Energy storage device health management method and system, electronic device, and storage medium

Abstract

The application provides a kind of energy storage equipment health management method and system, electronic equipment, storage medium, belong to energy storage management technical field, this method includes: the fusion of the plurality of health feature vectors of the management data of energy storage equipment, obtain comprehensive health index;First health state of energy storage equipment is obtained based on comprehensive health index and internal resistance of energy storage equipment, second health state of energy storage equipment is obtained based on the change trend of comprehensive health index and the historical comprehensive health index of current preset number, and the first health state and the second health state are fused, to obtain the target health state of energy storage equipment;Risk index of energy storage equipment is calculated based on target health state;Management strategy of energy storage equipment is determined based on risk index, and energy storage equipment is managed based on management strategy.The energy storage equipment health management method and system, electronic equipment, storage medium provided by the application can improve the reliability of energy storage equipment management.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of energy storage management, and more particularly relates to an energy storage device health management method and system, an electronic device, and a storage medium. BACKGROUND

[0002] With the popularization of renewable energy and the advancement of power grid modernization, energy storage devices play an increasingly important role in improving energy utilization efficiency and ensuring power grid stability. However, the performance of energy storage devices (especially electrochemical energy storage such as lithium-ion batteries, flow batteries, etc.) will decline with use and time, and the accurate assessment and effective management of the state of health (SOH) of the energy storage devices are directly related to the safety, reliability and economy of the energy storage system.

[0003] Traditional health management methods often rely on a single indicator or simple threshold judgment, resulting in inaccurate judgment of the health status of the energy storage device, and thus failing to achieve reliable management of the energy storage device. SUMMARY

[0004] The application aims to provide an energy storage device health management method and system, an electronic device, and a storage medium to improve the reliability of energy storage device management.

[0005] In a first aspect, an energy storage device health management method is provided, including: performing feature extraction on a management data set of the energy storage device to obtain a plurality of health feature vectors of a current cycle; the management data set is a standardized data set collected according to a cycle after preprocessing of original management data of the energy storage device; the cycle is a complete charge and discharge cycle of the energy storage device; the original management data is data generated by the energy storage device during the charge and discharge process;

[0006] Fusing the plurality of health feature vectors of the current cycle to obtain a comprehensive health index;

[0007] Determining a first health status of the energy storage device based on the comprehensive health index and the internal resistance of the energy storage device, determining a second health status of the energy storage device based on the comprehensive health index and a historical comprehensive health index, and fusing the first health status and the second health status to obtain a target health status of the energy storage device;

[0008] Calculating a risk index of the energy storage device based on the target health status;

[0009] Determining a management strategy for the energy storage device based on the risk index, and managing the energy storage device based on the management strategy.

[0010] In a second aspect, an energy storage device health management system is provided, including:

[0011] The feature extraction module is configured to extract features from a management data set of the energy storage device to obtain a plurality of health feature vectors of a current cycle; the management data set is a standardized data set collected according to a cycle after preprocessing of original management data of the energy storage device; the cycle is a complete charging and discharging cycle of the energy storage device; and the original management data is data generated by the energy storage device during the charging and discharging process.

[0012] The first fusion module is configured to fuse the plurality of health feature vectors of the current cycle to obtain a comprehensive health index.

[0013] The second fusion module is configured to determine a first health state of the energy storage device based on the comprehensive health index and an internal resistance of the energy storage device, determine a second health state of the energy storage device based on the comprehensive health index and a historical comprehensive health index, and fuse the first health state and the second health state to obtain a target health state of the energy storage device.

[0014] The risk calculation module is configured to calculate a risk index of the energy storage device based on the target health state.

[0015] The management module is configured to determine a management strategy of the energy storage device based on the risk index, and manage the energy storage device based on the management strategy.

[0016] In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the energy storage device health management method when executing the computer program.

[0017] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program implements the steps of the energy storage device health management method when executed by a processor.

[0018] The energy storage device health management method and system, the electronic device, and the storage medium provided by the embodiments of the present application have the following beneficial effects: the original data is preprocessed and health features are extracted to ensure data quality and comprehensiveness of features, and to lay a foundation for health evaluation. Secondly, the comprehensive health index comprehensively evaluates multi-dimensional features to make the health state intuitive. The double health state evaluation combining the internal resistance and the historical comprehensive health index takes into account the current health level and the degradation trend, and avoids one-dimensional evaluation bias. The risk index fuses the target health state and its change trend to quantify potential risks. Finally, a differentiated management strategy is formulated based on the risk level to realize closed-loop management from monitoring to control, which can early warn device failure, optimize operation and maintenance strategies, and prolong device life, thereby realizing reliable management of the energy storage device. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0020] Figure 1 A flowchart of a method for managing the health of an energy storage device according to an embodiment of the present application is provided.

[0021] Figure 2 A block diagram of a system for managing the health of an energy storage device according to an embodiment of the present application is provided.

[0022] Figure 3 A schematic block diagram of an electronic device according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0023] In the following description, specific details are set forth in order to provide a thorough understanding of the embodiments of the present application. However, persons skilled in the art will understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, systems, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0024] In order to make the purposes, technical solutions and advantages of the present application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0025] Reference will be made to Figure 1 , Figure 1 A flowchart of a method for managing the health of an energy storage device according to an embodiment of the present application is provided. The method can be executed by an electronic device and can include:

[0026] S101: Feature extraction is performed on a management data set of the energy storage device to obtain a plurality of health feature vectors of the current cycle. The management data set is a standardized data set collected according to a cycle after preprocessing of original management data of the energy storage device. The cycle is a complete charging and discharging period of the energy storage device. The original management data is data generated by the energy storage device during the charging and discharging process.

[0027] In this embodiment, the energy storage device is a device for storing electrical energy, such as a lithium battery, a lead-acid battery, a flow battery, etc.

[0028] The health feature vector is a multi-dimensional index (such as internal resistance growth rate, capacity retention rate, charge-discharge efficiency) extracted from single cycle data, representing the health state of the energy storage device. A one-dimensional index corresponds to a health feature vector. For example, the multiple health feature vectors in S101 can include a health feature vector corresponding to the internal resistance growth rate, a health feature vector corresponding to the capacity retention rate, and a health feature vector corresponding to the charge-discharge efficiency.

[0029] In this embodiment, the collected raw data (such as voltage fluctuation, temperature change) is preprocessed, including: eliminating sensor outliers (such as voltage jump, temperature jump); normalizing multi-source data (voltage, current, temperature) to a unified dimension; identifying complete charge-discharge cycles according to current direction (charging: current > 0 and state of charge of energy storage device rises; discharging: current < 0 and state of charge of energy storage device falls). In this embodiment, by preprocessing the collected raw data, the data quality is ensured, and sensor noise and communication anomalies and other interference are eliminated to improve the accuracy of the subsequent health state of the energy storage device.

[0030] Key features are extracted from each cycle, such as: capacity features, internal resistance features, voltage features, temperature features, etc.

[0031] S102: Fusion of multiple health feature vectors of the current cycle to obtain a comprehensive health index.

[0032] In this embodiment, the multiple health feature vectors contain different dimensions of health information, and it is more complex to directly use these high-dimensional features for decision-making. The comprehensive health index is generated by weighted fusion of multiple health features, generating a comprehensive health measurement value, making the expression of health status more intuitive and concise.

[0033] The comprehensive health index is a quantitative representation of the health state of the current cycle, and the higher the value, the better the health state.

[0034] This embodiment can fuse multiple health feature vectors of the cycle through a weighted average algorithm. Based on feature importance analysis, the weight of each feature vector is determined, and then the comprehensive health index is obtained by calculating the weighted sum. The calculation formula is:

[0035]

[0036] wherein, represents the comprehensive health index of the current cycle, represents the normalized value of the jth health feature vector in the current cycle, represents the weight of the jth health feature vector, and n represents the number of health features.

[0037] The closer the comprehensive health index value is to 1, the better the health status is, and the closer the comprehensive health index value is to 0, the worse the health status is.

[0038] In S103, the first health status of the energy storage device is determined based on the comprehensive health index and the internal resistance of the energy storage device, the second health status of the energy storage device is determined based on the comprehensive health index and the historical comprehensive health index, and the first health status and the second health status are fused to obtain the target health status of the energy storage device.

[0039] In this embodiment, the first health status of the energy storage device can be calculated based on the comprehensive health index and the internal resistance value measured in the current cycle. The internal resistance is an important indicator of the aging of the energy storage device (such as a lithium battery). An increase in the internal resistance of the energy storage device will lead to a decrease in energy efficiency and an increase in the risk of heating. In this embodiment, the internal resistance of the energy storage device can include the initial internal resistance of the energy storage device, the current internal resistance of the energy storage device, and the internal resistance of the energy storage device at the end of its life.

[0040] This embodiment can combine the comprehensive health index and the internal resistance to obtain the first health status (such as “good”, “general”, and “warning”) through a mapping relationship (such as a lookup table or formula calculation). In this embodiment, the first health status can be represented by the first health index. For example, if the first health index is greater than 0.7, the first health status is good; if the first health index is greater than 0.5 and not greater than 0.7, the first health status is general; and if the first health index is not greater than 0.5, the first health status is warning.

[0041] In this embodiment, the change trend (such as the slope and the difference value) of the current comprehensive health index and a preset number of historical comprehensive health indexes can be calculated. The change trend reflects the deterioration trend of the health status. Further, the second health status is calculated based on the change trend of the historical comprehensive health index and the comprehensive health index itself. For example, if the change trend of the historical comprehensive health index reflects a rapid decrease in the comprehensive health index, and the comprehensive health index value corresponding to the current cycle is low, the second health status is also relatively low. This reflects the dynamic health trend of the energy storage device, that is, the evolution speed of the health status. This embodiment considers the time dimension of the degradation of the device and can find possible signs of accelerated aging. Further, the second health status can also be represented by the second health index. For example, if the second health index is greater than 0.7, the second health status is good; if the second health index is greater than 0.5 and not greater than 0.7, the second health status is general; and if the second health index is not greater than 0.5, the second health status is warning.

[0042] In the embodiment, the first health state and the second health state can be combined by a weighted fusion method to obtain a target health state. The state comprehensively reflects the influence of the current health level, the historical degradation trend of the energy storage device and the change of the internal resistance on the health state of the energy storage device, and more comprehensively reflects the actual health condition of the device, avoiding one-sidedness of single-dimensional evaluation.

[0043] For example, if the first health state is 0.6, representing that the first health state is “general”, the second health state is 0.3, representing that the second health state is “warning”, and the respective weights of the first health state and the second health state are 0.5, then the fused health index is 0.45, representing that the target health state after fusion is “warning”.

[0044] S104: calculating a risk index of the energy storage device based on the target health state.

[0045] In the embodiment, the risk index is a comprehensive risk quantitative index for evaluating the current overall risk level of the energy storage device, and can be obtained from the target health state.

[0046] Specifically, the calculation of the risk index is based on the target health state, and the calculation formula of the risk index is:

[0047]

[0048] wherein, represents the risk index of the energy storage device, which is a quantitative result of the comprehensive health state and its change trend, and the greater the value is, the higher the risk is; represents the target health state, which is obtained by fusing the first health state and the second health state, and reflects the current comprehensive health level of the device; represents a weight coefficient of the change trend, which is used to adjust the influence degree of the historical change trend on the risk, when >0, the greater the change trend is, the greater the risk increment is; represents the number of time window cycles for calculating the historical change trend; represents the target health state value of the current cycle; represents the historical target health state value of the cycle at a distance of m cycles from the current cycle (for example, m=5, represents the historical target health state value corresponding to the cycle at a distance of 5 cycles from the current cycle), which is used to calculate the historical change trend; represents a nonlinear adjustment parameter of the change trend, which is used to control the nonlinear influence of the absolute value of the change trend on the risk, for example, when γ=1, it is a linear relationship, and when γ>1, it amplifies the influence of rapid change.

[0049] represents a basic risk term, which is based on the basic risk of the current health state, when The closer to 0 (the worse the health state), the greater the value, the higher the risk.

[0050] The change trend risk term represents the superimposed effect of the decline speed of the health state on the risk, The average absolute value of the change trend in m cycles measures the speed of the decline of the health state.

[0051] Through the risk index, the system manager can clearly understand the current risk state of the energy storage device, and accordingly take corresponding management measures.

[0052] S105: Determine the management strategy of the energy storage device based on the risk index, and manage the energy storage device based on the management strategy.

[0053] In this embodiment, according to the calculated risk index, the corresponding management strategy can be automatically selected or adjusted by referring to Table 1, management strategy comparison table.

[0054] Table 1 Management strategy comparison table

[0055]

[0056] In this embodiment, the control signal based on the management strategy can be sent to the control system (such as BMS, PCS, temperature control system) of the energy storage device, to realize the actual management and control of the energy storage device, so as to maximize its economic value and service life under the premise of ensuring the safety of the device.

[0057] From the above, first, the original data is preprocessed and health features are extracted in this embodiment to ensure data quality and feature comprehensiveness, laying a foundation for health assessment. Secondly, the comprehensive health index comprehensively evaluates multiple dimensions, making the health state intuitive. Combined with the double health state evaluation of internal resistance and historical change trend of comprehensive health index, the current health level and degradation trend are considered, avoiding one-sidedness of single dimension evaluation. The risk index integrates the target health state and its change trend, and quantifies the potential risk. Finally, based on the risk level, a differentiated management strategy is formulated to realize closed-loop management from monitoring to control, which can early warning of equipment failure, optimize operation and maintenance strategy, and prolong the service life of the equipment.

[0058] In an embodiment of the present application, the management data set is obtained in the following manner:

[0059] The original management data of the energy storage device is preprocessed to obtain initial management data, and the preprocessing includes data cleaning and noise filtering;

[0060] The initial management data is cyclically identified according to the direction of the current of the energy storage device in the charging and discharging process, and different types of initial management data in the current cycle are obtained; the categories include voltage, current, and temperature.

[0061] The time sequence of the different types of initial management data in the current cycle is aligned based on a preset time interval, and a management data set of the energy storage device is obtained.

[0062] In the embodiment, the original management data includes electrical parameters (voltage, current, SOC), physical parameters (temperature, pressure), environmental parameters (humidity, ambient temperature), etc. during the operation of the energy storage device, which can be collected in real time by corresponding sensors.

[0063] The quartile range method can be used in the embodiment to identify and delete data points (such as voltage jump and temperature rise) that deviate significantly from the normal range. Linear interpolation and moving average are used to fill short-time missing data, and long-time missing data is marked as invalid data segment. Then, a low-pass filter (such as a Butterworth filter) is used to eliminate high-frequency electromagnetic interference. After the above processing, the initial management data is obtained.

[0064] In the embodiment, complete charging (from the beginning of charging to the end of charging) and discharging (from the beginning of discharging to the end of discharging) cycle periods can be identified according to the current direction and the state of charge.

[0065] The mapping relationship between the current direction and the charging and discharging state is as follows:

[0066] Charging state, current direction is positive (flowing into the energy storage device), and the state of charge is continuously rising;

[0067] Discharging state, current direction is negative (flowing out of the energy storage device), and the state of charge is continuously falling.

[0068] When the current changes from negative to positive and the state of charge rises at a rate exceeding a threshold (such as 0.5% / min), the charging cycle start point is marked; when the current decreases to near 0 (such as <5% rated current) and the state of charge no longer changes, the charging cycle end point is marked; the complete cycle period is a continuous data segment from the charging start point to the charging end point, or from the discharging start point to the discharging end point. In the embodiment, the initial management data of different types (voltage data, current data, and temperature data) in the charging state stage in the current cycle and the initial management data of different types (voltage data, current data, and temperature data) in the discharging state stage in the current cycle are identified from the initial management data according to the current direction of the energy storage device in the charging and discharging process.

[0069] In the embodiment, different sensors (such as voltage sensors, temperature sensors) have different sampling frequencies (such as 10 Hz for voltage and 1 Hz for temperature), resulting in different data time sequences. In order to solve the problem of different data time sequences, in the embodiment, the time sequences of different types of initial management data in the current cycle are aligned, which can specifically include: aligning the management data of each type in the charging phase (for example, aligning the voltage data, current data and temperature data in the charging phase), and aligning the management data of each type in the discharging phase (for example, aligning the voltage data, current data and temperature data in the discharging phase).

[0070] Specifically, the embodiment can select the least common multiple frequency (such as 1 Hz) as the reference, and downsample the high-frequency data (such as taking the average value of voltage data per second), and upsample the low-frequency data (such as linear interpolation of temperature data). Through hardware clock synchronization (such as GPS time correction) or software timestamp correction, the clock deviation of the sensor is eliminated.

[0071] From the above, it can be concluded that the embodiment solves the problems of "large noise, chaotic time sequence and fuzzy cycle" of the operation data of the energy storage device by preprocessing the original management data and cycle recognition, and provides high-quality input data for the health management algorithm.

[0072] In an embodiment of the application, a plurality of health feature vectors of the current cycle are fused to obtain a comprehensive health index, including:

[0073] The plurality of health feature vectors are respectively normalized to obtain a plurality of target health feature vectors;

[0074] The plurality of target health feature vectors are assigned weights based on the influence degree of each target health feature vector on the health state of the energy storage device;

[0075] The plurality of health feature vectors and the respective weights are weighted and fused to obtain a comprehensive health index.

[0076] In the embodiment, the dimensions of different health feature vectors are different, such as voltage, current and temperature, etc. In order to eliminate the dimensional differences of different health features and make each feature comparable, the embodiment normalizes the plurality of health feature vectors to obtain a plurality of target health feature vectors.

[0077] In the embodiment, the plurality of health feature vectors can be mapped to a unified interval (such as [0, 1]) through mathematical transformation, such as through min-max normalization:

[0078]

[0079] wherein, value representing a target health feature vector, value representing a health feature vector, maximum value representing a health feature vector, minimum value representing a health feature vector.

[0080] For example, if the value of a health feature vector is 200 mV, the minimum value of the health feature vector is 100 mV, and the maximum value is 300 mV, after min-max normalization, it is (200-100) / (300-100)=0.5, that is, it is mapped to 0.5.

[0081] In this embodiment, different health features have different degrees of influence on the health state of the energy storage device (for example, internal resistance growth may be more critical than temperature fluctuation), and the importance needs to be quantified by weights.

[0082] This embodiment can assign weights to multiple target health feature vectors by principal component analysis, that is, by dimension reduction to extract principal components, and according to the variance contribution rate to determine the weights.

[0083] Suppose k target health feature vectors are V=[v1, v2, …, vk], and the corresponding weights are w=[w1, w2, …, wk], which satisfy k .

[0084] In this embodiment, the normalized feature vectors are linearly combined with the weights to obtain a comprehensive health index.

[0085]

[0086] wherein, CHI represents the comprehensive health index of the current cycle, vj represents the normalized value of the jth health feature vector in the current cycle, wj represents the weight of the jth health feature vector, and k represents the number of health features.

[0087] For example, if there are three feature vectors v1=0.6 (voltage), v2=0.8 (internal resistance), and v3=0.4 (temperature), and the corresponding weights are w1=0.25, w2=0.4, and w3=0.35, then: CHI=0.6×0.25+0.8×0.4+0.4×0.35=0.63.

[0088] The comprehensive health index converts multi-dimensional features into a value in the range of 0-1 through weighted fusion, and the higher the value, the better the health state of the energy storage device, and vice versa.

[0089] ​​​​​From the above, it can be concluded that the embodiment eliminates the dimensional difference by normalizing the health feature vector, makes the features comparable, assigns weights according to the influence of the features on the health state of the equipment, highlights the key indicators, generates a comprehensive health index by weighted fusion, quantifies the multi-dimensional health information into a single indicator, and makes the health state expression more intuitive.

[0090] In an embodiment of the present application, the first health state of the energy storage device is determined based on the comprehensive health index and the internal resistance of the energy storage device, comprising:

[0091] The first health state of the energy storage device is determined based on the comprehensive health index and the internal resistance of the energy storage device by the first formula;

[0092] The first formula is:

[0093]

[0094] Wherein, represents the first health state of the energy storage device, represents the initial internal resistance of the energy storage device, represents the current internal resistance of the energy storage device, represents the internal resistance of the energy storage device at the end of life, represents the comprehensive health index of the current cycle, represents the current actual capacity of the energy storage device, represents the nominal capacity of the energy storage device.

[0095] In the embodiment, represents the internal resistance degradation term, reflecting the degradation degree of the internal resistance of the energy storage device with the use cycle. represents the internal resistance of the energy storage device at the end of life, if is close to , the ratio tends to 1, indicating the deterioration of the health state.

[0096] represents the capacity attenuation term, reflecting the ratio of the actual capacity to the nominal capacity, when the energy storage device ages, , the ratio decreases, and the health state decreases.

[0097] In the embodiment, the health state of the energy storage device can be judged by the internal resistance growth and the capacity attenuation, but the correlation of the two is different in different aging stages. For example, the initial capacity attenuation may dominate the health degradation, while the internal resistance growth is more significant in the later stage.

[0098] In the embodiment, the internal resistance degradation term and the capacity attenuation term are both converted into dimensionless values in the interval [0, 1] by normalization processing, ensuring that different dimensional indicators can be directly weighted and summed.

[0099] For example: if , the internal resistance degradation term approaches 0 (optimal health state); if , the internal resistance degradation term is 1 (end of life); if , the capacity attenuation term is 1; if , the capacity attenuation term is 0.8.

[0100] The final calculated value range is [0, 1], and the lower the value, the better the health state.

[0101] From the above, it can be concluded that the embodiment fuses the internal resistance degradation and capacity attenuation indicators, and dynamically adjusts the weight using the comprehensive health index CHI, realizes multi-dimensional evaluation of the health state of the energy storage device, can adapt to the dominant failure mode in different aging stages, avoids one-sidedness of a single indicator, and improves the accuracy and working condition adaptability of health evaluation.

[0102] In an embodiment of the present application, the second health state of the energy storage device is determined based on the comprehensive health index and the historical comprehensive health index, comprising:

[0103] The second health state of the energy storage device is determined based on the comprehensive health index and the historical comprehensive health index, and through a second formula;

[0104] The second formula is:

[0105]

[0106] Wherein, represents the second health state of the energy storage device, represents the comprehensive health index of the current cycle, represents the change trend of the comprehensive health index in the time period from m-n cycle to m cycle, represents the historical comprehensive health index, represents the weight coefficient, represents the current actual capacity of the energy storage device, represents the nominal capacity of the energy storage device.

[0107] In the embodiment, represents the long short-term memory network, which captures the time sequence change rule of the comprehensive health index in the time period from m-n to m by using the long short-term memory network, processes the long-term dependency relationship through the memory unit, and reflects the degradation trend (such as accelerated attenuation or stable aging) of the health state of the energy storage device. The sequence of the comprehensive health index from m-n to the current time m is input into the long short-term memory network, and the long short-term memory network can output a value reflecting the dynamic change trend of the health state, which is used to predict the evolution direction of the future health state.

[0108] The weight coefficient The weights used to balance historical trends with the current state are applied when energy storage devices are in a stable aging phase. It can be set to a smaller value to focus on current health indicators; when the equipment is nearing the end of its lifespan or when operating conditions fluctuate greatly, it should be increased. It can enhance the impact of trend analysis and capture accelerated degradation characteristics in advance.

[0109] and They complement each other. When As the volume increases, the weight of trend analysis increases; conversely, the focus shifts more towards the current capacity and the real-time status of the overall health index.

[0110] In this embodiment, the entire lifecycle data of the energy storage device (such as charge-discharge cycles, CHI changes, and actual health status values) can be used to construct an optimization objective function, and the solution that minimizes the objective function can be obtained. Value. Specifically represented as:

[0111]

[0112] in, This represents the predicted second health status value obtained by calculating the i-th cyclic sample using the second formula. The second health status of the i-th cyclic sample is the measured value (which can be the real value obtained directly through experiments or sensors), and N represents the total number of cyclic samples.

[0113] From the above, it can be concluded that this embodiment achieves its intended effect through... By dynamically adjusting the weights of LSTM time-series trends and capacity status, and integrating historical CHI change trends with current capacity decay characteristics, the system not only captures dynamic trends of health degradation but also combines real-time capacity status to improve the foresight and accuracy of health assessment under complex operating conditions.

[0114] In one embodiment of this application, the first health state and the second health state are fused to obtain the target health state of the energy storage device, including:

[0115] Calculate the basic weights corresponding to the first and second health states based on their respective confidence levels.

[0116] Based on the correlation between the current operating characteristics of the energy storage device and the first and second health states, the operating condition matching factors for the first and second health states are calculated respectively. The operating characteristics represent the current operating conditions of the energy storage device. The correlation between the current operating characteristics of the energy storage device and the first health state is the degree of correlation between the current operating conditions of the energy storage device and the first health state. The correlation between the current operating characteristics of the energy storage device and the second health state is the degree of correlation between the current operating conditions of the energy storage device and the second health state.

[0117] calculate the average error of each of the historical first health state and the historical second health state, normalize based on the average error of each to obtain a historical performance factor corresponding to each of the first health state and the second health state;

[0118] fuse the base weight corresponding to the first health state, the operating condition matching factor and the historical performance factor to obtain a fusion weight corresponding to the first health state, and fuse the base weight corresponding to the second health state, the operating condition matching factor and the historical performance factor to obtain a fusion weight corresponding to the second health state;

[0119] weight fuse based on the first health state, the second health state and the fusion weight corresponding to each to obtain a target health state of the energy storage device.

[0120] In the embodiment, the confidence degree reflects the reliability of each health state evaluation method, which can be determined based on the theoretical basis or data source of the evaluation model. For example, the first health state can be obtained by a first model, the second health state can be obtained by a second model, and the confidence degree can reflect the confidence degree of the first model and the second model in predicting the health state.

[0121] Specifically, the base weight formula in the embodiment is represented as:

[0122]

[0123]

[0124] wherein, represents the base weight corresponding to the first health state, represents the base weight corresponding to the second health state, represents the confidence degree of the first health state, represents the confidence degree of the second health state.

[0125] In the embodiment, the operating condition feature represents the current operating condition of the energy storage device, such as temperature, charge-discharge rate, state of charge, etc. The correlation degree represents the correlation between the operating condition feature and each health state evaluation method.

[0126] For example: under high temperature operating condition, the change of internal resistance has a significant impact on , and the correlation degree of is high; under frequent charge-discharge operating condition, the CHI trend has a significant impact on , and the correlation degree of is high.

[0127] The embodiment can calculate the correlation degree of the current operating condition feature of the energy storage device with the first health state and the second health state respectively by cosine similarity.

[0128] In the embodiment, the average error represents the deviation of the historical evaluation result from the true value, reflecting the long-term stability of the health state judgment. The calculation formula of the average error is:

[0129]

[0130]

[0131] wherein, the average error of the historical first health state of the energy storage device, the average error of the historical second health state of the energy storage device, the first health state prediction value of the i th cycle sample calculated by the first formula.

[0132] The calculation formula of the historical performance factor is:

[0133]

[0134]

[0135] wherein, the historical performance factor of the first health state, the historical performance factor of the second health state.

[0136] In the embodiment, the basic weight corresponding to the first health state, the working condition matching factor and the historical performance factor are fused to obtain the fusion weight corresponding to the first health state, including: multiplying the basic weight corresponding to the first health state, the working condition matching factor and the historical performance factor and normalizing to obtain the fusion weight corresponding to the first health state. The fusion weight corresponding to the first health state is represented as:

[0137]

[0138] wherein, the fusion weight corresponding to the first health state, the working condition matching factor of the first health state, the working condition matching factor of the second health state.

[0139]

[0140] wherein, the fusion weight corresponding to the second health state.

[0141] The first health state and the second health state and the respective fusion weights are weighted and fused to obtain the target health state of the energy storage device. Specifically represented as:

[0142]

[0143] As can be seen from the above, this embodiment calculates the fusion weight by comprehensively considering confidence level, working condition matching degree and historical performance, and dynamically adjusts the influence of the first health state and the second health state, so as to make the target health state assessment more accurate.

[0144] In one embodiment of this application, the method further includes:

[0145] Based on the target health state and multiple health feature vectors, the health degradation cost for the next cycle and the current dynamic power envelope of the energy storage device are obtained. The health degradation cost is the loss cost caused by aging for each charge-discharge cycle of the energy storage device. The dynamic power envelope is the maximum allowable charge-discharge power boundary of the current energy storage device in a healthy state.

[0146] Based on health degradation costs and dynamic power envelope, the charging and discharging power of energy storage devices in each future cycle is predicted.

[0147] In this embodiment, the worse the health status, the higher the aging loss per cycle (e.g., accelerated capacity decay). A mathematical model is used to convert health indicators into economic costs (e.g., capacity loss per cycle is converted into replacement costs). For example, if the target health status shows 80% remaining capacity, combined with historical decay data, the cost corresponding to the capacity loss due to aging in the next cycle can be estimated.

[0148] In this embodiment, a deterioration in health status can lead to a decrease in the energy storage device's ability to withstand high-current charging and discharging (such as increased internal resistance and easy overheating).

[0149] This embodiment can establish a mapping relationship between health status and maximum allowable power through historical data or physical models (such as equivalent circuit models). For example, when the health status is below a threshold, the dynamic power envelope will shrink, limiting the charging and discharging power to avoid accelerating aging.

[0150] In this embodiment, to reduce aging costs, power planning should avoid excessive wear and tear on the health status (e.g., reducing the number of high-power charge and discharge cycles).

[0151] Power prediction must be within the power boundaries allowed by the current health status (e.g., the maximum charging power does not exceed the upper limit of the dynamic envelope).

[0152] In this embodiment, historical operating patterns and future load demands can be considered, and under cost and power constraints, an optimization algorithm (such as model predictive control) can be used to generate a charging and discharging power that balances economy and safety. For example, during periods of high health degradation costs, low-power charging and discharging is preferred, while when the dynamic power envelope is relatively loose, the power can be appropriately increased to meet load demands. Model predictive control can be expressed as:

[0153]

[0154] wherein, represents the predicted charging and discharging power of the future cycle (positive value represents charging, negative value represents discharging), represents the number of predicted future cycles; represents the health degradation cost of the i-th future cycle, which is a function of the dynamic power envelope represents the weight coefficient of balancing the health degradation cost and the power tracking error, which can be determined by experience, the larger the value is, the more the reference power demand is prioritized to be met; the smaller the value is, the more the aging cost is prioritized to be reduced; represents the reference power of the i-th future cycle. In the application process, the dynamic power envelope constraint condition needs to be met, that is:

[0155] wherein,

[0156] represents the maximum discharging power allowed based on the current health state at time t; represents the maximum charging power allowed based on the current health state at time t. From the above, it can be seen that, in the embodiment, the health degradation cost and the dynamic power envelope are calculated by the target health state and the health feature vector, the future charging and discharging power is predicted by combining the two, the aging loss is minimized while the power demand is met, the power boundary is dynamically adjusted, the balance between the service life and the economy of the energy storage device is realized, and the operation safety and the management scientificity are improved.

[0157] Corresponding to the energy storage device health management method of the above embodiment,

[0158] is a structural block diagram of an energy storage device health management system provided by an embodiment of the present application. For ease of illustration, only parts related to the embodiments of the present application are shown. With reference to Figure 2 , the energy storage device health management system 20 includes a feature extraction module 21, a first fusion module 22, a second fusion module 23, a risk calculation module 24, and a management module 25. Figure 2

[0159] The feature extraction module 21 is configured to perform feature extraction on a management data set of the energy storage device to obtain a plurality of health feature vectors of the current cycle. The management data set is a standardized data set collected according to cycles after preprocessing of original management data of the energy storage device. A cycle is a complete charging and discharging period of the energy storage device. The original management data is data generated by the energy storage device during the charging and discharging process.

[0160] ​The first fusion module 22 is configured to fuse the plurality of health feature vectors of the current cycle to obtain a comprehensive health index;

[0161] The second fusion module 23 is configured to determine a first health state of the energy storage device based on the comprehensive health index and an internal resistance of the energy storage device, determine a second health state of the energy storage device based on the comprehensive health index and a historical comprehensive health index, and fuse the first health state and the second health state to obtain a target health state of the energy storage device;

[0162] The risk calculation module 24 is configured to calculate a risk index of the energy storage device based on the target health state.

[0163] The management module 25 is configured to determine a management strategy of the energy storage device based on the risk index, and manage the energy storage device based on the management strategy.

[0164] In an embodiment of the present application, the energy storage device health management system 20 further comprises an acquisition module of management data set, and is specifically configured to:

[0165] The original management data of the energy storage device are preprocessed to obtain initial management data, and the preprocessing includes data cleaning and noise filtering;

[0166] The initial management data are cyclically identified according to the direction of the current of the energy storage device in the charging and discharging process to obtain different types of initial management data in the current cycle; the categories include voltage, current and temperature;

[0167] The different types of initial management data in the current cycle are time series aligned based on a preset time interval to obtain the management data set of the energy storage device.

[0168] In an embodiment of the present application, the first fusion module 22 is specifically configured to:

[0169] The plurality of health feature vectors are respectively normalized to obtain a plurality of target health feature vectors;

[0170] The plurality of target health feature vectors are assigned weights based on the influence degree of each of the plurality of target health feature vectors on the health state of the energy storage device;

[0171] The plurality of health feature vectors and the respective weights are weighted and fused to obtain the comprehensive health index.

[0172] In an embodiment of the present application, the second fusion module 23 is specifically configured to:

[0173] The first health state of the energy storage device is determined based on the comprehensive health index and the internal resistance of the energy storage device through a first formula;

[0174] The first formula is:

[0175]

[0176] wherein, represents a first health state of the energy storage device, represents an initial internal resistance of the energy storage device, represents a current internal resistance of the energy storage device, represents an internal resistance at the end of life of the energy storage device, represents a comprehensive health index of a current cycle, represents a current actual capacity of the energy storage device, represents a nominal capacity of the energy storage device.

[0177] In an embodiment of the present application, the second fusion module 23 is specifically further configured to:

[0178] determine a second health state of the energy storage device based on the comprehensive health index and the historical comprehensive health index and through a second formula;

[0179] wherein the second formula is:

[0180]

[0181] wherein, represents the second health state of the energy storage device, represents the comprehensive health index of the current cycle, represents a change trend of the comprehensive health index from the m-n cycle to the m cycle time period, represents the historical comprehensive health index, represents a weight coefficient, represents the current actual capacity of the energy storage device, represents the nominal capacity of the energy storage device.

[0182] In an embodiment of the present application, the second fusion module 23 is specifically further configured to:

[0183] respectively calculate a basic weight corresponding to each of the first health state and the second health state based on a confidence degree of each of the first health state and the second health state;

[0184] respectively calculate a working condition matching factor of each of the first health state and the second health state based on a correlation degree between a current working condition feature of the energy storage device and each of the first health state and the second health state; the working condition feature represents a current operating condition of the energy storage device; the correlation degree between the current working condition feature of the energy storage device and the first health state is a correlation degree between the current operating condition of the energy storage device and the first health state, and the correlation degree between the current working condition feature of the energy storage device and the second health state is a correlation degree between the current operating condition of the energy storage device and the second health state;

[0185] Calculate the average error of the first and second historical health states of the energy storage device, and normalize based on the average error to obtain the historical performance factors corresponding to the first and second health states.

[0186] The fusion weight corresponding to the first health state is obtained by fusing the basic weight, working condition matching factor and historical performance factor corresponding to the first health state; and the fusion weight corresponding to the second health state is obtained by fusing the basic weight, working condition matching factor and historical performance factor corresponding to the second health state.

[0187] The target health state of the energy storage device is obtained by weighted fusion based on the first health state, the second health state, and their respective fusion weights.

[0188] In one embodiment of this application, the energy storage device health management system 20 further includes: a power prediction module; specifically used for:

[0189] Based on the target health state and multiple health feature vectors, the health degradation cost for the next cycle and the current dynamic power envelope of the energy storage device are obtained. The health degradation cost is the loss cost caused by aging for each charge-discharge cycle of the energy storage device. The dynamic power envelope is the maximum allowable charge-discharge power boundary of the current energy storage device in a healthy state.

[0190] Based on health degradation costs and dynamic power envelope, the charging and discharging power of energy storage devices in each future cycle is predicted.

[0191] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of each module / unit in the above system embodiments, for example... Figure 2 The functions of the feature extraction module 21, the first fusion module 22, the second fusion module 23, the risk calculation module 24, and the management module 25 are shown.

[0192] It should be appreciated that in the embodiments of the present application, the processor 301 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0193] The input device 302 can include a touchpad, a fingerprint collection sensor (for collecting fingerprint information and direction information of a fingerprint of a user), a microphone, etc., and the output device 303 can include a display (LCD, etc.), a speaker, etc.

[0194] The memory 304 can include a read-only memory and a random access memory, and provide instructions and data for the processor 301. A part of the memory 304 can also include a non-volatile random access memory. For example, the memory 304 can also store first health state information, second health state information, etc.

[0195] In specific implementations, the processor 301, the input device 302 and the output device 303 described in the embodiments of the present application can execute the implementation manners described in the energy storage device health management method provided by the embodiments of the present application, and can also execute the implementation manners of the electronic device described in the embodiments of the present application, which will not be described herein again.

[0196] In another embodiment of the present application, a computer readable storage medium is provided, which stores a computer program. The computer program includes program instructions, which, when executed by a processor, implement all or part of the processes of the above-mentioned embodiment methods. The computer program can also instruct related hardware to complete the implementation. The computer program can be stored in a computer readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate form. The computer readable medium can include any entity or system capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0197] The computer readable storage medium can be an internal storage unit of the electronic device of any of the preceding embodiments, such as a hard disk or a memory of the electronic device. The computer readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the computer readable storage medium can include both the internal storage unit and the external storage device of the electronic device. The computer readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer readable storage medium can also be used to temporarily store data that has been output or will be output.

[0198] Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0199] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic device and the units described above can refer to the corresponding processes in the above-mentioned method embodiments, which will not be described here.

[0200] In several embodiments provided in the present application, it should be understood that the disclosed electronic device and method can be implemented in other manners. For example, the embodiments of the system described above are merely illustrative. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between the modules can be indirect coupling or communication connection through some interfaces, or can be in electrical, mechanical or other forms.

[0201] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0202] In addition, each functional module in each embodiment of the present application can be integrated in one processing unit, or each module can be physically present alone, or two or more modules can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0203] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for health management of energy storage equipment, characterized in that, The method comprises: feature extraction is performed on a management data set of the energy storage device to obtain a plurality of health feature vectors of a current cycle; the management data set is a standardized data set collected according to a cycle after preprocessing of original management data of the energy storage device; the cycle is a complete charging and discharging cycle of the energy storage device; and the original management data is data generated by the energy storage device during the charging and discharging process; a comprehensive health index is obtained by fusing the plurality of health feature vectors of the current cycle; a first health state of the energy storage device is determined based on the comprehensive health index and an internal resistance of the energy storage device, and a second health state of the energy storage device is determined based on the comprehensive health index and a historical comprehensive health index; a basic weight corresponding to each of the first health state and the second health state is calculated based on a confidence degree of each of the first health state and the second health state; the basic weight formula is represented as: wherein, represents a base weight corresponding to the first health status, represents a base weight corresponding to the second health status, represents a confidence of the first health status, represents a confidence of the second health status; a working condition matching factor of each of the first health state and the second health state is calculated based on a correlation degree between a current working condition feature of the energy storage device and each of the first health state and the second health state; the working condition feature represents a current operating condition of the energy storage device; the correlation degree between the current working condition feature of the energy storage device and the first health state is a correlation degree between the current operating condition of the energy storage device and the first health state, and the correlation degree between the current working condition feature of the energy storage device and the second health state is a correlation degree between the current operating condition of the energy storage device and the second health state; a historical performance factor corresponding to each of the first health state and the second health state is obtained by normalizing an average error of a historical first health state and a historical second health state of the energy storage device; the calculation formula of the historical performance factor is: wherein, a historical performance factor indicative of a first health state, a historical performance factor indicative of a second health state, an average error indicative of a historical first health state of the energy storage device, an average error indicative of a historical second health state of the energy storage device; a fusion weight corresponding to the first health state is obtained by fusing the basic weight, the working condition matching factor and the historical performance factor corresponding to the first health state based on the basic weight, and a fusion weight corresponding to the second health state is obtained by fusing the basic weight, the working condition matching factor and the historical performance factor corresponding to the second health state based on the basic weight; the fusion weight corresponding to the first health state is represented as: wherein, represents a fusion weight corresponding to the first health state, represents a working condition matching factor of the first health state, represents a working condition matching factor of the second health state; the fusion weight corresponding to the second health state is represented as: wherein, represents the fusion weight corresponding to the second health state; a target health state of the energy storage device is obtained by weighted fusion based on the first health state, the second health state and the fusion weights corresponding to the first health state and the second health state; a risk index of the energy storage device is calculated based on the target health state; a management strategy of the energy storage device is determined based on the risk index, and the energy storage device is managed based on the management strategy.

2. The energy storage device health management method of claim 1, wherein The acquisition method of the management data set comprises: preprocessing of the original management data of the energy storage device to obtain initial management data, wherein the preprocessing comprises data cleaning and noise filtering; cycle identification is performed on the initial management data according to the direction of the current of the energy storage device during the charging and discharging process to obtain different types of initial management data in a current cycle; the types include voltage, current and temperature; and The initial management data of different types in the current cycle period is time series aligned based on a preset time interval, and a management data set of the energy storage device is obtained.

3. The energy storage device health management method of claim 1, wherein The plurality of health feature vectors of the current cycle are fused to obtain a comprehensive health index, including: The plurality of health feature vectors are normalized respectively to obtain a plurality of target health feature vectors; The plurality of target health feature vectors are assigned weights based on the influence degree of each target health feature vector on the health state of the energy storage device; The plurality of health feature vectors and the respective weights are weighted and fused to obtain a comprehensive health index.

4. The energy storage device health management method of claim 1, wherein, The first health state of the energy storage device is determined based on the comprehensive health index and the internal resistance of the energy storage device, including: The first health state of the energy storage device is determined based on the comprehensive health index and the internal resistance of the energy storage device through a first formula; The first formula is: wherein, represents a first state of health of the energy storage device, represents an initial internal resistance of the energy storage device, represents a current internal resistance of the energy storage device, represents an internal resistance at the end of life of the energy storage device, represents a combined health index of the current cycle, represents a current real capacity of the energy storage device, represents a nominal capacity of the energy storage device.

5. The energy storage device health management method of claim 1, wherein, The second health state of the energy storage device is determined based on the comprehensive health index and the historical comprehensive health index, including: The second health state of the energy storage device is determined based on the comprehensive health index and the historical comprehensive health index through a second formula; The second formula is: wherein, represents a second health state of the energy storage device, represents a current cycle integrated health index, represents a change trend of the integrated health index from the m-n cycle to the m cycle time period, represents a historical integrated health index, represents a weight coefficient, represents a current actual capacity of the energy storage device, represents a nominal capacity of the energy storage device.

6. The energy storage device health management method of claim 1, wherein, Further comprising: A health degradation cost of the next cycle and a dynamic power envelope of the energy storage device are obtained based on the target health state and the plurality of health feature vectors; The health degradation cost is a loss cost caused by aging of the energy storage device for each cycle of charging and discharging; The dynamic power envelope is a maximum charging and discharging power boundary allowed by the current energy storage device in the health state; The charging and discharging power corresponding to each cycle of the energy storage device in the future is predicted based on the health degradation cost and the dynamic power envelope.

7. An energy storage device health management system, comprising: Comprising: A feature extraction module is configured to extract features from a management data set of the energy storage device to obtain a plurality of health feature vectors of the current cycle; The management data set is a standardized data set collected according to cycles after preprocessing of original management data of the energy storage device; The cycle is a complete charging and discharging cycle of the energy storage device; and the original management data is data generated by the energy storage device during the charging and discharging process; A first fusion module is configured to fuse the plurality of health feature vectors of the current cycle to obtain a comprehensive health index; A second fusion module is configured to determine a first health state of the energy storage device based on the comprehensive health index and the internal resistance of the energy storage device, and determine a second health state of the energy storage device based on the comprehensive health index and a historical comprehensive health index; The second fusion module is specifically configured to: Calculate a basic weight corresponding to each of the first health state and the second health state based on the confidence of each of the first health state and the second health state; The basic weight formula is represented as: wherein, represents a base weight corresponding to the first health status, represents a base weight corresponding to the second health status, represents a confidence of the first health status, represents a confidence of the second health status; The first health state and the second health state are respectively calculated based on a current working condition feature of the energy storage device and an association degree between the first health state and the second health state; the working condition feature represents a current operating condition of the energy storage device; the association degree between the working condition feature and the first health state is a correlation degree between the current operating condition of the energy storage device and the first health state, and the association degree between the working condition feature and the second health state is a correlation degree between the current operating condition of the energy storage device and the second health state; An average error of the historical first health state and the historical second health state of the energy storage device is calculated, and a historical performance factor corresponding to the first health state and the second health state is obtained based on the average error; The calculation formula of the historical performance factor is: wherein, a historical performance factor indicative of a first health state, a historical performance factor indicative of a second health state, an average error indicative of a historical first health state of the energy storage device, an average error indicative of a historical second health state of the energy storage device; The first health state and the second health state are respectively calculated based on a current working condition feature of the energy storage device and an association degree between the first health state and the second health state; the working condition feature represents a current operating condition of the energy storage device; the association degree between the working condition feature and the first health state is a correlation degree between the current operating condition of the energy storage device and the first health state, and the association degree between the working condition feature and the second health state is a correlation degree between the current operating condition of the energy storage device and the second health state; The first health state and the second health state are respectively calculated based on a current working condition feature of the energy storage device and an association degree between the first health state and the second health state; the working condition feature represents a current operating condition of the energy storage device; the association degree between the working condition feature and the first health state is a correlation degree between the current operating condition of the energy storage device and the first health state, and the association degree between the working condition feature and the second health state is a correlation degree between the current operating condition of the energy storage device and the second health state; wherein, represents a fusion weight corresponding to the first health state, represents a working condition matching factor of the first health state, represents a working condition matching factor of the second health state; The first health state and the second health state are respectively calculated based on a current working condition feature of the energy storage device and an association degree between the first health state and the second health state; the working condition feature represents a current operating condition of the energy storage device; the association degree between the working condition feature and the first health state is a correlation degree between the current operating condition of the energy storage device and the first health state, and the association degree between the working condition feature and the second health state is a correlation degree between the current operating condition of the energy storage device and the second health state; wherein, represents the fusion weight corresponding to the second health state; The first health state and the second health state are respectively calculated based on a current working condition feature of the energy storage device and an association degree between the first health state and the second health state; the working condition feature represents a current operating condition of the energy storage device; the association degree between the working condition feature and the first health state is a correlation degree between the current operating condition of the energy storage device and the first health state, and the association degree between the working condition feature and the second health state is a correlation degree between the current operating condition of the energy storage device and the second health state; The first health state and the second health state are respectively calculated based on a current working condition feature of the energy storage device and an association degree between the first health state and the second health state; the working condition feature represents a current operating condition of the energy storage device; the association degree between the working condition feature and the first health state is a correlation degree between the current operating condition of the energy storage device and the first health state, and the association degree between the working condition feature and the second health state is a correlation degree between the current operating condition of the energy storage device and the second health state; The first health state and the second health state are respectively calculated based on a current working condition feature of the energy storage device and an association degree between the first health state and the second health state; the working condition feature represents a current operating condition of the energy storage device; the association degree between the working condition feature and the first health state is a correlation degree between the current operating condition of the energy storage device and the first health state, and the association degree between the working condition feature and the second health state is a correlation degree between the current operating condition of the energy storage device and the second health state; 8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The first health state and the second health state are respectively calculated based on a current working condition feature of the energy storage device and an association degree 9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. ​

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