An electric vehicle mobile energy storage emergency service method
By collecting real-time electric vehicle status information and using multi-objective optimization algorithms to generate scheduling instructions, the problems of slow response speed and limited coverage of traditional emergency power supply modes are solved, realizing efficient emergency power supply for electric vehicles, rapid response, and reduced social costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NORTHEAST DIANLI UNIVERSITY
- Filing Date
- 2026-02-27
- Publication Date
- 2026-06-02
AI Technical Summary
Traditional emergency power supply models rely on fixed energy storage stations and emergency power generation vehicles, which have drawbacks such as slow response speed, limited coverage, and high equipment investment costs, making it difficult to efficiently utilize the energy storage potential of electric vehicles.
By collecting real-time electric vehicle status information and using a multi-objective optimization algorithm to generate optimal scheduling instructions, the system controls electric vehicles to perform discharge tasks, thereby achieving efficient matching between emergency power supply needs and electric vehicle resources.
It enables the spatial and temporal transfer and flexible allocation of emergency resources, allowing for rapid response to emergencies, shortening power outage time, improving power supply reliability, and minimizing social costs while meeting power supply needs.
Smart Images
Figure CN122136950A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electric vehicle charging technology, and in particular to a method for providing mobile energy storage emergency services for electric vehicles. Background Technology
[0002] Against the backdrop of the rapid development of the new energy industry and the construction of new power systems, electric vehicles are not only the core carrier of green transformation in the transportation sector, but their power batteries also possess the mobile energy storage characteristics of flexible charging and discharging, providing a brand-new solution for emergency power supply to the power grid. Traditional emergency power supply models rely on equipment such as fixed energy storage stations and emergency power generation vehicles, which suffer from pain points such as slow response speed, limited coverage, and high equipment investment costs.
[0003] Therefore, providing a mobile energy storage emergency service method for electric vehicles to overcome the difficulties of existing technologies is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0004] In view of this, the present invention provides a method for mobile energy storage emergency service for electric vehicles, which achieves efficient matching between emergency power supply demand and electric vehicle resources through multi-stage collaborative operation, fully taps the energy storage potential of dispatchable electric vehicles, and quickly responds to various emergency power supply needs.
[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for providing mobile energy storage emergency services for electric vehicles includes the following steps: Real-time collection of status information of dispatchable electric vehicles within the target service area; The collected status information is transmitted to the backend of the intelligent dispatch system via the mobile network, so that the system backend can detect the collected information and monitor the power and safety of the SOC status information. The current availability status of each electric vehicle is determined based on battery level and safety monitoring results. When the system receives an emergency power supply demand, it combines grid load deficit data and performs multi-objective optimization calculations based on vehicle location, available power, and response time from available electric vehicles to generate the optimal dispatch instruction. The corresponding electric vehicle is controlled to complete the discharge task based on the scheduling instructions.
[0006] Optionally, the status information collected includes: capturing real-time geographical location information of electric vehicles using an electric vehicle on-board positioning system, obtaining remaining battery power information of electric vehicles using an on-board terminal, and integrating power load characteristic curves of different regional power grids using a power grid data acquisition and monitoring system, thereby calculating the power supply deficit of each region and the geographical location information of the power deficit area.
[0007] Optional power and safety monitoring includes: The system assesses the battery availability of vehicles based on SOC data, identifying vehicles with current battery levels above a set threshold that are not in peak charging periods; it monitors the internal parameters of status information for safety, eliminating abnormal or risky vehicles; and it combines historical data and real-time information of electric vehicles to predict vehicle availability and battery level changes over a future period.
[0008] Optionally, determining the available status includes: Determine the types of dispatchable electric vehicles, including company vehicles, taxis, and private vehicles; For both company vehicles and private vehicles, vehicles with a SOC value higher than a set threshold are selected for discharge scheduling. For taxis, an additional check will be conducted to determine whether the vehicle is carrying passengers.
[0009] Optionally, generating optimal scheduling instructions includes: Calculate the power deficit in the region, perform an initial screening for different types of electric vehicles, and obtain a set of electric vehicles that are in a usable state. The electric vehicle set is then filtered a second time based on the current geographical location of the electric vehicles. The search area is expanded outward in kilometers, with the power deficit area as the center, and the estimated time for the electric vehicles in the set to arrive at the current electric vehicle is calculated. Key indicators include the vehicle's estimated response time to reach the target location, available discharge capacity, and distance from the grid connection point. A multi-objective optimization algorithm is adopted to generate the optimal scheduling instruction by minimizing the overall scheduling cost while meeting the requirements of total emergency power supply and time.
[0010] Optionally, the bee colony optimization algorithm can be used for multi-objective optimization. Three groups of bees were set up: lead bees, follow bees, and observation bees. Map the available vehicle set to a nectar source and initialize the leader bee, follower bee, and observer bee populations; Set the electric vehicle set as the honey source, and guide the bee stage to query the position vector of the initial solution; During the follower bee phase, assuming a new source of high-quality nectar is discovered, the probability of choosing the current source is obtained; During the observation bee phase, neighborhood searches and solution updates are performed. If no better solution is found, the leading bee becomes an observation bee and searches for new honey source locations. Each observation bee randomly selects a new solution from the solution space and updates the current best solution. If a new solution generated by an observation bee is better than the current best solution, the new solution replaces the current best solution.
[0011] Optionally, controlling the electric vehicle to perform related tasks includes: Dispatch instructions are sent to the vehicle owner's app and in-vehicle terminal via mobile network. Once the vehicle owner confirms, the dispatch process is initiated. Guide vehicles to designated power grid access points, connect to the grid via standard charging and discharging protocols, and receive real-time control from the intelligent dispatch system; The vehicle and power grid status are continuously monitored during the discharge process.
[0012] As can be seen from the above technical solution, compared with the prior art, the present invention provides a mobile energy storage emergency service method for electric vehicles, which has the following beneficial effects: 1) The present invention transforms a large number of dispersed electric vehicles into mobile energy storage power stations, realizing the spatiotemporal transfer and flexible dispatch of emergency resources, enabling rapid response to emergencies, shortening power outage time, and improving power supply reliability; 2) The present invention sets up a two-stage screening mechanism, which efficiently reduces the solution scale of the optimization problem, improves computational efficiency, and minimizes the overall social cost while meeting power supply needs; 3) The present invention achieves efficient matching between emergency power supply needs and electric vehicle resources through multi-stage collaborative operation, fully taps the energy storage potential of dispatchable electric vehicles, and can quickly respond to various emergency power supply needs. Attached Figure Description
[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0014] Figure 1 This is a flowchart of a mobile energy storage emergency service method for electric vehicles disclosed in this invention. Detailed Implementation
[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0016] Reference Figure 1 As shown, this invention discloses a method for providing mobile energy storage emergency services for electric vehicles, comprising the following steps: Real-time collection of status information of dispatchable electric vehicles within the target service area; The collected status information is transmitted to the backend of the intelligent dispatch system via the mobile network, so that the system backend can detect the collected information and monitor the power and safety of the SOC status information. The current availability status of each electric vehicle is determined based on battery level and safety monitoring results. When the system receives an emergency power supply demand, it combines grid load deficit data and performs multi-objective optimization calculations based on vehicle location, available power, and response time from available electric vehicles to generate the optimal dispatch instruction. The corresponding electric vehicle is controlled to complete the discharge task based on the scheduling instructions.
[0017] Furthermore, the status information collected includes: using the electric vehicle on-board positioning system to capture real-time geographical location information of electric vehicles, using the on-board terminal to obtain the remaining battery power information of electric vehicles, and using the power grid data acquisition and monitoring system to integrate the power load characteristic curves of different regions' power grids, thereby calculating the power supply deficit of each region's power grid and the geographical location information of the power deficit areas.
[0018] Specifically, the power grid data acquisition and monitoring system and distribution automation system are used to integrate the electricity load data of different regional power grids. Parameters such as active power, reactive power, voltage, and current in each zone are collected in real time to construct regional power grid load characteristic curves. By comparing the power supply capacity and electricity demand of the regional power grid, the power supply deficit of each regional power grid is calculated. When the power supply is less than the electricity load, the difference is the power deficit, and the geographical location of the power deficit area is also determined.
[0019] Furthermore, upon receiving the status information, a preliminary data check is first performed, specifically: Data integrity check: The backend system checks the data uploaded by each terminal against a preset information collection list to ensure its completeness and identify any missing key parameters. If data is missing, a retransmission command is immediately sent to the corresponding terminal. If multiple retransmissions fail to retrieve complete data, the vehicle or power grid monitoring node corresponding to that terminal is marked as "information abnormal" and temporarily excluded from the dispatchable range. Data validity check: Check whether the values of the uploaded data are within a reasonable range. Data that exceeds the reasonable range is judged as invalid data and is repaired by interpolating historical data or replacing it with the average value of data from similar terminals. If the proportion of invalid data exceeds the threshold, the terminal is marked as faulty and relevant maintenance personnel are notified to investigate. Data consistency detection: Compare the consistency of data uploaded by the same terminal at different times and the data associated with different terminals. If data inconsistencies are found, cross-validate the data from multiple sources to investigate whether there are terminal failures, transmission errors, or other issues, and correct them in a timely manner to ensure data reliability.
[0020] Further, power consumption and safety monitoring includes: The system assesses the battery availability of vehicles based on SOC data, identifying vehicles with current battery levels above a set threshold that are not in peak charging periods; it monitors the internal parameters of status information for safety, eliminating abnormal or risky vehicles; and it combines historical data and real-time information of electric vehicles to predict vehicle availability and battery level changes over a future period.
[0021] Specifically, safety monitoring is divided into battery safety, vehicle safety, and grid connection safety. Battery safety monitoring is used to monitor parameters such as battery temperature, individual cell voltage balance, and fault alarm information: temperature monitoring tracks temperature changes in various areas of the battery pack in real time; individual cell voltage balance monitoring calculates the voltage difference between individual cells in the battery pack; and fault alarm monitoring responds in real time to fault alarm information such as overvoltage, overcurrent, insulation abnormality, and battery bulging uploaded by the BMS. Vehicle safety monitoring is used to monitor the overall operating status of electric vehicles, including the status of the vehicle's braking system, control system, and on-board charging and discharging equipment. The grid connection safety prediction is used to predict the safety of electric vehicles after they are connected to the grid by combining parameters such as voltage, current and frequency at the regional grid connection point.
[0022] Furthermore, determining the available status includes: Determine the types of dispatchable electric vehicles, including company vehicles, taxis, and private vehicles; For both company vehicles and private vehicles, vehicles with a SOC value higher than a set threshold are selected for discharge scheduling. For taxis, an additional check will be conducted to determine whether the vehicle is carrying passengers.
[0023] Furthermore, since the status of electric vehicles and the grid load status are both changing in real time, a dynamic update mechanism for availability status is established to ensure the accuracy and timeliness of the available electric vehicle set. This mechanism specifically includes: Set a dynamic update frequency. When no emergency demand is triggered, update the availability status once at a certain interval. When an emergency demand is triggered, shorten the update time to ensure that changes in vehicle status and grid load can be captured in a timely manner. Real-time synchronization of SOC value changes, driving status, passenger status of taxis, fault alarm information, etc. for each vehicle, and updating of the available electric vehicle set, including: adding new vehicles that meet the judgment criteria to the set, and removing vehicles that no longer meet the criteria from the set. While updating the availability status, the vehicles in the set of available electric vehicles are dynamically prioritized based on factors such as vehicle location, available discharge capacity, and response time, combined with corresponding weights.
[0024] Furthermore, generating the optimal scheduling instruction includes: Calculate the power deficit in the region, perform an initial screening for different types of electric vehicles, and obtain a set of electric vehicles that are in a usable state. The electric vehicle set is then filtered a second time based on the current geographical location of the electric vehicles. The search area is expanded outward in kilometers, with the power deficit area as the center, and the estimated time for the electric vehicles in the set to arrive at the current electric vehicle is calculated. Key indicators include the vehicle's estimated response time to reach the target location, available discharge capacity, and distance from the grid connection point. A multi-objective optimization algorithm is adopted to generate the optimal scheduling instruction by minimizing the overall scheduling cost while meeting the requirements of total emergency power supply and time.
[0025] Furthermore, the initial screening includes capacity matching and type matching: Capacity matching: Calculate the actual available discharge capacity of each available vehicle, filter out vehicles whose available discharge capacity exceeds the minimum capacity threshold for emergency power supply, and combine emergency power supply priority to filter company vehicles first, then taxi vehicles, and finally private vehicles. Type matching: Select vehicles of suitable types for different emergency scenarios.
[0026] Furthermore, the multi-objective optimization algorithm employs the bee colony algorithm: Three groups of bees were set up: lead bees, follow bees, and observation bees. The available vehicle set is mapped to a nectar source, and the populations of leader bees, follower bees, and observer bees are initialized. Specifically, the total number of bees is set to N, and the number of leader bees and follower bees are each N / 2. Each vehicle in the candidate electric vehicle set is mapped to a nectar source, and each nectar source corresponds to a solution vector. The dimensions of the solution vector correspond to the multi-objective optimization index. The value of each dimension is the standardized value of the index corresponding to the vehicle, as well as the maximum number of iterations, the neighborhood search radius, and the threshold for abandoning the nectar source. The electric vehicle set is set as the honey source, and the position vector guiding the bee stage query for the initial solution is as follows: Each lead bee corresponds to a candidate vehicle. The multi-objective optimization index value of the vehicle is obtained, the position vector of the initial solution is constructed, and the fitness value of each initial solution is calculated by weighted summation. Each lead bee searches for new solutions in the neighborhood of the current solution. In the follower bee phase, assuming a new high-quality nectar source is discovered, the probability of selecting the current source is obtained. Specifically, the follower bee calculates the probability of selecting each nectar source based on the fitness value evaluated by the leader bee. The higher the fitness value of the nectar source, the greater the probability of it being selected, ensuring that the follower bee prioritizes following high-quality solutions. Based on the calculated selection probability, the follower bee randomly selects a nectar source to follow and searches for new solutions in the neighborhood of that nectar source. If a new high-quality nectar source is discovered, the position vector of that nectar source is updated, and the information of the high-quality solution is passed to the observer bee. After each follower bee completes the neighborhood search, the fitness values of the new solution and the current solution are compared, and the solution with the higher fitness value is retained to further optimize the quality of the solution. During the observation bee phase, neighborhood search and solution updates are performed. If no better solution is found, the leader bee becomes an observation bee and searches for new honey sources. Each observation bee randomly selects a new solution from the solution space and updates the current best solution. If a new solution generated by an observation bee is better than the current best solution, the new solution replaces the current best solution. Specifically, to ensure the algorithm can escape local optima and find the global optimum: the observation bees monitor the iteration of each nectar source. If a nectar source still cannot find a better solution after multiple iterations, it is determined to be a poor solution, and the corresponding leader bee becomes an observation bee, no longer focusing on searching for that nectar source. The transformed observation bee randomly selects a new solution from the solution space as a new nectar source and begins neighborhood search and solution updates, expanding the search range. The observation bee comprehensively evaluates all solutions in the entire colony and tracks the current best solution. If the fitness value of a new solution generated by an observation bee is higher than the current best solution, the new solution replaces the current best solution, and the global optimum record is updated. If the number of iterations reaches the maximum number of iterations and the optimal solution no longer changes, the search stops, and the global optimum set is output.
[0027] Furthermore, generating the optimal scheduling instruction includes: based on the globally optimal solution set output by the bee colony algorithm, and combined with constraints such as the total emergency power supply demand and the grid connection point capacity, generating the optimal scheduling instruction, specifically: From the set of globally optimal solutions, select vehicle combinations whose total available discharge capacity is greater than the total emergency power supply demand to ensure that the emergency power supply capacity requirements can be met. Based on the distance between the vehicle and the grid access point and the maximum carrying capacity of the access point, the selected vehicles are assigned to appropriate grid access points to ensure that the total discharge power of the vehicles connected to each access point does not exceed its maximum carrying capacity. The core information of the dispatch instruction includes: vehicle ID, location of the designated power grid access point, estimated arrival time, discharge power, discharge duration, and safety operation procedures. In generating the optimal dispatch instruction based on constraints, differentiated instructions are generated according to vehicle type. Among them, instructions for company vehicles are sent to the company's dispatch management platform, instructions for taxi vehicles are sent to the taxi operation platform, and instructions for private vehicles are sent to the vehicle owner's APP.
[0028] Furthermore, controlling electric vehicles to complete related tasks includes: Dispatch instructions are sent to the vehicle owner's app and in-vehicle terminal via mobile network. Once the vehicle owner confirms, the dispatch process is initiated. Guide vehicles to designated power grid access points, connect to the grid via standard charging and discharging protocols, and receive real-time control from the intelligent dispatch system; The vehicle and power grid status are continuously monitored during the discharge process.
[0029] Specifically, dispatch instructions are issued in an encrypted manner to ensure the security and integrity of the instruction information. After the instruction is issued, the system provides real-time feedback on the issuance status. For terminals that have not been delivered, the system continues to retry issuing the instruction and sends instruction reminders to the vehicle owner via SMS.
[0030] Furthermore, based on the vehicle's current location, the designated access point location, and real-time traffic conditions, the system generates an optimal driving route through a path planning algorithm. Route planning prioritizes routes with good road conditions, shortest distances, and least time consumption. The system receives real-time information on the vehicle's location, speed, and direction to monitor whether the vehicle is following the planned route. If the vehicle deviates from the planned route, a deviation alert is immediately sent to the owner's app, and a new optimal route is planned. If the vehicle's speed is abnormal, the system contacts the owner through the app to inquire about the situation and investigate potential issues such as malfunctions or congestion.
[0031] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for providing mobile energy storage emergency services for electric vehicles, characterized in that, Includes the following steps Real-time collection of status information of dispatchable electric vehicles within the target service area; The collected status information is transmitted to the backend of the intelligent dispatch system via the mobile network, so that the system backend can detect the collected information and monitor the power and safety of the SOC status information. The current availability status of each electric vehicle is determined based on battery level and safety monitoring results. When the system receives an emergency power supply demand, it combines grid load deficit data and performs multi-objective optimization calculations based on vehicle location, available power, and response time from available electric vehicles to generate the optimal dispatch instruction. The corresponding electric vehicle is controlled to complete the discharge task based on the scheduling instructions.
2. The method for providing mobile energy storage emergency services for electric vehicles according to claim 1, characterized in that, The collected status information includes: using the electric vehicle on-board positioning system to capture real-time geographical location information of electric vehicles, using the on-board terminal to obtain the remaining battery power information of electric vehicles, and using the power grid data acquisition and monitoring system to integrate the power load characteristic curves of different regions' power grids, thereby calculating the power supply deficit of each region's power grid and the geographical location information of the power deficit areas.
3. The method for providing mobile energy storage emergency services for electric vehicles according to claim 1, characterized in that, Battery and safety monitoring includes: The system assesses the battery availability of vehicles based on SOC data, identifying vehicles with current battery levels above a set threshold that are not in peak charging periods; it monitors the internal parameters of status information for safety, eliminating abnormal or risky vehicles; and it combines historical data and real-time information of electric vehicles to predict vehicle availability and battery level changes over a future period.
4. The method for providing mobile energy storage emergency services for electric vehicles according to claim 1, characterized in that, The available status is determined by: Determine the types of dispatchable electric vehicles, including company vehicles, taxis, and private vehicles; For both company vehicles and private vehicles, vehicles with a SOC value higher than a set threshold are selected for discharge scheduling. For taxis, an additional check will be conducted to determine whether the vehicle is carrying passengers.
5. The method for providing mobile energy storage emergency services for electric vehicles according to claim 4, characterized in that, Generating optimal scheduling instructions includes: Calculate the power deficit in the region, perform an initial screening for different types of electric vehicles, and obtain a set of electric vehicles that are in a usable state. The electric vehicle set is then filtered a second time based on the current geographical location of the electric vehicles. The search area is expanded outward in kilometers, with the power deficit area as the center, and the estimated time for the electric vehicles in the set to arrive at the current electric vehicle is calculated. Key indicators include the vehicle's estimated response time to reach the target location, available discharge capacity, and distance from the grid connection point. A multi-objective optimization algorithm is adopted to generate the optimal scheduling instruction by minimizing the overall scheduling cost while meeting the requirements of total emergency power supply and time.
6. The method for providing mobile energy storage emergency services for electric vehicles according to claim 5, characterized in that, The multi-objective optimization algorithm employs the bee colony algorithm, which includes: Three groups of bees were set up: lead bees, follow bees, and observation bees. Map the available vehicle set to a nectar source and initialize the leader bee, follower bee, and observer bee populations; Set the electric vehicle set as the honey source, and guide the bee stage to query the position vector of the initial solution; During the follower bee phase, assuming a new source of high-quality nectar is discovered, the probability of choosing the current source is obtained; During the observation bee phase, neighborhood searches and solution updates are performed. If no better solution is found, the leading bee becomes an observation bee and searches for new honey source locations. Each observation bee randomly selects a new solution from the solution space and updates the current best solution. If a new solution generated by an observation bee is better than the current best solution, the new solution replaces the current best solution.
7. The method for providing mobile energy storage emergency services for electric vehicles according to claim 1, characterized in that, Controlling electric vehicles to complete related tasks includes: Dispatch instructions are sent to the vehicle owner's app and in-vehicle terminal via mobile network. Once the vehicle owner confirms, the dispatch process is initiated. Guide vehicles to designated power grid access points, connect to the grid via standard charging and discharging protocols, and receive real-time control from the intelligent dispatch system; The vehicle and power grid status are continuously monitored during the discharge process.