Multi-level intelligent power supply and distribution method and energy system in extremely high altitude environment

Abstract

The invention discloses an extremely high altitude environment multi-level intelligent power supply and distribution method and an energy system, and belongs to the field of intelligent power supply and distribution, and the method comprises the steps: determining the load characteristics of load equipment and the power value of a load, grading different load equipment, constructing an equipment power database, arranging sensors at the key positions of the load equipment, and carrying out the classification of the load characteristics and the power value of the load; the method comprises the following steps: integrating sensors into a sensor cluster, and collecting environment data and real-time data of a load; the intelligent power distribution unit receives real-time data collected by the sensor cluster, preprocesses the data, integrates the data and constructs a sensor database; according to data in the equipment power database and the sensor database, an intelligent power distribution strategy is obtained in combination with a high-altitude environment coupling control algorithm; and the energy storage unit executes the control strategy according to the intelligent power distribution strategy and provides power for the load equipment. According to the invention, through the intelligent power distribution strategy, the influence of the high-altitude environment on the load characteristics is considered, and the load prediction accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent power supply and distribution, and in particular to a multi-level intelligent power supply and distribution method and energy system for an extremely high altitude environment. Background Art

[0002] In extremely high altitude environments, the lowest temperature can reach -55°C and the highest temperature can reach 15°C, with a large temperature difference between day and night; the air pressure is low, approximately 0.3 standard atmospheres (30KPa).

[0003] Due to the extremely low air pressure, extreme cold, extreme wind and strong ultraviolet rays in extremely high altitude areas, which pose great challenges to human survival, the comprehensive scientific observation and scientific research in this area are basically blank. Scientific research and observation instruments in extremely high altitude areas are restricted by the extreme environment. Technical personnel cannot arrive at the site in time for real-time technical maintenance. The equipment needs to run automatically in an unattended state. However, due to the influence of extreme cloudy weather, the power loss of the energy storage battery supporting the operation of the instruments and equipment will exceed the minimum threshold, which will lead to the loss or damage of the battery charging function, causing the instruments and equipment to stop operating. Therefore, it is necessary to solve the problem of self-recovery of power supply for instruments and equipment in extremely high altitude areas. It is necessary to enter the automatic sleep state before the battery power loss exceeds the minimum threshold, and restart the charging function when the charging conditions are met. Summary of the invention

[0004] One of the purposes of the present invention is to provide a multi-level intelligent power supply and distribution method in an extremely high altitude environment, so as to solve the problem in the prior art that due to the influence of the environment in extremely high altitude areas, the battery charging function of instruments and equipment is lost or damaged when they are unattended, causing the instruments and equipment to stop operating.

[0005] The present invention is implemented through the following technical scheme, a multi-level intelligent power supply and distribution method in an extremely high altitude environment, comprising the following steps: S100, determining load curves of different load devices, clarifying load characteristics, determining load power values, and grading different loads according to the load characteristics and load power values, and constructing the collected load characteristics and load power value data into an equipment power database, arranging sensors at key positions of load devices, and integrating different sensors arranged on the same load device into a sensor cluster for collecting environmental data and real-time data of the load; S200, an intelligent distribution unit, for receiving real-time data collected by the sensor cluster, and preprocessing the data, mapping the preprocessed data to a unified coordinate system grid, integrating the data and constructing a sensor database; S300, the intelligent distribution unit obtains an intelligent distribution strategy based on the data in the equipment power database and the sensor database, combined with a high altitude environment coupling control algorithm, and sends the intelligent distribution strategy to the energy storage unit; S400, the energy storage unit executes a control strategy according to the intelligent distribution strategy to provide power to the load device.

[0006] Furthermore, the sensor cluster may include: load sensors, air pressure sensors, temperature sensors, wind speed sensors and ultraviolet radiation sensors. The sensors transmit the collected data to the relay router in real time through the Internet of Things interface, and the collected data is preliminarily integrated at the relay router. The data of the same cluster is marked and sent to the back-end processing system.

[0007] Furthermore, the preprocessing of step S200 may include: denoising, completion and standardization of the sensor data, wherein the standardization uses Z-Score standardization to standardize the data, and aligns the timestamps of the data after the data is standardized.

[0008] Furthermore, the altitude environment coupling control algorithm may include: an environment-energy coupling field calculation process for determining the dynamic distribution law of the environment in the surrounding area of ​​the load equipment based on sensor data, thereby quantifying the impact of air pressure, wind speed, and ultraviolet rays on the heat dissipation efficiency of the load equipment; an energy system state calculation process for describing the battery charge state based on the dynamic process of external charging power, load distribution power, and environmental conditions; a target optimization control calculation process for obtaining the optimal battery charging and discharging control strategy based on the environment-energy coupling field calculation process and the energy system state calculation process to avoid the battery charge state entering the danger zone, while ensuring that the power requirements of high-priority load equipment are met first.

[0009] Furthermore, the environment-energy coupling field calculation process can be expressed by the following formula:

[0010] , where ρP cis the equivalent heat capacity of the battery; is the partial derivative of battery temperature with respect to time; k is thermal conductivity, k(T,P) is non-constant thermal conductivity, which is affected by battery temperature T and air pressure P; ∇T is temperature gradient, which indicates the rate of change of internal battery temperature; Q bat is the thermal power generated in the battery, I dis is the discharge current, R(T) is the internal resistance of the battery; h(P,v) is the convection heat transfer coefficient; T is the battery temperature; T i is the ambient temperature.

[0011] Furthermore, the energy system state calculation process can be expressed by the following formula:

[0012] , where SOC is the state of charge of the battery; η charge (T) is the charging efficiency; P in is the input power of the battery; β i (T) is the load temperature attenuation factor; L i is the load power, which is used to indicate the electric power consumed by different load devices; C eff (P) is the effective capacity of the battery; V bat The voltage of the battery. This item is usually a fixed value or varies slightly with load, temperature and other conditions. It is a standardized parameter used to convert power into SOC changes.

[0013] Furthermore, the decision function of the target optimization control calculation process can be expressed as follows:

[0014] , where u 1， u 2， u3 is the control variable; [t 0， t f ] is the time interval, t0 and t f Respectively represent the start and end time of optimization; SOC crit is the target state of charge; λ1 and λ2 are weight coefficients; w i is the load priority weight coefficient; is the load demand deviation.

[0015] Furthermore, the target optimization control calculation process may also include constraints, which may include: thermal runaway constraints, SOC constraints, and core load protection constraints.

[0016] Furthermore, the thermal runaway constraint can be: ,q max (UV) = , where q0 represents the maximum degradation coefficient under standard conditions; UV is the ultraviolet intensity.

[0017] Furthermore, the SOC constraint may be: , where P is the ambient air pressure and P0 is the reference air pressure.

[0018] Furthermore, the core load guarantee constraint may be: , where L1 is the core load; L 1,min (T) is the minimum core load power requirement at temperature T; L 1,nom is the nominal core load power.

[0019] Furthermore, the energy storage unit divides the power distribution circuit into three levels of loads according to the different priorities of the load devices.

[0020] Furthermore, the control strategy may include: S410, when the energy storage unit is fully charged, the BMS disconnects the charging switch, and the energy storage system only discharges to the outside until the SOC reaches ≤95%, and the BMS will close the charging switch again to allow energy replenishment; S420, when the energy storage unit can no longer supply power, the BMS will close the charging switch and disconnect all load power supply switches at the same time. The BMS enters an ultra-low power consumption mode and waits for input energy to be replenished until the energy storage unit meets the recovery conditions. The BMS restores power supply to the load according to the priority and power distribution strategy.

[0021] Furthermore, S420 may also include a recovery strategy, which includes: S421, the recovery strategy divides the energy storage battery power alarm into three levels: over-energy or severe insufficient energy, which is the first level alarm; insufficient energy with insufficient energy is the second level alarm; sufficient energy without over-energy, and nearly insufficient energy without insufficient energy alarm is the third level alarm; S422, when the energy storage unit is the first level alarm, the circuit of all load devices of the battery will be cut off, the energy storage unit is in a no-load state, the power supply to the load will stop, the BMS will work in local ultra-low power consumption, and the charging switch will be turned on; S423, when the energy storage unit is replenished to meet the working conditions, the voltage monitoring circuit will automatically work to supply power to the BMS. At this time, the BMS will restore the power supply to the load equipment according to the priority and power distribution strategy.

[0022] On the other hand, the present invention provides a multi-level intelligent power supply and distribution energy system for an extremely high altitude environment. The energy system includes an energy replenishment unit, an intelligent energy storage unit and an intelligent power distribution unit.

[0023] The energy replenishment unit is connected to the intelligent energy storage unit and is configured to provide electric energy replenishment for the intelligent energy storage unit; the intelligent energy storage unit is connected to the intelligent power distribution unit and is configured to execute the control strategy, store energy and provide power to the load equipment according to the intelligent power distribution strategy output by the intelligent power distribution unit.

[0024] The intelligent power distribution unit includes a database construction subunit and an intelligent power distribution strategy generation module, wherein the database construction subunit is configured to clarify the load characteristics of the load equipment and the power value of the load according to the load curves of different load equipment, classify different load equipment according to the load characteristics and the power values ​​of the load, construct the collected load characteristics and load power value data into a device power database, receive the environmental data and real-time data of the load sent by the sensor cluster arranged at the key position of the load equipment, pre-process the data, map the pre-processed data into a unified coordinate system grid, integrate the data and construct a sensor database; the intelligent power distribution strategy generation module is connected to the database construction subunit and is configured to obtain the intelligent power distribution strategy according to the data in the device power database and the sensor database in the database construction subunit, and send the intelligent power distribution strategy to the energy storage unit.

[0025] Furthermore, the intelligent power distribution strategy generation module includes: mutually coupled environment-energy coupling field submodule, energy system status submodule and target optimization control submodule, wherein the environment-energy coupling field submodule is configured to judge the dynamic distribution law of the environment in the surrounding area of ​​the load equipment according to the sensor data, and quantify the influence of air pressure, wind speed and ultraviolet rays on the heat dissipation efficiency of the load equipment; the energy system status submodule is configured to describe the battery charge state according to the dynamic process of external charging power, load distribution power and environmental conditions; the target optimization control submodule is configured to obtain the optimal battery charging and discharging control strategy according to the environment-energy coupling field submodule and the energy system status submodule.

[0026] Furthermore, the energy supplement unit includes a mains power input and a backup energy input, and the backup energy input includes a solar energy input or a wind energy input.

[0027] Furthermore, the environment-energy coupling field submodule is represented by the following formula:

[0028] , where ρP c is the equivalent heat capacity of the battery; is the partial derivative of battery temperature with respect to time; k is thermal conductivity, k(T,P) is non-constant thermal conductivity, which is affected by battery temperature T and air pressure P; ∇T is temperature gradient, which indicates the rate of change of internal battery temperature; Q bat is the thermal power generated in the battery, I dis is the discharge current, R(T) is the internal resistance of the battery; h(P,v) is the convection heat transfer coefficient; T is the battery temperature; T i is the ambient temperature.

[0029] Furthermore, the energy system status submodule is represented by the following formula:

[0030] , where SOC is the state of charge of the battery; η charge (T) is the charging efficiency; P in is the input power of the battery; β i (T) is the load temperature attenuation factor; L i is the load power, which is used to indicate the electric power consumed by different load devices; C eff (P) is the effective capacity of the battery; V bat The voltage of the battery. This item is usually a fixed value or varies slightly with load, temperature and other conditions. It is a standardized parameter used to convert power into SOC changes.

[0031] Furthermore, the target optimization control submodule includes a decision function and constraints.

[0032] Furthermore, the decision function is expressed as follows:

[0033] , where u 1， u 2， u3 is the control variable; [t 0， t f ] is the time interval, t0 and t f Respectively represent the start and end time of optimization; SOC crit is the target state of charge; λ1 and λ2 are weight coefficients; w i is the load priority weight coefficient; is the load demand deviation.

[0034] Furthermore, the constraints include: thermal runaway constraints, SOC constraints, and core load protection constraints, wherein the thermal runaway constraints are: ,q max (UV) = , where

[0035] q0 represents the maximum degradation coefficient under standard conditions; UV is the ultraviolet intensity.

[0036] The SOC constraint is: , where P is the ambient air pressure and P0 is the reference air pressure.

[0037] The core load guarantee constraints are: , where L1 is the core load; L 1,min (T) is the minimum core load power requirement at temperature T; L 1,nom is the nominal core load power.

[0038] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0039] 1. The present invention quantifies the impact of high-altitude environmental factors on the working performance of scientific research observation equipment by constructing a high-altitude environment coupling control algorithm, solves the problem that the prior art fails to fully consider the impact of the high-altitude environment on the equipment, and makes the power distribution process more accurate.

[0040] 2. The present invention arranges a sensor cluster to collect real-time environmental data and real-time load of scientific research observation equipment, and constructs the collected data into a sensor database and an equipment power database, thereby solving the problem of lack of real-time monitoring of equipment load and environmental data in the prior art, providing data support for dynamic optimization and adjustment of the power distribution process, and improving power supply reliability and operation and maintenance efficiency.

[0041] 3. The energy storage unit in the present invention divides the distribution circuit into three levels of loads according to priority, and executes the control strategy according to the intelligent distribution strategy, thereby realizing accurate demand-side response control and solving the problem that the existing demand-side response system fails to consider the impact of high-altitude environment on equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0043] Figure 1 This is a flow chart of the method provided in Example 1 of the present invention.

[0044] Figure 2 This is a system block diagram provided for Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The methods and calculation formulas of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0046] Example 1

[0047] In this embodiment, a multi-level intelligent power supply and distribution method for extremely high altitude environments is provided. Figure 1 The flowchart of the multi-level intelligent power supply and distribution method in this embodiment is shown. It can be seen from the figure that this embodiment includes the following steps:

[0048] Step 1: Determine the load curve according to the working characteristics of each different scientific research observation equipment, clarify the load characteristics, and determine the power value of important loads; classify the load characteristics and power values ​​of different scientific research observation equipment, and build the collected data into an equipment power database.

[0049] Specifically, the equipment power database can provide key data input for subsequent hierarchical management, and facilitate the definition of load weights in multi-objective optimization control equations. Time-varying parameters such as the minimum / maximum power, startup inrush current, and working cycle of the equipment collected by the sensor cluster can also be recorded in the equipment power database to facilitate subsequent accurate calculation of load weights.

[0050] Then, different sensors are selected according to the local specific altitude environment, geological environment and working characteristics of different scientific research observation equipment, and the selected sensors of various types are arranged at key positions of the scientific research observation equipment. The sensors arranged in the same scientific research observation equipment form a sensor cluster to collect environmental data and real-time data of the scientific research observation equipment.

[0051] Specifically, building a sensor cluster includes:

[0052] 1) In this embodiment, the sensor cluster should at least include a load sensor, an air pressure sensor, a temperature sensor, a wind speed sensor, and an ultraviolet radiation sensor, and the above sensors are installed at corresponding positions of the scientific research observation equipment.

[0053] 4) The collected data is transmitted to the relay router in real time through the IoT interface, where the collected data is preliminarily integrated, and the data of the same cluster is marked and sent to the back-end processing system.

[0054] Step 2: The intelligent distribution unit in the back-end processing system first preprocesses the received data and aligns the data's timestamps, then maps the preprocessed data into a unified coordinate system grid, integrates the data, and builds a sensor database.

[0055] Specifically, the preprocessing of the received data may include: noise reduction, completion and standardization of the sensor data. In this embodiment, Z-Score standardization is used to standardize the data. The data after Z-Score standardization can facilitate subsequent algorithm calculations and improve the operating efficiency of the algorithm.

[0056] Step 3: According to the factors affecting the high-altitude environment on scientific research and observation equipment, a high-altitude environment coupling control algorithm is constructed to quantify the impact of the altitude environment on scientific research and observation equipment. The data in the equipment power database and the sensor database are analyzed. At the same time, based on the real-time data collected by the sensor cluster, an intelligent power distribution strategy is obtained through the high-altitude environment coupling control algorithm.

[0057] Specifically, the high altitude environment coupling control algorithm in this embodiment consists of three parts: an environment-energy coupling field calculation process, an energy system state calculation process, and a multi-objective optimization control calculation process.

[0058] Among them, the environment-energy coupling field calculation process is used to describe the impact of the temperature field on the battery pack and scientific research observation equipment load in an extremely high altitude environment. The dynamic distribution law of the environment in the surrounding area of ​​the scientific research observation equipment is judged according to the sensor data, thereby quantifying the impact of air pressure, wind speed, and ultraviolet rays on the heat dissipation efficiency of the equipment, and providing thermal boundary conditions for subsequent energy control.

[0059] In this embodiment, the environment-energy coupling field can be expressed by the following formula:

[0060] ,

[0061] Where ρP c is the equivalent heat capacity of the battery (related to the SOC of the battery, in J / (m³·K)); is the partial derivative of the battery temperature with respect to time, indicating the rate of change of temperature with time; k is the thermal conductivity, k(T,P) is the non-constant thermal conductivity, which is affected by temperature T and air pressure P; ∇T is the temperature gradient, indicating the rate of change of the internal temperature of the battery; Q bat is the thermal power generated in the battery, I dis is the discharge current, R(T) is the internal resistance of the battery; h(P,v) is the convection heat transfer coefficient; T is the battery temperature; T i is the ambient temperature.

[0062] In this embodiment, the parameterized form of the thermal conductivity k can be:

[0063] , where k0 is the thermal conductivity under standard conditions; P0 is the reference air pressure.

[0064] In this embodiment, the internal resistance R(T) of the battery changes with the temperature T, which can be expressed by the following formula:

[0065] , where R0 is the internal resistance at the reference temperature; α is the temperature coefficient; T ref is the reference temperature;

[0066] In this embodiment, the convective heat transfer coefficient is affected by the air pressure P and the wind speed v. This coefficient determines the heat exchange rate between the battery surface and the environment and can be expressed in the following form:

[0067] , where h0 is the convective heat transfer coefficient under reference conditions; P0 is the reference air pressure; v0 is the reference wind speed.

[0068] It should be noted that in the environment-energy coupling field equation disclosed in this embodiment, the left side of the equation It represents the rate of change of temperature over time multiplied by the change of internal energy in the volume, which represents the time rate of change of the internal energy of the battery. The first term on the right side of the equation is It represents the heat change caused by heat conduction, the change of thermal conductivity temperature and pressure. The heat generated by the battery during operation depends on the internal resistance of the battery. It represents the convection heat dissipation process between the battery and the environment, and the coefficient h(P,v) changes with the air pressure and wind speed. The environment-energy coupling field equation in this embodiment describes the heat balance and temperature change of the battery under given environmental conditions by comprehensively considering environmental factors.

[0069] In this embodiment, the energy system state can be expressed by the following formula:

[0070] ,

[0071] Where SOC is the state of charge of the battery; η charge (T) is the charging efficiency; P in is the input power of the battery, which is generally the charging power, that is, the electrical energy input during the battery charging process; β i (T) is the load temperature attenuation factor; L i is the load power, which is used to represent the electric power consumed by different loads. A battery system can include multiple loads, corresponding to the power requirements of different power consumption scientific research observation equipment in the system; C eff (P) is the effective capacity of the battery; V bat The voltage of the battery. This item is usually a fixed value or varies slightly with load, temperature and other conditions. It is a standardized parameter used to convert power into SOC changes.

[0072] In this embodiment, the charging efficiency η charge (T), the change with temperature can be expressed by the following formula:

[0073] , where η0 is the charging efficiency at the reference temperature; T opt For the best working temperature.

[0074] In this embodiment, the load temperature attenuation factor β i (T), the change with temperature can be expressed by the following formula:

[0075] , where T ref is the reference temperature.

[0076] In this embodiment, the effective capacity C eff (P) is the effective battery capacity considering the influence of air pressure, which can be expressed by the following formula:

[0077] , where C nom is the nominal capacity of the battery, and P0 is the reference air pressure.

[0078] It should be noted that the energy system state equation in this embodiment describes the dynamic process of the battery state of charge (SOC) changing with external charging power, load distribution power and environmental conditions. By considering the impact of multiple factors such as input power, load power, temperature and altitude on the battery state of charge, the dynamic changes of the battery state of charge (SOC) under different charging and discharging conditions, ambient temperature and altitude pressure are systematically depicted and analyzed. Through this equation, the background control system can predict and update the SOC value in real time according to the real-time data of the sensor through the dynamic equation, which helps to more accurately consider the performance differences of the battery under different environmental conditions, improve the efficiency and safety of the charging process, and dynamically adjust the load distribution through the load temperature attenuation factor, thereby reducing the adverse effects of the environment in high altitude areas on battery performance.

[0079] In this embodiment, the target optimization control includes a power distribution decision function and constraint conditions, wherein the power distribution decision function minimizes the cost function to obtain the best battery charge and discharge control strategy to ensure that a specific target is achieved.

[0080] In this embodiment, the specific goals are: 1. Prevent SOC from entering the danger zone (primary goal), ensuring that the battery state of charge (SOC) is as close to the critical state as possible while preventing the battery from overcharging or over-discharging, thereby extending battery life and ensuring safety. 2. Ensure the priority of core loads (secondary goal), through load distribution strategies, improve the system's operating time, and ensure that critical loads have their power requirements met first.

[0081] The cost function takes into account the battery state of charge and load power distribution, and combines it with constraints to ultimately optimize system performance.

[0082] In this embodiment, the power distribution decision function can be expressed by the following formula:

[0083] ,

[0084] In the formula, u 1， u 2， u3 is the control variable, which can be set according to the actual local conditions; [t 0， t f ] is the time interval, t0 and t f Respectively represent the start and end time of optimization; SOC crit is the target (or critical) state of charge; λ1 and λ2 are weight coefficients; w i is the load priority weight coefficient. In this embodiment, there are three load priority weight coefficients (w 1， w 2， w3); It is the load demand deviation, that is, the difference between the actual load power and the required load power, which is used to indicate the deviation of the load demand.

[0085] In this embodiment, the load demand deviation It can be calculated by the following formula:

[0086] , where L i is the actual distributed load power; L i,req is the required load power.

[0087] In this implementation, constraints may include thermal runaway constraints, SOC constraints, and core load protection constraints. Thermal runaway constraints focus on the thermal management of the system to prevent thermal runaway caused by material degradation due to high temperature and ultraviolet intensity. SOC constraints take into account the impact of changes in plateau air pressure to ensure that the battery operates within a safe charging and discharging range. Core load protection is used to ensure that critical loads (for example, high-value scientific research observation equipment, communication equipment, control systems, etc.) can obtain basic power support under any circumstances to ensure that the basic functions of the entire system are not impaired.

[0088] Through the combined effect of constraints, the system can still operate safely and efficiently when environmental conditions, work requirements, etc. change. And through the comprehensive management of temperature, state of charge and load, the optimization control strategy can meet short-term operation needs while considering long-term reliability and safety. This multi-level constraint framework ensures the robustness of the entire energy system in the harsh environment of the plateau. Combining these constraints helps to develop reliable charging and discharging strategies and load management solutions, optimize system performance and maximize safety.

[0089] Among them, the thermal runaway constraint is used to prevent the negative value of the temperature gradient from exceeding the maximum allowable value at the boundary, thereby preventing thermal runaway, which can be expressed as follows:

[0090] ,q max (UV) = , where q0 represents the maximum degradation coefficient under standard conditions; UV is the intensity of ultraviolet rays, which affects the degradation rate of the material. It should be noted that the intensity of ultraviolet rays is high in high-altitude environments, which leads to accelerated aging of materials exposed to the external environment. By taking into account the impact of ultraviolet-induced material degradation on thermal runaway, the entire system can be ensured to operate safely in plateau environments, and the battery system can maintain efficient and stable thermal management performance during long-term operation and exposure to plateau environments.

[0091] The SOC constraint ensures that the battery's state of charge is within a safe range by taking into account the impact of altitude changes on the battery SOC and power requirements, avoiding excessive discharge or charging of the battery, damage to battery life, and potential safety issues. It can be expressed by the following formula:

[0092] , where P is the ambient air pressure and P0 is the reference air pressure.

[0093] The core load guarantee constraint can be expressed as follows:

[0094] , where L1 is the core load; L 1,min (T) is the minimum core load power requirement at temperature T; L 1,nom It is the nominal core load power. It should be noted that multiple core loads can be set according to actual conditions, and the core loads can be marked with priorities in the device power database and the sensor database to give priority to processing the data of the core loads.

[0095] It should be noted that the high-altitude environment coupling control algorithm provided in this embodiment forms an optimized control system through the close coupling between the environment-energy coupling field equation, the energy system state equation and the target optimization control equation, and through the mutual feedback and interaction between the various equations. The safety and efficiency of the system are comprehensively ensured from multiple aspects of thermal management, state of charge management and load distribution, and an optimization and control scheme for the battery system in the complex environment of the plateau is provided. This all-round comprehensive consideration and optimization can greatly improve the safety, reliability and operating efficiency of the system.

[0096] Step 4: The intelligent power distribution unit sends the obtained intelligent power distribution strategy to the energy storage unit. The energy storage unit divides the power distribution circuit into three levels of loads according to the different priorities of the load devices. Among them, the first-level load is the most core load (important), followed by the second-level load and the third-level load. The energy storage unit executes the control strategy according to the intelligent power distribution strategy to provide power to the load.

[0097] Specifically, in this embodiment, the energy storage unit control strategy may include the following contents:

[0098] a) When the energy storage unit is fully charged, the BMS disconnects the charging switch, and the energy storage system can only discharge externally at this time; until the SOC reaches ≤ 95%, the BMS will close the charging switch again to allow energy replenishment.

[0099] b) When the energy storage unit can no longer supply power, the BMS will close the charging switch and disconnect all load power switches SW1, SW2, SW3, and SW4. The BMS will enter ultra-low power consumption mode and wait for input energy to be replenished. When the energy storage unit meets the recovery conditions, the BMS will intelligently and orderly restore power to the load according to the priority and power distribution strategy. At the same time, the recovery strategy should meet the logical description of Article c.

[0100] c) When the energy storage unit is not overcharged or over-discharged, it will perform multi-level energy management, and the implementation logic is as follows:

[0101] i. The energy storage battery power alarm is divided into three levels: over-power, over-power level 1 alarm; sufficient power, no over-power alarm, no under-power alarm; nearly insufficient power, under-power level 3 alarm; insufficient power, under-power level 2 alarm; seriously insufficient power (undervoltage), under-power level 1 alarm.

[0102] ii. When the energy storage unit is low on energy and the first level alarm is triggered, all load circuits of the battery (including MCU power supply) will be cut off, the energy storage unit will be in a no-load state, the load will stop being powered, the BMS will work at a local ultra-low power consumption, and the charging switch will be turned on. Under this condition, you can wait for energy replenishment for a long time.

[0103] iii. When the energy storage unit is replenished to meet the working conditions, the voltage monitoring circuit automatically works to supply power to the BMS. At this time, the BMS restores the power supply to the load in an orderly manner according to the power distribution strategy; the power supply restoration logic for the load meets the description of the high-altitude multi-level self-recovery intelligent power supply and distribution energy protection threshold table in Table 1 according to the alarm signal.

[0104] Table 1. High-altitude multi-level self-recovery intelligent power supply and distribution energy protection threshold table

[0105]

[0106] In this embodiment, by considering the extreme conditions in the high-altitude environment and combining the use of sensor data, the energy supply of scientific research observation and research equipment in the extremely high-altitude environment area is aimed at, and the intelligent control of the power loss of the energy storage battery is realized through a mathematical model.

[0107] By acquiring key environmental variables in high-altitude environments through sensor data and combining these environmental variables with the state of energy storage batteries (energy systems), a control algorithm is constructed, and the data collected by the sensors is input into the model in real time to perform state prediction and control strategy adjustment, and the control strategy is updated in real time to achieve efficient energy management strategies. In addition, through multi-level management of loads, hierarchical power supply is provided according to the importance level, and the available energy supply is reasonably and accurately configured, and it can intelligently realize power-off self-protection, intelligent energy replenishment, and intelligent and orderly restoration of power supply to loads after energy recovery.

[0108] Example 2

[0109] In this embodiment, a multi-level intelligent power supply and distribution energy system for an extremely high altitude environment is disclosed. Figure 2 A system block diagram of the intelligent power supply and distribution energy system in this embodiment is shown. It can be seen from the figure that this embodiment includes an energy replenishment unit, an intelligent energy storage unit and an intelligent power distribution unit.

[0110] In this embodiment, the energy supplement unit is connected to the intelligent energy storage unit and is configured to provide electrical energy supplement for the intelligent energy storage unit.

[0111] Specifically, the energy supplement unit may include a mains power input and a backup energy input.

[0112] Among them, backup energy input can include solar panels installed around scientific research observation and research equipment, or electrical energy input provided by small wind power generation devices.

[0113] It should be noted that the energy input unit supports a variety of energy inputs, including but not limited to AC220V, AC380V, direct current, photovoltaic energy, wind turbine power generation energy, etc.; each charging controller of the power generation unit supports current-limited and voltage-limited charging to meet the safe charging requirements for energy storage batteries. The energy storage unit can be a component or system that can store electrical energy, such as a lead-acid battery, a lithium-ion battery, a farad capacitor, a supercapacitor, etc.

[0114] In this embodiment, the intelligent energy storage unit is connected to the intelligent power distribution unit and is configured to execute a control strategy, store energy and provide power to the load device according to the intelligent power distribution strategy output by the intelligent power distribution unit.

[0115] Specifically, in this embodiment, the control strategy may include the following contents:

[0116] 1) When the energy storage unit is fully charged, the battery management system (BMS) disconnects the charging switch, and the energy storage system only discharges to the outside until the SOC reaches ≤ 95%. The BMS will close the charging switch again to allow energy replenishment.

[0117] 2) When the energy storage unit can no longer supply power, the BMS will close the charging switch and disconnect all load power switches at the same time. The BMS enters ultra-low power consumption mode and waits for input energy to be replenished until the energy storage unit meets the recovery conditions. The BMS then resumes power supply to the load according to the priority and power distribution strategy.

[0118] Specifically, recovery strategies may include:

[0119] i. The recovery strategy divides the energy storage battery power alarm into three levels: over-energy or serious insufficient energy is the first level alarm; insufficient energy is the second level alarm; sufficient energy without over-energy, nearly insufficient energy without insufficient energy is the third level alarm;

[0120] ii. When the energy storage unit is in the first level alarm, the circuit of all load devices of the battery will be cut off, the energy storage unit will be in a no-load state, the power supply to the load will stop, the BMS will work in local ultra-low power consumption, and the charging switch will be turned on;

[0121] iii. When the energy storage unit is replenished to meet the working conditions, the voltage monitoring circuit automatically operates to supply power to the BMS. At this time, the BMS resumes power supply to the load equipment according to the priority and power distribution strategy.

[0122] In this embodiment, the intelligent power distribution unit includes a database construction subunit and an intelligent power distribution strategy generation module.

[0123] Among them, the database construction subunit is configured to clarify the load characteristics of the load equipment and the power value of the load according to the load curves of different load equipment, classify different load equipment according to the load characteristics and the power values ​​of the load, and construct the collected load characteristics and load power value data into an equipment power database.

[0124] Receive environmental data and real-time data of the load sent by sensor clusters arranged at key locations of the load equipment, pre-process the data, map the pre-processed data to a unified coordinate system grid, integrate the data and build a sensor database.

[0125] In this embodiment, the intelligent power distribution strategy generation module is connected to the database construction subunit and is configured to obtain an intelligent power distribution strategy based on data in the device power database and the sensor database in the database construction subunit, and send the intelligent power distribution strategy to the energy storage unit.

[0126] Specifically, the intelligent power distribution unit may include a database construction subunit and an intelligent power distribution strategy generation module.

[0127] Among them, the database construction subunit is configured to clarify the load characteristics of the load equipment and the power value of the load according to the load curves of different load equipment, classify different load equipment according to the load characteristics and the power values ​​of the load, construct the collected load characteristics and load power value data into an equipment power database, receive the environmental data and real-time data of the load sent by the sensor cluster arranged at the key position of the load equipment, pre-process the data, map the pre-processed data to a unified coordinate system grid, integrate the data and construct a sensor database.

[0128] The intelligent power distribution strategy generation module is connected to the database construction subunit and is configured to obtain an intelligent power distribution strategy based on data in the device power database and the sensor database in the database construction subunit, and send the intelligent power distribution strategy to the energy storage unit.

[0129] Specifically, in this embodiment, the intelligent power distribution strategy generation module may include: a mutually coupled environment-energy coupling field submodule, an energy system status submodule, and a target optimization control submodule.

[0130] The environment-energy coupling field submodule is configured to determine the dynamic distribution of the environment in the surrounding area of ​​the load device based on the sensor data, and quantify the impact of air pressure, wind speed, and ultraviolet light on the heat dissipation efficiency of the load device. In this embodiment, the environment-energy coupling field submodule can be expressed by the following formula:

[0131] ,

[0132] Where ρP c is the equivalent heat capacity of the battery; is the partial derivative of battery temperature with respect to time; k is thermal conductivity, k(T,P) is non-constant thermal conductivity, which is affected by battery temperature T and air pressure P; ∇T is temperature gradient, which indicates the rate of change of internal battery temperature; Q bat is the thermal power generated in the battery, I dis is the discharge current, R(T) is the internal resistance of the battery; h(P,v) is the convection heat transfer coefficient; T is the battery temperature; T i is the ambient temperature.

[0133] The energy system status submodule is configured to describe the battery charge state according to the dynamic process of external charging power, load distribution power and environmental conditions. In this embodiment, the energy system status submodule can be represented by the following formula:

[0134] ,

[0135] Where SOC is the state of charge of the battery; η charge (T) is the charging efficiency; P inis the input power of the battery; β i (T) is the load temperature attenuation factor; L i is the load power, which is used to indicate the electric power consumed by different load devices; C eff (P) is the effective capacity of the battery; V bat The voltage of the battery. This item is usually a fixed value or varies slightly with load, temperature and other conditions. It is a standardized parameter used to convert power into SOC changes.

[0136] The target optimization control submodule is configured to obtain the best battery charge and discharge control strategy according to the environment-energy coupling field submodule and the energy system state submodule. The target optimization control submodule includes a decision function and constraint conditions.

[0137] In this embodiment, the decision function can be expressed by the following formula:

[0138] ,

[0139] In the formula, u 1， u 2， u3 is the control variable; [t 0， t f ] is the time interval, t0 and t f Respectively represent the start and end time of optimization; SOC crit is the target state of charge; λ1 and λ2 are weight coefficients; w i is the load priority weight coefficient; is the load demand deviation.

[0140] In this embodiment, the constraint conditions may include: thermal runaway constraint, SOC constraint, and core load guarantee constraint.

[0141] Among them, the thermal runaway constraint is:

[0142] ,q max (UV) = ,

[0143] Where q0 represents the maximum degradation coefficient under standard conditions; UV is the ultraviolet intensity.

[0144] The SOC constraint is:

[0145] ,

[0146] Where P is the ambient air pressure and P0 is the reference air pressure.

[0147] The core load guarantee constraints are:

[0148] ,

[0149] Where L1 is the core load; L 1,min (T) is the minimum core load power requirement at temperature T; L 1,nom is the nominal core load power.

[0150] It should be noted that in this embodiment, the energy system is divided into a three-level structure: a multi-redundant energy input (supplement) unit, an intelligent energy storage unit, and an intelligent power distribution unit. It can achieve efficient scheduling of energy distribution when the system is short of energy in harsh plateau environments, and ensure the minimum conditions for the system to continue working as much as possible; through multi-level load management, the minimum energy demand for the normal operation of the system can be minimized.

[0151] In this embodiment, the intelligent power distribution strategy generation module of the intelligent power distribution unit generates an intelligent power distribution strategy to help the entire energy system achieve efficient energy scheduling management, which can effectively reduce the rigid demand for energy in the system (to ensure the minimum energy demand for the normal operation of the system), while fully improving the utilization efficiency of the existing energy supply. In extreme plateau environments and important applications, it can achieve accurate energy configuration management of the power supply and distribution system with self-protection and self-recovery capabilities.

[0152] At the same time, the energy storage battery power alarm is also divided into three levels: sufficient power, no alarm; nearly insufficient power, three-level alarm; insufficient power, two-level alarm; seriously insufficient power, one-level alarm. When there is no alarm, all distribution is output; in the three-level alarm state, the first and second-level loads of the distribution are supplied with energy; in the second-level alarm state, the first-level load of the distribution is supplied with energy; in the first-level alarm state, all load outputs are disconnected. It can achieve that when the system energy can no longer be supplied, the power supply and distribution system enters an extremely low power consumption silent state, and can wait for energy replenishment for as long as possible.

[0153] The energy replenishment unit has a backup power generation and charging subunit, which can work independently of the energy storage unit and does not consume the power of the energy storage unit; when the power generation and charging conditions are met, the energy storage unit can be automatically charged. When the energy storage unit is replenished to meet the working conditions, the voltage monitoring circuit automatically works to supply power to the control unit. At this time, the energy system resumes powering the load.

[0154] The energy system in this embodiment can efficiently dispatch energy distribution under the condition of insufficient system energy, and ensure the minimum conditions for the continuous operation of the system as much as possible. Through multi-level load management, the minimum energy demand to ensure the normal operation of the system is minimized; the power supply and distribution system enters an extremely low power consumption silent state, and can wait for energy replenishment for as long as possible. After the charging conditions are met, the energy input unit automatically replenishes the energy storage unit until the energy system restores its power supply capacity to the load in an orderly manner.

[0155] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-level intelligent power supply and distribution method in an extremely high altitude environment, characterized in that: The intelligent power supply and distribution method comprises: S100, determining load curves of different load devices, clarifying load characteristics, determining load power values, and classifying different load devices according to the load characteristics and load power values, and constructing the collected load characteristics and load power value data into a device power database, Place sensors at key locations of load equipment, and integrate different sensors placed on the same load equipment into a sensor cluster to collect environmental data and real-time data of the load; S200, the intelligent distribution unit receives the real-time data collected by the sensor cluster, pre-processes the data, maps the pre-processed data to a unified coordinate system grid, integrates the data and builds a sensor database; S300, the intelligent power distribution unit obtains an intelligent power distribution strategy based on the data in the equipment power database and the sensor database in combination with the high altitude environment coupling control algorithm, and sends the intelligent power distribution strategy to the energy storage unit; S400, the energy storage unit executes the control strategy according to the intelligent power distribution strategy to provide power to the load device.

2. The multi-level intelligent power supply and distribution method in an extremely high altitude environment according to claim 1, characterized in that: The sensor cluster includes: a load sensor, an air pressure sensor, a temperature sensor, a wind speed sensor and an ultraviolet radiation sensor. The sensor transmits the collected data to the relay router in real time through the Internet of Things interface. The collected data is initially integrated at the relay router, and the data of the same cluster is marked and sent to the back-end processing system.

3. The multi-level intelligent power supply and distribution method in an extremely high altitude environment according to claim 1, characterized in that: The preprocessing of step S200 includes: Noise reduction, completion and standardization of sensor data, The standardization uses Z-Score standardization to standardize the data, and aligns the timestamps of the data after the data standardization is completed.

4. The multi-level intelligent power supply and distribution method in an extremely high altitude environment according to claim 1, characterized in that: The altitude environment coupling control algorithm includes: Determine the dynamic distribution of the environment in the area around the load device based on the sensor data, and quantify the impact of air pressure, wind speed, and ultraviolet rays on the heat dissipation efficiency of the load device through the environmental-energy coupling field calculation process; The energy system state calculation process describes the battery state of charge based on the dynamic process of external charging power, load distribution power and environmental conditions; The target optimization control calculation process obtains the best battery charging and discharging control strategy according to the environment-energy coupling field calculation process and the energy system state calculation process, so as to avoid the battery charge state entering the danger zone, while ensuring that the power demand of high-priority load equipment is met first.

5. The multi-level intelligent power supply and distribution method in an extremely high altitude environment according to claim 4, characterized in that: The environment-energy coupling field calculation process is expressed by the following formula: ， Where ρP c is the equivalent heat capacity of the battery; is the partial derivative of battery temperature with respect to time; k is the thermal conductivity, k(T,P) is the non-constant thermal conductivity, which is affected by the battery temperature T and the air pressure P; ∇T is the temperature gradient, which indicates the rate of change of the internal temperature of the battery; Q bat is the thermal power generated in the battery, I dis is the discharge current, R(T) is the internal resistance of the battery; h(P,v) is the convection heat transfer coefficient; T is the battery temperature; T i is the ambient temperature; The energy system state calculation process is expressed by the following formula: ， Where SOC is the state of charge of the battery; η charge (T) is the charging efficiency; P in is the input power of the battery; β i (T) is the load temperature attenuation factor; L i is the load power, which is used to indicate the electric power consumed by different load devices; C eff (P) is the effective capacity of the battery; V bat The voltage of the battery, which is usually a fixed value or varies slightly with load, temperature and other conditions, and is used to convert power into a standardized parameter for SOC changes; The decision function of the target optimization control calculation process is expressed by the following formula: ， In the formula, u 1， u 2， u3 is the control variable; [t 0， t f ] is the time interval, t0 and t f Respectively represent the start and end time of optimization; SOC crit is the target state of charge; λ1 and λ2 are weight coefficients; w i is the load priority weight coefficient; is the load demand deviation.

6. The multi-level intelligent power supply and distribution method in an extremely high altitude environment according to claim 4, characterized in that: The target optimization control calculation process also includes constraints, which include: thermal runaway constraints, SOC constraints, and core load guarantee constraints, wherein: The thermal runaway constraint is: ， q max (UV) = , Where, q0 represents the maximum degradation coefficient under standard conditions; UV is the ultraviolet intensity; The SOC constraint is: , where P is the ambient air pressure; P0 is the reference air pressure; The core load guarantee constraints are: ， Where L1 is the core load; L 1,min (T) is the minimum core load power requirement at temperature T; L 1,nom is the nominal core load power.

7. The multi-level intelligent power supply and distribution method in an extremely high altitude environment according to claim 1, characterized in that: The energy storage unit divides the power distribution circuit into three levels of loads according to the different priorities of the load devices. The control strategy includes: S410, when the energy storage unit is fully charged, the BMS disconnects the charging switch, and the energy storage system only discharges to the outside until the SOC reaches ≤ 95%, and the BMS closes the charging switch again to allow energy replenishment; S420: When the energy storage unit can no longer supply power, the BMS will close the charging switch and disconnect all load power switches at the same time. The BMS enters the ultra-low power consumption mode and waits for the input energy to be replenished until the energy storage unit meets the recovery conditions. The BMS then resumes power supply to the load according to the priority and power distribution strategy.

8. The multi-level intelligent power supply and distribution method in an extremely high altitude environment according to claim 7, characterized in that: The S420 further includes a recovery strategy, which includes: S421, the recovery strategy divides the energy storage battery power alarm into three levels: over-energy or seriously insufficient energy is the first level alarm; insufficient energy is the second level alarm; sufficient energy without over-energy, nearly insufficient energy without insufficient energy is the third level alarm; S422, when the energy storage unit is in the first level alarm, the circuit of all load devices of the battery will be cut off, the energy storage unit will be in a no-load state, the power supply to the load will stop, the BMS will work in local ultra-low power consumption, and the charging switch will be turned on; S423. When the energy storage unit is charged to meet the working conditions, the voltage monitoring circuit automatically operates to supply power to the BMS. At this time, the BMS resumes power supply to the load equipment according to the priority and power distribution strategy.

9. A multi-level intelligent power supply and distribution energy system for extremely high altitude environment, comprising an energy replenishment unit, an intelligent energy storage unit and an intelligent power distribution unit, characterized in that: The energy supplement unit is connected to the intelligent energy storage unit and is configured to provide electric energy supplement for the intelligent energy storage unit; The intelligent energy storage unit is connected to the intelligent power distribution unit and is configured to execute a control strategy, store energy and provide power to the load device according to the intelligent power distribution strategy output by the intelligent power distribution unit; The intelligent power distribution unit includes a database construction subunit and an intelligent power distribution strategy generation module, wherein: The database construction subunit is configured to clarify the load characteristics of the load equipment and the power value of the load according to the load curves of different load equipment, classify the different load equipment according to the load characteristics and the power value of the load, and construct the collected load characteristics and load power value data into a device power database. Receive environmental data and real-time data of the load sent by sensor clusters arranged at key locations of the load equipment, pre-process the data, map the pre-processed data to a unified coordinate system grid, integrate the data and build a sensor database; The intelligent power distribution strategy generation module is connected to the database construction subunit and is configured to obtain an intelligent power distribution strategy based on data in the device power database and the sensor database in the database construction subunit, and send the intelligent power distribution strategy to the energy storage unit.

10. The extremely high altitude environment multi-level intelligent power supply and distribution energy system according to claim 9, characterized in that: The intelligent power distribution strategy generation module includes: a mutually coupled environment-energy coupling field submodule, an energy system state submodule and a target optimization control submodule, wherein: The environment-energy coupling field submodule is configured to determine the dynamic distribution law of the environment in the area surrounding the load device based on the data of the sensor, and quantify the influence of air pressure, wind speed, and ultraviolet rays on the heat dissipation efficiency of the load device; The energy system status submodule is configured to describe the battery state of charge based on the dynamic process of changes in external charging power, load distribution power, and environmental conditions; The target optimization control submodule is configured to obtain the best battery charge and discharge control strategy according to the environment-energy coupling field submodule and the energy system status submodule.

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