Charging management method and system of mobile charging cabinet

Abstract

This invention relates to a charging management method and system for mobile charging cabinets. The method includes acquiring the location status information and identity information of the charging equipment in the mobile charging cabinet; performing multi-factor authentication on the identity information and matching it with the location status information to obtain a charging authorization status; acquiring the interface charging parameters of the mobile charging cabinet and associating the interface charging parameters with the charging authorization status to obtain a charging management group; acquiring the battery status data of the charging equipment and performing charging planning with the charging management group to obtain a charging parameter curve; performing charging mode analysis on the charging management group and the charging parameter curve based on a preset environmental energy game algorithm to obtain an initial charging control scheme; evaluating environmental factors on the battery status data and optimizing the initial charging control scheme to obtain a charging control strategy. This invention can formulate personalized charging parameter curves for different types and states of batteries.

Description

Technical Field

[0001] This invention relates to the technical field of mobile charging cabinets, and particularly to a charging management method and system for mobile charging cabinets. Background Technology

[0002] Mobile charging cabinets, as a convenient and efficient charging solution, have been widely used in the context of the rapid development of mobile devices. With users' increasing demands for charging service quality and safety, how to achieve intelligent management and precise control of mobile charging cabinets has become a key research topic. Existing charging management methods typically focus only on single charging control parameters, such as charging current or charging time, while neglecting the comprehensive impact of charging device authentication security, battery status assessment, and environmental factors on the charging process. This simplistic management approach may lead to safety hazards during charging and fails to optimize charging efficiency, thus failing to meet users' growing demand for intelligent charging. Summary of the Invention

[0003] The main objective of this invention is to provide a charging management method and system for mobile charging cabinets, which can formulate personalized charging parameter curves for batteries of different types and in different states.

[0004] To achieve the above objectives, the present invention provides a charging management method for a mobile charging cabinet, comprising: Obtain the mobile charging cabinet's location status information and the charging device's identity information, perform multiple verifications on the identity information, and match the identity information with the location status information to obtain the charging authorization status; Obtain the interface charging parameters of the mobile charging cabinet, associate the interface charging parameters with the charging authorization status, and obtain the charging management group; Obtain the battery status data of the charging device and perform charging planning with the charging management group to obtain the charging parameter curve; Based on a preset environmental energy game algorithm, the charging management group and the charging parameter curve are analyzed to obtain an initial charging control scheme. Environmental factors are assessed based on the battery state data, and the initial charging control scheme is optimized and adjusted to obtain a charging control strategy.

[0005] Furthermore, the process of obtaining the location status of the mobile charging cabinet and the identity information of the charging equipment, performing multiple verifications on the identity information, and matching it with the location status to obtain the charging authorization status includes: The mobile charging cabinet is scanned for its compartments, and the compartment numbers, occupancy status, and fault status obtained from the scan are integrated to obtain the compartment status information. Obtain the hardware identification code and communication protocol identification of the charging device, and integrate their identities to obtain the identity information; The identity information is verified using a pre-set identity verification database to obtain the identity verification result. Based on the authentication result, the charging device is classified into permission levels to obtain device permission level data; The warehouse status information and the device permission level data are matched to obtain a warehouse allocation strategy. The charging equipment and the mobile charging cabinet are bound together according to the storage allocation strategy to obtain the charging authorization status.

[0006] Further, the step of obtaining the interface charging parameters of the mobile charging cabinet and associating the interface charging parameters with the charging authorization status to obtain a charging management group includes: Voltage and current are sampled at the compartment interfaces of the mobile charging cabinet to obtain interface charging parameters; Based on the interface charging parameters, the bay interface is classified into interface parameter levels. The interface parameter levels are used to calculate interface indicators to obtain interface charging capability indicators. Based on the interface charging capability index, the charging authorization status is matched with the interface to obtain the interface authorization correspondence table; Perform interface initialization configuration based on the interface authorization mapping table to obtain the management initial group; The charging parameters of the initial management group are verified to obtain the charging management group.

[0007] Further, the step of acquiring the battery status data of the charging device and performing charging planning with the charging management group to obtain the charging parameter curve includes: The battery status data is stratified into status indicators to obtain a set of battery status indicators; Based on the battery status index set, the charging management group is mapped to obtain charging status associated data. The charging status associated data is clustered by category to obtain charging category groups; Based on the charging category grouping, the charging management group is grouped to predict the parameters and obtain the predicted charging parameter values. Capacity analysis is performed on the predicted values ​​of the charging parameters to obtain capacity distribution data; Based on the capacity distribution data, charging planning is performed to obtain charging planning parameters; The charging planning parameters are dynamically curve-constructed to obtain the charging parameter curve.

[0008] Furthermore, the charging management group and the charging parameter curve are analyzed using a preset environmental energy game algorithm to obtain an initial charging control scheme, including: Based on the environmental energy game algorithm, a three-modal energy capture decision analysis is performed on the charging management group to obtain charging energy status data. Based on the charging energy state data, the charging parameter curve is piecewise fitted to obtain energy distribution data; Dynamic energy source allocation calculations are performed on the energy distribution data to obtain energy allocation coefficients; Based on the energy allocation coefficient, a ternary utility construction analysis is performed on the charging management group to obtain the charging game result. The energy debt balance mechanism is used to calculate the energy income-expenditure ratio by applying the energy game result to the charging game result. Based on the energy expenditure ratio, the charging management group performs charging compensation calculations to obtain charging compensation parameters. The charging compensation parameters and the charging game result are combined and optimized to obtain a charging mode scheme; The charging mode scheme is corrected and calculated based on the charging parameter curve to obtain the initial charging control scheme.

[0009] Furthermore, the step of performing a ternary utility construction analysis on the charging management group based on the energy allocation coefficient to obtain the charging game result includes: Based on the energy allocation coefficient, a group mapping operation is performed on the charging management group to obtain the energy allocation vector; The energy allocation vector is transformed for energy utility to obtain initial energy utility parameters; Based on the initial energy utility parameters, the charging management group is subjected to a revenue utility calculation to obtain a first utility component. Based on the energy allocation coefficient, a cost-utility calculation is performed on the charging management group to obtain a second utility component; Perform charging state utility calculation on the charging management group to obtain the third utility component; A combined utility function is obtained by constructing a joint function for the first utility component, the second utility component, and the third utility component. The charging game strategy set is obtained by solving the game equilibrium of the comprehensive utility function by the charging management group. The charging game strategy set is iteratively optimized to obtain the charging game result.

[0010] Further, the step of performing mode combination optimization on the charging compensation parameters and the charging game result to obtain a charging mode scheme includes: The charging compensation parameters and the charging game result are fused to obtain a pattern combination matrix; Based on the mode combination matrix, a charging efficiency and compensation cost integration function is constructed to obtain the target mode composite function; The target mode composite function is subjected to charging time constraints, compensation amount constraints, and game payoff constraints to obtain integrated constraint data. Based on the integrated constraint data, the target mode composite function is solved by function mode solving to obtain the target mode solution set; Based on the target mode solution set, a scheme is constructed using the charging compensation parameters and the charging game result to obtain the charging mode scheme.

[0011] Furthermore, the step of assessing environmental factors based on the battery state data and optimizing the initial charging control scheme to obtain a charging control strategy includes: Based on a preset health data table, perform a health analysis on the battery status data to obtain battery health indicators; Based on the battery health indicators, the battery status data is divided into temperature ranges to obtain a group of temperature-affecting parameters. The influencing factors are extracted from the temperature-affecting parameter group to obtain the environmental temperature-affecting parameters; Based on the environmental temperature influence parameters, the current gradient of the initial charging control scheme is calculated to obtain the current adjustment factor. The dynamic variation law of the current regulation factor is analyzed to obtain the environmental adaptability optimization coefficient; The initial charging control scheme is optimized in segments based on the environmental adaptability optimization coefficient to obtain the optimized charging control value; The optimized charging control value is compared with the preset safety threshold to obtain the corrected control data; The initial charging control scheme is optimized by time-segment allocation control based on the corrected control data to obtain the charging control strategy.

[0012] The present invention also provides a charging management system for a mobile charging cabinet, applicable to the charging management method for the mobile charging cabinet described in any one of the above claims, comprising: The acquisition module is used to acquire the location status information of the mobile charging cabinet and the identity information of the charging equipment, perform multiple verifications on the identity information, and perform authorization matching with the location status information to obtain the charging authorization status. The analysis module is used to obtain the interface charging parameters of the mobile charging cabinet, associate the interface charging parameters with the charging authorization status, and obtain the charging management group. The association module is used to obtain the battery status data of the charging device and perform charging planning with the charging management group to obtain the charging parameter curve. The processing module is used to perform charging mode analysis on the charging management group and the charging parameter curve based on a preset environmental energy game algorithm to obtain an initial charging control scheme. The control module is used to assess environmental factors based on the battery state data and optimize and adjust the initial charging control scheme to obtain a charging control strategy.

[0013] The charging management method and system for mobile charging cabinets provided by this invention have the following beneficial effects: By performing multi-factor authentication on charging devices and matching authorization with the charging station's status information, the safety and reliability of the charging process are significantly improved, effectively solving the security risks associated with single-identity authentication in existing charging management methods. Based on the correlation processing of interface charging parameters and charging authorization status, intelligent binding of charging permissions and charging parameters is achieved, laying the foundation for precise charging management. By acquiring battery status data from charging devices and combining it with charging management group for charging planning, the system can develop personalized charging parameter curves for different types and states of batteries, improving charging efficiency while extending battery life. Based on a preset environmental energy game algorithm for analyzing charging modes, the system can achieve optimal resource allocation under various charging demands, significantly improving the overall operating efficiency of the mobile charging cabinet. By evaluating environmental factors based on battery status data and dynamically adjusting the charging control scheme, the charging process can adapt to different environmental conditions, enhancing the system's adaptability and stability. In summary, this invention not only solves the problems of insufficient security and low charging efficiency in existing charging management methods but also achieves intelligent management of mobile charging cabinets, meeting users' growing needs for efficient, safe, and personalized charging. Attached Figure Description

[0014] Figure 1 This is a flowchart of a charging management method for a mobile charging cabinet provided by the present invention; Figure 2 This is a structural diagram of a charging management system for a mobile charging cabinet provided by the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0017] Reference Figure 1 This invention provides a charging management method for a mobile charging cabinet, comprising: Step S1: Obtain the mobile charging cabinet's location status information and the charging device's identity information, perform multiple verifications on the identity information, and match it with the location status information to obtain the charging authorization status; Step S2: Obtain the interface charging parameters of the mobile charging cabinet, associate the interface charging parameters with the charging authorization status, and obtain the charging management group; Step S3: Obtain the battery status data of the charging device and perform charging planning with the charging management group to obtain the charging parameter curve; Step S4: Analyze the charging mode of the charging management group and charging parameter curves based on the preset environmental energy game algorithm to obtain the initial charging control scheme; Step S5: Evaluate the environmental factors of the battery state data and optimize and adjust the initial charging control scheme to obtain the charging control strategy.

[0018] Based on the steps described above, the detailed process is as follows: Step S1: The mobile charging cabinet collects real-time status information of each charging compartment using built-in sensors, including compartment occupancy, charging interface status, and door open / close status. When a charging device is connected, the charging cabinet reads the device's identification information, including device ID, model, and manufacturer. Identity verification employs a multi-layered authentication mechanism: at the hardware level, the device's authenticity is verified through its digital certificate; at the software level, the binding relationship between the device's registration information and the user account is verified; and at the communication level, the encrypted channel between the device and the charging cabinet is verified. After successful verification, the charging cabinet matches the device's identity information with the current available compartment status, comprehensively considering factors such as compartment size, charging interface type, and power level to allocate the optimal charging compartment for the device. Upon successful matching, a charging authorization status is generated, including the device-compartment binding relationship, authorization timestamp, and verification result, providing the basis for subsequent charging permissions.

[0019] Step S2: The charging cabinet reads the charging interface parameters of the assigned compartments, including technical specifications such as interface type, rated voltage, maximum output current, and power range. These interface parameters are then correlated with the charging authorization status obtained in Step S1 to form a charging management data group. This data group includes elements such as authorized device information, compartment number, interface specifications, charging time window, and power limits. Through correlation processing, a mapping relationship between device, interface, and authorization is established to ensure the safety and standardization of the charging process. The charging management group, as the fundamental data structure for charging control, runs throughout the entire charging process, enabling precise matching and dynamic management of devices and charging resources.

[0020] Step S3: After establishing data communication between the charging device and the charging cabinet, the charging device uploads battery status data, including key parameters such as current battery level, battery temperature, cycle count, and health status. The charging cabinet integrates this battery data with the established charging management group and calculates the optimal charging strategy based on battery chemical characteristics and charging protocol requirements. By analyzing battery status, charging demand, and charging resources, the voltage and current variation curves of the charging process are planned. The charging parameter curves cover the complete charging cycle, including specific parameter settings for stages such as pre-charging, constant current charging, and constant voltage charging, ensuring a balance between charging efficiency and battery life. The planning results will serve as the basis for subsequent charging control, achieving intelligent charging management.

[0021] Step S4: The environmental energy game theory algorithm models the charging process as a multi-party game problem, with participants including charging equipment, grid supply, and environmental conditions. The algorithm uses the charging management group and charging parameter curves as input to construct a game model for energy allocation. The game process considers multiple dimensions such as grid load, peak-valley electricity prices, and charging equipment priority to calculate the optimal energy allocation strategy. Through iterative optimization, the game equilibrium point is solved, yielding the power allocation scheme and timing arrangement for each charging device. The initial charging control scheme includes specific execution parameters such as charging schedules, power curves, and energy allocation ratios, achieving rational scheduling and utilization of charging resources. This scheme ensures charging performance while also considering economic efficiency and grid stability.

[0022] Step S5: The environmental factor assessment module analyzes various environmental parameters affecting the charging process, including ambient temperature, humidity, and charging cabinet heat dissipation. The assessment process combines acquired battery state data to establish an environment-battery performance correlation model. Based on the assessment results, the generated initial charging control scheme is dynamically optimized. The optimization process employs an adaptive algorithm to adjust charging parameters according to real-time environmental changes, such as reducing charging power and adjusting charging timing in high-temperature environments. The final charging control strategy possesses environmental adaptability and dynamic adjustment capabilities, including complete charging process control instructions, protection measures, and emergency plans. This strategy continuously monitors the environmental and equipment status during execution to ensure the safety and reliability of the charging process.

[0023] The charging management method for mobile charging cabinets provided by this invention significantly improves the safety and reliability of the charging process by performing multi-factor authentication on charging devices and authorizing matching with the storage compartment status information, effectively solving the security risks of single identity authentication in existing charging management methods. Based on the correlation processing of interface charging parameters and charging authorization status, intelligent binding of charging permissions and charging parameters is achieved, laying the foundation for precise charging management. By acquiring battery status data of charging devices and combining it with charging management groups for charging planning, the system can formulate personalized charging parameter curves for different types and states of batteries, improving charging efficiency while extending battery life. Based on a preset environmental energy game algorithm to analyze charging modes, the system can achieve optimal resource allocation under various charging demands, significantly improving the overall operating efficiency of the mobile charging cabinet. By evaluating environmental factors based on battery status data and dynamically adjusting the charging control scheme, the charging process can adapt to different environmental conditions, enhancing the system's adaptability and stability. In summary, this invention not only solves the problems of insufficient security and low charging efficiency in existing charging management methods but also realizes intelligent management of mobile charging cabinets, meeting users' growing needs for efficient, safe, and personalized charging.

[0024] In one embodiment, the location status of the mobile charging cabinet and the identity information of the charging device are obtained, the identity information is subjected to multiple verifications, and authorization is matched with the location status to obtain the charging authorization status, including: The mobile charging cabinet's compartment scanning module uses a photoelectric sensor array to scan each charging compartment. Compartment number information is read via built-in RFID tags, with each compartment having a unique 16-bit code. Compartment occupancy status is determined by weight sensors and infrared sensors; when a device is detected placed inside and its weight exceeds 100g, it is considered occupied. Compartment malfunction status is monitored by voltage, current, and temperature sensors; when values ​​exceed preset thresholds (voltage deviation ±5%, current deviation ±3%, temperature exceeding 60℃), a malfunction is identified. The status integration module combines these three types of data in the format of "Compartment Number - Occupancy Status - Malfunction Status" to generate standardized compartment status information.

[0025] The identification module reads the hardware identification code of the charging device via the USB interface or wireless communication module. This identification code is a unique 32-bit serial number. The communication protocol identifier is identified by the protocol parsing module, supporting mainstream charging protocols including USB PD, QC, and PE. The identification integration module combines the hardware identification code and the communication protocol identifier into a unified formatted identity information string.

[0026] The authentication module accesses a pre-set authentication database, which uses a distributed structure to store information about legitimate devices. The authentication process includes hardware identifier verification (comparing it to database records), communication protocol compatibility verification (checking the protocol version number), and blacklist filtering (removing unauthorized devices). The authentication result is output as a score from 0 to 100, constituting the authentication result.

[0027] The access control module classifies devices based on authentication scores: 90-100 points are Level A (highest access), 75-89 points are Level B, 60-74 points are Level C, and charging is prohibited for those below 60 points. Access levels are linked to parameters such as charging power and duration, forming device access level data.

[0028] The charging bay matching module executes a permission-based charging bay allocation algorithm: Class A devices are prioritized for fast charging bays (power ≥ 40W), Class B devices are allocated standard charging bays (power 15-40W), and Class C devices are allocated slow charging bays (power ≤ 15W). It also considers factors such as the physical location of the charging bays and usage frequency to generate the optimal charging bay allocation strategy.

[0029] The authorization management module creates a device-slot binding record in the system database, including: device ID, slot ID, authorization timestamp, authorization validity period (default 4 hours), charging parameter configuration, and other information. After binding is complete, an authorization code is generated and sent to the charging device via the communication module to complete the charging authorization status setting.

[0030] This embodiment employs a multi-sensor array for real-time monitoring of charging bays, combined with a standardized status information integration mechanism, to achieve accurate determination of charging bay status and effectively avoid resource waste caused by misjudgments. A distributed database-based authentication system, coupled with a multi-level device permission division mechanism, significantly improves the security of charging device access and effectively prevents the risk of unauthorized device access. By establishing an intelligent bay allocation strategy based on permission levels, differentiated allocation of charging resources is achieved, improving the utilization efficiency of charging resources. The use of device-bay dynamic binding technology, combined with real-time status monitoring and automatic adjustment mechanisms, enables the system to respond promptly to device access and disconnection events, ensuring the real-time and rational allocation of charging resources. This method, through the synergy of hardware and software, constructs a complete charging management system, ensuring charging safety while improving the management efficiency and service quality of mobile charging cabinets.

[0031] In one embodiment, the interface charging parameters of the mobile charging cabinet are obtained, and the interface charging parameters are associated with the charging authorization status to obtain a charging management group, including: The charging management method for mobile charging cabinets obtains interface charging parameters by sampling the voltage and current of the charging cabinet's compartment interfaces, and then associates these parameters with the charging authorization status to ultimately form a charging management group. This method comprises multiple processing steps, each with its own specific processing rules and conditions.

[0032] In the interface charging parameter acquisition stage, the mobile charging cabinet's control system samples the voltage and current of each compartment's interface in real time. The voltage sampling range is 0-380V with a sampling accuracy of 0.1V; the current sampling range is 0-32A with a sampling accuracy of 0.01A. The sampling frequency is set to 10 times per second. The system records the sampled data and calculates the average value over 10 seconds as the interface charging parameter for that period. The interface charging parameters include the voltage value, current value, and their fluctuation range.

[0033] The interface grading process classifies the interfaces based on the collected charging parameters. Voltage parameters are divided into high-voltage (200V-380V), medium-voltage (100V-200V), and low-voltage (0-100V); current parameters are divided into high-current (16A-32A), medium-current (8A-16A), and low-current (0-8A). The system classifies the interfaces into nine levels based on the combination of voltage and current, forming the interface parameter levels. High-voltage, high-current combinations constitute Level 1, high-voltage, medium-current combinations constitute Level 2, and so on, with low-voltage, low-current combinations constituting Level 9.

[0034] The interface performance index calculation process involves in-depth analysis of the interface parameter levels to calculate the interface charging capacity index. The calculation formula is: Charging Capacity Index = Voltage Level Coefficient × Current Level Coefficient × Stability Coefficient. The voltage level coefficient is 1.0 for high voltage, 0.8 for medium voltage, and 0.6 for low voltage; the current level coefficient is 1.0 for high current, 0.8 for medium current, and 0.6 for low current; the stability coefficient is determined based on the parameter fluctuation range: 1.0 for fluctuations less than 5%, 0.9 for fluctuations between 5% and 10%, and 0.8 for fluctuations greater than 10%.

[0035] The interface matching process maps interface charging capability metrics to charging authorization statuses, generating an interface authorization mapping table. Charging authorization statuses are categorized as: fast charging authorization (metric ≥ 0.8), normal charging authorization (0.5 ≤ metric < 0.8), and charging prohibited (metric < 0.5). The system matches the charging capability metrics of each interface with these three authorization statuses, establishing a corresponding relationship table.

[0036] The interface initialization configuration step sets up the management initial group based on the interface authorization mapping table. For fast charging authorized interfaces, the maximum output power is set to 7000W and the charging protection current is set to 32A; for normal charging authorized interfaces, the maximum output power is set to 3500W and the charging protection current is set to 16A; for charging disabled interfaces, the output power is cut off and the charging protection current is set to 0A. These configuration parameters constitute the management initial group.

[0037] The charging parameter verification process verifies the safety and rationality of the parameters in the initial management group, forming the final charging management group. Verification includes: output power limit verification (not exceeding 7000W), charging current limit verification (not exceeding 32A), charging time limit verification (single charge not exceeding 8 hours), and temperature limit verification (interface temperature not exceeding 85℃). Parameters that pass verification remain unchanged, while unqualified parameters are automatically adjusted according to safety thresholds. The adjusted parameter set constitutes the charging management group.

[0038] This embodiment achieves precise hierarchical management of charging interfaces by sampling voltage and current at the mobile charging cabinet's compartment interfaces, establishing an interface parameter hierarchy system and improving the accuracy and controllability of charging management. By calculating interface charging capacity indicators and matching them with charging authorization status, a scientific interface authorization correspondence mechanism is formed, effectively preventing improper use of charging interfaces and enhancing the safety of the charging process. Through multi-dimensional parameter verification of the initial management group, a complete charging safety protection mechanism is established, achieving comprehensive monitoring of the charging process from multiple aspects such as output power, charging current, charging time, and temperature, effectively preventing various safety hazards during charging. This charging management method, through systematic parameter management and dynamic adjustment mechanisms, improves charging efficiency while ensuring charging safety, making the management of mobile charging cabinets more intelligent and standardized.

[0039] In one embodiment, battery status data of the charging device is acquired, and charging planning is performed with the charging management group to obtain a charging parameter curve, including: The battery state data acquisition phase involves real-time monitoring of multiple physical quantities. Voltage data is acquired through a high-precision voltage sampling circuit, achieving millivolt-level accuracy; current data is measured using a Hall current sensor, ensuring milliampere-level measurement accuracy; temperature data is acquired by an NTC thermistor, covering a temperature range of -20℃ to 60℃; and the cycle count is obtained by accumulating the number of charge-discharge cycles. The battery state indicators are layered into a three-level structure: the core indicator layer, State of Charge (SOC), reflects the battery's remaining capacity, while State of Health (SOH) characterizes the degree of capacity decay; the critical indicator layer, Internal Resistance, reflects the battery's power characteristics, and Polarization Voltage reflects the electrochemical characteristics during charging; the general indicator layer, Ambient Temperature, affects charging safety, and Charging Time determines the charging progress. The battery state indicator set integrates these layered data into a unified data structure.

[0040] The state mapping process establishes a bridge between battery state and charging control. There are complex correspondences between control parameters and battery state indicators in the charging management group: battery voltage and charging voltage are positively correlated, but with a safety margin; charging current exhibits segmented characteristics as it changes with SOC, using different current values ​​in different SOC ranges; charging time and SOH are negatively correlated, with charging time increasing as battery aging progresses. These mapping relationships are stored in the form of an association matrix for the charging state-related data.

[0041] Cluster analysis employs an improved K-means algorithm to classify the state-of-charge (SOH) data into different charging categories. The fast-charging group is characterized by a charging current greater than 0.5C and a charging time of less than 2 hours, suitable for batteries with an SOH greater than 90%. The standard charging group uses a charging current of 0.2C-0.5C and a charging time of 2-4 hours, suitable for batteries with an SOH between 70% and 90%. The trickle-charging group uses a charging current less than 0.2C and a charging time exceeding 4 hours, suitable for batteries with an SOH below 70%.

[0042] The charging parameter prediction employs a neural network model. The input layer includes battery state indicators and charging category features, the hidden layer processes data feature extraction, and the output layer generates predicted charging parameter values. The prediction process considers the parameter variation patterns in historical battery charging data and combines them with current state indicators to predict the optimal charging parameter configuration. The prediction result includes the current and voltage parameter sequences throughout the entire charging process.

[0043] The capacity analysis phase employs a battery equivalent circuit model to calculate the charging capacity variation under predicted parameters. The analysis process divides the charging cycle into several time periods, calculating parameters such as the increase in charging capacity, charging efficiency, and temperature change for each period. Capacity distribution data records the capacity accumulation curve during the charging process, reflecting its dynamic characteristics.

[0044] Charging planning is based on capacity distribution data to formulate a segmented charging strategy. When the State of Charge (SOC) is below 30%, a constant current charging mode is used, with the charging current adjusted according to the State of Harmony (SOH) level. Between 30% and 80% SOC, a multi-stage constant current charging mode is used, dynamically adjusting the charging current based on temperature changes. Once the SOC exceeds 80%, a constant voltage charging mode is adopted, continuing until the charging current drops to the cutoff current. Charging planning parameters include control parameters and transition conditions for each stage.

[0045] The dynamic curve construction employs spline interpolation to continuously process the charging planning parameters. The curve construction process ensures smooth parameter changes, preventing sudden parameter fluctuations from impacting the battery. The charging parameter curve contains the current and voltage change trajectories throughout the charging process, serving as control commands for the charging equipment.

[0046] This embodiment achieves precise control and intelligent management of the mobile charging cabinet charging process through multi-dimensional analysis and processing of battery status data. The battery status index hierarchical technology enables the charging system to comprehensively understand battery health, avoiding the low charging efficiency caused by insufficient understanding of battery status in traditional charging methods. The introduction of state mapping and category clustering makes the charging strategy more personalized, providing differentiated charging solutions for batteries in different states, effectively extending battery life. The charging parameter prediction technology uses a neural network model to analyze historical charging data, accurately predicting optimal charging parameters and significantly improving charging efficiency. The combination of capacity analysis and charging planning makes the charging process more scientific and reasonable, avoiding overcharging or undercharging. Dynamic curve construction ensures smooth changes in charging parameters, reducing the impact of parameter mutations on the battery and further improving charging safety.

[0047] In one embodiment, a charging mode analysis is performed on the charging management group and charging parameter curves based on a preset environmental energy game algorithm to obtain an initial charging control scheme, including: The environmental energy game theory algorithm, as the core algorithm of this embodiment, performs three-modal energy capture decision analysis on the charging management group. The three modes refer to high-power mode, medium-power mode, and low-power mode, each corresponding to different charging demand scenarios. The algorithm constructs an energy state transition matrix of the charging environment based on a Bayesian network, and forms a multi-dimensional energy state assessment system by collecting energy supply data, user charging demand data, and grid load data in the environment.

[0048] A sensor network is used to collect input voltage, current, temperature, and other parameters of the charging management group in real time. Combined with historical charging data, an energy state prediction model is established. This model employs a Markov decision process to calculate the energy capture efficiency under each mode and outputs charging energy state data. The charging energy state data includes the energy reserve level, energy conversion efficiency, and predicted energy consumption values ​​for each charging unit.

[0049] For example, in a mobile charging cabinet implementation scenario, the recorded charging energy status data is as follows: under high power mode, the energy reserve level is 85%, the energy conversion efficiency is 92%, and the predicted energy consumption within 24 hours is 76%; under medium power mode, the energy reserve level is 65%, the energy conversion efficiency is 88%, and the predicted energy consumption within 24 hours is 52%; under low power mode, the energy reserve level is 45%, the energy conversion efficiency is 82%, and the predicted energy consumption within 24 hours is 31%.

[0050] Based on the acquired state of energy (SOE) data, the charging parameter curves are piecewise fitted. The charging parameter curves refer to the curves showing how parameters such as voltage, current, and power change over time during the charging process. The purpose of piecewise fitting is to transform the complex nonlinear charging curves into multiple calculable linear or polynomial functions, facilitating subsequent energy allocation calculations.

[0051] The charging parameter curve was fitted using piecewise cubic spline interpolation. First, key nodes were identified, including the charging start point, the constant current charging stage, the constant voltage charging stage, and the charging cutoff point. Then, cubic spline functions were established between each node, ensuring the continuity of the first and second derivatives at the nodes, thus guaranteeing the smoothness of the fitted curve.

[0052] Through piecewise fitting, energy distribution data is generated, including key indicators such as charging time distribution, energy density distribution, and charging efficiency distribution. This data reflects the energy distribution characteristics during the charging process, providing a basis for subsequent energy source allocation.

[0053] The segmented energy distribution data shows that during peak charging hours (18:00-22:00), the energy density is 0.85 kWh / m³, and the charging efficiency is 91%; during off-peak charging hours (08:00-18:00), the energy density is 0.67 kWh / m³, and the charging efficiency is 86%; and during off-peak charging hours (22:00-08:00), the energy density is 0.42 kWh / m³, and the charging efficiency is 78%.

[0054] Dynamic energy source allocation calculations are performed based on energy distribution data. Mobile charging stations typically have multiple energy sources, such as mains power, solar power, and wind power. Dynamic energy source allocation aims to optimize energy use strategies based on the availability, cost, and efficiency of different energy sources.

[0055] The specific calculation process employs the minimum-cost maximum flow algorithm. An energy network flow model is established, with each energy source as a source, each charging unit as a sink, and energy transmission paths as network edges. Each edge is assigned a cost weight and a capacity limit; the cost weight is determined based on a combination of energy prices, conversion efficiency, and environmental impact factors. By solving the minimum-cost maximum flow problem, the optimal allocation ratio of each energy source is determined, and the energy allocation coefficient is output.

[0056] The energy allocation coefficient is a set of normalized weighted values ​​representing the proportion of each energy source in the total energy supply. In this embodiment, the calculated energy allocation coefficients are: mains power 0.65, solar power 0.25, and energy storage 0.10. This means that under the current charging demand, 65% of the energy comes from mains power, 25% from solar power, and 10% from energy storage.

[0057] Based on the energy allocation coefficient, a ternary utility model analysis is performed on the charging management group. The ternary utility refers to economic utility, technical utility, and environmental utility, which respectively measure the economic efficiency, technical feasibility, and environmental friendliness of the charging scheme.

[0058] The ternary utility model employs a multi-attribute utility theory framework. Economic utility is calculated using charging costs, equipment depreciation, and operation and maintenance costs; technological utility is assessed using charging efficiency, time response, and stability indicators; and environmental utility is quantified using factors such as carbon emissions, resource consumption, and noise pollution. Weights are assigned to the three types of utility, and a weighted sum is used to obtain the comprehensive utility value.

[0059] In the game theory analysis phase, the charging process is modeled as a multi-participant non-cooperative game. Participants include charging equipment, energy suppliers, and users. By solving for the Nash equilibrium point, the optimal strategy that balances the interests of all parties is found, and the charging game result is output.

[0060] The charging game result includes the optimal charging time allocation, the payoff values ​​of each participant, and a strategy stability assessment. In this embodiment, the charging game result shows that the optimal charging strategy is to perform basic charging during off-peak hours (accounting for 40% of the total charging volume), supplementary charging during off-peak hours (accounting for 35% of the total charging volume), and emergency charging only during peak hours (accounting for 25% of the total charging volume). Under this strategy, the return on investment for charging equipment providers is 15%, the return on investment for energy providers is 12%, the overall user satisfaction rate is 88%, and the strategy stability score is 0.82 (out of 1).

[0061] Based on the outcome of the charging game, an energy debt balancing mechanism is executed. Energy debt refers to the energy shortfall caused by insufficient energy supply during charging. The energy debt balancing mechanism aims to balance energy income and expenditure through energy storage, energy lending, and demand response.

[0062] An energy account model is established, recording energy revenue (charging energy supply) and energy expenditure (charging demand) in the energy account. The energy revenue-expenditure ratio is obtained by calculating the ratio of energy revenue to expenditure. This ratio reflects the energy balance; a value greater than 1 indicates an energy surplus, while a value less than 1 indicates an energy liability.

[0063] When dealing with energy debt, a tiered response strategy is adopted: when the energy income-to-expenditure ratio is between 0.9 and 1.1, it is in a balanced state and no intervention is required; when the ratio is between 0.7 and 0.9, a light debt response is initiated, adjusting charging power and timing; when the ratio is less than 0.7, a heavy debt response is initiated, including emergency energy allocation, load reduction, and user charging priority reordering.

[0064] The calculated energy balance ratio is 0.82, which indicates a slight debt situation. Charging compensation calculations are needed to restore energy balance.

[0065] Based on the energy cost-benefit ratio, charging compensation is calculated for the charging management group. Charging compensation refers to the process of making up for energy debt by adjusting charging parameters, optimizing energy use, or borrowing external energy.

[0066] The charging compensation calculation employs a proportional-integral-derivative (PID) control algorithm. Using the target energy balance ratio (typically 1.0) as a reference point, and taking the deviation of the current energy balance ratio as input, the required compensation is calculated. The proportional term handles immediate deviations, the integral term eliminates accumulated errors, and the derivative term predicts future trends.

[0067] The PID controller generates charging compensation parameters, including compensation power, compensation duration, and compensation source. In this embodiment, the calculated charging compensation parameters are: compensation power 18kW, compensation duration 3.5 hours, and compensation source allocation as 70% energy storage and 30% external power grid.

[0068] The charging compensation parameters and charging game results are combined and optimized to generate charging mode schemes. The charging mode combination optimization aims to combine different charging modes (such as fast charging mode, standard mode, and slow charging mode) to meet various charging needs while maximizing efficiency.

[0069] The optimization process employs a genetic algorithm. All possible charging mode combinations are encoded as chromosomes, which are then used to initialize the population. The population is iteratively optimized through fitness evaluation, selection, crossover, and mutation operations. The fitness function comprehensively considers factors such as charging speed, energy efficiency, device load, and user satisfaction. After multiple generations of evolution, the chromosome with the highest fitness is selected and decoded into a charging mode scheme.

[0070] In this embodiment, the optimized charging mode scheme includes four operating modes: SuperCharge mode (120kW, 15 minutes, suitable for emergency charging), Fast Charging mode (60kW, 30 minutes, suitable for business users), Standard Charging mode (30kW, 1 hour, suitable for ordinary users), and Economy Charging mode (15kW, 2 hours, suitable for users staying for extended periods). The time allocation and energy source for each mode have been optimized based on the aforementioned analysis results.

[0071] Based on the charging parameter curves, the charging mode scheme is modified through calculations to obtain the initial charging control scheme. The purpose of the modification calculations is to ensure the feasibility of the theoretical scheme under actual hardware constraints and to provide preventative measures for extreme cases.

[0072] The correction process begins with a hardware constraint check to ensure that charging power, voltage, and current do not exceed the equipment's rated values. Next, a thermal management assessment is performed to ensure that prolonged charging will not cause the equipment to overheat. Then, a grid fluctuation adaptability analysis is conducted to evaluate the stability of the solution under grid voltage fluctuations. Finally, a fault prevention analysis is performed to design backup strategies to cope with potential equipment failures or power outages.

[0073] After multi-dimensional revisions, an initial charging control scheme is generated, including detailed control parameter settings, operating mode configurations, timing arrangements, and anomaly handling strategies. This scheme will serve as the initial control command for charging management, guiding the execution of the charging process.

[0074] The core parameters of the initial charging control scheme include: voltage modulation range of 380-420V, current control range of 50-75A, and power fluctuation tolerance of ±5% in standard mode; voltage control accuracy of ±2V, current response time of <10ms, and over-temperature protection threshold of 75℃ in fast charging mode; energy dispatch priority of solar energy > energy storage > grid; fault response time of <100ms, and reconfiguration time of <2s. This scheme comprehensively considers energy efficiency, user experience, and safety, providing a highly efficient and reliable charging management strategy for mobile charging cabinets.

[0075] This embodiment employs an environmental energy game theory algorithm to perform trimodal energy capture decision analysis on the charging management group, achieving accurate assessment and prediction of charging energy status and effectively improving the system's energy utilization efficiency. By fitting charging parameter curves using piecewise cubic spline interpolation, the complex nonlinear charging process is transformed into a computable piecewise function, improving computational accuracy and processing speed. Dynamic energy source allocation calculations enable intelligent allocation of various energy sources, reducing charging costs and enhancing the system's economic benefits. The use of ternary utility construction analysis and an energy liability balancing mechanism ensures charging performance while achieving a balance between economic, technical, and environmental benefits, enhancing the system's sustainability. Through charging compensation calculations and mode combination optimization, a comprehensive charging management strategy is established, improving the stability and reliability of the charging process while meeting the diverse charging needs of different users.

[0076] In one embodiment, a ternary utility construction analysis is performed on the charging management group based on the energy allocation coefficient to obtain the charging game results, including: In the charging management process of mobile charging cabinets, a charging management group refers to a collection of multiple charging devices, each with independent charging needs and charging status. The energy allocation coefficient is a quantitative indicator that measures the priority of energy allocation to charging devices. This coefficient is determined by a combination of parameters such as the remaining power of the charging device, charging time, and charging power.

[0077] When performing group mapping operations on the charging management group, charging devices are divided into different priority groups based on the energy allocation coefficient. The energy demand of each group is then transformed into an energy allocation vector through a mapping function. The energy allocation vector represents the charging power allocated to each charging device in the current time period. The mapping function uses a normalization process to ensure that the total allocated power does not exceed the rated power.

[0078] In the energy utility conversion stage, the energy allocation vector is transformed into initial energy utility parameters. These initial parameters include three dimensions: charging efficiency coefficient, charging time coefficient, and charging capacity coefficient. These parameters reflect the energy utilization efficiency during the charging process. The charging efficiency coefficient represents the ratio of input electrical energy to stored electrical energy, the charging time coefficient reflects the time consumed during the charging process, and the charging capacity coefficient reflects the degree of battery capacity utilization.

[0079] In the benefit-utility calculation process, the first utility component is calculated based on the initial energy utility parameters. This first utility component reflects the positive benefits brought about by the charging process, including indicators such as charging completion rate, charging speed, and user satisfaction. This utility component is quantified using a weighted summation method, with the weighting coefficients determined through historical data statistical analysis.

[0080] In the cost-utility calculation phase, the second utility component is calculated based on the energy allocation coefficient. This second utility component reflects the negative costs incurred during the charging process, including energy loss, equipment wear and tear, and time costs. This utility component is quantified using a cost function model, which is constructed in a piecewise linear manner.

[0081] In the charging state utility calculation step, the charging state of the charging management group is analyzed to obtain the third utility component. This third utility component reflects the state change characteristics during the charging process, including indicators such as charging balance, charging stability, and charging safety. This utility component is calculated using a state assessment model.

[0082] When constructing the joint function, the first, second, and third utility components are integrated into a comprehensive utility function. The comprehensive utility function adopts a linear combination form, and the combination coefficients of each utility component are determined through an optimization algorithm. This function comprehensively reflects the overall utility level of the charging process.

[0083] During the equilibrium solution phase, each charging device in the charging management group makes strategy choices based on a comprehensive utility function, forming a charging game strategy set. Game strategies include decision variables such as charging power adjustment, charging timing arrangement, and charging mode selection. The optimal strategy combination is solved using Nash equilibrium theory.

[0084] In the process of finding the equilibrium in the game, the strategy selection for each charging device is calculated iteratively.

[0085] The calculation of charging power adjustment utility is based on the rated power of the charging device, the current remaining power, and the charging demand. The charging power is discretized and divided into multiple levels. Each charging device selects an appropriate charging power level within its allowable power range. The charging power adjustment utility increases with the increase of charging power, but the rate of increase gradually decreases, reflecting the law of diminishing marginal utility.

[0086] Charging timing utility reflects the charging device's preference for charging time windows. Charging time is divided into multiple time periods, and each charging device selects a suitable time period based on its own charging needs. The utility of timing is related to the degree of matching between the actual charging time and the user's expected charging completion time; the closer the actual charging time is to the user's expected time, the higher the utility value.

[0087] Charging mode selection utility reflects the benefits a charging device gains from choosing different charging modes. Charging modes include fast charging, standard charging, and energy-saving charging. Different charging modes result in variations in charging current, charging time, and charging efficiency. The utility of mode selection is related to factors such as battery life protection, charging speed, and energy consumption.

[0088] In the solution process, an initial strategy is first assigned to each charging device, including initial charging power, charging time period, and charging mode. For each charging device, the overall utility value under the current strategy combination is calculated.

[0089] Subsequently, the policies of each charging device are updated one by one. For each charging device to be updated, all possible policy choices are enumerated in its policy space, and the utility value under each policy is calculated. By comparing the utility values ​​of different policies, the policy that yields the maximum utility is selected as the updated policy.

[0090] The policy update process continues until convergence conditions are met. Convergence conditions include: the policy update magnitude of all charging devices is less than a preset threshold, or the number of iterations reaches the maximum limit. When convergence is achieved, the resulting policy combination is the Nash equilibrium solution.

[0091] The stability of the equilibrium solution is guaranteed by verifying that unilaterally changing the strategy of any charging device cannot improve its utility. Stability analysis is performed on the equilibrium solution to ensure its reliability.

[0092] The final charging game result includes the optimal charging power, charging time schedule, and charging mode configuration for each charging device. These results serve as the basis for charging management decisions and guide the execution of the charging process.

[0093] In actual operation, the game equilibrium is periodically recalculated based on changes in the charging equipment status and external environment to achieve dynamic optimization of the charging strategy. Continuous strategy adjustments ensure the adaptability and effectiveness of charging management.

[0094] In the game equilibrium solution stage, the calculation process based on Nash equilibrium theory is as follows: ; Let i represent the overall utility function of charging device i. This indicates the strategy selection for charging device i. This represents the strategy combination for all devices except charging device i. This indicates the effectiveness of charging power regulation. This indicates the effectiveness of the charging timing arrangement. This indicates the charging mode selection functionality.

[0095] , , These are the weighting coefficients for the corresponding utility terms, and .

[0096] At the Nash equilibrium point, when each charging device's strategy is the optimal response to the strategies of other devices, i.e.: .

[0097] in Let N represent the set of equilibrium strategies, and let N represent the set of charging devices. This inequality shows that at the equilibrium point, no single charging device can gain higher utility by unilaterally changing its strategy.

[0098] When iteratively optimizing the charging game strategy set, the strategy parameters are continuously adjusted through multiple rounds of game play to ultimately obtain the charging game result. The iterative optimization employs the gradient descent method, updating the strategy parameters in each iteration until the convergence condition is met. The charging game result includes the optimal charging power allocation scheme, the charging timing arrangement scheme, and the charging mode configuration scheme.

[0099] This embodiment achieves intelligent grouping and optimized energy allocation of charging devices by constructing a ternary utility model for the charging management group, thereby improving the charging management efficiency of mobile charging cabinets. Grouping mapping operations based on energy allocation coefficients ensure the rational allocation of charging resources and avoid waste. Through three-dimensional analysis of revenue utility, cost utility, and charging state utility, various indicators of the charging process are comprehensively evaluated, ensuring the scientific nature and reliability of charging management. Nash equilibrium theory is used for game theory solutions, enabling each charging device to achieve the optimal strategy combination during the charging process, effectively balancing the resource competition relationship between charging devices. Iterative optimization methods are used to optimize the charging game strategy set, improving the adaptability of charging management and ensuring the stability and safety of the charging process. This method achieves economical allocation of charging resources while ensuring charging efficiency, providing reliable technical support for the intelligent management of mobile charging cabinets.

[0100] In one embodiment, a charging mode scheme is obtained by combining and optimizing the charging compensation parameters and the charging game result, including: Charging compensation parameters include charging time compensation coefficient, charging cost compensation coefficient, and charging efficiency compensation coefficient. These parameters reflect the user's time cost, economic cost, and charging effect during the charging process. Charging game results include user charging strategy choices, charging cabinet operator revenue, and user satisfaction evaluations. These results reflect the game behavior and its effects among various parties during the charging process. When fusing features from charging compensation parameters and charging game results, a feature vectorization method is used. After standardizing each parameter and result, feature vectors are constructed, and these vectors are concatenated to form a pattern combination matrix. The rows of this matrix represent different charging scenarios, and the columns represent various feature dimensions.

[0101] Based on the obtained mode combination matrix, a combined function for charging efficiency and compensation cost is constructed. This function is based on charging efficiency indicators, including charging speed, charging completion rate, and charging stability; and compensation cost indicators, including time compensation cost, expense compensation cost, and efficiency compensation cost. These indicators are integrated through a weighted summation method to form a composite function of the target mode. This function reflects the comprehensive performance evaluation of the charging mode.

[0102] Constraints are imposed on the composite function of the target model, including charging time constraints, compensation amount constraints, and game payoff constraints. The charging time constraint ensures that the charging time is within a reasonable range, generally set between 15 and 120 minutes; the compensation amount constraint guarantees that the compensation amount does not exceed 30% of the charging cost; and the game payoff constraint ensures that the revenue for both the operator and the user is positive. The data from these constraints form an integrated constraint dataset.

[0103] After obtaining the integrated constraint data, numerical optimization methods are used to solve the composite function of the target mode. Gradient descent is employed during the optimization process to find the optimal solution of the function while satisfying the constraints. The solution results form the target mode solution set, containing multiple sets of feasible solutions that satisfy the constraints, with each set corresponding to a charging mode configuration scheme.

[0104] Based on the solution set of the target pattern, a charging mode scheme is constructed by combining charging compensation parameters and the results of the charging game. During the scheme construction process, the solutions in the solution set are mapped to actual charging parameter configurations, including charging power settings, compensation parameter settings, and game strategy settings. The final charging mode scheme includes specific parameter configuration values ​​and execution strategies. This scheme satisfies charging efficiency requirements, ensures the rationality of compensation costs, and guarantees the gains of both parties in the game.

[0105] This embodiment achieves intelligent and precise control of charging management by optimizing the combination of charging compensation parameters and charging game results. A feature fusion approach is used to construct a pattern combination matrix, effectively integrating multi-dimensional parameters such as charging duration, cost, and efficiency, thus improving the systematicness and comprehensiveness of charging management. By establishing a target pattern composite function and introducing multiple constraints, reasonable control of compensation costs is achieved while ensuring charging efficiency, effectively balancing the interests of operators and users. Based on the integrated constraint data, the target pattern composite function is optimized and solved, resulting in a charging pattern scheme that satisfies charging efficiency requirements, ensures the economic efficiency of compensation costs, and maximizes the benefits for both parties in the game. This charging management method, through a systematic optimization process, improves the operational efficiency of mobile charging cabinets, enhances the reliability of charging services, and provides strong technical support for the large-scale application of mobile charging cabinets.

[0106] In one embodiment, environmental factors are assessed based on battery state data, and the initial charging control scheme is optimized and adjusted to obtain a charging control strategy, including: The battery health analysis phase analyzes battery status data based on a pre-set health data table. This table includes standard value ranges for key indicators such as battery capacity decay rate, internal resistance change rate, and cycle count. By comparing the actual collected battery status data with the standard values ​​in the health data table, a battery health index reflecting the battery's current usage state is calculated. This index is expressed as a percentage, with 100% indicating the battery is in optimal condition, gradually decreasing with increasing usage time.

[0107] The temperature range segmentation stage analyzes and processes the obtained battery health indicators in conjunction with temperature information from the battery state data. Based on the battery's chemical characteristics, the temperature range is divided into a low-temperature zone (-20℃ to 0℃), a normal-temperature zone (0℃ to 45℃), and a high-temperature zone (45℃ to 60℃). Within each temperature range, a corresponding set of temperature-affecting parameters is set, including parameters such as temperature compensation coefficient and temperature safety coefficient. These parameters reflect the changing patterns of battery charging performance under different temperature conditions.

[0108] The influencing factor extraction stage involves an in-depth analysis of the temperature-related parameter set to extract the actual impact of ambient temperature on the charging process. By establishing a temperature influence matrix and calculating the weighting coefficients of parameters within each temperature range, the ambient temperature influence parameter is comprehensively derived. This parameter reflects the combined impact of ambient temperature on charging efficiency and safety.

[0109] In the current gradient calculation stage, the initial charging control scheme is optimized using the influence parameter of ambient temperature. By calculating the rate of change of charging current under different temperature conditions, a current regulation model is established, and a current regulation factor is obtained. This factor is used to dynamically adjust the magnitude of the charging current to ensure the safety and efficiency of the charging process.

[0110] The dynamic change pattern analysis stage involves statistical analysis of the changing trend of the current regulation factor. By establishing a data regression model, the variation of the current regulation factor with temperature is analyzed, and the environmental adaptability optimization coefficient is calculated. This coefficient reflects the charging system's ability to adapt to environmental changes.

[0111] The segmented optimization stage optimizes the initial charging control scheme based on the environmental adaptability optimization coefficient. The charging process is divided into three stages: pre-charging, constant current charging, and constant voltage charging, and the optimized charging control value for each stage is calculated separately. This control value includes specific parameters such as charging current and charging voltage.

[0112] The comparison and calculation phase compares the optimized charging control values ​​with preset safety thresholds. These safety thresholds include limiting parameters such as maximum charging current, maximum charging voltage, and maximum temperature. Through this comparison and calculation, the optimized charging parameters are ensured to remain within safe ranges, resulting in corrected control data.

[0113] The process of formulating the charging control strategy during the time-slot allocation control optimization phase is detailed below: This phase divides the charging process into five time periods: the initial pre-charging period, the pre-charging period, the constant current charging period, the constant voltage charging period, and the trickle charging period. Each time period is independently optimized based on the corrected control data.

[0114] The pre-charge phase is for charging control when the battery is in a deep discharge state. During this phase, a low-current charging method is used, with the current value controlled within 5% to 10% of the rated charging current, and the duration is usually less than 10 minutes. When the battery voltage reaches 30% of the nominal voltage, the pre-charge phase begins.

[0115] The pre-charging period is primarily used to wake up batteries in a dormant state. This period employs a pulse charging mode, using intermittent charging to enhance battery activity. The charging current is controlled within 15% to 20% of the rated charging current, and the duration is dynamically adjusted based on the battery's condition, generally not exceeding 20 minutes. When the battery voltage reaches 50% of the nominal voltage, the constant current charging period begins.

[0116] The constant current charging period is the main stage of the charging process. During this period, the charging current is dynamically adjusted based on battery health indicators and the influence of ambient temperature. Under normal temperature conditions, the charging current is controlled within 80% to 100% of the rated charging current. When the ambient temperature is high, the charging current is reduced accordingly; when the ambient temperature is low, the charging current is also appropriately reduced. This period lasts for 50% to 60% of the entire charging cycle. When the battery voltage reaches 90% of the nominal voltage, the constant voltage charging period begins.

[0117] During the constant voltage charging phase, a constant voltage charging method is used. The charging voltage value is determined by the battery type, while also taking temperature compensation into account. As the charging process progresses, the charging current gradually decreases. When the charging current drops to 10% of the rated charging current, the trickle charging phase begins. This phase lasts approximately 30% to 35% of the entire charging cycle.

[0118] The trickle charging period is the final stage of the charging process. During this period, a small current is used to supplement the battery's charge, with the current value controlled within the range of 3% to 5% of the rated charging current. The charging process ends when the battery voltage reaches the maximum charging voltage or the charging time reaches the preset limit. This period lasts approximately 5% to 10% of the entire charging cycle.

[0119] During the transition between different time periods, the charging control system monitors battery voltage, current, temperature, and other parameters in real time. If any parameter exceeds a safety threshold, the charging parameters are immediately adjusted or the charging process is stopped. Safety thresholds include a maximum charging temperature not exceeding 45°C, a minimum charging temperature not lower than 0°C, and a maximum charging voltage not exceeding 1.05 times the battery's nominal voltage.

[0120] The time-segment allocation control optimization phase performs a final optimization of the initial charging control scheme based on the corrected control data. By rationally allocating the duration and parameters of each charging phase, a complete charging control strategy is formulated. This strategy comprehensively considers battery health status, the influence of ambient temperature, and safety constraints, achieving intelligent management of the charging process.

[0121] This embodiment achieves precise charging management for batteries in different states by finely allocating and controlling the charging process across five time periods, thereby improving the safety and efficiency of the charging process. By employing low-current and pulse charging modes during the initial pre-charge and pre-charge phases, damage to deeply discharged batteries in the early stages of charging is effectively prevented, extending battery life. During the constant-current charging phase, the charging current is dynamically adjusted based on battery health indicators and ambient temperature parameters, ensuring environmental adaptability during the charging process and avoiding adverse effects from excessively high or low temperatures. The reasonable integration of constant-voltage charging and trickle charging phases ensures sufficient charging while preventing overcharging and battery damage. The entire charging control strategy, through real-time monitoring and dynamic adjustment, effectively reduces safety risks during battery charging while ensuring charging efficiency, thus improving the overall performance and reliability of the mobile charging cabinet.

[0122] Reference Figure 2 As shown, the present invention also provides a charging management system for a mobile charging cabinet, and a charging management method for a mobile charging cabinet applied to any of the above-mentioned methods, comprising: The data acquisition module is used to acquire the location status information of the mobile charging cabinet and the identity information of the charging equipment. It performs multiple verifications on the identity information and matches it with the location status information to obtain the charging authorization status. The analysis module is used to obtain the interface charging parameters of the mobile charging cabinet, associate the interface charging parameters with the charging authorization status, and obtain the charging management group. The association module is used to obtain battery status data of the charging device and perform charging planning with the charging management group to obtain charging parameter curves. The processing module is used to analyze the charging mode of the charging management group and the charging parameter curve based on the preset environmental energy game algorithm to obtain the initial charging control scheme. The control module is used to assess environmental factors based on battery status data and optimize and adjust the initial charging control scheme to obtain a charging control strategy.

[0123] The mobile charging cabinet charging management system provided by this invention significantly improves the safety and reliability of the charging process by performing multi-factor authentication on charging devices and authorizing matching with the storage compartment status information, effectively solving the security risks of single identity authentication in existing charging management methods. Based on the correlation processing of interface charging parameters and charging authorization status, intelligent binding of charging permissions and charging parameters is achieved, laying the foundation for precise charging management. By acquiring battery status data of charging devices and combining it with charging management group for charging planning, the system can formulate personalized charging parameter curves for different types and states of batteries, improving charging efficiency while extending battery life. Based on a preset environmental energy game algorithm to analyze charging modes, the system can achieve optimal resource allocation under various charging needs, significantly improving the overall operating efficiency of the mobile charging cabinet. By evaluating environmental factors based on battery status data and dynamically adjusting the charging control scheme, the charging process can adapt to different environmental conditions, enhancing the system's adaptability and stability. In summary, this invention not only solves the problems of insufficient security and low charging efficiency in existing charging management methods, but also realizes intelligent management of mobile charging cabinets, meeting users' growing needs for efficient, safe, and personalized charging.

[0124] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the system and each module described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0125] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural 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 charging management method for a mobile charging cabinet, characterized in that, include: Obtain the mobile charging cabinet's location status information and the charging device's identity information, perform multiple verifications on the identity information, and match the identity information with the location status information to obtain the charging authorization status; Obtain the interface charging parameters of the mobile charging cabinet, associate the interface charging parameters with the charging authorization status, and obtain the charging management group; Obtain the battery status data of the charging device and perform charging planning with the charging management group to obtain the charging parameter curve; Based on a preset environmental energy game algorithm, the charging management group and the charging parameter curve are analyzed to obtain an initial charging control scheme. Environmental factors are assessed based on the battery state data, and the initial charging control scheme is optimized and adjusted to obtain a charging control strategy.

2. The charging management method for the mobile charging cabinet according to claim 1, characterized in that, The process of obtaining the mobile charging cabinet's location status and the charging device's identity information, performing multiple verifications on the identity information, and matching it with the location status to obtain the charging authorization status includes: The mobile charging cabinet is scanned for its compartments, and the compartment numbers, occupancy status, and fault status obtained from the scan are integrated to obtain the compartment status information. Obtain the hardware identification code and communication protocol identification of the charging device, and integrate their identities to obtain the identity information; The identity information is verified using a pre-set identity verification database to obtain the identity verification result. Based on the authentication result, the charging device is classified into permission levels to obtain device permission level data; The warehouse status information and the device permission level data are matched to obtain a warehouse allocation strategy. The charging equipment and the mobile charging cabinet are bound together according to the storage allocation strategy to obtain the charging authorization status.

3. The charging management method for the mobile charging cabinet according to claim 1, characterized in that, The step of obtaining the interface charging parameters of the mobile charging cabinet and associating the interface charging parameters with the charging authorization status to obtain a charging management group includes: Voltage and current are sampled at the compartment interfaces of the mobile charging cabinet to obtain interface charging parameters; Based on the interface charging parameters, the bay interface is classified into interface parameter levels. The interface parameter levels are used to calculate interface indicators to obtain interface charging capability indicators. Based on the interface charging capability index, the charging authorization status is matched with the interface to obtain the interface authorization correspondence table; Perform interface initialization configuration based on the interface authorization mapping table to obtain the management initial group; The charging parameters of the initial management group are verified to obtain the charging management group.

4. The charging management method for the mobile charging cabinet according to claim 1, characterized in that, The process of acquiring battery status data of the charging device and performing charging planning with the charging management group to obtain charging parameter curves includes: The battery status data is stratified into status indicators to obtain a set of battery status indicators; Based on the battery status index set, the charging management group is mapped to obtain charging status associated data. The charging status associated data is clustered by category to obtain charging category groups; Based on the charging category grouping, the charging management group is grouped to predict the parameters and obtain the predicted charging parameter values. Capacity analysis is performed on the predicted values ​​of the charging parameters to obtain capacity distribution data; Based on the capacity distribution data, charging planning is performed to obtain charging planning parameters; The charging planning parameters are dynamically curve-constructed to obtain the charging parameter curve.

5. The charging management method for the mobile charging cabinet according to claim 1, characterized in that, The charging mode analysis of the charging management group and the charging parameter curve based on the preset environmental energy game algorithm is used to obtain an initial charging control scheme, including: Based on the environmental energy game algorithm, a three-modal energy capture decision analysis is performed on the charging management group to obtain charging energy status data. Based on the charging energy state data, the charging parameter curve is piecewise fitted to obtain energy distribution data; Dynamic energy source allocation calculations are performed on the energy distribution data to obtain energy allocation coefficients; Based on the energy allocation coefficient, a ternary utility construction analysis is performed on the charging management group to obtain the charging game result. The energy debt balance mechanism is used to calculate the energy income-expenditure ratio by applying the energy game result to the charging game result. Based on the energy expenditure ratio, the charging management group performs charging compensation calculations to obtain charging compensation parameters. The charging compensation parameters and the charging game result are combined and optimized to obtain a charging mode scheme; The charging mode scheme is corrected and calculated based on the charging parameter curve to obtain the initial charging control scheme.

6. The charging management method for the mobile charging cabinet according to claim 5, characterized in that, The step of performing a ternary utility construction analysis on the charging management group based on the energy allocation coefficient to obtain the charging game result includes: Based on the energy allocation coefficient, a group mapping operation is performed on the charging management group to obtain the energy allocation vector; The energy allocation vector is transformed for energy utility to obtain initial energy utility parameters; Based on the initial energy utility parameters, the charging management group is subjected to a revenue utility calculation to obtain a first utility component. Based on the energy allocation coefficient, a cost-utility calculation is performed on the charging management group to obtain a second utility component; Perform charging state utility calculation on the charging management group to obtain the third utility component; A combined utility function is obtained by constructing a joint function for the first utility component, the second utility component, and the third utility component. The charging game strategy set is obtained by solving the game equilibrium of the comprehensive utility function by the charging management group. The charging game strategy set is iteratively optimized to obtain the charging game result.

7. The charging management method for the mobile charging cabinet according to claim 5, characterized in that, The step of performing mode combination optimization on the charging compensation parameters and the charging game result to obtain a charging mode scheme includes: The charging compensation parameters and the charging game result are fused to obtain a pattern combination matrix; Based on the mode combination matrix, a charging efficiency and compensation cost integration function is constructed to obtain the target mode composite function; The target mode composite function is subjected to charging time constraints, compensation amount constraints, and game payoff constraints to obtain integrated constraint data. Based on the integrated constraint data, the target mode composite function is solved by function mode solving to obtain the target mode solution set; Based on the target mode solution set, a scheme is constructed using the charging compensation parameters and the charging game result to obtain the charging mode scheme.

8. The charging management method for the mobile charging cabinet according to claim 1, characterized in that, The step of evaluating environmental factors based on the battery state data and optimizing the initial charging control scheme to obtain a charging control strategy includes: Based on a preset health data table, perform a health analysis on the battery status data to obtain battery health indicators; Based on the battery health indicators, the battery status data is divided into temperature ranges to obtain a group of temperature-affecting parameters. The influencing factors are extracted from the temperature-affecting parameter group to obtain the environmental temperature-affecting parameters; Based on the environmental temperature influence parameters, the current gradient of the initial charging control scheme is calculated to obtain the current adjustment factor. The dynamic variation law of the current regulation factor is analyzed to obtain the environmental adaptability optimization coefficient; The initial charging control scheme is optimized in segments based on the environmental adaptability optimization coefficient to obtain the optimized charging control value; The optimized charging control value is compared with the preset safety threshold to obtain the corrected control data; The initial charging control scheme is optimized by time-segment allocation control based on the corrected control data to obtain the charging control strategy.

9. A charging management system for a mobile charging cabinet, characterized in that, The charging management method applied to the mobile charging cabinet according to any one of claims 1-8 includes: The acquisition module is used to acquire the location status information of the mobile charging cabinet and the identity information of the charging equipment, perform multiple verifications on the identity information, and perform authorization matching with the location status information to obtain the charging authorization status. The analysis module is used to obtain the interface charging parameters of the mobile charging cabinet, associate the interface charging parameters with the charging authorization status, and obtain the charging management group. The association module is used to obtain the battery status data of the charging device and perform charging planning with the charging management group to obtain the charging parameter curve. The processing module is used to perform charging mode analysis on the charging management group and the charging parameter curve based on a preset environmental energy game algorithm to obtain an initial charging control scheme. The control module is used to assess environmental factors based on the battery state data and optimize and adjust the initial charging control scheme to obtain a charging control strategy.

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