A system for quantifying the lifetime decay of electrochemical energy storage

Abstract

The present invention discloses an electrochemical energy storage life attenuation quantification system, which relates to the field of industrial energy storage technology. The technical solution solves the problem that capacity attenuation is difficult to accurately predict. By collecting data such as working temperature, current density, charge and discharge voltage, discharge depth, and working environment humidity, a capacity attenuation prediction model is used to fit and calculate the capacity attenuation index and provide early warning. At the same time, a cycle life control model is combined with a data set related to energy storage operation and a data set of impedance change, and a deep learning algorithm is used to generate a cycle life prediction index, and the load is dynamically adjusted to cope with complex operating conditions. In addition, the system monitors the AC impedance value, DC internal resistance value, electrode polarization impedance value, and electrolyte conductivity in real time, calculates the internal impedance change value, and compares it with the life attenuation warning threshold, provides optimization control suggestions, and alleviates the impact of impedance increase on performance.

Description

Technical Field

[0001] The invention relates to the technical field of industrial energy storage, and in particular to an electrochemical energy storage life attenuation quantification system. Background Art

[0002] The study of electrochemical energy storage life decay stems from the continuous development and demand for battery technology. The earliest electrochemical energy storage devices, such as lead-acid batteries, have certain advantages in energy storage, but over time, they gradually expose problems such as capacity decay and short cycle life. With the emergence of new electrochemical energy storage technologies such as lithium-ion batteries, the study of life decay has become a key issue. Research in this field mainly focuses on understanding the changes in electrode materials, electrolytes, and irreversible reactions produced during the cycle.

[0003] In industrial energy storage applications, the lifetime degradation of electrochemical energy storage can lead to multiple technical problems, as follows:

[0004] 1. Capacity decay is difficult to accurately predict: The capacity of the electrochemical energy storage system gradually decays with the increase of the number of charge and discharge cycles, and its decay rate is affected by many factors, such as operating temperature, current density, charge and discharge depth, etc. In industrial applications, how to accurately quantify the capacity decay trend of the energy storage system is a technical problem, especially when used for a long period of time, the decay rate may change suddenly, which is difficult to accurately predict in advance.

[0005] 2. Difficulty in quantifying cycle life attenuation: The cycle life of a battery is an important indicator of its performance, but different usage conditions (such as load fluctuations, discharge depth, ambient temperature, etc.) will accelerate or slow down the attenuation process, making the quantification of cycle life complicated. Industrial energy storage systems often need to remain stable under high load and long-term operation, so how to quantify the decline in their cycle life becomes a major challenge.

[0006] 3. Uncontrollable increase in internal impedance: In electrochemical energy storage systems, as the usage time increases, the internal impedance of the battery will gradually increase, resulting in a decrease in energy conversion efficiency. The increase in internal impedance is a key manifestation of life attenuation. However, since the growth of impedance is related to the complexity of electrochemical reactions, it is difficult to accurately quantify and predict it through simple models. Especially in long-term operation, this attenuation may lead to a significant reduction in system performance. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides an electrochemical energy storage life attenuation quantification system, which solves the technical shortcomings of the background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an electrochemical energy storage life attenuation quantification system, including a data acquisition module, an impedance monitoring module, a cycle life control module, a capacity attenuation prediction module and a life assessment module;

[0009] The data acquisition module is used to collect relevant data under different charge and discharge cycle conditions of the electrochemical energy storage system, obtain relevant data affecting life attenuation, and form a data set related to energy storage operation after preprocessing and dimensionless processing;

[0010] The impedance monitoring module monitors the changes in the internal impedance of the battery through an embedded sensor, records the impedance-related data inside the impedance battery in real time, and forms an impedance change data set; at the same time, it performs real-time analysis and calculation of the internal impedance of the energy storage system at different operating stages to construct an internal impedance change value Nkzb;

[0011] The cycle life control module establishes and introduces a cycle life control model, combines the energy storage operation related data set and the impedance change data set, uses a deep learning algorithm to quantitatively analyze the downward trend of the battery cycle life, obtains the cycle life prediction index Lfx, and dynamically adjusts the life attenuation under the operating load and environmental conditions;

[0012] The capacity attenuation prediction module is used to establish a capacity attenuation prediction model, and collect multi-dimensional environment-related data, load change-related data and historical-related data; and based on the capacity attenuation prediction model, the multi-dimensional environment-related data, load change-related data and historical-related data are combined to perform fitting calculations to obtain and evaluate the capacity attenuation index Qjx;

[0013] The life assessment module comprehensively analyzes the cycle life prediction index Lfx, the capacity decay index Qjx and the internal resistance change value Nkzb to form an overall energy storage life decay assessment value Se, and compares it with the preset life decay warning threshold Sth to obtain the optimization control suggestion of the current electrochemical energy storage system and output the control plan.

[0014] Preferably, the data acquisition module collects relevant data of the electrochemical energy storage system under different charge and discharge cycle conditions in real time by deploying sensors and monitoring equipment, and the sensors and monitoring equipment include temperature sensors, current sensors and charge and discharge monitoring equipment;

[0015] The energy storage operation related data set includes operating temperature Gwd, current density Gmd, charge and discharge voltage Gdy, discharge depth Gsd and operating environment humidity Ghs.

[0016] Preferably, the impedance monitoring module includes an impedance data acquisition unit and an impedance data processing unit;

[0017] The impedance data acquisition unit is used to monitor the impedance change inside the battery in real time. By installing the monitoring sensor at the key position of the energy storage system, the internal impedance of the battery is continuously monitored at different charge and discharge cycle stages and impedance-related data is collected in real time; then the collected impedance-related data is pre-processed and dimensionless processed to construct an impedance change data set;

[0018] The impedance change data set includes the AC impedance value Djz, the DC internal resistance value Dzl, the electrode polarization impedance value Djh and the electrolyte conductivity Dll.

[0019] Preferably, the impedance data processing unit extracts the impedance change data set to construct the following formula to calculate and obtain the internal impedance change value Nkzb. The specific calculation formula is as follows:

[0020] ;

[0021] Wherein, a1, a2, a3 and a4 represent the weight coefficients of the AC impedance value Djz, the DC internal resistance value Dzl, the electrode polarization impedance value Djh and the electrolyte conductivity Dll, respectively, and their specific values ​​are adjusted by the user.

[0022] Preferably, the cycle life control module includes a life control unit and a data analysis unit;

[0023] The life control unit is used to establish a cycle life control model. By dynamically collecting and analyzing relevant data information when the battery is working, the data information related to the cycle life in the model is updated in real time, so that the cycle life control model accurately reflects the life change trend of the battery under different working conditions; based on the deep learning algorithm, the energy storage operation related data and impedance change data are used as input to train and analyze the factors affecting the battery life attenuation.

[0024] Preferably, the data analysis unit is used to extract the energy storage operation related data set and fit it with the internal resistance change value Nkzb, and calculate the cycle life prediction index Lfx by the following formula:

[0025] ;

[0026] By comparing and evaluating the preset predicted cycle life threshold Q with the cycle life prediction index Lfx, the following evaluation content is generated:

[0027] When the cycle life prediction index Lfx ≥ the predicted cycle life threshold Q, it indicates that the current operating state of the battery is normal, the life decay is within a controllable range, and the remaining life of the battery meets the normal operating requirements of the system, and no adjustment is required.

[0028] When the cycle life prediction index Lfx is less than the predicted cycle life threshold Q, it indicates that the current operating state of the battery is abnormal and the life decay has exceeded the controllable range. At this time, adjustment measures are taken, including adjusting the load distribution, adjusting the charging algorithm and adjusting the working environment, and further predicting the decay trend of the battery capacity.

[0029] Preferably, the capacity decay prediction module includes a trend data acquisition unit and a capacity analysis unit;

[0030] The trend data acquisition unit is used to collect relevant trend data affecting battery capacity during the operation of the energy storage system in real time, including multi-dimensional environmental data, load change data and historical operation data;

[0031] The multi-dimensional environment related data include atmospheric pressure change value Sbh and light radiation intensity Sgf;

[0032] The load change data includes the power output fluctuation value Sbd and the peak load duration Sfz;

[0033] The historical data includes the number of cycle aging Slh of the battery during past operation;

[0034] The collected multi-dimensional environmental data, load change data and historical operation data are preprocessed and standardized to form an input data set for model analysis; then the input data set is used as input parameters to establish a capacity decay prediction model, and after the capacity decay prediction model is trained, the fitting calculation of the capacity decay trend is put into practice.

[0035] Preferably, the capacity analysis unit is used to extract and fit multi-dimensional environmental data, load change data and historical operation data to obtain the capacity attenuation index Qjx. The specific calculation formula is as follows:

[0036] ;

[0037] In the formula, Represents the natural logarithm, which is used to process the number of cycle aging Slh;

[0038] The preset capacity attenuation threshold W is compared with the capacity attenuation index Qjx for evaluation, and early warning is given for sudden attenuation. The specific contents are as follows:

[0039] When the capacity decay threshold W ≥ capacity decay index Qjx, it indicates that the current battery capacity decay trend is within the normal range, and the system continues to operate according to the current load and environmental conditions without special adjustments;

[0040] When the capacity decay threshold W is less than the capacity decay index Qjx, it indicates that the current battery capacity decay trend exceeds the normal range and the battery is currently in an accelerated decay state. At this time, a comprehensive analysis of the overall battery energy storage is further performed.

[0041] Preferably, the life assessment module includes a comprehensive life quantification unit and a comprehensive life assessment unit;

[0042] The comprehensive life quantification unit extracts the cycle life prediction index Lfx, the capacity attenuation index Qjx and the internal resistance change value Nkzb, and obtains the overall energy storage life attenuation evaluation value Se by calculating through the following formula:

[0043] ;

[0044] In the formula, e is a natural constant, represents an exponential function with the natural constant e as the base, i is an index variable, indicating the number of the current data point, Indicates the total number of data points involved in the calculation.

[0045] Preferably, the comprehensive life evaluation unit compares and evaluates the preset life decay warning threshold Sth with the overall energy storage life decay evaluation value Se, and obtains the optimization control suggestion of the current electrochemical energy storage system according to the evaluation content of the overall energy storage life decay evaluation value Se, and outputs the control scheme;

[0046] The specific process is as follows:

[0047] When the overall energy storage life decay assessment value Se ≥ life decay warning threshold Sth, it indicates that the current comprehensive life decay of the energy storage system is within an acceptable range, the battery health status is normal, and the system continues to work normally according to the existing operating conditions and load mode without the need for regulation or adjustment;

[0048] When the overall energy storage life decay assessment value Se is less than the life decay warning threshold Sth, it indicates that the current comprehensive life decay of the energy storage system exceeds the acceptable range. At this time, adjustment control measures are taken to slow down the decay rate and extend the overall service life of the system, including dynamically adjusting the load to the battery group in better condition according to the battery health status, adjusting the charging current, charging voltage and charging time, and starting or strengthening cooling or heating measures to adjust the working environment temperature of the battery.

[0049] The present invention provides an education management training system, method, computer equipment and storage medium. It has the following beneficial effects:

[0050] (1) This electrochemical energy storage life attenuation quantification system solves the problem of difficult to accurately predict capacity attenuation by combining a data acquisition module, an impedance monitoring module, a cycle life control module, a capacity attenuation prediction module, and a life assessment module. The system collects multi-dimensional environmental data and load change data of the energy storage system under different charge and discharge cycle conditions, especially key parameters such as operating temperature Gwd, current density Gmd, charge and discharge voltage Gdy, discharge depth Gsd, and working environment humidity Ghs, and uses a capacity attenuation prediction model to fit and calculate the battery's attenuation trend. The calculated capacity attenuation index Qjx can effectively capture the changing trend of capacity attenuation and provide early warning for sudden capacity attenuation; this comprehensive data analysis and fitting method can significantly improve the accuracy of capacity attenuation prediction and solve the problem of prediction difficulties caused by sudden changes in attenuation rate during long-term use.

[0051] (2) This is an electrochemical energy storage life attenuation quantification system. To address the difficulty in quantifying cycle life attenuation, this system establishes a cycle life control model through a cycle life control module, and combines the energy storage operation-related data set with the impedance change data set to quantify the downward trend of battery life through a deep learning algorithm. The system can adjust the parameters related to cycle life in the model in real time, such as the number of charge and discharge times, load fluctuations, ambient temperature, etc., generate a cycle life prediction index Lfx, and conduct comparative evaluation through a preset predicted cycle life threshold Q. Under high load or long-term operation, the system can dynamically adjust the battery's load distribution and operation strategy, and provide optimization control suggestions to meet the challenges of quantifying cycle life under complex operating conditions in industrial energy storage systems.

[0052] (3) This electrochemical energy storage life decay quantification system aims at the uncontrollable problem of internal impedance increase. This system uses the embedded sensor of the impedance monitoring module to monitor the changes in the internal impedance of the battery in real time, including the AC impedance value Djz, the DC internal resistance value Dzl, the electrode polarization impedance value Djh and the electrolyte conductivity Dll, and calculates the internal impedance change value Nkzb. With the long-term operation of the battery, the internal impedance will gradually increase. The system can construct a data set of impedance changes through real-time analysis of the impedance data, and conduct a comprehensive evaluation based on the cycle life and capacity decay data. Through the life evaluation module, the system can compare the overall energy storage life decay evaluation value Se with the preset life decay warning threshold Sth, and propose optimization control suggestions such as adjusting the charging current, charging voltage, charging time and ambient temperature, so as to alleviate the negative impact of the increase in internal impedance on the system performance and improve the energy conversion efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The present invention is a schematic diagram of the framework structure of an electrochemical energy storage life attenuation quantification system. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Example 1

[0056] See also Figure 1 , an electrochemical energy storage life attenuation quantification system, including a data acquisition module, an impedance monitoring module, a cycle life control module, a capacity attenuation prediction module and a life assessment module;

[0057] The data acquisition module is used to collect relevant data under different charge and discharge cycle conditions of the electrochemical energy storage system, obtain relevant data affecting life attenuation, and form a data set related to energy storage operation after preprocessing and dimensionless processing;

[0058] The impedance monitoring module monitors the changes in the internal impedance of the battery through an embedded sensor, records the impedance-related data inside the impedance battery in real time, and forms an impedance change data set; at the same time, the internal impedance of the energy storage system at different operation stages is analyzed and calculated in real time to construct the internal impedance change value Nkzb;

[0059] The cycle life control module establishes and introduces a cycle life control model, combines the energy storage operation related data set and the impedance change data set, uses a deep learning algorithm to quantitatively analyze the downward trend of the battery cycle life, obtains the cycle life prediction index Lfx, and dynamically adjusts the life attenuation under the operating load and environmental conditions;

[0060] The capacity attenuation prediction module is used to establish a capacity attenuation prediction model, and collect multi-dimensional environment-related data, load change-related data and historical-related data; and based on the capacity attenuation prediction model, the multi-dimensional environment-related data, load change-related data and historical-related data are combined to perform fitting calculations to obtain and evaluate the capacity attenuation index Qjx;

[0061] The life assessment module comprehensively analyzes the cycle life prediction index Lfx, the capacity decay index Qjx and the internal resistance change value Nkzb to form an overall energy storage life decay assessment value Se, and compares it with the preset life decay warning threshold Sth to obtain the optimization control suggestion of the current electrochemical energy storage system and output the control plan.

[0062] In this embodiment, the data acquisition module can accurately obtain relevant data affecting life attenuation by collecting multi-dimensional parameters such as working temperature Gwd, current density Gmd, charge and discharge voltage Gdy, discharge depth Gsd and working environment humidity Ghs, so as to ensure the accuracy of subsequent analysis; the impedance monitoring module can effectively capture the internal aging of the battery and generate an accurate internal impedance change value Nkzb by monitoring the AC impedance value Djz, DC internal resistance value Dzl, electrode polarization impedance value Djh and electrolyte conductivity Dll; the cycle life control module can dynamically adjust the operating load and environmental conditions through the cycle life control model and deep learning algorithm to reduce the battery life. The battery cycle life decay rate is calculated to generate the cycle life prediction index Lfx to ensure stable battery operation; the capacity decay prediction module can accurately predict the battery capacity decay trend and generate the capacity decay index Qjx through the collection and fitting calculation of multi-dimensional environmental data, load change data and historical operation data, and provide early warning of sudden decay; the life assessment module forms the overall energy storage life decay assessment value Se through comprehensive analysis of the cycle life prediction index Lfx, capacity decay index Qjx and internal impedance change value Nkzb, which can provide accurate battery health status assessment, compare with the life decay warning threshold Sth, output optimization control suggestions and regulation solutions, extend the overall life of the system and optimize operation efficiency.

[0063] Example 2

[0064] The data acquisition module collects relevant data of the electrochemical energy storage system under different charge and discharge cycle conditions in real time by deploying sensors and monitoring equipment, and the sensors and monitoring equipment include temperature sensors, current sensors and charge and discharge monitoring equipment;

[0065] The energy storage operation related data set includes operating temperature Gwd, current density Gmd, charge and discharge voltage Gdy, discharge depth Gsd and operating environment humidity Ghs.

[0066] In this embodiment, by deploying temperature sensors, current sensors and charge and discharge monitoring equipment, key data of the energy storage system under different charge and discharge cycle conditions can be monitored and collected in real time, including operating temperature Gwd, current density Gmd, charge and discharge voltage Gdy, discharge depth Gsd and working environment humidity Ghs; through the precise collection and integration of these parameters, the system can fully grasp the various factors that affect the life decay of the energy storage system; after pre-processing and dimensionless processing of these data, the energy storage operation-related data set is formed, which provides a reliable data basis for subsequent analysis; this enables the system to more accurately predict the battery life decay trend, optimize the operation strategy and improve the overall operation efficiency and stability of the system, while timely discovering abnormal conditions and making adjustments, effectively extending the battery life.

[0067] Example 3

[0068] The impedance monitoring module includes an impedance data acquisition unit and an impedance data processing unit;

[0069] The impedance data acquisition unit is used to monitor the impedance change inside the battery in real time. By installing the monitoring sensor at the key position of the energy storage system, the internal impedance of the battery is continuously monitored at different charge and discharge cycle stages and impedance-related data is collected in real time; then the collected impedance-related data is pre-processed and dimensionless processed to construct an impedance change data set;

[0070] The impedance change data set includes the AC impedance value Djz, the DC internal resistance value Dzl, the electrode polarization impedance value Djh and the electrolyte conductivity Dll.

[0071] The impedance data processing unit extracts the impedance change data set to construct the following formula to calculate and obtain the internal impedance change value Nkzb. The specific calculation formula is as follows:

[0072] ;

[0073] Wherein, a1, a2, a3 and a4 represent the weight coefficients of the AC impedance value Djz, the DC internal resistance value Dzl, the electrode polarization impedance value Djh and the electrolyte conductivity Dll, respectively, and their specific values ​​are adjusted by the user.

[0074] In this embodiment, through the coordinated work of the impedance data acquisition unit and the impedance data processing unit, the impedance change inside the battery can be monitored and collected in real time to ensure continuous monitoring of the battery at different charge and discharge cycle stages; by installing monitoring sensors at key positions of the energy storage system, the system can accurately obtain key parameters such as the AC impedance value Djz, the DC internal resistance value Dzl, the electrode polarization impedance value Djh and the electrolyte conductivity Dll; the AC impedance value Djz reflects the ion transmission performance of the battery at different frequencies; the DC internal resistance value Dzl is used to evaluate the electronic conduction efficiency of the battery; the electrode polarization impedance value Djh represents the resistance of the electrochemical reaction occurring on the electrode interface; the electrolyte conductivity Dll is used to measure the ionic conductivity of the battery electrolyte; these parameters are pre-processed and dimensionless to form an impedance change data set to ensure data standardization and comparability; the internal impedance change value Nkzb finally calculated by the formula provides key basic data for subsequent life decay analysis; this real-time impedance monitoring can not only help identify the aging trend of the battery, but also provide an accurate assessment of the health status of the battery, effectively improving the stability and service life of the energy storage system.

[0075] Example 4

[0076] The cycle life control module includes a life control unit and a data analysis unit;

[0077] The life control unit is used to establish a cycle life control model. By dynamically collecting and analyzing relevant data information when the battery is working, the data information related to the cycle life in the model is updated in real time, so that the cycle life control model accurately reflects the life change trend of the battery under different working conditions; based on the deep learning algorithm, the energy storage operation related data and impedance change data are used as input to train and analyze the factors affecting the battery life attenuation.

[0078] The data analysis unit is used to extract the energy storage operation related data set and fit it with the internal impedance change value Nkzb, and calculate the cycle life prediction index Lfx by the following formula:

[0079] ;

[0080] By comparing and evaluating the preset predicted cycle life threshold Q with the cycle life prediction index Lfx, the following evaluation content is generated:

[0081] When the cycle life prediction index Lfx ≥ the predicted cycle life threshold Q, it indicates that the current operating state of the battery is normal, the life decay is within a controllable range, and the remaining life of the battery meets the normal operating requirements of the system, and no adjustment is required.

[0082] When the cycle life prediction index Lfx is less than the predicted cycle life threshold Q, it indicates that the current operating state of the battery is abnormal and the life decay has exceeded the controllable range. At this time, adjustment measures are taken, including adjusting the load distribution, adjusting the charging algorithm and adjusting the working environment, and further predicting the decay trend of the battery capacity.

[0083] In this embodiment, through the synergy of the life control unit and the data analysis unit, a cycle life control model can be established and updated in real time; by dynamically collecting energy storage operation-related data and impedance change data of the battery during operation, including operating temperature Gwd, current density Gmd, charge and discharge voltage Gdy, discharge depth Gsd and internal impedance change value Nkzb, the life decay trend of the battery under different working conditions can be accurately reflected; based on the deep learning algorithm, the system can train and analyze these data, extract the cycle life prediction index Lfx, and ensure that the influencing factors of life decay can be accurately quantified; by comparing with the preset predicted cycle life threshold Q, the system can effectively determine whether the battery is in normal operating state; when the cycle life prediction index Lfx is lower than the predicted cycle life threshold Q, the system will timely adjust the working state of the battery by adjusting load distribution, optimizing charging algorithm and improving working environment; this module can effectively extend the service life of the battery and ensure efficient and safe operation of the system.

[0084] Example 5

[0085] The capacity decay prediction module includes a trend data acquisition unit and a capacity analysis unit;

[0086] The trend data acquisition unit is used to collect relevant trend data affecting battery capacity during the operation of the energy storage system in real time, including multi-dimensional environmental data, load change data and historical operation data;

[0087] The multi-dimensional environment related data include atmospheric pressure change value Sbh and light radiation intensity Sgf;

[0088] The load change data includes the power output fluctuation value Sbd and the peak load duration Sfz;

[0089] The historical data includes the number of cycle aging Slh of the battery during past operation;

[0090] The collected multi-dimensional environmental data, load change data and historical operation data are preprocessed and standardized to form an input data set for model analysis; then the input data set is used as input parameters to establish a capacity decay prediction model, and after the capacity decay prediction model is trained, the fitting calculation of the capacity decay trend is put into practice.

[0091] The capacity analysis unit is used to extract and fit multi-dimensional environmental data, load change data and historical operation data to obtain the capacity attenuation index Qjx. The specific calculation formula is as follows:

[0092] ;

[0093] In the formula, Represents the natural logarithm, which is used to process the number of cycle aging Slh;

[0094] The preset capacity attenuation threshold W is compared with the capacity attenuation index Qjx for evaluation, and early warning is given for sudden attenuation. The specific contents are as follows:

[0095] When the capacity decay threshold W ≥ capacity decay index Qjx, it indicates that the current battery capacity decay trend is within the normal range, and the system continues to operate according to the current load and environmental conditions without special adjustments;

[0096] When the capacity decay threshold W is less than the capacity decay index Qjx, it indicates that the current battery capacity decay trend exceeds the positive range and the battery is currently in an accelerated decay state. At this time, a comprehensive analysis of the overall battery energy storage is further performed.

[0097] In this embodiment, through the cooperation of the trend data acquisition unit and the capacity analysis unit, key trend data affecting the battery capacity during the operation of the energy storage system can be collected in real time; the multi-dimensional environment-related data include the atmospheric pressure change value Sbh and the light radiation intensity Sgf, which reflect the impact of the environment on the battery performance; the load change data include the power output fluctuation value Sbd and the peak load duration Sfz, which are used to evaluate the impact of load fluctuations on the battery; the historical data includes the number of cycle aging Slh, which represents the degree of aging of the battery in long-term use; after preprocessing and standardization, these data form an input data set for model analysis and are used to establish a capacity decay prediction model; the capacity analysis unit extracts the above data for fitting calculation, generates a capacity decay index Qjx, and compares and evaluates it with the preset capacity decay threshold W; timely discovers battery capacity decay problems, provides early warning, ensures stable operation of the system and extends battery life.

[0098] Example 6

[0099] The life assessment module includes a comprehensive life quantification unit and a comprehensive life assessment unit;

[0100] The comprehensive life quantification unit extracts the cycle life prediction index Lfx, the capacity attenuation index Qjx and the internal resistance change value Nkzb, and obtains the overall energy storage life attenuation evaluation value Se by calculating through the following formula:

[0101] ;

[0102] In the formula, e is a natural constant, represents an exponential function with the natural constant e as the base, i is an index variable, indicating the number of the current data point, Indicates the total number of data points involved in the calculation.

[0103] The comprehensive life evaluation unit compares and evaluates the preset life decay warning threshold Sth with the overall energy storage life decay evaluation value Se, and obtains the optimization control suggestion of the current electrochemical energy storage system according to the evaluation content of the overall energy storage life decay evaluation value Se, and outputs the control scheme;

[0104] The specific process is as follows:

[0105] When the overall energy storage life decay assessment value Se ≥ life decay warning threshold Sth, it indicates that the current comprehensive life decay of the energy storage system is within an acceptable range, the battery health status is normal, and the system continues to work normally according to the existing operating conditions and load mode without the need for regulation or adjustment;

[0106] When the overall energy storage life decay assessment value Se is less than the life decay warning threshold Sth, it indicates that the current comprehensive life decay of the energy storage system exceeds the acceptable range. At this time, adjustment control measures are taken to slow down the decay rate and extend the overall service life of the system, including dynamically adjusting the load distribution to the battery group in better condition according to the battery health status, adjusting the charging current, charging voltage and charging time, and starting or strengthening cooling or heating measures to adjust the working environment temperature of the battery.

[0107] In this embodiment, the life attenuation of the energy storage system can be comprehensively evaluated through the coordinated work of the comprehensive life quantification unit and the comprehensive life assessment unit; the comprehensive life quantification unit extracts the cycle life prediction index Lfx, the capacity attenuation index Qjx and the internal impedance change value Nkzb, and uses an exponential function to calculate and generate an overall energy storage life attenuation evaluation value Se; Lfx represents the cycle life status of the battery, Qjx reflects the attenuation trend of the battery capacity, and Nkzb reflects the change of the internal impedance of the battery, and the three provide a comprehensive life assessment; the comprehensive life assessment unit compares Se with the preset life attenuation warning threshold Sth to determine the current health status of the energy storage system; the advantage of the life assessment module is that it provides accurate battery life assessment and real-time regulation suggestions to ensure stable system operation and extend the battery life cycle.

[0108] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An electrochemical energy storage life attenuation quantification system, characterized by: It includes data acquisition module, impedance monitoring module, cycle life control module, capacity attenuation prediction module and life assessment module; The data acquisition module is used to collect relevant data under different charge and discharge cycle conditions of the electrochemical energy storage system, obtain relevant data affecting life attenuation, and form a data set related to energy storage operation after preprocessing and dimensionless processing; The impedance monitoring module monitors the changes in the internal impedance of the battery through an embedded sensor, records the impedance-related data inside the impedance battery in real time, and forms an impedance change data set; at the same time, it performs real-time analysis and calculation of the internal impedance of the energy storage system at different operating stages to construct an internal impedance change value Nkzb; The cycle life control module establishes and introduces a cycle life control model, combines the energy storage operation related data set and the impedance change data set, uses a deep learning algorithm to quantitatively analyze the downward trend of the battery cycle life, obtains the cycle life prediction index Lfx, and dynamically adjusts the life attenuation under the operating load and environmental conditions; The capacity attenuation prediction module is used to establish a capacity attenuation prediction model, and collect multi-dimensional environment-related data, load change-related data and historical-related data; and based on the capacity attenuation prediction model, the multi-dimensional environment-related data, load change-related data and historical-related data are combined to perform fitting calculations to obtain and evaluate the capacity attenuation index Qjx; The life assessment module comprehensively analyzes the cycle life prediction index Lfx, the capacity decay index Qjx and the internal resistance change value Nkzb to form an overall energy storage life decay assessment value Se, and compares it with the preset life decay warning threshold Sth to obtain the optimization control suggestion of the current electrochemical energy storage system and output the control plan.

2. The electrochemical energy storage life attenuation quantification system according to claim 1, characterized in that: The data acquisition module collects relevant data of the electrochemical energy storage system under different charge and discharge cycle conditions in real time by deploying sensors and monitoring equipment, and the sensors and monitoring equipment include temperature sensors, current sensors and charge and discharge monitoring equipment; The energy storage operation related data set includes operating temperature Gwd, current density Gmd, charge and discharge voltage Gdy, discharge depth Gsd and operating environment humidity Ghs.

3. The electrochemical energy storage life attenuation quantification system according to claim 2, characterized in that: The impedance monitoring module includes an impedance data acquisition unit and an impedance data processing unit; The impedance data acquisition unit is used to monitor the impedance change inside the battery in real time. By installing the monitoring sensor at the key position of the energy storage system, the internal impedance of the battery is continuously monitored at different charge and discharge cycle stages and impedance-related data is collected in real time; then the collected impedance-related data is pre-processed and dimensionless processed to construct an impedance change data set; The impedance change data set includes the AC impedance value Djz, the DC internal resistance value Dzl, the electrode polarization impedance value Djh and the electrolyte conductivity Dll.

4. The electrochemical energy storage life attenuation quantification system according to claim 3, characterized in that: The impedance data processing unit extracts the impedance change data set to construct the following formula to calculate and obtain the internal impedance change value Nkzb. The specific calculation formula is as follows: ； Wherein, a1, a2, a3 and a4 represent the weight coefficients of the AC impedance value Djz, the DC internal resistance value Dzl, the electrode polarization impedance value Djh and the electrolyte conductivity Dll, respectively, and their specific values ​​are adjusted by the user.

5. The electrochemical energy storage life attenuation quantification system according to claim 4, characterized in that: The cycle life control module includes a life control unit and a data analysis unit; The life control unit is used to establish a cycle life control model. By dynamically collecting and analyzing relevant data information when the battery is working, the data information related to the cycle life in the model is updated in real time, so that the cycle life control model accurately reflects the life change trend of the battery under different working conditions; based on the deep learning algorithm, the energy storage operation related data and impedance change data are used as input to train and analyze the factors affecting the battery life attenuation.

6. The electrochemical energy storage life attenuation quantification system according to claim 5, characterized in that: The data analysis unit is used to extract the energy storage operation related data set and fit it with the internal impedance change value Nkzb, and calculate the cycle life prediction index Lfx by the following formula: ； By comparing and evaluating the preset predicted cycle life threshold Q with the cycle life prediction index Lfx, the following evaluation content is generated: When the cycle life prediction index Lfx ≥ the predicted cycle life threshold Q, it indicates that the current operating state of the battery is normal, the life decay is within a controllable range, and the remaining life of the battery meets the normal operating requirements of the system, and no adjustment is required; When the cycle life prediction index Lfx is less than the predicted cycle life threshold Q, it indicates that the current operating state of the battery is abnormal and the life decay has exceeded the controllable range. At this time, adjustment measures are taken, including adjusting the load distribution, adjusting the charging algorithm and adjusting the working environment, and further predicting the decay trend of the battery capacity.

7. The electrochemical energy storage life attenuation quantification system according to claim 6, characterized in that: The capacity decay prediction module includes a trend data acquisition unit and a capacity analysis unit; The trend data acquisition unit is used to collect relevant trend data affecting battery capacity during the operation of the energy storage system in real time, including multi-dimensional environmental data, load change data and historical operation data; The multi-dimensional environment related data include atmospheric pressure change value Sbh and light radiation intensity Sgf; The load change data includes the power output fluctuation value Sbd and the peak load duration Sfz; The historical data includes the number of cycle aging Slh of the battery during past operation; The collected multi-dimensional environmental data, load change data and historical operation data are preprocessed and standardized to form an input data set for model analysis; then the input data set is used as input parameters to establish a capacity decay prediction model, and after the capacity decay prediction model is trained, the fitting calculation of the capacity decay trend is put into practice.

8. The electrochemical energy storage life attenuation quantification system according to claim 7, characterized in that: The capacity analysis unit is used to extract and fit multi-dimensional environmental data, load change data and historical operation data to obtain the capacity attenuation index Qjx. The specific calculation formula is as follows: ； In the formula, Represents the natural logarithm, which is used to process the number of cycle aging Slh; The preset capacity attenuation threshold W is compared with the capacity attenuation index Qjx for evaluation, and early warning is given for sudden attenuation. The specific contents are as follows: When the capacity decay threshold W ≥ capacity decay index Qjx, it indicates that the current battery capacity decay trend is within the normal range, and the system continues to operate according to the current load and environmental conditions without special adjustments; When the capacity decay threshold W is less than the capacity decay index Qjx, it indicates that the current battery capacity decay trend exceeds the normal range and the battery is currently in an accelerated decay state. At this time, a comprehensive analysis of the overall battery energy storage is further performed.

9. The electrochemical energy storage life attenuation quantification system according to claim 8, characterized in that: The life assessment module includes a comprehensive life quantification unit and a comprehensive life assessment unit; The comprehensive life quantification unit extracts the cycle life prediction index Lfx, the capacity attenuation index Qjx and the internal resistance change value Nkzb, and obtains the overall energy storage life attenuation evaluation value Se by calculating through the following formula: ； In the formula, e is a natural constant, represents an exponential function with the natural constant e as the base, i is an index variable, indicating the number of the current data point, Indicates the total number of data points involved in the calculation.

10. The electrochemical energy storage life attenuation quantification system according to claim 9, characterized in that: The comprehensive life evaluation unit compares and evaluates the preset life decay warning threshold Sth with the overall energy storage life decay evaluation value Se, and obtains the optimization control suggestion of the current electrochemical energy storage system according to the evaluation content of the overall energy storage life decay evaluation value Se, and outputs the control scheme; The specific process is as follows: When the overall energy storage life decay assessment value Se ≥ life decay warning threshold Sth, it indicates that the current comprehensive life decay of the energy storage system is within an acceptable range, the battery health status is normal, and the system continues to work normally according to the existing operating conditions and load mode without the need for regulation or adjustment; When the overall energy storage life decay assessment value Se is less than the life decay warning threshold Sth, it indicates that the current comprehensive life decay of the energy storage system exceeds the acceptable range. At this time, adjustment control measures are taken to slow down the decay rate and extend the overall service life of the system, including dynamically adjusting the load to the battery group in better condition according to the battery health status, adjusting the charging current, charging voltage and charging time, and starting or strengthening cooling or heating measures to adjust the working environment temperature of the battery.

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