A method for determining the consistency level of an energy storage battery pack

Abstract

The application provides a kind of energy storage battery pack consistency level determination method, it is related to energy storage battery performance evaluation technical field, specifically includes: based on the space structure calculation characteristic area environment benchmark temperature and battery body temperature rise set;Based on the cumulative equalization electric quantity in discharge cycle and capacity voltage coefficient calculation shielding voltage difference, add the measured voltage and shielding voltage difference to construct the reduction voltage set;Based on the mapping of reduction voltage set, obtain electrical consistency index, combined with body temperature rise set and temperature threshold evaluation, obtain thermal consistency index;According to the current rate characteristics of operating condition, dynamically adjust electrical weight and thermal weight, weighted calculation obtains comprehensive consistency score, and is substituted into rule interval matching output consistency level.The application constructs consistency evaluation system, realizes the accurate determination to the overall discrete state of energy storage battery pack.

Description

Technical Field

[0001] This invention relates to the field of energy storage battery performance evaluation technology, specifically a method for determining the consistency level of an energy storage battery pack. Background Technology

[0002] With the advancement of the "dual-carbon" target strategy, the scale of electrochemical energy storage systems has exploded. However, during long-term operation, individual cells inevitably exhibit inconsistencies in capacity, internal resistance, and temperature due to manufacturing processes, complex fluid thermal fields, and the interplay of charge and discharge topologies. This leads to the "weakest link" effect, accelerating lifespan degradation and even inducing electrothermal runaway under extreme conditions. Therefore, deeply integrating multi-dimensional data decoupling algorithms to accurately quantify and dynamically classify internal consistency has become a key path to achieving proactive safety defense throughout the entire lifecycle.

[0003] In the existing technology for consistency evaluation of energy storage battery packs, the patent with publication number CN115993555A proposes a typical multi-dimensional index fusion evaluation scheme: by collecting the voltage and temperature of individual cells, the range and standard deviation coefficients of voltage and temperature are extracted; after normalization, a linear transformation is used to map them into one-dimensional index parameters; an objective function is constructed based on historical operating data, and a genetic algorithm is used to optimize the main objective function composed of standard deviation and local density to adjust the weights; finally, the consistency level is output by substituting it into a preset interval.

[0004] However, this scheme exposes significant technical defects when faced with complex fluid thermal fields and dynamic operating conditions: First, the thermodynamic assessment is susceptible to external environmental biases such as wind speed attenuation and heat accumulation along the duct, and fails to decouple the actual endogenous temperature rise, which can easily lead to frequent misjudgments of remote batteries; Second, the frequent opening and closing of the equalization circuit and the ohmic voltage drop of the wiring harness mask the measured voltage, making it impossible to restore the true electromotive force state; Finally, the static weight ignores the drastic fluctuations in the current ratio under dynamic load conditions, and cannot adaptively adjust the electrical and thermodynamic weight balance, resulting in serious lag and drift in the assessment results.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method for determining the consistency level of energy storage battery packs, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for determining the consistency level of an energy storage battery pack, comprising the following steps: Step 1: Divide the internal space structure of the energy storage battery pack into characteristic regions, obtain the external ambient temperature and cooling system airflow data to calculate the ambient reference temperature of each characteristic region, collect the surface temperature of each individual battery, calculate the body temperature rise of each battery based on the surface temperature of each battery and the ambient reference temperature of the corresponding characteristic region, and construct the body temperature rise set of all individual batteries. Step 2: During the most recent complete discharge cycle, acquire the measured voltage, state of health, and state of charge of each individual battery cell. Integrate the balancing current and duration of the individual battery cells based on the battery management system to obtain the cumulative balancing capacity. Extract the corresponding capacity voltage coefficient based on the state of charge and state of health. Calculate the shielding voltage difference using the cumulative balancing capacity and capacity voltage coefficient. Add the measured voltage and the shielding voltage difference to obtain the restoration voltage and construct the set of restoration voltages for all individual batteries. Step 3: Calculate the voltage range and voltage standard deviation based on the reduced voltage set, and perform nonlinear mapping to obtain the electrical consistency index. Extract the maximum temperature rise value and average temperature rise value based on the body temperature rise set, and evaluate the thermal consistency index by combining multiple preset temperature thresholds. Step 4: Identify the current operating condition, dynamically adjust the corresponding electrical and thermal weights based on the current ratio characteristics of the current operating condition, and use the adjusted weights to weight the electrical consistency index and thermal consistency index respectively to calculate the comprehensive consistency score of the energy storage battery pack. Substitute the comprehensive consistency score into a pre-set rule range for matching and output the consistency level of the energy storage battery pack.

[0008] Furthermore, based on the internal spatial structure and cooling air duct design of the energy storage battery pack, with the air inlet of the cooling air duct as the origin of the coordinates, along the path of the cold air flow, and according to the arrangement boundary of each battery in the energy storage battery pack, the internal spatial structure is divided into different characteristic regions. Extract the geometric center point of the feature region and calculate the effective air duct distance from each geometric center point to the air inlet along the path of cold air flow; Obtain the thermal resistance coefficient and airflow damping index based on the physical structure calibration of the energy storage battery pack; The ambient temperature is collected by a temperature sensor outside the energy storage battery pack, and the airflow data of the cooling system is obtained by reading the airflow meter in the duct. Multiplying the effective duct distance by the thermal resistance coefficient yields the friction factor. The airflow data is subjected to a power operation on the duct fluid damping index to obtain the airflow damping factor. Divide the friction thermal resistance factor by the air volume damping factor to obtain the attenuation temperature rise of the characteristic region. The ambient temperature is superimposed with the decay temperature rise to obtain the ambient reference temperature of each characteristic region.

[0009] Furthermore, the surface temperature of each individual battery cell was collected; By finding the physical coordinates of a single battery cell and matching the ambient reference temperature of the corresponding feature area, the surface temperature of the single battery cell is subtracted from the ambient reference temperature of the corresponding feature area to obtain the body temperature rise of the single battery cell. By iterating through all individual cells in the energy storage battery pack, calculating the temperature rise of each individual cell, and mapping the temperature rise of the energy storage battery pack based on the physical coordinates of the individual cells, a set of temperature rises of the energy storage battery pack is obtained.

[0010] Furthermore, the operating status of the energy storage battery pack is monitored, and the continuous discharge process in which the energy storage battery pack enters the discharge condition and its state of charge drops from above the preset high threshold to below the low threshold is identified. This process is defined as a complete discharge cycle. During the complete discharge cycle, the measured voltage, health status and state of charge of each individual battery are collected according to the preset sampling step size. The sampling step size is used as the sampling interval to determine several discrete sampling times. At the same time, the resistance value of the equalization resistor in the equalization circuit and the real-time opening and closing status of the passive equalization switch of the individual battery are read. When the equalization switch is closed, the measured voltage is divided by the resistance value of the equalization resistor to obtain the equalization current; When the equalization switch is turned off, the equalization current is recorded as zero; The equalization current at each sampling time is multiplied by a preset sampling step size, and then integrated and accumulated in real time from the start of the complete discharge cycle to the current sampling time to obtain the cumulative equalization charge at each sampling time.

[0011] Furthermore, based on the state of charge and the health state, addressing is performed in a pre-constructed capacity-voltage mapping matrix to extract the corresponding capacity-voltage coefficients; Multiply the accumulated equalization power by the corresponding capacity voltage coefficient to obtain the shielding voltage difference; The measured voltage and the shielding voltage difference are added together to obtain the restored voltage of each individual cell at the current sampling time; Traverse all individual cells within the energy storage battery pack, calculate the restored voltage of each individual cell, and map them according to the physical address of each individual cell to obtain the set of restored voltages of the energy storage battery pack at the current sampling time.

[0012] Furthermore, the step of addressing in the pre-constructed capacity-voltage mapping matrix and extracting the corresponding capacity-voltage coefficients specifically includes: In the capacity-voltage mapping matrix, the coordinates of four adjacent grid nodes surrounding the state of charge and the state of health are locked, and the corresponding four reference capacity-voltage coefficients are extracted. The distance ratios of the charged state and the healthy state relative to the corresponding coordinates of the adjacent grid nodes are calculated respectively to obtain the first weighting factor and the second weighting factor; The four reference capacity voltage coefficients are linearly combined using the first weighting factor to obtain two intermediate transition coefficients; The capacity voltage coefficient is obtained by linearly combining the two intermediate transition coefficients using the second weighting factor.

[0013] Furthermore, the capacity-voltage mapping matrix is ​​constructed as follows: A two-dimensional matrix framework is established with the state of charge as the first feature dimension and the state of health as the second feature dimension. The first feature dimension and the second feature dimension are then divided into grids according to a preset step size to obtain multiple dimension addressing coordinate points. In the historical charge-discharge cycles of the energy storage battery pack, the terminal voltage sequence and cumulative charge sequence of individual cells under different health state gradients are obtained; The first derivative of the terminal voltage sequence with respect to the accumulated charge sequence is calculated to obtain the differential voltage curves corresponding to each health state gradient. Based on the coordinate points addressed by the dimension, the absolute value of the differential voltage value is extracted from the corresponding differential voltage curve as the capacity voltage coefficient. The capacity-voltage coefficients are inserted into the corresponding dimension-addressable coordinate points in the two-dimensional matrix frame to obtain the capacity-voltage mapping matrix.

[0014] Furthermore, the maximum and minimum restored voltage values ​​are extracted from the restored voltage set at the current sampling time, and the voltage range is calculated; Calculate the standard deviation of all restored voltage values ​​in the restored voltage set at the current sampling time to obtain the voltage standard deviation; Obtain the preset range penalty coefficient and standard deviation penalty coefficient, and multiply the voltage range and voltage standard deviation by the corresponding penalty coefficients respectively, and then add them together to obtain the electrical composite dispersion. The electrical consistency index is obtained by exponentiation using the natural base and the negative of the electrical composite dispersion. Extract the maximum and average temperature rise values ​​of the body temperature rise set; The difference between the preset thermal runaway critical threshold and the maximum temperature rise is divided by the thermal runaway critical threshold to obtain the local hotspot safety factor. If the maximum temperature rise is greater than or equal to the thermal runaway critical threshold, the local hotspot safety factor is recorded as zero. The difference between the preset aging temperature rise threshold and the average temperature rise value is divided by the aging temperature rise threshold to obtain the global heating baseline factor. If the average temperature rise value is greater than or equal to the aging temperature rise threshold, the global heating baseline factor is recorded as zero. The thermal consistency index is obtained by multiplying the local hotspot safety factor and the global heating baseline factor.

[0015] Furthermore, obtain the standard rated capacity of the energy storage battery pack; The total circuit charging and discharging current of the energy storage battery pack is collected in real time, and the current operating condition of the energy storage battery pack is identified based on the direction and amplitude of the total circuit charging and discharging current. Divide the absolute value of the total circuit charging and discharging current by the standard rated capacity to obtain the current ratio characteristic; Calculate the difference between the current rate characteristic and the preset median rate threshold, and multiply it by the preset rate sensitivity factor to obtain the rate bias characteristic variable; Obtain the preset normalized baseline value; Using the natural base as the base and the multiplier bias characteristic variable as the exponent, a power operation is performed to obtain the bias exponent term. The bias exponent term is added to the normalized benchmark value to obtain the weight denominator. The normalized benchmark value is divided by the weight denominator to obtain the electrical weight. The constant is subtracted from the electrical weight to obtain the thermal weight. The electrical consistency index is weighted using the electrical weight, and the thermal consistency index is weighted using the thermal weight. The weighted electrical consistency index and the weighted thermal consistency index are then added together to obtain the overall consistency score. The overall consistency score is substituted into a pre-set rule range for matching to obtain the consistency level of the energy storage battery pack.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention divides the internal spatial structure of the energy storage battery pack into characteristic regions, acquires external ambient temperature and cooling system airflow data to calculate the ambient reference temperature of each characteristic region, collects the surface temperature of each individual battery cell and calculates the body temperature rise of each battery cell, constructs a set of body temperature rises for all individual batteries, extracts the maximum and average temperature rise values ​​from it, and evaluates the thermodynamic consistency index by combining multiple preset temperature thresholds, thus eliminating environmental interference caused by uneven heat dissipation. Simultaneously, within the most recent complete discharge cycle, the measured voltage, health status, and state of charge of each individual battery cell are acquired, and the balancing current and duration of each individual battery cell are integrated to obtain the cumulative balancing charge. The capacity voltage coefficient is extracted based on the state of charge and health status, and the shielding voltage difference is calculated using the cumulative balancing charge and the capacity voltage coefficient. The measured voltage and the shielding voltage difference are added to obtain the restoration voltage, and a set of restoration voltages for all individual batteries is constructed, eliminating voltage masking caused by balancing. The voltage range and voltage standard deviation are calculated based on the set of restoration voltages, and the electrical consistency index is obtained through nonlinear mapping, thus restoring the true differences in electrochemical characteristics. This invention also identifies the current operating conditions and dynamically adjusts the corresponding electrical and thermal weights based on the current rate characteristics of the current operating conditions. The adjusted weights are then used to weight the electrical consistency index and the thermal consistency index respectively to calculate the comprehensive consistency score of the energy storage battery pack. The comprehensive consistency score is then substituted into a pre-set rule range for matching, and the consistency level of the energy storage battery pack is output. This adjustment process can adaptively adjust the weight ratio in real time according to the current rate characteristics. Under high current, it automatically reduces the weight of the electrical consistency index due to polarization distortion and increases the weight of the thermal consistency index due to significant heat generation. Under low current, it adjusts in the opposite way, which solves the limitations of consistency scoring hysteresis and level drift under complex operating conditions and ensures that the final output consistency level has extremely high accuracy. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall method flow of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0019] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0020] Example: Please see Figure 1 The present invention provides a technical solution: A method for determining the consistency level of an energy storage battery pack, comprising the following steps: Step 1: Divide the internal space structure of the energy storage battery pack into characteristic regions, obtain the external ambient temperature and cooling system airflow data to calculate the ambient reference temperature of each characteristic region, collect the surface temperature of each individual battery, calculate the body temperature rise of each battery based on the surface temperature of each battery and the ambient reference temperature of the corresponding characteristic region, and construct the body temperature rise set of all individual batteries.

[0021] Large containerized energy storage cabinets typically house thousands of individual batteries connected in series and parallel. As cold air flows within the cabinet, its velocity and temperature exhibit highly nonlinear rheological characteristics depending on spatial location. Batteries near the air inlet are constantly exposed to low-temperature air, while those further away are at a double disadvantage due to decreased airflow velocity and heat accumulation. Directly assessing consistency based on surface temperature will lead to frequent false alarms about thermal activity in distant, safe batteries.

[0022] In this embodiment, based on the internal spatial structure and cooling air duct design of the energy storage battery pack, with the air inlet of the cooling air duct as the origin of the coordinates, along the path of the cold air flow, the internal spatial structure is divided into different characteristic regions according to the arrangement boundary of each battery in the energy storage battery pack.

[0023] First, import the internal spatial structure and cooling air duct design drawings of the energy storage battery pack. Use the center section of the air inlet as the origin of the three-dimensional coordinate system. The mainstream direction of the cold air is defined as the fluid evolution path. Since the heat dissipation boundaries of battery modules at different physical locations inside the containerized energy storage cabinet are completely different, in order to achieve spatial hierarchical calculation, this embodiment divides the entire internal space structure into discrete feature regions that are non-overlapping and extend from near to far, based on the physical arrangement boundaries of individual batteries.

[0024] Extract the geometric center point of the feature region and calculate the effective air duct distance from each geometric center point to the air inlet along the path of cold air flow; The geometric center point of the three-dimensional solid formed by the geometric contours of all individual battery cells within a specific feature area is extracted. Due to the presence of flow around, splitting, and baffles within the cooling duct, the cold air does not actually flow in a straight line. Therefore, the actual cumulative path length traversed by the cold air streamline from the origin air inlet along the actual flow path of the duct to reach this geometric center point is calculated. This length is taken as the effective duct distance for that feature area. The magnitude of the effective duct distance directly reflects the degree of sensible heat accumulation and wind speed attenuation experienced by the cold air before reaching the area.

[0025] Obtain the thermal resistance coefficient and airflow damping index based on the physical structure calibration of the energy storage battery pack; The preset thermal resistance coefficient is set to a fixed physical constant. The preset duct fluid damping index is set to a dimensionless constant. Under standard discharge conditions of the energy storage cabinet, During discharge at the rated current rate, the average heat generation power of a single cell was determined by a thermal balance experiment. Within the range, when cooling air flows past the boundary of the densely packed individual battery cells, the rate of temperature rise of the fluid itself due to the absorption of convective heat per unit distance of the airflow through the air duct stabilizes after alignment by fluid thermal field simulation. .

[0026] The internal cooling duct structure of the container is extremely complex, with cold air exhibiting strong turbulence as it flows through various bends, battery gaps, and contraction sections. According to fluid mechanics and nonlinear heat transfer principles, the improvement in local convective heat transfer efficiency due to airflow volume exhibits diminishing marginal returns, while the frictional resistance loss caused by pressure drop along the duct is nonlinearly positively correlated with the square of the airflow volume. The combined weakening effect of these two factors on local cooling efficiency manifests as a typical nonlinear power function evolution in the temperature algebraic hedging model. Through fitting multi-objective measured data, this nonlinear power exponent is locked as... .

[0027] Among them, the thermal resistance coefficient is used to quantify the rate of temperature rise of the air itself due to the absorption of heat dissipated by batteries along the way when the air flows a unit distance; the duct fluid damping index is used to characterize the turbulent damping characteristics of the physical topology of a specific duct bend or contraction section on the nonlinear weakening of air volume delivery.

[0028] The ambient temperature is collected by a temperature sensor outside the energy storage battery pack, and the airflow data of the cooling system is obtained by reading the airflow meter in the duct. Multiplying the effective duct distance by the thermal resistance coefficient yields the friction factor. The airflow data is subjected to a power operation on the duct fluid damping index to obtain the airflow damping factor. Divide the friction thermal resistance factor by the air volume damping factor to obtain the attenuation temperature rise of the characteristic region. The formula for calculating the temperature rise attenuation is: In the formula, To reduce temperature rise, For effective air duct distance, The thermal resistance coefficient, For air volume data, The damping index of the air duct fluid.

[0029] The ambient temperature is superimposed with the decay temperature rise to obtain the ambient reference temperature of each characteristic region.

[0030] In practical engineering, even if a battery located at the far end of the airflow duct has no internal faults and its heat generation is perfectly normal, the air temperature reaching the far end of the duct is already higher because the cold air has absorbed a large amount of heat dissipated by the front-end batteries as it flows past. If no action is taken, directly comparing the higher temperature measured on the surface of the battery at the far end of the duct with the temperature of the battery near the airflow vent to calculate the temperature difference will mistakenly indicate that the batteries have undergone uniform degradation, leading to serious policy misreporting.

[0031] When the external airflow increases, the airflow damping factor increases, and the temperature rise automatically decreases, which aligns with the physical logic of rapid cooling with high airflow. Conversely, as the airflow path extends further, the thermal resistance factor along the path increases, and the temperature rise automatically increases, consistent with the physical scenario of heat accumulation in distant airflow. This achieves spatial environment decoupling for inconsistency state assessment, providing an absolutely reliable and highly confident data baseline for accurately deducting external heat dissipation bias and isolating the true internal temperature rise caused purely by the internal heating of individual battery cells.

[0032] In this embodiment, the surface temperature of each individual battery cell is collected; The current surface temperature of each individual battery is collected in real time by digital temperature sensors placed on the explosion-proof valves or physical surfaces of the side walls of each individual battery inside the energy storage battery cabinet.

[0033] By finding the physical coordinates of a single battery cell and matching the ambient reference temperature of the corresponding feature area, the surface temperature of the single battery cell is subtracted from the ambient reference temperature of the corresponding feature area to obtain the body temperature rise of the single battery cell. Based on the battery physical topology association control table, by inputting the unique hardware physical address coordinates of the individual battery to be queried, such as a certain slot, a certain layer, and a certain column, the system can determine which feature region the individual battery belongs to in physical space, and simultaneously retrieve the environmental reference temperature of that feature region at the current sampling time.

[0034] By iterating through all individual cells in the energy storage battery pack, calculating the temperature rise of each individual cell, and mapping the temperature rise of the energy storage battery pack based on the physical coordinates of the individual cells, a set of temperature rises of the energy storage battery pack is obtained.

[0035] The surface temperature of the single cell is subtracted from the ambient reference temperature of the corresponding characteristic area to calculate the body temperature rise of the single cell after the heat dissipation bias is removed.

[0036] The loop traversal mechanism is initiated to simultaneously perform the above-mentioned acquisition, matching and deduction calculations on all individual batteries inside the energy storage container. In the memory space, a multi-dimensional matrix is ​​constructed and the address is mapped based on the physical coordinates of each individual battery as the index key value. Finally, the body temperature rise set reflecting the true endogenous thermodynamic state of the entire cabinet of batteries is packaged and output.

[0037] In engineering practice, the surface temperature emitted by individual cells at different locations in an energy storage battery pack is essentially a result of the superposition of both the "external heat dissipation environment" and the "internal electrochemical ohmic heat and reaction heat".

[0038] Subtracting the ambient reference temperature of each region from the surface temperature is equivalent to forcibly pulling all individual batteries onto an ideal virtual zero-bias reference surface at the algorithm's underlying level, completely smoothing out external heat dissipation flow field interference. This spatial decoupling process eliminates the heat accumulation interference caused by uneven heat dissipation from the external environment, restoring the intrinsic nature of electrochemical heat generation. At this point, the body temperature rise of healthy batteries in any extreme position of the airflow can remain highly consistent, while the difference in body temperature rise between healthy and truly overheating and deteriorating individual batteries will be amplified sharply, giving the overall thermal consistency score an extremely high signal-to-noise ratio and completely solving the dilemma of false positives and false negatives caused by fluid thermal field interference.

[0039] Step 2: During the most recent complete discharge cycle, acquire the measured voltage, state of health, and state of charge of each individual cell. Integrate the balancing current and duration of the individual cells based on the battery management system to obtain the cumulative balancing capacity. Extract the corresponding capacity voltage coefficient based on the state of charge and state of health. Calculate the shielding voltage difference using the cumulative balancing capacity and capacity voltage coefficient. Add the measured voltage and the shielding voltage difference to obtain the restoration voltage, and construct the set of restoration voltages for all individual cells.

[0040] In this embodiment, the operating status of the energy storage battery pack is monitored, and the continuous discharge process in which the energy storage battery pack enters the discharge condition and its state of charge drops from above the preset high threshold to below the low threshold is identified. This process is defined as a complete discharge cycle. The system reads global bus data from the energy storage battery pack in real time. When the system current direction is detected to change to discharge and the amplitude is stable, the algorithm locks in the discharge conditions. At this time, the processor continuously compares the real-time state of charge (SOC) of the energy storage battery pack. When the SOC continuously decreases from above a preset high threshold to below a preset low threshold, this continuous and uninterrupted discharge history is defined as a complete discharge cycle.

[0041] As a preferred implementation, to ensure the complete coverage of the electrochemical polarization curve by the depth of charge / discharge, the preset high threshold is specifically set as follows: The preset low-order threshold is specifically set as follows: .

[0042] In this embodiment, the preset high-order threshold is specifically set as follows: When the state of charge of the battery pack is higher than At this time, the positive and negative electrode materials inside the single cell are in a period of intense electrochemical phase transition, usually accompanied by a strong voltage relaxation effect in the early stage of switching from charging to discharging conditions, resulting in high-density nonlinear thermodynamic fluctuations in the terminal voltage during this stage. The high threshold value is then set at... This effectively avoids the voltage polarization distortion region in the early stage of discharge on the time axis, ensuring that subsequent voltage reduction calculations can enter a stable and comparable electrochemical steady evolution range.

[0043] In this embodiment, the preset low-order threshold is specifically set as follows: When the state of charge of the battery pack is lower than At this point, the discharge curve of a lithium-ion battery will begin to slide towards the inflection point region at the tail end. Within this region, solid-phase diffusion and mass transport within the battery are drastically restricted, causing the polarization resistance to surge exponentially and non-linearly. At this time, even extremely small differences in capacity or impedance between individual cells will be excessively amplified into huge transient voltage drop discrepancies within this polarization distortion region. The low-threshold is then truncated at... It can not only fully extract the electrochemical polarization characteristics covering most of the main discharge plateau period, but also promptly cut off the abnormal voltage divergence interference caused by capacity depletion at the end, ensuring that the calculated electrical consistency index can highly focus on the real internal physical state differences.

[0044] During the complete discharge cycle, the measured voltage, health status and state of charge of each individual battery are collected according to the preset sampling step size. The sampling step size is used as the sampling interval to determine several discrete sampling times. At the same time, the resistance value of the equalization resistor in the equalization circuit and the real-time opening and closing status of the passive equalization switch of the individual battery are read. When the equalization switch is closed, the measured voltage is divided by the resistance value of the equalization resistor to obtain the equalization current; When the equalization switch is turned off, the equalization current is recorded as zero; Within the selected complete discharge cycle, the clock unit of the processing chip initiates a timing interrupt, using a preset sampling step size as the discrete-time driver. The preset sampling step size is 1. The system synchronously acquires the measured voltage, state of health, and state of charge of each individual battery cell at high frequency. Simultaneously, the processing chip reads the real-time on / off state of the passive equalization switches, such as the miniature MOSFETs, in the passive equalization circuit corresponding to each individual battery cell through the level states of the input / output pins or register flags, and retrieves the inherent equalization resistor values ​​from the hardware circuit.

[0045] At each discrete sampling moment, the algebraic calculation branch for the equalization current is adaptively switched based on the read state of the passive equalization switch. When the passive equalization switch is in the closed conducting state, the processor calls the floating-point divider to divide the current measured voltage by the fixed resistance value of the equalization resistor, thereby calculating the current equalization current.

[0046] The equalization current at each sampling time is multiplied by a preset sampling step size, and then integrated and accumulated in real time from the start of the complete discharge cycle to the current sampling time to obtain the cumulative equalization charge at each sampling time.

[0047] During the discharge process, the equalization current calculated at the current sampling moment is multiplied by a fixed sampling step size, and then dynamically and in real-time superimposed with the historical charge value accumulated since the start of the discharge in memory. The cumulative equalization charge at the current sampling moment is then calculated and output. The formula for calculating the cumulative equalization charge is: In the formula, This represents the cumulative equalization power at the current sampling time. This marks the beginning of a complete discharge cycle. The preset sampling step size, This is the equalization current at each sampling step point from the start of the complete discharge cycle to the current time. For time indexing.

[0048] Locked state of charge to This continuous discharge range is chosen because if sporadic shallow charge / discharge or short-term frequency-tuned micro-cycle data are mixed into the energy storage battery pack, the severe concentration polarization and open-circuit voltage relaxation behavior at the beginning and end of the battery pack will introduce extremely strong time-axis nonlinear noise, leading to voltage standard deviation distortion. Selecting this deep discharge time window with highly linear and monotonically decreasing characteristics can provide a unified macroscopic time benchmark with high comparability and thermodynamic closed-loop for consistency analysis throughout the entire life cycle.

[0049] The reason for real-time reading and integration of the passive balancing switch's on / off state to calculate battery charge is that passive balancing essentially reduces the charge of overcharged cells through short-circuit discharge via hardware resistors. When the balancing switch is closed, the bypass current of several hundred milliamps not only causes a significant difference in cumulative charge consumption between cells over a long period, but also induces an ohmic voltage drop during the conduction transient as current flows through the physical acquisition harness. This causes the raw measured voltage obtained by the battery management system to exhibit an upward or downward physical bending effect, known as voltage masking, which fails to reflect the true battery electromotive force.

[0050] Through the calculation formula of the cumulative equalization power in this embodiment, the processor does not rely on expensive series shunts. It can accurately digitally track the cumulative equalization power of each individual battery cell caused by hardware intervention on the time axis, based solely on the switching state of the underlying switches and the standard resistors. This transforms the cumulative equalization power from a macroscopic scalar into a dynamic time series variable that corresponds one-to-one with the current sampling time.

[0051] In this embodiment, based on the state of charge and the health state, addressing is performed in a pre-constructed capacity-voltage mapping matrix to extract the corresponding capacity-voltage coefficients; The system loads the state of charge (SOC) and state of health (SOH) data of each individual cell captured by hardware sensors at the current sampling time, and uses this data as the addressing coordinates for multidimensional feature retrieval. Because the terminal voltage of a single cell exhibits a strong nonlinear spatial distribution characteristic in its sensitivity to changes in internal charge under different remaining charge and aging degradation levels, it is necessary to accurately pinpoint the voltage evolution slope under the current physical state. This requires using the current SOC and SOH as a two-dimensional index to locate grid coordinates within a pre-constructed capacity-voltage mapping matrix, and extracting the capacity-voltage coefficient specific to the current state of that individual cell.

[0052] Multiply the accumulated equalization power by the corresponding capacity voltage coefficient to obtain the shielding voltage difference; The measured voltage and the shielding voltage difference are added together to obtain the restored voltage of each individual cell at the current sampling time; Traverse all individual cells within the energy storage battery pack, calculate the restored voltage of each individual cell, and map them according to the physical address of each individual cell to obtain the set of restored voltages of the energy storage battery pack at the current sampling time.

[0053] The aforementioned matrix addressing, shielding voltage difference calculation, and restore operations are performed synchronously on all individual cells within the energy storage battery pack. In dynamic memory, the processor aligns the calculated restore voltage of each individual cell with its unique physical address using key-value pairs and performs spatial topology mapping, ultimately packaging and outputting the set of restore voltages for the energy storage battery pack at the current sampling time.

[0054] In practical engineering, the passive balancing circuits frequently activated in energy storage battery management systems essentially artificially reduce the charge of certain individual cells by short-circuiting and discharging through parallel resistors. This management behavior has serious side effects: on the long-term axis, balanced cells consume more charge than unbalanced cells; on the micro-electrochemical level, due to the continuous slippage of the lithium-intercalated state in the lattice during the main plateau period of the battery discharge curve, the extra charge loss will inevitably cause a proportional monotonically decreasing intrinsic electromotive force of the battery.

[0055] By employing matrix transformation and power restoration logic, the shielding voltage difference is calculated by multiplying the accumulated equalization power by the capacity voltage coefficient and then added back, successfully reconstructing the voltage of the individual cells affected by equalization interference to the undisturbed reference plane. This process eliminates the voltage masking blind spots caused by the historical operation of the equalization circuit, achieving a true reconstruction of the physical state at the current sampling time. This provides an absolutely pure underlying data source, free from software interference, for subsequent steps to perform high-precision electrical consistency index calculations.

[0056] In this embodiment, the step of addressing in the pre-constructed capacity-voltage mapping matrix and extracting the corresponding capacity-voltage coefficient specifically includes: In the capacity-voltage mapping matrix, the coordinates of four adjacent grid nodes surrounding the state of charge and the state of health are locked, and the corresponding four reference capacity-voltage coefficients are extracted. The processor loads the current state of charge and health status of the individual cells captured by the operation monitoring module at the current sampling time.

[0057] Continuous states of charge and health are input into the grid positioning operator to determine their relative physical grid intervals within a two-dimensional matrix framework. This precisely locks the coordinates of four adjacent grid nodes surrounding the state of charge and health. Simultaneously, four reference capacity voltage coefficients corresponding to these four grid nodes are extracted from the storage cells of the two-dimensional matrix framework. Let the physical boundaries of the four locked adjacent grid node coordinates in the first feature dimension be... and The physical boundaries in the second feature dimension are respectively and The four corresponding reference capacity voltage coefficients are denoted as follows: , , as well as ; The distance ratios of the charged state and the healthy state relative to the corresponding coordinates of the adjacent grid nodes are calculated respectively to obtain the first weighting factor and the second weighting factor; The specific formulas for calculating the first weighting factor and the second weighting factor are as follows: In the formula, As the first weighting factor, As the second weighting factor, The state of charge at the current sampling time. This represents the health status at the current sampling time.

[0058] Using local relative distance calculation logic, the distance ratio between the current actual state of charge and health state and the coordinates of the locked adjacent grid nodes is calculated respectively. This distance ratio completes the normalization mapping within the local grid at the algebraic level, thereby solving for the first weight factor and the second weight factor.

[0059] The four reference capacity voltage coefficients are linearly combined using the first weighting factor to obtain two intermediate transition coefficients; The specific formula for calculating the two intermediate transition coefficients is as follows: In the formula, This is the first intermediate transition coefficient. This is the second intermediate transition coefficient.

[0060] The processor initiates a one-dimensional linear interpolation calculation in the first feature dimension. It uses the first weighting factor to perform pairwise linear combinations on the four reference capacity voltage coefficients located on the upper and lower grid boundaries, thereby compressing and eliminating one degree of freedom in the first feature dimension direction and calculating two intermediate transition coefficients.

[0061] The capacity voltage coefficient is obtained by linearly combining the two intermediate transition coefficients using the second weighting factor.

[0062] The processor initiates a quadratic linear interpolation calculation on the second feature dimension, and uses the second weighting factor to perform a final linear combination of the two intermediate transition coefficients just calculated, thereby completing the final algebraic convergence in the second feature dimension direction and accurately calculating the capacity voltage coefficient specific to the current real-time state of the individual battery cell.

[0063] In a preferred embodiment of the present invention, the pre-constructed capacity-voltage mapping matrix has a defined and finite discrete resolution. The gridded addressing range of the first feature dimension is preset to be... The specific preset step size for its spatial gridding is as follows: This solidifies the coordinates of 21 discrete grid nodes in the first dimension; the gridded addressing range of the second feature dimension is preset to... The specific preset step size for its spatial gridding is as follows: This results in the solidification of 11 discrete grid node coordinates in the second dimension. The entire capacity-voltage mapping matrix occupies 231 data addressing units in the static memory, where the reference capacity-voltage coefficient stored within each grid coordinate node is discretized and filled with values ​​pre-calibrated according to the battery's electrochemical characteristics.

[0064] The state of charge (SOC) and state of health (SCH) of a single cell are calculated in real time by the management chip as high-precision continuous floating-point numbers based on physical quantities such as current and terminal voltage. However, due to limitations in memory chip capacity and the time available for preliminary laboratory electrochemical testing, it is impossible to measure and store all the voltage change rates corresponding to an infinite number of consecutive SOC points into the chip. Therefore, the SOC must be discretized into a grid matrix.

[0065] By utilizing the first and second weighting factors as physical fits to the distance from the current actual point to the grid boundary, an algebraically continuous first-order smooth transition mechanism is constructed. This mechanism progressively fuses the characteristics of four adjacent reference nodes as the state undergoes micro-fluidic changes, realistically reproducing the inherent micro-electrochemical physical nature of lithium-ion batteries—the continuous and gradually changing slope of their open-circuit voltage curves under different residual charge and aging stages. This eliminates step numerical noise while ensuring extremely high accuracy and stability of the final determined consistency level.

[0066] In this embodiment, the capacity-voltage mapping matrix is ​​constructed as follows: A two-dimensional matrix framework is established with the state of charge as the first feature dimension and the state of health as the second feature dimension. The first feature dimension and the second feature dimension are then divided into grids according to a preset step size to obtain multiple dimension addressing coordinate points. A two-dimensional matrix framework is established, with the state of charge as the first feature dimension (e.g., the horizontal axis) and the health state as the second feature dimension (e.g., the vertical axis). Next, the algorithm discretizes these two continuous state feature dimensions into a grid according to a preset step size, thereby generating dimension-addressable coordinate points in two-dimensional space composed of multiple intersecting grid nodes. The preset grid-addressable range of the first feature dimension is as follows: The specific preset step size for its gridding is as follows: The gridded addressing range of the second feature dimension is specifically preset as follows: The specific preset step size for its gridding is as follows: 231 discrete coordinate points are solidified within the two-dimensional matrix framework.

[0067] In the historical charge-discharge cycles of the energy storage battery pack, the terminal voltage sequence and cumulative charge sequence of individual cells under different health state gradients are obtained; Before the energy storage battery pack is put into operation on site, standard discharge process data of individual cells under different health state gradients are obtained through historical operation database, thereby extracting and aligning high-density individual cell terminal voltage sequences and corresponding cumulative charge sequences.

[0068] Under each specific health state gradient, the individual cells are discharged at a standard constant current, and their terminal voltages are collected at high frequency to form a terminal voltage sequence. Simultaneously, the corresponding cumulative charge sequence is formed by ampere-hour integration.

[0069] The first derivative of the terminal voltage sequence with respect to the accumulated charge sequence is calculated to obtain the differential voltage curves corresponding to each health state gradient. The formula for calculating the differential voltage curve is: In the formula, This represents the differential value at each coordinate point on the differential voltage curve. Let be the coordinate point. The change in the terminal voltage sequence. This represents the change in the cumulative electricity sequence.

[0070] Based on the coordinate points addressed by the dimension, the absolute value of the differential voltage value is extracted from the corresponding differential voltage curve as the capacity voltage coefficient. The capacity-voltage coefficients are inserted into the corresponding dimension-addressable coordinate points in the two-dimensional matrix frame to obtain the capacity-voltage mapping matrix.

[0071] A two-dimensional matrix framework is constructed using the two characteristic dimensions of state of charge and state of health. In engineering practice, the terminal voltage change of a lithium battery depends on the insertion and extraction states of lithium ions within the positive and negative electrode materials. As the battery ages during long-term operation, the loss of active material and the accumulation of dead lithium cause irreversible degradation and slippage of the lattice physical structure of the electrode materials. This results in completely different intrinsic electromotive force jump slopes caused by the same change in charge at different aging stages.

[0072] In real-world operating scenarios, the original voltage-cumulative capacity curve of a lithium battery during its main discharge plateau is extremely flat. For every unit of charge released, the terminal voltage change is often only a few millivolts. This extremely weak signal is easily overwhelmed by sampling noise from the battery management system. Directly using the slope of the original voltage curve for parameter lookup results in resolution and signal-to-noise ratio that are insufficient for accurate reconstruction. However, by transforming the curve into a differential voltage curve through first-order differentiation, and utilizing the microscopic phase transition thermodynamics mechanism, the minute inclinations on the voltage curve can be nonlinearly amplified into dramatic peaks and troughs, thereby significantly improving the signal-to-noise ratio and accuracy of capacity-voltage coefficient identification.

[0073] When extracting the differential voltage value from the corresponding differential voltage curve, absolute value operation must be used. This is because, under the discharge condition of the energy storage battery, as the accumulated charge gradually increases, the battery's terminal voltage monotonically decreases. At this time, the differential value obtained by directly performing the first derivative will inevitably be negative. In order to facilitate the subsequent direct positive multiplication of the capacity voltage coefficient with the accumulated equalization charge to calculate the positive shielding voltage difference and directly add it back to the measured voltage for bias compensation, this embodiment reverses the algebraic sign of the differential slope to positive beforehand by using absolute value when constructing the matrix offline.

[0074] Step 3: Calculate the voltage range and voltage standard deviation based on the reduced voltage set, and perform nonlinear mapping to obtain the electrical consistency index. Extract the maximum temperature rise value and average temperature rise value based on the body temperature rise set, and evaluate the thermal consistency index by combining multiple preset temperature thresholds.

[0075] In this embodiment, the maximum and minimum restored voltage values ​​in the restored voltage set at the current sampling time are extracted, and the voltage range is calculated. Extract the set of restored voltages generated by the previous steps at the current sampling time. Using maximum and minimum value optimization operators, extract the maximum and minimum restored voltage values ​​from this set. Subtract these two values ​​to calculate the voltage range at the extreme voltage level.

[0076] Calculate the standard deviation of all restored voltage values ​​in the restored voltage set at the current sampling time to obtain the voltage standard deviation; Obtain the preset range penalty coefficient and standard deviation penalty coefficient, and multiply the voltage range and voltage standard deviation by the corresponding penalty coefficients respectively, and then add them together to obtain the electrical composite dispersion. In this embodiment, the preset standard deviation penalty coefficient is set to... The reason for its establishment is that the voltage standard deviation can provide a macroscopic statistical measure of the global voltage dispersion density and group aggregation degree of hundreds or thousands of individual batteries in an energy storage cabinet, reflecting the systemic degradation of the battery pack caused by manufacturing process fluctuations or gradual aging of the group during long-term evolution. It is set as... It can ensure that the baseline level of electrical indicators dominated by global discrete trends is maintained during daily variable load operation. It can not only play a good role in smoothing and filtering transient random noise at individual sampling points, but also more accurately amplify the group aging characteristics of the entire battery pack throughout its entire life cycle.

[0077] In this embodiment, the preset thermal runaway critical threshold is set as follows: The reason for its establishment is that, in the microscopic electrochemical thermodynamic evolution of lithium-ion batteries, This is the sensible thermal critical safety threshold, the point at which the solid electrolyte interface film inside the battery begins irreversible exothermic decomposition, inducing a violent chain of side reactions. It must be strictly locked at this threshold. It can establish an extremely tight safety defense boundary. Once the maximum temperature rise value is reached, the local hot spot safety factor will be immediately vetoed to zero, which is in line with the absolute safety scenario logic of active fire defense in energy storage power stations.

[0078] The electrical consistency index is obtained by exponentiation using the natural base and the negative of the electrical composite dispersion. The formula for calculating the electrical consistency index is as follows: In the formula, For electrical consistency index, The base is the natural number. This is the range penalty coefficient. The standard deviation penalty coefficient is... This is the maximum reduction voltage value. This is the minimum reduction voltage value. To restore the voltage set at the current sampling time, the first... The reduction voltage value of each individual cell. This refers to the serial number of a single battery cell. This represents the average restored voltage of all individual cells at the current sampling time. This represents the total number of individual battery cells.

[0079] Extract the maximum and average temperature rise values ​​of the body temperature rise set; The difference between the preset thermal runaway critical threshold and the maximum temperature rise is divided by the thermal runaway critical threshold to obtain the local hotspot safety factor. If the maximum temperature rise is greater than or equal to the thermal runaway critical threshold, the local hotspot safety factor is recorded as zero. The difference between the preset aging temperature rise threshold and the average temperature rise value is divided by the aging temperature rise threshold to obtain the global heating baseline factor. If the average temperature rise value is greater than or equal to the aging temperature rise threshold, the global heating baseline factor is recorded as zero. The thermal consistency index is obtained by multiplying the local hotspot safety factor and the global heating baseline factor.

[0080] The formula for calculating the thermal consistency index is as follows: In the formula, The thermal consistency index, As a local hotspot security factor, This is the global thermal baseline factor.

[0081] In this embodiment, the preset range penalty coefficient is set to... The reason for its establishment is that voltage range directly reflects the extreme degree of disconnect between the worst-performing individual cell in the battery pack and the rest. It is a key indicator used to capture transient and severe failures caused by localized micro-short circuits, lithium plating in individual cells, or disconnection of the acquisition harness. Therefore, it is set as... It can assign sufficient safety penalty weights to local sudden anomalies during algebraic recombination, ensuring that when individual faulty batteries experience sudden changes, it can sensitively reduce the electrical recombination dispersion, thus playing a timely local early safety warning role.

[0082] In this embodiment, the preset aging temperature rise threshold is set to... The reason for its establishment is that lithium-ion batteries maintain a stable optimal operating heat generation temperature range while balancing capacity utilization and cycle life. Between. Once the average temperature rise of the entire battery pack exceeds The rate of loss of internal active materials and thickening of side reactions will accelerate dramatically and exponentially, leading to a severe decline in the long-term cycle life of the power plant. This is set as... It can scientifically quantify the global long-term thermal degradation of the entire battery cabinet caused by air duct design defects or air conditioning failures. Once the line is reached, it triggers the global heating baseline factor to return to zero, which meets the physical scenario requirements of energy storage power stations for asset long-term life assessment and optimization.

[0083] In terms of electrical characteristics, voltage range directly reflects the extreme topological cracks between the worst and best individual cells in the battery pack, and is extremely sensitive to transient malignant changes caused by individual cells due to micro-short circuits, lithium plating, or severe disconnection. Voltage standard deviation, on the other hand, characterizes the overall dispersion density and aggregation degree of hundreds or thousands of battery cells in the entire pack, providing a macroscopic measure of the gradual and uniform aging degradation caused by minor fluctuations in the manufacturing process throughout the entire lifecycle. Using either one alone would lead to flaws in consistency scoring. This embodiment uses a weighted composite of range penalty coefficient and standard deviation penalty coefficient, and maps them using exponential operations with the natural base, successfully transforming electrical composite dispersion into a highly sensitive electrical consistency index. This ensures that the index is 1 when perfect consistency is achieved, and that when even a few malignant cells are mixed into the battery pack or overall drift occurs, the index drops sharply, providing high signal-to-noise ratio data input for the final fault classification.

[0084] In terms of thermal characteristics, the maximum temperature rise directly corresponds to whether there are individual local dead spots within the container that cause abnormal heat release due to localized internal resistance spikes, dendrite penetration, or mechanical abuse. Once the temperature rise exceeds the thermal runaway threshold, the local hot spot safety factor instantly drops to zero, conforming to the absolute safety veto logic. The average temperature rise, on the other hand, macroscopically corresponds to the overall thermal degradation of the entire container due to air duct design flaws, severe heat accumulation, or insufficient air conditioning efficiency. Once the average temperature rise of the entire container exceeds the aging temperature rise threshold, it means that although there is no immediate risk of explosion, the entire battery pack is in a degrading environment of accelerated aging, and the overall thermal baseline factor also drops to zero. This embodiment constructs an invisible double-layered safety defense network by performing algebraic overlap of the two factors. Due to the use of multiplicative logic, either the veto of local hot spots or the aging overheating of the overall environment will trigger a direct zeroing of the final thermal consistency index. This perfectly reflects the complex scenario of thermodynamic safety assessment that emphasizes both local thermal runaway prevention and global lifespan optimization. It solves the limitations of traditional scoring methods that rely too heavily on temperature indicators and cannot take into account both sudden safety and long-term aging, ensuring that the final consistency level has extremely high industrial confidence.

[0085] Step 4: Identify the current operating condition, dynamically adjust the corresponding electrical and thermal weights based on the current ratio characteristics of the current operating condition, and use the adjusted weights to weight the electrical consistency index and thermal consistency index respectively to calculate the comprehensive consistency score of the energy storage battery pack. Substitute the comprehensive consistency score into a pre-set rule range for matching and output the consistency level of the energy storage battery pack.

[0086] In this embodiment, the standard rated capacity of the energy storage battery pack is obtained; The total circuit charging and discharging current of the energy storage battery pack is collected in real time, and the current operating condition of the energy storage battery pack is identified based on the direction and amplitude of the total circuit charging and discharging current. The processor loads the standard rated capacity inherent in the underlying hardware of the energy storage battery cabinet. During online operation, the processor collects the total circuit charging and discharging current of the energy storage battery pack in real time through Hall current sensors or high-precision shunts on the total circuit. Based on the current flow direction and current amplitude of the total circuit charging and discharging current, the processor dynamically identifies the specific current operating condition of the energy storage battery pack, such as static float charging, low-current polarization, high-current peak-shaving discharge, or high-power charging.

[0087] Divide the absolute value of the total circuit charging and discharging current by the standard rated capacity to obtain the current ratio characteristic; Calculate the difference between the current rate characteristic and the preset median rate threshold, and multiply it by the preset rate sensitivity factor to obtain the rate bias characteristic variable; Obtain the preset normalized baseline value; Using the natural base as the base and the multiplier bias characteristic variable as the exponent, a power operation is performed to obtain the bias exponent term. The bias exponent term is added to the normalized benchmark value to obtain the weight denominator. The normalized benchmark value is divided by the weight denominator to obtain the electrical weight. The constant is subtracted from the electrical weight to obtain the thermal weight. The formula for calculating the electrical weight is: In the formula, For electrical weights, As a normalized baseline value, The base is the natural number. As a rate-sensitive factor, Characteristic of current ratio, This is the median multiple threshold.

[0088] Under different charge / discharge rates, the ohmic polarization voltage drop and Joule heat generation power of an energy storage battery do not evolve linearly, but rather exhibit a significant exponential and sharp increase. Using a traditional linear function to allocate weights would result in sluggish weight changes near critical operating conditions, failing to accurately reflect the abrupt changes in the electrochemical physical field. This formula creatively introduces a modified inverse S-shaped nonlinear function at the algorithm's underlying layer. This function shape provides a first-order continuous smooth sliding window, matching the nonlinear physical trajectory of the battery transitioning from a low-rate electrochemical equilibrium state to a high-rate, violently polarized thermal state. This ensures the smoothness of weight switching under drastic load conditions and avoids strategy jumps.

[0089] In the underlying mathematical logic of multidimensional index weighting, the weight values ​​must be strictly constrained. Within the closed interval, this formula constructs an adaptive convergent fraction by placing the bias exponent term, which includes the current variable, in the denominator and introducing a normalized reference value. From a mathematical limiting perspective: as the current multiplier approaches infinity, the denominator also approaches infinity, and the electrical weights can automatically and smoothly converge to the limit value. When the current ratio approaches a very small value, the exponential term decays, and the electrical weights smoothly converge to the limit value. .

[0090] The electrical consistency index is weighted using the electrical weight, and the thermal consistency index is weighted using the thermal weight. The weighted electrical consistency index and the weighted thermal consistency index are then added together to obtain the overall consistency score. The formula for calculating the overall consistency score is as follows: In the formula, To achieve a comprehensive consistency score, For electrical weights, For electrical consistency index, For thermal weighting, It is the thermal consistency index.

[0091] The overall consistency score is substituted into a pre-set rule range for matching to obtain the consistency level of the energy storage battery pack.

[0092] In this embodiment, the preset median multiple threshold is set as a dimensionless constant. The reason for its establishment is that, in the field of electrochemical energy storage engineering, mainstream high-capacity lithium iron phosphate energy storage batteries, such as single cells with a rated capacity of 280Ah, typically have standard constant current charge-discharge conditions and thermal management boundaries based on... The magnification ratio serves as the core of both design and operation. Below that... At high rates, the concentration polarization and ohmic polarization inside the battery are extremely weak, and the terminal voltage closely approximates the internal electromotive force state; above high rates... At higher rates, according to Joule's law, the heat generated by the battery's internal resistance exhibits a non-linear positive correlation with the square of the current, causing a sudden amplification of the thermodynamic heating gradient, which becomes the core physical quantity dominating the inconsistency. Therefore, the central symmetry axis of the non-linear weighting function is strictly locked at... It can perfectly align the electrochemical evolution boundary of energy storage batteries under light load steady state and heavy load hot state, ensuring the precise self-consistency of the weighting balance in the physical scenario.

[0093] In this embodiment, the preset rate sensitivity factor is set as a fixed nonlinear adjustment coefficient. The reason for its establishment is to determine the switching rate and sliding window slope of electrical and thermal weights as operating conditions change. In actual field operation, the change of the battery from the Ohmic voltage-dominated region to the Joule thermal-dominated region is a gradual physical process. If this sensitivity factor is set too large, the weighting function will degenerate into a steep, either-or step jump, causing the current ratio to... When there are slight fluctuations in the surrounding area, the electrical and thermal weights experience violent numerical oscillations, introducing significant computational noise. If this sensitivity factor is set too small, the weighting curve will tend to be flat and linear, resulting in a voltage weight that cannot quickly and effectively suppress polarization distortion under high-current heavy-load conditions. Through fitting large-scale laboratory data of polarization characteristics and heating gradients, this factor was determined to be [value missing]. This allows the variable weight function to exhibit a perfect S-shaped smooth and gradual transition, ensuring both the sensitivity of weight allocation during rheological changes and satisfying the fluid dynamics and thermodynamic constraints of numerical first-order continuous smoothness.

[0094] In this embodiment, the preset normalization reference value is set as a standard constant. The reason for its establishment is that this parameter is mainly used to construct the symmetric boundary and numerical convergence constraints of the adaptive fusion algorithm at the underlying algebraic level. When the normalized baseline value is set to... When combined with the aforementioned median multiplier threshold, it can be ensured that the actual current multiplier is exactly at the specified value. Under standard operating conditions, the bias exponent term is always equal to Thus, the electrical weights can be accurately calculated using fractional algebra. Thermal weighting is This achieves a perfect equal distribution and offsetting of the two-dimensional evaluation indicators. Simultaneously, the introduction of this benchmark value constrains the growth boundary of the entire weight denominator, ensuring that the online dynamically calculated electrical and thermal weights converge strictly and stably to a certain value under any extreme operating conditions. Within a closed interval, and the algebraic sum of the two is always equal to the constant one.

[0095] Meanwhile, the pre-defined consistency level evaluation rule interval is specifically divided into four consecutive, non-overlapping numerical segments: When the overall consistency score At that time, the consistency level of the matched output energy storage battery pack is "Level 1: Highly Consistent"; When the overall consistency score At that time, the consistency level of the matched output energy storage battery pack is "Level 2: Moderate consistency"; When the overall consistency score At that time, the consistency level of the matched output energy storage battery pack was "Level 3: Slight Inconsistency"; When the overall consistency score When the consistency level of the matched output energy storage battery pack is "Level 4: Severe inconsistency", an alarm will be triggered and a local active balancing or shutdown maintenance command will be issued.

[0096] When the energy storage system is in static float charging, low-current frequency regulation, or standby mode, i.e., the current rate characteristic is significantly less than the median rate threshold... The concentration polarization and ohmic polarization inside the battery are extremely weak. At this point, the reduction voltage calculated after charge reduction can closely approximate and restore the true equilibrium electromotive force of a single cell with extremely high fidelity. In this physical scenario, the voltage signal possesses a very high signal-to-noise ratio and determinism, enabling extremely precise capture of the subtle differences in charge and internal resistance within a single cell. However, due to the extremely small current, according to Joule's law, the electrochemical heat generation of the battery at this stage is extremely low, and the temperature rise across the entire cabinet is almost integrated with the external environment, resulting in a temperature signal filled with background noise and a significant decrease in its reliability. This embodiment, through the evolution of the above formula, when the current rate characteristic is less than... When the result of the ratio bias characteristic variable calculation is negative, it drives the bias exponent term to rapidly shift towards... Convergence. At this point, the weighted denominator approaches the normalized baseline value. The electrical weight automatically jumps and approaches The thermal weight adaptively decays to near This allows a high-confidence electrical consistency index to take full control of the scoring balance, eliminating noise-dense temperature signal interference at low currents.

[0097] Conversely, when energy storage power stations respond to sudden grid peak shaving and high-power peak shaving and valley filling under heavy load conditions, the intense electrochemical reactions cause nonlinear and unstable large dynamic polarization voltage drops at the lattice defects inside the positive and negative electrodes. Even with dynamic restoration, the terminal voltage signal will still be mixed with a large amount of transient dynamic distortion and nonlinear hysteresis under strong current impact. If a voltage lookup table is forcibly used for consistency scoring at this time, huge dispersion data drift will occur. However, large currents physically inevitably lead to intense Joule heating and side reaction heating. Since heat accumulation has an integral characteristic, even extremely small internal resistance imbalances or potential degradation risks between individual cells will be amplified into significant, high signal-to-noise ratio body temperature rise heating gradients under high current heating scenarios. This embodiment uses an algebraic model to determine when the current ratio characteristic is greater than At this point, the ratio bias characteristic variable becomes positive, and the bias exponent term spikes exponentially, causing the weight denominator to expand rapidly. At this time, the electrical weight is automatically and significantly suppressed, quickly dropping to near its normal value. Meanwhile, the thermal weight correspondingly climbs to nearly [a certain value]. This enables the thermodynamic consistency index, which has extremely significant physical characteristics and a strong ability to reflect internal heat generation changes, to automatically take over the evaluation under high current conditions, minimizing the weight of voltage indicators with polarization distortion.

[0098] The switching transition slope of the variable-weight sliding window is reasonably constrained by utilizing a multiplier-sensitive factor. It can adaptively and smoothly schedule the fusion balance of multi-dimensional indicators at the algorithm level, adapting to the drastic changes in power plant operating conditions. This completely eliminates the fatal limitation of traditional static weight evaluation techniques, which suffer from severe lag in consistency scores and rating drift during drastic load changes. The final synthesized dynamic comprehensive score is fed into four preset grading rule intervals for matching, ensuring that the final output consistency level has extremely high industrial confidence and judgment accuracy across all operating conditions and the entire life cycle.

[0099] Table 1: Consistency Level Data Table for Energy Storage Battery Packs This application constructs a consistency rating system for decoupling coupled flow field space and denoising control strategies. By introducing digital reconstruction of the intrinsic state of a single cell using bulk temperature rise and reduction voltage, it solves the risks of system-level strategy false alarms and rating lag caused by existing technologies relying on surface raw measurement data contaminated by multiple noises. This system utilizes a smooth S-shaped adaptive weighted quantization sliding window to set a rigid red line for cross-rate weighting of operating conditions, forcing the algorithm to dynamically suppress transient voltage distortions caused by concentration polarization and thermodynamic indicators with high weights highlighting significant heat generation characteristics during load scheduling. This eliminates the rating drift and false discrepancies caused by traditional static weighted evaluation from a full life-cycle perspective. Data calculations show that this application achieves a leap from superficial empirical statistics to electrochemical physics-driven judgment. Through precise underlying parameter bias compensation, it achieves a globally optimal balance between judgment accuracy and industrial confidence in complex real-world physical scenarios, ensuring the long-term robust operation of energy storage power stations in harsh turbulent thermal fields and frequent equilibrium control environments. Facing the maximum temperature difference between individual cells reaching [missing information], [missing information]. The original measured voltage range reached The severe false divergence mutation, the temperature rise range of the monomer body after cleaning and reconstruction in this application is only The voltage range is precisely reduced to This perfectly decouples the intermediate-layer indicators from the dynamic weights, ultimately stabilizing the overall consistency score. It outputs "First-class: Highly Consistent" with high confidence, penetrating the physical flow field bias and software control blind spots, and highly matching the objective physical truth value of the battery pack's actual health.

[0100] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0101] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0102] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0103] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for determining the consistency level of an energy storage battery pack, characterized in that, The specific steps include: Step 1: Divide the internal space structure of the energy storage battery pack into characteristic regions, obtain the external ambient temperature and cooling system airflow data to calculate the ambient reference temperature of each characteristic region, collect the surface temperature of each individual battery, calculate the body temperature rise of each battery based on the surface temperature of each battery and the ambient reference temperature of the corresponding characteristic region, and construct the body temperature rise set of all individual batteries. Step 2: During the most recent complete discharge cycle, acquire the measured voltage, state of health, and state of charge of each individual battery cell. Integrate the balancing current and duration of the individual battery cells based on the battery management system to obtain the cumulative balancing capacity. Extract the corresponding capacity voltage coefficient based on the state of charge and state of health. Calculate the shielding voltage difference using the cumulative balancing capacity and capacity voltage coefficient. Add the measured voltage and the shielding voltage difference to obtain the restoration voltage and construct the set of restoration voltages for all individual batteries. The specific steps for extracting the corresponding capacity voltage coefficient based on the state of charge and health state, and calculating the shielding voltage difference using the accumulated balanced charge and the capacity voltage coefficient are as follows: In the pre-constructed capacity-voltage mapping matrix, the coordinates of four adjacent grid nodes surrounding the state of charge and the state of health are locked, and the corresponding four reference capacity-voltage coefficients are extracted. The distance ratios of the charged state and the healthy state relative to the corresponding coordinates of the adjacent grid nodes are calculated respectively to obtain the first weighting factor and the second weighting factor; The four reference capacity voltage coefficients are linearly combined using the first weighting factor to obtain two intermediate transition coefficients; The capacity voltage coefficient is obtained by linearly combining the two intermediate transition coefficients using the second weighting factor. Multiply the accumulated equalization power by the corresponding capacity voltage coefficient to obtain the shielding voltage difference; Step 3: Calculate the voltage range and voltage standard deviation based on the reduced voltage set, and perform nonlinear mapping to obtain the electrical consistency index. Extract the maximum temperature rise value and average temperature rise value based on the body temperature rise set, and evaluate the thermal consistency index by combining multiple preset temperature thresholds. The process of extracting the maximum and average temperature rise values ​​based on the body temperature rise set, and then evaluating the thermal consistency index using multiple preset temperature thresholds, involves the following steps: Extract the maximum and average temperature rise values ​​of the body temperature rise set; The difference between the preset thermal runaway critical threshold and the maximum temperature rise is divided by the thermal runaway critical threshold to obtain the local hotspot safety factor. If the maximum temperature rise is greater than or equal to the thermal runaway critical threshold, the local hotspot safety factor is recorded as zero. The difference between the preset aging temperature rise threshold and the average temperature rise value is divided by the aging temperature rise threshold to obtain the global heating baseline factor. If the average temperature rise value is greater than or equal to the aging temperature rise threshold, the global heating baseline factor is recorded as zero. The thermal consistency index is obtained by multiplying the local hotspot safety factor and the global heating baseline factor. Step 4: Identify the current operating condition, dynamically adjust the corresponding electrical and thermal weights based on the current ratio characteristics of the current operating condition, and use the adjusted weights to weight the electrical consistency index and thermal consistency index respectively to calculate the comprehensive consistency score of the energy storage battery pack. Substitute the comprehensive consistency score into a pre-set rule range for matching and output the consistency level of the energy storage battery pack.

2. The method for determining the consistency level of an energy storage battery pack according to claim 1, characterized in that: Based on the internal spatial structure and cooling air duct design of the energy storage battery pack, with the air inlet of the cooling air duct as the origin of the coordinates, along the path of the cold air flow, and according to the arrangement boundary of each battery in the energy storage battery pack, the internal spatial structure is divided into different characteristic regions. Extract the geometric center point of the feature region and calculate the effective air duct distance from each geometric center point to the air inlet along the path of cold air flow; Obtain the thermal resistance coefficient and airflow damping index based on the physical structure calibration of the energy storage battery pack; The ambient temperature is collected by a temperature sensor outside the energy storage battery pack, and the airflow data of the cooling system is obtained by reading the airflow meter in the duct. Multiplying the effective duct distance by the thermal resistance coefficient yields the friction factor. The airflow data is subjected to a power operation on the duct fluid damping index to obtain the airflow damping factor. Divide the friction thermal resistance factor by the air volume damping factor to obtain the attenuation temperature rise of the characteristic region. The ambient temperature is superimposed with the decay temperature rise to obtain the ambient reference temperature of each characteristic region.

3. The method for determining the consistency level of an energy storage battery pack according to claim 2, characterized in that: Collect the surface temperature of each individual battery cell; By finding the physical coordinates of a single battery cell and matching the ambient reference temperature of the corresponding feature area, the surface temperature of the single battery cell is subtracted from the ambient reference temperature of the corresponding feature area to obtain the body temperature rise of the single battery cell. By iterating through all individual cells in the energy storage battery pack, calculating the temperature rise of each individual cell, and mapping the temperature rise of the energy storage battery pack based on the physical coordinates of the individual cells, a set of temperature rises of the energy storage battery pack is obtained.

4. The method for determining the consistency level of an energy storage battery pack according to claim 1, characterized in that: Monitor the operating status of the energy storage battery pack, identify the continuous discharge process when the energy storage battery pack enters the discharge condition and its state of charge drops from above the preset high threshold to below the low threshold, and define this process as a complete discharge cycle. During the complete discharge cycle, the measured voltage, health status and state of charge of each individual battery are collected according to the preset sampling step size. The sampling step size is used as the sampling interval to determine several discrete sampling times. At the same time, the resistance value of the equalization resistor in the equalization circuit and the real-time opening and closing status of the equalization switch of the individual battery are read. When the equalization switch is closed, the measured voltage is divided by the resistance value of the equalization resistor to obtain the equalization current; When the equalization switch is turned off, the equalization current is recorded as zero; The equalization current at each sampling time is multiplied by a preset sampling step size, and then integrated and accumulated in real time from the start of the complete discharge cycle to the current sampling time to obtain the cumulative equalization charge at each sampling time.

5. The method for determining the consistency level of an energy storage battery pack according to claim 1, characterized in that: The measured voltage and the shielding voltage difference are added together to obtain the restored voltage of each individual cell at the current sampling time; Traverse all individual cells within the energy storage battery pack, calculate the restored voltage of each individual cell, and map them according to the physical address of each individual cell to obtain the set of restored voltages of the energy storage battery pack at the current sampling time.

6. The method for determining the consistency level of an energy storage battery pack according to claim 1, characterized in that: The capacity-voltage mapping matrix is ​​constructed as follows: A two-dimensional matrix framework is established with the state of charge as the first feature dimension and the state of health as the second feature dimension. The first feature dimension and the second feature dimension are then divided into grids according to a preset step size to obtain multiple dimension addressing coordinate points. In the historical charge-discharge cycles of the energy storage battery pack, the terminal voltage sequence and cumulative charge sequence of individual cells under different health state gradients are obtained; The first derivative of the terminal voltage sequence with respect to the accumulated charge sequence is calculated to obtain the differential voltage curves corresponding to each health state gradient. Based on the coordinate points addressed by the dimension, the absolute value of the differential voltage value is extracted from the corresponding differential voltage curve as the capacity voltage coefficient. The capacity-voltage coefficients are inserted into the corresponding dimension-addressable coordinate points in the two-dimensional matrix frame to obtain the capacity-voltage mapping matrix.

7. The method for determining the consistency level of an energy storage battery pack according to claim 1, characterized in that: Extract the maximum and minimum restored voltage values ​​from the restored voltage set at the current sampling time, and calculate the voltage range; Calculate the standard deviation of all restored voltage values ​​in the restored voltage set at the current sampling time to obtain the voltage standard deviation; Obtain the preset range penalty coefficient and standard deviation penalty coefficient, and multiply the voltage range and voltage standard deviation by the corresponding penalty coefficients respectively, and then add them together to obtain the electrical composite dispersion. The electrical consistency index is obtained by exponentiation using the natural base and the negative of the electrical composite dispersion.

8. The method for determining the consistency level of an energy storage battery pack according to claim 1, characterized in that: Obtain the standard rated capacity of the energy storage battery pack; The total circuit charging and discharging current of the energy storage battery pack is collected in real time, and the current operating condition of the energy storage battery pack is identified based on the direction and amplitude of the total circuit charging and discharging current. Divide the absolute value of the total circuit charging and discharging current by the standard rated capacity to obtain the current ratio characteristic; Calculate the difference between the current rate characteristic and the preset median rate threshold, and multiply it by the preset rate sensitivity factor to obtain the rate bias characteristic variable; Obtain the preset normalized baseline value; Using the natural base as the base and the multiplier bias characteristic variable as the exponent, a power operation is performed to obtain the bias exponent term. The bias exponent term is added to the normalized benchmark value to obtain the weight denominator. The normalized benchmark value is divided by the weight denominator to obtain the electrical weight. The constant is subtracted from the electrical weight to obtain the thermal weight. The electrical consistency index is weighted using the electrical weight, and the thermal consistency index is weighted using the thermal weight. The weighted electrical consistency index and the weighted thermal consistency index are then added together to obtain the overall consistency score. The overall consistency score is substituted into a pre-set rule range for matching to obtain the consistency level of the energy storage battery pack.

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