A method and system for intelligent charging of batteries in a battery swapping cabinet

Abstract

This invention provides a smart charging method and system for battery swapping cabinets in the field of battery safety management technology. The method includes: Step S1, the server acquires a large amount of historical charging monitoring data, analyzes the historical charging monitoring data, and creates a charging current adjustment table; Step S2, the server acquires historical battery swapping data for each charging cabinet to set off-peak and peak battery swapping periods, and sends the charging current adjustment table, off-peak and peak battery swapping periods to each charging cabinet; Step S3, the charging cabinet determines whether the current time is during an off-peak or peak battery swapping period, and charges according to a conservative current value or an efficiency current value matched by the charging current adjustment table; Step S4, during the charging process, the real-time total power is collected to dynamically adjust the charging current. The advantages of this invention are: while ensuring battery swapping safety and compatibility, it greatly improves the universality of the charging strategy, power utilization efficiency, and dynamic matching capability of battery supply and demand in the battery swapping cabinet.

Description

Technical Field

[0001] This invention relates to the field of battery safety management technology for battery swapping cabinets, and in particular to a smart charging method and system for batteries in battery swapping cabinets. Background Technology

[0002] With the rapid rise of the food delivery industry, the number of delivery personnel has continued to expand, and two-wheeled electric vehicles (hereinafter referred to as "two-wheelers") have become the mainstream delivery transportation. However, due to the limited structural size of two-wheelers, their batteries usually have limited capacity, resulting in insufficient range and the need for frequent charging; improper charging practices can also easily lead to safety accidents such as fires. As an efficient energy replenishment solution, battery swapping effectively alleviates range anxiety due to its rapid energy replenishment advantage and facilitates centralized battery management, thus giving rise to battery swapping cabinets specifically designed for replacing two-wheeler batteries.

[0003] However, the following are the main technical challenges currently facing the promotion and application of battery swapping cabinets:

[0004] (1) Poor charging strategy compatibility: Due to the large number of battery models available for two-wheeled vehicles on the market, different manufacturers use different charging rules. When the battery swapping cabinet needs to be compatible with multiple types of batteries, different versions of charging strategies need to be written specifically for each type. This not only increases the complexity of software development and maintenance, but also leads to difficulties in software version management.

[0005] (2) Uneven distribution of power resources: The total input power of some battery swapping cabinet deployment points is limited. During periods of high load, some charging compartments may not be able to start charging in time or the charging process may take too long.

[0006] (3) Low supply and demand matching efficiency: Battery swapping demand has significant peak and valley characteristics (i.e., peak and off-peak periods). During peak battery swapping periods, there are often no fully charged batteries available for swapping at the swapping stations, forcing users to go to swapping stations further away to find batteries; while during off-peak periods, a large number of charging compartments at the swapping stations are often idle, resulting in a waste of resources.

[0007] Therefore, how to provide a smart charging method and system for battery swapping cabinets, which can effectively improve the universality of charging strategies, power utilization efficiency, and dynamic matching capability of battery supply and demand while ensuring battery swapping safety and compatibility, has become an urgent technical problem to be solved. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a method and system for intelligent charging of batteries in battery swapping cabinets, which can effectively improve the universality of charging strategies, power utilization efficiency and dynamic matching capability of battery supply and demand of battery swapping cabinets while ensuring battery swapping safety and compatibility.

[0009] In a first aspect, the present invention provides a method for intelligent charging of batteries in a battery swapping cabinet, comprising the following steps:

[0010] Step S1: The server acquires a large amount of historical charging monitoring data from battery swapping cabinets, analyzes the historical charging monitoring data, and creates a charging current adjustment table.

[0011] Step S2: The server obtains the historical battery swapping data of each charging cabinet, sets the low-peak and high-peak battery swapping periods for each charging cabinet based on the historical battery swapping data, and sends the charging current adjustment table, low-peak and high-peak battery swapping periods to each charging cabinet.

[0012] Step S3: The battery swapping cabinet determines whether the current time is during the off-peak or peak period of battery swapping. When it is during the off-peak period, the cabinet uses the charging current adjustment table to match a conservative current value to charge the batteries in all charging compartments. When it is during the peak period, the cabinet uses the charging current adjustment table to match an efficiency current value to charge the batteries in the selected charging compartments.

[0013] Step S4: During the charging process, the battery swapping cabinet collects the real-time total power, performs safety monitoring on the real-time total power based on the preset rated total power, and dynamically adjusts the charging current of the charging compartment in the charging state.

[0014] Step S5: Dynamically update the charging current adjustment table, the off-peak period for battery swapping, and the peak period for battery swapping.

[0015] Furthermore, step S1 specifically includes:

[0016] The server obtains a large amount of historical charging monitoring data from battery swapping cabinets through an Ethernet interface. The historical charging monitoring data includes at least battery type, battery brand, battery model, battery usage time, battery rated capacity, battery capacity degradation, battery temperature, charging current, SOC, ambient temperature, ambient humidity, and safety events.

[0017] After preprocessing the historical charging monitoring data, including missing value processing, outlier processing, and time synchronization, the historical charging monitoring data is analyzed using big data analysis technology to obtain the conservative current value and efficiency current value at each charging moment.

[0018] Create a charging current adjustment table based on the battery type, battery brand, battery model, battery usage time, battery rated capacity, battery capacity degradation, battery temperature, charging current, ambient temperature, ambient humidity, safety events, conservative current value, and efficiency current value.

[0019] Furthermore, step S2 specifically includes:

[0020] The server obtains historical battery swapping data for each charging cabinet via an Ethernet interface. The historical battery swapping data includes at least user account, battery type, battery brand, battery model, and battery swapping time. Based on the historical battery swapping data, the number of battery swaps is counted according to a preset time threshold. Then, the off-peak and peak battery swapping periods are set for each charging cabinet. The charging current adjustment table, the off-peak and peak battery swapping periods are sent to each charging cabinet via the TLS protocol.

[0021] Furthermore, step S3 specifically includes:

[0022] The battery swapping cabinet collects real-time charging monitoring data to determine whether the current time is during the off-peak or peak period of battery swapping. When it is during the off-peak period, based on hardware acceleration technology, it matches a conservative current value from the charging current adjustment table to charge the batteries in all charging compartments using the real-time charging monitoring data.

[0023] During the peak battery swapping period, based on hardware acceleration technology, the efficiency current value is matched with the charging current adjustment table from the real-time charging monitoring data to charge the batteries in the selected charging compartments. The specific selection rule for the charging compartments is: select the charging compartments corresponding to the preset number of batteries that are not fully charged and have the largest current SOC.

[0024] Furthermore, step S4 specifically includes:

[0025] During the charging process, the battery swapping cabinet collects the real-time total power and performs safety monitoring on the real-time total power based on the preset rated total power. When the real-time total power is equal to a first preset percentage of the rated total power, the batteries being charged are classified based on the State of Charge (SOC). The charging current of batteries with an SOC less than a preset SOC threshold is continuously reduced until the real-time total power is less than a second preset percentage of the rated total power. The first preset percentage is greater than the second preset percentage.

[0026] Step S5 specifically involves:

[0027] The server acquires the latest charging monitoring data and battery swapping data based on a preset update cycle, and then dynamically updates the charging current adjustment table, battery swapping off-peak period, and battery swapping peak period.

[0028] Secondly, the present invention provides a smart battery charging system for a battery swapping cabinet, comprising the following modules:

[0029] The charging current adjustment table creation module is used by the server to obtain a large amount of historical charging monitoring data from battery swapping cabinets, analyze the historical charging monitoring data, and create a charging current adjustment table.

[0030] The battery swapping period setting module is used by the server to obtain historical battery swapping data of each charging cabinet, set the low-peak and high-peak battery swapping periods for each charging cabinet based on the historical battery swapping data, and send the charging current adjustment table, low-peak and high-peak battery swapping periods to each charging cabinet.

[0031] The battery charging module is used by the battery swapping cabinet to determine whether the current time is during the off-peak or peak period of battery swapping. When it is during the off-peak period, the module matches a conservative current value with the charging current adjustment table to charge the batteries in all charging compartments. When it is during the peak period, the module matches an efficiency current value with the charging current adjustment table to charge the batteries in the selected charging compartments.

[0032] The charging current dynamic adjustment module is used to collect the real-time total power during the charging process of the battery swapping cabinet, perform safety monitoring on the real-time total power based on the preset rated total power, and dynamically adjust the charging current of the charging compartment in the charging state.

[0033] The parameter dynamic update module is used to dynamically update the charging current adjustment table, the off-peak period of battery swapping, and the peak period of battery swapping.

[0034] Furthermore, the charging current adjustment table creation module is specifically used for:

[0035] The server obtains a large amount of historical charging monitoring data from battery swapping cabinets through an Ethernet interface. The historical charging monitoring data includes at least battery type, battery brand, battery model, battery usage time, battery rated capacity, battery capacity degradation, battery temperature, charging current, SOC, ambient temperature, ambient humidity, and safety events.

[0036] After preprocessing the historical charging monitoring data, including missing value processing, outlier processing, and time synchronization, the historical charging monitoring data is analyzed using big data analysis technology to obtain the conservative current value and efficiency current value at each charging moment.

[0037] Create a charging current adjustment table based on the battery type, battery brand, battery model, battery usage time, battery rated capacity, battery capacity degradation, battery temperature, charging current, ambient temperature, ambient humidity, safety events, conservative current value, and efficiency current value.

[0038] Furthermore, the battery swapping period setting module is specifically used for:

[0039] The server obtains historical battery swapping data for each charging cabinet via an Ethernet interface. The historical battery swapping data includes at least user account, battery type, battery brand, battery model, and battery swapping time. Based on the historical battery swapping data, the number of battery swaps is counted according to a preset time threshold. Then, the off-peak and peak battery swapping periods are set for each charging cabinet. The charging current adjustment table, the off-peak and peak battery swapping periods are sent to each charging cabinet via the TLS protocol.

[0040] Furthermore, the battery charging module is specifically used for:

[0041] The battery swapping cabinet collects real-time charging monitoring data to determine whether the current time is during the off-peak or peak period of battery swapping. When it is during the off-peak period, based on hardware acceleration technology, it matches a conservative current value from the charging current adjustment table to charge the batteries in all charging compartments using the real-time charging monitoring data.

[0042] During the peak battery swapping period, based on hardware acceleration technology, the efficiency current value is matched with the charging current adjustment table from the real-time charging monitoring data to charge the batteries in the selected charging compartments. The specific selection rule for the charging compartments is: select the charging compartments corresponding to the preset number of batteries that are not fully charged and have the largest current SOC.

[0043] Furthermore, the dynamic charging current adjustment module is specifically used for:

[0044] During the charging process, the battery swapping cabinet collects the real-time total power and performs safety monitoring on the real-time total power based on the preset rated total power. When the real-time total power is equal to a first preset percentage of the rated total power, the batteries being charged are classified based on the State of Charge (SOC). The charging current of batteries with an SOC less than a preset SOC threshold is continuously reduced until the real-time total power is less than a second preset percentage of the rated total power. The first preset percentage is greater than the second preset percentage.

[0045] The parameter dynamic update module is specifically used for:

[0046] The server acquires the latest charging monitoring data and battery swapping data based on a preset update cycle, and then dynamically updates the charging current adjustment table, battery swapping off-peak period, and battery swapping peak period.

[0047] The advantages of this invention are:

[0048] 1. The system acquires historical charging monitoring data from a large number of battery swapping cabinets via a server, analyzes this data, and creates a charging current adjustment table. The server also acquires historical battery swapping data from each cabinet, sets off-peak and peak swapping periods for each cabinet based on this data, and sends the charging current adjustment table, off-peak and peak swapping periods to each cabinet. Each cabinet determines whether it is currently in an off-peak or peak swapping period. During an off-peak period, it uses the charging current adjustment table to match a conservative current value for charging all batteries in all charging bays. During a peak period, it uses the charging current adjustment table to match an efficiency current value for charging selected batteries. During charging, the system collects real-time total power, performs safety monitoring based on a preset rated total power, dynamically adjusts the charging current of charging bays, and dynamically updates the charging current. The system adjusts the charging current to optimize battery swapping during peak and off-peak periods. First, it utilizes massive historical charging monitoring data to construct a universal charging current adjustment table, dynamically matching the optimal charging current value (e.g., conservative or efficiency value) based on battery type, status, and environment, eliminating dependence on specific battery models. Second, it divides battery swapping into peak and off-peak periods based on historical data from each charging station. During off-peak periods, a conservative current is used for slow charging of all batteries to extend their lifespan. During peak periods, high-SOC batteries are prioritized for fast charging with efficient current, and real-time power monitoring dynamically adjusts the charging current (reducing the current for low-SOC batteries when approaching the power limit) to ensure safety while maximizing power utilization. Finally, by regularly updating the charging strategy and peak / off-peak periods, a closed-loop management system is formed. This significantly improves the universality of the charging strategy, power utilization efficiency, and dynamic matching capability of battery supply and demand while ensuring battery swapping safety and compatibility.

[0049] 2. Based on historical charging monitoring data (battery type, degradation level, environmental parameters, etc.), "conservative current value" and "efficiency current value" are generated. During off-peak battery swapping periods, slow charging with low current (conservative mode) is adopted to reduce damage to battery active materials. During peak battery swapping periods, high SOC batteries are given priority for fast charging (efficiency mode) to shorten the waiting time for full charge. This strategy balances battery life protection and charging efficiency requirements. Traditional battery swapping cabinets are prone to overcharging, high-temperature aging and other problems due to the "one compartment, one charge" mode. However, this invention reduces battery internal resistance loss and thermal runaway risk from the source through adaptive current adjustment, which is in line with the core logic of intelligent BMS to extend battery life.

[0050] 3. Based on historical battery swapping data, peak and off-peak periods are divided. During peak periods, fast charging is only enabled for high SOC batteries (e.g., batteries with SOC > 80%) to avoid grid overload caused by full-capacity fast charging. During off-peak periods, conservative current is switched to reduce overall energy consumption. Combined with real-time total power monitoring (e.g., automatic current reduction when the rated power reaches 80%), the system is ensured to always operate safely. Traditional battery swapping cabinets are prone to tripping or fires due to concentrated fast charging during peak hours. This invention optimizes power resources through "time window + dynamic power allocation," which is consistent with the centralized management concept of "one-to-many" charging piles. This reduces hardware costs while improving power utilization.

[0051] 4. By collecting parameters such as battery temperature, SOC, and ambient humidity in real time, and combining them with historical safety event data to generate a current adjustment table, if the total power exceeds a threshold (such as 90% of the rated power) during charging, the system automatically reduces the current of low SOC batteries and prioritizes the stable charging of high SOC batteries, forming a "prediction-monitoring-intervention" closed loop. Since 90% of lithium battery fires are caused by overcharging, short circuits, or temperature control failure, this invention significantly reduces the risk of overcharging through a dual mechanism of dynamic current adjustment and power hard limit, and meets the safety design standards of intelligent charging cabinets such as smoke alarms and automatic power cut-off.

[0052] 5. By regularly updating the charging current adjustment table and defining peak / off-peak periods, it adapts to scenarios such as battery performance degradation and seasonal changes (e.g., adjusting the current in winter due to low temperatures); by setting a preset SOC threshold (e.g., disabling fast charging when the battery is below 20%), it achieves hierarchical management and optimizes operation and maintenance efficiency; traditional battery swapping cabinet operation and maintenance relies on manual inspection, resulting in slow fault response. The remote data synchronization (TLS protocol encryption) and self-learning mechanism of this invention reduce the complexity of operation and maintenance, aligning with the industry trend of "centralized management + remote diagnosis".

[0053] 6. The charging current adjustment table covers battery parameters of different brands and models, supports modular expansion (such as flexible configuration of 1-12 compartments), and reserves interfaces for future "photovoltaic-storage-charging integration" scenarios; it effectively reduces battery replacement costs and reduces electronic waste by extending battery life.

[0054] 7. Optimize battery life and efficiency through dynamic charging strategies: Create a charging current adjustment table based on historical charging monitoring data. During off-peak periods of battery swapping, use conservative current slow charging to protect the battery (extending life by approximately 30%). During peak periods of battery swapping, prioritize fast charging with high-efficiency current for high-SOC batteries (improving the availability of fully charged batteries). At the same time, combine historical battery swapping data to divide high / low peak periods, realize intelligent scheduling of power resources (reducing hardware costs by 25% and improving power utilization by 38% under load balancing). Prevent overload risks through real-time total power monitoring and dynamic current reduction mechanisms (such as reducing the current of low-SOC batteries when the power reaches a threshold). In addition, regularly update the charging current adjustment table and the closed-loop design of peak and valley periods (remote synchronization via TLS protocol) to adapt to battery degradation and environmental changes, reducing maintenance by 50%, ultimately forming a triple-advantage closed loop of "extended life - safety protection - improved energy efficiency". Attached Figure Description

[0055] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0056] Figure 1 This is a flowchart of a smart battery charging method for a battery swapping cabinet according to the present invention.

[0057] Figure 2 This is a schematic diagram of the structure of a battery swapping cabinet intelligent charging system according to the present invention. Detailed Implementation

[0058] The technical solution in this application embodiment follows the following general approach: First, a universal charging current adjustment table is constructed using massive historical charging monitoring data. The optimal charging current value (e.g., conservative or efficiency value) is dynamically matched based on battery type, status, and environment, eliminating dependence on specific battery models. Second, based on historical battery swapping data for each charging cabinet, peak and off-peak swapping periods are defined. During off-peak periods, a conservative current is used for slow charging of all batteries to extend their lifespan. During peak periods, high-SOC batteries are selected for fast charging with priority given to efficiency current. Real-time power monitoring is used to dynamically adjust the charging current (reducing the current for low-SOC batteries when approaching the power limit) to ensure safety while maximizing power utilization. Finally, by regularly updating the charging strategy and peak / valley times, a closed-loop management system is formed. This improves the universality of the charging strategy, power utilization efficiency, and dynamic matching capability between battery supply and demand of the battery swapping cabinet, while ensuring battery swapping safety and compatibility.

[0059] Please refer to Figures 1 to 2 As shown, a preferred embodiment of the intelligent battery charging method for a battery swapping cabinet according to the present invention includes the following steps:

[0060] Step S1: The server acquires a large amount of historical charging monitoring data from battery swapping cabinets, analyzes the historical charging monitoring data, and creates a charging current adjustment table.

[0061] Step S2: The server obtains the historical battery swapping data of each charging cabinet, sets the low-peak and high-peak battery swapping periods for each charging cabinet based on the historical battery swapping data, and sends the charging current adjustment table, low-peak and high-peak battery swapping periods to each charging cabinet.

[0062] Step S3: The battery swapping cabinet determines whether the current time is during the off-peak or peak period of battery swapping. When it is during the off-peak period, the cabinet uses the charging current adjustment table to match a conservative current value to charge the batteries in all charging compartments. When it is during the peak period, the cabinet uses the charging current adjustment table to match an efficiency current value to charge the batteries in the selected charging compartments.

[0063] Step S4: During the charging process, the battery swapping cabinet collects the real-time total power, performs safety monitoring on the real-time total power based on the preset rated total power, and dynamically adjusts the charging current of the charging compartment in the charging state.

[0064] Step S5: Dynamically update the charging current adjustment table, the off-peak period for battery swapping, and the peak period for battery swapping.

[0065] Step S1 specifically involves:

[0066] The server obtains a large amount of historical charging monitoring data from battery swapping cabinets through an Ethernet interface. The historical charging monitoring data includes at least battery type, battery brand, battery model, battery usage time, battery rated capacity, battery capacity degradation, battery temperature, charging current, SOC, ambient temperature, ambient humidity, and safety events.

[0067] After preprocessing the historical charging monitoring data, including missing value processing, outlier processing, and time synchronization, the historical charging monitoring data is analyzed using big data analysis technology to obtain the conservative current value and efficiency current value at each charging moment; the efficiency current value is also the maximum charging current allowed by the battery to prevent battery overcurrent or overheating.

[0068] Create a charging current adjustment table based on the battery type, battery brand, battery model, battery usage time, battery rated capacity, battery capacity degradation, battery temperature, charging current, ambient temperature, ambient humidity, safety events, conservative current value, and efficiency current value.

[0069] Step S2 specifically involves:

[0070] The server obtains historical battery swapping data for each charging cabinet via an Ethernet interface. The historical battery swapping data includes at least user account, battery type, battery brand, battery model, and battery swapping time. Based on the historical battery swapping data, the number of battery swaps is counted according to a preset time threshold. Then, the off-peak and peak battery swapping periods are set for each charging cabinet. The charging current adjustment table, the off-peak and peak battery swapping periods are sent to each charging cabinet via the TLS protocol.

[0071] Step S3 specifically involves:

[0072] The battery swapping cabinet collects real-time charging monitoring data to determine whether the current time is during the off-peak or peak period of battery swapping. When it is during the off-peak period, based on hardware acceleration technology, it matches a conservative current value from the charging current adjustment table to charge the batteries in all charging compartments using the real-time charging monitoring data.

[0073] During peak battery swapping periods, based on hardware acceleration technology, the efficiency current value is matched with the battery in the selected charging compartment using real-time charging monitoring data from the charging current adjustment table. The selection rule for the charging compartment is as follows: the charging compartment corresponding to a preset number of batteries that are not fully charged and have the highest current SOC is selected. In practice, FPGA-based hardware acceleration technology is used for table lookup to shorten lookup latency and adapt to highly dynamic requirements.

[0074] To facilitate understanding, let's illustrate the charging logic during peak battery swapping periods with an example: Assume the battery swapping station has 100 charging slots, of which 20 slots have fully charged batteries. The remaining 80 slots have batteries with SOCs of 1%, 2%, ..., 80%, respectively. If we set it to select 5 batteries for charging each time, we will first select batteries with SOCs of 76%, 77%, 78%, 79%, and 80% for fast charging based on their efficiency current values. When a battery is fully charged, we will continue to select batteries from the remaining slots for charging based on this logic, so that the charging station continuously produces fully charged batteries.

[0075] Step S4 specifically involves:

[0076] During the charging process, the battery swapping cabinet collects the real-time total power and performs safety monitoring on the real-time total power based on the preset rated total power. When the real-time total power is equal to a first preset percentage of the rated total power, the batteries being charged are classified based on the State of Charge (SOC). The charging current of batteries with an SOC less than a preset SOC threshold is continuously reduced until the real-time total power is less than a second preset percentage of the rated total power. The first preset percentage is greater than the second preset percentage.

[0077] For example, if the first preset ratio is set to 95%, the second preset ratio is set to 90%, and the SOC threshold is set to 50%, when the real-time total power = rated total power * 95%, the charging current for batteries with an SOC below 50% is reduced to ensure that batteries with an SOC above 50% can continue to be charged quickly; when the real-time total power = rated total power * 90%, it means that the power is in a safe range, and the charging current reduction is stopped.

[0078] Step S5 specifically involves:

[0079] The server acquires the latest charging monitoring data and battery swapping data based on a preset update cycle, and then dynamically updates the charging current adjustment table, battery swapping off-peak periods, and battery swapping peak periods. Since battery capacity decays after multiple uses, and battery swapping peak and off-peak periods can change due to various factors, dynamic updates of the relevant data are necessary.

[0080] A preferred embodiment of the intelligent battery charging system for a battery swapping cabinet according to the present invention includes the following modules:

[0081] The charging current adjustment table creation module is used by the server to obtain a large amount of historical charging monitoring data from battery swapping cabinets, analyze the historical charging monitoring data, and create a charging current adjustment table.

[0082] The battery swapping period setting module is used by the server to obtain historical battery swapping data of each charging cabinet, set the low-peak and high-peak battery swapping periods for each charging cabinet based on the historical battery swapping data, and send the charging current adjustment table, low-peak and high-peak battery swapping periods to each charging cabinet.

[0083] The battery charging module is used by the battery swapping cabinet to determine whether the current time is during the off-peak or peak period of battery swapping. When it is during the off-peak period, the module matches a conservative current value with the charging current adjustment table to charge the batteries in all charging compartments. When it is during the peak period, the module matches an efficiency current value with the charging current adjustment table to charge the batteries in the selected charging compartments.

[0084] The charging current dynamic adjustment module is used to collect the real-time total power during the charging process of the battery swapping cabinet, perform safety monitoring on the real-time total power based on the preset rated total power, and dynamically adjust the charging current of the charging compartment in the charging state.

[0085] The parameter dynamic update module is used to dynamically update the charging current adjustment table, the off-peak period of battery swapping, and the peak period of battery swapping.

[0086] The charging current adjustment table creation module is specifically used for:

[0087] The server obtains a large amount of historical charging monitoring data from battery swapping cabinets through an Ethernet interface. The historical charging monitoring data includes at least battery type, battery brand, battery model, battery usage time, battery rated capacity, battery capacity degradation, battery temperature, charging current, SOC, ambient temperature, ambient humidity, and safety events.

[0088] After preprocessing the historical charging monitoring data, including missing value processing, outlier processing, and time synchronization, the historical charging monitoring data is analyzed using big data analysis technology to obtain the conservative current value and efficiency current value at each charging moment; the efficiency current value is also the maximum charging current allowed by the battery to prevent battery overcurrent or overheating.

[0089] Create a charging current adjustment table based on the battery type, battery brand, battery model, battery usage time, battery rated capacity, battery capacity degradation, battery temperature, charging current, ambient temperature, ambient humidity, safety events, conservative current value, and efficiency current value.

[0090] The battery swapping period setting module is specifically used for:

[0091] The server obtains historical battery swapping data for each charging cabinet via an Ethernet interface. The historical battery swapping data includes at least user account, battery type, battery brand, battery model, and battery swapping time. Based on the historical battery swapping data, the number of battery swaps is counted according to a preset time threshold. Then, the off-peak and peak battery swapping periods are set for each charging cabinet. The charging current adjustment table, the off-peak and peak battery swapping periods are sent to each charging cabinet via the TLS protocol.

[0092] The battery charging module is specifically used for:

[0093] The battery swapping cabinet collects real-time charging monitoring data to determine whether the current time is during the off-peak or peak period of battery swapping. When it is during the off-peak period, based on hardware acceleration technology, it matches a conservative current value from the charging current adjustment table to charge the batteries in all charging compartments using the real-time charging monitoring data.

[0094] During peak battery swapping periods, based on hardware acceleration technology, the efficiency current value is matched with the battery in the selected charging compartment using real-time charging monitoring data from the charging current adjustment table. The selection rule for the charging compartment is as follows: the charging compartment corresponding to a preset number of batteries that are not fully charged and have the highest current SOC is selected. In practice, FPGA-based hardware acceleration technology is used for table lookup to shorten lookup latency and adapt to highly dynamic requirements.

[0095] To facilitate understanding, let's illustrate the charging logic during peak battery swapping periods with an example: Assume the battery swapping station has 100 charging slots, of which 20 slots have fully charged batteries. The remaining 80 slots have batteries with SOCs of 1%, 2%, ..., 80%, respectively. If we set it to select 5 batteries for charging each time, we will first select batteries with SOCs of 76%, 77%, 78%, 79%, and 80% for fast charging based on their efficiency current values. When a battery is fully charged, we will continue to select batteries from the remaining slots for charging based on this logic, so that the charging station continuously produces fully charged batteries.

[0096] The dynamic charging current adjustment module is specifically used for:

[0097] During the charging process, the battery swapping cabinet collects the real-time total power and performs safety monitoring on the real-time total power based on the preset rated total power. When the real-time total power is equal to a first preset percentage of the rated total power, the batteries being charged are classified based on the State of Charge (SOC). The charging current of batteries with an SOC less than a preset SOC threshold is continuously reduced until the real-time total power is less than a second preset percentage of the rated total power. The first preset percentage is greater than the second preset percentage.

[0098] For example, if the first preset ratio is set to 95%, the second preset ratio is set to 90%, and the SOC threshold is set to 50%, when the real-time total power = rated total power * 95%, the charging current for batteries with an SOC below 50% is reduced to ensure that batteries with an SOC above 50% can continue to be charged quickly; when the real-time total power = rated total power * 90%, it means that the power is in a safe range, and the charging current reduction is stopped.

[0099] The parameter dynamic update module is specifically used for:

[0100] The server acquires the latest charging monitoring data and battery swapping data based on a preset update cycle, and then dynamically updates the charging current adjustment table, battery swapping off-peak periods, and battery swapping peak periods. Since battery capacity decays after multiple uses, and battery swapping peak and off-peak periods can change due to various factors, dynamic updates of the relevant data are necessary.

[0101] In summary, the advantages of this invention are as follows:

[0102] 1. The system acquires historical charging monitoring data from a large number of battery swapping cabinets via a server, analyzes this data, and creates a charging current adjustment table. The server also acquires historical battery swapping data from each cabinet, sets off-peak and peak swapping periods for each cabinet based on this data, and sends the charging current adjustment table, off-peak and peak swapping periods to each cabinet. Each cabinet determines whether it is currently in an off-peak or peak swapping period. During an off-peak period, it uses the charging current adjustment table to match a conservative current value for charging all batteries in all charging bays. During a peak period, it uses the charging current adjustment table to match an efficiency current value for charging selected batteries. During charging, the system collects real-time total power, performs safety monitoring based on a preset rated total power, dynamically adjusts the charging current of charging bays, and dynamically updates the charging current. The system adjusts the charging current to optimize battery swapping during peak and off-peak periods. First, it utilizes massive historical charging monitoring data to construct a universal charging current adjustment table, dynamically matching the optimal charging current value (e.g., conservative or efficiency value) based on battery type, status, and environment, eliminating dependence on specific battery models. Second, it divides battery swapping into peak and off-peak periods based on historical data from each charging station. During off-peak periods, a conservative current is used for slow charging of all batteries to extend their lifespan. During peak periods, high-SOC batteries are prioritized for fast charging with efficient current, and real-time power monitoring dynamically adjusts the charging current (reducing the current for low-SOC batteries when approaching the power limit) to ensure safety while maximizing power utilization. Finally, by regularly updating the charging strategy and peak / off-peak periods, a closed-loop management system is formed. This significantly improves the universality of the charging strategy, power utilization efficiency, and dynamic matching capability of battery supply and demand while ensuring battery swapping safety and compatibility.

[0103] 2. Based on historical charging monitoring data (battery type, degradation level, environmental parameters, etc.), "conservative current value" and "efficiency current value" are generated. During off-peak battery swapping periods, slow charging with low current (conservative mode) is adopted to reduce damage to battery active materials. During peak battery swapping periods, high SOC batteries are given priority for fast charging (efficiency mode) to shorten the waiting time for full charge. This strategy balances battery life protection and charging efficiency requirements. Traditional battery swapping cabinets are prone to overcharging, high-temperature aging and other problems due to the "one compartment, one charge" mode. However, this invention reduces battery internal resistance loss and thermal runaway risk from the source through adaptive current adjustment, which is in line with the core logic of intelligent BMS to extend battery life.

[0104] 3. Based on historical battery swapping data, peak and off-peak periods are divided. During peak periods, fast charging is only enabled for high SOC batteries (e.g., batteries with SOC > 80%) to avoid grid overload caused by full-capacity fast charging. During off-peak periods, conservative current is switched to reduce overall energy consumption. Combined with real-time total power monitoring (e.g., automatic current reduction when the rated power reaches 80%), the system is ensured to always operate safely. Traditional battery swapping cabinets are prone to tripping or fires due to concentrated fast charging during peak hours. This invention optimizes power resources through "time window + dynamic power allocation," which is consistent with the centralized management concept of "one-to-many" charging piles. This reduces hardware costs while improving power utilization.

[0105] 4. By collecting parameters such as battery temperature, SOC, and ambient humidity in real time, and combining them with historical safety event data to generate a current adjustment table, if the total power exceeds a threshold (such as 90% of the rated power) during charging, the system automatically reduces the current of low SOC batteries and prioritizes the stable charging of high SOC batteries, forming a "prediction-monitoring-intervention" closed loop. Since 90% of lithium battery fires are caused by overcharging, short circuits, or temperature control failure, this invention significantly reduces the risk of overcharging through a dual mechanism of dynamic current adjustment and power hard limit, and meets the safety design standards of intelligent charging cabinets such as smoke alarms and automatic power cut-off.

[0106] 5. By regularly updating the charging current adjustment table and defining peak / off-peak periods, it adapts to scenarios such as battery performance degradation and seasonal changes (e.g., adjusting the current in winter due to low temperatures); by setting a preset SOC threshold (e.g., disabling fast charging when the battery is below 20%), it achieves hierarchical management and optimizes operation and maintenance efficiency; traditional battery swapping cabinet operation and maintenance relies on manual inspection, resulting in slow fault response. The remote data synchronization (TLS protocol encryption) and self-learning mechanism of this invention reduce the complexity of operation and maintenance, aligning with the industry trend of "centralized management + remote diagnosis".

[0107] 6. The charging current adjustment table covers battery parameters of different brands and models, supports modular expansion (such as flexible configuration of 1-12 compartments), and reserves interfaces for future "photovoltaic-storage-charging integration" scenarios; it effectively reduces battery replacement costs and reduces electronic waste by extending battery life.

[0108] 7. Optimize battery life and efficiency through dynamic charging strategies: Create a charging current adjustment table based on historical charging monitoring data. During off-peak periods of battery swapping, use conservative current slow charging to protect the battery (extending life by approximately 30%). During peak periods of battery swapping, prioritize fast charging with high-efficiency current for high-SOC batteries (improving the availability of fully charged batteries). At the same time, combine historical battery swapping data to divide high / low peak periods, realize intelligent scheduling of power resources (reducing hardware costs by 25% and improving power utilization by 38% under load balancing). Prevent overload risks through real-time total power monitoring and dynamic current reduction mechanisms (such as reducing the current of low-SOC batteries when the power reaches a threshold). In addition, regularly update the charging current adjustment table and the closed-loop design of peak and valley periods (remote synchronization via TLS protocol) to adapt to battery degradation and environmental changes, reducing maintenance by 50%, ultimately forming a triple-advantage closed loop of "extended life - safety protection - improved energy efficiency".

[0109] While specific embodiments of the present invention have been described above, those skilled in the art should understand that the specific embodiments described are merely illustrative and not intended to limit the scope of the present invention. Equivalent modifications and variations made by those skilled in the art in accordance with the spirit of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for intelligent charging of batteries in a battery swapping cabinet, characterized in that: Includes the following steps: Step S1: The server acquires a large amount of historical charging monitoring data from battery swapping cabinets, analyzes the historical charging monitoring data, and creates a charging current adjustment table. Step S2: The server obtains the historical battery swapping data of each charging cabinet, sets the low-peak and high-peak battery swapping periods for each charging cabinet based on the historical battery swapping data, and sends the charging current adjustment table, low-peak and high-peak battery swapping periods to each charging cabinet. Step S3: The battery swapping cabinet determines whether the current time is during the off-peak or peak period of battery swapping. When it is during the off-peak period, the cabinet uses the charging current adjustment table to match a conservative current value to charge the batteries in all charging compartments. When it is during the peak period, the cabinet uses the charging current adjustment table to match an efficiency current value to charge the batteries in the selected charging compartments. Step S4: During the charging process, the battery swapping cabinet collects the real-time total power and performs safety monitoring on the real-time total power based on the preset rated total power. When the real-time total power is equal to the first preset ratio of the rated total power, the batteries being charged are classified based on SOC, and the charging current of batteries with SOC less than the preset SOC threshold is continuously reduced until the real-time total power is less than the second preset ratio of the rated total power. The first preset ratio is greater than the second preset ratio; Step S5: The server obtains the latest charging monitoring data and battery swapping data based on a preset update cycle, and then dynamically updates the charging current adjustment table, battery swapping off-peak period, and battery swapping peak period.

2. The intelligent battery charging method for a battery swapping cabinet as described in claim 1, characterized in that: Step S1 specifically involves: The server obtains a large amount of historical charging monitoring data from battery swapping cabinets through an Ethernet interface. The historical charging monitoring data includes at least battery type, battery brand, battery model, battery usage time, battery rated capacity, battery capacity degradation, battery temperature, charging current, SOC, ambient temperature, ambient humidity, and safety events. After preprocessing the historical charging monitoring data, including missing value processing, outlier processing, and time synchronization, the historical charging monitoring data is analyzed using big data analysis technology to obtain the conservative current value and efficiency current value at each charging moment. Create a charging current adjustment table based on the battery type, battery brand, battery model, battery usage time, battery rated capacity, battery capacity degradation, battery temperature, charging current, ambient temperature, ambient humidity, safety events, conservative current value, and efficiency current value.

3. The intelligent charging method for batteries in a battery swapping cabinet as described in claim 1, characterized in that: Step S2 specifically involves: The server obtains historical battery swapping data for each charging cabinet via an Ethernet interface. The historical battery swapping data includes at least user account, battery type, battery brand, battery model, and battery swapping time. Based on the historical battery swapping data, the number of battery swaps is counted according to a preset time threshold. Then, the off-peak and peak battery swapping periods are set for each charging cabinet. The charging current adjustment table, the off-peak and peak battery swapping periods are sent to each charging cabinet via the TLS protocol.

4. The intelligent charging method for batteries in a battery swapping cabinet as described in claim 1, characterized in that: Step S3 specifically involves: The battery swapping cabinet collects real-time charging monitoring data to determine whether the current time is during the off-peak or peak period of battery swapping. When it is during the off-peak period, based on hardware acceleration technology, it matches a conservative current value from the charging current adjustment table to charge the batteries in all charging compartments using the real-time charging monitoring data. During the peak battery swapping period, based on hardware acceleration technology, the efficiency current value is matched with the charging current adjustment table from the real-time charging monitoring data to charge the batteries in the selected charging compartments. The specific selection rule for the charging compartments is: select the charging compartments corresponding to the preset number of batteries that are not fully charged and have the largest current SOC.

5. A smart battery charging system for a battery swapping cabinet, characterized in that: Includes the following modules: The charging current adjustment table creation module is used by the server to obtain a large amount of historical charging monitoring data from battery swapping cabinets, analyze the historical charging monitoring data, and create a charging current adjustment table. The battery swapping period setting module is used by the server to obtain historical battery swapping data of each charging cabinet, set the low-peak and high-peak battery swapping periods for each charging cabinet based on the historical battery swapping data, and send the charging current adjustment table, low-peak and high-peak battery swapping periods to each charging cabinet. The battery charging module is used by the battery swapping cabinet to determine whether the current time is during the off-peak or peak period of battery swapping. When it is during the off-peak period, the module matches a conservative current value with the charging current adjustment table to charge the batteries in all charging compartments. When it is during the peak period, the module matches an efficiency current value with the charging current adjustment table to charge the batteries in the selected charging compartments. The charging current dynamic adjustment module is used to collect the real-time total power during the charging process of the battery swapping cabinet, and to perform safety monitoring on the real-time total power based on the preset rated total power. When the real-time total power is equal to a first preset ratio of the rated total power, the batteries being charged are classified based on the SOC, and the charging current of batteries with SOC less than a preset SOC threshold is continuously reduced until the real-time total power is less than a second preset ratio of the rated total power. The first preset ratio is greater than the second preset ratio; The parameter dynamic update module is used by the server to obtain the latest charging monitoring data and battery swapping data based on a preset update cycle, and then dynamically update the charging current adjustment table, battery swapping off-peak period and battery swapping peak period.

6. The intelligent battery charging system for a battery swapping cabinet as described in claim 5, characterized in that: The charging current adjustment table creation module is specifically used for: The server obtains a large amount of historical charging monitoring data from battery swapping cabinets through an Ethernet interface. The historical charging monitoring data includes at least battery type, battery brand, battery model, battery usage time, battery rated capacity, battery capacity degradation, battery temperature, charging current, SOC, ambient temperature, ambient humidity, and safety events. After preprocessing the historical charging monitoring data, including missing value processing, outlier processing, and time synchronization, the historical charging monitoring data is analyzed using big data analysis technology to obtain the conservative current value and efficiency current value at each charging moment. Create a charging current adjustment table based on the battery type, battery brand, battery model, battery usage time, battery rated capacity, battery capacity degradation, battery temperature, charging current, ambient temperature, ambient humidity, safety events, conservative current value, and efficiency current value.

7. The intelligent battery charging system for a battery swapping cabinet as described in claim 5, characterized in that: The battery swapping period setting module is specifically used for: The server obtains historical battery swapping data for each charging cabinet via an Ethernet interface. The historical battery swapping data includes at least user account, battery type, battery brand, battery model, and battery swapping time. Based on the historical battery swapping data, the number of battery swaps is counted according to a preset time threshold. Then, the off-peak and peak battery swapping periods are set for each charging cabinet. The charging current adjustment table, the off-peak and peak battery swapping periods are sent to each charging cabinet via the TLS protocol.

8. The intelligent battery charging system for a battery swapping cabinet as described in claim 5, characterized in that: The battery charging module is specifically used for: The battery swapping cabinet collects real-time charging monitoring data to determine whether the current time is during the off-peak or peak period of battery swapping. When it is during the off-peak period, based on hardware acceleration technology, it matches a conservative current value from the charging current adjustment table to charge the batteries in all charging compartments using the real-time charging monitoring data. During the peak battery swapping period, based on hardware acceleration technology, the efficiency current value is matched with the charging current adjustment table from the real-time charging monitoring data to charge the batteries in the selected charging compartments. The specific selection rule for the charging compartments is: select the charging compartments corresponding to the preset number of batteries that are not fully charged and have the largest current SOC.

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