A ship container battery full life cycle management method and system

Abstract

The application provides a ship container battery full life cycle management method and system, applied to the battery management technical field, aiming to solve the problem that it is difficult to accurately evaluate the battery state and effectively manage under the complex background of global dispersion of battery assets, complex and changeable operation environment, heterogeneous data sources and dynamic operation plan. The scheme realizes the fine and intelligent management of the whole process of the battery from factory to scrap through the construction of a data-driven, state-aware, dynamic decision-making and closed-loop feedback management process. Therefore, the application has the advantages of being able to realize the systematic management of the full life cycle of the ship container battery, overcoming the problem of scattered data and information island, and providing basic data support for subsequent battery state evaluation and decision making.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management, in particular to a ship container battery full life cycle management method and system. BACKGROUND

[0002] Ship container battery systems play an increasingly critical role in global maritime trade, providing power support for refrigerated containers, onboard equipment, and even ship propulsion. The flexible deployment of ship container batteries across regions and ships is a common phenomenon in modern shipping operations, but it also makes it difficult to comprehensively and real-time track and master the complete use history and maintenance records of specific battery units, forming an information silo.

[0003] During ship navigation, the onboard battery management system (BMS) continuously collects real-time operation data of battery units, including voltage, current, temperature, single cell pressure difference, charge and discharge cycles, and other key parameters. However, the transfer and logistics information of battery units are scattered in different logistics transportation systems, increasing the difficulty of data integration. As battery units experience different charging and discharging modes and continuous environmental stress in different ships and routes around the world, their internal performance gradually deteriorates, manifested as capacity decline, internal resistance increase, and single cell consistency deterioration. This performance degradation path is nonlinear and closely related to the operating conditions and environmental exposure experienced by the battery.

[0004] Traditional battery maintenance plans are often based on fixed time intervals or cumulative operating hours. This method based on static thresholds or simple cumulative quantities cannot accurately reflect the actual degradation rate and potential risks of batteries under the cumulative influence of specific complex operating conditions, which may lead to insufficient or excessive maintenance.

[0005] Based on inaccurate or insufficient battery state assessment and life prediction results, the shore-based management platform may face decision-making difficulties when formulating global deployment plans and maintenance strategies for batteries. In addition, ship operation plans themselves are dynamic, requiring battery deployment and maintenance plans to be quickly responsive and adjustable, while existing management systems often lack the ability to dynamically optimize based on real-time information.

[0006] Finally, when battery performance degradation requires retirement or step utilization, due to the lack of complete and reliable data on the detailed use history of batteries throughout their entire life cycle, it is difficult to accurately assess their residual value and the most suitable step utilization scenario, and it is also impossible to optimize the recycling process based on detailed use history data.

[0007] In view of the above problems, the existing technology needs to be improved. SUMMARY

[0008] In view of the above deficiencies of the prior art, the present application provides a ship container battery full life cycle management method and system, which has the advantages of being able to realize systematic management of the full life cycle of the ship container battery, overcoming the problems of data dispersion and information island, and providing basic data support for subsequent battery state evaluation and decision making.

[0009] In a first aspect, a ship container battery full life cycle management method is provided, the method comprising the steps of:

[0010] S1: obtaining the identity of each battery and battery life cycle data;

[0011] S2: integrating all the battery life cycle data according to the identity, and recording the cumulative operating conditions and environmental exposure information of each battery;

[0012] S3: evaluating the battery state according to the operating conditions and the environmental exposure information;

[0013] S4: obtaining real-time dynamic operation information, and generating a battery management decision according to the real-time dynamic operation information, the battery state, and preset business rules and constraints;

[0014] S5: issuing the battery management decision, receiving execution feedback information, and adjusting the battery management decision according to the execution feedback information.

[0015] The ship container battery full life cycle management method provided by the present application aims to solve the problem of being difficult to accurately evaluate the battery state and effectively manage the battery under the complex background of global dispersion of battery assets, complex and variable operating environment, heterogeneous data sources, and dynamic changes in operation plans. The scheme realizes fine and intelligent management of the whole process of the battery from factory to scrap by constructing a data-driven, state-aware, dynamic decision-making, and closed-loop feedback management process. Therefore, the present application has the advantages of being able to realize systematic management of the full life cycle of the ship container battery, overcoming the problems of data dispersion and information island, and providing basic data support for subsequent battery state evaluation and decision making.

[0016] Further, step S1 comprises:

[0017] S11: obtaining the identity of each battery;

[0018] S12: obtaining original operating data from the shipborne system, and processing the original operating data to generate a shipborne data summary;

[0019] S13: obtaining maintenance activity records and battery transfer event information from the port maintenance system and the logistics transportation system; the battery life cycle data at least includes the shipboard data summary, the maintenance activity records and the battery transfer event information.

[0020] The ship container battery full life cycle management method provided in the application aims to solve how to comprehensively and effectively obtain key information constituting battery life cycle data from scattered and heterogeneous data sources in ship container battery management, and provide reliable data basis for subsequent data integration, state evaluation and decision-making.

[0021] Further, step S2 comprises:

[0022] S21: according to the identity, integrating the shipboard data summary, the maintenance activity records and the battery transfer event information corresponding to each battery to obtain integrated battery life cycle data;

[0023] S22: according to the integrated battery life cycle data, extracting running parameters reflecting the running state of the battery and environmental parameters reflecting the environment of the battery;

[0024] S23: according to the preset working condition definition and the environmental exposure level definition, mapping the running parameters and the environmental parameters to corresponding running working conditions and environmental exposure levels;

[0025] S24: according to the running working conditions and the environmental exposure levels, recording the cumulative running working condition and environmental exposure information of each battery.

[0026] The ship container battery full life cycle management method provided in the application aims to solve how to effectively integrate heterogeneous battery life cycle data, and accurately extract, quantify and accumulate the running working conditions and environmental stresses experienced by the battery, so as to overcome the deficiencies of the prior art in processing complex data and accurately reflecting the actual stresses of the battery, and lay a solid foundation for subsequent accurate battery state evaluation.

[0027] Further, step S24 comprises:

[0028] S241: accumulating the duration of each battery under different running working conditions and environmental exposure levels;

[0029] S242: obtaining a preset stress weight, and calculating a comprehensive stress index of the cumulative influence of the running working conditions and the environmental exposure levels on the battery performance under the duration according to the preset stress weight;

[0030] S243: according to the duration and the comprehensive stress index, recording the cumulative running working condition and environmental exposure information of each battery.

[0031] The application provides a ship container battery full life cycle management method,

[0032] Further, step S3 comprises:

[0033] S31: obtaining real-time operation data of the battery according to the operation condition and the environmental exposure information;

[0034] S32: obtaining model information of the battery, and establishing a battery performance degradation model associated with the model information;

[0035] S33: evaluating the battery state according to the real-time operation data and the battery performance degradation model.

[0036] Further, step S32 comprises:

[0037] S321: obtaining refined stress historical data of the corresponding battery according to the model information;

[0038] S322: selecting a basic performance degradation model matched with the model information from a preset model library;

[0039] S323: individually adjusting the basic performance degradation model according to the refined stress historical data to obtain the battery performance degradation model.

[0040] Further, step S33 comprises:

[0041] S331: calculating a preliminary health state parameter of the battery at present by using the battery performance degradation model according to the real-time operation data;

[0042] S332: obtaining historical health state evaluation results and historical operation data of the battery;

[0043] S333: correcting the preliminary health state parameter according to the preliminary health state parameter, the historical health state evaluation results and the historical operation data to obtain a corrected health state parameter of the battery at present;

[0044] S334: predicting a remaining service life of the battery according to the corrected health state parameter, and generating an evaluation result of the battery state.

[0045] Further, step S4 comprises:

[0046] S41: predicting operation demand and resource availability in a future period of time according to the real-time dynamic operation information;

[0047] S42: determining a current performance level and potential risks of the battery according to the battery status;

[0048] S43: constructing a battery management function containing optimization targets and constraint conditions according to the operation demand and resource availability, the current performance level and potential risks of the battery, and preset business rules and constraint conditions;

[0049] S44: solving the battery management function according to a preset decision generation strategy to generate a candidate battery management decision scheme;

[0050] S45: evaluating the candidate battery management decision scheme according to preset risk assessment and benefit assessment criteria to determine the battery management decision.

[0051] Further, step S5 comprises:

[0052] S51: determining whether there is a deviation between an actual execution result of the battery management decision and an expected execution result according to the execution feedback information;

[0053] S52: if there is a deviation, obtaining updated information of actual operating conditions, actual environmental exposure information, actual maintenance activities, and actual location status of the battery;

[0054] S53: updating life cycle data, cumulative operating conditions and environmental exposure information, and battery status of the battery according to the updated information;

[0055] S54: adjusting the battery management decision according to the updated battery status, the actual execution result, and a preset decision adjustment rule.

[0056] The second aspect is a ship container battery full life cycle management system for implementing any of the above methods, the system comprising:

[0057] An acquisition module: acquiring an identity of each battery and battery life cycle data;

[0058] A recording module: integrating all the battery life cycle data according to the identity and recording cumulative operating conditions and environmental exposure information of each battery;

[0059] An evaluation module: evaluating battery status according to the operating conditions and the environmental exposure information;

[0060] A decision module: acquiring real-time dynamic operation information and generating a battery management decision according to the real-time dynamic operation information, the battery status, and preset business rules and constraint conditions;

[0061] The adjustment module: issuing the battery management decision, receiving execution feedback information, and adjusting the battery management decision according to the execution feedback information.

[0062] Beneficial effects: The ship container battery full life cycle management method and system provided by the present application aims to solve the problem of difficult accurate assessment of battery state and effective management under the complex background of global dispersion of battery assets, complex and variable operation environment, heterogeneous data sources and dynamic changes of operation plan. The scheme realizes the fine and intelligent management of the whole process of the battery from factory to scrap through the construction of a data-driven, state-aware, dynamic decision and closed-loop feedback management process. Therefore, the present application has the advantages of being able to realize the systematic management of the whole life cycle of the ship container battery, overcoming the problems of data dispersion and information island, and providing basic data support for subsequent battery state assessment and decision making. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 The flowchart of the ship container battery full life cycle management method provided by the present application.

[0064] Figure 2 The structure diagram of the ship container battery full life cycle management system provided by the present application.

[0065] Figure 3 The architecture diagram of the ship container battery full life cycle management system provided by the present application.

[0066] Label explanation: 201, acquisition module; 202, recording module; 203, evaluation module; 204, decision module; 205, adjustment module. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. The components of the embodiments of the present application described and indicated in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0068] It should be noted that similar reference numerals and letters refer to like items in the accompanying drawings, and once an item is defined in one drawing, it is not necessary to further define and explain it in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0069] Please refer to Figure 1 A ship container battery full life cycle management method, the method comprises the steps of:

[0070] S1: obtaining the identity of each battery and the battery life cycle data;

[0071] S2: according to the identity, integrate all the battery life cycle data, and record the cumulative running condition and environmental exposure information of each battery;

[0072] S3: according to the running condition and environmental exposure information, evaluate the battery state;

[0073] S4: obtain real-time dynamic operation information, and generate battery management decision according to real-time dynamic operation information, battery state and preset business rules and constraints;

[0074] S5: issue battery management decision, receive execution feedback information, and adjust battery management decision according to execution feedback information.

[0075] Among them, the identity refers to the mark for uniquely identifying each battery unit, which can be realized by serial number, bar code, two-dimensional code or electronic tag, such as the unique code given at the time of manufacturing or the installation of RFID chip, mainly to distinguish different battery individuals and ensure the accurate attribution of data.

[0076] The battery life cycle data refers to various information recorded in the whole process of battery from manufacturing, transportation, installation, operation, maintenance to disposal, which can include shipborne operation data, port maintenance record, logistics transfer information, test report, etc., such as voltage and current data collected by BMS, repair work order, location tracking record, mainly to provide a comprehensive view of the history of the battery.

[0077] The cumulative running condition and environmental exposure information refers to the data quantifying the stress accumulation degree of the battery under different running modes and environmental conditions, which can be realized by recording the cumulative time under specific running condition and exposure environment level and calculating the comprehensive stress index, such as recording the total time of large current charging and discharging under high temperature and high humidity environment or calculating the stress value weighted based on temperature, current, humidity and other parameters, mainly to reflect the real stress accumulation of the battery under actual complex environment, and provide basis for accurate evaluation of battery state.

[0078] Battery status refers to the evaluation results reflecting the current performance level, health degree, and remaining use potential of the battery, such as evaluating the percentage of the current capacity of the battery to the initial capacity or predicting the time when the battery can still meet certain performance requirements, mainly to provide quantitative indicators of the current availability and reliability of the battery.

[0079] Real-time dynamic operation information refers to immediate information reflecting changes in ship operation plans, task requirements, resource availability, etc. in the current and future period of time, which can include ship position, route plan, cargo type, port berthing time, shore power availability, etc., such as receiving update information from the ship dispatching system or notifications from the port service platform, mainly to provide immediate operation background and constraints required for making battery management decisions.

[0080] Battery management decisions refer to specific management instructions or recommendations generated for a specific battery or battery pack, which can include deployment instructions, maintenance plans, charging and discharging strategies, retirement recommendations, etc., such as deciding to transfer a certain battery from one ship to another or scheduling equalization maintenance for a certain battery, mainly to guide the actual use and maintenance activities of the battery.

[0081] Execution feedback information refers to the results after the actual execution of battery management decisions or the state change information caused thereby, which can include whether the decision is successfully executed, actual running data of the battery, maintenance effect, location update, etc., such as receiving confirmation information that the battery has been successfully installed to the designated location or change data of the internal resistance of the battery after maintenance, mainly to verify the decision effect and provide basis for subsequent adjustment.

[0082] The core innovation of the present application is that by integrating heterogeneous and dispersed battery life cycle data and quantifying cumulative running conditions and environmental exposure information, and then combining battery state evaluation based thereon, and further combining battery state evaluation results with real-time dynamic operation information to generate management decisions, and finally adjusting the decisions through execution feedback information, the problem of being difficult to accurately evaluate battery state and dynamically optimize management in complex and variable scenarios is solved, and the effects of improving battery asset utilization efficiency, reducing operation risk and maintenance cost are achieved.

[0083] As a preferred embodiment, the scheme of the present application is implemented as follows:

[0084] The serial number, running log, maintenance record, and location information of each battery are obtained from the ship-mounted battery management system, the port maintenance management system, and the logistics tracking platform through the data acquisition interface. These data are collected into a central database.

[0085] The battery serial number is used as the primary key to correlate and integrate data from different sources. From the integrated data, parameters such as charge and discharge current, temperature, humidity, and vibration of the battery are extracted. According to the pre-set working conditions and environmental exposure levels, the extracted parameters are mapped to the corresponding levels, and the time the battery stays at each level is accumulated. At the same time, according to these accumulated time and pre-set stress weight, the comprehensive stress index of each battery is calculated. Then, according to the model information of the battery, the corresponding performance degradation model is selected from the model library, and the historical operation data of the battery is used to calibrate the model.

[0086] The real-time operation data of the battery and the calculated comprehensive stress index are input into the calibrated performance degradation model to evaluate the current state of health (SOH) and predict the remaining useful life (RUL) of the battery. At the same time, real-time ship location, route, task priority, port service capacity, and other information are obtained from the ship scheduling system and the port operation system. The SOH and RUL of the battery, real-time operation information, and pre-set business objectives and constraints are input into an optimization decision engine to generate a battery deployment plan, maintenance recommendation, or charge and discharge strategy. For example, the decision engine may recommend moving a battery with low SOH from a high-load ship to a low-load ship, or scheduling preventive maintenance for a battery.

[0087] Finally, the generated decision is sent to the shipboard system or shore-based maintenance team through the instruction interface. The status report or maintenance completion confirmation information received from the execution system is used as feedback. If the feedback shows that the battery status does not meet the expected or the operation plan has been significantly adjusted, the system will automatically trigger data update and state re-evaluation processes, and adjust the original decision according to pre-set rules, such as advancing or postponing the maintenance plan.

[0088] Further, step S1 includes:

[0089] S11: Obtain the identity of each battery;

[0090] S12: Obtain the original operation data from the shipboard system and process the original operation data to generate a shipboard data summary;

[0091] S13: Obtain maintenance activity records and battery transfer event information from the port maintenance system and logistics transportation system; the battery life cycle data at least includes the shipboard data summary, maintenance activity records, and battery transfer event information.

[0092] The shipboard system refers to a collection of hardware and software installed on the ship, responsible for monitoring and managing the operation status of the battery, which can be implemented by a battery management system (BMS) or a ship integrated automation system.

[0093] Raw operational data refers to the real-time collected battery operational parameters without aggregation or processing, such as voltage, current, temperature, single cell voltage, charge and discharge power, etc., which can be realized by directly collecting electrical signals or digital signals with sensors. Processing refers to the operation of calculating, analyzing, screening or converting raw operational data, such as calculating statistical values, identifying abnormal events, data compression, etc., which can be realized by using data processing algorithms or data analysis models.

[0094] Onboard data summary refers to the refined data set after processing, which can reflect the key state and event of the battery during the operation on the ship, which can be realized by using statistical report, event log or feature parameter list.

[0095] Port maintenance system refers to the system deployed in the port or maintenance site for recording and managing battery maintenance, repair, replacement and other activities, which can be realized by using enterprise asset management system (EAM) or special maintenance work order system.

[0096] Logistics transportation system refers to the system for tracking the physical location, transportation status and transfer history of the battery, which can be realized by using global positioning system (GPS) tracking, warehouse management system (WMS) or transportation management system (TMS).

[0097] Maintenance activity record refers to the detailed record of various maintenance operations on the battery in the port or maintenance site, including maintenance time, place, content, performer, replacement parts, etc., which can be realized by using structured database record or electronic work order form.

[0098] Battery transfer event information refers to the record of the transfer of the battery between different physical locations, including transfer time, starting place, destination place, transportation mode, belonging ship or warehouse, etc., which can be realized by using logistics tracking data or asset allocation record.

[0099] In some specific embodiments, the identity is obtained by scanning a two-dimensional code or an RFID tag on the battery case. During the voyage of the ship, the on-board battery management system (BMS) collects the voltage, current, temperature and other data of the battery at a frequency of 1 second, and then calculates the average value, maximum value, minimum value and standard deviation of these data every hour, and sends these statistical values and the occurrence time, duration and maximum value of any abnormal events such as over-temperature and over-voltage as the on-board data summary to the shore-based management platform through the satellite communication link. When the battery is docked for maintenance, the maintenance personnel use a handheld terminal to record the maintenance content, replacement of parts and other information, which is synchronized to the port maintenance database on the shore through the network; at the same time, when the battery is unloaded from the ship or loaded onto another ship, the logistics system generates a transfer record containing information such as time, location, and involved ships / warehouses, which is automatically transmitted to the logistics database on the shore through the system interface. Finally, the shore-based management platform associates and integrates the on-board data summary obtained from the on-board system, the maintenance activity record obtained from the port maintenance system, and the battery transfer event information obtained from the logistics transportation system according to the identity of the battery, to form a complete life cycle data file of the battery.

[0100] Further, step S2 comprises:

[0101] S21: integrating the on-board data summary, the maintenance activity record and the battery transfer event information corresponding to each battery according to the identity to obtain the integrated battery life cycle data;

[0102] S22: extracting the operating parameters reflecting the operating state of the battery and the environmental parameters reflecting the environment in which the battery is located according to the integrated battery life cycle data;

[0103] S23: mapping the operating parameters and the environmental parameters to the corresponding operating conditions and environmental exposure levels according to the pre-defined operating condition definition and environmental exposure level definition;

[0104] S24: recording the cumulative operating condition and environmental exposure information of each battery according to the operating condition and the environmental exposure level.

[0105] Wherein, the integration refers to the aggregation, cleaning, alignment of data from different systems and different formats to form structured and consistent data, and the purpose is to solve the problem of scattered data and provide a complete data view for subsequent analysis.

[0106] Wherein, the operating parameters can include voltage, current, temperature, charge and discharge power, cycle number, internal resistance and other quantities reflecting the electrochemical and thermodynamic state of the battery. The environmental parameters can include environmental temperature, humidity, altitude, vibration intensity, impact number and other quantities reflecting the external environmental conditions of the battery. The extraction of these parameters is to quantify the internal and external stresses borne by the battery.

[0107] According to the preset working condition definition and environmental exposure level definition, it is defined in advance according to the battery type, application scenario, stress sensitivity and other factors. For example, high-rate charge and discharge working condition, high-temperature continuous discharge working condition, low-temperature storage environment, high-humidity salt spray environment, etc. These definitions provide standards for converting raw parameters into stress categories that can be compared and accumulated.

[0108] Mapping running parameters and environmental parameters to corresponding working conditions and environmental exposure levels means that according to the extracted running parameters and environmental parameters, the running condition and the environmental exposure level of the battery in a certain time period are determined by comparing the preset working condition definition and the environmental exposure level definition. This can convert raw data into standardized stress information.

[0109] Recording the cumulative running condition and environmental exposure information of each battery means recording the total amount of each battery experienced under different running conditions and environmental exposure levels in a certain way. This recording method is based on standardized working conditions and levels, and can reflect the cumulative exposure degree of the battery under different stress conditions.

[0110] As a specific implementation, a data integration platform can be deployed to connect shipborne systems, port maintenance systems, and logistics transportation systems through adapters. The platform aggregates data of the same battery into its corresponding life cycle file according to the battery identity in the received data and stores it in a database. The integration process can include data cleaning, format conversion, and data alignment. Processing services can be developed to extract parameters from integrated battery life cycle data on a regular or real-time basis. For example, voltage, current, and temperature sequences are parsed from the shipborne data summary, and the average current, maximum temperature, cumulative ampere-hour, and charge and discharge cycle number of each voyage are calculated. Maintenance type and occurrence time are identified from maintenance records. The origin, destination, and transportation duration are extracted from the transfer event, and the environmental temperature, humidity, and altitude of the corresponding time period and location are obtained by combining geographic information systems and weather databases.

[0111] A rule engine can be set in advance, for example, the rule can define: when the average discharge current exceeds the threshold value and the battery temperature exceeds the threshold value and lasts for a period of time, it is mapped to the high-load high-temperature discharge working condition. When the environmental temperature exceeds the threshold value and the relative humidity exceeds the threshold value and lasts for a period of time, it is mapped to the high-temperature high-humidity exposure level. The processing service inputs the extracted parameters into the rule engine, and outputs the corresponding working condition and environmental level label.

[0112] The database can be pre-set. Whenever a new period of operating condition and environmental exposure is determined, an entry is recorded in the database for the battery, containing battery ID, operating condition type, environmental level, start time, end time. Alternatively, it can also be summarized periodically, calculating the total duration of each battery under each operating condition and environmental level, and updating to the cumulative stress profile of the battery. For example, the recorded fields can include battery ID, high-load high-temperature discharge hours, high-temperature high-humidity exposure days, etc.

[0113] Further, step S24 comprises:

[0114] S241: Accumulate the duration of each battery under different operating conditions and environmental exposure levels;

[0115] S242: Obtain preset stress weights, and calculate the comprehensive stress index of the cumulative effect of operating conditions and environmental exposure levels on battery performance under the duration according to the preset stress weights;

[0116] S243: Record the cumulative operating condition and environmental exposure information of each battery according to the duration and the comprehensive stress index.

[0117] The method first accumulates the duration of each battery under different operating conditions and environmental exposure levels, which provides basic time information of the battery exposure to various conditions. Then, the preset stress weights are obtained, which reflect the relative influence of different operating conditions and environments on battery performance degradation. Based on the accumulated duration and the corresponding stress weights, the comprehensive stress index is calculated. This index quantifies the cumulative stress under different conditions by weighting, which can better reflect the actual performance damage than simply accumulating time. Finally, the duration and the comprehensive stress index are recorded together to form more comprehensive and more distinguishable cumulative operating condition and environmental exposure information. This recording method containing weighted stress information, combined with the aforementioned steps of integrating life cycle data according to identity, extracting operating and environmental parameters, and mapping parameters to operating condition and environmental level, can provide more reliable and more accurate input data for subsequent evaluation of battery status.

[0118] Specifically, assuming that there are n combinations of operating conditions and environmental exposure levels, denoted as wherein, represents the i-th operating condition, represents the j-th environmental exposure level. Then the duration under each combination is .

[0119] For each battery, the duration under different operating conditions and environmental exposure levels can be represented as wherein, is the total number of operating conditions, and n is the total number of environmental exposure levels.

[0120] The preset stress weights include operating condition weights and environmental exposure level weights .

[0121] The comprehensive stress index .

[0122] Different operating conditions and environmental exposure levels, the duration of the battery operating under different operating conditions and environmental exposure levels, and the comprehensive stress index are recorded as the cumulative operating condition and environmental exposure information of each battery.

[0123] In this way, the total stress load of the battery in the complex and variable use process can be more accurately evaluated, overcoming the problem that simple cumulative information cannot accurately reflect the cumulative impact of complex conditions, thereby improving the accuracy and effectiveness of the entire battery management method.

[0124] Further, step S3 includes:

[0125] S31: obtaining real-time operating data of the battery according to the operating condition and environmental exposure information;

[0126] S32: obtaining model information of the battery, and establishing a battery performance degradation model associated with the model information;

[0127] S33: evaluating the battery state according to the real-time operating data and the battery performance degradation model.

[0128] Among them, the battery performance degradation model refers to a mathematical model used to describe the change of battery performance with time, use condition and environmental factors, which can be realized by an empirical model. Evaluating the battery state refers to determining the current health status of the battery, such as capacity attenuation degree, internal resistance increase, power performance change or single consistency level, which can be realized by calculation based on mathematical model.

[0129] In one embodiment, obtaining real-time operating data of the battery according to operating condition and environmental exposure information can be collecting sensor data such as voltage, current and temperature of the battery by the on-board battery management system, and transmitting these data to the shore-based management platform through the communication network. Obtaining the model information of the battery can be scanning the two-dimensional code on the battery body or querying the model information associated with the battery identity from the database. Establishing a battery performance degradation model associated with the model information can select a Kalman filter-based capacity estimation model matched with the model from the preset model library.

[0130] Specifically, assuming that the health state of the battery is represented by capacitance and resistance, then: Let be the battery capacity at time t; Let be the internal resistance of the battery at time t.

[0131] The health state of a battery can be represented by a state vector. express: .

[0132] The changes in battery capacity and internal resistance over time can be described by a linear relationship: Where A is the state transition matrix, describing the natural degradation process of battery health. It is process noise, which can be assumed to be zero-mean Gaussian noise, representing random changes in the battery state.

[0133] Assuming the battery capacity decreases at a constant rate and the internal resistance increases at a constant rate, the state transition matrix A can be expressed as: Where α is the capacity decay rate, i.e., the proportion of capacity decay per unit time; β is the internal resistance growth rate, i.e., the proportion of internal resistance growth per unit time.

[0134] Real-time operating data of the battery (such as voltage) Current ,temperature The health status of a battery can be correlated with an observation equation, which is: .in, H is the observation vector, which includes at least voltage, current, and temperature; H is the observation matrix, which describes the relationship between the observation data and the battery health status. It is observation noise, assumed to be zero-mean Gaussian noise.

[0135] Assume the relationship between voltage, capacitance, and internal resistance is as follows: ,in, It is the open-circuit voltage of the battery. This is the voltage measurement error. Therefore, the observation matrix H can be expressed as: .

[0136] The battery state is estimated based on the Kalman filter algorithm, and its state vector is: The main steps in prediction include: establishing the prediction equation: ; .

[0137] in, For time t, based on time The predicted estimate of the state based on the observed data at time t. This is a vector containing the estimate of the state at time t.

[0138] In time Moment, based on time The optimal estimate of the state given the observations up to time t. This is a vector containing the best estimate of the state at time t given the observations up to time t The estimate of the state at time t.

[0139] The updated estimate of the state at time t given the observations at time t. The covariance matrix of the state estimate given the observations at time t, describing the uncertainty of the predicted state.

[0140] The updated estimate of the state at time t given the observations at time t. The covariance matrix of the state estimate given the observations at time t, describing the uncertainty of the predicted state. The covariance matrix of the state estimate given the observations at time t, describing the uncertainty of the predicted state. The uncertainty of the state estimate at time t.

[0141] Q is the covariance matrix of the process noise.

[0142] is the transpose of the state transition matrix A.

[0143] The update equation is established:

[0144] The Kalman gain equation: ;

[0145] The state update equation: ;

[0146] The covariance update equation: .

[0147] where, is a matrix representing the Kalman gain, which balances the credibility of the predicted value and the observed value. is the transpose of the observation matrix H.

[0148] is the observation vector, which is the vector obtained by actual observation.

[0149] The updated estimate of the state at time t given the observations at time t.

[0150] The updated state covariance matrix, which represents the uncertainty of the state estimate at time t given the observations at time t.

[0151] I is the identity matrix, representing the identity transformation that does not change over time.

[0152] Finally, the battery state is evaluated according to the real-time running data and the battery performance degradation model. The capacity of the battery can be dynamically estimated by inputting the real-time collected voltage, current and temperature data into the selected Kalman filter model and internal resistance Then, the current capacity attenuation percentage and internal resistance increase value of the battery are calculated according to the capacity and internal resistance of the battery, and these parameters are taken as the evaluation results of the battery health state.

[0153] For example, the capacity attenuation percentage of the battery is: wherein is the initial capacity of the battery.

[0154] The internal resistance increase value is: wherein, is the initial internal resistance of the battery.

[0155] By obtaining the real-time running data of the battery according to the running conditions and environmental exposure information, obtaining the model information of the battery and establishing the battery performance degradation model associated with the model information, and evaluating the battery state according to the real-time running data and the battery performance degradation model, the scheme can perform more accurate state evaluation according to the inherent characteristics of different battery models and the real-time performance of the batteries under complex running and environmental stress. The scheme overcomes the challenges brought by the differences between different battery models and the nonlinear influence of complex and variable running conditions and environmental stress on battery performance degradation, and improves the accuracy of battery state evaluation.

[0156] Further, the step S32 comprises:

[0157] S321: According to the model information, obtaining the refined stress history data of the corresponding battery;

[0158] S322: Selecting a basic performance degradation model matched with the model information from a preset model library;

[0159] S323: According to the refined stress history data, performing individual adjustment on the basic performance degradation model to obtain the battery performance degradation model.

[0160] The refined stress history data refers to detailed and high-granularity data records of a specific battery unit under different running conditions and environmental exposure in the actual use process, which can be realized by means of sensor collection, data recorder storage or integration from shipborne systems, port maintenance systems and other data sources.

[0161] The pre-set model library refers to a collection of various battery performance degradation models, which are pre-established for different battery models or types. The base performance degradation model refers to an initial model selected from the pre-set model library that matches a specific battery model, which reflects the performance degradation law of the battery model under typical or average stress conditions.

[0162] The personalized adjustment refers to modifying or optimizing the parameters, structure or prediction output of the selected base performance degradation model according to the refined stress history data of the specific battery unit, so as to more accurately reflect the actual degradation characteristics of the battery unit.

[0163] For example, in a specific embodiment, for a battery unit of a specific model, first, according to its identity and model information, all running data and environmental exposure records since its commissioning are extracted from the historical database, including but not limited to detailed time series data of charging and discharging current, voltage, temperature, ambient temperature, humidity, vibration, etc., constituting the refined stress history data of the battery unit.

[0164] Then, a base performance degradation model matching the model is selected from the pre-set model library, which may include an average degradation curve model based on the test of the battery model in the laboratory or under typical working conditions, such as an empirical model based on cycle number and temperature.

[0165] Finally, the selected base model is personalized adjusted using the obtained refined stress history data. For example, the actual historical degradation data of the battery unit (such as the change of capacity over time or cycle number) can be used as training data to optimize the key parameters in the base model through parameter fitting algorithms (such as least squares method or gradient descent method), so that the prediction results are more consistent with the actual degradation trajectory of the battery unit. After personalized adjustment, the performance degradation model of the specific battery unit is obtained.

[0166] Further, step S33 includes:

[0167] S331: calculating the preliminary health status parameter of the battery according to the real-time running data using the battery performance degradation model;

[0168] S332: obtaining the historical health status evaluation results and historical running data of the battery;

[0169] S333: correcting the preliminary health status parameter according to the preliminary health status parameter, the historical health status evaluation results and the historical running data, to obtain the corrected health status parameter of the battery;

[0170] S334: Based on the corrected health status parameter, predict the remaining useful life of the battery, and generate the evaluation result of the battery status.

[0171] The preliminary health status parameter refers to the battery health status indicator calculated based on the current or recent operating data of the battery and the performance degradation model, which has not been corrected by historical information, such as the initial capacity fade rate, internal resistance increase value, or health percentage estimate.

[0172] The historical health status evaluation result refers to the record generated by the system when it evaluates the status of the battery in the past, which can include a series of health status parameter values, remaining useful life prediction values, or related confidence indicators at different time points, reflecting the trajectory of the battery's health status change over time.

[0173] The historical operating data refers to the accumulated operating information of the battery throughout its life cycle, covering detailed records of different working conditions (such as the number of charge and discharge cycles, cumulative discharge energy, average / maximum charge and discharge current) and environmental exposure (such as cumulative high / low temperature exposure time, humidity, vibration intensity), which objectively reflect the stress experienced by the battery and the resulting degradation.

[0174] The correction process refers to the adjustment or correction of the preliminary health status parameter based on the historical health status evaluation result and the historical operating data, aiming to eliminate the errors caused by transient data fluctuations and incorporate the long-term cumulative degradation of the battery, making the evaluation result more consistent with the actual long-term performance of the battery.

[0175] The corrected health status parameter refers to the more accurate indicator reflecting the current true health level of the battery after the correction of historical information.

[0176] The remaining useful life refers to the time or cycle number that the battery is expected to continue to use before its performance degrades to a pre-set threshold under specific usage conditions.

[0177] The evaluation result of the battery status is the final output of comprehensive battery health status information, usually including the corrected health status parameter, the prediction of the remaining useful life, and possibly the confidence interval or risk prompt.

[0178] By introducing historical health state assessment results and historical operation data to correct the preliminary assessment results, the scheme can significantly improve the accuracy and stability of battery state assessment. This correction mechanism reduces the assessment errors caused by instantaneous data fluctuations or insufficient consideration of long-term cumulative effects by the model, making the obtained health state parameters more reliably reflect the true condition of the battery. Based on more accurate health state assessment, the prediction of the remaining service life of the battery is also more accurate. This provides a more solid data foundation for the whole life cycle management of ship container batteries, helps to make more reasonable and timely maintenance, deployment and disposal decisions, reduces operational risks, and optimizes asset utilization efficiency.

[0179] Further, step S4 comprises:

[0180] S41: predicting operation demand and resource availability in a future period of time according to real-time dynamic operation information;

[0181] S42: determining the current performance level and potential risks of the battery according to the battery state;

[0182] S43: constructing a battery management function containing optimization objectives and constraint conditions according to operation demand and resource availability, the current performance level and potential risks of the battery, and preset business rules and constraint conditions;

[0183] S44: solving the battery management function according to a preset decision generation strategy to generate a candidate battery management decision scheme;

[0184] S45: evaluating the candidate battery management decision scheme according to preset risk assessment and benefit assessment criteria to determine the battery management decision.

[0185] Among them, the real-time dynamic operation information refers to data reflecting the change of ship operation activities over time, which can include ship position, route information, port calling plan, cargo loading and unloading demand, port facility availability, etc.

[0186] Operation demand and resource availability refer to the demand for batteries and the availability of resources such as batteries, maintenance personnel, and charging facilities in a future period of time predicted according to real-time dynamic operation information.

[0187] The current performance level and potential risks of the battery refer to the actual capacity of the battery and the possibility of future failure or significant performance decline determined according to the battery state assessment results.

[0188] The preset business rules and constraint conditions refer to the regulations and restrictions that need to be followed in the battery management process, which can include safety standards, cost budget, maintenance strategy, transportation timeliness, battery technical specifications, etc.

[0189] The battery management function refers to a mathematical model or computational model formed by integrating operational requirements, resource availability, battery performance, potential risks, and business rules and constraints, used to describe battery management decision problems, including objectives to be optimized and restrictions to be met.

[0190] The preset decision generation strategy refers to an algorithm or method used to solve the battery management function and generate candidate decision schemes.

[0191] The candidate battery management decision scheme refers to a plurality of possible battery management schemes obtained by solving the battery management function, which can include battery deployment plans, maintenance arrangements, charging and discharging strategies, etc.

[0192] The preset risk assessment and benefit assessment criteria refer to standards for measuring the pros and cons of candidate battery management decision schemes, which can include the risk level, economic benefit, and impact on battery life of the scheme.

[0193] Specifically, the battery management function can be represented as an optimization problem:

[0194]

[0195] Where x is the decision variable vector, representing the battery management decision.

[0196] is the total cost function, including at least operational cost , maintenance cost and replacement cost .

[0197] represents the minimization objective function, and the objective function in this formula is .

[0198] is the weight factor, used to balance different costs.

[0199] is the battery state constraint function, is the upper limit of the battery state.

[0200] is the operational requirement constraint function, is the lower limit of the operational requirement.

[0201] is the resource availability constraint function, is the upper limit of the resource availability.

[0202] is the safety constraint function, is the upper limit of the safety constraint.

[0203] is a business rule constraint function.

[0204] The battery management function described above can be solved by linear programming, specifically:

[0205]

[0206] where c is a cost coefficient vector, denotes the transpose of c. denotes the minimization objective function, and the objective function in this formula is .

[0207] subject to is a commonly used term, meaning "subject to" or "satisfy the following conditions."

[0208] M is an inequality constraint coefficient matrix, b is an inequality constraint constant vector. B is an equality constraint coefficient matrix, and d is an equality constraint constant vector. 、 are the upper and lower bounds of the decision variable, respectively.

[0209] By solving the optimization problem described above, the optimal battery management decision scheme can be obtained, thereby achieving effective management and optimized operation of the battery.

[0210] Further, step S5 includes:

[0211] S51: According to the execution feedback information, determine whether there is a deviation between the actual execution result of the battery management decision and the expected execution result;

[0212] S52: If there is a deviation, obtain updated information of the actual operating conditions, actual environmental exposure information, actual maintenance activities, and actual location state of the battery;

[0213] S53: According to the updated information, update the battery's life cycle data, cumulative operating conditions and environmental exposure information, and battery state;

[0214] S54: According to the updated battery state, actual execution result, and preset decision adjustment rule, adjust the issued battery management decision.

[0215] Among them, the execution feedback information refers to the data recorded and summarized on the completion, problems encountered, and actual achieved effects of the issued battery management decision in the actual execution process. This information can be achieved in the form of structured reports, system logs, manually input confirmation information, etc.

[0216] The update information refers to a collection of data reflecting the latest actual situation of the battery, including its real running mode in a specific time period, real conditions of the environment it is in, actual maintenance operations performed, and its current physical location. These information can be obtained in the form of sensor data, system records, manually entered data, GPS data, etc.

[0217] The decision adjustment rule refers to a pre-set logic or strategy collection for guiding how to correct the original decision according to the actual situation. These rules can be implemented in the form of a rule base based on expert experience.

[0218] For example, in a specific implementation scenario, assume that the system issues a decision to transfer a certain battery from ship A to ship B and perform a deep discharge cycle on ship B as a maintenance operation. However, the actual execution feedback information shows that due to the temporary change of the route of ship A, the battery failed to be transferred to ship B as planned, but was transferred to a port warehouse for temporary storage. After receiving this feedback, the system determines through step S51 that the actual execution result (transferred to the port warehouse) deviates from the expected execution result (transferred to ship B and deep discharge). Subsequently, the system obtains the actual environmental exposure information of the battery during the storage period in the port warehouse (such as temperature and humidity records of the warehouse) and its current actual location state (port warehouse X) through step S52.

[0219] At the same time, the system may obtain whether there is a record of actual maintenance activities for the battery (such as whether an appearance inspection or cleaning has been performed) by querying the port maintenance system. Based on these update information, the system updates the life cycle data of the battery (adds the environmental exposure record of the port storage period), accumulates the running condition and environmental exposure information, and re-evaluates its battery state (for example, long-term storage in the warehouse may cause self-discharge, which requires correction of its current power and health status assessment) through step S53.

[0220] Finally, according to the updated battery state, actual execution result (stored in the port warehouse), and pre-set decision adjustment rule (for example, the rule stipulates that the battery needs to be charged or checked if it is stored in the port warehouse for more than a certain period of time), the system adjusts the original decision through step S54. The deep discharge cycle task in the original decision may be cancelled or postponed, and a new decision is generated, such as scheduling a state check and charging for the battery in the port warehouse, or reassigning it to another ship that is more suitable for its current state.

[0221] Please refer to Figure 2 , Figure 3 A ship container battery full life cycle management system for implementing any of the above methods, the system comprises:

[0222] The acquisition module 201 acquires the identity of each battery and the battery life cycle data.

[0223] The recording module 202 integrates all battery life cycle data according to the identity and records the cumulative operating conditions and environmental exposure information of each battery.

[0224] The evaluation module 203 evaluates the battery state according to the operating conditions and environmental exposure information.

[0225] The decision module 204 acquires real-time dynamic operation information and generates battery management decisions according to the real-time dynamic operation information, battery state, and preset business rules and constraints.

[0226] The adjustment module 205 issues battery management decisions, receives execution feedback information, and adjusts the battery management decisions according to the execution feedback information.

[0227] The acquisition module 201 is responsible for collecting battery-related data and can be implemented using data interfaces, communication modules, or data acquisition devices.

[0228] The recording module 202 is responsible for processing, integrating, and storing the acquired data and can be implemented using database systems, data processing servers, or storage devices.

[0229] The evaluation module 203 is responsible for analyzing the battery state and can be implemented using computing servers, analysis software, or model libraries.

[0230] The decision module 204 is responsible for generating management solutions and can be implemented using decision engines, optimization algorithms, or rule libraries.

[0231] The adjustment module 205 is responsible for managing decision execution and feedback and can be implemented using communication interfaces, control units, or feedback processing logic.

[0232] This system converts each key step in the ship container battery full life cycle management method into a functional module in the system, achieving automation and integration of complex management processes. The acquisition module 201 is responsible for collecting battery basic information and life cycle data from scattered heterogeneous data sources, providing input for subsequent processing.

[0233] The recording module 202 receives the acquired data and effectively integrates it based on the identity of the battery, building a complete historical file for each battery and extracting and recording cumulative operating conditions and environmental exposure information, overcoming the problem of scattered and heterogeneous data and providing historical stress data for accurate battery state evaluation.

[0234] The evaluation module 203 uses the cumulative information provided by the recording module and real-time data, combined with battery model and performance degradation model, to accurately evaluate the current health status and remaining life of the battery, so that the status evaluation is based on the actual stress and operation of the battery.

[0235] The decision module 204 receives the evaluation results and external real-time operation information, combined with business rules and constraint conditions, to generate optimized management decisions, so that the decisions can be adjusted according to the actual state of the battery and dynamic operation requirements.

[0236] The adjustment module 205 is responsible for the issuance of decisions and the reception of execution feedback, and dynamically adjusts the decisions according to the feedback information, so that the system has closed-loop control and adaptive ability, and can cope with uncertainties in the actual execution process.

[0237] Through the cooperative work of these modules, the system can automatically execute the complete process from data collection, integration, state evaluation to decision generation and feedback adjustment, and convert the complex method process into an operational and scalable system entity, so as to realize comprehensive, accurate and dynamic battery life cycle management in complex operation environment.

[0238] In this article, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between the entities or operations.

[0239] The above only describes the embodiments of the present application and does not limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for full life-cycle management of ship container batteries, characterized in that, Marine container batteries are used in global maritime trade, deployed across regions and vessels to provide power for refrigerated containers, shipboard equipment, and ship propulsion. The method includes the following steps: S1: Obtain the identification of each battery, and obtain the original operating data from the shipboard system. Process the original operating data to generate a shipboard data summary, and obtain maintenance activity records and battery transfer event information from the port maintenance system and logistics transportation system. S2: Based on the identity identifier, the shipborne data summary, the maintenance activity record, and the battery transfer event information corresponding to each battery are integrated to obtain the integrated battery life cycle data; Based on the integrated battery lifecycle data, operating parameters reflecting the battery's operating status and environmental parameters reflecting the environment in which the battery is located are extracted. Based on the preset operating condition definition and environmental exposure level definition, the operating parameters and environmental parameters are mapped to the corresponding operating conditions and environmental exposure levels; The cumulative duration of each battery under different operating conditions and environmental exposure levels; Obtain a preset stress weight, and calculate a comprehensive stress index based on the preset stress weight to determine the cumulative impact of the operating conditions and the environmental exposure level on battery performance during the duration of operation. Based on the duration and the comprehensive stress index, record the cumulative operating conditions and environmental exposure information of each battery. S3: Assess the battery status based on the operating conditions and environmental exposure information; S4: Obtain real-time dynamic operation information, and generate battery management decisions based on the real-time dynamic operation information, the battery status, and preset business rules and constraints; Step S4 includes: S41: Based on the real-time dynamic operation information, predict the operation demand and resource availability in the future period; S42: Based on the battery state, determine the current performance level and potential risks of the battery; S43: Based on the operational needs and resource availability, the current performance level and potential risks of the battery, and the preset business rules and constraints, construct a battery management function that includes optimization objectives and constraints; S44: Solve the battery management function according to the preset decision generation strategy to generate candidate battery management decision schemes; S45: Evaluate the candidate battery management decision schemes according to the preset risk assessment and benefit assessment criteria, and determine the battery management decision; S5: Issue the battery management decision, receive execution feedback information, and adjust the battery management decision according to the execution feedback information.

2. The method for full life-cycle management of ship container batteries according to claim 1, characterized in that, Step S3 includes: S31: Obtain real-time operating data of the battery based on the operating conditions and the environmental exposure information; S32: Obtain the battery model information and establish a battery performance degradation model associated with the model information; S33: Evaluate the battery status based on the real-time operating data and the battery performance degradation model.

3. The method for full life-cycle management of ship container batteries according to claim 2, characterized in that, Step S32 includes: S321: Based on the model information, obtain the refined historical stress data of the corresponding battery; S322: Select a basic performance degradation model that matches the model information from a preset model library; S323: Based on the refined stress history data, the basic performance degradation model is adjusted in a personalized manner to obtain the battery performance degradation model.

4. The method for full life-cycle management of ship container batteries according to claim 2, characterized in that, Step S33 includes: S331: Based on the real-time operating data, the battery performance degradation model is used to calculate the preliminary health status parameters of the battery. S332: Obtain the historical health status assessment results and historical operating data of the battery; S333: Based on the preliminary health status parameters, the historical health status assessment results, and the historical operating data, the preliminary health status parameters are corrected to obtain the current corrected health status parameters of the battery; S334: Based on the corrected health status parameters, predict the remaining lifespan of the battery and generate an assessment result of the battery status.

5. A method for full life-cycle management of ship container batteries according to claim 1, characterized in that, Step S5 includes: S51: Based on the execution feedback information, determine whether the actual execution result of the battery management decision deviates from the expected execution result; S52: If there is a deviation, then obtain the updated information of the battery's actual operating conditions, actual environmental exposure information, actual maintenance activities, and actual location status; S53: Based on the updated information, update the battery's life cycle data, cumulative operating conditions and environmental exposure information, and battery status; S54: Adjust the issued battery management decision based on the updated battery status, the actual execution result, and the preset decision adjustment rules.

6. A life-cycle management system for ship container batteries, characterized in that, The system for implementing the method according to any one of claims 1-5 comprises: Acquisition module: Acquires the identification of each battery, obtains raw operating data from the shipboard system, processes the raw operating data to generate a shipboard data summary, and obtains maintenance activity records and battery transfer event information from the port maintenance system and logistics transportation system; Recording module: Based on the identity identifier, integrate the shipborne data summary, maintenance activity record and battery transfer event information corresponding to each battery to obtain integrated battery life cycle data; Based on the integrated battery lifecycle data, operating parameters reflecting the battery's operating status and environmental parameters reflecting the environment in which the battery is located are extracted. Based on the preset operating condition definition and environmental exposure level definition, the operating parameters and environmental parameters are mapped to the corresponding operating conditions and environmental exposure levels; The cumulative duration of each battery under different operating conditions and environmental exposure levels; Obtain a preset stress weight, and calculate a comprehensive stress index based on the preset stress weight to determine the cumulative impact of the operating conditions and the environmental exposure level on battery performance during the duration of operation. Based on the duration and the comprehensive stress index, record the cumulative operating conditions and environmental exposure information of each battery. Evaluation module: Evaluates battery status based on the operating conditions and environmental exposure information; Decision module: Acquires real-time dynamic operation information and generates battery management decisions based on the real-time dynamic operation information, the battery status, and preset business rules and constraints; Adjustment module: issues the battery management decision, receives execution feedback information, and adjusts the battery management decision based on the execution feedback information.

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