A multi-feature active balancing method and system based on a battery energy storage system

Abstract

The application discloses a multi-feature active balancing method and system based on a battery energy storage system, acquires the state of charge of a single battery cell by collecting the single battery cell parameters of the current battery energy storage system, processes the single battery cell parameters and the state of charge, inputs the processed single battery cell parameters and the state of charge into a high-dimensional criterion model to obtain initial weights, adaptively adjusts the initial weights, calculates a current decision value by using a weighted summation algorithm, compares the current decision value with a preset balancing decision threshold, and when the current decision value is greater than the balancing decision threshold, enters an active balancing state and executes an active balancing process. The application obtains multi-dimensional feature initial weights by fusing voltage, current, temperature, SOC and other multi-dimensional feature parameters with the constructed high-dimensional criterion model, obtains a decision value after adaptively adjusting each weight, accurately determines the best balancing start time and target according to the decision value, and avoids invalid or harmful balancing.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of safe energy storage, in particular to a multi-feature active balancing method and system based on a battery energy storage system. BACKGROUND

[0002] With the increasing severity of energy and environmental challenges, lithium battery energy storage power stations, as clean and sustainable energy storage solutions, have been widely used. However, the inconsistency of single batteries within a battery pack can significantly reduce the capacity utilization of the entire pack and restrict its safety and cycle life, becoming one of the key factors limiting the development of energy storage power stations. Therefore, efficient and reliable lithium battery pack balancing technology is crucial for the battery management system (BMS) and has important significance for the sustainable development of the lithium battery energy storage industry.

[0003] During long-term operation, the inconsistency of single batteries in the battery cabin of the energy storage power station will gradually increase due to slight differences in the operating environment. At this time, a reasonable active balancing strategy plays a decisive role in ensuring that the battery cabin operates within the safety boundary, preventing overcharging, overdischarging, overheating, and other risks, thereby prolonging the system life and ensuring safe operation. However, the existing active balancing strategy generally has the problem of inaccurate balancing timing judgment (too early, too late, or unreasonable start), which leads to poor balancing effect and even threatens the safe and stable operation of the system. The current mainstream active balancing strategy uses single cell voltage difference or SOC difference as the balancing start judgment threshold, which may lead to unnecessary system balancing, misjudgment of the system state, such as active balancing when the cell is already in a potential fault state such as slight internal short circuit, which in turn may exacerbate the risk of battery thermal runaway, directly threatening the safe and stable operation of the entire battery system.

[0004] In the face of the trend of continuous progress of energy storage technology and continuous expansion of power station scale, the deficiencies of traditional balancing strategies in terms of high precision and high efficiency are increasingly prominent.

[0005] Therefore, it is a technical problem that needs to be solved by those skilled in the art to provide a multi-feature active balancing method and system based on a battery energy storage system to solve the above problems. SUMMARY

[0006] To solve the above technical problems, the purpose of the present application is to provide a multi-feature active balancing method based on a battery energy storage system, which has clear logic, simple operation, and integrates voltage, current, temperature, and SOC multi-dimensional feature parameters to realize adaptive balancing start decision, effectively improving the precision and efficiency of active balancing.

[0007] The technical solutions provided by the present application are as follows:

[0008] A multi-feature active balancing method based on a battery energy storage system, comprising the following steps:

[0009] collecting parameters of single cells of the current battery energy storage system to obtain state of charge of the single cells;

[0010] processing the parameters of the single cells and the state of charge of the single cells;

[0011] obtaining a current decision value according to the processed parameters of the single cells and the state of charge of the single cells and a weighted summation algorithm;

[0012] judging whether the current decision value is greater than an equalization decision threshold value;

[0013] if the judgment result is yes, entering an active equalization state;

[0014] The obtaining of the current decision value according to the processed parameters of the single cells and the state of charge of the single cells and the weighted summation algorithm comprises the following steps:

[0015] constructing a weight-range high-dimensional criterion model;

[0016] inputting the processed parameters of the single cells and the state of charge of the single cells into the weight-range high-dimensional criterion model to obtain corresponding initial weights;

[0017] adaptively adjusting the initial weights to obtain adjusted weights;

[0018] obtaining the current decision value according to the adjusted weights, the processed parameters of the single cells and the state of charge of the single cells and the weighted summation algorithm.

[0019] Preferably, before the collecting of the parameters of the single cells of the current battery energy storage system to obtain the state of charge of the single cells, the method further comprises the following steps:

[0020] performing a safety self-check to judge whether there is a fault condition;

[0021] if the judgment result is yes, obtaining the fault condition and entering a lockout equalization state.

[0022] Preferably, before the collecting of the parameters of the single cells of the current battery energy storage system to obtain the state of charge of the single cells, the method further comprises the following steps:

[0023] performing a safety self-check to judge whether the active equalization state is entered;

[0024] if the judgment result is yes, continuing to execute the current active equalization process;

[0025] When it is judged that there is no fault condition and it is not in the active balancing state, the current battery energy storage system is collected to obtain the state of charge of the single battery cell.

[0026] Preferably, the collection of the current battery energy storage system parameters of the single battery cell to obtain the state of charge of the single battery cell comprises the following steps:

[0027] The battery capacity and current of the single battery cell of the current battery energy storage system are collected.

[0028] According to the battery capacity, current and corrected ampere-hour integral algorithm of the single battery cell, the state of charge of the single battery cell is obtained.

[0029] Preferably, the processing of the single battery cell parameters and the state of charge of the single battery cell comprises the following steps:

[0030] The temperature and voltage of the single battery cell of the current battery energy storage system are collected.

[0031] The voltage, temperature, current and state of charge are normalized to obtain the corresponding voltage difference, temperature difference, current absolute value difference and state of charge difference.

[0032] Preferably, the processed single battery cell parameters and the state of charge of the single battery cell are input into the weight-difference high-dimensional criterion model to obtain the corresponding initial weight, specifically:

[0033] The voltage difference, temperature difference, current absolute value difference and state of charge difference are input into the weight-difference high-dimensional criterion model to obtain the corresponding initial voltage weight, initial temperature weight, initial current absolute value weight and initial state of charge weight.

[0034] Preferably, the adaptive adjustment of the initial weight, according to the adjusted weight, the processed single battery cell parameters and the state of charge of the single battery cell, uses the weighted summation algorithm to obtain the current decision value, comprising the following steps:

[0035] According to the voltage fault condition, temperature fault condition, current fault condition and state of charge fault condition, and the weight dynamic model, the initial voltage weight, the initial temperature weight, the initial current absolute value weight and the initial state of charge weight are adaptively adjusted.

[0036] According to the product of the adjusted voltage weight and the voltage difference, the product of the adjusted temperature weight and the temperature difference, the product of the adjusted current absolute value weight and the current absolute value difference, and the product of the adjusted state of charge weight and the state of charge difference, the current decision value is obtained.

[0037] The fault conditions include the voltage fault condition, the temperature fault condition, the current fault condition and the state of charge fault condition.

[0038] Preferably, after entering the active balancing state, the method further comprises the following steps:

[0039] According to the battery capacity, the current and the state of charge, a theoretical balancing time is calculated and obtained;

[0040] It is judged whether the active balancing state running time has reached the theoretical balancing time;

[0041] If the result of the judgment is yes, the active balancing state is exited.

[0042] Preferably, after judging whether the current decision value is greater than a balancing decision threshold, the method further comprises the following steps:

[0043] If the result of the judgment is no, a hibernation standby state is entered.

[0044] A multi-feature active balancing system based on a battery energy storage system, comprising:

[0045] A single cell parameter acquisition module is configured to collect single cell parameters of the current battery energy storage system and obtain a state of charge of the single cell;

[0046] A processing module is configured to process the single cell parameters and the state of charge of the single cell;

[0047] A current decision value acquisition module is configured to obtain a current decision value according to the processed single cell parameters and the state of charge of the single cell and a weighted summation algorithm;

[0048] A judgment module is configured to judge whether the current decision value is greater than a balancing decision threshold;

[0049] An active balancing execution module is configured to enter an active balancing state if the result of the judgment is yes.

[0050] The current decision value acquisition module comprises:

[0051] A high-dimensional criterion model construction submodule is configured to construct a weight-range high-dimensional criterion model;

[0052] An initial weight acquisition submodule is configured to input the processed single cell parameters and the state of charge of the single cell into the weight-range high-dimensional criterion model to obtain corresponding initial weights;

[0053] An adaptive weight adjustment submodule is configured to adaptively adjust the initial weights to obtain adjusted weights.

[0054] A current decision value acquisition submodule is configured to acquire the current decision value by using the weighted summation algorithm according to the adjusted weights and the processed single battery cell parameters and the state of charge of the single battery cell.

[0055] The multi-feature active balancing method based on the battery energy storage system provided by the application is characterized in that: the state of charge of the single battery cell is acquired by collecting the single battery cell parameters of the current battery energy storage system, the single battery cell parameters and the state of charge are processed, the initial weights are obtained by inputting the processed single battery cell parameters and the state of charge into a high-dimensional criterion model, the initial weights are adaptively adjusted, the current decision value is calculated by using a weighted summation algorithm, and when the current decision value is greater than a preset balancing decision threshold, the active balancing state is entered and the active balancing process is performed.

[0056] The application has the following advantages: the weights of the multi-dimensional feature parameters are obtained by fusing the multi-dimensional feature parameters such as voltage, current, temperature and SOC and the constructed high-dimensional criterion model, the decision value is calculated after the weights are adaptively adjusted, the best balancing starting time and target are accurately determined according to the decision value, and invalid or harmful balancing is avoided.

[0057] The application further discloses a multi-feature active balancing system based on a battery energy storage system, which has the same technical effects as the method because they belong to the same technical concept and solve the same technical problem, and will not be described here. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0059] Figure 1 The flowchart of the multi-feature active balancing method based on the battery energy storage system provided in the embodiments of the application is shown in the figure.

[0060] Figure 2 The flowchart of step S2 provided in the embodiments of the application is shown in the figure.

[0061] Figure 3 The flowcharts of steps B1 and B2 provided in the embodiments of the application are shown in the figure.

[0062] Figure 4A flow chart provided after step S4 in the embodiment of the present application;

[0063] Figure 5 A structural schematic diagram of a multi-feature active balancing system based on a battery energy storage system provided in the embodiment of the present application. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0065] The embodiments of the present application are written in a progressive manner.

[0066] The embodiments of the present application provide a multi-feature active balancing method and system based on a battery energy storage system. The technical problem that the existing technology has inaccurate balancing opportunity judgment (too early, too late or unreasonable start) and leads to poor balancing effect and even threatens the safe and stable operation of the system is solved. For the convenience of description, the steps are adaptively numbered below.

[0067] As shown in the figure, a multi-feature active balancing method based on a battery energy storage system comprises the following steps: Figure 1

[0068] S1. Collecting the single cell parameters of the current battery energy storage system to obtain the state of charge of the single cell;

[0069] S2. Processing the single cell parameters and the state of charge of the single cell;

[0070] S3. Obtaining the current decision value according to the processed single cell parameters and the state of charge of the single cell and a weighted summation algorithm;

[0071] S4. Judging whether the current decision value is greater than the balancing decision threshold value;

[0072] S5. If the judgment result is yes, entering the active balancing state;

[0073] As shown in the figure, step S3 comprises the following steps: Figure 2

[0074] A1. Constructing a weight-range high-dimensional criterion model;

[0075] A2. Inputting the processed single cell parameters and the state of charge of the single cell into the weight-range high-dimensional criterion model to obtain the corresponding initial weight;

[0076] ​​A3. The initial weight is adaptively adjusted to obtain an adjusted weight. A current decision value is obtained by using a weighted summation algorithm according to the adjusted weight, the processed single battery cell parameter and the state of charge of the single battery cell.

[0077] The battery energy storage system (BESS) in step S1 is a comprehensive system that stores electrical energy through batteries and releases it when needed. It is far more than just a set of batteries, but a complex engineering that integrates hardware, software and control; the single battery cell is the most basic energy storage unit of the battery system, and its core parameters directly reflect its immediate state, health level and safety boundary; the state of charge (SOC) refers to the percentage of the remaining available capacity in the battery. Its basic definition is: SOC = (remaining available capacity / current maximum available capacity) x 100%, wherein the remaining available capacity: the amount of electricity that the battery can discharge to the cut-off voltage under the current state and under certain conditions. The current maximum available capacity: this is a variable, not a fixed rated capacity. It will change with the aging of the battery, temperature, charge and discharge rate, etc.

[0078] The weighted summation in step S3 is to assign a specific importance weight to each number in a set of data, then multiply each number by its corresponding weight, and finally add all the products to get the total sum;

[0079] The balancing decision threshold in step S4 is a set of criteria and thresholds preset in the BMS software. When the inconsistency state of the battery cell monitored by the BMS meets these criteria, the balancing action is automatically triggered; when the inconsistency returns to an acceptable range, the balancing is stopped; the battery management system (BMS) is an electronic system that integrates hardware and software. It ensures that the battery pack works in a safe, efficient and reliable state through monitoring, protection, balancing, estimation and communication, and maximizes its service life;

[0080] In one embodiment, the balancing decision threshold is set to 0.65, i.e. if the current decision value is greater than 0.65, the active balancing state is entered.

[0081] The core idea of active balancing in step S5 is "peak clipping and valley filling", i.e. through an energy conversion circuit, energy is transferred from the battery cell with the highest voltage (or the most charge) to the battery cell with the lowest voltage (or the least charge), or to the entire battery pack or load.

[0082] Steps S1 to S5 are specific implementation details of the multi-feature active balancing method of the battery energy storage system, which is to collect the current single battery parameters of the battery energy storage system through the BMS system, calculate the state of charge of the single battery, then process the single battery parameters and the state of charge, combine the weighted summation algorithm, calculate the current decision value, compare the current decision value with the balancing decision threshold value preset in the BMS software, and when the current decision value is greater than the balancing decision threshold value, enter the active balancing state and execute the current active balancing process.

[0083] The high-dimensional criterion model in step A1 refers to a decision that depends on a large number of (hundreds or even more) variables or features, which together constitute a "high-dimensional feature space". The task of the model is to find the optimal decision boundary in this complex space;

[0084] Steps A1 to A3 are specific implementation details of step S3, which is to input the processed single battery parameters and the state of charge of the single battery into the high-dimensional criterion model to obtain the initial weight of each parameter, adaptively adjust the initial weight to obtain the adjusted weight, and obtain the current decision value by using the weighted summation algorithm according to the adjusted weight and each parameter.

[0085] Preferably, before collecting the current single battery parameters of the battery energy storage system and obtaining the state of charge of the single battery, the following steps are further included:

[0086] Performing a safety self-check to determine whether there is a fault condition;

[0087] If the result of the determination is yes, the fault condition is obtained and the balancing is locked.

[0088] The safety self-check refers to a series of automatic checks performed by the BMS on its hardware, software and external sensor status during power-on, operation or on demand. Its core goal is to discover and handle faults in time before they cause harm to the system; the balancing lock state refers to a control state in which the BMS actively and temporarily prohibits or suspends all battery balancing functions. It can be understood as a "pause instruction" or "safety lock" issued by the BMS to the balancing system; when the BMS is in the balancing lock state, whether the voltage difference or SOC difference between the batteries is large or small, whether it is passive balancing or active balancing, it will be forced to stop.

[0089] It should be noted that in some specific conditions, continuing to balance is not only useless, but may be harmful. The balancing lock is mainly based on the following considerations: safety; ensure the accuracy of state estimation; function priority and energy management.

[0090] The above step is a specific implementation detail before step S1, based on the above considerations, when a fault condition is found in the safety self-check process, the specific situation of the fault condition is obtained, and the lockout balancing state is entered. By introducing a real-time fault condition judgment mechanism, safety protection is provided for the limit condition of the balancing strategy start.

[0091] Preferably, before collecting the parameters of the single battery cells of the current battery energy storage system and obtaining the state of charge of the single battery cells, the following steps are further included:

[0092] Performing a safety self-check to determine whether it is in an active balancing state;

[0093] If the result of the determination is yes, then the current active balancing process is continued;

[0094] When it is determined that there is no fault condition and it is not in an active balancing state, collecting the parameters of the single battery cells of the current battery energy storage system and obtaining the state of charge of the single battery cells are performed.

[0095] The above step is a specific implementation detail before step S1, when it is found in the safety self-check process that the system is in an active balancing state, the active balancing process is not interrupted at this time, but the current active balancing process is continued; when it is found that the system is not in an active balancing state and no fault condition is detected, step S1 is executed.

[0096] Preferably, collecting the parameters of the single battery cells of the current battery energy storage system and obtaining the state of charge of the single battery cells include the following steps:

[0097] Collecting the battery capacity and current of the single battery cells of the current battery energy storage system;

[0098] Obtaining the state of charge of the single battery cells according to the battery capacity, current, and corrected ampere-hour integral algorithm of the single battery cells.

[0099] The battery capacity of the single battery cell refers to the total amount of charge that can be released by the battery cell from a full charge state (charging cutoff voltage) to an empty charge state (discharging cutoff voltage) under certain conditions; the current of the single battery cell refers to the size of the current flowing into or out of the single battery cell when the battery management system (BMS) performs the balancing function; for active balancing, this current can be the transfer current flowing out of the high-energy battery cell or flowing into the low-energy battery cell;

[0100] The algorithm with correction is the most mainstream, most practical and highest precision method for estimating state of charge (SOC) in the current battery management system (BMS). The core idea of the algorithm is that: taking the ampere-hour integral method as the main body of real-time tracking, at the same time, using other high-precision methods (mainly open-circuit voltage method) as "landmark", periodically correcting the two fatal weaknesses of ampere-hour integral, initial SOC error and capacity attenuation error; the algorithm with correction successfully combines open-loop ampere-hour integral method with closed-loop feedback correction mechanism, and skillfully solves the fundamental defects of pure integral method. It realizes the best balance between SOC estimation accuracy and robustness through double correction in time dimension (OCV correction) and capacity dimension (capacity learning), so it becomes the de facto standard algorithm in the field of BMS today.

[0101] The formula of the algorithm with correction is:

[0102] ;

[0103] Among them, is the state of charge at the moment, is the initial state of charge, is the coulomb efficiency coefficient, is the battery capacity of the single cell, is the current of the single cell (positive for charging, negative for discharging), is the correction factor.

[0104] The above steps are the specific implementation details of step S1, that is, the BMS system collects the current battery energy storage system single cell parameters, that is, the battery capacity and current of the single cell, and combines the algorithm with correction to calculate and obtain the state of charge of the single cell.

[0105] Preferably, the single cell parameters and the state of charge of the single cell are processed, including the following steps:

[0106] Collecting the temperature and voltage of the single cell of the current battery energy storage system;

[0107] Normalizing the voltage, temperature, current and state of charge to obtain the corresponding voltage difference, temperature difference, current absolute value difference and state of charge difference.

[0108] Among them, the parameters of the single cell also include: the temperature and voltage of the single cell;

[0109] ​Normalization processing is a very important preprocessing technique in the fields of data analysis, machine learning and signal processing. Its core idea is to scale a set of data according to a certain proportion, so that it falls within a specific, unified interval, usually [0, 1] or [-1, 1], without changing its original distribution and internal relationship; range is a very basic and important index in statistics and data analysis. It is the difference between the maximum and minimum values in a set of data, used to measure the fluctuation range or dispersion degree of the data.

[0110] The above steps are the specific implementation details of step S2, which normalize the parameters of the single battery cell: voltage, temperature, current and state of charge of the single battery cell, to obtain the corresponding voltage range, temperature range, current absolute value range and state of charge range;

[0111] In one embodiment, the minimum-maximum normalization method is selected to process the voltage, temperature, current and state of charge, to obtain the corresponding voltage range, temperature range, current absolute value range and state of charge range, as follows:

[0112] ;

[0113] ;

[0114] ;

[0115] ;

[0116] wherein, to correspond to the voltage range, temperature range, current absolute value range and state of charge range, respectively.

[0117] Preferably, the processed single battery cell parameters and the state of charge of the single battery cell are input into the weight-range high-dimensional criterion model to obtain the corresponding initial weights, specifically:

[0118] The voltage range, temperature range, current absolute value range and state of charge range are input into the weight-range high-dimensional criterion model to obtain the corresponding initial voltage weight, initial temperature weight, initial current absolute value weight and initial state of charge weight.

[0119] The above steps are the specific implementation details of step A2, which input the normalized parameters such as voltage range, temperature range, current absolute value range and state of charge range into the model to obtain the corresponding initial weights;

[0120] In one embodiment, the constructed weight-range high-dimensional criterion model is composed of high-dimensional feature quantity input and dynamic decision model;

[0121] The formula of the dynamic decision model is as follows:

[0122] ;

[0123] ;

[0124] wherein, is a weight, is a range, is an exponential function, is an index to be evaluated, is an evaluation object.

[0125] The high-dimensional criterion model introduces an exponential function, forming a comprehensive evaluation or decision model combining a range, a weight, and an exponential function. The model is used for balanced priority decision judgment.

[0126] The voltage range, the temperature range, the current absolute value range, and the state of charge range are input into the high-dimensional criterion model to obtain initial weights to .

[0127] As shown in Figure 3 , preferably, the initial weights are adaptively adjusted to obtain adjusted weights. According to the adjusted weights, the processed single cell parameters, and the state of charge of the single cell, a weighted summation algorithm is used to obtain a current decision value, including the following steps:

[0128] B1. According to the voltage fault condition, the temperature fault condition, the current fault condition, and the state of charge fault condition, and the weight dynamic model, the initial voltage weight, the initial temperature weight, the initial current absolute value weight, and the initial state of charge weight are adaptively adjusted.

[0129] B2. According to the product of the adjusted voltage weight and the voltage range, the product of the adjusted temperature weight and the temperature range, the product of the adjusted current absolute value weight and the current absolute value range, and the product of the adjusted state of charge weight and the state of charge range, a current decision value is obtained.

[0130] Wherein, the fault conditions include: voltage fault condition, temperature fault condition, current fault condition, and state of charge fault condition.

[0131] Steps B1 to B2 are specific implementation details of step A3, and are designed to obtain the adjusted corresponding weight after the initial weight of each parameter is obtained and the weight dynamic model is considered to exist in the fault condition to adaptively adjust the initial weight in the limit condition. The adjusted corresponding weight is obtained; the current decision value is calculated and obtained according to the product of the adjusted voltage weight and the voltage range, the product of the adjusted temperature weight and the temperature range, the product of the adjusted current absolute value weight and the current absolute value range, and the product of the adjusted state of charge weight and the state of charge range.

[0132] In one embodiment, considering the voltage imbalance, temperature gradient, large current charging, SOC difference and other limit conditions, the initial weight is adaptively adjusted according to the weight dynamic model to obtain the adjusted corresponding weight.

[0133] The weight dynamic model is shown in Table 1:

[0134] Table 1: Weight dynamic model

[0135]

[0136] The weight dynamic formula is as follows:

[0137]

[0138]

[0139]

[0140]

[0141] The current decision value is calculated according to the weighted sum formula, the adjusted weight and the corresponding range, and the formula is as follows:

[0142]

[0143] The current decision value is as follows:

[0144] As shown in Figure 4 , preferably, after the judgment result is yes, the following steps are further included after entering the active balancing state:

[0145] C1. According to the battery capacity, current and state of charge, the theoretical balancing time is calculated and obtained;

[0146] C2. Determine whether the active balancing state running time has reached the theoretical balancing time;

[0147] C3. If the judgment result is yes, exit the active balancing state.

[0148] ​​​​​​The theoretical equalization time in step C1 refers to the time required for the battery management system (BMS) to adjust the state of charge (SOC) of all battery cells in the battery pack to the target consistent range through the equalization circuit under ideal conditions;

[0149] Steps C1 to C3 are specific implementation details after step S4, i.e., after entering the active equalization state, calculating the theoretically required equalization time, determining whether the active equalization running state has reached the theoretical equalization time when executing the active equalization process, and if so, exiting the active equalization state and jumping to the safety self-check step for the next round of active equalization opening and closing judgment.

[0150] In one embodiment, calculating the theoretical equalization time includes the following steps:

[0151] Step 1: Calculate the amount of charge that needs to be transferred;

[0152] The total amount of charge that needs to be transferred is based on the SOC difference that needs to be "flattened" and the battery capacity.

[0153] The SOC that needs to be compensated for ,

[0154] The amount of charge that needs to be transferred = battery capacity × SOC that needs to be compensated for,

[0155] (unit: Ah).

[0156] Step 2: Calculate the time required for equalization;

[0157] According to the basic relationship between charge (Q) and current (I): time = charge / current,

[0158] Equalization time = amount of charge that needs to be transferred / equalization current,

[0159] ;

[0160] Substitute the formula in step 1:

[0161] ;

[0162] wherein, is the theoretical equalization time, is the battery capacity of the single cell, is the current of the single cell, is the state of charge difference between the most full cell and the most empty cell at the beginning of equalization, is the maximum state of charge difference that the target reaches after equalization is completed.

[0163] Preferably, after determining whether the current decision value is greater than the equalization decision threshold, the following steps are included:

[0164] If the result is no, the system enters a hibernation standby state.

[0165] If the current decision value is less than the balancing decision threshold, the system enters a hibernation standby state and waits for a command to wake up and jump to the safety self-check step for the next round of active balancing open-close judgment.

[0166] As shown in FIG. Figure 5 A multi-feature active balancing system based on a battery energy storage system includes:

[0167] A single cell parameter acquisition module is configured to collect single cell parameters of the current battery energy storage system and acquire a state of charge of the single cell.

[0168] A processing module is configured to process the single cell parameters and the state of charge of the single cell.

[0169] A current decision value acquisition module is configured to acquire a current decision value based on the processed single cell parameters and the state of charge of the single cell and a weighted summation algorithm.

[0170] A judgment module is configured to judge whether the current decision value is greater than a balancing decision threshold.

[0171] An active balancing execution module is configured to enter an active balancing state if the result is yes.

[0172] The current decision value acquisition module includes:

[0173] A high-dimensional criterion model construction submodule is configured to construct a weight-range high-dimensional criterion model.

[0174] An initial weight acquisition submodule is configured to input the processed single cell parameters and the state of charge of the single cell into the weight-range high-dimensional criterion model to obtain corresponding initial weights.

[0175] An adaptive weight adjustment submodule is configured to adaptively adjust the initial weights to obtain adjusted weights.

[0176] A current decision value acquisition submodule is configured to acquire the current decision value based on the adjusted weights, the processed single cell parameters, and the state of charge of the single cell using the weighted summation algorithm.

[0177] The multi-feature active balancing system based on the battery energy storage system proposed in the application, compared with the traditional method, the core innovation is that through the modular system architecture, by fusing voltage, current, temperature, SOC and other multi-dimensional feature parameters, a high-dimensional criterion model is constructed, the weights of the multi-dimensional feature parameters are obtained, the decision value is calculated after the adaptive adjustment of each weight, and the relationship between the decision value and the threshold value is realized. The best balancing starting time and target are accurately determined, and invalid or harmful balancing is avoided. In terms of improving the overall life, operation efficiency and safety protection level of the energy storage power station, it is significantly better than the existing scheme. The traditional active balancing method generally has the problem of inaccurate balancing time judgment (too early, too late or unreasonable start), which leads to poor balancing effect and even threatens the safe and stable operation of the system. The system acquires the current battery energy storage system cell parameters through the cell parameter acquisition module, acquires the state of charge of the cell, constructs a weight-range high-dimensional criterion model through the high-dimensional criterion model construction submodule in the current decision value acquisition module; the processed cell parameters and the state of charge of the cell are input into the weight-range high-dimensional criterion model through the initial weight acquisition submodule to obtain the corresponding initial weight; the initial weight is adaptively adjusted through the adaptive weight adjustment submodule; the current decision value is obtained by using the weighted summation algorithm according to the adjusted weight and the processed cell parameters and the state of charge of the cell through the current decision value acquisition submodule; whether the current decision value is greater than the balancing decision threshold is judged through the judgment module; and when the judgment result is yes, the active balancing state is entered through the active balancing execution module. The system accurately determines the best balancing starting time and target through the relationship between the decision value and the threshold value, avoids invalid or harmful balancing, and effectively improves the precision and efficiency of active balancing.

[0178] One or more embodiments in the present application are intended to cover all such alternatives, modifications and variations falling within the broad scope of the present application. Therefore, any omission, modification, equivalent replacement, improvement, etc. made in the spirit and principles of one or more embodiments in the present application shall be included in the protection scope of the present application.

[0179] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instructions and related hardware. The aforementioned program instructions can be stored in a computer-readable storage medium, and the program instructions execute the steps including the above-mentioned method embodiments when executed; and the aforementioned storage medium includes a mobile storage device, a read-only memory (ROM), a magnetic disc or an optical disc and various storage program codes.

[0180] If a flow diagram is used, the flow diagram is used to illustrate the operations according to embodiments of the present application. It should be understood that the operations in the flow diagram do not necessarily have to be performed in the order shown. Rather, various steps can be handled in reverse order or simultaneously. Additionally, other operations can be added or removed from the flow diagram without departing from the scope of the present application.

[0181] The above provides a multi-feature active balancing method and system based on a battery energy storage system. The above description of the disclosed embodiments enables those skilled in the art to carry out or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-feature active equalization method based on a battery energy storage system, characterized in that, Includes the following steps: Collect the parameters of individual cells in the current battery energy storage system to obtain the state of charge of the individual cells; The parameters of the individual battery cell and the state of charge of the individual battery cell are processed; Based on the processed parameters of the individual battery cell, the state of charge of the individual battery cell, and the weighted summation algorithm, the current decision value is obtained; Determine whether the current decision value is greater than the equilibrium decision threshold; If the judgment result is yes, then it enters an active equilibrium state; The step of obtaining the current decision value based on the processed parameters of the individual battery cell, the state of charge of the individual battery cell, and a weighted summation algorithm includes: Construct a weighted-range high-dimensional criterion model; The processed parameters of the individual battery cell and the state of charge of the individual battery cell are input into the weight-range high-dimensional criterion model to obtain the corresponding initial weights. The initial weights are adaptively adjusted to obtain the adjusted weights; Based on the adjusted weights, the processed individual cell parameters, and the state of charge of the individual cell, the weighted summation algorithm is used to obtain the current decision value.

2. The method according to claim 1, characterized in that, Before acquiring the individual cell parameters of the current battery energy storage system and obtaining the state of charge of the individual cell, the method further includes: Perform a safety self-check to determine if any fault conditions exist; If the judgment result is yes, the fault condition is obtained, and the lockout balance state is entered.

3. The method according to claim 2, characterized in that, Before acquiring the individual cell parameters of the current battery energy storage system and obtaining the state of charge of the individual cell, the method further includes: Perform a safety self-check to determine whether the active equilibrium state is in place. If the judgment result is yes, then continue to execute the current active balancing process; When it is determined that there is no fault condition and the system is not in an active balancing state, the system collects the individual cell parameters of the current battery energy storage system to obtain the state of charge of the individual cell.

4. The method according to claim 3, characterized in that, The process of collecting the parameters of individual cells in the current battery energy storage system and obtaining the state of charge of the individual cells includes: Collect the battery capacity and current of individual cells in the current battery energy storage system; The state of charge of a single battery cell is obtained based on its battery capacity, current, and a modified ampere-hour integral algorithm.

5. The method according to claim 4, characterized in that, The processing of the individual cell parameters and the state of charge of the individual cell includes the following steps: Collect the temperature and voltage of individual battery cells in the current battery energy storage system; The voltage, temperature, current, and state of charge are normalized to obtain the corresponding voltage range, temperature range, absolute current range, and state of charge range.

6. The method according to claim 5, characterized in that, The processed parameters of the individual battery cell and the state of charge of the individual battery cell are input into the weighted-range high-dimensional criterion model to obtain the corresponding initial weights, including: The voltage range, temperature range, absolute current range, and state of charge range are input into the weighted range high-dimensional criterion model to obtain the corresponding initial voltage weight, initial temperature weight, initial absolute current weight, and initial state of charge weight.

7. The method according to claim 6, characterized in that, The adaptive adjustment of the initial weights, and the acquisition of the current decision value using the weighted summation algorithm based on the adjusted weights, the processed individual cell parameters, and the individual cell's state of charge, includes: Based on voltage fault conditions, temperature fault conditions, current fault conditions, and state of charge fault conditions, as well as the weighted dynamic model, the initial voltage weight, the initial temperature weight, the initial current absolute value weight, and the initial state of charge weight are adaptively adjusted. The current decision value is obtained by multiplying the adjusted voltage weight by the voltage range, the adjusted temperature weight by the temperature range, the adjusted current absolute value weight by the current absolute value range, and the adjusted state of charge weight by the state of charge range. The fault conditions include: voltage fault condition, temperature fault condition, current fault condition and state of charge fault condition.

8. The method according to claim 5, characterized in that, After the condition that the judgment result is yes, the process of entering the active equilibrium state further includes: The theoretical balancing time is calculated based on the battery capacity, the current, and the state of charge. Determine whether the active equilibrium state has reached the theoretical equilibrium time; If the judgment result is yes, then exit the active equilibrium state.

9. The method according to claim 1, characterized in that, After determining whether the current decision value is greater than the equilibrium decision threshold, the method further includes: If the judgment result is negative, it will enter a hibernation standby state.

10. A multi-feature active balancing system based on a battery energy storage system, characterized in that, include: The single cell parameter acquisition module is used to collect the single cell parameters of the current battery energy storage system and obtain the state of charge of the single cell. The processing module is used to process the parameters of the individual battery cell and the state of charge of the individual battery cell; The current decision value acquisition module is used to acquire the current decision value based on the processed parameters of the individual cell, the state of charge of the individual cell, and a weighted summation algorithm. The judgment module is used to determine whether the current decision value is greater than the equilibrium decision threshold; The active balancing execution module is used to enter the active balancing state if the judgment result is yes. The current decision value acquisition module includes: The high-dimensional criterion model construction submodule is used to construct a weighted-range high-dimensional criterion model; The initial weight acquisition submodule is used to input the processed parameters of the individual cell and the state of charge of the individual cell into the weight-range high-dimensional criterion model to obtain the corresponding initial weights. An adaptive weight adjustment submodule is used to adaptively adjust the initial weights to obtain the adjusted weights. The current decision value acquisition submodule is used to acquire the current decision value by using the weighted summation algorithm based on the adjusted weights, the processed parameters of the individual cell and the state of charge of the individual cell.

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