An intelligent balancing control method and system for a new energy vehicle battery pack

Abstract

The present invention discloses an intelligent equalization control method and system for a new energy vehicle battery pack, which obtains the state of charge data of each battery cell from a battery management system, and calculates the state of charge difference value between battery cells by using a preset feature extraction algorithm; compares the state of charge difference value between battery cells with a first preset threshold, and if the state of charge difference value between battery cells is greater than the first preset threshold, determines the target battery cell combination; according to the target battery cell combination, uses a decision tree model to predict the dynamic energy transfer path between battery cells, and generates an energy transfer scheme; obtains real-time monitoring data through the energy transfer scheme, and calculates the energy transfer efficiency; compares the energy transfer efficiency with a second preset threshold, and if the energy transfer efficiency reaches the second preset threshold, triggers an active equalization circuit to perform energy distribution. The present invention improves the equalization efficiency and control accuracy, optimizes the energy distribution and reduces the energy consumption, and prolongs the service life of the battery pack.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and particularly discloses an intelligent equalization control method and system for a new energy vehicle battery pack. Background Art

[0002] The battery management system is crucial in electric vehicles and energy storage systems, directly affecting the performance, safety, and service life of the battery pack.

[0003] With the rapid development of the new energy industry, the equalization control of the battery pack has become a core technology to ensure battery consistency and extend the cycle life. Effective equalization control can not only improve energy utilization efficiency but also reduce safety hazards, providing a guarantee for large-scale commercial applications. However, many current equalization methods have obvious deficiencies in practical applications. Traditional passive equalization dissipates energy through resistors, with low efficiency and generating a large amount of heat; active equalization can transfer energy, but the control is complex and the cost is high. These limitations make it difficult for the battery pack to achieve efficient and accurate state-of-charge balance under complex working conditions, restricting the overall performance of the system.

[0004] In the field of battery equalization, the core challenges focus on how to efficiently achieve energy distribution among battery cells while taking into account the computational complexity and hardware cost of the system.

[0005] The difference in the state of charge between battery cells is the primary problem, which directly leads to overcharging or over-discharging of some battery cells and accelerates aging.

[0006] Due to the need for real-time monitoring and dynamic adjustment, existing methods often face the problem of a sharp increase in computational complexity when dealing with large-scale battery cell groups, increasing system latency and energy consumption.

[0007] Furthermore, the increase in computational complexity requires more powerful hardware support, which raises the design and maintenance costs of the system. Especially under dynamic working conditions, it is difficult to quickly respond to changes in the state of battery cells.

[0008] Therefore, how to reduce computational complexity and hardware cost while ensuring efficient energy transfer has become a key issue in battery equalization control research. Summary of the Invention

[0009] The present invention provides an intelligent equalization control method and system for a new energy vehicle battery pack, aiming to solve at least one of the defects existing in the above-mentioned prior art.

[0010] One aspect of the present invention relates to an intelligent equalization control method for a new energy vehicle battery pack, including the following steps:

[0011] Obtain the state-of-charge data of each battery cell from the battery management system, and calculate the difference value of the state of charge between battery cells using a preset feature extraction algorithm;

[0012] Compare the state-of-charge difference value between the battery cells with a first preset threshold. If the state-of-charge difference value between the battery cells is greater than the first preset threshold, determine the target battery cell combination;

[0013] According to the target battery cell combination, use a decision tree model to predict the dynamic energy transfer path between the battery cells and generate an energy transfer scheme;

[0014] Obtain real-time monitoring data through the energy transfer scheme and calculate the energy transfer efficiency;

[0015] Compare the energy transfer efficiency with a second preset threshold. If the energy transfer efficiency reaches the second preset threshold, trigger the active balancing circuit to perform energy distribution.

[0016] Further, the steps of obtaining the state-of-charge data of each battery cell from the battery management system and calculating the state-of-charge difference value between the battery cells using a preset feature extraction algorithm include:

[0017] Obtain the state-of-charge data of each battery cell from the battery management system, classify and store the data according to the battery cell numbers to obtain a state data set;

[0018] Use a preset feature extraction algorithm to extract the state-of-charge feature values of each battery cell from the state data set to obtain a feature value set;

[0019] Through the first difference calculation formula D(i,j)=|F(i)-F(j)|, calculate the state-of-charge difference value between the battery cells for the state-of-charge feature values of each battery cell in the feature value set, where D(i,j) represents the difference value between battery cell i and battery cell j, F(i) represents the feature value of battery cell i, and F(j) represents the feature value of battery cell j.

[0020] Further, the steps of comparing the state-of-charge difference value between the battery cells with a first preset threshold and determining the target battery cell combination if the state-of-charge difference value between the battery cells is greater than the first preset threshold include:

[0021] Obtain the voltage data of each battery cell from the battery management system, classify and store the voltage data according to the battery cell numbers to obtain a voltage data set;

[0022] Use a preset feature extraction algorithm to extract the voltage feature values of each battery cell from the voltage data set to obtain a voltage feature value set;

[0023] Through the second difference calculation formula V(i,j)=|V(i)-V(j)|, calculate the voltage difference value between the battery cells for the voltage feature values of each battery cell in the voltage feature value set to obtain a voltage difference value set, where V(i,j) represents the voltage difference value between battery cell i and battery cell j, V(i) represents the voltage feature value of battery cell i, and V(j) represents the voltage feature value of battery cell j;

[0024] If any voltage difference value in the set of voltage difference values is greater than the second preset threshold, determine the corresponding battery cells i and j as the target battery cell combination, and obtain the set of target battery cell combinations.

[0025] Further, according to the target battery cell combination, the steps of predicting the dynamic energy transfer path between battery cells using a decision tree model and generating an energy transfer plan include:

[0026] Obtain the operating data of each battery cell from the battery management system, store them classified according to the battery cell numbers, and extract the dynamic voltage characteristic values of the operating data using a preset feature extraction algorithm to obtain a set of dynamic voltage characteristic values;

[0027] If at least one characteristic value in the set of dynamic voltage characteristic values exceeds the preset dynamic threshold, classify and predict the target battery cell combination through the decision tree model to obtain a preliminary set of energy transfer paths;

[0028] According to the preliminary set of energy transfer paths, use a path optimization algorithm to sort the paths, calculate the transfer time and energy loss values for the paths, and obtain an optimized set of energy transfer paths;

[0029] Generate a corresponding energy transfer plan through the optimized set of energy transfer paths, determine the transfer priority and execution order of the target battery cell combination, and obtain the final energy transfer plan.

[0030] Further, the steps of obtaining real-time monitoring data through the energy transfer plan and calculating the energy transfer efficiency include:

[0031] Obtain the real-time monitoring data of the energy transfer plan, calculate the energy transfer efficiency of each path using a preset efficiency calculation formula, and obtain a set of energy transfer efficiencies;

[0032] Extract the paths with efficiencies lower than the preset threshold from the set of energy transfer efficiencies. If the efficiency of at least one path is lower than the threshold, use a path optimization algorithm to re-sort the paths to obtain an optimized set of paths;

[0033] For the optimized set of paths, calculate the transfer time and energy loss values of each path. If the transfer time exceeds the preset time threshold, eliminate the corresponding path to obtain a filtered set of paths;

[0034] According to the filtered set of paths, use a preset priority assignment algorithm to determine the transfer priority of each path and obtain the priority sorting result;

[0035] Based on the priority sorting result, combined with the dynamic voltage characteristics in the battery operating data, judge the execution order of each path. If the dynamic voltage characteristic value exceeds the preset range, adjust the execution order to obtain the final set of execution orders;

[0036] Extract the preferred path from the final execution order set, obtain its real-time monitoring data, and recalculate the energy transfer efficiency using the efficiency calculation formula.

[0037] Further, comparing the energy transfer efficiency with the second preset threshold, if the energy transfer efficiency reaches the second preset threshold, the steps of triggering the active equalization circuit for energy distribution include:

[0038] Obtain the energy transfer efficiency of each path from the real-time monitoring data, and calculate the energy transfer efficiency set of each path using the preset efficiency calculation formula; the efficiency calculation formula is E = P_out / P_in, where E represents the energy transfer efficiency, P_out represents the output power, and P_in represents the input power;

[0039] If the energy transfer efficiency of at least one path reaches the second preset threshold, generate a trigger signal through the preset trigger signal generation algorithm to determine the startup state of the active equalization circuit;

[0040] According to the trigger signal, use the control logic of the active equalization circuit to extract the dynamic voltage characteristics from the battery operation data, judge the energy distribution ratio of each path, and obtain the distribution ratio set;

[0041] Through the distribution ratio set, combined with the preset path selection algorithm, adjust the energy transmission amount of each path to determine the final energy distribution execution order set.

[0042] Another aspect of the present invention relates to an intelligent equalization control system for a new energy vehicle battery pack, which is used to implement the above-mentioned intelligent equalization control method for a new energy vehicle battery pack. The intelligent equalization control system for a new energy vehicle battery pack includes:

[0043] The first calculation module is used to obtain the state of charge data of each battery cell from the battery management system and calculate the state of charge difference value between battery cells using the preset feature extraction algorithm;

[0044] The determination module is used to compare the state of charge difference value between battery cells with the first preset threshold. If the state of charge difference value between battery cells is greater than the first preset threshold, determine the target battery cell combination;

[0045] The generation module is used to predict the dynamic energy transfer path between battery cells according to the target battery cell combination using the decision tree model and generate an energy transfer scheme;

[0046] The second calculation module is used to obtain real-time monitoring data through the energy transfer scheme and calculate the energy transfer efficiency;

[0047] A trigger module, configured to compare the energy transfer efficiency with a second preset threshold. If the energy transfer efficiency reaches the second preset threshold, the trigger module triggers the active balancing circuit to perform energy distribution.

[0048] Further, the first calculation module includes:

[0049] A first acquisition unit, configured to acquire the state-of-charge data of each battery cell from the battery management system, classify and store the data according to the battery cell numbers, and obtain a state data set;

[0050] A second acquisition unit, configured to extract the state-of-charge characteristic values of each battery cell from the state data set by using a preset feature extraction algorithm, and obtain a characteristic value set;

[0051] A first calculation unit, configured to calculate the state-of-charge difference value between battery cells for the state-of-charge characteristic values of each battery cell in the characteristic value set through a first difference calculation formula D(i,j)=|F(i)-F(j)|, where D(i,j) represents the difference value between battery cell i and battery cell j, F(i) represents the characteristic value of battery cell i, and F(j) represents the characteristic value of battery cell j.

[0052] Further, the determination module includes:

[0053] A third acquisition unit, configured to acquire the voltage data of each battery cell from the battery management system, classify and store the voltage data according to the battery cell numbers, and obtain a voltage data set;

[0054] A fourth acquisition unit, configured to extract the voltage characteristic values of each battery cell from the voltage data set by using a preset feature extraction algorithm, and obtain a voltage characteristic value set;

[0055] A second calculation unit, configured to calculate the voltage difference value between battery cells for the voltage characteristic values of each battery cell in the voltage characteristic value set through a second difference calculation formula V(i,j)=|V(i)-V(j)|, and obtain a voltage difference value set, where V(i,j) represents the voltage difference value between battery cell i and battery cell j, V(i) represents the voltage characteristic value of battery cell i, and V(j) represents the voltage characteristic value of battery cell j;

[0056] A fifth acquisition unit, configured to determine the corresponding battery cells i and j as a target battery cell combination if any voltage difference value in the voltage difference value set is greater than the second preset threshold, and obtain a target battery cell combination set.

[0057] Further, the generation module includes:

[0058] A sixth acquisition unit, configured to acquire the operation data of each battery cell from the battery management system, classify and store the operation data according to the battery cell numbers, and extract the dynamic voltage characteristic values of the operation data by using a preset feature extraction algorithm, and obtain a dynamic voltage characteristic value set;

[0059] A seventh acquisition unit, configured to, if at least one eigenvalue in the set of dynamic voltage eigenvalues exceeds a preset dynamic threshold, perform classification prediction on the target battery cell combination through a decision tree model to obtain a preliminary set of energy transfer paths;

[0060] An eighth acquisition unit, configured to, according to the preliminary set of energy transfer paths, sort the paths by using a path optimization algorithm, calculate the transfer time and energy loss value for the paths, and obtain an optimized set of energy transfer paths;

[0061] A ninth acquisition unit, configured to generate a corresponding energy transfer scheme through the optimized set of energy transfer paths, determine the transfer priority and execution order of the target battery cell combination, and obtain a final energy transfer scheme.

[0062] The beneficial effects achieved by the present invention are as follows:

[0063] The present invention provides an intelligent equalization control method and system for a new energy vehicle battery pack, obtains the state of charge data of each battery cell from a battery management system, and calculates the state of charge difference value between battery cells by using a preset feature extraction algorithm; compares the state of charge difference value between battery cells with a first preset threshold, and if the state of charge difference value between battery cells is greater than the first preset threshold, determines a target battery cell combination; according to the target battery cell combination, predicts the dynamic energy transfer path between battery cells by using a decision tree model, and generates an energy transfer scheme; obtains real-time monitoring data through the energy transfer scheme, and calculates the energy transfer efficiency; compares the energy transfer efficiency with a second preset threshold, and if the energy transfer efficiency reaches the second preset threshold, triggers an active equalization circuit to perform energy distribution. The intelligent equalization control method and system for a new energy vehicle battery pack provided by the present invention have the following specific beneficial effects:

[0064] I. Improve equalization efficiency and control accuracy

[0065] By calculating the state of charge (SOC) difference value between battery cells in real time through a feature extraction algorithm and combining a decision tree model to predict the dynamic energy transfer path, high-difference battery cell combinations can be accurately identified. Compared with traditional passive equalization that relies on a fixed threshold trigger, the quantification evaluation of the difference degree is realized through hierarchical threshold judgment (the first and second preset thresholds), so as to dynamically adjust the equalization strategy.

[0066] II. Optimize energy distribution and reduce energy consumption

[0067] By adopting an active equalization circuit combined with dynamic energy transfer path planning, the energy of high-SOC battery cells can be directly transferred to low-SOC battery cells instead of being dissipated through resistors, reducing energy loss. The real-time monitoring and feedback mechanism of energy transfer efficiency further ensures the minimization of energy consumption during the equalization process.

[0068] III. Prolong the service life of the battery pack

[0069] By continuously monitoring the SOC difference and quickly triggering balancing, it can effectively alleviate the "barrel effect" caused by inconsistent capacity attenuation among cells, and delay the aging of the overall battery pack. Combining with the analysis ability of the decision tree model for historical data, it can also predict the degradation trend of cell performance and achieve preventive balancing. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a schematic flowchart of an embodiment of an intelligent balancing control method for a new energy vehicle battery pack according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0072] As Figure 1 shown, a first embodiment of the present invention proposes an intelligent balancing control method for a new energy vehicle battery pack, including the following steps:

[0073] Step S100: Obtain the state of charge data of each cell from the battery management system, and calculate the state of charge difference value between cells by using a preset feature extraction algorithm.

[0074] The battery management system (Battery Management System, BMS) is a core control system used to monitor, manage and protect battery packs (such as lithium-ion batteries, lead-acid batteries, etc.), and is widely used in new energy vehicles, energy storage systems, consumer electronics and other fields. Its core goal is to ensure the safe, efficient and reliable operation of the battery pack and extend the battery life.

[0075] The state of charge (State of Charge, SOC) data is a core parameter that describes the ratio of the remaining battery charge to the full charge capacity.

[0076] The feature extraction algorithm is a mathematical method for identifying and extracting key indicators or patterns from raw data, used to simplify the data dimension and highlight the core features to support subsequent analysis or decision-making.

[0077] The state of charge (SOC) difference value between cells is a core indicator that measures the degree of dispersion of the remaining charge of each single cell in the same battery pack, defined as the difference (range) between the maximum and minimum SOC values of the cells in the group or quantitative parameters such as the statistical distribution standard deviation.

[0078] Step S200: Compare the state of charge difference value between cells with a first preset threshold. If the state of charge difference value between cells is greater than the first preset threshold, determine the target cell combination.

[0079] The target battery cell combination integrates individual battery cells through a specific series-parallel connection method to form a battery module that meets the consistency requirements, aiming to optimize the overall performance and adapt to the application scenario needs.

[0080] Step S300: According to the target battery cell combination, use a decision tree model to predict the dynamic energy transfer path between battery cells and generate an energy transfer plan.

[0081] The decision tree model is a supervised learning algorithm based on a tree structure. It realizes classification or regression tasks by recursively partitioning the data feature space. Its core logic is to simulate the human decision-making process and construct an interpretable prediction path with an "if-then" rule chain. In the field of battery management, this model is commonly used for battery cell state assessment, fault diagnosis, and balancing strategy optimization.

[0082] The dynamic energy transfer path refers to an intelligent management architecture that dynamically adjusts the energy flow direction based on the real-time system state (such as battery state of charge, load demand, input power fluctuation, etc.). Its core goal is to balance system efficiency and security by optimizing the energy distribution strategy.

[0083] The energy transfer plan is a set of strategies and technologies designed to achieve efficient energy flow and balanced distribution in a battery system or energy network. Its core lies in eliminating the energy difference between battery cells / components through a controllable energy transfer mechanism to ensure the overall performance and security of the system.

[0084] Step S400: Obtain real-time monitoring data through the energy transfer plan and calculate the energy transfer efficiency.

[0085] Real-time monitoring data refers to the operation parameters of the target system (such as energy network, industrial equipment) continuously collected by hardware devices such as sensors and intelligent terminals with a time resolution from milliseconds to seconds, and the data is instantaneously transmitted and analyzed through wired / wireless communication technologies to form a dynamic visual feedback and decision support for the system state.

[0086] The energy transfer efficiency refers to the ratio of the available energy effectively output to the total input energy during the energy transmission or conversion process, which is used to quantify the effectiveness of the energy transfer process.

[0087] Step S500: Compare the energy transfer efficiency with a second preset threshold. If the energy transfer efficiency reaches the second preset threshold, trigger the active balancing circuit for energy distribution.

[0088] The active balancing circuit is an electronic system that eliminates the energy difference between individual cells in a battery pack through controllable energy transfer technology. Its core lies in using active components (such as capacitors, inductors, or DC-DC converters) to achieve lossless or low-loss energy redistribution, rather than simply dissipating excess energy.

[0089] Energy distribution refers to the dynamic adjustment and optimal allocation of energy among different units within a system (such as battery cells, energy storage modules, or load devices) through active or passive control means to maintain the overall performance of the system, extend its lifespan, and improve energy utilization efficiency.

[0090] Furthermore, the intelligent equalization control method for the energy vehicle battery pack provided in this embodiment, step S100 includes:

[0091] Step S110: Obtain the state-of-charge data of each battery cell from the battery management system, classify and store the data according to the cell number, and obtain a state data set.

[0092] Obtaining the state-of-charge data of each battery cell from the battery management system is one of the core functions of the battery management system. The state of charge reflects the percentage of the remaining capacity of the battery cell in the total capacity, which directly affects the performance and safety of the battery pack.

[0093] Exemplarily, a battery pack of an electric vehicle contains 100 battery cells. The battery management system collects data of each battery cell in real time through voltage, current, and temperature sensors, generates state-of-charge values. For example, the state of charge of cell 1 is 80% and that of cell 2 is 78%. These data are stored in the database with timestamps and cell numbers as identifiers.

[0094] In a possible implementation manner, classify and store the state-of-charge data according to the cell number to generate a state data set.

[0095] It should be noted that classification storage needs to ensure data traceability. For example, the database creates a table with the cell number as the primary key to store the state-of-charge data of each battery cell. The data of cell 1 is stored in the record numbered 1, and the data of cell 2 is stored in the record numbered 2. This method facilitates subsequent data retrieval and analysis, and can quickly locate abnormal battery cells, improving management efficiency.

[0096] Step S120: Adopt a preset feature extraction algorithm to extract the state-of-charge feature values of each battery cell from the state data set to obtain a feature value set.

[0097] Specifically, adopt a preset feature extraction algorithm to extract the state-of-charge feature values from the state data set. The feature extraction algorithm can be a statistical analysis method, such as calculating the mean or change rate of the state of charge over a period of time.

[0098] In one embodiment, for cell 1, select the state-of-charge data in the past 1 hour, calculate the mean value of 79.5% and the standard deviation of 0.5% as the feature value. This feature value reflects the state-of-charge stability of the battery cell and helps to identify battery cells with abnormal fluctuations.

[0099] Preferably, feature extraction can also combine the influence of temperature to generate a comprehensive feature value, improving the analysis accuracy.

[0100] Step S130: Calculate the state-of-charge difference value between cells for the state-of-charge eigenvalue of each cell in the eigenvalue set through the first difference calculation formula D(i,j)=|F(i)-F(j)|, where D(i,j) represents the difference value between cell i and cell j, F(i) represents the eigenvalue of cell i, and F(j) represents the eigenvalue of cell j.

[0101] For example, calculate the state-of-charge difference value between cells through the difference calculation formula. The difference value reflects the balance of the state-of-charge between cells and is crucial for the equalization management of the battery pack.

[0102] In one embodiment, the eigenvalue of cell 1 is 79.5% and that of cell 2 is 78.5%, then the difference value is |79.5 - 78.5| = 1%. If the difference value exceeds the threshold, such as 2%, the equalization circuit is triggered to discharge the cell with a high state of charge. The difference value analysis can effectively prevent overcharging or over-discharging of cells and extend the battery life.

[0103] It can be understood that the above process needs to ensure the real-time and accuracy of data acquisition. For example, the sensor sampling frequency is set to 1 time per second, and the state-of-charge data is filtered by a filtering algorithm to remove noise to ensure the reliability of feature extraction.

[0104] In addition, the automated processing of classification storage and feature extraction can significantly reduce the cost of manual intervention and improve the system response speed.

[0105] In one possible implementation, the difference value result can be used to dynamically adjust the battery management strategy. For example, it is detected that the difference value between cell 3 and cell 4 is 3%, and the system automatically allocates more charging current to cell 4 with a lower state of charge.

[0106] This adaptive management can optimize the overall performance of the battery pack and reduce energy waste.

[0107] Preferably, the implementation of the above technical solution can significantly improve the safety and service life of the battery pack. For example, through real-time monitoring and difference analysis, cells with abnormal state of charge can be detected in advance to avoid the risk of thermal runaway.

[0108] At the same time, the equalization management is precisely regulated based on the difference value, reducing the performance deviation between cells and ensuring the stability of the battery pack in high-load scenarios. These technical effects jointly support the reliability and efficiency of the battery management system.

[0109] Furthermore, for the intelligent equalization control method of the energy vehicle battery pack provided in this embodiment, step S200 includes:

[0110] Step S210: Obtain the voltage data of each cell from the battery management system, classify and store the voltage data according to the cell number to obtain a voltage data set.

[0111] In a possible implementation, voltage data is classified and stored according to the cell number to generate a voltage data set.

[0112] It should be noted that classified storage needs to ensure the traceability and efficiency of data. Exemplarily, the database uses the cell number as the primary key to create a table to store voltage data. The voltage data of cell 1 is stored in the record numbered 1, and the voltage data of cell 2 is stored in the record numbered 2. This method is convenient for quickly retrieving and analyzing abnormal cells.

[0113] Obtaining the voltage data of each cell from the battery management system is one of the core functions of the battery management system. The voltage data reflects the working state of the cell and directly affects the performance of the battery pack. For example, an electric vehicle battery pack contains 50 cells. The battery management system collects the voltage data of each cell in real time through a high-precision voltage sensor. For example, the voltage of cell 1 is 3.65V and the voltage of cell 2 is 3.62V. These data are stored in the database with the time stamp and cell number as identifiers.

[0114] Step S220: Use a preset feature extraction algorithm to extract the voltage feature values of each cell from the voltage data set to obtain a voltage feature value set.

[0115] Specifically, a preset feature extraction algorithm is used to extract the voltage feature values of each cell from the voltage data set. The feature extraction algorithm can be a statistical analysis method, such as calculating the mean value or fluctuation range of the voltage over a period of time.

[0116] In one embodiment, for cell 1, the voltage data in the past 30 minutes is selected, and the calculated mean value is 3.64V, the maximum value is 3.66V, and the minimum value is 3.62V. The mean value is used as the voltage feature value. This feature value reflects the stability of the cell voltage and helps to identify abnormal cells.

[0117] Preferably, feature extraction can be combined with current data to generate a comprehensive feature value to improve the analysis accuracy.

[0118] Step S230: Through the second difference calculation formula V(i,j)=|V(i)-V(j)|, for the voltage feature values of each cell in the voltage feature value set, calculate the voltage difference value between cells to obtain a voltage difference value set, where V(i,j) represents the voltage difference value between cell i and cell j, V(i) represents the voltage feature value of cell i, and V(j) represents the voltage feature value of cell j.

[0119] Calculate the voltage difference value between cells through the second difference calculation formula to obtain a set of voltage difference values. For example, if the voltage characteristic value of cell 1 is 3.64V and that of cell 2 is 3.61V, the difference value is |3.64 - 3.61| = 0.03V. If the second preset threshold is 0.05V, this difference value does not exceed the threshold.

[0120] In one embodiment, if the characteristic value of cell 3 is 3.68V and that of cell 4 is 3.60V, the difference value is 0.08V, which exceeds the threshold, then cell 3 and cell 4 are determined as the target cell combination.

[0121] Step S240: If any voltage difference value in the set of voltage difference values is greater than the second preset threshold, determine the corresponding cells i and j as the target cell combination to obtain a set of target cell combinations.

[0122] It can be understood that the set of target cell combinations is used to identify pairs of cells with voltage imbalance and trigger subsequent management strategies.

[0123] In a possible implementation manner, the analysis of the set of voltage difference values can dynamically adjust the battery management strategy. For example, it is detected that the difference value between cell 5 and cell 6 is 0.07V, which exceeds the threshold, and the system automatically allocates more charging current to the cell 6 with a lower voltage. This adaptive management can optimize the performance of the battery pack.

[0124] Preferably, the automated processing of classification storage and feature extraction can reduce the cost of manual intervention and improve the system response speed. For example, the sensor sampling frequency is set to 2 times per second, and the voltage data is filtered by a filtering algorithm to remove noise to ensure the reliability of feature extraction.

[0125] This method can quickly locate the cells with abnormal voltage and improve the management efficiency.

[0126] Furthermore, for the intelligent equalization control method of the energy vehicle battery pack provided in this embodiment, step S300 includes:

[0127] Step S310: Obtain the operation data of each cell from the battery management system, classify and store it according to the cell number, and extract the dynamic voltage characteristic value of the operation data by using a preset feature extraction algorithm to obtain a set of dynamic voltage characteristic values.

[0128] Exemplarily, obtaining the operation data of each cell from the battery management system is a key link to realize battery performance optimization.

[0129] Operating data usually includes information such as voltage, current, and temperature. These data are collected in real time by high-precision sensors. For example, a battery pack of an electric vehicle contains 100 battery cells. The battery management system collects data once every second, records the voltage of cell 1 as 3.63V, the current as 2.5A, and the temperature as 25°C. These data are identified by cell number and timestamp to ensure traceability.

[0130] In a possible implementation, the operating data is stored classified according to the cell number for subsequent analysis. The database uses the cell number as the primary key to create a table to store the data. The operating data of cell 1 is stored in the record numbered 1, and that of cell 2 is stored in the record numbered 2.

[0131] It should be noted that the classified storage needs to ensure efficient retrieval, and an indexing mechanism is used to optimize the query speed. For example, querying the voltage data of cell 50 in the past hour can be completed in just a few milliseconds.

[0132] Specifically, a preset feature extraction algorithm is used to extract the dynamic voltage feature values to generate a set of dynamic voltage feature values. The feature extraction algorithm can be based on time series analysis to extract the fluctuation frequency or change rate of the voltage.

[0133] In an embodiment, for cell 1, the voltage data in the past 10 minutes is analyzed, and the voltage change rate is calculated as 0.01V / min, which is used as the dynamic voltage feature value. This feature value reflects the dynamic performance of the cell.

[0134] If the feature value of a certain cell is 0.03V / min, exceeding the preset dynamic threshold of 0.02V / min, it is marked as abnormal.

[0135] Step S320: If at least one feature value in the set of dynamic voltage feature values exceeds the preset dynamic threshold, classify and predict the target cell combination through a decision tree model to obtain a preliminary set of energy transfer paths.

[0136] Preferably, classify and predict the target cell combination through a decision tree model to generate a preliminary set of energy transfer paths. For example, the dynamic feature values of cell 3 and cell 4 are 0.025V / min and 0.015V / min respectively. The decision tree model predicts that energy transfer is required between the two, and generates the path "cell 3 → cell 4".

[0137] It can be understood that the decision tree quickly identifies the cell pairs that need to transfer energy through feature value comparison and historical data training.

[0138] Step S330: According to the preliminary set of energy transfer paths, use a path optimization algorithm to sort the paths, calculate the transfer time and energy loss value for each path, and obtain an optimized set of energy transfer paths.

[0139] In one embodiment, a path optimization algorithm is used to sort the preliminary energy transfer paths, and the transfer time and energy loss values are calculated. For example, for the path "Cell 3 → Cell 4", the expected transfer time is 5 minutes and the energy loss is 0.02 Wh; for another path "Cell 5 → Cell 6", the transfer time is 6 minutes and the loss is 0.03 Wh.

[0140] The optimization algorithm preferentially selects the path with lower loss to generate an optimized set of energy transfer paths.

[0141] Step S340: Generate a corresponding energy transfer plan through the optimized set of energy transfer paths, determine the transfer priority and execution order of the target cell combination, and obtain the final energy transfer plan.

[0142] For example, generate the final energy transfer plan through the optimized set of energy transfer paths, and determine the transfer priority and execution order. The path from Cell 3 to Cell 4 is preferentially executed due to lower loss, followed by the path from Cell 5 to Cell 6.

[0143] It should be noted that the plan can also adjust the priority in combination with the cell temperature data. For example, the priority of cells with higher temperature is reduced. This adaptive plan can effectively balance the cell state and improve the overall performance of the battery pack.

[0144] Furthermore, for the intelligent equalization control method of the energy vehicle battery pack provided in this embodiment, step S400 includes:

[0145] Step S410: Obtain the real-time monitoring data of the energy transfer plan, and calculate the energy transfer efficiency of each path using a preset efficiency calculation formula to obtain a set of energy transfer efficiencies.

[0146] Exemplarily, obtaining the real-time monitoring data of the energy transfer plan is the basis for ensuring the efficient operation of the battery pack. The real-time monitoring data includes the voltage, current, temperature of each cell, and the real-time state of the energy transfer path.

[0147] In one possible implementation, the battery management system collects data every second through high-precision sensors. For example, the voltage of Cell 7 in a certain battery pack is 3.65 V, the current is 2.3 A, and the temperature is 26 °C. These data are stored with timestamps and path numbers for subsequent efficiency calculation. It should be noted that real-time monitoring needs to ensure data consistency, and a verification mechanism is used to avoid data loss or errors.

[0148] Specifically, use a preset efficiency calculation formula to calculate the energy transfer efficiency of each path.

[0149] Efficiency calculation is usually based on the ratio of input energy to output energy. For example, for the path "Cell 7 → Cell 8", the input energy is 10 Wh and the output energy is 9.5 Wh, and the calculated efficiency is 95%. The efficiencies of all paths form the energy transfer efficiency set.

[0150] Step S420: Extract the paths with efficiencies lower than the preset threshold from the energy transfer efficiency set. If the efficiency of at least one path is lower than the threshold, use a path optimization algorithm to reorder the paths to obtain an optimized path set.

[0151] Preferably, extract the paths with efficiencies lower than the preset threshold (such as 90%) from the efficiency set. For example, the efficiency of the path "Cell 9 → Cell 10" is 88%, which is lower than the threshold and needs further optimization.

[0152] In one embodiment, use a path optimization algorithm to reorder the paths with efficiencies lower than the threshold. The path optimization algorithm can be based on factors such as energy loss and transmission distance. For example, for the path "Cell 9 → Cell 10", by adjusting the current distribution, the efficiency is increased to 92% after optimization. The optimized paths form a new set.

[0153] It should be noted that the optimization algorithm needs to balance efficiency and calculation speed to ensure real-time performance.

[0154] Step S430: For the optimized path set, calculate the transfer time and energy loss value of each path. If the transfer time exceeds the preset time threshold, eliminate the corresponding path to obtain a filtered path set.

[0155] It can be understood that for the optimized path set, calculate the transfer time and energy loss value of each path. For example, for the path "Cell 7 → Cell 8", the transfer time is 4 minutes and the loss is 0.015 Wh; for the path "Cell 9 → Cell 10", the transfer time is 7 minutes and the loss is 0.025 Wh.

[0156] If the transfer time exceeds the preset threshold (such as 5 minutes), eliminate the corresponding path to obtain a filtered path set. The path "Cell 9 → Cell 10" is eliminated due to exceeding the time limit.

[0157] Step S44: According to the filtered path set, use a preset priority allocation algorithm to determine the transfer priority of each path to obtain a priority sorting result.

[0158] For example, based on the filtered path set, use a priority allocation algorithm to determine the transfer priority.

[0159] The priority algorithm can sort according to the state of charge and efficiency of the cells. For example, the state of charge of Cell 7 is relatively high, and the path "Cell 7 → Cell 8" is given a high priority. The priority sorting result guides the path execution order.

[0160] Step S450: Based on the priority sorting result and combined with the dynamic voltage characteristics in the battery operation data, determine the execution order of each path. If the dynamic voltage characteristic value exceeds the preset range, adjust the execution order to obtain the final execution order set.

[0161] In a possible implementation, adjust the execution order in combination with the dynamic voltage characteristic value. For example, the dynamic voltage characteristic value of cell 8 is 0.02 V / min, which exceeds the preset range of 0.015 V / min, and its execution priority needs to be reduced. In the final execution order set, the path "cell 7 → cell 8" is ranked first.

[0162] Step S460: Extract the preferred path from the final execution order set, obtain its real-time monitoring data, and recalculate the energy transfer efficiency using the efficiency calculation formula.

[0163] Preferably, extract the preferred path from the final execution order set, re-obtain its real-time monitoring data, and calculate the efficiency.

[0164] For example, the latest data of the path "cell 7 → cell 8" shows that the input energy is 10.2 Wh, the output energy is 9.7 Wh, and the efficiency is 95.1%. This real-time verification ensures the accuracy of the solution.

[0165] It should be noted that dynamically adjusting the execution order can effectively adapt to the change of cell state and improve the robustness of the solution.

[0166] Furthermore, for the intelligent equalization control method of the energy vehicle battery pack provided in this embodiment, step S500 includes:

[0167] Step S510: Obtain the energy transfer efficiency of each path from the real-time monitoring data, and calculate the energy transfer efficiency set of each path using the preset efficiency calculation formula; the efficiency calculation formula is E = P_out / P_in, where E represents the energy transfer efficiency, P_out represents the output power, and P_in represents the input power.

[0168] Exemplarily, obtaining the energy transfer efficiency of each path from the real-time monitoring data is one of the core functions of the battery management system. The energy transfer efficiency reflects the effectiveness of energy transmission between each cell in the battery pack. The real-time monitoring data usually includes parameters such as voltage, current, and power. For example, the input power of the path "cell 1 → cell 2" in a certain battery pack is 100 W, the output power is 95 W, and the efficiency is calculated as 95% through the preset formula E = P_out / P_in. Similarly, the input power of the path "cell 3 → cell 4" is 120 W, the output power is 108 W, and the efficiency is 90%. These efficiency values form an efficiency set, providing a basis for subsequent optimization.

[0169] Step S520: If the energy transfer efficiency of at least one path reaches the second preset threshold, generate a trigger signal through a preset trigger signal generation algorithm, and determine the startup state of the active equalization circuit.

[0170] In a possible implementation, if the energy transfer efficiency of at least one path reaches the second preset threshold, such as 95%, the active equalization circuit is started through the trigger signal generation algorithm.

[0171] The generation of the trigger signal is based on a comprehensive judgment of efficiency data and cell state. For example, for the path "Cell 1 → Cell 2", the efficiency reaches 95%. The trigger algorithm analyzes that the state of charge of Cell 1 is 80% and that of Cell 2 is 60%, and generates a high-priority trigger signal to indicate the startup of the active equalization circuit to balance the energy distribution between cells. This mechanism ensures that the circuit operates preferentially on high-efficiency paths.

[0172] Step S530: According to the trigger signal, adopt the control logic of the active equalization circuit, extract the dynamic voltage characteristics from the battery operation data, judge the energy distribution ratio of each path, and obtain a set of distribution ratios.

[0173] Specifically, the control logic of the active equalization circuit extracts the dynamic voltage characteristics from the battery operation data for judging the energy distribution ratio. The dynamic voltage characteristics reflect the voltage change trend of the cell during operation. For example, the dynamic voltage characteristic value of Cell 1 is 0.01V / min, indicating that its state is stable and it is suitable as an energy output terminal;

[0174] The characteristic value of Cell 2 is 0.03V / min, indicating that its voltage changes rapidly and it needs to receive energy preferentially.

[0175] Based on this, the control logic determines that the energy distribution ratio of the path "Cell 1 → Cell 2" is 70% and that of the path "Cell 3 → Cell 4" is 30%, forming a set of distribution ratios. This distribution method effectively balances the energy requirements between cells.

[0176] Step S540: Through the set of distribution ratios, combined with a preset path selection algorithm, adjust the energy transmission amount of each path to determine the final set of energy distribution execution orders.

[0177] Preferably, the energy transmission amount of each path is adjusted through the set of distribution ratios combined with the path selection algorithm.

[0178] The path selection algorithm comprehensively considers efficiency, distance, and cell state. For example, for the path "Cell 1 → Cell 2", the algorithm preferentially allocates a higher transmission amount, such as 70W; for the path "Cell 3 → Cell 4", it allocates 30W.

[0179] After adjustment, determine the final set of energy distribution execution sequences. For example, the path "Cell 1 → Cell 2" is ranked first due to high efficiency and high priority, and the path "Cell 3 → Cell 4" follows. This sequence ensures that the priority of energy transfer is consistent with system requirements.

[0180] It can be understood that through multi-faceted analysis, the above embodiments form a rigorous logical chain from efficiency calculation to trigger signal generation, then to dynamic voltage feature extraction and path selection.

[0181] Each link supports each other to ensure the accuracy and efficiency of the energy distribution of the battery pack. For example, the generation of the trigger signal depends on the accuracy of the efficiency data, and the determination of the distribution ratio is based on the real-time analysis of the dynamic voltage characteristics. This multi-level collaborative work improves the overall performance of the system and provides a reliable guarantee for the efficient operation of the battery pack.

[0182] Another aspect of the present invention relates to an intelligent equalization control system for a new energy vehicle battery pack, which is used to implement the above-mentioned intelligent equalization control method for a new energy vehicle battery pack. The intelligent equalization control system for a new energy vehicle battery pack includes a first calculation module, a determination module, a generation module, a second calculation module, and a trigger module. Among them, the first calculation module is used to obtain the state-of-charge data of each cell from the battery management system and calculate the state-of-charge difference value between cells using a preset feature extraction algorithm; the determination module is used to compare the state-of-charge difference value between cells with a first preset threshold. If the state-of-charge difference value between cells is greater than the first preset threshold, determine the target cell combination; the generation module is used to predict the dynamic energy transfer path between cells using a decision tree model according to the target cell combination and generate an energy transfer plan; the second calculation module is used to obtain real-time monitoring data through the energy transfer plan and calculate the energy transfer efficiency; the trigger module is used to compare the energy transfer efficiency with a second preset threshold. If the energy transfer efficiency reaches the second preset threshold, trigger the active equalization circuit to perform energy distribution.

[0183] Furthermore, for the intelligent equalization control system of the new energy vehicle battery pack provided in this embodiment, the first calculation module includes a first acquisition unit, a second acquisition unit, and a first calculation unit. Among them, the first acquisition unit is used to obtain the state-of-charge data of each cell from the battery management system, classify and store the data according to the cell number, and obtain a state data set; the second acquisition unit is used to extract the state-of-charge feature values of each cell from the state data set using a preset feature extraction algorithm to obtain a feature value set; the first calculation unit is used to calculate the state-of-charge difference value between cells for the state-of-charge feature values of each cell in the feature value set through the first difference calculation formula D(i,j)=|F(i)-F(j)|, where D(i,j) represents the difference value between cell i and cell j, F(i) represents the feature value of cell i, and F(j) represents the feature value of cell j.

[0184] Preferably, for the intelligent equalization control system of the new energy vehicle battery pack provided in this embodiment, the determination module includes a third acquisition unit, a fourth acquisition unit, a second calculation unit, and a fifth acquisition unit. Among them, the third acquisition unit is used to acquire the voltage data of each battery cell from the battery management system, classify and store the voltage data according to the battery cell numbers, and obtain a voltage data set; the fourth acquisition unit is used to extract the voltage characteristic values of each battery cell from the voltage data set by using a preset feature extraction algorithm, and obtain a voltage characteristic value set; the second calculation unit is used to calculate the voltage difference value between battery cells for the voltage characteristic values of each battery cell in the voltage characteristic value set through the second difference calculation formula V(i,j)=|V(i)-V(j)|, and obtain a voltage difference value set, where V(i,j) represents the voltage difference value between battery cell i and battery cell j, V(i) represents the voltage characteristic value of battery cell i, and V(j) represents the voltage characteristic value of battery cell j; the fifth acquisition unit is used to determine the corresponding battery cells i and j as the target battery cell combination if any voltage difference value in the voltage difference value set is greater than the second preset threshold, and obtain a target battery cell combination set.

[0185] Furthermore, for the intelligent equalization control system of the new energy vehicle battery pack provided in this embodiment, the generation module includes a sixth acquisition unit, a seventh acquisition unit, an eighth acquisition unit, and a ninth acquisition unit. Among them, the sixth acquisition unit is used to acquire the operation data of each battery cell from the battery management system, classify and store it according to the battery cell numbers, and extract the dynamic voltage characteristic values of the operation data by using a preset feature extraction algorithm, and obtain a dynamic voltage characteristic value set; the seventh acquisition unit is used to perform classification prediction on the target battery cell combination through a decision tree model if at least one characteristic value in the dynamic voltage characteristic value set exceeds the preset dynamic threshold, and obtain a preliminary energy transfer path set; the eighth acquisition unit is used to sort the paths according to the preliminary energy transfer path set by using a path optimization algorithm, calculate the transfer time and energy loss value for the paths, and obtain an optimized energy transfer path set; the ninth acquisition unit is used to generate a corresponding energy transfer plan through the optimized energy transfer path set, determine the transfer priority and execution order of the target battery cell combination, and obtain a final energy transfer plan.

[0186] The intelligent equalization control method and system for a new energy vehicle battery pack provided in this embodiment, compared with the prior art, obtain the state of charge data of each battery cell from the battery management system, and calculate the state of charge difference value between battery cells by using a preset feature extraction algorithm; compare the state of charge difference value between battery cells with a first preset threshold, and if the state of charge difference value between battery cells is greater than the first preset threshold, determine the target battery cell combination; according to the target battery cell combination, use a decision tree model to predict the dynamic energy transfer path between battery cells and generate an energy transfer scheme; obtain real-time monitoring data through the energy transfer scheme and calculate the energy transfer efficiency; compare the energy transfer efficiency with a second preset threshold, and if the energy transfer efficiency reaches the second preset threshold, trigger the active equalization circuit to perform energy distribution. The intelligent equalization control method and system for a new energy vehicle battery pack provided in this embodiment have the following specific beneficial effects:

[0187] I. Improve equalization efficiency and control accuracy

[0188] By using the feature extraction algorithm to calculate the state of charge (SOC) difference value between battery cells in real time and combining the decision tree model to predict the dynamic energy transfer path, high-difference battery cell combinations can be accurately identified. Compared with the traditional passive equalization that relies on a fixed threshold trigger, the hierarchical threshold judgment (the first and second preset thresholds) realizes the quantitative evaluation of the difference degree, thereby dynamically adjusting the equalization strategy.

[0189] II. Optimize energy distribution and reduce energy consumption

[0190] By using the active equalization circuit combined with the dynamic energy transfer path planning, the energy of the high-SOC battery cells can be directly transferred to the low-SOC battery cells instead of being dissipated through resistors, reducing energy loss. The real-time monitoring and feedback mechanism of the energy transfer efficiency further ensures the minimization of energy consumption during the equalization process.

[0191] III. Prolong the service life of the battery pack

[0192] By continuously monitoring the SOC difference and quickly triggering equalization, the "barrel effect" caused by inconsistent capacity attenuation between battery cells is effectively alleviated, delaying the aging of the overall battery pack. Combining the analysis ability of the decision tree model for historical data, the degradation trend of battery cell performance can also be predicted to achieve preventive equalization.

[0193] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn of the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent equalization control method for a new energy vehicle battery pack, characterized in that, Including the following steps: Obtain the state-of-charge data of each battery cell from the battery management system, and calculate the state-of-charge difference value between battery cells using a preset feature extraction algorithm; Compare the state-of-charge difference value between the battery cells with a first preset threshold. If the state-of-charge difference value between the battery cells is greater than the first preset threshold, determine the target battery cell combination; According to the target battery cell combination, use a decision tree model to predict the dynamic energy transfer path between battery cells and generate an energy transfer scheme; Obtain real-time monitoring data through the energy transfer scheme and calculate the energy transfer efficiency; Compare the energy transfer efficiency with a second preset threshold. If the energy transfer efficiency reaches the second preset threshold, trigger the active balancing circuit to perform energy distribution; The steps of using a decision tree model to predict the dynamic energy transfer path between battery cells and generate an energy transfer scheme according to the target battery cell combination include: Obtain the operation data of each battery cell from the battery management system, classify and store the data according to the battery cell number, and extract the dynamic voltage feature values of the operation data using a preset feature extraction algorithm to obtain a set of dynamic voltage feature values; If at least one feature value in the set of dynamic voltage feature values exceeds a preset dynamic threshold, classify and predict the target battery cell combination through a decision tree model to obtain a preliminary set of energy transfer paths; According to the preliminary set of energy transfer paths, use a path optimization algorithm to sort the paths, calculate the transfer time and energy loss value for the paths, and obtain an optimized set of energy transfer paths; Generate a corresponding energy transfer scheme through the optimized set of energy transfer paths, determine the transfer priority and execution order of the target battery cell combination, and obtain the final energy transfer scheme.

2. The intelligent equalization control method for a new energy vehicle battery pack according to claim 1, characterized in that, The steps of obtaining the state-of-charge data of each battery cell from the battery management system and calculating the state-of-charge difference value between battery cells using a preset feature extraction algorithm include: Obtain the state-of-charge data of each battery cell from the battery management system, classify and store the data according to the battery cell number to obtain a state data set; Use a preset feature extraction algorithm to extract the state-of-charge feature values of each battery cell from the state data set to obtain a set of feature values; Through the first difference calculation formula D(i,j)=|F(i)-F(j)|, calculate the state-of-charge difference value between battery cells for the state-of-charge feature values of each battery cell in the set of feature values, where D(i,j) represents the difference value between battery cell i and battery cell j, F(i) represents the feature value of battery cell i, and F(j) represents the feature value of battery cell j.

3. The intelligent equalization control method for a new energy vehicle battery pack according to claim 1, wherein The steps of comparing the state-of-charge difference value between the battery cells with a first preset threshold. If the state-of-charge difference value between the battery cells is greater than the first preset threshold, determine the target battery cell combination include: Obtain the voltage data of each battery cell from the battery management system, classify and store the voltage data according to the battery cell number to obtain a voltage data set; Use a preset feature extraction algorithm to extract the voltage feature values of each battery cell from the voltage data set to obtain a set of voltage feature values; Using the second difference calculation formula V(i,j)=|V(i)-V(j)|, calculate the voltage difference values between the battery cells for the voltage characteristic values of each battery cell in the voltage characteristic value set, obtaining a voltage difference value set, where V(i,j) represents the voltage difference value between battery cell i and battery cell j, V(i) represents the voltage characteristic value of battery cell i, and V(j) represents the voltage characteristic value of battery cell j; If any voltage difference value in the voltage difference value set is greater than the second preset threshold, determine the corresponding battery cells i and j as the target battery cell combination, obtaining a target battery cell combination set.

4. The intelligent equalization control method for a new energy vehicle battery pack according to claim 1, wherein The steps of obtaining real-time monitoring data through the energy transfer scheme and calculating the energy transfer efficiency include: Obtain the real-time monitoring data of the energy transfer scheme, and calculate the energy transfer efficiency of each path using a preset efficiency calculation formula, obtaining an energy transfer efficiency set; Extract the paths with efficiencies lower than the preset threshold from the energy transfer efficiency set. If the efficiency of at least one path is lower than the threshold, use a path optimization algorithm to reorder the paths, obtaining an optimized path set; For the optimized path set, calculate the transfer time and energy loss value of each path. If the transfer time exceeds the preset time threshold, eliminate the corresponding path, obtaining a filtered path set; According to the filtered path set, use a preset priority assignment algorithm to determine the transfer priority of each path, obtaining a priority sorting result; Based on the priority sorting result, combined with the dynamic voltage characteristics in the battery operation data, judge the execution order of each path. If the dynamic voltage characteristic value exceeds the preset range, adjust the execution order, obtaining a final execution order set; Extract the preferred path from the final execution order set, obtain its real-time monitoring data, and recalculate the energy transfer efficiency using the efficiency calculation formula.

5. The intelligent equalization control method for a new energy vehicle battery pack according to claim 1, wherein, Compare the energy transfer efficiency with the second preset threshold. If the energy transfer efficiency reaches the second preset threshold, the steps to trigger the active equalization circuit for energy distribution include: Obtain the energy transfer efficiency of each path from the real-time monitoring data, and calculate using a preset efficiency calculation formula to obtain an energy transfer efficiency set for each path; the efficiency calculation formula is E = P_out / P_in, where E represents the energy transfer efficiency, P_out represents the output power, and P_in represents the input power, and calculate to obtain an energy transfer efficiency set for each path; If the energy transfer efficiency of at least one path reaches the second preset threshold, generate a trigger signal through a preset trigger signal generation algorithm to determine the startup state of the active equalization circuit; According to the trigger signal, use the control logic of the active equalization circuit to extract the dynamic voltage characteristics from the battery operation data and judge the energy distribution ratio of each path, obtaining a distribution ratio set; Based on the distribution ratio set, combined with a preset path selection algorithm, adjust the energy transmission amount of each path to determine the final energy distribution execution order set.

6. A smart balancing control system for a new energy vehicle battery pack, which is used to implement the smart balancing control method for a new energy vehicle battery pack as described in any one of claims 1 to 5, characterized in that, The intelligent equalization control system for the new energy vehicle battery pack includes: The first calculation module is used to obtain the state of charge data of each battery cell from the battery management system and calculate the state of charge difference value between battery cells by using a preset feature extraction algorithm; The determination module is used to compare the state of charge difference value between battery cells with a first preset threshold. If the state of charge difference value between battery cells is greater than the first preset threshold, a target battery cell combination is determined; The generation module is used to predict the dynamic energy transfer path between battery cells according to the target battery cell combination by using a decision tree model and generate an energy transfer scheme; The second calculation module is used to obtain real-time monitoring data through the energy transfer scheme and calculate the energy transfer efficiency; The triggering module is used to compare the energy transfer efficiency with a second preset threshold. If the energy transfer efficiency reaches the second preset threshold, an active balancing circuit is triggered to perform energy distribution.

7. The intelligent balancing control system for a new energy vehicle battery pack according to claim 6, wherein, The first calculation module includes: The first acquisition unit is used to obtain the state of charge data of each battery cell from the battery management system, classify and store the data according to the battery cell number, and obtain a state data set; The second acquisition unit is used to extract the state of charge characteristic values of each battery cell from the state data set by using a preset feature extraction algorithm and obtain a characteristic value set; The first calculation unit is used to calculate the state of charge difference value between battery cells for the state of charge characteristic values of each battery cell in the characteristic value set through a first difference calculation formula D(i,j)=|F(i)-F(j)|, where D(i,j) represents the difference value between battery cell i and battery cell j, F(i) represents the characteristic value of battery cell i, and F(j) represents the characteristic value of battery cell j.

8. The intelligent equalization control system for a new energy vehicle battery pack according to claim 6, wherein, The determination module includes: The third acquisition unit is used to obtain the voltage data of each battery cell from the battery management system, classify and store the voltage data according to the battery cell number, and obtain a voltage data set; The fourth acquisition unit is used to extract the voltage characteristic values of each battery cell from the voltage data set by using a preset feature extraction algorithm and obtain a voltage characteristic value set; The second calculation unit is used to calculate the voltage difference value between battery cells for the voltage characteristic values of each battery cell in the voltage characteristic value set through a second difference calculation formula V(i,j)=|V(i)-V(j)|, and obtain a voltage difference value set, where V(i,j) represents the voltage difference value between battery cell i and battery cell j, V(i) represents the voltage characteristic value of battery cell i, and V(j) represents the voltage characteristic value of battery cell j; The fifth acquisition unit is used to determine the corresponding battery cells i and j as the target battery cell combination if any voltage difference value in the voltage difference value set is greater than the second preset threshold, and obtain a target battery cell combination set.

9. The intelligent equalization control system for a new energy vehicle battery pack according to claim 6, wherein The generation module includes: The sixth acquisition unit is used to obtain the operation data of each battery cell from the battery management system, classify and store it according to the battery cell number, and extract the dynamic voltage characteristic values of the operation data by using a preset feature extraction algorithm to obtain a dynamic voltage characteristic value set; The seventh acquisition unit is used to classify and predict the target battery cell combination through a decision tree model if at least one characteristic value in the dynamic voltage characteristic value set exceeds a preset dynamic threshold, and obtain a preliminary energy transfer path set; An eighth acquisition unit, configured to sort the paths by using a path optimization algorithm according to the preliminary energy transfer path set, calculate transfer time and energy loss values for the paths, and obtain an optimized energy transfer path set; A ninth acquisition unit, configured to generate a corresponding energy transfer scheme through the optimized energy transfer path set, determine the transfer priority and execution order of the target battery cell combination, and obtain a final energy transfer scheme.

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