Solar charging control method and system with temperature compensation

Abstract

The application discloses a solar charging control method and system with temperature compensation, and relates to the technical field of solar charging, which comprises the following steps: collecting multi-source original operation data of a photovoltaic module and performing standardization processing to obtain a multi-source operation data sequence; based on the multi-source operation data sequence, fusing the temperature of the photovoltaic module, the temperature of an energy storage battery and the environmental temperature, and combining the influence of the output current of the photovoltaic module on the temperature disturbance to perform dynamic correction to obtain an equivalent temperature state; based on the equivalent temperature state and the multi-source operation data sequence, predicting the temperature variation trend of the photovoltaic module to generate a feedforward correction temperature parameter; and based on the feedforward correction temperature parameter, performing temperature compensation correction on the maximum power point reference voltage of the photovoltaic module. The application improves the accuracy of solar charging control, and takes into account the safe operation of the energy storage battery and the system operation stability.

Description

Technical Field

[0001] This invention relates to the field of solar charging technology, and in particular to a solar charging control method and system with temperature compensation. Background Technology

[0002] With the continuous development of photovoltaic power generation and energy storage technologies, solar charging systems have been widely used in communication base stations, off-grid power supply, and distributed energy. To improve the power generation efficiency of photovoltaic modules, existing solar charging control systems typically employ maximum power point tracking (MPPT) technology. This technology adjusts the operating point in real time based on parameters such as the output voltage and current of the photovoltaic modules, and uses temperature compensation to adjust the maximum power point reference voltage in conjunction with the temperature of the photovoltaic modules or the ambient temperature, thereby improving system operating efficiency and stability.

[0003] The actual thermal state of photovoltaic (PV) modules is influenced by a variety of factors, including ambient temperature, light intensity, output current, and internal heat generation, exhibiting dynamic coupling and thermal inertia characteristics. Most existing temperature compensation methods rely on single-source temperature information for correction, lacking comprehensive analysis of multi-source thermal information and thermal state change trends. When operating conditions change, these methods struggle to accurately reflect the actual thermal state of the PV modules, affecting the temperature compensation accuracy of the maximum power point reference voltage and consequently impacting the effectiveness of solar charging control. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a solar charging control method with temperature compensation to solve the technical problem that existing temperature compensation methods are unable to accurately reflect the dynamic thermal state of photovoltaic modules, resulting in insufficient accuracy of maximum power point reference voltage temperature compensation.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a solar charging control method with temperature compensation, comprising, Collect multi-source raw operating data of photovoltaic modules and perform standardization processing to obtain multi-source operating data sequences; Based on multi-source operating data sequences, the equivalent temperature state is obtained by integrating the temperature of photovoltaic modules, the temperature of energy storage batteries and the ambient temperature, and dynamically correcting the effect of the output current of photovoltaic modules on temperature disturbances. Based on the equivalent temperature state and multi-source operation data sequence, the temperature change trend of photovoltaic modules is predicted, and feedforward correction temperature parameters are generated. Based on the feedforward correction temperature parameter, the maximum power point reference voltage of the photovoltaic module is corrected by temperature compensation to obtain the temperature-compensated maximum power point voltage reference value. The maximum power point voltage reference value is input into the maximum power point tracking control module to dynamically adjust the operating point of the photovoltaic module and obtain the optimal combination of operating voltage and current. Based on the optimal combination of operating voltage and current, combined with the state of charge and temperature information of the energy storage battery, the charging current is subjected to safety constraint control to obtain the final charging current control command.

[0007] In a preferred embodiment of the temperature-compensated solar charging control method of the present invention, the multi-source operating data sequence includes: The system collects information on photovoltaic module output voltage, photovoltaic module output current, light intensity, ambient temperature, and energy storage battery temperature to obtain multi-source raw operating data. It then performs time synchronization and standardization processing to obtain a multi-source operating data sequence.

[0008] In a preferred embodiment of the temperature-compensated solar charging control method of the present invention, the equivalent temperature state includes: Multi-source thermal response information is extracted from the output voltage of the photovoltaic module, the output current of the photovoltaic module, the temperature of the energy storage battery, and the ambient temperature to obtain the thermal response components of the photovoltaic module, the thermal response components of the energy storage battery, and the ambient thermal response components, respectively. Based on the dynamic coupling relationship between the output current and output voltage of the photovoltaic module, the heat generation behavior caused by the internal current flow of the photovoltaic module is identified, and the internal heat generation characterization quantity is established in combination with the change of the output power of the photovoltaic module. Based on the internal heat generation characterization quantity, environmental thermal response component, and energy storage battery thermal response component within multiple consecutive sampling periods, a dynamic heat exchange relationship between the heat input, heat output, and heat storage of the photovoltaic module is constructed to obtain the current thermal state of the photovoltaic module. The heat accumulation index is calculated based on the dynamic balance between heat input and heat output. The heat accumulation index is used to identify whether the photovoltaic module is in the heat accumulation stage, the heat stability stage, or the heat release stage. Based on different thermal states, corresponding temperature reliability adjustment strategies are established. When the photovoltaic module is in the heat accumulation stage, the contribution weight of the photovoltaic module thermal response component is increased. When the photovoltaic module is in the thermal stability stage, the fusion ratio between the photovoltaic module thermal response component, the energy storage battery thermal response component and the environmental thermal response component is coordinated. When the photovoltaic module is in the heat release stage, the contribution weight of the environmental thermal response component and the energy storage battery thermal response component is increased. The current change rate characteristic is obtained by continuous periodic sampling of the output current of the photovoltaic module. The voltage drift characteristic is obtained based on the relationship between the output voltage of the photovoltaic module and the reference voltage. The electrothermal coupling response characteristic is constructed based on the current change rate characteristic and the voltage drift characteristic. The internal heat generation dynamic mapping relationship is established based on the electrothermal coupling response characteristics, and the thermal inertia adjustment factor is calculated in combination with the heat accumulation index. The dynamic fusion weights under different thermal states are corrected by using a thermal inertia adjustment factor. The corrected dynamic fusion weights are then applied to the thermal response components of the photovoltaic module, the thermal response components of the energy storage battery, and the environmental thermal response components to reconstruct the real-time thermal state of the photovoltaic module and generate an equivalent temperature state.

[0009] In a preferred embodiment of the solar charging control method with temperature compensation described in this invention, the feedforward correction temperature parameter includes: Extract the light intensity, photovoltaic module output current, ambient temperature and equivalent temperature status from multiple consecutive sampling times in the multi-source operation data sequence; Based on the relationship between adjacent sampling times, we construct the input signals of light change, current change, and environmental thermal boundary. Combined with the thermal state information in the equivalent temperature state, we calculate the current heat accumulation rate, heat release rate, and thermal inertia response of the photovoltaic module. The transient energy conversion intensity of the photovoltaic module is constructed based on the input signals of light change and current change, and the environmental heat dissipation constraint is established based on the input signal of environmental thermal boundary. A thermal dynamic evolution model of photovoltaic modules is established by taking the transient energy conversion intensity of photovoltaic modules as the heat generation driving term, the environmental heat dissipation constraint as the heat loss constraint term, the heat accumulation rate as the thermal state evolution term. The heat transfer rate in the thermal dynamic evolution model is dynamically adjusted according to the degree of thermal inertia response. When the degree of thermal inertia response increases, the prediction weight of heat accumulation is increased, and when the degree of thermal inertia response decreases, the prediction weight of heat dissipation is increased. The thermal state change trend within multiple sampling periods is recursively derived based on the thermal dynamic evolution model, thus obtaining the future thermal state sequence. Based on the future thermal state sequence, the trend of maximum power point voltage change in multiple sampling periods is predicted, and a dynamic mapping relationship between the future thermal state and the maximum power point voltage is established. The future temperature compensation requirement is calculated based on the dynamic mapping relationship, and the corresponding feedforward correction temperature parameters are generated.

[0010] In a preferred embodiment of the temperature-compensated solar charging control method of the present invention, the maximum power point voltage reference value after temperature compensation includes: The feedforward correction temperature parameter is input into the temperature compensation. The temperature compensation input parameter is obtained by using the feedforward correction temperature parameter and the maximum power point voltage temperature coefficient in the temperature characteristic curve of the photovoltaic module. The temperature compensation input parameters and the maximum power point reference voltage of the photovoltaic module are input into the temperature voltage mapping. The maximum power point reference voltage of the photovoltaic module is offset and corrected through the linear proportional relationship in the temperature voltage mapping correction process to obtain the temperature compensation correction amount. The temperature compensation correction is superimposed on the maximum power point reference voltage of the photovoltaic module to obtain the maximum power point voltage reference value after temperature compensation.

[0011] As a preferred embodiment of the temperature-compensated solar charging control method of the present invention, the optimal operating voltage and current combination includes: A periodic voltage perturbation is applied to the target tracking voltage to obtain the perturbed voltage command. The perturbed voltage command is then sent to the DC-DC converter to adjust the duty cycle and collect the adjusted photovoltaic module output current and photovoltaic module output voltage to obtain the adjusted instantaneous output power. By comparing the instantaneous output power after adjustment with the historical output power of the previous cycle, the voltage disturbance direction is updated according to the power increase / decrease relationship to obtain the voltage disturbance amount for the next cycle. The voltage perturbation of the next cycle is applied to the target tracking voltage to obtain a new perturbation voltage command, and the voltage and current of the current photovoltaic module operating point are locked to obtain the optimal combination of operating voltage and current.

[0012] In a preferred embodiment of the solar charging control method with temperature compensation described in this invention, the final charging current control command includes: Extract the state of charge and temperature information of the energy storage battery from the multi-source operation data sequence, and record the current value in the optimal operating voltage and current combination as the initial charging current. The energy storage battery temperature information is compared with the preset temperature safety threshold. When the energy storage battery temperature information exceeds the preset temperature safety threshold, the initial charging current is inversely limited according to the excess amount to obtain the temperature-constrained charging current. The state of charge of the energy storage battery is compared with a preset safe charge threshold. When the state of charge of the energy storage battery exceeds the preset safe charge threshold, the charging current after temperature constraint is reduced in a stepwise manner according to the extent of the excess, so as to obtain the final charging current control command.

[0013] Secondly, the present invention provides a solar charging control system with temperature compensation, including a processing module that collects multi-source raw operating data of photovoltaic modules and performs standardized processing to obtain a multi-source operating data sequence. The fusion module, based on multi-source operating data sequences, integrates the temperature of photovoltaic modules, the temperature of energy storage batteries, and the ambient temperature, and dynamically corrects the effect of photovoltaic module output current on temperature disturbances to obtain the equivalent temperature state. The prediction module predicts the temperature change trend of photovoltaic modules based on equivalent temperature state and multi-source operating data sequences, and generates feedforward correction temperature parameters. The correction module performs temperature compensation correction on the maximum power point reference voltage of the photovoltaic module based on the feedforward correction temperature parameter, and obtains the temperature-compensated maximum power point voltage reference value. The combined module inputs the maximum power point voltage reference value into the maximum power point tracking control module to dynamically adjust the operating point of the photovoltaic module and obtain the optimal combination of operating voltage and current. The control module, based on the optimal combination of operating voltage and current, combined with the state of charge and temperature information of the energy storage battery, performs safety constraint control on the charging current to obtain the final charging current control command.

[0014] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the temperature-compensated solar charging control method as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the temperature-compensated solar charging control method as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By collecting multi-source operating data such as photovoltaic module output voltage, output current, light intensity, ambient temperature, and energy storage battery temperature, an equivalent temperature state including heat input, heat output, and heat accumulation characteristics is constructed, realizing a comprehensive characterization of the actual thermal state of the photovoltaic module. This allows temperature compensation to more accurately reflect the dynamic thermal characteristics of the photovoltaic module under different operating conditions. By establishing a thermal dynamic evolution model based on the equivalent temperature state, the future thermal state change trend is predicted, and feedforward correction temperature parameters are generated, achieving advance compensation of the maximum power point reference voltage. This transforms the temperature compensation process from current state correction to predictive dynamic correction, improving the adaptability of the reference voltage during maximum power point tracking. Furthermore, maximum power point tracking is performed using the temperature-compensated maximum power point reference voltage, and the charging current is safely constrained and controlled based on the energy storage battery's state of charge and temperature information. This improves the accuracy of solar charging control while also ensuring the safe operation of the energy storage battery and the stability of the system. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a solar charging control method with temperature compensation.

[0019] Figure 2 This is a schematic diagram of a solar charging control system with temperature compensation. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Reference Figures 1-2 As one embodiment of the present invention, this embodiment provides a solar charging control method with temperature compensation, comprising the following steps: S1. Collect multi-source raw operating data of photovoltaic modules and perform standardization processing to obtain multi-source operating data sequences.

[0024] S1.1 Collect information on photovoltaic module output voltage, photovoltaic module output current, light intensity, ambient temperature, and energy storage battery temperature to obtain multi-source raw operating data. Then, perform time synchronization processing and standardization processing to obtain a multi-source operating data sequence.

[0025] Furthermore, light intensity data is acquired through a light sensing unit, ambient temperature data through an ambient temperature sensing unit, and energy storage battery temperature data through an energy storage battery temperature sampling unit. These data are collected synchronously with the output voltage and current data of the photovoltaic modules within the same sampling period. All operating parameters are aligned to a unified time reference, and interpolation corrections are applied to address sampling time offsets, ensuring a correspondence between data from different sources at the same time point. Subsequently, all operating parameters are standardized, converting parameters with different dimensions and fluctuation ranges to a unified expression scale, thus constructing a multi-source operating data sequence.

[0026] S2. Based on the multi-source operation data sequence, the photovoltaic module temperature, energy storage battery temperature and ambient temperature are integrated, and the influence of the photovoltaic module output current on temperature disturbance is dynamically corrected to obtain the equivalent temperature state.

[0027] S2.1 Extract multi-source thermal response information from the output voltage of the photovoltaic module, the output current of the photovoltaic module, the temperature of the energy storage battery and the ambient temperature, and obtain the thermal response components of the photovoltaic module, the thermal response components of the energy storage battery and the ambient temperature respectively.

[0028] Furthermore, based on the instantaneous relationship between the output voltage and output current of the photovoltaic module, information on power change is obtained. Combined with the corresponding relationship between power change and heat generation, voltage fluctuation components and current fluctuation components are converted into thermal response feature expressions of the photovoltaic module. At the same time, thermal response information of the energy storage battery is extracted from the temperature of the energy storage battery. The thermal response characteristics of the energy storage battery as the temperature changes during the charging and discharging process are used to characterize the thermal state evolution trend of the energy storage battery. Environmental thermal response information is extracted from the ambient temperature. The environmental thermal response expression is formed by the influence relationship between the ambient temperature and the heat dissipation boundary conditions.

[0029] Specifically, the output voltage of the photovoltaic module, the output current of the photovoltaic module, the temperature of the energy storage battery, and the ambient temperature are matched according to the same time reference, so that the thermal response information is expressed synchronously within the same time segment. The relationship between electrical and thermal quantities is uniformly transformed by a mapping method based on the rate of change, so as to obtain the thermal response components of the photovoltaic module, the thermal response components of the energy storage battery, and the ambient thermal response components. This allows thermal influencing factors from different sources to form a distinguishable thermal response structure under the same expression framework, thus obtaining multi-source thermal response information.

[0030] S2.2 Based on the dynamic coupling relationship between the output current and the output voltage of the photovoltaic module, identify the heat generation behavior caused by the internal current flow of the photovoltaic module, and establish internal heat generation characterization quantity in combination with the change of output power of the photovoltaic module.

[0031] Furthermore, based on the synchronous change relationship between the photovoltaic module output current and output voltage during continuous sampling, the current change trajectory and voltage change trajectory are obtained, and the dynamic coupling characteristics are characterized by the correspondence between the current change amplitude and the voltage change amplitude. When the photovoltaic module output current increases while the photovoltaic module output voltage decreases or fluctuations increase, this type of coupling change is corresponding to an enhanced internal current flow state, and the correlation between the voltage drop rate and the current rise rate is used as the basis for heat generation identification. During the synchronous change of the photovoltaic module output current and output voltage, the coupling consistency between voltage and current is determined by comparing the power change trend. When the power shows continuous change and the current driving effect dominates, the power loss at the corresponding moment is converted into the basis for internal heat generation characterization. Specifically, the change in the output power of the photovoltaic module between adjacent sampling periods is taken as the energy conversion difference, and this difference is correlated and mapped with the current change response to identify the heat generation behavior caused by the internal current flow of the photovoltaic module. The current change response characteristics and voltage change response characteristics are combined to affect the power change, so that the energy loss in the internal current flow process is collected in a unified form to obtain the internal heat generation characterization quantity. This allows the thermal effect in the current flow process inside the photovoltaic module to be quantified, and the internal heat generation characterization quantity to be obtained.

[0032] It should be noted that by utilizing the dynamic coupling relationship between the output current and output voltage of the photovoltaic module, the influence of current changes on voltage response is transformed into a basis for identifying heat generation. This allows energy loss during the current flow process within the photovoltaic module to be indirectly characterized through the linkage between voltage and current changes, achieving a mapping expression from electrical coupling to heat generation behavior. By introducing the change in photovoltaic module output power as a representation of energy conversion differences, an irreversible loss during the energy change process can be correlated with the heat generation process. By jointly constraining the current change response and voltage change response, the heat generation behavior caused by internal current flow can be stably expressed in the dynamic coupling relationship. This ensures that power loss under different operating conditions can be attributed to the heat generation process, improving the adaptability and consistent expression capability of the internal heat generation characterization quantity to complex operating conditions.

[0033] S2.3. Based on the internal heat generation characterization quantity, environmental thermal response component and energy storage battery thermal response component within multiple consecutive sampling periods, construct the dynamic heat exchange relationship between the heat input, heat output and heat storage of the photovoltaic module to obtain the current thermal state of the photovoltaic module.

[0034] Furthermore, multiple consecutive sampling periods refer to multiple adjacent time segments formed in a continuous time series according to a unified sampling interval. By acquiring the internal heat generation characterization quantity, the environmental thermal response component, and the energy storage battery thermal response component in each sampling period, the three types of thermal response information form a continuous change sequence in the time dimension. When constructing the dynamic heat exchange relationship between the heat input, heat output, and heat storage of photovoltaic modules based on the synchronous change relationship of the three types of thermal response information in multiple consecutive sampling periods, the cumulative change of the internal heat generation characterization quantity in the continuous sampling period is used as the source of the time evolution of the heat input quantity, the fluctuation change of the environmental thermal response component in the continuous sampling period is used as the source of the boundary constraint of the heat output quantity, and the lag change of the energy storage battery thermal response component in the continuous sampling period is used as the source of the coupled response of the heat storage quantity. In each sampling period, the three types of thermal response information are matched accordingly so that they form a corresponding relationship in the same time series.

[0035] Specifically, by comparing the changing trends of the internal heat generation characteristic quantity and the environmental thermal response component between adjacent sampling periods, the process of increasing or decreasing heat input is identified. The heat accumulation or release process is characterized by the delayed change features of the energy storage battery's thermal response component in continuous sampling periods, forming a dynamic balance between heat input, heat output, and heat storage. When the internal heat generation characteristic quantity continuously increases in multiple consecutive sampling periods while the environmental thermal response component remains relatively stable, the state dominated by heat input corresponds to the heat accumulation process. When the environmental thermal response component continuously increases while the internal heat generation characteristic quantity tends to decrease, the state dominated by heat output corresponds to the heat release process. When the energy storage battery's thermal response component shows continuous change in continuous sampling periods and the internal heat generation characteristic quantity and the environmental thermal response component show alternating fluctuations, the state dominated by heat storage corresponds to the thermal equilibrium process. By summarizing the evolutionary relationships of these three states in continuous sampling periods, the current thermal state of the photovoltaic module is obtained.

[0036] S2.4 Calculate the heat accumulation index based on the dynamic balance between heat input and heat output, and identify whether the photovoltaic module is in the heat accumulation stage, the heat stabilization stage or the heat release stage based on the heat accumulation index.

[0037] Specifically, the time series relationship between heat input and heat output is obtained within a continuous sampling period. Using the current sampling time as a benchmark, the heat input and heat output corresponding to multiple historical sampling times are cumulatively correlated, so that the heat changes in different time segments can be compared under the same reference axis. The difference between the heat input and heat output at the first sampling time is used as the basic heat balance difference term, and the difference term is recursively expressed by the stepwise superposition process of the continuous sampling period.

[0038] Specifically, when the heat input is consistently greater than the heat output over multiple consecutive sampling times, the heat accumulation index shows a continuously increasing trend, corresponding to the gradual accumulation of heat inside the photovoltaic module; when the heat input and heat output change relatively consistently over consecutive sampling periods, the heat accumulation index tends to stabilize, corresponding to the thermal equilibrium state of the photovoltaic module; when the heat output gradually increases over multiple consecutive sampling times and exceeds the trend of heat input, the heat accumulation index shows a decreasing trend, corresponding to the heat release state of the photovoltaic module. By classifying and judging the dynamic change trend of the heat accumulation index, the heat accumulation stage, the thermal stability stage, and the heat release stage can be distinguished.

[0039] The expression for the heat accumulation index is: ; in, For the first The heat accumulation index at each sampling time At the current sampling time, For the first Thermal input at each sampling time For the first Heat output at each sampling time It is a positive number.

[0040] It should be noted that the heat input is acquired separately within the continuous sampling period. With heat output The time series, and the current sampling time Using this as a benchmark, the ratio of the difference between the two to their sum is calculated to obtain the current heat accumulation index. This index is then calibrated through numerous experiments or selected based on the system's thermal response characteristics and engineering experience. .

[0041] when Greater than When (a preset positive threshold) is reached, it indicates that the current heat input is greater than the heat output, and the photovoltaic module is in the heat accumulation stage; when | |≤ When the heat input and heat output are basically balanced, the photovoltaic module is in a thermally stable stage; when <- When the heat output exceeds the heat input, the photovoltaic module is in the heat release phase.

[0042] By continuously monitoring changes in the sign and size, the transition process of photovoltaic modules between three states—heat accumulation, thermal stability, and heat release—can be dynamically identified.

[0043] S2.5 Establish corresponding temperature reliability adjustment strategies according to different thermal state stages. When the photovoltaic module is in the heat accumulation stage, increase the contribution weight of the photovoltaic module thermal response component. When the photovoltaic module is in the thermal stability stage, coordinate the fusion ratio between the photovoltaic module thermal response component, the energy storage battery thermal response component and the environmental thermal response component. When the photovoltaic module is in the heat release stage, increase the contribution weight of the environmental thermal response component and the energy storage battery thermal response component.

[0044] Furthermore, based on the heat accumulation index, the thermal state of the photovoltaic module is staged. When the heat accumulation index shows a continuous upward trend across multiple sampling times, it is determined to be in the heat accumulation stage; when the heat accumulation index maintains a relatively stable fluctuation across consecutive sampling times, it is determined to be in the thermal stability stage; and when the heat accumulation index shows a continuous downward trend across consecutive sampling times, it is determined to be in the heat release stage. Temperature reliability adjustment strategies corresponding to different thermal state stages are constructed separately. The photovoltaic module's thermal response component is used as the primary temperature source. By increasing the proportion of the photovoltaic module's thermal response component in the equivalent temperature expression, the reliability of the photovoltaic module is improved. Information on heat generation caused by internal current flow dominates the expression. During the thermal stability phase, the thermal response components of the photovoltaic module, the energy storage battery, and the environment are coordinated and integrated. The contribution ratio of each component is adjusted according to the consistency of their changes in continuous sampling periods, so that the three heat sources maintain a relative balance in the equivalent temperature expression. During the heat release phase, the environmental thermal response component and the energy storage battery thermal response component are used as the main temperature references. By increasing the contribution weight of the environmental thermal response component and the energy storage battery thermal response component, the external heat dissipation effect and the heat exchange effect of the energy storage battery dominate the temperature expression.

[0045] Specifically, the thermal state of photovoltaic modules is divided into stages by the thermal accumulation index, enabling the temperature reliability adjustment strategy to dynamically switch according to the thermal state evolution trend, avoiding the problem that the fixed weight method cannot adapt to changes in thermal state; by strengthening the weight of the thermal response component of the photovoltaic module in the thermal accumulation stage, the heat generation behavior caused by internal current flow can dominate the temperature expression, improving the sensitivity of the internal heating process; by coordinating the fusion ratio among the thermal response components of the photovoltaic module, the thermal response component of the energy storage battery, and the environmental thermal response component in the thermal stability stage, the multi-source thermal information can form a consistent expression in a balanced state, enhancing the stability of the temperature state; by increasing the contribution weight of the environmental thermal response component and the thermal response component of the energy storage battery in the heat release stage, the external heat dissipation and heat exchange of the energy storage battery can dominate the temperature expression, more accurately reflecting the heat release process; and by dynamically adjusting the weight of different thermal response components based on the stage identification results.

[0046] It should be noted that the thermal state of photovoltaic modules is identified in stages based on the heat accumulation index. A continuous upward trend in the heat accumulation index indicates the heat accumulation stage; a relatively stable fluctuating trend indicates the thermal stability stage; and a continuous downward trend indicates the heat release stage. In the heat accumulation stage, the photovoltaic module's thermal response component is used as the dominant heat source. The contribution weight of the photovoltaic module's thermal response component is enhanced based on the heat accumulation index, while the weights of the energy storage battery's thermal response component and the environmental thermal response component are reduced, ensuring that the internal flow heat effect dominates the equivalent temperature representation. In the thermal stability stage, the contribution weights of the photovoltaic module's thermal response component and the energy storage battery's thermal response component are further enhanced. The response components and environmental thermal response components are processed for consistency coordination. Based on the consistency of changes among the three types of thermal response components, their respective contribution weights are adaptively balanced and allocated to maintain a stable fusion relationship of multi-source thermal information in the equivalent temperature expression. During the heat release stage, the contribution weights of the environmental thermal response components and the energy storage battery thermal response components are increased, while the corresponding weights of the photovoltaic module thermal response components are decreased, so that the external heat dissipation effect and the energy storage battery heat exchange effect dominate the equivalent temperature expression. The weight allocation results under different thermal state stages are dynamically corrected through a thermal inertia adjustment factor, so that the contribution ratio of each thermal response component is continuously updated with the evolution of the thermal state, forming a temperature reliability adjustment strategy based on staged control of the thermal state.

[0047] S2.6. Based on continuous periodic sampling of the output current of the photovoltaic module, the current change rate characteristic is obtained. Based on the relationship between the output voltage of the photovoltaic module and the reference voltage, the voltage drift characteristic is obtained. Based on the current change rate characteristic and the voltage drift characteristic, the electrothermal coupling response characteristic is constructed.

[0048] Furthermore, the output current of the photovoltaic module is acquired cycle by cycle within continuous sampling time. The current change rate characteristic is determined by the difference in current change between adjacent sampling cycles. The difference between the current value at the current sampling time and the current value at the previous sampling time is used as the basis for change. The current change rate is expressed by the correspondence between the change amplitude and the time interval, so that the current change process forms a continuous response characteristic in the time dimension. In the process of acquiring the voltage drift characteristic, the output voltage of the photovoltaic module is synchronously compared with the reference voltage within continuous sampling time. The voltage drift direction and drift amplitude are determined by the deviation relationship between the output voltage at the current sampling time and the reference voltage. The voltage deviation change is continuously tracked within continuous sampling time, so that the change process of voltage deviating from the reference state forms a time series. The current change rate characteristic and the voltage drift characteristic are synchronously correlated. The current change rate characteristic is used as the internal energy change driving characteristic, and the voltage drift characteristic is used as the output state offset characteristic. By matching the two types of characteristics in the same sampling period, a coupling relationship is formed between the energy disturbance caused by the current change and the voltage offset. The current change rate characteristic and the voltage drift characteristic work together to form an electrothermal coupling response characteristic. Specifically, the output current change process of the photovoltaic module is obtained based on continuous periodic sampling, enabling the current change rate feature to be dynamically expressed in the time series, thus continuously characterizing the internal energy change process of the photovoltaic module. By using the deviation between the output voltage of the photovoltaic module and the reference voltage as the voltage drift feature, the degree of deviation of the photovoltaic module's operating state can be quantified in a unified form, making the output state change traceable. By correlating the current change rate feature with the voltage drift feature, a coupled expression relationship is formed between the current-driven change and the voltage response change, enabling the electrical change process to correspond to the thermal disturbance behavior. By constructing electrothermal coupled response features, the internal energy change process and the output state deviation process of the photovoltaic module can be uniformly expressed in the same time dimension, enhancing the comprehensive characterization capability of the photovoltaic module's operating state changes.

[0049] S2.7. Establish the internal heat generation dynamic mapping relationship based on the electrothermal coupling response characteristics, and calculate the thermal inertia adjustment factor in combination with the heat accumulation index.

[0050] Furthermore, when establishing the dynamic mapping relationship of internal heat generation based on the electrothermal coupling response characteristics, the change sequence of the electrothermal coupling response characteristics is time-correlated with the internal heat generation characterization quantity, so that the electrothermal coupling response characteristics are used as input changes and the internal heat generation characterization quantity is used as output response quantity, and a recursive correlation is formed between adjacent sampling times, realizing the continuous mapping from electrical changes to heat generation changes; at the same time, the internal heat generation change process is matched according to the comprehensive change amplitude of the current change rate characteristic quantity, voltage drift characteristic quantity, and power change quantity, so that the change of the electrothermal coupling response characteristics can correspond to the change of the internal heat generation characterization quantity. According to the strengthening or weakening trend of the electrothermal coupling response characteristics, the increasing or decreasing state of the internal heat generation characterization quantity is respectively corresponding to the increasing or decreasing state of the internal heat generation characterization quantity, realizing the synchronous expression of the heat generation process; the thermal inertia adjustment factor is calculated by combining the change trend of the heat accumulation index and the change amplitude of the internal heat generation characterization quantity, and the thermal inertia adjustment factor is obtained.

[0051] Specifically, by establishing a dynamic mapping relationship between the electrothermal coupling response characteristics and the internal heat generation characterization quantities, the electrical change process can directly correspond to the heat generation evolution process, realizing a continuous mapping expression from electrical signal changes to thermal state changes. This makes the internal heat generation characterization no longer dependent on a single sampling moment. By introducing the comprehensive changes of current change rate characteristics, voltage drift characteristics, and power changes into the mapping relationship, different electrical disturbance factors can act uniformly on the heat generation expression process, improving the adaptability of the internal heat generation characterization quantities to complex operating conditions. By introducing the heat accumulation index as an inertial constraint quantity into the calculation of the thermal inertia adjustment factor, the thermal inertia adjustment factor has the ability to adjust the trend of thermal state changes. By synergistically correlating the changes of internal heat generation characterization quantities with the heat accumulation trend, the thermal inertia adjustment factor can reflect the transition process of the photovoltaic module's thermal state from accumulation to release, enhancing the stability and continuity of the thermal state fusion process.

[0052] It should be noted that during the operation of photovoltaic modules, transient changes in electrical quantities (current surges, voltage drops, power fluctuations) can trigger internal Joule heating effects. These effects, however, cannot be immediately reflected in the surface temperature due to the thermal inertia of the module's encapsulation materials. By constructing an electrothermal coupling response feature vector, a recursive mapping relationship from electrical changes to heat generation characteristics is established. The heat accumulation index is introduced as an inertial constraint quantity into the adjustment factor calculation, enabling refined perception of the internal thermal dynamics of the photovoltaic module. When establishing the internal heat generation dynamic mapping relationship, a recursive function is used to express the correspondence between the electrothermal coupling response characteristics and the internal heat generation characteristics. The internal heat generation characteristics at the current sampling moment are jointly determined by the internal heat generation characteristics at the previous sampling moment and the electrothermal coupling response characteristics. The electrothermal coupling response characteristics serve as the input driving term, and the internal heat generation characteristics serve as the state output term. A time decay factor is introduced to characterize the thermal inertia delay effect, allowing the heat generation changes to reflect both cumulative and hysteretic characteristics. Simultaneously, the mapping gain is adjusted according to the heat accumulation index, enabling the heat generation mapping relationship to exhibit different response sensitivities under different thermal states.

[0053] Assume the electrothermal coupling response characteristics are as follows: ; in, For the first Electrothermal coupling response characteristics at each sampling time. For the first The characteristic quantity of the rate of change of current at each sampling time. For the first Voltage drift characteristics at each sampling time, For the first The change in electrical power at each sampling time.

[0054] The combination of current change rate, voltage drift, and power change constitutes a set of electrothermal coupling response characteristics, considering the following: Capture the speed information of transient current. Joule heating is proportional to the square of the current. The faster the current changes, the stronger the transient thermal shock.

[0055] By capturing changes in the internal impedance of the component, when the current increases suddenly, the voltage drop across the internal series resistor increases, and the voltage drift reflects the conditions for heat generation.

[0056] It provides a comprehensive measurement from an energy perspective, unifying changes in current and voltage into a single power change, avoiding the dimensional mismatch problem when directly weighting and fusing multidimensional features. The combination of these three features enables the electrothermal coupling response characteristics to comprehensively characterize the thermal excitation potential of electrical disturbances from different perspectives.

[0057] The internal heat generation characterization quantity can be defined as: ; in, For the first Internal heat generation characterization quantity at each sampling time This is a characterization of internal heat generation at the previous sampling time. The gain coefficient for electro-thermal conversion. For the first The change in electrical power at each sampling time.

[0058] Electrothermal coupling response characteristics include There are three components, but in the final recursive formula, we choose... As a driving force, it is based on the following physical insights: Joule heating is essentially the dissipation of electrical power across a resistor. It already implicitly contains comprehensive information about current and voltage changes, using Avoid and The difficulty in parameter tuning and the problem of dimension mismatch when assigning weight coefficients separately.

[0059] It is a discrete-time integrator, whose physical meaning is heat accumulation. Joule heat continuously accumulates inside the component, and the equivalent temperature only decreases when the heat dissipation process removes it. The recursive structure naturally simulates the thermal inertia effect: even at a certain moment... It's very small, as long as it remains positive. This will continue to grow, reflecting the long-term effects of heat accumulation.

[0060] The thermal inertia adjustment factor is: ; in, For the first Thermal inertia adjustment factor at each sampling time. The Sigmoid normalization function, For internal heat generation weighting coefficients, For the weighting coefficient of thermal accumulation intensity, As a current indicator of heat accumulation, For the heat accumulation trend weighting coefficient, This is the heat accumulation index at the previous sampling time.

[0061] S2.8. The dynamic fusion weights under different thermal states are corrected by using the thermal inertia adjustment factor, and the corrected dynamic fusion weights are applied to the thermal response components of the photovoltaic module, the thermal response components of the energy storage battery, and the environmental thermal response components respectively to reconstruct the real-time thermal state of the photovoltaic module and generate the equivalent temperature state.

[0062] Furthermore, the thermal state stage of the photovoltaic module is determined based on the thermal accumulation index. When the thermal accumulation index continues to rise, it corresponds to the thermal accumulation stage; when the thermal accumulation index remains stable, it corresponds to the thermal stability stage; and when the thermal accumulation index continues to fall, it corresponds to the thermal release stage. The thermal inertia adjustment factor is used for dynamic fusion weight correction. The weight ratio of the thermal response components of the photovoltaic module, the thermal response components of the energy storage battery, and the environmental thermal response components is adaptively adjusted according to different thermal state stages. In the thermal accumulation stage, the weight of the thermal response component of the photovoltaic module is strengthened; in the thermal stability stage, the three types of thermal response components are balanced and fused; and in the thermal release stage, the weights of the environmental thermal response component and the thermal response component of the energy storage battery are increased. The real-time thermal state of the photovoltaic module is reconstructed by weighted fusion of the three types of thermal response components to generate an equivalent temperature state.

[0063] Specifically, a thermal inertia adjustment factor is introduced into the fusion weight correction process under different thermal state stages, enabling the contribution ratios of the photovoltaic module thermal response component, the energy storage battery thermal response component, and the environmental thermal response component to adaptively adjust with the evolution of the thermal state, avoiding the problem that the fixed weight method cannot reflect changes in the thermal state; by strengthening the weight of the photovoltaic module thermal response component during the heat accumulation stage, the heat generation effect caused by internal current flow dominates in the equivalent temperature expression, improving the sensitivity to the internal heat generation process; by achieving the balance correction of multi-source thermal response components during the thermal stability stage, different heat sources form a stable synergistic relationship during the fusion process, improving the consistency of the thermal state expression; by enhancing the weight of the environmental thermal response component and the energy storage battery thermal response component during the heat release stage, the external heat dissipation and heat exchange process of the energy storage battery are fully reflected; and the equivalent temperature state can be dynamically updated with changes in thermal inertia.

[0064] It should be noted that when using the thermal inertia adjustment factor to correct the dynamic fusion weights, the thermal inertia adjustment factor is input as a weight adjustment coefficient into the fusion weight update process. The basic weights of the photovoltaic module thermal response component, the energy storage battery thermal response component, and the environmental thermal response component are multiplicatively corrected, and the sum of the corrected weights is kept constant through normalization constraints. When the thermal inertia adjustment factor increases, the weight gain coefficient of the photovoltaic module thermal response component is increased, so that its proportion in the fusion result is increased. When the thermal inertia adjustment factor decreases, the weight gain coefficients of the environmental thermal response component and the energy storage battery thermal response component are increased, so that the influence of external heat dissipation is enhanced, thereby achieving dynamic balance adjustment of the fusion weights.

[0065] The weight gain coefficient of the photovoltaic module's thermal response component is dynamically adjusted based on the degree of change in the thermal accumulation index. The increase rule is as follows: using the thermal accumulation index as the driving force for weight enhancement, when the thermal accumulation index is in the thermal accumulation stage and continuously increases with the sampling period, the weight gain coefficient corresponding to the photovoltaic module's thermal response component is gradually increased, so that the contribution ratio of this component in the fusion increases with the enhancement of internal heat generation. The gain adjustment range is determined based on the difference between the thermal accumulation index at the current sampling time and the thermal accumulation index at the previous sampling time. When the increase in the thermal accumulation index is large, the adjustment rate of the weight gain coefficient is increased; when the increase in the thermal accumulation index is small, the adjustment rate of the weight gain coefficient is decreased to avoid sudden weight changes. Simultaneously, upper and lower limits are set for the weight gain coefficient to ensure that it changes continuously within a preset range, guaranteeing that the weight of the photovoltaic module's thermal response component can monotonically increase with the degree of thermal accumulation and maintaining the stability of the multi-source thermal response fusion process.

[0066] S3. Based on the equivalent temperature state and multi-source operation data sequence, predict the temperature change trend of photovoltaic modules and generate feedforward correction temperature parameters.

[0067] S3.1 Extract the light intensity, photovoltaic module output current, ambient temperature and equivalent temperature status from multiple consecutive sampling times in the multi-source operation data sequence.

[0068] Furthermore, when extracting light intensity, photovoltaic module output current, ambient temperature, and equivalent temperature state from multiple consecutive sampling moments in the multi-source operational data sequence, the operating parameters are extracted in temporal order within the continuous sampling period. This ensures that light intensity, photovoltaic module output current, ambient temperature, and equivalent temperature state form a corresponding sequence relationship on the same time axis. The equivalent temperature state is used as the thermal state characterization benchmark and synchronously correlated with changes in light intensity and photovoltaic module output current. By extracting the trends in light intensity and photovoltaic module output current changes from multiple consecutive sampling moments, the light intensity change input signal and the current change input signal form a continuous expression in the time dimension. Simultaneously, the ambient temperature change process is used as the basic source of the ambient thermal boundary input signal, enabling changes in the external thermal environment to correspond with the operating state of the photovoltaic module. The changes in light intensity, photovoltaic module output current, ambient temperature, and equivalent temperature state are uniformly time-aligned, ensuring that each input signal forms a consistent expression structure within the continuous sampling period.

[0069] S3.2. Based on the relationship between adjacent sampling times, construct the light change input signal, current change input signal and environmental thermal boundary input signal, and combine the thermal state information in the equivalent temperature state to calculate the current heat accumulation rate, heat release rate and thermal inertia response degree of the photovoltaic module.

[0070] Furthermore, the difference in the output current of the photovoltaic module between adjacent sampling times is used as the current change input signal, and the trend of the ambient temperature change in continuous sampling times is used as the ambient thermal boundary input signal. This allows the three types of input signals to form a synchronous change relationship in the time series. Combined with the thermal state information in the equivalent temperature state, the current thermal state is expressed in a correlated manner, so that the light change input signal, current change input signal, and ambient thermal boundary input signal can participate in the thermal process description together with the thermal state information. By comprehensively analyzing the change trends of the light change input signal, current change input signal, and ambient thermal boundary input signal in the continuous sampling period, the heat input enhancement process, heat release enhancement process, and thermal inertia change process of the photovoltaic module under different operating conditions are identified, and the current heat accumulation rate, heat release rate, and thermal inertia response degree of the photovoltaic module are calculated respectively.

[0071] Specifically, by constructing input signals for light change, current change, and environmental thermal boundary based on the relationship between adjacent sampling times, multi-source operational information is transformed from static quantities to dynamic quantities, enhancing the ability to characterize the temporal sequence of photovoltaic module operation. By introducing equivalent temperature state into the input signal construction process, a coupled expression basis is formed between thermal state information and changes in light, current, and environment, enabling thermal state evolution to be synchronously correlated with external input changes. By calculating the heat accumulation rate, heat release rate, and thermal inertia response based on the change trend, the thermal process of photovoltaic modules is transformed from a state description to a rate of change description, enhancing the distinguishability and continuous expression of the thermal state evolution process. Finally, thermal input changes, thermal output changes, and thermal inertia changes are uniformly mapped to rate expressions.

[0072] The expression for the rate of thermal accumulation is: ; in, For the first The heat accumulation rate at each sampling time For the first The equivalent temperature state at each sampling time. For the first The equivalent temperature state at each sampling time. The sampling period.

[0073] The expression for the heat release rate is: ; in, For the first The heat release rate at each sampling time.

[0074] The expression for the degree of thermal inertial response is: ; in, For the first The degree of thermal inertial response at each sampling time For the first The heat accumulation rate at each sampling time For the first The heat release rate at each sampling time.

[0075] It should be noted that, due to and The results are extracted unidirectionally for the rate of temperature rise and the rate of temperature fall, respectively. At most one of them is non-zero at the same sampling time. The value range is [0, 1): when the temperature rises, A positive value less than 1 indicates a greater advantage of heat accumulation rate over heat dissipation capacity; when the temperature decreases or remains constant, =0 indicates that the state is in heat release or thermal equilibrium. As a switch-type weighting factor in the feedforward prediction model, it distinguishes between accumulation-dominated and non-accumulation states.

[0076] S3.3 Construct the transient energy conversion intensity of the photovoltaic module based on the light change input signal and the current change input signal, and establish the environmental heat dissipation constraint quantity according to the environmental thermal boundary input signal.

[0077] Furthermore, when constructing the transient energy conversion intensity of a photovoltaic module based on the light change input signal and the current change input signal, the amplitude of the change in the light change input signal between adjacent sampling times is extracted so that the change in light input can reflect the external energy input disturbance. The output current change input signal of the photovoltaic module is used as the internal energy output response characterization. By synchronizing the light change input signal and the current change input signal, the external energy input change and the internal current response change are coupled in the same time series. When the light change input signal shows an increasing trend and the current change input signal increases synchronously, this state corresponds to the transient energy input enhancement process. When the light change input signal increases but the current change input signal response lags, this state corresponds to the energy conversion constraint process. When the light change input signal fluctuates and the current change input signal fluctuates synchronously, this state corresponds to the energy conversion dynamic adjustment process.

[0078] Specifically, the transient energy conversion intensity of photovoltaic modules is uniformly characterized through the above three types of change relationships, so that the transient conversion process between changes in light input and changes in current output can be consistently expressed. When establishing environmental heat dissipation constraints based on the environmental thermal boundary input signal, the trend of environmental temperature change in continuous sampling time is extracted, so that the environmental temperature change process is used as the source of external heat dissipation condition change, and the rate of environmental temperature change is used as the basis for heat dissipation intensity change. When the environmental temperature shows a decreasing trend, it corresponds to the heat dissipation enhancement state; when the environmental temperature remains stable, it corresponds to the heat dissipation equilibrium state; and when the environmental temperature shows an increasing trend, it corresponds to the heat dissipation weakening state. The environmental heat dissipation constraints are formed by uniformly mapping different heat dissipation states.

[0079] S3.4. Using the transient energy conversion intensity of the photovoltaic module as the heat generation driving term, the environmental heat dissipation constraint as the heat loss constraint term, and the heat accumulation rate as the thermal state evolution term, establish a thermal dynamic evolution model for the photovoltaic module.

[0080] Furthermore, by using the change in transient energy conversion intensity during continuous sampling periods as the input source of heat generation, the energy conversion process formed by the combined effects of light change and current response is mapped to the heat generation driving process. The environmental heat dissipation constraint is used as the external heat exchange boundary condition, allowing the heat dissipation enhancement or weakening process corresponding to changes in environmental temperature to participate in the expression of heat loss. At the same time, the heat accumulation rate is used as the basis for the evolution of the thermal state over time, enabling the thermal state change process to be recursively expressed in continuous sampling periods. When the transient energy conversion intensity increases and the environmental heat dissipation constraint decreases, it corresponds to the heat generation-dominated state. When the environmental heat dissipation constraint increases and the transient energy conversion intensity decreases, it corresponds to the heat loss-dominated state. When both fluctuate simultaneously, the intermediate transition state is expressed through the heat accumulation rate, enabling the changes in the thermal state of the photovoltaic module to form a unified dynamic evolution relationship among heat generation, heat loss, and heat accumulation.

[0081] S3.5. Dynamically adjust the heat transfer rate in the thermal dynamic evolution model according to the degree of thermal inertia response. When the degree of thermal inertia response increases, increase the prediction weight of heat accumulation; when the degree of thermal inertia response decreases, increase the prediction weight of heat dissipation.

[0082] Furthermore, the degree of thermal inertia response is determined based on the relationship between the rate of heat accumulation and the rate of heat release, and is used as the basis for adjusting the heat transfer rate. By judging the changing trend of the degree of thermal inertia response in continuous sampling periods, when the degree of thermal inertia response shows an increasing trend, the change in thermal state is attributed to the process dominated by heat accumulation. At this time, the heat transfer rate is reduced to strengthen the prediction weight of heat accumulation, making the evolution of thermal state more inclined to the expression of the accumulation process. When the degree of thermal inertia response shows a decreasing trend, the change in thermal state is attributed to the process dominated by heat dissipation. At this time, the heat transfer rate is increased to strengthen the prediction weight of heat dissipation, making the evolution of thermal state more inclined to the expression of the release process. In specific implementation, the heat transfer rate is used as an adjustment parameter for the change of thermal state between adjacent sampling periods. By adjusting the prediction weights of heat accumulation and heat dissipation inversely, the thermal dynamic evolution model exhibits different evolution rates under different thermal inertia conditions.

[0083] S3.6. Based on the thermal dynamic evolution model, the trend of thermal state change within multiple future sampling periods is recursively deduced to obtain the future thermal state sequence.

[0084] Furthermore, when recursively predicting the thermal state change trend over multiple sampling periods based on the thermal dynamic evolution model, the thermal state at the current sampling moment is used as the initial state input. Combined with the changing relationship between the heat generation driving term and the heat loss constraint term in consecutive sampling periods, the evolution process of the thermal state in subsequent sampling periods is progressively recursively predicted. This ensures that the thermal state of each sampling period is derived from the thermal state of the previous sampling period, forming a continuous thermal state sequence. The weight allocation of the heat accumulation process and the heat dissipation process is adjusted according to the degree of thermal inertia response, allowing the evolution direction under different thermal state stages to be adjusted with changes in thermal inertia. When the degree of thermal inertia response is strong, the future thermal state sequence exhibits a continuous accumulation characteristic; when the degree of thermal inertia response is weak, the future thermal state sequence exhibits a rapid release characteristic, thus obtaining the future thermal state sequence.

[0085] S3.7. Based on the future thermal state sequence, predict the trend of the maximum power point voltage change in multiple sampling periods in the future, and establish a dynamic mapping relationship between the future thermal state and the maximum power point voltage. Calculate the future temperature compensation requirement based on the dynamic mapping relationship, and generate the corresponding feedforward correction temperature parameters.

[0086] Specifically, the thermal evolution trend is characterized by the magnitude of thermal state changes, and this trend is mapped to the maximum power point voltage (MPP) change process. An increase in thermal state corresponds to a decrease in MPP, and a decrease in thermal state corresponds to an increase in MPP, forming a correlation between future thermal state and MPP. The relationship between the current sampling moment's MPP and future thermal state changes is superimposed, creating a recursive change sequence of MPP in the continuous prediction sampling period. When establishing the dynamic mapping relationship between future thermal state and MPP, future thermal state is used as the input variable, and MPP as the output variable. A benchmark mapping is formed by combining the MPP at a reference temperature, ensuring that different future thermal states correspond to different MPP changes. This constructs a dynamic mapping relationship. Based on the deviation between the predicted future MPP and the MPP at the reference temperature, future temperature compensation requirements are calculated, ensuring that the temperature compensation requirements reflect the impact of future thermal state changes on MPP. Corresponding feedforward correction temperature parameters are then generated based on these temperature compensation requirements.

[0087] The expression for the maximum power point voltage is: ; in, For the first The maximum power point voltage for each predicted sampling period The maximum power point voltage at the current sampling time. For the first The future thermal state value for each predicted sampling period This is the current thermal state value. This is the temperature-to-voltage conversion factor.

[0088] Take the current sampling time Maximum power point voltage Based on the current thermal state value, the future thermal prediction model is used to obtain the next thermal state value. The thermal state prediction values ​​for each sampling period are used to calculate the temperature difference between the two. Multiplying this temperature difference by the temperature-voltage conversion factor yields the voltage compensation amount corresponding to the temperature change. It carries the dimensional property of V / ℃, and the temperature difference in ℃ is related to... The denominator ℃ cancels out, and the result is naturally converted into the voltage dimension V. The voltage compensation is superimposed on the current maximum power point voltage to obtain the maximum power point voltage at the future prediction time, realizing a quantitative mapping from the thermal domain to the voltage domain.

[0089] because It has been mapped to the equivalent temperature dimension through the heat capacity model, therefore the temperature difference term The unit is ℃, and After multiplying by V / ℃, the unit is automatically converted to V, achieving dimensional closure from the thermal domain to the voltage domain.

[0090] The dynamic mapping expression is: ; in, This is a dynamic mapping function from the future thermal state to the voltage at the maximum power point. The maximum power point voltage at the reference temperature. For future thermal states, This is a reference temperature.

[0091] The expression for future temperature compensation demand is: ; in, For the first The future temperature compensation requirement for each forecast period. The maximum power point voltage at the reference temperature. The temperature-voltage conversion factor is the predicted future maximum power point voltage.

[0092] S4. Based on the feedforward correction temperature parameters, perform temperature compensation correction on the maximum power point reference voltage of the photovoltaic module to obtain the temperature-compensated maximum power point voltage reference value.

[0093] S4.1 Input the feedforward correction temperature parameter into the temperature compensation, and use the feedforward correction temperature parameter and the maximum power point voltage temperature coefficient in the photovoltaic module temperature characteristic curve to calculate and obtain the temperature compensation input parameter.

[0094] Furthermore, when the feedforward correction temperature parameter is input into the temperature compensation process, it is correlated with the maximum power point voltage temperature coefficient in the photovoltaic module temperature characteristic curve. This allows the feedforward correction temperature parameter to reflect the degree of influence of photovoltaic module temperature change on maximum power point voltage. Through this correlation, the thermal state prediction result is transformed into a voltage correction basis quantity. By matching the change trend of the feedforward correction temperature parameter in the continuous prediction sampling period with the change characteristics of the maximum power point voltage temperature coefficient, the temperature influence under different thermal state change conditions can correspond to different voltage correction intensities.

[0095] The expression for the temperature compensation input parameter is:

[0096] in, Input parameters for temperature compensation, This is for feedforward correction of temperature parameters.

[0097] S4.2 Input the temperature compensation input parameters and the maximum power point reference voltage of the photovoltaic module into the temperature voltage mapping. Through the linear proportional relationship in the temperature voltage mapping correction process, the maximum power point reference voltage of the photovoltaic module is offset and corrected to obtain the temperature compensation correction amount.

[0098] Furthermore, by mapping the temperature compensation input parameters to the maximum power point reference voltage in the same mapping relationship, the impact of temperature changes on voltage changes can be reflected in a unified expression structure. During the temperature-voltage mapping process, the voltage correction ratio coefficient is determined based on the correspondence between the change amplitude of the temperature compensation input parameters and the change amplitude of the maximum power point reference voltage, so that different voltage correction intensities correspond to different temperature change conditions, and the relationship between temperature and voltage is uniformly expressed through a linear proportional relationship. After obtaining the voltage correction ratio coefficient, the coefficient is processed with the maximum power point reference voltage of the photovoltaic module, so that the voltage deviation caused by temperature changes can be output in the form of a correction amount, forming the temperature compensation correction amount.

[0099] Specifically, by incorporating the temperature compensation input parameters and the maximum power point reference voltage of the photovoltaic module into a temperature-voltage mapping relationship, temperature changes and voltage changes can be expressed in a unified mapping structure, achieving a direct conversion of temperature effects into a voltage correction process. By introducing a linear proportional relationship in the mapping process, different temperature compensation input parameters correspond to different voltage correction ratio coefficients, enabling a quantifiable expression of the impact of temperature changes on voltage. By calculating the voltage correction ratio coefficient with the maximum power point reference voltage, the temperature effect can be directly applied to the voltage reference value, forming a temperature compensation correction amount.

[0100] S4.3. The temperature compensation correction is superimposed on the maximum power point reference voltage of the photovoltaic module to obtain the maximum power point voltage reference value after temperature compensation.

[0101] Furthermore, when superimposing the temperature compensation correction amount with the maximum power point reference voltage of the photovoltaic module, the temperature compensation correction amount is input as a voltage offset into the expression process of the maximum power point reference voltage. This allows the voltage offset caused by temperature changes to directly affect the reference voltage value. The direction of change of the temperature compensation correction amount is matched with the trend of change of the maximum power point reference voltage, so that the voltage correction process is adjusted synchronously with the direction of temperature change. When the temperature compensation correction amount shows an increasing trend, it corresponds to the upward shift of the maximum power point reference voltage. When the temperature compensation correction amount shows a decreasing trend, it corresponds to the downward shift of the maximum power point reference voltage. This allows the impact of temperature changes on the maximum power point reference voltage of the photovoltaic module to be expressed in a direct weighted form, generating a temperature-compensated maximum power point voltage reference value.

[0102] S5. Input the maximum power point voltage reference value into the maximum power point tracking control module to dynamically adjust the operating point of the photovoltaic module and obtain the optimal combination of operating voltage and current. S5.1. Apply a periodic voltage perturbation to the target tracking voltage to obtain the perturbed voltage command. Send the perturbed voltage command to the DC-DC converter to adjust the duty cycle and collect the adjusted photovoltaic module output current and photovoltaic module output voltage to obtain the adjusted instantaneous output power. Furthermore, when applying a periodic voltage disturbance to the target tracking voltage, the periodically changing voltage disturbance is superimposed on the target tracking voltage to form a disturbed voltage command. This disturbed voltage command is then input to the DC-DC converter, causing the DC-DC converter to adjust its duty cycle according to the disturbed voltage command, thereby changing the operating point of the photovoltaic module. By collecting the relationship between the output current and output voltage of the photovoltaic module, the adjusted instantaneous output power is obtained. The instantaneous output power is used as the power response result of the current sampling period, enabling the voltage disturbance to form a feedback expression at the power output level. This establishes a correspondence between the voltage disturbance and the power response, resulting in the adjusted instantaneous output power.

[0103] S5.2 By comparing the instantaneous output power after adjustment with the historical output power of the previous cycle, the voltage disturbance direction is updated according to the power increase / decrease relationship to obtain the voltage disturbance amount for the next cycle. Furthermore, by comparing the adjusted instantaneous output power with the historical output power of the previous cycle, the difference between the instantaneous output power of the current sampling cycle and the output power of the previous sampling cycle is analyzed. The effectiveness of the voltage disturbance direction is determined based on the power increase / decrease relationship. When the instantaneous output power increases compared to the previous cycle, the current voltage disturbance direction is maintained, allowing the disturbance to continue evolving along the favorable power increase direction. When the instantaneous output power decreases compared to the previous cycle, the voltage disturbance direction is updated in the opposite direction, adjusting the disturbance to the power increase direction, forming the voltage disturbance amount for the next cycle. In this way, the voltage disturbance direction can be dynamically corrected according to the power change results, and the voltage disturbance amount can be updated.

[0104] S5.3 Apply the voltage perturbation amount of the next cycle to the target tracking voltage to obtain a new perturbation voltage command, and lock the voltage and current of the current photovoltaic module operating point to obtain the optimal operating voltage and current combination.

[0105] Furthermore, when applying the voltage perturbation amount for the next cycle to the target tracking voltage, the updated voltage perturbation amount is superimposed on the target tracking voltage to form a new perturbation voltage command, which is then input to the DC-DC converter for duty cycle adjustment, causing the photovoltaic module's operating point to move towards the new voltage-current combination state. During the operating point locking process, when the instantaneous output power reaches a stable change state, the current output voltage and output current of the photovoltaic module are recorded and used as the current optimal operating voltage and current combination, so that this combination corresponds to the optimal output state under the current perturbation conditions, thus completing the determination of the optimal operating voltage and current combination and achieving the target of operating point locking.

[0106] S6. Based on the optimal combination of operating voltage and current, and combined with the state of charge and temperature information of the energy storage battery, the charging current is subjected to safety constraint control to obtain the final charging current control command.

[0107] S6.1 Extract the state of charge and temperature information of the energy storage battery from the multi-source operation data sequence, and record the current value in the optimal operating voltage and current combination as the initial charging current.

[0108] Furthermore, when extracting the state of charge and temperature information of the energy storage battery from the multi-source operating data sequence, the operating data of the energy storage battery is extracted synchronously in continuous sampling time, so that the changes in state of charge and temperature change form a corresponding relationship in the time series, and serve as the basis for characterizing the operating state of the energy storage battery. On this basis, the current value in the optimal combination of operating voltage and current is used as the initial charging current, and this current value is used as the basic input quantity for charging control. Together with the current operating state parameters of the energy storage battery, it constitutes the initial charging condition, thus completing the determination of the initial charging current.

[0109] S6.2. Compare the energy storage battery temperature information with the preset temperature safety threshold. When the energy storage battery temperature information exceeds the preset temperature safety threshold, the initial charging current is inversely limited according to the excess amount to obtain the temperature-constrained charging current.

[0110] Furthermore, when comparing the energy storage battery temperature information with a preset temperature safety threshold, the change in the energy storage battery temperature during a continuous sampling period is analyzed. When the energy storage battery temperature is below the preset temperature safety threshold, the initial charging current remains unchanged. When the energy storage battery temperature exceeds the preset temperature safety threshold, the initial charging current is inversely limited according to the magnitude of the exceedance, so that the higher the temperature, the lower the charging current, forming a temperature-constrained charging current. In this way, the charging current can be dynamically adjusted with changes in the energy storage battery temperature, achieving temperature safety constraint control. S6.3. Compare the state of charge of the energy storage battery with the preset power safety threshold. When the state of charge of the energy storage battery exceeds the preset power safety threshold, the charging current after temperature constraint is reduced in a stepwise manner according to the extent of the excess, so as to obtain the final charging current control command.

[0111] Furthermore, when comparing the state of charge (SOC) of the energy storage battery with a preset safe charge threshold, the trend of the SOC's change over a continuous sampling period is analyzed. When the SOC is below the preset safe charge threshold, the charging current is maintained after temperature constraints. When the SOC exceeds the preset safe charge threshold, the charging current after temperature constraints is reduced in a stepwise manner based on the extent of the excess. This causes the charging current to decrease gradually as the SOC increases, forming the final charging current control command. This allows the charging process to be dynamically adjusted based on the SOC, in addition to temperature safety constraints, achieving dual safety control of the charging current.

[0112] This embodiment also provides a solar charging control system with temperature compensation, including: a processing module that collects multi-source raw operating data of photovoltaic modules and performs standardized processing to obtain a multi-source operating data sequence; The fusion module, based on multi-source operating data sequences, integrates the temperature of photovoltaic modules, the temperature of energy storage batteries, and the ambient temperature, and dynamically corrects the effect of photovoltaic module output current on temperature disturbances to obtain the equivalent temperature state. The prediction module predicts the temperature change trend of photovoltaic modules based on equivalent temperature state and multi-source operating data sequences, and generates feedforward correction temperature parameters. The correction module performs temperature compensation correction on the maximum power point reference voltage of the photovoltaic module based on the feedforward correction temperature parameter, and obtains the temperature-compensated maximum power point voltage reference value. The combined module inputs the maximum power point voltage reference value into the maximum power point tracking control module to dynamically adjust the operating point of the photovoltaic module and obtain the optimal combination of operating voltage and current. The control module, based on the optimal combination of operating voltage and current, combined with the state of charge and temperature information of the energy storage battery, performs safety constraint control on the charging current to obtain the final charging current control command.

[0113] This embodiment also provides a computer device applicable to the case of a solar charging control method with temperature compensation, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the solar charging control method with temperature compensation as proposed in the above embodiment.

[0114] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0115] This embodiment also provides a storage medium on which a computer program is stored. When executed by a processor, the program implements the temperature-compensated solar charging control method as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0116] In summary, this invention collects multi-source operational data, including photovoltaic module output voltage, output current, irradiance, ambient temperature, and energy storage battery temperature, to construct an equivalent temperature state encompassing heat input, heat output, and heat accumulation characteristics. This enables a comprehensive characterization of the actual thermal state of the photovoltaic module, allowing temperature compensation to more accurately reflect the dynamic thermal characteristics of the photovoltaic module under different operating conditions. By establishing a thermal dynamic evolution model based on the equivalent temperature state, future thermal state change trends are predicted, and feedforward correction temperature parameters are generated. This achieves advance compensation of the maximum power point reference voltage, transforming the temperature compensation process from current state correction to predictive dynamic correction, thus improving the adaptability of the reference voltage during maximum power point tracking. Furthermore, maximum power point tracking is performed using the temperature-compensated maximum power point reference voltage, and the charging current is safely constrained and controlled based on the energy storage battery's state of charge and temperature information. This improves the accuracy of solar charging control while ensuring the safe operation of the energy storage battery and the stability of the system.

[0117] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A solar charging control method with temperature compensation, characterized in that: include, Collect multi-source raw operating data of photovoltaic modules and perform standardization processing to obtain multi-source operating data sequences; Based on multi-source operating data sequences, the equivalent temperature state is obtained by integrating the temperature of photovoltaic modules, the temperature of energy storage batteries and the ambient temperature, and dynamically correcting the effect of the output current of photovoltaic modules on temperature disturbances. Based on the equivalent temperature state and multi-source operation data sequence, the temperature change trend of photovoltaic modules is predicted, and feedforward correction temperature parameters are generated. Based on the feedforward correction temperature parameter, the maximum power point reference voltage of the photovoltaic module is corrected by temperature compensation to obtain the temperature-compensated maximum power point voltage reference value. The maximum power point voltage reference value is input into the maximum power point tracking control module to dynamically adjust the operating point of the photovoltaic module and obtain the optimal combination of operating voltage and current. Based on the optimal combination of operating voltage and current, combined with the state of charge and temperature information of the energy storage battery, the charging current is subjected to safety constraint control to obtain the final charging current control command.

2. The solar charging control method with temperature compensation as described in claim 1, characterized in that: The multi-source operational data sequence includes, The system collects information on photovoltaic module output voltage, photovoltaic module output current, light intensity, ambient temperature, and energy storage battery temperature to obtain multi-source raw operating data. It then performs time synchronization and standardization processing to obtain a multi-source operating data sequence.

3. The solar charging control method with temperature compensation as described in claim 2, characterized in that: The equivalent temperature state includes, Multi-source thermal response information is extracted from the output voltage of the photovoltaic module, the output current of the photovoltaic module, the temperature of the energy storage battery, and the ambient temperature to obtain the thermal response components of the photovoltaic module, the thermal response components of the energy storage battery, and the ambient thermal response components, respectively. Based on the dynamic coupling relationship between the output current and output voltage of the photovoltaic module, the heat generation behavior caused by the internal current flow of the photovoltaic module is identified, and the internal heat generation characterization quantity is established in combination with the change of the output power of the photovoltaic module. Based on the internal heat generation characterization quantity, environmental thermal response component, and energy storage battery thermal response component within multiple consecutive sampling periods, a dynamic heat exchange relationship between the heat input, heat output, and heat storage of the photovoltaic module is constructed to obtain the current thermal state of the photovoltaic module. The heat accumulation index is calculated based on the dynamic balance between heat input and heat output. The heat accumulation index is used to identify whether the photovoltaic module is in the heat accumulation stage, the heat stability stage, or the heat release stage. Based on different thermal states, corresponding temperature reliability adjustment strategies are established. When the photovoltaic module is in the heat accumulation stage, the contribution weight of the photovoltaic module thermal response component is increased. When the photovoltaic module is in the thermal stability stage, the fusion ratio between the photovoltaic module thermal response component, the energy storage battery thermal response component and the environmental thermal response component is coordinated. When the photovoltaic module is in the heat release stage, the contribution weight of the environmental thermal response component and the energy storage battery thermal response component is increased. The current change rate characteristic is obtained by continuous periodic sampling of the output current of the photovoltaic module. The voltage drift characteristic is obtained based on the relationship between the output voltage of the photovoltaic module and the reference voltage. The electrothermal coupling response characteristic is constructed based on the current change rate characteristic and the voltage drift characteristic. The internal heat generation dynamic mapping relationship is established based on the electrothermal coupling response characteristics, and the thermal inertia adjustment factor is calculated in combination with the heat accumulation index. The dynamic fusion weights under different thermal states are corrected by using a thermal inertia adjustment factor. The corrected dynamic fusion weights are then applied to the thermal response components of the photovoltaic module, the thermal response components of the energy storage battery, and the environmental thermal response components to reconstruct the real-time thermal state of the photovoltaic module and generate an equivalent temperature state.

4. The solar charging control method with temperature compensation as described in claim 3, characterized in that: The feedforward correction temperature parameters include, Extract the light intensity, photovoltaic module output current, ambient temperature and equivalent temperature status from multiple consecutive sampling times in the multi-source operation data sequence; Based on the relationship between adjacent sampling times, we construct the input signals of light change, current change, and environmental thermal boundary. Combined with the thermal state information in the equivalent temperature state, we calculate the current heat accumulation rate, heat release rate, and thermal inertia response of the photovoltaic module. The transient energy conversion intensity of the photovoltaic module is constructed based on the input signals of light change and current change, and the environmental heat dissipation constraint is established based on the input signal of environmental thermal boundary. A thermal dynamic evolution model of photovoltaic modules is established by taking the transient energy conversion intensity of photovoltaic modules as the heat generation driving term, the environmental heat dissipation constraint as the heat loss constraint term, the heat accumulation rate as the thermal state evolution term. The heat transfer rate in the thermal dynamic evolution model is dynamically adjusted according to the degree of thermal inertia response. When the degree of thermal inertia response increases, the prediction weight of heat accumulation is increased, and when the degree of thermal inertia response decreases, the prediction weight of heat dissipation is increased. The thermal state change trend within multiple sampling periods is recursively derived based on the thermal dynamic evolution model, thus obtaining the future thermal state sequence. Based on the future thermal state sequence, the trend of maximum power point voltage change in multiple sampling periods is predicted, and a dynamic mapping relationship between the future thermal state and the maximum power point voltage is established. The future temperature compensation requirement is calculated based on the dynamic mapping relationship, and the corresponding feedforward correction temperature parameters are generated.

5. The solar charging control method with temperature compensation as described in claim 4, characterized in that: The temperature-compensated maximum power point voltage reference value includes, The feedforward correction temperature parameter is input into the temperature compensation, and the temperature compensation input parameter is obtained by using the feedforward correction temperature parameter and the maximum power point voltage temperature coefficient in the temperature characteristic curve of the photovoltaic module. The temperature compensation input parameters and the maximum power point reference voltage of the photovoltaic module are input into the temperature voltage mapping. The maximum power point reference voltage of the photovoltaic module is offset and corrected through the linear proportional relationship in the temperature voltage mapping correction process to obtain the temperature compensation correction amount. The temperature compensation correction is superimposed on the maximum power point reference voltage of the photovoltaic module to obtain the maximum power point voltage reference value after temperature compensation.

6. The solar charging control method with temperature compensation as described in claim 5, characterized in that: The optimal operating voltage and current combination includes, A periodic voltage perturbation is applied to the target tracking voltage to obtain the perturbed voltage command. The perturbed voltage command is then sent to the DC-DC converter to adjust the duty cycle and collect the adjusted photovoltaic module output current and photovoltaic module output voltage to obtain the adjusted instantaneous output power. By comparing the instantaneous output power after adjustment with the historical output power of the previous cycle, the voltage disturbance direction is updated according to the power increase / decrease relationship to obtain the voltage disturbance amount for the next cycle. The voltage perturbation of the next cycle is applied to the target tracking voltage to obtain a new perturbation voltage command, and the voltage and current of the current photovoltaic module operating point are locked to obtain the optimal combination of operating voltage and current.

7. The solar charging control method with temperature compensation as described in claim 6, characterized in that: The final charging current control command includes: Extract the state of charge and temperature information of the energy storage battery from the multi-source operation data sequence, and record the current value in the optimal combination of operating voltage and current as the initial charging current. The energy storage battery temperature information is compared with the preset temperature safety threshold. When the energy storage battery temperature information exceeds the preset temperature safety threshold, the initial charging current is inversely limited according to the excess amount to obtain the temperature-constrained charging current. The state of charge of the energy storage battery is compared with a preset safe charge threshold. When the state of charge of the energy storage battery exceeds the preset safe charge threshold, the charging current after temperature constraint is reduced in a stepwise manner according to the extent of the excess, so as to obtain the final charging current control command.

8. A solar charging control system with temperature compensation, based on the solar charging control method with temperature compensation according to any one of claims 1 to 7, characterized in that: This includes a processing module that collects multi-source raw operating data from photovoltaic modules and performs standardized processing to obtain a multi-source operating data sequence; The fusion module, based on multi-source operating data sequences, integrates the temperature of photovoltaic modules, the temperature of energy storage batteries, and the ambient temperature, and dynamically corrects the effect of photovoltaic module output current on temperature disturbances to obtain the equivalent temperature state. The prediction module predicts the temperature change trend of photovoltaic modules based on equivalent temperature state and multi-source operating data sequences, and generates feedforward correction temperature parameters. The correction module performs temperature compensation correction on the maximum power point reference voltage of the photovoltaic module based on the feedforward correction temperature parameter, and obtains the temperature-compensated maximum power point voltage reference value. The combined module inputs the maximum power point voltage reference value into the maximum power point tracking control module to dynamically adjust the operating point of the photovoltaic module and obtain the optimal combination of operating voltage and current. The control module, based on the optimal combination of operating voltage and current, combined with the state of charge and temperature information of the energy storage battery, performs safety constraint control on the charging current to obtain the final charging current control command.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the solar charging control method with temperature compensation as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the solar charging control method with temperature compensation as described in any one of claims 1 to 7.

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