Dynamic energy distribution optimization method for photovoltaic car charger

Abstract

The invention provides a dynamic energy distribution optimization method and system for a photovoltaic car charger, and relates to the technical field of dynamic energy distribution. The method comprises the following steps: acquiring vehicle running state data, analyzing load characteristics of a navigation module, a power module and a safety monitoring module, generating load characteristic data, and generating a weight distribution table by adopting a weight distribution model; according to the weight distribution table and the photovoltaic output power, a priority ranking algorithm is adopted to generate a preliminary distribution scheme; obtaining an electric energy supply stability index, generating a dynamic energy adjustment parameter, optimizing an electric energy distribution proportion, and generating an optimized electric energy distribution scheme; and according to the optimized electric energy distribution scheme, evaluating a satisfaction degree, generating a competition balance parameter, and generating a final distribution scheme by adopting an adaptive adjustment algorithm. Dynamic balance of electric energy distribution can be achieved, the satisfaction degree of navigation and safety monitoring is improved, and the electric energy utilization efficiency and the operation stability of the vehicle under complex working conditions are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of dynamic energy distribution technology, and in particular to a dynamic energy distribution optimization method and system for a photovoltaic vehicle charger. Background Technology

[0002] Currently, new energy vehicles are widely used globally, and photovoltaic vehicle chargers, as an innovative technology that uses solar energy to provide power to vehicles, are becoming an important force in promoting green travel. The dynamic energy distribution of its internal power source directly determines the operating efficiency and stability of the vehicle's electrical system.

[0003] Existing technologies, when dealing with multiple load demands, often rely on fixed allocation rules or simple priority ranking, lacking a comprehensive consideration of the dynamic characteristics of the load. This is particularly true when handling power competition among multiple circuit modules, failing to effectively balance the real-time demands and long-term operational requirements of different loads. In the face of rapidly changing energy supply, they lack adaptive adjustment capabilities and struggle to optimize allocation strategies based on load characteristics in a short period. Therefore, how to achieve dynamic power allocation through intelligent mechanisms, and design an adaptive power allocation system based on dynamic competition parameters to balance load importance weights and real-time competition scores when the output power of the photovoltaic charger is insufficient, has become a key issue in optimizing the controllable circuit management of energy vehicles.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a dynamic energy distribution optimization method and system for a photovoltaic vehicle charger, in order to solve the problem that the existing technology lacks adaptive adjustment capability and is difficult to optimize the distribution strategy according to the load characteristics in a short time, so as to achieve dynamic balance of power distribution, improve the satisfaction of navigation and safety monitoring, and significantly improve the power utilization efficiency and operational stability of the vehicle under complex working conditions.

[0006] This invention provides a dynamic energy distribution optimization method for a photovoltaic vehicle charger, comprising:

[0007] Acquire vehicle operating status data;

[0008] Based on the vehicle operating status data, the load characteristics of the navigation module, power module, and safety monitoring module are analyzed to generate load characteristic data;

[0009] Based on the load characteristic data, a weight allocation table is generated using a weight allocation model;

[0010] Obtain the real-time power demand of the load, and generate a preliminary allocation scheme based on the weight allocation table and the real-time power demand of the load using a priority sorting algorithm;

[0011] Based on the preliminary allocation plan, obtain the power supply stability index, and generate dynamic energy adjustment parameters based on the power supply stability index;

[0012] Based on the aforementioned dynamic energy adjustment parameters, the power distribution ratio is optimized to generate an optimized power distribution scheme.

[0013] Based on the optimized power allocation scheme, the satisfaction level is evaluated, and competitive equilibrium parameters are generated;

[0014] Based on the aforementioned competitive balance parameters, an adaptive adjustment algorithm is used to generate the final allocation scheme.

[0015] In some optional embodiments, the step of generating a weight allocation table based on the load characteristic data using a weight allocation model further includes:

[0016] Based on the load characteristic data, the dynamic change rate of the load is calculated using time series analysis, and the dynamic change trend of the load is obtained based on the dynamic change rate of the load.

[0017] Based on the aforementioned dynamic load change trend, real-time monitoring technology is used to measure the load response time of the navigation module, power module, and safety monitoring module.

[0018] Based on the load response time, a linear regression algorithm is used to predict the load demand of the navigation module, power module, and safety monitoring module to obtain the load demand distribution. If the load demand distribution exceeds a preset demand threshold, the navigation module, power module, and safety monitoring module are prioritized to generate a preliminary priority factor.

[0019] Based on the preliminary priority factor and the preset weight allocation model, the final priority factor is calculated using the weighted average method, and a weight allocation table is generated based on the final priority factor.

[0020] In some optional embodiments, the step of analyzing the load characteristics of the navigation module, power module, and safety monitoring module based on the vehicle operating status data to generate load characteristic data further includes:

[0021] Based on the navigation module parameters in the vehicle operating status data, the load characteristics of the navigation module are calculated according to a preset path planning algorithm;

[0022] Based on the power module parameters in the vehicle operating status data, the support vector machine algorithm is used to analyze the power demand under acceleration conditions and obtain the power module load characteristics.

[0023] The sensor refresh frequency of the safety monitoring module is obtained, the relationship between the sensor refresh frequency and vehicle operating status data is analyzed, the working mode of the safety monitoring module is determined by the decision tree algorithm, and the load characteristics of the safety monitoring module are obtained based on the working mode.

[0024] The load characteristic data is obtained by fusing the load characteristics of the navigation module, the power module, and the safety monitoring module using a weighted average method.

[0025] In some optional embodiments, the step of generating a preliminary allocation scheme using a priority ranking algorithm based on the weight allocation table and the photovoltaic output power further includes:

[0026] Photovoltaic output power data is obtained from the photovoltaic system. If the photovoltaic output power data is lower than a preset output power threshold, a data preprocessing method is used to filter the photovoltaic output power data to obtain smooth power data.

[0027] If the smoothed power data is lower than the preset balanced power threshold, the weight allocation adjustment mechanism is triggered, and the weight coefficients of the navigation module, power module and safety monitoring module are obtained from the weight allocation table.

[0028] Obtain the real-time power demand of the load, and calculate the initial competition scores of the navigation module, power module, and safety monitoring module based on the weighting coefficients and the real-time power demand of the load.

[0029] Based on the initial competition score, the navigation module, power module, and safety monitoring module are sorted using a priority ranking algorithm to obtain a priority sequence;

[0030] Based on the priority sequence, the power allocation ratio of the navigation module, power module, and safety monitoring module is calculated, and a preliminary allocation scheme is obtained based on the power allocation ratio.

[0031] In an optional embodiment, the step of optimizing the power allocation ratio based on the dynamic energy adjustment parameters to generate an optimized power allocation scheme further includes:

[0032] Based on the dynamic energy parameters, the optimized competition score is calculated.

[0033] If the optimized competition score is lower than the preset competition score threshold, a fast response mechanism is triggered to adjust the power allocation ratio and obtain an optimized power allocation scheme.

[0034] In an optional embodiment, the step of evaluating satisfaction and generating competitive balance parameters based on the optimized power allocation scheme further includes:

[0035] Obtain satisfaction data for the navigation module, power module, and safety monitoring module. Normalize the satisfaction data for these modules using a preset standardization processing method to obtain a standardized satisfaction dataset.

[0036] Based on the standardized satisfaction dataset, principal component analysis algorithm is used to reduce the dimensionality of the navigation module satisfaction data, power module satisfaction data, and safety monitoring satisfaction data to obtain a dimensionality-reduced feature set.

[0037] Based on the reduced feature set, the power distribution balance is calculated. If the power distribution balance is lower than the preset balance threshold, the power distribution scheme is obtained by adjusting the distribution weight.

[0038] Based on the optimized power allocation scheme, the competition score deviation of each node is calculated, and the competition score deviation is classified using the support vector machine algorithm to determine the stability of the competition score.

[0039] Based on the stability of the competition score, the operating status of the navigation module, the operating status of the power module, and the operating status of the safety monitoring module, the competition balance parameters are calculated.

[0040] In an optional embodiment, the final allocation scheme is generated using an adaptive adjustment algorithm based on the competition balance parameters, and further includes:

[0041] Based on the aforementioned competition balance parameters, the real-time demand priorities of the navigation module, power module, and safety monitoring module are determined.

[0042] Based on the real-time demand priority, calculate the latest power allocation ratio of the navigation module, power module and safety monitoring module;

[0043] Based on the latest power allocation ratio, an adaptive adjustment algorithm is used to generate the final allocation scheme.

[0044] This invention provides a dynamic energy distribution optimization system for a photovoltaic vehicle charger, comprising:

[0045] The status acquisition module is used to acquire vehicle operating status data;

[0046] The load analysis module is used to analyze the load characteristics of the navigation module, power module, and safety monitoring module based on the vehicle operating status data, and generate load characteristic data.

[0047] The weight allocation module is used to generate a weight allocation table based on the load characteristic data using a weight allocation model.

[0048] The preliminary allocation module is used to obtain the real-time power demand of the load and generate a preliminary allocation scheme based on the weight allocation table and the real-time power demand of the load using a priority sorting algorithm.

[0049] The power supply analysis module is used to obtain power supply stability indicators based on the preliminary allocation scheme, and generate dynamic energy adjustment parameters based on the power supply stability indicators.

[0050] An optimization allocation module is used to optimize the power allocation ratio based on the dynamic energy adjustment parameters and generate an optimized power allocation scheme.

[0051] The scheme evaluation module is used to evaluate the satisfaction level and generate competitive balance parameters based on the optimized power allocation scheme.

[0052] The adjustment and update module is used to generate the final allocation scheme based on the competition balance parameters using an adaptive adjustment algorithm.

[0053] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention.

[0054] The dynamic energy distribution optimization method and system for a photovoltaic vehicle charger of the present invention have the following beneficial effects:

[0055] This method acquires vehicle operating status data, analyzes the load characteristics of the navigation module, power module, and safety monitoring module to generate load characteristic data, and uses a weighted allocation model to generate a weighted allocation table. Based on the weighted allocation table and photovoltaic output power, a priority ranking algorithm is used to generate a preliminary allocation scheme. Power supply stability indicators are acquired to generate dynamic energy adjustment parameters, optimize the power allocation ratio, and generate an optimized power allocation scheme. Based on the optimized power allocation scheme, satisfaction is evaluated, competitive balance parameters are generated, and an adaptive adjustment algorithm is used to generate the final allocation scheme. This invention can achieve dynamic balance in power allocation, improve the satisfaction of navigation and safety monitoring, and significantly improve the power utilization efficiency and operational stability of vehicles under complex operating conditions. Attached Figure Description

[0056] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0057] Figure 1 This is a flowchart of a dynamic energy distribution optimization method for a photovoltaic vehicle charger according to an embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of the dynamic energy distribution optimization system of a photovoltaic vehicle charger according to an embodiment of the present invention. Detailed Implementation

[0059] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0060] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0061] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined. Therefore, the actual execution order may change depending on the specific circumstances.

[0062] like Figure 1 As shown, this embodiment of the invention provides a dynamic energy distribution optimization method for a photovoltaic vehicle charger. This method achieves dynamic balance of power distribution through an adaptive adjustment algorithm, improves the satisfaction of navigation and safety monitoring, and significantly improves the power utilization efficiency and operational stability of the vehicle under complex operating conditions.

[0063] S11. Status Acquisition: Acquire vehicle operating status data. Specifically, real-time vehicle speed and acceleration are acquired through speed and acceleration sensors to determine the motion state. Power module parameters such as motor power, battery output voltage, current, and state of charge are read from nodes like the motor controller and battery management system via the controller area network. Ambient light and temperature data are collected through ambient light and temperature sensors. Simultaneously, data such as the path calculation complexity and screen brightness of the navigation module are specifically acquired, as well as the refresh rate and operating mode of sensors such as LiDAR and cameras in the safety monitoring module. The collected data is transmitted to the central processing unit via CAN bus, Ethernet, and other vehicle network protocols.

[0064] S12. Load Analysis: Based on the vehicle operating status data obtained in step S11, analyze the load characteristics of the navigation module, power module, and safety monitoring module to generate load characteristic data. Use Dijkstra's algorithm to parse map data, combined with a single calculation time of 0.1 seconds and a fixed power consumption model, to output energy consumption data. Based on the motor parameters during acceleration, use the Support Vector Machine (SVM) algorithm to analyze the correlation between battery voltage, current transient values, and acceleration duration, generating load characteristic data. Trigger sensor refresh rates based on environmental parameters, and use a decision tree algorithm to optimize the sampling strategy: when the monitoring accuracy is below a threshold, dynamically adjust the LiDAR sampling frequency and data processing priority to output optimized safety monitoring accuracy data. Finally, fuse the three types of data using a weighted average method to generate structured load characteristic data.

[0065] S13. Weight Allocation: Based on the load characteristic data, a weight allocation table is generated using a weight allocation model. Specifically, based on the collected load characteristic data, including the power utilization percentages of the navigation module, power module, and safety monitoring module, a time series analysis method is used to calculate the dynamic load change rate. Specifically, a differential algorithm is used to calculate the load change trend curve second by second and take the average. Based on this trend curve, real-time monitoring technology is used to measure the task processing latency of each module to determine the quantified value of the load response time. Key indicators such as power peak and periodic fluctuations are extracted from the load response time and load characteristic data, and a linear regression algorithm is used to predict the future load demand distribution of each module. If the predicted load demand distribution exceeds a preset threshold, the navigation module, power module, and safety monitoring module are prioritized to generate preliminary priority factors. Based on the preliminary priority factors and the preset weight allocation model, a weighted average method is used to fuse the preliminary factors with the AHP feature vector at a 7:3 ratio to calculate the final priority factors. A weight allocation table is generated using the final factors and stored in the priority database of the vehicle ECU. The system resource allocation strategy is dynamically adjusted based on the weight allocation table to optimize data processing efficiency.

[0066] S14. Preliminary Allocation: Obtain the real-time power demand of the load. Based on the weight allocation table and the real-time power demand, a preliminary allocation scheme is generated using a priority ranking algorithm. Specifically, when the photovoltaic output power data is lower than a preset output power threshold, the system uses a data preprocessing method to filter the photovoltaic output power to obtain smoothed power data. Then, the smoothed power data is compared with the preset threshold. If it is confirmed to be lower than the preset threshold, the allocation adjustment mechanism is triggered. After triggering, the system reads the weight coefficients of the navigation module, power module, and safety monitoring module from the weight allocation table, and then calculates their real-time competition scores based on their respective real-time power demand. Based on these scores, the system uses a priority ranking algorithm to sort the navigation module, power module, and safety monitoring module to obtain a priority sequence. Then, based on this sequence and the weight coefficients, the power allocation ratio of the navigation module, power module, and safety monitoring module is calculated to generate a preliminary allocation scheme.

[0067] S15. Power Supply Analysis: Based on the preliminary allocation scheme, obtain the power supply stability index and generate dynamic energy adjustment parameters based on the power supply stability index. Specifically, extract the photovoltaic charger output capacity time series from the preliminary allocation scheme, perform spectral analysis on the time series using Fast Fourier Transform, and if a sudden drop frequency higher than the system's set threshold is identified, the system gradually shifts the time series with a fixed-width sliding window, calculating the difference between the power mean in each window and the mean in the previous window, thus obtaining a continuous fluctuation amplitude sequence, and using the variance of this sequence as the power supply stability index. Input this index and historical power values ​​simultaneously into a Kalman filter, and use the state equation and observation equation for iterative updates to predict the short-term trend of the power supply in the next period; if the prediction result shows that the supply is still unstable, then use linear regression to model the relationship between fluctuation amplitude and available power to obtain the initial value of the dynamic energy adjustment coefficient. Then, fine-tune the dynamic energy adjustment parameters within the feasible region using an iterative optimization algorithm.

[0068] S16. Optimized Allocation: Based on the aforementioned dynamic energy adjustment parameters, the power allocation ratio is optimized to generate an optimized power allocation scheme. Specifically, firstly, by real-time acquisition of the system's dynamic energy parameters and load response time, a real-time competition score reflecting the current system competition status is calculated. If this initial score is lower than a preset safety or performance threshold, it indicates that the existing power allocation cannot meet critical needs, and a rapid response mechanism is triggered. The core of this mechanism is to dynamically adjust the power allocation priority, prioritizing the most critical loads, thereby generating a preliminary optimized power allocation scheme. Based on this, the power focus is redirected to the navigation module, power module, and safety monitoring module to obtain the actual power supply data for critical circuits. Subsequently, the support vector machine (SVM) algorithm is used to analyze the power supply data of the navigation module circuit and classify the distribution of its load among the sub-units. If the classification result indicates that the load is unbalanced, the priority ranking of the internal circuits of the navigation module needs to be further adjusted to redistribute power and form an updated power allocation scheme. According to this scheme, the system continuously monitors the operating status of the safety monitoring module and applies a decision tree algorithm to analyze the overall operating data of the critical circuits to determine their stability. When the decision tree determines that the system is operating stably, it records the current power allocation scheme and the corresponding load response time, which serves as the final optimized allocation scheme after multiple verifications and adjustments. The entire process realizes a closed loop of refined power management based on real-time data feedback, machine learning-assisted decision-making, and dynamic priority adjustment.

[0069] S17. Scheme Evaluation: Based on the optimized power allocation scheme, the satisfaction level is evaluated, and competitive balance parameters are generated. Specifically, the system first collects raw data on the satisfaction levels of the navigation module, power module, and safety monitoring module in real time. The raw data is then transformed into a dimensionless standardized satisfaction dataset using a preset standardization algorithm. Next, multi-dimensional feature vectors are extracted from this dataset, and principal component analysis is used for dimensionality reduction to eliminate redundant information and generate a dimensionality-reduced feature set representing the core features. Based on this feature set, the system calculates the power allocation balance. If this value is lower than a preset threshold, the power allocation weights of the navigation module, power module, and safety monitoring module are dynamically adjusted to form an optimized allocation scheme. Subsequently, for the new scheme, the system calculates the deviation value of the competitive score for each node and uses a support vector machine algorithm to classify the deviation (e.g., into three categories: "stable," "fluctuating," and "abnormal"), thereby determining the overall stability of the competitive score. Finally, key indicators such as fluctuation frequency and abnormal amplitude are extracted from the stability assessment results. These are then combined with the operating status of the navigation module, power module, and safety monitoring module through linear weighted fusion to generate competitive equilibrium parameters, providing a quantitative basis for subsequent power allocation decisions.

[0070] S18. Adjustment and Update: Based on the aforementioned competitive balance parameters, an adaptive adjustment algorithm is used to generate the final allocation scheme. Specifically, the system determines the current needs of the navigation module, power module, and safety monitoring module based on the competitive balance parameters. If the load on the navigation module is higher than that on the power module or safety monitoring module, power is preferentially allocated to the navigation module; if the safety monitoring module detects an anomaly, power is immediately reallocated to the safety monitoring module. After generating a module priority sequence through a preset threshold, a dynamic frequency adjustment strategy is initiated, and a linear regression algorithm is used to predict the power demand change trend of each module, outputting an initial power allocation ratio. This ratio is input into a particle swarm optimization algorithm to iteratively calculate the optimal allocation solution under total power constraints. If the deviation between the optimization result and the initial value exceeds a preset threshold, the calculation is recalculated; if the requirements are met, a real-time adjustment scheme is output.

[0071] Through the above steps, this embodiment can achieve dynamic balance of power distribution, improve the satisfaction of navigation and safety monitoring, and significantly improve the power utilization efficiency and operational stability of vehicles under complex operating conditions.

[0072] In some embodiments, based on the above embodiments, the process of generating a weight allocation table using a weight allocation model according to the load characteristic data includes the following steps:

[0073] Based on the load characteristic data, the dynamic change rate of the load is calculated using time series analysis, and the dynamic change trend of the load is obtained based on the dynamic change rate of the load.

[0074] Based on the aforementioned dynamic load change trend, real-time monitoring technology is used to measure the load response time of the navigation module, power module, and safety monitoring module.

[0075] Based on the load response time, a linear regression algorithm is used to predict the load demand of the navigation module, power module, and safety monitoring module to obtain the load demand distribution. If the load demand distribution exceeds a preset threshold, the navigation module, power module, and safety monitoring module are prioritized to generate a preliminary priority factor.

[0076] Based on the preliminary priority factor and the preset weight allocation model, the final priority factor is calculated using the weighted average method, and a weight allocation table is generated based on the final priority factor.

[0077] Assume the load characteristics of the navigation module, power module, and safety monitoring module are as follows: navigation module load value 50, power module load value 70, and safety monitoring module load value 30, in percentage occupancy.

[0078] The load dynamic change rate is calculated using the time series differencing method: with a sampling interval of 1 second, the load data for the most recent 5 seconds is obtained. The load sequence for the navigation module is [48, 49, 50, 50, 51], for the power module it is [68, 69, 70, 71, 72], and for the safety monitoring module it is [28, 29, 30, 30, 31]. The load dynamic change rate algorithm is: (current value - previous value) / previous value, calculating the change rate per second and taking the average value. Therefore, the change rate of the navigation module is [(49-48) / 48,(50-49) / 49,(50-50) / 50,(51-50) / 50] = [0.0208,0.0204,0,0.02], and the average change rate is (0.0208+0.0204+0+0.02) / 4 = 0.0153. The change rate of the power module is [0.0147, 0.0145, 0.0143, 0.0141], with an average change rate of 0.0144; the change rate of the safety monitoring module is [0.0357, 0.0345, 0, 0.0333], with an average change rate of 0.0259.

[0079] Load response time is calculated by simulating task processing latency, assuming a response time of 0.2 seconds for the navigation module, 0.3 seconds for the power module, and 0.15 seconds for the safety monitoring module. The weight allocation model uses the analytic hierarchy process (AHP): the preset priority matrix is ​​1.5 for the navigation module versus the power module, 2 for the navigation system versus the safety monitoring module, and 1.33 for the power module versus the safety monitoring module. The calculated eigenvectors yield a weight allocation table: navigation system 0.46, power module 0.31, and safety monitoring module 0.23. Analysis shows that the navigation system, due to its high weight and high rate of change, requires priority resource allocation, followed by the power module. The safety monitoring module, due to its low response time and relatively high rate of change, requires dynamic resource allocation adjustment. This logical chain optimizes system resource scheduling through load characteristics, rate of change, response time, and weight allocation.

[0080] In some embodiments, based on the above embodiments, the process of analyzing the load characteristics of the navigation module, power module, and safety monitoring module based on the vehicle operating status data to generate load characteristic data includes the following steps:

[0081] Based on the vehicle operating status data, the load characteristics of the navigation module are calculated according to a preset path planning algorithm;

[0082] Based on the load characteristics of the navigation module, the power demand under acceleration is analyzed using the support vector machine algorithm to obtain the load characteristics of the power module.

[0083] The sensor refresh frequency of the safety monitoring module is obtained, the relationship between the sensor refresh frequency and vehicle operating status data is analyzed, and the safety monitoring load characteristic data is obtained by using a decision tree algorithm.

[0084] Assuming the vehicle is traveling at a high speed of 120 km / h, triggering the navigation module to calculate the path in real time, the power module is in acceleration mode, and the safety monitoring module maintains high-frequency sensor refresh. The navigation module's load demand analysis uses Dijkstra's algorithm to calculate the optimal path. Assuming the map data contains 10,000 nodes, a single path calculation requires processing 1,000 nodes, resulting in a computational complexity of O(n^2), with each calculation taking approximately 0.1 seconds. The navigation module consumes 10W, consuming 0.01 kWh per hour. During acceleration (0-100 km / h, completed in 5 seconds), the power module's peak motor power is 200kW with an efficiency of 85%, resulting in an actual power consumption of 200kW × 5s ÷ 3600s = 0.278 kWh. Considering a battery voltage of 400V, the current is approximately 588A. The safety monitoring module includes a LiDAR and a camera, with a refresh rate of 20Hz. A single data processing time is 0.05 seconds, consuming 5W, and consuming 0.005 kWh per hour. Load characteristic data is analyzed over time to extract the peak power and cycle of each system. The navigation module updates the path every 5 seconds, the power module accelerates every 10 seconds, and the safety monitoring module runs continuously.

[0085] Based on comprehensive analysis, the total electrical energy demand for one hour of high-speed driving is 0.01kWh + 0.278kWh + 0.005kWh = 0.293kWh. This data is stored in the vehicle's ECU and uploaded to the cloud via the MQTT protocol for subsequent optimization of route planning and energy consumption management, forming a closed-loop control logic.

[0086] In some embodiments, based on the above embodiments, the process of generating a preliminary allocation scheme using a priority ranking algorithm according to the weight allocation table and photovoltaic output power includes the following steps:

[0087] Photovoltaic output power data is obtained from the photovoltaic system. If the photovoltaic output power data is lower than a preset output power threshold, a data preprocessing method is used to filter the photovoltaic output power data to obtain smooth power data.

[0088] If the smoothed power data is lower than the preset balanced power threshold, the weight allocation adjustment mechanism is triggered, and the weight coefficients of the navigation module, power module and safety monitoring module are obtained from the weight allocation table.

[0089] Obtain the real-time power demand of the load, and calculate the initial competition scores of the navigation module, power module, and safety monitoring module based on the weighting coefficients and the real-time power demand of the load.

[0090] Based on the initial competition score, the navigation module, power module, and safety monitoring module are sorted using a priority ranking algorithm to obtain a priority sequence;

[0091] Based on the priority sequence, the power allocation ratio of the navigation module, power module, and safety monitoring module is calculated, and a preliminary allocation scheme is obtained based on the power allocation ratio.

[0092] Assuming the photovoltaic output power is 500W, which is lower than the preset threshold of 800W, the power allocation process is triggered. The weighting coefficients are obtained as follows: navigation module weight 0.3, power module weight 0.5, and safety monitoring module weight 0.2. The load power is obtained as follows: navigation module 200W, power module 1000W, and safety monitoring module 300W. The initial competition scores are calculated as follows: navigation module score = 0.3 × 200 = 60, power module score = 0.5 × 1000 = 500, and safety monitoring module score = 0.2 × 300 = 60. A priority sorting algorithm is used (sorted in descending order of score), resulting in power module (500), navigation module (60), and safety monitoring module (60). The power allocation ratio is adjusted based on the score ratio: the power module accounts for 500 / (500+60+60)≈0.806, thus receiving 500×0.806=403W of power; the navigation module and safety monitoring module each account for 60 / (500+60+60)≈0.097, thus receiving 500×0.097=48.5W of power each. The preliminary allocation plan is: power module 403W, navigation module 48.5W, safety monitoring module 48.5W.

[0093] Through the above methods, this embodiment can ensure that power allocation meets priorities, has rigorous logic, and is suitable for low-power scenarios.

[0094] In some embodiments, based on the above embodiments, the process of optimizing the power allocation ratio and generating an optimized power allocation scheme based on the dynamic energy adjustment parameters includes the following steps:

[0095] Based on the dynamic energy parameters, the optimized competition score is calculated.

[0096] If the optimized competition score is lower than a preset threshold, a fast response mechanism is triggered to adjust the power allocation ratio and obtain an optimized power allocation scheme.

[0097] Assuming the navigation module currently has a load of 120W, the safety monitoring module currently has a load of 80W, and the total available power of the system is 300W. Based on the collected data, the system uses a weighted allocation algorithm to set the priority weight of the navigation module to 0.6 and the priority weight of the safety monitoring module to 0.4, calculating the initial allocated power: the navigation module receives 300 × 0.6 = 180W, and the safety monitoring module receives 300 × 0.4 = 120W. Subsequently, the system uses a response time optimization algorithm to monitor the real-time response latency of the two modules, assuming a latency of 50ms for the navigation module and 30ms for the safety monitoring module, with a target latency threshold of 40ms. Analysis reveals that the navigation module's latency exceeds the standard, requiring increased power to reduce latency. The system uses a dynamic adjustment formula:

[0098]

[0099] in This is the new power value allocated to the adjusted navigation module. This refers to the power value allocated to the navigation module before adjustment; exceeding the limit results in delay. =50-40=10ms.

[0100] The new power of the navigation module can be calculated. =180×(1+10 / 100)=198W. The remaining power 300-198=102W is allocated to the safety monitoring module. A fast response mechanism is adopted through a real-time feedback controller, which checks the load change every 10ms. If a sudden increase in the navigation module load to 150W is detected, the allocation ratio is recalculated to ensure that the total power does not exceed the limit. After the update, the navigation module is allocated 210W and the safety monitoring module is allocated 90W. Finally, the system verifies the allocation scheme and confirms that the navigation module latency is reduced to 38ms and the safety monitoring module latency is maintained at 30ms, both meeting the threshold requirements. The overall competition score is improved by 20%, and the optimized allocation scheme is to allocate 210W to the navigation module and 90W to the safety monitoring module.

[0101] Through the above methods, this embodiment can achieve automated adjustment, ensuring rigorous logic and strong real-time performance.

[0102] In some embodiments, based on the above embodiments, the process of evaluating satisfaction and generating competitive equilibrium parameters according to the optimized power allocation scheme includes the following steps:

[0103] Obtain navigation module satisfaction data and safety monitoring module satisfaction data, and normalize the navigation module satisfaction data and safety monitoring module satisfaction data using a preset standardization processing method to obtain a standardized satisfaction dataset;

[0104] Based on the standardized satisfaction dataset, principal component analysis algorithm is used to reduce the dimensionality of navigation system satisfaction and safety monitoring satisfaction data to obtain a dimensionality-reduced feature set.

[0105] Based on the reduced feature set, the power distribution balance is calculated. If the power distribution balance is lower than a preset threshold, the power distribution scheme is obtained by adjusting the distribution weight.

[0106] Based on the optimized power allocation scheme, the competition score deviation of each node is calculated, and the competition score deviation is classified using the support vector machine algorithm to determine the stability of the competition score.

[0107] Based on the stability of the competition score, the stability of the navigation path, and the coverage of safety monitoring, the competition balance parameters are calculated.

[0108] Assume that the total available power of the photovoltaic charging system of an electric vehicle is 1000W, the power requirement of the navigation module is 700W, and the power requirement of the safety monitoring module is 300W. The system first collects actual operating data through onboard sensors: the navigation module's path planning success rate is 8.5 out of 10, and the response time is 0.3 seconds; the safety monitoring module's obstacle recognition rate is 98%, and the false alarm rate is 2% (both measured values). Based on a pre-defined standardized calculation formula, the system automatically calculates the navigation satisfaction score S1 = 0.7 × 8.5 + 0.3 × 1 / 0.3 = 6.95, and the safety satisfaction score S2 = 0.8 × 0.98 + 0.2 × 0.98 = 0.98.

[0109] The system further analyzes the real-time power distribution of 10 circuit modules. Assuming the monitored values ​​are [10, 12, 9, 11, 10, 8, 13, 9, 11, 12] kW, the power distribution balance is calculated using the standard deviation as E = 1 - 1.414 / 10.5 = 0.865. Simultaneously, assuming the real-time competition scores of 5 functional modules are [85, 90, 88, 87, 92] points, the score deviation is calculated as D = 2.218 / 88.4 = 0.025. Substituting these parameters into the competition balance parameter formula C = 0.4 × 6.95 + 0.3 × 0.98 + 0.2 × 0.865 - 0.1 × 0.025 = 3.2445, which exceeds the preset threshold of 3.0, triggering a dynamic adjustment mechanism.

[0110] In some embodiments, based on the above embodiments, the process of generating the final allocation scheme using an adaptive adjustment algorithm based on the competition balance parameters includes:

[0111] Based on the aforementioned competition balance parameters, the real-time demand priorities of the navigation module, power module, and safety monitoring module are determined.

[0112] Based on the real-time demand priority, calculate the latest power allocation ratio of the navigation module, power module and safety monitoring module;

[0113] Based on the latest power allocation ratio, an adaptive adjustment algorithm is used to generate the final allocation scheme.

[0114] Based on the assumptions and calculation results of the previous embodiment, when the system detects that the actual demand of the navigation module suddenly increases to 750W due to the vehicle entering complex terrain, the control algorithm automatically increases the priority weight α of the navigation module from 0.5 to 0.6. Through iterative calculation using the particle swarm optimization algorithm, assuming five iterations, under the constraint of a total power of 1000W, the objective function E value is optimized from the initial 2500 to 1800, finally outputting the optimized allocation scheme: 630W allocated to the navigation module and 370W allocated to the safety monitoring module. Real-time system monitoring shows that the adjusted navigation response time is 0.35 seconds, meeting the requirement of ≤0.4 seconds; the false alarm rate of safety monitoring is reduced to 0.9%, meeting the requirement of ≤1%; the critical circuit assurance rate reaches 96%; the entire dynamic adjustment process is completed within 200ms, and the final execution command is output through the PWM signal of the power management IC.

[0115] Through actual testing and comparison, this dynamic allocation scheme improves the power utilization rate by 23% compared with the fixed ratio allocation, effectively verifying the system's adaptive capability under sudden operating conditions.

[0116] like Figure 2 As shown, this embodiment of the invention provides a dynamic energy distribution optimization system for a photovoltaic vehicle charger, which achieves dynamic balance of power distribution through an adaptive adjustment algorithm.

[0117] The status acquisition module M201 is used to acquire vehicle operating status data;

[0118] The load analysis module M202 is used to analyze the load characteristics of the navigation module, power module and safety monitoring module based on the vehicle operating status data, and generate load characteristic data.

[0119] The weight allocation module M203 is used to generate a weight allocation table based on the load characteristic data using a weight allocation model.

[0120] The preliminary allocation module M204 is used to obtain the real-time power demand of the load and generate a preliminary allocation scheme based on the weight allocation table and the real-time power demand of the load using a priority sorting algorithm.

[0121] The power supply analysis module M205 is used to obtain the power supply stability index according to the preliminary allocation scheme, and generate dynamic energy adjustment parameters based on the power supply stability index.

[0122] The optimization allocation module M206 is used to optimize the power allocation ratio based on the dynamic energy adjustment parameters and generate an optimized power allocation scheme.

[0123] The scheme evaluation module M207 is used to evaluate the satisfaction level and generate competitive balance parameters based on the optimized power allocation scheme.

[0124] The adjustment and update module M208 is used to generate the final allocation scheme based on the competition balance parameters using an adaptive adjustment algorithm.

[0125] The M201 status acquisition module acquires three core types of vehicle operating status data in real time via the vehicle's onboard sensor bus: navigation module load, power module load, and safety monitoring load. This industrial big data is continuously uploaded to the cloud at a sampling interval of 1 second, forming a time-series database.

[0126] The load analysis module M202 performs in-depth processing on this raw data: first, it uses a support vector machine algorithm to analyze the power module's energy demand curve during acceleration; then, it uses a decision tree algorithm to optimize the sensor refresh rate of the safety monitoring module; finally, it outputs a load characteristic dataset containing dynamic power consumption characteristics, response time characteristics, and accuracy requirements. For example, in a high-speed driving scenario, the total energy consumption of the three systems in one hour is: Navigation 0.01 + Power 0.278 + Safety 0.005 = 0.293 kWh.

[0127] The weight allocation module M203 uses the Analytic Hierarchy Process (AHP) to process load characteristic data: it calculates the dynamic change rate through a 5-second time window, constructs a judgment matrix based on the response time, and generates a weight allocation table after consistency verification. When the photovoltaic output power is lower than the threshold, the preliminary allocation module M204 initiates a competition mechanism: it calculates real-time scores based on weights and peak power, generates an initial allocation scheme after priority sorting, and then adjusts it to power according to the minimum power guarantee rule. When the power supply analysis module M205 detects a sudden drop in power, it uses PID control to dynamically adjust parameters to stabilize the target power. The optimization allocation module M206 dynamically increases the navigation power, and finally outputs the optimized allocation scheme for the navigation module and the safety monitoring module.

[0128] The scheme evaluation module M207 first evaluates the comprehensive indicators of the optimized scheme: based on the navigation user score of 8.5 points and the response time of 0.3 seconds, the satisfaction level S1=6.95 is calculated, and combined with the 98% recognition rate of safety monitoring, S2=0.98 is obtained; the balance degree E=0.865 is obtained by analyzing the power allocation of 10 circuit modules; the deviation D=0.025 is calculated by the competitive scores of 5 modules [85,90,88,87,92], and finally the competitive balance parameter C=3.2445 is synthesized. When the parameter C exceeds the threshold of 3.0, the self-adjusting update module M208 is triggered: with a total power of 1000W as a constraint, the particle swarm optimization algorithm is used to optimize the objective function E=0.6×(630-750)²+0.4×(370-300)²=1800. After 3 iterations, the final scheme of allocating 630W to the navigation module and 370W to the safety monitoring module is locked. During implementation, closed-loop monitoring with a 200ms cycle verified that the navigation delay remained at 0.35s (<0.4s threshold), the false alarm rate was 0.9% (<1% threshold), and the critical circuit reliability reached 96%. If the reliability rate was lower than 95% (e.g., 90%) during a certain operation, load characteristic data (e.g., the requirement of adding a 100W load D) was collected again, and iterative optimization was performed until the system stability requirements were met.

[0129] In some embodiments, based on the above-described dynamic energy distribution optimization system, a processor coupled to a memory storing executable program code is further included. The processor invokes the executable program code stored in the memory to execute some or all of the steps in the dynamic energy distribution optimization method for a photovoltaic vehicle charger disclosed in Embodiment 1 of the present invention.

[0130] In some embodiments, based on the above-described dynamic energy distribution optimization system, a computer storage medium stores computer instructions, which, when invoked, are used to execute the steps in the dynamic energy distribution optimization method for a photovoltaic vehicle charger disclosed in Embodiment 1 of the present invention.

[0131] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0132] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0133] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing dynamic energy distribution in a photovoltaic vehicle charger, characterized in that, include: Acquire vehicle operating status data, and based on the vehicle operating status data, analyze the load characteristics of the navigation module, power module, and safety monitoring module to generate load characteristic data; Based on the load characteristic data, a weight allocation table is generated using a weight allocation model; Obtain the real-time power demand of the load, and generate a preliminary allocation scheme based on the weight allocation table and the real-time power demand of the load using a priority sorting algorithm; Based on the preliminary allocation plan, obtain the power supply stability index, and generate dynamic energy adjustment parameters based on the power supply stability index; Based on the aforementioned dynamic energy adjustment parameters, the power distribution ratio is optimized to generate an optimized power distribution scheme. Based on the optimized power allocation scheme, the satisfaction level is evaluated, and competitive equilibrium parameters are generated; Based on the aforementioned competitive balance parameters, an adaptive adjustment algorithm is used to generate the final allocation scheme.

2. The method as described in claim 1, characterized in that, The step of generating a weight allocation table based on the load characteristic data using a weight allocation model includes: Based on the load characteristic data, the dynamic change rate of the load is calculated using time series analysis, and the dynamic change trend of the load is obtained based on the dynamic change rate of the load. Based on the aforementioned dynamic load change trend, real-time monitoring technology is used to measure the load response time of the navigation module, power module, and safety monitoring module. Based on the load response time, a linear regression algorithm is used to predict the load demand of the navigation module, power module, and safety monitoring module to obtain the load demand distribution. If the load demand distribution exceeds a preset demand threshold, the navigation module, power module, and safety monitoring module are prioritized to generate a preliminary priority factor. Based on the preliminary priority factor and the preset weight allocation model, the final priority factor is calculated using the weighted average method, and a weight allocation table is generated based on the final priority factor.

3. The method as described in claim 1, characterized in that, The step of analyzing the load characteristics of the navigation module, power module, and safety monitoring module based on the vehicle operating status data to generate load characteristic data includes: Based on the navigation module parameters in the vehicle operating status data, the load characteristics of the navigation module are calculated according to a preset path planning algorithm; Based on the power module parameters in the vehicle operating status data, the support vector machine algorithm is used to analyze the power demand under acceleration conditions and obtain the power module load characteristics. The sensor refresh frequency of the safety monitoring module is obtained, the relationship between the sensor refresh frequency and vehicle operating status data is analyzed, the working mode of the safety monitoring module is determined by the decision tree algorithm, and the load characteristics of the safety monitoring module are obtained based on the working mode. The load characteristic data is obtained by fusing the load characteristics of the navigation module, the power module, and the safety monitoring module using a weighted average method.

4. The method as described in claim 1, characterized in that, The step of obtaining the real-time power demand of the load, and generating a preliminary allocation scheme based on the weight allocation table and the real-time power demand of the load using a priority sorting algorithm, includes: Photovoltaic output power data is obtained from the photovoltaic system. If the photovoltaic output power data is lower than a preset output power threshold, a data preprocessing method is used to filter the photovoltaic output power data to obtain smooth power data. If the smoothed power data is lower than the preset balanced power threshold, the weight allocation adjustment mechanism is triggered, and the weight coefficients of the navigation module, power module and safety monitoring module are obtained from the weight allocation table. Obtain the real-time power demand of the load, and calculate the initial competition scores of the navigation module, power module, and safety monitoring module based on the weighting coefficients and the real-time power demand of the load. Based on the initial competition score, the navigation module, power module, and safety monitoring module are sorted using a priority ranking algorithm to obtain a priority sequence; Based on the priority sequence, the power allocation ratio of the navigation module, power module, and safety monitoring module is calculated, and a preliminary allocation scheme is obtained based on the power allocation ratio.

5. The method as described in claim 1, characterized in that, The step of optimizing the power allocation ratio and generating an optimized power allocation scheme based on the dynamic energy adjustment parameters includes: Based on the dynamic energy parameters, the optimized competition score is calculated. If the optimized competition score is lower than the preset competition score threshold, a fast response mechanism is triggered to adjust the power allocation ratio and obtain an optimized power allocation scheme.

6. The method as described in claim 1, characterized in that, The step of evaluating satisfaction and generating competitive equilibrium parameters based on the optimized power allocation scheme includes: Obtain satisfaction data for the navigation module, power module, and safety monitoring module. Normalize the satisfaction data for these modules using a preset standardization processing method to obtain a standardized satisfaction dataset. Based on the standardized satisfaction dataset, principal component analysis algorithm is used to reduce the dimensionality of the navigation module satisfaction data, power module satisfaction data, and safety monitoring satisfaction data to obtain a dimensionality-reduced feature set. Based on the reduced feature set, the power distribution balance is calculated. If the power distribution balance is lower than the preset balance threshold, the power distribution scheme is obtained by adjusting the distribution weight. Based on the optimized power allocation scheme, the competition score deviation of each node is calculated, and the competition score deviation is classified using the support vector machine algorithm to determine the stability of the competition score. Based on the stability of the competition score, the operating status of the navigation module, the operating status of the power module, and the operating status of the safety monitoring module, the competition balance parameters are calculated.

7. The method as described in claim 1, characterized in that, The step of generating the final allocation scheme using an adaptive adjustment algorithm based on the competition balance parameters includes: Based on the aforementioned competition balance parameters, the real-time demand priorities of the navigation module, power module, and safety monitoring module are determined. Based on the real-time demand priority, calculate the latest power allocation ratio of the navigation module, power module and safety monitoring module; Based on the latest power allocation ratio, an adaptive adjustment algorithm is used to generate the final allocation scheme.

8. A dynamic energy distribution optimization system for a photovoltaic vehicle charger, characterized in that, The system includes: The status acquisition module is used to acquire vehicle operating status data; The load analysis module is used to analyze the load characteristics of the navigation module, power module, and safety monitoring module based on the vehicle operating status data, and generate load characteristic data. The weight allocation module is used to generate a weight allocation table based on the load characteristic data using a weight allocation model. The preliminary allocation module obtains the real-time power demand of the load and generates a preliminary allocation scheme based on the weight allocation table and the real-time power demand of the load using a priority sorting algorithm. The power supply analysis module is used to obtain power supply stability indicators based on the preliminary allocation scheme, and generate dynamic energy adjustment parameters based on the power supply stability indicators. An optimization allocation module is used to optimize the power allocation ratio based on the dynamic energy adjustment parameters and generate an optimized power allocation scheme. The scheme evaluation module is used to evaluate the satisfaction level and generate competitive balance parameters based on the optimized power allocation scheme. The adjustment and update module is used to generate the final allocation scheme based on the competition balance parameters using an adaptive adjustment algorithm.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 7.

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