Photovoltaic energy power domain vehicle control method

Abstract

The invention discloses a photovoltaic energy power domain vehicle control method, which relates to the technical field of new energy automobiles, and comprises the following steps: based on photovoltaic panel energy collection efficiency and data collected by a sensor, performing analysis through a deep learning model, predicting an energy demand value, and generating an energy distribution proportion through an intelligent algorithm; energy management operation is carried out through an energy distribution strategy, the energy collection condition of the photovoltaic panel and the energy consumption condition of the vehicle are continuously monitored and fed back to the central processing unit, and the energy distribution strategy is optimized. According to the invention, environment data and vehicle state data are combined, the angle of the photovoltaic panel is dynamically adjusted by using the sun trajectory calculator so as to directly face sunlight, and the output power is measured in real time and the energy collection efficiency is recorded by using the current and voltage sensor.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to a photovoltaic energy powered vehicle control method. Background Art

[0002] As the global demand for clean energy continues to rise, solar energy, as a representative of sustainable development, has received increasing attention in the field of transportation, especially in electric vehicles. Photovoltaic energy-powered vehicle control technology has been developed since the end of the 20th century. Initial research focused on how to efficiently integrate photovoltaic panels into vehicles and test their energy collection capabilities in static environments. Early attempts typically involved installing photovoltaic panels at fixed angles to capture solar energy. However, this approach was inefficient because it failed to take into account changes in the sun's position over time and geographical location.

[0003] Despite significant progress, current photovoltaic energy control systems still have some limitations. First, most existing systems rely on preset schedules or basic algorithms to adjust the angle of photovoltaic panels, which limits their ability to use environmental data and vehicle status data for precise adjustments. Second, existing energy management systems lack effective prediction mechanisms for future energy demand and supply. This can lead to problems with battery overcharging or undercharging, which can affect vehicle performance and user experience. Summary of the invention

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

[0005] Therefore, the present invention provides a photovoltaic energy powered vehicle control method to solve the problem of inaccurate photovoltaic panel angle adjustment in the prior art for effectively predicting future energy demand.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, the present invention provides a photovoltaic energy-powered vehicle control method, which includes initializing settings and collecting environmental data and vehicle status data through integrated sensors; based on the environmental data and vehicle status data, calculating the optimal photovoltaic panel angle through a solar trajectory calculator, dynamically adjusting the photovoltaic panel angle, and recording the energy collection efficiency of the photovoltaic panel in real time; based on the photovoltaic panel energy collection efficiency and the data collected by the sensor, analyzing through a deep learning model, predicting the energy demand value in the future, and generating an energy allocation strategy through an intelligent algorithm; performing energy management operations through the energy allocation strategy, continuously monitoring the energy collection status of the photovoltaic panel and the energy consumption status of the vehicle, and feeding back to the central processing unit to optimize the energy allocation strategy.

[0008] As a preferred solution of the photovoltaic energy powered vehicle control method of the present invention, the initialization setting includes obtaining current position information, presetting the basic angle of the photovoltaic panel, battery status check, software version check and sensor calibration.

[0009] As a preferred solution of the photovoltaic energy powered vehicle control method of the present invention, the integrated sensors include GPS, light sensors, temperature sensors, speed and acceleration sensors and BMS sensors.

[0010] As a preferred solution of the photovoltaic energy power domain vehicle control method of the present invention, wherein: the environmental data includes geographical location, solar radiation intensity, weather conditions, temperature, wind speed and wind direction;

[0011] The vehicle status data includes the vehicle parking direction, the current angle of the photovoltaic panel, the battery power status, the vehicle load condition and the historical energy consumption data.

[0012] As a preferred solution of the photovoltaic energy power domain vehicle control method of the present invention, wherein: the photovoltaic panel angle is dynamically adjusted and the energy collection efficiency of the photovoltaic panel is recorded in real time. The specific steps are as follows:

[0013] The optimal photovoltaic panel angle is calculated based on the sun trajectory calculator, and the tilt angle and azimuth angle of the photovoltaic panel are changed by rotating and moving the mechanical structure so that the photovoltaic panel faces the sunlight;

[0014] The output voltage and current of the photovoltaic panel are measured by current and voltage sensors, the actual output power is calculated, and the theoretical maximum power is defined based on the technical specifications provided by the photovoltaic panel manufacturer. The actual output power is compared with the theoretical maximum power to obtain the energy collection efficiency and record it.

[0015] As a preferred solution of the photovoltaic energy power domain vehicle control method of the present invention, wherein: the data collected based on the photovoltaic panel energy collection efficiency and the sensor is analyzed by a deep learning model, and the energy demand value is predicted. The specific steps are as follows:

[0016] Based on the energy collection efficiency of photovoltaic panels and the data collected by sensors, data cleaning, normalization and data segmentation are performed, and photovoltaic energy management features are extracted from the processed data;

[0017] Build a time series based on historical data, use the energy collection efficiency, light intensity and temperature in the historical data as input features, and divide it into training set and test set;

[0018] Define the LSTM model structure, use the training set data to train the LSTM model structure, and use the test set to evaluate the generalization ability of the LSTM model;

[0019] Based on the trained LSTM model, the energy demand value is predicted.

[0020] As a preferred solution of the photovoltaic energy power domain vehicle control method of the present invention, wherein: the energy distribution strategy is generated by an intelligent algorithm, and the specific steps are as follows:

[0021] Define the energy balance at a certain moment based on the difference between the power generated by the photovoltaic panels and the power stored in the battery and the energy demand value;

[0022] Based on the energy demand value, the predicted energy demand value, the energy balance status at a certain moment and the photovoltaic energy management characteristics are used as inputs to predict the energy allocation ratio of each component in the next time step through an intelligent algorithm;

[0023] According to the energy allocation ratio, the current output power of the photovoltaic panel and the remaining power of the battery are divided into various components. Through real-time monitoring and dynamic adjustment, energy usage is optimized according to the energy allocation ratio, and an effective energy allocation strategy is generated.

[0024] As a preferred solution of the photovoltaic energy power domain vehicle control method of the present invention, wherein: the energy management operation is performed through the energy allocation strategy, the energy collection of the photovoltaic panel and the energy consumption of the vehicle are continuously monitored, and the energy allocation strategy is optimized by feeding back to the central processing unit. The specific steps are as follows:

[0025] Energy distribution strategy generated by intelligent algorithm, dynamically adjusting the energy supply of each component according to data of light intensity, remaining battery capacity and vehicle operation status;

[0026] The data collected by the sensors are transmitted to the central processing unit, which analyzes the received data, generates an energy allocation strategy for adjustment, and evaluates the energy collection efficiency of the photovoltaic panels and the energy consumption of the vehicle.

[0027] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the photovoltaic energy power domain vehicle control method as described in the first aspect of the present invention is implemented.

[0028] In a fourth aspect, 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, any step of the photovoltaic energy power domain vehicle control method as described in the first aspect of the present invention is implemented.

[0029] The beneficial effects of the present invention are as follows: by combining environmental data and vehicle status data, the solar trajectory calculator is used to dynamically adjust the angle of the photovoltaic panel to face the sunlight, and the current and voltage sensors are used to measure the output power and record the energy collection efficiency in real time. Based on these data, the LSTM model is used to predict future energy demand and supply, and the intelligent algorithm dynamically generates the energy distribution ratio of each component based on this. This method not only improves energy utilization efficiency, but also extends battery life and enhances the overall performance and user experience of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0031] Figure 1 This is a flow chart of the photovoltaic energy powered vehicle control method in Example 1.

[0032] Figure 2 This is a flow chart for predicting energy demand values ​​in Example 1. DETAILED DESCRIPTION

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0035] 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0036] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a photovoltaic energy power domain vehicle control method, comprising the following steps:

[0037] S1: Perform initialization settings and collect environmental data and vehicle status data through integrated sensors.

[0038] S1.1: Perform initialization settings including obtaining current location information, presetting the basic angle of the photovoltaic panel, battery status check, software version check and sensor calibration.

[0039] It should be noted that the initialization setup will first obtain the current location information of the device to determine the best operating parameters. Next, the basic angle of the photovoltaic panel is preset according to the geographical location to maximize the efficiency of light reception. At the same time, it is necessary to check the status of the battery to ensure that it is working properly and can store energy stably. Then the software version is checked to ensure that the system is running the latest software version to avoid possible errors or performance issues. Finally, the sensor calibration is carried out to ensure the accuracy of data collection by accurately calibrating each sensor, providing reliable data support for the efficient operation of the system.

[0040] S1.2: The integrated sensors include GPS, light sensor, temperature sensor, speed and acceleration sensor and BMS sensor.

[0041] Furthermore, the integrated sensor suite includes GPS for positioning and navigation, light sensors to detect ambient light intensity, temperature sensors to monitor temperature changes during device operation, speed and acceleration sensors to track the device's motion status, and BMS sensors to monitor battery health and charging and discharging efficiency. These sensors work together to provide comprehensive environmental and status information monitoring.

[0042] S2: Based on environmental data and vehicle status data, the optimal photovoltaic panel angle is calculated through the sun track calculator, the photovoltaic panel angle is dynamically adjusted, and the energy collection efficiency of the photovoltaic panel is recorded in real time.

[0043] S2.1: The environmental data include geographical location, solar radiation intensity, weather conditions, temperature, and wind speed and direction;

[0044] The vehicle status data includes the vehicle parking direction, the current angle of the photovoltaic panel, the battery power status, the vehicle load condition and the historical energy consumption data;

[0045] Based on the environmental data and vehicle status data, the optimal photovoltaic panel angle is calculated by the sun track calculator, and the expression is:

[0046] θ=(α s (φ,t)+k1·v+k2·ΔT,A z (φ,t)+k3·a+k4·I s );

[0047] Among them, θ represents the most suitable photovoltaic panel angle, t represents the current time, φ represents the dimensional coordinate of the vehicle's location, and α s(φ,t) represents the solar altitude angle calculated based on dimension φ and time t, v represents the speed of the vehicle, k1 represents the influence coefficient of adjusting the speed v on the tilt angle of the photovoltaic panel, ΔT represents the temperature change value of the environment, k2 represents the influence coefficient of adjusting the temperature change value ΔT on the tilt angle of the photovoltaic panel, A z (φ,t) represents the solar azimuth angle calculated based on latitude φ and time t, k3 represents the influence coefficient of adjusting acceleration a on the azimuth angle of the photovoltaic panel, a represents the acceleration of the vehicle, I s Indicates light intensity, k4 indicates adjusting light intensity I s The influence coefficient of the azimuth angle of the photovoltaic panel.

[0048] S2.2: Record the energy collection efficiency of photovoltaic panels in real time.

[0049] Furthermore, the solar track calculator calculates the most suitable angle for the photovoltaic panel to absorb solar energy, and can adjust the inclination and azimuth of the photovoltaic panel by controlling the rotating and moving mechanical structure to ensure that the photovoltaic panel is accurately facing the sunlight, thereby maximizing the efficiency of light reception and optimizing the energy collection process. This automated process dynamically adjusts the angle of the photovoltaic panel according to the changes in the position of the sun to adapt to the solar track at different times and locations, thereby improving energy utilization efficiency.

[0050] S2.3: Measure the output voltage and current of the photovoltaic panel through the current and voltage sensor, calculate the actual output power, and define the theoretical maximum power based on the technical specifications provided by the photovoltaic panel manufacturer. Compare the actual output power with the theoretical maximum power, obtain the energy collection efficiency and record it.

[0051] It should be noted that by measuring the output voltage and current of the photovoltaic panel through the current and voltage sensor, the actual output power can be calculated, and the theoretical maximum power can be defined according to the technical specifications provided by the photovoltaic panel manufacturer. Then the actual output power is compared and analyzed with the theoretical maximum power to obtain the energy collection efficiency. The results of the whole process will be recorded for subsequent performance evaluation and optimization adjustment. This series of steps helps to accurately understand the working efficiency of the photovoltaic panel and ensure the maximum utilization of energy.

[0052] S3: Based on the energy collection efficiency of photovoltaic panels and the data collected by sensors, the deep learning model is used to analyze and predict the energy demand value, and the energy allocation strategy is generated through intelligent algorithms.

[0053] S3.1: Based on the energy collection efficiency of photovoltaic panels and the data collected by sensors, data cleaning, normalization and data segmentation are performed, and photovoltaic energy management features are extracted from the processed data.

[0054] Furthermore, based on the energy collection efficiency of photovoltaic panels and the data collected by sensors, data cleaning is first performed to remove outliers and inaccurate data, and then the cleaned data is normalized to ensure that various data indicators are within a comparable range. Subsequently, data segmentation is performed to facilitate the analysis of performance at different stages or conditions, and key features that are helpful for photovoltaic energy management are extracted from these processed data, such as the optimal angle, cleanliness effect, temperature coefficient, etc., to provide data support for improving energy collection efficiency and optimization.

[0055] S3.2: Construct a time series based on historical data, use the energy collection efficiency, light intensity and temperature in the historical data as input features, and divide it into training set and test set.

[0056] A time series is constructed based on historical data, and the energy collection efficiency, light intensity and temperature in past records are used as input features. Future performance is predicted by analyzing the changing patterns of these features over time. The data is divided into training and test sets to facilitate the evaluation of the accuracy and reliability of the model. The whole process aims to use past data to optimize photovoltaic energy management and improve energy collection efficiency.

[0057] S3.3: Define the LSTM model structure, use the training set data to train the LSTM model structure, and use the test set to evaluate the generalization ability of the LSTM model.

[0058] Furthermore, the LSTM model structure is defined, and the model is trained using the training set data. The patterns and regularities in the time series are learned by inputting features, and then the generalization ability of the model is evaluated using the test set to ensure that it can also accurately predict on unseen data. The whole process aims to improve the accuracy and reliability of photovoltaic energy management.

[0059] S3.4: Based on the trained LSTM model, predict the energy demand value. The specific expression is:

[0060]

[0061] in, represents the energy demand value at the future time t, t represents the different time nodes in the sequence, W y represents the weight matrix of the output layer, b y represents the bias term of the output layer, h represents the hidden state, and h t represents the hidden state of LSTM at time step t, W h Indicates the hidden state of LSTM, h t The weight matrix of s represents the decoder state, W srepresents the weight matrix in the decoder state s, v represents the weight vector in the attention mechanism, T represents the transpose operation of the vector, and v T represents the transposed form of the weight vector v in the attention mechanism, h n represents the hidden state of the last time step after LSTM has processed all inputs, b y represents the bias term of the output layer, n represents the length of the input sequence, b represents the bias term of the nonlinear transformation, and k represents the index of all time steps of the input sequence.

[0062] S3.5: Define the energy balance at a certain moment based on the difference between the power generated by the photovoltaic panels and the power stored in the battery and the energy demand value.

[0063] Furthermore, based on the difference between the electric energy generated by the photovoltaic panels and the electric energy stored in the battery and the energy demand value, the energy balance status at a certain moment can be defined, that is, by comparing the electric energy generated by the photovoltaic panels in real time, the available electric energy stored in the battery, and the energy demand value at that moment, it can be determined whether the current energy supply and demand balance has been achieved. If the electric energy generated by the photovoltaic panels plus the electric energy released by the battery meets the immediate energy demand, it is considered to be in an energy balance state; otherwise, there is an energy surplus or shortage, and the energy management strategy needs to be adjusted to cope with different energy demands and supply changes. This process helps to accurately control energy distribution and ensure efficient use of renewable energy resources.

[0064] S3.6: Based on the predicted energy demand value, the predicted energy demand value, the energy balance status at a certain moment and the photovoltaic energy management characteristics are used as inputs, and the energy allocation ratio of each component in the next time step is predicted through an intelligent algorithm. The specific expression is:

[0065]

[0066] Among them, t+1 represents the next time step immediately following it, α(t+1) represents the energy allocation ratio at time t+1, and A represents the optimization algorithm based on reinforcement learning. represents the predicted energy supply, Δt represents the time interval from time t to t+1, It represents the predicted value of energy supply at the future time point t+Δt obtained by the prediction model, represents the predicted energy demand, It represents the predicted value of energy demand at the future time point t+Δt obtained by the prediction model, E b represents energy balance, E b (t) represents the energy balance at time t, X t represents the photovoltaic energy management characteristics collected at time t.

[0067] S3.7: According to the energy allocation ratio, the current output power of the photovoltaic panel and the remaining power of the battery are divided into each component. Through real-time monitoring and dynamic adjustment, the energy usage is optimized according to the energy allocation ratio, and an effective energy allocation strategy is generated.

[0068] Furthermore, according to the energy allocation ratio, the current output power of the photovoltaic panels and the remaining power of the battery are reasonably divided among the various components. By real-time monitoring of the energy usage of each component and dynamically adjusting the allocation ratio, the optimal use of energy is ensured. This process flexibly allocates energy according to the needs and priorities of different components, aiming to maximize the use of electricity generated by photovoltaic panels and stored in batteries, while ensuring that key components receive sufficient energy support. The entire mechanism generates an effective energy allocation strategy through continuous monitoring and intelligent adjustment, thereby improving overall energy utilization efficiency.

[0069] S4: Perform energy management operations through energy allocation strategies, continuously monitor the energy collection of photovoltaic panels and the energy consumption of the vehicle, and feed back to the central processing unit to optimize the energy allocation strategy.

[0070] S4.1: Based on the energy allocation strategy generated by the intelligent algorithm, the energy supply of each component is dynamically adjusted according to the data of light intensity, remaining battery capacity and vehicle operating status.

[0071] It should be noted that the energy allocation strategy is generated based on the intelligent algorithm, and the energy supply of each component is dynamically adjusted according to the real-time monitored light intensity, battery remaining capacity and vehicle operating status data. This process first relies on advanced optimization algorithms, which can analyze and process data from multiple sources, including current light conditions, battery charge and discharge status, and vehicle driving conditions, so as to develop a scientific and reasonable energy allocation plan. By continuously monitoring the changes in these key parameters, changes in environmental and operating conditions can be identified in a timely manner. For example, when the light is strong during the day, photovoltaic energy is used to power the vehicle and charge the battery; when the light is insufficient or the battery is low, limited energy resources are reallocated to ensure the energy supply to key components, such as giving priority to the power needs of electronic equipment related to driving safety.

[0072] Furthermore, this dynamic adjustment mechanism can also predict energy demand and production in the future, and make corresponding energy storage or release decisions in advance to avoid energy shortages or waste. For example, when it is expected to enter a tunnel or drive at night, the battery's energy output is increased, while the energy consumption of non-essential components is reduced. This method not only improves the efficiency of energy use, but also extends the service life of the battery, reduces overall operating costs, and makes the entire energy management more intelligent and efficient. In this way, even in complex and changeable actual operating environments, the effective distribution and use of energy can be guaranteed, the use of renewable energy can be maximized, and all key components can be ensured to have sufficient and stable energy supply.

[0073] S4.2: The data collected by the sensor is transmitted to the central processing unit, which analyzes the received data, generates an energy allocation strategy for adjustment, and evaluates the energy collection efficiency of the photovoltaic panel and the energy consumption of the vehicle.

[0074] It should be noted that the data collected by the sensors, including information such as light intensity, temperature, remaining battery capacity and vehicle operating status, are transmitted to the central processing unit, which conducts in-depth analysis of these data to identify the current energy production and consumption patterns. Based on the analysis results, an energy allocation strategy is generated or adjusted to ensure that the energy generated by the photovoltaic panels and the energy stored in the battery can be efficiently and reasonably allocated to each component to meet the immediate energy needs and optimize the overall energy utilization efficiency.

[0075] Furthermore, by comparing the actual output power of the photovoltaic panel with the theoretical maximum power, its energy collection efficiency is evaluated and possible performance bottlenecks or room for improvement are identified. The vehicle's energy consumption will also be comprehensively evaluated based on factors such as driving distance, speed, and load to determine the rationality of energy use and provide a reference for future energy management. The entire process aims to maximize the utilization of photovoltaic energy through precise data analysis and intelligent decision-making, while ensuring efficient and stable operation of the vehicle under various conditions, achieving the goal of energy conservation and emission reduction and extending battery life. By continuously cycling this process, the energy allocation strategy can be continuously optimized, the ability to cope with different environments and operating conditions can be improved, and the optimal configuration and use of energy resources can be ensured.

[0076] This embodiment also provides a computer device, which is suitable for the case of a photovoltaic energy power domain vehicle control method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the photovoltaic energy power domain vehicle control method proposed in the above embodiment.

[0077] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0078] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the photovoltaic energy power domain vehicle control method proposed in the above embodiment is implemented; 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 (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0079] In summary, the present invention combines environmental data and vehicle status data, uses a solar trajectory calculator to dynamically adjust the angle of the photovoltaic panel to face the sunlight, and uses a current and voltage sensor to measure the output power and record the energy collection efficiency in real time. Based on these data, an LSTM model is used to predict future energy demand and supply, and an intelligent algorithm dynamically generates the energy distribution ratio of each component based on this. This method not only improves energy utilization efficiency, but also extends battery life and enhances the overall performance and user experience of the vehicle.

[0080] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A photovoltaic energy powered vehicle control method, characterized in that: include, Perform initialization settings and collect environmental data and vehicle status data through integrated sensors; Based on environmental data and vehicle status data, the optimal photovoltaic panel angle is calculated through the sun track calculator, the photovoltaic panel angle is dynamically adjusted, and the energy collection efficiency of the photovoltaic panel is recorded in real time; Based on the energy collection efficiency of photovoltaic panels and the data collected by sensors, the deep learning model is used to analyze and predict the energy demand value, and the energy allocation strategy is generated through intelligent algorithms; Energy management operations are performed through energy allocation strategies, which continuously monitor the energy collection of photovoltaic panels and the energy consumption of the vehicle, and provide feedback to the central processing unit to optimize the energy allocation strategy.

2. The photovoltaic energy powered vehicle control method according to claim 1, characterized in that: The initialization settings include obtaining current position information, presetting the basic angle of the photovoltaic panel, battery status check, software version check and sensor calibration.

3. The photovoltaic energy powered vehicle control method according to claim 2, characterized in that: The integrated sensors include GPS, light sensor, temperature sensor, speed and acceleration sensor and BMS sensor.

4. The photovoltaic energy powered vehicle control method according to claim 3, characterized in that: The environmental data include geographical location, solar radiation intensity, weather conditions, temperature, and wind speed and direction; The vehicle status data includes the vehicle parking direction, the current angle of the photovoltaic panel, the battery power status, the vehicle load condition and the historical energy consumption data.

5. The photovoltaic energy powered vehicle control method according to claim 4, characterized in that: The specific steps of dynamically adjusting the angle of the photovoltaic panel and recording the energy collection efficiency of the photovoltaic panel in real time are as follows: The optimal photovoltaic panel angle is calculated based on the sun trajectory calculator, and the tilt angle and azimuth angle of the photovoltaic panel are changed by rotating and moving the mechanical structure so that the photovoltaic panel faces the sunlight; The output voltage and current of the photovoltaic panel are measured by current and voltage sensors, the actual output power is calculated, and the theoretical maximum power is defined based on the technical specifications provided by the photovoltaic panel manufacturer. The actual output power is compared with the theoretical maximum power to obtain the energy collection efficiency and record it.

6. The photovoltaic energy powered vehicle control method according to claim 5, characterized in that: The energy collection efficiency of photovoltaic panels and the data collected by sensors are analyzed through a deep learning model to predict the energy demand value. The specific steps are as follows: Based on the energy collection efficiency of photovoltaic panels and the data collected by sensors, data cleaning, normalization and data segmentation are performed, and photovoltaic energy management features are extracted from the processed data; Build a time series based on historical data, use the energy collection efficiency, light intensity and temperature in the historical data as input features, and divide it into training set and test set; Define the LSTM model structure, use the training set data to train the LSTM model structure, and use the test set to evaluate the generalization ability of the LSTM model; Based on the trained LSTM model, the energy demand value is predicted.

7. The photovoltaic energy powered vehicle control method according to claim 6, characterized in that: The energy allocation strategy is generated by the intelligent algorithm, and the specific steps are as follows: Defining the energy balance status based on the difference between the electricity generated by the photovoltaic panels and the electricity stored in the batteries and the energy demand value; Based on the energy demand value, the predicted energy demand value, the energy balance status at a certain moment and the photovoltaic energy management characteristics are used as inputs to predict the energy allocation ratio of each component in the next time step through an intelligent algorithm; According to the energy allocation ratio, the current output power of the photovoltaic panel and the remaining power of the battery are divided into various components. Through real-time monitoring and dynamic adjustment, energy usage is optimized according to the energy allocation ratio, and an effective energy allocation strategy is generated.

8. The photovoltaic energy powered vehicle control method according to claim 7, characterized in that: The energy management operation is performed through the energy allocation strategy, the energy collection of the photovoltaic panel and the energy consumption of the vehicle are continuously monitored, and the feedback is given to the central processing unit to optimize the energy allocation strategy. The specific steps are as follows: Energy distribution strategy generated by intelligent algorithm, dynamically adjusting the energy supply of each component according to data of light intensity, remaining battery capacity and vehicle operation status; The data collected by the sensors are transmitted to the central processing unit, which analyzes the received data, generates an energy allocation strategy for adjustment, and evaluates the energy collection efficiency of the photovoltaic panels and the energy consumption of the vehicle.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the photovoltaic energy power domain vehicle control method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the photovoltaic energy power domain vehicle control method according to any one of claims 1 to 7 are implemented.

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