Charging and discharging control method and system of photovoltaic power supply self-service intelligent cabinet

Abstract

The invention provides a charging and discharging control method and system for a photovoltaic power supply self-service intelligent cabinet, and relates to the technical field of new energy power supply and intelligent retail, and the method comprises the following steps: S1, data collection: collecting output data and illumination data of a photovoltaic power generation system, and state data and historical load data of a battery system in real time; s2, performing short-term prediction; and S3, mode decision making: calculating an energy balance value through the difference between the predicted generated power and the predicted load power demand, and dynamically entering different power supply modes according to the energy balance value and the state of the battery system. According to the method, prediction of the energy supply and demand relation is achieved, charge and discharge balance of the battery is guaranteed through dynamic selection of the charging target, and running of the selling function is guaranteed while balance of refrigeration and energy saving is guaranteed through gradual load reduction.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy power supply and intelligent retail technology, in particular to a charging and discharging control method and system of a photovoltaic power supply unmanned vending intelligent cabinet. BACKGROUND

[0002] The photovoltaic power supply unmanned vending intelligent cabinet is an automatic retail device using solar energy as the main energy source, which converts light energy into electrical energy through the installation of solar panels on the top or side of the cabinet body, and is equipped with energy storage batteries to store excess energy to ensure that the device can continue to operate at night or in insufficient light.

[0003] However, in the prior art, the traditional intelligent cabinet needs to be connected to the power grid, the deployment scene is limited, and the refrigeration system accounts for more than 80% of the total machine power consumption. When the battery is directly powered, the endurance is insufficient, and there is no hierarchical protection strategy when the battery is depleted, which leads to system downtime or inability to sell, and there are technical defects. SUMMARY

[0004] The purpose of the present application is to solve the problems existing in the prior art, provide a charging and discharging control method and system of a photovoltaic power supply unmanned vending intelligent cabinet, realize the prediction of energy supply and demand relationship, and dynamically select the charging target to balance the charging and discharging of the battery. The use of gradual load reduction ensures the balance between refrigeration and energy saving while ensuring the operation of the vending function.

[0005] In order to achieve the above purpose, the present application adopts the following technical scheme: a charging and discharging control method of a photovoltaic power supply unmanned vending intelligent cabinet, comprising the following steps: S1, data acquisition: real-time acquisition of output data of a photovoltaic power generation system, illumination data, state data of a battery system and historical load data; S2, short-term prediction: S2.1, based on the change trend of the illumination data, predict the power generation trend in the future set time; S2.2, based on the historical load data, predict the power demand of the load; S3, mode decision: calculate the energy balance value by the difference between the predicted power generation and the predicted load power demand, and dynamically enter different power supply modes according to the energy balance value and the state of the battery system; Among them, the state of charge remaining capacity value threshold of the preset six auxiliary batteries is set, when the system is determined to be in an energy surplus state, the photovoltaic charging mode is entered, and the dynamic charging target selection is performed: the main battery is charged by default; If it is detected that the state of charge remaining capacity value of the auxiliary battery is lower than the first preset threshold, a part of the charging power is allocated to trickle charge the auxiliary battery.

[0006] In a preferred embodiment, during the mode decision-making step, when the system determines that it is in an energy deficit state, it enters the battery power supply mode and executes the battery call priority protocol: it prioritizes calling the auxiliary battery to output power to make up for the power gap; if the remaining power of the auxiliary battery is lower than the second preset threshold or cannot provide the required power, it switches to power supply by the main battery.

[0007] In a preferred embodiment, a cooling-stage load easing control strategy is also implemented in the battery-powered mode: If the remaining charge of the main battery is greater than or equal to the third preset threshold and the health status of the battery in good condition is higher than the preset level, and the temperature inside the cabinet is higher than the set upper limit, then the refrigeration system is allowed to operate at full power. If any of the following conditions occur: the remaining charge of the main battery is less than or equal to the fourth preset threshold, the health status of the battery is lower than a preset level, or the power deficit exceeds the battery's maximum continuous output capacity, then graded load reduction will be triggered, specifically as follows: Main battery remaining power ≤ fourth preset threshold: compressor operates at reduced frequency; Battery health status is below preset level: Turn off compressor; Power deficit exceeds battery's maximum continuous output capacity: Shut down non-core loads.

[0008] In a preferred implementation, in the mode decision, regardless of the mode, if the remaining power of the main battery is detected to be lower than the fifth preset threshold, the system immediately enters the safety protection mode: all non-core loads are hard-cut off. If the auxiliary battery is in place and its remaining power is higher than the sixth preset threshold, the auxiliary battery supplies power to the core control and communication module to maintain the device's online status and send alarm information.

[0009] In a preferred embodiment, during the trickle charging process of the auxiliary battery, the charging power of the auxiliary battery is limited to less than 10% of the total charging power.

[0010] The charging and discharging control system of the photovoltaic-powered unmanned vending smart cabinet includes: The data acquisition module is used to collect data from photovoltaics, batteries, and loads in real time. The forecasting module is used to make short-term forecasts of power generation and load demand based on the collected data. The decision control module is used to calculate the energy balance value and control each unit to perform corresponding actions based on the decision results. The decision control module includes a dynamic charging target selection unit. When there is an energy surplus, the dynamic charging target selection unit controls the charging circuit to prioritize charging the main battery. When the remaining power of the auxiliary battery is detected to be lower than a first preset threshold, the dynamic charging target selection unit controls the charging circuit to allocate part of the power to trickle charge the auxiliary battery.

[0011] In a preferred embodiment, the decision control module further includes a battery priority unit, which prioritizes the discharge of the auxiliary battery circuit to make up for the power gap when the energy deficit is in a state of energy deficit, and switches to the main battery circuit when the auxiliary battery power is insufficient.

[0012] In a preferred embodiment, the decision control module further includes a cooling degradation control unit. In battery-powered mode, the cooling degradation control unit outputs a control signal to perform graded load reduction of the cooling system based on the remaining battery power, battery health status, and power shortage.

[0013] In a preferred embodiment, the system further includes a universal battery switching module, which comprises: The hardware anti-reverse current circuit is connected in series in the auxiliary battery circuit to physically prevent current from charging the auxiliary battery. The software read-only management unit is used to logically prevent the allocation of charging commands to the auxiliary battery after the auxiliary battery has been certified. Intelligent authentication and arc-free access circuitry is used to communicate with the auxiliary battery BMS and enable its safe and smooth access to the system bus.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: 1. In this invention, by combining multi-source data acquisition with short-term prediction algorithms, the energy supply and demand relationship in the next 2 to 3 minutes is predicted, thereby triggering the optimal control strategy in advance. This effectively avoids the risk of system oscillation or downtime caused by light fluctuations or sudden load changes in traditional solutions. Furthermore, by selecting dynamic charging targets, the main battery is prioritized for charging when there is an energy surplus, while the low-charge auxiliary battery is given current-limited trickle charging. This ensures both the energy reserves of the main energy storage unit and the real-time availability of the emergency power supply. The battery priority protocol, which forces the auxiliary battery to discharge first when there is an energy deficit, significantly reduces the number of cycles of the main battery and extends the battery life.

[0015] 2. In this invention, the refrigeration graded load reduction control triggers a three-level progressive load reduction through multi-parameter coupling, reducing compressor power consumption while ensuring slow temperature changes inside the cabinet. This greatly balances the contradiction between preservation and energy saving. Furthermore, through the coordinated control of six-level remaining power thresholds and seamless switching of safety protection mechanisms, it not only achieves off-grid self-sufficiency but also allows maintenance personnel to quickly restore the equipment to full operation by replacing the auxiliary battery with a plug-and-play battery. This solves the problems of deployment limitations, energy consumption contradictions, and lack of emergency response in traditional equipment in scenarios without mains power, greatly improving the commercial operation feasibility in areas without grid coverage, such as scenic spots and parks. Attached Figure Description

[0016] Figure 1The flowchart of the charging and discharging control method for the photovoltaic-powered unmanned vending smart cabinet proposed in this invention is shown below; Figure 2 This is a schematic diagram illustrating the charging and discharging control system of the photovoltaic-powered unmanned vending smart cabinet proposed in this invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0018] like Figure 1 and Figure 2 As shown, the present invention provides a technical solution: a charging and discharging control system for a photovoltaic-powered unmanned vending smart cabinet, comprising: The data acquisition module is used to collect real-time data from photovoltaic systems, batteries, and loads. It includes a photovoltaic power generation data acquisition submodule, a battery system data acquisition submodule, and a load power consumption data acquisition submodule. Specifically: Photovoltaic power generation data acquisition submodule: Used to monitor the output characteristics of photovoltaic modules and the lighting environment in real time; employs high-precision closed-loop Hall sensors to measure the output voltage Vpv and output current Ipv of photovoltaic modules respectively, and calculates the instantaneous photovoltaic power generation in real time: Ppv=Vpv*Ipv; uses a illuminance sensor to directly output the digital illuminance value Lux through the I2C bus, and calculates the gradient of illuminance change: ΔLux / Δt; outputs the acquired Ppv, Vpv, Ipv, Lux, and ΔLux / Δt to the MCU; Battery system data acquisition submodule: Used to acquire detailed status information of the main battery and each auxiliary battery; the main battery and each auxiliary battery are equipped with a smart battery management chip, which communicates with the MCU via the I2C-compatible SMBus protocol. Digital temperature sensors are additionally placed at key thermal nodes outside the battery pack for cross-validation and redundancy backup with the internal temperature readings of the smart battery management chip; the MCU communicates with the main battery smart battery management chip and each online auxiliary battery smart battery management chip in turn at a frequency of 1Hz through the SMBus host controller, directly reading battery status parameters from the smart battery management chips. State of charge remaining charge: charge percentage, a core decision parameter; Battery Health Status: Battery health status, used to assess actual capacity; Internal temperature Tbatt: from the thermistor inside the battery BMS; Voltage Vbatt and current Ibatt: charging and discharging current, with accuracy far exceeding that of external shunts; Based on the read state of charge remaining capacity, state of health battery health status, and internal temperature Tbatt, the actual usable capacity of the battery is calculated as: Cusable = remaining capacity * battery health status * k(Tbatt), where k(Tbatt) is a temperature compensation coefficient lookup table pre-stored in the MCU; Load power consumption data acquisition submodule: Used to monitor total load power consumption and accurately identify high-power loads, such as the operating status of the refrigeration compressor; at the inverter's AC output, an open-type AC current transformer is used to measure the total load current Iload; a high-frequency response current clamp is connected in series on the power supply branch of the refrigeration compressor, specifically for capturing the compressor's transient current waveform; the cabinet temperature Tcabinet is measured; the MCU samples the total load current signal at a frequency of 10Hz through the ADC and calculates the total load power Pload; by analyzing the current waveform characteristics, it accurately determines whether the compressor is in the start-up, running, or shutdown state; it continuously monitors Tcabinet and compares it with the system's set upper and lower temperature limits to generate a cooling demand signal; The raw data collected by the photovoltaic power generation data acquisition submodule, the battery system data acquisition submodule, and the load power consumption data acquisition submodule are integrated and optimized. The forecasting module is used to make short-term forecasts of power generation and load demand based on the collected data. It includes a short-term power generation forecasting submodule and a load demand forecasting submodule, specifically: The short-term power generation forecast submodule is used to predict the photovoltaic power generation trend Ppvforecast for the next 2-3 minutes based on current and historical sunlight and power generation data. It adopts a lightweight autoregressive integral moving average model. During operation, it will periodically use the latest data to fine-tune the model error, realize slow adaptive learning, and make the prediction increasingly closer to the actual deployment environment. In real-time prediction, Ppv and ΔLux / Δt are input, and the photovoltaic power generation forecast value Ppvforecast for the next 2-3 minutes is output after calculation. Load demand forecasting submodule: Based on historical operating data and real-time status, it is used to predict the start probability and expected running time of the refrigeration compressor within a specific time period. It is constructed using a hybrid forecasting method based on rules and statistical learning. The inputs are the real-time cabinet temperature Tcabinet, real-time clock information, historical load power curve and compressor historical start and stop records. After calculation, it outputs the start probability of the refrigeration compressor Pcompressoron and the expected energy consumption Eloadforecast for a future period. The decision control module is used to calculate the energy balance value and control each unit to perform corresponding actions based on the decision results. It includes an energy balance calculation unit and a multi-mode decision engine, specifically: Energy balance calculation unit: used to perform real-time comparison of energy supply and demand, and calculate the energy balance value EnergyBalance in each control cycle: EnergyBalance=Ppvforecast-Pload; Multi-mode decision engine: Based on data from the EnergyBalance, data acquisition module, and prediction module, it switches between operating modes. Specifically: State 1: When EnergyBalance>+50W and the remaining power of the main battery is <95%, the charging circuit is controlled to use all the surplus energy to charge the main battery. If the remaining power of any auxiliary battery is detected to be ≤ the first preset threshold, an instruction is sent to the charging circuit to allocate about 10% of the total charging power to trickle charge the auxiliary battery, while the remaining power is still used to charge the main battery. State 2: When EnergyBalance<-50W, or Ppv≈0, and the remaining power of the main battery is >20% or the remaining power of any auxiliary battery is >20%, calculate the power gap Pgap: Pgap=|EnergyBalance|, and execute the battery call priority protocol and cooling graded load reduction control; State 3: Regardless of the mode, as long as the remaining power of the main battery is detected to be less than the fifth preset threshold, the power supply to all non-core loads, such as cooling, lighting, and display, will be forcibly cut off through the relay control circuit. If the auxiliary battery is present and its remaining power is greater than the sixth preset threshold, the decision engine controls the power path switching circuit to supply power only to the core components such as the MCU, 4G communication module, and card payment module by the auxiliary battery, so as to maintain the device's online status and payment capability. Among them, the first preset threshold is 15%, the second preset threshold is 20%, the third preset threshold is 55%, the fourth preset threshold is 45%, the fifth preset threshold is 10%, and the sixth preset threshold is 5%; Furthermore, the decision control module includes a dynamic charging target selection unit. When there is an energy surplus, the dynamic charging target selection unit controls the charging circuit to prioritize charging the main battery. When the remaining power of the auxiliary battery is detected to be below a first preset threshold, the dynamic charging target selection unit controls the charging circuit to allocate a portion of its power to trickle charge the auxiliary battery. Specifically: The dynamic charging target selection unit first obtains the latest data from the data acquisition module, then executes the default logic to maximize the charging of the main battery. At the same time, the unit continuously scans the remaining power status of all auxiliary batteries. If the remaining power of any auxiliary battery is less than or equal to a first preset threshold, the unit immediately calculates the charging power allocated to the auxiliary battery, then reduces the power of the main battery charging channel while activating the auxiliary battery charging channel and initiating the buck trickle charging of the corresponding auxiliary battery charging channel. During the trickle charging process, the unit continuously monitors the charging current and voltage of the auxiliary battery through the ADC to ensure that it strictly follows the predetermined low-voltage, low-current mode. Once any abnormality is detected, the unit immediately stops charging the auxiliary battery and records the fault code. Furthermore, the decision control module also includes a battery priority unit for executing the battery priority protocol. In a state of energy deficit, the battery priority unit prioritizes the discharge of the auxiliary battery circuit to compensate for the power shortfall, and switches to the main battery circuit when the auxiliary battery is low on power. Specifically: The battery priority unit first obtains the current total load power, current photovoltaic power generation, remaining capacity of all online batteries, health status of all batteries, maximum continuous discharge current capability, and real-time power gap data from the data acquisition module. Then, based on the conditions that the battery is in place, its remaining capacity is greater than the second preset threshold, and there is no fault flag, it determines whether the auxiliary battery can be used as a discharge source. Then, all available auxiliary batteries are placed into an "available resource pool" and sorted from high to low remaining capacity, with the auxiliary battery to be called as the first priority. The auxiliary battery with the highest remaining capacity is selected from the "available resource pool" as the first discharge unit. If the remaining capacity of the currently discharging auxiliary battery drops to ≤ the second preset threshold, the voltage of the auxiliary battery drops sharply due to high current discharge and cannot provide sufficient power, or the auxiliary battery fails, the main battery is switched to power supply. If a single auxiliary battery cannot meet the power gap, multiple auxiliary batteries are activated simultaneously for parallel discharge. Furthermore, the decision control module also includes a cooling degradation control unit for performing cooling staged load reduction control. In battery-powered mode, this cooling degradation control unit outputs control signals to perform staged load reduction of the cooling system based on the remaining battery charge, battery health status, and power deficit. Specifically: The cooling degradation control unit continuously monitors the remaining charge of the main battery, the battery health status, and the power gap. When any of the following conditions are met: remaining charge of the main battery ≤ fourth preset threshold, battery health status ≤ 70%, or power gap Pgap > maximum continuous output capacity of the battery * 0.8, graded derating is executed. Graded derating is divided into three levels: Level 1 load reduction: Significantly reduces compressor power consumption while maintaining its continuous operation, preventing the cabinet temperature from rising rapidly; Secondary load reduction: Under primary load reduction conditions, when the remaining charge of the main battery continues to decrease or the power gap Pgap continues to increase, the power supply circuit of the compressor is completely cut off. Level 3 load reduction: In Level 2 load reduction mode, when the remaining power of the main battery continues to decrease or the power gap Pgap continues to increase, the backlight brightness of the display screen is adjusted from 100% to the lowest visibility, and all power supply relays of non-core loads are turned off, maintaining only the main control MCU, payment module and 4G module at the lowest power consumption. When the remaining charge of the main battery recovers and the power gap Pgap decreases, the load reduction measures are gradually lifted in reverse order. In addition, a universal battery switching module is included, which comprises: Hardware anti-reverse current circuit: connected in series in the auxiliary battery circuit, used to physically prevent current from charging the auxiliary battery, ensuring the insurmountability of the auxiliary battery's "discharge only, no charge" principle at the physical hardware level. Software read-only management unit: After certifying the auxiliary battery, it is used to logically prevent the allocation of charging commands to the auxiliary battery, forming a double protection with the hardware anti-reverse current unit; Intelligent authentication and arc-free access circuit: used to communicate with the auxiliary battery BMS and realize its safe and smooth access to the system bus, safely complete the electrical connection and disconnection of the auxiliary battery, avoid sparks and surge current impacts at the moment of connection, extend connector life, and ensure operational safety.

[0019] In this embodiment, efficient off-grid operation and intelligent energy management of photovoltaic-powered unmanned vending smart cabinets are achieved through multi-module collaboration. Specifically, a closed-loop control logic of photovoltaic-storage-utilization is constructed through high-precision data acquisition, short-term prediction algorithms, and multi-objective optimization decision-making. The data acquisition module uses closed-loop Hall sensors and digital light intensity sensors to monitor photovoltaic output characteristics and light change trends in real time. It communicates with the battery BMS via the SMBus protocol to obtain multi-dimensional parameters and calculates the actual available capacity in combination with the temperature compensation coefficient. At the same time, it accurately identifies the compressor's working status through current waveform analysis. The prediction module uses a lightweight autoregressive integral moving average model to predict power generation in the short term and combines a hybrid algorithm based on rules and statistical learning to predict load demand, providing a forward-looking decision-making basis for the system. The decision control module dynamically switches between three states by calculating the energy balance value. The battery universal switching module achieves dual anti-charging protection through hardware anti-reverse current circuits and software read-only management. It achieves arc-free safe access by combining intelligent authentication and pre-charging circuits. Finally, the system achieves complete off-grid operation and extends battery life through intelligent load reduction strategies through the collaborative control of the sixth-order remaining power threshold. Example

[0020] like Figure 1 and Figure 2As shown, based on the charging and discharging control system of the photovoltaic-powered unmanned vending smart cabinet provided in Embodiment 1, a charging and discharging control method for the photovoltaic-powered unmanned vending smart cabinet is proposed. This method presets a threshold value for the remaining charge of the sixth-order auxiliary battery, and includes the following steps: S1. Data Acquisition: Real-time acquisition of output data, solar irradiance data, battery system status data, and historical load data of the photovoltaic power generation system. Specifically: The system reads Hall sensor values ​​through an analog-to-digital converter to calculate real-time photovoltaic voltage Vpv and photovoltaic current Ipv, thereby obtaining real-time photovoltaic power generation Ppv. Simultaneously, it reads the light intensity sensor value Lux and calculates its gradient change ΔLux / Δt over one minute. Furthermore, it communicates with the battery management systems of the main and auxiliary batteries via I2C / SMBus interfaces to read their remaining charge, battery health status, voltage, temperature Tbatt, and maximum discharge current. Then, it measures the total load current Iload through a sampling resistor and an analog-to-digital converter. Next, it reads the cabinet temperature Tcabinet through a temperature sensor and monitors the compressor operating current through a current clamp to determine the compressor status. Finally, the collected raw data is processed by a moving average filtering algorithm to eliminate noise interference and obtain stable and reliable data for subsequent decision-making. S2, Short-term forecast: S2.1 Power Generation Trend Prediction: Based on the trend of changes in solar irradiance data, predict the power generation trend within a set time period in the future. Specifically, based on the current Ppv and ΔLux / Δt, run a lightweight autoregressive integral moving average prediction model to predict the power generation trend Ppv forecast for the next 2 to 3 minutes. S2.2 Load power demand prediction: predict the load power demand based on historical load data. Specifically, based on historical data, use a learning algorithm to predict the high probability start time and duration of the refrigeration compressor. S3. Mode Decision: The energy balance value is calculated based on the difference between the predicted power generation and the predicted load demand. Based on the energy balance value and the state of the battery system, different power supply modes are dynamically selected. Specifically: When the system determines that it is in an energy deficit state, it enters battery power mode and executes the battery priority protocol: it prioritizes the auxiliary battery to output power to make up for the power shortage; if the remaining power of the auxiliary battery is lower than the second preset threshold or cannot provide the required power, it switches to main battery power, wherein: In the battery-powered mode, a cooling-stage load reduction control strategy is also implemented: If the remaining charge of the main battery is greater than or equal to the third preset threshold and the health status of the battery in good condition is higher than the preset level, and the temperature inside the cabinet is higher than the set upper limit, then the refrigeration system is allowed to operate at full power. If any of the following conditions occur: the remaining charge of the main battery is less than or equal to the fourth preset threshold, the health status of the battery is lower than a preset level, or the power deficit exceeds the battery's maximum continuous output capacity, then graded load reduction will be triggered, specifically as follows: Level 1 load reduction: Main battery remaining power ≤ fourth preset threshold: Compressor operates at reduced frequency; Level 2 load reduction: When the battery's health status is below a preset level, the compressor is turned off. Level 3 derating: When the power deficit exceeds the battery's maximum continuous output capacity, shut down non-core loads; Regardless of the mode, if the remaining power of the main battery is detected to be lower than the fifth preset threshold, the system will immediately enter the safety protection mode: all non-core loads will be hard-cut off. If the auxiliary battery is in place and its remaining power is higher than the sixth preset threshold, the auxiliary battery will supply power to the core control and communication modules to maintain the online status of the device and send alarm information. When the system determines that it is in an energy surplus state, it enters the photovoltaic charging mode and performs dynamic charging target selection: by default, it prioritizes charging the main battery; if the remaining charge value of the auxiliary battery is detected to be lower than the first preset threshold, a portion of the charging power is allocated to trickle charge the auxiliary battery, and the charging power of the auxiliary battery is limited to less than 10% of the total charging power.

[0021] In this embodiment, intelligent energy scheduling is achieved through a six-level remaining power threshold system and a three-layer architecture based on multi-source data fusion, short-term prediction, and multi-mode collaborative decision-making. During the data acquisition phase, data from Hall sensors and light intensity sensors are read via ADC to calculate photovoltaic power Ppv and the light intensity gradient ΔLux / Δt in real time. Multi-dimensional parameters are obtained from the battery management system (BMS) via the SMBus protocol, and the actual usable battery capacity is dynamically calculated using a temperature compensation coefficient. Simultaneously, the compressor status is accurately identified through current waveform analysis. All data undergoes moving average filtering preprocessing to ensure reliability. In the short-term prediction phase, a lightweight ARIMA model is used to process Ppv and ΔLux / Δt data, generating a power generation prediction Ppvforecast for the next 2-3 minutes. Combined with historical data and real-time temperature, machine learning algorithms predict the compressor start-stop probability and energy consumption, providing feedforward signals for decision-making. In the mode decision-making phase, three states are dynamically triggered by calculating the energy balance value: dynamic charging target selection is executed when there is energy surplus; battery priority protocol and multi-parameter coupled cooling graded load reduction are activated when there is energy deficit; and non-core loads are hard-cut off in emergency situations, with auxiliary batteries maintaining core functions.

[0022] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments for application in other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A charging and discharging control method for a photovoltaic-powered unmanned vending machine, characterized in that: Includes the following steps: S1. Data Acquisition: Real-time acquisition of output data, illumination data, battery system status data, and historical load data of the photovoltaic power generation system; S2, Short-term forecast: S2.1 Based on the trend of changes in sunlight data, predict the trend of power generation within a set time period in the future; S2.2 Predict the power demand of the load based on historical load data; S3, Mode Decision: Calculate the energy balance value by the difference between the predicted power generation and the predicted load power demand, and dynamically enter different power supply modes based on the energy balance value and the state of the battery system. The system presets a threshold for the remaining state of charge of the sixth-order auxiliary battery. When the system determines that it is in an energy surplus state, it enters the photovoltaic charging mode and performs dynamic charging target selection: by default, it prioritizes charging the main battery; if the remaining state of charge of the auxiliary battery is detected to be lower than the first preset threshold, a portion of the charging power is allocated to trickle charge the auxiliary battery.

2. The charging and discharging control method for the photovoltaic-powered unmanned vending machine intelligent cabinet according to claim 1, characterized in that: In the mode decision-making step, when the system determines that it is in an energy deficit state, it enters the battery power supply mode and executes the battery call priority protocol: it prioritizes calling the auxiliary battery to output power to make up for the power gap; if the remaining power of the auxiliary battery is lower than the second preset threshold or cannot provide the required power, it switches to the main battery for power supply.

3. The charging and discharging control method for the photovoltaic-powered unmanned vending machine intelligent cabinet according to claim 2, characterized in that: In the battery-powered mode, a cooling-stage load easing control strategy is also implemented: If the remaining charge of the main battery is greater than or equal to the third preset threshold and the health status of the battery in good condition is higher than the preset level, and the temperature inside the cabinet is higher than the set upper limit, then the refrigeration system is allowed to operate at full power. If any of the following conditions occur: the remaining charge of the main battery is less than or equal to the fourth preset threshold, the health status of the battery is lower than a preset level, or the power deficit exceeds the battery's maximum continuous output capacity, then graded load reduction will be triggered, specifically as follows: Main battery remaining power ≤ fourth preset threshold: compressor operates at reduced frequency; Battery health status is below preset level: Turn off compressor; Power deficit exceeds battery's maximum continuous output capacity: Shut down non-core loads.

4. The charging and discharging control method for the photovoltaic-powered unmanned vending machine intelligent cabinet according to claim 1, characterized in that: In the mode decision-making process, regardless of the mode, if the remaining power of the main battery is detected to be lower than the fifth preset threshold, the system will immediately enter the safety protection mode: all non-core loads will be hard-cut off. If the auxiliary battery is in place and its remaining power is higher than the sixth preset threshold, the auxiliary battery will supply power to the core control and communication module to maintain the online status of the device and send alarm information.

5. The charging and discharging control method for the photovoltaic-powered unmanned vending machine intelligent cabinet according to claim 1, characterized in that: During the trickle charging process of the auxiliary battery, the charging power of the auxiliary battery is limited to less than 10% of the total charging power.

6. A charging and discharging control system for a photovoltaic-powered unmanned vending machine, comprising the charging and discharging control method for a photovoltaic-powered unmanned vending machine according to any one of claims 1-5, including: The data acquisition module is used to collect data from photovoltaics, batteries, and loads in real time. The forecasting module is used to make short-term forecasts of power generation and load demand based on the collected data. The decision control module is used to calculate the energy balance value and control each unit to perform corresponding actions based on the decision results. The decision control module includes a dynamic charging target selection unit. When there is an energy surplus, the dynamic charging target selection unit controls the charging circuit to prioritize charging the main battery. When the remaining power of the auxiliary battery is detected to be lower than a first preset threshold, the dynamic charging target selection unit controls the charging circuit to allocate part of the power to trickle charge the auxiliary battery.

7. The charging and discharging control system of the photovoltaic-powered unmanned vending machine intelligent cabinet according to claim 6, characterized in that: The decision control module also includes a battery priority unit. When there is an energy deficit, the battery priority unit prioritizes the discharge of the auxiliary battery circuit to make up for the power gap, and switches to the main battery circuit when the auxiliary battery power is insufficient.

8. The charging and discharging control system of the photovoltaic-powered unmanned vending machine intelligent cabinet according to claim 6, characterized in that: The decision control module also includes a cooling degradation control unit. In battery-powered mode, the cooling degradation control unit outputs control signals to perform graded load reduction of the cooling system based on the remaining battery power, battery health status, and power shortage.

9. The charging and discharging control system of the photovoltaic-powered unmanned vending machine intelligent cabinet according to claim 6, characterized in that: The system also includes a universal battery switching module, which comprises: The hardware anti-reverse current circuit is connected in series in the auxiliary battery circuit to physically prevent current from charging the auxiliary battery. The software read-only management unit is used to logically prevent the allocation of charging commands to the auxiliary battery after the auxiliary battery has been certified. Intelligent authentication and arc-free access circuitry is used to communicate with the auxiliary battery BMS and enable its safe and smooth access to the system bus.

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