Energy storage system adaptive management method and energy storage system

Abstract

The application provides an energy storage system adaptive management method and an energy storage system, and relates to the technical field of energy storage system control. The method comprises the following steps: acquiring a plurality of sensor parameters; generating an environment harshness index based on the plurality of sensor parameters; controlling the charging rate of a battery based on the environment harshness index; when the environment harshness index is greater than a first threshold value or after the battery charging is completed, evaluating the battery damage condition, and performing battery self-repairing according to the battery damage condition. The method provided by the application helps to solve the problems of low charging efficiency of the energy storage system, serious battery damage and high safety risk in an extreme environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage system control, and particularly relates to an energy storage system adaptive management method and an energy storage system. BACKGROUND

[0002] An energy storage system is composed of an electric core, a BMS (Battery Management System), a PCS (Power Conversion System), and the like. The BMS is responsible for real-time monitoring of key parameters such as voltage, current, and temperature of a battery pack, and performs key functions such as battery balancing, thermal management, and overcharge / overdischarge protection, and is a core component for ensuring safe, stable, and long-term operation of the battery. The PCS is a core component for bidirectional conversion and power regulation of AC / DC. The BMS, PCS, and other power electronic devices contain a large number of precision components, and their performance will drift or even fail with changes in environmental temperature, humidity, dust, and the like.

[0003] The internal part of the electric core is composed of positive and negative electrodes, electrolyte, and diaphragm, and the electrochemical reaction rate, material stability, electrolyte conductivity, and SEI film growth of the electric core are all extremely sensitive to environmental temperature. In addition, humidity, salt spray, and the like in the environment will also erode the system structure, affect electrical insulation, and bring safety risks. In extreme environments (such as extreme cold, high temperature, high humidity, high radiation, and the like), the charging efficiency and service life of the energy storage system (such as lithium batteries and supercapacitors) will be significantly affected. The related technical solutions cannot solve the problems of low charging efficiency, severe battery damage, and high safety risks of the energy storage system in extreme environments. SUMMARY

[0004] The present application provides an energy storage system adaptive management method and an energy storage system, which helps to solve the problems of low charging efficiency, severe battery damage, and high safety risks of the energy storage system in extreme environments.

[0005] In a first aspect, the present application provides an energy storage system adaptive management method, comprising:

[0006] obtaining a plurality of sensor parameters;

[0007] generating an environment severity index based on the plurality of sensor parameters;

[0008] controlling the charging rate of the battery based on the environment severity index;

[0009] when the environment severity index is greater than a first threshold or the battery charging is completed, evaluating the battery damage condition, and performing battery self-repairing according to the battery damage condition.

[0010] In one possible implementation, the plurality of sensor parameters at least include a current ambient temperature, a relative humidity, a chloride ion concentration, a radiation dose rate, and spectral data; the environment severity index is generated based on the plurality of sensor parameters, including:

[0011] calculating a temperature offset coefficient based on the current ambient temperature and a standard reference temperature;

[0012] calculating a humidity corrosion coefficient based on the relative humidity and the chloride ion concentration;

[0013] calculating an electrolyte crystallinity coefficient based on the spectral data;

[0014] generating the environment severity index based on the temperature offset coefficient, the humidity corrosion coefficient, the radiation dose rate, and the electrolyte crystallinity coefficient in combination with respective weights.

[0015] In one possible implementation, the charging rate of the battery is controlled based on the environment severity index, including:

[0016] when the environment severity index is less than or equal to a second threshold value, the battery is charged at a first current in constant current charging;

[0017] when the environment severity index is greater than the second threshold value and less than or equal to a third threshold value, the battery is charged at a second current in ramp charging;

[0018] when the environment severity index is greater than the third threshold value, the battery is charged at a third current in constant current charging;

[0019] the first current is greater than the second current, the second current is greater than the third current, the third threshold value is greater than the first threshold value, and the first threshold value is greater than the second threshold value.

[0020] In one possible implementation, the plurality of sensor parameters include electrochemical impedance spectroscopy data, battery surface deformation data, current data, and state of charge data of the battery; the battery damage condition is evaluated, and the battery self-repair is performed according to the battery damage condition, including:

[0021] feature extraction and algorithm analysis are performed based on the electrochemical impedance spectroscopy data, the battery surface deformation data, the current data, and the state of charge data of the battery to obtain a crystalline coverage rate and a stress value;

[0022] when the crystalline coverage rate is greater than 5% or the stress value is greater than 50 kPa,

[0023] the battery damage type and the battery damage level are determined according to the crystalline coverage rate and the stress value;

[0024] the battery self-repair is performed according to the battery damage type and the battery damage level.

[0025] In one possible implementation, determining the battery damage type and the battery damage level according to the crystallization coverage and the stress value includes:

[0026] calculating a ratio K of the crystallization coverage and the stress value;

[0027] when K<0.5, the battery damage type is stress damage, and the battery damage level is determined according to the stress value; or

[0028] when K>10, the battery damage type is crystallization damage, and the battery damage level is determined according to the crystallization coverage; or

[0029] when 0.5≤K≤2, the battery damage type is a mixed type, a first battery damage level is determined according to the stress value, a second battery damage level is determined according to the crystallization coverage, the highest level of the first battery damage level and the second battery damage level is taken, and the highest level is added by one level as the battery damage level; or

[0030] when 2

[0031] In one possible implementation, the battery damage type includes crystallization damage, the battery damage level includes a crystallization damage level, and the crystallization damage level is divided into five levels; battery self-repairing according to the battery damage type and the battery damage level includes:

[0032] if the crystallization damage level is the first or second level, micro-current activation is performed;

[0033] if the crystallization damage level is the third level, a resonant electromagnetic field is started to decompose the crystallization;

[0034] if the crystallization damage level is the fourth or fifth level, a repair agent is injected, and a resonant electromagnetic field is combined for collaborative repair.

[0035] In one possible implementation, the battery damage type includes stress damage, the battery damage level includes a stress damage level, and the stress damage level is divided into five levels; battery self-repairing according to the battery damage type and the battery damage level includes:

[0036] if the stress damage level is the first or second level, micro-current conditioning is performed;

[0037] if the stress damage level is the third level, shape memory polymer thermal shrinkage is performed to repair cracks;

[0038] If the stress damage level is level four or five, high-pressure shape memory polymer thermal shrinkage repair of the crack is performed, and conductive adhesive is injected to fill the crack.

[0039] One possible implementation of the method also includes:

[0040] The battery damage level is adjusted based on battery temperature or the number of charge-discharge cycles.

[0041] One possible implementation of the method also includes:

[0042] By using a long short-term memory network to analyze historical data of multiple sensor parameters, the weights of temperature offset coefficient, humidity corrosion coefficient, radiation dose rate, and electrolyte crystallinity coefficient are dynamically adjusted.

[0043] One possible implementation of the method also includes:

[0044] When at least one sensor fails, a Bayesian network estimation model is used to fill in the missing sensor parameters.

[0045] One possible implementation of the method also includes:

[0046] The microcurrent intensity is dynamically updated based on the current ambient temperature.

[0047] In one possible implementation, the method further includes the following steps before generating the environmental severity index based on multiple sensor parameters:

[0048] Multiple sensor parameters are preprocessed and feature extracted. The preprocessing includes spatiotemporal alignment and anomaly detection.

[0049] In one possible implementation, the multiple sensor parameters include at least the current ambient temperature, relative humidity, and absorbed radiation dose. The method also includes:

[0050] If the current ambient temperature is less than or equal to the first temperature threshold, preheating is performed using alternating current, and electromagnetic resonance is coupled to target and decompose the crystals.

[0051] If the current ambient temperature is greater than or equal to the second temperature threshold, then active heat dissipation is achieved by utilizing the thermoelectric inverse effect to recover waste heat. The second temperature threshold is greater than the first temperature threshold.

[0052] If the relative humidity is greater than or equal to the humidity threshold, nitrogen pulse drying is performed, and the insulation breakdown voltage is increased simultaneously.

[0053] If the absorbed radiation dose is greater than or equal to the radiation threshold, the tungsten alloy shielding layer is automatically activated and switched to discrete pulse discharge.

[0054] Secondly, this application provides an energy storage system, including: a battery management system, an energy storage device, multiple sensors, and a battery repair device; the multiple sensors are disposed inside and outside the energy storage device, and the multiple sensors are used to collect multiple sensor parameters; the battery repair device is used to repair battery damage; the battery management system includes a processor and a memory, the memory is used to store computer programs; the processor is used to run the computer programs to implement the adaptive management method of the energy storage system as described in the first aspect.

[0055] The beneficial effects of this application are as follows:

[0056] This application provides an adaptive management method and system for an energy storage system. The method involves acquiring multiple sensor parameters; generating an environmental severity index based on these parameters; controlling the battery charging rate based on the environmental severity index; assessing battery damage when the environmental severity index exceeds a first threshold or after battery charging is complete; and performing battery self-repair based on the damage condition. This achieves dynamic switching of the adaptive charging protocol for the energy storage system under the coupling effect of multiple parameters, and also monitors the internal chemical state of the battery in real time, enabling self-repair of battery damage and ensuring the safe operation of the energy storage system. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the energy storage system provided in the embodiments of this application;

[0058] Figure 2 This is a schematic diagram of the layered architecture of an energy storage system provided in an embodiment of this application;

[0059] Figure 3 A flowchart illustrating the adaptive management method for an energy storage system provided in an embodiment of this application;

[0060] Figure 4 A schematic diagram of the inference process of a Bayesian network estimation model;

[0061] Figure 5 A schematic diagram illustrating the calculation process for crystallization coverage and stress values ​​provided in the embodiments of this application;

[0062] Figure 6 A schematic diagram of the process for generating repair instructions provided in an embodiment of this application;

[0063] Figure 7 A flowchart illustrating the selection of a repair solution provided in an embodiment of this application;

[0064] Figure 8 A schematic diagram of the process for updating the microcurrent intensity provided in an embodiment of this application;

[0065] Figure 9 This is a schematic diagram of a battery self-repair process provided in an embodiment of this application. Detailed Implementation

[0066] In this embodiment of the application, unless otherwise stated, the character " / " indicates that the preceding and following objects are in an OR relationship. For example, A / B can represent A or B. "AND / OR" describes the relationship between the associated objects, indicating that three relationships can exist. For example, A AND / OR B can represent: A existing alone, A and B existing simultaneously, and B existing alone.

[0067] It should be noted that the terms "first" and "second" used in the embodiments of this application are used only for distinguishing descriptive purposes and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated, nor should they be construed as indicating or implying order.

[0068] In the embodiments of this application, "at least one" means one or more, and "more than one" means two or more. Furthermore, "at least one of the following" or similar expressions refer to any combination of these items, which may include any combination of a single item or a plurality of items. For example, at least one of A, B, or C can represent: A, B, C, A and B, A and C, B and C, or A, B, and C. Each of A, B, and C can be an element itself or a set containing one or more elements.

[0069] In this application, terms such as "exemplary," "in some embodiments," and "in another embodiment" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the term "exemplary" is intended to present the concept in a concrete manner.

[0070] In the embodiments of this application, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction, their meanings are consistent. Similarly, in the embodiments of this application, "communication" and "transmission" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction, their meanings are consistent. For example, transmission can include sending and / or receiving, and can be a noun or a verb.

[0071] In the embodiments of this application, the term "equal to" can be used in conjunction with "greater than" to apply to technical solutions employing the condition of "greater than", and can also be used in conjunction with "less than" to apply to technical solutions employing the condition of "less than". It should be noted that when "equal to" is used with "greater than", it cannot be used with "less than"; and when "equal to" is used with "less than", it cannot be used with "greater than".

[0072] Extreme environments (such as extreme cold, high temperature, high humidity, and high radiation) have a significant impact on the charging efficiency and lifespan of energy storage systems (such as lithium batteries and supercapacitors). For example, in low-temperature environments, the increased viscosity of the electrolyte leads to a decrease in ion mobility, which easily induces lithium dendrite growth, and conventional constant current charging may directly cause irreversible damage to the battery; in high-temperature environments, the internal resistance of the battery increases, increasing the risk of SEI film (Solid-Electrolyte Interphase) decomposition and thermal runaway. Current temperature control systems rely on external heat dissipation, which is energy-intensive and has a slow response; in high-humidity environments, there are problems such as circuit corrosion and insulation failure, which are difficult to completely solve through traditional sealing solutions; under high radiation, gamma rays increase the viscosity of the electrolyte and cause a sharp drop in conductivity. The single-event effect leads to an increase in the soft error rate of the BMS (soft error rate refers to the probability or frequency of transient, non-destructive failures), and logic confusion causes control function paralysis.

[0073] Most related technologies employ threshold control based on a single environmental parameter (such as temperature), which is relatively mature in terms of adaptability to single environments and static management. Examples include using thermistors to monitor temperature and limit charging rates; pulse preheating strategies in low-temperature environments; liquid cooling systems for heat dissipation in high-temperature environments; and dynamic temperature equalization. However, in extreme environments, batteries are affected by the coupled effects of multiple environmental parameters (such as temperature, humidity, and radiation), leading to higher safety risks. Related technologies lack solutions for responding to multiple parameters and do not address the real-time need for repairing the internal chemical state of batteries under extreme conditions. Significant gaps remain in areas such as multi-parameter dynamic response and embedded self-healing.

[0074] Based on the above problems, this application proposes an adaptive management method and energy storage system for energy storage systems, which helps to solve the problems of low charging efficiency, severe battery damage and high safety risks in energy storage systems under extreme environments.

[0075] Now combined Figures 1-9 The adaptive management method for energy storage systems provided in the embodiments of this application will be described.

[0076] Figure 1 This is a schematic diagram of the energy storage system provided in an embodiment of this application. Figure 1As shown, the energy storage system includes: a Battery Management System (BMS), energy storage devices, multiple sensors, and a battery repair device. The multiple sensors include Sensor 1, Sensor 2, and Sensor 3. Sensor 1 (e.g., a temperature sensor) is located inside the energy storage device, while Sensor 2 and Sensor 3 (e.g., a microwave radiometer, a humidity sensor, etc.) are located outside the energy storage device. These sensors collect various parameters. The battery repair device is used to repair battery damage. The sensors upload the collected parameters to the BMS. The BMS generates an Environmental Degradation Index (EEI) based on these parameters and controls the charging rate of the batteries in the energy storage device based on the EEI. When the EEI exceeds a first threshold or charging is complete, the battery damage is assessed, and the battery repair device is controlled to perform self-repair based on the battery damage condition. It should be noted that... Figure 1 Taking one energy storage device and three sensors as an example, in other embodiments, the number of energy storage devices and sensors can be set according to actual needs.

[0077] Figure 2 This is a schematic diagram of the layered architecture of an energy storage system provided in an embodiment of this application. Figure 2 As shown, the layered architecture of the energy storage system includes a perception layer, a decision-making layer, an execution layer, and a closed-loop feedback layer.

[0078] The perception layer includes a sensor network and multi-source data fusion. The sensor network includes multiple sensors, and multi-source data fusion refers to aligning the parameters collected by multiple sensors in time and space to form a fused data stream.

[0079] The decision-making layer includes an EEI computing engine and three modes (including mode A, mode B and mode C). The EEI computing engine refers to performing EEI calculations based on the data fused from multiple sensors. Based on the EEI, the three modes are divided into three traffic distributions: mode A executes a performance-first strategy, mode B executes a repair-performance balancing strategy, and mode C executes a survival protection strategy.

[0080] The execution layer includes a 1C charger and peak discharge unit in Mode A, a 0.5C ramp charging and waste heat recovery system in Mode B, and a 0.1C current limiter and chlorine sealing system in Mode C. The 1C charger refers to the battery being charged at a constant current rate of 1C. The 1C charger supports low-temperature fast charging, with a charging efficiency >60% at -50℃. The peak discharge unit can provide a specific repair procedure for sudden high-load demands, providing instantaneous ultra-high power current. 0.5C ramp charging refers to the battery being charged at a medium current intensity (0.5C) using a slow, gradual current increase, essentially a "ramp" charging. The waste heat recovery system can utilize the Peltier effect to generate electricity, converting the heat generated by the energy storage device itself (chips, batteries, power transistors, etc.) back into electrical energy, with some of the electrical energy then used to charge the battery, achieving energy recovery. The 0.1C current limiter refers to the battery being charged at a constant current rate of 0.1C. The chlorine sealing system can protect the battery with a 0.5kPa micro-positive pressure, isolating it from salt spray.

[0081] The closed-loop feedback layer includes state monitoring. When the battery charging ends or the EEI is greater than the first threshold, the battery state is monitored, the battery damage is assessed, and repair methods such as repair agent injection, microcurrent activation and targeted therapy are performed according to the battery damage. After repair, the success of the repair is verified, and repair parameters are continuously optimized to improve the repair effect.

[0082] Figure 3 A flowchart illustrating the adaptive management method for an energy storage system provided in this application embodiment is shown, specifically including the following steps:

[0083] Step S31: Obtain multiple sensor parameters.

[0084] Specifically, multiple sensors are installed inside and outside the energy storage device. These sensors include voltage and current sensors, salt spray sensors, MEMS temperature and humidity arrays, microwave radiometers, gamma dosimeters, miniature fiber optic spectrometers (1400-2500nm±0.1nm), flexible strain sensors (±2μm), and broadband impedance spectrometers (0.1Hz-100kHz). Among them, voltage and current sensors are used to collect battery voltage and current; salt spray sensors are used to collect chloride ion concentration; MEMS temperature and humidity arrays are used to collect ambient temperature and relative humidity; microwave radiometers use non-contact detection to detect the internal temperature field distribution and electrolyte drying degree of the battery; gamma dosimeters are used to monitor the ambient gamma radiation intensity (i.e., radiation dose rate) and cumulative absorbed dose (i.e., radiation absorbed dose) to assess the damage of ionizing radiation to battery materials; miniature fiber optic spectrometers are used to collect spectral data (including wavelength arrays and corresponding absorbance arrays) to analyze electrolyte crystallization in real time; flexible strain sensors are used to collect battery surface deformation data to capture electrode expansion; and broadband impedance spectrometers collect EIS data (Electrochemical Impedance Spectroscopy) to diagnose the state of the SEI film.

[0085] In some embodiments, the acquired sensor parameters include current ambient temperature, relative humidity, chloride ion concentration, radiation dose rate, radiation absorbed dose, spectral data, EIS data, current, voltage, and SOC (State of Charge).

[0086] In some optional embodiments, prior to step S22, the adaptive management method for energy storage systems provided in this application further includes: preprocessing and feature extraction of multiple sensor parameters, wherein the preprocessing includes spatiotemporal alignment and abnormal data detection.

[0087] To achieve interference-resistant data design, edge computing units are typically integrated into sensor nodes for data preprocessing. Optionally, preprocessing includes spatiotemporal alignment and anomaly detection. Spatiotemporal alignment refers to spatiotemporal synchronization, which aligns multiple sensor parameters in both time and spatial dimensions. In the time dimension, PTP (Precise Time Protocol) precision clock synchronization (±100μs) and 10Hz resampling (cubic spline interpolation) are used for time alignment. In the spatial dimension, a homogeneous transformation matrix is ​​used to unify the coordinate system (error <2mm). Anomaly detection includes: using the RANSAC algorithm to remove outliers other than ±3σ, or using sensor cross-validation algorithms (such as temperature-stress coupling verification) and Kalman filtering algorithms for data self-verification, achieving an electromagnetic shielding effectiveness of 60dB at 1 GHz electromagnetic wave frequency and a data integrity rate >99% in extremely cold environments of -50℃ or 95%RH salt spray environments.

[0088] Feature extraction includes generating 3D thermograms using Kriging space interpolation to study the temperature field; locating hotspots using PCA (Principal Component Analysis) algorithm to study stress distribution; and fusing spectral and impedance data using a Bayesian network estimation model to study electrolyte state.

[0089] Step S32: Generate an environmental severity index based on multiple sensor parameters.

[0090] Multiple sensor parameters collected by multiple sensors are fused to generate an environmental severity index, which is used to characterize the degree of impact of the environment on the battery state.

[0091] Specifically, the temperature deviation coefficient is calculated based on the current ambient temperature and the standard reference temperature; the humidity corrosion coefficient is calculated based on the relative humidity and chloride ion concentration; the electrolyte crystallinity coefficient is calculated based on spectral data; and the environmental severity index is generated by combining the temperature deviation coefficient, humidity corrosion coefficient, radiation dose rate, and electrolyte crystallinity coefficient with their respective weights.

[0092] The expression for the environmental severity index is as follows:

[0093] EEI=a Tdev+b Hcorr+c Rrad +d Ccrystal;

[0094] Tdev = |Tenv - Tstandard|;

[0095] Hcorr = RH^k × [ ];

[0096] Ccrystal = (A_1430 - A_baseline) / K_cal.

[0097] Where EEI represents the environmental severity index, Tdev is the temperature deviation coefficient, Hcorr is the humidity corrosion coefficient, Rrad is the radiation dose rate (unit: μSv / h), Ccrystal is the electrolyte crystallinity coefficient, Tenv is the current ambient temperature, Tstandard is the standard reference temperature (usually 25℃), RH is the relative humidity percentage, and k is a constant. [A] represents the chloride ion concentration (mg / m³), and A_1430 represents the absorbance at a wavelength of 1430 nm. At the characteristic peak of 1430 nm), A_baseline is the baseline absorbance (reference wavelength such as 1300 nm), K_cal is the calibration coefficient (determined experimentally), and a, b, c, and d are the respective weights of temperature offset coefficient, humidity corrosion coefficient, radiation dose rate, and electrolyte crystallinity coefficient.

[0098] Optionally, a = 0.4 ± 0.1, b = 0.3 ± 0.05, c = 0.2 ± 0.05, d = 0.1 ± 0.02.

[0099] Optionally, a+b+c+d=1. The sum of the weights of the temperature offset coefficient, humidity corrosion coefficient, radiation dose rate, and electrolyte crystallinity coefficient is equal to 1, which facilitates normalization and gives the weighted EEI calculation results a unified scale, making them easier to understand and compare.

[0100] Compared with a single threshold control method, this application integrates four-dimensional parameters—temperature offset coefficient, humidity corrosion coefficient, radiation dose rate, and electrolyte crystallinity coefficient—to calculate EEI. This allows for the control of the battery charging process in conjunction with various environmental factors, thereby improving the battery's operating efficiency in extreme environments.

[0101] In some optional embodiments, the adaptive management method for energy storage systems provided in this application further includes: using a Long Short-Term Memory (LSTM) network to analyze historical data of multiple sensor parameters and dynamically adjusting the weights (a, b, c, d) of temperature offset coefficient, humidity corrosion coefficient, radiation dose rate, and electrolyte crystallinity coefficient.

[0102] By analyzing historical data of multiple sensor parameters and obtaining their changing trends, the weights of temperature offset coefficient, humidity corrosion coefficient, radiation dose rate, and electrolyte crystallinity coefficient can be adjusted. This helps to dynamically adjust the environmental severity index, enabling the energy storage system to better cope with dynamic environmental changes.

[0103] In some optional embodiments, the adaptive management method for energy storage systems provided in this application further includes: when at least one sensor fails, using a Bayesian network estimation model to fill in the missing sensor parameters.

[0104] Bayesian network estimation models are a data fusion technique driven by probabilistic graphical models. When sensors fail, they achieve intelligent estimation of missing parameters through multi-source data association and reasoning. At its core, it reconstructs the system state through a network of conditional probability relationships, overcoming the limitations of traditional mean-based imputation.

[0105] Figure 4 This is a schematic diagram of the inference process of a Bayesian network estimation model, as shown below. Figure 4 As shown, when a sensor fails (either one or more of multiple sensors), the Bayesian network estimation model is activated. Available data is input, including ambient temperature, historical sensor data (including historical data from available sensors and historical data from the failed sensor before its failure), and data from relevant sensor nodes (such as data from neighboring nodes). Probabilistic inference is then performed, using the available data and probabilistic inference algorithms (such as connection tree algorithms, variable elimination, or approximate sampling algorithms) to calculate the posterior probability distribution of the missing sensor parameters. Confidence assessment refers to outputting the posterior probability value corresponding to the estimated value.

[0106] This application establishes a safety redundancy mechanism. When a sensor fails, it switches to a Bayesian network estimation mode, using data from other relevant sensors combined with the Bayesian network estimation model to estimate missing values ​​in real time, ensuring data integrity and providing a data foundation for calculating the environmental severity index, achieving a false judgment rate of <0.1%.

[0107] Step S33: Control the battery charging rate based on the environmental severity index.

[0108] The Environmental Severity Index (EEI) calculated through the above steps quantifies battery risk, enabling different charging modes at different risk levels.

[0109] In some embodiments, when the environmental severity index is less than or equal to the second threshold, the battery is charged at a constant current with the first current; when the environmental severity index is greater than the second threshold and less than or equal to the third threshold, the battery is charged in a ramp manner with the second current, and the ramp charging means that the current is gradually increased to the second current at a fixed slope; when the environmental severity index is greater than the third threshold, the battery is charged at a constant current with the third current; wherein, the first current is greater than the second current, the second current is greater than the third current, the third threshold is greater than the first threshold, and the first threshold is greater than the second threshold.

[0110] Optionally, the first current can be any value in the range of 0.7C to 1C, the second current can be any value in the range of 0.3C to 0.7C, and the third current can be any value in the range of 0 to 0.3C.

[0111] Optionally, the first threshold is 5, the second threshold is 3, and the third threshold is 6. The first threshold, the second threshold, and the third threshold can be set according to expert knowledge or engineering experience, and the present application does not limit this.

[0112] When the environmental severity index is less than or equal to the second threshold, the system is in a low-risk state. At this time, the battery is charged at a constant current with a large current (the first current) to improve the charging efficiency; when the environmental severity index is greater than the second threshold and less than or equal to the third threshold, the system is in a medium-risk state. At this time, the battery is charged in a ramp manner with a medium current, which can not only ensure the charging efficiency but also ensure the system safety; when the environmental severity index is greater than the third threshold, the system is in a high-risk state. At this time, the battery is charged at a constant current with a small current to minimize the safety risk of the system.

[0113] For example, when EEI ≤ 3, it is a low risk, and 1C fast charging is enabled, that is, the battery is charged at a constant current with 1C; when 3 < EEI ≤ 6, it is a medium risk, and 0.5C current limiting and partial repair are started, that is, the battery is charged in a "ramp" manner with a slowly rising current to a medium current intensity (0.5C), which can not only ensure the charging speed but also minimize the impact and heat generation; when EEI > 6, it is a high risk, and 0.1C survival mode is triggered, that is, the battery is charged at a constant current with 0.1C.

[0114] In some optional embodiments, environmental gradient prediction can also be combined to predict the risk level in advance, that is, the risk level is judged in advance according to EEI. For example, when the temperature change rate > 5°C / min, the risk level is predicted 3 - 5 seconds in advance. Through the risk level prediction, risk management can be actively carried out to achieve the safe, efficient, and sustainable operation of the energy storage system.

[0115] Step S34, when the environmental severity index is greater than the first threshold or the battery charging is completed, evaluate the battery damage condition, and perform battery self-repair according to the battery damage condition.

[0116] This application detects and assesses battery damage during charging (EEI>5) or after charging is complete, and actively repairs internal battery damage. During the battery repair process, the BMS receives repair instructions, then selects and executes the appropriate repair scheme by parsing the repair instructions. Optionally, the repair instructions include the battery damage type and battery damage level.

[0117] In some optional embodiments, multiple sensor parameters include electrochemical impedance spectroscopy data, battery surface deformation data, current data, and battery state of charge data; assessing battery damage and performing battery self-repair based on the damage status includes: performing feature extraction and algorithm analysis based on electrochemical impedance spectroscopy data, battery surface deformation data, current data, and battery state of charge data to obtain crystallization coverage and stress values; when the crystallization coverage > 5% or the stress value > 50 kPa, determining the battery damage type and battery damage level based on the crystallization coverage and stress value; and performing battery self-repair based on the battery damage type and battery damage level.

[0118] Figure 5 This is a schematic diagram illustrating the calculation process for crystallization coverage and stress values ​​provided in the embodiments of this application, as shown below. Figure 5 As shown, after feature extraction of EIS data collected by broadband impedance spectrometer, surface deformation data collected by flexible strain sensor, and SOC and current data collected by current and voltage sensor, the data are input into machine learning model, mechanical and electrochemical model for algorithm analysis, and output crystallization coverage and internal stress value respectively.

[0119] This application obtains the crystallization coverage and internal stress value of the battery by performing feature extraction and algorithm analysis on multiple sensor parameters, and assesses the battery damage status based on the crystallization coverage and stress value. When the crystallization coverage > 5% or the stress value > 50 kPa, it indicates that the battery has a certain degree of damage. It is necessary to determine the battery damage type and damage level based on the crystallization coverage and stress value, and to perform battery self-repair based on the battery damage type and damage level.

[0120] In some optional embodiments, determining the type and level of battery damage based on the crystallization coverage rate and stress value includes: calculating the ratio K of the crystallization coverage rate to the stress value; when K < 0.5, the type of battery damage is stress damage, and the level of battery damage is determined according to the stress value; or when K > 10, the type of battery damage is crystallization damage, and the level of battery damage is determined according to the crystallization coverage rate; or when 0.5 ≤ K ≤ 2, the type of battery damage is a mixed type, the first level of battery damage is determined according to the stress value, the second level of battery damage is determined according to the crystallization coverage rate, the highest level of the first level of battery damage and the second level of battery damage is taken, and the highest level plus one level is used as the level of battery damage; or when 2 < K ≤ 10, the growth rates of the crystallization coverage rate and the stress value are judged based on the historical data of multiple sensor parameters. If the growth rate of the crystallization coverage rate is greater than the growth rate of the stress value, the type of battery damage is crystallization damage, and the level of battery damage is determined according to the crystallization coverage rate; if the growth rate of the stress value is greater than the growth rate of the crystallization coverage rate, the type of battery damage is stress damage, and the level of battery damage is determined according to the stress value.

[0121] Figure 6 The flowchart of generating a repair instruction provided by an embodiment of the present application is as Figure 6 shown. When the battery charging ends or EEI > 5, the battery is subjected to damage detection. When the crystallization coverage rate > 5% or the stress value > 50 kPa, calculate the ratio K of the crystallization coverage rate to the stress value, and determine the type of battery damage according to K. The types of battery damage include crystallization damage (type = crystal), stress damage (type = stress), and mixed type (type = hybrid). If the type of battery damage is crystallization damage, it is classified according to the crystallization coverage rate, that is, the level of battery damage is determined according to the crystallization coverage rate; if the type of battery damage is stress damage, it is classified according to the stress value, that is, the level of battery damage is determined according to the stress value; if the type of battery damage is a mixed type, take the highest level of the two + 1 as the level of battery damage, that is, classify according to the crystallization coverage rate and the stress value respectively, and take the highest level of the two plus one level as the level of battery damage.

[0122] Continue to refer to Figure 6, when K > 10, the battery damage type is crystallization damage, and the battery damage level is determined according to the crystallization coverage rate; when K < 0.5, the battery damage type is stress damage, and the battery damage level is determined according to the stress value; when 0.5 ≤ K ≤ 2, the battery damage type is a mixed type, the first battery damage level is determined according to the stress value, the second battery damage level is determined according to the crystallization coverage rate, the higher level of the first battery damage level and the second battery damage level is taken, and the higher level plus one level is used as the battery damage level; when 2 < K ≤ 10, based on historical data decision, if the crystallization growth is fast recently (the growth rate of the crystallization coverage rate is greater than the growth rate of the stress value), the battery damage type is crystallization damage, and the battery damage level is determined according to the crystallization coverage rate; if the stress growth is fast recently (the growth rate of the stress value is greater than the growth rate of the crystallization coverage rate), the battery damage type is stress damage, and the battery damage level is determined according to the stress value. If there is no historical data, the battery damage type is crystallization damage, and the battery damage level is determined according to the crystallization coverage rate.

[0123] In some optional embodiments, the energy storage system adaptive management method provided by the present application further includes: correcting the battery damage level according to the battery temperature or the number of battery charge and discharge cycles.

[0124] Optionally, when the battery temperature > 45°C, the battery damage level +1; when the battery temperature < 0°C, the battery damage level +0.5; when 0°C ≤ battery temperature ≤ 45°C, the battery damage level remains unchanged.

[0125] Optionally, when the number of battery charge and discharge cycles > 80% (i.e., 1600 times) of the design life (i.e., the preset number of charge and discharge cycles, such as 2000 times), the battery damage level +1; when the number of cycles > 120% (i.e., 2400 times) of the design life, the battery damage level +2; when the number of cycles < 80% (i.e., 1600 times) of the design life (such as 2000 times), the battery damage level remains unchanged.

[0126] In the present application, correcting the battery damage level according to the battery temperature or the number of battery charge and discharge cycles helps to more accurately judge the battery damage situation, more targeted repair of battery damage, and ensure system safety.

[0127] In some optional embodiments, the battery damage type includes crystallization damage, the battery damage level includes the crystallization damage level, and the crystallization damage level is divided into 5 levels.

[0128] Table 1. Crystallization grading comparison table

[0129]

[0130] As shown in Table 1, when the crystal coverage is 5%~8%, the crystal damage level is level 1; when the crystal coverage is 8%~12%, the crystal damage level is level 2; when the crystal coverage is 12%~18%, the crystal damage level is level 3; when the crystal coverage is 18%~25%, the crystal damage level is level 4; and when the crystal coverage is >25%, the crystal damage level is level 5.

[0131] In some optional embodiments, the battery damage type includes stress damage, and the battery damage level includes stress damage level, which is divided into 5 levels.

[0132] Table 2. Stress Rating Comparison Table

[0133]

[0134] As shown in Table 2, when the stress value is 50~70 kPa, the stress damage level is level 1; when the stress value is 70~90 kPa, the stress damage level is level 2; when the stress value is 90~110 kPa, the stress damage level is level 3; when the stress value is 110~130 kPa, the stress damage level is level 4; and when the stress value is >>130 kPa, the stress damage level is level 5.

[0135] Example 1: The crystallization coverage is 25%, the stress value is 100 kPa, K = 25 / 100 = 0.25 < 0.5, the battery damage type is stress damage, and according to Table 2, when the stress value is 100 kPa, the stress damage level is level 3.

[0136] Example 2: The crystallization coverage is 55%, the stress value is 55 kPa, K = 55 / 55 = 1, and the battery damage type is mixed. According to Table 1, the damage level corresponding to a crystallization coverage of 55% is level 5, and according to Table 2, the damage level corresponding to a stress value of 55 kPa is level 1. The highest level for both is level 5, and the highest level plus level 1 equals level 6. The battery damage type is crystallization damage. Since the highest crystallization damage level in this embodiment is level 5, the final determined crystallization damage level is level 5.

[0137] It should be noted that the unit of crystallization coverage is "%" and the range is 0-100%, while the unit of stress value is "kPa". When calculating the ratio K of crystallization coverage and stress value, the unit is not taken into account.

[0138] After receiving the repair instruction, the BMS needs to parse the repair instruction and perform battery self-repair based on the battery damage type and level in the repair instruction.

[0139] Specifically, Figure 7 This is a flowchart illustrating the selection of a repair solution provided in an embodiment of this application, as shown below. Figure 7As shown, after the BMS receives and parses the repair command, it first determines the damage type, and then performs battery self-repair according to the battery damage level, including:

[0140] When the damage type is crystallization damage, if the crystallization damage level is level one or two, microcurrent activation is performed; if the crystallization damage level is level three, resonant electromagnetic field decomposition crystallization is initiated (e.g., 10MHz resonant electromagnetic field decomposition). If the crystallization damage level is level four or five, a repair agent is injected, and synergistic repair is achieved by combining it with a resonant electromagnetic field. The electromagnetic field may not only decompose the crystals but also promote the diffusion of the repair agent in the electrolyte and its reaction efficiency with the electrode surface.

[0141] When the damage type is stress damage, if the stress damage level is level one or two, microcurrent conditioning is performed; if the stress damage level is level three, shape memory polymer thermal shrinkage repair of the crack is performed; if the stress damage level is level four or five, high-pressure shape memory polymer thermal shrinkage repair of the crack is performed, and conductive adhesive is injected to fill the crack.

[0142] When the damage type is mixed, stress damage repair and crystallization damage repair are performed, and finally the repair effect is verified.

[0143] After executing the repair plan, determine whether the repair was successful. If successful, update the battery health status; if unsuccessful, optimize the parameters.

[0144] This application sets different damage levels for different types of battery damage, which can accurately determine the damage status of the battery and carry out differentiated repair according to different damage levels, thereby improving the repair effect of the battery.

[0145] In some optional embodiments, reinforcement learning models (Q-learning) can be used to analyze the repair effect, i.e., to determine whether the repair is successful. Specifically, the impedance spectrum changes are monitored in real time, and a spectrometer is used to verify whether crystallization has been eliminated. The SEI film impedance recovery rate and the 1430nm absorbance decrease rate are analyzed. When the SEI film impedance recovery rate is >90% and the 1430nm absorbance decrease rate is >95%, the battery repair is considered successful.

[0146] In some optional embodiments, the optimized parameters include microcurrent intensity, remedial agent dosage ratio, SMP remediation temperature and pressure, etc. Optionally, the adaptive management method for energy storage systems provided in this application embodiment further includes: dynamically updating the microcurrent intensity based on the current ambient temperature.

[0147] Figure 8 This is a schematic diagram of the process for updating the microcurrent intensity provided in an embodiment of this application, as shown below. Figure 8As shown, the ambient temperature and reference microcurrent parameters (i.e., reference strength) are obtained. The ambient temperature is corrected to obtain an accurate temperature. When the temperature is >45℃ (high temperature), the microcurrent strength is 80% of the reference strength; when the temperature is 25℃ < temperature ≤ 45℃, the microcurrent strength is the reference strength; when the temperature is 0℃ < temperature ≤ 25℃ (low temperature), the microcurrent strength is 120% of the reference strength; when the temperature is <0℃ (extreme low temperature), the microcurrent strength is 150% of the reference strength, and the battery is heated first.

[0148] In this application, real-time monitoring of the battery's internal chemical state helps analyze the battery's repair effectiveness. When the repair effect is unsatisfactory, the repair parameters are optimized to achieve closed-loop repair of battery damage. Furthermore, the micro-current intensity can be dynamically updated based on the current ambient temperature, which helps optimize the battery repair scheme and improve the repair effect under extreme environments (such as extreme low temperatures).

[0149] Figure 9 This is a schematic diagram of a battery self-healing process provided in an embodiment of this application, as shown below. Figure 9 As shown, the control center (e.g., BMS) sends a "open valve (0.1 ml / Ah)" command to the repair agent storage tank, which then injects the repair agent (e.g., lithium fluorobenzoate repair solution (0.1 ml / Ah)) into the battery. The control center also sends a "start 0.05C pulse" command to the microcurrent generator, which applies a 0.05C, 10Hz alternating electric field to the battery to promote repair agent diffusion. After executing the above repair scheme, the battery returns an impedance spectrum change report to the control center. The control center then sends a "update repair parameters" command to the reinforcement learning module. This application employs closed-loop repair to achieve active healing of battery damage, which helps improve battery repair performance.

[0150] When batteries are in extreme environments, such as extreme cold, high temperature, high humidity, and high radiation, safety protection measures need to be implemented for the batteries in order to ensure the safe operation of the equipment.

[0151] In some optional embodiments, the multiple sensor parameters include at least the current ambient temperature, relative humidity, and radiation absorbed dose. The adaptive management method for the energy storage system provided in this application embodiment further includes: if the current ambient temperature is less than or equal to a first temperature threshold, preheating is performed using alternating current, and electromagnetic resonance is coupled to target decomposition and crystallization; if the current ambient temperature is greater than or equal to a second temperature threshold, active heat dissipation is performed using thermoelectric inversion effect to achieve waste heat recovery, wherein the second temperature threshold is greater than the first temperature threshold; if the relative humidity is greater than or equal to a humidity threshold, nitrogen pulse drying is performed, and the breakdown voltage of the insulation layer is increased simultaneously; if the radiation absorbed dose is greater than or equal to a radiation threshold, the tungsten alloy shielding layer is automatically activated, and discrete pulse discharge is switched.

[0152] Optionally, if the current ambient temperature is less than or equal to a first temperature threshold (e.g., -40℃), indicating that the energy storage device is in an extremely cold environment, a 1kHz alternating current is used for preheating (-40℃ to -20℃ / 30s), coupled with 10MHz electromagnetic resonance for targeted decomposition and crystallization, achieving a preheating efficiency of up to 98%. If the current ambient temperature is greater than or equal to a second temperature threshold (e.g., 80℃), indicating that the energy storage device is in a high-temperature environment, the thermoelectric inversion effect (the Peltier coefficient of the thermoelectric inversion effect module is 1.2V / K) is used for active heat dissipation to achieve waste heat recovery. The thermal conversion to electrical energy recovery rate is >25%; if the relative humidity is greater than or equal to the humidity threshold (e.g., 95%RH (including condensation)), it indicates that the energy storage device is in a high humidity environment, so a 0.5MPa nitrogen pulse drying is performed (<10ms response), and the insulation breakdown voltage is simultaneously increased to 3kV / mm; if the radiation absorbed dose is greater than or equal to the radiation threshold (100krad(Si)), it indicates that the energy storage device is in a high radiation environment, so the tungsten alloy shielding layer is automatically activated (γ attenuation rate 99%), and it is switched to discrete pulse discharge (e.g., 5%SOC / time).

[0153] In summary, the adaptive management method and energy storage system provided in this application acquire multiple sensor parameters; generate an environmental severity index based on the multiple sensor parameters; control the battery charging rate based on the environmental severity index; assess battery damage when the environmental severity index exceeds a first threshold or after battery charging is completed, and perform battery self-repair based on the battery damage. This achieves dynamic switching of the adaptive charging protocol of the energy storage system under the coupling effect of multiple parameters, and monitors the internal chemical state of the battery in real time. Self-repair is performed for battery damage, such as 10MHz resonant decomposition and crystallization, nitrogen micro-positive pressure sealing, repair agent injection, and micro-current activation, ensuring the safe operation of the energy storage system.

[0154] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for adaptive management of an energy storage system, characterized in that, The method comprises: obtaining a plurality of sensor parameters; generating an environment harshness index based on the plurality of sensor parameters; controlling the charging rate of the battery based on the environment harshness index; when the environment harshness index is greater than a first threshold value or after the battery is fully charged, evaluating the battery damage condition and performing battery self-repair according to the battery damage condition; the plurality of sensor parameters at least include current environmental temperature, relative humidity, chloride ion concentration, radiation dose rate and spectral data; the environment harshness index is generated based on the plurality of sensor parameters, comprising: calculating a temperature offset coefficient based on the current environmental temperature and a standard reference temperature; calculating a humidity corrosion coefficient based on the relative humidity and the chloride ion concentration; calculating an electrolyte crystallinity coefficient based on the spectral data; combining the respective weights of the temperature offset coefficient, the humidity corrosion coefficient, the radiation dose rate and the electrolyte crystallinity coefficient to generate the environment harshness index; controlling the charging rate of the battery based on the environment harshness index, comprising: when the environment harshness index is less than or equal to a second threshold value, the battery is charged at a first current; when the environment harshness index is greater than the second threshold value and less than or equal to a third threshold value, the battery is charged at a second current; when the environment harshness index is greater than the third threshold value, the battery is charged at a third current; the first current is greater than the second current, the second current is greater than the third current, the third threshold value is greater than the first threshold value, and the first threshold value is greater than the second threshold value; the plurality of sensor parameters include electrochemical impedance spectroscopy data, battery surface deformation data, current data and state of charge data of the battery; the battery damage condition is evaluated and the battery self-repair is performed according to the battery damage condition, comprising: feature extraction and algorithm analysis based on the electrochemical impedance spectroscopy data, the battery surface deformation data, the current data and the state of charge data of the battery to obtain crystalline coverage and stress value; when the crystalline coverage is > 5% or the stress value is > 50kPa, determining the battery damage type and the battery damage level according to the crystalline coverage and the stress value; performing battery self-repair according to the battery damage type and the battery damage level.

2. The adaptive management method of an energy storage system according to claim 1, wherein, determining the battery damage type and the battery damage level according to the crystalline coverage and the stress value, comprising: calculating the ratio K of the crystalline coverage and the stress value; when K < 0.5, the battery damage type is stress damage, and the battery damage level is determined according to the stress value; or when K > 10, the battery damage type is crystalline damage, and the battery damage level is determined according to the crystalline coverage; or when 0.5 ≤ K ≤ 2, the battery damage type is a mixed type, the first battery damage level is determined according to the stress value, the second battery damage level is determined according to the crystalline coverage, the highest level of the first battery damage level and the second battery damage level is taken, and the highest level is added by one level as the battery damage level; or When 2 < K ≤ 10, the growth rates of the crystallization coverage and the stress value are determined according to the historical data of the plurality of sensor parameters, if the growth rate of the crystallization coverage is greater than the growth rate of the stress value, the battery damage type is crystallization damage, and the battery damage level is determined according to the crystallization coverage; if the growth rate of the stress value is greater than the growth rate of the crystallization coverage, the battery damage type is stress damage, and the battery damage level is determined according to the stress value.

3. The adaptive management method of an energy storage system of claim 1, wherein, The battery damage type includes crystallization damage, and the battery damage level includes a crystallization damage level, which is divided into five levels; the battery self-repairing according to the battery damage type and the battery damage level includes: If the crystallization damage level is the first or second level, micro-current activation is performed; If the crystallization damage level is the third level, a resonant electromagnetic field is started to decompose the crystallization; If the crystallization damage level is the fourth or fifth level, a repair agent is injected, and a resonant electromagnetic field is used for cooperative repair.

4. The adaptive management method of an energy storage system of claim 1, wherein, The battery damage type includes stress damage, and the battery damage level includes a stress damage level, which is divided into five levels; the battery self-repairing according to the battery damage type and the battery damage level includes: If the stress damage level is the first or second level, micro-current conditioning is performed; If the stress damage level is the third level, a shape memory polymer thermal contraction is performed to repair cracks; If the stress damage level is the fourth or fifth level, a high-pressure shape memory polymer thermal contraction is performed to repair cracks, and a conductive adhesive is injected to fill the cracks.

5. The adaptive management method of an energy storage system according to any of claims 1-4, characterized in that, The method further includes: The battery damage level is corrected according to the battery temperature or the number of battery charge and discharge cycles.

6. The adaptive management method of an energy storage system of claim 1, wherein, The method further includes: The historical data of the plurality of sensor parameters are analyzed by using a long short-term memory network to dynamically adjust the weights of the temperature offset coefficient, the humidity corrosion coefficient, the radiation dose rate, and the electrolyte crystallinity coefficient.

7. The adaptive management method of an energy storage system of claim 1, wherein, The method further includes: When at least one sensor fails, a Bayesian network estimation model is used to fill in the missing sensor parameters.

8. The adaptive management method of an energy storage system according to claim 3 or 4, wherein, The method further includes: The micro-current intensity is dynamically updated based on the current environmental temperature.

9. The adaptive management method of an energy storage system of claim 1, wherein, Before the environmental severity index is generated based on the plurality of sensor parameters, the method further includes: The plurality of sensor parameters are preprocessed and feature extracted, and the preprocessing includes time-space alignment and abnormal data detection.

10. The adaptive management method of an energy storage system of claim 1, wherein, The plurality of sensor parameters at least include the current environmental temperature, the relative humidity, and the radiation absorbed dose, and the method further includes: If the current environmental temperature is less than or equal to a first temperature threshold, alternating current is used for preheating, and an electromagnetic resonance target is coupled to decompose the crystallization; If the current environmental temperature is greater than or equal to a second temperature threshold, a thermoelectric inverse effect is used for active heat dissipation to realize waste heat recovery, and the second temperature threshold is greater than the first temperature threshold; If the relative humidity is greater than or equal to a humidity threshold, nitrogen pulse drying is performed, and the insulation layer breakdown voltage is simultaneously increased; If the radiation absorbed dose is greater than or equal to a radiation threshold, then automatically activate the tungsten alloy shielding layer and switch to a discrete pulsed discharge.

11. An energy storage system characterized by, Comprise: A battery management system, an energy storage device, a plurality of sensors, and a battery repair device; The plurality of sensors are arranged inside and outside the energy storage device, and are used to collect a plurality of sensor parameters; the battery repair device is used to repair battery damage; the battery management system comprises a processor and a memory, and the memory is used to store a computer program; the processor is used to run the computer program to realize the adaptive management method of the energy storage system according to any one of claims 1-10.

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