A non-transaction multi-electric vehicle cluster mutual assistance peak load shifting and valley filling energy storage scheduling system based on member level and credit dynamic weight and supporting user-defined vehicle plan verification

Abstract

This invention discloses a transaction-free multi-vehicle cluster mutual assistance peak shaving and valley filling energy storage dispatching system based on membership level and dynamic credit weights, supporting user-defined vehicle usage plan verification. It belongs to the field of user-side new energy storage dispatching technology. This invention completely abandons the traditional V2G energy storage system's capital transaction and electricity arbitrage model, constructing a fully compliant underlying architecture based on pure equity exchange. Through vehicle pre-emptive safety self-inspection, 24-hour travel and load predictive dispatching, user-initiated pre-entry of customized vehicle usage plans, automatic system comparison of actual execution, direct linkage of performance behavior with credit rewards and penalties, joint optimization of V2H / V2V electricity consumption, irreversible essential electricity demand protection, and credit ecosystem self-governance, this invention addresses industry pain points such as compliance risks, insufficient security, passive dispatching, lack of planning, ecosystem imbalance, and insufficient constraints in traditional technologies. This invention features a unique core innovation: a multi-level membership system with fixed privileges, a real-time dynamic credit weighting system (including plan fulfillment points), and a two-factor differentiated charging and discharging scheduling mechanism. Combined with a global-user dynamic strategy game architecture, it achieves a top-tier energy storage mutual aid ecosystem characterized by "long-term contribution preservation, short-term activity incentives, precise plan fulfillment, priority for high-quality users, and a positive ecological cycle." Simultaneously, it integrates a complete intelligent system encompassing user big data reports, AI predictive strategy recommendations, one-click cloud-based switching of user multi-scenario strategies, and one-click global template maintenance for administrators. This invention achieves a disruptive upgrade across seven dimensions: compliance, security, intelligence, accuracy, fairness, commercial viability, and operational efficiency. It boasts extremely high barriers to entry, is unavoidable, and can be directly scaled for commercial application, possessing high patent licensing stability and industrialization value.

Description

Technical Field

[0001] This invention belongs to the fields of user-side distributed energy storage, smart community energy dispatching, electric vehicle V2G / V2H bidirectional energy interaction, and virtual power plant user-side collaborative technology. Specifically, it relates to a multi-electric vehicle cluster mutual assistance energy storage peak shaving and valley filling system that features zero-fund compliant mutual assistance, vehicle pre-entry safety access, 24-hour load and travel forecasting, fixed privileges for membership levels, dynamic credit scheduling weights, user-defined vehicle usage plan input and execution verification, global-user two-layer strategy game, multi-scenario strategy template switching, and global one-click operation and maintenance by administrators. Background Technology

[0002] Existing technologies for mutual energy storage of electric vehicle clusters and V2G peak-valley scheduling in residential communities have the following core shortcomings: 1. Compliance flaws: All vehicle-to-vehicle mutual assistance and community energy storage dispatch in the industry rely on electricity bill settlement, price arbitrage, and user electricity sales transactions, which are unlicensed commercial electricity transactions that violate the national electricity monopoly red line and cannot be legally implemented, inspected, or commercialized.

[0003] 2. Lack of cluster security mechanisms: There is no mandatory network access self-test, no battery health screening, and no faulty vehicle interception. Cluster operation is prone to overheating, differential pressure disorder, power distribution overload, and thermal runaway.

[0004] 3. Passive and inefficient scheduling mode: Traditional systems are reactive scheduling systems with no load forecasting, no travel prediction, and egalitarian resource allocation. Highly idle vehicles are not fully utilized, and frequently used vehicles are ineffectively damaged.

[0005] 4. Complete lack of ecological fairness: The existing technology lacks a differentiated rights system, and there is no way to restrain users from "free-riding and mutual assistance, only taking and not giving out". Users who contribute more and have more idle resources have no priority, and the ecosystem is extremely prone to collapse.

[0006] 5. The reward and punishment mechanism is decoupled from the scheduling: Traditional points are only used for recording and do not participate in scheduling, so they cannot achieve resource allocation or incentivize continuous contributions.

[0007] 6. Rigid strategy system: There is only a single fixed strategy, which cannot adapt to multiple scenarios of user commuting, home, long distance, and high power consumption. The operation and maintenance backend parameters are fixed and cannot be switched to global mode with one click.

[0008] 7. Lack of differentiation among user levels and no privileges for premium users: The existing technology lacks a membership tier system and a priority privilege system, and all users are treated the same, making it impossible to form a business operation ecosystem of "more contribution, more rights, and higher priority".

[0009] 8. Unable to proactively obtain user travel plans and relies on passive prediction: The existing system relies solely on historical data for prediction, and cannot inform users of temporary travel or long-distance plans in advance; there is no custom car use plan input, no execution comparison, and no credit linkage mechanism, which leads to inaccurate scheduling, incorrect battery reservation, and waste of mutual assistance resources, while also failing to constrain dishonest behavior.

[0010] In summary, existing technologies suffer from insufficient compliance, security, intelligence, unfair ecosystem, outdated operation and maintenance, and a lack of proactive planning interaction and credit punishment mechanisms. The industry lacks a top-tier energy storage scheduling architecture that provides: compliance assurance, security closed loop, predictive scheduling, dynamic credit weighting, tiered membership privileges, user-initiated vehicle usage plans, plan-actual execution comparison, credit reward and punishment linkage, two-layer strategy game, personalization, operability, and commercial viability. Summary of the Invention

[0011] Purpose of the invention Addressing all the shortcomings of existing technologies, this invention, based on a zero-transaction compliance mutual assistance system, integrates AI predictive scheduling, secure access closed loop, user multi-strategy templates, administrator global strategy library, and membership level + credit dual-factor scheduling. Furthermore, it adds the ability for users to proactively input custom usage plans in advance, the system to compare actual execution, and direct linkage between performance / default on credit score adjustments. This achieves a fully closed-loop ecosystem of "user proactive planning - precise system scheduling - behavioral credit-based scheduling - differentiated scheduling," balancing scheduling accuracy, user freedom, ecosystem constraints, and commercial sustainability.

[0012] Core Technology Innovation Architecture (13 Closed Loops) 1. Zero capital, zero transactions, pure equity swap compliant underlying layer (the only legal model in the industry) 2. Vehicle front-mounted fully automated safety self-inspection and access mechanism 3. 24-hour travel probability + AI predictive scheduling of community load 4. User-defined car usage plans can be pre-entered, edited, saved, and reused periodically. 5. Comparison and verification of plan and actual execution, automatic judgment of performance / breach of trust. 6. The fulfillment rate of vehicle usage plans is directly linked to credit score rewards and penalties. 7. Tiered User Membership System (Fixed Priority Tiers) 8. Two-tiered differentiated charging and discharging scheduling based on membership level and dynamic credit weight. 9. V2H / V2V Joint Optimization Allocation Model 10. Global-User Dynamic Strategy Game Mechanism 11. Priority will be given to ensuring essential household electricity needs are met, even if they are irreversible. 12. User big data reports + AI-powered personalized predictive recommendation strategies and cloud-based switching of strategies across multiple scenarios. 13. Administrator global policy template library, one-click network-wide operation and maintenance, version rollback. Technical solution

[0013] 1. Zero-transaction compliant mutual aid underlying layer (core compliance barrier) The system prohibits any electricity bill settlement, fund transactions, user electricity sales, or peak-valley arbitrage. All mutual assistance activities only quantify electricity contribution and vehicle usage plan fulfillment, converting them into membership level points plus dynamic credit weights to achieve the exchange of rights. There are no commercial transaction attributes, and it fully complies with national power regulatory compliance requirements.

[0014] 2. Fully automated safety self-check before vehicle registration. Before participating in cluster energy storage, vehicles must undergo mandatory testing including: battery health (SOH), individual cell voltage difference, temperature, fault codes, BMS operating conditions, and remaining capacity compatibility. Vehicles failing these tests are immediately intercepted and prohibited from joining the network, thus eliminating cluster safety risks at the source.

[0015] 3. AI-powered 24 / 7 predictive adaptive scheduling The system predicts based on historical big data: the probability of each vehicle's travel in the next 24 hours, the overall peak and valley load fluctuations of the community, the peak power consumption periods of users' households' V2H, and the storage capacity of off-peak electricity; it also pre-allocates charging strategies, pre-plans mutual discharge margins, and pre-recommends the optimal scenario mode for users.

[0016] 4. User-defined car usage plans are pre-entered into the system. Users can proactively enter their car usage plans for the next 1-7 days in advance via a mobile app. Supports: - Enter travel date, departure / return time, minimum battery level, and travel purpose (commuting / long-distance / staying at home). - Supports saving as a recurring schedule template (fixed commute on weekdays, fixed stay-at-home on weekends). - Edit, modify, cancel, and add plans at any time - The plan can be linked to user-saved multi-scenario policies, enabling one-click activation. The system takes user-initiated plans as the highest scheduling reference and combines AI predictions for double protection.

[0017] 5. Automatic comparison and verification of planned and actual execution. The system monitors the vehicle's real-time status: actual travel time, SOC (State of Charge) changes, parking duration, and V2H (Vehicle-to-Everything) power consumption; it automatically compares this data with the user's pre-entered planned usage scheme and determines the appropriate usage. - Full compliance: Actual actions are largely consistent with the plan. - Slight deviation: minor time / battery charge deviation - Serious breach of trust: failure to travel as planned, failure to report unplanned long-distance trips, and insufficient reserve power leading to dispatch disorder. 6. Rewards and penalties are directly linked to the fulfillment rate of vehicle usage plans and credit scores. The system automatically executes the credit linkage mechanism: - Timely fulfillment and accurate planning: Credit score increase, improved scheduling weight, and accelerated member growth. - Minor deviation: Credit score remains unchanged or slightly reduced. - Repeated serious breaches of trust and frequent failure to report temporary changes: credit score drops significantly, mutual assistance permissions are restricted, and priority for energy replenishment is reduced. - Proactively modifying and reporting your plans in advance: This will not be considered a breach of trust and will not result in point deductions. The goal is to achieve the following: the more accurate the plan, the higher the credit score, the higher the priority, and the greater the mutual benefits. This incentivizes users to proactively plan and manage their vehicle usage, significantly improving the accuracy of the system's dispatching.

[0018] 7. Tiered User Membership System (Fixed Tier Privileges) Level 5 membership is automatically promoted based on a combination of historical accumulated mutual assistance contributions, activity level, and car rental plan fulfillment rate: Memberships range from Level 1 (Regular) to Level 5 (Supreme Privilege); the higher the level, the higher the priority for peak power replenishment, mutual discharge queuing, battery loss compensation, disaster backup power, and AI strategy adaptation.

[0019] 8. Two-tiered differentiated scheduling based on membership level and dynamic credit score. Overall Priority = Membership Level Weight × Dynamic Credit Coefficient (including performance score) × Idle Space Suitability Coefficient - Long-term high priority + recent high mutual assistance + high fulfillment rate = highest charging and discharging priority - High rating but frequent breaches of trust: Weighting declines - Low level but high contribution and high performance: can dynamically overtake others. 9. V2H / V2V Joint Power Allocation Optimization Under the premise of ensuring sufficient power for basic household needs and backup power for travel, surplus power is intelligently allocated to participate in mutual energy storage, maximizing the utilization rate of off-peak electricity and the benefits to users.

[0020] 10. Global-User Dynamic Strategy Game Mechanism Administrators implement global policies to lock in the bottom line of security; within the security boundary, users are allocated resources based on membership and credit weights and can freely execute personal scenario policies; when the load is heavy, high-level, high-credit, and high-performance users are given priority.

[0021] 11. Priority will be given to irreversible, essential electricity needs. Household V2H essential electricity needs > Vehicle backup power for next day's trip (based on user plan) > Group mutual discharge 12. Credit-based autonomous ecosystem Daily quantification of mutual aid contributions and plan fulfillment status, updating credit scores and membership level progress; users with zero contributions and frequent defaults have their permissions restricted at each level; users with emergency discharge and high-precision plans receive extra rewards.

[0022] 13. User big data analysis + exportable reports + AI prediction and recommendation A new car usage plan fulfillment analysis report has been added, which, together with car usage habits, household electricity consumption, battery health, credit rating, and membership growth, constitutes five categories of exportable reports (PDF / Excel); AI combines user-initiated plans with historical data to generate predictive optimal strategies, which can be activated and saved with one click in the APP.

[0023] 14. Cloud-based templates for multi-scenario user strategies + one-click switching anytime It supports saving an unlimited number of scenario strategies to the cloud, and can be bound to a fixed car use plan template for one-click switching and linkage.

[0024] 15. One-click maintenance of the administrator's global policy template library. The backend has multiple built-in global cluster modes: daily, maximum energy storage, battery protection, maintenance current limiting, extreme weather, disaster recovery, and new user onboarding. It supports one-click switching across the entire network, version rollback, operation logging, and regulatory compatibility. Beneficial effects

[0025] 1. Industry-leading compliance barriers: Pure equity swaps and zero-capital transactions completely circumvent the regulatory red lines of the power monopoly.

[0026] 2. Complete closed-loop security mechanism: pre-emptive self-inspection + dynamic risk control + threshold protection to eliminate cluster risks.

[0027] 3. Intelligent and precise scheduling: Driven by both AI prediction and user-initiated planning, scheduling errors are significantly reduced.

[0028] 4. A unique three-dimensional incentive system of membership + credit + performance: fixed level determines long-term privileges, dynamic credit determines short-term incentives, and planned performance determines the credibility of scheduling, creating a positive cycle of the ecosystem that will never collapse.

[0029] 5. Closed-loop behavioral constraints: The car usage plan is directly linked to credit, solving the problem of resource waste caused by users' temporary changes.

[0030] 6. Global-User Dynamic Game Theory: A perfect balance between security and experience, fairness and efficiency, and control and freedom.

[0031] 7. Full-scenario adaptive: Users can switch between multiple templates and administrators can perform global operations and maintenance, adapting to all working conditions and scenarios.

[0032] 8. Strong commercial applicability: The membership level + performance credit system can be directly used for community operation, property management cooperation, and energy platform commercialization. Detailed Implementation

[0033] Step 1: Authorize vehicle access to the network, perform fully automated pre-security self-check, and include qualified vehicles in the cluster resource pool.

[0034] Step 2: Users can pre-enter their custom car usage plan for the next 1-7 days in the APP, and can save periodic templates and bind corresponding charging and discharging strategies.

[0035] Step 3: The system collects historical data and user-initiated plans to predict the probability of vehicle travel and the load trend of the community in the next 24 hours, and dynamically divides dynamic and static energy storage nodes.

[0036] Step 4: During off-peak hours at night, implement a predictive and plan-driven optimal energy storage strategy to prioritize the electricity consumption for users' planned travel.

[0037] Step 5: The system reads the user's fixed membership level + real-time credit score (including planned performance score) and generates a comprehensive scheduling priority weight.

[0038] Step 6: During daytime peak power periods, the system executes differentiated mutual charging and discharging scheduling based on dual-factor weights, strictly adhering to the user-entered plan as the basis for backup power.

[0039] Step 7: The system compares the user's planned usage with their actual usage behavior in real time, automatically determines whether the user has fulfilled their obligations, deviated from the plan, or defaulted on their credit score, and updates the credit score in real time.

[0040] Step 8: The system strictly enforces the highest priority of household essential needs and planned travel backup power, and does not allow mutual dispatch to encroach on private power.

[0041] Step 9: Users can freely switch between custom strategy templates such as daily, weekend, long-distance, and high-power home use according to their own scenarios.

[0042] Step 10: The administrator can switch the global cluster operation mode of the community with one click based on the weather, load, maintenance, and emergency scenarios.

[0043] Step 11: The system quantifies mutual assistance contributions and plan fulfillment in real time, and updates credit scores and member level growth progress.

[0044] Step 12: When the power grid fails, it automatically enters the disaster recovery and supply guarantee mode, giving priority to ensuring the basic power supply of high-level, high-credit, and high-performance users.

[0045] Step 13: Automatically generate multi-dimensional user analysis reports daily (including car rental plan fulfillment reports), and use AI to predict and generate the optimal strategy and push it for one-click activation.

[0046] Step 14: The system learns and iterates daily to continuously optimize prediction accuracy, plan adaptation model and differentiated scheduling algorithm.

Claims

1. A transaction-free multi-electric vehicle cluster mutual assistance peak shaving and valley filling energy storage dispatching system based on membership level and dynamic credit weights, supporting user-defined vehicle usage plan verification, characterized in that... include: The module includes: Network Security Self-Check Module, Node Travel Prediction and Hierarchy Module, User-Customized Vehicle Use Plan Input and Management Module, Plan-Actual Execution Comparison and Verification Module, Plan Performance Credit Reward and Punishment Linkage Module, Member Level Grading Privilege Module, Credit Dynamic Weight Calculation Module, Two-Factor Differentiated Charging and Discharging Scheduling Module, V2H / V2V Joint Optimization Module, Priority Guarantee Module for Essential Electricity Demand Module, Dynamic Strategy Game Theory Module, Big Data Report and AI Prediction Recommendation Module, User Multi-Strategy Cloud Switching Module, and Administrator Global Strategy Operation and Maintenance Module. The network access safety self-test module is used to automatically screen battery health SOH, single cell pressure difference, temperature, fault codes, BMS operating conditions and remaining power compatibility before electric vehicles participate in cluster energy storage, intercept abnormal vehicles, and only allow qualified vehicles to be included in the cluster resource pool. The node travel prediction and stratification module is used to predict the probability of a vehicle's travel in the next 24 hours based on historical vehicle usage data and user-initiated vehicle usage plans, and dynamically divide them into static energy storage nodes and dynamic demand nodes. The user-defined car use plan entry and management module is used to support users to enter their car use plans for the next 1-7 days in advance, including travel time, minimum reserved battery power, and travel type. It supports saving plans as periodic templates and can bind corresponding charging and discharging strategies to achieve linkage; The plan-actual execution comparison and verification module is used to collect real-time data on vehicle travel, battery level changes, and parking status, and automatically compare it with the user's preset vehicle use plan to determine four behavior types: full performance, minor deviation, serious breach of trust, and proactive reporting of changes. The plan performance credit reward and punishment linkage module is used to automatically adjust the user's credit value according to the comparison results: full performance adds points, minor deviations do not adjust, serious dishonesty deducts points, and proactively reporting changes in advance does not deduct points. The credit value is directly related to the scheduling weight. The membership level grading privilege module is used to comprehensively evaluate the user's membership level from 1 to 5 based on the user's historical mutual assistance contribution, activity level, and vehicle use plan fulfillment rate. The higher the level, the higher the priority of charging and discharging, energy replenishment, and disaster backup power. The credit dynamic weight calculation module is used to update the dynamic credit coefficient in real time by combining user mutual assistance behavior and plan fulfillment. The dual-factor differentiated charging and discharging scheduling module is used to calculate the comprehensive scheduling priority based on the fixed weight of membership level, dynamic credit coefficient, and vehicle idle adaptation coefficient, so as to realize the priority allocation of resources for high-level, high-credit, and high-performance users. The V2H / V2V joint optimization module is used to allocate surplus electricity to participate in cluster mutual energy storage while ensuring self-use needs are met. The essential power demand priority backup module is used to solidify the priority: household V2H self-use load > vehicle planned travel backup power reserve > cluster mutual discharge; The dynamic strategy game module is used to allocate resources according to the user's comprehensive priority, with the administrator's global security policy as the boundary, and the user executes personalized scenario strategies within the limit. The big data reporting and AI prediction recommendation module is used to generate multi-dimensional reports including vehicle usage plan fulfillment analysis, and AI predicts the optimal energy storage strategy and supports one-click activation. The user multi-strategy cloud switching module is used to store multiple sets of scenario strategies in the cloud, supports one-click switching, and is linked with the car use plan. The administrator's global policy operation and maintenance module is used to build multiple global scheduling templates in the background, enabling one-click switching across the entire network, version rollback, and operation and maintenance record keeping. The system involves no fund transactions, no electricity bill settlement, and no user electricity arbitrage. It only converts quantified mutual assistance contributions and plan fulfillment into membership levels and credit weights, achieving compliant mutual assistance through pure equity exchange.

2. The system according to claim 1, characterized in that, The user-defined car use plan entry and management module allows users to edit, modify, and cancel entered plans at any time. Proactively reporting plan changes in advance will not be considered a breach of trust and will not trigger credit score deductions.

3. The system according to claim 1, characterized in that, The comprehensive priority calculation formula of the dual-factor differentiated charging and discharging scheduling module includes fixed weights for membership level, real-time dynamic coefficients for credit, and vehicle idleness adaptation coefficients. Long-term membership level determines basic privileges, while short-term performance and mutual assistance behaviors dynamically adjust scheduling priority.

4. The system according to claim 1, characterized in that, Under heavy load conditions, the system prioritizes allocating mutual aid resources to users with high membership levels, high credit ratings, and high plan fulfillment rates; under light load conditions, it grants users the authority to execute personalized strategies.

5. The system according to claim 1, characterized in that, The administrator's global policy operation and maintenance module has multiple built-in global operation templates for daily operation, energy storage maximization, battery protection, maintenance current limiting, extreme weather, disaster recovery, and new user guidance. It supports one-click switching across the entire network, operation logging, and reporting of regulatory data.

6. The system according to claim 1, characterized in that, The big data reporting and AI prediction recommendation module supports exporting reports in PDF and Excel formats. AI generates personalized energy storage strategies based on user driving plans, historical behavior, and battery status, and can save them as user-owned strategy templates with one click.

7. A transaction-free multi-trolley cluster mutual assistance peak shaving and valley filling energy storage scheduling method based on the system described in any one of claims 1-6, characterized in that, Includes the following steps: 1) Electric vehicles undergo a pre-entry safety self-inspection upon network access, and are included in the cluster resource pool after passing the inspection; 2) Users pre-enter their vehicle usage plans for the next 1-7 days, save periodic templates, and bind charging and discharging strategies; 3) The system combines historical data with user plans, and uses AI to predict the probability of 24-hour travel and peak and valley loads in the community to divide energy storage nodes; 4) During off-peak hours, priority will be given to ensuring backup power for users' planned travel, and surplus power will be allocated to participate in cluster energy storage; 5) Read the user's membership level and real-time credit value, and calculate the overall scheduling priority; 6) During peak power periods, differentiated mutual charging and discharging will be implemented according to priority, strictly adhering to the priority rules for essential electricity demand; 7) Real-time comparison of user plans and actual vehicle usage behavior, and automatic updates to credit scores; 8) Users can freely switch personalized scenario strategies, and administrators can switch global operating modes as needed; 9) Generate multi-dimensional data reports for users daily, push the optimal energy storage strategy with AI, and iteratively optimize the scheduling model through system self-learning.

8. The scheduling method according to claim 7, characterized in that, When the power grid fails, the system automatically switches to disaster recovery mode, prioritizing the basic electricity needs of users with high membership levels, high credit ratings, and high plan fulfillment rates.

9. The scheduling method according to claim 7, characterized in that, Users who fail to report temporary changes to their vehicle usage plans, resulting in wasted dispatch resources, will be deemed to have committed a serious breach of trust and will be subject to credit point deductions, restrictions on mutual assistance permissions, and downgrades in energy replenishment priority.

10. The scheduling method according to claim 7, characterized in that, The system quantifies users' mutual assistance contributions and plan fulfillment status daily, automatically updates credit scores, and promotes membership level upgrades.

11. The scheduling method according to claim 7, characterized in that, The dynamic strategy game method is as follows: the administrator locks the bottom line for the safe operation of the cluster, the system allocates resource quotas based on the member level and credit weight, and users independently execute personalized charging and discharging strategies within the safe quota.