Intelligent charging system and method based on illumination intensity adaptive adjustment

Abstract

The invention provides an intelligent charging system and method based on illumination intensity adaptive adjustment. The intelligent charging system and method are applied to a photovoltaic energy storage system comprising a photovoltaic cell, a DC-DC converter, a storage battery charging mode control module and a storage battery. The method comprises the following steps: firstly, acquiring current illumination intensity, an incident light angle and a working environment temperature, and sending the current illumination intensity, the incident light angle and the working environment temperature to a photovoltaic cell and storage battery charging mode control module; the photovoltaic cell outputs corresponding voltage and current based on the parameters and a temperature compensation mechanism; and the storage battery charging mode control module converts the output of the photovoltaic battery into charging voltage and current for the storage battery, and dynamically determines a constant-voltage or constant-current charging mode in combination with the illumination intensity, the environment temperature, the incident light angle and the charging parameters, so that the self-adaptive charging of the storage battery is realized. Through multi-parameter cooperative adjustment, the charging mode is accurately matched with the environment condition and the storage battery state, and the charging efficiency and the system stability can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy battery charging control, in particular to an intelligent charging system and method based on adaptive adjustment of light intensity. BACKGROUND

[0002] The commonly used photovoltaic battery-energy storage system currently usually adopts a photovoltaic battery in parallel with an energy storage capacitor as an energy collection device, and a lithium ion / sodium ion battery pack as a rechargeable battery to realize collection and conversion of light energy into electrical energy for storage. The collected electrical energy can provide power supply for various small-power electronic devices, and is widely used in the industries and fields of automatic street lamps, sensor networks, security and protection, automobiles, and field sites. The lithium ion / sodium ion battery used as an energy storage system does not have a direct requirement for energy density, but usually requires a battery cycle life of more than 3500 times to pursue a long service life and low cost of the entire photovoltaic-energy storage device. The charging strategy directly affects the battery cycle life, and high-power and unstable power current input can greatly reduce the battery cycle life.

[0003] Constant current constant voltage charging is a charging mode commonly used for energy storage / power batteries, and its advantages include efficient and accurate charging control and prolonging battery life. Constant current charging is a method of charging through a constant current input, and a dynamic output voltage is used to maintain a constant charging current. The constant current constant voltage charging mode has multiple advantages such as flexibility, stability, protection function, and high efficiency. The actual output power of a photovoltaic battery is closely related to factors such as its own conversion efficiency, light area, light intensity, and load matching characteristics. Under the same conditions, when the external light environment changes, the output power of the photovoltaic battery also changes. Especially under different environmental light intensities (such as sunny, cloudy, and rainy days), the input characteristics of the charging battery in the energy collection circuit are dynamically changed in a large range. In particular, when the environmental light intensity is low, the output power of the photovoltaic battery is very small, far lower than the normal input power of the charging circuit. This results in unstable long-term output power of the photovoltaic battery, a large difference in energy collection efficiency between different working time periods, and further affects the service life of the battery. SUMMARY

[0004] To solve the above technical problems, the technical solution adopted by the present application is as follows: According to the first aspect of the present application, an intelligent charging method based on adaptive adjustment of light intensity is provided, which is applied to a photovoltaic energy storage system including a photovoltaic battery, a DC-DC converter, a battery charging mode control module, and a battery. The method includes the following steps: S100, the current light intensity irradiating the photovoltaic battery, the current incident light angle received by the photovoltaic battery, and the current working environment temperature of the photovoltaic battery are obtained and sent to the photovoltaic battery and the battery charging mode control module.

[0005] S200, the photovoltaic cell outputs corresponding voltage and current based on the current light intensity, the current working environment temperature and the current incident light angle, respectively as the current output voltage and the current output current and sends to the battery charging mode control module.

[0006] S300, the DC-DC converter converts the received current output voltage and current output current into the current input voltage and current input current for charging the battery, and the battery charging mode control module determines the current charging mode of the battery based on the current light intensity, the current working environment temperature, the current incident light angle, the current input current and the current input voltage, and charges the battery based on the determined charging mode, wherein the charging mode includes constant voltage charging mode and constant current charging mode.

[0007] The present application has at least the following beneficial effects: (1) Improve charging efficiency: by real-time acquisition of light intensity, incident light angle and other environmental parameters, combined with temperature compensation mechanism to dynamically adjust the photovoltaic cell output, and through the battery charging mode control module to convert the charging parameters suitable for the battery, the charging process is accurately matched with the environmental energy supply capacity, avoiding the problem of energy waste or insufficient charging in fixed mode, significantly improving the photovoltaic energy utilization rate.

[0008] (2) Protect the service life of the battery: the battery charging mode control module dynamically switches the constant voltage / constant current mode based on multiple parameters, uses the constant current mode for fast charging when the battery power is low, and automatically switches to the constant voltage mode when the battery is close to full to avoid overcharging, reduce the polarization and capacity attenuation of the battery, and prolong the cycle life.

[0009] (3) Enhance environmental adaptability: for complex environmental conditions such as light intensity fluctuation, temperature change, incident angle deviation, etc., through real-time parameter acquisition and mode adaptive adjustment, the system can work stably under different weather conditions (such as sunny, cloudy) and time periods (such as morning, evening, noon), which widens the application scenarios of photovoltaic energy storage system.

[0010] (4) Intelligent control: without manual intervention, the battery charging mode control module automatically completes parameter acquisition, mode judgment and charging adjustment, realizing the full-process automation of environmental perception, parameter conversion and mode decision, reducing the operation complexity and improving the intelligent level of the system.

[0011] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced in the following. Obviously, the drawings in the following description only represent some of the embodiments of the present application, and other drawings can be obtained by those of ordinary skill in the art without any creative effort based on these drawings.

[0013] Figure 1 The flow chart of the intelligent charging method based on the self-adaptive adjustment of the illumination intensity provided in the embodiments of the present application is shown in FIG. 1. Figure 2 The schematic diagram of the charging circuit of the energy storage system for the photovoltaic cell obtained by the simulation using MATLAB is shown in FIG. 2. Figures 3a to 3c The logic schematic diagram of the charging mode decision is shown in FIG. 3. Figures 4a to 4d The simulation and emulation results of the system in the embodiments using MATLAB to adjust the constant current / constant voltage mode according to different illumination environments are shown in FIG. 4. DETAILED DESCRIPTION

[0014] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments only represent some of the embodiments of the present application, but not all the embodiments. Based on the embodiments of the present application, all the other embodiments obtained by those of ordinary skill in the art without any creative effort belong to the scope of protection of the present application.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this description, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise. The term "and / or" includes any and all combinations of one or more of the associated listed items.

[0016] It is noted that some example embodiments are described as a process or method depicted as a flowchart. Although the flowchart describes the steps as occurring in a sequence, many of the steps can be performed concurrently, in parallel, or simultaneously. In addition, the order of the steps can be re-arranged. A process can terminate when its operations are completed, but could also terminate without reaching the end of the process, for example, if it is stopped or aborted. The processes can correspond to methods, functions, routines, subroutines, subprograms, etc.

[0017] The application aims to provide a charging method and system for adjusting constant current / constant voltage mode according to different ambient light intensities. The method of using constant current and constant voltage charging for a suitable period of time can reduce the capacity loss characteristics caused by battery polarization to solve the problem of lithium ion / sodium ion battery charging rate instability in the prior art photovoltaic battery-energy storage system, thereby improving the service life of the battery.

[0018] The embodiment of the application provides an intelligent charging method based on adaptive adjustment of light intensity, which is applied to a photovoltaic energy storage system including a photovoltaic battery, a DC-DC converter, a battery charging mode control module and a battery, as shown in Figure 1 The method can include the following steps: S100, acquiring the current light intensity of the irradiated photovoltaic battery, the current incident light angle received by the photovoltaic battery and the current working environment temperature of the photovoltaic battery and sending them to the photovoltaic battery and the battery charging mode control module.

[0019] In the embodiment of the application, the light intensity, the incident light angle and the environment temperature can be acquired by a light signal receiving device. The light signal receiving device mainly serves as a part of an auxiliary system and is responsible for the function of detecting light conditions in the photovoltaic system. The device can include a light sensor, a sun position sensor, a signal processing unit and an environment monitoring auxiliary device, etc. The light sensor (such as a photodiode, a photoresistor) can change the resistance according to the light intensity, convert the light signal into an electrical signal, and specifically can adopt a crystalline silicon light sensor installed on the surface of the photovoltaic panel to collect the current light intensity of the irradiated photovoltaic battery in real time, with a sampling frequency of 1 time / second, a measurement range of the light sensor of 0-2000 W / m² and an accuracy of ±5%.

[0020] The incident light angle refers to the included angle between the incident light and the normal line of the photovoltaic panel. The sun position sensor includes an azimuth angle / altitude angle sensor and a four-quadrant photoelectric detector, which can acquire the angle of the incident light received by the photovoltaic battery and determine the direction of the sun through light spot offset. Specifically, a two-axis sun position sensor can be adopted to first collect the azimuth angle and the altitude angle of the incident light received by the photovoltaic battery, and then convert them into the included angle between the incident light and the normal line of the photovoltaic panel (i.e. the current incident light angle) in combination with the installation inclination and orientation of the photovoltaic panel. The measurement range of the sensor is -90°-90°, and the resolution is 0.1°. An NTC temperature sensor attached to the back plate of the photovoltaic battery is used to collect the current working environment temperature of the photovoltaic battery. The measurement range of the sensor is -40℃-85℃, the accuracy is ±0.5℃, and the light intensity, the incident light angle and the environment temperature are sampled synchronously (1 time / second).

[0021] The environment monitoring auxiliary device includes a temperature sensor, a temperature / wind speed sensor, etc., and is mainly responsible for monitoring the temperature of the photovoltaic panel to achieve the purpose of safe operation of the auxiliary system.

[0022] After the preliminary filtering of the above-mentioned raw data, for example, removing transient pulse interference, the sensors synchronously send the data to the control unit of the photovoltaic cell and the system main control circuit through the SPI communication protocol, and the sending period is consistent with the sampling frequency (1 time / second).

[0023] In the embodiment of the present application, the illumination intensity is the illumination intensity after preprocessing, and the preprocessing includes outlier filtering and data smoothing. The outlier filtering limits the illumination intensity in the physically feasible range of 0-1200 W / m², any value below 0 is set to 0, and any value above 1200 is set to 1200. The data smoothing is processed by a Savitzky-Golay filter, the window length of the filter is 61 data points, and the polynomial order is 3, so as to eliminate environmental noise while retaining the trend characteristics of the data.

[0024] Specifically, after the original illumination intensity is collected by a crystalline silicon light sensor, it first enters the outlier filtering link, and the core purpose is to exclude unreasonable data caused by sensor errors, transient interference or extreme environment: Filtering rule: based on the physical range of the illumination intensity in the actual application scene of the photovoltaic system (natural illumination intensity usually fluctuates within 0-1200 W / m², extreme midday strong light may approach 1000 W / m² for a short time, and overcast weak light is mostly within 50-300 W / m²), the original data is forcibly limited within the range of 0-1200 W / m²: If the original collection value is less than 0 (such as negative deviation caused by transient short circuit of the sensor circuit), it is directly corrected to 0; If the original collection value is greater than 1200 W / m² (such as misreading of the sensor caused by direct strong light for a moment, electromagnetic interference causes a sharp peak), it is directly corrected to 1200 W / m²; The original data within 0-1200 W / m² remains unchanged.

[0025] The outlier filtering can avoid the interference of abnormal values (such as -50 W / m² or 1500 W / m²) on the subsequent algorithm (for example, causing the MPPT algorithm to misjudge as "strong light" or "no light" state), and ensure the physical reasonableness of the input data.

[0026] The intermediate data after outlier filtering enters the data smoothing link and is further processed by a Savitzky-Golay filter (also known as "polynomial sliding window filter") to eliminate high-frequency noise and retain the trend characteristics of the illumination intensity. The filter parameters are designed as follows: Window length: set to 61 data points, combined with a sampling frequency of 1 / s, corresponding to an actual time window of 61 seconds (i.e. each smoothing process is based on the historical data of the last 61 seconds). The reason for choosing 61 points is: ① an odd window length facilitates polynomial fitting centered on the current data point; ② a window of 61 seconds can cover short noise periods (such as 30 seconds of rapid cloud cover) without excessively lagging behind moderate changes in light (such as 5 minutes of gradual light change), balancing noise filtering effect and real-time performance; Polynomial order: a 3rd order polynomial is used, which is fitted to the 61 data points in the window by least squares, which can retain the linear trend of the light intensity (such as uniform enhancement at sunrise) and adapt to nonlinear trends (such as fluctuating rise in cloudy weather), avoiding the over-smoothing of low-order polynomials (1-2 order) or over-fitting of high-order polynomials (≥4 order).

[0027] The sliding window update mechanism is: The preprocessing submodule opens a circular buffer area for the light intensity data, and automatically removes the oldest data point from the buffer area every time a new data point is collected (1 / s), always retaining the last 61 data points; then, a 3rd order polynomial fitting is performed on the 61 data points in the window with the current latest data point as the center, and the output value of the fitted curve at the current data point is taken as the "smoothed light intensity".

[0028] Data smoothing can effectively filter high-frequency environmental noise (such as sensor mechanical jitter, wind-induced spot flicker, etc. 10 seconds of instantaneous fluctuations), while retaining the core trend of the light intensity (such as a continuous light enhancement / weakening for more than 1 minute), making the output preprocessed light intensity closer to the actual environmental light change pattern.

[0029] In the embodiment of the present application, the light intensity preprocessing strictly follows the order of "first abnormal value filtering, then data smoothing": the original data is first filtered by physical range limitation to remove invalid values, and then the sliding window polynomial fitting is used to eliminate noise, and the final output of the preprocessed light intensity is used as the basis for subsequent calculation, such as the effective light intensity for correcting the influence of incident light angle, or as the input parameter of the irradiance efficiency ratio (IR) in the environmental efficiency coefficient, to ensure that the core algorithm is based on high-quality data. Through this preprocessing process, the system's perception accuracy of light intensity is improved to ±3% (original sensor accuracy ±5%), providing reliable data support for photovoltaic cell output adjustment and charging mode judgment.

[0030] S200, the photovoltaic cell outputs corresponding voltage and current based on the current light intensity, the current working environment temperature and the current incident light angle, respectively as the current output voltage and the current output current and sends to the DC-DC converter.

[0031] The open-circuit voltage of the photovoltaic cell decreases with temperature rise (temperature coefficient is about -0.3% / ℃), and the short-circuit current slightly increases with temperature rise. The control unit corrects the output parameters through a temperature compensation formula: The temperature compensation formula is: Tc=1-k×(T-T0), Tc is the temperature compensation coefficient, T is the working environment temperature, T0 is the reference temperature (for example, 25℃), and k is the preset coefficient (for example, 0.0045); The original output (voltage and current generated by photovoltaic effect) of the photovoltaic cell needs to be processed by the internal circuit to form a stable current output voltage and current output, wherein: the DC-DC conversion circuit converts the unstable original output (which may fluctuate with light and angle) of the photovoltaic cell into a constant range of direct current voltage (such as 12V~24V) and current (such as 0.5A~10A), to ensure that the output parameters meet the input requirements of the battery charging mode control module; the reverse connection and protection circuit prevents the current from flowing back in the reverse direction (such as the battery discharging to the photovoltaic cell) through a diode or a MOSFET switch, and triggers protection when the voltage is too high (≥25V) or the current is too high (≥12A), to limit the output parameters within a safe range.

[0032] S300, the DC-DC converter converts the received current output voltage and current output into current input voltage and current input for charging the battery, and the battery charging mode control module determines the current charging mode of the battery based on the current light intensity, the current working environment temperature, the current incident light angle, the current current of the battery and the current voltage of the battery, and charges the battery based on the determined charging mode, wherein the charging mode includes constant voltage charging mode and constant current charging mode.

[0033] In the embodiment of the application, the DC-DC converter converts the photovoltaic output voltage into a current input voltage close to the current voltage of the battery, such as 13V when the current voltage of the battery is 12V, which is slightly higher to form a charging voltage difference; and simultaneously converts the photovoltaic output current into a current input current matched with the capacity of the battery, such as 10A~20A, i.e. 0.1C~0.2C, for a 100Ah battery. Further, in an illustrative embodiment, in S300, the current charging mode of the battery is determined based on the current light intensity, the current working environment temperature, the current incident light angle, the current input current and the current input voltage, and specifically includes: S310, if the current working environment temperature is greater than or equal to the temperature safety threshold, the current charging mode is determined to be the constant voltage charging mode, and the maximum charging current is limited to be less than or equal to the preset current threshold; if the current working environment temperature is less than the temperature safety threshold, S320 is executed.

[0034] In the embodiment of the present application, the temperature safety threshold can be 45℃. The preset current threshold can be 0.1C of the rated capacity of the battery, such as 10A corresponding to a 100Ah battery.

[0035] In S320, based on the received current illumination intensity, current working environment temperature and current incident light angle, a current environment efficiency coefficient is calculated, and based on the current voltage of the battery, a current dynamic threshold is calculated; S330 is performed.

[0036] In the embodiment of the present application, the environment efficiency coefficient satisfies the following condition: SE = w1 x IR + w2 x AE + w3 x TF, wherein SE is the environment efficiency coefficient, IR is the irradiance efficiency ratio, IR = min (ID / ID ideal , 1), wherein ID is the illumination intensity, ID ideal is the ideal illumination intensity, and the value under standard test conditions is 1000W / m². w1 is the irradiance weight; AE is the incident angle efficiency factor, AE = cos a, a is the incident light angle, w2 is the incident angle weight, TF is the temperature efficiency factor, TF = 1-k x (T-T0), T is the working environment temperature, T0 is the reference temperature, for example, 25℃. k is a preset coefficient, the value is 0.0045 / ℃, w3 is the temperature weight, w1 + w2 + w3 = 1.

[0037] The dynamic threshold satisfies the following condition: DH = c1 + c2 x VR; wherein DH is the dynamic threshold, VR is the voltage ratio, VR = V / V0, V is the current voltage of the battery, V0 is the maximum charging voltage of the battery, for example, the maximum charging voltage of the lead-acid battery is 14.4V, c1 and c2 are preset coefficients, satisfying c1 + c2 ≤ 1, for example, c1 = 0.6, c2 = 0.2.

[0038] In one illustrative embodiment, w1, w2 and w3 are fixed weights, for example, w1 = 0.5, w2 = 0.3, w3 = 0.2.

[0039] In another illustrative embodiment, w1, w2 and w3 are determined based on the state value SOH of the battery, which is determined based on the charge and discharge cycle number, voltage attenuation rate and internal resistance change information of the battery. The charge and discharge cycle number is the cumulative number of complete cycles of the battery from full charge to discharge cutoff voltage and then full charge; the voltage attenuation rate is the difference between the current full charge voltage and the initial full charge voltage as a percentage of the initial full charge voltage; the internal resistance change is the ratio of the current internal resistance to the initial internal resistance.

[0040] Further, the w1, w2 and w3 are determined based on the state value SOH of the battery, specifically including: If SOH≥d1, set w1=w10, w2=w20, w3=w30, w10, w20 and w30 are initial irradiance weight, initial incidence angle weight and initial temperature weight respectively; d1 is a first threshold value. In an illustrative embodiment, d1=0.9.

[0041] In the embodiment of the application, w10=0.5, w20=0.3, and w30=0.2.

[0042] If d2≤SOH<d1, set w1=w10-△w, w2=w20, w3=w30+△w, △w is a preset unit weight, for example, 0.1; d2 is a second threshold value; in an illustrative embodiment, d2=0.8.

[0043] If SOH<d2, set w1=w10-2△w, w2=w20, w3=w30+2△w, and reduce the maximum charging current of the constant current mode to a preset multiple of the rated value, for example, 0.8. The rated value is 0.2C of the battery capacity.

[0044] Further, SOH=f1×(1-AC)+f2×(1-VD)+f3×(1-RC), wherein AC is a cycle number attenuation coefficient, f1 is a cycle number weight, VD is a voltage attenuation rate, f2 is a voltage attenuation weight, RC is a resistance growth rate, and f3 is a resistance change weight, and f1+f2+f3=1 is satisfied, for example, f1=0.4, f2=0.3, and f3=0.3.

[0045] In the formula, the cycle number attenuation coefficient is equal to min(actual cycle number / rated cycle life, 1.0), the rated cycle life is in times, for example, 3500 times. The voltage attenuation rate is equal to (initial full voltage-current full voltage) / initial full voltage, the initial full voltage is the maximum charging voltage of the battery in a brand-new state. The resistance growth rate is equal to (current resistance-initial resistance) / initial resistance, the initial resistance is the internal resistance of the battery in a brand-new state; the current resistance is calculated by the currently recorded charging and discharging voltage and current data, that is, resistance=voltage change / current change.

[0046] S330, if the current environmental efficiency coefficient is greater than the current dynamic threshold value, and the current voltage of the battery is less than a preset voltage threshold value, it is determined that the current charging mode is the constant current charging mode, otherwise, it is determined that the current charging mode is the constant voltage charging mode.

[0047] In the embodiment of the application, the preset voltage threshold value is 95% of the maximum charging voltage.

[0048] In the embodiment of the present application, PID control (proportional-integral-derivative control) is performed on the current input voltage and current to achieve constant voltage and constant current charging. The core value of PID control is to achieve stable output of constant current charging (CC) and constant voltage charging (CV) through real-time dynamic adjustment, and the entire control logic needs to meet the requirement of public sufficiency to ensure that ordinary personnel in the technical field can understand and reproduce the control process based on the description. Among them: In the constant current mode, the target current is used as the feedback value to adjust the PWM duty cycle to stabilize the input current and achieve constant current charging of the battery; In the constant voltage mode, the target voltage is used as the feedback value to adjust the PWM duty cycle to stabilize the input voltage and achieve constant voltage charging of the battery.

[0049] Specifically, from the overall closed-loop feedback logic, PID control first completes real-time acquisition of key parameters through sensors. In the constant current mode, the focus is on the current input current of the charging circuit, and in the constant voltage mode, the focus is on the current voltage across the battery. Then, the actual feedback value collected is compared with the preset target value to calculate the deviation, where the deviation in the constant current mode is the difference between the preset target charging current (such as 0.2C or 0.5C current set according to the battery capacity) and the actual input current, and the deviation in the constant voltage mode is the difference between the preset voltage threshold (i.e. 95% of the maximum charging voltage of the battery) and the actual battery voltage. Then, the deviation is subjected to proportional, integral, and differential operations in three stages to form a total control amount. Finally, the total control amount is converted into the duty cycle of the PWM (pulse width modulation) signal, and the on and off time of the power devices such as MOSFET or IGBT in the charging circuit is adjusted to achieve precise control of the input battery current or voltage, until the deviation approaches zero, achieving the goal of constant current or constant voltage charging.

[0050] When the trigger condition of "environmental efficiency coefficient greater than dynamic threshold and current battery voltage less than preset voltage threshold" is met, the constant current charging mode will be entered. In this mode, the PID control takes maintaining the stability of the charging current as the core goal. The proportional element (P) directly outputs an adjustment signal according to the size of the current deviation, and the larger the deviation, the stronger the adjustment. It can quickly respond to changes in current caused by fluctuations in photovoltaic power. For example, when photovoltaic power suddenly drops, causing the actual charging current to drop from 10A to 8A, the proportional element will quickly amplify the 2A deviation, increase the PWM duty cycle, and quickly restore the current. The integral element (I) operates on the accumulated value of the deviation over time, effectively eliminating the static deviation of the system after stabilization, and avoiding the problem of a small residual deviation between the actual current and the target current when relying solely on the proportional element. For example, it ensures that the current is accurately maintained at 10A after stabilization, rather than 9.9A. The derivative element (D) predicts the trend of the current deviation by calculating the rate of change of the deviation, and adjusts it in advance to prevent overshooting of the current during the recovery process. For example, when the actual current quickly drops from 10A to 7A, the derivative element will capture the trend of the rapidly increasing deviation and output an inhibition signal in advance to prevent the subsequent proportional and integral elements from over-adjusting and causing the current to surge to more than 11A. The PWM duty cycle is positively correlated with the PID output control quantity, and the control quantity increases, the duty cycle increases, the power device conduction time extends, and the charging current increases. Conversely, the duty cycle decreases, the current decreases, and the constant charging current is achieved through this dynamic adjustment mechanism.

[0051] When the constant current charging trigger condition is not met, i.e., the current battery voltage reaches or exceeds the preset voltage threshold, the constant voltage charging mode will be switched to. At this time, the control target is to maintain the battery voltage stable at the preset threshold to avoid overvoltage damage. In this mode, the proportional element (P) adjusts the logic to voltage deviation, and to prevent voltage overshoot, the proportional coefficient (Kp) is usually set smaller than in the constant current mode to ensure that when photovoltaic power suddenly increases, the PWM duty cycle can be adjusted smoothly to maintain voltage stability. The integral element (I) still plays a role in eliminating static deviation, ensuring that the battery voltage is accurately stabilized at the preset threshold (e.g., 51.87V for a maximum charging voltage of 54.6V), and avoiding the voltage remaining at 51.8V or other values that do not meet the threshold after stabilization. The derivative element (D) has a higher risk of damage to the battery due to voltage overshoot, so its derivative coefficient (Kd) is set larger to enhance its ability to predict and suppress voltage mutation trends. For example, when photovoltaic power suddenly increases, the PWM duty cycle is reduced in advance to prevent the voltage from rising above 52V. At the same time, as the battery SOC (state of charge) increases, its internal resistance gradually increases, and the charging current naturally decreases. The PID control continuously adjusts the PWM duty cycle to maintain the voltage at the preset threshold, achieving a "constant voltage, reduced current" charging process until the charging is complete.

[0052] From the practical application value, PID control has obvious advantages in the system: first, adaptive anti-interference ability, can quickly respond to the power fluctuation caused by the photovoltaic panel due to cloud cover and other factors, through the synergistic effect of the three links, avoid the current or voltage fluctuation, protect the battery from impact; Second, no static error precise control, meet the different charging stage of the battery on the current or voltage precision requirements, adapt to the "CC-CV" or "CC-CV-trickle" charging curve of different types of batteries such as lead-acid battery, lithium battery; Third, smooth mode switching ability, when the system switches between constant current and constant voltage mode, the proportion, integral, differential coefficient can be adjusted through the preset parameter transition strategy, avoid the current or voltage mutation in the switching moment. In the aspect of parameter setting, it is necessary to adjust according to the type of battery and charging stage-constant current fast charging stage, moderate proportion coefficient to ensure response speed, small integral coefficient to reduce current fluctuation, moderate differential coefficient to suppress impact; Constant voltage floating charge stage, small proportion coefficient, large integral coefficient, large differential coefficient, respectively corresponding to the needs of anti-overshoot, static error elimination, anti-mutation. In engineering practice, the experience method (first adjust the proportion, then adjust the integral, and finally adjust the differential) or Ziegler-Nichols setting method is commonly used to complete parameter optimization, to ensure that the system is balanced between response speed and stability, without overshoot, no shock and fast convergence.

[0053] (Embodiment) In order to verify the feasibility of the intelligent charging method based on the adaptive adjustment of the light intensity in the application, first, the light simulation data system conforming to the actual environment is constructed, the natural light change law, environmental interference factors and physical constraints are simulated, and real and effective input data support is provided for the charging mode decision, so as to avoid the disconnection between theoretical derivation and actual scene.

[0054] (I) Light simulation data generation logic The simulation data takes the natural light change law as the core, adopts the three-layer construction method of "basic sinusoidal curve + random disturbance + physical constraint", and the specific implementation is as follows: 1. Core parameter definition Data length (hours): default 24 hours, covering the complete day-night cycle; Noise intensity (noise_level): the value range is 0-1, representing the intensity of environmental random disturbance (such as instantaneous light spot jitter, sensor error), and the default is 0.2 (moderate interference level); Data generation frequency: 1 data point per minute, total data quantity t=hours×60 (24 hours correspond to 1440 data points), matching the parameter acquisition frequency of the actual photovoltaic system.

[0055] 2. Basic light curve construction The sinusoidal function is used to simulate the change of light intensity under the trajectory of the sun day and night. The function expression is: base = 1000 * sin(pi * t / hours).

[0056] Where base is the basic light intensity (unit: W / m²), t is the time series (unit: hours). The function takes values from 0 (sunrise) to 1000 W / m² (midday peak, close to the maximum natural light of sunny noon), and then down to 0 (sunset) in the range of t∈[0, hours], perfectly fitting the nonlinear variation law of actual day and night light intensity.

[0057] 3. Environmental interference simulation Random noise: Random noise noise is generated by Gaussian distribution function, expression: noise = np.random.normal(0, noise_level * 1000, len(t)).

[0058] Where the noise standard deviation is controlled by noise_level * 1000 (such as noise_level = 0.2, standard deviation is 200 W / m²), to ensure that the noise scale matches the light intensity magnitude (0-1200 W / m²), simulate the light fluctuation of cloudy weather, sensor circuit noise and other random interference. len(t) is the number of data points contained in the time series "t". For example, when "hours = 24", "t" contains "24 * 60 = 1440" data points, and "len(t) = 1440" at this time.

[0059] Cloud cover: Set 5 random cloud cover per day, each lasting 60 minutes, and the shielding intensity is randomly taken from 200-800 W / m² (simulate the different shielding effects of thin clouds and thick clouds), expression: cloud_effect[start:start+60] = -np.random.uniform(200, 800).

[0060] Where start is the start time of the shielding (randomly taken between 0 and len(t)-60), and cloud_effect is the light intensity attenuation value caused by the shielding (negative value indicates light reduction).

[0061] 4. Physical constraints and data correction There is a clear physical boundary for light intensity in nature, so the synthesized data is truncated: irradiance = np.clip(base + noise + cloud_effect, 0, 1200).

[0062] Where irradiance is the final simulated light intensity, which is limited to the range of 0-1200 W / m² by the np.clip function (0 corresponds to no light, 1200 W / m² corresponds to extreme light, covering all scenarios of sunny, cloudy, rainy, snowy, etc.).

[0063] (B) Simulation data preprocessing and analysis The generated light data needs to be preprocessed to eliminate invalid values and interference, extract the core trend, and provide high-quality input for charging mode decision-making. The preprocessing process includes outlier filtering, data smoothing, trend analysis, and temperature compensation, as follows: 1. Outlier filtering Physically infeasible data is removed by boundary constraints: values less than 0 in irradiance are corrected to 0, and values greater than 1200 are corrected to 1200, to avoid extreme outliers (such as a sensor misreporting 1500 W / m²) leading to charging mode misjudgment.

[0064] 2. Data smoothing Savitzky-Golay filter (window length 61 minutes, 3rd order polynomial) is used to smooth the data, with the function: processed['smooth'] = savgol_filter(processed['irradiance'], window_length=61, polyorder=3) This method preserves the core trend of light intensity (such as sustained light enhancement / weakening for more than 1 hour) while eliminating high-frequency noise (such as transient fluctuations within 10 seconds), outputting the smoothed sequence smooth.

[0065] 3. Trend analysis The long-term change trend of light intensity is extracted through a linear regression model, constructing the equation: Y = β0 + β1 × X.

[0066] Where the independent variable X is the data point time index (0, 1, 2, …, N-1), and the dependent variable Y is the smoothed light intensity smooth; the intercept β0 and slope β1 are fitted by least squares, outputting the trend line sequence trend, which is used to judge whether the overall light is increasing (β1 > 0) or decreasing (β1 < 0), providing a preliminary basis for charging mode switching, and N is equal to the total number of data points in the analysis period, such as 24 hours of collection corresponding to N = 1440, with index up to 1439.

[0067] 4. Temperature compensation mechanism The influence of the introduction temperature on the efficiency of the photovoltaic system is compensated by a coefficient formula: temp_factor = 1 - 0.005 x (temperature - 25).

[0068] Wherein, temperature is the temperature of the photovoltaic panel (unit: ℃), 25 ℃ is the reference temperature; the efficiency of the photovoltaic panel is reduced by 0.5% for each 1 ℃ rise in temperature (in line with the temperature characteristics of monocrystalline silicon photovoltaic modules), and the final photovoltaic output power needs to be corrected in combination with the temperature compensation coefficient: output_power = irradiance x efficiency x temp_factor, wherein efficiency is the conversion efficiency of the photovoltaic panel, and is taken as 0.18 by default.

[0069] II. ANSYS simulation verification: feasibility verification of charging mode under multiple scenarios To further verify the effectiveness of the charging method of the application under complex environments, a photovoltaic- energy storage system simulation model is established by using ANSYS, the charging process under different light, temperature and incident angle conditions is reproduced, the output voltage of the photovoltaic cell, the input voltage and current change curve of the storage battery are output, and the rationality of the constant current / constant voltage mode switching is directly verified.

[0070] (I) ANSYS simulation model construction 1. Model module composition Photovoltaic cell module: a monocrystalline silicon photovoltaic module (size 1600 mm x 900 mm, conversion efficiency 0.18) is used, and the input parameters are the simulated generated light intensity irradiance, photovoltaic panel temperature temperature and incident light angle θ (angle with the normal line of the photovoltaic panel); Energy storage battery module: a lithium ion battery pack (rated voltage 12V, capacity 100Ah, cycle life requirement ≥3500 times) is selected, and the maximum charging voltage is set to 14.4V and the maximum charging current is set to 10A; Battery charging mode control module: a DC-DC conversion circuit (Buck architecture, conversion efficiency ≥92%), a PID controller and a charging mode decision unit are integrated, and the "environmental efficiency coefficient-dynamic threshold" decision logic of the application is implanted.

[0071] 2. Core parameter configuration Environmental efficiency coefficient (SE): calculated according to the formula SE = 0.5 x IR + 0.3 x AE + 0.2 x TF, wherein: IR = min(irradiance / 800, 1.0) (800 W / m² is the ideal light intensity); AE = cos(θ) (incident light angle efficiency factor, in line with the cosine law); TF = 1 - 0.005 x (temperature - 25) (temperature efficiency factor); Dynamic threshold (DH): calculated by formula DH = 0.6 + 0.2 x (battery_voltage / 14.4) (battery_voltage is the current voltage of the battery); Charging mode switching condition: when SE > DH and battery_voltage < 14.4 x 0.95 (i.e. 13.68V), switch to constant current mode (CC); otherwise, constant voltage mode (CV).

[0072] (II) Simulation scenarios and result verification Four typical environmental scenarios were selected for simulation, covering ideal conditions, moderate conditions, high temperature and harsh conditions, and high temperature boundary conditions. The simulation time was 10 seconds (corresponding to the actual scene of rapidly changing environmental parameters), and the results are shown in Table 1: Table 1: (III) Core conclusions of simulation results Mode switching rationality: In ideal conditions (strong light, moderate temperature, small angle), the system automatically switches to constant current mode, achieving fast charging; in non-ideal conditions such as weak light, high temperature, and large angle, it runs stably in constant voltage mode, avoiding battery life decay caused by current fluctuations or overcharging, and meeting the design goal of "adapting to the environment and protecting the battery".

[0073] Parameter stability: In constant current mode, current fluctuation is ≤0.2A, and in constant voltage mode, voltage fluctuation is ≤0.1V, both meeting the stability requirements of battery charging (current fluctuation ≤1A, voltage fluctuation ≤0.5V).

[0074] Safety protection: When the temperature is high (≥45℃), the current limiting protection is automatically triggered (maximum current ≤5A), avoiding further temperature rise of the battery; when the battery voltage approaches full charge (≥13.68V), it is forced to switch to constant voltage mode, preventing capacity loss caused by overcharging.

[0075] III. Core value of simulation data and simulation verification The invention proves the feasibility and practicality of the charging method through "simulation data generation + ANSYS simulation" dual verification method, which is specifically reflected in: Data authenticity: The simulation data is based on natural light rules (sine curve), actual interference factors (noise, cloud cover) and physical constraints (0-1200W / m²), which are not subjective and can effectively reproduce sunny, cloudy and rainy weather conditions. Method universality: ANSYS simulation covers 4 typical environmental scenarios, verifying the effectiveness of the charging mode decision logic (environmental efficiency coefficient-dynamic threshold) under different light, temperature, and incident angle, indicating that the method can adapt to complex and variable outdoor environments. Performance reliability: simulation results show that the system charging efficiency is ≥90% in constant current mode, voltage stability is ≥99% in constant voltage mode, and battery cycle life is expected to be improved to more than 3800 times (8.6% higher than traditional fixed mode), achieving the invention goal of "improving efficiency and prolonging life".

[0076] In summary, simulation data and ANSYS simulation not only provide quantitative support for theoretical methods, but also verify the feasibility of the method through the reproduction of actual scenarios, avoiding "paper talk", and laying a solid foundation for subsequent physical prototype development.

[0077] Four, MATLAB simulation verification: charging mode adaptability in short-term dynamic environment To fully verify the feasibility and reliability of the charging method of the invention, MATLAB / Simulink is used to build a simulation system to verify from two dimensions of "decision logic implementation" and "dynamic response effect", and to completely reproduce the charging mode self-adaptive adjustment process of the photovoltaic-energy storage system under different environments.

[0078] (One) Overall architecture of simulation model The MATLAB simulation model takes "environmental parameter input-decision logic processing-charging parameter output" as the core link, including four modules: Environmental parameter input module: generates dynamic light intensity, photovoltaic panel temperature, and incident angle cosine value (simulates natural environmental changes); Photovoltaic cell module (PVArray): outputs corresponding voltage (12-24V) based on input parameters, matching the power characteristics of single-crystal silicon photovoltaic components; Buck down-conversion circuit module: converts photovoltaic output voltage to charging voltage (10.5-14.4V) suitable for storage batteries, controlled by PWM signal output; Core control module: includes "charging mode decision unit" (corresponding to Figure 3) and "parameter adjustment unit", realizing constant current (CC) / constant voltage (CV) mode switching and charging parameter stable control; Battery module (Battery): selects 12V lithium-ion battery pack (capacity 100Ah), real-time feedback input voltage (U2), current (I2), and state of charge (SOC).

[0079] (Two) Analysis of charging mode decision logic Figure 2The photovoltaic cell charging circuit schematic diagram for the energy storage system obtained by using MATLAB for simulation. Figure 2 It can be divided into four modules, wherein the photovoltaic cell module in module 1 simulates the working mode of the photovoltaic cell. When the external environment changes, the time-varying light intensity and temperature curve are input, and the module can output the corresponding voltage. Subsequently, the output voltage is input into the battery module in module 3 after being stepped down by the DC-DC converter (Buck circuit) in module 2. Among them, the voltmeter U2 measures the battery input voltage, and the voltmeter I2 measures the battery input current. The output results of U2 and I2 are simultaneously input into the battery charging mode control module in module 4, which can make decisions on the charging mode of the circuit according to the input values of light intensity, light angle, temperature, current, voltage, etc. The specific charging mode decision-making logic schematic diagram can be seen in detail in the attached Figures 3a to 3c .

[0080] Subsequently, the battery charging mode control module outputs the duty cycle signal to the PWM generator, and finally the PWM generator outputs the PWM signal to the Mosfet switch according to the corresponding duty cycle signal to control the output current and output voltage of the Buck circuit, and finally control the input current and input voltage of the battery. Oscilloscope 2 is used to observe the battery input voltage U2, input current I2, and state of charge SOC of the battery. Oscilloscope 1 is used to observe the voltage on the input side of the DC-DC converter, the current on the output side, and the output voltage.

[0081] In order to better illustrate the implementation process of the above-mentioned application, MATLAB is used to simulate the core calculation logic. Figures 3a to 3c For the charging mode decision-making logic schematic diagram, it can be divided into seven modules. In the figure, the "temperature threshold" system, the "constant voltage control" system, the "environmental efficiency coefficient" system and the "constant current control" system are all enable subsystems. If the input is 1, the subsystem runs, and if the input is 0, the subsystem stops. The functions and logic sequences of each module are as follows: 1. Module 1: Battery voltage threshold judgment (anti-overcharge priority) Core function: Based on the real-time input voltage of the battery (U2, collected by the voltmeter), it is judged whether it is necessary to directly enter the constant voltage mode to avoid overcharge damage to the battery.

[0082] Judgment logic: Calculate the ratio of "U2 to the maximum charging voltage of the battery (13.0658V)", if the ratio > 0.95 (i.e. U2≥13.0658V×0.95≈12.4125V, close to full charge state), output "1" to the Constantvoltage module, the system directly enters the constant voltage mode; if the ratio ≤ 0.95, output "0", enter the next step of decision-making.

[0083] Technical significance: "Battery voltage safety" as the highest priority judgment condition, priority through constant voltage mode to stabilize the voltage, avoid capacity attenuation caused by overcharge.

[0084] 2. Module 2: Duty cycle signal once constraint Core function: Range limit the PWM duty cycle control signal output by module 1, guarantee the stable operation of Buck circuit.

[0085] Constraint logic: Force the duty cycle signal to be between 0 and 1 (0 corresponds to the circuit completely off, 1 corresponds to the circuit completely on).

[0086] Technical significance: Filter out-of-range signals caused by sensor false positives and circuit noise, prevent voltage / current out of control, and provide a safe signal basis for subsequent decision-making.

[0087] 3. Module 3: Temperature threshold judgment (safety protection) Core function: Based on the working temperature of photovoltaic panels (collected by temperature sensor), determine whether to switch to constant voltage mode, to cope with the impact of high temperature environment on the system.

[0088] Judgment logic: If the collected temperature is greater than 45°C (temperature safety threshold), output "1" to the Constant Voltage module to start constant voltage mode; if the temperature is less than or equal to 45°C, output "0" to enter the next step of decision-making.

[0089] Technical significance: High temperature will cause photovoltaic panel efficiency to decline (efficiency decreases by 0.5% per 1°C increase) and battery charging and discharging performance to decline. By switching modes based on temperature threshold, balance system efficiency and device safety.

[0090] 4. Module 4: Duty cycle signal secondary constraint Core function: Consistent with module 2 logic, further limit the duty cycle signal output by module 3 to the range of 0-1.

[0091] Technical significance: Form "double signal constraint", further filter abnormal signals after temperature judgment, reduce the risk of circuit out of control.

[0092] 5. Module 5: Environmental efficiency coefficient and dynamic threshold calculation (core quantitative basis) Core function: Integrate multi-dimensional parameters to calculate "environmental efficiency coefficient (SE)" and "dynamic threshold (DH)", provide quantitative decision-making indicators for mode switching.

[0093] Calculation logic: Environmental efficiency coefficient: SE = 0.5 × IR + 0.3 × AE + 0.2 × TF, where: IR (irradiance efficiency ratio) = min (actual light intensity / ideal light intensity (800 W / m²), 1.0); AE (angle of incidence efficiency factor) = cos (angle of incidence) (angle is the angle between incident light and the normal of the photovoltaic panel); TF (temperature efficiency factor) = 1 - 0.005 x (actual temperature - 25°C) (25°C is the reference temperature); Dynamic threshold: DH = 0.6 + 0.2 x (U2 / 13.0658V) (adjusted by real-time battery voltage, DH increases when fully charged, tends to constant voltage mode; when the battery is depleted, DH decreases, tends to constant current mode).

[0094] Technical significance: Convert environmental parameters such as light, angle, temperature and battery state into comparable quantitative values, avoid subjectivity of mode switching, and improve decision accuracy.

[0095] 6. Module 6: Efficiency coefficient and threshold comparison (final mode decision) Core function: Determine the final charging mode by comparing SE and DH, which is the "core judgment link" of decision logic.

[0096] Judgment logic: If SE > DH (indicating that the current environment energy supply is sufficient for fast charging), output "1" to the Constantcurrent module to start constant current mode; if SE ≤ DH (indicating insufficient environmental energy or the battery is close to full), output "1" to the Constantvoltage module to start constant voltage mode.

[0097] Technical significance: Realize the dual consideration of "environmental adaptability" and "battery state", ensure efficient fast charging in strong light, suitable temperature and optimal angle, and stable slow charging in weak light, high temperature and poor angle.

[0098] 7. Module 7: Final duty cycle signal constraint Core function: Third 0-1 range limit on the duty cycle signal output by module 6.

[0099] Technical significance: After the final mode is determined, the last signal constraint ensures that the control signal received by the Buck circuit completely meets the hardware operation requirements, avoiding charging parameter fluctuations caused by abnormal signals.

[0100] The 7 modules progress in the order of "voltage safety judgment → temperature safety judgment → efficiency and threshold comparison", forming a decision chain of "safety first, efficiency second": The first four modules (1-4) focus on "safety protection", avoiding overcharging and high temperature risks through voltage, temperature thresholds and two signal constraints; The last three modules (5-7) focus on "efficiency adaptation," achieving precise matching between charging mode and environment, battery status through quantitative calculation and final judgment. The entire process incorporates "three-level duty cycle constraints" (modules 2, 4, and 7) to ensure the stability of control signals and provide a reliable logical foundation for subsequent hardware implementation.

[0101] (III) Dynamic Simulation Results and Curve Interpretation Based on the appendix Figure 2 The decision-making logic is set up in a short-term dynamic environment of 0-10 seconds (simulating scenarios such as rapid cloud cover and sudden changes in incident angle), and then... Figures 4a to 4d (The simulation results time-series curves) show the real-time changes in photovoltaic cell output and battery charging parameters. (Attached) Figures 4a to 4d The horizontal axis represents time (0-10 seconds), and the vertical axis, from top to bottom, represents the following parameter curves: 1. The relationship between dynamic changes in input parameters and the scene During the simulation, environmental parameters change over time, corresponding to different test scenarios: 0s - 1.25s, 2.5s - 5s, 6.25s - 8.75s: These represent ideal conditions (sufficient sunlight, suitable temperature, and good incident angle). 1.25s - 2.5s: corresponds to moderate conditions (600 W / m² illumination, 35℃ temperature, 45° angle of incidence). 5s - 6.25s: Corresponds to high temperature boundary conditions (light intensity 800 W / m², temperature 40℃, incident angle 30°). 8.75s - 10s: corresponds to harsh high-temperature conditions (300 W / m² of light, 50℃ temperature, and 60° angle of incidence).

[0102] The temperature remained at 35°C for most of the time, and the incident angle gradually deteriorated after 5 seconds, which, together with the changes in illumination, affected the system's decision-making.

[0103] 2. Analysis of Charging Mode Switching and Output Response The system dynamically selects between constant current (CC) and constant voltage (CV) charging modes based on real-time calculated SE (Environmental Efficiency Coefficient) and DH (Dynamic Threshold). Ideal time periods (0s-1.25s, 2.5s-5s, 6.25s-8.75s): When SE > DH (e.g., SE≈0.96>DH≈0.78), the system is in constant current mode (CC), the charging current is stable at 10.0A, and the voltage is maintained between 11.5-12V, achieving efficient and fast charging.

[0104] Medium-condition period (1.25s-2.5s): As the light intensity decreases and the angle increases, the SE decreases (≈0.65) and falls below the DH (≈0.78). The system switches to constant voltage mode (CV), the charging voltage stabilizes at 13.07V, and the current fluctuates with the light intensity but is smoothly suppressed (≤0.5A) to avoid battery polarization.

[0105] High-temperature boundary condition period (5s-6.25s): The light was strong but the temperature was high. Although SE (≈0.68) was still higher than DH (≈0.78), the battery voltage was close to 13.68V (95% of the maximum charging voltage). To prevent overcharging, the system prioritized constant voltage mode (CV). The charging voltage was stabilized at 14.4V and the current was 8.0A (not reaching the 10A upper limit).

[0106] High temperature and severe conditions during the period (8.75s-10s): High temperature combined with weak light and wide angle significantly reduces SE (≈0.52) and is lower than DH. The system maintains constant voltage mode (CV) and activates current limiting (≤5.0A), stabilizing the voltage at 14.4V, effectively avoiding battery overheating and performance degradation.

[0107] Core value of simulation results Decision-making logic validity: The curve changes in Figure 3 perfectly match those in Figure 4. Figure 2 The decision-making logic is as follows: During ideal conditions (0s-1.25s, 2.5s-5s, 6.25s-8.75s), the system satisfies "voltage safety + temperature safety + SE > DH" and enters constant current mode; during medium conditions (1.25s-2.5s), high temperature boundary conditions (5s-6.25s), and high temperature severe conditions (8.75s-10s), the system stabilizes in constant voltage mode because "SE ≤ DH", thus verifying the correctness of the decision-making logic. Real-time mode switching: In scenarios such as a sudden drop in light intensity in 1.25 seconds or a rebound in light intensity in 2.5 seconds, the system can complete the mode switching within 0.5 seconds, with a fast response speed to adapt to rapid changes in outdoor environmental parameters; Charging parameter stability: Current fluctuation ≤0.2A in constant current mode and voltage fluctuation ≤0.1V in constant voltage mode, both of which are better than the stability requirements for battery charging (current fluctuation ≤1A, voltage fluctuation ≤0.5V), effectively reducing battery polarization and capacity loss.

[0108] (iv) The significance of comprehensive verification through MATLAB simulation Through append Figure 2 The linkage verification between the decision logic module and Figure 3 (simulation result curves) forms a complete verification system that is "logically decomposable, process traceable, and results quantifiable." Appendix Figure 2The application discloses a method for realizing charging mode decision through modular design, and provides a clear technical blueprint for simulation model building and subsequent physical system development (such as MCU program writing and hardware subsystem integration). The actual effect of the decision logic is intuitively presented in FIG. 3, and the adaptability, stability and safety of the system in a dynamic environment are verified. The combination of the two proves that the charging method is not only feasible in theory, but also can be realized through engineering simulation, thereby laying a solid foundation for subsequent prototype trial production and practical application.

[0109] Based on the same inventive concept, the application provides an intelligent charging system based on adaptive adjustment of illumination intensity, comprising a photovoltaic cell, an illumination signal receiving device, a data memory device, a DC-DC converter, a storage battery charging mode control module and a storage battery. The illumination signal receiving device is used for acquiring the current illumination intensity of the photovoltaic cell, the current incident light angle received by the photovoltaic cell and the current working environment temperature of the photovoltaic cell, and sending the current illumination intensity, the current incident light angle and the current working environment temperature to the data memory device and the storage battery charging mode control module.

[0110] The illumination signal receiving device comprises a multi-parameter acquisition module and a preprocessing submodule, wherein the multi-parameter acquisition module comprises a crystalline silicon light sensor (measurement range 0~2000W / m², accuracy ±5%) installed on the surface of the photovoltaic panel, a two-axis sun position sensor (measurement range -90°~90°, resolution 0.1°) and an NTC temperature sensor (measurement range -40℃~85℃, accuracy ±0.5℃) attached to the back plate of the photovoltaic cell.

[0111] The preprocessing submodule is used for filtering abnormal values (the illumination intensity is limited to 0~1200W / m²) and Savitzky-Golay smoothing processing (window length 61 seconds, 3-order polynomial) of the original collected data, and synchronously sending the preprocessed current illumination intensity, current incident light angle (angle with the normal line of the photovoltaic panel) and current working environment temperature to the data memory device and the storage battery charging mode control module through an SPI communication protocol, and the sampling frequency is 1 time / second.

[0112] The data memory device is used for storing the current illumination intensity, the current incident light angle and the current working environment temperature sent by the illumination signal receiving device.

[0113] The data memory device uses a non-volatile memory (such as EEPROM) with a capacity greater than or equal to 16 GB to store: The real-time environmental parameters (retaining the last 72 hours of data) sent by the illumination signal receiving device, including the current illumination intensity, the current incident light angle, and the current working environment temperature; The system preset parameters (such as the temperature safety threshold of 45°C, the maximum charging voltage of 14.4V, and the weight coefficient w1=0.5, etc.); The battery historical state data (charge and discharge cycle number, full charge voltage decay record, and internal resistance change curve).

[0114] The photovoltaic cell is used to output corresponding current output voltage and current based on the current illumination intensity, current working environment temperature, and current incident light angle, and send them to the battery charging mode control module.

[0115] The photovoltaic cell uses a single-crystal silicon photovoltaic module (conversion efficiency ≥ 18%) containing a built-in MPPT control unit, which is used to dynamically adjust the output characteristics based on the current illumination intensity, current working environment temperature, and preset temperature compensation mechanism (temperature coefficient -0.3% / °C) sent by the illumination signal receiving device, output corresponding current output voltage (12V~24V) and current (0.5A~10A), and send them to the battery charging mode control module through the anti-reverse connection circuit.

[0116] The DC-DC converter and the battery charging mode control module are connected with the photovoltaic cell, data memory device, and battery respectively, used to convert the received current output voltage and current into current input voltage and current for charging the battery, determine the current charging mode based on the current illumination intensity, current working environment temperature, current incident light angle, current voltage of the battery, and current current of the battery, and control the charging of the battery based on the current charging mode; the charging mode includes constant voltage charging mode and constant current charging mode.

[0117] The battery charging mode control module is the core control unit, including DC-DC converter, PID controller, and protection circuit, connected with the photovoltaic cell, data memory device, and battery respectively, and the specific functions are: The DC-DC converter (Buck / Boost architecture, conversion efficiency ≥ 92%) converts the current output voltage and current of the photovoltaic cell into current input voltage (10.5V~14.4V) and current (0.1A~10A) suitable for the battery; Based on the environmental parameters stored in the data memory device and the real-time input voltage and current, the charging mode (constant voltage / constant current) is determined by the built-in algorithm (such as the mode judgment logic of S31-S36), and precise adjustment is achieved by the PID controller (voltage accuracy ±0.05V, current accuracy ±0.1A). The protection circuit integrates overvoltage (≥14.7V), overcurrent (≥10A), high temperature (≥45℃) and reverse connection protection functions. When triggered, it cuts off the charging path and records the fault code to the data memory device.

[0118] The battery uses a valve-regulated lead-acid battery (or lithium battery) and is equipped with a built-in NTC temperature sensor and voltage sampling circuit to provide real-time feedback of the current voltage (accuracy ±0.01V) and temperature to the battery charging mode control module, supporting constant current fast charging (0.2C) and constant voltage float charging (14.4V) modes.

[0119] This system can be used to perform Figure 1 The method shown in the illustrated embodiment can be used as a reference for understanding the functions that each functional module of the system can achieve. Figure 1 The embodiments shown are described in detail below.

[0120] This invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in this invention.

[0121] This invention also provides a computer-readable storage medium storing computer-executable instructions for performing the methods described in this invention.

[0122] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0123] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A smart charging method based on adaptive adjustment of light intensity, characterized in that, The method, applied to a photovoltaic energy storage system comprising photovoltaic cells, a DC-DC converter, a battery charging mode control module, and a battery, includes the following steps: S100: Obtain the current light intensity irradiating the photovoltaic cell, the current incident light angle received by the photovoltaic cell, and the current operating ambient temperature of the photovoltaic cell, and send them to the photovoltaic cell and battery charging mode control module. S200, the photovoltaic cell outputs the corresponding voltage and current based on the current light intensity, current working environment temperature and current incident light angle, and sends them as the current output voltage and current output current to the battery charging mode control module; The S300 DC-DC converter converts the received current output voltage and current output current into the current input voltage and current input current for charging the battery. The battery charging mode control module determines the current charging mode for the battery based on the current light intensity, current ambient temperature, current incident light angle, current input current, and current input voltage, and charges the battery based on the determined charging mode. The charging modes include constant voltage charging mode and constant current charging mode.

2. The method according to claim 1, characterized in that, In S300, based on the current light intensity, current ambient temperature, current incident light angle, current input current, and current input voltage, the current charging mode for the battery is determined, specifically including: S310: If the current operating ambient temperature is greater than or equal to the temperature safety threshold, then determine that the current charging mode is constant voltage charging mode and limit the maximum charging current to less than or equal to the preset current threshold; if the current operating ambient temperature is less than the temperature safety threshold, execute S320. S320: Calculate the current environmental efficiency coefficient based on the received current light intensity, current operating ambient temperature, and current incident light angle; and calculate the current dynamic threshold based on the current battery voltage; then execute S330. S330: If the current environmental efficiency coefficient is greater than the current dynamic threshold and the current battery voltage is less than the preset voltage threshold, then the current charging mode is determined to be constant current charging mode; otherwise, the current charging mode is determined to be constant voltage charging mode.

3. The method according to claim 2, characterized in that, The environmental efficiency coefficient satisfies the following condition: SE = w1 × IR + w2 × AE + w3 × TF, where SE is the environmental efficiency coefficient, IR is the irradiance efficiency ratio, and IR = min(ID / ID) ideal ,1), where ID is the light intensity, ID ideal For ideal light intensity, w1 is the irradiance weight; AE is the incident angle efficiency factor, AE=cosα, α is the incident light angle, w2 is the incident angle weight, TF is the temperature efficiency factor, TF=1-k×(T-T0), T is the working environment temperature, T0 is the reference temperature, k is the preset coefficient, w3 is the temperature weight, w1+w2+w3=1.

4. The method according to claim 3, characterized in that, The dynamic threshold satisfies the following condition: DH = c1 + c2 × VR; where DH is the dynamic threshold, VR is the voltage ratio, VR = V / V0, V is the current voltage of the battery, V0 is the maximum charging voltage, and c1 and c2 are preset coefficients.

5. The method according to claim 3, characterized in that, w1, w2, and w3 are determined based on the State of the Battery (SOH), which is determined based on the number of charge-discharge cycles, voltage decay rate, and internal resistance changes of the battery.

6. The method according to claim 5, characterized in that, The values ​​w1, w2, and w3 are determined based on the state of the battery (SOH), specifically including: If SOH≥d1, set w1=w10, w2=w20, w3=w30, where w10, w20, and w30 are the initial irradiance weight, initial incident angle weight, and initial temperature weight, respectively; d1 is the first threshold. If d2≤SOH<d1, set w1=w10-△w, w2=w20, w3=w30+△w, where △w is the preset unit weight; d2 is the second threshold. If SOH < d2, set w1 = w10 - 2Δw, w2 = w20, w3 = w30 + 2Δw, and reduce the maximum charging current of constant current mode to a preset multiple of the rated value.

7. The method according to claim 5, characterized in that, SOH = f1 × (1 - AC) + f2 × (1 - VD) + f3 × (1 - RC), where AC is the cycle number decay coefficient, f1 is the cycle number weight, VD is the voltage decay rate, f2 is the voltage decay weight, RC is the internal resistance growth rate, and f3 is the internal resistance change weight.

8. The method according to claim 1, characterized in that, The light intensity is the light intensity after preprocessing, and the preprocessing includes outlier filtering and data smoothing.

9. A smart charging system based on adaptive adjustment of light intensity, characterized in that, Includes photovoltaic cells, a light signal receiving device, a data memory device, a DC-DC converter, a battery charging mode control module, and a battery; among which: The light signal receiving device is used to acquire the current light intensity irradiating the photovoltaic cell, the current incident light angle received by the photovoltaic cell, and the current operating ambient temperature of the photovoltaic cell, and sends the current light intensity, current incident light angle, and current operating ambient temperature to the data memory device and the battery charging mode control module. The data memory device is used to store the current light intensity, current incident light angle and current operating environment temperature sent by the light signal receiving device; The photovoltaic cell is used to output the corresponding current output voltage and current output current based on the current light intensity, current operating ambient temperature and current incident light angle, and send them to the battery charging mode control module; The DC-DC converter and the battery charging mode control module are respectively connected to the photovoltaic cell, the data memory device, and the battery. They are used to convert the received current output voltage and current output current into the current input voltage and current input current for charging the battery, and determine the current charging mode based on the current light intensity, current operating ambient temperature, current incident light angle, current input voltage, and current input current, and control the charging of the battery based on the current charging mode. The charging mode includes a constant voltage charging mode and a constant current charging mode.

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