Battery replacement dynamic decision method, decision system, electronic device and storage medium

Abstract

This application provides a dynamic decision-making method, decision-making system, electronic device, and storage medium for battery swapping, belonging to the field of electric vehicle battery swapping technology. The dynamic decision-making method includes: acquiring multi-dimensional data including at least battery SOH data, battery SOC data, user demand priority, and battery swapping station inventory status; based on the multi-dimensional data, calculating a matching score for the batteries to be allocated using a pre-built decision model, wherein the decision model assigns weights to various influencing factors and performs weighted calculations based on at least the degree of fit between SOH data, SOC data and user demand, user demand priority, and battery swapping station inventory status; outputting a recommended battery swapping scheme based on the calculated matching score, and updating the recommended battery swapping scheme in response to changes in the multi-dimensional data. This method, by acquiring multi-dimensional data and making dynamic decisions, can effectively improve user experience, extend battery life, and improve the operational efficiency of battery swapping stations.

Description

Technical Field

[0001] This application belongs to the field of electric vehicle battery swapping technology, specifically relating to a dynamic decision-making method, decision-making system, electronic device, and storage medium for battery swapping. Background Technology

[0002] With the increasing popularity of electric vehicles, battery swapping has gradually become an important energy replenishment method due to its efficient and convenient charging experience. Currently, electric vehicle battery swapping decisions are primarily based on the battery's remaining state of charge (SOC), only meeting the user's basic charging range needs. This SOC-centric decision-making model has significant limitations, mainly in the following two aspects:

[0003] I. Insufficient user experience Current methods do not incorporate the critical parameter of battery State of Health (SOH) into the decision-making system. This could lead to situations where fully charged batteries with low SOH are assigned to users during actual operation, resulting in accelerated range degradation and decreased charging efficiency during subsequent use, severely impacting the user's actual driving experience and satisfaction.

[0004] II. High operational risks of battery swapping stations From an operational perspective, existing decision-making methods lack a systematic analysis of the correlation between State of Health (SOH) and battery swapping frequency and cycle life. This may lead to batteries with lower health being used too frequently, thereby accelerating their performance degradation, shortening the lifespan of the entire battery pack, and increasing operating costs.

[0005] Furthermore, existing algorithms mostly employ static decision-making logic, which cannot effectively adapt to real-time user needs (such as emergency power replenishment and scheduled battery swapping) and dynamic fluctuations in the inventory status of battery swapping stations (such as shortages of high-health batteries and backlogs of batteries awaiting maintenance). Static decision-making models result in inflexible resource allocation and low operational efficiency.

[0006] In summary, existing battery swapping decision-making methods rely on a single basis, making it difficult to achieve an effective balance between improving user experience and ensuring the long-term, economical, and efficient operation of battery swapping stations. Summary of the Invention

[0007] The purpose of this application is to solve the problems existing in the prior art and provide a dynamic decision-making method, decision-making system, electronic device and storage medium for battery swapping, which can effectively improve user experience, extend battery life and improve the operational efficiency of battery swapping stations.

[0008] This application is achieved through the following technical solution: The first aspect of this application provides a battery swapping dynamic decision-making method based on battery health status, the battery swapping dynamic decision-making method comprising: Acquire multi-dimensional data, including at least battery SOH data, battery SOC data, user demand priority, and battery swapping station inventory status; Based on the multi-dimensional data, a matching score is calculated for the batteries to be allocated through a pre-built decision model. The decision model assigns weights to various influencing factors and performs weighted calculations based on at least the SOH data, the degree of fit between the SOC data and user needs, the priority of user needs, and the inventory status of the battery swapping station. Based on the calculated matching score, a recommended battery swapping solution is output, and the recommended battery swapping solution is updated in response to changes in the multi-dimensional data.

[0009] Preferably, the calculation formula of the decision model is: S = α W s + β W c + γ W p + δ W i in, S This indicates the battery swapping matching score. α , β , γ , δ Let represent the weight coefficients of each corresponding influencing factor, and α+β+γ+δ=1. W s Indicates the SOH weight. W c Indicates the SOC adaptation weights. W p Indicates user priority weight. W i This indicates the inventory balance weight.

[0010] Preferably, the SOH weight is determined based on the health range to which the battery's SOH value belongs; the SOC adaptation weight is determined based on the deviation between the battery's current SOC value and the target SOC value calculated based on user needs; the user priority weight is determined based on the priority level of the user's needs; and the inventory balancing weight is determined based on the inventory proportion of batteries in each health range in the battery swapping station.

[0011] Preferably, the weighting coefficient can be dynamically adjusted according to the operation strategy or real-time operation status of the battery swapping station.

[0012] Preferably, based on the calculated matching score, a recommended battery swapping scheme is output, including: Batteries to be assigned are selected based on the matching scores from highest to lowest. If the SOH value of the selected battery is lower than the preset health threshold, it will be verified based on the user's battery selection history. If the verification results indicate that users tend to reject batteries with low health, then the battery with the second highest matching score is selected as the recommended battery swapping solution.

[0013] Preferably, the triggering condition for updating the recommended battery swapping scheme is that at least one of the following changes: the battery's SOH data, SOC data, user demand priority, and battery swapping station inventory status.

[0014] Preferably, the battery swapping dynamic decision-making method periodically repeats the process of acquiring multi-dimensional data and calculating a matching score based on the multi-dimensional data to continuously update the recommended battery swapping scheme.

[0015] A second aspect of this application provides a battery swapping dynamic decision-making system based on battery health status, the battery swapping dynamic decision-making system comprising: The data acquisition module is used to collect multi-dimensional data in real time, including at least the battery's SOH data, battery's SOC data, user demand priority, and the inventory status of the battery swapping station. The dynamic decision-making module is used to calculate the matching score for the batteries to be allocated based on the multi-dimensional data and through a pre-built decision-making model. The decision-making model assigns weights to various influencing factors and performs weighted calculations based on at least the SOH data, the degree of fit between the SOC data and user needs, the priority of user needs, and the inventory status of the battery swapping station. The scheme management module is used to output recommended battery swapping schemes based on the calculated matching score, and to update the recommended battery swapping schemes in response to changes in the multi-dimensional data.

[0016] A third aspect of this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program executed by the processor, the computer program, when executed by the processor, causes a device equipped with the processor to perform a battery swapping dynamic decision-making method based on battery health status as described in any of the preceding claims.

[0017] A fourth aspect of this application provides a storage medium storing a computer program that runs on a computer and, when running, causes the computer to perform the battery swapping dynamic decision-making method based on battery health status as described in any of the preceding claims.

[0018] Compared with the prior art, the beneficial effects of this application are: the battery swapping dynamic decision-making method provided by this application, by acquiring multi-dimensional data including battery SOH data, battery SOC data, user demand priority and swapping station inventory status and making dynamic decisions, can effectively improve user experience, extend battery life and improve the operational efficiency of swapping stations. Attached Figure Description

[0019] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof.

[0020] Figure 1 This is a flowchart illustrating a battery swapping dynamic decision-making method based on battery health status, according to some embodiments of this application. Figure 2 This is a flowchart illustrating a battery swapping dynamic decision-making method based on battery health status, as described in some other embodiments of this application. Figure 3 This is a schematic diagram of the structure of a battery swapping dynamic decision-making system based on battery health status according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of an electronic device according to some embodiments of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application more apparent, exemplary embodiments according to this application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein. Based on the embodiments of this application described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of this application.

[0022] To address the problems of existing methods for electric vehicle battery swapping decisions relying primarily on remaining battery capacity while neglecting battery health, user demand priorities, and swapping station inventory status, which negatively impacts user experience, shortens battery pack lifespan, increases operating costs, and reduces operational efficiency, this application proposes a dynamic battery swapping decision-making method, decision-making system, electronic device, and storage medium. These methods effectively improve user experience, extend battery life, and enhance the operational efficiency of swapping stations. The following detailed description, in conjunction with the accompanying drawings, further illustrates this application.

[0023] The dynamic decision-making method provided in this application can be completed independently by a single computing device (such as a server) or collaboratively by multiple interconnected devices or system modules. The core of the method lies in data flow and decision logic, and its physical implementation relies on hardware devices with corresponding data processing and communication capabilities. Several embodiments are described in detail below, but the scope of protection of this application is not limited thereto.

[0024] The following is for reference Figure 1 This application describes a battery swapping dynamic decision-making method based on battery health status in some embodiments.

[0025] In these embodiments, the method can be executed by a cloud-based decision service platform within the system, which mainly includes: a user terminal, one or more battery swapping stations, and the cloud-based decision service platform. Each battery swapping station is equipped with a battery management system, battery inventory, and charging equipment, and maintains a communication connection with the cloud platform.

[0026] Figure 1 This is a flowchart illustrating some embodiments of the battery swapping dynamic decision-making method based on battery health status, such as... Figure 1 As shown, the battery swapping dynamic decision-making method based on battery health status in this application includes at least the following steps S100 to S300.

[0027] Step S100: Obtain multi-dimensional data including at least the battery's SOH data, battery's SOC data, user demand priority, and battery swapping station inventory status.

[0028] Among them, the battery's State of Health (SOH) data represents the battery's health status, usually expressed as a percentage, reflecting the degree of capacity degradation or internal resistance increase compared to its brand-new state. SOH data is a core indicator for evaluating the battery's long-term performance and lifespan. The State of Charge (SOC) data represents the battery's remaining capacity, usually expressed as a percentage, reflecting the battery's current available energy reserves. SOC data is a core basis for users to judge range. User demand priority indicates the urgency or special requirements of users for battery swapping services, such as regular battery swapping, scheduled battery swapping, or emergency battery swapping. Its level can be set according to factors such as user type, appointment time, or current traffic conditions. The battery swapping station inventory status indicates the quantity and type of available batteries within the station, as well as their SOH and SOC distribution. Inventory status is a core factor affecting the operational efficiency of battery swapping stations and battery turnover.

[0029] Step S200: Based on the multi-dimensional data, calculate the matching score for the batteries to be allocated using a pre-built decision model. The decision model assigns weights to various influencing factors and performs weighted calculations based on at least the SOH data, the SOC data and the degree of adaptation to user needs, the priority of user needs and the inventory status of the battery swapping station.

[0030] The decision model refers to a set of algorithms or rules that have been built to process multi-dimensional data and calculate a matching score. This model aims to comprehensively evaluate multiple influencing factors to make battery allocation decisions. The matching score is a quantitative indicator calculated by the decision model based on various influencing factors, used to measure the degree of fit between the batteries to be allocated and current user needs and the battery swapping station's operational strategy. A higher score indicates a better matching degree.

[0031] Step S300: Based on the calculated matching score, output a recommended battery swapping scheme and update the recommended battery swapping scheme in response to changes in the multi-dimensional data.

[0032] The recommended battery swapping scheme refers to the battery allocation result suggested by the decision model based on the matching score, which is recommended to the user. This scheme aims to provide users with batteries whose performance meets their needs, while also taking into account the operational efficiency of the battery swapping station.

[0033] Specifically, the platform receives real-time user demand data from user terminals and battery status and inventory data from various battery swapping stations via a communication interface. The platform then uses its internally deployed decision-making model algorithm to calculate the matching score for all batteries to be allocated based on the acquired data. The platform sends the recommended battery solution with the highest score to the corresponding user terminal for display and simultaneously issues battery scheduling instructions to the control system of the corresponding battery swapping station. The platform continuously monitors data changes and triggers recalculation and updates at preset intervals. When a user initiates a battery swap request via the app, the request data (including priority) is uploaded to the cloud platform. Simultaneously, the platform polls the target battery swap station for real-time data. After making a decision, a recommendation is sent to the user's app for confirmation, and a work order is generated and sent to the battery swap station. The battery swap station's robotic arm then prepares the battery according to the work order.

[0034] It should be noted that the above method is not limited to being implemented on a cloud platform. Other possible implementation methods include: Edge computing mode: All or part of the steps of the method (such as real-time data acquisition and rapid dynamic updates) can be integrated into the local edge computing equipment or station control system of the battery swapping station to reduce network latency and achieve faster local response.

[0035] Vehicle-station collaborative mode: The vehicle terminal can be responsible for collecting and providing its own high-precision SOH and SOC data, and receiving matching results from the server or station.

[0036] Distributed collaborative model: For example, data collection is completed by the station and vehicle, preliminary screening is performed by the station, while complex multi-objective optimization and global inventory balancing decisions are completed by the cloud, forming a cloud-edge-device collaborative decision-making system.

[0037] Regardless of which method is used, all implementations fall within the protection scope of the present invention.

[0038] This application acquires multi-dimensional data such as battery SOH, SOC, user demand priority, and battery swapping station inventory status, and calculates a matching score based on a decision model to output and dynamically update recommended battery swapping solutions. This solves the problem of existing battery swapping decisions relying solely on remaining battery capacity while ignoring battery health, user demand, and inventory dynamics. This method improves the user battery swapping experience, avoids allocating batteries with low health levels, extends the overall battery pack lifespan, reduces operational risks, and enhances the flexibility and operational efficiency of battery swapping station resource allocation.

[0039] In some embodiments of this application, the triggering condition for updating the recommended battery swapping scheme is that at least one of the following changes: the battery's SOH data, SOC data, user demand priority, and battery swapping station inventory status.

[0040] Specifically, the trigger condition for updating the recommended battery swapping plan refers to the basis or signal by which the system determines when to re-execute the battery swapping decision process. Its purpose is to ensure the timeliness and effectiveness of the recommended battery swapping plan, avoid unnecessary consumption of computing resources, and prevent inaccurate decisions due to outdated data. This trigger condition can be implemented in various ways. For example, an event listening mechanism can be used, where the system receives an event notification and initiates the update process when a specific data source reports a data change; alternatively, the system can use a data verification mechanism to periodically check whether key data differs from the data at the time of the last decision, and trigger an update if a difference exists.

[0041] When any one of the four key data categories mentioned above—SOH data, SOC data, user demand priority, and battery swapping station inventory status—changes, it is considered a trigger for an update. This ensures the comprehensiveness and real-time nature of decision-making, avoiding situations where decisions fail due to a single data change and a lack of timely updates, while also avoiding the lag of updating only after all data has changed.

[0042] Next, refer to Figure 2 This application describes a battery swapping dynamic decision-making method based on battery health status, according to other embodiments of this application. This method can be used in the intelligent operation and management system of electric vehicle battery swapping stations.

[0043] like Figure 2 As shown, the battery swapping dynamic decision-making method based on battery health status includes: Step S1: Multi-dimensional data collection.

[0044] The system collects three types of core data in real time, providing basic input for dynamic decision-making: ① Battery Data: For each battery awaiting allocation within the swapping station, its health status, remaining charge, and other key parameters are acquired in real time through its Battery Management System (BMS). Specifically, this includes: Real-time SOH data: Parameters characterizing the battery's state of health, such as percentage values. In one embodiment, this data is calculated and provided by the BMS with an accuracy of ±2%.

[0045] Current SOC data: A parameter characterizing the remaining battery capacity.

[0046] Cycle count: The number of full charge and full discharge cycles that the battery has completed.

[0047] Last maintenance time: Records the timestamp of the last deep calibration or maintenance performed on the battery.

[0048] ② User demand data: Information related to battery swapping requests is collected through user terminals (such as mobile apps), including: Scheduled battery swap time: The user's planned battery swap time.

[0049] Target driving range: The driving range that users expect to achieve after battery swapping.

[0050] Urgency level of vehicle use: The user-defined priority of their needs. In a preferred embodiment, it is set to three levels: Normal, Urgent, and Scheduled.

[0051] Historical battery swapping preferences: Data on users' historical behavior recorded by the system, such as whether they have repeatedly refused to accept batteries with low health.

[0052] ③ Battery swapping station operation data: Real-time operation status information is obtained from the battery swapping station inventory management system, mainly including: Available battery inventory consists of the total number of batteries that can be immediately used for battery swapping, and can be further classified and counted according to health range, such as: number of high SOH batteries (SOH≥90%), number of medium SOH batteries (80%≤SOH<90%), and number of low SOH batteries (SOH<80%).

[0053] Battery charging queue status: For example, the estimated completion time of a battery that is being charged or the average waiting time in the queue.

[0054] Step S2: Calculation of dynamic decision model.

[0055] The system employs a weighted allocation-multi-objective optimization algorithm to construct a battery swapping decision model. The core of this model is to calculate a battery swapping matching score for each battery to be assigned, thereby achieving optimal matching under multiple objectives. The calculation formula for the decision model is as follows: S = α W s + β W c + γ W p + δ W i in, S This indicates the battery swapping matching score. α , β , γ , δ Let represent the weight coefficients of each corresponding influencing factor, and α+β+γ+δ=1. W s Indicates the SOH weight. W c Indicates the SOC adaptation weights.W p Indicates user priority weight. W i This indicates the inventory balance weight.

[0056] The methods for determining each weighting item and coefficient are as follows: Dynamic weighting coefficients α, β, γ, and δ: These can be automatically adjusted according to preset battery swapping station operation strategies. For example, during morning and evening peak hours, to ensure user experience, the weight of the user priority coefficient γ can be automatically increased; when battery inventory is low in a certain health range, the weight of the inventory balance coefficient δ is increased to prevent this type of battery from being quickly depleted.

[0057] SOH weight W s Determined based on the battery's SOH data. In one embodiment, a setting can be made for high SOH batteries (SOH ≥ 90%). W s Between 0.8 and 1.0; medium SOH batteries (80% ≤ SOH < 90%) W s Between 0.5 and 0.7; low SOH batteries (SOH < 80%) W s It ranges from 0.2 to 0.4. This measure aims to prioritize allocating users with healthier batteries.

[0058] SOC adaptation weight W c Based on the user-provided target driving range, estimate the required target SOC value. Calculate the deviation between the current battery SOC and the target SOC; the smaller the deviation, the better. W c The higher the value, the better. For example, you can set the deviation to ≤5%. W c The value is 1.0, and for every 5% increase in deviation, W c The corresponding decrease is 0.2.

[0059] User priority weight W p This directly maps the urgency of user needs. For example, you can set an urgency priority corresponding to... W p The priority level is 1.0, with reservation priority at 0.7 and normal priority at 0.5. Through the three-tiered priority matching of emergency, reservation, and normal needs, emergency users can reduce battery swapping waiting time by more than 90%, meeting the needs of diverse usage scenarios.

[0060] Inventory balance weight W iThis is used to optimize inventory structure and prevent overstocking or shortages of certain types of batteries. For example, when the inventory ratio of batteries in any of the high, medium, or low SOH ranges in the system exceeds 50%, adjustments can be made to the inventory of batteries in that range. W i The value is reduced by 0.1-0.3 to promote the rational consumption of this type of battery and achieve inventory balance.

[0061] Step S3: Initial scheme generation and dynamic adjustment.

[0062] The decision model calculates the matching score of all batteries to be allocated within the station based on real-time collected data. S The battery with the highest score will be selected as the initial recommended battery swapping solution.

[0063] To address real-time changes, the system employs a dynamic adjustment mechanism. When triggering conditions are met (e.g., updates to battery swapping station inventory data, changes in user requirements, or changes in battery status), the system will re-execute the data collection and decision calculation process to update the recommended solution. In a preferred embodiment, the system automatically recalculates at fixed intervals (e.g., every 30 seconds) to ensure that the recommended solution remains consistent with the latest state.

[0064] Preferably, the system performs user preference verification on the initially recommended battery swapping scheme. For example, when the SOH data of the initially recommended battery is lower than a preset threshold (e.g., 80%), the system will call the user's historical battery swapping preference data for secondary verification. If the historical data shows that the user has rejected batteries with too low SOH, the system will automatically abandon the initial scheme and instead select the battery with the second highest matching score and an SOH not lower than the threshold as the recommended scheme, in order to improve user acceptance.

[0065] Step S4: Result Output and Feedback.

[0066] The system outputs the final recommended battery swapping plan to the relevant terminals. On one hand, it displays key information about the recommended battery, such as SOH, SOC, and estimated driving range, to the user terminal for confirmation. On the other hand, it provides feedback on the plan and inventory optimization suggestions to the battery swapping station operation and management system. For example, it suggests prioritizing the allocation of low SOH batteries to fixed-route vehicles that are less sensitive to battery degradation, reducing allocation to individual users; thereby achieving intelligent and refined management of battery inventory.

[0067] The technical solution described in this embodiment, by introducing SOH as the core decision-making dimension and constructing a dynamic closed-loop system, can bring the following beneficial effects: Significantly enhances user experience: Prioritizing high SOH batteries fundamentally avoids the range anxiety caused by "full charge, low battery health" batteries. High SOH batteries can reduce subsequent range degradation (actual tests show a 15%-20% reduction in range) and ensure stable charging efficiency. Simultaneously, the user priority response mechanism greatly shortens the waiting time for users with urgent needs.

[0068] Optimize the operational efficiency of battery swapping stations: By intelligently adjusting the usage frequency of batteries with different health levels through inventory balancing weights, the overuse of low-SOH batteries is avoided, which can extend the overall cycle life of the battery pack by 10%-15% and reduce long-term maintenance and replacement costs. The dynamic adjustment mechanism can adapt to inventory fluctuations in real time (such as the addition of batteries after charging is completed and the removal of faulty batteries), avoiding inventory backlog or shortage, and improving the overall operational efficiency of battery swapping stations by more than 25%.

[0069] It has high versatility and adaptability: The dynamic weighting coefficient mechanism enables the model to flexibly adapt to the operation strategies of different battery swapping stations, the demand characteristics of different time periods, and the changing inventory status, making it highly versatile.

[0070] This application also provides a battery swapping dynamic decision-making system based on battery health status, such as... Figure 3 As shown, the battery swapping dynamic decision-making system 300 includes: The data acquisition module 310 is used to collect multi-dimensional data in real time, including at least the battery's SOH data, battery's SOC data, user demand priority, and the inventory status of the battery swapping station. The dynamic decision module 320 is used to calculate the matching score for the battery to be allocated based on the multi-dimensional data and through a pre-built decision model. The decision model assigns weights to various influencing factors and performs weighted calculations based on at least the SOH data, the degree of fit between the SOC data and user needs, the priority of user needs, and the inventory status of the battery swapping station. The scheme management module 330 is used to output a recommended battery swapping scheme based on the calculated matching score, and to update the recommended battery swapping scheme in response to changes in the multi-dimensional data.

[0071] The battery swapping dynamic decision-making system of this application first acquires multi-dimensional data through a data acquisition module. Specifically, the battery's SOH (State of Health) data can be monitored in real time and uploaded to the cloud platform through the battery management system, or collected through periodic testing equipment. The battery's SOC (State of Charge) data can also be acquired in real time by the battery management system. User demand priorities can be manually selected by the user in the battery swapping app, such as selecting "normal battery swapping" or "emergency battery swapping," or the system can determine this based on the user's historical behavior, membership level, and other information. Battery swapping station inventory status data can be statistically analyzed in real time through the battery swapping station management system, including the number of available batteries in the station, and the SOH and SOC values ​​of each battery. This data can be centrally stored in a data center for subsequent decision-making.

[0072] Furthermore, the dynamic decision-making module, based on the acquired multi-dimensional data, calculates a matching score for the batteries to be allocated using a decision model. This decision model can be implemented using various algorithms. For example, it can establish a series of rules based on expert experience to qualitatively or semi-quantitatively evaluate influencing factors such as the suitability of SOH data and SOC data to user needs, user demand priority, and inventory balance requirements. Different scores are assigned based on the evaluation results, and these scores are then simply summed or averaged to obtain the matching score. For instance, batteries with an SOH value above 80% can receive higher scores, batteries with an SOC value close to user expectations can receive higher scores, high-priority users can receive higher scores, and when the quantity of batteries with a certain health level in the inventory is low, allocating that type of battery receives additional points. In this way, the decision model can comprehensively consider multiple factors, avoiding the limitations of single-indicator decision-making.

[0073] Therefore, the solution management module outputs recommended battery swapping solutions based on the calculated matching score. Specifically, it sorts all batteries to be assigned according to their matching scores from highest to lowest, and provides the battery with the highest score as the recommended solution to the user.

[0074] Through the above technical solution, this application solves the problem that existing battery swapping decisions rely solely on remaining battery power while ignoring battery health status, user demand, and inventory dynamics. This method can improve the user's battery swapping experience, avoid allocating batteries with low health levels, extend the overall lifespan of the battery pack, reduce operational risks, and improve the flexibility and operational efficiency of battery swapping station resource allocation.

[0075] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program that is executed by the processor. When the computer program is executed by the processor, it causes the device equipped with the processor to perform the battery swapping dynamic decision-making method based on battery health status as described in any of the above embodiments.

[0076] Next, refer to Figure 4 This describes an example electronic device 400 for implementing the battery health state-based dynamic decision-making method of the embodiments of this application.

[0077] like Figure 4 As shown, the electronic device 400 includes a processor 410, a memory 420, and a communication interface 430. The processor 410, memory 420, and communication interface 430 can be interconnected and communicate via a communication bus 440 and / or other forms of connection mechanisms (not shown).

[0078] It should be noted that Figure 4 The components and structure of the electronic device 400 shown are merely exemplary and not limiting; the electronic device may also have other components and structures as needed.

[0079] Optionally, the communication interface 430 may also include a transmitter and / or a receiver.

[0080] The processor 410 may be a microcontroller unit (MCU), a central processing unit (CPU), a digital signal processor (DSP), a microcontroller and embedded device, or other processing units with data processing capabilities and / or instruction execution capabilities.

[0081] The memory 420 can be various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM), cache memory, synchronous dynamic random access memory (SDRAM), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may also be stored on the computer-readable storage medium, and the memory 420 can execute the program instructions to implement the battery health state-based dynamic decision-making method described in the embodiments of this application above.

[0082] This application also provides a storage medium storing a computer program that runs on a computer. When the computer program runs, it causes the computer to execute the battery swapping dynamic decision-making method based on battery health status as described in any of the above embodiments.

[0083] In this application, the electronic device and storage medium provided in the embodiments are used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0084] Finally, it should be noted that the above technical solution is only one implementation method of this application. For those skilled in the art, based on the application methods and principles disclosed in this application, it is easy to make various types of improvements or modifications, and not limited to the methods described in the specific implementation methods above. Therefore, the methods described above are only preferred and have no limiting significance.

Claims

1. A battery swapping dynamic decision-making method based on battery health status, characterized in that: The battery swapping dynamic decision-making method includes: Acquire multi-dimensional data, including at least battery SOH data, battery SOC data, user demand priority, and battery swapping station inventory status; Based on the multi-dimensional data, a matching score is calculated for the batteries to be allocated through a pre-built decision model. The decision model assigns weights to various influencing factors and performs weighted calculations based on at least the SOH data, the degree of fit between the SOC data and user needs, the priority of user needs, and the inventory status of the battery swapping station. Based on the calculated matching score, a recommended battery swapping solution is output, and the recommended battery swapping solution is updated in response to changes in the multi-dimensional data.

2. The battery swapping dynamic decision-making method based on battery health status according to claim 1, characterized in that: The calculation formula for the decision model is as follows: S = α W s + β W c + γ W p + δ W i in, S This indicates the battery swapping matching score. α , β , γ , δ Let represent the weight coefficients of each corresponding influencing factor, and α+β+γ+δ=1. W s Indicates the SOH weight. W c Indicates the SOC adaptation weights. W p Indicates user priority weight. W i This indicates the inventory balance weight.

3. The battery swapping dynamic decision-making method based on battery health status according to claim 2, characterized in that: The SOH weight is determined based on the health range to which the battery's SOH value belongs; the SOC adaptation weight is determined based on the deviation between the battery's current SOC value and the target SOC value calculated based on user needs; the user priority weight is determined based on the priority level of the user's needs; and the inventory balance weight is determined based on the inventory ratio of batteries in each health range in the battery swapping station.

4. The battery swapping dynamic decision-making method based on battery health status according to claim 2, characterized in that: The weighting coefficients can be dynamically adjusted based on the operation strategy or real-time operation status of the battery swapping station.

5. The battery swapping dynamic decision-making method based on battery health status according to claim 1, characterized in that: Based on the calculated matching score, a recommended battery swapping solution is output, including: Batteries to be assigned are selected based on the matching scores from highest to lowest. If the SOH value of the selected battery is lower than the preset health threshold, it will be verified based on the user's battery selection history. If the verification results indicate that users tend to reject batteries with low health, then the battery with the second highest matching score is selected as the recommended battery swapping solution.

6. The battery swapping dynamic decision-making method based on battery health status according to claim 1, characterized in that: The triggering condition for updating the recommended battery swapping scheme is that at least one of the following changes: the battery's SOH data, SOC data, user demand priority, and battery swapping station inventory status.

7. The battery swapping dynamic decision-making method based on battery health status according to claim 1, characterized in that: The battery swapping dynamic decision-making method periodically repeats the process of acquiring multi-dimensional data and calculating a matching score based on the multi-dimensional data to continuously update the recommended battery swapping scheme.

8. A battery swapping dynamic decision-making system based on battery health status, characterized in that, The battery swapping dynamic decision-making system includes: The data acquisition module is used to collect multi-dimensional data in real time, including at least the battery's SOH data, battery's SOC data, user demand priority, and the inventory status of the battery swapping station. The dynamic decision-making module is used to calculate the matching score for the batteries to be allocated based on the multi-dimensional data and through a pre-built decision-making model. The decision-making model assigns weights to various influencing factors and performs weighted calculations based on at least the SOH data, the degree of fit between the SOC data and user needs, the priority of user needs, and the inventory status of the battery swapping station. The scheme management module is used to output recommended battery swapping schemes based on the calculated matching score, and to update the recommended battery swapping schemes in response to changes in the multi-dimensional data.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program executed by the processor, the computer program, when executed by the processor, causing the device equipped with the processor to perform the battery swapping dynamic decision-making method based on battery health status as described in any one of claims 1-8.

10. A storage medium, characterized in that, The storage medium stores a computer program that runs on a computer and, when running, causes the computer to execute the battery swapping dynamic decision-making method based on battery health status as described in any one of claims 1-8.

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