New energy automobile battery swap station battery scheduling method with battery health detection function

Abstract

The invention relates to a new energy automobile battery swap station battery dispatching method with a battery health detection function. The method comprises the steps of battery health detection model establishment, real-time detection and data synchronization, real-time detection and data synchronization and dispatching instruction execution and feedback. The method has remarkable advantages in the aspect of battery health detection, the battery state can be judged more accurately through multi-parameter fusion monitoring and intelligent algorithm analysis, compared with a traditional detection method, the detection accuracy is improved, potential problems can be captured in time through comprehensive analysis of multiple parameters, and after the potential problems of the battery are found in advance, the detection accuracy is improved. According to the method, corresponding maintenance measures can be taken, so that the service life of the battery is prolonged, the replacement cost of the battery is reduced, the use efficiency and economy of the battery are effectively improved, the operation of the battery replacement station can be remarkably optimized through a reasonable battery scheduling strategy, accurate scheduling can be performed according to user requirements and battery states, and the waiting time of users is shortened.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle technology, and in particular to a battery scheduling method for new energy vehicle battery swapping stations with battery health detection function. Background Technology

[0002] In recent years, with increasing global attention to environmental protection and sustainable development, the new energy vehicle market has experienced rapid growth. According to data from the China Association of Automobile Manufacturers, from January to October 2025, my country's production and sales of new energy vehicles reached 13.005 million and 12.986 million units respectively, representing year-on-year increases of 37.8% and 37.5%. New energy vehicle sales accounted for 47.3% of total new vehicle sales. The rapid development of new energy vehicles not only helps reduce reliance on traditional fuels and lower carbon emissions, but also promotes the transformation and upgrading of the automotive industry. In the development of new energy vehicles, the construction of charging infrastructure has always been a key issue. Traditional charging methods, whether AC slow charging or DC fast charging, suffer from long charging times, causing considerable inconvenience to users, especially during long-distance travel or when time is limited. Surveys show that fast charging for most pure electric vehicles takes 30 minutes to an hour, while slow charging can take several hours or even longer, resulting in significant waiting time for users. To address this problem, battery swapping has emerged. Battery swapping refers to a method of rapid energy replenishment by replacing the depleted battery in an electric vehicle with a fully charged one.

[0003] Despite the numerous advantages of battery swapping, current battery scheduling methods at swapping stations still have some shortcomings. In terms of battery allocation, there is an issue of uneven battery supply. Regarding battery health monitoring, existing technologies are not yet perfect. Traditional battery health monitoring mainly relies on parameters such as voltage, current, and temperature collected by the battery management system (BMS) to estimate the battery's health status. This method has limitations and cannot accurately reflect the actual health condition of the battery. Moreover, most existing monitoring technologies perform tests under static conditions, failing to monitor real-time health changes during dynamic use, thus making it difficult to meet the needs of efficient swapping station operation. In conclusion, in order to fully leverage the advantages of the battery swapping model and improve the operational efficiency and service quality of battery swapping stations, it is of great practical significance and urgency to develop a battery scheduling method for new energy vehicle battery swapping stations with battery health detection capabilities. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A battery scheduling method for new energy vehicle battery swapping stations with battery health detection function includes the following steps: S1): Establish a battery health detection model: By collecting historical charge and discharge data and real-time operating parameters of the battery, construct a health status assessment model based on deep learning; The battery health quantification value SOH is calculated using the following formula: ,in C current C represents the current actual capacity of the battery. rated α is the rated capacity of the battery; α is the attenuation correction coefficient, ranging from 0.08 to 0.12; n is the number of characteristic parameters; w i Let be the weight of the i-th feature parameter, and satisfy . Pi is the normalized value of the i-th feature parameter; S2): Real-time detection and data synchronization: A distributed sensor network is set up within the battery swapping station to collect real-time parameters of the stocked batteries and the batteries of vehicles to be replaced. The detection data is synchronized to the cloud scheduling platform via a 5G communication module, with a synchronization frequency of no less than once per minute. During the real-time parameter collection process, the consistency of the battery cell voltage is recorded as follows: ,when At that time, a voltage consistency warning is triggered; ,in This represents the voltage difference between individual battery cells. This represents the real-time voltage values ​​of k individual cells within the battery pack. S3): Dynamic scheduling decision generation: The scheduling platform classifies the batteries in the battery swapping station according to the battery health status in step 1 and generates scheduling instructions; wherein battery classification includes: Class A: Health level ≥ 90%. Class A batteries are prioritized for allocation to vehicles with urgent pre-orders and range requirements ≥ 500km. Grade B: Health level is 70%-89%, Grade B batteries are allocated to regular orders; Grade C: Health level is 50%-69%. Grade C batteries are used for energy storage peak shaving or battery swapping for low-speed logistics vehicles. Grade D: Health level <50%, Grade D batteries trigger the retirement and recycling process; The formula for adjusting the charging current in the dynamic scheduling decision is as follows:

[0005] ,in I charging The dynamically adjusted charging current; I ratedq represents the battery's rated charging current; q is the number of health degradation levels, meaning that every 10% decrease in health corresponds to one degradation level, and the charging current decreases by 9% for each level. S4): Scheduling instruction execution and feedback: The robotic arm of the battery swapping station completes the battery replacement operation according to the scheduling instructions, updates the battery's cycle count and health data, and feeds the execution results back to the scheduling platform to form a closed-loop management; S5): Cross-site battery scheduling mechanism: When the battery inventory of a certain health level of a single battery swapping station is lower than the warning threshold, A level < 5 batteries, B level < 10 batteries, the scheduling platform calculates the battery inventory and transportation costs of battery swapping stations within 30 kilometers, generates a cross-site allocation plan, prioritizes the allocation of batteries with fewer cycles in the same health level, and updates the geographical location information and ownership records of the batteries simultaneously. The dispatching platform calculates transportation costs using the following formula: ,in C transport d represents the unit battery transportation cost; d represents the inter-station transportation distance; β represents the basic transportation rate, ranging from 1.2 to 1.8; N cycle This indicates the number of battery cycles.

[0006] S6): Full-scenario scheduling decision: S61): Digital traceability of the entire battery lifecycle: Assign a unique blockchain identifier to each battery and record data for the entire process of production, warehousing, battery swapping, maintenance, and decommissioning and recycling; S62): Personalized scheduling driven by user profiles: By collecting user travel habits, vehicle parameters, and historical battery swapping preferences, a three-dimensional user profile is constructed, and differentiated battery and charging strategies are matched according to user type; S63): Dynamic response to grid interaction: Real-time communication with the regional power grid dispatch center to achieve two-way interaction between charging and discharging based on grid load dynamics and battery health status; S64): Emergency dispatch and support: Establish a tiered emergency response mechanism for extreme weather, power outages, and equipment failures.

[0007] Furthermore, the construction process of the battery health detection model includes: S11): Data preprocessing: outlier removal and normalization of the collected raw parameters; The normalization formula is: ,in P i X is the normalized value of the i-th feature parameter; i This represents the original acquired value of the i-th feature parameter; X max Xmin These are the historical maximum and minimum values ​​of the feature parameter, respectively. S12): Feature engineering: Extract the battery's voltage fluctuation coefficient, temperature gradient change rate, internal resistance growth trend, and charge / discharge depth distribution characteristics as model input variables; S13): Model training: An improved long short-term memory network is adopted, an attention mechanism is introduced to strengthen the weights of key features, and historical faulty battery data is used as negative samples for supervised training, so that the model's prediction error of health status is ≤3%.

[0008] Furthermore, the real-time detection in step 2 also includes battery safety status monitoring, specifically comprising the following steps: S21): Detects the consistency of battery cell voltage; triggers an alarm when the difference exceeds 50mV. S22): The degree of shell deformation is detected using a laser rangefinder sensor; S23): Gas escape concentration. Equipped with a gas sensor to detect electrolyte volatiles. When any indicator exceeds the safety threshold, the dispatch platform immediately marks the battery as "not for vehicle replacement" and initiates the isolation storage procedure.

[0009] Furthermore, step 3, the dynamic scheduling decision generation, also includes battery charging plan optimization, specifically the following steps: S31): Based on peak and valley electricity price periods, charging time periods are divided. During valley periods, Class C batteries are prioritized to be charged to 50%-60% SOC for participation in grid peak shaving. S32): For Class A and Class B batteries, based on the predicted number of pre-orders for the next day, the batteries are charged to 90%-95% SOC during the normal charging period. During the charging process, a combination of pulse charging and constant voltage charging is used. The charging current is dynamically adjusted according to the battery health. For every 10% decrease in battery health, the charging current is reduced by 8%-10%.

[0010] Furthermore, the user's scheduled battery swap request processing includes: collecting the vehicle model, current battery health, and expected driving range through the user's APP; the scheduling platform matching at least two backup batteries that meet the health requirements based on the vehicle model battery compatibility database; and completing battery preheating or precooling to the 25℃-35℃ range 15 minutes before the user's arrival to improve the vehicle's initial driving range performance after battery swapping.

[0011] Furthermore, the battery health stratification results will be dynamically updated over time, based on the following criteria: re-testing health after each battery swap cycle, conducting a full inventory battery deep test weekly, and mandatory re-testing within 24 hours after a battery is detected to have experienced extreme operating conditions.

[0012] Furthermore, the scheduling instruction execution process includes an error prevention mechanism: before grabbing the battery, the robotic arm reads the battery ID through the RFID tag and compares it with the target battery ID in the scheduling instruction. If they do not match, the operation is paused and an alarm is issued. After the replacement is completed, the compatibility between the new battery and the vehicle's BMS system is verified through the battery interface communication. The battery swapping process can only end after the verification is successful.

[0013] Furthermore, the cross-station transfer scheme also needs to consider health protection during battery transportation: the transport vehicle is equipped with a constant temperature compartment, with the temperature controlled between 15℃ and 40℃, and shockproof fixing devices. Battery status data is collected every 30 minutes during transportation. If a voltage drop of more than 0.5V or a temperature fluctuation of more than 10℃ occurs, the receiving station is immediately notified to adjust the post-receive testing process.

[0014] Furthermore, it also includes a battery health predictive maintenance module: based on the battery health change trend and remaining lifespan prediction, it generates a maintenance plan 7 days in advance, performs equalization charging on batteries with a health decline rate exceeding 2% per month, adopts active equalization technology to control the voltage difference of each cell within 10mV, and automatically triggers an early warning prompt to purchase a new battery for batteries with a remaining lifespan of less than 300 cycles.

[0015] Furthermore, the data recorded by the blockchain identifier in step 6 includes production batch, initial health status, users of each battery swap, maintenance records, and extreme operating condition experience information, which are used for quality accountability and supplementing health monitoring model data; The scheduling logic for the three-dimensional user profile is as follows: For high-frequency long-distance users: prioritize matching with Grade A batteries with a cycle count of <50 cycles and a health rate of ≥95%, and complete pre-charging and temperature adjustment 30 minutes in advance; For daily commuters: Use a Class B battery or a Class A battery with 50-100 charge cycles, and prioritize charging during off-peak electricity pricing periods. New users: By default, they are assigned Grade A batteries with a health level of ≥90%; The power grid interaction logic includes: During peak grid load: Class C batteries discharge in reverse to the grid, with a discharge depth ≤30%. The formula for calculating the depth of discharge is: ,in SOC initial SOC final These represent the discharge start and end charging states, respectively. In the event of an emergency power outage: Suspend charging of Class A batteries for non-urgent orders; During off-peak grid load periods: Class C battery energy storage charging, batteries with a health level ≥80% perform a "shallow charge and shallow discharge" cycle; The tiered emergency response mechanism is specifically as follows: Extreme weather: Prioritize battery swapping needs for rescue vehicles and public transportation vehicles, and suspend orders from non-essential private users; Power outage: Activate backup power supply, prioritizing charging of Class A batteries; Equipment failure: Switch to manual battery swapping channel and dispatch mobile battery swapping vehicles within a 30-kilometer radius for support.

[0016] 1. This invention offers significant advantages in battery health monitoring. Through multi-parameter fusion monitoring and intelligent algorithm analysis, it can more accurately determine battery status, improving accuracy compared to traditional testing methods. The detection technology of this invention can promptly identify potential problems through comprehensive analysis of multiple parameters. Early detection of potential battery issues allows for appropriate maintenance measures to be taken, thereby extending battery life and reducing battery replacement costs. This effectively improves battery efficiency and economy.

[0017] 2. A well-designed battery scheduling strategy can significantly optimize the operation of battery swapping stations. On one hand, it allows for precise scheduling based on user demand and battery status, reducing user waiting time. During peak hours, pre-allocating fully charged batteries can shorten the average waiting time for users, greatly improving the user experience. On the other hand, optimizing charging plans can reduce operating costs. Charging during off-peak hours reduces electricity costs; simultaneously, a reasonable battery allocation and charging strategy can improve battery utilization efficiency, reducing battery idleness and overcharging, further lowering operating costs. Furthermore, efficient battery scheduling can improve the service quality of battery swapping stations, enhance user trust and acceptance of the battery swapping model, and promote the development of the new energy vehicle battery swapping industry. Attached Figure Description

[0018] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the battery scheduling method for new energy vehicle battery swapping stations with battery health detection function according to the present invention. Figure 2 This is a flowchart of an embodiment of the present invention; Figure 3 This is a system framework diagram for implementing the present invention. Detailed Implementation

[0020] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby providing a clearer and more explicit definition of the scope of protection of the present invention. Specific Implementation Example 1: like Figures 1-3 The method for battery scheduling at a new energy vehicle battery swapping station with battery health detection function is shown, characterized by the following steps: Step 1: Establish a battery health detection model By collecting historical charge and discharge data and real-time operating parameters of the battery, a deep learning-based health status assessment model is constructed, outputting a quantitative value of battery health (SOH) and a predicted value of remaining useful life (RUL). The quantitative value of battery health (SOH) is calculated using the following formula:

[0022] C current The current actual capacity of the battery (Ah) is obtained through capacity testing experiments; C rated α represents the battery's rated capacity (Ah), which is the battery's factory calibration value; α is the attenuation correction coefficient, ranging from 0.08 to 0.12, adaptively adjusted according to the battery type (ternary lithium / lithium iron phosphate); n represents the number of characteristic parameters; w i The weights of the i-th feature parameter are obtained through training using an attention mechanism, satisfying the following conditions: Pi is the normalized value of the i-th feature parameter; the construction process of the battery health detection model includes: Data preprocessing: Outlier removal and normalization are performed on the collected raw parameters. The normalization formula is as follows:

[0023] P i X is the normalized value of the i-th feature parameter; i This represents the original acquired value of the i-th feature parameter; X max X min These are the historical maximum and minimum values ​​of the feature parameter, respectively. Feature engineering: Extract the battery's voltage fluctuation coefficient, temperature gradient change rate, internal resistance growth trend, and depth of charge / discharge (DOD) distribution characteristics as model input variables; Model training: An improved Long Short-Term Memory (LSTM) network is adopted, and an attention mechanism is introduced to strengthen the weights of key features. Historical faulty battery data is used as negative samples for supervised training, so that the model's prediction error of health status is ≤3%.

[0024] Step 2: Real-time detection and data synchronization The battery swapping station is equipped with a distributed sensor network to collect real-time parameters of the stock batteries and the batteries of the vehicles to be replaced. The detection data is synchronized to the cloud scheduling platform through a 5G communication module, with a synchronization frequency of no less than once per minute. The formula for determining the battery cell voltage consistency warning in real-time detection is as follows:

[0025] This represents the voltage difference between individual battery cells (mV). The real-time voltage (mV) of k cells within the battery pack; when At that time, a voltage consistency warning is triggered; The real-time detection also includes battery safety status monitoring, specifically: detecting the consistency of battery cell voltage, triggering an early warning when the difference exceeds 50mV, detecting the degree of casing deformation using a laser rangefinder, and detecting the concentration of gas escaping by using a gas sensor to detect electrolyte volatiles. When any indicator exceeds the safety threshold, the dispatch platform immediately marks the battery as "not for vehicle replacement" and initiates the isolation storage procedure.

[0026] Step 3: Dynamic Scheduling Decision Generation The scheduling platform generates scheduling instructions based on the following parameters: the real-time health stratification results of the battery inventory at the battery swapping station (≥90% is Grade A, 70%-89% is Grade B, 50%-69% is Grade C, and <50% is Grade D); user's scheduled battery swapping request including vehicle model compatibility information, expected swapping time, charging pile load status, and battery charging efficiency curve. Grade A batteries are prioritized for allocation to vehicles with urgent scheduled orders and range requirements ≥500km; Grade B batteries are allocated to regular orders; Grade C batteries are used for energy storage peak shaving or low-speed logistics vehicle battery swapping; and Grade D batteries trigger the retirement and recycling process. The battery health stratification results are dynamically updated over time, based on the following criteria: re-checking health after each battery swapping cycle, conducting a full inventory battery deep inspection weekly (with an added battery capacity test), and mandatory re-inspection within 24 hours after a battery is detected to have experienced extreme operating conditions.

[0027] The formula for adjusting the charging current in the dynamic scheduling decision is:

[0028] I charging The dynamically adjusted charging current (A); I rated The rated charging current of the battery is (A); q is the health degradation level number, i.e. Each 10% decrease in health corresponds to one degradation level, with a 9% reduction in charging current for each level. The dynamic scheduling decision generation also includes battery charging plan optimization, specifically: Based on the peak-valley electricity price period, the charging period is divided into charging periods. During the valley period, Class C batteries are prioritized to be charged to 50%-60% SOC (State of Charge) for participation in grid peak shaving. Based on the predicted number of pre-orders for Grade A and Grade B batteries, they are charged to 90%-95% SOC during the normal charging period. During the charging process, a combination of pulse charging and constant voltage charging is used, and the charging current is dynamically adjusted according to the battery health. For every 10% decrease in battery health, the charging current is reduced by 8%-10%.

[0029] The user's scheduled battery swap request processing includes: collecting the vehicle model, current battery health, and expected driving range through the user's APP; the dispatching platform matching at least two backup batteries that meet the health requirements based on the vehicle model battery compatibility database; and completing battery preheating (winter) or precooling (summer) to the 25℃-35℃ range 15 minutes before the user's arrival to improve the initial driving range performance of the vehicle after the battery swap.

[0030] Step 4: Execution and Feedback of Scheduling Instructions The robotic arm at the battery swapping station completes the battery replacement operation according to the scheduling instructions, while updating the battery's cycle count and health data, and feeding the execution results back to the scheduling platform to form a closed-loop management system. The execution of the scheduling instructions includes an error prevention mechanism: before grabbing the battery, the robotic arm reads the battery ID through an RFID tag and compares it with the target battery ID in the scheduling instructions. If they do not match, the operation is paused and an alarm is issued. After the replacement is completed, the compatibility between the new battery and the vehicle's BMS system is verified through battery interface communication. The battery swapping process can only end after the verification is successful.

[0031] Step 5, Cross-site Battery Scheduling Mechanism: When the battery inventory of a certain health level of a single battery swapping station is lower than the warning threshold (A level < 5 batteries, B level < 10 batteries), the scheduling platform calculates the battery inventory and transportation costs of battery swapping stations within a 30-kilometer radius, generates a cross-site allocation plan, prioritizes the allocation of batteries with fewer cycles in the same health level, and simultaneously updates the geographical location information and ownership records of the batteries. The dispatching platform calculates transportation costs using the following formula:

[0032] Where: C transport Unit battery transportation cost (yuan); d is the inter-station transportation distance (km); β is the basic transportation rate (yuan / (km・block)), ranging from 1.2 to 1.8, dynamically adjusted according to road grade; N cycleThis refers to the number of battery cycles (times). The inter-station transfer plan also needs to consider health protection during battery transportation: the transport vehicle is equipped with a temperature-controlled compartment, with the temperature controlled between 15℃ and 40℃, and shockproof fixing devices. Battery status data is collected every 30 minutes during transportation. If a voltage drop of more than 0.5V or a temperature fluctuation of more than 10℃ occurs, the receiving station is immediately notified to adjust the post-receive testing process.

[0033] Step Six: Full-Scenario Scheduling Decision 1) Digital traceability of the entire battery life cycle: Each battery is assigned a unique blockchain identifier to record data throughout the entire process of production, warehousing, battery swapping, maintenance, and decommissioning and recycling; the data recorded by the blockchain identifier includes production batch, initial health, users of each battery swap, maintenance records, and extreme operating condition experience information, which is used for quality accountability and supplementing health monitoring model data; 2) User-profile-driven personalized scheduling: By collecting user travel habits, vehicle parameters, and historical battery swapping preferences, a three-dimensional user profile is constructed, and differentiated battery and charging strategies are matched according to user type; the scheduling logic of the three-dimensional user profile is as follows: For high-frequency long-distance users: prioritize matching with Grade A batteries with a cycle count of <50 cycles and a health rate of ≥95%, and complete pre-charging and temperature adjustment 30 minutes in advance; For daily commuters: Use a Class B battery or a Class A battery with 50-100 charge cycles, and prioritize charging during off-peak electricity pricing periods. New users: By default, they are assigned Grade A batteries with a health level of ≥90%; 3) Dynamic Response to Power Grid Interaction: Real-time communication with the regional power grid dispatch center, enabling bidirectional charging / discharging interaction based on power grid load dynamics and battery health status; the power grid interaction logic includes: During peak grid load: Class C batteries discharge in reverse to the grid, with a discharge depth ≤ 30%. The discharge depth calculation formula is as follows: ,in SOC initial SOC final These represent the discharge start and end charging states, respectively. In the event of an emergency power outage: Suspend charging of Class A batteries for non-urgent orders; During off-peak grid load periods: Class C battery energy storage charging, batteries with a health level ≥80% perform a "shallow charge and shallow discharge" cycle; 4) Emergency Dispatch and Support: Establish a tiered emergency response mechanism for extreme weather, power outages, and equipment failures. The tiered emergency response mechanism is as follows: Extreme weather: Prioritize battery swapping needs for rescue vehicles and public transportation vehicles, and suspend orders from non-essential private users; Power outage: Activate backup power supply, prioritizing charging of Class A batteries; Equipment failure: Switch to manual battery swapping channel and dispatch mobile battery swapping vehicles within a 30km radius for support. It also includes step seven, the battery health predictive maintenance module: based on the battery health change trend and remaining lifespan prediction, a maintenance plan is generated 7 days in advance, and equalization charging is performed on batteries whose health decline rate exceeds 2% per month. Active equalization technology is used to control the voltage difference of each cell within 10mV. For batteries with a remaining lifespan of less than 300 cycles, an early warning prompt to purchase a new battery is automatically triggered.

[0034] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A battery scheduling method for new energy vehicle battery swapping stations with battery health detection function, characterized in that, Includes the following steps: S1): Establish a battery health detection model: By collecting historical charge and discharge data and real-time operating parameters of the battery, construct a health status assessment model based on deep learning; The battery health quantification value SOH is calculated using the following formula: ,in C current C represents the current actual capacity of the battery. rated α is the rated capacity of the battery; α is the attenuation correction coefficient, with a value range of 0.08-0.12; n is the number of characteristic parameters. w i Let be the weight of the i-th feature parameter, and satisfy . Pi is the normalized value of the i-th feature parameter; S2): Real-time detection and data synchronization: A distributed sensor network is set up within the battery swapping station to collect real-time parameters of the stocked batteries and the batteries of vehicles to be replaced. The detection data is synchronized to the cloud scheduling platform via a 5G communication module, with a synchronization frequency of no less than once per minute. During the real-time parameter collection process, the consistency of the battery cell voltage is recorded as follows: ,when At that time, a voltage consistency warning is triggered; ,in This represents the voltage difference between individual battery cells. This represents the real-time voltage values ​​of k individual cells within the battery pack. S3): Dynamic scheduling decision generation: The scheduling platform classifies the batteries in the battery swapping station according to the battery health status in step 1 and generates scheduling instructions. The battery grading includes: Class A: Health level ≥ 90%. Class A batteries are prioritized for allocation to vehicles with urgent pre-orders and range requirements ≥ 500km. Grade B: Health level is 70%-89%, Grade B batteries are allocated to regular orders; Grade C: Health level is 50%-69%. Grade C batteries are used for energy storage peak shaving or battery swapping for low-speed logistics vehicles. Grade D: Health level <50%, Grade D batteries trigger the retirement and recycling process; The formula for adjusting the charging current in the dynamic scheduling decision is as follows: ， ,in I charging The dynamically adjusted charging current; I rated q represents the battery's rated charging current; q is the number of health degradation levels, meaning that every 10% decrease in health corresponds to one degradation level, and the charging current decreases by 9% for each level. S4): Scheduling instruction execution and feedback: The robotic arm of the battery swapping station completes the battery replacement operation according to the scheduling instructions, updates the battery's cycle count and health data, and feeds the execution results back to the scheduling platform to form a closed-loop management; S5): Cross-site battery scheduling mechanism: When the battery inventory of a certain health level of a single battery swapping station is lower than the warning threshold, A level < 5 batteries, B level < 10 batteries, the scheduling platform calculates the battery inventory and transportation costs of battery swapping stations within 30 kilometers, generates a cross-site allocation plan, prioritizes the allocation of batteries with fewer cycles in the same health level, and updates the geographical location information and ownership records of the batteries simultaneously. The dispatching platform calculates transportation costs using the following formula: ,in C transport d represents the unit battery transportation cost; d represents the inter-station transportation distance; β represents the basic transportation rate, ranging from 1.2 to 1.8; N cycle This indicates the number of battery cycles completed. S6): Full-scenario scheduling decision: S61): Digital traceability of the entire battery lifecycle: Assign a unique blockchain identifier to each battery and record data for the entire process of production, warehousing, battery swapping, maintenance, and decommissioning and recycling; S62): Personalized scheduling driven by user profiles: By collecting user travel habits, vehicle parameters, and historical battery swapping preferences, a three-dimensional user profile is constructed, and differentiated battery and charging strategies are matched according to user type; S63): Dynamic response to grid interaction: Real-time communication with the regional power grid dispatch center to achieve two-way interaction between charging and discharging based on grid load dynamics and battery health status; S64): Emergency dispatch and support: Establish a tiered emergency response mechanism for extreme weather, power outages, and equipment failures.

2. The battery scheduling method for a new energy vehicle battery swapping station with battery health detection function according to claim 1, characterized in that, The construction process of the battery health detection model includes: S11): Data preprocessing: outlier removal and normalization of the collected raw parameters; The normalization formula is: ,in P i X is the normalized value of the i-th feature parameter; i This represents the original acquired value of the i-th feature parameter; X max X min These are the historical maximum and minimum values ​​of the feature parameter, respectively. S12): Feature engineering: Extract the battery's voltage fluctuation coefficient, temperature gradient change rate, internal resistance growth trend, and charge / discharge depth distribution characteristics as model input variables; S13): Model training: An improved long short-term memory network is adopted, an attention mechanism is introduced to strengthen the weights of key features, and historical faulty battery data is used as negative samples for supervised training, so that the model's prediction error of health status is ≤3%.

3. The battery scheduling method for a new energy vehicle battery swapping station with battery health detection function according to claim 1, characterized in that, Step 2, in addition to real-time detection, also includes battery safety status monitoring, specifically comprising the following steps: S21): Detects the consistency of battery cell voltage; triggers an alarm when the difference exceeds 50mV. S22): The degree of shell deformation is detected using a laser rangefinder sensor; S23): Gas escape concentration. Equipped with a gas sensor to detect electrolyte volatiles. When any indicator exceeds the safety threshold, the dispatch platform immediately marks the battery as "not for vehicle replacement" and initiates the isolation storage procedure.

4. A battery scheduling method for a new energy vehicle battery swapping station with battery health detection function according to claim 1, characterized in that, Step 3, the dynamic scheduling decision generation, also includes battery charging plan optimization. The specific steps are as follows: S31): Based on peak and valley electricity price periods, charging time periods are divided. During valley periods, Class C batteries are prioritized to be charged to 50%-60% SOC for participation in grid peak shaving. S32): For Class A and Class B batteries, based on the predicted number of pre-orders for the next day, the batteries are charged to 90%-95% SOC during the normal charging period. During the charging process, a combination of pulse charging and constant voltage charging is used. The charging current is dynamically adjusted according to the battery health. For every 10% decrease in battery health, the charging current is reduced by 8%-10%.

5. A battery scheduling method for a new energy vehicle battery swapping station with battery health detection function according to claim 1, characterized in that, The user's scheduled battery swap request processing includes: collecting the vehicle model, current battery health, and expected driving range through the user's APP; the scheduling platform matching at least two backup batteries that meet the health requirements based on the vehicle model battery compatibility database; and completing battery preheating or precooling to the 25℃-35℃ range 15 minutes before the user's arrival to improve the vehicle's initial driving range performance after the battery swap.

6. A battery scheduling method for a new energy vehicle battery swapping station with battery health detection function according to claim 1, characterized in that, The battery health stratification results will be dynamically updated over time. The update criteria include: re-testing the health status after each battery swap cycle, conducting a full inventory battery deep test once a week, and mandatory re-testing within 24 hours when a battery is detected to have experienced extreme operating conditions.

7. A battery scheduling method for a new energy vehicle battery swapping station with battery health detection function according to claim 1, characterized in that, The scheduling instruction execution process includes a fault prevention mechanism: before grabbing the battery, the robotic arm reads the battery ID through the RFID tag and compares it with the target battery ID in the scheduling instruction. If they do not match, the operation is paused and an alarm is issued. After the replacement is completed, the compatibility between the new battery and the vehicle's BMS system is verified through the battery interface communication. The battery swapping process can only end after the verification is successful.

8. A battery scheduling method for a new energy vehicle battery swapping station with battery health detection function according to claim 1, characterized in that, The cross-station transfer plan also needs to consider health protection during battery transportation: the transport vehicle is equipped with a constant temperature compartment, with the temperature controlled between 15℃ and 40℃, and shockproof fixing devices. Battery status data is collected every 30 minutes during transportation. If the voltage drops by more than 0.5V or the temperature fluctuates by more than 10℃, the receiving station is immediately notified to adjust the testing process after receiving the battery.

9. A battery scheduling method for a new energy vehicle battery swapping station with battery health detection function according to claim 1, characterized in that, It also includes a battery health predictive maintenance module: based on the battery health change trend and remaining lifespan prediction, it generates a maintenance plan 7 days in advance, performs equalization charging on batteries with a health decline rate of more than 2% per month, adopts active equalization technology to keep the voltage difference between individual cells within 10mV, and automatically triggers an early warning prompt to purchase new batteries for batteries with a remaining lifespan of less than 300 cycles.

10. A battery scheduling method for a new energy vehicle battery swapping station with battery health detection function according to claim 1, characterized in that, The data recorded by the blockchain identifier in step 6 includes production batch, initial health status, users of each battery swap, maintenance records, and extreme working condition experience information, which are used for quality accountability and supplementing health detection model data. The scheduling logic for the three-dimensional user profile is as follows: For high-frequency long-distance users: prioritize matching with Grade A batteries with a cycle count of <50 cycles and a health rate of ≥95%, and complete pre-charging and temperature adjustment 30 minutes in advance; For daily commuters: Use a Class B battery or a Class A battery with 50-100 charge cycles, and prioritize charging during off-peak electricity pricing periods. New users: By default, they are assigned Grade A batteries with a health level of ≥90%; The power grid interaction logic includes: During peak grid load: Class C batteries discharge in reverse to the grid, with a discharge depth ≤30%. The formula for calculating the depth of discharge is: ,in SOC initial SOC final These represent the discharge start and end charging states, respectively. In the event of an emergency power outage: Suspend charging of Class A batteries for non-urgent orders; During off-peak grid load periods: Class C battery energy storage charging, batteries with a health level ≥80% perform "shallow charge and shallow discharge" cycles; The tiered emergency response mechanism is specifically as follows: Extreme weather: Prioritize battery swapping needs for rescue vehicles and public transportation vehicles, and suspend orders from non-essential private users; Power outage: Activate backup power supply, prioritizing charging of Class A batteries; Equipment failure: Switch to manual battery swapping channel and dispatch mobile battery swapping vehicles within a 30-kilometer radius for support.

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