A multi-channel adaptive UAV battery cryogenic insulation device and control method

Abstract

This invention relates to the field of battery temperature control technology, specifically disclosing a multi-channel adaptive low-temperature insulation device and control method for drone batteries. The method includes an insulation box, which, from the outside to the inside, comprises a structural support layer, a reflective layer, a main insulation layer, a nano-insulation layer, and an inner liner layer; multiple heating units; multiple temperature detection units for detecting the temperature at various points within the battery insulation zone; an ambient temperature detection unit located on the outside of the insulation box for detecting the external ambient temperature; and a control unit connected to the heating units, temperature detection units, and ambient temperature detection units for executing an adaptive multi-step model predictive control algorithm to precisely control the temperature of the battery insulation zone. Through the five-layer insulation structure design of the insulation box, the heating and temperature measurement module, the dual power supply module, the control module, and the adaptive multi-step model predictive control method, stable operation of drone batteries can be achieved in extreme low-temperature environments.

Description

Technical Field

[0001] This invention relates to the field of battery temperature control technology, specifically a multi-channel adaptive low-temperature insulation device and control method for UAV batteries. Background Technology

[0002] With the development of drone technology, drone applications are gradually increasing in various production and daily life sectors. The operating environment of drone batteries is a crucial factor restricting drone applications, with ambient temperature being a key focus of current research in the drone application field. In extreme low-temperature environments, the performance of drone batteries (especially lithium iron phosphate batteries) deteriorates significantly, internal resistance increases, and discharge capacity decreases, potentially leading to drones failing to start or permanent battery damage in severe cases.

[0003] To address this issue, current drone batteries in low-temperature environments often employ a thermostatic chamber structure combined with a heating device to ensure operation at a suitable temperature and extend flight time. Traditional thermostatic chamber structures typically consist of only single or double-layer insulation, using polystyrene or polyurethane as the supporting and insulating materials. Their insulation properties cannot guarantee a stable operating temperature range of 0-25°C for drone batteries in low-temperature environments. Current drone battery heating devices mostly employ single-point or single-channel heating control methods, characterized by simple structure, large control hysteresis, and poor temperature uniformity. Their temperature control methods often use PID control, which is slow to respond to environmental disturbances and multi-point temperature differences, making it difficult to achieve high-precision multi-point coordinated control. For ground preheating of drone batteries and energy storage management in low-temperature environments, existing technologies still have shortcomings in energy utilization efficiency, heating uniformity, and adaptive control. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-channel adaptive low-temperature insulation device and control method for UAV batteries to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A multi-channel adaptive drone battery low-temperature insulation device includes:

[0007] The insulated box body comprises, from the outside to the inside, a structural support layer, a reflective layer, a main insulation layer, a nano-insulation layer, and an inner liner layer.

[0008] Multiple heating units are evenly arranged on the outer surface of the inner liner layer;

[0009] Multiple temperature detection units are located inside the inner liner layer to detect the temperature at various points within the battery insulation zone.

[0010] An ambient temperature detection unit is located on the outside of the insulation box and is used to detect the external ambient temperature.

[0011] The control unit, connected to the heating unit, temperature detection unit and ambient temperature detection unit, is used to execute an adaptive multi-step model predictive control algorithm to precisely control the temperature of the battery insulation zone.

[0012] The control method for a multi-channel adaptive UAV battery low-temperature insulation device includes the following steps:

[0013] Establish a temperature control prediction model for drone batteries;

[0014] Real-time acquisition of temperature data from multiple points within the battery insulation zone and the ambient temperature, and calculation of the environmental disturbance coefficient;

[0015] The model parameters are updated based on the recursive least squares method;

[0016] Construct a multi-step prediction objective function that includes temperature deviation and power change;

[0017] Solve the objective function optimization problem under the constraints of power, temperature uniformity, and isothermal accuracy.

[0018] Based on the optimization results, power allocation and compensation control are performed on each heating channel.

[0019] As a further embodiment of the present invention, the structural support layer is a PVC material layer;

[0020] The reflective layer is an aluminized polyester film;

[0021] The main insulation layer is a vacuum insulation board, and its outer side is filled with polyurethane foam.

[0022] The nano-insulation layer is a nano-aerogel felt;

[0023] The inner liner is made of aluminum alloy.

[0024] As a further embodiment of the present invention, the heating unit comprises several PTC ceramic heating elements arranged in a distributed manner on the outer surface of the inner liner.

[0025] As a further embodiment of the present invention, the temperature detection unit comprises a plurality of first temperature sensors, the installation positions of which correspond to the distribution positions of the heating unit;

[0026] The ambient temperature detection unit is a second temperature sensor.

[0027] As a further embodiment of the present invention, the temperature control prediction model is expressed as follows:

[0028] ;

[0029] Where T(k+1) is the spatial temperature state vector at time k+1, A(k) is the system thermal inertia, T(k) is the spatial temperature state vector at time k, B(k) is the influence matrix of several PTC heating channels, P(k) is the heating power distribution vector at time k, E(k) is the environmental disturbance coefficient matrix, and T env (k) represents the ambient temperature.

[0030] As a further aspect of the present invention, the parameter update based on the recursive least squares method is achieved through the following formula:

[0031] ;

[0032] ;

[0033] ;

[0034] in, For the system parameter estimation vector, Let C(k) be the regression vector, C(k) be the covariance, and λ be the forgetting factor.

[0035] As a further embodiment of the present invention, the multi-step prediction objective function is:

[0036] ;

[0037] Where J is the prediction objective function; T ref The target temperature; Q represents the change in adjacent heating power; T For temperature deviation weighting; Q P Weighting for changes in heating power.

[0038] As a further embodiment of the present invention, the power constraint, temperature uniformity constraint, and isothermal accuracy constraint are expressed as follows:

[0039] ;

[0040] Among them, P i (k) represents the current power of the i-th channel; P max This represents the maximum output power of a single channel; T i (k),T j (k) represents the temperature at any two corresponding locations in the channels.

[0041] As a further embodiment of the present invention, the power compensation control adopts the following formula:

[0042] ;

[0043] in, T represents the corrected output power of the i-th channel; avg (k) is the average temperature of all channels; is the thermal balance gain coefficient; T(k) is the temperature distribution vector.

[0044] Compared with the prior art, the beneficial effects of the present invention are: through the five-layer insulation structure design of the insulation box, the heating and temperature measurement module, the dual power supply module, the control module, and the adaptive multi-step model predictive control method, the stable operation of the drone battery can be achieved in extreme low temperature environments.

[0045] By introducing channel heating power constraints, temperature uniformity constraints, and constant temperature accuracy constraints as optimization constraints for the prediction objective function, and based on the recursive least squares method, adaptive updating of model parameters is achieved. This enables real-time adjustment of model parameters and dynamic balance control of the temperature in various areas of the UAV battery insulation zone, ensuring the insulation performance of the insulation box under extreme low temperature conditions.

[0046] It can monitor the solar cell power input and system power supply battery status information in real time, establish energy balance constraints, ensure the stability and reliability of system operation, and improve the system power supply energy utilization efficiency. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0048] Figure 1 Detailed views of the insulated box;

[0049] Figure 2 This is the front view of the insulated box;

[0050] Figure 3 Diagram showing the installation location of the PTC ceramic heating element;

[0051] Figure 4 Diagram showing the installation location of the PT100 temperature sensor;

[0052] Figure 5 This is a system functional block diagram;

[0053] Figure 6 This is a control architecture diagram of the method of the present invention;

[0054] Figure 7 Flowchart of the adaptive multistep model predictive control algorithm;

[0055] Figure 8 Temperature loss curves of each layer in the battery insulation box at an ambient temperature of -40℃.

[0056] Figure 9 Temperature response curves of each layer of the battery insulation box under controlled temperature conditions at an ambient temperature of -40℃. Figure 1 ;

[0057] Figure 10 Temperature response curves of each layer of the battery insulation box under controlled temperature conditions at an ambient temperature of -40℃. Figure 2 ;

[0058] Figure 11 Temperature values ​​of each layer of the battery insulation box at different times under controlled temperature conditions at an ambient temperature of -40℃.

[0059] Figure 12 The power compensation process of the system power supply section;

[0060] Figure 13 Temperature control comparison curves of the method of this invention and the PID method.

[0061] The components include: 1. Outer PVC trolley case body; 10. Outer PVC trolley case top cover; 2. Case body reflective layer; 20. Top cover reflective layer; 3. Case body main insulation layer; 30. Top cover main insulation layer; 4. Case body nano-insulation layer; 40. Top cover nano-insulation layer; 5. Case inner liner layer; 50. Top cover inner liner layer; 51. Aluminum alloy frame; 52. Aluminum alloy heat sink; 53. First temperature sensor; 54. PTC ceramic heating element; 6. Microcontroller; 7. ABS material panel; 8. Battery fixing slot; 9. Solar panel; 11. Second temperature sensor. Detailed Implementation

[0062] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0063] In this embodiment of the invention, a multi-channel adaptive drone battery low-temperature insulation device is provided, the method comprising:

[0064] A multi-channel adaptive drone battery low-temperature insulation device, the method comprising:

[0065] The insulated box body comprises, from the outside to the inside, a structural support layer, a reflective layer, a main insulation layer, a nano-insulation layer, and an inner liner layer.

[0066] Multiple heating units are evenly arranged on the outer surface of the inner liner layer;

[0067] Multiple temperature detection units are located inside the inner liner layer to detect the temperature at various points within the battery insulation zone.

[0068] An ambient temperature detection unit is located on the outside of the insulation box and is used to detect the external ambient temperature.

[0069] The control unit, connected to the heating unit, temperature detection unit, and ambient temperature detection unit, is used to execute an adaptive multi-step model predictive control algorithm for precise control of the temperature of the battery insulation zone.

[0070] like Figures 1 to 5 As shown, in this embodiment, a thermal insulation structure is provided, including an outer PVC suitcase cover 10 and an outer PVC suitcase body 1. The outer PVC suitcase cover 10 and the outer PVC suitcase body 1 are fastened together by snap-fit. The outer PVC suitcase cover 10 is provided with a reflective layer 20, a main thermal insulation layer 30, a nano-insulation layer 40, and an inner liner layer 50 in sequence. The outer PVC suitcase body 1 is provided with a suitcase body in sequence. The enclosure comprises a reflective layer 2, a main insulation layer 3, a nano-insulation layer 4, and an inner liner layer 5. The reflective layer 20 on the top cover is fastened to the reflective layer 2 on the enclosure. The main insulation layer 30 on the top cover is fastened to the main insulation layer 3 on the enclosure. The nano-insulation layer 40 on the top cover is fastened to the nano-insulation layer 4 on the enclosure. The inner liner layer 50 on the top cover is fastened to the inner liner layer 5 on the enclosure. The inner liner layer 5 contains a controller 6, an ABS panel 7, and a battery fixing slot 8.

[0071] An aluminum alloy frame 51 and an aluminum alloy heat sink 52 are provided on the outer surface of the inner liner layer 5 of the box. The aluminum alloy frame 51 serves to fix the aluminum alloy heat sink 52 and support the ABS panel of the instrument. A PTC ceramic heating element 54 is installed on the aluminum alloy heat sink 52. The aluminum alloy heat sink 52 is used to fix the PTC ceramic heating element 54 and increase the heat dissipation area. Three PTC ceramic heating elements 54 are arranged on the left and right long sides and the top and bottom sides of the outer surface of the inner liner layer 5 of the box, and two are arranged on each of the short sides. Several first temperature sensors 53 are arranged inside the inner liner layer 50 of the top cover. Three sensors are arranged on the left and right long sides and the top and bottom sides of the top cover inner liner layer 50, and two are arranged on each of the short sides.

[0072] The internal dimensions of the drone battery insulation box's structural support layer (PVC layer) are 597×427×272mm. Functionally, the internal space is divided into two parts: one part is the control unit area, measuring 80×427×272mm, mainly used to install the control system hardware, including the heating and temperature measurement module, power supply module, and control module; the power supply module provides power to the system, using a dual power supply method of battery pack power and solar panel power; the other part is the battery insulation area, measuring 517×427×270mm, used to install the various insulation materials and place the drone battery. The aluminum alloy inner liner of the battery insulation area adopts a snap-on design for battery slots of multiple external sizes, which can meet the insulation requirements of drone batteries with a maximum length of 450mm, a width of 350mm, and a height of 200mm.

[0073] In a preferred embodiment of the present invention, the structural support layer is a PVC material layer;

[0074] The reflective layer is an aluminized polyester film;

[0075] The main insulation layer is a vacuum insulation board, and its outer side is filled with polyurethane foam.

[0076] The nano-insulation layer is a nano-aerogel felt;

[0077] The inner liner is made of aluminum alloy.

[0078] In this embodiment, the insulated box has a five-layer composite structure. The outer layer is a structural support layer made of PVC material (3mm thick, thermal conductivity 0.25W / m·K), which is waterproof, moisture-proof, and impact-resistant. This layer mainly provides mechanical support. In addition, the photovoltaic solar panel 9 used for powering the drone battery insulated box is fixed on the top of the PVC outer surface. The second layer is a reflective layer, which is an aluminized polyester film (0.1mm thick, thermal conductivity 0.05W / m·K) used to reflect internal heat radiation and reduce heat loss. The third layer is the main insulation layer, a 20mm thick vacuum insulation board (thermal conductivity 0.008W / m·K) filled with polyurethane foam on the outside. The fourth layer is a nano-insulation layer, a 5mm thick nano-aerogel felt (thermal conductivity 0.014W / m·K). The fifth layer is an inner liner layer (thermal conductivity 170W / m·K) made of 3mm thick aluminum alloy.

[0079] like Figure 3 As shown, in a preferred embodiment of the present invention, the heating unit consists of several PTC ceramic heating elements 54 arranged in a distributed manner on the outer surface of the inner liner.

[0080] In this embodiment, there are 16 PTC ceramic heating elements 54. The 16 PTC ceramic heating elements 54 (each with a power of 38W) are evenly distributed on the outer surface of the inner liner. There are 3 elements on the front, back, top, and bottom of the rectangular structure, and 2 elements on the left and right sides. The heating elements are bonded to the inner liner with thermally conductive silicone grease to ensure uniform heating.

[0081] like Figure 4 and Figure 5 As shown, in a preferred embodiment of the present invention, the temperature detection unit comprises a plurality of first temperature sensors 53, the installation positions of which correspond to the distribution positions of the heating unit;

[0082] The ambient temperature detection unit is a second temperature sensor 11.

[0083] In this embodiment, both the first temperature sensor 53 and the second temperature sensor 11 are PT100 temperature sensors. Sixteen PT100 temperature sensors are installed inside the inner liner to measure the temperature of the battery insulation area. Their installation method is the same as that of the PTC ceramic heating element 54. One PT100 temperature sensor is installed outside the battery insulation box to measure the ambient temperature.

[0084] Heating and temperature measurement module: includes signal conditioning circuit, mainly used for signal amplification, shaping, power amplification, etc.; 16 first temperature sensors 53 for measuring the temperature of the battery insulation area, and 1 second temperature sensor 11 for measuring the ambient temperature; the power amplification circuit is used to drive the PTC ceramic heating element 54; the 16-channel PTC ceramic heating element 54 is used to heat each area of ​​the battery insulation area.

[0085] Power supply module: The main power supply is a 24V lithium iron phosphate battery pack, and the auxiliary power supply is a 300×300 mm solar panel 9;

[0086] Control module: Includes microcontroller 6, using STM32F410; PWM pulse modulation circuit. Microcontroller 6 collects 16 temperature data points from the battery insulation zone and 1 ambient temperature data point. Based on the system control algorithm, it uses PWM waves to control the heating of PTC ceramic heating element 54, achieving temperature control of the battery insulation zone under low-temperature conditions.

[0087] like Figure 6 and Figure 7 As shown, the present invention also provides a control method based on a multi-channel adaptive UAV battery low-temperature insulation device, comprising the following steps:

[0088] Establish a temperature control prediction model for drone batteries;

[0089] Real-time acquisition of temperature data from multiple points within the battery insulation zone and the ambient temperature, and calculation of the environmental disturbance coefficient;

[0090] The model parameters are updated based on the recursive least squares method;

[0091] Construct a multi-step prediction objective function that includes temperature deviation and power change;

[0092] Solve the objective function optimization problem under the constraints of power, temperature uniformity, and isothermal accuracy.

[0093] Based on the optimization results, power allocation and compensation control are performed on each heating channel.

[0094] In this embodiment, a multi-channel UAV battery low-temperature insulation control system is built based on the established temperature control prediction model, which can also be called the battery low-temperature insulation model.

[0095] The temperature of the 16-channel battery insulation zone measured by the first temperature sensor 53 is compared with the set target temperature to obtain the temperature difference, and the environmental disturbance coefficient E(k) is calculated based on the measured external ambient temperature.

[0096] Based on the S2 step and recursive least squares method, the model parameters A(k), B(k), and E(k) are adaptively updated to predict the future temperature T(k+1).

[0097] Based on the S3 step, the temperature change weight, the power change amount and power change weight parameters of adjacent heating areas are calculated. The channel heating power constraint, temperature uniformity constraint and constant temperature accuracy constraint are introduced to optimize the prediction objective function J.

[0098] The power allocation vector P(k) is obtained after optimization based on the predicted objective function. After temperature balance power compensation, the temperature of the UAV's insulation zone is controlled according to the battery low-temperature insulation model. By repeating the above operations, the temperature of the UAV's battery insulation zone can be controlled in real time.

[0099] In a preferred embodiment of the present invention, the temperature control prediction model is expressed as follows:

[0100] ;

[0101] Wherein, T(k+1) is the spatial temperature state vector at time k+1; A(k) is the system thermal inertia description, reflecting the relationship between temperature change and its own state; T(k) is the spatial temperature state vector at time k, containing temperature data from 16 measurement points; B(k) is the influence matrix of 16 PTC heating channels, representing the contribution coefficient of each channel's heating power to the temperature rise; P(k) is the heating power allocation vector at time k, representing the heating power settings for the 16 heating channels; E(k) is the environmental disturbance coefficient matrix, describing the influence of external environmental temperature changes on the system; T env(k) represents the ambient temperature, which is acquired by the second temperature sensor 11.

[0102] In a preferred embodiment of the present invention, the parameter update based on the recursive least squares method is achieved through the following formula:

[0103] ;

[0104] ;

[0105] ;

[0106] In this embodiment, ;

[0107] in, This is the system parameter estimation vector, which includes estimates of thermal inertia and coupling coefficients; The regression vector contains past temperatures and heating power; C(k) is the covariance; λ is the forgetting factor (0.95~0.99), used to balance data interference; T(k) is the actual measured temperature value, and the model parameters A(k) and B(k) can be adjusted in real time based on the recursive least squares method.

[0108] As a preferred embodiment of the present invention, N is sought based on the prediction objective function J. P The optimal output P(k) is obtained in one step, and the parameters are optimized. The objective function of the multi-step prediction is:

[0109] ;

[0110] Where J is the prediction objective function; T ref The target temperature; Q represents the change in adjacent heating power; T For temperature deviation weighting; Q P Weighting for changes in heating power.

[0111] In a preferred embodiment of the present invention, to ensure the accuracy and stability of temperature control, a channel heating power constraint is introduced. The power constraint, temperature uniformity constraint, and constant temperature accuracy constraint are expressed as follows:

[0112] ;

[0113] Among them, P i (k) represents the current power of the i-th channel; P max This represents the maximum output power of a single channel; T i (k),T j (k) represents the temperature at any two corresponding locations of the channels, and ±1℃ represents the steady-state temperature error range of the method.

[0114] In this embodiment,

[0115] In a preferred embodiment of the present invention, a temperature balance power compensation mechanism is used to correct the output power of the PTC heating channel, and the power compensation control adopts the following formula:

[0116] ;

[0117] in, T represents the corrected output power of the i-th channel; avg (k) is the average temperature of all channels; The gain coefficient is 0.05~0.2; T(k) is the temperature distribution vector. When the temperature in a certain area of ​​the UAV battery insulation zone is too low, the method of this invention automatically increases the heating power of the corresponding PTC heating channel in that area; conversely, it decreases the heating power to achieve dynamic temperature balance control of each area.

[0118] In this embodiment, the system power supply is controlled using energy balance constraints. Adjustments are made based on the output power of the solar panel 9 and the battery output power, taking into account the overall system application, to ensure the stability of the overall system operation. The energy balance constraints can be described as follows:

[0119] ;

[0120] Among them, P total (k) represents the total power requirement of the system; P bat (k) represents the real-time output power of the system's power supply battery; P solar (k) represents the real-time output power of solar panel 9; η represents the solar energy utilization efficiency (0~1).

[0121] To verify the effectiveness of the present invention, the following experiments and analyses were conducted in this embodiment:

[0122] The experimental environment was Matlab R2020b platform. To verify the performance of the adaptive multistep model predictive control method in cryogenic insulation of UAV batteries, simulation tests were conducted under different temperature conditions.

[0123] The battery insulation box was tested for heat loss under conditions of -40℃ external environment, +25℃ battery insulation zone, and no PTC heating temperature control. The drone battery dimensions are 450×350×200mm. The drone battery insulation box has a 5-layer structure, and the parameters of each layer are as follows:

[0124] The structure consists of: a support layer (PVC material) 3mm thick with a thermal conductivity of 0.25W / m·K; a reflective layer (aluminized polyester film) 0.1mm thick with a thermal conductivity of 0.05W / m·K; a main insulation layer (vacuum insulation board) 20mm thick with a thermal conductivity of 0.008W / m·K, with an outer polyurethane foam insulation layer; a nano-insulation layer (nano-aerogel felt) 5mm thick with a thermal conductivity of 0.014W / m·K; and an inner liner layer (aluminum alloy) 3mm thick with a thermal conductivity of 170W / m·K. Under conditions of -40℃ external environment without heating control, the initial temperature of the battery insulation area in the insulated box is +25℃. The heat loss temperature drop curve is shown below. Figure 8 As shown in the figure, the temperatures are all the outer surface temperatures of each layer.

[0125] Under heating control conditions, the system thermal inertia matrix A(k) is constructed through thermal coupling coefficients. The thermal conductivity of the materials is taken according to empirical values: its self-heating retention coefficient is 0.96, the coupling coefficient between the PVC support layer and the reflective layer is 0.025, the coupling coefficient between the reflective layer and the main insulation layer is 0.065, the coupling coefficient between the main insulation layer and the nano-insulation layer is 0.093, the coupling coefficient between the nano-insulation layer and the inner liner is 0.37, and the coupling coefficient between the inner liner and the battery insulation space is 0.080.

[0126] The B(k) matrix represents the power contribution coefficients, mapping the power input of the 16 PTC heating channels to the system temperature response. The heat capacity of each layer is calculated using the following formula. :

[0127] ;

[0128] Where ρ is density, cp is specific heat capacity, V is layer volume, and m represents the layer number. Specific heat capacity cp is empirically assigned as follows: 0.9 kJ / (kg·℃) for the support layer (PVC material layer), 1.2 kJ / (kg·℃) for aluminized polyester film, 1.0 kJ / (kg·℃) for vacuum insulation board, 0.8 kJ / (kg·℃) for nano-aerogel felt, and 0.9 kJ / (kg·℃) for aluminum alloy. Density The values ​​for each layer are taken according to typical values ​​based on the material type, and the volume of layer V is calculated based on the dimensions of each layer. Due to the high thermal conductivity of aluminum alloy, heating primarily occurs in the aluminum alloy layer (layer 5, m=5). The thermal coupling coefficient B(k) for each heating channel is constructed based on the heat capacity of each layer.

[0129] ;

[0130] In the formula, dt is the time interval of the continuous-time discretization, with a value of 1 second.

[0131] Only the outer layer (PVC) and the drone battery directly participate in heat exchange; other layers are indirectly affected through heat conduction. Environmental disturbances originate from the convective heat transfer coefficient h. env (m) mainly includes the heat exchange between the external environment and the PVC outer layer, as well as between the battery and the inner liner within the insulation zone. Based on empirical values, the convective heat transfer coefficient between the PVC outer layer and the external environment is 200 W / m²·K, and the convective heat transfer coefficient between the battery and the inner liner is 25 W / m²·K. The environmental disturbance matrix E(k) is calculated using the following formula:

[0132] ;

[0133] After completing the parameter setting and construction of the overall thermal insulation model, the ambient temperature was set to -40℃, and the initial temperatures of each material layer and the battery were consistent with the ambient temperature. The target temperature for the battery insulation zone was set to +25℃. The battery insulation zone was heated using an adaptive multi-step model predictive control method, and the control process was as follows: Figure 9 and Figure 10 As shown, the simulation lasted for 120 minutes. At 28.43 minutes, the temperature of the aluminum alloy inner liner rose to +29.89℃. At 46.77 minutes, the temperature of the battery insulation area tended to stabilize and the temperature error was kept within ±1℃.

[0134] With an ambient temperature of -40℃, and under system temperature control, the temperature values ​​of each layer of the battery insulation box at different times are as follows: Figure 11 As shown.

[0135] The system adopts a coordinated power supply strategy of photovoltaic solar panels 9 and batteries. Within each control cycle (0.5 seconds), the system collects photovoltaic power generation and battery status data. During control, dynamic power allocation is performed according to the principle of "photovoltaic priority, battery supplementation," comprehensively utilizing the electrical energy provided by the battery pack and solar panels 9 to ensure efficient and energy-saving operation of the system. Photovoltaic power is simulated to generate fluctuations under actual sunlight conditions. The ambient temperature is set to -40℃, and the initial temperature of each layer of material and the battery insulation area is consistent with the ambient temperature. The target temperature of the battery insulation area is +25℃. The system's photovoltaic solar panel 9 output power and battery pack output power utilization history within 2 hours after the heating system is started are shown below. Figure 12 As shown.

[0136] like Figure 13 As shown, the AMPC method involved in this invention is compared with the empirical PID control method. The temperature condition of the insulation box is -25℃, and the target temperature is +25℃. Considering the large inertia characteristics of temperature control in the battery insulation zone, the empirical PID parameters are set to kp=80.0, ki=0.80, and kd=25.0. Figure 13As shown, at 15 minutes, the temperature rise was 7.86℃ for the AMPC method and 6.48℃ for the empirical PID method, with the AMPC method achieving a temperature rise 1.38℃ higher than the empirical PID method. After 27.32 minutes, the control entered a relative equilibrium stage, with the maximum relative temperature error of 4.35% for AMPC and 14.36% for PID. Within a 120-minute control period, the proportion of the time range within which the target temperature was ±1℃ (+25℃) was 71.5% for the AMPC method and 44.5% for the PID method.

[0137] This invention primarily addresses the issue of maintaining a constant temperature for drone batteries in low-temperature environments, based on a drone battery insulation box and a drone battery low-temperature insulation control system. The main technologies used include: a five-layer drone battery insulation box structure designed based on thermal conduction effects. The outer four layers effectively prevent heat loss, while the inner layer and heating unit provide real-time heat replenishment to the drone battery insulation area; the control unit employs a dual power supply technology using both batteries and solar panels, enabling energy optimization and compensation, effectively ensuring the stability and reliability of the system's power supply; and an adaptive multi-step model predictive control method that uses an adaptive parameter update mechanism and multi-channel heating collaborative control technology to comprehensively control the ambient temperature of the drone battery's location under low-temperature conditions. Through the comprehensive application of these technologies, stable operation of the drone battery under constant temperature insulation in low-temperature environments is guaranteed.

[0138] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-channel adaptive low-temperature insulation device for UAV batteries, characterized in that, include: The insulated box body comprises, from the outside to the inside, a structural support layer, a reflective layer, a main insulation layer, a nano-insulation layer, and an inner liner layer. Multiple heating units are evenly arranged on the outer surface of the inner liner layer; Multiple temperature detection units are located inside the inner liner layer to detect the temperature at various points within the battery insulation zone. An ambient temperature detection unit is located on the outside of the insulation box and is used to detect the external ambient temperature. The control unit, connected to the heating unit, temperature detection unit and ambient temperature detection unit, is used to execute an adaptive multi-step model predictive control algorithm to precisely control the temperature of the battery insulation zone. The control method for a multi-channel adaptive UAV battery low-temperature insulation device includes the following steps: Establish a temperature control prediction model for drone batteries; Real-time acquisition of temperature data from multiple points within the battery insulation zone and the ambient temperature, and calculation of the environmental disturbance coefficient; The model parameters are updated based on the recursive least squares method; Construct a multi-step prediction objective function that includes temperature deviation and power change; Solve the objective function optimization problem under the constraints of power, temperature uniformity, and isothermal accuracy. Based on the optimization results, power allocation and compensation control are performed on each heating channel.

2. The multi-channel adaptive UAV battery low-temperature insulation device according to claim 1, characterized in that, The structural support layer is a PVC material layer; The reflective layer is an aluminized polyester film; The main insulation layer is a vacuum insulation board, and its outer side is filled with polyurethane foam. The nano-insulation layer is a nano-aerogel felt; The inner liner is made of aluminum alloy.

3. The multi-channel adaptive UAV battery low-temperature insulation device according to claim 1, characterized in that, The heating unit consists of several PTC ceramic heating elements arranged in a distributed manner on the outer surface of the inner liner.

4. The multi-channel adaptive UAV battery low-temperature insulation device according to claim 1, characterized in that, The temperature detection unit consists of several first temperature sensors, whose installation positions correspond to the distribution positions of the heating unit; The ambient temperature detection unit is a second temperature sensor.

5. The control method for a multi-channel adaptive UAV battery low-temperature insulation device according to claim 1, characterized in that, The temperature control prediction model is expressed as follows: ； Where T(k+1) is the spatial temperature state vector at time k+1, A(k) is the system thermal inertia, T(k) is the spatial temperature state vector at time k, B(k) is the influence matrix of several PTC heating channels, P(k) is the heating power distribution vector at time k, E(k) is the environmental disturbance coefficient matrix, and T env (k) represents the ambient temperature.

6. The control method for a multi-channel adaptive UAV battery low-temperature insulation device according to claim 5, characterized in that, Parameter updates based on recursive least squares are achieved through the following formula: ； ； ； in, For the system parameter estimation vector, Let C(k) be the regression vector, C(k) be the covariance, and λ be the forgetting factor.

7. The control method for a multi-channel adaptive UAV battery low-temperature insulation device according to claim 6, characterized in that, The multi-step prediction objective function is: ； Where J is the prediction objective function; T ref The target temperature; Q represents the change in adjacent heating power; T Temperature deviation weight; Q P Weighting for changes in heating power.

8. The control method for a multi-channel adaptive UAV battery low-temperature insulation device according to claim 7, characterized in that, The power constraint, temperature uniformity constraint, and isothermal accuracy constraint are expressed as follows: ； Among them, P i (k) represents the current power of the i-th channel; P max This represents the maximum output power of a single channel; T i (k),T j (k) represents the temperature at any two corresponding locations in the channels.

9. The control method for a multi-channel adaptive UAV battery low-temperature insulation device according to claim 8, characterized in that, The power compensation control uses the following formula: ； in, T represents the corrected output power of the i-th channel; avg (k) is the average temperature of all channels; is the thermal balance gain coefficient; T(k) is the temperature distribution vector.

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