Multi-device cooperative charging system and method based on w algorithm

Abstract

The application provides a multi-device cooperative charging system and method based on W algorithm, and relates to the technical field of cooperative charging. The application comprises a charging data collection module for collecting charging data; a weight assignment module for analyzing key factors affecting charging and replacing decisions and assigning weights by using W algorithm; a comprehensive evaluation module for calculating component matrices of each vehicle and each site, and assigning weights to the component matrices according to a qualified weight table; a charging and replacing distribution decision module for distributing each vehicle to a site with the maximum evaluation value, and updating the use state of charging piles; and a real-time monitoring module for monitoring the site state and vehicle progress in real time, and judging whether a change event occurs, and if so, adjusting the matching of vehicles and sites. The application constructs an intelligent charging network with pile station cooperation, dynamically optimizes vehicle distribution through a weight self-adaptive algorithm driven by real-time data, and realizes the most efficient, fastest responding and power grid friendly energy supplement scheduling.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of cooperative charging technology, in particular to a multi-device cooperative charging system based on a W algorithm and a multi-device cooperative charging method based on a W algorithm. BACKGROUND

[0002] With electric two-wheelers becoming the daily commuting tool for hundreds of millions of people, a huge demand for charging has suddenly emerged, especially during the peak period of evening residential area electricity consumption. The disordered charging mode of one vehicle per pile has led to a sharp rise in local power grid load and a sharp increase in safety hazards. The behavior of flying wire charging and battery home charging has caused a large number of fire accidents, becoming a serious urban safety management problem. These pain points show that traditional and isolated charging facilities have been unable to meet the demand for safe, efficient and large-scale applications.

[0003] Traditional systems usually simply connect multiple charging piles for static power distribution and queuing management. Their cooperative ability is weak, charging piles and battery swap stations operate independently, and global optimization across resources cannot be achieved. They use fixed rules and cannot dynamically adjust strategies according to grid state and inventory conditions. They lack adaptive ability, and when equipment fails or demand surges, manual intervention is often required, which cannot quickly perform local rescheduling, affecting the overall efficiency and stability of the system. Against this background, the key technologies of Internet of Things, cloud computing and intelligent algorithms have matured, providing a possibility for solutions. SUMMARY

[0004] The present application provides a multi-device cooperative charging system based on a W algorithm to solve the lack of adaptive ability in the prior art, which often requires manual intervention when equipment fails or demand surges, and cannot quickly perform local rescheduling.

[0005] In one aspect, the present application provides a multi-device cooperative charging system based on a W algorithm, comprising:

[0006] A charging data collection module collects charging pile positions, power and usage status to obtain a charging data set.

[0007] A weight assignment module is used to analyze key factors affecting charging / swap decisions based on the charging data set and to assign weights using the weight adaptive adjustment mechanism of the W algorithm according to the operation strategy to obtain a qualified weight table.

[0008] A comprehensive evaluation module is used to calculate the component matrix of each vehicle and each site, and to assign weights to the component matrix according to the qualified weight table to obtain a comprehensive evaluation matrix.

[0009] A charging and swapping distribution decision module is used to assign each vehicle to the site with the maximum evaluation value according to the comprehensive evaluation matrix, update the usage status of the charging pile or the battery inventory data of the battery swap station, and repeat until all vehicles to be replenished are assigned.

[0010] A real-time monitoring and dynamic adjustment module is configured to monitor the station state and vehicle progress in real time when the energy is supplemented, and determine whether a change event occurs, and if so, adjust the matching of the vehicle and the station.

[0011] According to the multi-device cooperative charging system based on the W algorithm provided by the application, in the weight assignment module, the specific steps of assigning weights by using the weight self-adaptive adjustment mechanism of the W algorithm are as follows:

[0012] Factors affecting the charging and battery swapping decision are screened from the charging data set, and are layered according to core rigidity and operation efficiency, and classified key factors are output.

[0013] On the basis of the operation strategy, the AHP algorithm is used according to the classified key factor list to assign basic weights to different scenes, to clearly define the initial proportion of each factor, and to output the initial weights.

[0014] Real-time data are collected, and according to the classified key factors and the initial weights, factors that need to be adjusted and the change amplitudes are identified, and the factors to be adjusted and the dynamic change values thereof are output.

[0015] According to the factors to be adjusted and the change values, the W algorithm is used to iteratively optimize the weights, and the initial weights are dynamically adjusted, and a dynamic weight table is output.

[0016] The dynamic weight table is used to check whether the core factor weights meet the standards, and if not, the adjustment is traced back, and a qualified weight table is output.

[0017] According to the multi-device cooperative charging system based on the W algorithm provided by the application, the specific steps of using the W algorithm to iteratively optimize the weights are as follows:

[0018] According to the factors to be adjusted and the dynamic change values thereof, and the initial weight table, an upper limit of the number of iterations and a convergence threshold value are set, the current weights are initialized as the initial weights, and an iteration environment configuration containing parameters is output.

[0019] According to the dynamic change values of the factors to be adjusted, an influence coefficient is calculated for each factor to be adjusted according to a preset rule, and a factor coefficient corresponding table is output.

[0020] According to the initial weights and the influence coefficients, a temporary weight is calculated using the W algorithm formula, and the temporary weight is output.

[0021] According to the temporary weight table, it is checked whether the threshold value rule is met, and if not, the temporary weight is compressed in proportion, and a constrained temporary weight is output.

[0022] The difference between the constrained temporary weight and the temporary weight is compared, and it is determined whether the difference is less than the convergence threshold value, and if so, a dynamic weight table is output. Otherwise, the constrained temporary weight is taken as a new current weight, and the temporary weight is recalculated.

[0023] The W algorithm-based multi-device cooperative charging system provided by the application comprises a comprehensive evaluation module, a charging data set, a data preparation unit, a component matrix calculation unit, and a comprehensive evaluation value calculation unit.

[0024] The data preparation unit is configured to extract associated data of each vehicle and each site from the charging data set, wherein the associated data comprises vehicle-site location data, vehicle power data, site inventory data, and energy supplement consumption time data.

[0025] The component matrix calculation unit is configured to calculate a component matrix according to the associated data.

[0026] The comprehensive evaluation value calculation unit is configured to calculate a vehicle-site comprehensive evaluation matrix according to the component matrix and a dynamic weight table.

[0027] The W algorithm-based multi-device cooperative charging system provided by the application comprises a component matrix calculation unit.

[0028] The distance calculation subunit is configured to calculate an actual distance according to the vehicle-site location data by using a Haversine formula, standardize the actual distance into a distance component, convert the distance component into an expected arrival time length by combining a road network congestion coefficient, and output a distance component matrix.

[0029] The emergency degree calculation subunit is configured to calculate an emergency degree component according to the vehicle power data and a user emergency mark, and output an emergency degree component matrix.

[0030] The battery replacement adaptation calculation subunit is configured to judge a vehicle battery specification and a site inventory matching degree according to the site inventory data, superimpose an inventory sufficiency degree, and sum up the matching degree and the sufficiency degree into a battery replacement adaptation component, and output an adaptation component matrix.

[0031] The efficiency component calculation subunit is configured to utilize the energy supplement consumption time data, superimpose a queuing time length correction according to a charging time length score, and output an efficiency component matrix.

[0032] The W algorithm-based multi-device cooperative charging system provided by the application comprises a charging and battery replacement distribution decision module.

[0033] The vehicle priority sorting unit is configured to extract vehicle emergency degree data from the comprehensive evaluation matrix and sort the vehicle emergency degree data, and output a priority distribution vehicle list.

[0034] The resource distribution unit is configured to extract candidate sites of the top three evaluation values of each vehicle from the comprehensive evaluation matrix, mark resource states of the candidate sites, check whether the candidate sites meet vehicle requirements to reserve effective sites, distribute the effective sites with the highest evaluation values to each vehicle and record relationships, and output a preliminary distribution table.

[0035] The resource state updating unit is configured to update corresponding site states according to the preliminary distribution table, and obtain a site resource state table.

[0036] The allocation verification unit compares the priority allocation vehicle list with the preliminary allocation table to check for any unallocated vehicles. If any unallocated vehicles are found, the process returns to the second step to re-filter candidate sites. If all allocations are completed, the final allocation results and the updated site resource table are output.

[0037] The multi-device collaborative charging system based on the W algorithm provided by the present invention includes a resource allocation unit comprising:

[0038] The candidate site selection subunit is used to extract the top three sites with the highest evaluation values ​​for each vehicle from the comprehensive evaluation matrix as candidates, mark the current resource status of the sites, and output a table of candidate sites for each vehicle.

[0039] The resource compatibility verification subunit is used to verify whether the resources of each candidate station in the vehicle candidate station correspondence table meet the vehicle requirements. If they do, the candidate station is retained; otherwise, stations that do not meet the conditions are removed, and a list of valid candidate stations for vehicles is output.

[0040] The allocation execution subunit is used to allocate the highest-valued valid station to each vehicle based on the valid candidate station list, record the allocation relationship, and output a preliminary allocation table.

[0041] The multi-device collaborative charging system based on the W algorithm provided by the present invention includes a real-time monitoring and dynamic adjustment module comprising:

[0042] The real-time data acquisition unit is used to continuously collect station status, vehicle progress and new vehicle information, and output real-time dynamic datasets.

[0043] The change event identification unit is used to compare the real-time dynamic dataset with the data from the previous moment to determine whether a sudden event has occurred. If so, it outputs a list of change events.

[0044] The impact scope definition unit is used to analyze the range of vehicles and stations affected by the event based on the list of change events, and output a list of affected vehicles and related stations.

[0045] The dynamic evaluation recalculation unit is used to recalculate the comprehensive evaluation value of affected vehicles and available stations based on the real-time dataset, generate a new vehicle-station evaluation matrix, and output temporary adjustment evaluation results.

[0046] The adjustment scheme generation unit is used to redistribute affected vehicles based on the temporary assessment results, update the site resource pre-occupancy status, and output a dynamic adjustment scheme.

[0047] The formula for calculating the comprehensive evaluation value of the multi-device collaborative charging system based on the W algorithm provided by the present invention is as follows:

[0048]

[0049] In the formula, Let v be the distance from vehicle v to station s. To meet the vehicle's energy replenishment needs, The instantaneous available power or effective power of station s. , , Weighting coefficients for distance, energy replenishment requirements, and instantaneous available power or efficiency at the site. Regarding vehicle refueling needs The function, For information about the instantaneous available power or efficiency of site s The function, Regarding distance The function.

[0050] This invention also provides a multi-device collaborative charging method based on the W algorithm, including:

[0051] S1: Collect charging pile location, power and usage status, battery swapping station geography, inventory and battery swapping efficiency, vehicle location, power level and battery specifications to obtain charging dataset.

[0052] S2: Analyze the key factors affecting charging / swapping decisions based on the charging dataset, and assign weights according to the operational strategy using the W algorithm's weight adaptive adjustment mechanism to obtain a qualified weight table.

[0053] S3: Calculate the component matrix of each vehicle and each station, and assign weights to each component matrix according to the qualified weight table to obtain a comprehensive evaluation.

[0054] S4: Based on the comprehensive evaluation matrix, assign each vehicle to the station with the highest evaluation value, update the usage status of the charging pile or the battery inventory data of the battery swapping station, until all vehicles waiting to be recharged have been assigned.

[0055] S5: Monitors the station status and vehicle progress in real time during refueling, and determines whether there are changes in new vehicles, station failures, or inventory. If so, it adjusts the matching of vehicles and stations.

[0056] The multi-device collaborative charging system based on the W algorithm provided by this invention constructs a smart charging network with charging pile-station collaboration. Through a real-time data-driven weighted adaptive algorithm, it dynamically optimizes vehicle allocation, achieving the highest global efficiency, fastest response, and grid-friendly energy dispatching. The beneficial effects achieved are as follows:

[0057] The multi-device collaborative charging system designed in this invention has the core advantage of completely surpassing the limitations of traditional charging management models through a highly integrated, intelligent, and dynamically responsive architecture. It achieves a paradigm shift from single, static, and passive resource allocation to multi-dimensional, dynamic, and proactive energy service scheduling. This system integrates dispersed charging piles and battery swapping stations into a unified pile-station collaborative network, and with its sophisticated modular design, it demonstrates outstanding advantages in efficiency, resilience, user experience, and grid integration.

[0058] This invention constructs a global real-time data view, integrating heterogeneous data such as geographic location, power status, battery inventory, and vehicle demand into a unified charging dataset, breaking down information silos. This enables the system to make decisions from a global perspective, rather than focusing on local optimizations.

[0059] This invention introduces a weighted adaptive adjustment mechanism based on the W algorithm. This mechanism quantifies operational strategies into key performance indicators (KPIs) and dynamically adjusts the weights of key factors such as distance, urgency, inventory, and efficiency by continuously comparing the deviations between actual and target values. This guides vehicles towards orderly charging or battery swapping. When battery swapping stations in a certain area have sufficient battery inventory, the system reduces the inventory weight and increases the efficiency weight, encouraging users to prioritize battery swapping. This feedback-based continuous learning and optimization capability ensures that the system can proactively adapt to changes in the external environment and consistently make efficient decisions around core operational objectives.

[0060] This invention can readily handle various emergencies. When charging piles malfunction, battery swapping stations run out of stock, or new vehicles suddenly flood the system, it can quickly detect the anomalies and accurately pinpoint the affected vehicles and stations, rather than blindly performing a global reset. It recalculates the overall assessment value only for the affected parts. This localized, precise, and rapid response mechanism greatly reduces the impact of sudden failures on the overall system, ensuring service continuity and stability, and avoiding the risk of system-wide paralysis caused by a single disruption.

[0061] This invention transforms dispersed charging activities into a controllable collective resource through intelligent scheduling. When the grid load is too high, the system can automatically reduce charging power or guide users to use battery swapping by adjusting weights, thus shaving the grid peak. During off-peak hours, charging is encouraged to fill the valleys. This capability transforms the charging network from a consumer of the energy system into an active contributor to stability, providing support for grid stability under a high proportion of renewable energy integration, and aligning with the development direction of new power systems. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0063] Fig. 1 This is a schematic diagram of a multi-device collaborative charging system based on the W algorithm provided in an embodiment of the present invention;

[0064] Fig. 2 This is a flowchart illustrating the multi-device collaborative charging method based on the W algorithm provided in this embodiment of the invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0066] The following is combined with Figs. 1-2 This invention describes a multi-device collaborative charging system and method based on the W algorithm.

[0067] like Fig. 1 As shown, the multi-device collaborative charging system based on the W algorithm provided in this embodiment of the invention includes:

[0068] The charging data collection module collects information on charging pile location, power, and usage status; battery swapping station geography, inventory, and swapping efficiency; and vehicle location, battery level, and battery specifications to obtain a charging dataset.

[0069] Record the specific location information of each charging station, clarifying its specific coordinates or area, to facilitate subsequent judgment of its distance from the vehicle. Obtain the charging power, current usage status, and remaining available charging power of each charging station, which is related to its ability to undertake vehicle charging tasks and charging efficiency.

[0070] Clearly defining the geographical location of each battery swapping station, its distribution, and its connection to surrounding roads and frequently visited areas of users facilitates assessment of vehicle accessibility. It also involves tracking the inventory of each type of battery at the stations to understand the current availability of fully charged batteries for swapping, directly impacting the ability to meet vehicle swapping needs. Furthermore, it's crucial to understand the efficiency of the swapping operations, such as the average time required for each swap, as swapping times can vary between stations due to differences in equipment and operator proficiency. Using the vehicle's positioning system, the current location of each electric two-wheeler can be determined, and the remaining battery power can be checked to assess the urgency of the vehicle's power needs. Lower remaining power indicates a more urgent need for charging or swapping. The type and specifications of the vehicle's battery must be clearly identified to ensure compatibility with the station's backup batteries, and the estimated charging time at charging stations can also be estimated.

[0071] The collected data on charging piles, battery swapping stations, and vehicles are organized and summarized to form a clear and organized data set.

[0072] The weighting module assigns weights based on four key factors: charging data output distance, urgency of energy replenishment, battery swapping inventory, and charging / swapping efficiency. It utilizes the W algorithm's adaptive weighting adjustment mechanism. Distance factor: This considers the physical distance between the vehicle and the charging pile or battery swapping station. A shorter distance means less travel time for the user, higher convenience, and reduced energy consumption during the journey, making it a crucial factor.

[0073] Urgency Factors: Based on the vehicle's remaining battery power, vehicles with very low remaining power urgently need charging. The method that can quickly meet their needs should be prioritized, whether it's charging or battery swapping. Battery Swapping Inventory Factors: For battery swapping stations, the number of fully charged batteries in stock determines whether a vehicle can be successfully swapped. If inventory is insufficient, even if the distance is suitable, a battery swapping option may not be available. Therefore, this factor significantly impacts battery swapping decisions. Charging Efficiency Factors: Considering the estimated charging time at a charging station and the average swapping time at a battery swapping station, choosing a shorter charging method is more beneficial for users, reducing waiting time and improving vehicle utilization efficiency.

[0074] Factors influencing charging and battery swapping decisions are filtered from the charging dataset, categorized by core rigidity and operational efficiency, and key factors are output for each category.

[0075] While core rigid factors determine whether vehicles can successfully undergo charging and battery swapping operations, operational efficiency is equally crucial, affecting the smoothness and timeliness of the entire process. Effective station inventory is a key aspect; the quantity of batteries of different specifications and health conditions in the battery swapping station directly impacts the timely availability of suitable batteries for arriving vehicles. Inventory changes over time based on vehicle swapping activity and battery replenishment / maintenance. Charging pile load reflects the current utilization of the charging piles; excessive load indicates potential queuing for charging, and this value dynamically varies depending on vehicle charging demand at different times. Estimated charging time comprehensively considers the vehicle's travel time to the station, station queuing time, and the actual charging / swapping operation time.

[0076] The peak-valley difference in charging electricity prices means that charging costs will vary significantly depending on the time of day. Operators need to consider how to meet user demand while reasonably guiding users to charge during off-peak hours to reduce costs. Battery swapping costs involve the performance degradation of the battery after each swap and during daily use and charging / discharging, which is closely related to the battery's health and cycle life.

[0077] Based on the operational strategy, the AHP algorithm is used to assign basic weights to different scenarios according to the list of key classification factors, clarify the initial proportion of each factor, and output the initial weights.

[0078] The Analytic Hierarchy Process (AHP) is used to assign basic weights to the key classification factors in different scenarios. The AHP algorithm first needs to build a hierarchical structure model, placing the overall goal of charging and swapping decisions at the top layer. The middle layer consists of the previously defined core rigidity, operational efficiency, user experience, and cost control factors. The bottom layer consists of the specific key factors, such as vehicle battery urgency and effective site inventory.

[0079] By analyzing historical data or operational experience, pairwise comparisons are made between factors at each level to construct a judgment matrix. For example, in emergency scenarios, comparative analysis suggests that core rigid factors are more important than other factors, assigning a importance score of 5 to core rigid factors compared to operational efficiency factors, indicating that in this situation, the importance of core rigid factors is significantly higher than that of operational efficiency factors.

[0080] Using the eigenvector method, the judgment matrix is ​​calculated to determine the weight of each factor in the corresponding scenario, thus clarifying the initial proportion of each factor. For different scenarios, such as normal operation, peak hours, and off-peak hours, the process of constructing the judgment matrix and calculating weights is repeated. This ultimately generates an initial weight table containing different scenario labels and the specific proportion of each factor in the corresponding scenario, providing an initial reference standard for dynamically adjusting the weights based on actual conditions.

[0081] Data is collected in real time. Based on the key classification factors and initial weights, the factors that need to be adjusted and the magnitude of change are identified, and the factors to be adjusted and their dynamic change values ​​are output.

[0082] Regarding vehicle battery urgency, the system obtains the remaining battery charge (SOC) value from the vehicle's battery management system in real time, updating the data every few minutes to accurately monitor real-time changes in vehicle battery charge. For station inventory, the inventory management system within the swapping station records the entry and exit of each battery type and the current remaining quantity in real time, while also monitoring battery health and cycle count information to accurately determine the true state of effective inventory. For charging pile load, the system uses a built-in monitoring module to statistically analyze the number of vehicles currently charging and the charging power, calculating the charging pile's load rate, and dynamically updating this data as vehicles enter and leave. Similarly, for factors such as estimated arrival time, user preferences, charging electricity prices, and battery swapping costs, there are corresponding data sources and collection methods to obtain the latest actual situation.

[0083] Based on the initial weighting table, the collected real-time data was analyzed and compared. Using the importance and proportion of each factor as set by the initial weights as a reference standard, it was examined which factors showed significant deviations from the applicable scenarios for the initial weights. For example, if the initial weights were set to maintain a certain inventory level for a specific battery type at a battery swapping station under normal operating conditions to maintain the corresponding operational efficiency weighting, but real-time data showed a sudden and significant drop in the inventory of that battery type, far below the expected level, this means that the actual situation of this factor has deviated from the scenario on which the initial weights were based, and adjustments are needed.

[0084] Based on the factors to be adjusted and their changes, the W algorithm is used to iteratively optimize the weights, and the initial weights are dynamically adjusted to output a dynamic weight table.

[0085] Based on initial weights, which represent the importance distribution of various factors under a given operational strategy and typical scenario, the initial weights serve as the reference benchmark for the entire weight adjustment. The adjustment amount is calculated by combining the dynamic changes of the factors to be adjusted, and the weights are updated iteratively. In the first iteration, the initial weights are calculated with the calculated adjustment amount to obtain the initially adjusted weight values. In each iteration, all factors to be adjusted and their changes are re-examined, and the weights of each factor are continuously adjusted according to the rules of the W algorithm, while also comprehensively considering the mutual influence and weight balance among the factors. Through multiple rounds of such iterative calculations and adjustments, the weight allocation is continuously optimized, allowing the weights of each factor to dynamically adapt to changes in the actual situation, ultimately forming a dynamic weight table containing the weight values ​​of each factor after multiple iterations of optimization.

[0086] Based on the dynamic changes of the factors to be adjusted, the influence coefficient of each factor to be adjusted is calculated according to the preset rules, and the factor coefficient correspondence table is output.

[0087] This set of preset rules is based on a large amount of past charging and swapping operation data, analysis of actual business scenarios, and professional experience in related fields. It aims to reasonably reflect the relationship between the magnitude of factor changes and their impact on weight adjustments. For each factor to be adjusted, based on its specific dynamic change value, the corresponding impact coefficient is accurately calculated according to its respective preset rules. Then, the factor name and the calculated impact coefficient are matched one by one and compiled into a standardized factor coefficient correspondence table.

[0088] The temporary weights are calculated using the W algorithm formula based on the initial weights and influence coefficients, and then output as temporary weights.

[0089] The system checks whether the temporary weights meet the threshold rules. If they exceed the limits, they are compressed proportionally, and the constrained temporary weights are output. The system rigorously checks the temporary weights of each factor according to the preset threshold rules to ensure the rationality and balance of the entire weight system. This prevents situations where certain factors have excessively high or low weights, leading to deviations in charging / swapping decisions. If any non-compliance with the rules is found, adjustments such as proportional compression are performed promptly, and the constrained temporary weights table is finally output.

[0090] Compare the difference between the constrained temporary weights and the current temporary weights. Determine if the difference is less than the convergence threshold. If so, output the dynamic weight table. Otherwise, use the constrained temporary weights as the new current weights and recalculate the temporary weights.

[0091] Use a dynamic weight table to check whether the weights of core factors meet the standards. If they exceed the standards, backtrack and adjust them to output a qualified weight table.

[0092] The W algorithm, based on an optimization logic combining weighted summation and gradient descent, aims to maximize charging efficiency, minimize equipment safety risks, and balance grid load. It achieves multi-objective collaborative optimization through dynamic weight allocation. Its core formula is expressed as:

[0093]

[0094] In the formula: O bj To comprehensively optimize the target value, a larger value indicates a better solution. ω1, ω2, and ω3 are weighting coefficients for charging efficiency, equipment safety, and grid load, respectively, and can be dynamically adjusted according to the scenario. E is the charging efficiency, calculated by dividing the actual charging power by the average of the equipment's rated charging power. S is the safety risk coefficient, calculated by weighting parameters such as battery temperature, voltage fluctuation, and overcharge risk. L is the grid load deviation coefficient, calculated by dividing the current total charging power by the grid's rated carrying capacity; the closer it is to 1, the more unbalanced the load.

[0095] The comprehensive evaluation module is used to calculate the distance component, the urgency component based on the power consumption, the battery swapping compatibility component based on the inventory, and the efficiency component based on the time consumption for each vehicle and each station according to the weights, and then sum up these components to obtain the comprehensive evaluation value.

[0096] The data preparation unit is used to extract the correlation data between each vehicle and each station from the charging dataset. The correlation data includes vehicle and station location data, vehicle power data, station inventory data, and charging time data.

[0097] The component matrix calculation unit is used to calculate individual component matrices based on the associated data.

[0098] The distance calculation subunit calculates the actual distance based on vehicle and station location data using the Haversine formula, standardizes it into distance components, and combines this with the road network congestion coefficient to convert it into estimated arrival time, outputting a distance component matrix. The actual distance from the vehicle to the charging pile or battery swapping station is calculated using the straight-line distance formula, and then multiplied by the reciprocal of the distance weight to obtain the evaluation value component corresponding to the distance factor.

[0099] The urgency calculation subunit is used to calculate the urgency component based on vehicle battery data and user emergency markers, and output an urgency component matrix. The evaluation value component corresponding to this factor is obtained by multiplying the urgency weight of the charging situation by the reciprocal of the vehicle's remaining battery capacity.

[0100] The battery swapping adaptation calculation subunit is used to determine the matching degree between vehicle battery specifications and battery swapping station inventory based on station inventory data, and to add inventory adequacy, summing the results into a battery swapping adaptation component, which is then output as an adaptation component matrix. For each battery swapping station, the evaluation value component corresponding to that factor is obtained by multiplying the battery swapping inventory weight by the number of fully charged batteries of the corresponding vehicle battery type in the station's inventory.

[0101] The efficiency component calculation subunit utilizes the charging time data, adjusts it by adding queuing time correction to the charging time score, and outputs an efficiency component matrix. For charging piles, the charging efficiency weight is multiplied by the reciprocal of the estimated charging time. For battery swapping stations, this weight is multiplied by the reciprocal of the average battery swapping time to obtain their respective evaluation value components.

[0102] The comprehensive evaluation value calculation unit calculates the comprehensive evaluation matrix of the vehicle and the station based on the component matrix and the dynamic weight table. The evaluation value components corresponding to each factor calculated above are added together to obtain the comprehensive evaluation value of each vehicle for each charging pile and battery swapping station. This value comprehensively reflects the quality of the vehicle's choice of charging pile or battery swapping station.

[0103] The charging and battery swapping allocation decision module is used to allocate each vehicle to the station with the highest evaluation value based on the comprehensive evaluation value, and update the usage status of the charging pile or the battery inventory data of the battery swapping station in the first step until all vehicles waiting to be recharged have been allocated.

[0104] The vehicle priority ranking unit performs in-depth work by precisely extracting the urgency data for each vehicle from the comprehensive evaluation matrix. This data mainly includes key information such as the vehicle's remaining battery charge (SOC) and whether the user has marked an emergency charging need. Then, the vehicles are sorted according to preset priority rules. For example, vehicles with SOC < 15% and marked as emergency are assigned to priority level 1, vehicles with only an emergency marking on their SOC are assigned to priority level 2, and so on. The final output is a list of vehicles to be allocated, ranked from highest to lowest priority.

[0105] The resource allocation unit integrates the entire process of candidate site selection, resource compatibility verification, and allocation execution. It calls upon a comprehensive evaluation matrix, and the candidate site selection subunit extracts the top three sites with the highest evaluation values ​​for each vehicle as candidates, detailing the current resource status of these sites, such as the number of available charging piles, and the battery inventory and specifications of the battery swapping stations, forming a vehicle-to-candidate-site mapping table. The resource compatibility verification subunit checks each candidate site in this mapping table to ensure its resources match the vehicle's needs, such as charging pile interface compatibility and whether the battery swapping station has compatible batteries with sufficient inventory. Sites meeting the criteria are retained, while those not meeting the criteria are removed, outputting a vehicle-to-valid-candidate-site table. The allocation execution subunit, based on this valid candidate-site table, allocates the highest-value valid site to each vehicle, records the vehicle-site mapping, and outputs a preliminary allocation table.

[0106] The candidate site filtering subunit is used to extract the top three sites with the highest evaluation values ​​for each vehicle from the comprehensive evaluation matrix as candidates, mark the current resource status of the sites, and output a vehicle candidate site correspondence table. It accurately selects the top three sites with the highest evaluation values ​​for each vehicle from the comprehensive evaluation matrix. First, it iterates through all site evaluation data corresponding to each vehicle in the matrix, sorts them by evaluation value from highest to lowest, and extracts the information of the top three sites, including site ID and type. Simultaneously, this subunit connects to the site resource database in real time, marking the current resource status of these candidate sites—such as the idle / occupied / faulty status of charging piles, interface type, current load rate, battery inventory quantity of battery swapping stations, number of available swapping bays, and real-time queuing time—and finally integrates these to form a vehicle candidate site correspondence table, clearly presenting the association between each vehicle and the three candidate sites, as well as the site resource details.

[0107] The resource compatibility verification subunit checks whether the resources of each candidate site in the candidate site table meet the vehicle's requirements. If so, the candidate site is retained; otherwise, sites that do not meet the requirements are removed, and a list of valid candidate sites for the vehicle is output. Using the vehicle candidate site correspondence table as input, targeted resource compatibility verification is performed on each candidate site. For charging pile candidate sites, the verification includes whether the interface type matches the vehicle's charging interface, whether the charging pile power meets the vehicle's maximum charging power requirement, and whether it is currently idle or ready to serve. For battery swapping station candidate sites, the focus is on verifying whether the station has batteries that perfectly match the vehicle's battery specifications, whether the effective inventory of batteries of that specification is greater than 1, and whether the battery swapping station has available capacity. After verifying each site, all candidate sites that meet the compatibility requirements are retained, and those that do not meet the requirements are removed. Finally, a list of valid candidate sites for the vehicle is output, ensuring that subsequently allocated sites have actual service capabilities.

[0108] The allocation execution subunit is used to allocate the highest-valued valid station to each vehicle based on the valid candidate station list, record the allocation relationship, and output a preliminary allocation table. For each vehicle, it iterates through its valid candidate station list, prioritizing the station with the highest evaluation value as the allocation target—if multiple stations have the same evaluation value, they are further filtered based on the quality of their resource status. After determining the allocation station, this subunit records the binding relationship between the vehicle ID and the station ID, and also marks the allocation time and pre-occupied resource type information, ultimately generating a preliminary allocation table to provide basic data support for subsequent resource status updates and allocation verification.

[0109] The resource status update unit updates the status of corresponding stations based on the preliminary allocation result table, resulting in a station resource status table. The core task of this unit is to update the resource status of the corresponding stations in real time based on the preliminary allocation result table. If a vehicle is allocated to a charging pile, the charging pile's status is changed from idle to reserved. If allocated to a battery swapping station, the battery inventory of the corresponding specification at that station is reduced accordingly. After these operations, the latest station resource status table is obtained, ensuring the accuracy of station resource information.

[0110] The allocation verification unit compares the priority allocation vehicle list with the preliminary allocation table to check for any unallocated vehicles and returns to re-filter candidate sites. If all allocations are completed, the final allocation result and an updated site resource table are output. This unit is responsible for checking the completion status of the allocation work. It compares the priority allocation vehicle list with the preliminary allocation table to check for any unallocated vehicles. If there are unallocated vehicles, it means that the current candidate sites may not be able to meet the demand, so it returns to the second step of the resource allocation unit to re-filter candidate sites and perform subsequent operations. If all vehicles have been allocated, the final allocation result and an updated site resource table are output, completing the entire charging and swapping allocation process.

[0111] The real-time monitoring and dynamic adjustment module is used to monitor the station status and vehicle progress during refueling, determine whether there are changes in new vehicles, station failures, or inventory, and adjust the matching of vehicles and stations accordingly.

[0112] The real-time data acquisition unit is used to continuously collect station status, vehicle progress and new vehicle information, and output real-time dynamic datasets.

[0113] The change event identification unit compares the real-time dynamic dataset with the data from the previous moment to determine whether a sudden event has occurred; if so, it outputs a list of change events. During vehicle charging or battery swapping, the unit continuously monitors the operational status of each charging pile and battery swapping station, such as whether the charging pile is malfunctioning, whether the charging power is stable, and whether the battery swapping operation at the station is smooth and whether the battery inventory is replenished in a timely manner.

[0114] The system tracks vehicle refueling progress, including real-time changes in battery level during charging and whether battery swapping has been successfully completed, to keep abreast of the overall refueling dynamics.

[0115] The impact scope definition unit is used to analyze the range of vehicles and stations affected by the event based on the list of change events, and output a list of affected vehicles and related stations.

[0116] The dynamic evaluation recalculation unit is used to recalculate the comprehensive evaluation value of affected vehicles and available stations based on the real-time dataset, generate a new vehicle-station evaluation matrix, and output temporary adjustment evaluation results.

[0117] When dynamic events occur in the system, including site failures, new vehicle additions, and changes in battery inventory, a recalculation is performed for the affected vehicles and all available sites.

[0118] The formula for calculating the energy replenishment demand of vehicle v is as follows:

[0119]

[0120] In the formula, C v For vehicle battery capacity and SOC v This represents the current remaining battery level.

[0121] instantaneous available power or effective power of site s for:

[0122]

[0123] In the formula, For maximum power, Power already used For fully charged battery inventory, For standard battery capacity, This represents the average battery swapping time. This indicates the energy that a battery swapping station can provide per unit time, making its dimensions consistent with those of a charging pile.

[0124] The formula for calculating the comprehensive evaluation value is as follows:

[0125]

[0126] In the formula, Let v be the distance from vehicle v to station s. To meet the vehicle's energy replenishment needs, The instantaneous available power or effective power of station s. , , These are weighting coefficients for distance, energy replenishment demand, and instantaneous available power or efficiency at the site. They are used to measure the importance of distance, energy replenishment demand, and instantaneous available power or efficiency at the site in the comprehensive evaluation. Their values ​​can be adjusted according to the actual application scenario and needs to reflect the weight of different factors on the comprehensive evaluation results. Regarding vehicle refueling needs The function transforms the energy replenishment demand into the numerical form required for comprehensive evaluation, reflecting its impact on the comprehensive evaluation results. For information about the instantaneous available power or efficiency of site s The function processes the instantaneous available power or efficiency of a site so that it can be used in the calculation of the comprehensive evaluation value. For example, the higher the available power, the larger the function value, and the more obvious the positive effect on the comprehensive evaluation. Regarding distance The function is used to process the original parameter of distance and transform it into a numerical form that contributes to the overall evaluation.

[0127] The adjustment scheme generation unit is used to redistribute affected vehicles based on the temporary assessment results, update the site resource pre-occupancy status, and output a dynamic adjustment scheme.

[0128] If new vehicles join the energy replenishment demand queue, a charging pile or battery swapping station suddenly malfunctions and cannot operate normally, or there are significant changes in the battery inventory of the battery swapping station, it is necessary to recalculate the comprehensive evaluation value of each vehicle for each charging pile and battery swapping station according to the above steps, and make new charging allocation decisions to ensure that the charging pile-battery swapping coordination can always efficiently and reasonably meet the energy replenishment needs of vehicles and flexibly respond to various changes in actual operation.

[0129] In summary, this embodiment provides a multi-device collaborative charging system based on the W algorithm. By constructing a charging pile-station collaborative intelligent charging network, and using a real-time data-driven weighted adaptive algorithm to dynamically optimize vehicle allocation, it achieves the highest global efficiency, fastest response, and grid-friendly energy dispatching. The beneficial effects achieved are as follows:

[0130] The multi-device collaborative charging system designed in this invention has the core advantage of completely surpassing the limitations of traditional charging management models through a highly integrated, intelligent, and dynamically responsive architecture. It achieves a paradigm shift from single, static, and passive resource allocation to multi-dimensional, dynamic, and proactive energy service scheduling. This system integrates dispersed charging piles and battery swapping stations into a unified pile-station collaborative network, and with its sophisticated modular design, it demonstrates outstanding advantages in efficiency, resilience, user experience, and grid integration.

[0131] Example 2: Please refer to Fig. 2 This embodiment provides a multi-device collaborative charging scheme based on the W algorithm, which can be controlled using the multi-device collaborative charging system based on the W algorithm in Embodiment 1, including:

[0132] S1: Collect charging pile location, power and usage status, battery swapping station geography, inventory and battery swapping efficiency, vehicle location, power level and battery specifications to obtain charging dataset.

[0133] S2: Analyze the key factors affecting charging / swapping decisions based on the charging dataset, and assign weights according to the operational strategy using the W algorithm's weight adaptive adjustment mechanism to obtain a qualified weight table.

[0134] S3: Calculate the component matrix of each vehicle and each station, and assign weights to each component matrix according to the qualified weight table to obtain a comprehensive evaluation.

[0135] S4: Based on the comprehensive evaluation matrix, assign each vehicle to the station with the highest evaluation value, update the usage status of the charging pile or the battery inventory data of the battery swapping station, until all vehicles waiting to be recharged have been assigned.

[0136] S5: Monitors the station status and vehicle progress in real time during refueling, and determines whether there are changes in new vehicles, station failures, or inventory. If so, it adjusts the matching of vehicles and stations.

[0137] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment 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, such as ROM / RAM, magnetic disk, optical disk, including several instructions to cause a computer device (which may be a personal computer, server, or network device) to execute the methods of various embodiments or some parts of embodiments.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-device collaborative charging system based on the W algorithm, characterized in that, include: The charging data collection module collects information on the location, power, and usage status of charging stations to obtain a charging dataset. The weight assignment module is used to analyze the key factors affecting charging / swapping decisions based on the charging dataset, and assign weights according to the operation strategy using the weight adaptive adjustment mechanism of the W algorithm to obtain a qualified weight table. The comprehensive evaluation module is used to calculate the component matrix of each vehicle and each station, and to assign weights to each component matrix according to the qualified weight table to obtain the comprehensive evaluation matrix. The charging and swapping allocation decision module is used to allocate each vehicle to the station with the highest evaluation value according to the comprehensive evaluation matrix, and update the usage status of the charging pile or the battery inventory data of the swapping station until all vehicles waiting to be recharged have been allocated. The real-time monitoring and dynamic adjustment module is used to monitor the station status and vehicle progress during refueling in real time, determine whether there are any changes, and adjust the matching between vehicles and stations if so. In the weight assignment module, the specific steps for assigning weights using the W algorithm's adaptive weight adjustment mechanism are as follows: Factors influencing charging and battery swapping decisions are filtered from the charging dataset, categorized by core rigidity and operational efficiency, and key factors are output for each category. Based on the operational strategy, the AHP algorithm is used to assign basic weights to different scenarios according to the list of key classification factors, clarify the initial proportion of each factor, and output the initial weights. Data is collected in real time. Based on the key classification factors and initial weights, the factors that need to be adjusted and the magnitude of change are identified, and the factors to be adjusted and their dynamic change values ​​are output. Based on the factors to be adjusted and their changes, the weights are iteratively optimized using the W algorithm, and dynamically adjusted in combination with the initial weights to output a dynamic weight table. Use the dynamic weight table to check whether the weights of the core factors meet the standards. If they exceed the standards, backtrack and adjust, and output a qualified weight table. The specific steps for iteratively optimizing weights using the W algorithm are as follows: Based on the dynamic changes of the factors to be adjusted and the initial weight table, set the upper limit of the number of iterations and the convergence threshold, initialize the current weights as the initial weights, and output the iterative environment configuration with parameters. Based on the dynamic change values ​​of the factors to be adjusted, the influence coefficient of each factor to be adjusted is calculated according to the preset rules, and the factor coefficient correspondence table is output. The temporary weights are calculated using the W algorithm formula based on the initial weights and influence coefficients, and the temporary weights are output. The temporary weights are checked against the threshold rules. If they exceed the threshold, they are compressed proportionally, and the constrained temporary weights are output. Compare the difference between the constrained temporary weights and the current temporary weights, and determine whether the difference is less than the convergence threshold. If so, output the dynamic weight table; otherwise, use the constrained temporary weights as the new current weights and recalculate the temporary weights.

2. The multi-device collaborative charging system based on the W algorithm according to claim 1, characterized in that, The comprehensive evaluation module includes: The data preparation unit is used to extract the correlation data between each vehicle and each station from the charging dataset. The correlation data includes vehicle and station location data, vehicle power data, station inventory data, and charging time data. A component matrix calculation unit is used to calculate a component matrix based on the associated data; The comprehensive evaluation value calculation unit calculates the comprehensive evaluation matrix of the vehicle station based on the component matrix and the dynamic weight table.

3. The multi-device collaborative charging system based on the W algorithm according to claim 2, characterized in that, The component matrix calculation unit includes: The distance calculation subunit is used to calculate the actual distance based on vehicle and station location data using the Haversine formula, standardize it into distance components, combine it with the road network congestion coefficient to convert it into the estimated arrival time, and output the distance component matrix. An emergency calculation subunit is used to calculate emergency components based on the vehicle battery data and user emergency markers, and output an emergency component matrix. The battery swapping adaptation calculation subunit is used to determine the matching degree between the vehicle battery specifications and the battery swapping station inventory based on the station inventory data, and to add the inventory adequacy. The sum is the battery swapping adaptation component, and the adaptation component matrix is ​​output. The efficiency component calculation subunit is used to utilize the energy replenishment time data, add queuing time correction according to the charging time score, and output the efficiency component matrix.

4. The multi-device collaborative charging system based on the W algorithm according to claim 1, characterized in that, The charging / swapping allocation decision module includes: The vehicle priority sorting unit is used to extract vehicle urgency data from the comprehensive evaluation matrix and sort it, and output a priority allocation vehicle list. The resource allocation unit is used to extract the top three candidate stations for each vehicle's evaluation value from the comprehensive evaluation matrix and mark their resource status. It verifies whether these candidate stations meet the vehicle's requirements to retain valid stations. Then, it allocates the highest-value valid station to each vehicle, records the relationship, and outputs a preliminary allocation table. The resource status update unit is used to update the status of the corresponding site according to the preliminary allocation table to obtain the site resource status table. The allocation verification unit is used to compare the priority allocation vehicle list with the preliminary allocation table to see if there are any unallocated vehicles, and then return to the second step to re-filter candidate sites; if all allocations are completed, the final allocation result and the updated site resource table are output.

5. The multi-device collaborative charging system based on the W algorithm according to claim 4, characterized in that, The resource allocation unit includes: The candidate site filtering subunit is used to extract the top three sites with the highest evaluation values ​​for each vehicle from the comprehensive evaluation matrix as candidates, mark the current resource status of the sites, and output a vehicle candidate site correspondence table. The resource compatibility verification subunit is used to verify whether the resources of each candidate station in the vehicle candidate station correspondence table meet the vehicle requirements. If they do, the candidate station is retained; otherwise, the station that does not meet the conditions is removed, and the valid candidate station table for the vehicle is output. The allocation execution subunit is used to allocate the valid station with the highest evaluation value to each vehicle according to the valid candidate station table, record the allocation relationship, and output a preliminary allocation table.

6. The multi-device collaborative charging system based on the W algorithm according to claim 1, characterized in that, The real-time monitoring and dynamic adjustment module includes: The real-time data acquisition unit is used to continuously collect station status, vehicle progress and new vehicle information, and output real-time dynamic datasets. The change event identification unit is used to compare the real-time dynamic dataset with the data of the previous moment to determine whether a sudden event has occurred; if so, it outputs a list of change events. The impact scope definition unit is used to analyze the range of vehicles and stations affected by the event based on the list of change events, and output a list of affected vehicles and associated stations. The dynamic evaluation recalculation unit is used to recalculate the comprehensive evaluation value of the affected vehicles and available stations based on the affected vehicles and the real-time dataset, generate a new vehicle-station evaluation matrix, and output temporary evaluation results. The adjustment scheme generation unit is used to redistribute affected vehicles based on the temporary assessment results, update the site resource pre-occupancy status, and output a dynamic adjustment scheme.

7. The multi-device collaborative charging system based on the W algorithm according to claim 6, characterized in that, The formula for calculating the comprehensive evaluation value is as follows: In the formula, Let v be the distance from vehicle v to station s. To meet the vehicle's energy replenishment needs, The instantaneous available power or effective power of station s. , , Weighting coefficients for distance, energy replenishment requirements, and instantaneous available power or efficiency at the site. Regarding vehicle refueling needs The function, For information about the instantaneous available power or efficiency of site s The function, Regarding distance The function.

8. A multi-device collaborative charging method based on the W algorithm, wherein the multi-device collaborative charging system based on the W algorithm as described in any one of claims 1 to 7 is characterized in that, The collaborative charging method includes: S1: Collect charging pile location, power and usage status, battery swapping station geography, inventory and battery swapping efficiency, vehicle location, battery power and battery specifications to obtain charging dataset; S2: Analyze the key factors affecting charging / swapping decisions based on the charging dataset, assign weights according to the operational strategy using the W algorithm's adaptive weight adjustment mechanism, and obtain a qualified weight table; S3: Calculate the component matrix of each vehicle and each station, and assign weights to each component matrix according to the qualified weight table to obtain a comprehensive evaluation; S4: Based on the comprehensive evaluation matrix, assign each vehicle to the station with the highest evaluation value, update the usage status of the charging pile or the battery inventory data of the battery swapping station, until all vehicles waiting to be recharged have been assigned. S5: Monitors the station status and vehicle progress in real time during refueling, and determines whether there are changes in new vehicles, station failures, or inventory. If so, it adjusts the matching of vehicles and stations.

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