Solar-powered aircraft energy storage multi-stage combined charging optimization method under weight index

Abstract

The application discloses a solar aircraft energy storage multi-stage combined charging optimization method under a weight index, and belongs to the technical field of solar aircraft energy storage optimization, and comprises the following steps: S1, predicting a photovoltaic power curve of the day according to the geographical position and date of the solar aircraft; S2, initializing a multi-stage combined charging strategy according to the photovoltaic power curve of the day, and calculating the internal parameters of the battery through a coupled battery model; and S3, obtaining an optimal direction with different tendencies based on three optimization indexes of the weighted charging anxiety, the battery health and the energy efficiency, performing an optimization algorithm optimization process, and obtaining an optimal solution. The solar aircraft energy storage multi-stage combined charging optimization method under the weight index solves the problem that the charging speed of the solar aircraft energy storage system is improved and the battery aging is slowed down under the photovoltaic power limitation, and provides a multi-stage combined charging strategy.

Description

Technical Field

[0001] This invention relates to the field of energy storage optimization technology for solar-powered aircraft, and in particular to a multi-stage combined charging optimization method for energy storage of solar-powered aircraft under a weighted index. Background Technology

[0002] With the development of the aerospace industry, solar-powered unmanned aerial vehicles (UAVs) have played a vital role in fields such as communications and agriculture due to their advantages of high flight altitude and long endurance. To meet the mission requirements of day-and-night flight, the UAVs need to deploy efficient and stable energy management strategies, while considering mission requirements and system health. During the daytime flight, the UAV stores the energy generated by photovoltaic cells in an energy storage unit to enable continued flight at night when sunlight is insufficient. Lithium-ion batteries, as the main component of the energy storage unit, are charged during the day and discharged at night. However, in repeated charge-discharge cycles, lithium-ion batteries are prone to performance degradation, affecting the reliability of the UAV. Therefore, properly designed energy storage battery charging control is particularly important. On the one hand, controlling the charging rate directly affects the UAV's performance in complex missions. On the other hand, a carefully designed charging strategy can also effectively consider the battery's health and extend the lifespan of the energy storage device.

[0003] It is important to note that, unlike traditional ground-based charging, the available charging power in a solar-powered aircraft is limited due to fluctuations in photovoltaic (PV) power. The main factors influencing the PV power curve are the current acceptable solar irradiance and ambient temperature, the specific values ​​of which are determined by the aircraft's geographical location, altitude, and the date and time. Generally, solar irradiance gradually increases after sunrise, peaks at noon, and then decreases until sunset. Therefore, during the charging process of a solar-powered aircraft's batteries, the available charging power gradually increases from zero, and the charging strategy should follow this pattern.

[0004] Given the limitations of photovoltaic power, the goal of battery charging strategies should be to achieve faster charging speeds while minimizing lithium battery aging. Currently, the most commonly used charging strategies are constant voltage and constant current charging. While these methods are simple to control and deploy, they do not consider the internal characteristics of the battery, leading to rapid battery aging. Furthermore, the initial constant current phase cannot meet the charging needs of the morning, and the final constant voltage trickle charging phase significantly prolongs the charging time. Model-based intelligent charging strategies have become increasingly popular in recent years, automatically controlling the charging current based on the battery's evolution trend to slow down performance degradation. However, due to the hardware resource limitations of solar-powered aircraft, intelligent charging control applications are costly and difficult to deploy.

[0005] Battery models are essential for accurately examining battery evolution trends and internal characteristics. However, neither precise and complex electrochemical models nor easily identifiable equivalent circuit models are sufficient for analyzing the energy storage units of solar-powered aircraft. Furthermore, the low temperatures of the aircraft's environment must be considered in relation to the battery's performance and lifespan. During charging, the battery's charging rate, temperature changes, and capacity decay are interdependent; therefore, evaluating the charging strategy of solar-powered aircraft solely based on charging time and performance indicators is inadequate.

[0006] Based on the above analysis, for solar-powered unmanned aerial vehicle energy storage units, finding a charging strategy that can adapt to photovoltaic power limitations and comprehensively improve the charging speed and health of the energy storage unit has significant engineering implications. Summary of the Invention

[0007] The purpose of this invention is to provide a multi-stage combined charging optimization method for solar-powered aircraft energy storage under weighted indexes, which solves the problem of improving charging speed and slowing down battery aging under photovoltaic power limitations in solar-powered aircraft energy storage systems. It provides a multi-stage combined charging strategy based on a coupled battery model that considers charging anxiety and battery health.

[0008] To achieve the above objectives, this invention provides a multi-stage combined charging optimization method for solar-powered aircraft energy storage under weighted indices, comprising the following steps:

[0009] S1. Predict the photovoltaic power curve for the day based on the geographical location and date of the solar-powered aircraft;

[0010] S2. Randomly initialize the multi-stage combined charging strategy based on the photovoltaic power curve of the day, and calculate the internal parameters of the battery by coupling the battery model;

[0011] S3. Based on the three optimization indicators of weighted charging anxiety, battery health and energy efficiency, different optimization directions are obtained, and the optimization algorithm is used to find the optimal solution.

[0012] Preferably, in step S2, the multi-stage combined charging strategy includes three stages of constant current charging and one stage of pulse charging, as detailed below:

[0013] (1) When the SoC is located at 0 to In between, through Charge;

[0014] (2) In the SoC located to In between, through Charge;

[0015] (3) In the SoC located To (1-ΔSoC) p When between ) , through Charge;

[0016] (4) When SoC is higher than (1-ΔSoC) p When charging is performed, the battery is intermittently pulse-charged at a 1C rate. During the constant charging time, the battery voltage rises and falls after charging stops. When the battery voltage drops to the upper limit voltage, the battery is charged with the same current value, and the next pulse cycle begins.

[0017] The SoC range is equal for all three stages of the constant current charging process. For the i-stage of the constant current charging process, ΔSoC p It is the SoC range for the pulse charging process.

[0018] Preferably, in step S3, the three optimization indicators—charging anxiety, battery health, and energy efficiency—are as follows:

[0019] The charging anxiety index is composed of the total time and the time of each stage. Since the charging time reaches 10,000 seconds, in order to balance the order of magnitude with other optimization objectives, the total time is divided by the time of the battery at a constant current of 0.5C. The total time is divided into three stages of constant current charging and one stage of pulse charging. The total time is calculated as follows:

[0020]

[0021] Among them, t w Represents the total time. t is the time of the i-th constant current charging stage. p t represents the duration of the pulse charging phase. 0.5C This represents the charging time of the battery at a constant charging rate of 0.5C.

[0022] To measure the charging speed at different stages, a linear regression was performed between the charging current of the four stages and the final SoC of each stage. The magnitude of the regression coefficient was used to characterize the charging speed before and after each stage. The formula for the linear regression coefficient is as follows:

[0023]

[0024] Among them, X k For each stage's end SoC, Y k The charging current is for the four stages. and The expected values ​​of both are given; substituting the linear regression coefficients into the exponential function, the charging anxiety index is obtained as follows:

[0025] Ja =κ1e b t w

[0026] Wherein, κ1 is the compensation coefficient of the charging anxiety index;

[0027] Battery health indicators are determined by the battery's state of health and are obtained by calculating the current change in the battery's soH (energy dissipation rate). The battery health indicators are:

[0028] J h =κ2(1-SoH)

[0029] Where κ2 is the battery health index compensation coefficient, and SoH is the battery health level. The formula for calculating SoH is:

[0030]

[0031] Where Q is the current available capacity, Q e This refers to the factory output capacity;

[0032] The energy efficiency index is determined by the total energy consumed during charging; the higher the total energy, the greater the battery loss. The energy efficiency index is defined as follows:

[0033]

[0034] Where κ3 represents the energy efficiency index compensation coefficient, E b It is the total energy absorbed by the battery, E p This represents the theoretical maximum output energy of the photovoltaic system.

[0035] Preferably, in step S3, the objective function of the optimization problem is defined as:

[0036]

[0037] Where α, β and γ are the weight coefficients of the three indicators, and α+β+γ=1;

[0038] By adjusting the three weighting coefficients, different optimization directions are determined; when the weight of the charging anxiety index is the largest, the optimization direction is to minimize charging time; when the weight of the battery health index is the largest, the optimization direction is to maximize battery health.

[0039] Therefore, the present invention adopts the above-mentioned weighted index-based multi-stage combined charging optimization method for solar-powered aircraft energy storage, and its technical effects are as follows:

[0040] (1) The method proposed in this invention realizes the charging optimization of solar-powered aircraft under different lighting conditions, which can improve the mission capability of the aircraft.

[0041] (2) The multi-stage combined charging strategy proposed in this invention can adapt to charging power limitations, achieve efficient charging through a relatively simple control method, and delay battery aging.

[0042] (3) The method proposed in this invention is based on a coupled battery model and designs indicators such as charging anxiety, battery health and energy efficiency, which can more objectively characterize the advantages and disadvantages of different charging strategies and provide clear indicators for the optimization process.

[0043] (4) The method proposed in this invention can change the weighting coefficient according to the mission requirements of the aircraft, thereby changing the optimization direction and achieving different levels of charging speed and aging ratio.

[0044] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0045] Figure 1 This is a flowchart of the charging optimization algorithm;

[0046] Figure 2 This is a diagram of a multi-stage combined charging strategy;

[0047] Figure 3 This is a battery coupling model diagram;

[0048] Figure 4 A comparison chart of charging power and photovoltaic power;

[0049] Figure 5 A comparison chart showing the optimization effects of charging; among them, Figure 5 (a) in the figure is a voltage change diagram during the charging process; Figure 5 (b) in the figure shows the current change during the charging process; Figure 5 (c) in the diagram shows the change in state of charge during the charging process; Figure 5 (d) in the figure represents the change in battery health during the charging process. Detailed Implementation

[0050] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0051] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0052] Example 1

[0053] like Figure 1 The diagram shows a flowchart of a multi-stage combined charging optimization method for solar-powered aircraft energy storage under a weighted index, according to the present invention. The specific steps are as follows:

[0054] I. Predicting the photovoltaic power curve for the day based on the flight conditions of the solar-powered aircraft. This embodiment uses a photovoltaic intensity calculation model and a photovoltaic cell engineering model for calculation. The change curve of sunlight intensity is calculated based on the geographical location and time of the aircraft, and then the current photovoltaic power curve is calculated based on the characteristics of the photovoltaic cells.

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061] E = I 0n τsin(θ)

[0062] Among them, I SC and I 0n The values ​​represent the external intensity of solar radiation and the average radiation intensity at the current altitude; r and r0 represent the actual distance and average distance, respectively; ε represents the flatness of the Earth; α represents the day angle; L represents the latitude of the spacecraft. ω, δ, and θ represent the hour angle, solar elevation angle, and zenith angle, respectively; τ is the projection coefficient; and Day is the number of days that have passed in the current year. The solar irradiance E is calculated based on these parameters.

[0063] Photovoltaic cells convert solar energy into electrical energy through the photovoltaic effect. For ease of calculation, photovoltaic cells typically use an engineering model that uniquely determines the photovoltaic current-voltage characteristic curve using four parameters: short-circuit current, open-circuit voltage, maximum power current, and maximum power voltage. The maximum power under the current irradiance can be obtained by calculating the product of the maximum power point voltage and current. The output characteristics of photovoltaic cells may change under varying irradiance and temperature conditions; therefore, compensation equations are needed to correct for these parameter values ​​under different environments.

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] Among them, Sr and T r These are the reference battery temperature and the reference solar irradiance, respectively. and This represents a coefficient under reference temperature and intensity conditions. This represents the maximum power of the photovoltaic cell. The compensation coefficients a, b, and c are constants obtained through numerous experiments. ΔS and ΔT represent the differences between the actual solar irradiance or temperature and the reference value, respectively.

[0070] Second, randomly initialize the combined charging strategy based on the photovoltaic power curve to ensure that the generated charging power remains within the maximum available power limit.

[0071] A stochastic charging strategy is generated based on photovoltaic power curve limitations and charging voltage, current, and temperature constraints. This strategy includes three stages of constant-current charging and a final pulse charging phase. The current during the three constant-current charging stages and the SoC range of the pulse charging are controllable variables. The SoC ranges for the three constant-current charging stages are equal, with specific values ​​depending on the pulse charging range. The charging speed during the constant-current stages is related to the charging current rate. During the pulse charging phase, the battery is intermittently charged with a pulsed current. The battery voltage rises within a constant charging time and then slowly decreases after charging stops. When the battery voltage drops to its upper limit, it is charged with the same current value, starting the next pulse cycle. The battery is considered fully charged when the duty cycle of the pulse process is less than 55% of a single charging cycle. The controlled variables are the current during the three constant-current stages and the SoC interval of the pulse charging stage, expressed as follows:

[0072]

[0073] Among them, I i The i-stage of the constant current charging process is presented by ΔSoC. p This represents the SoC range for the pulse charging process; the larger the value, the earlier the pulse process begins. Therefore, the SoC nodes for the four stages are:

[0074]

[0075] like Figure 2 The diagram shown illustrates a typical charging strategy under the current power limitations.

[0076] Third, the charging strategy is simulated using a battery model to obtain the charging anxiety, battery health, and energy efficiency under the charging strategy.

[0077] Coupled battery model such as Figure 3As shown, the charging strategy generated in step two is used to simulate the entire charging process using the battery model, obtaining parameters such as charging time, temperature change, and capacity decay under this strategy. The calculation formula is:

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] Among them, t p The system step size is represented by the subscripts k and k+1, which represent discrete-time steps. Q e This is the initial capacity of the battery. R1, R2 and C1, C2 represent two sets of polarization resistors and polarization capacitors, and their polarization voltages are... and This characterizes the transient response within the battery caused by polarization and diffusion effects. k Q is the system input current at time k; a positive value indicates charging, and a negative value indicates discharging. l This represents the capacity that has already been lost. and Represents the terminal voltage and open-circuit voltage at time k, R0 is the ohmic internal resistance, m is the mass of the battery, c is the specific heat capacity of the battery, h is the heat transfer coefficient, A is the heat dissipation area, and T is the open-circuit voltage. f For ambient temperature, Let n be the battery temperature, and n be the number of resistors in the ECM. Entropy coefficient E. e Related to the current SoC; E a The activation energy is given by R and z, which are the gas constant and aging exponent, respectively. α represents the aging acceleration coefficient caused by current, and B(I) is a pre-exponential factor. Battery capacity decay can be calculated from these values. And battery health SoH.

[0084] In this embodiment, the battery sample is a Panasonic NCR18650B battery, and its coupling model parameters have been obtained through identification experiments. The entropy coefficient polynomial in the temperature model is:

[0085] E e =12.52SoC 5 -36.84SoC 4 +29.72SoC 3 +1.51SoC 2 -14.24SoC +5.13

[0086] The pre-exponential factor polynomial in the aging model is:

[0087] B(I)=-48.23I 3 +1221I 2 -9312.2I+33042

[0088] Table 1 Battery Model Parameters

[0089] parameter numerical values unit capacity 3.25 Ah weight 0.0475 kg Specific heat capacity 800 <![CDATA[J / kg 2 ·K]]> surface area 0.04317 <![CDATA[m 2 ]]> Ambient temperature 298.15 K Capacity and throughput order 0.55 - activation energy 371700 J / mol Temperature coefficient 112.12 J / mol·A gas constant 8.314 mol·A

[0090] Fourth, set weight coefficients according to task requirements to obtain the final optimization index.

[0091] By changing the weighting coefficients, the direction and result of the optimization can be altered, resulting in charging strategies with different tendencies. This embodiment provides three different weighting directions: fast mode, balanced mode, and healthy mode.

[0092] (1) Fast mode: Charge the battery as quickly as possible, focusing on charging anxiety during the charging process rather than battery health degradation. The faster the overall charging speed and the less time spent in the previous charging stage, the less charging anxiety there will be. The coefficients are set as follows: α = 0.6, β = 0.3, γ = 0.1.

[0093] (2) Health mode: The charging process can maintain the battery's health level as much as possible without fast charging. The coefficients are set as follows: α = 0.3, β = 0.6, γ = 0.1.

[0094] (3) Balanced mode: To mitigate the two extreme situations mentioned above, a balanced mode that takes into account both speed and health is provided. The coefficients are designed as α = 0.45, β = 0.45, and γ = 0.1.

[0095] This embodiment illustrates the impact of weight adjustment on the optimization direction through three typical weight values. In practice, the weight coefficients can be adjusted according to task requirements and system conditions, and are not limited to the three working modes designed in this embodiment.

[0096] Fifth, based on various optimization indicators, an optimization algorithm is used to find the optimal solution, which is the optimal charging strategy under the current working conditions.

[0097] To compare the differences between the various modes, the manufacturer-recommended conventional constant current-constant voltage charging method was also used under photovoltaic-limited conditions. Figure 4 The power curves for charging strategies with different optimization directions and constant current-constant voltage modes are all below the envelope curve of photovoltaic power limitation. Figure 5This shows the changes in voltage (a), current (b), state of charge (c), and battery health (d) during the charging process. It can be seen that the start-up time differs depending on the weighting of the charging modes. The fast mode charges very quickly, while the health mode, which focuses on battery aging, requires a relatively longer time. Due to limitations in photovoltaic power, the charging start-up performance varies between modes. The fast mode starts later due to its higher initial charging power, while the health mode starts charging earlier. It is worth noting that the fast mode can still complete charging relatively quickly. In terms of battery health trends, the health mode shows the least battery degradation, while the fast mode shows the greatest reduction in SoH (Solar Hysteresis). The moderate mode falls between the two. The manufacturer-recommended constant current-constant voltage mode shows the greatest battery degradation.

[0098] Therefore, this invention adopts the above-mentioned multi-stage combined charging optimization method for solar-powered aircraft energy storage under a weighted index, which solves the problem of improving charging speed and slowing down battery aging under photovoltaic power limitation of solar-powered aircraft energy storage system, and provides a multi-stage combined charging strategy based on a coupled battery model that considers charging anxiety and battery health.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing multi-stage combined charging of solar-powered aircraft energy storage under a weighted index, characterized in that, Includes the following steps: S1. Predict the photovoltaic power curve for the day based on the geographical location and date of the solar-powered aircraft; S2. Randomly initialize the multi-stage combined charging strategy based on the photovoltaic power curve of the day, and calculate the internal parameters of the battery by coupling the battery model; S3. Based on the three optimization indicators of weighted charging anxiety, battery health and energy efficiency, different optimization directions are obtained, and the optimization algorithm is used to find the optimal solution. In step S2, the multi-stage combined charging strategy includes three stages of constant current charging and one stage of pulse charging, as detailed below: (1) When the SoC is located at 0 to In between, through Charge; (2) When the SoC is located to In between, through Charge; (3) When the SoC is located to In between, through Charge; (4) In SoC higher than During this process, the battery is intermittently pulse-charged at a 1C rate. Within a constant charging time, the battery voltage rises and falls after charging stops. When the battery voltage drops to the upper limit voltage, the battery is charged with the same current value, and the next pulse cycle begins. The SoC range is equal for all three stages of the constant current charging process. For the constant current charging process stage, It refers to the SoC range of the pulse charging process; In step S3, the three optimization metrics—charging anxiety, battery health, and energy efficiency—are as follows: The charging anxiety index is composed of the total time and the time of each stage. Since the charging time reaches 10,000 seconds, in order to balance the order of magnitude with other optimization objectives, the total time is divided by the time of the battery at a constant current of 0.5C. The total time is divided into three stages of constant current charging and one stage of pulse charging. The total time is calculated as follows: in, Represents the total time. It is the first The duration of each constant current charging phase Indicates the duration of the pulse charging phase. This represents the charging time of the battery at a constant charging rate of 0.5C. To measure the charging speed at different stages, a linear regression was performed between the charging current of the four stages and the final SoC of each stage. The magnitude of the regression coefficient was used to characterize the charging speed before and after each stage. The formula for the linear regression coefficient is as follows: in, At the end of each stage of the SoC, The charging current is for the four stages. and The expected values ​​of both are given; substituting the linear regression coefficients into the exponential function, the charging anxiety index is obtained as follows: in, It is the compensation coefficient for the charging anxiety index; Battery health indicators are determined by the battery's state of health and are obtained by calculating the current change in the battery's soH (energy dissipation rate). The battery health indicators are: in, SoH is the battery health index compensation coefficient, representing battery health. The formula for calculating SoH is: in, Current available capacity This refers to the factory output capacity; The energy efficiency index is determined by the total energy consumed during charging; the higher the total energy, the greater the battery loss. The energy efficiency index is defined as follows: in, This represents the energy efficiency index compensation coefficient. It is the total energy absorbed by the battery. This represents the theoretical maximum output energy of the photovoltaic system; In step S3, the objective function of the optimization problem is defined as: in , and These are the weighting coefficients of the three indicators, and ; By adjusting the three weighting coefficients, different optimization directions are determined; when the weight of the charging anxiety index is the largest, the optimization direction is to minimize charging time; when the weight of the battery health index is the largest, the optimization direction is to maximize battery health.

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