New energy vehicle flexible energy supplementing method based on mobile energy storage robot

Abstract

The present application relates to the technical field of new energy automobile charging, in particular to a new energy automobile flexible energy supplementing method based on a mobile energy storage robot, which relies on a dual-mode energy storage supplementing network composed of an energy storage type, delivery type robot and a cloud collaborative platform, and realizes efficient energy supplementing through four core steps: robot dynamic role switching based on multi-factor marginal value evaluation, adaptation to local supply and demand changes; whole life cycle tracking and relay supply of standardized energy capsules, realization of energy closed-loop circulation; group path optimization of energy carpooling, clustering and merging of space and time close demands to generate optimized routes; distributed ledger driven energy assetization operation, completion of trusted automatic settlement. Through demand merging and dynamic role allocation, the present application improves the flexibility and service efficiency of the energy supplementing network, and provides a flexible and efficient mobile energy supplementing solution for new energy automobiles.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle charging technology, and more specifically, to a flexible energy replenishment method for new energy vehicles based on a mobile energy storage robot. Background Technology

[0002] Energy replenishment for new energy vehicles is a key supporting area for the development of the new energy vehicle industry. Fixed charging piles suffer from problems such as limited layout and insufficient flexibility. Mobile energy storage and replenishment technology, which can be dispatched on demand, has become an important direction for solving the problem of terminal energy replenishment.

[0003] Existing mobile energy replenishment technologies have significant shortcomings: First, robot roles are mostly fixed, making it impossible to adjust functions according to changes in regional energy replenishment needs. This easily leads to resource mismatch issues such as a shortage of delivery robots and idle storage robots in some areas. Second, energy replenishment request processing adopts a one-to-one model, failing to integrate dispersed demands, resulting in redundant robot mileage and low service efficiency. Third, energy trading and settlement rely on centralized platforms, leading to insufficient transparency of transaction information and a lack of efficient multi-party benefit distribution mechanisms. These problems constrain the operational efficiency and sustainability of mobile energy replenishment networks.

[0004] To address this, this invention proposes a flexible energy replenishment method for new energy vehicles based on mobile energy storage robots. It constructs a dual-mode reconfigurable robot and cloud-based collaborative energy replenishment system, and solves the pain points of existing technologies through core technologies such as demand merging and dynamic role allocation. Summary of the Invention

[0005] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a flexible energy replenishment method for new energy vehicles based on mobile energy storage robots.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The flexible energy replenishment method for new energy vehicles based on mobile energy storage robots includes the following steps: Step 1: Dynamic role switching based on marginal value assessment: By quantitatively assessing and dynamically monitoring the marginal value of the robot under different roles, the energy replenishment resources can be flexibly allocated to adapt to changes in energy supply and demand in local areas. Step 2: Internal energy logistics management based on standardized energy capsules: Through full life cycle status tracking of standardized energy units, low-cost warehousing strategies, and efficient relay replenishment mechanisms, closed-loop circulation of energy within the replenishment network is achieved; Step 3: Group route optimization method based on energy carpooling: By spatiotemporal clustering of energy replenishment requests, calculation of route affinity, and route planning with time windows, combined with user incentive measures, service efficiency and user participation are improved; Step 4: Energy asset operation based on distributed ledger: Through on-chain asset mapping of physical energy units, automatic transaction settlement driven by smart contracts, and incentives for collaborative contributions, the credibility and efficiency of energy replenishment transactions are realized.

[0007] Furthermore, the method relies on a dual-mode energy storage and replenishment network, which consists of two types of reconfigurable mobile robots and a cloud-based collaborative platform. The two types of robots are based on a unified mobile chassis platform and are defined as energy storage robots and energy delivery robots, respectively, through pluggable functional modules. The former focuses on obtaining electricity from the power grid and charging standardized energy units, while the latter focuses on directly providing charging or battery swapping services for new energy vehicles. The cloud-based collaborative platform is responsible for real-time scheduling, path planning, transaction settlement, and status monitoring of the entire network. Energy transfer between the two types of robots is achieved through standardized energy capsules.

[0008] Furthermore, the dynamic role switching based on marginal value assessment includes three sub-steps: state information synchronization, marginal value calculation, and switching decision and execution. The robot periodically reports its own state vector to the cloud, and the cloud calculates the marginal value of the robot after switching roles based on the state vector. Then, by comparing the sum of regional marginal values, the optimal robot's role switching command is triggered.

[0009] Furthermore, the internal energy logistics management based on standardized energy capsules includes three sub-steps: standardized energy capsule state modeling, energy storage robot procurement and storage, and energy delivery robot relay replenishment. A unique state vector is established for each standardized energy capsule to achieve full life cycle tracking. The energy storage robot formulates a charging strategy based on the grid electricity price and capsule health status. When the energy delivery robot's power is insufficient, it initiates a replenishment request and matches the optimal replenishment point to complete the energy replacement.

[0010] Furthermore, the state vector of the standardized energy capsule includes the current power, rated capacity, health status, geographical coordinates, and holder information. Its health status is obtained by weighting and correcting the number of charge-discharge cycles, internal resistance changes, and temperature decay coefficient.

[0011] Furthermore, the optimal refueling point for the energy delivery robot is determined by balancing the additional travel loss of the energy delivery robot after completing the current task with the cost of the energy storage robot traveling to the refueling point.

[0012] Furthermore, the energy-based carpooling group route optimization includes three sub-steps: request clustering and route proximity calculation, dynamic energy shuttle route generation, and user incentives and endurance guarantee. Clustering and route proximity calculation are performed on energy replenishment requests that are similar in time and space. Based on the clustering results, an approximately optimal service route is generated. At the same time, price incentives are provided to improve user participation, and the energy consumption of the route is monitored in real time to ensure the continuity of service.

[0013] Furthermore, the dynamic energy shuttle route generation model the clustered requests as a vehicle routing problem with a time window, uses a heuristic algorithm to construct an initial path and optimizes it through local search to generate a service path that satisfies the time window and power constraints.

[0014] Furthermore, the energy asset operation based on distributed ledger includes two sub-steps: asset mapping and state on-chaining, and smart contract micro-transactions. Each physical standardized energy capsule is mapped to a digital asset on the blockchain managed by a smart contract. Its key state information includes current power, holder identity, and transaction history. The key state information is regularly updated to the blockchain through the Internet of Things module, and the updated data must be signed by the holder's private key.

[0015] Furthermore, the smart contract microtransaction includes two core logics: energy credit creation and service settlement allocation. Energy credit creation involves the energy storage robot calling a smart contract function to generate energy credits corresponding to the amount of charge after charging a standardized energy capsule. Service settlement allocation involves the energy delivery robot using a standardized energy capsule to complete a user's energy replenishment service. After deducting the platform service fee from the service revenue, the remaining portion is allocated proportionally to the energy credit holder of the standardized energy capsule, i.e., the energy storage robot, and the operator of the energy delivery robot.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. To address the resource misallocation problem caused by fixed robot roles in existing systems, this invention employs a dynamic role switching method based on multi-factor marginal value assessment. This method comprehensively considers factors such as revenue density, energy potential, and the physical cost of role switching to quantify the marginal value of a robot after role switching. By comparing the total marginal value of robots with different roles within a region, the system triggers the replacement of the optimal robot's functional modules and role reconfiguration, enabling flexible allocation of energy storage resources. This adapts to changes in energy supply and demand in localized areas, avoiding overcapacity or shortage in certain regions and significantly improving the overall response efficiency of the energy replenishment network. 2. To address the issue of redundant robot journeys caused by scattered energy replenishment requests, this invention employs a group path optimization method based on energy carpooling. It clusters energy replenishment requests that are spatially and temporally similar and calculates their route proximity, merging multiple scattered requests into an optimized journey for a single energy delivery robot. This method, combined with a path planning algorithm with a time window, reduces unnecessary robot mileage and increases the service coverage of a single robot. Simultaneously, a price incentive mechanism is implemented to increase user participation, ensure the continuity and economy of energy replenishment services, and effectively reduce the overall operating cost of the energy replenishment network. Attached Figure Description

[0017] Figure 1A flowchart of a flexible energy replenishment method for new energy vehicles based on mobile energy storage robots; Figure 2 This is a schematic diagram of the cloud-based collaborative platform and two types of robots working collaboratively according to the present invention; Figure 3 A flowchart for generating an approximately optimal serial service path for this invention. Detailed Implementation

[0018] Example, refer to Figure 1 This embodiment presents a flexible energy replenishment method for new energy vehicles based on mobile energy storage robots. This method constructs a dual-mode energy storage and replenishment network consisting of two types of reconfigurable mobile robots and a cloud-based collaborative platform. The two types of robots are based on a unified mobile chassis platform, and their roles are defined through pluggable functional modules. Energy Buffering Robot (EBR): The core functional module is a multi-machine parallel charging management module, which enables it to focus on obtaining power from the grid at the optimal cost, charging standardized energy units, and providing energy replenishment for another type of robot; Energy Express Robot (EER): Its core functional module is the vehicle service interaction module, which allows it to focus on directly providing charging or battery swapping services for new energy vehicles. like Figure 2 As shown, the cloud-based collaborative platform is responsible for real-time scheduling, path planning, transaction settlement, and status monitoring across the entire network. Energy transfer between the two types of robots is achieved through a standardized physical medium—the Standardized Energy Capsule (SEC). The system's core innovation lies in four collaborative mechanisms: dynamic role switching, internal energy logistics management, group path optimization, and asset-based operation; specifically, it includes the following steps: Step 1: Dynamic role switching based on marginal value assessment.

[0019] By assessing the marginal value of robots in different roles in real time, flexible resource allocation can be achieved, alleviating the supply-demand imbalance of energy replenishment services in local areas. The overall process is as follows: status information synchronization → marginal value calculation → switching decision and execution. The robot periodically uploads its status to the cloud, and the cloud calculates the marginal value and triggers the role switching command, ensuring that resource allocation dynamically adapts to changes in demand.

[0020] S11. Status information synchronization: By periodically reporting its own status to the cloud, the robot provides real-time and accurate input data for subsequent marginal value calculations, ensuring the cloud's ability to perceive the status of resources across the entire network.

[0021] Each robot periodically reports its state vector to the cloud platform. ;in, Geographic coordinates; For EER, the remaining available energy refers to the total SEC energy it carries; for EBR, it refers to the total charged SEC energy that it can allocate. This is the identifier for the currently loaded functional module. The current task queue status is categorized as idle, serving, and charging in this embodiment. S12, Marginal Value Calculation: By quantifying the benefits, capabilities, and costs of robots switching roles, marginal value indicators are generated, providing a quantitative basis for subsequent switching decisions and ensuring the economy and feasibility of role switching.

[0022] The cloud platform calculates the marginal value for each robot unit in playing another potential role; for a robot currently playing the role of EBR... Its marginal value when converted into an EER role The calculation formula is as follows: ; in, This indicates predictions based on historical data and real-time requests in the robot. Location Future time period within the surrounding service radius The expected service revenue density (yuan / hour·square kilometer) (e.g., 5 minutes to 60 minutes); its value is determined by a spatiotemporal kernel density estimation algorithm, specifically by performing kernel density fitting on historical service request data from the past 30 to 90 days, and then weighting and correcting it by combining the spatiotemporal distribution of real-time requests; Represents robots The average energy cost estimate for moving from the current location to the nearest service hotspot area is given in yuan / km. This value is obtained by multiplying the robot's energy consumption per unit distance by the current grid electricity price (yuan / kWh). Represents robots The current available energy and the minimum energy threshold required to perform a typical EER task The difference (e.g., 5kWh to 20kWh) reflects its service potential, in kWh. Represents robots Current location to the nearest modular automated exchange station The distance represents the physical cost of role switching, measured in kilometers; The weighting coefficient, with a value ranging from 0 to 1, is obtained by training on historical operational data from the past 6 to 12 months through offline reinforcement learning, and is used to balance the three factors of revenue, capability, and cost. For the robot whose current role is EER Its marginal value when converted into an EBR role The calculation formula is as follows: ; in, Represents robots Current location The real-time electricity price of the power grid, in yuan / kWh, is obtained by collecting the electricity price data released by the power grid in real time through the IoT module on the robot. The collection cycle is consistent with the robot's status reporting cycle. Represents robots Current location Within a radius of 3 to 5 kilometers, the potential demand intensity for providing energy replenishment services to other EERs is measured in times per hour. This value is determined by a spatiotemporal kernel density estimation algorithm. Kernel density fitting is performed on EER energy replenishment request data from the past 30 to 90 days, and weighted correction is made in conjunction with the real-time energy replenishment request distribution. Represents robots The difference between the rated storable energy capacity and the current remaining energy, minus the minimum energy storage space required to perform a typical EBR storable charging task. The remaining capacity (e.g., 5kWh~20kWh) reflects its storage energy potential, expressed in kWh; among which... The total capacity of standardized energy capsules that can be carried after EER is converted to EBR is determined. This represents the current remaining available energy of the EER. Represents robots Current location to the nearest modular automated exchange station The distance represents the physical cost of role switching, measured in kilometers; The weighting coefficient, with a value ranging from 0 to 1, is obtained by training on historical operating data from the past 6 to 12 months through offline reinforcement learning. It is used to balance three factors: electricity price demand revenue, storage energy capacity, and conversion physical cost. When calculating the above marginal value, all relevant input parameters are normalized by dividing by the corresponding preset benchmark value to make them dimensionless coefficients before being substituted into the formula. S13, Switching Decisions and Execution: By dynamically monitoring and comparing the marginal value of the entire network, the optimal role switching action is triggered to achieve precise allocation of resources and ensure the supply and demand balance of energy replenishment service capabilities in local areas.

[0023] The cloud platform periodically (every 5 minutes in this embodiment) scans the entire network of robots; for each region, it calculates the sum of the marginal values ​​of all EERs within that region. The sum of the marginal values ​​of all EBRs If a region satisfies If so, it is determined that the EER service capacity in the area is insufficient, among which The preset dynamic threshold ranges from 0.1 to 1.0 and is positively correlated with the regional demand intensity. The system will collect EBRs from this region or neighboring regions. In the middle, select the one that satisfies and The robot with the highest value Give it a role-switching command; robot Navigate to the nearest exchange station, the mechanical replacement function module becomes the vehicle service interaction module, and the identity is reset in the software. Then, it is included in the service scheduling pool as an EER.

[0024] Step 2: Internal energy logistics management based on standardized energy capsules.

[0025] Standardized energy capsules enable closed-loop energy flow between EBRs and EERs, solving the problem of efficient energy allocation within the energy replenishment network. The overall process is: SEC status modeling → EBR procurement and storage → EER energy relay replenishment. SEC status information is synchronized to the cloud in real time. The cloud schedules EBR charging based on electricity prices and demand, and matches the optimal replenishment point for EERs, ensuring efficient and controllable energy flow.

[0026] S21, SEC State Model: This step provides a digital foundation for energy logistics management. By establishing a unique state vector for each SEC, it enables full lifecycle tracking of energy units, ensuring the traceability and controllability of energy flow.

[0027] Each SEC has a unique digital identifier. and state vector .in, Current electricity consumption, in kWh; Rated capacity, in kWh; The coordinates of the current geographical location; Record the ID of the current holder, i.e., EBR or EER; For a healthy state, the value ranges from 0 to 1. It is derived based on the ratio of the SEC rated capacity to the current actual usable capacity, combined with weighted corrections for parameters such as charge / discharge cycle count, internal resistance change, and temperature decay coefficient; details are as follows: ; in, , This indicates the cumulative number of charge-discharge cycles for the SEC. Indicates the SEC design cycle life. This represents the cyclic decay coefficient, which ranges from 0 to 1 and is calibrated by offline aging experiments. In this embodiment, the default value is 0.8. , This indicates the current internal resistance within the SEC. This indicates the initial internal resistance of the SEC at the time of manufacture. This represents the internal resistance attenuation coefficient, with a value ranging from 0 to 1, calibrated by offline aging experiments. In this embodiment, the default value is 0.7. , This indicates the current operating temperature of the SEC (unit: °C). This indicates the optimal operating temperature for the SEC (typical value: 25℃). Indicates the maximum permissible temperature deviation. This represents the temperature decay coefficient, with a value ranging from 0 to 1, calibrated by offline aging experiments. In this embodiment, the default value is 0.6. Represents the weighting coefficients, satisfying Default value: This is used to balance the weights of the effects of cycle number, internal resistance, and temperature on health status, and can be iteratively optimized based on operational data. Meanwhile, the BMS (Battery Management System) built into EBR / EER collects charge and discharge data in real time. After every 50 charge and discharge cycles or every 30 days, it is calibrated by combining offline aging test data to ensure accuracy. After each charge and discharge cycle, the SOH value is automatically updated, and the data is synchronized to the cloud and stored on the blockchain. S22 and EBR's Procurement and Warehousing Strategies: By scheduling EBRs for centralized charging during periods of low electricity prices, energy acquisition costs are reduced, and charging strategies are optimized based on the health status of SECs to extend their lifespan, providing low-cost and highly reliable energy reserves for subsequent energy replenishment.

[0028] EBR was dispatched during periods of low electricity prices (electricity price) Centralized charging is conducted in grid charging areas during off-peak hours, with the charging decision objective being to minimize unit energy acquisition cost and consider SEC lifetime; for EBR management... Each SEC, its charging cutoff capacity Determined by the following formula: ; in, This indicates the remaining time window for the current low electricity price, in hours. This indicates the maximum power of the EBR charging module, in kW; This represents the average charging efficiency, with a value ranging from 0.8 to 0.95. This indicates the number of SECs used for parallel charging, with a value ranging from 1 to 8. This represents the SOH attenuation factor (calibrated through aging experiments), with a value range of 0~1; when hour, The value is set to 0.5~1.0 to reduce charging power and extend SEC lifespan; S23, EER energy relay replenishment: By matching the optimal replenishment point for low-power EERs, efficient energy relay is achieved, ensuring the service continuity of EERs and improving the overall service capability of the energy replenishment network.

[0029] when When the remaining service capacity (estimated by the total SEC power it carries) falls below a threshold, a replenishment request is initiated; the cloud platform matches it with the nearest available SEC with sufficient inventory. Supply points Determining is an optimization problem, the goal of which is to make After completing the current service task, proceed to... Additional travel loss, and Go to The sum of costs is minimized; the near-optimal rendezvous point can be obtained by solving the following equation: ; in, express Location of the next planned service point; This represents the average moving speed of the two types of robots, expressed in kilometers per hour. express The remaining available electricity, in kWh; This represents the EER (Electric Energy Equivalent) coefficient, expressed in kilometers per kWh. This represents a set of feasible supply areas, dynamically determined by the current power grid and traffic conditions. This represents a weighting coefficient, ranging from 0 to 1, used to balance the costs of both parties; during replenishment, and exist The robotic arm can quickly replace a low-charge SEC with a fully charged SEC, with the replacement time not exceeding 5 minutes.

[0030] Step 3: Group route optimization method based on energy carpooling.

[0031] By merging recharge requests that are similar in time and space, an optimized service path is generated, which improves the service efficiency of a single EER and reduces operating costs. The overall process is request clustering and route calculation → dynamic energy shuttle route generation → user incentives and endurance guarantee. After user requests are clustered, a service path is generated. The system monitors the energy consumption of the path in real time and triggers recharge. At the same time, price incentives are used to increase user participation.

[0032] S31. Request clustering and pathfinding calculation: By performing spatiotemporal clustering and route evaluation on user requests, a foundation is provided for generating optimized service paths, ensuring the economy and rationality of merging services.

[0033] Set the current time window (In this embodiment, the timeframe is 10 to 30 minutes.) Within a certain area, there are N charging requests, each request... Includes vehicle location and flexible service hours (For example, the time interval between the earliest and latest service times is 30 to 60 minutes); the system first uses a density-based spatial clustering algorithm (such as DBSCAN) to... Initial grouping was performed, with cluster radii set between 0.5 km and 2 km; for each cluster... Calculate any two requests within it. Convenience between : ; in, This represents the actual path distance, in kilometers. This serves as a virtual departure or assembly point for the EER in this region. The smaller the value, the more convenient the route is between the two request points compared to serving from the hub alone; S32. Dynamic Energy Shuttle Route Generation: By modeling the clustered requests as a vehicle routing problem with time windows, near-optimal service paths are generated, ensuring a balance between service efficiency and time constraints.

[0034] For clustering The service path planning is modeled as a variant of the Vehicle Routing Problem with Time Windows (VRPTW), with the objective function being to minimize the total travel distance while satisfying the time window constraints at each point and the EER's own battery level constraints. The system uses a heuristic algorithm (in this embodiment, the Clarke-Wright method combined with local search is used) to quickly generate a near-optimal serial service path. ,in For service order; such as Figure 3 As shown, the specific implementation process is as follows: First, the initial service path is constructed based on the Clarke-Wright cost-saving method. The core idea is to prioritize merging request pairs by calculating the path saving distance between two requests. The cost-saving distance formula is defined as follows: ,in For merge request and The larger the value, the more significant the path shortening after merging, and the more valuable the merging process. Secondly, initialize the path set and cluster it. Each request is treated as an independent path, meaning each path contains only a node and a single request point. ; Subsequently, all request pairs are sorted from largest to smallest by the distance saved, and the corresponding paths are attempted to be merged sequentially. During merging, time window constraints must be checked (the service time of each request in the merged path must fall within its specified time window). The path merging process is completed if the cumulative energy consumption of the merged path does not exceed the safe threshold of the current available energy of the EER (within the specified range). Otherwise, the request pair is skipped. Finally, local search optimization is performed on the initial merging path (using a 2-opt or 3-opt strategy). By swapping the service order of two or three requests in the path, path intersections and redundant road segments are eliminated, and the total travel distance is further shortened. Finally, an approximately optimal serial service path that satisfies all constraints is obtained. S33, User Incentive Mechanism and Shuttle Bus Endurance Guarantee: By using price incentives to increase user acceptance of the carpooling model and by monitoring route energy consumption in real time, we can ensure the continuity of service tasks and improve user experience and service reliability.

[0035] The system offers users two options: dedicated fast charging and carpooling for energy replenishment. Users who choose carpooling will receive a price discount. Its service hours will be scheduled along the route. Within the calculated time window, the total energy consumption of the EER performing the energy shuttle mission along its route. Real-time prediction; if ( To ensure safety, a value ranging from 0.7 to 0.9 is used. The system will then proceed along the path... Insert a relay supply point with EBR (calculated in the same way as S23) to ensure that the mission is completed without interruption.

[0036] Step 4: Energy asset operation based on distributed ledger.

[0037] By digitizing SEC assets and managing them on a distributed ledger, the network achieves trusted, automated settlement and incentives for transactions, improving operational transparency and collaborative efficiency. The overall process involves asset mapping and on-chain status recording → smart contract microtransactions. SEC status and transaction information are recorded on the blockchain in real time, and smart contracts automatically execute settlement and allocation, ensuring the trustworthiness and sustainability of operations.

[0038] S41. Asset Mapping and State On-Chain: This step provides a digital foundation for the operation of energy assets. By mapping physical SECs to on-chain digital assets, it enables credible and transparent tracking of asset status, providing a basis for subsequent transactions and settlements.

[0039] Each physical SEC (ID is) On the blockchain, this corresponds to a digital asset managed by a smart contract. Critical status (battery level) Holders Transaction history The data is updated regularly to the blockchain via the IoT module, with an update cycle of 1 to 5 minutes. The update requires the holder's private key signature to ensure authenticity.

[0040] S42. Smart contract-based microtransaction protocol: By automatically executing energy transactions and settlements through smart contracts, human intervention is eliminated, transaction efficiency and credibility are improved, and the timely and accurate distribution of benefits to all parties is ensured.

[0041] The core transaction logic is implemented by the following smart contract functions: Energy Credit Casting: When For SEC From battery power Charged to It calls the contract function. After the contract signature is verified, it becomes... account increase Energy credit, of which The benchmark is based on the average grid price during the charging period, and the unit is yuan / kWh; Service settlement and allocation: When Using SEC Revenue is generated by providing charging services to users. Locked Call Automatic contract execution: 1) ( The platform service fee (ranging from 5% to 15%) is allocated proportionally to the current SEC digital assets. The energy credit holder, i.e., the EBR that charges it; 2) Assigned to The operator's account.

[0042] Furthermore, the overall workflow of this method is summarized as follows: The cloud-based collaborative platform integrates the algorithm modules corresponding to the four methods and steps mentioned above, forming a unified scheduling engine. The workflow is briefly described below: 1) The platform receives a user request, triggers the group route optimization module, and generates a carpooling route or assigns a separate EER; 2) Monitor the status of all EER / SEC in real time. When energy replenishment is needed, trigger the internal energy logistics management module to dispatch EBR for relay energy replenishment. 3) Periodically run dynamic role switching assessments and initiate role switching commands when resource mismatch occurs; 4) Energy service transfer and service delivery events trigger the corresponding smart contracts in the asset operation module to complete automatic settlement and recording.

[0043] Through the deep integration of the above technologies, the entire system achieves end-to-end optimization of the power replenishment network resources, service efficiency, and operational reliability.

[0044] Through the detailed description of the above embodiments, the flexible energy replenishment method for new energy vehicles based on mobile energy storage robots of the present invention, by constructing a core architecture of a dual-mode reconfigurable robot and a cloud-based collaborative platform, integrates four major mechanisms: dynamic role switching, standardized energy capsule logistics, energy carpooling route optimization, and distributed ledger operation, forming a highly efficient and collaborative mobile energy replenishment system. This method highlights two core technical features: demand merging and dynamic role allocation based on multi-factor marginal utility. It solves both the problem of travel redundancy caused by dispersed demand and the pain point of resource mismatch caused by fixed roles, achieving flexible allocation of energy replenishment resources, closed-loop controllable energy flow, and reliable and efficient transaction settlement, providing a flexible and economical mobile energy replenishment solution for new energy vehicles.

[0045] The above formulas are all dimensionless calculations, and the preset parameters in the formulas should be set by those skilled in the art according to the actual situation.

[0046] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0047] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0048] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0049] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0050] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0051] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0052] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A flexible energy replenishment method for new energy vehicles based on mobile energy storage robots, characterized in that, The method flow is as follows: Step 1: Dynamic role switching based on marginal value assessment: By quantitatively assessing and dynamically monitoring the marginal value of the robot under different roles, the elastic allocation of energy replenishment resources can be realized to adapt to changes in energy supply and demand in local areas. Step 2: Internal energy logistics management based on standardized energy capsules: Through full life cycle status tracking of standardized energy units, low-cost warehousing strategies, and efficient relay replenishment mechanisms, closed-loop circulation of energy within the replenishment network is achieved; Step 3: Group route optimization method based on energy carpooling: By spatiotemporal clustering of energy replenishment requests, calculation of route affinity, and route planning with time windows, combined with user incentive measures, service efficiency and user participation are improved; Step 4: Energy asset operation based on distributed ledger: Through on-chain asset mapping of physical energy units, automatic transaction settlement driven by smart contracts, and incentives for collaborative contributions, the credibility and efficiency of energy replenishment transactions are realized.

2. The flexible energy replenishment method for new energy vehicles based on a mobile energy storage robot according to claim 1, characterized in that, The method relies on a dual-mode energy storage and replenishment network, which consists of two types of reconfigurable mobile robots and a cloud-based collaborative platform. The two types of robots are based on a unified mobile chassis platform and are defined as energy storage robots and energy delivery robots, respectively, through pluggable functional modules. The former focuses on obtaining electricity from the grid and charging standardized energy units, while the latter focuses on directly providing charging or battery swapping services for new energy vehicles. The cloud-based collaborative platform is responsible for real-time scheduling, path planning, transaction settlement, and status monitoring of the entire network. Energy transfer between the two types of robots is achieved through standardized energy capsules.

3. The flexible energy replenishment method for new energy vehicles based on a mobile energy storage robot according to claim 2, characterized in that, The dynamic role switching based on marginal value assessment includes three sub-steps: state information synchronization, marginal value calculation, and switching decision and execution. The robot periodically reports its own state vector to the cloud. The cloud calculates the marginal value of the robot after switching roles based on the state vector. Then, by comparing the sum of regional marginal values, the optimal robot's role switching command is triggered.

4. The flexible energy replenishment method for new energy vehicles based on a mobile energy storage robot according to claim 2, characterized in that, The internal energy logistics management based on standardized energy capsules includes three sub-steps: standardized energy capsule state modeling, energy storage robot procurement and storage, and energy delivery robot energy relay replenishment. A unique state vector is established for each standardized energy capsule to achieve full life cycle tracking. The energy storage robot formulates a charging strategy based on the grid electricity price and capsule health status. When the energy delivery robot's power is insufficient, it initiates a replenishment request and matches the optimal replenishment point to complete the energy replacement.

5. The flexible energy replenishment method for new energy vehicles based on a mobile energy storage robot according to claim 4, characterized in that, The state vector of the standardized energy capsule includes the current power, rated capacity, health status, geographical coordinates, and holder information. Its health status is obtained by weighted correction based on the number of charge-discharge cycles, internal resistance changes, and temperature decay coefficient.

6. The flexible energy replenishment method for new energy vehicles based on a mobile energy storage robot according to claim 4, characterized in that, The optimal refueling point for the energy delivery robot is determined by balancing the additional travel loss of the energy delivery robot after completing the current task with the cost of the energy storage robot traveling to the refueling point.

7. The flexible energy replenishment method for new energy vehicles based on a mobile energy storage robot according to claim 2, characterized in that, The group route optimization based on energy carpooling includes three sub-steps: request clustering and route proximity calculation, dynamic energy shuttle route generation, and user incentives and endurance guarantee. Clustering of energy replenishment requests that are similar in time and space and calculating their routeability, generating an approximate optimal service path based on the clustering results, providing price incentives to increase user engagement, and monitoring path energy consumption in real time to ensure service continuity.

8. The flexible energy replenishment method for new energy vehicles based on a mobile energy storage robot according to claim 6, characterized in that, The dynamic energy shuttle route generation modeled the clustered requests as a vehicle routing problem with a time window, used a heuristic algorithm to construct an initial route and optimized it through local search to generate a service route that satisfies the time window and power constraints.

9. The flexible energy replenishment method for new energy vehicles based on a mobile energy storage robot according to claim 2, characterized in that, The energy asset operation based on distributed ledger includes two sub-steps: asset mapping and on-chain status, and smart contract micro-transactions. Each physical standardized energy capsule is mapped to a digital asset on the blockchain managed by a smart contract. Its key status information includes current power, holder identity, and transaction history. The key status information is updated to the blockchain regularly through the Internet of Things module, and the updated data must be signed by the holder's private key.

10. The flexible energy replenishment method for new energy vehicles based on a mobile energy storage robot according to claim 8, characterized in that, The smart contract microtransactions include two core logics: energy credit creation and service settlement allocation. Energy credit creation involves the energy storage robot calling a smart contract function to generate energy credits corresponding to the amount of charge after charging a standardized energy capsule. Service settlement allocation involves the energy delivery robot using the standardized energy capsule to complete a user's energy replenishment service. After deducting the platform service fee from the service revenue, the remaining portion is distributed proportionally to the energy credit holder of the standardized energy capsule, i.e., the energy storage robot, and the operator of the energy delivery robot.

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