in design, small in calculation amount and easy to implement, but the control rule of the algorithm is too dependent on the experience of an engineer. Hybrid unmanned aerial vehicle has the adaptability of promotion unmanned aerial vehicle that multiple operational mode can be very big to different operating modes, but engine response time compares the lithium cell longer for unmanned aerial vehicle is at the mode switch in-process, and the fluctuation appears in the voltage, influences power transmission's stationarity.
The energy management strategy based on the optimization algorithm optimizes the control parameters by establishing an objective function, and is divided into energy management strategies based on the global and instantaneous optimization algorithms. The global optimization energy management strategy aims at the whole flight working condition, takes fuel economy as a main target, and dynamically distributes each energy source output by applying optimal control theories such as a dynamic programming algorithm (DP) and a genetic algorithm, so that the full flight process performance of the unmanned aerial vehicle is optimal. The global optimization energy management strategy can theoretically realize optimal control, but needs to predict the whole flight working condition, and has larger calculated amount. The management strategy of the minimum equivalent fuel consumption (ECMS) is to equate the electric energy consumption of the battery to the fuel consumption of the engine, control the output power of the battery and the engine to ensure that the instantaneous equivalent fuel consumption is minimum, but the charge-discharge equivalent conversion coefficient can only be suitable for a specific working condition. In the prior art, the corresponding optimal equivalent conversion coefficient is solved according to each set working condition, and then the corresponding equivalent conversion coefficient is determined by identifying the actual operating working condition. However, energy balance among various energy consumption in the hybrid power system is not researched in the above method, so that the energy of a lithium battery of the unmanned aerial vehicle is too sufficient or the unmanned aerial vehicle is exhausted when a flight task is finished, and the safe flight of the unmanned aerial vehicle is influenced.
From the above literature analysis, most of the existing hybrid power energy management strategies for unmanned aerial vehicles or vehicles have a single control target, mainly pursue system economy, and neglect the balance among energy consumption and the dynamic performance of a switching process.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides an energy self-adaptive control method and system for a hybrid power system of an unmanned aerial vehicle, which are used for realizing fuel economy in a hybrid system and considering energy consumption balance and dynamic performance in a switching process.
In order to solve the technical problem, an embodiment of the present invention provides an energy adaptive control method for a hybrid power system of an unmanned aerial vehicle, where the method includes:
acquiring real-time signal data of a control object, wherein the real-time signal data comprises acceleration, load power and state information of a lithium battery;
performing power optimization search processing in an optimal power search model of the fuel engine based on the load power in the real-time signal data to obtain an optimal working point of the fuel engine;
estimating and processing in a load power estimation model based on the load power in the real-time signal data to obtain the estimated load power of the next time period;
performing self-adaptive adjustment processing in a self-adaptive adjustment model based on the acceleration in the real-time signal data, the state information of the lithium battery and the estimated load power to obtain the optimal weight of the state of charge of the lithium battery and the mechanical weight of the fuel engine;
performing rolling optimization design processing in a multi-target capacity management model based on the optimal working point of the fuel engine, the lithium battery state of charge optimization weight and the fuel engine mechanical weight to generate the lithium battery power of the control object at the next moment and the fuel engine power of the control object at the next moment;
and adjusting and controlling the control object by using the power of the lithium battery at the next moment and the power of the fuel engine at the next moment.
Optionally, the control object comprises a lithium battery, a fuel engine, a generator and a motor; the lithium battery is connected with the motor, the fuel engine is connected with the generator, and the generator is respectively connected with the lithium battery and the motor;
the power relationship of the control object is as follows:
P fc (t)+P bat (t)＝P L (t)；
wherein, P fc (t) represents the output power of the generator at time t; p bat (t) represents the output power of the lithium battery at the time t; p L (t) represents the load power of the control object at time t; when the instantaneous load power is greater than the instantaneous output power of the generator at the end of the fuel engine, the output power of the lithium battery is positive; when the instantaneous power of the load is smaller than the instantaneous output power of the generator at the end of the fuel engine, the output power of the energy storage battery is negative.
Optionally, before performing power optimization search processing in an optimal power search model of the fuel engine based on the load power in the real-time signal data, the method further includes:
establishing a fuel consumption efficiency curve of the fuel engine, wherein the fuel consumption efficiency curve is an efficiency curve fitted by a second-order polynomial, and the fitting equation is as follows:
η＝a 1 P fc +a 2 P fc 2 ；
wherein η represents the fuel consumption efficiency of the fuel engine; a is 1 、a 2 Are all parameters representing the fuel consumption efficiency curve of the fuel engine, and a 1 、a 2 Will change with speed, environmental changes; p fc Indicating power generation at the end of a fuel engineThe output power of the machine.
Optionally, the performing power optimization search processing in an optimal power search model of the fuel engine based on the load power in the real-time signal data includes:
the fitting equation of the fuel consumption efficiency curve is rewritten as follows:
Figure BDA0003613138660000041
recording the input as U and the output as Z, and inputting U T (k)＝[P(k),P 2 (k)]Output z (k) ═ η; considering the Gaussian measurement noise v (k) N (0, σ), the above equation is expressed as:
Z(k)＝a 1 (k)U 1 (k)+a 2 U 2 (k)+v(k)；
the log-likelihood function obtained by combining the maximum likelihood algorithm principle is as follows:
Figure BDA0003613138660000042
the log-likelihood function of the above equation is minimized,
Figure BDA0003613138660000043
the minimum value is required to be obtained;
and v (k) ═ z (k) — a 1 (k)U 1 (k)-a 2 U 2 (k) Substitution can obtain:
Figure BDA0003613138660000044
order:
Figure BDA0003613138660000045
Figure BDA0003613138660000046
x(k)＝θ(k) T ；
due to Z 2 (k) When an extreme value is obtained as an observable value, a certain constant is obtained, and then:
Figure BDA0003613138660000047
wherein v (k) represents gaussian noise; k represents time, where k is 1, …, Q; q represents a log-likelihood function value.
Optionally, the estimating, in a load power estimation model, based on the load power in the real-time signal data includes:
and filtering the load power in the real-time signal data by adopting a load power filter, and taking the filtered load current value as an estimated value, wherein the estimated value is obtained according to the Lagrange median theorem:
Figure BDA0003613138660000048
by performing approximation processing on the differential term, there are:
Figure BDA0003613138660000051
wherein, P L (t-τ 1 ) And P L (t-τ 2 ) The known value of the load power is obtained through two first-order inertia links as follows:
Figure BDA0003613138660000052
wherein, P L (s) is the load power P L (t) laplace transform; the load power at the next moment is expressed as:
Figure BDA0003613138660000053
wherein L is - Representing the inverse laplace transform.
Optionally, the performing adaptive adjustment processing in an adaptive adjustment model based on the acceleration in the real-time signal data, the state information of the lithium battery, and the estimated load power includes:
and carrying out weight coefficient self-adaptive adjustment processing on the lithium battery charge state optimization target constraint item and the fuel engine mechanical property optimization target constraint item by adopting fuzzy control in a self-adaptive adjustment model based on the acceleration in the real-time signal data, the state information of the lithium battery and the estimated load power.
Optionally, the rolling optimization design processing is performed in a multi-objective capacity management model based on the optimal operating point of the fuel engine, the optimal weight of the state of charge of the lithium battery, and the mechanical weight of the fuel engine, and includes:
power II P of fuel engine from economical point of view fc (k)-P opt II, minimizing as a first optimization target;
from the safety point of view, the state of charge of the lithium battery is limited between the minimum value and the maximum value, and the state of charge is at the expected value
Figure BDA0003613138660000054
The accessory fluctuates, then
Figure BDA0003613138660000055
Minimization is taken as a second optimization objective;
compared with a lithium battery, the fuel engine has slow response speed from the aspect of smoothness, and the output power fluctuation of the fuel engine is gentle, so that the minimum | [ delta ] P fc (k) II as a third optimization target;
and performing rolling optimization design processing based on the first optimization objective, the second optimization objective and the third optimization objective in a simultaneous manner.
Optionally, the simultaneous rolling optimization design based on the first optimization goal, the second optimization goal and the third optimization goal is as follows:
Figure BDA0003613138660000056
f 1 (x(k))＝[‖P L (k+1|k)-P opt ‖,…‖P L (k+n|k)-P opt ‖] T ；
Figure BDA0003613138660000061
f 3 (x(k))＝[‖ΔP fc (k+1|k)‖,…‖ΔP fc (k+n-1|k)‖] T ；
wherein F (x (k)) ε R 3n Representing an optimization objective vector; f. of 1 (x (k)) represents an economic objective function; f. of 2 (x (k)) represents a security objective function; alpha represents a security weight; f. of 3 (x (k)) represents a smoothness objective function; beta represents a smoothness weight; f. of 1 (x(k))∈R n Indicating the operating efficiency of the fuel engine at the time k; f. of 2 (x(k))∈R n Representing the wave band of the lithium battery at the moment k; f. of 3 (x(k))∈R n Representing the fuel engine power band at time k.
Optionally, the constraint conditions of the rolling optimization design process are described as follows:
x(k+i+1|k)＝Ax(k+i|k)+Bu(k+i|k)+Cd(k+i|k)；
wherein the state variable x (i) is defined as x (i) ═ P fc (i)V s (i)V f (i)V soc (i)] T Entry of
Figure BDA0003613138660000064
d (k) representing the location load power of the multi-objective capacity management model; x (k + i | k) and u (k + i | k) represent the estimated state quantity and input variables at the k + i-th time; the coefficient matrix A, B, C has:
Figure BDA0003613138660000062
B＝[T 0 0 0] T ；
Figure BDA0003613138660000063
wherein, C s And C f Representing the equivalent capacitance of the lithium battery; c b Represents a controlled source capacitance; r s And R f Representing the equivalent resistance of the lithium battery; r sd Represents a controlled source resistance; t represents time; u represents the voltage across the capacitor.
In addition, the embodiment of the invention also provides an energy self-adaptive control system of a hybrid power system of the unmanned aerial vehicle, and the system comprises:
a data acquisition module: the system comprises a control object, a load, a lithium battery, a power supply and a power supply, wherein the control object is used for acquiring real-time signal data of the control object, and the real-time signal data comprises acceleration, load power and state information of the lithium battery;
a power optimization module: the system is used for carrying out power optimization searching processing in an optimal power searching model of the fuel engine based on the load power in the real-time signal data to obtain an optimal working point of the fuel engine;
an estimation module: the load power estimation module is used for estimating and processing the load power in the real-time signal data in a load power estimation model to obtain the estimated load power of the next time period;
self-adaptive adjusting module: the system is used for carrying out self-adaptive adjustment processing in a self-adaptive adjustment model based on the acceleration in the real-time signal data, the state information of the lithium battery and the estimated load power to obtain the optimized weight of the state of charge of the lithium battery and the mechanical weight of the fuel engine;
a rolling optimization module: the system comprises a multi-target capacity management model, a lithium battery state-of-charge optimization model and a control object, wherein the multi-target capacity management model is used for performing rolling optimization design processing on the basis of the optimal working point of the fuel engine, the lithium battery state-of-charge optimization weight and the fuel engine mechanical weight to generate the lithium battery power of the control object at the next moment and the fuel engine power of the control object at the next moment;
adjusting the control module: and the control system is used for adjusting and controlling the control object by utilizing the power of the lithium battery at the next moment and the power of the fuel engine at the next moment.
In the embodiment of the invention, the basic idea of rolling optimization is adopted to realize the optimization control of the hybrid power system, dynamically distribute the output power of various energy sources, and effectively ensure the economy, smoothness and safety of the unmanned aerial vehicle in the flying process; the fuel economy in the hybrid system can be realized, and the balance of energy consumption and the dynamic performance of the switching process are considered.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a hybrid power system energy adaptive control method of an unmanned aerial vehicle in an embodiment of the invention;
fig. 2 is a schematic structural composition diagram of a hybrid power system energy adaptive control system of the unmanned aerial vehicle in the embodiment of the invention;
fig. 3 is an equivalent circuit diagram of a lithium battery in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In a first embodiment, please refer to fig. 1, where fig. 1 is a schematic flow chart of a hybrid power system energy adaptive control method of an unmanned aerial vehicle according to an embodiment of the present invention.
As shown in fig. 1, a hybrid power system energy adaptive control method for an unmanned aerial vehicle, the method includes:
s11: acquiring real-time signal data of a control object, wherein the real-time signal data comprises acceleration, load power and state information of a lithium battery;
in the specific implementation process of the invention, the control object comprises a lithium battery, a fuel engine, a generator and a motor; the lithium battery is connected with the motor, the fuel engine is connected with the generator, and the generator is respectively connected with the lithium battery and the motor; the power relationship of the control object is as follows:
P fc (t)+P bat (t)＝P L (t)；
wherein, P fc (t) represents the output power of the generator at time t; p bat (t) represents the output power of the lithium battery at the time t; p L (t) represents the load power of the control object at time t; when the instantaneous load power is greater than the instantaneous output power of the generator at the end of the fuel engine, the output power of the lithium battery is positive; when the instantaneous power of the load is smaller than the instantaneous output power of the generator at the end of the fuel engine, the output power of the energy storage battery is negative.
Specifically, the hybrid power system adopts a series hybrid power system, classifies the fuel engine and the power demand, and provides conditions for the continuous operation of the fuel engine in the optimal fuel economy area.
A control target lithium battery, a fuel engine, a generator, and a motor; the fuel engine is connected with the generator, and the generator is respectively connected with the lithium battery and the motor; defining the output power of a generator connected with a fuel engine at the moment t as P fc (t)；P bat (t) represents the output power of the lithium battery at the time t; p L (t) represents the load power of the control object at time t; when the instantaneous load power is greater than the instantaneous output power of the generator at the end of the fuel engine, the output power of the lithium battery is positive; when the instantaneous power of the load is less than the instantaneous output of the generator at the end of the fuel engineWhen the power is output, the output power of the energy storage battery is negative; the power relationship of the control object is as follows:
P fc (t)+P bat (t)＝P L (t)；
wherein lithium cell equivalent circuit is shown in fig. 3, in order to avoid the unmanned aerial vehicle flight in-process lithium cell overcharge or overdischarge phenomenon to appear, consequently the change condition of lithium cell SOC is paid close attention to in this application energy control strategy's design. The lithium battery equivalent circuit is shown in fig. 3, in the circuit, the left half part is used for simulating the dynamic change process of the SOC and representing the mutual influence between the state of charge and the port current, and the right half part is used for simulating the change situation of the internal voltage of the lithium battery when the current fluctuates.
The state equation of the lithium battery is as follows:
Figure BDA0003613138660000091
Figure BDA0003613138660000092
Figure BDA0003613138660000093
V B (t)＝V OC (t)-V s (t)-V f (t)-R 0 i B (t)；
wherein i B (t) and V B (t) represents the output current and output voltage of the lithium battery, when the current value is positive, discharge is indicated, otherwise, charge is indicated, R 0 Represents the internal resistance, V, of a lithium battery OC (t) represents an open circuit voltage of the lithium battery; v soc (t) represents the state of charge of the lithium battery; v soc (t)∈[0，1]；C s And C f Representing the equivalent capacitance of the lithium battery; c b Represents a controlled source capacitance; r s And R f Representing the equivalent resistance of the lithium battery; r sd Representing the controlled source resistance.
S12: performing power optimization search processing in an optimal power search model of the fuel engine based on the load power in the real-time signal data to obtain an optimal working point of the fuel engine;
in a specific implementation process of the present invention, before performing power optimization search processing in an optimal power search model of a fuel engine based on load power in the real-time signal data, the method further includes: establishing a fuel consumption efficiency curve of the fuel engine, wherein the fuel consumption efficiency curve is an efficiency curve fitted by a second-order polynomial, and the fitting equation is as follows:
η＝a 1 P fc +a 2 P fc 2 ；
wherein η represents the fuel consumption efficiency of the fuel engine; a is 1 、a 2 Are all parameters representing the fuel consumption efficiency curve of the fuel engine, and a 1 、a 2 Will change with speed, environmental changes; p fc Representing the output power of the generator at the end of the fuel engine.
Further, the power optimization search processing in the optimal power search model of the fuel engine based on the load power in the real-time signal data comprises:
the fitting equation of the fuel consumption efficiency curve is rewritten as follows:
Figure BDA0003613138660000101
recording the input as U and the output as Z, and inputting U T (k)＝[P(k),P 2 (k)]Output z (k) ═ η; considering the Gaussian measurement noise v (k) N (0, σ), the above equation is expressed as:
Z(k)＝a 1 (k)U 1 (k)+a 2 U 2 (k)+v(k)；
the log-likelihood function obtained by combining the maximum likelihood algorithm principle is as follows:
Figure BDA0003613138660000102
the log-likelihood function of the above formula is minimized,
Figure BDA0003613138660000103
the minimum value is required to be obtained;
and v (k) ═ z (k) — a 1 (k)U 1 (k)-a 2 U 2 (k) Substitution can obtain:
Figure BDA0003613138660000104
order:
Figure BDA0003613138660000105
Figure BDA0003613138660000106
x(k)＝θ(k) T ；
due to Z 2 (k) When an extreme value is obtained as an observable value, a certain constant is obtained, and then:
Figure BDA0003613138660000107
wherein v (k) represents gaussian noise; k represents time, where k is 1, …, Q; q represents a log-likelihood function value.
Specifically, the fuel engine system consumption characteristic, in the oil-electricity hybrid power system, energy control strategy design purpose can enough satisfy anticipated performance target, can also reduce unmanned aerial vehicle's fuel consumption, improves energy utilization efficiency, lets fuel engine work in high-efficient region. Based on the goal of reducing fuel consumption, firstly, a fuel consumption efficiency model of the fuel engine needs to be established, and a fuel efficiency curve can be fitted by a second-order polynomial, as follows:
η＝a 1 P fc +a 2 P fc 2 ；
wherein η represents the fuel consumption efficiency of the fuel engine; a is 1 、a 2 Are all parameters representing the fuel consumption efficiency curve of the fuel engine, and a 1 、a 2 Will change with speed, environmental changes; p fc Representing the output power of the generator at the end of the fuel engine. In a hybrid system, a fuel engine and a synchronous generator with a rectifier are generally taken as a whole, namely a fuel engine module under a direct current network. Aiming at the fuel consumption efficiency calculation of the module, the new fuel consumption characteristics of the synchronous generator and the rectifier obtained after power loss are considered, the form of the new fuel consumption characteristics is the same as that of the new fuel consumption characteristics, and only the correction coefficient is needed.
In the application, a maximum likelihood method is used for solving a parameter theta (k) ═ a to be estimated in a fuel consumption efficiency curve 1 (k),a 2 (k)](ii) a The maximum likelihood method is to construct a likelihood function related to the measured data and the unknown parameters, and obtain the parameter identification value of the model by maximizing the function; the fitting equation of the fuel consumption efficiency curve is rewritten as follows:
Figure BDA0003613138660000111
recording the input as U and the output as Z, and inputting U T (k)＝[P(k),P 2 (k)]Output z (k) ═ η; considering the Gaussian measurement noise v (k) N (0, σ), the above equation is expressed as:
Z(k)＝a 1 (k)U 1 (k)+a 2 U 2 (k)+v(k)；
the log-likelihood function obtained by combining the maximum likelihood algorithm principle is as follows:
Figure BDA0003613138660000112
the log-likelihood function of the above equation is minimized,
Figure BDA0003613138660000113
need to minimize the acquisitionA value;
and v (k) ═ z (k) — a 1 (k)U 1 (k)-a 2 U 2 (k) Substitution can obtain:
Figure BDA0003613138660000114
order:
Figure BDA0003613138660000115
Figure BDA0003613138660000116
x(k)＝θ(k) T ；
due to Z 2 (k) When an extreme value is obtained as an observable value, a certain constant is obtained, and then:
Figure BDA0003613138660000117
wherein v (k) represents gaussian noise; k represents time, where k is 1, …, Q; q represents a log-likelihood function value.
S13: estimating and processing in a load power estimation model based on the load power in the real-time signal data to obtain the estimated load power of the next time period;
in a specific implementation process of the present invention, the estimating process in a load power estimation model based on the load power in the real-time signal data includes:
and filtering the load power in the real-time signal data by adopting a load power filter, and taking the filtered load current value as an estimated value, wherein the estimated value is obtained according to the Lagrange median theorem:
Figure BDA0003613138660000121
by performing approximation processing on the differential term, there are:
Figure BDA0003613138660000122
wherein, P L (t-τ 1 ) And P L (t-τ 2 ) Is a known value of the load power, obtained by two first-order inertia links as follows:
Figure BDA0003613138660000123
wherein, P L (s) is the load power P L (t) laplace transform; the load power at the next instant is expressed as:
Figure BDA0003613138660000124
wherein L is - Representing the inverse laplace transform.
Specifically, in order to achieve a good energy management effect, the load power of the unmanned aerial vehicle in a future period of time needs to be accurately estimated. In the application, a load power filter is designed for filtering the existing load power data, and the filtered load current value is used as an estimated value for a subsequent energy management control strategy. According to the lagrange median theorem, the following can be obtained:
Figure BDA0003613138660000125
by performing approximation processing on the differential term, there are:
Figure BDA0003613138660000126
wherein, P L (t-τ 1 ) And P L (t-τ 2 ) Is a known value of the load power, obtained by two first-order inertia links as follows:
Figure BDA0003613138660000131
wherein, P L (s) is the load power P L (t) laplace transform; the load power at the next moment is expressed as:
Figure BDA0003613138660000132
wherein L is - Representing the inverse laplace transform.
S14: performing self-adaptive adjustment processing in a self-adaptive adjustment model based on the acceleration in the real-time signal data, the state information of the lithium battery and the estimated load power to obtain the optimal weight of the state of charge of the lithium battery and the mechanical weight of the fuel engine;
in a specific implementation process of the present invention, the adaptively adjusting process performed in an adaptively adjusting model based on the acceleration in the real-time signal data, the state information of the lithium battery, and the estimated load power includes: and carrying out weight coefficient self-adaptive adjustment processing on the lithium battery charge state optimization target constraint item and the fuel engine mechanical property optimization target constraint item by adopting fuzzy control in a self-adaptive adjustment model based on the acceleration in the real-time signal data, the state information of the lithium battery and the estimated load power.
Specifically, in order to obtain a certain constraint effect and well match the running state at the same time, and realize global optimum, the weight coefficients of a lithium battery charge state optimization target constraint term and an engine dynamic characteristic optimization target constraint term are subjected to fuzzy control in the application; in order to ensure the safety of the lithium battery, prolong the service life of the lithium battery and ensure that the state of charge is within a certain range, a fuzzy control rule is compiled as shown in table 1 according to the discharging depth DoD of the lithium battery and the load power requirement, wherein the DoD is the difference between the current value and the expected value.
TABLE 1
Figure BDA0003613138660000133
In order to ensure smoothness and inhibit severe fluctuation of the fuel engine, the constraint term in the expectation formula plays a role in the state that the power of the fuel engine is changed greatly; where power is steady, it is desirable to target reduced losses and control of battery state of charge. Unmanned aerial vehicle acceleration a can match this expectation effect better, according to SOC state and acceleration a demand, compiles fuzzy control rule as table 2.
TABLE 2
Figure BDA0003613138660000141
S15: performing rolling optimization design processing in a multi-target capacity management model based on the optimal working point of the fuel engine, the lithium battery state of charge optimization weight and the fuel engine mechanical weight to generate the lithium battery power of the control object at the next moment and the fuel engine power of the control object at the next moment;
in the specific implementation process of the invention, the rolling optimization design treatment based on the optimal working point of the fuel engine, the lithium battery state of charge optimization weight and the fuel engine mechanical weight in the multi-objective capacity management model comprises the following steps: power II P of fuel engine from economical point of view fc (k)-P opt II, minimizing as a first optimization target; from the safety point of view, the state of charge of the lithium battery is limited between the minimum value and the maximum value, and the state of charge is at a desired value
Figure BDA0003613138660000142
The accessory fluctuates, then
Figure BDA0003613138660000143
Minimization is taken as a second optimization objective; the response speed of the fuel engine is lower than that of a lithium battery from the aspect of smoothness, and the fuel engine outputs workThe rate fluctuation is gentle, and then the minimum | [ delta ] P fc (k) II as a third optimization target; and performing rolling optimization design processing based on the first optimization objective, the second optimization objective and the third optimization objective in a simultaneous manner.
Further, the simultaneous rolling optimization design based on the first optimization goal, the second optimization goal and the third optimization goal is as follows:
Figure BDA0003613138660000144
f 1 (x(k))＝[‖P L (k+1|k)-P opt ‖,…‖P L (k+n|k)-P opt ‖] T ；
Figure BDA0003613138660000145
f 3 (x(k))＝[‖ΔP fc (k+1|k)‖,…‖ΔP fc (k+n-1|k)‖] T ；
wherein F (x (k)) E R 3n Representing an optimization objective vector; f. of 1 (x (k)) represents an economic objective function; f. of 2 (x (k)) represents a security objective function; alpha represents a security weight; f. of 3 (x (k)) represents a smoothness objective function; beta represents a smoothness weight; f. of 1 (x(k))∈R n Indicating the operating efficiency of the fuel engine at the time k; f. of 2 (x(k))∈R n Representing the wave band of the lithium battery at the moment k; f. of 3 (x(k))∈R n Representing the fuel engine power band at time k.
Further, the constraint conditions of the rolling optimization design process are described as follows:
x(k+i+1|k)＝Ax(k+i|k)+Bu(k+i|k)+Cd(k+i|k)；
wherein the state variable x (i) is defined as x (i) ═ P fc (i)V s (i)V f (i)V soc (i)] T Entry of
Figure BDA0003613138660000154
d (k) representing the location load power of the multi-objective capacity management model; x (k + i | k) and u (k + i | k) represent the estimated state quantity and input variables at the k + i-th time; the coefficient matrix A, B, C has:
Figure BDA0003613138660000151
B＝[T 0 0 0] T ；
Figure BDA0003613138660000152
wherein, C s And C f Representing the equivalent capacitance of the lithium battery; c b Represents a controlled source capacitance; r s And R f Representing the equivalent resistance of the lithium battery; r sd Represents a controlled source resistance; t represents time; u represents the voltage across the capacitor.
Specifically, energy management system designs in this application are primarily directed to economy, safety, and smoothness as control objectives. Based on this goal, the performance index for optimal management of a fuel engine/lithium battery hybrid system can be described as follows:
(1) from the economical point of view, the engine works at the optimum working point, the fuel conversion efficiency is the highest, and the power | P of the fuel engine is enabled to be the fc (k)-P opt II, minimizing as a first optimization target;
(2) from a safety point of view, the state of charge of lithium batteries must be strictly limited between a minimum and a maximum value. Setting the state of charge at a desired value
Figure BDA0003613138660000155
The accessory fluctuates, then
Figure BDA0003613138660000153
Minimization is taken as a second optimization objective;
(3) from the aspect of smoothness, the response speed of the engine is higher than that of a lithium batterySlow, the smaller the rate of change of engine power, the better, which means that the engine output power fluctuates more smoothly. Therefore, minimize | Δ P fc (k) II as a third optimization target;
based on the performance indicators, the multi-objective optimization problem is described as follows:
Figure BDA0003613138660000161
f 1 (x(k))＝[‖P L (k+1|k)-P opt ‖,…‖P L (k+n|k)-P opt ‖] T ；
Figure BDA0003613138660000162
f 3 (x(k))＝[‖ΔP fc (k+1|k)‖,…‖ΔP fc (k+n-1|k)‖] T ；
wherein F (x (k)) E R 3n Representing an optimization objective vector; f. of 1 (x (k)) represents an economic objective function; f. of 2 (x (k)) represents a security objective function; alpha represents a security weight; f. of 3 (x (k)) represents a smoothness objective function; beta represents a smoothness weight; f. of 1 (x(k))∈R n Indicating the operating efficiency of the fuel engine at the time k; f. of 2 (x(k))∈R n Representing the wave band of the lithium battery at the moment k; f. of 3 (x(k))∈R n Representing the fuel engine power band at time k. The constraints of the optimization problem can be described as:
x(k+i+1|k)＝Ax(k+i|k)+Bu(k+i|k)+Cd(k+i|k)；
wherein the state variable x (i) is defined as x (i) ═ P fc (i)V s (i)V f (i)V soc (i)] T Entry of
Figure BDA0003613138660000165
d (k) representing the location load power of the multi-objective capacity management model; x (k + i | k) and u (k + i | k) denote the estimated state quantity and input variable at the k + i-th time(ii) a The coefficient matrix A, B, C has:
Figure BDA0003613138660000163
B＝[T 0 0 0] T ；
Figure BDA0003613138660000164
wherein, C s And C f Representing the equivalent capacitance of the lithium battery; c b Represents a controlled source capacitance; r s And R f Representing the equivalent resistance of the lithium battery; r sd Represents a controlled source resistance; t represents time; u represents the voltage across the capacitor.
S16: and adjusting and controlling the control object by using the power of the lithium battery at the next moment and the power of the fuel engine at the next moment.
In the specific implementation process of the invention, the power of the lithium battery at the next moment and the power of the fuel engine at the next moment can be used for adjusting and controlling the control object (unmanned aerial vehicle).
In the embodiment of the invention, the basic idea of rolling optimization is adopted to realize the optimization control of the hybrid power system, dynamically distribute the output power of various energy sources, and effectively ensure the economy, smoothness and safety of the unmanned aerial vehicle in the flying process; the fuel economy in the hybrid system can be realized, and the balance of energy consumption and the dynamic performance of the switching process are considered.
In a second embodiment, fig. 2 is a schematic structural composition diagram of an energy adaptive control system of a hybrid power system of an unmanned aerial vehicle in the embodiment of the present invention.
As shown in fig. 2, a hybrid power system energy adaptive control system for an unmanned aerial vehicle, the system comprising:
the data obtaining module 21: the system comprises a control object, a load, a lithium battery, a power supply and a power supply, wherein the control object is used for acquiring real-time signal data of the control object, and the real-time signal data comprises acceleration, load power and state information of the lithium battery;
in the specific implementation process of the invention, the control object comprises a lithium battery, a fuel engine, a generator and a motor; the lithium battery is connected with the motor, the fuel engine is connected with the generator, and the generator is respectively connected with the lithium battery and the motor; the power relationship of the control object is as follows:
P fc (t)+P bat (t)＝P L (t)；
wherein, P fc (t) represents the output power of the generator at time t; p bat (t) represents the output power of the lithium battery at the time t; p L (t) represents the load power of the control object at time t; when the instantaneous load power is greater than the instantaneous output power of the generator at the end of the fuel engine, the output power of the lithium battery is positive; when the instantaneous power of the load is smaller than the instantaneous output power of the generator at the end of the fuel engine, the output power of the energy storage battery is negative.
Specifically, the hybrid power system adopts a series hybrid power system, classifies the fuel engine and the power demand, and provides conditions for the continuous operation of the fuel engine in the optimal fuel economy area.
A control target lithium battery, a fuel engine, a generator, and a motor; the fuel engine is connected with the generator, and the generator is respectively connected with the lithium battery and the motor; defining the output power of a generator connected with a fuel engine at the moment t as P fc (t)；P bat (t) represents the output power of the lithium battery at the time t; p L (t) represents the load power of the control object at time t; when the instantaneous load power is greater than the instantaneous output power of the generator at the end of the fuel engine, the output power of the lithium battery is positive; when the instantaneous load power is smaller than the instantaneous output power of the generator at the end of the fuel engine, the output power of the energy storage battery is negative; the power relationship of the control object is as follows:
P fc (t)+P bat (t)＝P L (t)；
wherein lithium cell equivalent circuit is shown in fig. 3, in order to avoid the unmanned aerial vehicle flight in-process lithium cell overcharge or overdischarge phenomenon to appear, consequently the change condition of lithium cell SOC is paid close attention to in this application energy control strategy's design. The lithium battery equivalent circuit is shown in fig. 3, in the circuit, the left half part is used for simulating the dynamic change process of the SOC and representing the mutual influence between the state of charge and the port current, and the right half part is used for simulating the change situation of the internal voltage of the lithium battery when the current fluctuates.
The state equation of the lithium battery is as follows:
Figure BDA0003613138660000181
Figure BDA0003613138660000182
Figure BDA0003613138660000183
V B (t)＝V OC (t)-V s (t)-V f (t)-R 0 i B (t)；
wherein i B (t) and V B (t) represents the output current and output voltage of the lithium battery, discharge is indicated when the current value is positive, charge is indicated when the current value is negative, and R represents 0 Represents the internal resistance, V, of a lithium battery OC (t) represents an open circuit voltage of the lithium battery; v soc (t) represents the state of charge of the lithium battery; v soc (t)∈[0，1]；C s And C f Representing the equivalent capacitance of the lithium battery; c b Represents a controlled source capacitance; r s And R f Representing the equivalent resistance of the lithium battery; r sd Representing the controlled source resistance.
The power optimization module 22: the system is used for carrying out power optimization searching processing in an optimal power searching model of the fuel engine based on the load power in the real-time signal data to obtain an optimal working point of the fuel engine;
in a specific implementation process of the present invention, before performing power optimization search processing in an optimal power search model of a fuel engine based on load power in the real-time signal data, the method further includes: establishing a fuel consumption efficiency curve of the fuel engine, wherein the fuel consumption efficiency curve is an efficiency curve fitted by a second-order polynomial, and the fitting equation is as follows:
η＝a 1 P fc +a 2 P fc 2 ；
wherein η represents the fuel consumption efficiency of the fuel engine; a is 1 、a 2 Are all parameters representing the fuel consumption efficiency curve of the fuel engine, and a 1 、a 2 Will change with speed, environmental changes; p is fc Representing the output power of the generator at the end of the fuel engine.
Further, the power optimization search processing in the optimal power search model of the fuel engine based on the load power in the real-time signal data comprises:
the fitting equation of the fuel consumption efficiency curve is rewritten as follows:
Figure BDA0003613138660000191
recording the input as U and the output as Z, and inputting U T (k)＝[P(k),P 2 (k)]Output z (k) ═ η; considering the Gaussian measurement noise v (k) N (0, σ), the above equation is expressed as:
Z(k)＝a 1 (k)U 1 (k)+a 2 U 2 (k)+v(k)；
the log-likelihood function obtained by combining the maximum likelihood algorithm principle is as follows:
Figure BDA0003613138660000192
the log-likelihood function of the above equation is minimized,
Figure BDA0003613138660000193
the minimum value is required to be obtained;
and v (k) ═ z (k) — a 1 (k)U 1 (k)-a 2 U 2 (k) Substitution can obtain:
Figure BDA0003613138660000194
order:
Figure BDA0003613138660000195
Figure BDA0003613138660000196
x(k)＝θ(k) T ；
due to Z 2 (k) When an extreme value is obtained as an observable value, a certain constant is obtained, and then:
Figure BDA0003613138660000197
wherein v (k) represents gaussian noise; k represents time, where k is 1, …, Q; q represents a log-likelihood function value.
Specifically, the fuel engine system consumption characteristic, in the oil-electricity hybrid power system, energy control strategy design purpose can enough satisfy anticipated performance target, can also reduce unmanned aerial vehicle's fuel consumption, improves energy utilization efficiency, lets fuel engine work in high-efficient region. Based on the goal of reducing fuel consumption, firstly, a fuel consumption efficiency model of the fuel engine needs to be established, and a fuel efficiency curve can be fitted by a second-order polynomial, as follows:
η＝a 1 P fc +a 2 P fc 2 ；
wherein η represents the fuel consumption efficiency of the fuel engine; a is 1 、a 2 Are all parameters representing the fuel consumption efficiency curve of the fuel engine, and a 1 、a 2 Will change with speed, environmental changes; p fc Representing the output power of the generator at the end of the fuel engine. In the mixingIn a hybrid power system, a fuel engine and a synchronous generator with a rectifier are generally taken as a whole, namely a fuel engine module under a direct current network. Aiming at the fuel consumption efficiency calculation of the module, the new fuel consumption characteristics of the synchronous generator and the rectifier obtained after power loss are considered, the form of the new fuel consumption characteristics is the same as that of the new fuel consumption characteristics, and only the correction coefficient is needed.
In the application, a maximum likelihood method is used for solving a parameter theta (k) ═ a to be estimated in a fuel consumption efficiency curve 1 (k),a 2 (k)](ii) a The maximum likelihood method is to construct a likelihood function related to the measured data and the unknown parameters, and obtain the parameter identification value of the model by maximizing the function; the fitting equation of the fuel consumption efficiency curve is rewritten as follows:
Figure BDA0003613138660000201
recording the input as U and the output as Z, and inputting U T (k)＝[P(k),P 2 (k)]Output z (k) ═ η; considering the Gaussian measurement noise v (k) N (0, σ), the above equation is expressed as:
Z(k)＝a 1 (k)U 1 (k)+a 2 U 2 (k)+v(k)；
the log-likelihood function obtained by combining the maximum likelihood algorithm principle is as follows:
Figure BDA0003613138660000202
the log-likelihood function of the above equation is minimized,
Figure BDA0003613138660000203
the minimum value is required to be obtained;
and v (k) ═ z (k) — a 1 (k)U 1 (k)-a 2 U 2 (k) Substitution can obtain:
Figure BDA0003613138660000204
order:
Figure BDA0003613138660000205
Figure BDA0003613138660000206
x(k)＝θ(k) T ；
due to Z 2 (k) When an extreme value is obtained as an observable value, a certain constant is obtained, and then:
Figure BDA0003613138660000207
wherein v (k) represents gaussian noise; k represents time, where k is 1, …, Q; q represents a log-likelihood function value.
The estimation module 23: the load power estimation module is used for estimating and processing the load power in the real-time signal data in a load power estimation model to obtain the estimated load power of the next time period;
in a specific implementation process of the present invention, the estimating process in the load power estimation model based on the load power in the real-time signal data includes:
and filtering the load power in the real-time signal data by adopting a load power filter, and taking the filtered load current value as an estimated value, wherein the estimated value is obtained according to the Lagrange median theorem:
Figure BDA0003613138660000211
by performing approximation processing on the differential term, there are:
Figure BDA0003613138660000212
wherein, P L (t-τ 1 ) And P L (t-τ 2 ) The known value of the load power is obtained through two first-order inertia links as follows:
Figure BDA0003613138660000213
wherein, P L (s) is the load power P L (t) laplace transform; the load power at the next moment is expressed as:
Figure BDA0003613138660000214
wherein L is - Representing the inverse laplace transform.
Specifically, in order to achieve a good energy management effect, the load power of the unmanned aerial vehicle in a future period of time needs to be accurately estimated. In the application, a load power filter is designed for filtering the existing load power data, and the filtered load current value is used as an estimated value for a subsequent energy management control strategy. According to the lagrange median theorem, the following can be obtained:
Figure BDA0003613138660000215
by performing approximation processing on the differential term, there are:
Figure BDA0003613138660000216
wherein, P L (t-τ 1 ) And P L (t-τ 2 ) The known value of the load power is obtained through two first-order inertia links as follows:
Figure BDA0003613138660000221
wherein, P L (s) is the load power P L (t) laplace transform; the load power at the next moment is expressed as:
Figure BDA0003613138660000222
wherein L is - Representing the inverse laplace transform.
The adaptive adjustment module 24: the system is used for carrying out self-adaptive adjustment processing in a self-adaptive adjustment model based on the acceleration in the real-time signal data, the state information of the lithium battery and the estimated load power to obtain the optimized weight of the state of charge of the lithium battery and the mechanical weight of the fuel engine;
in a specific implementation process of the present invention, the adaptively adjusting process performed in an adaptively adjusting model based on the acceleration in the real-time signal data, the state information of the lithium battery, and the estimated load power includes: and carrying out weight coefficient self-adaptive adjustment processing on the lithium battery charge state optimization target constraint item and the fuel engine mechanical property optimization target constraint item by adopting fuzzy control in a self-adaptive adjustment model based on the acceleration in the real-time signal data, the state information of the lithium battery and the estimated load power.
Specifically, in order to obtain a certain constraint effect and well match the running state at the same time, and realize global optimum, the weight coefficients of a lithium battery charge state optimization target constraint term and an engine dynamic characteristic optimization target constraint term are subjected to fuzzy control in the application; in order to ensure the safety of the lithium battery, prolong the service life of the lithium battery and ensure that the state of charge is within a certain range, a fuzzy control rule is compiled as shown in table 1 according to the discharging depth DoD of the lithium battery and the load power requirement, wherein the DoD is the difference between the current value and the expected value.
TABLE 1
Figure BDA0003613138660000223
In order to ensure smoothness and inhibit severe fluctuation of the fuel engine, the constraint term in the expectation formula plays a role in the state that the power of the fuel engine is changed greatly; where power is steady, it is desirable to target reduced losses and control of battery state of charge. Unmanned aerial vehicle acceleration a can match this expectation effect better, according to SOC state and acceleration a demand, compiles fuzzy control rule as table 2.
TABLE 2
Figure BDA0003613138660000231
The rolling optimization module 25: the system comprises a multi-target capacity management model, a lithium battery state-of-charge optimization model and a control object, wherein the multi-target capacity management model is used for performing rolling optimization design processing on the basis of the optimal working point of the fuel engine, the lithium battery state-of-charge optimization weight and the fuel engine mechanical weight to generate the lithium battery power of the control object at the next moment and the fuel engine power of the control object at the next moment;
in the specific implementation process of the invention, the rolling optimization design treatment based on the optimal working point of the fuel engine, the lithium battery state of charge optimization weight and the fuel engine mechanical weight in the multi-objective capacity management model comprises the following steps: power II P of fuel engine from economical point of view fc (k)-P opt II, minimizing as a first optimization target; from the safety point of view, the state of charge of the lithium battery is limited between the minimum value and the maximum value, and the state of charge is at a desired value
Figure BDA0003613138660000232
The accessory fluctuates, then
Figure BDA0003613138660000233
Minimization is taken as a second optimization objective; compared with a lithium battery, the fuel engine has slow response speed from the aspect of smoothness, and the output power fluctuation of the fuel engine is gentle, so that the minimum | [ delta ] P fc (k) II as a third optimization target; and performing rolling optimization design processing based on the first optimization objective, the second optimization objective and the third optimization objective in a simultaneous manner.
Further, the simultaneous rolling optimization design based on the first optimization goal, the second optimization goal and the third optimization goal is as follows:
Figure BDA0003613138660000234
f 1 (x(k))＝[‖P L (k+1|k)-P opt ‖,…‖P L (k+n|k)-P opt ‖] T ；
Figure BDA0003613138660000235
f 3 (x(k))＝[‖ΔP fc (k+1|k)‖,…‖ΔP fc (k+n-1|k)‖] T ；
wherein F (x (k)) E R 3n Representing an optimization objective vector; f. of 1 (x (k)) represents an economic objective function; f. of 2 (x (k)) represents a security objective function; alpha represents a security weight; f. of 3 (x (k)) represents a smoothness objective function; beta represents a smoothness weight; f. of 1 (x(k))∈R n Indicating the operating efficiency of the fuel engine at the time k; f. of 2 (x(k))∈R n Representing the wave band of the lithium battery at the moment k; f. of 3 (x(k))∈R n Representing the fuel engine power band at time k.
Further, the constraint conditions of the rolling optimization design process are described as follows:
x(k+i+1|k)＝Ax(k+i|k)+Bu(k+i|k)+Cd(k+i|k)；
wherein the state variable x (i) is defined as x (i) ═ P fc (i)V s (i)V f (i)V soc (i)] T Entry of
Figure BDA0003613138660000245
d (k) representing the location load power of the multi-objective capacity management model; x (k + i | k) and u (k + i | k) represent the estimated state quantity and input variables at the k + i-th time; the coefficient matrix A, B, C has:
Figure BDA0003613138660000241
B＝[T 0 0 0] T ；
Figure BDA0003613138660000242
wherein, C s And C f Representing the equivalent capacitance of the lithium battery; c b Represents a controlled source capacitance; r s And R f Representing the equivalent resistance of the lithium battery; r sd Represents a controlled source resistance; t represents time; u represents the voltage across the capacitor.
Specifically, energy management system designs in this application are primarily directed to economy, safety, and smoothness as control objectives. Based on this goal, the performance index for optimal management of a fuel engine/lithium battery hybrid system can be described as follows:
(1) from the economical point of view, the engine works at the optimum working point, the fuel conversion efficiency is the highest, and the power | P of the fuel engine is enabled to be the fc (k)-P opt II, minimizing as a first optimization target;
(2) from a safety point of view, the state of charge of lithium batteries must be strictly limited between a minimum and a maximum value. Setting the state of charge at a desired value
Figure BDA0003613138660000243
The accessories fluctuate, then
Figure BDA0003613138660000244
Minimization is taken as a second optimization objective;
(3) from the aspect of smoothness, the response speed of the engine is lower than that of a lithium battery, and the smaller the change rate of the engine power is, the better the change rate is, and the more gradual the fluctuation of the output power of the engine is. Therefore, minimize | Δ P fc (k) II as a third optimization target;
based on the performance indexes, the multi-objective optimization problem is described as follows:
Figure BDA0003613138660000251
f 1 (x(k))＝[‖P L (k+1|k)-P opt ‖,…‖P L (k+n|k)-P opt ‖] T ；
Figure BDA0003613138660000252
f 3 (x(k))＝[‖ΔP fc (k+1|k)‖,…‖ΔP fc (k+n-1|k)‖] T ；
wherein F (x (k)) E R 3n Representing an optimization objective vector; f. of 1 (x (k)) represents an economic objective function; f. of 2 (x (k)) represents a security objective function; alpha represents a security weight; f. of 3 (x (k)) represents a smoothness objective function; beta represents a smoothness weight; f. of 1 (x(k))∈R n Indicating the operating efficiency of the fuel engine at the time k; f. of 2 (x(k))∈R n Representing the wave band of the lithium battery at the moment k; f. of 3 (x(k))∈R n Representing the fuel engine power band at time k. The constraints of the optimization problem can be described as:
x(k+i+1|k)＝Ax(k+i|k)+Bu(k+i|k)+Cd(k+i|k)；
wherein the state variable x (i) is defined as x (i) ═ P fc (i)V s (i)V f (i)V soc (i)] T Entry of
Figure BDA0003613138660000255
d (k) representing the location load power of the multi-objective capacity management model; x (k + i | k) and u (k + i | k) represent the estimated state quantity and input variables at the k + i-th time; the coefficient matrix A, B, C has:
Figure BDA0003613138660000253
B＝[T 0 0 0] T ；
Figure BDA0003613138660000254
wherein, C s And C f Representing the equivalent capacitance of the lithium battery; c b Represents a controlled source capacitance; r s And R f Representing the equivalent resistance of the lithium battery; r sd Represents a controlled source resistance; t represents time; u represents the voltage across the capacitor.
The regulation control module 26: and the control system is used for adjusting and controlling the control object by utilizing the power of the lithium battery at the next moment and the power of the fuel engine at the next moment.
In the specific implementation process of the invention, the power of the lithium battery at the next moment and the power of the fuel engine at the next moment can be used for adjusting and controlling the control object (unmanned aerial vehicle).
In the embodiment of the invention, the basic idea of rolling optimization is adopted to realize the optimization control of the hybrid power system, dynamically distribute the output power of various energy sources, and effectively ensure the economy, smoothness and safety of the unmanned aerial vehicle in the flying process; the fuel economy in the hybrid system can be realized, and the balance of energy consumption and the dynamic performance of the switching process are considered.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
In addition, the hybrid power system energy adaptive control method and system of the unmanned aerial vehicle provided by the embodiment of the invention are introduced in detail, a specific embodiment is adopted herein to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

1. An energy adaptive control method for a hybrid power system of an unmanned aerial vehicle, the method comprising:
acquiring real-time signal data of a control object, wherein the real-time signal data comprises acceleration, load power and state information of a lithium battery;
performing power optimization searching processing in an optimal power searching model of the fuel engine based on the load power in the real-time signal data to obtain an optimal working point of the fuel engine;
estimating and processing in a load power estimation model based on the load power in the real-time signal data to obtain the estimated load power of the next time period;
performing self-adaptive adjustment processing in a self-adaptive adjustment model based on the acceleration in the real-time signal data, the state information of the lithium battery and the estimated load power to obtain the optimal weight of the state of charge of the lithium battery and the mechanical weight of the fuel engine;
performing rolling optimization design processing in a multi-target capacity management model based on the optimal working point of the fuel engine, the lithium battery state of charge optimization weight and the fuel engine mechanical weight to generate the lithium battery power of the control object at the next moment and the fuel engine power of the control object at the next moment;
and adjusting and controlling the control object by using the power of the lithium battery at the next moment and the power of the fuel engine at the next moment.
2. The hybrid power system energy adaptive control method according to claim 1, characterized in that the control object lithium battery, fuel engine, generator, and motor; the lithium battery is connected with the motor, the fuel engine is connected with the generator, and the generator is respectively connected with the lithium battery and the motor;
the power relationship of the control object is as follows:
P fc (t)+P bat (t)＝P L (t)；
wherein, P fc (t) represents the output power of the generator at time t; p bat (t) represents the output power of the lithium battery at the time t; p is L (t) represents the load power of the control object at time t; when the instantaneous load power is greater than the instantaneous output power of the generator at the end of the fuel engine, the output power of the lithium battery is positive; when the instantaneous power of the load is smaller than the instantaneous output power of the generator at the end of the fuel engine, the output power of the energy storage battery is negative.
3. The adaptive hybrid power system energy control method according to claim 1, wherein before performing a power optimization search process in a fuel engine optimum power search model based on the load power in the real-time signal data, the method further comprises:
establishing a fuel consumption efficiency curve of the fuel engine, wherein the fuel consumption efficiency curve is an efficiency curve fitted by a second-order polynomial, and the fitting equation is as follows:
η＝a 1 P fc +a 2 P fc 2 ；
wherein η represents the fuel consumption efficiency of the fuel engine; a is 1 、a 2 Are all parameters representing the fuel consumption efficiency curve of the fuel engine, and a 1 、a 2 Will change with speed, environmental changes; p fc Representing the output power of the generator at the end of the fuel engine.
4. The adaptive hybrid power system energy control method according to claim 1, wherein the performing a power optimization search process in a fuel engine optimum power search model based on the load power in the real-time signal data comprises:
the fitting equation of the fuel consumption efficiency curve is rewritten as follows:
Figure FDA0003613138650000021
recording the input as U and the output as Z, and inputting U T (k)＝[P(k),P 2 (k)]Output z (k) ═ η; considering the Gaussian measurement noise v (k) N (0, σ), the above equation is expressed as:
Z(k)＝a 1 (k)U 1 (k)+a 2 U 2 (k)+v(k)；
the log-likelihood function obtained by combining the maximum likelihood algorithm principle is as follows:
Figure FDA0003613138650000022
the log-likelihood function of the above formula is minimized,
Figure FDA0003613138650000023
the minimum value is required to be obtained;
and v (k) z (k) -a 1 (k)U 1 (k)-a 2 U 2 (k) Substitution can obtain:
Figure FDA0003613138650000024
order:
Figure FDA0003613138650000031
Figure FDA0003613138650000032
x(k)＝θ(k) T ；
due to Z 2 (k) When an extreme value is obtained as an observable value, a certain constant is obtained, and then:
Figure FDA0003613138650000033
wherein v (k) represents gaussian noise; k represents time, where k is 1, …, Q; q represents a log-likelihood function value.
5. The adaptive energy control method for a hybrid power system according to claim 1, wherein the estimating in a load power estimation model based on the load power in the real-time signal data comprises:
and filtering the load power in the real-time signal data by adopting a load power filter, and taking the filtered load current value as an estimated value, wherein the estimated value is obtained according to the Lagrange median theorem:
Figure FDA0003613138650000034
by performing approximation processing on the differential term, there are:
Figure FDA0003613138650000035
wherein, P L (t-τ 1 ) And P L (t-τ 2 ) The known value of the load power is obtained through two first-order inertia links as follows:
Figure FDA0003613138650000036
wherein, P L (s) is the load power P L (t) laplace transform; the load power at the next moment is expressed as:
Figure FDA0003613138650000037
wherein L is - Representing the inverse laplace transform.
6. The energy adaptive control method for the hybrid power system according to claim 1, wherein the adaptive adjustment processing in an adaptive adjustment model based on the acceleration in the real-time signal data, the state information of the lithium battery and the estimated load power comprises:
and carrying out weight coefficient self-adaptive adjustment processing on the lithium battery charge state optimization target constraint item and the fuel engine mechanical property optimization target constraint item by adopting fuzzy control in a self-adaptive adjustment model based on the acceleration in the real-time signal data, the state information of the lithium battery and the estimated load power.
7. The energy adaptive control method for the hybrid power system according to claim 1, wherein the rolling optimization design process based on the optimal operating point of the fuel engine, the state of charge optimization weight of the lithium battery and the mechanical weight of the fuel engine in a multi-objective capacity management model comprises the following steps:
power II P of fuel engine from economical point of view fc (k)-P opt II, minimizing as a first optimization target;
from the safety point of view, the state of charge of the lithium battery is limited between the minimum value and the maximum value, and the state of charge is at a desired value
Figure FDA0003613138650000044
The accessories fluctuate, then
Figure FDA0003613138650000043
Minimization is taken as a second optimization objective;
compared with a lithium battery, the fuel engine has slow response speed from the aspect of smoothness, and the output power fluctuation of the fuel engine is gentle, so that the minimum | [ delta ] P fc (k) II as a third optimization target;
and performing rolling optimization design processing based on the first optimization objective, the second optimization objective and the third optimization objective in a simultaneous manner.
8. The hybrid system energy adaptive control method according to claim 7, wherein the simultaneous rolling optimization design process based on the first, second and third optimization objectives is as follows:
Figure FDA0003613138650000041
f 1 (x(k))＝[‖P L (k+1|k)-P opt ‖,…‖P L (k+n|k)-P opt ‖] T ；
Figure FDA0003613138650000042
f 3 (x(k))＝[‖ΔP fc (k+1|k)‖,…‖ΔP fc (k+n-1|k)‖] T ；
wherein F (x (k)) E R 3n Representing an optimization objective vector; f. of 1 (x (k)) represents an economic objective function; f. of 2 (x (k)) represents a security objective function; alpha represents a security weight; f. of 3 (x (k)) represents a smoothness objective function; beta represents a smoothness weight; f. of 1 (x(k))∈R n Indicating the operating efficiency of the fuel engine at the time k; f. of 2 (x(k))∈R n Representing the wave band of the lithium battery at the moment k; f. of 3 (x(k))∈R n Representing the fuel engine power band at time k.
9. The hybrid power system energy adaptive control method according to claim 8, wherein the constraints of the rolling optimization design process are described as:
x(k+i+1|k)＝Ax(k+i|k)+Bu(k+i|k)+Cd(k+i|k)；
wherein the state variable x (i) is defined as x (i) ═ P fc (i)V s (i)V f (i)V soc (i)] T Entry of
Figure FDA0003613138650000051
d (k) representing the location load power of the multi-objective capacity management model; x (k + i | k) and u (k + i | k) represent the estimated state quantity and input variables at the k + i-th time; the coefficient matrix A, B, C has:
Figure FDA0003613138650000052
B＝[T 0 0 0] T ；
Figure FDA0003613138650000053
wherein, C s And C f Representing the equivalent capacitance of the lithium battery; c b Represents a controlled source capacitance; r s And R f Representing the equivalent resistance of the lithium battery; r sd Represents a controlled source resistance; t represents time; u represents the voltage across the capacitor.
10. An unmanned aerial vehicle's hybrid power system energy adaptive control system, characterized in that, the system includes:
a data acquisition module: the system comprises a control object, a load, a lithium battery, a power supply and a power supply, wherein the control object is used for acquiring real-time signal data of the control object, and the real-time signal data comprises acceleration, load power and state information of the lithium battery;
a power optimization module: the system is used for carrying out power optimization searching processing in an optimal power searching model of the fuel engine based on the load power in the real-time signal data to obtain an optimal working point of the fuel engine;
an estimation module: the load power estimation module is used for estimating and processing the load power in the real-time signal data in a load power estimation model to obtain the estimated load power of the next time period;
self-adaptive adjusting module: the system is used for carrying out self-adaptive adjustment processing in a self-adaptive adjustment model based on the acceleration in the real-time signal data, the state information of the lithium battery and the estimated load power to obtain the optimized weight of the state of charge of the lithium battery and the mechanical weight of the fuel engine;
a rolling optimization module: the system comprises a multi-target capacity management model, a lithium battery state-of-charge optimization model and a control object, wherein the multi-target capacity management model is used for performing rolling optimization design processing on the basis of the optimal working point of the fuel engine, the lithium battery state-of-charge optimization weight and the fuel engine mechanical weight to generate the lithium battery power of the control object at the next moment and the fuel engine power of the control object at the next moment;
adjusting the control module: and the control system is used for adjusting and controlling the control object by utilizing the power of the lithium battery at the next moment and the power of the fuel engine at the next moment.
CN202210439401.1A 2022-04-25 2022-04-25 Hybrid power system energy self-adaptive control method and system of unmanned aerial vehicle Pending CN114802774A (en)

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