Low pressure energy storage system at extreme low temperatures

Abstract

The application relates to the technical field of battery management or energy storage system control, in particular to a low-voltage energy storage system under extreme low temperature; the system comprises a multi-dimensional state sensing unit, a joint estimation unit, a dynamic risk quantification unit and an adaptive decision unit; the system identifies temperature data pollution by solving a temperature gradient index; the core is to calculate the core temperature according to the pollution mark and the lowest reading, and to quantify SOH damage and SOC, and then to calculate a comprehensive risk index and a safety distance; when the model collapses or the safety distance is insufficient, the system will adaptively start self-heating or refuse to discharge; the application realizes active identification of sensor data pollution, can avoid misjudgment caused by relying on false high data, and ensures the conservativeness and safety of estimation.

Description

Technical Field

[0001] This invention relates to the field of battery management technology or energy storage system control technology, specifically to a low-pressure energy storage system under extreme low temperatures. Background Technology

[0002] Battery Management Systems (BMS) face severe challenges in energy storage systems operating in extreme low-temperature environments. Deployed temperature sensors are susceptible to external asymmetric thermal shocks, leading to data contamination and inflated readings. Traditional BMS algorithms lack the ability to perceive data reliability, making reliance on such data prone to optimistic misjudgments. Low temperatures cause instantaneous capacity loss in cells, and traditional State of Charge (SOC) estimation does not correct for this effect, resulting in systematic errors in SOC estimation and failing to accurately reflect the true available capacity. Under these dual challenges of data uncertainty and state estimation failure, BMS struggles to make safe discharge or heating decisions, potentially causing the system to operate at dangerous boundaries, threatening system safety and long-term lifespan. Therefore, achieving accurate estimation of system state and safe adaptive decision-making under extreme low-temperature and sensor uncertainties is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a low-pressure energy storage system for extreme low temperatures. Specifically, the technical solution of this invention includes:

[0004] The multi-dimensional state sensing unit is used to collect the raw dataset and calculate the temperature gradient index; compare the temperature gradient index with the preset normal temperature gradient threshold to obtain the temperature data pollution indicator; and combine the temperature data pollution indicator, the mean and the lowest reading of the raw dataset to deduce the estimated core temperature.

[0005] The joint estimation unit is used to quantify irreversible fractional damage of SOH based on the estimated core temperature and the collected discharge current, and update the current SOH score; it combines the current SOH score with the estimated core temperature to obtain a dynamic available capacity benchmark; and it estimates the current SOC score based on the dynamic available capacity benchmark.

[0006] The dynamic risk quantification unit is used to generate a power risk index based on the current SOC score; a temperature risk index based on the estimated core temperature; a model collapse risk index based on the temperature data contamination indicator; and to combine the power risk index, temperature risk index, and model collapse risk index, and perform a weighted summation to obtain a comprehensive risk index; to calculate the quantified safe distance index based on the comprehensive risk index; and to calculate the safe distance change rate of the quantified safe distance index.

[0007] The adaptive decision unit outputs a self-heating start decision signal when the model collapse risk index is greater than zero and the estimated core temperature is less than the preset upper temperature limit; otherwise, it outputs a discharge rejection decision signal when the quantized safety distance index is less than the preset first safety threshold or the safety distance change rate is less than the preset first change rate threshold; otherwise, it outputs a discharge allow decision signal.

[0008] Preferably, the calculation process for estimating the core temperature includes:

[0009] When the temperature data contamination flag is zero, the mean of the original dataset is set as the estimated core temperature.

[0010] When the temperature data contamination flag is one, the lowest reading of the original dataset is set as the estimated core temperature.

[0011] Preferably, the quantification process for irreversible fractional damage to SOH includes:

[0012] Get the amount of charge in the current time step;

[0013] Based on the estimated core temperature, preset reference temperature, charge quantity, preset temperature sensitivity coefficient, and basic capacity damage rate, the irreversible fractional damage of SOH is calculated.

[0014] Preferably, the process for revising the dynamic available capacity baseline includes:

[0015] Based on the estimated core temperature, a pre-defined lookup table or fitting function is queried to obtain the low-temperature capacity reduction factor;

[0016] The dynamic available capacity baseline is obtained by multiplying the nominal capacity, the current SOH fraction, and the low-temperature capacity reduction factor.

[0017] Preferably, the calculation process for the electricity risk index includes:

[0018] The ratio is obtained by dividing the difference between the preset SOC safety boundary and the power lock boundary by the difference between the current SOC score and the power lock boundary.

[0019] The ratio is then used to perform an exponential calculation based on a preset SOC risk sensitivity coefficient to obtain the electricity risk index.

[0020] Preferably, the calculation process for the temperature risk index includes:

[0021] The difference between the preset temperature safety boundary and the preset temperature lockout boundary is set as a molecule;

[0022] The difference between the estimated core temperature and the temperature lockout boundary is set as the denominator;

[0023] Divide the numerator by the denominator to obtain the ratio, and then perform an exponential calculation on the ratio using a preset temperature risk sensitivity coefficient to obtain the temperature risk index.

[0024] Preferably, the calculation process for the model collapse risk index includes:

[0025] The model collapse risk index is obtained by multiplying the temperature data contamination marker with the preset contamination risk factor.

[0026] Preferably, the quantitative safety distance index is obtained by solving the inverse of the comprehensive risk index;

[0027] The rate of change of safe distance is calculated by quantifying the safe distance index using the first difference over a preset time step.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. This system can effectively identify sensor data contamination caused by external thermal shock by calculating the temperature gradient index and comparing it with a threshold. When identifying contamination, the system automatically uses the lowest temperature reading as the core temperature estimate, avoiding optimistic misjudgments caused by relying on artificially high data, and ensuring the conservatism and safety of subsequent estimates.

[0030] 2. This system combines the estimated core temperature with the health status score to dynamically correct the available capacity benchmark. This correction takes into account the instantaneous capacity loss of the cells caused by low temperature, and solves the problem of systematic error caused by the lack of capacity correction in traditional SOC estimation at low temperature, so that the estimated SOC score can accurately reflect the actual available power.

[0031] 3. This system innovatively quantifies the state of sensor data contamination into a model collapse risk index, and then weights and sums it with power risk and temperature risk to obtain a comprehensive risk. This multi-dimensional risk quantification method enables the system to perceive the confidence level of its own estimation results for the first time, providing a unified quantitative basis for making robust decisions.

[0032] 4. By introducing a quantitative safety distance index and its rate of change, this system enables decision-making units to predict the dynamic development trend of risks. When data contamination is detected, the system prioritizes self-heating to restore data credibility. When the data is credible, it intervenes in advance based on the safety distance and its rate of change to reject high-risk discharges, thus achieving closed-loop adaptive safety management under uncertainty. Attached Figure Description

[0033] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0034] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0036] Example 1:

[0037] Please see Figure 1 Low-pressure energy storage systems for extreme low temperatures include:

[0038] The multi-dimensional state sensing unit is used to collect the raw dataset and calculate the temperature gradient index; compare the temperature gradient index with the preset normal temperature gradient threshold to obtain the temperature data pollution indicator; and combine the temperature data pollution indicator, the mean and the lowest reading of the raw dataset to deduce the estimated core temperature.

[0039] The joint estimation unit is used to quantify irreversible fractional damage of SOH based on the estimated core temperature and the collected discharge current, and update the current SOH score; it combines the current SOH score with the estimated core temperature to obtain a dynamic available capacity benchmark; and it estimates the current SOC score based on the dynamic available capacity benchmark.

[0040] The dynamic risk quantification unit is used to generate a power risk index based on the current SOC score; a temperature risk index based on the estimated core temperature; a model collapse risk index based on the temperature data contamination indicator; and to combine the power risk index, temperature risk index, and model collapse risk index, and perform a weighted summation to obtain a comprehensive risk index; to calculate the quantified safe distance index based on the comprehensive risk index; and to calculate the safe distance change rate of the quantified safe distance index.

[0041] The adaptive decision unit outputs a self-heating start decision signal when the model collapse risk index is greater than zero and the estimated core temperature is less than the preset upper temperature limit; otherwise, it outputs a discharge rejection decision signal when the quantized safety distance index is less than the preset first safety threshold or the safety distance change rate is less than the preset first change rate threshold; otherwise, it outputs a discharge allow decision signal.

[0042] The low-pressure energy storage system under extreme low temperature conditions in this embodiment includes an energy storage body and an algorithm-based battery management system control unit. The control unit integrates a multi-dimensional state perception unit, a joint estimation unit, a dynamic risk quantification unit, and an adaptive decision-making unit to form a closed-loop management system to cope with the challenges of extreme low temperature. Extreme low temperature in this invention refers to an ambient temperature below -20°C, such as a temperature environment in the range of -40°C to -20°C.

[0043] Multidimensional state sensing unit

[0044] This unit is designed to sense the temperature distribution within the energy storage system in real time and identify data contamination in sensor readings under extreme low-temperature environments, generating a reliable core temperature reading. As input for subsequent estimation and decision-making;

[0045] This unit collects raw datasets through a multi-point NTC temperature sensor array deployed inside the energy storage system. ;

[0046] Temperature gradient index Solution: This unit calculates the temperature gradient index. This index characterizes the uniformity and symmetry of the system's thermal state; Defined as the original dataset The highest reading in With the lowest reading difference;

[0047] ;

[0048] in, and All data are real-time measurements from NTC sensors; this metric is designed to distinguish between normal internal heat dissipation and external asymmetric thermal shock.

[0049] Temperature data contamination indicator Generation: This unit will generate the temperature gradient index With the preset normal temperature gradient threshold By comparison, a pollution indicator for temperature data was obtained. ; It is a binary flag used to quickly identify data that has become unreliable due to contamination by external asymmetric heat sources;

[0050] ;

[0051] in, The setting is based on the modeling of the system's thermophysical state, representing the maximum normal temperature difference that can be generated by internal self-heating alone under extreme low temperature conditions; This was achieved by running different self-heating strategies and recording data under standard -40°C undisturbed conditions. The statistical extreme values ​​were obtained through offline calibration.

[0052] Estimate core temperature The calculation: This unit combines temperature data with pollution indicators. The mean of the original dataset With the lowest reading The estimated core temperature was calculated. For the specific calculation logic, please refer to the implementation method.

[0053] Joint estimation unit

[0054] This unit aims to achieve a health status fraction of the energy storage system under high and low temperature coupling environments. and fraction of state of charge Dynamics, coupling, and accurate estimation;

[0055] SOH Update: This unit is based on estimated core temperature. and the collected discharge current Quantification of irreversible fractional damage in SOH The score is then subtracted from the SOH score of the previous period to update the current health status score. That is, the SOH fraction This enables modeling of the cumulative damage caused by low temperature and current over long-term lifespan.

[0056] Dynamic available capacity baseline Correction: This unit incorporates the current SOH score. With estimated core temperature The dynamic available capacity baseline is obtained by correction. ; As the denominator of the ampere-hour integral, it solves the systematic error caused by instantaneous capacity loss in traditional SOC estimation under low temperature environment;

[0057] SOC estimation: This unit uses the ampere-hour integral method, based on a dynamic available capacity baseline. As the current total capacity, and combined with the collected discharge current By integrating, the fraction of the current state of charge is estimated. That is, SOC score ;

[0058] Dynamic Risk Quantification Unit

[0059] This unit aims to quantify the multiple failure risks of energy storage systems into a single risk indicator. And use this indicator to establish the concept of safety margin;

[0060] Itemized risk generation: This unit is based on the current SOC score. Generate a power risk index Based on the estimated core temperature Generate a temperature risk index Pollution indicators based on temperature data Generate a model collapse risk index ;

[0061] Overall Risk Index This unit incorporates the electricity risk index. Temperature risk index Model crash risk index The weighted sum is then used to obtain the comprehensive risk index. ;

[0062] ;

[0063] in, It is derived from the linear weighted sum of three dimensionless risk components and is used to quantify the degree of danger of the system approaching the multidimensional failure boundary; risk weight coefficient It maximizes the long-term SOH score by executing a deep reinforcement learning model offline. The objective function is trained to determine its optimal relative weights;

[0064] Safe distance quantification: This unit is based on a comprehensive risk index. Calculate the quantitative safety distance index ,in yes The reciprocal of;

[0065] Rate of change of safe distance: This unit calculates the rate of change of safe distance to quantify the safe distance index. This characterizes the dynamic trend of risk changes;

[0066] Adaptive Decision Unit

[0067] This unit is designed to execute adaptive control logic for risk hedging based on quantified multidimensional risks and safety distances;

[0068] Rule 1: When the model crash risk index Greater than zero and estimated core temperature Less than the preset upper temperature limit At that time, output the self-heating start decision signal. ; This is a preset upper limit for the battery's tolerance temperature, typically set according to the cell manufacturer's safety specifications; this rule aims to proactively eliminate sources of data contamination. To restore data credibility;

[0069] Rule 2: Otherwise, when quantifying the safe distance index Less than the preset first safety threshold or rate of change of safe distance Less than the preset first rate of change threshold At that time, output a rejection discharge decision signal. ; and The value is obtained by analyzing the long-term SOH fraction. Target protection and discharge permission decision signals The requirements were weighed and obtained through offline model optimization. It is a negative threshold, indicating a safe distance. It is shrinking at an unacceptable rate;

[0070] Rule 3: Otherwise, output a discharge permission decision signal. ;

[0071] This system identifies data contamination. This avoids optimistic misjudgments based on inflated sensor readings and forces the adoption of a conservative core temperature. ;based on Joint dynamic correction of state of health (SOH) and state of charge (SOC) This eliminates the systematic error in SOC estimation caused by low temperature; ultimately, by using a model collapse risk index... Integrate into comprehensive risk model and based on safe distance and its rate of change By implementing risk hedging, the system can operate continuously, reliably, and safely under the dual challenges of extreme low temperatures and sensor uncertainties, and maximize the long-term lifespan of the battery.

[0072] Example 2:

[0073] The process of estimating the core temperature includes:

[0074] When the temperature data contamination flag is zero, the mean of the original dataset is set as the estimated core temperature.

[0075] When the temperature data contamination flag is one, the lowest reading of the original dataset is set as the estimated core temperature.

[0076] This embodiment calculates and estimates the core temperature using the multi-dimensional state sensing unit in Embodiment 1. Specific details; this calculation logic uses temperature data contamination indicators. As a logic switch, it is designed to address the issue of artificially high sensor data caused by external asymmetric thermal shock, ensuring... It is a conservative estimate that is closest to the actual thermal state inside the battery cell;

[0077] Logic 1: When temperature data is contaminated A mean of zero indicates that the sensor data is reliable; in this case, the mean of the original dataset is... Set to estimate core temperature ; It is the original dataset The arithmetic mean of the data, under reliable conditions, most accurately represents the overall average thermal state of the energy storage system;

[0078] Logic 2: When temperature data is contaminated A value of 1 indicates that the sensor data is unreliable; in this case, the system adopts the most conservative strategy, using the lowest reading from the original dataset. Set to estimate core temperature ; yes The minimum value in the range is taken as the temperature closest to the frozen state inside the battery cell;

[0079] This deduction logic can be uniformly expressed in a weighted sum form:

[0080] ;

[0081] pass Flag bit for core temperature By employing a conservative switching mechanism, the system effectively avoids relying on erroneous optimistic temperature readings when external asymmetric thermal shocks cause artificially high temperature readings. Making risky discharge decisions; this conservative extrapolation under data contamination greatly improves the system's safety margin in extreme low-temperature environments.

[0082] Example 3:

[0083] The quantification process of irreversible fractional damage to SOH includes:

[0084] Get the amount of charge in the current time step;

[0085] Based on the estimated core temperature, preset reference temperature, charge quantity, preset temperature sensitivity coefficient, and basic capacity damage rate, the irreversible fractional damage of SOH is calculated.

[0086] This embodiment quantifies the irreversible fractional damage of SOH using the joint estimation unit in Embodiment 1. The specific details aim to establish a core temperature... and charge Coupled damage accumulation model;

[0087] Get the amount of charge in the current time step By analyzing the collected discharge current At time step By performing an ampere-hour integral within the time frame, the following calculation is obtained. ; It is the absolute value of the charge flowing through the battery cell, which serves as an excitation factor leading to irreversible damage to the SOH (Sodium Hydrochloric Oxide).

[0088] Calculation of irreversible fractional damage in SOH Based on estimated core temperature Preset reference temperature , charge and preset temperature sensitivity coefficient and basic capacity damage rate The irreversible fractional damage of SOH was calculated. ;

[0089] ;

[0090] in, It is a dimensionless loss of SOH fraction; It is a preset reference temperature; in this embodiment, the reference temperature is... Specifically refers to the ambient temperature at which the nominal capacity is specified in the battery cell manufacturer's specifications, such as 25°C;

[0091] and It was pre-calibrated through accelerated life testing; this calibration process was based on a set of experimental data. ;in, To maintain a constant temperature SOH loss measured when the load reaches a specific fraction of capacity loss. This corresponds to the cumulative charge amount; and The model was fitted using nonlinear least squares regression analysis on the experimental dataset to ensure optimal fit on the calibration dataset.

[0092] The term is the temperature acceleration factor of damage, which accurately simulates the exponential amplification effect of low temperature on irreversible damage to the battery cell.

[0093] This quantification process innovatively establishes the core temperature. Long-term damage from SOH The coupled model accurately captures the nonlinear, exponential amplification effect of extreme low temperatures on battery SOH damage by introducing an exponential temperature acceleration factor, providing a high-precision and high-reliability long-term health status benchmark for subsequent life protection decisions.

[0094] Example 4:

[0095] The process of revising the dynamic available capacity baseline includes:

[0096] Based on the estimated core temperature, a pre-defined lookup table or fitting function is queried to obtain the low-temperature capacity reduction factor;

[0097] The dynamic available capacity baseline is obtained by multiplying the nominal capacity, the current SOH fraction, and the low-temperature capacity reduction factor.

[0098] This embodiment modifies the dynamic available capacity benchmark of the joint estimation unit in Embodiment 1. Specific details;

[0099] Obtain the low-temperature capacity reduction factor Based on estimated core temperature Query the preset lookup table or fitting function to obtain the low-temperature capacity reduction factor. ; It is dimensionless, referring to the core temperature. The instantaneous discharge capacity of a battery cell is the fractional ratio of its nominal capacity to its instantaneous discharge capacity at different constant temperatures. The instantaneous discharge capacity of the battery cell is measured. Capacity relative to standard temperature And obtain, that is This is an inherent characteristic of system performance;

[0100] Calculate the dynamic available capacity baseline : nominal capacity Current SOH score Low-temperature capacity reduction factor Multiply to obtain the dynamic available capacity baseline. ;

[0101] ;

[0102] in, Simultaneously represented as a score of long-term health status and represent transient reversible damage at low temperatures. Factor correction;

[0103] This correction process dynamically adjusts the denominator of the SOC estimate by coupling long-term and instantaneous factors. This solves the problem of instantaneous capacity loss caused by neglecting low temperatures in traditional SOC estimation, and greatly improves the current SOC score. The accuracy of the estimation ensures that the power displayed by the BMS at extremely low temperatures is the actual usable power.

[0104] Example 5:

[0105] The calculation process for the electricity risk index includes:

[0106] The ratio is obtained by dividing the difference between the preset SOC safety boundary and the power lock boundary by the difference between the current SOC score and the power lock boundary.

[0107] The ratio is then used to perform an exponential calculation based on a preset SOC risk sensitivity coefficient to obtain the electricity risk index.

[0108] Electricity Risk Index Characterizes the degree of danger the system is from the power lock boundary; the difference between the preset SOC safety boundary and the power lock boundary is divided by the difference between the current SOC score and the power lock boundary to obtain the ratio;

[0109] The ratio is then used to calculate the exponent of the preset SOC risk sensitivity coefficient to obtain the electricity risk index;

[0110] Calculate the ratio: The preset SOC safety boundary... With respect to the preset power lock boundary The difference is set as the numerator; the current SOC score is... With power lock boundary The difference is set as the denominator; the ratio is obtained by dividing the numerator by the denominator.

[0111] Exponential calculation: and then apply a preset SOC risk sensitivity coefficient to the ratio. The electricity risk index is obtained through index calculation. ;

[0112] ;

[0113] in, It is a dimensionless coefficient greater than 1; The value is optimized offline, so that... exist near To meet specific risk response time targets in a timely manner, ensuring that the decision-making system intervenes in advance; It is the preset SOC safety boundary, representing the power level at which the system should begin to avoid risks;

[0114] Through index The calculation ensures that the current SOC score is... Approaching the battery lockout boundary At that time, the power risk index It exhibits non-linear and rapid growth, forming a strong risk warning signal to prevent sudden system failure under extremely low power conditions.

[0115] Example 6:

[0116] The calculation process for the temperature risk index includes:

[0117] The difference between the preset temperature safety boundary and the preset temperature lockout boundary is set as a molecule;

[0118] The difference between the estimated core temperature and the temperature lockout boundary is set as the denominator;

[0119] Divide the numerator by the denominator to obtain the ratio, and then perform an exponential calculation on the ratio using a preset temperature risk sensitivity coefficient to obtain the temperature risk index.

[0120] Temperature risk index Characterizes the degree of danger the system faces from the temperature lockout boundary;

[0121] Setting molecules: setting the preset temperature safety boundary With respect to the preset temperature lock-in boundary The difference is set as the molecule. ; It is the lowest temperature threshold at which the battery cell can operate safely. It is the upper boundary temperature at which the performance of the battery cell begins to decline significantly. Both are usually set based on the physical characteristics and safety limits of the battery cell.

[0122] Define the denominator: the estimated core temperature. Temperature lockout boundary The difference is set as the denominator. ;

[0123] Calculation and exponential operation: Divide the numerator by the denominator to obtain the ratio, and then apply the ratio to a preset temperature risk sensitivity coefficient. The temperature risk index is obtained through index calculation. ;

[0124] ;

[0125] in, It is a dimensionless coefficient, and its value is obtained through offline optimization. exist near When the specific risk response time objective is met;

[0126] This risk model achieves core temperature. Temperature risk The exponential effect; when estimating core temperature Approaching the temperature lockout boundary When, the denominator Approaching zero, leading to temperature risks. Rapid dispersion accurately simulates the exponential suppression effect of low temperature on the cell's discharge capability, forcing the system to take protective measures before the critical temperature point.

[0127] Example 7:

[0128] The calculation process for the model collapse risk index includes:

[0129] The model collapse risk index is obtained by multiplying the temperature data contamination marker with the preset contamination risk factor.

[0130] Model crash risk index Characterizes the confidence level of the BMS algorithm in the data input;

[0131] Calculate the risk index of model collapse : Contamination of temperature data With preset pollution risk factors Multiplying them together yields the model collapse risk index. ;

[0132] ;

[0133] in, It is a dimensionless, pre-defined high-value constant; The value is set high enough to ensure Greater than the weighted sum of all other risk items, i.e., when hour, Becoming a comprehensive risk The primary factor forces the system to enter the highest level of alert.

[0134] The introduction of this index will contaminate temperature data. Integrating it into the decision-making system solves the problem that traditional BMS algorithms cannot detect a decline in their own estimation confidence; through high-value factors This ensures that once data corruption is detected, the system will immediately implement risk hedging strategies, thus guaranteeing the robustness of the decision-making system.

[0135] Example 8:

[0136] The quantitative safety distance index is obtained by solving the inverse of the comprehensive risk index;

[0137] The rate of change of safe distance is calculated by quantifying the safe distance index using the first difference over a preset time step.

[0138] This embodiment calculates the quantified safety distance index using the dynamic risk quantification unit in Embodiment 1. and its rate of change Specific details;

[0139] Quantitative safety distance index Solution:

[0140] Quantitative safety distance index Through comprehensive risk index The reciprocal is obtained by solving;

[0141] ;

[0142] in, yes The reciprocal of quantifies the safety margin of the system from the multidimensional failure boundary;

[0143] Rate of change of safe distance Calculation:

[0144] Rate of change of safe distance By quantifying the safe distance index At the preset time step The first-order difference within is calculated;

[0145] ;

[0146] in, The preset time step should be matched with the decision cycle of the BMS control unit; for example, it can be set to 100 milliseconds. Characterizing safety margin The dynamic trend of change is a key input for making risk hedging decisions;

[0147] By integrating risk Converted into quantified safe distance This provides a clear concept of margin for decision-making; rate of change of safety distance. The introduction of this technology enables the decision-making system to sense whether danger is rapidly approaching, thereby allowing it to intervene in advance and implement emergency hedging strategies, avoiding a passive response mode.

[0148] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A low-pressure energy storage system for extreme low temperatures, characterized in that, include: A multi-dimensional state sensing unit is used to collect raw datasets and calculate temperature gradient indicators. The temperature gradient index is compared with the preset normal temperature gradient threshold to obtain the temperature data pollution indicator; and the core temperature is estimated by combining the temperature data pollution indicator, the mean and the lowest reading of the original dataset. The joint estimation unit is used to quantify irreversible fractional damage of SOH based on the estimated core temperature and the collected discharge current, and update the current SOH fraction; combined with the current SOH fraction and the estimated core temperature, the dynamic available capacity benchmark is obtained. Based on the dynamic available capacity benchmark, the current SOC score is estimated. The dynamic risk quantification unit is used to generate a power risk index based on the current SOC score. A temperature risk index is generated based on the estimated core temperature. Based on the pollution indicators in the temperature data, a model collapse risk index is generated; The combined risk index is obtained by weighting and summing the power risk index, temperature risk index, and model collapse risk index. The quantitative safety distance index is calculated based on the comprehensive risk index. And calculate the rate of change of the safe distance in the quantitative safe distance index; The adaptive decision unit is used to output a self-heating decision signal when the model collapse risk index is greater than zero and the estimated core temperature is less than the preset temperature limit. Otherwise, when the quantized safety distance index is less than the preset first safety threshold or the safety distance change rate is less than the preset first change rate threshold, a discharge rejection decision signal is output. Otherwise, output a discharge permission decision signal; Temperature gradient index Defined as the original dataset The highest reading in With the lowest reading The difference; temperature gradient index With the preset normal temperature gradient threshold By comparison, a contamination marker for temperature data was obtained. ; ； The process of estimating the core temperature includes: When the temperature data contamination flag is zero, the mean of the original dataset is set as the estimated core temperature. When the temperature data contamination flag is one, the lowest reading of the original dataset is set as the estimated core temperature. The process of revising the dynamic available capacity baseline includes: Based on the estimated core temperature, a pre-defined lookup table or fitting function is queried to obtain the low-temperature capacity reduction factor. The dynamic available capacity baseline is obtained by multiplying the nominal capacity, the current SOH fraction, and the low-temperature capacity reduction factor. The quantitative safety distance index is calculated by taking the inverse of the comprehensive risk index.

2. The low-pressure energy storage system under extreme low temperatures according to claim 1, characterized in that, The quantification process of irreversible fractional damage to SOH includes: Get the amount of charge in the current time step; Based on the estimated core temperature, preset reference temperature, charge quantity, preset temperature sensitivity coefficient, and basic capacity damage rate, the irreversible fractional damage of SOH is calculated.

3. The low-pressure energy storage system under extreme low temperatures according to claim 1, characterized in that, The calculation process for the electricity risk index includes: The ratio is obtained by dividing the difference between the preset SOC safety boundary and the power lock boundary by the difference between the current SOC score and the power lock boundary. The ratio is then used to perform an exponential calculation based on a preset SOC risk sensitivity coefficient to obtain the electricity risk index.

4. The low-pressure energy storage system under extreme low temperatures according to claim 1, characterized in that, The calculation process for the temperature risk index includes: The difference between the preset temperature safety boundary and the preset temperature lockout boundary is set as a molecule; The difference between the estimated core temperature and the temperature lockout boundary is set as the denominator; Divide the numerator by the denominator to obtain the ratio, and then perform an exponential calculation on the ratio using a preset temperature risk sensitivity coefficient to obtain the temperature risk index.

5. The low-pressure energy storage system under extreme low temperatures according to claim 1, characterized in that, The calculation process for the model collapse risk index includes: The model collapse risk index is obtained by multiplying the temperature data contamination marker with the preset contamination risk factor.

6. The low-pressure energy storage system under extreme low temperatures according to claim 1, characterized in that, The rate of change of safe distance is calculated by quantifying the safe distance index using the first difference over a preset time step.

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