An electric ship battery replacement scheduling management system and method

Abstract

The application discloses an electric ship battery replacement scheduling management system, which comprises a ship end data acquisition unit, a shore end battery replacement management and control unit and a cloud end intelligent scheduling platform. The ship end data acquisition unit is installed on an electric ship matched with the system. The shore end battery replacement management and control unit is installed on a ship battery replacement station matched with the system. The ship end data acquisition unit and the shore end battery replacement management and control unit interact data with the cloud end intelligent scheduling platform through a network, and data interaction between the ship end data acquisition unit and the shore end battery replacement management and control unit is realized through the cloud end intelligent scheduling platform. The application constructs a ship-shore-cloud integrated intelligent management and control system, realizes unified access, integration and analysis of multi-source data, software-defined control architecture, facilitates system iteration and upgrading, realizes multi-unit collaborative intelligent scheduling, improves battery replacement efficiency, perfects battery full life cycle intelligent management and control, improves safety and resource utilization, improves intelligent adaptability of the battery replacement network, and adapts to multi-scene requirements.

Description

Technical Field

[0001] This invention relates to the field of intelligent scheduling of electric ship battery swapping networks and digital management of the entire battery lifecycle. Background Technology

[0002] With the rapid development of the new energy shipbuilding industry, the large-scale application of electric ships has placed higher demands on the digitalization level, scheduling efficiency, and battery lifecycle management capabilities of battery swapping networks. For example, the published document with application publication number CN116485140A, application publication date 2023-07-25, and patent title "A Ship Inland Waterway Battery Swapping Scheduling Method and Device" discloses a battery scheduling method that includes the following steps: obtaining the ship's model, route information, load, and location information of each battery swapping station within the route; obtaining the ship's location information, speed, battery energy consumption, and weather information; calculating the time it takes for the ship to reach each battery swapping station and the remaining battery power for the remaining driving time based on the obtained data; and determining the target battery swapping station and swapping time based on the calculation results, and sending the ID and location information of the target battery swapping station to the ship, while simultaneously sending the ship's ID and swapping time to the target battery swapping station.

[0003] Similar current industry-wide battery swapping methods suffer from the following technical shortcomings:

[0004] Battery swapping scheduling lacks multi-dimensional coordination: data from units such as ships, battery swapping stations, robotic arms, and batteries are fragmented, making it impossible to achieve dynamic optimization of berth, robotic arm, and battery resources, which can easily lead to problems such as long ship waiting times and low resource utilization.

[0005] Battery management lacks full lifecycle traceability: data from battery production, assembly, use, maintenance, and disposal cannot be shared, making it difficult to accurately assess battery health status and to make scientific decisions regarding battery fault early warning, lifespan prediction, and secondary utilization.

[0006] The battery swapping network lacks intelligent adaptability: it cannot adjust its scheduling strategy based on dynamic data such as ship routes, navigation status, and remaining battery power, making it difficult to cope with the different battery swapping needs of different ships, thus limiting battery swapping efficiency and user experience. Summary of the Invention

[0007] The technical problem to be solved by this invention is to realize a system that integrates ship-shore-cloud collaboration, intelligent scheduling, and digital intelligent management and control of the entire battery life cycle, thereby breaking through the technical bottleneck of existing battery swapping networks.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows: an electric ship battery swapping scheduling and management system, the system including a ship-side data acquisition unit, a shore-side battery swapping control unit, and a cloud-based intelligent scheduling platform. The ship-side data acquisition unit is installed on an electric ship compatible with the system, and the shore-side battery swapping control unit is installed at a ship battery swapping station compatible with the system. Both the ship-side data acquisition unit and the shore-side battery swapping control unit interact with the cloud-based intelligent scheduling platform through a network, and the cloud-based intelligent scheduling platform enables data interaction between the ship-side data acquisition unit and the shore-side battery swapping control unit.

[0009] The ship-end data acquisition unit is equipped with a data acquisition module for collecting basic ship information, navigation status, battery status, and battery swapping requirements.

[0010] Basic vessel information: Vessel ID, vessel type, rated power, battery configuration specifications;

[0011] Navigation status data: real-time location, route information, speed, heading, and remaining range;

[0012] Battery status data: Individual cell voltage, individual cell temperature, state of charge (SOC), health status, charge / discharge current, and fault alarm information of the marine battery pack.

[0013] Battery swapping demand data: time of battery swapping request initiated by the ship, expected battery swapping priority, and preference for target battery swapping stations.

[0014] The shore-based battery swapping control unit is deployed within each electric ship battery swapping station, configured for each station, and is used to manage the hardware resources and battery swapping process within the station, including:

[0015] Resource acquisition module: Real-time acquisition of the berth status of the battery swapping station, the working status of the robotic arm, the storage status of each battery pack in the battery, the charging progress, and the operation and maintenance logs;

[0016] Local control module: Receives cloud scheduling instructions, controls the robotic arm to precisely dock with the ship's battery compartment, completes battery installation and replacement, and manages battery charging and discharging;

[0017] Edge computing module: It performs local preprocessing on real-time data collected from the ship and shore, and, in combination with local resource load, feeds back schedulable resource information and local scheduling suggestions to the cloud, thereby reducing the computing pressure on the cloud.

[0018] The cloud-based intelligent scheduling platform is built on a cloud server and serves as the core decision-making and control hub of the system. It includes an intelligent scheduling subsystem, a battery lifecycle management subsystem, and a data visualization and decision analysis subsystem.

[0019] The intelligent scheduling subsystem has a built-in multi-dimensional resource scheduling model to realize dynamic and coordinated scheduling of ship, berth, robotic arm, and battery resources. The specific execution steps are as follows:

[0020] 1) Data access and integration: Real-time reception of navigation status and battery swapping demand data collected from each ship, as well as resource load data of each shore-based battery swapping station;

[0021] 2) Data demand and resource matching: Based on the ship's battery swapping request time, route priority, and remaining battery power, combined with the berth vacancy rate of each battery swapping station, the workload of the robotic arm, and the number of available battery packs, a genetic algorithm is used to perform resource matching and generate a preliminary battery swapping scheduling plan.

[0022] 3) Dynamic optimization and adjustment: Based on the real-time navigation position of the ship, the deviation of the route, and the sudden maintenance tasks of the battery swapping station, the scheduling plan is dynamically iterated and optimized to ensure that the ship's battery swapping waiting time is the shortest and the resource utilization rate is the highest.

[0023] 4) Digital command issuance and execution feedback: The optimized scheduling commands are issued to the corresponding ship-side and shore-side battery swapping control units, and the battery swapping execution progress feedback is received in real time. Emergency scheduling adjustments are made for abnormal situations.

[0024] The battery lifecycle management subsystem constructs a digital twin model of the battery lifecycle to achieve intelligent digital control of the entire process from battery production to disposal. Its functions include:

[0025] 1) Full lifecycle data traceability: Assign a unique identifier to each battery pack, integrate data from all stages of battery production, assembly, use, maintenance, and disposal, and form a traceable and searchable digital archive;

[0026] 2) Precise health status assessment: Based on the collected real-time data such as battery charging and discharging data, temperature, and voltage, combined with the LSTM neural network prediction model, the battery health status is calculated in real time, the remaining battery life is predicted, and early warnings are given for battery abnormalities.

[0027] 3) Intelligent decision-making for operation and maintenance and secondary utilization: Based on the battery health status assessment results, operation and maintenance suggestions are automatically generated; retired batteries are disassembled, tested and graded for performance, and combined with scenarios such as energy storage needs and ship backup power needs, the direction of secondary utilization of batteries is scientifically planned to maximize the value utilization of battery resources.

[0028] 4) Intelligent battery scheduling: Based on the distribution of battery pack health and charging progress of batteries at each battery swapping station, and combined with the battery grade requirements of ship battery swapping needs, optimize the charging and discharging sequence and allocation strategy of battery packs within the battery to ensure that high-health batteries are given priority to supply high-priority ship battery swapping needs.

[0029] The data visualization and decision analysis subsystem utilizes big data analytics to deeply mine and visualize battery swapping network operation data and battery lifecycle data, including:

[0030] 1) Visualization of the battery swapping network operation status: Display the real-time resource load of each battery swapping station, the progress of ship battery swapping, and the charging and discharging status of batteries in the form of a map, and generate reports on core indicators such as the operating efficiency of the battery swapping network, resource utilization rate, and ship waiting time.

[0031] 2) Battery life cycle analysis report: Automatically generates reports on battery health trend analysis, failure pattern analysis, and tiered utilization benefit analysis, providing data support for resource expansion, battery procurement, and operation and maintenance strategy optimization of the battery swapping network;

[0032] 3) Intelligent decision support: Based on historical operation data and prediction models, predict the future resource demand, battery health status and operation and maintenance costs of the battery swapping network, and provide intelligent decision-making suggestions to operation and maintenance personnel.

[0033] The management methods based on the electric ship battery swapping scheduling and management system include intelligent scheduling methods, battery life cycle management methods, and data visualization and decision analysis methods.

[0034] The intelligent scheduling method includes: data access and integration, demand and resource matching, dynamic optimization and adjustment, and instruction issuance and execution feedback.

[0035] The battery lifecycle management method includes: full lifecycle data traceability, accurate health status assessment, intelligent decision-making for operation and maintenance and secondary utilization, and intelligent battery scheduling.

[0036] The data visualization and decision analysis methods include: visualization of the battery swapping network operation status, battery life cycle analysis report, and intelligent decision support.

[0037] This invention constructs an integrated ship-shore-cloud digital intelligent management and control system, realizing unified access, integration and analysis of multi-source data, software-defined control architecture, facilitating system iteration and upgrades, realizing multi-unit collaborative intelligent scheduling, improving battery swapping efficiency, perfecting digital intelligent management and control throughout the battery life cycle, improving safety and resource utilization, enhancing the intelligent adaptability of the battery swapping network, and adapting to the needs of multiple scenarios. Attached Figure Description

[0038] The following is a brief explanation of the content represented by each figure in this specification:

[0039] Figure 1 This is a block diagram of the overall architecture of the electric ship battery swapping dispatch management system.

[0040] Figure 2 A detailed block diagram for the electric ship battery swapping dispatch management system;

[0041] Figure 3 A flowchart for the battery lifecycle management process;

[0042] Figure 4 A flowchart for the scheduling and management of battery swapping for electric ships. Detailed Implementation

[0043] The following description, with reference to the accompanying drawings, details the specific implementation of the present invention, including the shape and structure of each component, the relative positions and connections between the parts, the function and working principle of each part, the manufacturing process, and the operation and use methods, to help those skilled in the art to have a more complete, accurate, and in-depth understanding of the inventive concept and technical solution of the present invention.

[0044] The three core problems of electric ship battery swapping networks are inefficient resource scheduling, lack of full battery life cycle management, and insufficient intelligent adaptability of the battery swapping network. The goal is to achieve coordinated optimization of resources such as ships, battery swapping stations, robotic arms, and batteries, as well as precise control and intelligent operation and maintenance of batteries throughout their entire life cycle.

[0045] An intelligent scheduling and battery lifecycle digital management system for electric ship battery swapping networks includes a ship-side data acquisition unit, a shore-side battery swapping control unit, and a cloud-based digital scheduling platform. The three components achieve low-latency data interaction and collaborative control through a 5G / edge computing network.

[0046] Shipboard Data Acquisition Unit

[0047] Deployed on electric ships, it is used to collect core ship data in real time, including:

[0048] Basic vessel information: Vessel ID, vessel type, rated power, battery configuration specifications;

[0049] Navigation status data: real-time location, route information, speed, heading, and remaining range;

[0050] Battery status data: Individual cell voltage, individual cell temperature, SOC (state of charge), SOH (state of health), charging and discharging current, and fault alarm information of the marine battery pack;

[0051] Battery swapping demand data: time of battery swapping request initiated by the ship, expected battery swapping priority, and preference for target battery swapping stations.

[0052] shore-side battery swapping control unit

[0053] Deployed within each electric ship battery swapping station, configured for each station, it manages the hardware resources and battery swapping process within the station, including:

[0054] Resource acquisition module: Real-time acquisition of the berth status of the battery swapping station, the working status of the robotic arm, the storage status of each battery pack in the battery, the charging progress, and the operation and maintenance logs;

[0055] Local control module: Receives cloud scheduling instructions, controls the robotic arm to precisely dock with the ship's battery compartment, completes battery installation and replacement, and manages battery charging and discharging;

[0056] Edge computing module: It performs local preprocessing on real-time data collected from the ship and shore, and, in combination with local resource load, feeds back schedulable resource information and local scheduling suggestions to the cloud, thereby reducing the computing pressure on the cloud.

[0057] Cloud-based intelligent dispatch platform

[0058] Built on a cloud server, it serves as the core decision-making and control hub of the system, including an intelligent scheduling subsystem, a battery lifecycle management subsystem, and a data visualization and decision analysis subsystem;

[0059] Intelligent scheduling subsystem

[0060] The system incorporates a multi-dimensional resource scheduling model to achieve dynamic and coordinated scheduling of resources such as ships, berths, robotic arms, and batteries. The specific execution steps are as follows:

[0061] Data access and integration: Real-time reception of navigation status and battery swapping demand data collected from each ship, as well as resource load data of each shore-based battery swapping station;

[0062] Demand and resource matching: Based on the ship's battery swapping request time, route priority, and remaining battery power, combined with the berth vacancy rate of each battery swapping station, the workload of the robotic arm, and the number of available battery packs, a genetic algorithm is used to perform resource matching and generate a preliminary battery swapping scheduling plan.

[0063] Dynamic optimization and adjustment: Based on the real-time navigation position of the ship, the deviation of the route, and the sudden maintenance tasks of the battery swapping station, the scheduling plan is dynamically iterated and optimized to ensure that the ship's battery swapping waiting time is the shortest and the resource utilization rate is the highest.

[0064] Command issuance and execution feedback: The optimized scheduling commands (including target battery swapping stations, berth allocation, and robotic arm scheduling sequence) are issued to the corresponding ship-side and shore-side battery swapping control units. Real-time feedback on the battery swapping execution progress is received, and emergency scheduling adjustments are made for abnormal situations (such as battery swapping station failures or temporary ship rerouting).

[0065] Battery lifecycle management subsystem

[0066] Build a digital twin model of the entire battery lifecycle to achieve intelligent digital management and control of the entire battery process from production to disposal. Specific functions are as follows:

[0067] Full lifecycle data traceability: Assign a unique identifier (RFID / BeiDou positioning) to each battery pack, and integrate data from all stages of battery production (model, parameters, quality inspection report), assembly (assembly time, ship compatibility information), use (charging and discharging data, fault records, number of battery replacements), maintenance (maintenance time, maintenance content, replaced parts), and scrapping (scrapping time, disassembly and testing results, tiered utilization direction) to form a traceable and searchable digital archive;

[0068] Accurate health status assessment: Based on the collected real-time data such as battery charging and discharging data, temperature, and voltage, combined with the LSTM neural network prediction model, the battery health status (SOH) is calculated in real time, the remaining battery life is predicted, and early warnings are given for battery anomalies (such as sudden temperature rise and single cell voltage imbalance).

[0069] Intelligent decision-making for operation and maintenance and secondary utilization: Based on the battery health status assessment results, automatic operation and maintenance suggestions (such as routine maintenance, fault repair, and replacement) are generated; retired batteries are disassembled, tested, and graded for performance; combined with scenarios such as energy storage needs and ship backup power needs, the direction of secondary utilization of batteries is scientifically planned to maximize the value utilization of battery resources.

[0070] Intelligent battery scheduling: Based on the distribution of battery pack health and charging progress of batteries at each battery swapping station, and combined with the battery grade requirements of ship battery swapping needs, the charging and discharging sequence and allocation strategy of battery packs within the battery are optimized to ensure that high-health batteries are given priority to supply high-priority ship battery swapping needs.

[0071] Data Visualization and Decision Analysis Subsystem

[0072] Through big data analytics, we conduct in-depth analysis and visualization of battery swapping network operation data and battery lifecycle data, including:

[0073] Visualization of the battery swapping network operation status: Display the real-time resource load of each battery swapping station, the progress of ship battery swapping, and the charging and discharging status of batteries in the form of a map, and generate reports on core indicators such as the operating efficiency of the battery swapping network, resource utilization rate, and ship waiting time;

[0074] Battery lifecycle analysis report: Automatically generates reports on battery health trend analysis, failure pattern analysis, and tiered utilization benefit analysis, providing data support for resource expansion, battery procurement, and operation and maintenance strategy optimization of the battery swapping network;

[0075] Intelligent decision support: Based on historical operating data and predictive models, it predicts the future resource demand, battery health status and operation and maintenance costs of the battery swapping network, and provides intelligent decision-making suggestions to operation and maintenance personnel (such as capacity expansion suggestions, battery procurement suggestions and operation and maintenance cycle optimization suggestions).

[0076] Intelligent scheduling subsystem execution process

[0077] Data access and integration: The cloud platform receives real-time data on the navigation status, battery status, and battery swapping needs of ships A, B, and C, as well as resource load data of battery swapping station 1 and battery swapping station 2.

[0078] Demand and resource matching: Based on Ship A's high priority battery swapping demand and remaining range (only 80km, less than the route mileage), combined with the berth vacancy rate of Battery Swapping Station 1 (2 / 3), robotic arm workload (1 / 2), and number of high-health battery packs (10 sets), Ship A is prioritized to be matched to berth 3 of Battery Swapping Station 1; Ship B's battery swapping priority is "medium" and its remaining range is sufficient (50km), so it is matched to berth 1 of Battery Swapping Station 2;

[0079] Dynamic optimization and adjustment: Ship C temporarily changes course due to operational needs and plans to leave its current berth. The cloud platform receives the course change data in real time and adjusts Ship C's battery swapping needs to battery swapping station 2. At the same time, Ship A increases its sailing speed and is expected to arrive at battery swapping station 1 within 10 minutes. The cloud platform adjusts the working sequence of robotic arm 2 in advance to ensure that battery swapping operations can be carried out immediately after Ship A arrives.

[0080] Command Issuance and Execution Feedback: Ship A is given the command "Proceed to Berth 3 of Battery Swapping Station 1, battery swap in 10 minutes," and Battery Swapping Station 1 is given the command "Robotic Arm 2, standby, ready to swap batteries for Ship A." Ship B is given the command "Proceed to Berth 1 of Battery Swapping Station 2, battery swap in 15 minutes," and Battery Swapping Station 2 is given the command "Robotic Arm 1, standby, ready to swap batteries for Ship B." Real-time battery swapping progress is received from each battery swapping station. Ship A arrives at Battery Swapping Station 1 in 10 minutes, and Robotic Arm 2 completes the battery swap, taking 8 minutes. Ship B arrives at Battery Swapping Station 2 in 15 minutes, and Robotic Arm 1 completes the battery swap, taking 10 minutes. After Ship C changes course, the cloud platform matches it with Berth 1 of Battery Swapping Station 1, and the battery swap begins upon Ship C's arrival, taking 9 minutes.

[0081] Battery lifecycle management subsystem execution process

[0082] Full lifecycle data traceability: Assign a unique ID "BT001" to the 100kWh battery pack installed on Ship A, integrating its production information (manufactured in January 2025, model BT-100, quality inspection qualified), assembly information (assembled to Ship A in March 2025, suitable for inland waterway cargo ships), usage information (cumulative battery swaps 50 times, normal charge and discharge data, no fault records), and maintenance information (routine maintenance completed in January 2026, cooling fan replaced);

[0083] Accurate health status assessment: Based on real-time charge / discharge data and temperature data of the battery pack, the LSTM neural network prediction model calculates that the current SOH of battery pack A is 88%, the remaining life is predicted to be 2 years, and there are no abnormal alarms; battery pack C has one abnormal charge / discharge record, and its SOH is calculated to be 85%, with a remaining life predicted to be 1.5 years, triggering a "minor abnormality" warning. The maintenance personnel are advised to "strengthen the inspection of battery pack C and record charge / discharge data".

[0084] Intelligent decision-making for operation and maintenance and secondary utilization: Based on the SOH value of the battery pack of Ship A, an operation and maintenance suggestion of "routine maintenance, inspection once every 3 months" is generated; after the battery pack of Ship C triggers an early warning, an operation and maintenance suggestion of "strengthening inspection, if more than 2 abnormal records are found, it is recommended to replace" is generated; after disassembling and testing the 20 battery packs with a health of 70%-80% in Battery Swapping Station 1, they are planned to be used for the secondary utilization scenario of "backup power for low-speed electric ships in inland waterways";

[0085] Intelligent battery scheduling: Based on the battery pack health distribution of Battery Swapping Station 1, battery packs with a health level ≥90% are prioritized for allocation to high-priority vessels such as Ship A and Ship C, while battery packs with a health level of 80%-90% are allocated to medium-priority vessels such as Ship B, ensuring the rational utilization of battery resources.

[0086] Data visualization and decision analysis subsystem execution flow

[0087] Visualization of the battery swapping network's operational status: The cloud platform generates an operational report for the Wuhu battery swapping network, showing that the resource utilization rate of swapping station 1 is 85%, the resource utilization rate of swapping station 2 is 70%, and the average waiting time for ships to swap batteries is 9 minutes, which is 40% lower than the industry average (15 minutes).

[0088] Battery lifecycle analysis report: A health trend analysis report for ship A battery pack was generated, showing that its SOH value slowly decreased from 90% to 88%, with a stable downward trend; a fault pattern analysis report for ship C battery pack was generated, showing that its charging and discharging abnormalities mostly occurred in "high temperature environment (≥35℃)", and it is recommended to optimize the battery pack cooling system;

[0089] Intelligent decision support: Based on historical operation data, it is predicted that the demand for ship battery swapping in Wuhu's battery swapping network will increase by 20% in the next month. It provides operation and maintenance personnel with expansion and procurement suggestions such as "adding 1 berth to the battery swapping station and purchasing 10 sets of high-health battery packs". It also suggests optimizing the battery charging and discharging sequence and adjusting the charging cycle of high-health battery packs to the off-peak period at night to reduce electricity costs.

[0090] Based on the above description of the electric ship battery swapping scheduling and management system, the core advantages of this invention include:

[0091] (1) Achieve multi-unit collaborative intelligent scheduling and improve battery swapping efficiency: Through the genetic algorithm optimization of the cloud-based intelligent scheduling platform, the dynamic matching and optimized scheduling of ship, battery swapping station, robotic arm and battery resources are realized, breaking the status quo of data fragmentation of each unit, greatly shortening the waiting time for ship battery swapping, and improving the utilization rate of battery swapping network resources; according to actual tests, the average waiting time for ship battery swapping can be reduced by more than 40%, and the utilization rate of battery swapping station resources can be increased by more than 35%, solving the problem of inefficient existing battery swapping scheduling.

[0092] (2) Improve the intelligent management and control of the entire battery life cycle and enhance safety and resource utilization: Construct a digital twin model and LSTM neural network prediction model for the entire battery life cycle to achieve full data traceability, accurate health status assessment and early warning of faults for the entire process from production to scrapping. The accuracy rate of battery fault warning can reach more than 95%, effectively avoiding safety hazards caused by battery faults. At the same time, scientifically plan battery operation and maintenance and tiered utilization strategies to extend battery life by 10%-15%, reduce battery use and operation and maintenance costs, and solve the problems of existing battery management deficiencies and resource waste.

[0093] (3) Improve the intelligent adaptability of the battery swapping network to meet the needs of multiple scenarios: Combine the real-time navigation status of ships, route information, and differences in battery swapping needs, dynamically adjust the scheduling strategy to adapt to the battery swapping needs of electric ships in different scenarios such as inland waterways and coastal areas. At the same time, it supports the coordinated scheduling of multiple battery swapping stations to meet the needs of large-scale expansion of the battery swapping network and solve the problem of insufficient adaptability of the existing system.

[0094] (4) Construct an integrated ship-shore-cloud digital management and control system to realize the full-process digitalization and intelligentization of the battery swapping network, which is in line with the enterprise's digital transformation strategy, reduces manual intervention, reduces the workload of maintenance personnel, and improves the operation and maintenance management efficiency of the battery swapping network;

[0095] (5) Achieve unified access, integration and analysis of multi-source data, break down data silos, provide accurate data support for the operation and maintenance management and strategy optimization of the battery swapping network, and promote the transformation of the battery swapping network from "experience management" to "data-driven";

[0096] (6) The software-defined control architecture facilitates system iteration and upgrades. It can flexibly expand functional modules according to the expansion of enterprise business (such as adding new ship types or new battery swapping stations), adapt to the business change requirements in the process of digital transformation, and reduce system upgrade costs.

[0097] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution of the present invention, or the direct application of the inventive concept and technical solution of the present invention to other occasions without modification, are all within the protection scope of the present invention.

Claims

1. An electric ship battery swapping dispatch and management system, characterized in that: The system includes a ship-side data acquisition unit, a shore-side battery swapping control unit, and a cloud-based intelligent dispatching platform. The ship-side data acquisition unit is installed on an electric vessel compatible with the system, and the shore-side battery swapping control unit is installed at a ship-side battery swapping station compatible with the system. Both the ship-side data acquisition unit and the shore-side battery swapping control unit exchange data with the cloud-based intelligent dispatching platform via a network, and the cloud-based intelligent dispatching platform enables data exchange between the ship-side data acquisition unit and the shore-side battery swapping control unit.

2. The electric ship battery swapping dispatch management system according to claim 1, characterized in that: The ship-end data acquisition unit is equipped with a data acquisition module for collecting basic ship information, navigation status, battery status, and battery swapping requirements. Basic vessel information: Vessel ID, vessel type, rated power, battery configuration specifications; Navigation status data: real-time location, route information, speed, heading, and remaining range; Battery status data: Individual cell voltage, individual cell temperature, state of charge (SOC), health status, charge / discharge current, and fault alarm information of the marine battery pack. Battery swapping demand data: time of battery swapping request initiated by the ship, expected battery swapping priority, and preference for target battery swapping stations.

3. The electric ship battery swapping dispatch management system according to claim 2, characterized in that: The shore-based battery swapping control unit is deployed within each electric ship battery swapping station, configured for each station, and is used to manage the hardware resources and battery swapping process within the station, including: Resource acquisition module: Real-time acquisition of the berth status of the battery swapping station, the working status of the robotic arm, the storage status of each battery pack in the battery, the charging progress, and the operation and maintenance logs; Local control module: Receives cloud scheduling instructions, controls the robotic arm to precisely dock with the ship's battery compartment, completes battery installation and replacement, and manages battery charging and discharging; Edge computing module: It performs local preprocessing on real-time data collected from the ship and shore, and, in combination with local resource load, feeds back schedulable resource information and local scheduling suggestions to the cloud, thereby reducing the computing pressure on the cloud.

4. The electric ship battery swapping dispatch management system according to claim 1, 2 or 3, characterized in that: The cloud-based intelligent scheduling platform is built on a cloud server and serves as the core decision-making and control hub of the system. It includes an intelligent scheduling subsystem, a battery lifecycle management subsystem, and a data visualization and decision analysis subsystem.

5. The electric ship battery swapping dispatch management system according to claim 4, characterized in that: The intelligent scheduling subsystem has a built-in multi-dimensional resource scheduling model to realize dynamic and coordinated scheduling of ship, berth, robotic arm, and battery resources. The specific execution steps are as follows: 1) Data access and integration: Real-time reception of navigation status and battery swapping demand data collected from each ship, as well as resource load data of each shore-based battery swapping station; 2) Data demand and resource matching: Based on the ship's battery swapping request time, route priority, and remaining battery power, combined with the berth vacancy rate of each battery swapping station, the workload of the robotic arm, and the number of available battery packs, a genetic algorithm is used to perform resource matching and generate a preliminary battery swapping scheduling plan. 3) Dynamic optimization and adjustment: Based on the real-time navigation position of the ship, the deviation of the route, and the sudden maintenance tasks of the battery swapping station, the scheduling plan is dynamically iterated and optimized to ensure that the ship's battery swapping waiting time is the shortest and the resource utilization rate is the highest. 4) Digital command issuance and execution feedback: The optimized scheduling commands are issued to the corresponding ship-side and shore-side battery swapping control units, and the battery swapping execution progress feedback is received in real time. Emergency scheduling adjustments are made for abnormal situations.

6. The electric ship battery swapping dispatch management system according to claim 5, characterized in that: The battery lifecycle management subsystem constructs a digital twin model of the battery lifecycle to achieve intelligent digital control of the entire process from battery production to disposal. Its functions include: 1) Full lifecycle data traceability: Assign a unique identifier to each battery pack, integrate data from all stages of battery production, assembly, use, maintenance, and disposal, and form a traceable and searchable digital archive; 2) Precise health status assessment: Based on the collected real-time data such as battery charging and discharging data, temperature, and voltage, combined with the LSTM neural network prediction model, the battery health status is calculated in real time, the remaining battery life is predicted, and early warnings are given for battery abnormalities. 3) Intelligent decision-making for operation and maintenance and secondary utilization: Based on the battery health status assessment results, operation and maintenance suggestions are automatically generated; retired batteries are disassembled, tested and graded for performance, and combined with scenarios such as energy storage needs and ship backup power needs, the direction of secondary utilization of batteries is scientifically planned to maximize the value utilization of battery resources. 4) Intelligent battery scheduling: Based on the distribution of battery pack health and charging progress of batteries at each battery swapping station, and combined with the battery grade requirements of ship battery swapping needs, optimize the charging and discharging sequence and allocation strategy of battery packs within the battery to ensure that high-health batteries are given priority to supply high-priority ship battery swapping needs.

7. The electric ship battery swapping dispatch management system according to claim 6, characterized in that: The data visualization and decision analysis subsystem utilizes big data analytics to deeply mine and visualize battery swapping network operation data and battery lifecycle data, including: 1) Visualization of the battery swapping network operation status: Display the real-time resource load of each battery swapping station, the progress of ship battery swapping, and the charging and discharging status of batteries in the form of a map, and generate reports on core indicators such as the operating efficiency of the battery swapping network, resource utilization rate, and ship waiting time. 2) Battery life cycle analysis report: Automatically generates reports on battery health trend analysis, failure pattern analysis, and tiered utilization benefit analysis, providing data support for resource expansion, battery procurement, and operation and maintenance strategy optimization of the battery swapping network; 3) Intelligent decision support: Based on historical operation data and prediction models, predict the future resource demand, battery health status and operation and maintenance costs of the battery swapping network, and provide intelligent decision-making suggestions to operation and maintenance personnel.

8. A management method based on the electric ship battery swapping scheduling management system according to any one of claims 1-7, characterized in that: This includes intelligent scheduling methods, battery lifecycle management methods, and data visualization and decision analysis methods.

9. The management method according to claim 8, characterized in that: The intelligent scheduling method includes: data access and integration, demand and resource matching, dynamic optimization and adjustment, and instruction issuance and execution feedback. The battery lifecycle management method includes: full lifecycle data traceability, accurate health status assessment, intelligent decision-making for operation and maintenance and secondary utilization, and intelligent battery scheduling. The data visualization and decision analysis methods include: visualization of the battery swapping network operation status, battery life cycle analysis report, and intelligent decision support.

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