Household energy storage battery management system and management method

Abstract

The invention provides a household energy storage battery management system and method, and the method comprises the steps: constructing a constant-voltage charging curve of a household energy storage battery in a constant-voltage charging stage, and determining the saturation health characteristics of the household energy storage battery according to the constant-voltage charging curve; the method comprises the following steps: acquiring relaxation voltage data of a household energy storage battery in a relaxation stage, determining a relaxation aging coefficient through the relaxation voltage data, and performing abrupt change extraction on the relaxation voltage data so as to obtain abrupt change polarization characteristics of the household energy storage battery in the relaxation stage; constructing a health assessment vector according to the saturation health feature, the relaxation aging coefficient and the abrupt polarization feature; and performing state evaluation on the household energy storage battery according to the health evaluation vector to obtain a health state factor of the household energy storage battery, and further adjusting a charging and discharging strategy of the household energy storage battery based on the health state factor. By adopting the scheme, health assessment can be carried out through the characteristics of the operation data of the household energy storage battery in different stages, so that the accuracy and credibility of health state assessment are improved.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and more specifically, to a household energy storage battery management system and management method. Background Art

[0002] Home energy storage battery management refers to the technology and management process for comprehensively monitoring, regulating, and optimizing the operating status, charging and discharging behavior, safety performance, and lifespan of energy storage batteries within a home energy system. With the rapid development of distributed photovoltaic power generation, home energy autonomy, and floating electricity price mechanisms, home energy storage systems (HESS) are becoming increasingly popular among households, becoming a key enabler for localized renewable energy consumption, achieving energy independence, and achieving electricity economy.

[0003] However, in actual use, household energy storage batteries often experience partial charging and discharging (partial cycling) and intermittent operation. Health degradation is often nonlinear and has hysteresis. This makes the traditional method of relying on full-cycle charge and discharge testing to determine battery health status impractical in practical applications. Therefore, how to conduct health assessments based on the characteristics of household energy storage battery operating data at different stages to improve the accuracy and reliability of health assessments is a challenge facing the industry. Summary of the Invention

[0004] The present application provides a household energy storage battery management system and management method, which can perform health assessment based on the characteristics of household energy storage battery operation data at different stages to improve the accuracy and credibility of health status assessment.

[0005] In a first aspect, the present application provides a household energy storage battery management method, the management method comprising the following steps: Constructing a constant voltage charging curve for a household energy storage battery in a constant voltage charging stage, and determining a saturation health characteristic of the household energy storage battery based on the constant voltage charging curve; Collecting relaxation voltage data of a household energy storage battery during a relaxation stage, determining a relaxation aging coefficient of the household energy storage battery through the relaxation voltage data, performing mutation extraction on the relaxation voltage data, and thereby obtaining a mutation polarization characteristic of the household energy storage battery during the relaxation stage; Constructing a health assessment vector of the household energy storage battery based on the saturation health feature, the relaxation aging coefficient, and the sudden polarization feature; The state of the household energy storage battery is evaluated according to the health assessment vector to obtain a health state factor of the household energy storage battery, and then the charge and discharge strategy of the household energy storage battery is adjusted based on the health state factor.

[0006] Preferably, constructing a constant voltage charging curve for a household energy storage battery in the constant voltage charging stage specifically includes: Collect charging current data of household energy storage batteries during the constant voltage charging stage; The charging current data is curve fitted to obtain a constant voltage charging curve of the household energy storage battery in the constant voltage charging stage.

[0007] Preferably, determining the saturation health characteristics of the household energy storage battery according to the constant voltage charging curve specifically includes: Dividing the constant voltage charging curve into equal current intervals to obtain a plurality of constant voltage charging segments; All constant voltage charging segments are screened to obtain the saturation charging segment of the household energy storage battery during the constant voltage charging stage; Health features are extracted from the saturated charging segment to obtain saturated health features of the household energy storage battery.

[0008] Preferably, the relaxation voltage data of the household energy storage battery during the relaxation stage is collected by an integrated data collector.

[0009] Preferably, determining the relaxation aging coefficient of the household energy storage battery through the relaxation voltage data specifically includes: Determining the distribution information amount of each relaxation voltage point in the relaxation voltage data; Determine the aging contribution of each relaxation voltage point based on the corresponding distribution information; The relaxation aging coefficient of household energy storage batteries is determined based on the aging contribution of all relaxation voltage points.

[0010] Preferably, constructing a health assessment vector for a household energy storage battery based on the saturated health feature, the relaxation aging coefficient and the sudden polarization feature is to use the feature vector composed of the saturated health feature, the relaxation aging coefficient and the sudden polarization feature as the health assessment vector for the household energy storage battery.

[0011] Preferably, adjusting the charge and discharge strategy of the household energy storage battery based on the health status factor specifically includes: Get the pre-set health status level interval; The health status factor is mapped to the health status level interval, and then the charging and discharging strategy of the household energy storage battery is adjusted according to the health status level of the health status factor.

[0012] In a second aspect, the present application provides a household energy storage battery management system for executing a household energy storage battery management method, wherein the household energy storage battery management system includes a charge and discharge control unit, and the charge and discharge control unit includes: A health feature extraction module is used to construct a constant voltage charging curve of the household energy storage battery in the constant voltage charging stage, and determine the saturation health feature of the household energy storage battery according to the constant voltage charging curve; A polarization feature extraction module is used to collect relaxation voltage data of a household energy storage battery during the relaxation stage, determine the relaxation aging coefficient of the household energy storage battery through the relaxation voltage data, perform mutation extraction on the relaxation voltage data, and thereby obtain the mutation polarization characteristics of the household energy storage battery during the relaxation stage; An evaluation vector construction module, configured to construct a health evaluation vector for a household energy storage battery based on the saturation health feature, the relaxation aging coefficient, and the sudden polarization feature; A strategy control module is used to perform a status assessment on the household energy storage battery according to the health assessment vector, obtain a health status factor of the household energy storage battery, and then adjust the charge and discharge strategy of the household energy storage battery based on the health status factor.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned household energy storage battery management method.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned method for managing a household energy storage battery.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: In a household energy storage battery management system and management method provided in the present application, a constant-voltage charging curve is constructed for the household energy storage battery in the constant-voltage charging stage, and the saturation health characteristics of the household energy storage battery are determined based on the constant-voltage charging curve; relaxation voltage data of the household energy storage battery in the relaxation stage is collected, and the relaxation aging coefficient of the household energy storage battery is determined based on the relaxation voltage data, and mutation extraction is performed on the relaxation voltage data to obtain the mutation polarization characteristics of the household energy storage battery in the relaxation stage; a health assessment vector of the household energy storage battery is constructed based on the saturation health characteristics, the relaxation aging coefficient and the mutation polarization characteristics; the state of the household energy storage battery is evaluated based on the health assessment vector to obtain the health status factor of the household energy storage battery, and the charging and discharging strategy of the household energy storage battery is adjusted based on the health status factor.

[0016] It can be seen that in this application, first, by constructing a constant-voltage charging curve for household energy storage batteries in the constant-voltage charging stage and further extracting saturated health characteristics, the energy acceptance capacity and internal polarization behavior of the battery in the later stage of charging can be accurately characterized, thereby identifying whether the battery has health degradation phenomena such as capacity decay, slow response or increased internal resistance; then, by collecting the relaxation voltage data of household energy storage batteries in the relaxation stage and further extracting the relaxation aging coefficient and sudden polarization characteristics, the intrinsic electrochemical changes of the battery during the static recovery process can be more comprehensively reflected, which can significantly improve the sensitivity and granularity of state assessment, and can also enhance The model's adaptability to complex degradation paths; furthermore, constructing a health assessment vector for household energy storage batteries based on saturated health characteristics, relaxation aging coefficients, and mutation polarization characteristics helps to comprehensively characterize the state change characteristics of the battery in different operating stages from multiple dimensions, thereby achieving a more accurate and reliable health status assessment; finally, by evaluating the state of household energy storage batteries according to the health assessment vector and obtaining the health status factor, and then adjusting the charge and discharge strategy based on this factor, more accurate battery management can be achieved, extending the battery life and optimizing system performance, which helps to improve the accuracy and credibility of the health status assessment.

[0017] To sum up, the technical solution adopted in this application can perform health assessment based on the characteristics of the operating data of household energy storage batteries at different stages, so as to improve the accuracy and credibility of the health status assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 is an exemplary flow chart of a household energy storage battery management method according to some embodiments of the present application; Figure 2 is an exemplary flow chart for determining the relaxation aging coefficient of a household energy storage battery according to some embodiments of the present application; Figure 3 is an exemplary flow chart for adjusting the charge and discharge strategy of a household energy storage battery according to some embodiments of the present application; Figure 4 is a schematic diagram of exemplary hardware and / or software of a charge and discharge control unit according to some embodiments of the present application; Figure 5 It is a structural diagram of a computer device for implementing a household energy storage battery management method according to some embodiments of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] The embodiment of the present application provides a household energy storage battery management system and management method, the core of which is to construct a constant voltage charging curve for the household energy storage battery in the constant voltage charging stage, and determine the saturation health characteristics of the household energy storage battery based on the constant voltage charging curve; collect the relaxation voltage data of the household energy storage battery in the relaxation stage, determine the relaxation aging coefficient of the household energy storage battery through the relaxation voltage data, perform mutation extraction on the relaxation voltage data, and then obtain the mutation polarization characteristics of the household energy storage battery in the relaxation stage; construct a health assessment vector of the household energy storage battery based on the saturation health characteristics, the relaxation aging coefficient and the mutation polarization characteristics; perform a status assessment on the household energy storage battery based on the health assessment vector, obtain the health status factor of the household energy storage battery, and then adjust the charge and discharge strategy of the household energy storage battery based on the health status factor. The above scheme can be used to perform health assessment based on the characteristics of the operating data of the household energy storage battery at different stages to improve the accuracy and credibility of the health status assessment.

[0022] In order to better understand the above technical solution, the following will be combined with the accompanying drawings and specific implementation methods to describe the above technical solution in detail. Figure 1 , which is an exemplary flow chart of a household energy storage battery management method according to some embodiments of the present application. The management method mainly includes the following steps: In step S1, a constant voltage charging curve of a household energy storage battery in a constant voltage charging stage is constructed, and a saturation health characteristic of the household energy storage battery is determined according to the constant voltage charging curve.

[0023] In some embodiments, the constant voltage charging curve of the household energy storage battery in the constant voltage charging stage can be constructed in the following manner, namely: Collect charging current data of household energy storage batteries during the constant voltage charging stage; The charging current data is curve fitted to obtain a constant voltage charging curve of the household energy storage battery in the constant voltage charging stage.

[0024] In specific implementation, first, it is necessary to determine the start of the constant voltage stage of the household energy storage battery. In constant voltage charging control, when the battery voltage gradually rises to the set upper limit (for example, 4.2V for lithium batteries) and remains stable, it indicates that the constant voltage stage has begun; then, after the household energy storage battery enters the constant voltage stage, the collection of charging current data of the household energy storage battery is immediately started until the constant voltage charging stage ends, thereby obtaining the charging current data of the household energy storage battery in the constant voltage charging stage. The charging current data consists of a series of charging current values sorted by timestamps; finally, the exponential decay model can be selected as the curve fitting model in this application, so that the charging current data can be curve fitted using the least squares method to obtain the constant voltage charging curve of the household energy storage battery in the constant voltage charging stage.

[0025] In some embodiments, the saturation health characteristics of the household energy storage battery can be determined according to the constant voltage charging curve in the following manner, namely: Dividing the constant voltage charging curve into equal current intervals to obtain a plurality of constant voltage charging segments; All constant voltage charging segments are screened to obtain the saturation charging segment of the household energy storage battery during the constant voltage charging stage; Health features are extracted from the saturated charging segment to obtain saturated health features of the household energy storage battery.

[0026] In specific implementation, first, the constant voltage charging curve can be divided into equal current intervals, that is, the current in the constant voltage charging curve is segmented from the initial maximum value to the cutoff minimum value according to a fixed current interval, wherein the current interval can be preset based on historical experience, so that the constant voltage charging curve can be divided into multiple constant voltage charging segments; then, all constant voltage charging segments can be screened, that is, the average current corresponding to each constant voltage charging segment is compared, so that the constant voltage charging segment with the smallest average current is used as the saturated charging segment of the household energy storage battery in the constant voltage charging stage, wherein the saturated charging segment refers to a constant voltage charging segment in which the current has dropped to a low level and the charging rate is very slow; finally, the saturated charging segment can be health characterized Extraction, that is, calculating the duration of the saturated charging segment and the saturation attenuation of the saturated charging segment, wherein the saturation attenuation indicates the severity of the current change in the saturated charging segment. The smaller the saturation attenuation, the weaker the absorption capacity of the household energy storage battery and the lower the health state. The duration of the saturated charging segment and the inverse of the saturation attenuation can be weighted and summed, and the final calculation result can be used as the saturated health feature of the household energy storage battery. The saturated health feature indicates the health of the household energy storage battery. The larger the value of the saturated health feature, the more serious the internal polarization of the household energy storage battery, the battery capacity decays, and the health state deteriorates. The corresponding weights can be set according to historical experiments and data analysis, which will not be repeated here.

[0027] It should be noted that by constructing a constant-voltage charging curve for household energy storage batteries in the constant-voltage charging stage and further extracting saturation health characteristics, the energy acceptance capacity and internal polarization behavior of the battery in the later stage of charging can be accurately characterized, thereby identifying whether the battery has health degradation phenomena such as capacity attenuation, slow response or increased internal resistance.

[0028] In step S2, relaxation voltage data of the household energy storage battery in the relaxation stage is collected, the relaxation aging coefficient of the household energy storage battery is determined through the relaxation voltage data, and mutation extraction is performed on the relaxation voltage data to obtain the mutation polarization characteristics of the household energy storage battery in the relaxation stage.

[0029] It should be noted that the relaxation phase is a natural recovery process in which a household energy storage battery is in an open-circuit state after stopping charging or discharging. During the relaxation phase, the voltage of the household energy storage battery will slowly change due to internal ion redistribution and electrode polarization relaxation, which is called the relaxation voltage. The dynamic change characteristics of the relaxation voltage are highly sensitive to the battery health status. Therefore, collecting voltage data during the relaxation phase can provide data support for subsequent health assessments. In specific implementation, the relaxation voltage data of the household energy storage battery during the relaxation phase can be collected through an integrated data collector. The integrated data collector refers to a voltage monitoring device embedded in or connected to the household energy storage system. The integrated data collector has multiple modules (precision analog-to-digital converter, communication interface, external transmission module and MCU main control unit) that can monitor the battery current. When the charging or discharging is completed and the current drops to a set threshold and remains stable for a certain period of time (e.g., 30 seconds), the household energy storage battery is considered to have entered the relaxation state. Therefore, the household energy storage battery can be periodically sampled through the integrated data collector to obtain the relaxation voltage data of the household energy storage battery during the relaxation phase.

[0030] Preferably, in some embodiments, reference Figure 2 As shown in FIG, this figure is an exemplary flow chart for determining the relaxation aging coefficient of a household energy storage battery in some embodiments of the present application. In this embodiment, determining the relaxation aging coefficient of a household energy storage battery by using the relaxation voltage data can be implemented by the following steps: In step S21, the distribution information amount of each relaxation voltage point in the relaxation voltage data is determined; In step S22, the aging contribution of each relaxation voltage point is determined based on the corresponding distribution information amount; In step S23, the relaxation aging coefficient of the household energy storage battery is determined according to the aging contribution of all relaxation voltage points.

[0031] In specific implementation, first, the distribution information amount of each relaxation voltage point in the relaxation voltage data can be determined, wherein the distribution information amount represents the importance and rarity of the relaxation voltage point relative to the overall data. For each relaxation voltage point in the relaxation voltage data, the inverse of the relaxation voltage point value can be calculated, and the logarithm of the calculation result with base 2 is taken, and the final result is used as the distribution information amount of the relaxation voltage point. The distribution information amount of each relaxation voltage point in the relaxation voltage data can be obtained in the above manner; then, the aging contribution of each relaxation voltage point can be determined based on the corresponding distribution information amount, wherein the aging contribution represents the contribution of the relaxation voltage point to the aging of the household energy storage battery. The contribution degree of information can be obtained by taking the product of the value of the relaxation voltage point and the distribution information of the relaxation voltage point as the aging contribution degree of the relaxation voltage point. The aging contribution degree of each relaxation voltage point can be obtained in the above manner. Finally, the relaxation aging coefficient of the household energy storage battery can be determined according to the aging contribution degrees of all relaxation voltage points. The relaxation aging coefficient represents the aging degree of the household energy storage battery based on the relaxation process. The larger the relaxation aging coefficient, the more obvious the internal polarization phenomenon of the household energy storage battery and the higher the degree of health degradation. In actual implementation, the sum of the aging contribution degrees of all relaxation voltage points can be used as the relaxation aging coefficient of the household energy storage battery.

[0032] In some embodiments, the relaxation voltage data is subjected to mutation extraction to obtain the mutation polarization characteristics of the household energy storage battery in the relaxation stage, specifically in the following manner, namely: Performing curve fitting on the relaxation voltage data to obtain a relaxation voltage curve of the household energy storage battery in the relaxation stage; Performing polarization extraction on the relaxation voltage curve to obtain a polarization dissipation curve of the household energy storage battery in the relaxation stage; The mutation characteristics of the polarization dissipation curve are extracted to obtain the mutation polarization characteristics of the household energy storage battery in the relaxation stage.

[0033] In specific implementation, first, an exponential decay model can be selected as the curve fitting model in this application, so that the relaxation voltage data can be curve fitted using the least squares method to obtain the relaxation voltage curve of the household energy storage battery in the relaxation stage; then, the relaxation voltage curve can be polarized, that is, the relaxation voltage curve is first-order differentiated, and the result is used as the polarization dissipation curve of the household energy storage battery in the relaxation stage. The voltage recovery phenomenon caused by electrochemical imbalance in the household energy storage battery can be reflected through polarization extraction; finally, the polarization dissipation curve can be subjected to mutation feature extraction, that is, the maximum curvature of the polarization dissipation curve is calculated, and the calculation result is used as the mutation polarization feature of the household energy storage battery in the relaxation stage, wherein the mutation polarization feature is a feature used to represent the degree of drastic change in the polarization behavior of the household energy storage battery. The mutation phenomenon of the household energy storage battery in the relaxation stage may be caused by internal resistance mutation, lithium dendrite formation, and active material heterogeneity, and is an important precursor to the decline in the health of the household energy storage battery.

[0034] It should be noted that by collecting the relaxation voltage data of household energy storage batteries in the relaxation stage and further extracting the relaxation aging coefficient and mutation polarization characteristics, the intrinsic electrochemical changes of the battery during the static recovery process can be more comprehensively reflected. The data in the relaxation stage are not subject to external charging and discharging disturbances, and can reveal the true state of the battery's internal polarization, charge distribution reconstruction and interface behavior, and effectively capture the kinetic hysteresis and nonlinear degradation characteristics caused by aging. Therefore, incorporating the characteristics of the relaxation stage into the health assessment can not only significantly improve the sensitivity and granularity of the status assessment, but also enhance the model's adaptability to complex degradation paths, thereby improving the accuracy and credibility of the overall health status judgment.

[0035] In step S3, a health assessment vector of the household energy storage battery is constructed based on the saturation health feature, the relaxation aging coefficient, and the sudden polarization feature.

[0036] In some embodiments, constructing a health assessment vector for a household energy storage battery based on the saturated health feature, the relaxation aging coefficient, and the sudden polarization feature is to use a feature vector composed of the saturated health feature, the relaxation aging coefficient, and the sudden polarization feature as the health assessment vector for the household energy storage battery.

[0037] In specific implementation, first, in order to eliminate dimensional differences and distribution inconsistencies, minimum-maximum normalization or standard deviation normalization can be used to eliminate the dimensional differences between saturation health characteristics, relaxation aging coefficients, and mutation polarization characteristics, thereby ensuring that all feature dimensions are within a unified scale (such as the 0-1 interval), which is convenient for subsequent model processing and evaluation; then, the saturation health characteristics, relaxation aging coefficients, and mutation polarization characteristics can be feature spliced, so that the spliced feature vector can be used as the health assessment vector of household energy storage batteries.

[0038] It should be noted that constructing a health assessment vector for household energy storage batteries based on saturation health characteristics, relaxation aging coefficients, and mutation polarization characteristics helps to comprehensively characterize the state change characteristics of the battery at different operating stages from multiple dimensions, thereby achieving a more accurate and reliable health status assessment. Specifically, the saturation health characteristics, relaxation aging coefficients, and mutation polarization characteristics cover key operating conditions such as battery charging, static state, and polarization response. They are complementary and time-related, and can enhance the health assessment model's perception of complex aging mechanisms, reduce the risk of misjudgment due to changes in a single characteristic, and thus significantly improve the accuracy and robustness of the assessment results.

[0039] In step S4, the state of the household energy storage battery is evaluated according to the health assessment vector to obtain a health state factor of the household energy storage battery, and then the charge and discharge strategy of the household energy storage battery is adjusted based on the health state factor.

[0040] In some embodiments, performing status assessment on the household energy storage battery according to the health assessment vector is inputting the health assessment vector into a health status estimation model based on a random forest to perform status assessment on the household energy storage battery, thereby obtaining a health status factor of the household energy storage battery.

[0041] In specific implementation, first, a large amount of historical sample data can be collected, and the historical sample data can be used to train a health status estimation model based on random forest. When constructing the random forest model, the replacement sampling method can be used to construct it, which can enable the random forest to run efficiently when processing large-scale data sets, thereby completing the health status estimation more quickly. The replacement sampling method introduces randomness and diversity, reduces the influence of noise in the training data of the model, effectively avoids overfitting, and thus improves the accuracy and credibility of the health status estimation; then, the health assessment vector can be input into the health status estimation model based on random forest to perform status assessment on the household energy storage battery. Each decision tree in the health status estimation model independently outputs a prediction result (such as health level or SOH value), and the final result is synthesized by majority voting (classification) to obtain the health status factor of the household energy storage battery. The health status factor is an indicator used to represent the health level of the household energy storage battery.

[0042] Preferably, in some embodiments, reference Figure 3 As shown in FIG, this figure is an exemplary flow chart for adjusting the charge and discharge strategy of a household energy storage battery in some embodiments of the present application. In this embodiment, adjusting the charge and discharge strategy of a household energy storage battery based on the health status factor can be implemented by the following steps: In step S41, a preset health status level interval is obtained; In step S42, the health status factor is mapped to the health status level interval, and then the charging and discharging strategy of the household energy storage battery is adjusted according to the health status level of the health status factor.

[0043] In specific implementation, the health status level interval can be pre-set and optimized according to the battery model, usage environment and historical data. It will not be repeated here. In this application, the health status level interval includes the following four intervals: When the health factor is between 90 and 100, the health level of the home energy storage battery is 1, indicating that the home energy storage battery is in a healthy state, the battery performance is good, and it can operate according to the standard strategy; When the health factor is between 75 and 89, the health level of the home energy storage battery is II, indicating that the home energy storage battery is in a slightly aged state. It is recommended to moderately reduce the load and optimize the charging strategy. When the health factor is between 50 and 74, the health level of the household energy storage battery is III, indicating that the household energy storage battery is in a moderate aging state and the battery is obviously degraded. The charging rate and discharge depth should be reduced. The health status factor is between 0 and 49. At this time, the health status level of the household energy storage battery is IV, indicating that the household energy storage battery is in a serious aging state and the battery is nearing the end of its life. Its use should be restricted or replaced.

[0044] In addition, in specific implementation, the health status factor can be mapped to the health status level interval, so that the health status level of the health status factor can be obtained. According to the matched health status level, the charge and discharge control parameters of the energy storage system are automatically adjusted, for example: When the health status factor is at Level I, it indicates that the household energy storage battery is in the best working condition, and performance indicators such as capacity and internal resistance meet the design requirements. At this stage, a normal charge and discharge strategy can be adopted, that is, the charging voltage is maintained at the rated upper limit (such as 4.2V), the maximum recommended constant current charging current (such as 0.5-1C) is allowed, the discharge depth can be set to a higher level (80-90%), no forced sleep or delayed scheduling is performed, and peak and valley load response is supported.

[0045] When the health status factor is at Level II: it indicates that the household energy storage battery has experienced a certain degree of performance degradation, but the overall performance is still acceptable. At this stage, mild protection measures should be taken to slow down the degradation rate, that is, lowering the charge cut-off voltage by 0.05-0.1V (such as to 4.1V to reduce polarization reaction), slightly reducing the charging current to 80% of the rated value (such as 0.5C), limiting the discharge depth to 70-80% (to avoid over-discharge), and if the system allows, some low-load operation cycles can be added (such as charging during non-peak hours).

[0046] When the health status factor is at level III: it indicates that the performance of the household energy storage battery has declined significantly, and active control is required to avoid overheating or aggravation of nonlinear aging effects. That is, the maximum charging voltage can be further reduced to 4.0V or lower, the current in the constant current charging stage can be limited to less than 50% (such as 0.2-0.3C), the discharge depth is strictly controlled at around 60% (to prevent deep discharge from causing damage to the negative electrode structure), the charge and discharge cycles need to be balanced more frequently, and the system dispatching side should reduce the load pressure.

[0047] When the health status factor is at Level IV: it indicates that the household energy storage battery is nearing the end of its life and is no longer suitable as a primary energy unit. It should enter the retirement buffer state or be planned for replacement. That is, the charging voltage can be immediately reduced to a safe and conservative value (such as 3.9-4.0V, no longer pursuing full charge), the current in the constant current stage is limited to the minimum (such as 0.1-0.2C), the discharge depth is controlled within 50%, and only basic backup or emergency functions are supported. Participation in high-power scheduling or peak load response can be suspended, and a retirement condition alarm is set.

[0048] It should be noted that by evaluating the status of household energy storage batteries based on the health assessment vector and obtaining the health status factor, and then adjusting the charge and discharge strategy based on the factor, more accurate battery management can be achieved, extending the battery life and optimizing system performance. By combining the operating data characteristics of the battery at different stages for health assessment, not only can the actual health status of the battery be reflected in real time, but the charge and discharge strategy can also be dynamically adjusted according to the battery degradation characteristics to avoid performance loss or failure caused by excessive use or unreasonable charge and discharge processes. The evaluation method based on stage characteristics helps to improve the accuracy and credibility of health status assessment.

[0049] It can be seen that in this application, first, by constructing a constant-voltage charging curve for household energy storage batteries in the constant-voltage charging stage and further extracting saturated health characteristics, the energy acceptance capacity and internal polarization behavior of the battery in the later stage of charging can be accurately characterized, thereby identifying whether the battery has health degradation phenomena such as capacity decay, slow response or increased internal resistance; then, by collecting the relaxation voltage data of household energy storage batteries in the relaxation stage and further extracting the relaxation aging coefficient and sudden polarization characteristics, the intrinsic electrochemical changes of the battery during the static recovery process can be more comprehensively reflected, which can significantly improve the sensitivity and granularity of state assessment, and can also enhance The model's adaptability to complex degradation paths; furthermore, constructing a health assessment vector for household energy storage batteries based on saturated health characteristics, relaxation aging coefficients, and mutation polarization characteristics helps to comprehensively characterize the state change characteristics of the battery in different operating stages from multiple dimensions, thereby achieving a more accurate and reliable health status assessment; finally, by evaluating the state of household energy storage batteries according to the health assessment vector and obtaining the health status factor, and then adjusting the charge and discharge strategy based on this factor, more accurate battery management can be achieved, extending the battery life and optimizing system performance, which helps to improve the accuracy and credibility of the health status assessment.

[0050] To sum up, the technical solution adopted in this application can perform health assessment based on the characteristics of the operating data of household energy storage batteries at different stages, so as to improve the accuracy and credibility of the health status assessment.

[0051] In addition, in another aspect of the present application, in some embodiments, the present application provides a household energy storage battery management system, which includes a charge and discharge control unit, referring to Figure 4 , which is a schematic diagram of exemplary hardware and / or software of a charge-discharge control unit according to some embodiments of the present application. The charge-discharge control unit includes: a health feature extraction module 401, a polarization feature extraction module 402, an evaluation vector construction module 403, and a strategy control module 404, which are described as follows: Health feature extraction module 401, in this application, the health feature extraction module 401 is mainly used to construct a constant voltage charging curve of the household energy storage battery in the constant voltage charging stage, and determine the saturation health feature of the household energy storage battery according to the constant voltage charging curve; Polarization feature extraction module 402, in this application, polarization feature extraction module 402 is mainly used to collect relaxation voltage data of the household energy storage battery in the relaxation stage, determine the relaxation aging coefficient of the household energy storage battery through the relaxation voltage data, perform mutation extraction on the relaxation voltage data, and then obtain the mutation polarization characteristics of the household energy storage battery in the relaxation stage; Evaluation vector construction module 403, in this application, the evaluation vector construction module 403 is mainly used to construct a health evaluation vector of the household energy storage battery based on the saturation health characteristics, the relaxation aging coefficient and the sudden polarization characteristics; The strategy control module 404 in this application is mainly used to perform a status evaluation on the household energy storage battery according to the health evaluation vector, obtain the health status factor of the household energy storage battery, and then adjust the charge and discharge strategy of the household energy storage battery based on the health status factor.

[0052] The above describes in detail an example of a household energy storage battery management system and management method provided by an embodiment of the present application. It can be understood that, in order to realize the above functions, the corresponding device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0053] In some embodiments, the present application also provides a computer device, which includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned household energy storage battery management method.

[0054] In some embodiments, reference Figure 5 , the dotted line in the figure indicates that the unit or module is optional. The figure is a structural diagram of a computer device according to a household energy storage battery management method provided in an embodiment of the present application. The household energy storage battery management method described in the above embodiment can be Figure 5 The computer device shown in the figure is implemented, and the computer device includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device can be a terminal device, a server or a chip.

[0055] Processor 501 may be a general-purpose processor or a dedicated processor. For example, processor 501 may be a central processing unit (CPU). The CPU may be used to control the computer device, execute software programs, and process data from the software programs. The computer device may also include a communication unit 505 for inputting (receiving) and outputting (transmitting) signals.

[0056] For example, the computer device may be a chip, the communication unit 505 may be an input and / or output circuit of the chip, or the communication unit 505 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other device.

[0057] For another example, the computer device may be a terminal device or a server, and the communication unit 505 may be a transceiver of the terminal device or the server, or the communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0058] The computer device may include one or more memories 502, on which a program 504 is stored. Program 504 can be executed by processor 501 to generate instructions 503, causing processor 501 to execute the method described in the above method embodiment according to instructions 503. Optionally, memory 502 may also store data (such as a target audit model). Optionally, processor 501 may also read data stored in memory 502. This data may be stored at the same storage address as program 504, or at a different storage address.

[0059] The processor 501 and the memory 502 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of a terminal device.

[0060] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 501. The processor 501 can be a central processing unit, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0061] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] For example, in some embodiments, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned household energy storage battery management method when executing.

[0063] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0064] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A household energy storage battery management method, characterized in that: The management method comprises the following steps: Constructing a constant voltage charging curve for a household energy storage battery in a constant voltage charging stage, and determining a saturation health characteristic of the household energy storage battery based on the constant voltage charging curve; Collecting relaxation voltage data of a household energy storage battery during a relaxation stage, determining a relaxation aging coefficient of the household energy storage battery through the relaxation voltage data, performing mutation extraction on the relaxation voltage data, and thereby obtaining a mutation polarization characteristic of the household energy storage battery during the relaxation stage; Constructing a health assessment vector of a household energy storage battery based on the saturation health feature, the relaxation aging coefficient, and the sudden polarization feature; The state of the household energy storage battery is evaluated according to the health assessment vector to obtain a health state factor of the household energy storage battery, and then the charge and discharge strategy of the household energy storage battery is adjusted based on the health state factor.

2. A household energy storage battery management method according to claim 1, characterized in that: Constructing a constant voltage charging curve for a household energy storage battery during the constant voltage charging stage specifically includes: Collect charging current data of household energy storage batteries during the constant voltage charging stage; The charging current data is curve fitted to obtain a constant voltage charging curve of the household energy storage battery in the constant voltage charging stage.

3. A household energy storage battery management method according to claim 1, characterized in that: Determining the saturation health characteristics of the household energy storage battery according to the constant voltage charging curve specifically includes: Dividing the constant voltage charging curve into equal current intervals to obtain a plurality of constant voltage charging segments; All constant voltage charging segments are screened to obtain the saturation charging segment of the household energy storage battery during the constant voltage charging stage; Health features are extracted from the saturated charging segment to obtain saturated health features of the household energy storage battery.

4. A household energy storage battery management method according to claim 1, characterized in that: The relaxation voltage data of household energy storage batteries during the relaxation stage is collected through an integrated data collector.

5. A household energy storage battery management method according to claim 1, characterized in that: Determining the relaxation aging coefficient of the household energy storage battery using the relaxation voltage data specifically includes: Determining the distribution information amount of each relaxation voltage point in the relaxation voltage data; Determine the aging contribution of each relaxation voltage point based on the corresponding distribution information; The relaxation aging coefficient of household energy storage batteries is determined based on the aging contribution of all relaxation voltage points.

6. A household energy storage battery management method according to claim 1, characterized in that: Constructing a health assessment vector for a household energy storage battery based on the saturated health feature, the relaxation aging coefficient, and the sudden polarization feature is to use a feature vector composed of the saturated health feature, the relaxation aging coefficient, and the sudden polarization feature as the health assessment vector for the household energy storage battery.

7. A household energy storage battery management method according to claim 1, characterized in that: Adjusting the charging and discharging strategy of the household energy storage battery based on the health status factor specifically includes: Get the pre-set health status level interval; The health status factor is mapped to the health status level interval, and then the charging and discharging strategy of the household energy storage battery is adjusted according to the health status level of the health status factor.

8. A household energy storage battery management system, configured to execute a household energy storage battery management method according to any one of claims 1 to 7, wherein the household energy storage battery management system comprises a charge and discharge control unit, characterized in that: The charge and discharge control unit includes: A health feature extraction module is used to construct a constant voltage charging curve of the household energy storage battery in the constant voltage charging stage, and determine the saturation health feature of the household energy storage battery according to the constant voltage charging curve; A polarization feature extraction module is used to collect relaxation voltage data of a household energy storage battery during the relaxation stage, determine the relaxation aging coefficient of the household energy storage battery through the relaxation voltage data, perform mutation extraction on the relaxation voltage data, and thereby obtain the mutation polarization characteristics of the household energy storage battery during the relaxation stage; An evaluation vector construction module, configured to construct a health evaluation vector for a household energy storage battery based on the saturation health feature, the relaxation aging coefficient, and the sudden polarization feature; A strategy control module is used to perform a status assessment on the household energy storage battery according to the health assessment vector, obtain a health status factor of the household energy storage battery, and then adjust the charge and discharge strategy of the household energy storage battery based on the health status factor.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes a household energy storage battery management method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions or codes, which, when executed on a computer, enable the computer to implement a household energy storage battery management method as described in any one of claims 1 to 7.

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