Lithium battery pack electric energy metering and energy efficiency analysis integrated system and method

Abstract

The application belongs to the technical field of battery management and testing, and discloses a lithium battery pack electric energy metering and energy efficiency analysis integrated system and method, which comprises the following steps: acquiring electrical parameters of each single battery in a lithium battery pack to generate a battery state feature set; constructing a corresponding electric energy distribution path based on the battery state feature set; acquiring an initial electric energy metering result according to the electric energy distribution path; dynamically adjusting the electric energy distribution path in combination with the real-time working state of the lithium battery pack to obtain an electric energy metering correction path; and correcting the initial electric energy metering result through the electric energy metering correction path to obtain an accurate electric energy metering result, so as to realize electric energy metering and energy efficiency analysis of the lithium battery pack, and greatly improve the long-term stability of the lithium battery pack.

Description

Technical Field

[0001] This invention relates to the field of battery management and testing technology, and more specifically, to an integrated system and method for energy metering and energy efficiency analysis of lithium battery packs. Background Technology

[0002] With the rapid development and widespread application of new energy technologies, lithium battery packs, as core energy storage units, have become key components of electric vehicles, smart grids, and renewable energy systems. Accurately measuring the state of energy of battery packs and analyzing their energy efficiency performance in real time is of great significance for optimizing energy utilization, extending battery life, and improving system safety. In order to achieve precise management of battery packs and maximize their economic value, in recent years, energy monitoring and efficiency evaluation technologies based on battery management systems have been widely used. These technologies monitor multiple parameters such as voltage, current, and temperature, and combine them with algorithm models and data processing to achieve dynamic evaluation and predictive analysis of the state of charge, health status, and energy conversion efficiency of battery packs.

[0003] However, existing lithium battery pack energy metering technologies have significant limitations in addressing the uneven charging and discharging issues of large-scale parallel battery systems, a problem particularly pronounced in high-power fast charging and long-cycle applications. Specifically, when large battery packs consist of multiple individual cells connected in parallel, differences in internal resistance and aging inconsistencies inevitably exist between these cells, and these differences gradually increase with the number of charge-discharge cycles. For instance, in electric vehicle power battery systems, when it is necessary to simultaneously monitor multiple parameters such as current distribution, thermal runaway risk, and cycle efficiency of each individual cell within the battery pack, the data consistency and time synchronization between traditional bus-based sampling methods and distributed monitoring technologies are difficult to achieve. To ensure this, the imbalance in power metering not only leads to deviations in the overall capacity estimation of the battery pack, but also poses a risk of local overcharging or over-discharging under high-rate charging and discharging conditions. The current mainstream approach to solving this problem is to introduce complex balancing circuits and adaptive state estimation algorithms to mitigate the impact of imbalance through forced current sharing and mathematical model correction. However, this hardware-based compensation solution not only increases system complexity and cost, but also requires a large amount of computing resources and frequent calibration processes, making it difficult to maintain long-term stability in real-world applications. It cannot meet the growing demand for high-precision energy efficiency management and has high power consumption, ultimately limiting the optimized application and full lifecycle management of lithium battery packs in large-scale energy storage systems.

[0004] In view of this, the present invention proposes an integrated system and method for lithium battery pack power metering and energy efficiency analysis to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: an integrated method for lithium battery pack energy metering and energy efficiency analysis, comprising: Step S1: Obtain the electrical parameters of each individual cell in the lithium battery pack, identify the internal resistance characteristics and capacity decay characteristics of each individual cell through preset feature extraction rules, and generate a battery state feature set; Step S2: Based on the battery state feature set and combined with the preset battery characteristic database, construct the corresponding power distribution path; Step S3: Calculate the real-time current distribution value of each individual battery cell according to the power distribution path to obtain the initial power metering result; Step S4: Dynamically adjust the power distribution path based on the real-time operating status of the lithium battery pack to obtain the power metering correction path; Step S5: Correct the initial energy metering result through the energy metering correction path to obtain an accurate energy metering result, so as to realize the energy metering and energy efficiency analysis of the lithium battery pack.

[0006] An integrated system for lithium battery pack energy metering and energy efficiency analysis includes: Data acquisition module: acquires the electrical parameters of each individual cell in the lithium battery pack, identifies the internal resistance characteristics and capacity decay characteristics of each individual cell through preset feature extraction rules, and generates a battery state feature set; Allocation construction module: Based on the battery state feature set and combined with the preset battery characteristic database, construct the corresponding power allocation path; Metering module: Based on the power distribution path, calculate the real-time current distribution value of each individual battery cell to obtain the initial power metering result; Correction module: Dynamically adjusts the power distribution path based on the real-time operating status of the lithium battery pack to obtain a power metering correction path; Analysis module: Through the energy metering correction path, the initial energy metering result is corrected to obtain accurate energy metering result, so as to realize the energy metering and energy efficiency analysis of lithium battery pack.

[0007] The technical effects and advantages of the integrated system and method for lithium battery pack energy metering and energy efficiency analysis of this invention are as follows: This invention presents the internal resistance and capacity decay characteristics of each individual battery cell through a battery state feature set, accurately reflecting the actual operating state of each individual battery cell in the battery pack and providing basic data for energy metering. The uneven current distribution caused by differences in internal resistance and inconsistent aging among individual batteries in a lithium battery pack is accurately described by constructing an energy distribution path based on the battery state feature set and a pre-set battery characteristic database, avoiding metering deviations caused by ignoring individual cell differences in traditional methods. In practical applications, the dynamic changes in the battery pack's operating state lead to continuous adjustments in the current distribution relationship. By dynamically adjusting the energy distribution path based on the real-time operating state, a corrected energy metering path is obtained. This approach reduces the impact of battery state changes on energy metering results. In high-power fast charging and long-cycle applications, lithium battery packs may experience uneven load rates and temperature distributions in individual cells. By correcting the initial energy metering results based on the energy metering correction path, accurate measurement of the energy state of each individual cell within the battery pack is achieved. This avoids the risk of local overcharging or over-discharging caused by uneven energy metering, improving the accuracy of lithium battery pack energy metering and the reliability of energy efficiency analysis. It eliminates the need for complex balancing circuits and adaptive state estimation algorithms, reducing system complexity and computational resource requirements, and effectively supporting the optimized application and full lifecycle management of lithium battery packs in large-scale energy storage systems. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the integrated method for lithium battery pack power metering and energy efficiency analysis of the present invention; Figure 2 This is a schematic diagram of the integrated lithium battery pack energy metering and energy efficiency analysis system of the present invention. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] Example 1 Please see Figure 1 As shown, the integrated method for lithium battery pack energy metering and energy efficiency analysis in this embodiment includes: Step S1: Obtain the electrical parameters of each individual cell in the lithium battery pack, identify the internal resistance characteristics and capacity decay characteristics of each individual cell through preset feature extraction rules, and generate a battery state feature set; Step S2: Based on the battery state feature set and combined with the preset battery characteristic database, construct the corresponding power distribution path; Step S3: Calculate the real-time current distribution value of each individual battery cell according to the power distribution path to obtain the initial power metering results; Step S4: Dynamically adjust the power distribution path based on the real-time operating status of the lithium battery pack to obtain the power metering correction path; Step S5: Correct the initial energy metering results through the energy metering correction path to obtain accurate energy metering results, so as to realize the energy metering and energy efficiency analysis of the lithium battery pack.

[0011] In this embodiment, firstly, the electrical parameters of each individual cell in the lithium battery pack are acquired. These electrical parameters are then analyzed using preset feature extraction rules to extract the internal resistance and capacity decay characteristics of each individual cell, generating a battery state feature set. The preset feature extraction rules can be electrochemical or statistical models. These rules are calibrated using a large amount of historical test data to obtain accurate features. The feature extraction rules extract the battery's internal resistance characteristics by analyzing the voltage, current, and temperature changes under different operating conditions. The battery's capacity decay is then assessed based on its charge-discharge curve and cycle count, generating capacity decay characteristics. Integrating the internal resistance and capacity decay characteristics into a battery state feature set comprehensively reflects the health status and performance characteristics of individual cells, providing fundamental data for constructing energy distribution paths and improving the accuracy of energy metering.

[0012] Specifically, the electrical parameters of each individual cell in the lithium battery pack are obtained, and the internal resistance and capacity decay characteristics of each individual cell are identified through preset feature extraction rules to generate a battery state feature set, including: By collecting data on the terminal voltage, charging and discharging current, and temperature of each individual cell, the electrical parameters of each individual cell in the lithium battery pack can be obtained. Based on electrical parameters, the internal resistance characteristics and capacity decay characteristics of each individual cell are calculated using preset feature extraction rules. By integrating internal resistance characteristics and capacity decay characteristics, a battery state feature set is generated.

[0013] In this embodiment, firstly, the data acquisition unit of the battery management system collects the terminal voltage, charge / discharge current, and temperature data of each individual cell in the lithium battery pack in real time. The terminal voltage data reflects the battery's state of charge and health status; the charge / discharge current data reflects the battery's workload; and the temperature data affects the battery's performance and safety. The collected data are combined to obtain the electrical parameters of each individual cell. By collecting the battery's multi-dimensional electrical parameters, the battery's working state and performance characteristics can be understood from different perspectives, avoiding analytical biases caused by single data points, and providing a comprehensive data foundation for analyzing the battery's state.

[0014] Specifically, based on the collected electrical parameters, the internal resistance and capacity decay of the battery are analyzed, and the internal resistance characteristics and capacity decay characteristics of each individual battery cell are calculated through preset feature extraction rules. The collected electrical parameters are preprocessed, including filtering, noise reduction, and standardization, to obtain preprocessed electrical parameters. The preset feature extraction rules can be methods such as equivalent circuit simulation and electrochemical impedance spectroscopy analysis.

[0015] For example, the feature extraction rule in this embodiment adopts the equivalent circuit simulation method. This simulation method treats the battery as a combination of resistors, capacitors, and inductors. By analyzing the voltage, current, and temperature response of the battery under different operating conditions, the internal resistance value of the battery is calculated. The preset internal resistance calculation formula is: ,in This is the change in voltage. The change in current The internal resistance is defined by Ohm's law, which states that when the current changes, the battery terminal voltage changes accordingly, and the ratio of these changes is the internal resistance value. The feature extraction rules are calibrated using extensive historical test data to ensure the accuracy of the internal resistance calculation. Simultaneously, based on the battery's charge / discharge curve and cycle count, combined with battery capacity decay data, the degree of capacity decay is assessed. The preset capacity decay calculation method is the ratio of the absolute value of the difference between the initial capacity and the current capacity to the initial capacity. This calculation method is based on the premise that the battery's usable capacity gradually decreases with increasing usage time and cycle count. By comparing the difference between the current capacity and the initial capacity, the degree of battery capacity decay can be quantified.

[0016] By extracting internal resistance and capacity decay characteristics, the health status and performance characteristics of batteries can be accurately assessed, providing a basis for formulating corresponding power distribution strategies.

[0017] Specifically, by combining internal resistance characteristics and capacity decay characteristics, a battery state feature set is generated. The internal resistance characteristics and capacity decay characteristics are standardized in format and integrated into the data. Combined with other battery characteristic parameters (such as self-discharge rate, charge and discharge efficiency, etc.), a comprehensive and systematic battery state description is generated, providing more comprehensive battery information. When formulating power allocation strategies and calculating current allocation ratios, it can better reflect the actual state of the battery and improve the accuracy of power metering.

[0018] For example, the internal resistance characteristic of single cell 2 is 48mΩ and the capacity decay characteristic is 5%. After format unification and integration, the generated battery state characteristics are {internal resistance: 48mΩ; capacity decay rate: 5%; rated capacity: 2850mAh; charge / discharge efficiency: 95.2%; self-discharge rate: 3% / month}.

[0019] Furthermore, based on the battery state feature set obtained from the analysis, a matching process is performed in a pre-set battery characteristic database to select current distribution patterns that match the internal resistance and capacity characteristics of each individual battery. These patterns are then combined according to the principle of optimal energy efficiency to obtain the energy distribution path. Constructing an energy distribution path based on the battery state feature set allows for the customization of corresponding energy distribution schemes for the battery pack, ensuring that the current distribution matches the actual performance of each individual battery. This helps improve the overall energy efficiency of the battery pack, avoids overcharging and discharging of certain batteries, extends the battery pack's lifespan, and also enables more accurate measurement of the battery pack's energy consumption.

[0020] Specifically, based on the battery state feature set and combined with a pre-set battery characteristic database, a corresponding power distribution path is constructed, including: The internal resistance and capacity values ​​of each individual cell are extracted from the battery state feature set to construct a battery characteristic vector; Based on a pre-set battery characteristic database, each individual battery cell is used as a distribution node. The current distribution relationship between individual batteries cells is analyzed according to the internal resistance difference, and connections are established between the distribution nodes to construct an energy distribution network. Based on the battery characteristic vector, the corresponding current distribution ratio is calculated in the power distribution network to determine the distribution node that meets the overall efficiency of the battery pack. Based on the connection relationship between nodes in the power distribution network, the corresponding power transmission path is searched starting from the input node to obtain a set of candidate distribution paths. The matching value between each path in the candidate allocation path set and the battery characteristic vector is calculated by configuring the allocation efficiency calculation rules, and the path with the highest matching value is selected as the power allocation path in the candidate allocation path set.

[0021] In this embodiment, the internal resistance value of each individual cell is extracted based on the internal resistance characteristics in the battery state feature set; the actual capacity value of the battery is calculated based on the capacity decay characteristics and the rated capacity of the battery; the extracted internal resistance value and capacity value are combined to construct a battery characteristic vector; by constructing the battery characteristic vector, an energy distribution strategy adapted to the battery state can be quickly matched.

[0022] Based on a pre-set battery characteristic database, each individual battery cell is used as an allocation node. The current allocation relationship between individual batteries is analyzed according to the internal resistance difference, and the allocation relationship is used as the connection edge between allocation nodes to construct an energy allocation network. By analyzing various battery performance and current allocation strategies in the battery characteristic database, the optimal current allocation ratio under different combinations of internal resistance and capacity is determined. The pre-set battery characteristic database contains a large amount of characteristic data and corresponding current allocation strategies for batteries of different models and states. By matching with actual battery parameters, the most suitable current allocation scheme for the current battery pack state can be found.

[0023] Based on the battery characteristic vector, the corresponding current distribution ratio is calculated in the power distribution network, and distribution nodes that meet the overall efficiency of the battery pack are selected. Based on the connection relationship between nodes in the power distribution network, starting from the input node, the corresponding power transmission path is searched to form a candidate distribution path set. According to the internal resistance and capacity values ​​in the battery characteristic vector, characteristic matching is performed respectively. When the battery internal resistance is low and the capacity is high, the battery can handle a larger current load, which is beneficial to improving the overall efficiency of the battery pack. Combining the results of internal resistance matching and capacity matching, the distribution node that meets the overall efficiency of the battery pack is determined. Starting from the input node, combined with the connection edges between the distribution nodes in the power distribution network, the corresponding power transmission path is searched to form a candidate distribution path set. By selecting multiple power distribution paths that meet the overall efficiency of the battery pack, the internal resistance and capacity characteristics of the battery are fully considered, providing a reference for power distribution schemes, avoiding the problem that a single fixed distribution path cannot adapt to changes in battery state, and improving the flexibility and adaptability of power distribution.

[0024] After selecting the candidate allocation path set, the matching degree between each path in the candidate allocation path set and the battery characteristic vector is analyzed, the corresponding matching value is calculated, and the path with the highest matching value is selected as the power allocation path. By calculating the matching value and selecting the corresponding power allocation path, the power allocation scheme that best matches the current state of the battery pack can be selected, ensuring that the current allocation matches the performance characteristics of each individual battery cell, improving the overall energy efficiency and lifespan of the battery pack, and also improving the accuracy of power metering, providing reliable data for energy efficiency analysis.

[0025] For example, for calculating internal resistance matching efficiency, a matching efficiency calculation rule is set based on the degree of deviation between the internal resistance value of each allocation node and the average internal resistance of the battery pack. For instance, if the deviation rate between the internal resistance value of the allocation node and the average internal resistance is less than 5%, the matching efficiency is 1; if the deviation rate is between 5% and 15%, the matching efficiency is 0.8; if the deviation rate is between 15% and 30%, the matching efficiency is 0.6; and if the deviation rate is greater than 30%, the matching efficiency is 0.4. The internal resistance matching efficiencies of all allocation nodes in the path are summed to obtain the total internal resistance matching efficiency of the path. For calculating capacity utilization efficiency, a matching efficiency calculation rule is set based on the ratio of the capacity value of the allocation node to the nominal capacity of the battery pack. For instance, when the capacity value of the allocation node is greater than or equal to 95% of the nominal capacity of the battery pack, the capacity utilization efficiency is 1; if the capacity value is between 85% and 95%, the capacity utilization efficiency is 0.9; if the capacity value is between 75% and 85%, the capacity utilization efficiency is 0.7; and if the capacity value is less than 75%, the capacity utilization efficiency is 0.5. The total capacity utilization efficiency of the path is obtained by summing the capacity utilization efficiencies of all allocated nodes in the path. The total internal resistance matching efficiency and the total capacity utilization efficiency are combined and weighted by summing, such as when the total internal resistance matching efficiency accounts for 60% and the total capacity utilization efficiency accounts for 40%, to calculate the final matching value between each path and the battery characteristic vector.

[0026] Furthermore, the current distribution relationship between individual cells is analyzed based on the difference in internal resistance, including: Calculate the deviation rate between the internal resistance of each individual cell and the average internal resistance of the battery pack. When the deviation rate is greater than or equal to a preset deviation threshold, the corresponding individual cell is marked as a high internal resistance cell; when the deviation rate is less than the preset deviation threshold, the corresponding individual cell is marked as a low internal resistance cell. The current distribution relationship between individual cells is determined based on the internal resistance marking. Low internal resistance cells receive a higher current distribution ratio, while high internal resistance cells receive a lower current distribution ratio. Cells within the same capacity class are distributed current according to the inverse ratio of their internal resistance, while cells between different capacity classes are distributed current according to the product of their capacity ratio and the inverse ratio of their internal resistance.

[0027] In this embodiment, the deviation rate between the internal resistance value of each individual cell and the average internal resistance of the battery pack is calculated to evaluate the consistency level of the battery internal resistance. The calculation formula is as follows: ;in, The deviation rate, For the internal resistance of a single monomer, The average internal resistance is used. When the deviation rate is greater than or equal to a preset deviation threshold of 15%, the corresponding individual cell is marked as a high internal resistance cell; when the deviation rate is less than the preset deviation threshold of 15%, the corresponding individual cell is marked as a low internal resistance cell. The deviation threshold of 15% is set based on the battery performance dividing point obtained through the analysis of a large amount of battery test data. When the internal resistance deviation exceeds 15%, the performance difference of the batteries increases significantly, and different current distribution strategies need to be adopted.

[0028] By labeling high-internal-resistance and low-internal-resistance batteries, it is possible to quickly identify batteries with significant performance differences within a battery pack, providing a basis for developing differentiated current allocation strategies and improving the overall performance and lifespan of the battery pack.

[0029] Specifically, the current distribution among individual cells is determined based on their internal resistance. Cells with low internal resistance receive a higher proportion of current, while those with high internal resistance receive a lower proportion. This distribution method reduces energy loss in the battery pack and improves energy utilization efficiency. For cells within the same capacity class, the current distribution ratio is calculated inversely to their internal resistance; the lower the internal resistance, the higher the current allocated. The calculation formula is as follows: ;in, For the current distribution ratio, For the first The internal resistance of each individual battery cell. This allocation method is based on the principle of parallel circuits, in which the current in each branch is inversely proportional to the resistance of that branch.

[0030] For batteries of different capacity levels, both internal resistance and capacity factors need to be considered. The current distribution ratio is calculated as the product of the capacity ratio and the inverse ratio of internal resistance. The calculation formula is as follows: ;in For the current distribution ratio, For the first The capacity of a single battery cell The average is the average capacity of the battery pack. This allocation method is based on the fact that the battery's load-bearing capacity is proportional to its capacity. Batteries with larger capacities can handle more current loads. At the same time, the impact of internal resistance on energy loss must also be considered.

[0031] By determining the current distribution relationship based on internal resistance marking, differentiated current distribution strategies can be formulated for batteries with different performance characteristics, optimizing the overall performance of the battery pack, reducing energy loss, extending the battery pack's lifespan, and improving the accuracy of energy metering.

[0032] For example, a lithium battery pack consists of three individual cells. The internal resistance and capacity data of each individual cell are as follows: Cell 1: internal resistance 55mΩ, capacity 2700mAh; Cell 2: internal resistance 48mΩ, capacity 2850mAh; Cell 3: internal resistance 60mΩ, capacity 2650mAh. The average internal resistance is calculated as: (55+48+60)÷3=54.33mΩ. The internal resistance deviation rate of each individual cell is calculated as follows: Cell 1: |55-54.33|÷54.33=1.23%, less than 15%, marked as a low internal resistance cell; Cell 2: |48-54.33|÷54.33=11.65%, less than 15%, marked as a low internal resistance cell; Cell 3: |60-54.33|÷54.33=10.44%, less than 15%, marked as a low internal resistance cell. Since all three batteries are low internal resistance batteries, the current distribution ratio is calculated according to the inverse ratio of internal resistance and the ratio of capacity: Single cell 1: (2700 / 2733.33)×(1 / 55)÷[(2700 / 2733.33)×(1 / 55)+(2850 / 2733.33)×(1 / 48)+(2650 / 2733.33)×(1 / 60)]=0.35, which is 35%; then the similar single cell 2 is 42%; and the single cell 3 is 23%.

[0033] Furthermore, based on the power distribution path, the real-time current distribution value of each individual battery cell is calculated to obtain the initial power metering results, including: Based on the power distribution path, a corresponding first current calculation method is matched for each distribution node in the preset metering algorithm library; Based on the battery characteristic vector, the parameters of the first current calculation method are adjusted to obtain the second current calculation method; According to the order of the distribution nodes in the power distribution path, the current value of each node is calculated by the second current calculation method to obtain the initial power metering result.

[0034] In this embodiment, based on the power distribution path, a corresponding current calculation method is matched to each distribution node in a preset metering algorithm library to obtain a suitable first current calculation method. The preset metering algorithm library stores various types of current calculation methods, each applicable to specific battery states and operating conditions, including current distribution methods based on internal resistance and current calculation methods based on state estimation. The applicable conditions of each calculation method in the metering algorithm library are analyzed, including applicable internal resistance range, capacity range, temperature range, and other information. The characteristics of the distribution node are compared with the applicable conditions of the calculation method, and the internal resistance range is matched first. Ensure the calculation method is applicable to the internal resistance characteristics of the distribution node; then match the capacity range to analyze whether the calculation method is applicable to the capacity state of the node; finally consider the temperature range to ensure the calculation method is effective at the operating temperature of the node; when the applicable conditions of the calculation method match the characteristics of the distribution node, use this method as the first current calculation method for that distribution node, and match the corresponding first current calculation method for each distribution node in the power distribution path in the metering algorithm library; by matching the first current calculation method, the current distribution value of each individual battery can be quickly calculated according to the power distribution path, improving the efficiency and accuracy of power metering.

[0035] After matching the first current calculation method, the battery characteristic vector is compared and analyzed with the relevant parameters of the first current calculation method. The calculation parameters are adjusted according to the battery's internal resistance characteristics, and the compensation coefficient is adjusted according to the battery's capacity characteristics. The parameters of the first current calculation method are adjusted to obtain a second current calculation method that better reflects the actual state of the battery. Adjusting the parameters of the first current calculation method based on the battery characteristic vector can fully consider the performance differences of different batteries, making the calculation method better adapt to the actual state of each individual battery. By adjusting the calculation parameters and compensation coefficient, the accuracy of current calculation is improved, and the metering error caused by inconsistent battery states is reduced, which helps to improve the accuracy and reliability of power metering.

[0036] For example, the first current calculation method is a current distribution method based on internal resistance, and its standard calculation formula is: ,in This represents the current value of a single cell. This represents the total current of the battery pack. Based on the internal resistance and capacity values ​​in the battery characteristic vector, the calculation formula is adjusted. The adjusted formula is as follows: ,in This is an adjustment factor, ranging from 0 to 1, determined based on the actual state of the battery. For battery packs with significant differences in internal resistance, increase the value. Value, enhance the influence of internal resistance on current distribution; for battery packs with large capacity differences, reduce The value enhances the impact of capacity on current distribution. By adjusting the parameters, the calculation method is better adapted to the actual state of the battery pack.

[0037] Specifically, following the order of the nodes in the power distribution path, the current value of each node is calculated using a second current calculation method to obtain the initial power metering result. For example, if the power distribution path is "input current, current distribution (cell 2 accounts for 42%, cell 1 accounts for 35%, cell 3 accounts for 23%), output current", the corresponding second current calculation methods are "current distribution method based on internal resistance and capacity", "current calculation method based on state estimation", and "temperature-compensated current distribution method". Based on the total current of the battery pack and the characteristic parameters of each cell, the corresponding second current calculation method is applied to calculate the real-time current value of each cell. Combined with the battery terminal voltage, the power and energy consumption of each cell are calculated to obtain the initial power metering result. By applying the optimized current calculation method, the power consumption of each cell can be measured more accurately, providing reliable data for subsequent energy efficiency analysis.

[0038] Furthermore, by dynamically adjusting the power distribution path based on the real-time operating status of the lithium battery pack, a power metering correction path is obtained, including: Based on the real-time operating status of the lithium battery pack, the path adjustment area is identified by analyzing the load rate and temperature distribution of the distribution nodes in the power distribution path. The path adjustment area is dynamically adjusted by adjusting the allocation weights, correcting measurement deviations, or reconstructing the allocation path to obtain the first corrected path. The power distribution path is updated based on the first correction path to obtain the power metering correction path.

[0039] In this embodiment, based on the real-time operating status of the lithium battery pack, the load rate and temperature distribution of the distribution nodes in the power distribution path are analyzed. The load rate of the distribution nodes represents the ratio of the actual current of a single battery cell to its rated current. An excessively high load rate may lead to battery overheating or accelerated aging. The temperature distribution reflects the temperature status of each single battery cell in the battery pack. Uneven temperature can affect battery performance and safety. By analyzing the load rate and temperature distribution of the distribution nodes, the parts of the current power distribution path that do not match the real-time operating status of the battery pack are identified, and path adjustment areas are obtained. Based on the collected real-time operating data of the battery pack, the real-time operating status of the battery pack is analyzed. The power distribution path is divided according to battery modules or parallel groups, and the average load rate and temperature of each group are calculated. By comparing the load rate and temperature of different groups, areas with abnormal load rates (too high or too low) or abnormal temperatures are identified. When the load rate of a certain area exceeds 85% or the temperature exceeds 45°C, that area is designated as a path adjustment area. When the load rate difference between different areas exceeds 20% or the temperature difference exceeds 10°C, the relevant areas can also be designated as path adjustment areas. These thresholds are set based on technical standards for safe battery operation and the analysis results of extensive battery test data. When the load rate and temperature exceed these thresholds, the battery's performance and lifespan will be significantly affected, requiring path adjustments to ensure battery safety and performance. By identifying the path adjustment area, parts that do not match the real-time operating state of the battery pack can be located, avoiding blindly modifying the entire path and improving the targeting and efficiency of path adjustments.

[0040] The path adjustment area is dynamically adjusted by modifying allocation weights, correcting metering deviations, or reconstructing allocation paths to obtain the first corrected path. Adjusting allocation weights dynamically adjusts the current distribution ratio based on the real-time temperature and load of the batteries, reducing the current burden on batteries with high temperatures or high loads. Correcting metering deviations corrects metering errors caused by differences in battery state of charge, ensuring that the metering results accurately reflect the actual energy state of the batteries. Reconstructing the allocation path rebuilds the power distribution network based on changes in battery performance, adapting to changes in battery pack operating conditions. Based on the specific conditions of the path adjustment area and the real-time operating status of the battery pack, an appropriate adjustment method is selected for optimization, resulting in the adjusted first corrected path. By dynamically adjusting the path adjustment area, the power distribution strategy can be flexibly adjusted according to the real-time operating status of the battery pack, meeting the needs of safe and efficient battery pack operation, promptly responding to conditions such as temperature increases and load changes, and improving the accuracy and adaptability of power metering.

[0041] Specifically, based on the first correction path, the power distribution path is updated, replacing the original path adjustment area to obtain the power metering correction path. The replaced power distribution path is then checked to verify the overall logic of the path and the correctness of the relationships between the distribution nodes. The path is then optimized, for example, by adjusting the connection relationships between distribution nodes and supplementing necessary metering parameters, to obtain the complete power metering correction path. Integrating the first correction path into the power distribution path to obtain the power metering correction path ensures the integrity and coherence of the power distribution adjustment, better adapts to the real-time operating status of the battery pack, improves the quality and accuracy of power metering, and helps the battery pack operate more safely and efficiently, achieving dynamic adaptive adjustment of power metering.

[0042] Furthermore, by adjusting the allocation weights, correcting measurement deviations, or reconstructing the allocation path, the path adjustment area is dynamically adjusted to obtain the first correction path, including: Based on the real-time operating status of the lithium battery pack, analyze the real-time temperature and state of charge of each individual cell. Based on the real-time temperature, the allocation weight of the corresponding nodes in the path adjustment area is adjusted to obtain the first adjustment area; When the difference in state of charge exceeds a preset difference threshold, the metering deviation of the corresponding node is corrected to obtain the second adjustment area; By analyzing the compatibility between the real-time operating status and the power distribution network, the distribution paths with a compatibility less than a preset compatibility threshold are reconstructed to obtain the third adjustment area. By combining the first adjustment region, the second adjustment region, and the third adjustment region, the first correction path is obtained.

[0043] In this embodiment, based on the real-time operating status of the lithium battery pack, the current temperature and state of charge of each individual cell are analyzed. Temperature is a key factor affecting battery performance and safety; excessively high temperatures can accelerate battery aging and even cause safety issues. The state of charge reflects the energy storage level of the battery, and an imbalance in the state of charge of each individual cell can affect the performance and lifespan of the battery pack. By analyzing real-time temperature and state of charge, direction is provided for path adjustment, thereby optimizing the power distribution strategy and improving the safety and performance of the battery pack.

[0044] Specifically, based on the real-time temperature of each individual battery cell, the impact of temperature on battery performance is analyzed. When the battery temperature rises, the internal resistance changes, affecting the accuracy of the current distribution ratio. Therefore, the distribution weights need to be adjusted. In the path adjustment region, the distribution weights of corresponding nodes are adjusted according to temperature changes. Generally, the higher the temperature, the lower the distribution weight should be to reduce the load on the high-temperature battery and prevent further temperature increases. The formula for calculating the adjustment coefficient is: ,in, This is the temperature adjustment coefficient. Real-time battery temperature. This is a reference temperature (usually 25℃). This is the maximum permissible temperature (usually 55℃). This is the temperature sensitivity coefficient (ranging from 0.1 to 0.5). The formula is based on the relationship curve between battery temperature and performance. As temperature increases, battery safety and lifespan are affected, necessitating a reduction in its load. After adjusting the allocation weights, a first adjustment zone including temperature compensation is obtained. By adjusting the allocation weights, it is possible to respond to changes in battery temperature, appropriately reduce the load on high-temperature batteries, lower the risk of hot spots, improve the safety and balance of the battery pack, and allow energy metering to better adapt to the real-time temperature state of the battery.

[0045] Specifically, when the difference in the state of charge (SCC) of the batteries exceeds a preset threshold, it indicates an imbalance in the energy state of the individual cells in the battery pack, which may lead to energy metering deviations. These deviations need to be corrected, and the metering deviations at the corresponding nodes are adjusted to obtain a second adjustment zone. In this embodiment, the preset SCC difference threshold is 8%. This threshold is set based on the battery management system's balance control standards and the analysis results of a large amount of battery usage data. When the SCC difference exceeds 8%, the performance and lifespan of the battery pack will be significantly affected, requiring metering deviation correction. The metering deviation correction formula is: ,in Represents the correction factor. This represents the average state of charge of the battery pack. This refers to the state of charge of a single battery cell. To correct the sensitivity coefficient (ranging from 0.05 to 0.2), when the state of charge of a single cell is lower than the average value, its measurement value is appropriately increased, and vice versa. By correcting the measurement deviation, the energy state of each cell can be balanced, improving the overall performance and lifespan of the battery pack, while also enhancing the accuracy of energy metering.

[0046] Specifically, based on the adaptability of the battery pack's real-time operating state to the power distribution network, allocation paths with adaptability less than a preset adaptability threshold are reconstructed to obtain a third adjustment region. When the adaptability is less than the preset adaptability threshold, it indicates that the current allocation path is no longer suitable for the battery pack's operating state. By reconstructing the allocation path, the power distribution strategy is optimized to better adapt to the battery pack's real-time operating state. The corresponding adaptability is obtained by calculating the cosine similarity between the battery pack's real-time operating state vector and each allocation path vector in the power distribution network. In this embodiment, the preset adaptability threshold is 0.85. This threshold is set based on the effectiveness evaluation standard of the power distribution strategy. When the adaptability is less than 0.85, it indicates that the matching degree between the allocation path and the actual battery state is insufficient, and path reconstruction is required. For allocation paths with a fit degree less than 0.85, a new path with a higher fit degree to the real-time operating state of the battery pack and more suitable for the current operating conditions is searched in the power allocation network and replaced, and the connection relationship between nodes is updated. By analyzing the fit degree between the real-time operating state and the power allocation network to reconstruct the allocation path, it is possible to ensure that the power allocation strategy matches the actual operating state of the battery pack, thereby improving the accuracy and adaptability of power metering.

[0047] The path contents of the first, second, and third adjustment regions are merged, duplicate parts are removed, and valid allocation nodes and connections are retained. The integrated path is then reviewed to check the rationality of the relationships between allocation nodes, and the connections between nodes are adjusted to obtain the final first corrected path. By dynamically adjusting the path in different ways, fully considering factors such as battery temperature, state of charge, and adaptability to operating conditions, a power distribution strategy more consistent with the real-time operating status of the battery pack is provided. This helps improve the accuracy of power metering, enhance the safety and performance of the battery pack, and comprehensively improve the energy efficiency and lifespan of the battery pack.

[0048] Furthermore, the initial energy metering results are corrected through an energy metering correction path to obtain accurate energy metering results, thereby enabling energy metering and energy efficiency analysis of the lithium battery pack, including: Based on the electricity metering correction path, the initial electricity metering result is corrected in stages to obtain the corresponding first correction result; The first correction results are integrated to obtain accurate power metering results, so as to realize power metering and energy efficiency analysis of lithium battery packs.

[0049] In this embodiment, the initial energy metering results are graded and corrected according to the energy metering correction path. The initial energy metering results are corrected according to different dimensions. Based on the adjustment requirements for each part in the energy metering correction path, the metering results at different levels are specifically corrected, so that each metering result can better reflect the actual energy state of the battery, thus obtaining the first correction result. By graded correction of the initial energy metering results, the correction can be more detailed and comprehensive, improving the accuracy of the metering results. This avoids the inaccuracy problem caused by general adjustments to the metering results, improving the effect and efficiency of the correction, and helping to improve the quality and reliability of energy metering.

[0050] Specifically, the first correction results are integrated according to the corresponding logical relationships and energy balance principles to form a complete and consistent set of electricity metering results, resulting in accurate electricity metering results. Based on the relationships between the nodes in the electricity metering correction path and the energy balance principle of the battery pack (input energy is the sum of output energy and energy loss), the integration method of the first correction results is determined. For example, in an electric vehicle battery pack, the energy consumption of each individual battery cell should be counted first, and then the overall energy efficiency of the battery pack should be calculated. The metering data in the first correction results are integrated sequentially, checking whether the data between adjacent metering cycles is continuous and whether the energy balance is satisfied. Unreasonable data is adjusted to ensure the continuity and consistency of the metering results. The relationships between the metering results are optimized to obtain accurate electricity metering results. By integrating the correction results, the systematic nature and consistency of electricity metering are ensured, which helps to understand the energy status of the battery pack more comprehensively and accurately, better supporting the energy efficiency analysis and health management of the battery pack, improving the battery pack's utilization efficiency and lifespan, and enhancing the supporting and guiding role of electricity metering in battery management.

[0051] Furthermore, based on the electricity metering correction path, the initial electricity metering results are corrected in stages to obtain the corresponding first correction results, including: Based on the corrected allocation nodes in the electricity metering correction path, the corresponding correction algorithm is matched in the preset metering algorithm library to obtain the correction calculation method. The correction calculation method is applied to the corresponding metering data in the initial power metering results, and the power distribution ratio and metering cycle of each individual battery cell are adjusted to obtain the first correction result.

[0052] In this embodiment, based on the corrected allocation nodes in the energy metering correction path, algorithm matching is performed in a preset metering algorithm library to find the corresponding correction algorithm and obtain the correction calculation method. The characteristic parameters and operating status of each corrected allocation node in the energy metering correction path are analyzed to obtain the required characteristics of the correction node. The required characteristics of the correction node are compared with the applicable conditions of the correction algorithms in the metering algorithm library, prioritizing matching the applicable scope to ensure that the correction algorithm is suitable for the operating status of the node. Next, the correction accuracy is matched to analyze whether the algorithm can meet the accuracy requirements of the node. Finally, considering computational complexity, an algorithm with higher computational efficiency is selected based on meeting the first two requirements. When the applicable conditions of the correction algorithm match the required characteristics of the correction node, the algorithm is used as the correction calculation method corresponding to that correction node, and a corresponding correction calculation method is matched for each corrected allocation node in the energy metering correction path. By matching correction calculation methods, a suitable correction algorithm for each allocation node can be found quickly and efficiently, ensuring the accuracy and reliability of the correction results.

[0053] The matched correction calculation method is applied to the corresponding metering data in the initial energy metering results, and the energy allocation ratio and metering cycle of each individual battery are adjusted to obtain the first correction result. In the initial energy metering results, the metering data corresponding to the correction allocation node in the energy metering correction path are found, and the corresponding correction calculation method is applied to these metering data to perform data correction. According to the real-time operating status of the battery pack, the energy allocation ratio of each individual battery is adjusted to make the allocation ratio more consistent with the actual performance state of the battery. Combining the operating mode of the battery pack and the frequency of data changes, the metering cycle is adjusted. When the battery state changes rapidly, the metering cycle is shortened and the data sampling frequency is increased. When the battery state is relatively stable, the metering cycle is appropriately extended to reduce the amount of data processing. After data correction, allocation ratio adjustment and metering cycle adjustment, the complete first correction result is obtained. By applying the correction calculation method to the initial energy metering results and adjusting the allocation ratio and metering cycle, the performance and reliability of the battery management system can be improved.

[0054] For example, during the charging process of a lithium battery pack for an electric vehicle, the initial energy metering results show that the temperature of cell 3 rises to 42℃, and its state of charge (SOC) is 68%, significantly lower than that of other cells. Applying a temperature compensation correction algorithm, the temperature correction coefficient is calculated as: 1 - [(42-25) / 55] × 0.3 = 0.923. Applying a state of charge (SOC) balance correction algorithm, the SOC correction coefficient is calculated as: 1 + [(74.25-68) / 74.25] × 0.1 = 1.008. Applying the correction coefficient to the current distribution ratio of cell 3, the original distribution ratio was 23%, and after correction, it is 23% × 0.923 × 1.008 = 21.4%. Simultaneously, due to the higher temperature of cell 3, its metering cycle is shortened from 60 seconds to 30 seconds to more closely monitor its state changes, resulting in the corrected energy metering results.

[0055] This embodiment presents the internal resistance and capacity decay characteristics of each individual battery cell through a battery state feature set, accurately reflecting the actual operating state of each individual battery cell in the battery pack and providing basic data for energy metering. The uneven current distribution caused by differences in internal resistance and inconsistent aging among the individual cells in the lithium battery pack is accurately described by constructing an energy distribution path based on the battery state feature set and a preset battery characteristic database, avoiding metering deviations caused by ignoring individual cell differences in traditional methods. In practical applications, the dynamic changes in the battery pack's operating state lead to continuous adjustments in the current distribution relationship. By dynamically adjusting the energy distribution path based on the real-time operating state, energy metering corrections are obtained. This approach reduces the impact of battery state changes on energy metering results. In high-power fast charging and long-cycle applications, lithium battery packs may experience uneven load rates and temperature distributions in individual cells. By correcting the initial energy metering results based on the energy metering correction path, accurate measurement of the energy state of each individual cell within the battery pack is achieved. This avoids the risk of local overcharging or over-discharging caused by uneven energy metering, improving the accuracy of lithium battery pack energy metering and the reliability of energy efficiency analysis. It eliminates the need for complex balancing circuits and adaptive state estimation algorithms, reducing system complexity and computational resource requirements, and effectively supporting the optimized application and full lifecycle management of lithium battery packs in large-scale energy storage systems.

[0056] Example 2 Please see Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. An integrated system for lithium battery pack energy metering and energy efficiency analysis is provided, including: Data acquisition module: acquires the electrical parameters of each individual cell in the lithium battery pack, identifies the internal resistance characteristics and capacity decay characteristics of each individual cell through preset feature extraction rules, and generates a battery state feature set; Allocation Module: Based on the battery state feature set and combined with the preset battery characteristic database, construct the corresponding power allocation path; Metering module: Based on the power distribution path, calculate the real-time current distribution value of each individual battery cell to obtain the initial power metering result; Correction module: Dynamically adjusts the power distribution path based on the real-time operating status of the lithium battery pack to obtain the power metering correction path; Analysis module: By correcting the initial energy metering results through the energy metering correction path, the module obtains accurate energy metering results to realize energy metering and energy efficiency analysis of lithium battery packs.

[0057] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0058] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0059] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0060] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0061] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0062] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0063] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0064] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. An integrated method for energy metering and energy efficiency analysis of lithium battery packs, characterized in that, include: Step S1: Obtain the electrical parameters of each individual cell in the lithium battery pack, identify the internal resistance characteristics and capacity decay characteristics of each individual cell through preset feature extraction rules, and generate a battery state feature set; Step S2: Based on the battery state feature set and combined with the preset battery characteristic database, construct the corresponding power distribution path; including: extracting the internal resistance and capacity values ​​of each individual battery cell according to the battery state feature set, and constructing a battery characteristic vector; Based on a pre-set battery characteristic database, each individual battery cell is used as an allocation node. The current distribution relationship between individual batteries is analyzed according to the internal resistance difference, including: calculating the deviation rate between the internal resistance value of each individual battery cell and the average internal resistance of the battery pack; when the deviation rate is greater than or equal to a preset deviation threshold, the corresponding individual battery cell is marked as a high internal resistance battery; when the deviation rate is less than the preset deviation threshold, the corresponding individual battery cell is marked as a low internal resistance battery; the current distribution relationship between individual batteries is determined based on the internal resistance marking, wherein low internal resistance batteries bear a higher current distribution ratio, high internal resistance batteries bear a lower current distribution ratio, batteries within the same capacity level are current-distributed according to the inverse ratio of internal resistance, and batteries between different capacity levels are current-distributed according to the product of capacity ratio and inverse internal resistance ratio; and connections are established between allocation nodes to construct a power distribution network. Based on the battery characteristic vector, the corresponding current distribution ratio is calculated in the power distribution network to determine the distribution node that meets the overall efficiency of the battery pack. Based on the connection relationship between the nodes in the power distribution network, the corresponding power transmission path is searched starting from the input node to obtain a set of candidate distribution paths. The matching value between each path in the candidate allocation path set and the battery characteristic vector is calculated by configuring allocation efficiency calculation rules. The path with the highest matching value in the candidate allocation path set is selected as the power allocation path. The allocation efficiency calculation rules include: for internal resistance matching efficiency, assigning a value to the internal resistance matching efficiency of each allocation node according to the deviation between the internal resistance value of the allocation node and the average internal resistance of the battery pack, and summing the internal resistance matching efficiencies of all allocation nodes in the path to obtain the total internal resistance matching efficiency of the path; for capacity utilization efficiency, assigning a value to the capacity utilization efficiency of each allocation node according to the ratio between the capacity value of the allocation node and the nominal capacity of the battery pack, and summing the capacity utilization efficiencies of all allocation nodes in the path to obtain the total capacity utilization efficiency of the path; and weighted summing the total internal resistance matching efficiency and the total capacity utilization efficiency of each path to obtain the matching value between each path and the battery characteristic vector. Step S3: Calculate the real-time current distribution value of each individual battery cell according to the power distribution path to obtain the initial power metering result; Step S4: Dynamically adjust the power distribution path based on the real-time operating status of the lithium battery pack to obtain the power metering correction path; Step S5: Correct the initial energy metering result through the energy metering correction path to obtain an accurate energy metering result, so as to realize the energy metering and energy efficiency analysis of the lithium battery pack.

2. The integrated method for lithium battery pack energy metering and energy efficiency analysis according to claim 1, characterized in that, The process involves acquiring the electrical parameters of each individual cell in the lithium battery pack, identifying the internal resistance and capacity decay characteristics of each individual cell using preset feature extraction rules, and generating a battery state feature set, including: By collecting data on the terminal voltage, charging and discharging current, and temperature of each individual cell, the electrical parameters of each individual cell in the lithium battery pack can be obtained. Based on the electrical parameters, the internal resistance characteristics and capacity decay characteristics of each individual battery cell are calculated using preset feature extraction rules. By integrating the internal resistance characteristics and capacity decay characteristics, a battery state feature set is generated.

3. The integrated method for lithium battery pack energy metering and energy efficiency analysis according to claim 1, characterized in that, The step of calculating the real-time current distribution value of each individual battery cell based on the power distribution path to obtain the initial power metering result includes: Based on the power distribution path, a corresponding first current calculation method is matched for each distribution node in the preset metering algorithm library; Based on the battery characteristic vector, the parameters of the first current calculation method are adjusted to obtain the second current calculation method; According to the order of the distribution nodes in the power distribution path, the current value of each node is calculated by the second current calculation method to obtain the initial power metering result.

4. The integrated method for lithium battery pack energy metering and energy efficiency analysis according to claim 1, characterized in that, The dynamic adjustment of the power distribution path based on the real-time operating status of the lithium battery pack to obtain a power metering correction path includes: Based on the real-time operating status analysis of the lithium battery pack, the load rate and temperature distribution of the distribution nodes in the power distribution path are analyzed to identify the path adjustment area. The path adjustment area is dynamically adjusted by adjusting the allocation weights, correcting measurement deviations, or reconstructing the allocation path to obtain the first corrected path. The power distribution path is updated based on the first correction path to obtain the power metering correction path.

5. The integrated method for lithium battery pack energy metering and energy efficiency analysis according to claim 4, characterized in that, The step of dynamically adjusting the path adjustment area by adjusting the allocation weights, correcting measurement deviations, or reconstructing the allocation path to obtain the first correction path includes: Based on the real-time operating status of the lithium battery pack, analyze the real-time temperature and state of charge of each individual cell. Based on the real-time temperature, the allocation weight of the corresponding nodes in the path adjustment area is adjusted to obtain the first adjustment area; When the difference in the state of charge exceeds a preset difference threshold, the metering deviation of the corresponding node is corrected to obtain the second adjustment area; By analyzing the compatibility between the real-time working state and the power distribution network, the distribution paths with a compatibility less than a preset compatibility threshold are reconstructed to obtain the third adjustment region. By combining the first adjustment region, the second adjustment region, and the third adjustment region, the first correction path is obtained.

6. The integrated method for lithium battery pack energy metering and energy efficiency analysis according to claim 1, characterized in that, The process of correcting the initial energy metering result through the energy metering correction path to obtain an accurate energy metering result, thereby realizing energy metering and energy efficiency analysis of the lithium battery pack, includes: Based on the electricity metering correction path, the initial electricity metering result is corrected in stages to obtain the corresponding first correction result; The first correction results are integrated to obtain accurate power metering results, so as to realize power metering and energy efficiency analysis of lithium battery packs.

7. The integrated method for lithium battery pack energy metering and energy efficiency analysis according to claim 6, characterized in that, The step of performing graded corrections on the initial energy metering results according to the energy metering correction path to obtain the corresponding first correction result includes: Based on the corrected allocation nodes in the electricity metering correction path, the corresponding correction algorithm is matched in the preset metering algorithm library to obtain the correction calculation method. The correction calculation method is applied to the corresponding metering data in the initial power metering results, and the power distribution ratio and metering cycle of each individual battery cell are adjusted to obtain the first correction result.

8. An integrated system for lithium battery pack energy metering and energy efficiency analysis, used to implement the integrated method for lithium battery pack energy metering and energy efficiency analysis as described in any one of claims 1 to 7, characterized in that, include: Data acquisition module: acquires the electrical parameters of each individual cell in the lithium battery pack, identifies the internal resistance characteristics and capacity decay characteristics of each individual cell through preset feature extraction rules, and generates a battery state feature set; Allocation construction module: Based on the battery state feature set and combined with the preset battery characteristic database, construct the corresponding power allocation path; Metering module: Based on the power distribution path, calculate the real-time current distribution value of each individual battery cell to obtain the initial power metering result; Correction module: Dynamically adjusts the power distribution path based on the real-time operating status of the lithium battery pack to obtain a power metering correction path; Analysis module: Through the energy metering correction path, the initial energy metering result is corrected to obtain accurate energy metering result, so as to realize the energy metering and energy efficiency analysis of lithium battery pack.

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