Industrial and commercial energy storage battery active balancing method and storage medium

Abstract

The present application relates to a kind of commercial energy storage battery active balancing method and storage medium, comprising: constructing battery safety work area, the dynamic characteristic parameter of each battery unit is collected in real time, using long short-term memory network training working condition migration model, predict next time unit working condition vector;Current working condition vector is mapped to global feature space to generate dynamic working condition point, calculate working condition migration direction.Combined with battery safety work area to determine the equilibrium critical point, and then calculate the equilibrium potential of each unit and global unbalance degree;When all working condition points are located in safety area and global unbalance degree is over threshold value, charge quantity is transferred by bidirectional DC-DC or switch matrix;If there is working condition point outside battery safety work area, then reduce input current, if necessary, switch parallel branch balance impedance.By dynamic working condition prediction and potential quantization, realize accurate active balancing and improve system safety, effectively deal with battery inconsistency problem.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of battery equalization, in particular to a kind of industrial and commercial energy storage battery active equalization method and storage medium. BACKGROUND

[0002] With the expansion of industrial and commercial energy storage system, the performance imbalance problem caused by the difference of manufacturing process, aging degree and running environment of single battery in battery pack is increasingly prominent. The existing active equalization technology mainly relies on the static threshold value of voltage or state of charge (SOC) to judge, and realizes energy redistribution through passive response strategy. However, such technology has significant defects:

[0003] Single parameter limitation: only voltage or SOC is used as the basis for equalization, ignoring the coupling effect of key characteristic parameters such as temperature gradient, internal resistance drift, dynamic current path voltage drop, leading to the disconnection of equalization decision and actual safety boundary of battery.

[0004] Lack of working condition predictability: traditional methods cannot predict the migration trend of battery, and only make lag compensation after imbalance occurs, which is difficult to prevent the battery pack from entering dangerous working conditions such as overvoltage, overheating or internal resistance mutation.

[0005] Insufficient strategy adaptability: the existing technology uses fixed threshold to trigger equalization, which cannot distinguish the differentiated regulation needs of battery pack, and is easy to cause invalid equalization or safety risk.

[0006] The above defects lead to two technical problems of energy storage system: first, equalization lag exacerbates the risk of battery aging and even thermal runaway; second, the timing and energy transfer amount of equalization action lack scientific basis, causing system energy efficiency loss and life reduction. SUMMARY

[0007] In view of the deficiencies of the prior art, the embodiments of the present application provide an industrial and commercial energy storage battery active equalization method, which comprises the following steps: collecting historical working condition data of energy storage battery pack, and constructing battery safe working area based on the historical working condition data;

[0008] Real-time acquisition of basic characteristic parameters of each battery unit at each time point in a preset time window, and acquisition of dynamic characteristic parameters of each battery unit at each time point in a preset time window according to the basic characteristic parameters, and arranging the dynamic characteristic parameters in ascending order of time to generate a dynamic characteristic sequence of each battery unit;

[0009] Training long short-term memory network model as working condition migration model based on dynamic characteristic sequence;

[0010] extracting dynamic characteristic parameters of each battery unit at the current time to generate a unit working condition vector of each battery unit at the current time, and mapping the unit working condition vector to a global characteristic space after normalization to generate a dynamic working condition point of each battery unit at the current time; and the origin of the global characteristic space represents that all battery units of the battery pack are in an ideal balanced state;

[0011] inputting the unit working condition vector of each battery unit at the current time into a working condition migration model to output a unit working condition vector of each battery unit at the next time;

[0012] taking the vector difference between the unit working condition vector at the current time and the unit working condition vector at the next time as a working condition migration vector, and taking the direction of the working condition migration vector as a working condition migration direction;

[0013] if the dynamic working condition point is located in the battery safe working area, selecting a point with the minimum Euclidean distance among intersection points of a ray along the working condition migration direction and the boundary of the battery safe working area as a balanced critical point; if the dynamic working condition point is located outside the battery safe working area, taking the nearest point from the dynamic working condition point to the boundary of the battery safe working area as the balanced critical point;

[0014] calculating a normalized Euclidean distance from the dynamic working condition point to the balanced critical point, and calculating a balanced potential energy of each battery unit according to the normalized Euclidean distance; obtaining a maximum value and a standard deviation of the balanced potential energies of all battery units, and then performing weighted summation on the maximum value and the standard deviation to obtain a global imbalance degree;

[0015] when all dynamic working condition points are located in the battery safe working area and the global imbalance degree is greater than a preset imbalance degree threshold, performing the following steps: selecting a battery unit with the minimum balanced potential energy as a balanced target unit, and selecting a battery unit with the maximum balanced potential energy as a balanced source unit;

[0016] determining a transferred charge amount according to a difference between the balanced potential energies of the balanced source unit and the balanced target unit, and transferring the transferred charge amount from the balanced source unit to the balanced target unit through a bidirectional DC-DC or a switch matrix;

[0017] when there is a dynamic working condition point located outside the battery safe working area, performing the following steps:

[0018] adjusting an input current of the energy storage battery pack according to the balanced potential energy, and switching a parallel branch balancing impedance if there is still a dynamic working condition point located outside the battery safe working area after adjusting the input current.

[0019] According to a preferred embodiment, the basic characteristic parameters include SOC values, voltage values, temperature values and internal resistance values; and the dynamic characteristic parameters include temperature rise rates, charge current integral quantities, current path voltage drops, voltage drop amplitudes, internal resistance change rates and self-discharge rates during a static period.

[0020] According to a preferred embodiment, constructing the battery safe operating area based on historical operating condition data comprises:

[0021] Traversing the dynamic characteristic parameters, taking the dynamic characteristic parameter being traversed as a target dynamic characteristic parameter;

[0022] Increasing or decreasing the value of the target dynamic characteristic parameter step by step according to a preset boundary test step size, until the abnormal state of the battery cell triggering voltage over-limit, temperature alarm or internal resistance mutation;

[0023] Recording the value of the target dynamic characteristic parameter when triggering the abnormal state as a safety boundary point of the target dynamic characteristic parameter;

[0024] Repeating the above steps until the safety boundary point corresponding to each dynamic characteristic parameter is obtained;

[0025] Mapping all the safety boundary points to a global characteristic space with the dynamic characteristic parameters as the coordinate axes;

[0026] Connecting all the mapped safety boundary points to form a closed multi-dimensional convex polyhedral boundary;

[0027] Verifying the boundary continuity of the multi-dimensional convex polyhedral boundary; if the Euclidean distance between adjacent safety boundary points exceeds a preset tolerance, inserting a linear interpolation point between the two points;

[0028] Defining the space surrounded by the multi-dimensional convex polyhedral boundary as the initial battery safe operating area.

[0029] According to a preferred embodiment, constructing the battery safe operating area based on historical operating condition data comprises:

[0030] Identifying a plurality of historical operating condition points from the historical operating condition data, each of which is constructed by the voltage, temperature, current, SOC and internal resistance value of a single battery cell at the same time;

[0031] According to the preset voltage threshold, temperature threshold, internal resistance deviation threshold and BMS alarm record, screening the historical operating condition points: the historical operating condition points triggering any abnormal condition are abnormal operating condition points, and the historical operating condition points in the standard charging and discharging interval and without alarm record are normal operating condition points;

[0032] Counting the number of normal operating condition points located within the initial battery safe operating area, calculating the proportion of the number in the total number of normal operating condition points, and if the proportion is less than a preset proportion threshold, determining that the initial battery safe operating area fails the first verification;

[0033] Checking whether there is any abnormal operating condition point located within the initial battery safe operating area, and if there is, determining that the initial battery safe operating area fails the second verification;

[0034] If the initial battery safe working area fails to pass either of the first verification and the second verification, the boundary test step is halved, and the generation process of the safe boundary point is re-executed based on the halved boundary test step, and then the initial battery safe working area is reconstructed by using the newly generated safe boundary point;

[0035] The above-mentioned safe boundary point generation, initial battery safe working area construction and initial battery safe working area verification steps are repeatedly executed until the reconstructed initial battery safe working area passes both the first verification and the second verification, at which time it is determined as the final battery safe working area.

[0036] According to a preferred embodiment, determining the transferred charge amount according to the difference in equilibrium potential energy between the equilibrium source unit and the equilibrium target unit comprises:

[0037] Calculating the difference in equilibrium potential energy between the equilibrium source unit and the equilibrium target unit;

[0038] Calculating the capacity correction coefficient according to the rated capacity and the SOC value of the equilibrium target unit;

[0039] Calculating the voltage difference between the equilibrium source unit and the equilibrium target unit;

[0040] Calculating the basic transferred charge amount based on the difference in potential energy, the capacity correction coefficient and the voltage difference;

[0041] Obtaining a safety coefficient and a maximum transferred charge amount set in advance, and determining the final transferred charge amount according to the basic transferred charge amount, the safety coefficient and the maximum transferred charge amount;

[0042] If the voltage difference is less than the voltage difference threshold value, the final transferred charge amount is transferred from the equilibrium source unit to the equilibrium target unit through the switch matrix; otherwise, the final transferred charge amount is transferred from the equilibrium source unit to the equilibrium target unit through the bidirectional DC-DC;

[0043] After the charge transfer is completed, the SOC value and the voltage value of the equilibrium source unit and the equilibrium target unit are updated.

[0044] According to a preferred embodiment, reducing the input current of the energy storage battery pack according to the equilibrium potential energy comprises:

[0045] The battery unit whose dynamic working condition point exceeds the boundary of the battery safe working area is recorded as an out-of-bound unit;

[0046] The average equilibrium potential energy of all out-of-bound units is calculated;

[0047] The Euclidean distance between the global feature space origin and each safe boundary point of the battery safe working area is calculated, and the smallest Euclidean distance is taken as the boundary threshold value;

[0048] The current adjustment ratio is calculated according to the average of the equalization potential and the boundary threshold value;

[0049] The adjustment target current is calculated according to the current input current and the current adjustment ratio;

[0050] The input current is adjusted to the adjustment target current in a preset current change rate step-by-step manner, and the dynamic operating points of the out-of-bound cells are monitored in real time;

[0051] If all the dynamic operating points of the out-of-bound cells return to the battery safe operating area after the current adjustment, the adjustment target current is maintained for operation;

[0052] If there are still out-of-bound cells, a parallel branch balance impedance switching operation is performed.

[0053] According to a preferred embodiment, the specific steps of performing the parallel branch balance impedance switching operation include:

[0054] Identifying the parallel branches in which all the out-of-bound cells are located to form a to-be-adjusted branch set;

[0055] For each parallel branch in the to-be-adjusted branch set, the following operations are performed:

[0056] The balance impedance value of the parallel branch is increased by a preset impedance step;

[0057] The dynamic operating points of each battery cell in the parallel branch are updated in real time;

[0058] The equalization potential of each battery cell in the parallel branch is calculated;

[0059] If the dynamic operating points of all the battery cells in the parallel branch are located within the battery safe operating area, the increase of the balance impedance of the parallel branch is stopped and the current impedance value is recorded;

[0060] If the balance impedance of the parallel branch is increased to a preset limit impedance, and there are still battery cells with dynamic operating points exceeding the boundary of the battery safe operating area in the parallel branch, the corresponding parallel branch is marked as a fault branch;

[0061] The balance impedance values of the non-adjusted branches are adjusted synchronously to maintain the total input current of the energy storage battery pack as the adjustment target current;

[0062] If there is a fault branch, a protection shutdown mechanism of the energy storage battery pack is triggered;

[0063] If the dynamic operating points of all the out-of-bound cells return to the battery safe operating area, the current balance impedance values of the branches are maintained for operation.

[0064] According to a preferred embodiment, training a long short-term memory network model as an operating condition migration model based on the dynamic characteristic sequence includes:

[0065] extracting a dynamic characteristic parameter of each battery unit at each time point from the dynamic characteristic sequence, and generating a unit working condition vector of each battery unit at each time point according to the dynamic characteristic parameter of each battery unit at each time point;

[0066] Taking the unit working condition vector of the same battery unit at the previous moment as an input sample and the unit working condition vector at the next moment as an output label, a training sample pair is formed;

[0067] A long short-term memory network model is constructed, wherein: the number of input layer neurons is equal to the feature dimension of the unit working condition vector; the number of output layer neurons is the same as that of the input layer neurons;

[0068] The training sample pair is input into the long short-term memory network model in chronological order, the unit working condition vector at the previous moment is taken as the input, and the unit working condition vector at the next moment is taken as the real label, the network parameters are optimized through the back propagation algorithm, and the mean square error between the predicted working condition vector and the real label is minimized;

[0069] The training is stopped when any of the following conditions is met: the prediction error of the verification set decreases by less than a preset convergence threshold for a plurality of consecutive training periods; the total number of training periods reaches a preset maximum iteration number.

[0070] The application also provides a storage medium, which stores a readable and writable program, and the program is executed to realize the battery active balancing method.

[0071] The application has the following beneficial effects:

[0072] 1. The application dynamically constructs a battery safe working area, monitors the deviation state of each battery unit in the feature space in real time, and accurately identifies potential risks. Based on the migration trend, the risk of thermal runaway is prevented, and the irreversible damage caused by the operation of the battery in the non-battery safe working area is reduced.

[0073] 2. The application uses the working condition migration direction to combine the dynamic characteristic sequence and the global imbalance degree analysis, accurately locates the source unit and the target unit to be optimized, and determines the optimal transfer charge amount, avoids the blind energy dissipation of the traditional balancing, and the balancing process is gentle and progressive, reducing the impact on the battery. BRIEF DESCRIPTION OF DRAWINGS

[0074] Figure 1 A flowchart of an active balancing method for industrial and commercial energy storage batteries is provided for an exemplary embodiment. DETAILED DESCRIPTION

[0075] The exemplary embodiments will be described in detail herein with reference to several drawings. Descriptions of well-known functions and constructions can be omitted to help concentrate on assets of the exemplary embodiments. The following description is presented to enable any person skilled in the art to make and use the application. Descriptions of specific devices and methods are included to provide a thorough understanding of embodiments of the application. However, it will be apparent to those skilled in the art that the application can be practiced without these specific details. In some instances, well-known structures have not been described in detail in order to avoid obscuring the application. Those skilled in the art will appreciate that the exemplary embodiments can be practiced by employing both series and parallel architectures. The different figures illustrate different aspects of the exemplary embodiments and can be employed as independent implementations or as sub-components of other implementations. The implementations can be implemented as part of a computer system.

[0076] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0077] It is to be understood that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It is to be understood that the term "and / or" as used herein encompasses all possible combinations of particular items listed apart from disjunctively worded limitations of various claims, so that if a particular claim is divided into disjunctively worded subclaims, references in that particular claim to "and / or" should be understood as an open-ended disjunction, meaning that additional claim limitations can be present in subclaims not referenced in the particular claim. It is to be understood that the terms "comprises", "comprising", "includes", "including" and "have" or "has" when used herein, specify the presence of stated features, integers, steps, operations, elements, components or members, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, members, or groups thereof.

[0078] Referring to Figure 1 The commercial energy storage battery active balancing method of the present application comprises:

[0079] S1, collecting historical working condition data of the energy storage battery pack, and constructing a battery safe working area based on the historical working condition data.

[0080] Optionally, the energy storage battery pack is composed of a plurality of battery units.

[0081] In a preferred embodiment, constructing the battery safe working area based on the historical working condition data comprises:

[0082] Traversing the dynamic characteristic parameters, taking the dynamic characteristic parameter being traversed as a target dynamic characteristic parameter;

[0083] Increasing or decreasing the value of the target dynamic characteristic parameter step by step according to a preset boundary test step size until the battery unit triggers an abnormal state of voltage overrun, temperature alarm or internal resistance mutation;

[0084] Recording the value of the target dynamic characteristic parameter when the abnormal state is triggered as a safety boundary point of the target dynamic characteristic parameter;

[0085] Repeating the above steps until the safety boundary point corresponding to each dynamic characteristic parameter is obtained;

[0086] mapping all the safety boundary points to a global feature space with dynamic feature parameters as coordinate axes;

[0087] connecting all the mapped safety boundary points to form a closed multi-dimensional convex polyhedral boundary;

[0088] verifying the boundary continuity of the multi-dimensional convex polyhedral boundary; if the Euclidean distance between adjacent safety boundary points exceeds a preset tolerance, linear interpolation points are inserted between the two points;

[0089] defining the space enclosed by the multi-dimensional convex polyhedral boundary as an initial battery safe operating area.

[0090] Further, constructing the battery safe operating area based on historical working condition data comprises:

[0091] identifying a plurality of historical working condition points from the historical working condition data, each of which is constructed from the voltage, temperature, current, SOC and internal resistance value of a single battery cell at the same time;

[0092] screening the historical working condition points according to preset voltage threshold, temperature threshold, internal resistance deviation threshold and BMS alarm record: the historical working condition points triggering any abnormal condition are abnormal working condition points, and the historical working condition points in the standard charging and discharging interval and without alarm record are normal working condition points;

[0093] counting the number of normal working condition points located within the initial battery safe operating area, and calculating the proportion of the number in the total number of normal working condition points; if the proportion is less than a preset proportion threshold, the initial battery safe operating area is determined to fail the first verification;

[0094] checking whether any abnormal working condition point is located within the initial battery safe operating area; if so, the initial battery safe operating area is determined to fail the second verification;

[0095] if the initial battery safe operating area fails any of the first verification and the second verification, the boundary test step is halved, and the generation process of the safety boundary points is re-executed based on the halved boundary test step, and then the initial battery safe operating area is reconstructed using the newly generated safety boundary points;

[0096] repeating the above safety boundary point generation, initial battery safe operating area construction and initial battery safe operating area verification steps until the reconstructed initial battery safe operating area passes both the first verification and the second verification, at which time it is determined as the final battery safe operating area.

[0097] Optionally, the dynamic feature parameters include temperature rise rate, charging current integral quantity, current path voltage drop, voltage drop amplitude, internal resistance change rate and static period self-discharge rate.

[0098] Optionally, the preset tolerance, the preset proportion threshold and the boundary test step are set in advance according to actual conditions.

[0099] S2, real-time collection of basic characteristic parameters of each battery unit at each time point in a preset time window, acquisition of dynamic characteristic parameters of each battery unit at each time point in the preset time window according to the basic characteristic parameters, and generation of a dynamic characteristic sequence of each battery unit in ascending order of time according to the dynamic characteristic parameters.

[0100] Optionally, the basic characteristic parameters include SOC values, voltage values, temperature values and internal resistance values; and the dynamic characteristic parameters include temperature rise rates, charge current integral quantities, current path voltage drops, voltage drop amplitudes, internal resistance change rates and static period self-discharge rates.

[0101] Optionally, the dynamic characteristic sequence is a dynamic characteristic parameter of the battery unit arranged in time sequence.

[0102] S3, training of a long short-term memory network model as a working condition migration model based on the dynamic characteristic sequence.

[0103] Preferably, the training of the long short-term memory network model as the working condition migration model based on the dynamic characteristic sequence comprises:

[0104] extracting dynamic characteristic parameters of each battery unit at each time point from the dynamic characteristic sequence, and generating a unit working condition vector of each battery unit at each time point according to the dynamic characteristic parameters of each battery unit at each time point;

[0105] taking the unit working condition vector of the same battery unit at the previous time as an input sample and the unit working condition vector at the next time as an output label to form a training sample pair;

[0106] constructing the long short-term memory network model, wherein: the number of input layer neurons is equal to the feature dimension of the unit working condition vector; and the number of output layer neurons is the same as the number of input layer neurons;

[0107] inputting the training sample pairs into the long short-term memory network model in time sequence, taking the unit working condition vector at the previous time as an input and the unit working condition vector at the next time as a real label, optimizing network parameters through a back propagation algorithm to minimize the mean square error between the predicted working condition vector and the real label;

[0108] stopping the training when any of the following conditions is met: the descending amplitude of the prediction error of the validation set for consecutive multiple training periods is less than a preset convergence threshold; and the total number of training periods reaches a preset maximum iteration number.

[0109] Optionally, the preset convergence threshold and the preset maximum iteration number are set in advance according to actual conditions.

[0110] S4, extract the dynamic characteristic parameters of each battery unit at the current time to generate a unit working condition vector of each battery unit at the current time, and map the normalized unit working condition vector to a global feature space to generate a dynamic working condition point of each battery unit at the current time.

[0111] Optionally, the dynamic working condition point represents the actual state of the battery unit at present.

[0112] Optionally, the origin of the global feature space represents that all battery units of the battery pack are in an ideal balanced state.

[0113] S5, input the unit working condition vector of each battery unit at the current time into the working condition migration model to output the unit working condition vector of each battery unit at the next time; the vector difference between the unit working condition vector at the current time and the unit working condition vector at the next time is taken as a working condition migration vector, and the direction of the working condition migration vector is taken as a working condition migration direction.

[0114] S6, if the dynamic working condition point is located within the battery safe working area, the point with the smallest Euclidean distance among the intersection points of the ray along the working condition migration direction and the boundary of the battery safe working area is selected as a balanced critical point; if the dynamic working condition point is located outside the battery safe working area, the nearest point of the dynamic working condition point to the boundary of the battery safe working area is taken as a balanced critical point.

[0115] Optionally, the balanced critical point is a theoretical limit point closest to the safety boundary under the current state and the predicted trend of the battery unit. The balanced critical point represents a critical state closest to the safety boundary that the battery unit is allowed to reach under certain conditions, and is a reference point for calculating the balanced potential energy.

[0116] S7, calculate the normalized Euclidean distance from the dynamic working condition point to the balanced critical point, and calculate the balanced potential energy of each battery unit according to the normalized Euclidean distance; obtain the maximum value and the standard deviation of the balanced potential energy of all battery units, and then perform weighted summation on the maximum value and the standard deviation to obtain a global imbalance degree.

[0117] Optionally, the balanced potential energy reflects the degree to which the current state of the battery unit deviates from the "safe critical state", and the greater the balanced potential energy, the closer the dynamic working condition point is to the balanced critical point (i.e. very close to the safety boundary), and the state is dangerous and needs to be pulled back urgently. The smaller the balanced potential energy, the farther the dynamic working condition point is from the balanced critical point (i.e. far from the safety boundary, closer to the ideal origin), and the state is safe and close to the ideal.

[0118] Optionally, the greater the balanced potential energy, the more unbalanced the battery unit is, which may face safety risks due to overcharging, overdischarging, rapid temperature rise, etc., and needs to be preferentially used as a charge output side to participate in active balancing.

[0119] Optionally, the balanced potential energy = 1 - normalized Euclidean distance.

[0120] Optionally, the weighted sum of the maximum value and the standard deviation is used to obtain the global imbalance degree, and the weighted coefficient of the maximum value is generally 0.7-0.9, and the weighted coefficient of the standard deviation is generally 0.3-0.1.

[0121] Optionally, the global imbalance degree is used to quantify the overall imbalance degree of the entire battery pack.

[0122] Optionally, the global imbalance degree effectively and comprehensively reflects the overall imbalance state and safety risk level of the battery pack by fusing the two key dimensions of “the most dangerous cell state” and “the overall state dispersion”.

[0123] Optionally, the maximum value of the equalization potential energy represents the battery cell with the most dangerous state and closest to the safety boundary in the energy storage battery pack. A large equalization potential energy value means that at least one battery cell is in danger, the overall system risk is high, and equalization must be triggered. This is a key indicator to ensure safety.

[0124] Optionally, the standard deviation of the equalization potential energy represents the dispersion degree of the state of all battery cells relative to their respective critical points. A large standard deviation means that the state difference between battery cells is large, and the overall coordination is poor. Even if there is no extremely dangerous cell, equalization is needed to improve overall consistency.

[0125] S8, when all dynamic operating points are located within the battery safe operating area, and the global imbalance degree is greater than a preset imbalance degree threshold, the following steps are performed: selecting a battery cell with the minimum equalization potential energy as an equalization target cell, and a battery cell with the maximum equalization potential energy as an equalization source cell; determining a transferred charge amount according to the equalization potential energy difference between the equalization source cell and the equalization target cell, and transferring the transferred charge amount from the equalization source cell to the equalization target cell through a bidirectional DC-DC or a switch matrix.

[0126] Optionally, the preset imbalance degree threshold is obtained according to experimental calibration.

[0127] Optionally, determining the transferred charge amount according to the equalization potential energy difference between the equalization source cell and the equalization target cell includes:

[0128] calculating the equalization potential energy difference between the equalization source cell and the equalization target cell;

[0129] calculating a capacity correction coefficient according to the rated capacity and the SOC value of the equalization target cell;

[0130] calculating the voltage difference between the equalization source cell and the equalization target cell;

[0131] calculating a basic transferred charge amount based on the potential energy difference, the capacity correction coefficient, and the voltage difference;

[0132] obtaining a preset safety factor and a preset maximum transfer charge amount, and determining a final transfer charge amount according to the basic transfer charge amount, the safety factor and the maximum transfer charge amount;

[0133] If the voltage difference is less than the voltage difference threshold, the final transfer charge amount is transferred from the balancing source unit to the balancing target unit through the switch matrix; otherwise, the final transfer charge amount is transferred from the balancing source unit to the balancing target unit through the bidirectional DC-DC converter.

[0134] After the charge transfer is completed, the SOC value and the voltage value of the balancing source unit and the balancing target unit are updated.

[0135] Optionally, a capacity correction factor is calculated according to the rated capacity and the SOC value of the balancing target unit, by the following formula:

[0136]

[0137] wherein C nom is the rated capacity of the balancing target unit, SOC min is the SOC value of the balancing target unit, SOC max is the SOC value of the balancing source unit, and K c is the capacity correction factor.

[0138] Optionally, the basic transfer charge amount is calculated based on the potential difference, the capacity correction factor and the voltage difference, by the following formula:

[0139]

[0140] wherein ΔE is the potential difference, ΔV is the voltage difference, ε is a constant for preventing zero or extreme small value, K c is the capacity correction factor, and Q base is the basic transfer charge amount.

[0141] Optionally, the final transfer charge amount is determined according to the basic transfer charge amount, the safety factor and the maximum transfer charge amount, by the following formula:

[0142] Q transfer = min (α × Q base , Q max )

[0143] wherein Q transfer is the final transfer charge amount, α is the safety factor, Q base is the basic transfer charge amount, and Q max is the preset maximum transfer charge amount.

[0144] Optionally, the maximum transfer charge amount is the maximum charge amount allowed to be transferred by the battery pack system.

[0145] S9, when there is a dynamic operating point outside the battery safe operating area, the following steps are performed: reducing the input current of the energy storage battery pack according to the equalization potential energy, and if there is still a dynamic operating point outside the battery safe operating area after adjusting the input current, switching the parallel branch balancing impedance.

[0146] Optionally, reducing the input current of the energy storage battery pack according to the equalization potential energy comprises:

[0147] The battery cells whose dynamic operating points exceed the boundary of the battery safe operating area are recorded as border crossing cells;

[0148] The average equalization potential energy of all border crossing cells is calculated;

[0149] The Euclidean distances between the global feature space origin and each safe boundary point of the battery safe operating area are calculated, and the smallest Euclidean distance is taken as a boundary threshold;

[0150] The current adjustment ratio is calculated according to the average equalization potential energy and the boundary threshold;

[0151] The adjustment target current is calculated according to the current input current and the current adjustment ratio;

[0152] The input current is adjusted to the adjustment target current in a preset current change rate step, and the dynamic operating points of the border crossing cells are monitored in real time;

[0153] If all dynamic operating points return to the battery safe operating area after current adjustment, the adjustment target current is maintained for operation;

[0154] If there are still border crossing cells, the operation of switching the parallel branch balancing impedance is performed.

[0155] Optionally, the current adjustment ratio is calculated according to the average equalization potential energy and the boundary threshold, and the formula is as follows

[0156]

[0157] Wherein, K I is the current adjustment ratio, E avg is the average equalization potential energy, E bound is the boundary threshold, and β is a preset maximum adjustment coefficient.

[0158] Optionally, the adjustment target current is calculated according to the current input current and the current adjustment ratio, and the formula is as follows:

[0159] I adj = I in × K I

[0160] Wherein, I in is the current input current, K I is the current adjustment ratio, and Iadj to adjust the target current.

[0161] According to a preferred embodiment, the specific steps of performing the switching shunt branch balancing impedance operation include:

[0162] Identifying the shunt branch where all the out-of-bound cells are located to form a set of shunt branches to be adjusted;

[0163] For each shunt branch in the set of shunt branches to be adjusted, the following operations are performed:

[0164] Increasing the balancing impedance value of the shunt branch by a preset impedance step;

[0165] Real-time updating the dynamic operating points of each battery cell in the shunt branch;

[0166] Calculating the equalization potential energy of each battery cell in the shunt branch;

[0167] If the dynamic operating points of all battery cells in the shunt branch are located within the battery safe operating area, stop increasing the balancing impedance of the shunt branch and record the current impedance value;

[0168] If the balancing impedance of the shunt branch is increased to a preset limit impedance, and there are still battery cells with dynamic operating points exceeding the boundary of the battery safe operating area in the shunt branch, the corresponding shunt branch is marked as a fault branch;

[0169] Synchronously adjusting the balancing impedance value of the non-adjustment shunt branch to keep the total input current of the energy storage battery pack as the adjustment target current;

[0170] If there is a fault branch, triggering the protection shutdown mechanism of the energy storage battery pack;

[0171] If the dynamic operating points of all out-of-bound cells return to the battery safe operating area, maintaining the current balancing impedance values of each shunt branch for operation.

[0172] The present application dynamically constructs a battery safe operating area, real-time monitors the deviation state of each battery cell in the feature space, and accurately identifies potential risks. Based on the preventive regulation of migration trend, the risk of thermal runaway is reduced, and the irreversible damage caused by the operation of the battery in the non-battery safe operating area is reduced. The present application uses the migration direction of the operating condition combined with the dynamic feature sequence and global imbalance degree analysis to accurately locate the source cell and target cell to be optimized and determine the optimal transfer charge amount, avoiding the blind energy dissipation of traditional balancing, and the balancing process is gentle and progressive, reducing the impact on the battery.

[0173] The embodiment also provides a storage medium, and the storage medium stores a readable and writable program, and the program is executed to realize the battery active balancing method. Specifically, the battery active balancing method provided by the embodiment can be compiled into a program or software and stored on the storage medium. In actual use, the program stored on the storage medium is used to execute each step of the battery active balancing method.

[0174] Computer program instructions for carrying out operations of the present application can be in assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, and procedural programming languages such as the "C" language or similar programming languages. Computer readable program instructions can be executed entirely on a user computer, partially on a user computer, as a separate software package, partially on a user computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, connected to the Internet through an Internet service provider). In some embodiments, by utilizing the state information of computer readable program instructions to personalize customize electronic circuits, such as programmable logic circuits, field programmable gate arrays (FPGA) or programmable logic arrays (PLA), the electronic circuits can execute computer readable program instructions to implement various aspects of the present application.

[0175] The non-transitory computer readable storage medium described in the present application stores computer instructions, and when the computer instructions are executed by a processor, the processor executes the above method.

[0176] A person of ordinary skill in the art can understand that all or part of the steps in the above method can be instructed by a program to complete related hardware (for example, a processor, an FPGA, an ASIC, etc.), and the program can be stored in a readable storage medium, such as a read-only memory, a magnetic disk or an optical disk, etc. All or part of the steps of the above embodiment can also be implemented using one or more integrated circuits. Accordingly, each module in the above embodiment can be implemented in the form of hardware, for example, by an integrated circuit to implement its corresponding function, or can be implemented in the form of a software function module, for example, by a processor executing a program / instruction stored in a memory to implement its corresponding function. The embodiment of the present application is not limited to any specific form of combination of hardware and software.

[0177] In addition, each function unit in each embodiment herein can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0178] When the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions herein, essentially or in the form of a contribution to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment herein. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various other media that can store program codes.

[0179] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for active balancing of industrial and commercial energy storage batteries, characterized in that, Includes the following steps: Collect historical operating condition data of energy storage battery packs and construct a battery safety operating area based on the historical operating condition data; The basic characteristic parameters of each battery cell are collected in real time at each time point within a preset time window. Based on the basic characteristic parameters, the dynamic characteristic parameters of each battery cell at each time point within the preset time window are obtained. The dynamic characteristic parameters are then arranged in ascending order of time to generate a dynamic characteristic sequence of each battery cell. The basic characteristic parameters include SOC value, voltage value, temperature value, and internal resistance value; the dynamic characteristic parameters include temperature rise rate, charging current integral, current path voltage drop, voltage drop amplitude, internal resistance change rate, and resting period self-discharge rate. A long short-term memory network model trained based on dynamic feature sequences is used as a working condition transfer model. The dynamic feature parameters of each battery cell at the current moment are extracted to generate the cell operating condition vector of each battery cell at the current moment. After normalization, the cell operating condition vector is mapped to the global feature space to generate the dynamic operating condition point of each battery cell at the current moment. The origin of the global feature space represents that all battery cells of the battery pack are in an ideal equilibrium state. Input the cell operating condition vector of each battery cell at the current time into the operating condition transfer model to output the cell operating condition vector of each battery cell at the next time. The difference between the current unit working condition vector and the next unit working condition vector is taken as the working condition transition vector, and the direction of the working condition transition vector is taken as the working condition transition direction. If the dynamic operating point is located within the battery safe operating area, select the point with the smallest Euclidean distance from the intersection of the ray along the operating point migration direction and the boundary of the battery safe operating area as the equilibrium critical point. If the dynamic operating point is located outside the battery safe operating area, the closest point from the dynamic operating point to the boundary of the battery safe operating area is taken as the equilibrium critical point. Calculate the normalized Euclidean distance from the dynamic operating point to the equilibrium critical point, and calculate the equilibrium potential energy of each battery cell based on the normalized Euclidean distance; obtain the maximum value and standard deviation of the equilibrium potential energy of all battery cells, and then perform a weighted summation of the maximum value and standard deviation to obtain the global imbalance degree. When all dynamic operating points are within the battery safe operating area and the global imbalance is greater than the preset imbalance threshold, the following steps are performed: select the battery cell with the smallest equalization potential energy as the equalization target cell and the battery cell with the largest equalization potential energy as the equalization source cell. The amount of charge to be transferred is determined based on the difference in equilibrium potential energy between the equilibrium source unit and the equilibrium target unit, and the amount of charge to be transferred from the equilibrium source unit to the equilibrium target unit is transferred through a bidirectional DC-DC converter or a switching matrix. When a dynamic operating point exists outside the battery's safe operating range, perform the following steps: The input current of the energy storage battery pack is reduced based on the equilibrium potential energy. If, after adjusting the input current, there is still a dynamic operating point outside the safe operating area of ​​the battery, the parallel branch impedance is switched to balance the impedance.

2. The active balancing method according to claim 1, characterized in that, The battery safety working area is constructed based on historical operating condition data, including: Traverse the dynamic feature parameters and use the dynamic feature parameter being traversed as the target dynamic feature parameter. The target dynamic characteristic parameter value is gradually increased or decreased according to the preset boundary test step size until the battery cell triggers an abnormal state such as over-limit voltage, temperature alarm or internal resistance change. Record the values ​​of the target's dynamic characteristic parameters when an abnormal state is triggered, and use them as the safety boundary points of the target's dynamic characteristic parameters; Repeat the above steps until the safe boundary point corresponding to each dynamic feature parameter is obtained; Map all safety boundary points to a global feature space with dynamic feature parameters as coordinate axes; Connecting all mapped safe boundary points forms a closed multidimensional convex polyhedron boundary. Verify the boundary continuity of the multidimensional convex polyhedron boundary; if the Euclidean distance between adjacent safe boundary points exceeds the preset tolerance, then insert a linear interpolation point between the two points. The space enclosed by the boundary of the multidimensional convex polyhedron is defined as the initial battery safe operating area.

3. The active balancing method according to claim 2, characterized in that, The battery safety working area is constructed based on historical operating condition data, including: Several historical operating condition points are identified by using historical operating condition data. Each historical operating condition point is constructed from the voltage, temperature, current, SOC and internal resistance of a single battery cell at the same time. Based on preset voltage threshold, temperature threshold, internal resistance deviation threshold and BMS alarm records, historical operating points are filtered: historical operating points that trigger any abnormal condition are abnormal operating points, and historical operating points that are in the standard charging and discharging range and have no alarm records are normal operating points. The number of normal operating points that are within the initial battery safe operating area is counted, and the proportion of the number of normal operating points is calculated. If the proportion is less than a preset proportion threshold, it is determined that the initial battery safe operating area has not passed the first verification. Check if any abnormal operating point is located within the initial battery safe operating area. If so, the initial battery safe operating area is deemed to have failed the second verification. If the initial battery safe working area fails either the first or the second verification, the boundary test step size is halved, and the safe boundary point generation process is re-executed based on the halved boundary test step size, thereby reconstructing the initial battery safe working area using the newly generated safe boundary points. Repeat the above steps of generating safety boundary points, constructing the initial battery safe working area, and verifying the initial battery safe working area until the reconstructed initial battery safe working area passes both the first and second verifications. At this point, it is determined as the final battery safe working area.

4. The active balancing method according to claim 3, characterized in that, The amount of transferred charge is determined based on the difference in equilibrium potential energy between the equilibrium source unit and the equilibrium target unit, including: Calculate the difference in equilibrium potential energy between the equilibrium source unit and the equilibrium target unit; Calculate the capacity correction factor based on the rated capacity and SOC value of the target unit; Calculate the voltage difference between the equalization source unit and the equalization target unit; The amount of basic transferred charge is calculated based on the potential energy difference, capacity correction factor, and voltage difference. Obtain the preset safety factor and maximum transferred charge, and determine the final transferred charge based on the base transferred charge, safety factor, and maximum transferred charge; If the voltage difference is less than the voltage difference threshold, the final transferred charge is transferred from the equalization source unit to the equalization target unit through the switching matrix; otherwise, the final transferred charge is transferred from the equalization source unit to the equalization target unit through bidirectional DC-DC. After the charge transfer is completed, update the SOC and voltage values ​​of the equalization source cell and the equalization target cell.

5. The active balancing method according to claim 4, characterized in that, Reducing the input current of the energy storage battery pack based on the equilibrium potential energy includes: Battery cells whose dynamic operating point exceeds the boundary of the battery safe operating area are recorded as out-of-bounds cells. Calculate the average equilibrium potential energy of all out-of-bounds units; Calculate the Euclidean distance between the origin of the global feature space and each safety boundary point of the battery safety working area, and use the minimum Euclidean distance as the boundary threshold; The current adjustment ratio is calculated based on the average equilibrium potential energy and the boundary threshold. Calculate the target current to be adjusted based on the current input current and the current adjustment ratio; The input current is adjusted to the target current in a stepwise manner using a preset current change rate, and the dynamic operating point of the out-of-range unit is monitored in real time. If all dynamic operating points return to the battery's safe operating range after current adjustment, then maintain the operation at the adjusted target current. If there are still out-of-bounds units, then perform a switch to balance the impedance of the parallel branches.

6. The active balancing method according to claim 5, characterized in that, The specific steps for performing the switching of parallel branch impedance balancing operation include: Identify all parallel branches containing out-of-bounds units and form a set of branches to be adjusted; For each parallel branch in the set of branches to be adjusted, perform the following operations: The balance impedance value of the parallel branch is increased by a preset impedance step size; The dynamic operating point of each battery cell in the parallel branch is updated in real time; Calculate the equilibrium potential energy of each battery cell in the parallel branch; If the dynamic operating point of all battery cells in the parallel branch is within the safe operating range of the battery, then stop increasing the balancing impedance of the parallel branch and record the current impedance value. If the balance impedance of the parallel branch increases to the preset limit impedance, and there are still battery cells in the parallel branch whose dynamic operating point exceeds the boundary of the battery safe operating area, then the corresponding parallel branch will be marked as a faulty branch. Synchronously adjust the balance impedance value of the non-adjusted branch to keep the total input current of the energy storage battery pack at the adjustment target current; If a faulty branch exists, the protection shutdown mechanism of the energy storage battery pack will be triggered; If the dynamic operating points of all out-of-bounds units return to the battery's safe operating range, then the current balanced impedance values ​​of each branch will be maintained.

7. The active balancing method according to claim 6, characterized in that, Long Short-Term Memory (LSTM) network models trained based on dynamic feature sequences are used as condition transfer models, including: Extract the dynamic feature parameters of each battery cell at each time point from the dynamic feature sequence, and generate the cell operating condition vector of each battery cell at each time point based on the dynamic feature parameters of each battery cell at each time point; The training sample pairs are formed by using the cell condition vector of the same battery cell at the previous moment as the input sample and the cell condition vector at the next moment as the output label. Construct a long short-term memory network model, wherein: the number of neurons in the input layer is equal to the feature dimension of the unit condition vector; the number of neurons in the output layer is the same as the number of neurons in the input layer. The training sample pairs are input into the Long Short-Term Memory network model in chronological order, with the previous time step unit load vector as the input and the next time step unit load vector as the true label. The network parameters are optimized through the backpropagation algorithm to minimize the mean square error between the predicted load vector and the true label. Training will stop when any of the following conditions are met: the prediction error on the validation set decreases by less than a preset convergence threshold for several consecutive training cycles; or the total number of training cycles reaches a preset maximum number of iterations.

8. A storage medium having a readable and writable program stored thereon, characterized in that, When the program is executed, it implements the active balancing method as described in any one of claims 1-7.

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