114 results about "User charge" patented technology

The invention relates to the technical field of electric vehicle charging, in particular to a non-inductive guidance intelligent charging system and method for electric vehicle users, and the method comprises the steps: researching the charging behavior time-varying characteristics of private vehicles and commercial vehicles and a prediction model based on a user charging rule and a decision mechanism; the selection preference, waiting time, unit time cost and price sensitivity of the user to the charging pile are analyzed, and a charging behavior guiding difficulty and evaluation model is established; combining different service charges and load data to construct a service charge-load elastic model; a multi-period pricing mechanism facing peak load shifting and considering user space-time guidance is provided; establishing a power grid interaction analysis and dynamic grouping recommendation method under multiple scenes of the power grid; and in combination with SOC and real-time position information, a response capability prediction model is constructed, and the multi-element regulation and control requirements are met. According to the invention, new requirements can be met in time, and efficient operation of the charging service is ensured.

The invention relates to the technical field of power system optimization, and discloses a charging load estimation and charging mode optimization method based on an electric vehicle. According to the method, a source load storage-medium voltage distribution network collaborative planning framework is initialized, and charging station geographical distribution and user charging behavior modes are integrated; collecting charging load sequence data in real time, and judging a charging load control state through a volatility analysis algorithm; when the charging load control is in an unstable state, synchronously analyzing voltage fluctuation characteristics and charging power change characteristics of the power distribution network, and generating a charging efficiency evaluation value; dividing the charging effect into three levels of high efficiency, conventional and low efficiency based on the evaluation value; for a conventional grade charging effect, a time sequence decomposition technology is adopted to extract a load trend component, and an overall load stability index is calculated; and fusing the charging efficiency evaluation value and the overall load stability index, and outputting a charging power adjustment instruction. The method effectively improves the operation stability and the charging efficiency of the power distribution network.

An electric vehicle charging station recommendation method and system, applied to the related field of charging station recommendation. The method comprises: acquiring user charging features and influence degree values of user charging evaluation features on user charging preferences, wherein the user charging evaluation features are constructed on the basis of the user's evaluation information on charging stations (S1); calculating the user's comprehensive scores for different charging stations on the basis of the influence degree values, and constructing a user preference matrix by using the user's comprehensive scores for different charging stations as elements (S2); and decomposing the user preference matrix by means of a singular value decomposition method, acquiring potential matching relationships between the user and charging station features, and recommending a charging station to the user on the basis of the potential matching relationships (S3). The method solves the problem in the prior art that charging stations cannot be personalizedly recommended for different users, thereby improving user experience.

This invention relates to the field of power system demand response technology, and provides a user demand response method based on master-slave game theory under cost control constraints. The method includes: establishing a demand response model using master-slave game theory, where electricity retailers design time-of-use pricing strategies, and electricity users respond based on a convex utility function; the utility function includes a quantification term for electricity usage satisfaction and a calculation term for electricity costs; the electricity retailers' revenue function includes four functions: user electricity costs, long-term market electricity purchase costs, current market electricity purchase costs, and conditional risk value, coupled with the characteristics of the electricity spot market and risk management requirements; proving the existence of a Nash equilibrium solution using backward induction; and optimizing the strategy based on a slime mold optimization algorithm. This invention provides important support for electricity retailers to formulate flexible pricing mechanisms and tap demand-side resources in terms of cost control for users with high response potential and optimization of peak-valley load differences.

The invention provides a charging management method, a charging management system and electronic equipment, and the method comprises the steps: obtaining the user charging information of a to-be-charged vehicle, and determining a user behavior coefficient; collecting state information of a charging environment, and determining an environment state coefficient based on the state information; acquiring safety information of the charging facility, and determining a facility safety coefficient based on the safety information; based on the user behavior coefficient, the environment state coefficient and the real-time working parameters of the to-be-charged vehicle, the charging adaptation degree of the to-be-charged vehicle under the real-time working parameters is determined; and correcting the reference charging current based on the charging adaptation degree and the facility safety coefficient to obtain a target charging current of the to-be-charged vehicle. In the mode, the reference charging current can be dynamically corrected by integrating the charging demand of the user, the environment state and the facility safety, so that the accurate adaptation of the charging strategy to the personalized demand of the user and the real-time health state of the battery is realized on the premise of ensuring the safety of the field facility and the stability of the power grid.

This invention discloses an AI intelligent charging control method, system, and mobile terminal. The method includes: collecting user charging data and marking records of continuous charging for more than a first duration after the battery is fully charged as valid charging records; activating an AI charging strategy when the cumulative number of valid records reaches a preset number and the current battery level is higher than a preset threshold; calculating pre-charging time, disconnection time, and recharging time based on the preset number of valid records; pausing charging when the battery level reaches a first preset level; and resuming charging to full charge when the recharging time is triggered. The system includes corresponding modules. This invention achieves intelligent pause and precise recharging by autonomously learning user charging habits, automatically fully charging the battery before the user disconnects it, effectively reducing the battery's immersion time in the high-charge range and extending battery cycle life. Simultaneously, through mechanisms such as screen brightness detection and low-temperature protection, it considers both emergency use needs and charging safety, achieving fully autonomous intelligent control without manual user intervention, thus improving the user experience.

The application relates to the technical field of electric digital processing, in particular to a pure electric vehicle replacement quantity prediction method and device. The method comprises the following steps: for each pure electric vehicle in a pure electric vehicle set, acquiring user charging behavior data, average temperature of a region where the vehicle is located and current battery parameters; determining an actual attenuation rate of the battery according to the current battery parameters; determining a remaining service life of the battery according to the actual attenuation rate and a current health degree of the battery; determining a charging index according to the user charging behavior data; determining a replacement confidence degree of each pure electric vehicle according to the charging index, the remaining service life of the battery and the average temperature of the region where the vehicle is located; and predicting a replacement quantity of the pure electric vehicles in the pure electric vehicle set according to the replacement confidence degree of each pure electric vehicle. The application scientifically and reasonably predicts the replacement quantity of the pure electric vehicles based on the user charging behavior data, the battery parameters and the temperature.

This invention relates to the field of charging station operation optimization and resource allocation technology, and particularly to a comprehensive operation management method and system for charging stations based on multi-dimensional user segmentation. The comprehensive operation management method for charging stations based on multi-dimensional user segmentation includes the following steps: S1: Acquiring user charging demand data and user value-added demand data, and constructing a complete mutual information set based on the user charging demand data and user value-added demand data; S2: Determining a first mutual information set and a second mutual information set based on the complete mutual information set, performing multi-dimensional user segmentation based on the first mutual information set and the second mutual information set, and constructing a charging station operation dimension combination library. This invention integrates user charging and value-added demand data, constructs a complete mutual information set for multi-dimensional segmentation, introduces directional demand conversion indicators, and dynamically matches strategies to achieve adaptive adjustment of resource allocation and continuous optimization of the strategy library through a closed loop.

The present application relates to the field of new energy vehicle direct current fast charging and charging power distribution control technology, and discloses a kind of intelligent charging pile charging power distribution management method.Connection charging user number is numbered, the remaining amount of charge needed is obtained according to the difference between target electric quantity and current electric quantity and a bidding factor is generated, the number of power modules and charging guns, maximum output power and connection relationship are initialized, each user charging power and module number are proportionally distributed according to the bidding factor sorting and combined with the total power available to the system, so that the power distribution takes into account the urgency, fairness and efficiency;And through temperature model and connection detection, module over-temperature peeling and smooth hot plug are realized, and single-gun and system power upper limit constraint and power scaling are realized, then according to the number of users and bidding, a unified settlement price is determined, the actual charging quantity is charged and the user and module state is updated, the full user is removed, the state information is transmitted, the cycle settlement closed loop and the next cycle safe continuous scheduling are realized.

The application discloses a user charging and changing behavior multi-dimensional feature space-time quantification method considering cross-domain factors, relies on user travel law, charging and changing service level, power grid price level, traffic network data, and meteorological factors of the place, and objectively quantifies the charging behavior of the user. In the method, not only the influence of the user charging behavior on the surrounding road traffic is proposed, the blank in the field is made up, but also the influence of the charging behavior on the area is fully considered, the traditional single evaluation dimension is transcended, the basic ability construction of vehicle-road-network coordination optimization can be better supported, the scientificity and precision of charging and changing infrastructure planning are improved, the user behavior modeling theory is enriched, and cross-domain technology integration is promoted.

This application relates to a method, apparatus, vehicle, and storage medium for assessing the driving life of a vehicle battery. The method includes: acquiring the current charging mode, current temperature, and current energy throughput of the battery to be assessed; inputting the current charging mode, current temperature, and current energy throughput into a pre-built driving aging model to obtain the capacity degradation rate of the battery to be assessed, wherein the pre-built driving aging model is trained using the charging mode, charging temperature, and energy throughput of multiple target vehicles distributed in multiple target regions; and assessing the driving life of the battery to be assessed based on the capacity degradation rate to obtain the assessment result of the driving life of the battery to be assessed. This solves the problems of limited empirical models for assessing battery driving life in related technologies, which are often singular and do not consider the impact of user charging habits on battery life, leading to low accuracy in battery driving life assessment, and improves the accuracy of battery driving life prediction.

The application provides a charging pile remote regulation and control method and system based on a cloud platform, and belongs to the technical field of new energy infrastructure and smart grid technology.The method comprises the following steps: collecting historical charging data of charging pile users and historical operation data of power grids in the regions where the users are located, generating user charging original data sets and power grid original operation data sets; and using a data mining algorithm to construct a user behavior and power grid state double-dimensional model and generate double-dimensional model data; through the construction of the user behavior and power grid state double-dimensional model, the user charging habits and the real-time operation characteristics of the power grid can be accurately captured, detailed and comprehensive data support is provided for the regulation and control of the charging pile, the problem of inaccurate regulation and control caused by single data dimension in the traditional mode is effectively avoided, and the scientificity and rationality of the charging regulation and control decision are improved.

The application provides an orderly charging method, device and equipment of an electric vehicle and a storage medium. It relates to the technical field of big data. The method comprises the following steps: taking multiple charging powers of the electric vehicle at different time points as particles of an initial population of an NSGA2 algorithm, and substituting the particles into a target optimization function of the lowest charging cost and a target optimization function of user charging satisfaction; obtaining an external non-dominated solution set by calculation; evaluating the evolution state of the NSGA2 algorithm according to the probability value of the external non-dominated solution set, including a convergence state and a diversity state; improving the diversity by population conversion if the algorithm is in the diversity state; obtaining a particle with the minimum potential energy value and calculating a global optimal value; selecting a compromise solution as a target charging power in the global optimal value; and performing orderly charging of the electric vehicle according to the target charging power. The method reduces the charging cost of the user and improves the charging satisfaction.

The invention discloses a charging load regulation and control method, platform, system and device for a charging station. The method comprises the steps of obtaining power data of a charging gun in a charging state in the charging station; according to the power data of the charging gun and the SOC value of the battery in the charging state, predicting and calculating to obtain a charging power parameter value in a preset regulation and control interval; and regulating and controlling the current charging load of the charging station according to the regulation and control plan parameter value and the charging power parameter value issued by the power grid dispatching system. According to the invention, the charging power parameter value of the charging gun is predicted, and the power limit value of the charging gun is calculated, so that the load of the charging station is accurately regulated and controlled, and the charging expectation of a user and the operation limit of a power grid are met. The technical effects of realizing non-inductive response and improving the participation heat of the charging operator in the power grid demand response are achieved.

The invention relates to the technical field of highway charging piles, in particular to a unified consumption linkage service system for highway charging piles, and the system comprises a user interaction module which is used for receiving user position data and a charging pile operation instruction submitted by a user; the charging pile data acquisition module is used for acquiring real-time operation data of each charging pile in the expressway service area; the scheduling verification module is used for generating a charging scheduling instruction; the order processing module is used for calculating the charging metering data of the charging pile corresponding to the target charging pile identifier; the payment management module is used for updating the payment state of the charging order data based on the payment confirmation information; and the invoice filing module is used for outputting charging pile filing information when the payment state of the target to-be-invoiced order is paid and the invoicing state in the to-be-invoiced order is not invoiced. According to the invention, unified consumption linkage service management can be carried out on a plurality of charging piles in the expressway service area, and the response efficiency to the charging request of the user is improved.

The embodiment of the invention discloses a parking fee reduction and remission processing method, system and device, electronic equipment and a storage medium. The method comprises the steps that charging information reported when a vehicle is charged is acquired; determining the charging state of the vehicle according to the charging information; in response to the situation that the charging state represents that the vehicle is abnormal in charging, deduction data are determined, and the charging abnormality is order hanging abnormality or gun jumping abnormality; and transmitting the deduction data so that parking fee deduction is performed on the vehicle according to the deduction data. Therefore, in the embodiment, the reduction and remission data is determined in time when the charging abnormity occurs, and the parking fee reduction and remission is executed on the vehicle based on the reduction and remission data, so that the parking fee reduction and remission failure caused by the charging abnormity can be avoided, and the charging experience of a user is improved.

The invention discloses a user charging behavior multi-dimensional feature space-time quantification method considering cross-domain factors, which objectively quantifies the charging behavior of a user based on user travel rules, charging service level, power grid price level, traffic network data and meteorological factors of the location. According to the method, the influence of the charging behavior of the user on surrounding road traffic is put forward, the blank in the field is made up, the influence of the charging behavior on the area where the charging behavior is located is fully considered, a traditional single evaluation dimension is exceeded, basic capability construction of vehicle-road-network collaborative optimization can be well supported, and the method has good application prospects. The scientificity and precision of the charging and battery swap infrastructure planning are improved, the user behavior modeling theory is enriched, and cross-field technology fusion is promoted.

The invention relates to the technical field of charging and discharging, and relates to a user demand prediction-based charging pile dynamic power distribution method, which comprises the following steps of: obtaining multi-modal data corresponding to a user charging demand and power grid capacity limitation; transmitting the multi-modal data to a pre-constructed demand prediction model to dynamically allocate weights for features corresponding to the multi-modal data through a soft attention mechanism, fusing the features corresponding to the multi-modal data, and outputting a charging demand prediction value in a future preset time period; on the basis of a preset multi-objective function and constraint conditions, according to the charging demand predicted value and power grid capacity limitation, a differential evolution algorithm is adopted to distribute the power of the charging pile cluster, and an optimal power distribution scheme is obtained; correcting the optimal power distribution scheme according to the actual charging state and the fluctuation amplitude of the power grid fluctuation data; and taking the charging piles as distributed storage units, and performing bidirectional power management on the distributed storage units according to the corrected optimal power distribution scheme.

The invention discloses a health degree evaluation method, a recommendation method, equipment, a medium, a product and a vehicle, and the health degree evaluation method comprises the steps: obtaining a charging health degree score value based on index data and a charging health degree score index system; wherein the charging health degree scoring index system is constructed based on the charging data; the index data is charging data corresponding to each index in the charging health degree scoring index system. According to the method, the health degree of the charging behavior of the user is quantitatively evaluated in a specific numerical value mode, so that the user visually knows whether the charging behavior is healthy or not, the accuracy of charging health evaluation and the charging experience of the user are improved, a training algorithm model with a huge data volume does not need to be used, the development cost is low, and the user experience is improved. The calculation process is simple.

The invention discloses a new energy vehicle charging management method and system, and relates to the technical field of new energy vehicle charging management, and the method comprises the following steps: collecting a vehicle historical charging record of a current user, and defining a common charging position based on the collected vehicle historical charging record, constructing a user charging behavior habit model through a neural network in combination with historical charging records of the vehicle; when a user vehicle is charged, outputting a reference charging position and a reference charging electric quantity through a user charging behavior habit model based on the current charging position of the user and the electric quantity of a battery, and generating a reference charging position charging strategy and a common charging position charging strategy in combination with the common charging position of the current user vehicle; and saving amounts under different charging strategies are calculated. According to the new energy automobile charging management method and system, the personalized charging strategy can be generated based on the charging behavior habits of the user, the cost advantage can be clearly presented, and meanwhile the battery maintenance requirement is considered.

This invention provides a method and system for real-time mobile phone charging optimization using the W algorithm in dynamic environments, relating to the field of mobile phone charging optimization technology. The method includes: establishing a unique identifier association between the device to be charged and available charging resources; acquiring device usage status, subsequent schedule information, device relationships, and dynamic environmental variables; calculating a comprehensive priority score based on the W algorithm; outputting resource scheduling decisions for different resource scenarios; continuously tracking changes in device parameters, battery level, and resource status; and optimizing the basic weights of priority evaluation indicators. This invention achieves precise matching of charging resources by combining the comprehensive priority evaluation of the W algorithm with scenario-based scheduling strategies. A dynamic recalculation mechanism based on triggering conditions such as battery level changes, schedule adjustments, and resource anomalies, along with continuous iterative optimization of weight factors, improves the real-time performance, adaptability, and resource utilization of charging scheduling in dynamic environments, effectively reducing device waiting time and enhancing the user charging experience.

The invention relates to the technical field of charging pile electric energy management and load control, in particular to a dynamic load balancing control method and system based on real-time power collection. In the method, a station-level controller establishes a charging session with each charging pile, obtains real-time power and user setting information, divides residual charging tasks into a core task segment, a target task segment and an opportunity task segment, and updates the completion progress of each task segment along with time. And the controller generates rigid, semi-elastic and high-elastic tags for each charging session according to the task segment type and the remaining time length from the predicted station leaving time, and the rigid, semi-elastic and high-elastic tags represent the power adjustable degree of each charging session. And when it is judged that the total power needs to be adjusted according to the in-station power and the power constraint condition, the power adjustment priority is determined, and a target power distribution result is generated and issued to each charging pile according to the elastic label in combination with the charging mode and the charging pile adjustment fatigue, so that dynamic load balancing is realized, and the power distribution safety margin and the user charging experience are improved.

The invention belongs to the technical field of electric vehicle charging scheduling, and particularly discloses a vehicle network interaction strategy-oriented electric vehicle charging scheduling method and system, and the method comprises the following steps: determining EVA operation income data and user charging cost data; based on the EVA operation income data and the user charging cost data, establishing a target function by taking maximization of the EVA operation income and minimization of the user charging cost as targets; determining constraint conditions of the target function; and solving the target function based on the constraint condition to obtain an electric vehicle charging scheduling scheme. According to the method, the problem of large-scale electric vehicle access of the power grid can be solved, the V2G response willingness of the user can be accurately predicted, and the method can adapt to dynamic charging scheduling schemes of different scenes and requirements, so that the problems of economical efficiency and safe operation of power grid operation are solved.

The invention discloses a charging and discharging operation mode control method for an energy storage user, which solves the problem that in the prior art, the electricity cost is increased due to the fact that the demand is increased when the peak-valley arbitrage income is met, and comprises the following steps: obtaining user energy consumption data and energy storage parameters; generating an energy storage initial operation strategy according to the user type and the environment factor; and dynamically optimizing the energy storage initial operation strategy according to the demand control model and the economical efficiency optimal control model, and outputting a next-day operation strategy considering the electric charge cost. And the most suitable charging and discharging operation strategy can be formulated according to the actual condition and demand of the user, so that the energy cost of the user is reduced to the greatest extent.

This invention relates to the field of power management technology, specifically to an energy management method for charging pile clusters, comprising the following steps: S1: predicting the short-term power demand and renewable energy availability of each charging pile; S2: adjusting the charging power of each charging pile using a dynamic allocation algorithm; S3: encouraging users to charge during off-peak hours based on a preset pricing algorithm; S4: predicting user charging demand patterns; S5: monitoring power quality in real time during the charging process and automatically adjusting charging equipment parameters using preset power regulation technology; S6: automatically activating a preset backup energy supply scheme in emergency situations of grid failures. This invention optimizes energy allocation through real-time monitoring and intelligent adjustment of charging strategies, improving grid efficiency, reducing costs, and ensuring the continuity and security of charging services through a backup energy system, thereby significantly improving the operational efficiency of charging infrastructure and user satisfaction.

This invention relates to artificial intelligence technology and discloses a method, apparatus, device, and storage medium for selecting charging pile sites. The method includes: based on charging data of electric vehicles within a target area, statistically analyzing the set of vehicles with the highest charging demand and constructing a distribution map of the largest charging vehicles; based on charging records of each charging station, obtaining the user distribution range of each charging station; based on the charging convenience level of users in each region under the combined influence of different charging stations, constructing a user charging convenience map, and obtaining a charging difficulty map based on a preset threshold; obtaining the central area of ​​the charging difficulty map through clustering, acquiring the set of human geographical environment types in the central area, using a pre-constructed charging area scale prediction model for prediction, and selecting the location corresponding to the smallest environmental type as the charging pile site selection area. This invention can improve the effectiveness of charging pile site selection and increase the coordination between charging pile site selection and scale.

This invention discloses a charging and swapping user behavior pattern analysis system and method, relating to the field of electric vehicle charging management technology. It acquires and establishes a multi-source data pool, merges the multi-source data pool, analyzes user charging and swapping behavior patterns and calculates the probability of abnormal behavior, establishes dynamic scheduling rules based on user charging and swapping behavior patterns, performs resource allocation and service classification, optimizes the model and establishes a response mechanism, and generates real-time scheduling instructions. This invention, through multi-source data collection and analysis, accurately predicts user charging and swapping behavior, dynamically optimizes resource allocation, improves charging station operating efficiency and user satisfaction, promotes the integration of virtual power plants, constructs a vehicle-to-grid strategy optimization module, quantifies the potential of users to participate in grid peak shaving, and optimizes regional energy dispatch. Furthermore, it has an anomaly detection and response mechanism to ensure safety and stability, and promotes the development of charging and swapping services towards high efficiency, intelligence, and sustainability.

The invention provides an FSE-OPL-DenseNet electric vehicle charging recommendation system based on sparse network sharing, which comprises a data preprocessing module, a model training module and a recommendation output module, real-time data transmission among the modules is realized through data interfaces, the data preprocessing module is used for preprocessing obtained historical charging order data of a user to obtain preprocessed training data; the model training module is deployed on the GPU server and is used for training an FSE-OPL-DenseNet model according to the preprocessed training data so as to obtain a trained FSE-OPL-DenseNet model; and the recommendation output module is integrated on an API interface of a mobile terminal or a cloud terminal, and is used for receiving a charging request of a user in real time, generating an access probability distribution result of the user to each candidate charging interest site according to a trained FSE-OPL-DenseNet model, and generating a charging interest site recommendation list based on the access probability distribution result. According to the invention, the accuracy and reliability of charging station recommendation are improved.

This invention provides a remote monitoring, management, and control method and system for new energy vehicle charging piles. It achieves real-time monitoring of charging gun status, abnormal noise, and electrical parameters by deploying multimodal sensors. Edge computing and dynamic threshold cleaning reduce data redundancy and improve transmission efficiency. A self-attention mechanism and Drools rule engine are combined for multi-source data fusion and root cause analysis to dynamically assess risk levels. Dynamic power allocation based on health status and grid load optimizes resource utilization. Peak-valley dynamic pricing and an incentive mechanism guide off-peak charging. A hash chain ensures the immutability of maintenance data. Scheduling paths are optimized based on urgency and engineer resources. User feedback and extreme weather data iterative models enhance system robustness. This achieves intelligent management of the entire charging pile lifecycle, improving operation and maintenance efficiency, safety, and user charging experience.