A zero-code configuration method and system for an energy storage BMS product

Abstract

This invention relates to the technical field of energy storage BMS product configuration, and provides a zero-code configuration method and system for energy storage BMS products. The method includes: acquiring battery type information and deployment location information of the energy storage system; acquiring environmental data of the corresponding deployment location based on the deployment location information; acquiring battery characteristic rules for the corresponding battery type based on the battery type information; assessing the safety risks of configuration parameters set by the operator based on the battery type information, deployment location environmental data, and battery characteristic rules, and obtaining parameter safety risk assessment results; and issuing early warning information and providing adjustment suggestions based on the parameter safety risk assessment results. This invention improves the operational safety and reliability of energy storage systems.

Description

Technical Field

[0001] This invention relates to the technical field of energy storage BMS product configuration, and specifically to a zero-code configuration method and system for energy storage BMS products. Background Technology

[0002] In modern energy storage systems, configuring the Battery Management System (BMS) is a crucial step in ensuring its safe and efficient operation. To simplify this complex process, zero-code configuration methods have emerged, allowing technicians to set various operating parameters of the BMS through an intuitive graphical interface without writing code.

[0003] However, with the rapid development of battery technology and the increasing diversity of energy storage system deployment environments, traditional no-code configuration tools have revealed their inherent limitations when dealing with the dynamic relationship between parameter verification logic and actual physical conditions. Especially when facing new battery chemistry systems and extreme environmental conditions, the verification logic of configuration tools may fail to adapt in a timely manner, resulting in configurations that appear logically compliant but actually harbor serious safety risks in actual physical operation. These risks are not only difficult for operators to detect but also difficult to trace through conventional means, severely impacting the long-term reliability and safety of energy storage systems.

[0004] When an energy storage system is deployed and begins operation in a cold environment, the BMS attempts to charge the nickel-cobalt-manganese (NiCoMn) batteries at a value configured as the "maximum charging current limit." However, due to the significant increase in internal resistance of the NiCoMn cells at low temperatures, even a seemingly "safe" charging current can trigger unexpected temperature rises in localized areas. This localized overheating, once reaching or exceeding the battery material's tolerance limits, can trigger a thermal runaway warning from the temperature sensor probes attached to the cells or busbars within the BMS. Upon receiving such a warning, the BMS immediately activates an emergency shutdown protection mechanism, causing an unexpected power outage for the entire energy storage system, thus affecting its normal operation.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] This application discloses a zero-code configuration method and system for energy storage BMS products, aiming to solve the limitations of existing zero-code configuration methods for energy storage BMS products in handling the dynamic correlation between parameter verification logic and actual physical conditions. In particular, under new battery chemistry systems and extreme environmental conditions, the verification logic of the configuration tool cannot adapt in time, resulting in configuration parameters that are logically compliant but hide serious safety risks in actual physical operation.

[0007] The technical solution of this application is as follows:

[0008] Firstly, this application discloses a zero-code configuration method for an energy storage BMS product, including:

[0009] Obtain information on the battery type and deployment location of the energy storage system;

[0010] Based on the deployment location information, obtain the deployment environment data for the corresponding deployment location;

[0011] Based on the battery type information, obtain the battery characteristic rules for the corresponding battery type;

[0012] Based on battery type information, deployment site environmental data, and battery characteristic rules, the safety risks of the configuration parameters set by the operator are assessed, and the parameter safety risk assessment results are obtained.

[0013] Based on the results of the parameter safety risk assessment, early warning information is issued and adjustment suggestions are provided.

[0014] This technical solution enables dynamic safety risk assessment of energy storage BMS product configuration parameters, effectively identifying and warning of potential safety hazards. It solves the shortcomings of traditional zero-code configuration tools that cannot fully consider physical limitations and dynamic changes in battery characteristics, thereby improving the operational safety and reliability of energy storage systems.

[0015] Furthermore, based on the above, this application also proposes a step for assessing the security risks of operator-set configuration parameters based on battery type information, deployment site environmental data, and battery characteristic rules, and obtaining parameter security risk assessment results, including:

[0016] Based on the deployment location information, obtain the micro-topographic features of the energy storage system's deployment location;

[0017] Based on the micro-topographic features, the environmental data of the deployment site are corrected to obtain corrected local extreme temperature data and corrected local low temperature duration data;

[0018] Based on the battery type information, load the corresponding battery non-uniform response rules;

[0019] Based on the corrected local extreme temperature data, configuration parameters, and battery non-uniform response rules, the maximum temperature difference that can be generated inside the battery cluster is analyzed.

[0020] Based on the corrected local extreme temperature data, the corrected local low temperature duration data, the maximum temperature difference, configuration parameters, and battery non-uniform response rules, the comprehensive risk index of each battery cluster within the energy storage system is calculated as the parameter safety risk assessment result.

[0021] This technical solution can improve the accuracy and relevance of parameter safety risk assessment by introducing micro-topographic features to correct environmental data and combining it with battery non-uniform response rules. This effectively avoids potential risks caused by local environmental differences and uneven battery characteristics.

[0022] Based on this, this application further proposes that, after calculating the comprehensive risk index of each battery cluster within the energy storage system as the parameter safety risk assessment result based on the corrected local extreme temperature data, the corrected local low temperature duration data, the maximum temperature difference, configuration parameters, and battery non-uniform response rules, the application also includes:

[0023] Based on the parameter safety risk assessment results, battery clusters with potential localized hidden damage risks are identified, and damage risk identification results are obtained.

[0024] Based on the damage risk identification results, the configuration parameters are compared with the deployment site environmental data and battery characteristic rules to obtain the risk comparison results.

[0025] This technical solution can further identify battery clusters with potential localized hidden damage risks, and by comparing configuration parameters with environmental data and battery characteristic rules, it provides a clearer basis for risk tracing and adjustment, enhancing the depth and effectiveness of risk management.

[0026] In some preferred embodiments, this application also proposes that, based on the results of a parametric safety risk assessment, the step of identifying battery clusters with potential localized, hidden damage risks, and obtaining the damage risk identification results, includes:

[0027] Acquire information on the switching of operating modes or changes in environmental conditions of the energy storage system;

[0028] Based on information about switching operating modes or changes in environmental conditions, obtain the corresponding risk threshold adjustment strategies;

[0029] Adjust the risk threshold adjustment strategy based on the current battery health status;

[0030] Adjust the risk assessment threshold for parameter safety risk assessment according to the revised risk threshold adjustment strategy;

[0031] The comprehensive risk index corresponding to the parameter safety risk assessment results is compared with the adjusted risk judgment threshold to identify battery clusters with local hidden damage risks, thus obtaining the damage risk identification results.

[0032] This technical solution enables dynamic adjustment of risk assessment thresholds based on operating modes, environmental changes, and battery health status, making risk identification more flexible and accurate. It avoids misjudgments or omissions that may occur with fixed thresholds, thereby improving the adaptability and reliability of risk identification.

[0033] As an optional solution, this application also proposes that, based on the results of the parameter safety risk assessment, the steps for identifying battery clusters with potential localized hidden damage risks and obtaining the damage risk identification results include:

[0034] Acquire information on the energy storage system's operating mode switching and changes in the remaining battery life cycle;

[0035] Adjust the corresponding risk assessment thresholds based on operating mode switching information and changes in battery remaining life cycle information;

[0036] The comprehensive risk index corresponding to the parameter safety risk assessment results is compared with the adjusted risk judgment threshold to identify battery clusters with local hidden damage risks, thus obtaining the damage risk identification results.

[0037] This technical solution allows for the adjustment of risk assessment thresholds by combining operating modes and remaining battery lifespan information, making risk identification more closely aligned with the actual operating state and lifespan of the battery, thereby improving the accuracy and foresight of risk identification.

[0038] Furthermore, this application proposes to compare the comprehensive risk index corresponding to the parameter safety risk assessment results with the adjusted risk judgment threshold to identify battery clusters with potential localized hidden damage risks. The steps for obtaining the damage risk identification results include:

[0039] Based on the adjusted risk assessment threshold, a risk confirmation interval is defined; this risk confirmation interval includes the upper limit threshold for entering a risk state and the lower limit threshold for leaving a risk state.

[0040] When the comprehensive risk index corresponding to the parameter safety risk assessment result exceeds the upper limit threshold for the first time, the duration timer is started.

[0041] Within the preset time set by the duration timer, it is determined whether the comprehensive risk index continues to remain above the upper limit threshold, and the result of exceeding the upper limit is obtained;

[0042] When the result of the exceeding the upper limit is yes, the corresponding battery cluster is confirmed to enter a risk state;

[0043] When the overall risk index of a battery cluster that is already in a risky state falls below the lower threshold for the first time, a duration timer is started.

[0044] Within the preset time set by the duration timer, it is determined whether the comprehensive risk index continues to remain below the lower limit threshold, and the result of exceeding the lower limit is obtained.

[0045] When the lower limit judgment result is yes, the corresponding battery cluster is confirmed to be out of risk.

[0046] This technical solution avoids misjudgments caused by instantaneous fluctuations by introducing a risk confirmation interval and duration timer, making the confirmation of risk status more robust and reliable, and improving the accuracy and anti-interference ability of risk identification.

[0047] Based on the above, this application further proposes that when the comprehensive risk index exhibits the characteristic of rising at a rate greater than the rising rate threshold and then suddenly dropping within a preset time set by the duration timer, and the duration does not trigger a preset persistence judgment, the step of determining whether the comprehensive risk index continues to remain greater than the upper limit threshold within the preset time set by the duration timer, and obtaining the upper limit judgment result, includes:

[0048] Obtain information on the current operating mode of the energy storage system;

[0049] Based on the operating mode information, identify whether the energy storage system is in a transient operating condition;

[0050] When the energy storage system is identified to be in a transient operating condition, the instantaneous rate of change of the comprehensive risk index is obtained;

[0051] The instantaneous rate of change is compared with a preset transient risk threshold to obtain the instantaneous risk comparison result, which is used as the result of exceeding the upper limit judgment.

[0052] When the instantaneous risk comparison result indicates that the instantaneous rate of change exceeds the transient risk threshold, a transient risk warning is triggered to ensure a timely response to real potential risks.

[0053] This technical solution enables the assessment of the sudden rise and fall of risk indices under transient operating conditions by introducing instantaneous change rate and transient risk threshold, thereby triggering timely transient risk warnings and ensuring rapid response to real potential risks, avoiding the lag that may be caused by traditional continuous assessment.

[0054] To enhance functionality, this application also proposes that when the instantaneous risk comparison result indicates that the instantaneous rate of change exceeds the transient risk threshold, a transient risk warning is triggered to ensure a timely response to real potential risks. The steps include:

[0055] Retrieve the configuration parameters that trigger the transient risk warning;

[0056] Obtain information on the current operating mode and battery health status of the energy storage system;

[0057] Based on configuration parameters, operating mode information, and battery health status information, risk source indication information and physical impact analysis information are generated.

[0058] Based on risk source indication information and physical impact analysis information, generate configuration parameter adjustment suggestions;

[0059] Based on the configuration parameters, adjust the suggested information and generate operation guidance information;

[0060] Send and display risk source indication information, physical impact analysis information, configuration parameter adjustment suggestions, and operation guidance information to operators.

[0061] This technical solution not only triggers early warnings but also further analyzes the source of risk and its physical impact, providing specific adjustment suggestions and operational guidelines. This helps operators quickly understand the problem and take effective measures, significantly improving the efficiency and accuracy of risk management.

[0062] To improve the solution, this application also proposes steps for sending and displaying risk source indication information, physical impact analysis information, configuration parameter adjustment suggestions, and operation guidance information to operators, including:

[0063] Obtain the status of the communication link between the energy storage system and the operator;

[0064] Based on the communication link status, select the transmission channel and the corresponding priority and redundancy of the transmission channel;

[0065] Based on risk source indication information, physical impact analysis information, configuration parameter adjustment suggestion information, and operation guidance information, extract core risk summary information;

[0066] Based on the selected transmission channel, configure the corresponding priority and redundancy, and send the core risk summary information;

[0067] Send and display risk source indication information, physical impact analysis information, configuration parameter adjustment suggestions, and operation guidance information through at least two sensory prompts.

[0068] This technical solution enables the selection of appropriate transmission channels, priorities, and redundancy based on the communication link status, and employs multiple sensory prompts to ensure that risk information can be efficiently, reliably, and intuitively conveyed to operators, thereby improving the delivery rate of early warning information and the operator's response speed.

[0069] Secondly, this application also discloses a zero-code configuration system for energy storage BMS products, used to perform zero-code configuration of energy storage BMS products, including:

[0070] The system information acquisition module is used to acquire information about the battery type and deployment location of the energy storage system.

[0071] The environmental data acquisition module is used to acquire the environmental data of the corresponding deployment location based on the deployment location information.

[0072] The battery characteristic acquisition module is used to obtain the battery characteristic rules of the corresponding type of battery based on the battery type information.

[0073] The parameter safety assessment module is used to assess the safety risks of the configuration parameters set by the operator based on battery type information, deployment site environmental data, and battery characteristic rules, and obtain parameter safety risk assessment results.

[0074] The early warning information issuing module is used to issue early warning information and provide adjustment suggestions based on the parameter safety risk assessment results.

[0075] This technical solution provides an integrated system that enables automated safety risk assessment and early warning for zero-code configuration of energy storage BMS products. It effectively solves the safety hazards of traditional configuration tools in complex environments and new battery application scenarios, and improves the overall safety and operating efficiency of energy storage systems.

[0076] Beneficial effects

[0077] The zero-code configuration method for energy storage BMS products disclosed in this application obtains battery type and deployment location information of the energy storage system, and based on this, acquires environmental data and battery characteristic rules of the deployment location. It can then perform a safety risk assessment on the configuration parameters set by the operator based on this information, and issue early warning information and adjustment suggestions based on the assessment results. This method effectively solves the limitations of existing zero-code configuration tools in handling the dynamic correlation between parameter verification logic and actual physical conditions, especially for new battery chemistry systems and extreme environmental conditions. It can avoid the problem of configuration parameters being logically compliant but hiding serious safety risks in actual physical operation. Through dynamic assessment and early warning mechanisms, this application can promptly detect and alert to potential safety hazards, thereby significantly improving the operational safety and reliability of the energy storage system and overcoming the shortcomings caused by information transmission gaps and lagging verification logic in traditional methods. Attached Figure Description

[0078] Figure 1 This is a flowchart of a zero-code configuration method for an energy storage BMS product according to one embodiment of the present invention;

[0079] Figure 2 This is a flowchart of a zero-code configuration method for an energy storage BMS product according to another embodiment of the present invention;

[0080] Figure 3This is a system block diagram of a zero-code configuration system for an energy storage BMS product according to another embodiment of the present invention;

[0081] Explanation of reference numerals in the attached figures:

[0082] 1. Zero-code configuration system for energy storage BMS products; 11. System information acquisition module; 12. Environmental data acquisition module; 13. Battery characteristic acquisition module; 14. Parameter safety assessment module; 15. Early warning information issuance module. Detailed Implementation

[0083] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0084] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0085] Traditional zero-code configuration methods for existing energy storage BMS products have inherent limitations in handling the dynamic relationship between parameter verification logic and actual physical conditions. Especially when facing new battery chemistry systems and extreme environmental conditions, the verification logic of the configuration tool may fail to adapt in a timely manner. This can lead to configurations that appear logically compliant but actually harbor serious safety risks during physical operation. These risks are not only difficult for operators to detect but also difficult to trace through conventional means, severely impacting the long-term reliability and safety of the energy storage system.

[0086] To address this, this application proposes a zero-code configuration method for energy storage BMS products, combining... Figure 1 As shown, it includes:

[0087] S1, obtain battery type information and deployment location information of the energy storage system;

[0088] S2, based on the deployment location information, obtain the deployment environment data of the corresponding deployment location;

[0089] S3, based on the battery type information, obtain the battery characteristic rules for the corresponding battery type;

[0090] S4, based on battery type information, deployment site environmental data and battery characteristic rules, assesses the security risks of the configuration parameters set by the operator and obtains the parameter security risk assessment results;

[0091] S5 issues early warning information and provides adjustment suggestions based on the parameter safety risk assessment results.

[0092] To better understand the zero-code configuration method for energy storage BMS products proposed in this application, some key terms involved will be explained below.

[0093] "Energy storage system" generally refers to a system that can store energy and release it when needed, such as a battery energy storage system composed of battery clusters, battery management system (BMS), energy management system (EMS), etc.

[0094] "Battery type information" refers to parameters such as the type, chemical composition, rated voltage, and capacity of the batteries used in the energy storage system. For example, it could be lithium iron phosphate batteries, nickel-cobalt-manganese batteries, etc.

[0095] "Deployment location information" refers to the geographical location information of the actual installation and operation of the energy storage system, such as latitude and longitude, altitude, climate zone, etc.

[0096] "Deployment site environmental data" refers to environmental parameters related to the deployment location of the energy storage system, such as historical temperature data, humidity data, wind speed data, and solar radiation intensity data. This data can be obtained from meteorological databases, geographic information systems, or on-site sensors.

[0097] "Battery characteristic rules" refer to a set of rules describing the performance, safety boundaries, and aging characteristics of a specific type of battery under different operating conditions. Examples include charge / discharge rate limits, internal resistance variation patterns, and thermal runaway triggering conditions at different temperatures.

[0098] "Configuration parameters" refer to the BMS operating parameters set by the operator in the zero-code configuration interface, such as maximum charging current, minimum discharge voltage, and equalization strategy.

[0099] "Parameter security risk assessment results" refer to the quantitative or qualitative results of the security risks that the configuration parameters may cause, obtained through comprehensive analysis of the configuration parameters.

[0100] "Warning information" refers to the prompt information issued by the system to the operator when a potential security risk is detected.

[0101] "Adjustment suggestion information" refers to the suggestions provided by the system to operators based on the risk assessment results, which are used to correct configuration parameters to reduce risks.

[0102] The zero-code configuration method for energy storage BMS products proposed in this application aims to overcome the limitations of traditional zero-code configuration tools in handling the dynamic correlation between parameter verification logic and actual physical conditions. Specifically, the method is implemented through the following steps:

[0103] First, the system obtains information about the battery type and deployment location of the energy storage system. For example, operators can input battery type information by selecting from a preset list of battery models in the configuration interface, or provide deployment location information by inputting the geographical coordinates of the energy storage system. Alternatively, the system can automatically obtain this information by interfaceing with the energy storage system's asset management system (AMS).

[0104] Secondly, based on the deployment location information, the system obtains the corresponding environmental data for the deployment location. For example, the system can query environmental data such as average temperature, extreme maximum / minimum temperature, and sunshine duration over the past ten years or even longer from a global meteorological database based on the input geographical coordinates. As another approach, the system can also obtain or periodically update the environmental data of the deployment location by interacting with local weather stations or environmental monitoring equipment.

[0105] Secondly, based on the battery type information, the system obtains the corresponding battery characteristic rules. For example, the system can load characteristic rules such as charge-discharge curves, internal resistance characteristics, thermal runaway thresholds, and cycle life of the identified battery type (e.g., lithium iron phosphate battery) from a preset battery database. Alternatively, battery characteristic rules can also be generated by an expert system or machine learning model after analyzing laboratory test data or field operation data.

[0106] Subsequently, based on battery type information, deployment site environmental data, and battery characteristic rules, the safety risks of the configuration parameters set by the operator are assessed, resulting in a parameter safety risk assessment. For example, when the operator sets a "maximum charging current" parameter, the system will comprehensively consider the battery characteristic rules (such as low-temperature charging limitations) of the current battery type under deployment site environmental data (such as low temperature) to determine whether the charging current will cause local overheating or lithium plating risks. As another implementation method, this assessment process can employ a multi-factor weighted analysis model to comprehensively score each input data, thereby quantifying the safety risks of the configuration parameters.

[0107] Finally, based on the parameter safety risk assessment results, the system issues warning messages and provides adjustment suggestions. For example, if the assessment results indicate that a certain configuration parameter poses a high risk, the system will immediately display a red warning box on the configuration interface, along with the warning message "Maximum charging current is too high, which may lead to low-temperature lithium plating risk." Simultaneously, the system will provide the adjustment suggestion message "It is recommended to adjust the maximum charging current to below X amperes." Alternatively, warning messages can also be sent to the operator via SMS, email, or voice notification, and adjustment suggestions can be displayed intuitively in the form of charts or dynamic sliders for easy operator adjustments.

[0108] Optional, combined Figure 2 As shown, the steps by which S4 assesses the security risks of operator-set configuration parameters based on battery type information, deployment site environmental data, and battery characteristic rules to obtain parameter security risk assessment results include:

[0109] S41, Based on the deployment location information, obtain the micro-topographic features of the energy storage system's deployment location;

[0110] S42, based on micro-topographic features, corrects the environmental data of the deployment site to obtain corrected local extreme temperature data and corrected local low temperature duration data;

[0111] S43, Based on the battery type information, load the corresponding battery non-uniform response rules;

[0112] S44, based on the corrected local extreme temperature data, configuration parameters and battery non-uniform response rules, analyzes the maximum temperature difference that can be generated inside the battery cluster;

[0113] S45 calculates the comprehensive risk index of each battery cluster within the energy storage system as the parameter safety risk assessment result based on the corrected local extreme temperature data, the corrected local low temperature duration data, the maximum temperature difference, configuration parameters, and battery non-uniform response rules.

[0114] Specifically, acquiring the micro-topographic features of an energy storage system deployment site refers to obtaining subtle geographical features such as local landforms, vegetation, and buildings that influence environmental factors like airflow, sunlight, and heat dissipation through methods such as high-precision Geographic Information System (GIS) data, LiDAR scanning, UAV aerial photography combined with image recognition technology, or on-site surveys. Among these, correcting the deployment site's environmental data based on these micro-topographic features to obtain corrected local extreme temperature data and corrected local low-temperature duration data is crucial. While deployment site environmental data typically comes from weather stations and represents regional averages, the introduction of micro-topographic features allows for more refined correction of these data. For example, leeward slopes may lead to increased local temperatures, while sunny slopes may increase solar radiation intensity, thus affecting local extreme temperatures and the duration of low temperatures. The corrected data more accurately reflects the actual local environmental conditions of each battery cluster in the energy storage system. In practical applications, based on battery type information, the corresponding battery non-uniform response rules refer to the performance differences exhibited by different battery cells within a battery cluster under the same external conditions due to factors such as manufacturing differences, aging levels, and location differences, under charging / discharging and temperature variations. These differences include internal resistance, capacity, self-discharge rate, and thermal characteristics. These rules can be established and stored based on battery model, batch, historical operating data, and experimental test results. Furthermore, based on corrected local extreme temperature data, configuration parameters, and battery non-uniform response rules, the maximum temperature difference that can occur within the battery cluster is analyzed. The maximum temperature difference within the battery cluster is a key indicator for assessing battery safety; excessive temperature differences may lead to accelerated battery performance degradation, capacity inconsistency, and even thermal runaway. This analysis combines corrected local extreme temperature data, operator-set configuration parameters (such as charge / discharge rate and balancing strategy), and battery non-uniform response rules, using thermal models or simulation tools for prediction. Therefore, based on the corrected local extreme temperature data, the corrected local low temperature duration data, the maximum temperature difference, configuration parameters, and battery non-uniform response rules, a comprehensive risk index is calculated for each battery cluster within the energy storage system. This comprehensive risk index serves as a parameter for safety risk assessment. It is a quantitative indicator used to comprehensively measure the safety risk of a battery cluster under specific configuration parameters. Its calculation comprehensively considers the corrected local extreme temperature data, the corrected local low temperature duration data, the maximum temperature difference within the battery cluster, configuration parameters, and battery non-uniform response rules. This index can be constructed using various methods such as weighted averaging, fuzzy logic, and machine learning models to reflect the contribution of different risk factors to overall safety.

[0115] In some preferred embodiments, a large energy storage power station is assumed to be deployed in an area with complex micro-topography, where some battery containers are located on the leeward side of buildings, while others are in open areas. First, the system obtains the micro-topographical features of each container based on its precise deployment location information, such as the obstruction effect of buildings on airflow and localized shading areas. Based on these micro-topographical features, the regional deployment site environmental data obtained from weather stations is corrected to obtain more accurate data on local extreme temperatures and the duration of localized low temperatures within each container. For example, containers on the leeward side may be corrected to have higher local extreme temperatures and longer durations of low temperatures. Simultaneously, based on the specific battery type used (e.g., lithium iron phosphate batteries), the system loads battery non-uniform response rules for that type of battery under different operating conditions (such as high-rate charge and discharge). These rules may indicate that battery cells in the central region of the battery cluster are more prone to heating under specific conditions. Subsequently, based on the corrected local extreme temperature data, operator-set charge and discharge rates and other configuration parameters, and the loaded battery non-uniform response rules, the system analyzes the maximum temperature difference that may occur within each battery cluster. For example, a battery cluster inside a container on the leeward side might be analyzed to have a large maximum temperature difference due to locally high temperatures and non-uniform internal battery response. Ultimately, these corrected local environmental data, maximum temperature differences, configuration parameters, and battery non-uniform response rules are combined to calculate a comprehensive risk index for each battery cluster. In this way, even under seemingly normal overall environmental conditions, the system can identify potential local overheating or undercooling risks in a particular battery cluster under specific micro-topography or configuration parameter combinations, thus providing more targeted warnings and adjustment suggestions.

[0116] Optionally, after calculating the comprehensive risk index of each battery cluster within the energy storage system as the parameter safety risk assessment result based on the corrected local extreme temperature data, the corrected local low temperature duration data, the maximum temperature difference, configuration parameters, and battery non-uniform response rules, the method further includes:

[0117] Based on the parameter safety risk assessment results, battery clusters with potential localized hidden damage risks are identified, and damage risk identification results are obtained.

[0118] Based on the damage risk identification results, the configuration parameters are compared with the deployment site environmental data and battery characteristic rules to obtain the risk comparison results.

[0119] Specifically, identifying battery clusters with potential hidden damage risks involves further analyzing the overall risk index (the parameter safety risk assessment result) after obtaining it. This analysis aims to determine if certain battery clusters, while having an overall risk index within an acceptable range, exhibit or are about to experience subtle, cumulative damage risks within their internal or localized areas. Such damage may stem from various factors, including battery aging, localized overheating / overcooling, and uneven current distribution, and may not initially lead to a significant increase in the overall risk index. The goal is to identify and provide early warnings of potential localized problems that could lead to long-term performance degradation or safety hazards through deeper analysis.

[0120] The process involves comparing configuration parameters with deployment site environmental data and battery characteristic rules to obtain risk comparison results. This can be understood as follows: after identifying battery clusters with potential localized, hidden damage risks, the system uses the damage risk identification results as a trigger to cross-validate and compare the currently set configuration parameters with pre-acquired deployment site environmental data and battery characteristic rules. For example, the comparison may include checking whether the configuration parameters are within the safe operating range allowed by the environmental data and battery characteristic rules, or whether there are configurations that contradict these rules. The purpose is to accurately locate the specific configuration parameters that lead to localized, hidden damage risks through this comparison, and to provide data support and decision-making basis for subsequent adjustment recommendations.

[0121] In some preferred embodiments, a specific example is given below. Assume that in an energy storage power station, an operator configures a set of parameters, and after calculation using the method described above, obtains the comprehensive risk index for each battery cluster. At this point, the system discovers that although the comprehensive risk index of a certain battery cluster does not exceed the overall risk threshold, its historical operating data shows an abnormally high frequency of local temperature fluctuations, and its internal resistance growth rate is higher than other battery clusters under low-temperature conditions. Based on the parameter safety risk assessment results, the system identifies a localized, hidden damage risk in this battery cluster and obtains the damage risk identification result.

[0122] Furthermore, based on the damage risk identification results, the system compares the current operator-set configuration parameters such as charging cut-off voltage, discharging cut-off voltage, and maximum charging / discharging current with historical ambient temperature data, altitude data, and the low-temperature performance degradation rules and cycle life characteristic rules for this type of battery at the deployment location. For example, the comparison results may show that, in the extreme low-temperature environment of the current deployment location, the maximum discharge current set by the operator exceeds the safe operating range of this type of battery under low-temperature conditions, or the charging cut-off voltage is set too high, which may accelerate the risk of lithium plating in localized areas. Through this comparison, the system can obtain detailed risk comparison results, clearly indicating which configuration parameter(s) are inconsistent with the environmental or battery characteristic rules, thus leading to localized hidden damage risks. For example, the system may prompt "The discharge current parameter of battery cluster A is set too high, which is incompatible with the extreme low-temperature environment of the deployment location in winter, and may lead to localized lithium plating risk."

[0123] Optionally, based on the parameter safety risk assessment results, the steps for identifying battery clusters with potential localized hidden damage risks and obtaining damage risk identification results include:

[0124] Acquire information on the switching of operating modes or changes in environmental conditions of the energy storage system;

[0125] Based on information about switching operating modes or changes in environmental conditions, obtain the corresponding risk threshold adjustment strategies;

[0126] Adjust the risk threshold adjustment strategy based on the current battery health status;

[0127] Adjust the risk assessment threshold for parameter safety risk assessment according to the revised risk threshold adjustment strategy;

[0128] The comprehensive risk index corresponding to the parameter safety risk assessment results is compared with the adjusted risk judgment threshold to identify battery clusters with local hidden damage risks, thus obtaining the damage risk identification results.

[0129] Specifically, operating mode switching information refers to the transition of the energy storage system between different operating states, such as switching from charging mode to discharging mode, or from standby mode to operating mode. Environmental condition change information refers to changes in the external environment in which the energy storage system operates, such as changes in ambient temperature, humidity, and air pressure. This information can be collected in real time by the energy storage system's sensors or obtained from external data sources. The risk threshold adjustment strategy can be understood as a set of rules or algorithms, preset or dynamically generated based on specific operating modes or environmental conditions, used to guide the adjustment of risk judgment thresholds. For example, in extreme low-temperature environments, battery performance may degrade, and the risk judgment threshold may need to be lowered to improve sensitivity. In practical applications, battery health status refers to the current performance and lifespan of the battery, such as the State of Health (SOH), internal resistance, and capacity decay. This information can be obtained through the diagnostic functions of the Battery Management System (BMS). Adjusting the risk threshold adjustment strategy based on the current battery health status aims to make risk judgment more personalized and accurate; for example, for batteries with poor health status, the risk threshold may need to be further tightened. Therefore, the risk assessment threshold for parameter safety risk assessment is dynamically adjusted to adapt to the complexity and variability of actual energy storage system operation. The adjusted risk assessment threshold is then compared with the comprehensive risk index corresponding to the parameter safety risk assessment results. When the comprehensive risk index exceeds the adjusted risk assessment threshold, the battery cluster is considered to have a risk of localized, hidden damage, thus obtaining the damage risk identification result.

[0130] In some preferred embodiments, a specific example is given below. Assume an energy storage system deployed in a northern region, operating in two modes: a daily charge / discharge mode and a winter low-temperature operation mode. When the energy storage system switches from the daily charge / discharge mode to the winter low-temperature operation mode, the system first obtains the operation mode switching information. Based on this information, the system loads a preset low-temperature risk threshold adjustment strategy. This strategy may indicate that in low-temperature environments, due to reduced battery activity and increased internal resistance, the overall risk assessment threshold needs to be lowered by 10% to improve sensitivity to potential low-temperature damage risks. Simultaneously, the system obtains the current battery health status information for each battery cluster. For example, the state of health (SOH) of one battery cluster has dropped to 80%, while other battery clusters remain above 95%. Based on this information, the system further modifies the low-temperature risk threshold adjustment strategy. For a battery cluster with an SOH of 80%, its risk assessment threshold may be further lowered by 5% on top of the original 10% reduction to more rigorously monitor its operational risks at low temperatures. Thus, the risk assessment threshold for parameter safety risk assessment is dynamically adjusted, and the risk assessment threshold may differ for battery clusters with different health states. Subsequently, the comprehensive risk index of each battery cluster is compared with its adjusted risk assessment threshold. For example, in low-temperature mode, the comprehensive risk index of a battery cluster with a state of harmlessness (SOH) of 80% may exceed its dynamically adjusted lower risk assessment threshold even before reaching the traditional static threshold. This allows for timely identification of potential localized hidden damage risks and the issuance of an early warning. This dynamic adjustment mechanism ensures the accuracy and timeliness of risk identification, effectively improving the safe operation level of the energy storage system.

[0131] Optionally, based on the parameter safety risk assessment results, the steps for identifying battery clusters with potential localized hidden damage risks and obtaining damage risk identification results include:

[0132] Acquire information on the energy storage system's operating mode switching and changes in the remaining battery life cycle;

[0133] Adjust the corresponding risk assessment thresholds based on operating mode switching information and changes in battery remaining life cycle information;

[0134] The comprehensive risk index corresponding to the parameter safety risk assessment results is compared with the adjusted risk judgment threshold to identify battery clusters with local hidden damage risks, thus obtaining the damage risk identification results.

[0135] Specifically, operating mode switching information refers to the current operating state of the energy storage system or the operating state it is about to switch to, such as charging mode, discharging mode, standby mode, peak shaving mode, and frequency regulation mode. Different operating modes have different impacts on the battery's stress level and potential risks. Battery remaining life cycle change information refers to the changes in the battery's health status over time or under usage conditions, such as the battery's cycle life, calendar life, internal resistance changes, and capacity decay. This information reflects the battery's aging degree and performance degradation.

[0136] Adjusting the corresponding risk assessment threshold refers to dynamically modifying the critical value used to determine whether there is a risk of localized, hidden damage to the battery cluster based on the obtained operating mode switching information and changes in the remaining battery life cycle. For example, when the energy storage system is operating under high load and high stress (such as rapid charging and discharging), or when the battery's remaining life cycle is short and its health condition is poor, the risk assessment threshold can be appropriately lowered to improve the sensitivity of risk identification and ensure that even minor risk signs can be detected in a timely manner. Conversely, in low load or standby mode, and when the battery's health condition is good, the risk assessment threshold can be appropriately raised to avoid unnecessary false alarms.

[0137] In practical applications, the comprehensive risk index corresponding to the parameter safety risk assessment results is compared with the adjusted risk judgment threshold. The purpose is to make a more accurate judgment on the risk assessment results based on the actual operating status of the current energy storage system and the health status of the batteries, so as to more accurately identify battery clusters with local hidden damage risks.

[0138] In some preferred embodiments, suppose that a battery cluster in an energy storage system has a comprehensive risk index of 0.7 after parameter safety assessment. If a fixed risk assessment threshold (e.g., 0.8) is used, the battery cluster may not be identified as having a risk. However, with the solution of this application, the system first obtains that the battery cluster is currently in a high-intensity discharge frequency modulation operation mode, and its remaining battery life cycle information shows that the battery cluster has used 80% of its design life. Based on this information, the system dynamically adjusts the risk assessment threshold from 0.8 to 0.65. At this time, the comprehensive risk index of 0.7 is compared with the adjusted risk assessment threshold of 0.65. Since 0.7 is greater than 0.65, the system will identify that the battery cluster has a local hidden damage risk and issue a corresponding warning. This dynamic adjustment mechanism ensures that potential problems can be captured more sensitively when the battery is in a high-risk operating condition or aging state, so as to take timely intervention measures and avoid risk omissions that may be caused by threshold mismatch.

[0139] Optionally, the steps of comparing the comprehensive risk index corresponding to the parameter safety risk assessment results with the adjusted risk judgment threshold to identify battery clusters with potential localized hidden damage risks and obtaining damage risk identification results include:

[0140] Based on the adjusted risk assessment threshold, a risk confirmation interval is defined; the risk confirmation interval includes the upper threshold for entering a risk state and the lower threshold for leaving a risk state.

[0141] When the comprehensive risk index corresponding to the parameter safety risk assessment result exceeds the upper limit threshold for the first time, the duration timer is started.

[0142] Within the preset time set by the duration timer, it is determined whether the comprehensive risk index continues to remain above the upper limit threshold, and the result of exceeding the upper limit is obtained;

[0143] When the result of the exceeding the upper limit is yes, the corresponding battery cluster is confirmed to enter a risk state;

[0144] When the overall risk index of a battery cluster that is already in a risky state falls below the lower threshold for the first time, a duration timer is started.

[0145] Within the preset time set by the duration timer, it is determined whether the comprehensive risk index continues to remain below the lower limit threshold, and the result of exceeding the lower limit is obtained.

[0146] When the lower limit judgment result is yes, the corresponding battery cluster is confirmed to be out of risk.

[0147] Specifically, the risk confirmation interval refers to the numerical range used to define the transition of a battery cluster's risk state. Its purpose is to introduce a certain hysteresis to avoid frequent switching of risk states due to minor fluctuations in the comprehensive risk index. This risk confirmation interval consists of two key thresholds: an upper threshold for entering a risk state and a lower threshold for leaving a risk state. The upper threshold determines whether a battery cluster is likely to enter a risk state, while the lower threshold determines whether a battery cluster already in a risk state can safely leave it. Typically, the upper threshold is higher than the lower threshold to form a "risk confirmation band," thereby enhancing the stability of the judgment.

[0148] The duration timer is a mechanism used to measure the duration for which the comprehensive risk index continuously meets specific conditions. When the comprehensive risk index first exceeds the upper threshold, the timer is activated and begins recording the duration. Within the preset time, the system continuously checks whether the comprehensive risk index remains above the upper threshold. If the comprehensive risk index remains above the upper threshold within this preset time, the result of exceeding the upper threshold is "yes," and only then can the corresponding battery cluster be confirmed as truly entering a risky state. Similarly, when the comprehensive risk index of a battery cluster already in a risky state first falls below the lower threshold, the duration timer is activated again and checks whether the comprehensive risk index remains below the lower threshold within the preset time. If this condition is continuously met, the result of exceeding the lower threshold is "yes," confirming that the battery cluster has escaped the risky state. The preset time can be flexibly configured according to the actual application scenario, battery type, and risk level; for example, it can be set to several seconds, tens of seconds, or longer to balance the timeliness of risk response and the suppression of false alarms.

[0149] In some preferred embodiments, a specific example is given below. Suppose that during the operation of a battery cluster in an energy storage system, its overall risk index fluctuates over a period of time. For example, the upper limit threshold for entering a risky state is set to 80, the lower limit threshold for leaving a risky state is set to 70, and the preset time of the duration timer is 5 seconds.

[0150] Specifically, if the overall risk index of a battery cluster rises from 65 to 82 and remains there for 3 seconds before dropping back to 75, the system will not confirm that the battery cluster has entered a risk state since the duration has not reached the preset 5 seconds, thus avoiding a potential false alarm.

[0151] Furthermore, if the overall risk index of the battery cluster rises from 65 to 85 and remains above 80 for 7 seconds, the duration timer will determine that the duration has exceeded the preset 5 seconds, and the system will confirm that the battery cluster has entered a risk state and issue a corresponding warning.

[0152] Subsequently, assuming the overall risk index of the battery cluster, which was already in a risky state, drops from 85 to 68 and remains below 70 for 6 seconds, the duration timer determines that the duration has exceeded the preset 5 seconds, and the system will confirm that the battery cluster has been removed from the risky state.

[0153] However, if the overall risk index drops from 85 to 68, but only lasts for 2 seconds before rising back to 78, the system will not confirm that the battery cluster has escaped the risk state because the duration has not reached the preset 5 seconds. This avoids frequent switching of risk states due to short-term rebounds and ensures the stability of risk assessment.

[0154] Through the above mechanism, the proposed solution can effectively cope with the complex fluctuations of the comprehensive risk index in actual operation and provide a more robust and accurate risk identification capability.

[0155] Optionally, when the comprehensive risk index exhibits the characteristic of rising at a rate greater than the rising rate threshold and then suddenly dropping within a preset time set by the duration timer, and the duration does not trigger a preset persistence judgment, the steps to determine whether the comprehensive risk index continues to remain greater than the upper limit threshold within the preset time set by the duration timer, and to obtain the result of exceeding the upper limit judgment, include:

[0156] Obtain information on the current operating mode of the energy storage system;

[0157] Based on the operating mode information, identify whether the energy storage system is in a transient operating condition;

[0158] When the energy storage system is identified to be in a transient operating condition, the instantaneous rate of change of the comprehensive risk index is obtained;

[0159] The instantaneous rate of change is compared with a preset transient risk threshold to obtain the instantaneous risk comparison result, which is used as the result of exceeding the upper limit judgment.

[0160] When the instantaneous risk comparison result indicates that the instantaneous rate of change exceeds the transient risk threshold, a transient risk warning is triggered to ensure a timely response to real potential risks.

[0161] Specifically, when the comprehensive risk index exhibits a rapid increase exceeding a preset threshold within a predetermined time period, followed by a rapid decrease, and this fluctuation does not persist to the point of triggering a routine continuity assessment, the system will activate the transient risk identification mechanism. The current operating mode information of the energy storage system can be understood as the system's current working state, such as charging mode, discharging mode, standby mode, or grid ancillary service mode (e.g., peak shaving, frequency regulation). This information can be obtained through internal sensors of the BMS system or external control commands.

[0162] In practical applications, based on operating mode information, it is possible to identify whether an energy storage system is in a transient condition. A transient condition refers to a situation where the system state changes drastically within a short period of time. For example, when the system rapidly switches from standby mode to high-power discharge mode, or when a grid fault occurs, the system needs to respond quickly and adjust its output power. Identifying transient conditions can be achieved through preset rule sets, state machine models, or machine learning models trained on historical data. When a transient condition is detected, the system acquires the instantaneous rate of change of the comprehensive risk index. The instantaneous rate of change refers to the speed at which the comprehensive risk index changes within a very short time, and can be calculated by differencing or differentiating the comprehensive risk index. Its purpose is to capture the rapid fluctuation characteristics of the risk index.

[0163] Subsequently, the instantaneous rate of change is compared with a preset transient risk threshold to obtain the instantaneous risk comparison result. The transient risk threshold is a pre-set critical value based on system design, battery characteristics, and safety requirements, used to determine whether the instantaneous rate of change has reached a level requiring a warning. If the instantaneous rate of change exceeds this threshold, a transient risk is considered to exist, and this comparison result is used as the result of exceeding the upper limit. When the instantaneous risk comparison result indicates that the instantaneous rate of change exceeds the transient risk threshold, the system will trigger a transient risk warning. This warning mechanism aims to respond promptly to potential risks that, although short in duration, change drastically and may lead to serious consequences, thereby ensuring the safe operation of the energy storage system.

[0164] In some preferred embodiments, a specific example is given below. Suppose that an energy storage system is providing grid frequency regulation services, and due to a sudden change in grid commands, the system switches from high-power charging to high-power discharging in a very short time, accompanied by rapid fluctuations in ambient temperature. Under this transient condition, the comprehensive risk index of the battery cluster calculated by the energy storage BMS system may rapidly spike from a normal level within seconds, reaching a peak value far exceeding the upper limit threshold, but then quickly drop back. The entire process may be completed within a preset time (e.g., 30 seconds) set by the duration timer.

[0165] If relying solely on the aforementioned continuous judgment mechanism, the system may not trigger a risk warning because the comprehensive risk index remains above the upper limit threshold for less than 30 seconds, thus missing the opportunity to intervene in potential damage to the battery cluster.

[0166] However, according to the solution of this application, when the system identifies a transient operating condition of grid frequency regulation, it immediately obtains the instantaneous rate of change of the comprehensive risk index. If this instantaneous rate of change (e.g., an increase of 10 risk units per second) exceeds a preset transient risk threshold (e.g., an increase of 5 risk units per second), the system will immediately trigger a transient risk warning even if the duration of the comprehensive risk index does not reach the preset value. For example, the system will issue a warning message of "transient overload risk of battery cluster X" and provide adjustment suggestions such as "immediately reduce charging and discharging power" or "check the internal temperature sensor of battery cluster X". In this way, this application can ensure timely and effective identification and response to rapidly changing potential risks under transient operating conditions, thereby avoiding irreversible damage to the battery cluster caused by short-term drastic fluctuations.

[0167] Optionally, when the instantaneous risk comparison result indicates that the instantaneous rate of change exceeds the transient risk threshold, a transient risk warning is triggered to ensure a timely response to real potential risks. The steps include:

[0168] Retrieve the configuration parameters that trigger the transient risk warning;

[0169] Obtain information on the current operating mode and battery health status of the energy storage system;

[0170] Based on configuration parameters, operating mode information, and battery health status information, risk source indication information and physical impact analysis information are generated.

[0171] Based on risk source indication information and physical impact analysis information, generate configuration parameter adjustment suggestions;

[0172] Based on the configuration parameters, adjust the suggested information and generate operation guidance information;

[0173] Send and display risk source indication information, physical impact analysis information, configuration parameter adjustment suggestions, and operation guidance information to operators.

[0174] Specifically, when a transient risk warning is triggered, the system first obtains the configuration parameters that caused the warning. These configuration parameters can be set by the operator during zero-code configuration, such as the upper limit of charging current, depth of discharge, and temperature control threshold. These are key factors that directly affect the operating behavior of the energy storage system. Simultaneously, the system also obtains information about the current operating mode of the energy storage system, such as whether it is in charging, discharging, standby, or equalization mode, as well as battery health status information, such as the battery's SOH (State of Health) and SOC (State of Charge). This information collectively constitutes the system context when the risk occurs.

[0175] Based on the acquired configuration parameters, operating mode information, and battery health status information, the system can generate risk source indication information and physical impact analysis information. The risk source indication information aims to clearly identify which configuration parameter(s) are improperly set, or, under the current operating mode and battery health status, the combination of these parameters leads to the occurrence of transient risks. For example, excessively high charging current parameters in low-temperature environments might lead to the risk of lithium plating inside the battery. The physical impact analysis information further elaborates on the specific physical damage or performance impact that this risk may cause to the battery or energy storage system, such as excessively high battery temperature, abnormal voltage, and accelerated capacity decay.

[0176] In practical applications, based on the generated risk source indication information and physical impact analysis information, the system can intelligently generate configuration parameter adjustment suggestions. These suggestions are targeted at the configuration parameters that cause the risk and aim to eliminate or reduce the risk by adjusting these parameters. For example, if the risk source indication is excessive charging current, the suggestion information may include reducing the charging current value. Furthermore, based on the configuration parameter adjustment suggestion information, the system also generates operation guidance information. The operation guidance information consists of specific, actionable steps that instruct the operator on how to adjust the configuration parameters according to the suggestions, such as "Enter the BMS configuration interface, find the charging current setting, and adjust it from X amps to Y amps."

[0177] Therefore, all this detailed information regarding the sources of risk, physical impact analysis, configuration parameter adjustment recommendations, and operational guidelines will be sent and displayed to the operator. This ensures that the operator not only knows that there is a risk, but also fully understands the ins and outs of the risk and obtains a clear solution.

[0178] In some preferred embodiments, a specific example is given below. Suppose that after a zero-code configuration of an energy storage system, the operator sets a high charging current parameter. During system operation, due to a sudden drop in ambient temperature and the system entering fast charging mode, the overall risk index within the battery cluster rises sharply within a short period of time at a rate exceeding the rise rate threshold, triggering a transient risk warning.

[0179] At this point, the proposed solution will be activated immediately. First, the system will obtain the configuration parameters that caused this warning, namely the "high charging current parameter" set by the operator. Simultaneously, the system will obtain the current operating mode information ("fast charging mode") and battery health status information (e.g., battery SOH is 90%). Based on this information, the system generates risk source indication information, such as "charging current parameter is too high, incompatible with the current low-temperature environment and fast charging mode," and generates physical impact analysis information, such as "may lead to lithium plating inside the battery, long-term impact on battery life, and even the risk of thermal runaway."

[0180] Furthermore, based on this risk information, the system generates configuration parameter adjustment suggestions, such as "It is recommended to adjust the charging current parameter from 1C to 0.8C." Subsequently, the system generates specific operation guidance information, such as "Please enter the BMS configuration interface, select 'Charging Parameter Settings,' modify the 'Maximum Charging Current' to 0.8C, and save the configuration." Finally, this detailed risk source indication information, physical impact analysis information, configuration parameter adjustment suggestions, and operation guidance information will be sent and displayed to the operator via the BMS management interface or SMS / email. After receiving this clear and comprehensive information, the operator can quickly understand the nature of the risk and adjust the parameters according to the guidance, thereby promptly eliminating potential safety hazards and ensuring the safe and stable operation of the energy storage system.

[0181] Optionally, the steps of sending and displaying risk source indication information, physical impact analysis information, configuration parameter adjustment suggestions, and operation guidance information to operators include:

[0182] Obtain the status of the communication link between the energy storage system and the operator;

[0183] Based on the communication link status, select the transmission channel and its corresponding priority and redundancy;

[0184] Based on risk source indication information, physical impact analysis information, configuration parameter adjustment suggestion information, and operation guidance information, extract core risk summary information;

[0185] Based on the selected transmission channel, configure the corresponding priority and redundancy, and send the core risk summary information;

[0186] Send and display risk source indication information, physical impact analysis information, configuration parameter adjustment suggestions, and operation guidance information through at least two sensory prompts.

[0187] Specifically, acquiring the communication link status between the energy storage system and the operator refers to the system's real-time monitoring and evaluation of the quality and availability of various communication paths used for information transmission. This includes assessments of network latency, packet loss rate, bandwidth, and connection stability, aiming to provide a basis for selecting the optimal transmission channel. Selecting a transmission channel based on the communication link status, along with its corresponding priority and redundancy, can be understood as the system dynamically selecting the most suitable channel for transmitting risk information based on the current communication link evaluation results. For example, when the network connection is stable and bandwidth is sufficient, detailed information can be displayed through the BMS management platform interface; when the network is unstable or an emergency notification is needed, information can be sent via SMS, email, or voice call. Simultaneously, to ensure the reliable transmission of critical information, different priorities can be configured for the selected transmission channels. For example, emergency warning information is given the highest priority to ensure its priority transmission. Redundancy refers to sending information through multiple channels simultaneously or through backup channels to prevent information loss due to a single channel failure. In practical applications, extracting core risk summary information based on risk source indication information, physical impact analysis information, configuration parameter adjustment suggestions, and operation guidance information involves the system intelligently analyzing and refining the generated detailed risk information to identify the most critical and urgent risk elements and recommendations. For example, it can extract concise phrases or codes such as "Battery cluster A overheating risk" and "Recommendation to adjust charging upper limit to 4.1V." The purpose is to quickly convey core information within limited display space or in emergency notification scenarios, avoiding information overload. Furthermore, based on the selected sending channel and configured with corresponding priorities and redundancy, sending the core risk summary information means sending the extracted core risk summary information through the channel previously selected based on the communication link status, strictly adhering to the priority and redundancy strategies configured for that channel. For example, the core risk summary information can be sent to the operator's mobile device via a high-priority SMS channel, while simultaneously being sent to their email address via a redundant email channel, ensuring that the information reaches the operator quickly and reliably. Furthermore, sending and displaying risk source indication information, physical impact analysis information, configuration parameter adjustment suggestions, and operation guidance information through at least two sensory cues means that when the system presents complete risk information to the operator, it does not rely solely on a single visual presentation, but combines multiple sensory stimuli to enhance the operator's perception and response speed. For example, risk information can be displayed on the BMS management interface with flashing red text (visual cues), while simultaneously playing an alarm sound (auditory cues), or even using vibration devices (tactile cues) for reminders. The purpose is to ensure that operators can promptly notice and understand risk information in different working environments.

[0188] In some preferred embodiments, a specific example is given below. Assume that during the operation of the energy storage system, the parameter safety assessment module detects a sudden spike in the comprehensive risk index of a certain battery cluster A, triggering a transient risk warning. At this time, the system first obtains the communication link status between the energy storage system and the operator. If the network connection between the BMS management platform and the operator's PC is stable, but it is also detected that the operator may be performing other operations, the system will select the BMS management platform interface as the primary transmission channel and configure it with high priority. Simultaneously, to increase redundancy and ensure emergency notification, the system will also select the operator's mobile phone SMS channel as an auxiliary channel and configure it with the highest priority. Next, the system will extract core risk summary information from the generated risk source indication information (e.g., "internal short circuit risk of battery cluster A"), physical impact analysis information (e.g., "may lead to thermal runaway, rapid temperature increase"), configuration parameter adjustment suggestion information (e.g., "immediately disconnect battery cluster A and reduce the charging current limit"), and operation guidance information (e.g., "refer to emergency operation manual step A for processing"), such as "Emergency! Battery cluster A short circuit, risk of thermal runaway, please handle immediately!". Subsequently, the system will send this core risk summary information to the operator via SMS with the highest priority. Simultaneously, on the BMS management platform interface, the system will display complete risk source indication information, physical impact analysis information, configuration parameter adjustment suggestions, and operation guidance information in a flashing red warning box (visual cues), accompanied by a continuous alarm sound (auditory cues). Through this multi-channel, multi-sensory, and hierarchical information delivery method, operators can quickly perceive the urgency of the risk, rapidly understand the situation through the core summary information, and then accurately handle the situation through detailed information and operation guidance, thereby effectively preventing potential safety incidents.

[0189] This application proposes a zero-code configuration system for energy storage BMS products, used to perform zero-code configuration of energy storage BMS products, combined with... Figure 3 As shown, the zero-code configuration system 1 for the energy storage BMS product includes:

[0190] System information acquisition module 11 is used to acquire battery type information and deployment location information of the energy storage system;

[0191] The environmental data acquisition module 12 is used to acquire the deployment environment data of the corresponding deployment location based on the deployment location information.

[0192] The battery characteristic acquisition module 13 is used to acquire the battery characteristic rules of the corresponding type of battery based on the battery type information.

[0193] The parameter safety assessment module 14 is used to assess the safety risks of the configuration parameters set by the operator based on battery type information, deployment site environmental data and battery characteristic rules, and obtain parameter safety risk assessment results.

[0194] The early warning information issuing module 15 is used to issue early warning information and provide adjustment suggestions based on the parameter safety risk assessment results.

[0195] To better understand the zero-code configuration system for energy storage BMS products proposed in this application, some key modules involved will be explained below.

[0196] The system information acquisition module is used to acquire battery type information and deployment location information of the energy storage system. The acquisition of battery type information and deployment location information has already been described in the above embodiments and will not be repeated here. It is important to emphasize that this system information acquisition module can be implemented as a software component, such as a user interface (UI) module, allowing the operator to manually input or select battery type information and deployment location information. Alternatively, this module can also be a data interface module, configured to communicate with the energy storage system's asset management system (AMS) to automatically acquire this information.

[0197] The environmental data acquisition module is used to acquire environmental data for the corresponding deployment location based on the deployment location information. The acquisition of environmental data based on deployment location information has already been described in the above embodiments and will not be repeated here. It should be emphasized that this environmental data acquisition module can be configured to connect to an external meteorological database or Geographic Information System (GIS) service via a network protocol (e.g., HTTP / HTTPS) to query and download historical or real-time environmental data. Alternatively, this module can also integrate a sensor data processing unit to directly receive and parse real-time data streams from on-site environmental sensors (e.g., temperature sensors, humidity sensors).

[0198] The battery characteristic acquisition module is used to obtain battery characteristic rules for the corresponding battery type based on battery type information. The content of obtaining battery characteristic rules based on battery type information has already been described in the above embodiments and will not be repeated here. It is important to emphasize that this battery characteristic acquisition module can be implemented as a rule base management system, which pre-stores detailed characteristic rules for various battery types (e.g., lithium iron phosphate batteries, nickel-cobalt-manganese batteries), including charge-discharge curves, internal resistance characteristics, thermal runaway thresholds, etc. When battery type information is received, this module can efficiently retrieve and load the corresponding rule set from the rule base. Alternatively, this module can also interact with an external battery model service, which dynamically generates or provides the required battery characteristic rules based on the battery type.

[0199] The parameter safety assessment module is used to evaluate the safety risks of configuration parameters set by the operator based on battery type information, deployment site environmental data, and battery characteristic rules, and obtain parameter safety risk assessment results. The content of assessing the safety risks of configuration parameters based on battery type information, deployment site environmental data, and battery characteristic rules has already been described in the above embodiments, and will not be repeated here. It is important to emphasize that this parameter safety assessment module can be built as a core computing service, which encapsulates complex risk assessment algorithms and models. This service receives input data from the system information acquisition module, environmental data acquisition module, and battery characteristic acquisition module, and performs calculations according to preset assessment logic or dynamic models, thereby outputting parameter safety risk assessment results. For example, this module can use an algorithm based on multi-factor weighted analysis to comprehensively score various input data to quantify the safety risks of configuration parameters.

[0200] The early warning information issuing module is used to issue early warning information and provide adjustment suggestions based on the parameter security risk assessment results. The content of issuing early warning information and providing adjustment suggestions based on the parameter security risk assessment results has already been described in the above embodiments and will not be repeated here. It is important to emphasize that this early warning information issuing module can be implemented as a message notification service, responsible for converting the assessment results into user-understandable early warning information and adjustment suggestions. It can be configured with multiple output channels, such as displaying directly in the configuration tool through a graphical user interface (GUI), or sending notifications to external systems (such as SMS platforms or email servers) through a communication interface. Furthermore, this module can also generate detailed risk reports for further analysis by operators.

[0201] The zero-code configuration system for energy storage BMS products proposed in this application achieves dynamic and intelligent safety risk assessment of the configuration parameters set by the operator by deeply integrating and comprehensively analyzing the battery type information, deployment location information, deployment location environmental data, and battery characteristic rules of the energy storage system.

[0202] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A zero-code configuration method for an energy storage BMS product, characterized in that, include: Obtain information on the battery type and deployment location of the energy storage system; Based on the deployment location information, obtain the deployment environment data for the corresponding deployment location; Based on the battery type information, obtain the battery characteristic rules for the corresponding battery type; Based on the battery type information, deployment site environmental data, and battery characteristic rules, the safety risks of the configuration parameters set by the operator are assessed, and the parameter safety risk assessment results are obtained. Based on the safety risk assessment results of the parameters, issue early warning information and provide adjustment suggestions; The step of assessing the security risks of the configuration parameters set by the operator based on the battery type information, deployment site environmental data, and battery characteristic rules, and obtaining the parameter security risk assessment result, includes: Based on the deployment location information, obtain the micro-topographic features of the energy storage system's deployment location; Based on the micro-topographic features, the environmental data of the deployment site are corrected to obtain corrected local extreme temperature data and corrected local low temperature duration data; Based on the battery type information, load the corresponding battery non-uniform response rule; Based on the corrected local extreme temperature data, configuration parameters, and battery non-uniform response rules, the maximum temperature difference that can be generated inside the battery cluster is analyzed. Based on the corrected local extreme temperature data, the corrected local low temperature duration data, the maximum temperature difference, configuration parameters, and battery non-uniform response rules, the comprehensive risk index of each battery cluster within the energy storage system is calculated as the parameter safety risk assessment result.

2. The zero-code configuration method for an energy storage BMS product according to claim 1, characterized in that, After the step of calculating the comprehensive risk index of each battery cluster within the energy storage system as the parameter safety risk assessment result based on the corrected local extreme temperature data, the corrected local low temperature duration data, the maximum temperature difference, configuration parameters, and battery non-uniform response rules, the method further includes: Based on the safety risk assessment results of the parameters, battery clusters with potential localized hidden damage risks are identified, and damage risk identification results are obtained. Based on the damage risk identification results, the configuration parameters are compared with the deployment site environmental data and the battery characteristic rules to obtain the risk comparison results.

3. The zero-code configuration method for an energy storage BMS product according to claim 2, characterized in that, The step of identifying battery clusters with potential localized hidden damage based on the safety risk assessment results of the parameters, and obtaining the damage risk identification results, includes: Acquire information on the switching of operating modes or changes in environmental conditions of the energy storage system; Based on the operation mode switching information or the environmental condition change information, obtain the corresponding risk threshold adjustment strategy; The risk threshold adjustment strategy is adjusted based on the current battery health status. Adjust the risk assessment threshold for parameter safety risk assessment according to the revised risk threshold adjustment strategy; The comprehensive risk index corresponding to the safety risk assessment results of the parameters is compared with the adjusted risk judgment threshold to identify battery clusters with local hidden damage risks, and the damage risk identification results are obtained.

4. The zero-code configuration method for an energy storage BMS product according to claim 2, characterized in that, The step of identifying battery clusters with potential localized hidden damage based on the safety risk assessment results of the parameters, and obtaining the damage risk identification results, includes: Acquire information on the energy storage system's operating mode switching and changes in the remaining battery life cycle; Based on the operating mode switching information and the battery remaining life cycle change information, adjust the corresponding risk judgment threshold; The comprehensive risk index corresponding to the safety risk assessment results of the parameters is compared with the adjusted risk judgment threshold to identify battery clusters with local hidden damage risks, and the damage risk identification results are obtained.

5. A zero-code configuration method for an energy storage BMS product according to claim 4, characterized in that, The step of comparing the comprehensive risk index corresponding to the parameter safety risk assessment result with the adjusted risk judgment threshold to identify battery clusters with local hidden damage risk and obtaining damage risk identification results includes: Based on the adjusted risk assessment threshold, a risk confirmation interval is defined; the risk confirmation interval includes an upper threshold for entering a risk state and a lower threshold for leaving a risk state. When the comprehensive risk index corresponding to the safety risk assessment result of the parameter exceeds the upper limit threshold for the first time, a duration timer is started. Within a preset time set by the duration timer, it is determined whether the comprehensive risk index continues to remain above the upper limit threshold, and an upper limit judgment result is obtained; When the result of the above-limit judgment is yes, the corresponding battery cluster is confirmed to be in a risk state; When the overall risk index of a battery cluster that is already in a risky state falls below the lower threshold for the first time, a duration timer is started. Within a preset time set by the duration timer, it is determined whether the comprehensive risk index remains below the lower limit threshold, and an over-lower limit judgment result is obtained; When the lower limit judgment result is yes, the corresponding battery cluster is confirmed to be out of risk.

6. A zero-code configuration method for an energy storage BMS product according to claim 5, characterized in that, When the comprehensive risk index exhibits the characteristic of rising at a rate greater than the rising rate threshold and then suddenly dropping within a preset time set by the duration timer, and the duration does not trigger a preset persistence judgment, the step of determining whether the comprehensive risk index continues to remain greater than the upper limit threshold within the preset time set by the duration timer to obtain the upper limit judgment result includes: Obtain information on the current operating mode of the energy storage system; Based on the operating mode information, identify whether the energy storage system is in a transient operating condition; When the energy storage system is identified as being in a transient operating condition, the instantaneous rate of change of the comprehensive risk index is obtained; The instantaneous rate of change is compared with a preset transient risk threshold to obtain an instantaneous risk comparison result, which is used as the result of exceeding the upper limit judgment. When the instantaneous risk comparison result indicates that the instantaneous rate of change exceeds the transient risk threshold, a transient risk warning is triggered to ensure a timely response to real potential risks.

7. A zero-code configuration method for an energy storage BMS product according to claim 6, characterized in that, The step of triggering a transient risk warning when the instantaneous risk comparison result indicates that the instantaneous rate of change exceeds the transient risk threshold, in order to ensure a timely response to real potential risks, includes: Obtain the configuration parameters that trigger the transient risk warning; Obtain information on the current operating mode and battery health status of the energy storage system; Based on the configuration parameters, operating mode information, and battery health status information, risk source indication information and physical impact analysis information are generated. Based on the risk source indication information and physical impact analysis information, generate configuration parameter adjustment suggestion information; Based on the configuration parameters, adjust the suggested information and generate operation guidance information; Send and display the risk source indication information, physical impact analysis information, configuration parameter adjustment suggestion information, and operation guidance information to the operator.

8. A zero-code configuration method for an energy storage BMS product according to claim 7, characterized in that, The steps of sending and displaying the risk source indication information, physical impact analysis information, configuration parameter adjustment suggestion information, and operation guidance information to the operator include: Obtain the status of the communication link between the energy storage system and the operator; Based on the communication link status, a transmission channel and the corresponding priority and redundancy of the transmission channel are selected; Based on the risk source indication information, physical impact analysis information, configuration parameter adjustment suggestion information, and operation guidance information, core risk summary information is extracted. Based on the selected transmission channel, configure the corresponding priority and redundancy, and send the core risk summary information; The risk source indication information, physical impact analysis information, configuration parameter adjustment suggestion information, and operation guidance information are sent and displayed through at least two sensory prompts.

9. A zero-code configuration system for an energy storage BMS product, used to execute the zero-code configuration method for an energy storage BMS product as described in any one of claims 1 to 8, characterized in that, include: The system information acquisition module is used to acquire information about the battery type and deployment location of the energy storage system. The environmental data acquisition module is used to acquire the deployment location environmental data of the corresponding deployment location based on the deployment location information. The battery characteristic acquisition module is used to acquire the battery characteristic rules of the corresponding type of battery based on the battery type information. The parameter safety assessment module is used to assess the safety risks of the configuration parameters set by the operator based on the battery type information, deployment site environmental data, and battery characteristic rules, and obtain the parameter safety risk assessment results. The early warning information issuing module is used to issue early warning information and provide adjustment suggestions based on the safety risk assessment results of the parameters; The process of assessing the security risks of operator-set configuration parameters based on battery type information, deployment site environmental data, and battery characteristic rules, and obtaining parameter security risk assessment results, includes: Based on the deployment location information, obtain the micro-topographic features of the energy storage system's deployment location; Based on the micro-topographic features, the environmental data of the deployment site are corrected to obtain corrected local extreme temperature data and corrected local low temperature duration data; Based on the battery type information, load the corresponding battery non-uniform response rule; Based on the corrected local extreme temperature data, configuration parameters, and battery non-uniform response rules, the maximum temperature difference that can be generated inside the battery cluster is analyzed. Based on the corrected local extreme temperature data, the corrected local low temperature duration data, the maximum temperature difference, configuration parameters, and battery non-uniform response rules, the comprehensive risk index of each battery cluster within the energy storage system is calculated as the parameter safety risk assessment result.

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