Voltage threshold optimization method for energy storage device

Abstract

The invention discloses an energy storage device voltage threshold optimization method, and belongs to the technical field of energy storage device management. The method solves the technical problems that the service life is shortened, the performance is reduced and the safety risk is increased due to the fact that a fixed voltage threshold value cannot adapt to diversified aging modes in the charging and discharging process of an existing energy storage device. The method comprises the following steps: querying a pre-established aging mode mapping table according to the current SOH, SOC and temperature, and judging a dominant aging mode; corresponding adjustment strategies are executed for different aging modes to adjust voltage prefabrication; and controlling the charging and discharging process by using the adjusted voltage threshold. The method is mainly used for dynamically optimizing the charging and discharging strategy of the energy storage device, delaying the aging process, prolonging the service life, and improving the safety and reliability.

Description

Technical Field

[0001] This invention relates to the field of energy storage device management technology. More specifically, this invention relates to a method for optimizing the voltage threshold of an energy storage device. Background Technology

[0002] Energy storage devices, especially lithium-ion batteries, are typically managed and controlled through preset voltage thresholds during charging and discharging. These voltage thresholds are crucial for preventing overcharging and over-discharging, ensuring safe and stable operation. Currently, most energy storage systems employ fixed charging and discharging cutoff voltages. This fixed-threshold management method is based on the battery's characteristics under ideal or standard operating conditions, aiming to balance energy output with basic safety requirements.

[0003] However, the aging mechanism of energy storage devices is not static during actual operation. Battery aging is a complex electrochemical process, influenced by various factors such as charge / discharge rate, ambient temperature, and state-of-charge (SEI) cycle range. Under different external operating conditions and internal states, the dominant aging mode changes. Common aging modes include lithium plating on the negative electrode, loss of active material on the positive electrode, and growth of the solid electrolyte interphase (SEI) film. Each aging mode has a different path and degree of impact on battery life and performance degradation. For example, charging under low-temperature, high-SOC conditions easily triggers lithium plating on the negative electrode, accelerating capacity decay and potentially posing safety risks; while loss of active material on the positive electrode may be related to stress and side reactions under high voltage; and SEI film growth is typically more significant under high-temperature conditions.

[0004] Existing fixed voltage threshold management methods have certain shortcomings. The main problem lies in the lack of ability to identify and respond to the dominant aging modes. Because the set voltage threshold is static, it cannot be dynamically adjusted according to the actual health status, operating conditions, and the dominant aging process currently occurring in the battery. When the battery is under conditions that easily trigger specific aging modes (such as lithium plating), the fixed charging voltage threshold may be too high, thus accelerating this aging process; conversely, when the aging mode changes, the fixed threshold may limit the battery's energy throughput, failing to fully utilize its potential performance. This rigid management strategy makes it difficult to proactively intervene in and mitigate the battery aging process, potentially leading to suboptimal battery life and even posing safety hazards under certain operating conditions.

[0005] The primary cause of this problem lies in the complexity and multi-factor coupling of the battery aging process. The difficulty in achieving dynamic threshold optimization lies in accurately and in real-time determining which aging mode is currently dominant. This requires a deep understanding of the battery's internal state. However, directly monitoring the specific aging responses within the battery is extremely difficult. While externally observable parameters such as state of health and state of charge can indirectly reflect the battery's state, establishing a reliable and effective mapping between these parameters and specific aging modes, and making precise threshold adjustment decisions accordingly, is a challenge. Past technologies have often struggled to overcome this bottleneck: the lack of an effective mechanism to reliably infer the dominant aging mode based on measurable parameters and finely adjust the voltage threshold accordingly. Therefore, there is room for optimization in the management strategies of energy storage devices to adapt to complex and variable aging behaviors. Summary of the Invention

[0006] One object of the present invention is to solve at least the above-mentioned problems and to provide at least the advantages that will be described later.

[0007] Another objective of this invention is to provide a method for optimizing the voltage threshold of energy storage devices. This method addresses the problem that existing energy storage device management methods, which use fixed voltage thresholds, cannot adapt to the dynamic changes in the dominant aging modes caused by variations in operating conditions (such as different SOCs and temperatures) during actual operation. Fixed thresholds exacerbate specific aging modes (such as lithium plating, cathode material loss, and SEI film growth), failing to achieve targeted lifespan extension and safety assurance.

[0008] To achieve these objectives and other advantages of the present invention, a method for optimizing the voltage threshold of an energy storage device is provided, comprising the following steps: S1. Real-time acquisition of operating data of the energy storage device, including voltage, current and temperature; based on the operating data, calculate the current state of health (SOH) and current state of charge (SOC) of the energy storage device; S2. Based on the current SOH, SOC and temperature, query the pre-established aging mode mapping table to determine the dominant aging mode under the current operating conditions of the energy storage device; the dominant aging modes include negative electrode lithium plating dominant aging, positive electrode active material loss dominant aging, and solid electrolyte interface film growth dominant aging. S3. If the aging is determined to be dominated by lithium plating on the negative electrode, the first adjustment strategy is executed: when the SOC is higher than the first preset value and the temperature is lower than the second preset value, the charging voltage threshold is reduced by a first magnitude, while the discharge voltage threshold remains unchanged; if the aging is determined to be dominated by loss of positive electrode active material, the second adjustment strategy is executed: when the SOC is in the first preset SOC range, the charging voltage threshold is reduced by a second magnitude, while when the SOC is in the second preset SOC range, the discharge voltage threshold is increased by a third magnitude; if the aging is determined to be dominated by solid electrolyte interface film growth, the third adjustment strategy is executed: when the temperature is higher than the third preset value, the charging voltage threshold is reduced by a fourth magnitude, and the discharge voltage threshold is increased by a fourth magnitude; wherein, the first preset value, the second preset value, the third preset value, the first preset SOC range, and the second preset SOC range are thresholds or ranges preset based on the data distribution characteristics in the aging mode mapping table. S4. Use the adjusted charging voltage threshold and discharging voltage threshold to control the charging and discharging process of the energy storage device.

[0009] Preferably, in the energy storage device voltage threshold optimization method, the method for constructing and updating the pre-established aging mode mapping table in step S2 is as follows: Under laboratory conditions, accelerated aging experiments were conducted on the same type of energy storage device to obtain aging data under different combinations of SOH, SOC, temperature and current rate. The dominant aging mode was determined by disassembly and verification, and a basic mapping table was formed. In actual operation, the real-time operating data of the energy storage device is continuously recorded, and the determination result of the dominant aging mode is obtained based on the basic mapping table and the real-time operating data; the health status of the energy storage device is calibrated periodically by electrochemical impedance spectroscopy or incremental capacity analysis technology, and the calibration results are obtained. The calibration results are compared with the judgment results. When the deviation between the judgment results and the calibration results continues to exceed the preset tolerance for multiple calibration cycles, the basic mapping table is corrected using the calibration data.

[0010] Preferably, in the energy storage device voltage threshold optimization method, step S3, determining the first amplitude, second amplitude, third amplitude, and fourth amplitude includes the following steps: a) Set a base adjustment value for the first amplitude, the second amplitude, the third amplitude and the fourth amplitude respectively. The base adjustment value is a fixed voltage value and the range is 1mV to 10mV. b) Real-time acquisition of the charging and discharging current I of the energy storage device, and according to formula C rate =I / C n Calculate the current rate C rate C n This refers to the rated capacity of the energy storage device; c) The absolute value of the current current rate |Crate | As input, a predefined gain coefficient mapping table is consulted to obtain the corresponding gain coefficient K; where the gain coefficient mapping table specifies that when |C rate When |C ≤ 0.5, K = 1.0; when 0.5 < |C rate When |C| ≤ 1.0, K = 1.2; when 1.0 < |C| rate When |C ≤ 1.5, K = 1.5; when |C rate When |>1.5, K=2.0; d) Multiply the base adjustment value determined in step a) by the gain coefficient K determined in step c), and use the product as the adjustment range corresponding to the current adjustment strategy to be executed.

[0011] Preferably, the energy storage device voltage threshold optimization method further includes: S5. After each voltage threshold adjustment, monitor the internal resistance growth rate or capacity decay rate of the energy storage device as an aging rate feedback indicator within a complete charge-discharge cycle or a preset time period. If the aging rate feedback index decreases compared to before the adjustment, the current adjustment strategy is deemed effective, and the current dominant aging mode determination result and the adjustment range obtained in step d) are maintained. If the aging rate feedback index does not decrease or even increases, return to step S2 to re-determine the dominant aging mode: If the re-determined dominant aging mode is the same as the mode determined before this adjustment, when executing step S3 again, reduce the corresponding basic adjustment value by a preset step voltage value, which is 10% to 50% of the corresponding basic adjustment value, and then calculate the new adjustment range according to steps b) to d); If the re-determined dominant aging mode changes, execute step S3 based on the new dominant aging mode, and use the basic adjustment value set for the new mode, and then calculate the new adjustment range according to steps b) to d).

[0012] Preferably, in the energy storage device voltage threshold optimization method, step S5 also records each adjustment strategy, adjustment range, and corresponding aging rate feedback index change to form a historical optimization record. Regularly conduct statistical analysis of historical optimization records to establish a model of the correlation between the adjustment magnitude and the improvement effect of aging rate; When the improvement effect of the corresponding aging rate feedback index approaches zero or fluctuates within a small positive and negative range for N consecutive times after the base adjustment value is reduced multiple times for the same dominant aging mode, it is determined that the optimization for the current dominant aging mode has reached a local optimum. The current voltage threshold combination is locked, and the time interval for the next execution of step S2 for re-determination is extended to M times the original interval; where N≥3 and M>1.

[0013] Preferably, in the third adjustment strategy of the energy storage device voltage threshold optimization method, the execution of reducing the charging voltage threshold by a fourth magnitude and increasing the discharging voltage threshold by a fourth magnitude is specifically as follows: when the temperature is higher than a third preset value and continues for a first preset duration, the operation of reducing the charging voltage threshold is executed first; subsequently, if the absolute value of the average charging and discharging current is detected to be lower than the preset current threshold within a second preset duration, the operation of increasing the discharging voltage threshold is executed.

[0014] Preferably, in the energy storage device voltage threshold optimization method, in step S3, executing the first adjustment strategy, the second adjustment strategy, or the third adjustment strategy also requires satisfying a common additional condition: the current rate C calculated in real time. rate The absolute value of the voltage threshold is lower than the safe current rate threshold corresponding to each aging mode; if the absolute value of the current rate exceeds the safe current rate threshold, the voltage threshold adjustment is paused and the original voltage threshold is maintained.

[0015] Preferably, in the energy storage device voltage threshold optimization method, step S5 includes a safety verification step before reducing the corresponding basic adjustment value by a preset step voltage value: calculating the reduced basic adjustment value and confirming that it is not lower than the minimum adjustment voltage threshold set for the dominant aging mode; if it is lower than the minimum adjustment voltage threshold, it will not be reduced further, and it will be directly determined that the optimization for the current dominant aging mode has reached a local optimum.

[0016] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0017] The present invention has at least the following beneficial effects: 1. This invention achieves precise intervention in the aging process of energy storage devices by real-time determination of the dominant aging mode and execution of corresponding dynamic voltage threshold adjustment strategies. Compared to fixed threshold methods, it can effectively delay the most significant aging mechanisms, such as suppressing lithium plating on the negative electrode by reducing the charging voltage or mitigating cathode material loss by adjusting the SOC operating range, thereby significantly extending the device's lifespan while ensuring safety. This mode-based differentiated control enhances the adaptability and reliability of energy storage systems under different operating conditions.

[0018] 2. This invention effectively overcomes the impact of initial battery differences and long-term performance drift on the accuracy of aging mode determination by combining a laboratory-based mapping table with calibration results from actual operation through a dynamic update mechanism. The online calibration capability ensures that the mapping table can track the actual aging characteristics of the battery over a long period of time, so that the determination results of the dominant aging mode always maintain high reliability, providing an accurate data foundation for subsequent voltage threshold optimization and improving the robustness of the entire method for long-term application.

[0019] 3. This invention introduces a gain coefficient based on the current current rate to dynamically calculate the adjustment range, ensuring that the optimization of the voltage threshold matches the severity of actual operating conditions. Increasing the adjustment range at high current rates can more effectively suppress rapid aging; using a smaller range at low current rates avoids unnecessary limitations on energy throughput due to over-adjustment. This adaptive adjustment mechanism makes the optimization strategy more refined and efficient.

[0020] 4. By introducing closed-loop control based on aging rate feedback, this invention constructs a self-learning optimization loop of "judgment-execution-verification-correction", which can directly verify the effectiveness of the adjustment strategy. If the effect is not good, it can automatically trigger re-judgment or fine-tuning of the adjustment strategy, avoiding performance degradation or risks caused by single misjudgment or improper adjustment, greatly improving the safety and reliability of the optimization process, and enabling the system to have self-optimization and fault tolerance capabilities.

[0021] 5. This invention, through historical data analysis and local optimum identification, can intelligently determine when the optimization effect has reached saturation. Once a local optimum is identified, the parameters are locked and the judgment interval is extended, avoiding unnecessary calculations and adjustments when the marginal benefit of optimization is extremely low. This saves the computing resources of the BMS, reduces system instability that may be caused by frequent parameter changes, and enables the system to run in a stable and efficient state.

[0022] 6. This invention implements the adjustment of charging and discharging thresholds in SEI film growth mode in stages, and adds current condition judgment to achieve a smooth transition in the adjustment process. The charging threshold is processed first to cope with the impact of high temperature, and the discharging threshold is adjusted when the system load is light. This effectively avoids the instantaneous impact on system energy management that may be caused by changing two thresholds at the same time under high temperature and high power conditions, and improves the stability of control and system stability.

[0023] 7. By setting a safe current rate threshold as an additional condition, this invention ensures that voltage threshold adjustment is only performed under safe operating conditions where the current is relatively stable. This effectively prevents control loop oscillations, sudden changes in electrical stress, or safety hazards that may be caused by adjusting the voltage threshold during dynamic high-current transients, enhancing the applicability and safety of the method in actual complex dynamic operating environments.

[0024] 8. This invention sets an effective lower limit for reducing the adjustment range by setting a minimum adjustment voltage threshold and performing safety verification. This prevents the adjustment range from being too small to lose its optimization significance, or from introducing additional losses due to frequent small voltage fluctuations. It ensures that each adjustment action has a clear physical meaning and practical effect, maintaining the rigor and effectiveness of the optimization process.

[0025] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0026] The present invention will be further described in detail below with reference to embodiments, so that those skilled in the art can implement it based on the description.

[0027] It should be understood that terms such as “having,” “comprising,” and “including” as used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0028] It should be noted that, unless otherwise specified, the experimental methods described in the following implementation plan are all conventional methods, and the reagents and materials described are all commercially available unless otherwise specified.

[0029] In the description of this invention, the terms "lateral", "longitudinal", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship, and are only for the convenience of describing this invention and simplifying the description. They do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0030] This invention provides a method for optimizing the voltage threshold of an energy storage device, comprising the following steps: S1. Real-time acquisition of operating data of the energy storage device, including voltage, current and temperature; based on the operating data, calculate the current state of health (SOH) and current state of charge (SOC) of the energy storage device; SOH (State of Health): usually defined as the percentage of the current maximum available capacity to the rated capacity; SOC (State of Charge): usually defined as the percentage of the remaining energy to the current maximum available capacity; S2. Based on the current SOH, SOC and temperature, query the pre-established aging mode mapping table to determine the dominant aging mode under the current operating conditions of the energy storage device; the dominant aging modes include negative electrode lithium plating dominant aging, positive electrode active material loss dominant aging, and solid electrolyte interface film growth dominant aging. S3. If the aging is determined to be dominated by lithium plating on the negative electrode, the first adjustment strategy is executed: when the SOC is higher than the first preset value and the temperature is lower than the second preset value, the charging voltage threshold is reduced by a first magnitude, while the discharge voltage threshold remains unchanged; if the aging is determined to be dominated by loss of positive electrode active material, the second adjustment strategy is executed: when the SOC is in the first preset SOC range, the charging voltage threshold is reduced by a second magnitude, while when the SOC is in the second preset SOC range, the discharge voltage threshold is increased by a third magnitude; if the aging is determined to be dominated by solid electrolyte interface film growth, the third adjustment strategy is executed: when the temperature is higher than the third preset value, the charging voltage threshold is reduced by a fourth magnitude, and the discharge voltage threshold is increased by a fourth magnitude; wherein, the first preset value, the second preset value, the third preset value, the first preset SOC range, and the second preset SOC range are thresholds or ranges preset based on the data distribution characteristics in the aging mode mapping table. S4. Use the adjusted charging voltage threshold and discharging voltage threshold to control the charging and discharging process of the energy storage device.

[0031] This technical solution relates to the field of energy storage device management technology, specifically addressing a method for optimizing the voltage threshold of energy storage devices. In existing technologies, energy storage devices are typically managed using fixed charging and discharging voltage thresholds. This method, based on ideal conditions or standard operating conditions, cannot adapt to the dynamic changes in dominant aging modes caused by variations in operating conditions during actual operation. Fixed thresholds lack flexibility during battery aging. When the battery is under conditions that easily trigger specific aging modes (such as lithium plating on the negative electrode, loss of active material on the positive electrode, or growth of the solid electrolyte interfacial film), a fixed voltage threshold may exacerbate the aging process, leading to shortened battery life, performance degradation, and increased safety risks. The specific technical challenge addressed by this solution is how to enable the voltage threshold to dynamically adapt to changes in the actual aging modes of the battery, achieving targeted intervention for different aging modes, thereby extending battery life and improving safety.

[0032] In the above technical solution, the State of Health (SOH) of the energy storage device refers to the ratio of the current capacity to the initial capacity of the battery, reflecting the degree of battery aging; the State of Charge (SOC) refers to the ratio of the remaining battery charge to the total capacity, representing the battery's charging level; the dominant aging mode refers to the mechanism that plays a major role in battery aging under certain operating conditions, including lithium plating-dominated aging of the negative electrode, loss of active material of the positive electrode, and growth of solid electrolyte interfacial film-dominated aging. These modes correspond to different internal electrochemical reaction paths; the voltage threshold refers to the upper and lower limits of the voltage set during charging and discharging, used to control the charging and discharging behavior of the battery; the aging mode mapping table is a lookup table based on experimental data that associates SOH, SOC, temperature, and aging mode, used to determine the current dominant aging mode in real time.

[0033] In the above technical solution, the battery management system collects real-time operating data of the energy storage device, including voltage, current, and temperature. The system uses this data to calculate the current State of Health (SOH) and State of Charge (SOC). Then, the system uses the current SOH, SOC, and temperature values ​​as input conditions to query a pre-stored aging mode mapping table. This mapping table is a database built in a laboratory environment through extensive accelerated aging tests on the same type of battery, systematically changing parameters such as SOH, SOC, temperature, and current rate. After the tests, the batteries are disassembled and analyzed to determine their dominant aging mode. The query process matches the currently measured parameters with the parameter ranges in the database, thereby outputting the most likely dominant aging mode determination result. After obtaining the dominant aging mode determination result, the system enters the core voltage threshold adjustment step S3. If the aging is determined to be dominated by negative electrode lithium plating, the system checks whether the current SOC is higher than a preset critical value and whether the temperature is lower than another preset critical value. The system will only activate the first adjustment strategy when both conditions are met simultaneously. Specifically, this involves lowering the upper limit of the charging voltage threshold by a calculated margin, while keeping the discharging voltage threshold unchanged. This adjustment aims to prevent excessively high charging voltages under high SOC and low-temperature conditions, thereby suppressing lithium plating at the negative electrode. If aging is determined to be primarily caused by loss of positive electrode active material, the system will execute the second adjustment strategy. This strategy is more refined, making differentiated adjustments based on different SOC ranges. When the SOC is in a preset high range, the system lowers the charging voltage threshold to reduce the stress of high voltage on the positive electrode material; when the SOC is in a preset low range, the system appropriately raises the discharging voltage threshold to prevent excessive battery discharge from exacerbating positive electrode material loss. If aging is determined to be primarily caused by solid electrolyte interface film growth, the system will monitor whether the temperature remains consistently above a preset safe temperature value. When high-temperature conditions are met, the system executes the third adjustment strategy, simultaneously adjusting the charging and discharging voltage thresholds by a fourth margin, lowering the charging voltage threshold and raising the discharging voltage threshold, aiming to mitigate the negative impact of high temperatures on electrolyte interface stability. All of the above adjustment margins are not fixed values ​​but are dynamically calculated. The system acquires the charging and discharging current in real time, calculates the current rate, and then looks up the corresponding coefficient K according to a preset gain coefficient mapping table. This coefficient reflects the severity of the current operating conditions. The final adjustment range is equal to the product of the preset base adjustment value for this aging mode and the gain coefficient K. This allows for a greater adjustment force under harsh operating conditions such as high-rate charging and discharging, so as to more effectively suppress rapid aging.

[0034] The above technical solution effectively addresses the diversity of battery aging modes through dynamic voltage threshold adjustment. Compared to fixed threshold methods, this method can precisely suppress the dominant aging mechanism, such as reducing the charging voltage under lithium-electrode deposition conditions to avoid safety risks associated with lithium deposition, or adjusting the threshold under high-temperature conditions to mitigate interfacial film growth, thereby significantly extending battery life. Simultaneously, the optimization loop based on real-time data and feedback enhances the system's adaptability, avoiding performance fluctuations caused by misjudgments or changes in operating conditions, and improving the safety and reliability of the energy storage device. The entire method indirectly controls internal aging modes through measurable external parameters, eliminating the need for complex internal sensors, reducing implementation costs, and increasing practical value. The detailed design and execution of step S3 ensures the timeliness and accuracy of intervention measures, enabling the optimization strategy to closely align with the actual state changes of the battery, achieving a leap from passive protection to active life extension.

[0035] In another technical solution, the method for constructing and updating the pre-established aging mode mapping table in step S2 of the energy storage device voltage threshold optimization method is as follows: Under laboratory conditions, accelerated aging experiments were conducted on the same type of energy storage device to obtain aging data under different combinations of SOH, SOC, temperature and current rate. The dominant aging mode was determined by disassembly and verification, and a basic mapping table was formed. In actual operation, the real-time operating data of the energy storage device is continuously recorded, and the determination result of the dominant aging mode is obtained based on the basic mapping table and the real-time operating data; the health status of the energy storage device is calibrated periodically by electrochemical impedance spectroscopy or incremental capacity analysis technology, and the calibration results are obtained. The calibration results are compared with the judgment results. When the deviation between the judgment results and the calibration results continues to exceed the preset tolerance (e.g., mode judgment error or SOH deviation exceeds 5%) for multiple calibration cycles (e.g., three consecutive calibration cycles), the basic mapping table is corrected using the calibration data.

[0036] The specific technical challenge of this solution is: how to establish and maintain a mapping table that accurately reflects the dominant aging modes of energy storage devices under actual complex operating environments. The mapping table initially built based on laboratory data may gradually become inaccurate due to individual battery differences, long-term performance drift, and deviations between actual operating conditions and laboratory conditions, leading to a decrease in the reliability of aging mode determination.

[0037] In existing technologies, aging mode mapping relationships typically rely on initial laboratory models and lack mechanisms for continuous verification and updating during actual operation. Such static mapping tables struggle to adapt to the slow performance changes of batteries throughout their lifespan, and the accuracy of their determinations cannot be guaranteed over time.

[0038] In the above technical solution, the basic mapping table refers to an initial database established under controlled laboratory conditions through systematic accelerated aging experiments and post-disassembly analysis. This database reflects the correspondence between a specific type of energy storage device and the dominant aging mode under different combinations of health states, states of charge, temperature, and current rate parameters. Real-time operating data refers to the time-series data such as voltage, current, and temperature continuously collected by the battery management system during actual use of the energy storage device. Calibration refers to the process of relatively accurately measuring and evaluating the actual health state and aging mechanism inside the energy storage device using specific diagnostic techniques, such as electrochemical impedance spectroscopy or incremental capacity analysis. The results serve as a reference benchmark to verify the accuracy of the mapping table's judgment. The preset tolerance refers to the maximum allowable deviation between the mapping table's judgment result and the calibration result; exceeding this limit indicates that the judgment is unreliable.

[0039] The aforementioned technical solution provides a complete mapping table construction and dynamic update mechanism, with the mapping table construction beginning in the laboratory phase. In the laboratory, accelerated aging experiments are conducted on a batch of energy storage device samples of the same model as the target application. The experimental design covers a wide range of operating conditions, including different initial health states, state-of-charge cycle ranges, ambient temperature gradients, and charge / discharge current rates. By continuously running and monitoring the aging trajectory of the samples, and interrupting the experiment at predetermined aging nodes, the samples are disassembled, and physical and chemical analysis methods are used to identify their internal dominant aging modes. For example, lithium plating on the electrode surface is observed using scanning electron microscopy, or the loss of positive electrode active material is detected through component analysis. All these conditional parameters are mapped one-to-one with the finally determined dominant aging mode, thus forming the basic mapping table for initial judgment. After deployment in actual applications, the system enters a continuous learning and updating phase. The battery management system continuously records the real-time operating data of the energy storage device. When aging mode judgment is required, the system inputs the current health state, state of charge, and temperature values ​​into the basic mapping table for querying, obtaining a preliminary dominant aging mode judgment result. Simultaneously, the system periodically initiates a calibration process. This process is not continuous, but rather triggered at specific time intervals or when significant changes in certain key parameters are detected. During the calibration process, the system controls the energy storage device to execute a specific diagnostic charge-discharge sequence and collects its voltage and current response data. This data is then processed using electrochemical impedance spectroscopy or incremental capacity analysis to derive calibration results regarding the current internal state and main aging mechanisms. These calibration results are considered a reference value that more closely approximates the actual situation under the current state. The system compares the judgment result from the mapping table with the calibration result. If the deviation exceeds a preset tolerance range, the system records a mismatch event. Update decisions are not based on a single mismatch but introduce continuous judgment logic. Only when the deviation between the judgment result and the calibration result exceeds the tolerance for multiple consecutive calibration cycles, indicating that the deviation is not accidental but systematic, does the system determine that the basic mapping table no longer accurately reflects the actual aging characteristics of the energy storage device. At this point, the system initiates a calibration procedure, using recently obtained reliable calibration data to correct or weight and update the data points in the corresponding areas of the basic mapping table, making the mapping relationship more closely match the actual behavior of the individual battery. In this way, the mapping table evolves from a static initial model into a dynamic knowledge base capable of tracking the aging characteristics of a specific battery.

[0040] The aforementioned scheme, through a dynamic update mechanism combining laboratory baseline data with calibration results from actual operation, ensures the long-term accuracy of the aging mode determination criteria, effectively overcoming the impact of differences in battery production batches, initial performance dispersion, and characteristic drift during long-term operation on the determination accuracy. This online self-calibration capability enables the system to adapt to changes in the battery throughout its entire lifespan, avoiding misjudgments caused by outdated mapping tables. This lays a solid and reliable foundation for subsequent precise adjustment of voltage thresholds, significantly improving the robustness and durability of the entire optimization method in practical applications.

[0041] In another technical solution, the method for optimizing the voltage threshold of the energy storage device, step S3, involves determining the first amplitude, the second amplitude, the third amplitude, and the fourth amplitude, which includes the following steps: a) Set a base adjustment value for the first amplitude, the second amplitude, the third amplitude and the fourth amplitude respectively. The base adjustment value is a fixed voltage value and the range is 1mV to 10mV (this range is determined based on the typical BMS voltage control accuracy and the minimum voltage change required for effective intervention). b) Real-time acquisition of the charging and discharging current I of the energy storage device, and according to formula C rate =I / C n Calculate the current rate C rate C n This refers to the rated capacity of the energy storage device; c) The absolute value of the current current rate |C rate | As input, a predefined gain coefficient mapping table is consulted to obtain the corresponding gain coefficient K; where the gain coefficient mapping table specifies that when |C rate When |C ≤ 0.5, K = 1.0; when 0.5 < |C rate When |C| ≤ 1.0, K = 1.2; when 1.0 < |C| rate When |C ≤ 1.5, K = 1.5; when |C rate When |>1.5, K=2.0 (this mapping relationship is empirically set based on the accelerating effect of increased current rate on aging rate, and is configurable); C rate (Current rate): The ratio of charging / discharging current to rated capacity; d) Multiply the base adjustment value determined in step a) by the gain coefficient K determined in step c), and use the product as the adjustment range corresponding to the current adjustment strategy to be executed.

[0042] The specific technical challenge addressed by this solution is determining the precise adjustment range of the voltage threshold so that the adjustment effectively suppresses aging without affecting the normal performance of the energy storage device due to excessive adjustment. In existing technologies, the adjustment range is often fixed, which cannot adapt to the varying aging rates caused by dynamic changes in current rate during actual operation.

[0043] Existing technologies typically set a fixed value for adjusting the voltage threshold. This simplistic approach ignores the significant impact of charge / discharge current rates on the aging process. Under high current rate conditions, the battery aging reaction is more severe, and a fixed, small adjustment may not be sufficient to produce an effective suppression effect; while under low current rate conditions, excessively large adjustments may unnecessarily limit the battery's energy throughput.

[0044] In the above technical solution, the base adjustment value is a preset base voltage adjustment amount to cope with specific aging modes, representing the recommended adjustment level under standard or medium load conditions. The current rate is a ratio of the charging / discharging current to the battery's rated capacity, reflecting the intensity of charging and discharging. The gain coefficient is a multiplier determined based on the current actual current rate, used to scale the base adjustment value, aiming to match the final adjustment magnitude with the severity of the current operating conditions. The gain coefficient mapping table is a predefined lookup table that specifies the gain coefficient values ​​corresponding to different current rate ranges.

[0045] The above technical solution provides a refined method for dynamically calculating the adjustment range. First, for the first, second, and third adjustment strategies, a common or independent base adjustment value is set for their respective required first, second, third, and fourth ranges. These base adjustment values ​​are fixed small voltage values, typically in the millivolt range, determined based on electrochemical knowledge and experimental data, serving as the reference quantity for adjustment. Subsequently, the system enters the dynamic calculation phase. The battery management system collects the instantaneous current value flowing through the energy storage device in real time and, combined with the rated capacity of the energy storage device, calculates the current rate. This current rate is a dimensionless value that directly reflects the intensity of charging and discharging. Then, the system uses the absolute value of the calculated current rate as input to query a predefined gain coefficient mapping table stored in the system. This mapping table divides the continuous current rate range into several key intervals and assigns a specific gain coefficient to each interval. For example, when the current rate is very low, the gain coefficient is 1, meaning a standard base adjustment value is used. As the current rate increases to a medium level, the gain coefficient moderately increases to 1.2 or 1.5. When the current rate is very high, the gain coefficient can increase to 2.0, indicating that double the control force is needed. After finding the corresponding gain coefficient K, the system performs a multiplication operation, multiplying the base adjustment value determined in the previous steps by the newly found gain coefficient K. The resulting product is the actual adjustment magnitude that should be performed. This final magnitude is a voltage value, which will be directly used for subsequent modification operations of the charging or discharging voltage threshold.

[0046] The aforementioned technical solution achieves adaptive and refined control of the adjustment intensity based on a dynamic amplitude calculation mechanism of the current rate. This elevates voltage threshold optimization from a static, one-size-fits-all strategy to a dynamic strategy correlated with real-time operating load. Under harsh conditions such as high-rate charging and discharging, the system automatically applies a greater adjustment intensity, thereby more effectively curbing rapid aging reactions and enhancing the timeliness and effectiveness of optimization measures. Under light load conditions, the system employs a relatively gentle adjustment amplitude, avoiding unnecessary restrictions on the normal energy exchange capacity of the energy storage device and maintaining system efficiency. This method ensures that the intensity of optimization intervention is proportional to the actual aging risk, significantly improving the intelligence and economy of the entire voltage threshold optimization process, ensuring that battery life is extended while maintaining its availability.

[0047] In another technical solution, the energy storage device voltage threshold optimization method further includes: S5. Within a complete charge-discharge cycle or a preset time period (e.g., 24 hours) after each voltage threshold adjustment, monitor the internal resistance growth rate or capacity decay rate of the energy storage device as an aging rate feedback indicator; Complete charge-discharge cycle: refers to the charge-discharge process that takes until the cumulative charge-discharge amount reaches 100% of the rated capacity; Internal resistance growth rate: the increase in the internal resistance of the battery per unit time, which can be periodically measured by electrochemical impedance spectroscopy (EIS) or DC internal resistance method (DCR); Capacity decay rate: the decrease in the usable capacity of the battery per unit time, calculated by ampere-hour integration method combined with periodic full-capacity calibration; If the aging rate feedback index decreases compared to before the adjustment, the current adjustment strategy is deemed effective, and the current dominant aging mode determination result and the adjustment range obtained in step d) are maintained. If the aging rate feedback index does not decrease or even increases, return to step S2 to re-determine the dominant aging mode: If the re-determined dominant aging mode is the same as the mode determined before this adjustment, when executing step S3 again, reduce the corresponding basic adjustment value by a preset step voltage value, which is 10% to 50% of the corresponding basic adjustment value (for example, 20% of the corresponding basic adjustment value), and then calculate the new adjustment range according to steps b) to d); If the re-determined dominant aging mode changes, execute step S3 based on the new dominant aging mode, and use the basic adjustment value set for the new mode, and then calculate the new adjustment range according to steps b) to d).

[0048] The specific technical challenge addressed by this solution is: how to ensure the actual effectiveness of the voltage threshold adjustment strategy and how to automatically and safely correct it when the strategy is ineffective, avoiding continuous adverse effects on the energy storage device due to a single misjudgment or improper adjustment. In existing technologies, once voltage threshold adjustment is implemented, there is usually a lack of direct verification of the adjustment effect and a feedback-based closed-loop optimization mechanism, which may cause the system to operate in a suboptimal or even accelerated aging state for a long time.

[0049] Most existing technologies employ open-loop control, meaning that the strategy is continuously applied after adjustments are made according to preset rules. The limitation of this approach is that it assumes the initial judgments and adjustments are always correct, and it cannot address strategy failures caused by individual battery differences, sudden changes in operating conditions, or slight inaccuracies in the mapping table. If the adjustment strategy itself is flawed, its negative impact will accumulate over time.

[0050] In the above technical solutions, a complete charge-discharge cycle refers to the time period during which the energy storage device, starting from a certain state of charge, undergoes a complete charge and discharge process and returns to near its initial state of charge. The preset time period is a fixed-length window set by the system when it is impossible or inconvenient to wait for a complete cycle, used to evaluate the adjustment effect. The internal resistance growth rate refers to the increase in the internal resistance of the energy storage device per unit time, and is one of the sensitive indicators reflecting the battery aging rate. The capacity decay rate refers to the decrease in the usable capacity of the energy storage device per unit time, and is a direct indicator of battery life degradation. The aging rate feedback index is a collective term for the aforementioned internal resistance growth rate or capacity decay rate, used as a quantitative basis for judging the effectiveness of the adjustment strategy. The step voltage value is a preset fixed voltage value used to reduce the base adjustment value, determining the fineness of the optimization algorithm during fine-tuning.

[0051] The above technical solution introduces a complete closed-loop feedback and correction process. After each voltage threshold adjustment, the system does not immediately perform the next adjustment but enters an effect monitoring period. This monitoring period can be a complete charge-discharge cycle or a preset fixed duration. During this period, the system continuously monitors and calculates the selected aging rate feedback index. For example, it calculates the change in internal resistance by analyzing the voltage-current curve, or estimates the capacity decay by using the ampere-hour integration method combined with reference point comparison, and calculates the average rate of change of this index during the monitoring period. After the monitoring period ends, the system compares the calculated aging rate with the historical aging rate within the same duration before the adjustment. If the comparison result shows that the aging rate has decreased, it proves that the current adjustment strategy is effective. The system will then maintain the current dominant aging mode determination result and the adjustment magnitude calculation method being used, and continue to apply the current optimization strategy. If the comparison result shows that the aging rate has not decreased significantly, or has even increased, it indicates that the adjustment has failed to achieve the expected effect, or may even have had a negative impact. At this time, the system will immediately activate the correction mechanism. The correction process first returns to the dominant aging mode determination step, i.e., re-collecting current data and querying the mapping table to obtain a fresh dominant aging mode determination result. The system compares this newly determined mode with the old mode used when the adjustment was just performed. If the re-determined dominant aging mode is the same as before, it indicates that the aging mode identification may be correct, but the adjustment intensity may be mismatched. In this case, the system will not simply repeat the previous adjustment, but will adopt a more cautious strategy. When executing the adjustment strategy again, it will first subtract a preset step voltage value from the base adjustment value corresponding to the aging mode. This step size is usually a percentage of the base adjustment value. Then, using this reduced new base adjustment value, combined with the gain coefficient calculated from the real-time current rate, a new, more moderate adjustment amplitude is obtained and applied. This gradual narrowing of the adjustment intensity helps to find the optimal point of action. If the re-determined dominant aging mode has changed, it indicates that the initial determination may have been incorrect, and a different aging mechanism is currently occurring. The system will immediately terminate the original strategy and switch to a new, targeted adjustment strategy based on the newly determined aging mode, calling the preset basic adjustment value, and calculating the gain coefficient in conjunction with the current current rate.

[0052] The aforementioned technical solution, based on a feedback-driven closed-loop control mechanism, endows the system with self-verification and autonomous optimization capabilities. It constructs a complete cycle of judgment, execution, verification, and correction, transforming the optimization strategy from a one-way execution command into a dynamic learning process that adjusts dynamically based on actual results. This significantly enhances the robustness and security of the entire method. When the adjusted strategy is effective, the system executes it resolutely; when the strategy is ineffective or produces adverse effects, the system can promptly detect the problem and intelligently distinguish whether it stems from pattern misjudgment or inappropriate intensity, thereby taking targeted corrective measures. This mechanism effectively prevents long-term performance degradation caused by a single erroneous decision, ensuring that the energy storage device always operates under active and effective intervention measures, and significantly improving the reliable achievement rate of the lifespan extension target.

[0053] In another technical solution, the energy storage device voltage threshold optimization method, in step S5, also records each adjustment strategy, adjustment range and corresponding aging rate feedback index change to form a historical optimization record. Regularly conduct statistical analysis of historical optimization records to establish a model of the correlation between the adjustment magnitude and the improvement effect of aging rate; When, for the same dominant aging mode, after the base adjustment value has been reduced multiple times, the improvement effect of the corresponding aging rate feedback index is close to zero for N consecutive times (e.g., the absolute value of the improvement rate is <0.5%) or fluctuates within a small positive and negative range, it is determined that the optimization for the current dominant aging mode has reached a local optimum, the current voltage threshold combination is locked, and the time interval for the next execution of step S2 for re-determination is extended to M times the original interval; where N≥3 and M>1.

[0054] The specific technical challenge addressed by this solution is: how to intelligently determine whether the voltage threshold optimization process has reached the optimal effect under the current conditions (i.e., local optimum), and after reaching it, avoid unnecessary and inefficient subsequent adjustment calculations, thereby saving system resources and maintaining the stability of control parameters.

[0055] In existing technologies, feedback-based optimization loops may continue indefinitely, even when subsequent adjustments have little effect on slowing down aging. This continuous adjustment operation, with near-zero marginal benefits, not only wastes the computing resources of the battery management system but may also introduce unnecessary system fluctuations due to frequent, minor changes in parameters.

[0056] In the above technical solutions, historical optimization records refer to the operation log dataset formed by continuously saving the voltage threshold adjustment strategy executed by the system each time, the specific adjustment range adopted, and the changes in the aging rate feedback index monitored after the adjustment. The correspondence model refers to the approximate pattern or trend between the adjustment range and the achieved aging rate improvement effect, extracted through statistical analysis of the accumulated historical optimization records. This model is usually qualitative or semi-quantitative and is used to guide subsequent optimization decisions. Local optimum refers to the best aging suppression effect achievable by adjusting the voltage threshold under the current operating conditions and system understanding for a specific dominant aging mode. Further fine-tuning parameters near this state will result in very limited performance improvement or instability. Locking the voltage threshold combination means that after the system determines that a local optimum has been reached, it stops further adjusting the set of charge and discharge voltage thresholds, maintaining a fixed control strategy. Extending the re-judgment interval means that after determining optimization saturation, the system actively lengthens the cycle of each dominant aging mode query and judgment, thereby reducing the system's computational load.

[0057] Based on the closed-loop optimization established by the above technical solution, a long-term learning and optimization saturation judgment mechanism has been added. The system faithfully records complete information for each optimization operation, including the dominant aging mode determined at that time, the type of adjustment strategy executed, the specific adjustment magnitude used, and the specific change in the aging rate feedback index obtained during the subsequent monitoring period compared to before the adjustment. This data is continuously added to the historical optimization record. The system periodically, for example, after accumulating dozens of optimization records, initiates a background analysis process. This process mainly categorizes and analyzes multiple optimization data under the same dominant aging mode, attempting to find the general pattern between the adjustment magnitude and the improvement effect of the aging rate. As the optimization process continues, especially when the base adjustment value for a specific aging mode is gradually reduced multiple times according to the mechanism of the previous technical solution, the system pays special attention to its aftereffects, observing how the improvement effect of the aging rate changes after each reduction in adjustment intensity. If the system detects that, in several consecutive optimizations, despite varying adjustment magnitudes, the improvement in the aging rate becomes negligible—meaning the change is close to zero or fluctuates within a small positive or negative range without a clear trend—it indicates that further adjustments are unlikely to bring stable and meaningful performance improvements. At this point, the system does not simply continue looping but makes a high-level decision: determining that the voltage threshold optimization for the dominant aging mode under the current operating conditions has reached a local optimum. Once this determination is made, the system immediately takes two measures. The first is to lock the currently used combination of charging and discharging voltage thresholds, ceasing any further automatic adjustments, allowing the energy storage device to operate stably under what is currently considered the optimal parameters. The second is to significantly extend the time interval between the next triggering of a re-determination of the dominant aging mode, reducing the determination frequency to a fraction of its original value. This means the system transitions from an active, frequent optimization search state to a stable, low-power monitoring operation state.

[0058] The aforementioned technical solution, based on an optimization saturation judgment mechanism using historical data learning, significantly enhances the system's intelligence and economy. It enables the optimization process to converge automatically, preventing it from falling into an ineffective computational loop after the optimization potential is exhausted, thus saving valuable BMS computing resources and energy consumption. Simultaneously, locking parameters and reducing the decision frequency provides long-term stable operating conditions for the energy storage device, reducing potential risks caused by frequent changes in control parameters and enhancing the overall stability of the system. This allows the method of this invention to not only actively seek optimization but also know when to stop, achieving a good balance between optimization efficiency and operational stability, which is beneficial for the energy storage device to maintain reliable operation in the medium to long term of its life cycle.

[0059] In another technical solution, the energy storage device voltage threshold optimization method, in the third adjustment strategy, specifically executes the fourth magnitude of reducing the charging voltage threshold and the fourth magnitude of increasing the discharging voltage threshold as follows: when the temperature is higher than the third preset value and lasts for a first preset duration (e.g., 10 min), the operation of reducing the charging voltage threshold is executed first; then, if the absolute value of the average charging and discharging current is detected to be lower than the preset current threshold (e.g., 0.2C) within a second preset duration (e.g., 5 min), the operation of increasing the discharging voltage threshold is executed.

[0060] The specific technical challenge addressed by this solution is: when implementing the adjustment strategy of solid electrolyte interface film growth-dominated aging mode, how to avoid the instantaneous impact on energy management of the energy storage system that may be caused by simultaneously changing the charging and discharging voltage thresholds, and ensure a smooth transition of the control process and system stability.

[0061] In existing technologies, if a specific aging mode (such as SEI film growth) is detected and the charge / discharge voltage threshold needs to be adjusted simultaneously, these two operations are usually performed immediately or almost simultaneously. This synchronous adjustment may instantly change the system's energy input and output limits under high load or high temperature conditions, causing oscillations in the control loop of the battery management system or external energy conversion system, or causing unnecessary disturbances to the load equipment and the power grid.

[0062] In the above technical solution, the first preset duration refers to the shortest continuous time required for the temperature to remain above the threshold triggering the third adjustment strategy. This is used to confirm that the high-temperature condition is not an instantaneous fluctuation, but a stable state requiring intervention. The second preset duration is a time window used by the system to observe and evaluate the current operating state after performing the operation of reducing the charging voltage threshold. The absolute value of the average charging and discharging current is the average of the absolute current values ​​calculated within the second preset duration, used to characterize the average load level of the system during this period. The preset current threshold is a safe current threshold used to determine whether the system is under relatively low load and suitable for adjusting the discharge threshold.

[0063] The aforementioned technical solution provides refined timing and condition management for voltage threshold adjustment during the solid electrolyte interphase (SEI) membrane growth-dominated aging mode. When the system determines that the current aging is SEI membrane growth-dominated and the temperature is higher than the third preset value, it does not immediately adjust the charge and discharge thresholds simultaneously. First, the system initiates a continuous temperature monitoring and confirmation phase. Only when the high-temperature condition remains stable for more than the first preset duration does the system confirm the need for intervention. The first step of the intervention is to reduce the charging voltage threshold. This operation aims to directly address the negative impact of high temperature on the charging process; reducing the upper limit of the charging voltage helps mitigate interfacial side reactions. After lowering the charging voltage threshold, the system does not immediately adjust the discharge threshold but enters a waiting and observation period, i.e., the second preset duration. During this period, the system continuously monitors the charging and discharging current of the energy storage device and calculates its average absolute value. This average value is significant for assessing the current load intensity of the system. Only when the calculated average absolute value of the charging and discharging current is lower than the preset current threshold does the system determine that it is currently in a relatively light load and relatively "quiet" operating condition. At this point, the system will execute the third step, i.e., increase the discharge voltage threshold according to the fourth step. This step-by-step execution strategy separates two parameter changes that might otherwise occur simultaneously and significantly impact the system's energy management boundaries in time. By addressing the charging side first and then adjusting the discharging side when the system load is lighter, sufficient time is given for the system to digest and adapt to each parameter change, avoiding the instantaneous impact that could result from the superposition of the two changes.

[0064] The above-described technical solution significantly improves the smoothness of control actions and the robustness of the system through a step-by-step implementation method with conditional judgments. It decomposes a complex operation that could cause system disturbances into two orderly and relatively mild single-step operations, and ensures that the second step operation only occurs during low-load periods when the system has strong tolerance through load current judgment. This method effectively prevents management system instability or impact on the external power grid that may occur under harsh operating conditions of high temperature and high power due to bidirectional changes in voltage thresholds. It ensures the continuity and smoothness of energy management of the energy storage device, demonstrating the invention's high regard for system operational stability while pursuing aging suppression effects, enabling the optimized control strategy to be safely and seamlessly integrated into actual operation.

[0065] In another technical solution, the energy storage device voltage threshold optimization method, in step S3, requires that the execution of the first adjustment strategy, the second adjustment strategy, or the third adjustment strategy also meet a common additional condition: the current rate C calculated in real time. rateThe absolute value is lower than the safe current rate threshold corresponding to each aging mode (the safe current rate threshold for each mode is set based on the safe charge / discharge rate and system stability requirements in the battery specification, for example, it can be uniformly set to 1.0C); if the absolute value of the current rate exceeds the safe current rate threshold, the voltage threshold adjustment will be paused and the original voltage threshold will remain unchanged.

[0066] The specific technical challenge addressed by this solution is: how to prevent voltage threshold adjustment from being performed under transient conditions of dynamic high-current charging and discharging in energy storage devices, so as to avoid instability in the control loop, sudden changes in electrical stress, or potential safety risks caused by improper adjustment timing.

[0067] In existing technologies, the triggering conditions for voltage threshold adjustment strategies are typically based only on slowly changing parameters such as health status, state of charge, and temperature, while ignoring the magnitude of instantaneous charge and discharge current. Changing the voltage threshold under high current rate conditions may instantly alter the power limit, causing control oscillations in the battery management system or connected power converter, or imposing additional electrical stress on the battery itself.

[0068] In the above technical solution, the current rate is the ratio of the charging / discharging current to the rated capacity calculated in real time, instantly reflecting the severity of the load. The safe current rate threshold is a preset current rate limit value for each dominant aging mode, representing the maximum safe current level that allows voltage threshold adjustment under that aging mode. "Pause execution" refers to a protective state where the system temporarily suspends the predetermined voltage threshold adjustment operation and maintains the existing parameters unchanged when the current operating condition is detected as not meeting safety requirements. The original voltage threshold refers to the charging and discharging voltage settings currently used by the system before a new round of optimization judgment.

[0069] The above technical solution adds a crucial safety gating condition to the execution of the voltage threshold adjustment strategy. Before executing the first, second, or third adjustment strategy, the system does not immediately apply the adjustment but first performs an additional safety check. The system collects the instantaneous current of the energy storage device in real time and calculates the current absolute value of the current rate. Simultaneously, the system internally stores safe current rate thresholds set for different dominant aging modes. These thresholds are based on electrochemical safety and system control stability considerations and are typically set to relatively conservative values. The system compares the real-time calculated absolute value of the current rate with the safe current rate threshold corresponding to the currently determined aging mode. If the comparison result shows that the absolute value of the current rate is lower than the safe threshold, it indicates that the system is in a relatively stable current and moderate load condition, and performing voltage threshold adjustment is safe. The system will then execute the predetermined adjustment strategy normally. Conversely, if the absolute value of the current rate is detected to exceed the safe current rate threshold, it indicates that the energy storage device is in a dynamic process of high-rate charging and discharging. At this time, the system will immediately trigger a protection mechanism, suspending any voltage threshold adjustment operation. No matter how ideal the adjustment strategy determined based on health status, state of charge, and temperature may be, the system will maintain the original, unadjusted voltage threshold until the current rate falls back to within a safe range. This mechanism ensures that parameter modifications only occur when the system's electrical state is relatively stable.

[0070] The safety features added to the above technical solution significantly enhance the applicability and safety of the voltage threshold optimization method in real-world dynamic operating environments. This effectively avoids the risk of control instability that may arise from adjusting the system's operating boundaries during periods of drastic current fluctuations, and prevents potential instantaneous shocks to the battery management system and power hardware. This makes the optimization process more robust and reliable, ensuring that interventions to delay aging do not come at the cost of introducing new operational risks. This mechanism reflects the invention's comprehensive consideration of system-level safety, enabling advanced optimization algorithms to be safely applied in complex and ever-changing real-world engineering scenarios.

[0071] In another technical solution, the energy storage device voltage threshold optimization method, in step S5, before reducing the corresponding basic adjustment value by a preset step voltage value, also includes a safety verification step: calculating the reduced basic adjustment value and confirming that it is not lower than the minimum adjustment voltage threshold set for the dominant aging mode (this threshold is set based on the BMS effective control resolution and to avoid frequent small fluctuations in the threshold, for example, 5mV); if it is lower than the minimum adjustment voltage threshold, it will no longer be reduced, and it will be directly determined that the optimization for the current dominant aging mode has reached a local optimum.

[0072] The specific technical challenge addressed by this solution is: how to prevent the adjustment value from becoming too small during the optimization process of gradually narrowing the voltage adjustment range, so as to lose the actual optimization significance, or cause additional losses to the energy storage device due to the frequent slight fluctuations in the voltage threshold caused by it.

[0073] In existing technologies, feedback-based optimization algorithms may continuously reduce the adjustment step size, theoretically approaching zero infinitely. However, this can have adverse effects in engineering practice: when the adjustment range becomes small enough, the aging suppression effect may become unmeasurable or even drowned out by system noise. Furthermore, frequent, minute fluctuations in the voltage threshold may interfere with the stable control of the battery management system or cause unnecessary stress on power electronic devices.

[0074] In the above technical solution, the safety verification step is a logical judgment performed before reducing the base adjustment value, aiming to ensure that the reduced value remains within a valid and safe range. The reduced base adjustment value refers to the new reference value, after step voltage reduction, that is planned to be applied to the next adjustment. The minimum adjustment voltage threshold is a lower limit for voltage adjustment preset for each dominant aging mode, representing the minimum adjustment considered to still have clear optimization significance. This threshold is determined based on a comprehensive consideration of electrochemical principles, control precision, and engineering practice.

[0075] The aforementioned technical solution embeds a crucial safety valve within its fine-tuning mechanism. When the system determines, based on feedback, that the base adjustment value corresponding to a dominant aging mode needs to be reduced, it does not immediately apply the reduced value. Before executing the reduction operation, the system performs a virtual calculation: subtracting a preset step voltage value from the current base adjustment value to obtain a "reduced base adjustment value." Then, the system compares this calculated new value with a "minimum adjustment voltage threshold" pre-set for that specific aging mode. This minimum adjustment voltage threshold is a positive, non-zero fixed voltage value, defining the "minimum effective unit" for optimization intervention. If the comparison result shows that the reduced base adjustment value is still greater than or equal to this minimum threshold, the system considers the reduction safe and meaningful, and thus confirms and saves the new value for subsequent adjustment magnitude calculations. Conversely, if the calculation finds that the reduced base adjustment value is already below the specified minimum adjustment voltage threshold, the system immediately interrupts the reduction operation, avoiding the excessively small value and maintaining the current base adjustment value. More importantly, based on this condition, the system directly makes a judgment: the optimization for the current dominant aging mode has reached a local optimum. Subsequently, the system will skip further fine-tuning attempts and directly enter the state, that is, lock the current voltage threshold combination and extend the re-judgment interval. This verification step plays a key braking role when the optimization process reaches the boundary of physical or engineering effectiveness.

[0076] The security verification mechanism provided by the above technical solution ensures the rigor and practical effectiveness of the optimization process, guaranteeing that each voltage threshold adjustment has a clear and perceptible physical meaning, thus avoiding the optimization process from falling into a state of infinite mathematical refinement that becomes ineffective in engineering. By setting and adhering to a minimum effective adjustment amount, it prevents control commands from jittering within the noise range due to excessively small adjustment amplitudes, thereby maintaining the stability and authority of the battery management system's control signals. This enhances the engineering practical value of the entire optimization algorithm, ensuring that every decision it makes is clear, powerful, and beneficial, ultimately contributing to the long-term stability and reliability of the energy storage device.

[0077] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0078] When the program is executed by the processor, it controls the battery management system to perform the following steps: Real-time data collection of voltage, current and temperature operation of the energy storage device, and calculation of the current state of health (SOH) and current state of charge (SOC) of the energy storage device based on the operation data; Based on the current SOH, SOC and temperature, query the aging mode mapping table pre-established in the storage medium or system memory to determine the current dominant aging mode. The dominant aging mode includes negative electrode lithium plating dominant aging, positive electrode active material loss dominant aging, and solid electrolyte interface film growth dominant aging. If the aging is determined to be dominated by lithium plating on the negative electrode, a first adjustment instruction is generated: when the SOC is higher than the first preset value and the temperature is lower than the second preset value, the charging voltage threshold is reduced by a first magnitude dynamically calculated based on the current current rate, while the discharge voltage threshold remains unchanged. If the aging is determined to be dominated by loss of positive electrode active material, a second adjustment instruction is generated: when the SOC is in the first preset SOC range, the charging voltage threshold is reduced by a second magnitude dynamically calculated based on the current current rate, and when the SOC is in the second preset SOC range, the discharge voltage threshold is increased by a third magnitude dynamically calculated based on the current current rate. If it is determined that the aging is dominated by the growth of the solid electrolyte interface film, a third adjustment instruction is generated: when the temperature is higher than the third preset value, the charging voltage threshold is reduced by a fourth magnitude dynamically calculated based on the current current rate, and the discharging voltage threshold is increased by a fourth magnitude dynamically calculated based on the current current rate. The generated adjustment command is sent to the charge and discharge control unit to control the charging and discharging process of the energy storage device using the adjusted voltage threshold; The step of dynamically calculating the adjustment range based on the current current rate includes: obtaining the current charging and discharging current to calculate the current rate, determining the gain coefficient according to a predefined gain coefficient mapping table, and multiplying the gain coefficient by the basic adjustment value preset for the corresponding adjustment strategy to obtain the final adjustment range.

[0079] Example 1 Example: Implementation and effect verification of voltage threshold optimization method for lithium-ion battery energy storage system I. Background of the Implementation Examples This embodiment uses a commercial lithium-ion battery energy storage system for smoothing the output power of a photovoltaic power station as an example. The system consists of multiple 100Ah rated capacity, 3.7V rated voltage cells connected in series. During daily operation, the system needs to cope with drastic load fluctuations caused by changes in solar radiation, with the charge / discharge current rate dynamically varying between 0.1C and 2C, and the ambient temperature range approximately -10℃ to 50℃. Traditional fixed voltage threshold management strategies are insufficient to effectively delay battery aging under these complex operating conditions; therefore, the dynamic optimization method described in this invention is adopted. In this embodiment, the BMS sampling frequency is no less than 10Hz, and the voltage measurement accuracy is no less than 1mV to ensure data validity.

[0080] II. Specific Implementation Process Step 1: System Initialization and Mapping Table Construction Before the system was put into operation, accelerated aging experiments were first conducted on battery samples of the same model in the laboratory. The experiment systematically changed the combination of parameters such as battery health state, state of charge, ambient temperature, and charge / discharge current rate, and disassembled and electrochemically analyzed the batteries at each aging node to accurately identify the dominant aging mode. Based on this data, a detailed aging mode mapping table was constructed and pre-stored in the battery management system's memory. The core logic of this mapping table is as follows: for example, when the system detects that the battery is in a high state of charge and low temperature environment, it tends to determine that "negative electrode lithium plating" is the dominant aging mode; while when it is in a high temperature environment, it tends to determine that "solid electrolyte interface film growth" is the dominant aging mode.

[0081] Step 2: Real-time data acquisition and status determination During system operation, the battery management system continuously collects voltage, current, and temperature data for each battery. Based on this data, two key state parameters are calculated in real time: state of charge, which is updated in real time using the ampere-hour integration method; and state of health, which is estimated and updated weekly through full-capacity calibration or electrochemical impedance spectroscopy analysis.

[0082] Step 3: Triggering the Main Aging Mode Query and Adjustment Strategy Suppose that at a certain moment, the system's real-time data is as follows: health status 92%, state of charge 85%, temperature 8℃, and charging current 20A. The system inputs these parameters into a pre-stored aging mode mapping table for lookup. According to the mapping rules, the system determines that the current dominant aging mode is "negative electrode lithium plating" based on the high state of charge combined with the low temperature conditions.

[0083] Step 4: Dynamic adjustment range calculation and safe execution After determining the mode, the system triggers the first adjustment strategy. First, a safety check is performed: the current current rate is calculated to be 0.2C, which is lower than the preset safety threshold of 2.0C, allowing adjustment. Next, the specific adjustment range is calculated: the preset base adjustment value is 5mV, and based on the current moderate current rate, the gain coefficient is found to be 1.0, therefore the final adjustment range is 5mV. Subsequently, the system slightly reduces the upper limit of the charging voltage threshold from the standard 4.200V to 4.195V, while the lower limit of the discharge voltage threshold remains unchanged. This adjustment aims to avoid excessively high charging voltage under high charge state and low temperature conditions, thereby suppressing lithium plating reaction at the negative electrode from the source.

[0084] Step 5: Closed-loop feedback and strategy optimization During the 24-hour monitoring period following voltage adjustment, the system focuses on the rate of increase in battery internal resistance as an aging feedback indicator. Monitoring results show that the daily rate of increase in internal resistance decreased from 0.10 milliohms to 0.08 milliohms after adjustment, indicating that the adjustment strategy is effective (internal resistance is obtained by calculating DCR through short pulse injection at a fixed SOC point daily). The system therefore maintains the current judgment and adjustment range. Conversely, if the rate of increase in internal resistance does not decrease or even increases, the system will automatically return to the second step to re-determine the aging mode. If the re-determination mode remains unchanged, the adjustment range will be appropriately reduced for fine-tuning during the next adjustment; if the mode changes, the new adjustment strategy will be switched immediately. Through this closed loop of "judgment-execution-verification-correction," the system achieves autonomous learning and optimization.

[0085] III. Verification of Experimental Results To objectively evaluate the effectiveness of this method, a comparative test was conducted over a period of six months in a controlled experimental environment.

[0086] The test setup included two identical battery systems (of the same model with consistent initial performance): one group was managed using a traditional fixed voltage threshold (4.2V for charging and 2.8V for discharging), while the other group applied the dynamic optimization method of this invention.

[0087] Both battery packs were subjected to the same simulated daily operating conditions, including varying temperature cycling and charge / discharge loads; specific tests included: Environment and Load: Both battery systems were placed in the same temperature chamber and subjected to identical simulated daily load profiles. These load profiles simulated real-world applications, dynamically varying the battery charge / discharge current rates between 0.1C and 2C. The ambient temperature was set to cycle daily from -5°C to 45°C to simulate diurnal and seasonal temperature variations.

[0088] Measurement and Calibration: To accurately obtain aging data, a standard charge-discharge test was performed on both sets of batteries monthly to calibrate their actual capacity; at the same time, the internal resistance was measured monthly using electrochemical impedance spectroscopy when the battery was at 50% state of charge.

[0089] After testing equivalent to 500 complete charge-discharge cycles, the performance difference between the two groups of batteries was very significant. The experimental group of batteries using the method of this invention exhibited a significantly higher capacity retention rate than the control group. At the end of the test, the capacity of the control group batteries decayed to 80% of their initial capacity, while the experimental group maintained above 85%, demonstrating a slower aging rate.

[0090] In terms of internal resistance changes, the experimental group also performed excellently. The internal resistance of the control group battery increased by about 80% throughout the entire test cycle, while the internal resistance increase of the experimental group was controlled within 50%, indicating that its internal chemical side reactions and structural degradation were more effectively suppressed.

[0091] The final disassembly analysis provided direct evidence for the above data. Visible lithium metal deposition was observed at the negative electrode of the control group battery, and significant loss of active material was also observed at the positive electrode. In contrast, the internal structure of the experimental group battery remained more intact, with uniform and mild signs of aging, demonstrating that the dynamic optimization strategy successfully intervened in the main aging mechanisms.

[0092] Throughout the test, the system using the optimization method operated smoothly without any safety issues such as voltage control oscillations or overcharging / over-discharging caused by threshold adjustments, thus verifying the reliability and safety of the method in practical applications.

[0093] This embodiment fully demonstrates that the voltage threshold dynamic optimization method provided by the present invention can intelligently sense the real-time status and aging mode of the battery and implement precise intervention. By dynamically adjusting the charge and discharge voltage threshold, it effectively slows down the degradation of battery capacity and the increase of internal resistance, increasing the battery's service life by more than 20%, while ensuring the safe and stable operation of the system. This provides an effective technical solution for achieving efficient, economical, and safe operation of the energy storage system throughout its entire life cycle.

[0094] The number of devices and processing scale described herein are for the purpose of simplifying the description of the invention. Applications, modifications, and variations of the invention will be readily apparent to those skilled in the art.

[0095] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details.

Claims

1. A method for optimizing the voltage threshold of an energy storage device, characterized in that, Includes the following steps: S1. Real-time acquisition of operating data of the energy storage device, including voltage, current and temperature; based on the operating data, calculate the current state of health (SOH) and current state of charge (SOC) of the energy storage device; S2. Based on the current SOH, SOC and temperature, query the pre-established aging mode mapping table to determine the dominant aging mode under the current operating conditions of the energy storage device; the dominant aging modes include negative electrode lithium plating dominant aging, positive electrode active material loss dominant aging, and solid electrolyte interface film growth dominant aging. S3. If it is determined that the aging is dominated by lithium plating on the negative electrode, the first adjustment strategy is executed: when the SOC is higher than the first preset value and the temperature is lower than the second preset value, the charging voltage threshold is reduced by the first magnitude, and the discharge voltage threshold is kept unchanged; if it is determined that the aging is dominated by loss of positive electrode active material, the second adjustment strategy is executed: when the SOC is in the first preset SOC range, the charging voltage threshold is reduced by the second magnitude, and when the SOC is in the second preset SOC range, the discharge voltage threshold is increased by the third magnitude. If it is determined that the aging is dominated by the growth of the solid electrolyte interface film, the third adjustment strategy is executed: when the temperature is higher than the third preset value, the charging voltage threshold is reduced by the fourth magnitude and the discharging voltage threshold is increased by the fourth magnitude; wherein, the first preset value, the second preset value, the third preset value, the first preset SOC range and the second preset SOC range are thresholds or ranges preset based on the data distribution characteristics in the aging mode mapping table. S4. Use the adjusted charging voltage threshold and discharging voltage threshold to control the charging and discharging process of the energy storage device.

2. The energy storage device voltage threshold optimization method as described in claim 1, characterized in that, The specific method for constructing and updating the pre-established aging mode mapping table in step S2 is as follows: Under laboratory conditions, accelerated aging experiments were conducted on the same type of energy storage device to obtain aging data under different combinations of SOH, SOC, temperature and current rate. The dominant aging mode was determined by disassembly and verification, and a basic mapping table was formed. In actual operation, the real-time operating data of the energy storage device is continuously recorded, and the determination result of the dominant aging mode is obtained based on the basic mapping table and the real-time operating data. The health status of energy storage devices is calibrated periodically using electrochemical impedance spectroscopy or incremental capacity analysis techniques to obtain calibration results; The calibration results are compared with the judgment results. When the deviation between the judgment results and the calibration results continues to exceed the preset tolerance for multiple calibration cycles, the basic mapping table is corrected using the calibration data.

3. The energy storage device voltage threshold optimization method as described in claim 2, characterized in that, In step S3, determining the first amplitude, second amplitude, third amplitude, and fourth amplitude includes the following steps: a) Set a base adjustment value for the first amplitude, the second amplitude, the third amplitude and the fourth amplitude respectively. The base adjustment value is a fixed voltage value and the range is 1mV to 10mV. b) Real-time acquisition of the charging and discharging current I of the energy storage device, and according to formula C rate =I / C n Calculate the current rate C rate C n This refers to the rated capacity of the energy storage device. c) The absolute value of the current current rate |C rate | As input, a predefined gain coefficient mapping table is consulted to obtain the corresponding gain coefficient K; where the gain coefficient mapping table specifies that when |C rate When |C ≤ 0.5, K = 1.0; when 0.5 < |C rate When |C| ≤ 1.0, K = 1.2; when 1.0 < |C| rate When |C ≤ 1.5, K = 1.5; when |C rate When |>1.5, K=2.0; d) Multiply the base adjustment value determined in step a) by the gain coefficient K determined in step c), and use the product as the adjustment range corresponding to the current adjustment strategy to be executed.

4. The energy storage device voltage threshold optimization method as described in claim 3, characterized in that, Also includes: S5. After each voltage threshold adjustment, monitor the internal resistance growth rate or capacity decay rate of the energy storage device as an aging rate feedback indicator within a complete charge-discharge cycle or a preset time period. If the aging rate feedback index decreases compared to before the adjustment, the current adjustment strategy is deemed effective, and the current dominant aging mode determination result and the adjustment range obtained in step d) are maintained. If the aging rate feedback index does not decrease or even increases, return to step S2 to re-determine the dominant aging mode: If the re-determined dominant aging mode is the same as the mode determined before this adjustment, when executing step S3 again, reduce the corresponding basic adjustment value by a preset step voltage value, which is 10% to 50% of the corresponding basic adjustment value, and then calculate the new adjustment range according to steps b) to d); If the re-determined dominant aging mode changes, execute step S3 based on the new dominant aging mode, and use the basic adjustment value set for the new mode, and then calculate the new adjustment range according to steps b) to d).

5. The energy storage device voltage threshold optimization method as described in claim 4, characterized in that, In step S5, the adjustment strategy, adjustment range, and corresponding changes in the aging rate feedback index are also recorded for each adjustment to form a historical optimization record. Regularly conduct statistical analysis of historical optimization records to establish a model showing the correlation between the adjustment magnitude and the improvement effect of aging rate; When the improvement effect of the corresponding aging rate feedback index approaches zero or fluctuates within a small positive and negative range for N consecutive times after the base adjustment value is reduced multiple times for the same dominant aging mode, it is determined that the optimization for the current dominant aging mode has reached a local optimum. The current voltage threshold combination is locked, and the time interval for the next execution of step S2 for re-determination is extended to M times the original interval; where N≥3 and M>1.

6. The energy storage device voltage threshold optimization method as described in claim 3, characterized in that, In the third adjustment strategy, the specific execution of lowering the charging voltage threshold by the fourth magnitude and raising the discharging voltage threshold by the fourth magnitude is as follows: when the temperature is higher than the third preset value and continues for the first preset duration, the operation of lowering the charging voltage threshold is executed first. Subsequently, if the absolute value of the average charging and discharging current is found to be lower than the preset current threshold within the second preset time period, the operation of increasing the discharge voltage threshold will be performed.

7. The energy storage device voltage threshold optimization method as described in claim 1, characterized in that, In step S3, executing the first adjustment strategy, the second adjustment strategy, or the third adjustment strategy also requires satisfying a common additional condition: the current current rate C calculated in real time. rate The absolute value of the voltage threshold is lower than the safe current rate threshold corresponding to each aging mode; if the absolute value of the current rate exceeds the safe current rate threshold, the voltage threshold adjustment is paused and the original voltage threshold is maintained.

8. The energy storage device voltage threshold optimization method as described in claim 4, characterized in that, In step S5, before reducing the corresponding basic adjustment value by a preset step voltage value, a safety verification step is also included: calculating the reduced basic adjustment value and confirming that it is not lower than the minimum adjustment voltage threshold set for the dominant aging mode; if it is lower than the minimum adjustment voltage threshold, it will no longer be reduced, and it will be directly determined that the optimization for the current dominant aging mode has reached a local optimum.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-8.