A multi-level intelligent power supply and distribution method and energy system for extremely high altitude environments

Abstract

The present invention discloses a multi-level intelligent power supply and distribution method and energy system for extremely high altitude environments, belonging to the field of intelligent power supply and distribution. The method includes: determining the load characteristics and power values of load devices, classifying different load devices, constructing a device power database, arranging sensors at key positions of the load devices, integrating the sensors into a sensor cluster, and collecting environmental data and real-time data of the load; the intelligent power distribution unit receives the real-time data collected by the sensor cluster, preprocesses the data, integrates the data and constructs a sensor database; according to the data in the device power database and the sensor database, combined with the high altitude environment coupling control algorithm, an intelligent power distribution strategy is obtained; the energy storage unit executes the control strategy according to the intelligent power distribution strategy to provide power for the load devices. Through the intelligent power distribution strategy, the present invention takes into account the influence of the high altitude environment on the load characteristics and improves the accuracy of load prediction.

Description

Technical Field

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

[0002] In an extremely high altitude environment, the lowest temperature can reach -55°C, the highest temperature can reach 15°C, and the temperature difference between day and night is large; the air pressure is low, and the atmospheric pressure is about 0.3 standard atmospheric pressures (30 KPa).

[0003] Due to the harsh environmental conditions such as extremely low air pressure, extremely cold, strong wind, and strong ultraviolet rays in the extremely high altitude area, it poses a great challenge to human survival. Therefore, the comprehensive scientific observation and scientific research in this area are basically blank. The scientific research observation instruments in the extremely high altitude area are restricted by the extreme environment, and technicians cannot reach the site in time for real-time technical maintenance. The equipment needs to operate automatically in an unattended state. However, affected by the extremely continuous cloudy weather, the power loss of the energy storage battery supporting the operation of the instrument equipment exceeds the minimum threshold, which will cause the battery charging function to be lost or damaged, resulting in the instrument equipment stopping running. Therefore, it is necessary to solve the problem of self-recovery of the power supply of the instrument equipment in the extremely high altitude area, enter the automatic sleep state before the power loss of the battery exceeds the minimum threshold, and restart the charging function after 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 to solve the problem that the battery charging function of the instrument equipment is lost or damaged in an unattended state due to the environmental impact in the extremely high altitude area, resulting in the instrument equipment stopping running in the prior art.

[0005] The present invention is realized through the following technical solutions. A multi-level intelligent power supply and distribution method for extremely high altitude environments includes the following steps: S100. Determine the load curves of different load devices, clarify the load characteristics, determine the power values of the loads, classify different loads according to the load characteristics and power values of the loads, construct an equipment power database with the collected load characteristic and power value data of the loads, arrange sensors at key positions of the load devices, and integrate different sensors arranged on the same load device into a sensor cluster for collecting environmental data and real-time data of the loads; S200. An intelligent power distribution unit is used to receive the real-time data collected by the sensor cluster, preprocess the data, map the preprocessed data to a unified coordinate system grid, integrate the data, and construct a sensor database; S300. The intelligent power distribution unit obtains an intelligent power distribution strategy according to the data in the equipment power database and the sensor database, in combination with a 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 for the load devices.

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

[0007] Further, the preprocessing in step S200 may include: noise reduction, complementation, and standardization processing of the sensor data. The standardization uses Z-Score standardization to standardize the data, and aligns the timestamps of the data after the data is completed with the standardization processing.

[0008] Further, the altitude environment coupling control algorithm may include: an environment-energy coupling field calculation process for quantifying the influence of barometric pressure, wind speed, and ultraviolet rays on the heat dissipation efficiency of the load device by judging the environmental dynamic distribution law in the surrounding area of the load device according to the data of the sensors; an energy system state calculation process for describing the state of charge of the battery according to the dynamic process of the external charging power, load distribution power, and environmental conditions; a target optimization control calculation process for obtaining the best battery charge and discharge control strategy according to the environment-energy coupling field calculation process and the energy system state calculation process, avoiding the state of charge of the battery from entering the dangerous area, and at the same time ensuring that the power requirements of high-priority load devices are preferentially met.

[0009] Further, 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 the battery temperature with respect to time; k is the thermal conductivity, k(T,P) is the non-constant thermal conductivity, affected by the battery temperature T and the air pressure P; ∇T is the temperature gradient, representing the rate of change of the internal temperature of the battery; Q bat is the thermal power generated inside the battery, I dis is the discharge current, R(T) is the internal resistance of the battery; h(P,v) is the convective heat transfer coefficient; T is the battery temperature; T i is the ambient temperature.

[0011] Furthermore, the calculation process of the energy system state 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 decay factor; L i is the load power, used to represent the electric power consumed by different load devices; C eff (P) is the effective capacity of the battery; V bat is the voltage of the battery, which is usually a fixed value or varies slightly with conditions such as load and temperature, and is used as a standardized parameter for converting power into changes in SOC.

[0013] Furthermore, the decision function of the target optimization control calculation process can be expressed by the following formula:

[0014] , where u 1， u 2， u 3 is the control variable; [t 0， t f is the time interval, t 0 and t f respectively represent the start and end times of the optimization; SOC crit is the target state of charge; λ 1 and λ 2 are the 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 constraint conditions, and the constraint conditions may include: thermal runaway constraint, SOC constraint, and core load guarantee constraint.

[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] Further, the SOC constraint can be: , where P is the ambient air pressure; P 0 is the reference air pressure.

[0018] Further, the core load guarantee constraint can be: , where L 1 is the core load; L 1,min (T) is the minimum core load power demand at temperature T; L 1,nom is the nominal core load power.

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

[0020] Further, the control strategy can include: S410. When the energy storage unit is fully charged, the BMS disconnects the charging switch, and the energy storage system only discharges externally at this time until SOC ≤ 95%, and the BMS will close the charging switch again to allow energy replenishment; S420. When the energy storage unit cannot continue to supply power, the BMS will close the charging switch and disconnect all load power supply switches at the same time. The BMS enters the ultra-low power consumption mode and waits for the replenishment of input energy until the energy storage unit's energy meets the recovery conditions, and the BMS restores the power supply to the load according to the priorities and power distribution strategy.

[0021] Further, S420 can also include a recovery strategy, and the recovery strategy includes: S421. The recovery strategy divides the energy storage battery power alarm into three levels: over-energy or severely insufficient energy and under-energy, which is the first-level alarm; insufficient energy and under-energy is the second-level alarm; sufficient energy without over-energy, and the energy is close to insufficient without under-energy alarm is the third-level alarm; S422. When the energy storage unit is in the first-level alarm, the circuits of all load devices of the battery will be cut off, the energy storage unit is in a no-load state, stops supplying power to the load, and the BMS works in local ultra-low power consumption and turns on the charging switch; S423. When the energy of 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 devices according to the priorities and power distribution strategy.

[0022] On the other hand, the present invention provides a multi-level intelligent power supply and distribution energy system for extremely high altitude environments, and 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 electrical 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 a control strategy, store energy and provide power for load devices 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. Among them, the database construction subunit is configured to clarify the load characteristics and power values of load devices according to the load curves of different load devices, classify different load devices according to the load characteristics and power values of the load, construct a device power database with the collected load characteristic and power value data of the load, receive the environmental data and real-time data of the load sent by the sensor cluster arranged at the key positions of the load devices, preprocess the data, map the preprocessed data to 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 an 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: an environment-energy coupling field submodule, an energy system state submodule, and a target optimization control submodule that are mutually coupled. Among them, the environment-energy coupling field submodule is configured to judge the environmental dynamic distribution law of the surrounding area of the load device according to 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 state submodule is configured to describe the state of charge of the battery according to the dynamic process of external charging power, load distribution power, and environmental condition changes; 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.

[0026] Furthermore, the energy replenishment unit includes a mains input and a backup energy input, and the backup energy input includes a solar 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 the battery temperature with respect to time; k is the thermal conductivity, k(T,P) is a non-constant thermal conductivity affected by the battery temperature T and air pressure P; ∇T is the temperature gradient, representing the rate of change of the internal temperature of the battery; Q bat is the thermal power generated inside the battery, I disis the discharge current, R(T) is the internal resistance of the battery; h(P,v) is the convective heat transfer coefficient; T is the battery temperature; T i is the ambient temperature.

[0029] Furthermore, the energy system state sub-module 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 decay factor; L i is the load power, which is used to represent the electric power consumed by different load devices; C eff (P) is the effective capacity of the battery; V bat is the voltage of the battery. This item is usually a fixed value or varies slightly with conditions such as load and temperature, and is used as a standardized parameter for converting power into changes in SOC.

[0031] Furthermore, the target optimization control sub-module includes a decision function and constraint conditions.

[0032] Furthermore, the decision function is represented by the following formula:

[0033] , where u 1， u 2， u 3 is the control variable; [t 0， t f is the time interval, t 0 and t f respectively represent the start and end times of optimization; SOC crit is the target state of charge; λ 1 and λ 2 are the weight coefficients; w i is the load priority weight coefficient; is the load demand deviation.

[0034] Furthermore, the constraint conditions include: thermal runaway constraint, SOC constraint, and core load guarantee constraint. Among them, the thermal runaway constraint is: , q max (UV) = , where

[0035] q 0 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; P 0 is the reference air pressure.

[0037] The core load guarantee constraint is as follows: , where L 1 is the core load; L 1,min (T) is the minimum core load power demand 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. By constructing a coupling control algorithm for high-altitude environments, the present invention quantifies the impact of high-altitude environmental factors on the working performance of scientific research observation equipment, solves the problem that the prior art fails to fully consider the impact of high-altitude environments on equipment, and makes the power distribution process more accurate.

[0040] 2. By arranging a sensor cluster to collect environmental data and the real-time load of scientific research observation equipment in real time, and constructing a sensor database and an equipment power database with the collected data, the present invention solves the problem that the prior art lacks real-time monitoring of equipment load and environmental data, provides data support for the dynamic optimization and adjustment of the power distribution process, and improves power supply reliability and operation and maintenance efficiency.

[0041] 3. The energy storage unit in the present invention divides the power distribution circuit into three levels of loads according to priority and executes control strategies according to intelligent power distribution strategies, realizing accurate demand-side response control and solving the problem that the prior demand-side response system fails to consider the impact of high-altitude environments on equipment. Description of the Drawings

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

[0043] Figure 1 is the flowchart of the method provided by Embodiment 1 of the present invention.

[0044] Figure 2 is the system block diagram provided by Embodiment 2 of the present invention. Detailed Embodiments

[0045] To make the objectives, 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 some, but not all, of the embodiments of the present invention. The methods and calculation formulas of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0046] Embodiment 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 of each different scientific research and observation device according to its working characteristics, clarify the load characteristics, and determine the power values of important loads; classify different scientific research and observation devices according to their load characteristics and power values of the loads, and construct the collected data into a device power database.

[0049] Specifically, the device power database can provide key data input for subsequent hierarchical management, facilitating the definition of load weights in the multi-objective optimization control equation. It can also record time-varying parameters such as the minimum / maximum power, starting impact current, and working cycle of the devices collected by the sensor cluster into the device power database to facilitate accurate calculation of the load weights in the future.

[0050] Then, select different sensors according to the specific altitude environment, geological environment, and working characteristics of different scientific research and observation devices in the local area. Arrange the selected various types of sensors at key positions of the scientific research and observation devices. The sensors arranged on the same scientific research and observation device form a sensor cluster for collecting environmental data and real-time data of the scientific research and observation devices.

[0051] Specifically, constructing the sensor cluster includes,

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

[0053] 4) Transmit the collected data to the relay router in real time through the Internet of Things interface. Perform preliminary integration of the collected data at the relay router, mark the data of the same cluster, and send it to the backend processing system.

[0054] Step 2: The intelligent power distribution unit in the backend processing system first preprocesses the received data and aligns the timestamps of the data, and then maps the preprocessed data to a unified coordinate system grid, integrates the data, and constructs a sensor database.

[0055] Specifically, the preprocessing of the received data can include: noise reduction, complementation, 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 operation efficiency of the algorithm.

[0056] Step 3: According to the influencing factors of the scientific research observation equipment in the high-altitude environment, construct a high-altitude environment coupling control algorithm to quantify the influence of the altitude environment on the scientific research observation equipment, analyze it in combination with the data in the equipment power database and the sensor database, and at the same time, according to the real-time data collected by the sensor cluster, obtain an intelligent power distribution strategy 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 influence of the temperature field on the battery pack and the load of the scientific research observation equipment in the extremely high-altitude environment, judge the environmental dynamic distribution law of the surrounding area of the scientific research observation equipment according to the sensor data, so as to quantify the influence of air pressure, wind speed, and ultraviolet rays on the equipment heat dissipation efficiency, and provide 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] In the formula, ρP c is the equivalent heat capacity of the battery (related to the SOC of the battery, unit: 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 a non-constant thermal conductivity, 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 inside the battery, I dis is the discharge current, R(T) is the internal resistance of the battery; h(P,v) is the convective heat transfer coefficient; T is the battery temperature; T i is the ambient temperature.

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

[0063] , in the formula, k 0 is the thermal conductivity under standard conditions; P 0 is the reference air pressure.

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

[0065] , in the formula, R 0 is the internal resistance at the reference temperature; α is the temperature coefficient; Tref 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 h 0 is the convective heat transfer coefficient under reference conditions; P 0 is the reference air pressure; v 0 is the reference wind speed.

[0068] It should be noted that in the environmental-energy coupling field equation disclosed in this embodiment, the left side of the equation represents the change rate of temperature with time multiplied by the change in internal energy within the volume, representing the time change rate of the battery's internal energy. The first term on the right side of the equation represents the change in heat brought about by heat conduction, the thermal conductivity, temperature, and pressure changes. The second term on the right side of the equation represents the heat generated during the operation of the battery, which depends on the internal resistance of the battery. The third term on the right side of the equation represents the convective heat dissipation process between the battery and the environment. The coefficient h(P, v) changes with the air pressure and wind speed. The environmental-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 state of the energy system 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, 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, used to represent the electrical power consumed by different loads. A battery system can include multiple loads, corresponding to the power requirements of different power-consuming scientific research observation equipment within the system; C eff (P) is the effective capacity of the battery; V bat is the voltage of the battery. This item is usually a fixed value or changes slightly with conditions such as load and temperature, and is used as a standardized parameter for converting power into changes in SOC.

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

[0073] , where η 0 is the charging efficiency at the reference temperature; T opt is the optimal operating temperature.

[0074] In this embodiment, the load temperature decay factor β i (T), the variation 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) of the effective battery capacity considering the influence of air pressure can be expressed by the following formula:

[0077] , where C nom is the nominal battery capacity, and P 0 is the reference air pressure.

[0078] It should be noted that the state equation of the energy system in this embodiment describes the dynamic process of the state of charge (SOC) of the battery changing with external charging power, load distribution power, and environmental conditions. By considering the influence of multiple factors such as input power, load power, temperature, and altitude on the state of charge of the battery, the dynamic changes of the state of charge (State of Charge, SOC) of the battery under different charge and discharge conditions, environmental temperatures, and altitude air pressures are systematically depicted and analyzed. Through this equation, the background control system can predict and update the SOC value in real time through the dynamic equation according to the real-time data of the sensor, 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 decay factor, thereby reducing the adverse impact of the environment in high-altitude areas on the battery performance.

[0079] In this embodiment, the target optimization control includes a power distribution decision function and constraint conditions. Among them, the power distribution decision function obtains the optimal battery charge and discharge control strategy by minimizing the cost function to ensure the realization of specific goals.

[0080] In this embodiment, the specific goals are: 1. Avoid the SOC entering the dangerous area (main goal), ensure that the state of charge (SOC) of the battery is as close as possible to the critical state while preventing overcharging or over-discharging of the battery, thereby extending the battery life and ensuring safety. 2. Ensure the core load priority (secondary goal), improve the running time of the system through the load distribution strategy, and at the same time ensure that the critical load first meets its power demand.

[0081] The cost function takes into account the state of charge of the battery and the load power distribution, and at the same time is combined with the constraint conditions to finally optimize the 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， u 3 is a control variable and can be set according to the local actual situation; [t 0， t f is a time interval, t 0 and t f respectively represent the start and end times of optimization; SOC crit is the target (or critical) state of charge; λ 1 and λ 2 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， w 3 ); is the load demand deviation, that is, the difference between the actual load power and the demanded load power, which is used to represent the deviation of the load demand.

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

[0086] , in the formula, L i is the actually allocated load power; L i,req is the demanded load power.

[0087] In this implementation, the constraint conditions can include thermal runaway constraints, SOC constraints, and core load guarantee constraints. The thermal runaway constraint focuses on the thermal management of the system to prevent thermal runaway caused by the degradation of materials due to high temperature and ultraviolet intensity. The SOC constraint takes into account the influence of plateau air pressure changes to ensure that the battery operates within a safe charge and discharge range. The core load guarantee is used to ensure that critical loads (such as high-value scientific research observation equipment, communication equipment, control systems, etc.) can obtain basic power support under any circumstances, and to ensure that the basic functions of the entire system are not damaged.

[0088] Through the combined action of the constraints, it is ensured that 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 optimized control strategy can not only meet the short-term operation needs but also consider long-term reliability and safety. This multi-level constraint framework ensures the robustness of the entire energy system in the harsh plateau environment. Combining these constraints helps to formulate reliable charge and discharge strategies and load management plans, optimize system performance, and maximize safety.

[0089] Among them, the thermal runaway constraint is used to represent avoiding the negative value of the temperature gradient exceeding the maximum allowable value at the boundary to prevent thermal runaway, which can be expressed by the following formula:

[0090] , q max (UV) = , where q 0 represents the maximum degradation coefficient under standard conditions; UV is the ultraviolet intensity, which affects the degradation rate of the material. It should be noted that in the high-altitude environment, the ultraviolet irradiation intensity is high, resulting in an accelerated aging rate of the materials exposed to the external environment. By considering the impact of preventing the degradation of materials caused by ultraviolet rays on thermal runaway, it is ensured that the entire system can operate safely in the plateau environment, and the battery system can maintain efficient and stable thermal management performance during long-term operation and exposure to the plateau environment.

[0091] The SOC constraint ensures that the state of charge of the battery is within a safe range by considering the impact of altitude changes on the battery SOC and power demand, avoiding over-discharging or over-charging of the battery, damaging the battery life, and causing potential safety problems, which can be expressed by the following formula:

[0092] , where P is the ambient air pressure; P 0 is the reference air pressure.

[0093] The core load guarantee constraint can be expressed by the following formula:

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

[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 equations. From multiple aspects such as thermal management, state of charge management, and load distribution, it comprehensively ensures the safety and efficiency of the system, and provides an optimization and control scheme for the battery system in the complex environment of the plateau. This multi-faceted 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 for the load.

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

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

[0099] b) Under the condition that the energy of the energy storage unit cannot continue to supply power, the BMS will close the charging switch and at the same time disconnect all load power supply switches SW1, SW2, SW3, and SW4; the BMS will enter the ultra-low power consumption mode and wait for the replenishment of input energy; until the energy of the energy storage unit meets the recovery condition, the BMS will intelligently and orderly resume power supply to the load according to the priority and power distribution strategy, and the recovery strategy should meet the logical description of Article c.

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

[0101] i. The battery charge alarm of the energy storage unit is divided into three levels: over energy, over energy first-level alarm; sufficient power, no over energy alarm and no under energy alarm; power approaching insufficiency, under energy third-level alarm; power insufficiency, under energy second-level alarm; severe power insufficiency (under voltage), under energy first-level alarm.

[0102] ii. When the energy storage unit has an under energy first-level alarm, it will cut off all load circuits of the battery (including the power supply of the MCU). The energy storage unit is in a no-load state, stops supplying power to the load, and the BMS works at a local ultra-low power consumption and opens the charging switch. Under this condition, it can wait for energy replenishment for a long time.

[0103] iii. When the power of 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 orderly resumes power supply to the load according to the power distribution strategy; the logic for resuming power supply to 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, for the energy supply of scientific research observation and research equipment in the extremely high-altitude environment area, intelligent control of the power loss of the energy storage battery is achieved through a mathematical model.

[0107] By obtaining the key environmental variables in the high-altitude environment from sensor data, and at the same time combining these environmental variables with the state of the energy storage battery (energy system), a control algorithm is constructed, and the data collected by the sensor is input into the model in real time for state prediction and control strategy adjustment, and the control strategy is updated in real time to achieve an efficient energy management strategy. And through multi-level management of the load, power is supplied in hierarchical levels according to the importance level, the available energy supply is rationally and precisely configured, and power-off self-protection, intelligent energy replenishment, and intelligent and orderly resumption of power supply to the load after energy recovery can be intelligently achieved.

[0108] Embodiment 2

[0109] In this embodiment, a multi-level intelligent power supply and distribution energy system for extremely high-altitude environment is disclosed. Figure 2 The 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 replenishment unit is connected to the intelligent energy storage unit and is configured to provide electrical energy replenishment for the intelligent energy storage unit.

[0111] Specifically, the energy replenishment unit may include mains input and backup energy input.

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

[0113] It should be noted that the energy input unit supports multiple energy inputs, including but not limited to AC220V, AC380V, direct current, photovoltaic energy, wind power generation energy, etc.; each charging controller of the power generation unit supports current-limiting and voltage-limiting charging to meet the safe charging requirements for energy storage batteries. The energy storage unit can be a component or system capable of storing electrical energy, such as lead-acid batteries, lithium-ion batteries, farad capacitors, supercapacitors, 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 according to the intelligent power distribution strategy output by the intelligent power distribution unit, store energy and provide power for load devices.

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

[0116] 1) When the energy storage unit is fully charged, the battery management system (BMS) disconnects the charging switch. At this time, the energy storage system only discharges externally until the state of charge (SOC) ≤ 95%, and the BMS will close the charging switch again to allow energy replenishment.

[0117] 2) When the energy of the energy storage unit cannot continue to supply power, the BMS will close the charging switch and at the same time disconnect all load power supply switches. The BMS enters the ultra-low power consumption mode and waits for the replenishment of input energy until the energy of the energy storage unit meets the recovery condition, and the BMS restores the power supply to the load according to the priority and power distribution strategy.

[0118] Specifically, the recovery strategy may include:

[0119] i. The recovery strategy divides the battery power alarm of the energy storage battery into three levels: over-energy or severely insufficient energy or under-energy, which is the first-level alarm; insufficient energy with under-energy is the second-level alarm; sufficient energy without over-energy, and the energy is close to insufficient without under-energy alarm is the third-level alarm;

[0120] ii. When the energy storage unit is in the first-level alarm, the circuits of all load devices of the battery will be cut off. The energy storage unit is in a no-load state, stops supplying power to the load, and the BMS works with ultra-low power consumption locally and opens the charging switch;

[0121] iii. When the energy of 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 devices according to the priority and power distribution strategy.

[0122] In this embodiment, the intelligent power distribution unit includes a database construction sub-unit 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 devices and the power values of the loads according to the load curves of different load devices, classify different load devices according to the load characteristics and the power values of the loads, and construct the load characteristic and power value data collected into a device power database.

[0124] Receive the environmental data and real-time data of the load sent by the sensor cluster arranged at the key positions of the load devices, preprocess the data, map the preprocessed data to a unified coordinate system grid, integrate the data and construct 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 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.

[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 devices and the power values of the loads according to the load curves of different load devices, classify different load devices according to the load characteristics and the power values of the loads, and construct the load characteristic and power value data collected 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 positions of the load devices, preprocess the data, map the preprocessed 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 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.

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

[0130] Among them, the environment-energy coupling field sub-module is configured to judge the environmental dynamic distribution law in the surrounding area of the load device according to 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. In this embodiment, the environment-energy coupling field sub-module can be represented by the following formula:

[0131] ,

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

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

[0134] ,

[0135] 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, used to represent the electric power consumed by different load devices; C eff (P) is the effective capacity of the battery; V bat is the voltage of the battery, which is usually a fixed value or varies slightly with conditions such as load and temperature, and is a standardized parameter for converting power into changes in SOC.

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

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

[0138] ,

[0139] where u 1， u 2， u 3 is the control variable; [t 0， t f is the time interval, t 0 and t f represent the start and end times of the optimization respectively; SOC crit is the target state of charge; λ 1 and λ 2 are the weight coefficients; w iis 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] In the formula, q 0 It indicates 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; P 0 is the reference air pressure.

[0147] The core load guarantee constraints are:

[0148] ,

[0149] Where, L 1 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; power approaching insufficiency, level-three alarm; insufficient power, level-two alarm; severely insufficient power, level-one alarm. In the no-alarm state, all power distributions are output; in the level-three alarm state, power is supplied to the first-level and second-level loads; in the level-two alarm state, power is supplied to the first-level load; in the level-one alarm state, all load outputs are disconnected. It can achieve that under the condition that the system energy cannot continue to be supplied, the power supply and distribution system enters an extremely low-power 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 sub-unit, 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, it can automatically charge the energy storage unit. When the power of 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 power supply to the load.

[0154] The energy system in this embodiment can efficiently schedule energy allocation under the condition of insufficient system energy, and ensure the lowest conditions for the system to continue working as much as possible. Through multi-level load management, the minimum energy demand for ensuring the normal operation of the system is minimized; the power supply and distribution system enters an extremely low-power 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 energy to the energy storage unit until the energy system orderly resumes the power supply capacity to the load.

[0155] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope 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 real-time data of the environment and 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; The high 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.

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 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 is the battery voltage, which can be a fixed value or vary with load and temperature, and is a standardized parameter used to convert power into 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.

5. The multi-level intelligent power supply and distribution method in an extremely high altitude environment according to claim 1, 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 pressure; P0 is the reference 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.

6. 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.

7. The multi-level intelligent power supply and distribution method in an extremely high altitude environment according to claim 6, 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.

8. 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 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; 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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