Intelligent safety storage cabinet management and control system for lithium ion batteries

Abstract

The application discloses a kind of lithium ion battery intelligent safe storage cabinet management and control systems, it is related to battery safety control technology field, and the core operating parameter of electric core parameter acquisition module is collected in real time, and data basis is provided for subsequent control;SOC-SOH coupling calculation module is based on specific algorithm real-time calculation SOC and SOH, constructs coupling constraint equation, and the correlation of the state of electric core and storage voltage is quantified;Subsection adaptive control module is divided according to coupling constraint equation Control interval, different matching voltage and equalization parameter, effectively avoid the overvoltage risk of aging electric core under high SOC;Periodic inspection update module is iteratively calculated coupling parameter according to fixed cycle, dynamically updates control threshold, adapts the attenuation characteristics of electric core whole cycle;Voltage overage soft current-limiting closed-loop control module limits equalization current amplitude and change rate through PID closed-loop adjustment, to prevent overexcited action damage electric core;Equalization execution module executes accurate voltage regulation and equalization action, completes closed-loop control.

Description

Technical Field

[0001] This invention relates to the field of battery safety management technology, specifically to a smart safety storage cabinet management system for lithium-ion batteries. Background Technology

[0002] Lithium-ion batteries, with their significant advantages such as high energy density and long cycle life, have been widely used in many fields such as new energy storage, electric vehicles, and portable devices. In the lithium-ion battery industry chain, the safety management of finished battery storage and backup storage is crucial, as it directly determines the battery's lifespan and safety. As the core equipment for static battery storage, lithium-ion battery storage cabinets bear the important responsibility of ensuring battery storage safety and performance stability. Among them, voltage control and equalization control are the core technologies for the safety protection of lithium-ion battery storage cabinets, playing a key role in maintaining the battery's health, preventing battery safety accidents, and extending battery life.

[0003] Existing lithium-ion battery storage cabinet management technologies generally adopt a fixed voltage threshold management mode, pre-setting a uniform upper limit for the resting voltage and equalization current parameters. Furthermore, they lack a dynamic adjustment mechanism throughout the storage process. This traditional technology has several significant technical defects: First, it does not fully consider the coupling effect between the cell's state of charge (SOC) and state of health (SOH). In actual storage, aging cells at high SOC are easily over-voltaged by the fixed voltage threshold, triggering electrochemical side reactions such as electrolyte decomposition and lithium plating. This not only accelerates cell aging but may even lead to thermal runaway, seriously threatening battery storage safety. Second, it lacks a full-cycle parameter update mechanism. During battery storage, cell performance continuously degrades, and the fixed threshold cannot be adjusted in time according to changes in cell state, resulting in a gradual deterioration in management effectiveness and failing to effectively guarantee battery safety throughout the entire storage cycle. Third, the equalization control lacks current-limiting protection measures. When the voltage exceeds acceptable limits, the equalization module often acts too aggressively, and large current surges can easily cause cell polarization damage, further reducing battery storage safety. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a smart safety storage cabinet management system for lithium-ion batteries. This system can collect core operating parameters of the battery cells in real time through a cell parameter acquisition module, providing a data foundation for subsequent management. A SOC-SOH coupling calculation module calculates SOC and SOH in real time based on a specific algorithm and constructs coupling constraint equations to quantify the correlation between cell state and storage voltage. A segmented adaptive management module divides management intervals according to the coupling constraint equations, and differentiates voltage and balancing parameters to effectively avoid the risk of overvoltage in aging cells under high SOC. A periodic inspection and update module iteratively calculates coupling parameters at fixed intervals and dynamically updates management thresholds to adapt to the full-cycle degradation characteristics of the battery cells. A voltage over-tolerance soft current limiting closed-loop control module limits the amplitude and rate of change of the balancing current through PID closed-loop adjustment to prevent over-excitation from damaging the battery cells. A balancing execution module performs precise voltage regulation and balancing actions to complete closed-loop management.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a lithium-ion battery intelligent safety storage cabinet management and control system, the system comprising: a cell parameter acquisition module, a SOC-SOH coupling calculation module, a segmented adaptive management and control module, a periodic inspection and update module, a voltage over-tolerance soft current limiting closed-loop control module, and an equalization execution module; The cell parameter acquisition module collects the voltage, current, temperature and internal resistance operating parameters of each lithium-ion cell in the storage cabinet in real time. The SOC-SOH coupled solution module is communicatively connected to the cell parameter acquisition module. Based on the acquired operating parameters, it calculates the SOC and SOH values ​​of a single cell in real time and constructs the SOC-SOH coupled constraint equation. The segmented adaptive control module is communicatively connected to the SOC-SOH coupled solution module. According to the coupling constraint equation, the cell static storage voltage is divided into three control intervals, and the target static voltage and equalization current threshold are adaptively matched for different control intervals. The periodic inspection and update module is connected to the SOC-SOH coupling calculation module and the segmented adaptive control module respectively. It recalculates the SOC-SOH coupling parameters according to a fixed inspection cycle and updates the voltage threshold and equalization current threshold of each control interval simultaneously. The voltage deviation soft current limiting closed-loop control module is communicatively connected to the segmented adaptive control module and the equalization execution module. It monitors the cell voltage deviation in real time, generates a soft current limiting control signal, and limits the output current amplitude and rate of change of the equalization execution module. The equalization execution module receives control signals and performs cell voltage equalization and static voltage adjustment actions.

[0006] Furthermore, in the SOC-SOH coupled calculation module, the SOC calculation is based on the ampere-hour integration method, which accumulates the charge and discharge charge of the battery cell in real time, and calculates the remaining charge percentage by combining the rated capacity of the battery cell. The open circuit voltage is collected during the static and stable period of the battery cell, and the integration error is corrected by comparing it with the standard OCV-SOC curve of the battery cell. At the same time, a temperature compensation factor is introduced to correct the charge measurement deviation in high and low temperature environments in real time.

[0007] Furthermore, in the SOC-SOH coupled calculation module, the SOH calculation adopts a fusion calculation of capacity decay method and internal resistance characterization method. By comparing the actual usable capacity of the cell with the factory rated capacity, the capacity decay ratio is obtained. The degree of aging is quantified by combining the increase of the cell's AC internal resistance compared with the initial internal resistance. After eliminating abnormal data such as instantaneous fluctuations and acquisition interference, a weighted fitting is performed to output an accurate SOH value.

[0008] Furthermore, in the SOC-SOH coupled calculation module, after completing the independent calculation of SOC and SOH, a coupling constraint relationship between the two is constructed. SOH is used as a weighting coefficient to couple the SOC value with the static voltage limit. A full parameter iterative calculation is completed every 100ms, and the coupled calculation results are updated in real time. At the same time, the cell reference parameters are recalibrated in response to the periodic inspection command to eliminate performance drift and calculation errors caused by long-term storage.

[0009] Furthermore, in the SOC-SOH coupled solution module, the SOC and SOH coupled constraint equations are constructed using the real-time calculated state of charge (SOC) and state of health (SOH) of the battery cell as core variables, combined with the collected battery cell temperature, AC internal resistance, and reference electrochemical parameters. Based on the standard static reference voltage of the battery cell, a SOC nonlinearity correction coefficient and a SOH attenuation weighting coefficient are introduced, while a temperature compensation term is superimposed to achieve multi-physical quantity coupling constraints. The SOC correction coefficient is generated by fitting the battery cell's OCV-SOC characteristic curve and decreases gradually with increasing SOC value. The SOH attenuation coefficient is determined by the ratio of the battery cell's real-time SOH to its factory-rated SOH; the deeper the battery cell's aging, the smaller this coefficient becomes. During the construction process, abnormal data such as instantaneous charging and discharging disturbances and collected noise are removed. A weighted fitting method is used to smooth coefficient fluctuations. The equation parameters are iterated and updated every 100ms. During periodic inspections, the battery cell's reference capacity and initial internal resistance parameters are recalibrated to correct the reference coefficients of the equations, eliminating performance drift errors during long-term battery cell storage.

[0010] Furthermore, in the SOC-SOH coupled solution module, the expression for the SOC and SOH coupled constraint equations is as follows: ,in, It is the upper limit voltage for real-time safe static placement of the battery cell, which is directly used as the basis for dividing and regulating the three control zones; This refers to the factory-standard static reference voltage for the battery cell, and the nominal safe storage voltage provided. It is a nonlinear correction coefficient for the state of charge, which is obtained by fitting the cell OCV-SOC characteristic curve. It gradually decreases as the SOC increases, and is used to suppress the risk of side reactions caused by excessively high voltage threshold under high SOC. It is the health status degradation weighting coefficient, which is the ratio of real-time SOH to rated SOH. The higher the degree of cell aging, the smaller this coefficient is, which simultaneously lowers the upper limit of the safe voltage of the aging cell. It is the temperature compensation coefficient, calculated based on the real-time surface temperature T of the battery cell, and is used to compensate for the influence of high and low temperature environments on the voltage characteristics of the battery cell.

[0011] Furthermore, the segmented adaptive control module establishes real-time communication with the SOC-SOH coupling calculation module via the CAN bus, continuously receiving the transmitted real-time calculated values ​​of single-cell SOC and SOH, as well as the voltage constraint upper limit calculated by the coupling constraint equation. The division process follows coupled constraint logic, combining the cell's factory electrochemical parameters with actual storage test data to clarify the division criteria for the three control intervals: the healthy adaptation segment corresponds to SOH ≥ 90%, at which point the cell performance is good, and the output of the coupled constraint equation is... Based on the benchmark, the target static voltage and rated equalization current are matched; the degradation warning segment corresponds to 70%≤SOH<90%, indicating slight aging of the cell. Lower the target resting voltage by 5%-10%, and simultaneously reduce the equalization current amplitude to minimize cell polarization damage; the aging voltage limit section corresponds to SOH < 70%, indicating severe cell aging, requiring forced adjustment. Set the target resting voltage to 80%, enable minimum equalization current, and prohibit the storage of high-charge states with SOC > 60%.

[0012] Furthermore, the periodic inspection and update module triggers a full-parameter recalculation process according to a preset fixed inspection cycle. When the inspection is triggered, it first synchronously retrieves the latest data on single-cell voltage, charging and discharging current, surface temperature, and AC internal resistance uploaded by the cell parameter acquisition module. Invalid data is removed through data validity verification, and the filtered valid parameters are transmitted to the SOC-SOH coupled calculation module to recalculate the current SOC and SOH values ​​of a single cell, as well as the SOC nonlinearity correction coefficient, SOH attenuation weight coefficient, and temperature compensation coefficient in the coupled constraint equation, thus completing the full-parameter iteration of the coupled constraint equation. The system updates the parameters. After the update is completed, it recalculates the reference static voltage threshold and corresponding equalization current threshold for the three control intervals—health adaptation, attenuation warning, and aging voltage limiting—based on the new coupling constraint equations and the cell's factory reference parameters. Simultaneously, it stores the updated threshold parameters for each interval in the local parameter library and sends them to the segmented adaptive control module. If the coupling parameter calculation is abnormal or the updated threshold exceeds the cell's safe operating limit, it automatically uses the previously stored valid threshold and triggers an abnormal alarm signal to be fed back to the main control unit, thus achieving dynamic adaptation of control parameters throughout the entire storage cycle.

[0013] Furthermore, the voltage deviation soft current limiting closed-loop control module acquires the target static voltage threshold issued by the segmented adaptive control module in real time, and simultaneously collects the actual operating voltage of the battery cell and the equalization output current data returned by the equalization execution module. By calculating the battery cell voltage deviation value in real time, when the deviation value exceeds the preset safety range, a soft current limiting control signal is generated based on the incremental PID adjustment algorithm. The amplitude of the equalization output current is linearly adjusted according to the magnitude of the voltage deviation to avoid large current step output. At the same time, the rate of change of the equalization current is limited to prevent the equalization action from being too aggressive and causing polarization damage to the battery cell. As the voltage deviation gradually falls back to the safe range, the control signal synchronously and smoothly adjusts the current output until the deviation is eliminated and the rated equalization parameters are restored.

[0014] Furthermore, the voltage over-tolerance soft current limiting closed-loop control module generates a soft current limiting control signal based on an incremental PID adjustment algorithm, the algorithm formula of which is: ,in, This represents the incremental adjustment of the equalization current within the current control cycle, i.e., the change in current amplitude output from the soft current limiting module to the equalization execution module, used to smoothly adjust the equalization output magnitude; k is the current control cycle number, representing the timing node for PID calculation. This is the voltage deviation value for the current cycle. , These are the voltage deviation values ​​for the previous control cycle and the two previous control cycles, respectively, used to reflect the trend of deviation changes; It is a proportional coefficient that quickly adjusts the equalizing current increment based on the magnitude of the voltage deviation. These are integral coefficients used to eliminate steady-state error; It is a differential coefficient that makes advance adjustments based on the rate of change of voltage deviation, suppresses sudden changes in the equilibrium current, and ensures a stable rate of change of current.

[0015] Compared with existing technologies, this intelligent safety storage cabinet management system for lithium-ion batteries has the following advantages: This invention constructs a static voltage control model with coupled constraints of SOC and SOH to quantify the coupled influence of cell health and state of charge, fundamentally avoiding side reactions such as lithium plating and electrolyte decomposition caused by overvoltage in aging cells under high SOC conditions, thus significantly reducing storage safety risks. It employs a three-stage partitioned adaptive control strategy, configuring voltage and balancing parameters differently for cells with varying degrees of aging, balancing storage safety and cell consistency. Furthermore, it integrates a periodic inspection and dynamic update mechanism, iteratively updating coupled parameters and control thresholds throughout the entire storage cycle to adapt to the continuous degradation characteristics of cells, achieving long-term stable control. Combined with soft current limiting closed-loop control, it effectively suppresses balancing current surges.

[0016] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 A structural block diagram of a smart safety storage cabinet control system for lithium-ion batteries; Figure 2 A flowchart of a SOC-SOH coupled calculation module for a lithium-ion battery intelligent safety storage cabinet management system. Figure 3 This is a flowchart of a smart safety storage cabinet management system for lithium-ion batteries. Detailed Implementation

[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0020] This invention provides a smart safety storage cabinet management system for lithium-ion batteries, such as... Figure 1As shown, the system includes a cell parameter acquisition module, a SOC-SOH coupling calculation module, a segmented adaptive control module, a periodic inspection and update module, a voltage tolerance soft current limiting closed-loop control module, and an equalization execution module. The cell parameter acquisition module collects the core operating parameters of the cell in real time, providing a data foundation for subsequent control. The SOC-SOH coupling calculation module calculates SOC and SOH in real time based on a specific algorithm and constructs coupling constraint equations to quantify the correlation between cell state and storage voltage. The segmented adaptive control module divides the control interval according to the coupling constraint equations, and differentiates the voltage and equalization parameters to effectively avoid the risk of overvoltage in aging cells under high SOC. The periodic inspection and update module iteratively calculates the coupling parameters at fixed intervals and dynamically updates the control thresholds to adapt to the cell's full-cycle degradation characteristics. The voltage tolerance soft current limiting closed-loop control module limits the amplitude and rate of change of the equalization current through PID closed-loop adjustment to prevent over-excitation from damaging the cell. The equalization execution module performs precise voltage regulation and equalization actions to complete the closed-loop control. The cell parameter acquisition module is deployed at the corresponding acquisition node of each cell in the storage cabinet to achieve synchronous real-time acquisition of all dimensions of operating parameters. For the static storage condition of the cells, a high-precision synchronous sampling mechanism is adopted to continuously acquire four core operating parameters of a single cell: terminal voltage, circuit current, surface temperature, and AC internal resistance. During the sampling process, a dual processing mechanism of hardware filtering and digital filtering is built-in to eliminate power frequency interference, line noise, and instantaneous acquisition spikes, ensuring the validity of the original data.

[0021] Among them, voltage acquisition covers the entire storage voltage range of the battery cell and uses differential sampling to eliminate common-mode interference; current acquisition focuses on the resting micro-current and the balanced operating current to achieve accurate identification of microampere-level current; temperature acquisition uses sampling points attached to the surface of the battery cell to provide real-time feedback on the thermal state of the battery cell; internal resistance acquisition uses the AC injection method to complete the internal resistance detection in the undisturbed state of the battery cell, avoiding the impact of charging and discharging operations on the storage state of the battery cell. After all the acquired parameters are preprocessed, they are packaged and uploaded in real time to the SOC-SOH coupled calculation module to provide standardized data input for subsequent state calculation.

[0022] like Figure 2 As shown, the SOC-SOH coupled solution module receives valid operating parameters uploaded by the cell parameter acquisition module, independently completes accurate calculation of the state of charge (SOC) and state of health (SOH), and constructs multi-physical quantity coupled constraint equations to realize dynamic binding of state parameters and safety voltage limits.

[0023] SOC calculation uses the ampere-hour integration method to accumulate the micro-leakage charge during the cell's resting phase and the charge during equalization operation in real time. Combined with the cell's rated capacity, the remaining charge percentage is calculated in real time to complete the initial SOC value calculation. To eliminate the accumulation error, a cell resting stability judgment condition is set. When the cell voltage and current fluctuations are lower than the preset stability threshold, it is judged as a resting stability period. At this time, the cell open circuit voltage (OCV) is collected, and the factory-preset OCV-SOC standard characteristic curve of the cell is retrieved. The SOC integration error is corrected in real time through curve interpolation fitting.

[0024] Meanwhile, a temperature compensation factor is introduced to construct a temperature correction model. Based on the real-time surface temperature of the cell, three temperature ranges are divided: low temperature, normal temperature, and high temperature. The corresponding charge metering compensation coefficients are matched to the ranges to perform real-time dynamic correction of charge metering deviations caused by changes in the electrolyte ion migration rate under high and low temperature environments, ensuring the accuracy of SOC calculation across the entire temperature range.

[0025] SOH calculation employs a combined calculation mechanism of capacity decay method and internal resistance characterization method, taking into account both capacity aging and internal resistance aging dimensions to improve the accuracy of aging status identification. Specifically, the capacity decay method obtains the actual usable capacity of the cell through periodic inspections, and calculates the ratio with the cell's factory rated capacity to quantify the cell's capacity decay ratio. The internal resistance characterization method collects the cell's real-time AC internal resistance, compares it with the cell's initial factory internal resistance, calculates the internal resistance increase value, and quantifies the degree of electrochemical impedance aging of the cell.

[0026] The calculation process incorporates abnormal data removal logic to identify and filter abnormal capacity and internal resistance data caused by instantaneous current fluctuations, acquisition noise, and poor contact. It performs weighted fitting on two sets of effective aging characterization data, adaptively allocates weight coefficients according to the cell storage time, and finally outputs a fluctuation-free, high-precision SOH value, accurately reflecting the aging degree of the cell throughout its entire life cycle.

[0027] After completing the independent calculations of SOC and SOH, this module constructs multi-physical quantity coupled constraint equations and establishes a quantitative mapping relationship between state parameters and the cell's safe resting voltage limit. The equation expression is as follows: In the formula, The upper limit voltage for real-time safe static placement of the battery cell serves as the core basis for subsequent segmented management; This is the factory standard static reference voltage for the battery cell, and the nominal safe storage reference value for the battery cell. The nonlinearity correction coefficient for the state of charge is generated by piecewise fitting of the OCV-SOC characteristic curve. The coefficient value decreases gradually as the SOC value increases. It is used to suppress the risk of side reactions such as electrolyte decomposition and lithium deposition caused by excessively high voltage threshold in the high SOC range. The health status degradation weighting coefficient is the ratio of the cell's real-time SOH to its rated SOH. The deeper the cell's aging, the smaller the coefficient value, which simultaneously reduces the upper limit of the safe voltage of the aging cell and avoids the risk of high-voltage storage failure of the aging cell. The temperature compensation coefficient is calculated using polynomial fitting based on the real-time temperature T to compensate for voltage deviations caused by shifts in the electrochemical characteristics of the battery cell under high and low temperature environments.

[0028] The equation parameters are smoothed by weighted fitting, and a full parameter iteration update is completed every 100ms. At the same time, in response to the periodic inspection command, the core reference parameters such as the cell reference capacity and initial internal resistance are recalibrated, the equation reference coefficients are corrected, and the performance drift and cumulative calculation error caused by long-term static storage of the cell are completely eliminated.

[0029] The segmented adaptive control module communicates in real time with the SOC-SOH coupled solution module via an industrial communication bus, continuously receiving the calculated SOC and SOH values ​​of a single cell and the output of the coupling constraint equations. Voltage limits are controlled using a three-stage zoned management strategy based on the degree of cell aging. The target resting voltage and equalization current threshold are adaptively matched to achieve differentiated and precise control.

[0030] Based on the cell's factory electrochemical parameters and long-term storage test data, three control zones are defined according to the SOH value. The control logic for each zone is as follows: Health Adaptation Segment: Corresponds to SOH≥90%, the cell's electrochemical performance is intact, with no obvious aging degradation. Using the Ulim output from the coupled constraint equation as the benchmark value, the standard target static voltage and rated equalization current are matched, and routine equalization control is performed to ensure cell storage consistency. Attenuation warning segment: corresponding to 70%≤SOH<90%, the cell shows slight capacity decay and increased internal resistance. Based on Ulim, the target resting voltage is reduced by 5%-10%, and the equalization current amplitude is reduced proportionally to reduce the cell polarization effect during the equalization process and avoid accelerated aging. Aging voltage limit stage: corresponding to SOH < 70%, the cell is severely aged, the electrochemical stability is greatly reduced, and forced to... The target resting voltage is set to 80%, and the minimum equalization current is used to perform low stress equalization. At the same time, the high SOC storage limit is triggered to prohibit the long-term storage of the cell in a high-charge state of SOC>60%, thus eliminating the risk of thermal runaway.

[0031] All interval control parameters are synchronized in real time to the voltage over-tolerance soft current limiting closed-loop control module, serving as the reference threshold for closed-loop regulation.

[0032] The periodic inspection and update module triggers the recalculation of all system parameters and dynamic updates of thresholds according to a preset fixed inspection cycle, which solves the problems of cell performance drift and control parameter mismatch during long-term storage and realizes adaptive calibration of parameters throughout the entire storage cycle.

[0033] After the inspection is triggered, the module first retrieves the latest full parameters from the cell parameter acquisition module, performs data validity verification, and eliminates offline data, out-of-limit data, and interference data through threshold judgment and fluctuation identification logic; the filtered valid parameters are then transmitted to the SOC-SOH coupled solution module, driving the module to recalculate the SOC and SOH values ​​and synchronously iteratively update the coupled constraint equations. , , All coefficients.

[0034] After the parameter calculation is completed, based on the updated coupling constraint equations, the reference static voltage threshold and equalization current threshold for the three intervals of health adaptation segment, attenuation warning segment, and aging voltage limiting segment are recalculated. The updated parameters are synchronously stored in the local non-volatile parameter library and sent to the segmented adaptive control module for real-time replacement. If the parameter is abnormal in this calculation or the updated threshold exceeds the cell's safe operating limit, the module automatically locks the previous valid threshold and uses it. At the same time, an abnormal alarm signal is generated and uploaded to the main control unit to avoid control failure caused by invalid parameters and to ensure the continuity and security of the storage process.

[0035] The voltage deviation soft current limiting closed-loop control module obtains the target static voltage threshold issued by the segmented adaptive control module in real time, and synchronously collects the actual cell voltage and equalization output current returned by the equalization execution module to construct a voltage deviation closed-loop adjustment mechanism. Based on the incremental PID algorithm, soft current limiting control is realized to eliminate the impact damage of large current equalization on the cell.

[0036] The deviation between the actual cell voltage and the target voltage is calculated in real time. When the deviation exceeds the preset safety range, soft current limiting regulation is initiated. An incremental PID control algorithm is used to generate the control signal. The algorithm expression is as follows: In the formula, The current equalization current adjustment increment is used to smooth the output amplitude of the control current in the current control cycle; k is the timing number of the control cycle. , , These represent the voltage deviation values ​​for the current cycle, the previous cycle, and the two cycles prior, respectively, characterizing the trend of deviation change. This is a proportional coefficient that rapidly adjusts the current increment based on the deviation amplitude. The integral coefficient continuously eliminates steady-state voltage error, ensuring equalization accuracy. The differential coefficient is used to adjust the deviation rate in advance and suppress sudden current changes.

[0037] During the control process, the module linearly adjusts the equalization current amplitude according to the magnitude of the voltage deviation, prohibiting large current step output; at the same time, it limits the upper limit of the current change rate to ensure smooth current rise and fall and avoid polarization damage; when the voltage deviation gradually falls back to the safe range, the control signal synchronously and smoothly decays until the deviation is completely eliminated, and then automatically restores the rated equalization parameters to achieve shock-free closed-loop regulation.

[0038] The equalization execution module receives the current limiting control signal from the voltage deviation soft current limiting closed-loop control module, and performs cell individual voltage equalization and precise adjustment of static voltage. It adopts a lossless equalization topology and supports adaptive switching of multiple modes such as charging equalization, discharging equalization and passive equalization. Based on the cell voltage deviation status and current limiting command, it precisely controls the on / off state of the equalization circuit and the current output.

[0039] During execution, the soft current limiting command is strictly followed to limit the amplitude and rate of change of the output current, without any overly aggressive adjustment actions. After equalization is completed, the actual voltage and output current data of the battery cells are transmitted back to the closed-loop control module in real time, forming a complete data feedback link to ensure the closed-loop nature and accuracy of the closed-loop control, and ultimately achieve consistent voltage regulation and safe static management of all battery cells in the storage cabinet.

[0040] like Figure 3 As shown, the specific workflow of the intelligent safety storage cabinet management system for lithium-ion batteries provided by this invention is as follows: Multi-parameter synchronous acquisition: The cell parameter acquisition module acquires the voltage, current, temperature and internal resistance parameters of all cells in real time, and uploads them to the SOC-SOH coupling solution module after filtering and preprocessing. SOC-SOH Coupled Calculation: The coupled calculation module completes high-precision SOC and SOH calculations with a period of 100ms, iteratively updates the coupled constraint equations, and outputs the upper limit of the cell's real-time safe static voltage. Segmented adaptive parameter matching: The control module divides the cell control range according to the SOH value and adaptively matches the corresponding target static voltage and equalization current threshold. Periodic dynamic calibration and update: The inspection module triggers full parameter recalculation according to a preset period, updates the control threshold, and locks safety parameters and issues an alarm when an anomaly occurs; Soft current limiting closed-loop regulation: The closed-loop control module calculates the voltage deviation in real time and generates a soft current limiting signal through an incremental PID algorithm to smoothly regulate the equalization current; Low-stress equalization execution: The equalization execution module receives current limiting commands, performs cell voltage equalization adjustment, provides real-time feedback of operating data, and completes closed-loop management.

[0041] This embodiment uses an industrial energy storage power station backup lithium-ion battery cell storage cabinet as an application scenario. The storage cabinet contains 200 series-connected lithium-ion battery cells for long-term static storage of emergency backup power for the power station. The ambient temperature fluctuation range is 0℃-45℃, the system inspection cycle is set to 24 hours, and the core calculation cycle is 100ms. The specific implementation process is as follows: During the normal data acquisition phase: The cell parameter acquisition module continuously acquires the individual voltage, leakage current, surface temperature and AC internal resistance of 200 cells for 24 hours. After double filtering, 3 sets of abnormal acquisition data are removed, and the valid parameters are uploaded in real time. State calculation phase: The SOC-SOH coupled calculation module completes the calculation of all cells, identifies 150 cells with SOH≥90% (health adaptation segment), 40 cells with 70%≤SOH<90% (attenuation warning segment), and 10 cells with SOH<70% (aging voltage limit segment), and simultaneously calculates the Ulim of each cell and completes parameter iteration; Partition control phase: The segmented adaptive control module configures standard voltage and rated equalization current for healthy cells, lowers target voltage by 8% and reduces equalization current for slightly aged cells, and limits voltage to 80% Ulim for severely aged cells, enables minimum current and prohibits storage with SOC > 60%. Inspection and calibration phase: Periodic inspection is triggered at midnight every day. The module re-collects all parameters and calibrates the coupling equation coefficients, and updates the control thresholds. Among them, the parameters of 2 battery cells drifted. The system used the old thresholds and triggered a minor alarm. There was no control interruption. Closed-loop balancing phase: When the voltage deviation of 12 cells is detected to be outside the safe range, the voltage over-tolerance soft current limiting module starts incremental PID regulation, linearly limiting the amplitude and rate of change of the balancing current, without any sudden current surge. Final stage of execution: The equalization module completes low-stress voltage equalization, and the voltage deviation of all cells falls back to the safe range. The equalization is automatically stopped, and the cells return to the static storage state. The entire process is free of cell polarization damage and safety risks, and achieves long-term stable storage of batch cells.

[0042] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A smart safety storage cabinet management system for lithium-ion batteries, characterized in that, The system includes: Cell parameter acquisition module: Real-time acquisition of voltage, current, temperature and internal resistance operating parameters of each lithium-ion cell in the storage cabinet; SOC-SOH Coupled Calculation Module: Based on operating parameters, calculates the values ​​of single cell state of charge (SOC) and state of health (SOH) in real time, and constructs the SOC-SOH coupled constraint equations; In the SOC-SOH coupled solution module, the SOC and SOH coupled constraint equations are constructed using the real-time calculated state of charge (SOC) and state of health (SOH) of the battery cell as core variables, combined with the collected battery cell temperature, AC internal resistance, and reference electrochemical parameters. The SOC nonlinearity correction coefficient and SOH attenuation weighting coefficient are introduced based on the standard static reference voltage of the battery cell, and a temperature compensation term is superimposed to achieve multi-physical quantity coupling constraints. The SOC correction coefficient is generated by fitting the battery cell's OCV-SOC characteristic curve and decreases gradually as the SOC value increases. The SOH attenuation coefficient is determined by the ratio of the battery cell's real-time SOH to its factory-rated SOH; the deeper the battery cell's aging, the smaller this coefficient becomes. During the construction process, abnormal data such as instantaneous charging and discharging disturbances and collected noise are removed. A weighted fitting method is used to smooth coefficient fluctuations. The equation parameters are iterated and updated every 100ms. Furthermore, during periodic inspections, the battery cell's reference capacity and initial internal resistance parameters are recalibrated to correct the reference coefficients of the equations, eliminating performance drift errors caused by long-term battery cell storage. Segmented adaptive control module: Divides the cell static storage voltage into three control intervals according to the coupling constraint equation, and adaptively matches the target static voltage and equalization current threshold for different control intervals; Periodic inspection update module: Recalculates the SOC-SOH coupling parameters according to a fixed inspection cycle, and synchronously updates the voltage threshold and equalization current threshold of each control zone; Voltage deviation soft current limiting closed-loop control module: Real-time monitoring of cell voltage deviation, generating soft current limiting control signal; Equalization Execution Module: Receives control signals and performs cell voltage equalization and static voltage regulation actions.

2. The intelligent safety storage cabinet management system for lithium-ion batteries according to claim 1, characterized in that, In the SOC-SOH coupled calculation module, the SOC calculation is based on the ampere-hour integration method, which accumulates the charge and discharge of the battery cell in real time. It combines the rated capacity of the battery cell to calculate the remaining power ratio. During the stable period of the battery cell, the open circuit voltage is collected. The integration error is corrected by comparing it with the standard OCV-SOC curve of the battery cell. At the same time, a temperature compensation factor is introduced to correct the charge measurement deviation under high and low temperature environments in real time.

3. The intelligent safety storage cabinet management system for lithium-ion batteries according to claim 1, characterized in that, In the SOC-SOH coupled calculation module, the SOH calculation adopts the fusion calculation of capacity decay method and internal resistance characterization method. The capacity decay ratio is obtained by comparing the actual usable capacity of the cell with the factory rated capacity. The degree of aging is quantified by the increase of the cell's AC internal resistance compared with the initial internal resistance. After removing abnormal data such as instantaneous fluctuations and acquisition interference, the data is weighted and fitted to output an accurate SOH value.

4. The intelligent safety storage cabinet management system for lithium-ion batteries according to claim 1, characterized in that, In the SOC-SOH coupled calculation module, after completing the independent calculation of SOC and SOH, a coupling constraint relationship between the two is constructed. SOH is used as a weighting coefficient to couple the SOC value with the static voltage limit. A full parameter iterative calculation is completed every 100ms, and the coupled calculation results are updated in real time. At the same time, the cell reference parameters are recalibrated in response to the periodic inspection command to eliminate the performance drift and calculation error caused by long-term storage.

5. The intelligent safety storage cabinet management system for lithium-ion batteries according to claim 1, characterized in that, In the SOC-SOH coupled solution module, the expression for the SOC and SOH coupled constraint equations is as follows: ,in, It is the upper limit voltage for real-time safe static placement of the battery cell, which is directly used as the basis for dividing and regulating the three control zones; This refers to the factory-standard static reference voltage for the battery cell, and the nominal safe storage voltage provided. It is a nonlinear correction coefficient for the state of charge, which is obtained by fitting the cell OCV-SOC characteristic curve. It gradually decreases as the SOC increases, and is used to suppress the risk of side reactions caused by excessively high voltage threshold under high SOC. It is the health status degradation weighting coefficient, which is the ratio of real-time SOH to rated SOH. The higher the degree of cell aging, the smaller this coefficient is, which simultaneously lowers the upper limit of the safe voltage of the aging cell. It is the temperature compensation coefficient, calculated based on the real-time surface temperature T of the battery cell, and is used to compensate for the influence of high and low temperature environments on the voltage characteristics of the battery cell.

6. The intelligent safety storage cabinet management system for lithium-ion batteries according to claim 5, characterized in that, The segmented adaptive control module establishes real-time communication with the SOC-SOH coupling calculation module via the CAN bus, and continuously receives the transmitted real-time calculation values ​​of single-cell SOC and SOH, as well as the voltage constraint upper limit calculated by the coupling constraint equation. The division process follows coupled constraint logic, combining the cell's factory electrochemical parameters with actual storage test data to clarify the division criteria for the three control intervals: the healthy adaptation segment corresponds to SOH ≥ 90%, at which point the cell performance is good, and the output of the coupled constraint equation is... Based on the standard target resting voltage and rated equalization current; The degradation warning stage corresponds to 70% ≤ SOH < 90%, indicating slight aging of the battery cell. Lower the target resting voltage by 5%-10%, and simultaneously reduce the equalization current amplitude to minimize cell polarization damage; the aging voltage limit section corresponds to SOH < 70%, indicating severe cell aging, requiring forced adjustment. Set the target resting voltage to 80%, enable minimum equalization current, and prohibit the storage of high-charge states with SOC > 60%.

7. The intelligent safety storage cabinet management system for lithium-ion batteries according to claim 1, characterized in that, The periodic inspection and update module triggers a full parameter recalculation process according to a preset fixed inspection cycle. When the inspection is triggered, it first synchronously retrieves the latest data on single cell voltage, charging and discharging current, surface temperature and AC internal resistance uploaded by the cell parameter acquisition module. Invalid data is removed through data validity verification, and the filtered valid parameters are transmitted to the SOC-SOH coupled calculation module to recalculate the current SOC and SOH values ​​of a single cell and the SOC nonlinearity correction coefficient, SOH attenuation weight coefficient and temperature compensation coefficient in the coupled constraint equation, thus completing the full parameter iterative update of the coupled constraint equation. After the update is completed, the reference static voltage thresholds and corresponding equalization current thresholds for the three control intervals—health adaptation segment, attenuation warning segment, and aging voltage limiting segment—are recalculated based on the new coupling constraint equations and the cell's factory reference parameters. At the same time, the updated threshold parameters for each interval are synchronously stored in the local parameter library and sent to the segmented adaptive control module. If the coupling parameter calculation is abnormal or the updated threshold exceeds the cell's safe operating limit, the previously stored valid threshold will be automatically used, and an abnormal alarm signal will be triggered and fed back to the main control unit, realizing dynamic adaptation of control parameters throughout the entire storage cycle.

8. The intelligent safety storage cabinet management system for lithium-ion batteries according to claim 1, characterized in that, The voltage deviation soft current limiting closed-loop control module acquires the target static voltage threshold issued by the segmented adaptive control module in real time, and simultaneously collects the actual operating voltage and equalization output current data of the battery cell returned by the equalization execution module. It calculates the battery cell voltage deviation value by real-time differential calculation. When the deviation value exceeds the preset safety range, it generates a soft current limiting control signal based on the incremental PID adjustment algorithm. The amplitude of the equalization output current is linearly adjusted according to the magnitude of the voltage deviation to avoid large current step output. At the same time, it limits the rate of change of the equalization current to prevent the equalization action from being too aggressive and causing polarization damage to the battery cell. As the voltage deviation gradually falls back to the safe range, the control signal synchronously and smoothly adjusts the current output until the deviation is eliminated and the rated equalization parameters are restored.

9. A lithium-ion battery intelligent safety storage cabinet management system according to claim 8, characterized in that, The voltage deviation soft current limiting closed-loop control module generates a soft current limiting control signal based on an incremental PID regulation algorithm. The algorithm formula is as follows: ,in, This represents the incremental adjustment of the equalization current within the current control cycle, i.e., the change in current amplitude output from the soft current limiting module to the equalization execution module, used to smoothly adjust the equalization output magnitude; k is the current control cycle number, representing the timing node for PID calculation. This is the voltage deviation value for the current cycle. , These are the voltage deviation values ​​for the previous control cycle and the two previous control cycles, respectively, used to reflect the trend of deviation changes; It is a proportional coefficient that quickly adjusts the equalizing current increment based on the magnitude of the voltage deviation. These are integral coefficients used to eliminate steady-state error; It is a differential coefficient that makes advance adjustments based on the rate of change of voltage deviation, suppresses sudden changes in the equilibrium current, and ensures a stable rate of change of current.

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