A battery pack charging energy consumption balancing optimization method

Abstract

The application discloses a battery pack charging energy consumption equalization optimization method, and relates to the technical field of battery management. The method establishes a charging energy consumption contribution function for each battery monomer in the battery pack in the charging process dimension, which is used for describing the energy consumption intensity and its change rule at different charging process positions; based on the energy consumption contribution function of each monomer, a battery pack energy consumption distribution state is constructed at each position of the charging process, and the energy consumption distribution dispersion is calculated to quantify the charging energy consumption equalization degree among the monomers; further, the energy consumption distribution dispersion is continuously analyzed in the charging process range, and it is determined whether the energy consumption imbalance presents a convergence trend or an amplification trend; when it is determined that there is an amplification trend, a charging process reconstruction mapping is generated with the target of reducing the energy consumption distribution dispersion, and the distribution structure of the energy consumption contribution in the charging process is controlled and adjusted; finally, the energy consumption distribution dispersion is recalculated based on the reconstructed energy consumption contribution function, and when it satisfies the preset equalization condition, the charging energy consumption equalization optimization is determined to be completed.

Description

Technical Field

[0001] This invention relates to the field of battery management and energy optimization technology, specifically to a method for optimizing the energy consumption balance of battery pack charging. Background Technology

[0002] With the development of electric vehicles, energy storage systems, and various electrical devices, battery packs are widely used as the core energy carrier. Battery packs are usually composed of multiple battery cells connected in series or parallel. During charging, it is necessary to manage each battery cell in a unified manner to ensure charging safety and energy utilization efficiency.

[0003] In actual charging, due to differences in manufacturing processes, internal resistance characteristics, aging levels, and operating environments among individual battery cells, even under the same charging conditions, the energy consumption generated by each battery cell during charging often varies. This difference in energy consumption may gradually accumulate during charging, leading to an uneven distribution of energy consumption within the battery pack, affecting overall charging efficiency, and potentially accelerating the performance degradation of some battery cells.

[0004] Most existing battery pack charging balancing technologies focus on voltage, current, or state of charge (SOC) as the adjustment targets, reducing the SOC differences between individual battery cells by adjusting charging parameters or introducing balancing circuits. While these technologies can improve charging consistency to some extent, their focus is primarily on SOC parameters, making it difficult to directly reflect and control the actual energy consumption differences generated by individual battery cells during charging.

[0005] Furthermore, existing technologies typically perform equalization only after detecting deviations in the state of individual battery cells, lacking an analysis and judgment mechanism for the evolution of energy consumption distribution during charging. When energy imbalance amplifies during charging, traditional post-event adjustment methods struggle to promptly suppress the continued expansion of energy consumption differences, easily leading to localized high energy consumption and reducing the overall charging efficiency of the battery pack. Simultaneously, some charging equalization schemes are highly dependent on specific charging processes or hardware structures, limiting their versatility and adaptability across different battery pack types or application scenarios.

[0006] Therefore, how to effectively analyze and optimize the uneven energy distribution during battery charging without relying on complex hardware structures remains a problem that urgently needs to be solved in existing technologies. Summary of the Invention

[0007] This invention provides a method for optimizing energy consumption balancing during battery pack charging, comprising:

[0008] S10. In the dimension of charging process, a charging energy consumption contribution function is established for each battery cell in the battery pack to characterize the energy consumption intensity and its variation characteristics generated by the battery cell at different charging process positions.

[0009] S20. Based on the charging energy consumption contribution function of each battery cell, calculate the energy consumption distribution state of the battery pack at each position in the charging process, and measure the degree of charging energy consumption balance among different battery cells through energy consumption distribution discretization.

[0010] S30. Perform continuous analysis on the dispersion of energy consumption distribution within the charging process range to determine whether the uneven energy consumption during charging shows a convergence trend or an amplification trend.

[0011] S40. When it is determined that the imbalance of charging energy consumption has an amplifying trend, with the goal of reducing the dispersion of energy consumption distribution, a reconstruction mapping of the charging energy consumption contribution function is generated to adjust the distribution structure of energy consumption contribution in the charging process.

[0012] S50. Recalculate the energy consumption distribution dispersion based on the reconstructed and mapped charging energy consumption contribution function. When the energy consumption distribution dispersion meets the preset equilibrium condition, complete the charging energy consumption equilibrium optimization.

[0013] The battery pack charging energy consumption balancing optimization method described above includes, in the dimension of the charging process, establishing a charging energy consumption contribution function for each battery cell in the battery pack, used to characterize the energy consumption intensity and its variation characteristics generated by the battery cell at different charging process positions, including:

[0014] S101. Perform process modeling on the battery pack charging process, construct charging process parameters that characterize the evolution position of the charging process, and use them to uniformly describe the positional relationship of different charging stages in the same process coordinate system.

[0015] S102. At different charging process positions, calculate the energy consumption characteristics of the battery cell based on the charging voltage, current and corresponding duration of the battery cell.

[0016] S103. Based on energy consumption characteristics and charging process parameters, generate a charging energy consumption contribution function that reflects the relationship between energy consumption intensity and the charging process.

[0017] The battery pack charging energy consumption balancing optimization method described above includes calculating the energy consumption distribution state of the battery pack at various points in the charging process based on the charging energy consumption contribution function of each individual battery cell, and quantifying the degree of charging energy consumption balancing among different battery cells through energy consumption distribution discretization, including:

[0018] S201. Extract the energy consumption contribution value of each battery cell at each position in the charging process to form a set of energy consumption contributions of multiple cells in parallel.

[0019] S202. Construct a group energy consumption distribution state that reflects the energy consumption differences within the battery pack based on the energy consumption contribution set;

[0020] S203. Calculate the energy consumption distribution state of the group to obtain the energy consumption distribution dispersion, which is used to quantify the degree of balance of charging energy consumption among different battery cells.

[0021] The battery pack charging energy consumption balancing optimization method described above includes continuously analyzing the dispersion of energy consumption distribution within the charging process range to determine whether the charging energy consumption imbalance exhibits a convergence trend or an amplification trend during the charging process, including:

[0022] S301. Perform continuous or segmented statistics on the energy consumption distribution dispersion within the charging process range to form a sequence of dispersion changes with the charging process.

[0023] S302. Extracting trend features reflecting the direction and rate of change based on discrete sequence;

[0024] S303. Based on trend characteristics, it is determined that the uneven charging energy consumption shows a convergence trend or an amplification trend during the charging process.

[0025] The battery pack charging energy consumption balancing optimization method described above, wherein, when it is determined that the charging energy consumption imbalance has an amplifying trend, a reconstructed mapping of the charging energy consumption contribution function is generated with the goal of reducing the dispersion of energy consumption distribution, and the distribution structure of energy consumption contribution during the charging process is adjusted, including:

[0026] S401. With reducing the dispersion of energy consumption distribution as the optimization objective, construct reconstruction constraints to constrain the adjustment range and direction of energy consumption.

[0027] S402. Under the premise of satisfying the reconstruction constraints, generate the reconstruction mapping relationship of the charging energy consumption contribution function in the charging process dimension.

[0028] S403. Based on the reconstructed mapping relationship, the distribution structure of the charging energy consumption contribution function in the charging process is adjusted to suppress the amplification trend of energy consumption imbalance.

[0029] The battery pack charging energy consumption balancing optimization method described above includes recalculating the energy consumption distribution dispersion based on the reconstructed and mapped charging energy consumption contribution function. Charging energy consumption balancing optimization is completed when the energy consumption distribution dispersion meets a preset balancing condition. This includes:

[0030] S501. Based on the reconstructed and mapped charging energy consumption contribution function, recalculate the energy consumption distribution dispersion within the charging process range;

[0031] S502. Compare the recalculated energy consumption distribution dispersion with the preset equilibrium conditions to make a judgment.

[0032] S503. When the energy consumption distribution dispersion meets the preset balance condition, the charging energy consumption balance optimization is completed.

[0033] In the battery pack charging energy consumption balancing optimization method described above, when reconstructing the charging energy consumption contribution function, the reconstruction mapping simultaneously satisfies the following constraints:

[0034] Throughout the entire charging process, the integral value of the charging energy consumption contribution function corresponding to each battery cell remains unchanged to ensure the conservation of the total charging energy consumption of each battery cell.

[0035] The reconstructed mapping only affects the location and density of energy consumption distribution in the charging process dimension, and does not change the overall sequence of the charging process.

[0036] Reconstruction mapping weakens the trend of uneven amplification of charging energy consumption by suppressing the peak growth of energy consumption distribution dispersion within the local charging process interval.

[0037] The present invention also provides a battery pack charging energy consumption balancing optimization system, comprising:

[0038] The energy consumption modeling module is used to establish a charging energy consumption contribution function for each battery cell in the battery pack in the dimension of charging process, so as to characterize the energy consumption intensity and its variation characteristics generated by the battery cell at different charging process positions.

[0039] The distribution evaluation module is used to calculate the energy consumption distribution state of the battery pack at each position in the charging process based on the charging energy consumption contribution function of each battery cell, and generate the energy consumption distribution dispersion to quantify the degree of charging energy consumption balance among different battery cells.

[0040] The trend determination module is used to continuously analyze the dispersion of energy consumption distribution within the charging process range and determine whether the uneven charging energy consumption shows a convergence trend or an amplification trend during the charging process.

[0041] The reconstruction and optimization module is used to generate a reconstruction mapping of the charging energy consumption contribution function with the goal of reducing the dispersion of energy consumption distribution when it is determined that there is an amplification trend of charging energy consumption imbalance. This module adjusts the distribution structure of energy consumption contribution in the charging process.

[0042] The equalization determination module is used to recalculate the energy consumption distribution dispersion based on the reconstructed and mapped charging energy consumption contribution function, and output the determination result that the charging energy consumption equalization optimization is completed when the energy consumption distribution dispersion meets the preset equalization conditions.

[0043] The beneficial effects achieved by this invention are as follows: This invention models the charging energy consumption of individual battery cells in the charging process dimension, constructs a charging energy consumption contribution function, and quantitatively analyzes the balance of charging energy consumption within the battery pack based on the energy consumption distribution dispersion, thereby determining the evolution trend of energy consumption imbalance during the charging process. On this basis, by reconstructing the charging energy consumption contribution function, only the distribution structure of energy consumption in the charging process is adjusted, achieving charging energy consumption balance optimization while keeping the total charging energy consumption of individual cells and the charging process sequence unchanged. This effectively suppresses the amplification trend of charging energy consumption imbalance, improves the overall charging efficiency and consistency of the battery pack, and enhances the applicability of the solution under different battery packs and charging scenarios. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0045] Figure 1 This is a flowchart of a battery pack charging energy consumption balancing optimization method provided in Embodiment 1 of this application;

[0046] Figure 2 This is a schematic diagram of a battery pack charging energy consumption balancing optimization system provided in Embodiment 2 of this application. Detailed Implementation

[0047] 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, not all, of the embodiments of the present invention. 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.

[0048] Example 1

[0049] like Figure 1 As shown, Embodiment 1 of this application provides a battery pack charging energy consumption balancing optimization method, including the following steps:

[0050] S10. In the dimension of charging process, a charging energy consumption contribution function is established for each battery cell in the battery pack to characterize the energy consumption intensity and its variation characteristics generated by the battery cell at different charging process positions.

[0051] This step describes the energy consumption of each battery cell in the battery pack during the charging process within a unified charging process framework. By mapping the charging process to the charging process dimension and combining the actual energy consumption performance of battery cells at different stages of the process, a charging energy consumption contribution function reflecting the relationship between energy consumption and the charging process is constructed. Specifically, it includes the following sub-steps:

[0052] S101. Perform process modeling on the battery pack charging process, construct charging process parameters that characterize the evolution position of the charging process, and use them to uniformly describe the positional relationship of different charging stages in the same process coordinate system.

[0053] First, the entire charging process of the battery pack is modeled as a continuous evolution, from the start to the end of charging. By mapping different stages of the charging process to a unified charging process coordinate system, different charging stages can still be described within the same process dimension, even if there are differences in charging time or stage divisions. This allows for subsequent processing, where energy consumption changes during charging can be characterized based on the location of the charging process rather than specific time points.

[0054] S102. At different charging process positions, calculate the energy consumption characteristics of the battery cell based on the charging voltage, current and corresponding duration of the battery cell.

[0055] After modeling the charging process, the energy consumption of each battery cell in the battery pack is calculated at different charging process positions. Within the time interval corresponding to each charging process position, the energy consumption of the battery cell at that process position is quantified by combining the charging voltage, charging current, and duration of that interval, resulting in a corresponding energy consumption characteristic. This energy consumption characteristic reflects the energy consumption intensity of the battery cell at different charging process positions, providing a basis for analyzing the energy consumption differences between different battery cells.

[0056] S103. Based on energy consumption characteristics and charging process parameters, generate a charging energy consumption contribution function that reflects the relationship between energy consumption intensity and the charging process.

[0057] After obtaining the energy consumption characteristics corresponding to each charging process position, the energy consumption characteristics are organized according to the order of the charging process to form a continuously changing relationship in the charging process dimension. Based on this, the energy consumption characteristics are associated with the charging process position to generate a charging energy consumption contribution function to characterize the change of battery cell energy consumption intensity with the charging process, thereby describing the energy consumption contribution of the battery cell in the entire charging process in the charging process dimension.

[0058] S20. Based on the charging energy consumption contribution function of each battery cell, calculate the energy consumption distribution state of the battery pack at each position in the charging process, and measure the degree of charging energy consumption balance among different battery cells through energy consumption distribution discretization.

[0059] This step is used to provide a grouped description of the energy consumption differences within the battery pack along the charging process. Specifically, it consists of the following sub-steps:

[0060] S201. Extract the energy consumption contribution value of each battery cell at each position in the charging process to form a set of energy consumption contributions of multiple cells in parallel.

[0061] Sample or traverse multiple process positions along the charging process. At any charging process position... Above, read the first number of cells in the battery pack respectively. Energy consumption contribution function of each battery cell The function value is used as the energy consumption contribution value of the single cell at that process position. To ensure consistency in the comparison criteria of different battery cells at the same process position, the selected process position and sampling granularity are consistent with the process modeling in step S10, so that each process position can obtain an energy consumption contribution set composed of multiple cells in parallel. .

[0062] S202. Construct a group energy consumption distribution state that reflects the energy consumption differences within the battery pack based on the energy consumption contribution set;

[0063] The energy consumption contribution set is transformed into a group distribution form to reflect the internal energy consumption difference structure of the battery pack at this stage of the process. Considering that the absolute energy consumption levels of different battery packs or different stages may differ, directly using... The absolute value of the distribution affects the comparison, therefore, in this embodiment, a normalization method is used to construct the group energy consumption distribution state. Specifically, at the process location... The energy consumption contribution value of each battery cell is proportionalized according to the total energy consumption within the group to obtain the energy consumption proportion sequence. The sequence satisfies the condition that the sum is 1, and is used to describe the group energy consumption distribution of the battery pack at the process location.

[0064] S203. Calculate the energy consumption distribution state of the group to obtain the energy consumption distribution dispersion, which is used to quantify the degree of balance of charging energy consumption among different battery cells.

[0065] To quantify the degree of energy consumption balance among different battery cells, energy consumption distribution dispersion is introduced. This makes it sensitive to both "local spike-like imbalances" and "overall concentrated imbalances." Therefore, dispersion is defined as a combined index of mean square deviation and information deviation, with the following calculation relationship: ,in, Indicates the number of individual battery cells in the battery pack; Indicates the position of the charging process; Indicates the first Individual battery cells at the charging process position Energy consumption contribution value; This indicates the position of the i-th battery cell in the charging process. The energy consumption percentage is used to determine the energy consumption distribution of the group. A positive stable term; The mean square deviation term is used to characterize the current energy consumption distribution relative to the perfect equilibrium distribution. The degree of deviation; This is the information deviation term, used to characterize the degree of concentration of energy consumption distribution; the more concentrated the distribution, the larger this term becomes. This is a weighting coefficient used to adjust the relative weights of the mean square deviation term and the information deviation term in the dispersion. Through the processing in steps S201–S203, the corresponding energy consumption distribution dispersion can be obtained at each point in the charging process. This allows for a quantitative description of the balance of charging energy consumption within the battery pack using a unified process dimension.

[0066] S30. Perform continuous analysis on the dispersion of energy consumption distribution within the charging process range to determine whether the uneven energy consumption during charging shows a convergence trend or an amplification trend.

[0067] This step analyzes and judges the changing trend of the uneven charging energy consumption within the battery pack from the perspective of the overall charging process. By continuously observing the dispersion of energy consumption distribution within the charging process, it identifies whether the uneven energy consumption gradually alleviates or continuously intensifies during charging. Specifically, it includes the following sub-steps:

[0068] S301. Perform continuous or segmented statistics on the energy consumption distribution dispersion within the charging process range to form a sequence of dispersion changes with the charging process.

[0069] Continuous sampling or statistical analysis is performed at multiple process locations along the charging process, or within preset process intervals. At each charging process location, the energy consumption distribution dispersion is read and arranged according to the chronological order of the charging process, forming a sequence reflecting the change of energy consumption distribution dispersion as the charging process progresses. This dispersion sequence describes the change in the degree of energy consumption imbalance within the battery pack throughout the entire charging process, enabling subsequent analysis to be based on the overall evolution within the charging process range, rather than being limited to the instantaneous state at a single process location.

[0070] S302. Extracting trend features reflecting the direction and rate of change based on discrete sequence;

[0071] After obtaining the sequence of dispersion changes with the charging process, trend features are extracted from this sequence to characterize the direction and intensity of energy imbalance during the charging process. Specifically, by analyzing the overall changes of the dispersion sequence along the charging process dimension, it is identified whether the dispersion gradually increases or decreases with the charging process, or remains relatively stable within a certain range. When extracting trend features, the directionality, magnitude, and persistence of the dispersion changes are comprehensively considered to avoid interference from local fluctuations or short-term anomalies in the overall trend judgment.

[0072] S303. Based on trend characteristics, it is determined that the uneven charging energy consumption shows a convergence trend or an amplification trend during the charging process.

[0073] When the energy consumption distribution dispersion shows a continuous decrease or gradual weakening change characteristic within the charging process range, it is determined that the charging energy consumption imbalance shows a convergence trend; when the energy consumption distribution dispersion shows a continuous increase or gradual strengthening change characteristic within the charging process range, it is determined that the charging energy consumption imbalance shows an amplification trend.

[0074] If the trend is not obvious or the change is small, the energy consumption imbalance within the current charging process range can be considered to be in a relatively stable state, and this can serve as a reference for whether to trigger an adjustment in the energy consumption distribution structure in the future.

[0075] S40. When it is determined that the imbalance of charging energy consumption has an amplifying trend, with the goal of reducing the dispersion of energy consumption distribution, a reconstruction mapping of the charging energy consumption contribution function is generated to adjust the distribution structure of energy consumption contribution in the charging process.

[0076] When it is determined that the imbalance in charging energy consumption has an amplifying trend, an adjustment to the energy consumption distribution structure is triggered. The goal of this adjustment is not to change the total energy consumption level of individual cells, but rather to reorganize the location and distribution density of energy consumption contributions along the charging process dimension. This aims to reduce the dispersion of energy consumption distribution within the charging process, especially avoiding the formation of continuously increasing peaks in dispersion within local process intervals, thereby suppressing the amplifying trend of energy consumption imbalance. Specifically, this involves the following sub-steps:

[0077] S401. With reducing the dispersion of energy consumption distribution as the optimization objective, construct reconstruction constraints to constrain the adjustment range and direction of energy consumption.

[0078] To optimize energy consumption distribution by reducing its dispersion, reconfiguration constraints are established to limit the magnitude and direction of energy consumption adjustments. The constraint design emphasizes three points: first, the total energy consumption of each battery cell remains consistent before and after reconfiguration, avoiding "false equilibrium" achieved by altering the total energy consumption; second, adjustments occur only in the charging process dimension, without disrupting the order of charging processes; and third, the magnitude and rate of change in process positions are limited to prevent excessive rearrangement that could lead to instability or difficulty in implementation.

[0079] S402. Under the premise of satisfying the reconstruction constraints, generate the reconstruction mapping relationship of the charging energy consumption contribution function in the charging process dimension.

[0080] To achieve controllable distribution rearrangement along the charging process dimension, a monotonic charging process reconstruction mapping is introduced. And based on this mapping, the original charging energy consumption contribution function Reconstructed The mapping and reconstruction process is given by the following optimization definition: , , , ,in, This indicates the charging progress position; This represents the charging process reconstruction mapping function, which is used to reparameterize the original charging process position to adjust the energy consumption distribution structure along the charging process dimension. This represents the charging process weighting function, which is used to adjust the degree of influence of different charging process intervals on the discreteness optimization objective. This represents the dispersion of energy consumption distribution calculated based on the reconfigured energy consumption contribution function, used to measure the position of the battery pack in the charging process after reconfiguration. The degree of unevenness in charging energy consumption at various locations; This represents the peak suppression weight coefficient, which is used to adjust the influence of the exponential dispersion penalty term on the overall optimization objective function. This represents the dispersion amplification factor, used to enhance the suppression effect of the optimization process on local high-energy-consumption discrete intervals; This represents the mapping magnitude constraint weight, used to limit the overall offset of the reconstructed mapping relative to the original charging process position; This represents the constraint weight for the rate of change of the mapping, used to limit the stretching or compression of the reconstructed mapping in the charging process dimension; Represents reconstructed mapping Regarding the charging progress position The first derivative of is used to describe the change in charging process density during the reconfiguration process; This represents the lower bound of the mapping derivative, used to ensure that the reconstruction mapping remains strictly monotonic in the charging process dimension, thereby avoiding the reversal of the charging process order; This indicates the maximum allowed process offset, used to limit the maximum adjustment range of the reconstructed mapping to the original charging process position; This indicates the position of the i-th battery cell in the charging process. The charging energy consumption contribution function; This indicates the position of the i-th battery cell in the charging process. The reconstructed energy consumption contribution function; This represents the numerically stable term; N represents the total number of battery cells in the battery pack. This indicates the position of the i-th battery cell in the charging process. The energy consumption percentage at each location is used to construct the reconstructed group energy consumption distribution status. This represents the dispersion tradeoff coefficient, used to adjust the relative weights of the mean square deviation term and the information deviation term in the calculation of energy consumption distribution dispersion.

[0081] S403. Based on the reconstructed mapping relationship, the distribution structure of the charging energy consumption contribution function in the charging process is adjusted to suppress the amplification trend of energy consumption imbalance.

[0082] Under the charging process reconstruction mapping relationship determined in step S402, the charging energy consumption contribution function of each battery cell is reconstructed, so that the distribution position and distribution density of energy consumption contribution in the charging process dimension are adjusted in a controlled manner. This adjustment only affects the distribution structure of energy consumption in the charging process, without changing the total energy consumption of the battery cell in the entire charging process, nor disrupting the sequential relationship of the charging process.

[0083] During the reconfiguration process, the focus is on the changes in energy consumption distribution dispersion within local intervals of the charging process. This is used to determine whether the current reconfiguration mapping effectively suppresses the continued amplification trend of energy consumption imbalance. When the reconfigured energy consumption distribution structure exhibits characteristics of limited dispersion growth and mitigation of local peaks within the charging process range, the current reconfiguration mapping is determined to be an effective mapping, and the charging energy consumption contribution function corresponding to this mapping is used as the input result for subsequent evaluation steps.

[0084] If, after reconfiguration, the energy consumption distribution structure still exhibits obvious signs of local dispersion clustering or increased imbalance, then, while keeping the established reconfiguration constraints unchanged, the parameter configuration of the reconfiguration mapping is adjusted, and a new charging process reconfiguration mapping for adjusting the energy consumption distribution structure is generated to promote the energy consumption distribution structure to evolve in the direction of suppressing the trend of imbalance amplification.

[0085] S50. Recalculate the energy consumption distribution dispersion based on the reconstructed and mapped charging energy consumption contribution function. When the energy consumption distribution dispersion meets the preset equilibrium condition, complete the charging energy consumption equilibrium optimization.

[0086] This step is used to uniformly evaluate the reconstruction results after the charging energy consumption contribution function has been reconstructed and adjusted, and to determine whether the current charging process has met the preset energy consumption balance requirements. Specifically, it includes the following sub-steps:

[0087] S501. Based on the reconstructed and mapped charging energy consumption contribution function, recalculate the energy consumption distribution dispersion within the charging process range;

[0088] Based on the charging energy consumption contribution function obtained after reconstruction mapping, the energy consumption distribution at each stage of the charging process is re-evaluated, and the energy consumption distribution dispersion covering the entire charging process is calculated to reflect the energy consumption difference level between individual battery cells during the current charging process. This evaluation result serves as an overall reflection of the reconstruction effect and is no longer used to guide the adjustment of the energy consumption distribution structure, but only to determine whether the current charging state has achieved good energy consumption balance.

[0089] S502. Compare the recalculated energy consumption distribution dispersion with the preset equilibrium conditions to make a judgment.

[0090] The energy consumption distribution dispersion obtained from the reassessment is compared with the pre-set balance judgment conditions to check whether the dispersion of energy consumption distribution during the current charging process is within the allowable range. The balance judgment conditions are used to limit the acceptable level of energy consumption imbalance of the battery pack during charging, and their setting can be determined in combination with battery pack design specifications or existing operating experience.

[0091] S503. When the energy consumption distribution dispersion meets the preset balance condition, the charging energy consumption balance optimization is completed.

[0092] When the energy consumption distribution dispersion meets the preset balance judgment condition, the energy consumption distribution in this charging process is considered to have reached a relatively balanced state, the charging energy consumption balance optimization is determined to be complete, and the corresponding optimization process ends. When the balance condition is not met, the non-compliance status and corresponding evaluation results are output for reference in subsequent charging processes or to trigger the next round of optimization process.

[0093] Example 2

[0094] like Figure 2 As shown, Embodiment 2 of this application provides a battery pack charging energy consumption balancing optimization system, including:

[0095] The energy consumption modeling module is used to establish a charging energy consumption contribution function for each battery cell in the battery pack along the charging process dimension, so as to characterize the energy consumption intensity and its variation characteristics of the battery cell at different stages of the charging process. Specifically, it includes the following sub-modules:

[0096] The charging process modeling submodule performs process-oriented modeling of the battery pack's single charging process, mapping the start to end of charging to continuous charging process coordinates and establishing a correspondence between the process positions during charging and the actual sampling intervals. The sequential order of the charging process is preserved during process modeling, ensuring that each process position can serve as a unified reference for subsequent energy consumption statistics.

[0097] The energy consumption characteristic calculation submodule reads the charging voltage, charging current, and duration of individual battery cells within the time interval corresponding to different charging process positions, and calculates the energy consumption characteristic of each battery cell at that process position. The energy consumption characteristic is organized on a per-cell basis, reflecting the differences in energy consumption intensity among individual cells at the same process position, providing a data foundation for subsequently constructing the energy consumption contribution function.

[0098] The energy consumption contribution function generation submodule organizes the energy consumption characteristics corresponding to each charging process position according to the process sequence, forming a continuous changing relationship across the charging process dimension. It then associates the energy consumption characteristics with the process position to generate the charging energy consumption contribution function for each battery cell. Each battery cell generates a corresponding energy consumption contribution curve to describe its energy consumption contribution distribution throughout the entire charging process.

[0099] The distribution evaluation module is used to calculate the energy consumption distribution of the battery pack at various points in the charging process based on the charging energy consumption contribution function of each individual battery cell, and to generate the energy consumption distribution dispersion to quantify the degree of energy consumption balance among different battery cells. Specifically, it includes the following sub-modules:

[0100] The contribution value extraction submodule samples or traverses multiple process positions along the charging process. At any process position, it reads the energy consumption contribution function value of each battery cell and uses it as the energy consumption contribution value of that cell at that process position. The contribution values ​​are aggregated side-by-side by cell to form a contribution set, allowing direct comparison of the energy consumption differences between cells at the same process position.

[0101] The group distribution construction submodule transforms the contribution set into a group energy consumption distribution state. Considering that the overall energy consumption level of the battery pack may vary at different stages, this submodule performs intra-group normalization on the contribution values ​​to obtain an energy consumption percentage sequence, ensuring that the sum of the percentage sequences remains consistent. This guarantees that the distribution comparisons between different process locations and different battery packs have a consistent caliber.

[0102] The dispersion calculation submodule calculates the dispersion of energy consumption distribution based on the energy consumption ratio sequence to quantify the degree of energy consumption balance of the battery pack at this stage of the process. The dispersion index is designed to simultaneously consider the "degree of deviation from uniform distribution" and the "degree of energy consumption concentration" in order to remain sensitive to both local spike imbalances and overall concentrated imbalances. The obtained dispersion results are then output to the trend determination module for subsequent continuous analysis.

[0103] The trend determination module is used to continuously analyze the dispersion of energy consumption distribution within the charging process range, and determine whether the uneven energy consumption during charging exhibits a convergence trend or an amplification trend. Specifically, it includes the following sub-modules:

[0104] The discrete sequence construction submodule continuously samples or statistically analyzes the discreteness along the charging process to form a sequence of discreteness changes with the charging process, and preserves the process order relationship of the sequence so that the evolution of discreteness can be stably tracked.

[0105] The trend feature extraction submodule analyzes the dispersion sequence, extracts trend features that reflect the direction and intensity of change, identifies whether the dispersion gradually increases, gradually decreases, or remains relatively stable within the charging process range, and takes into account the persistence of change to avoid short-term fluctuations from misleading the overall trend judgment.

[0106] The trend determination submodule outputs trend determination results based on trend characteristics. When the dispersion shows a continuous increasing or gradually increasing characteristic, the energy consumption imbalance is determined to be amplifying; when the dispersion shows a continuous decreasing or gradually decreasing characteristic, the energy consumption imbalance is determined to be converging; when the trend characteristics are not obvious, a relatively stable determination result is output, and the determination result is used as one of the triggering conditions for the reconstruction optimization module.

[0107] The reconstruction and optimization module, when it is determined that the imbalance in charging energy consumption has an amplifying trend, aims to reduce the dispersion of energy consumption distribution by generating a reconstruction map of the charging energy consumption contribution function, thereby adjusting the distribution structure of energy consumption contribution during the charging process. Specifically, it includes the following sub-modules:

[0108] The reconstruction constraint generation submodule establishes reconstruction constraints with the goal of reducing dispersion. The constraints include at least: maintaining the total energy consumption of each battery cell consistent throughout the entire charging process; ensuring that the reconstruction mapping remains monotonic in the charging process dimension to avoid reversing the process order; and limiting the offset magnitude and intensity of process position changes to avoid excessive rearrangement that could lead to instability or difficulty in implementation.

[0109] The reconstruction mapping solution submodule generates a charging process reconstruction mapping while satisfying reconstruction constraints. This mapping is then associated with the dispersion objective to guide the reorganization of the energy consumption contribution function along the charging process dimension. The mapping solution process balances overall dispersion reduction with local peak suppression and allows different weights to be assigned to different charging process intervals, enabling optimization to focus on process segments with more significant dispersion amplification.

[0110] The distributed structure reconstruction submodule reconstructs the energy consumption contribution function of each battery cell based on the reconstruction map, thereby controlling the distribution position and density of energy consumption contribution along the charging process dimension. This submodule focuses on whether the reconstructed distributed structure exhibits the characteristics of "limited dispersion growth and mitigation of local peaks." When the reconstruction effect is insufficient to suppress amplification, the mapping parameter configuration is adjusted without exceeding the established constraints, and the mapping is regenerated to complete the reconstruction of the distributed structure until a stable reconstruction result suitable for final evaluation is formed.

[0111] The energy balance determination module is used to recalculate the energy consumption distribution dispersion based on the reconstructed and mapped charging energy consumption contribution function, and outputs a determination result indicating that the charging energy consumption balance optimization is complete when the energy consumption distribution dispersion meets the preset balance conditions. Specifically, it includes the following sub-modules:

[0112] The discreteness recalculation submodule re-evaluates the energy consumption distribution at each process location along the charging process based on the final reconstructed energy consumption contribution function, and calculates the energy consumption distribution discreteness covering the entire charging process range to reflect the overall equilibrium level achieved by the current charging process after reconstruction.

[0113] The equilibrium condition comparison submodule compares the recalculated dispersion results with preset equilibrium judgment conditions to check whether the dispersion is within the allowable range. The equilibrium judgment conditions can be determined by system configuration parameters, design indicators, or historical experience, and are used to give an "acceptable upper limit of imbalance".

[0114] The judgment output submodule completes when the dispersion meets the balance condition, outputs the judgment result "optimization completed" and ends the current charging energy consumption balance optimization process; when the dispersion does not meet the balance condition, it outputs the non-compliance status and corresponding evaluation result, which can be used as a reference for subsequent charging processes or to trigger the next round of optimization process.

[0115] Corresponding to the above embodiments, the present invention provides a computer storage medium, including: at least one memory and at least one processor;

[0116] The memory is used to store one or more program instructions;

[0117] A processor is used to run one or more program instructions to execute a battery pack charging energy consumption balancing optimization method.

[0118] Corresponding to the above embodiments, this embodiment of the invention provides a computer-readable storage medium containing one or more program instructions, which are executed by a processor to provide a battery pack charging energy consumption balancing optimization method.

[0119] The embodiments disclosed in this invention provide a computer-readable storage medium storing computer program instructions. When the computer program instructions are executed on a computer, the computer performs the above-described battery pack charging energy consumption balancing optimization method.

[0120] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0121] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0122] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0123] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0124] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0125] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0126] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using a combination of hardware and software. When applied as software, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0127] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing energy consumption balancing during battery pack charging, characterized in that, include: S10. In the dimension of charging process, a charging energy consumption contribution function is established for each battery cell in the battery pack to characterize the energy consumption intensity and its variation characteristics generated by the battery cell at different charging process positions. S20. Based on the charging energy consumption contribution function of each battery cell, calculate the energy consumption distribution state of the battery pack at each position in the charging process, and quantify the degree of charging energy consumption balance among different battery cells through energy consumption distribution dispersion; the energy consumption distribution dispersion is a combined index of mean square deviation and information deviation, and its calculation relationship is as follows: ,in, Indicates the number of individual battery cells in the battery pack; Indicates the position of the charging process; Indicates the first Individual battery cells at the charging process position Energy consumption contribution value; Indicates the first Individual battery cells at the charging process position The energy consumption percentage is used to determine the energy consumption distribution of the group. A positive stable term; The mean square deviation term is used to characterize the current energy consumption distribution relative to the perfect equilibrium distribution. The degree of deviation; This is the information deviation term, used to characterize the degree of concentration of energy consumption distribution; the more concentrated the distribution, the larger this term becomes. This is a weighting coefficient used to adjust the relative weights of the mean square deviation term and the information deviation term in the dispersion; S30. Perform continuous analysis on the dispersion of energy consumption distribution within the charging process range to determine whether the uneven energy consumption during charging shows a convergence trend or an amplification trend. S40. When it is determined that the imbalance of charging energy consumption has an amplifying trend, with the goal of reducing the dispersion of energy consumption distribution, a reconstruction mapping of the charging energy consumption contribution function is generated to adjust the distribution structure of energy consumption contribution in the charging process. S50. Recalculate the energy consumption distribution dispersion based on the reconstructed and mapped charging energy consumption contribution function. When the energy consumption distribution dispersion meets the preset equilibrium condition, complete the charging energy consumption equilibrium optimization.

2. The battery pack charging energy consumption balancing optimization method as described in claim 1, characterized in that, In terms of the charging process, a charging energy consumption contribution function is established for each individual battery cell in the battery pack to characterize the energy consumption intensity and its variation characteristics at different stages of the charging process. This is specifically divided into the following sub-steps: S101. Perform process modeling on the battery pack charging process, construct charging process parameters that characterize the evolution position of the charging process, and use them to uniformly describe the positional relationship of different charging stages in the same process coordinate system. S102. At different charging process positions, calculate the energy consumption characteristics of the battery cell based on the charging voltage, current and corresponding duration of the battery cell. S103. Based on energy consumption characteristics and charging process parameters, generate a charging energy consumption contribution function that reflects the relationship between energy consumption intensity and the charging process.

3. The battery pack charging energy consumption balancing optimization method as described in claim 1, characterized in that, Based on the charging energy consumption contribution function of each battery cell, the energy consumption distribution state of the battery pack is calculated at each point in the charging process. The degree of charging energy consumption balance among different battery cells is then quantified by discretizing the energy consumption distribution. This process is divided into the following sub-steps: S201. Extract the energy consumption contribution value of each battery cell at each position in the charging process to form a set of energy consumption contributions of multiple cells in parallel. S202. Construct a group energy consumption distribution state that reflects the energy consumption differences within the battery pack based on the energy consumption contribution set; S203. Calculate the energy consumption distribution state of the group to obtain the energy consumption distribution dispersion, which is used to quantify the degree of balance of charging energy consumption among different battery cells.

4. The battery pack charging energy consumption balancing optimization method as described in claim 1, characterized in that, Continuous analysis of the energy consumption distribution dispersion within the charging process range is performed to determine whether the uneven energy consumption during charging exhibits a convergence or amplification trend. This is specifically divided into the following sub-steps: S301. Perform continuous or segmented statistics on the energy consumption distribution dispersion within the charging process range to form a sequence of dispersion changes with the charging process. S302. Extracting trend features reflecting the direction and rate of change based on discrete sequence; S303. Based on trend characteristics, it is determined that the uneven charging energy consumption shows a convergence trend or an amplification trend during the charging process.

5. The battery pack charging energy consumption balancing optimization method as described in claim 1, characterized in that, When it is determined that the imbalance in charging energy consumption has an amplifying trend, a reconstructed mapping of the charging energy consumption contribution function is generated with the goal of reducing the dispersion of energy consumption distribution. This adjusts the distribution structure of energy consumption contribution during the charging process, specifically through the following sub-steps: S401. With reducing the dispersion of energy consumption distribution as the optimization objective, construct reconstruction constraints to constrain the adjustment range and direction of energy consumption. S402. Under the premise of satisfying the reconstruction constraints, generate the reconstruction mapping relationship of the charging energy consumption contribution function in the charging process dimension. S403. Based on the reconstructed mapping relationship, the distribution structure of the charging energy consumption contribution function in the charging process is adjusted to suppress the amplification trend of energy consumption imbalance.

6. The battery pack charging energy consumption balancing optimization method as described in claim 1, characterized in that, The energy consumption distribution dispersion is recalculated based on the reconstructed and mapped charging energy consumption contribution function. When the energy consumption distribution dispersion meets the preset equilibrium condition, the charging energy consumption equilibrium optimization is completed. Specifically, it consists of the following sub-steps: S501. Based on the reconstructed and mapped charging energy consumption contribution function, recalculate the energy consumption distribution dispersion within the charging process range; S502. Compare the recalculated energy consumption distribution dispersion with the preset equilibrium conditions to make a judgment. S503. When the energy consumption distribution dispersion meets the preset balance condition, the charging energy consumption balance optimization is completed.

7. The battery pack charging energy consumption balancing optimization method as described in claim 1, characterized in that, When reconstructing the charging energy consumption contribution function, the reconstruction mapping must simultaneously satisfy the following constraints: Throughout the entire charging process, the integral value of the charging energy consumption contribution function corresponding to each battery cell remains unchanged to ensure the conservation of the total charging energy consumption of each battery cell. The reconstructed mapping only affects the location and density of energy consumption distribution in the charging process dimension, and does not change the overall sequence of the charging process. Reconstruction mapping weakens the trend of uneven amplification of charging energy consumption by suppressing the peak growth of energy consumption distribution dispersion within the local charging process interval.

8. A battery pack charging energy consumption balancing optimization system, characterized in that, To perform the battery pack charging energy consumption balancing optimization method as described in any one of claims 1-7, comprising: The energy consumption modeling module is used to establish a charging energy consumption contribution function for each battery cell in the battery pack in the dimension of charging process, so as to characterize the energy consumption intensity and its variation characteristics generated by the battery cell at different charging process positions. The distribution evaluation module is used to calculate the energy consumption distribution state of the battery pack at each position in the charging process based on the charging energy consumption contribution function of each battery cell, and generate the energy consumption distribution dispersion to quantify the degree of charging energy consumption balance among different battery cells. The trend determination module is used to continuously analyze the dispersion of energy consumption distribution within the charging process range and determine whether the uneven charging energy consumption shows a convergence trend or an amplification trend during the charging process. The reconstruction and optimization module is used to generate a reconstruction mapping of the charging energy consumption contribution function with the goal of reducing the dispersion of energy consumption distribution when it is determined that there is an amplification trend of charging energy consumption imbalance. This module adjusts the distribution structure of energy consumption contribution in the charging process. The equalization determination module is used to recalculate the energy consumption distribution dispersion based on the reconstructed and mapped charging energy consumption contribution function, and output the determination result that the charging energy consumption equalization optimization is completed when the energy consumption distribution dispersion meets the preset equalization conditions.

Images